SWE-Dev/SWE-Dev-1 · Datasets at Hugging Face

repo stringlengths 5 69	instance_id stringlengths 11 74	base_commit stringlengths 40 40	patch stringlengths 169 823k	problem_statement stringlengths 22 84.7k	hints_text stringlengths 0 274k	created_at timestamp[ns]date 2013-07-02 23:04:30 2024-12-13 21:22:22	FAIL_TO_PASS sequencelengths 0 0	PASS_TO_PASS sequencelengths 0 0
discsim/frank	discsim__frank-173	0f9f073a0f4ca947ac7b43c891c838488fff67ee	diff --git a/frank/radial_fitters.py b/frank/radial_fitters.py index e54bd503..d5bd58da 100644 --- a/frank/radial_fitters.py +++ b/frank/radial_fitters.py @@ -320,7 +320,7 @@ def predict(self, u, v, I=None, geometry=None, block_size=10**5): geometry = self._geometry if geometry is not None: - u, v = self._geometry.deproject(u, v) + u, v = geometry.deproject(u, v) q = np.hypot(u, v) V = self._predict(q, I, block_size)	Predict using a different geometry Hi! I think that there is a small error when trying to predict the visibilities using a different geometry. Right now the code is: ``` if geometry is not None: u, v = self._geometry.deproject(u, v) ``` but I think that the correct one should be: ``` if geometry is not None: u, v = geometry.deproject(u, v) ``` Thanks!!	Hi @anibalsierram Thanks! I agree this a bug, we will fix it ASAP.	2021-11-29T17:02:59	[]	[]
discsim/frank	discsim__frank-161	670ebdd823060a6bd26790d8cc81d3d9c16ff34d	diff --git a/.circleci/config.yml b/.circleci/config.yml deleted file mode 100644 index 2e710daa..00000000 --- a/.circleci/config.yml +++ /dev/null @@ -1,125 +0,0 @@ -# Python CircleCI 2.0 configuration file -# -# Check https://circleci.com/docs/2.0/language-python/ for more details -# -version: 2.1 - -jobs: - test: - parameters: - python-version: - type: string - docker: - # specify the version you desire here - - image: circleci/python:<<parameters.python-version>> - - # Specify service dependencies here if necessary - # CircleCI maintains a library of pre-built images - # documented at https://circleci.com/docs/2.0/circleci-images/ - # - image: circleci/postgres:9.4 - - working_directory: ~/repo - - steps: - - checkout - - # Download and cache dependencies - - restore_cache: - keys: - - v1-dependencies-{{ checksum "requirements.txt" }} - # fallback to using the latest cache if no exact match is found - - v1-dependencies- - - run: - name: install package - command: \| - python3 -m venv venv - . venv/bin/activate - pip install .[test] - - save_cache: - paths: - - ./venv - key: v1-dependencies-{{ checksum "requirements.txt" }} - # run tests - - run: - name: run tests - command: \| - . venv/bin/activate - mkdir -p test-reports - py.test --junitxml=test-reports/junit.xml frank/tests.py - - store_test_results: - path: test-reports - destination: test-reports - - docs-build: - docker: - - image: circleci/python:3.7 - steps: - - checkout - - run: - name: Install package - command: \| - python3 -m venv venv - . venv/bin/activate - sudo apt-get install pandoc - pip install jupyter_client - pip install ipykernel - python3 -m ipykernel install --user - pip install .[docs-build] .[test] - - run: - name: Build docs - command: \| - . venv/bin/activate - cd docs/ && make html - - run: - name: Coverage - command: \| - . venv/bin/activate - coverage run --source=frank -m pytest frank/tests.py - coverage report - mkdir -p docs/_build/html/coverage - coverage html -d docs/_build/html/coverage - coverage-badge -f -o docs/_build/html/coverage/badge.svg - - - persist_to_workspace: - root: docs/_build - paths: html - - docs-deploy: - docker: - - image: node:8.10.0 - steps: - - checkout - - attach_workspace: - at: docs/_build - - run: - name: Disable jekyll builds - command: touch docs/_build/html/.nojekyll - - run: - name: Install and configure dependencies - command: \| - npm install -g --silent [email protected] - git config user.email "[email protected]" - git config user.name "ci-build" - - add_ssh_keys: - fingerprints: - - "6a:8d:f2:c3:f1:0a:97:9c:86:3b:02:e7:fd:ed:ea:a6" - - run: - name: Deploy docs to gh-pages branch - command: gh-pages --dotfiles --message "[skip ci] Updates" --dist docs/_build/html - -workflows: - version: 2.1 - build: - jobs: - - test: - matrix: - parameters: - python-version: ["3.6", "3.7", "3.8"] - - docs-build - - docs-deploy: - requires: - - test - - docs-build - filters: - branches: - only: master diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md index b7c63512..7539f0d5 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.md +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -1,30 +1,27 @@ --- name: Bug report about: Create a report to help us improve -title: "[BUG] " -labels: bug [e.g.: 'bug', 'enhancement', 'question'] -assignees: '' [Leave blank unless you want to suggest specifically @rbooth200, @jeffjennings and/or @mtazzari.] +title: '' +labels: bug +assignees: '' --- -Describe the bug -A clear and concise description of what the bug is. +Describe the bug - +1. A clear and concise description of what the bug is. +2. Include the full Traceback of the error. -To Reproduce -Steps to reproduce the behavior: -1. provide input parameters (or attach parameters file) -2. report the failing command [e.g., `python -m frank.fit uvtdata.npz`] +To reproduce - +1. Include a simple, standalone code snippet that demonstrates the issue. +2. If relevant, provide the input parameters for the frank fit (or attach the .json parameter file). -Expected behavior +Expected behavior - A clear and concise description of what you expected to happen. -Screenshots/Logs -If applicable, add screenshots to help explain your problem. -Even better, paste the full Traceback of the error. +Your setup - + - frank version (check with `python -c 'import frank; print(frank.__version__)'`): + - Python version: + - Operating system: -Desktop (please complete the following information): - - OS: [e.g. MacOS] - - frank version [run `python -c 'import frank; print(frank.__version__)'` and paste here the output] - -Additional context -Add any other context about the problem here (e.g., any peculiarity of the data or of the problem you're trying to solve?) +Additional context - +Add any other context about the problem (e.g., any peculiarity of the data or of the problem you're trying to solve?). diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md new file mode 100644 index 00000000..8170920f --- /dev/null +++ b/.github/ISSUE_TEMPLATE/feature_request.md @@ -0,0 +1,20 @@ +--- +name: Feature request +about: Suggest an idea for frank +title: '' +labels: enhancement +assignees: '' + +--- + +Is your feature request related to a problem? Please describe - +A clear and concise description of what the problem is. + +Describe the solution you'd like - +A description of what you want to happen. + +Describe alternatives you've considered - +A description of any alternative solutions or features you've considered. + +Additional context - +Add any other context or screenshots about the feature request. diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml new file mode 100644 index 00000000..5d69d36d --- /dev/null +++ b/.github/workflows/docs.yml @@ -0,0 +1,105 @@ +name: docs + +# Run this workflow when a review is requested on a PR that targets the master +# branch, or the PR is closed +on: + pull_request: + types: [review_requested, closed] + branches: [master] + +# Prevent multiple PRs from building/deploying the docs at the same time +concurrency: + group: ${{ github.workflow }} + +jobs: + docs-build: + name: Build docs + runs-on: ubuntu-latest + + steps: + - name: Checkout repo + uses: actions/checkout@v2 + + - name: Set up Python + uses: actions/setup-python@v2 + with: + python-version: 3.7 + + - name: Display Python version + run: python -c "import sys; print(sys.version)" + + - name: Install docs dependencies + run: \| + sudo apt-get install pandoc + pip install setuptools --upgrade + pip install .[test,docs-build] + + - name: Make docs + run: \| + pip install . + cd docs + make html + + # check code coverage, store results in the built docs. + # coverage.py creates a .gitignore with '*' where it's run; remove it + # to keep the cooverage report and badge on gh-pages + - name: Coverage + # only continue if the PR is not just closed, but also merged + if: github.event.pull_request.merged == true + run: \| + coverage run --source=frank -m pytest frank/tests.py + coverage report + mkdir -p docs/_build/html/coverage + coverage html -d docs/_build/html/coverage + rm docs/_build/html/coverage/.gitignore + coverage-badge -f -o docs/_build/html/coverage/badge.svg + + # upload the built docs as an artifact so the files can be accessed + # by a subsequent job in the workflow. + # only store the artifact for 'retention-days' + - name: Upload docs artifact + if: github.event.pull_request.merged == true + uses: actions/upload-artifact@v3 + with: + name: built_docs + path: docs/_build/html + retention-days: 1 + + docs-deploy: + name: Deploy docs + needs: docs-build + if: github.event.pull_request.merged == true + runs-on: ubuntu-latest + + steps: + - name: Checkout repo + uses: actions/checkout@v2 + + # download the previously uploaded 'built_docs' artifact + - name: Download docs artifact + uses: actions/download-artifact@v3 + id: download + with: + name: built_docs + path: docs/_build/html + + # - name: Echo download path + # run: echo ${{steps.download.outputs.download-path}} + + - name: Disable jekyll builds + run: touch docs/_build/html/.nojekyll + + # - name: Display docs file structure + # run: ls -aR + # working-directory: docs/_build/html + + - name: Install and configure dependencies + run: \| + npm install -g --silent [email protected] + + - name: Deploy docs to gh-pages branch + run: \| + git remote set-url origin https://git:${GITHUB_TOKEN}@github.com/${GITHUB_REPOSITORY}.git + npx gh-pages --dotfiles --dist docs/_build/html --user "github-actions-bot <[email protected]>" --message "Update docs [skip ci]" + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} diff --git a/LICENSE.txt b/LICENSE.txt index b059d641..65c5ca88 100644 --- a/LICENSE.txt +++ b/LICENSE.txt @@ -1,621 +1,165 @@ - GNU GENERAL PUBLIC LICENSE - Version 3, 29 June 2007 - - Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/> - Everyone is permitted to copy and distribute verbatim copies - of this license document, but changing it is not allowed. - - Preamble - - The GNU General Public License is a free, copyleft license for -software and other kinds of works. - - The licenses for most software and other practical works are designed -to take away your freedom to share and change the works. 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Such new +versions will be similar in spirit to the present version, but may +differ in detail to address new problems or concerns. + + Each version is given a distinguishing version number. If the +Library as you received it specifies that a certain numbered version +of the GNU Lesser General Public License "or any later version" +applies to it, you have the option of following the terms and +conditions either of that published version or of any later version +published by the Free Software Foundation. If the Library as you +received it does not specify a version number of the GNU Lesser +General Public License, you may choose any version of the GNU Lesser +General Public License ever published by the Free Software Foundation. + + If the Library as you received it specifies that a proxy can decide +whether future versions of the GNU Lesser General Public License shall +apply, that proxy's public statement of acceptance of any version is +permanent authorization for you to choose that version for the +Library. diff --git a/MANIFEST.in b/MANIFEST.in index 543949de..6e62abde 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1,4 +1,4 @@ -include README.md AUTHORS.rst HISTORY.rst LICENSE.txt requirements.txt +include README.md AUTHORS.rst HISTORY.rst LICENSE.txt global-include default_parameters.json parameter_descriptions.json frank.mplstyle prune docs prune .ci diff --git a/README.md b/README.md index 12b53b55..24ad5021 100644 --- a/README.md +++ b/README.md @@ -3,43 +3,50 @@ </p> <p align="center"> + <!-- current release --> <a href="https://github.com/discsim/frank/releases"> <img src="https://img.shields.io/github/release/discsim/frank/all.svg"> </a> + <!-- current version on pypi --> <a href="https://pypi.python.org/pypi/frank"> <img src="https://img.shields.io/pypi/v/frank.svg"> - </a> - - <a href="https://discsim.github.io/frank/blob/master/HISTORY.rst"> - <img src="https://img.shields.io/badge/Changelog-detailed-blue"/> - - <br/> - <a href="https://discsim.github.io/frank/"> - <img src="https://img.shields.io/badge/docs-Read%20em!-blue.svg?style=flat"/> <br/> - <a href="https://academic.oup.com/mnras/advance-article/doi/10.1093/mnras/staa1365/5838058?guestAccessKey=7f163a1f-c12f-4771-8e54-928636794a5b"> - <img src="https://img.shields.io/badge/paper-MNRAS-blue.svg"> + <!-- changelog --> + <a href="https://github.com/discsim/frank/blob/master/HISTORY.rst"> + <img src="https://img.shields.io/badge/changelog-detailed-blue"/> </a> + <!-- zenodo --> <a href="https://doi.org/10.5281/zenodo.3832064"> <img src="https://zenodo.org/badge/DOI/10.5281/zenodo.3832064.svg" alt="DOI"> - </a> <br/> - <a href="https://circleci.com/gh/discsim/frank"> - <img src="https://circleci.com/gh/discsim/frank.svg?style=shield"> + <!-- tests --> + <a href="https://github.com/discsim/frank/actions/workflows/tests.yml"> + <img src="https://github.com/discsim/frank/actions/workflows/tests.yml/badge.svg"> </a> + <!-- docs build --> + <a href="https://github.com/discsim/frank/actions/workflows/docs.yml"> + <img src="https://github.com/discsim/frank/actions/workflows/docs.yml/badge.svg"/> + </a> + + <!-- coverage --> <a href="https://discsim.github.io/frank/coverage/index.html"> <img src="https://discsim.github.io/frank/coverage/badge.svg"> - </a> <br/> + <!-- paper --> + <a href="https://academic.oup.com/mnras/advance-article/doi/10.1093/mnras/staa1365/5838058?guestAccessKey=7f163a1f-c12f-4771-8e54-928636794a5b"> + <img src="https://img.shields.io/badge/paper-MNRAS-blue.svg"> + </a> + + <br/> + <!-- license --> <a href="https://www.gnu.org/licenses/lgpl-3.0"> - <img src="https://img.shields.io/badge/License-LGPL%20v3-blue.svg" - [![License: LGPL v3](https://img.shields.io/badge/License-LGPL%20v3-blue.svg"> + <img src="https://img.shields.io/badge/license-LGPL%20v3-blue.svg"> </a> </p> diff --git a/frank/utilities.py b/frank/utilities.py index 831d540a..84427da2 100644 --- a/frank/utilities.py +++ b/frank/utilities.py @@ -456,7 +456,7 @@ def estimate_weights(u, v=None, V=None, nbins=300, log=True, use_median=False): In each case the variance of V in the uv-bins is used to estimate the weights. The first call will use q = np.hypot(u, v) in the uv-bins. The second and third calls are equivalent to the first with u=0. - + """ logging.info(' Estimating visibility weights') @@ -594,6 +594,7 @@ def sweep_profile(r, I, project=False, phase_shift=False, geom=None, axis=0, ----- Sign convention: a negative geom.dRA shifts the source to the right in the image + """ if project: inc, pa, dra, ddec = geom.inc, geom.PA, geom.dRA, geom.dDec diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index 5406d671..00000000 --- a/requirements.txt +++ /dev/null @@ -1,3 +0,0 @@ -numpy>=1.12.0 -matplotlib>=3.1.0 -scipy>=0.18.0 diff --git a/setup.cfg b/setup.cfg new file mode 100644 index 00000000..361f2040 --- /dev/null +++ b/setup.cfg @@ -0,0 +1,57 @@ +# to set version dynamically: https://github.com/pypa/setuptools/issues/1724#issuecomment-627241822 + +[metadata] +name = frank +version = attr: frank.__version__ +author = Richard Booth, Jeff Jennings, Marco Tazzari +author_email = [email protected] +description = Frankenstein, the flux reconstructor +long_description = file: README.md +long_description_content_type = text/markdown +license = LGPLv3 +license_file = LICENSE.txt +include_package_data = True +url = https://github.com/discsim/frank +project_urls = + Bug Tracker = https://github.com/discsim/frank/issues +classifiers = + Development Status :: 5 - Production/Stable + License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3) + Intended Audience :: Developers + Intended Audience :: Science/Research + Operating System :: MacOS :: MacOS X + Operating System :: POSIX :: Linux + Programming Language :: Python :: 3.6 + Programming Language :: Python :: 3.7 + Programming Language :: Python :: 3.8 + Programming Language :: Python :: 3.9 +keywords = + science + astronomy + interferometry + +[options] +packages = frank + +# python_requires docs: https://packaging.python.org/guides/distributing-packages-using-setuptools/#python-requires +python_requires = >=3.6 + +# PEP 440 - pinning package versions: https://www.python.org/dev/peps/pep-0440/#compatible-release +install_requires = + numpy>=1.12 + matplotlib>=3.1.0 + scipy>=0.18.0 + +# extras_require syntax: +# https://setuptools.readthedocs.io/en/latest/userguide/declarative_config.html?highlight=options.extras_require#configuring-setup-using-setup-cfg-files +[options.extras_require] +test = pytest + coverage==6.2 + coverage-badge==1.1.0 + +docs-build = sphinx + sphinxcontrib-fulltoc + sphinx_rtd_theme + nbsphinx + jupyter_client + ipykernel diff --git a/setup.py b/setup.py index cc0a49a1..2ca29466 100644 --- a/setup.py +++ b/setup.py @@ -1,37 +1,6 @@ #!/usr/bin/env python # coding=utf-8 -from setuptools import setup, find_packages +from setuptools import setup -with open("frank/__init__.py", "r") as f: - for line in f: - if line.startswith('__version__'): - version = line.split('=')[1].strip().strip('"\'') - -setup( - name="frank", - version=version, - packages=find_packages(), - include_package_data=True, - author="Richard Booth, Jeff Jennings, Marco Tazzari", - author_email="[email protected]", - description="Frankenstein, the flux reconstructor", - long_description_content_type="text/markdown", - long_description=open('README.md').read(), - python_requires='>=3', - install_requires=[line.rstrip() for line in open("requirements.txt", "r").readlines()], - extras_require={ - 'test' : ['pytest', 'coverage', 'coverage-badge'], - 'docs-build' : ['sphinx', 'sphinxcontrib-fulltoc', 'sphinx_rtd_theme', 'nbsphinx'], - }, - license="GPLv3", - url="https://github.com/discsim/frank", - keywords=["science", "astronomy", "interferometry"], - classifiers=[ - "Development Status :: 5 - Production/Stable", - "Intended Audience :: Developers", - "Intended Audience :: Science/Research", - "Operating System :: OS Independent", - "License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)", - "Programming Language :: Python :: 3", - ] -) +# setup configuration is in `setup.cfg` +setup()	circleci tests queuing The circleci tests for any given push are still constantly failing because the tests are being queued (not running) with the notice, "This job was waitlisted due to your concurrency limits". The tests are delayed from starting by 5 - 9 minutes, then time out at ~10 minutes and so 'fail'. You have to re-run tests multiple times to get all of them to pass, which can take 30 minutes - > 1 hr for a single push. The circleci account plan says we can have "1 concurrent job with access to Linux and Windows builds on Small and Medium sizes". I googled and don't know why this queuing started happening seemingly randomly. I found this - https://support.circleci.com/hc/en-us/articles/360055741771-Build-Not-Running-due-to-concurrency-limit-but-no-other-job-is-running - but I don't see any background jobs that are running which would cause the tests to queue. Unless someone sees a quick fix, we should open a support ticket on circleci.	Hey, I was just about to open an issue to migrate to Github Actions. I just did it for both uvplot and galario. It's just great, tests are run immediately and it's completely free for public projects. I'll open an issue asap. Opened issue #157 Awesome, thank you! Sounds much faster and easier I'll have a look in the next days and will submit a PR.	2021-03-17T20:36:36	[]	[]
ToucanToco/fastexcel	ToucanToco__fastexcel-304	152ff50011da173be5c59e09ba3bd4d7b0f20cad	diff --git a/python/fastexcel/__init__.py b/python/fastexcel/__init__.py index 50320e6..d1e6380 100644 --- a/python/fastexcel/__init__.py +++ b/python/fastexcel/__init__.py @@ -233,7 +233,8 @@ def load_sheet( - if `skip_rows` is a number, it skips the specified number of rows from the start of the sheet. :param schema_sample_rows: Specifies how many rows should be used to determine - the dtype of a column. + the dtype of a column. Cannot be 0. A specific dtype can be + enforced for some or all columns through the `dtypes` parameter. If `None`, all rows will be used. :param dtype_coercion: Specifies how type coercion should behave. `coerce` (the default) will try to coerce different dtypes in a column to the same one, @@ -336,7 +337,8 @@ def load_table( If `header_row` is `None`, it skips the number of rows from the start of the sheet. :param schema_sample_rows: Specifies how many rows should be used to determine - the dtype of a column. + the dtype of a column. Cannot be 0. A specific dtype can be + enforced for some or all columns through the `dtypes` parameter. If `None`, all rows will be used. :param dtype_coercion: Specifies how type coercion should behave. `coerce` (the default) will try to coerce different dtypes in a column to the same one, diff --git a/src/types/python/excelreader.rs b/src/types/python/excelreader.rs index 0be6ca8..f6c8549 100644 --- a/src/types/python/excelreader.rs +++ b/src/types/python/excelreader.rs @@ -350,6 +350,14 @@ impl ExcelReader { eager: bool, py: Python<'_>, ) -> PyResult<PyObject> { + // Cannot use NonZeroUsize in the parameters, as it is not supported by pyo3 + if let Some(0) = schema_sample_rows { + return Err(FastExcelErrorKind::InvalidParameters( + "schema_sample_rows cannot be 0, as it would prevent dtype inferring".to_string(), + ) + .into()) + .into_pyresult(); + } let sheet = idx_or_name .try_into() .and_then(\|idx_or_name\| match idx_or_name { @@ -420,6 +428,14 @@ impl ExcelReader { eager: bool, py: Python<'_>, ) -> PyResult<PyObject> { + // Cannot use NonZeroUsize in the parameters, as it is not supported by pyo3 + if let Some(0) = schema_sample_rows { + return Err(FastExcelErrorKind::InvalidParameters( + "schema_sample_rows cannot be 0, as it would prevent dtype inferring".to_string(), + ) + .into()) + .into_pyresult(); + } self.build_table( name.to_string(), header_row,	schema_sample_rows=0 results in a table filled with null values When reading a excel file and setting the schema_sample_rows to 0 results in a table of the correct height, but all values are set to null. If the schema_sample_rows is set to n and the first n values in a column are empty, then the remainder of the values in this column are also filled with null values. This means that the values that are present in this column, are replaced with null values. This is causing a loss of data, while this can be fixed by defaulting to a column dtype of type string. For example, xlsx2csv has this also as default.	I don't really understand. `schema_sample_rows` sets the number of rows used to infer the type of the columns. If you set it to 0, we can't infer anything because no data is read hence the `null` type being generated I understand that the type cannot be inferred, but why would you replace all the values with null then? I have a table filled with values, but I do not want to have any types inferred as I want to assign them by myself. However, the columns are all filled with null values instead of the values that are present in the column. I would be fine with the fact that the column dtype is null, but then all the values are kept as strings for example. In that way I would be able to cast the columns by myself, to the values that I want them to be and prevent the extra overhead of inferring the dtype. For example the following excel on the left with schema_sample_rows = 0 results in the table on the right after reading it with all dtypes being null. ![image](https://github.com/ToucanToco/fastexcel/assets/44165272/9b18dac5-4fcb-42f6-8472-a305789e729c) Another example is the following excel on the left with schema_sample_rows = 1 results in the table on the right after reading it with the dtypes of the first three columns being string and the last column being of dtype null. ![image](https://github.com/ToucanToco/fastexcel/assets/44165272/51296571-450e-48d0-8833-691dd135b5f8) I do not see why this is wanted behavior to remove all data from the column if the dtype cannot be inferred. Could you please elaborate why this is the default behavior and how to prevent the replacement in way that is not casting all 16384 columns with the dtypes parameter to type string. I ran into this as well. I have very inconsistent excel files and the requirement is to load all data as strings. Since I don't know how many columns there are, I am currently doing a 3-step process: load the first row, build a dtype map based on the width of the row, then load the sheet using the map. The time hit for the double load is noticeable for larger worksheets. I am certainly open to better solutions. I have tried some combinations of `Polars` and `fastexcel` and always run into trouble... dropped columns/null data at one end and `unsupported column type combination: {DateTime, String}` at the other. I was also going to open a feature request to pass a single `dtype` to apply to all columns. ```python ws = wb.load_sheet_by_name(name=sheet_name, header_row=None, n_rows=1, schema_sample_rows=0) type_map = {i: "string" for i in range(ws.width + 1)} ws = wb.load_sheet_by_name(name=sheet_name, header_row=None, dtypes=type_map) ``` The solution that I have used with polars is by creating the `type_map` for all 16384 columns as this is the limit of an excel file. Of course I am not creating it for the all columns but for my purpose with polars I have set it for the first 100 columns. This returns the correct dataframe. The extra columns in the `type_map` are not created at all, so therefore you are able to create a `type_map` without knowing the width of the worksheet in the first place. @Tim-Kracht @niekrongen we made some improvements by supporting most mix types. Does it solve your problems? I don't want to add another option but if a way to have all columns in string is really needed, we'll work on a solution (like https://github.com/ToucanToco/fastexcel/issues/257) @lukapeschke I reckon we should forbid `0` as value. It makes no sense. And then we can close this issue. WDYT? @PrettyWood I agree, having `schema_sample_rows=0` should result in an `InvalidParameters` error	2024-10-15T15:13:22	[]	[]
ToucanToco/fastexcel	ToucanToco__fastexcel-297	8c9559fe753f27b38f6b952613ea65d82ce74745	diff --git a/python/fastexcel/__init__.py b/python/fastexcel/__init__.py index 382eda6..c406883 100644 --- a/python/fastexcel/__init__.py +++ b/python/fastexcel/__init__.py @@ -208,7 +208,7 @@ def load_sheet( , header_row: int \| None = 0, column_names: list[str] \| None = None, - skip_rows: int = 0, + skip_rows: int \| None = None, n_rows: int \| None = None, schema_sample_rows: int \| None = 1_000, dtype_coercion: Literal["coerce", "strict"] = "coerce", @@ -227,8 +227,11 @@ def load_sheet( If `None`, all rows are loaded :param skip_rows: Specifies how many rows should be skipped after the `header_row`. Any rows before the `header_row` are automatically skipped. - If `header_row` is `None`, it skips the number of rows from the - start of the sheet. + If `header_row` is `None`: + - if `skip_rows` is `None` (default): it skips all empty rows + at the beginning of the sheet. + - if `skip_rows` is a number, it skips the specified number + of rows from the start of the sheet. :param schema_sample_rows: Specifies how many rows should be used to determine the dtype of a column. If `None`, all rows will be used. @@ -372,7 +375,7 @@ def load_sheet_eager( , header_row: int \| None = 0, column_names: list[str] \| None = None, - skip_rows: int = 0, + skip_rows: int \| None = None, n_rows: int \| None = None, schema_sample_rows: int \| None = 1_000, dtype_coercion: Literal["coerce", "strict"] = "coerce", @@ -405,7 +408,7 @@ def load_sheet_by_name( , header_row: int \| None = 0, column_names: list[str] \| None = None, - skip_rows: int = 0, + skip_rows: int \| None = None, n_rows: int \| None = None, schema_sample_rows: int \| None = 1_000, dtype_coercion: Literal["coerce", "strict"] = "coerce", @@ -434,7 +437,7 @@ def load_sheet_by_idx( , header_row: int \| None = 0, column_names: list[str] \| None = None, - skip_rows: int = 0, + skip_rows: int \| None = None, n_rows: int \| None = None, schema_sample_rows: int \| None = 1_000, dtype_coercion: Literal["coerce", "strict"] = "coerce", diff --git a/python/fastexcel/_fastexcel.pyi b/python/fastexcel/_fastexcel.pyi index 33ba39f..41a2b2e 100644 --- a/python/fastexcel/_fastexcel.pyi +++ b/python/fastexcel/_fastexcel.pyi @@ -104,7 +104,7 @@ class _ExcelReader: , header_row: int \| None = 0, column_names: list[str] \| None = None, - skip_rows: int = 0, + skip_rows: int \| None = None, n_rows: int \| None = None, schema_sample_rows: int \| None = 1_000, dtype_coercion: Literal["coerce", "strict"] = "coerce", @@ -119,7 +119,7 @@ class _ExcelReader: , header_row: int \| None = 0, column_names: list[str] \| None = None, - skip_rows: int = 0, + skip_rows: int \| None = None, n_rows: int \| None = None, schema_sample_rows: int \| None = 1_000, dtype_coercion: Literal["coerce", "strict"] = "coerce", diff --git a/src/types/python/excelreader.rs b/src/types/python/excelreader.rs index d36f9aa..d0b0b69 100644 --- a/src/types/python/excelreader.rs +++ b/src/types/python/excelreader.rs @@ -7,8 +7,8 @@ use arrow::{pyarrow::ToPyArrow, record_batch::RecordBatch}; use pyo3::{prelude::PyObject, pyclass, pymethods, Bound, IntoPy, PyAny, PyResult, Python}; use calamine::{ - open_workbook_auto, open_workbook_auto_from_rs, Data, DataRef, Range, Reader, ReaderRef, - Sheet as CalamineSheet, Sheets, Table, + open_workbook_auto, open_workbook_auto_from_rs, Data, DataRef, HeaderRow, Range, Reader, + ReaderRef, Sheet as CalamineSheet, Sheets, Table, }; use crate::{ @@ -73,6 +73,19 @@ impl ExcelSheets { ) } + fn with_header_row(&mut self, header_row: HeaderRow) -> &mut Self { + match self { + Self::File(sheets) => { + sheets.with_header_row(header_row); + self + } + Self::Bytes(ref mut sheets) => { + sheets.with_header_row(header_row); + self + } + } + } + fn worksheet_range_ref(&mut self, name: &str) -> FastExcelResult<Range<DataRef>> { match self { ExcelSheets::File(Sheets::Xlsx(sheets)) => Ok(sheets.worksheet_range_ref(name)?), @@ -164,7 +177,7 @@ impl ExcelReader { sheet_meta: CalamineSheet, header_row: Option<usize>, column_names: Option<Vec<String>>, - skip_rows: usize, + skip_rows: Option<usize>, n_rows: Option<usize>, schema_sample_rows: Option<usize>, dtype_coercion: DTypeCoercion, @@ -173,14 +186,28 @@ impl ExcelReader { eager: bool, py: Python<'_>, ) -> PyResult<PyObject> { - let header = Header::new(header_row, column_names); + // calamine `header_row` is the first row of the range to be read. + // For us `header_row` can be `None` (meaning there is no header and we should start reading + // the data at the beginning) + let calamine_header_row = match (header_row, skip_rows) { + (None, None) \| (Some(0), None) => HeaderRow::FirstNonEmptyRow, + (None, Some(_)) => HeaderRow::Row(0), + (Some(row), _) => HeaderRow::Row(row as u32), + }; + // And our header row is simply the first row of the data if defined. + let data_header_row = header_row.and(Some(0)); + + let header = Header::new(data_header_row, column_names); let selected_columns = Self::build_selected_columns(use_columns).into_pyresult()?; + if eager && self.sheets.supports_by_ref() { let range = self .sheets + .with_header_row(calamine_header_row) .worksheet_range_ref(&sheet_meta.name) .into_pyresult()?; - let pagination = Pagination::new(skip_rows, n_rows, &range).into_pyresult()?; + let pagination = + Pagination::new(skip_rows.unwrap_or(0), n_rows, &range).into_pyresult()?; Self::load_sheet_eager( &range.into(), pagination, @@ -195,9 +222,11 @@ impl ExcelReader { } else { let range = self .sheets + .with_header_row(calamine_header_row) .worksheet_range(&sheet_meta.name) .into_pyresult()?; - let pagination = Pagination::new(skip_rows, n_rows, &range).into_pyresult()?; + let pagination = + Pagination::new(skip_rows.unwrap_or(0), n_rows, &range).into_pyresult()?; let sheet = ExcelSheet::try_new( sheet_meta, range.into(), @@ -298,7 +327,7 @@ impl ExcelReader { *, header_row = 0, column_names = None, - skip_rows = 0, + skip_rows = None, n_rows = None, schema_sample_rows = 1_000, dtype_coercion = DTypeCoercion::Coerce, @@ -312,7 +341,7 @@ impl ExcelReader { idx_or_name: &Bound<'_, PyAny>, header_row: Option<usize>, column_names: Option<Vec<String>>, - skip_rows: usize, + skip_rows: Option<usize>, n_rows: Option<usize>, schema_sample_rows: Option<usize>, dtype_coercion: DTypeCoercion, @@ -337,14 +366,14 @@ impl ExcelReader { } } IdxOrName::Idx(idx) => self - .sheet_metadata .get(idx) + .sheet_metadata + .get(idx) .ok_or_else(\|\| FastExcelErrorKind::SheetNotFound(IdxOrName::Idx(idx)).into()) - .with_context(\|\| { format!( - "Sheet index {idx} is out of range. File has {} sheets.", - self.sheet_metadata.len() - ) - }) - , + .with_context(\|\| format!( + "Sheet index {idx} is out of range. File has {} sheets.", + self.sheet_metadata.len() + ) + ), }) .into_pyresult()?.to_owned();	header_row does not work as expected if there are blank rows If I put expect the header on e.g. row index 15, but there are blank rows (at least at the beginning of the sheet), I cannot do `header_row=15`. It looks like they are ignored, so I have to put in a lower index.	Hello! can you please provide an excel sheet example? I will try to create a basic sheet to reproduce. It seems the issue is not actually with blank rows, but there is some other reason I am having to put in a much lower index than I expect. It looks like it does have to do with empty rows in some case.. not exactly clear to me, but it's very easy to reproduce with this file: [TestFastExcelRead.xlsx](https://github.com/ToucanToco/fastexcel/files/15015950/TestFastExcelRead.xlsx) I have the header row on row 8 (0-based; 9 in excel) but have to specify it as 6. You can also remove that "Note 1" cell to change things. ```python def test_row_issue(): reader = read_excel("...TestFastExcelRead.xlsx") ws = reader.load_sheet("Sheet1", header_row=6) df = ws.to_pandas() ``` I'll try to dig into it this weekend seem related to #209 see https://github.com/ToucanToco/fastexcel/issues/209#issuecomment-2239211891	2024-10-11T22:38:02	[]	[]
ToucanToco/fastexcel	ToucanToco__fastexcel-292	5145063f439f8b2801c73e9800a0ec0bf8e3693a	diff --git a/src/data.rs b/src/data.rs index 553d10d..8da9ada 100644 --- a/src/data.rs +++ b/src/data.rs @@ -81,6 +81,8 @@ mod array_impls { use calamine::{CellType, DataType, Range}; use chrono::NaiveDate; + use crate::types::dtype::excel_float_to_string; + pub(crate) fn create_boolean_array<DT: CellType + DataType>( data: &Range<DT>, col: usize, @@ -142,6 +144,8 @@ mod array_impls { cell.get_datetime_iso().map(str::to_string) } else if cell.is_bool() { cell.get_bool().map(\|v\| v.to_string()) + } else if cell.is_float() { + cell.get_float().map(excel_float_to_string) } else { cell.as_string() } diff --git a/src/types/dtype.rs b/src/types/dtype.rs index 9af844a..32b3dd9 100644 --- a/src/types/dtype.rs +++ b/src/types/dtype.rs @@ -280,6 +280,31 @@ pub(crate) fn get_dtype_for_column<DT: CellType + Debug + DataType>( } } +/// Convert a float to a nice string to mimic Excel behaviour. +/// +/// Excel can store a float like 29.02 set by the user as "29.020000000000003" in the XML. +/// But in fact, the user will see "29.02" in the cell. +/// Excel indeed displays decimal numbers with 8 digits in a standard cell width +/// and 10 digits in a wide cell. Like this: +/// +/// Format = 0.000000000 \| Unformatted, wide cell \| Unformatted, standard width +/// ---------------------\|--------------------------\|---------------------------- +/// 1.123456789 \| 1.123456789 \| 1.123457 +/// 12.123456789 \| 12.12345679 \| 12.12346 +/// ... \| ... \| ... +/// 123456.123456789 \| 123456.1235 \| 123456.1 +/// +/// Excel also trims trailing zeros and the decimal point if there is no fractional part. +/// +/// We do not distinguish between wide cells and standard cells here, so we retain at most +/// nine digits after the decimal point and trim any trailing zeros. +pub(crate) fn excel_float_to_string(x: f64) -> String { + format!("{x:.9}") + .trim_end_matches('0') + .trim_end_matches('.') + .to_string() +} + #[cfg(test)] mod tests { use calamine::{Cell, Data as CalData}; @@ -394,4 +419,12 @@ mod tests { FastExcelErrorKind::UnsupportedColumnTypeCombination(_) )); } + + #[rstest] + #[case(29.020000000000003, "29.02")] + #[case(10000_f64, "10000")] + #[case(23.0, "23")] + fn test_excel_float_to_string(#[case] x: f64, #[case] expected: &str) { + assert_eq!(excel_float_to_string(x), expected.to_string()); + } }	29.02 cell cast to 29.020000000000003 string ## Steps to reproduce See the Excel file [decimal-numbers.xlsx](https://github.com/user-attachments/files/17162995/decimal-numbers.xlsx). It contains a column with two decimals: \| Decimals \| \|--------\| \| 28.14 \| \| 29.02 \| Read the Excel file using fastexcel: ```python read_excel("decimal-numbers.xlsx").load_sheet_by_name("Sheet1").to_polars() ``` ``` shape: (2, 1) ┌──────────┐ │ Decimals │ │ --- │ │ f64 │ ╞══════════╡ │ 28.14 │ │ 29.02 │ └──────────┘ ``` Looks fine. Then read the Excel file while casting to strings: ```python read_excel("decimal-numbers.xlsx").load_sheet_by_name("Sheet1", dtypes={0: 'string'}).to_polars() ``` ``` shape: (2, 1) ┌────────────────────┐ │ Decimals │ │ --- │ │ str │ ╞════════════════════╡ │ 28.14 │ │ 29.020000000000003 │ └────────────────────┘ ``` The expected result was that the strings would be the same as shown to the user in the Excel file: ``` shape: (2, 1) ┌──────────┐ │ Decimals │ │ --- │ │ str │ ╞══════════╡ │ 28.14 │ │ 29.02 │ └──────────┘ ``` # Wrap-up I understand this looks like an issue due to floating point precision, and I'm not sure if this: 1. could be fixed in fastexcel 2. could be fixed in the underlying calamine. 3. cannot be fixed at all, since it's a fundamental property of the Excel file format or parsing process. What's the motivation for filing this bug: In our system, we have highly heterogeneous data, so we have to read all Excel values as strings. However, if users see 29.02 in their Excel files, but `29.020000000000003` in our system, that's highly confusing and surprising to users. What do you think?	Hello @severinh Always great to have a clear bug issue with file and clear explanation thank you! I checked quickly and indeed it feels hard to fix on `fastexcel` side In `src/data.rs`, in `create_string_array` function we simply do ```rs cell.as_string() ``` Even with ```rs cell.get_float().map(\|v\| v.to_string()) ``` or ```rs cell.as_f64() ``` we get the same behavior with this floating precision issue and AFAIK there is no way for us to get more info on the original input. I'll try to dig on calamine side tonight EDIT: quick check on calamine ```rs inner: [ SharedString( "Decimals", ), Float( 28.14, ), Float( 29.020000000000003, ), ], ``` when reading the xml content we get ```rs BytesText { content: Borrowed("29.020000000000003") } ``` so I guess it goes even further directly in the xml content 😞 The floating point number in the Excel file is 29.020000000000003: ```xml <row r="2" spans="1:1" x14ac:dyDescent="0.2"> <c r="A2" s="1"> <v>28.14</v> </c> </row> <row r="3" spans="1:1" x14ac:dyDescent="0.2"> <c r="A3" s="1"> <v>29.020000000000003</v> </c> </row> ``` For the display (and storage) of floating point numbers Excel uses something very similar to the `printf()` format option `%.16G`: ```python $ python >>> print("%.16G" % 29.020000000000003) 29.02 ``` That format option isn't supported by Rust in [std::fmt](https://doc.rust-lang.org/std/fmt/) however.	2024-10-02T16:50:08	[]	[]
ToucanToco/fastexcel	ToucanToco__fastexcel-287	79a7f17b8654438c691872522fca70b9ec7d9898	diff --git a/python/fastexcel/__init__.py b/python/fastexcel/__init__.py index 0899cea..8a8e33a 100644 --- a/python/fastexcel/__init__.py +++ b/python/fastexcel/__init__.py @@ -1,6 +1,7 @@ from __future__ import annotations import sys +import typing from typing import TYPE_CHECKING, Callable, Literal if sys.version_info < (3, 10): @@ -31,6 +32,7 @@ __version__, _ExcelReader, _ExcelSheet, + _ExcelTable, ) from ._fastexcel import read_excel as _read_excel @@ -41,6 +43,14 @@ SheetVisible: TypeAlias = Literal["visible", "hidden", "veryhidden"] +def _recordbatch_to_polars(rb: pa.RecordBatch) -> pl.DataFrame: + import polars as pl + + df = pl.from_arrow(data=rb) + assert isinstance(df, pl.DataFrame) + return df + + class ExcelSheet: """A class representing a single sheet in an Excel File""" @@ -104,16 +114,83 @@ def to_polars(self) -> "pl.DataFrame": Requires the `polars` extra to be installed. """ - import polars as pl - - df = pl.from_arrow(data=self.to_arrow()) - assert isinstance(df, pl.DataFrame) - return df + return _recordbatch_to_polars(self.to_arrow()) def __repr__(self) -> str: return self._sheet.__repr__() +class ExcelTable: + """A class representing a single table in an Excel file""" + + def __init__(self, table: _ExcelTable) -> None: + self._table = table + + @property + def name(self) -> str: + """The name of the table""" + return self._table.name + + @property + def sheet_name(self) -> str: + """The name of the sheet this table belongs to""" + return self._table.sheet_name + + @property + def width(self) -> int: + """The table's width""" + return self._table.width + + @property + def height(self) -> int: + """The table's height""" + return self._table.height + + @property + def total_height(self) -> int: + """The table's total height""" + return self._table.total_height + + @property + def offset(self) -> int: + """The table's offset before data starts""" + return self._table.offset + + @property + def selected_columns(self) -> list[ColumnInfo]: + """The table's selected columns""" + return self._table.selected_columns + + @property + def available_columns(self) -> list[ColumnInfo]: + """The columns available for the given table""" + return self._table.available_columns + + @property + def specified_dtypes(self) -> DTypeMap \| None: + """The dtypes specified for the table""" + return self._table.specified_dtypes + + def to_arrow(self) -> pa.RecordBatch: + """Converts the table to a pyarrow `RecordBatch`""" + return self._table.to_arrow() + + def to_pandas(self) -> "pd.DataFrame": + """Converts the table to a Pandas `DataFrame`. + + Requires the `pandas` extra to be installed. + """ + # We know for sure that the table will yield exactly one RecordBatch + return self.to_arrow().to_pandas() + + def to_polars(self) -> "pl.DataFrame": + """Converts the table to a Polars `DataFrame`. + + Requires the `polars` extra to be installed. + """ + return _recordbatch_to_polars(self.to_arrow()) + + class ExcelReader: """A class representing an open Excel file and allowing to read its sheets""" @@ -196,6 +273,7 @@ def table_names(self, sheet_idx_or_name: str \| int \| None = None) -> list[str]: """ return self._reader.table_names(sheet_idx_or_name) + @typing.overload def load_table( self, name: str, @@ -208,7 +286,37 @@ def load_table( dtype_coercion: Literal["coerce", "strict"] = "coerce", use_columns: list[str] \| list[int] \| str \| Callable[[ColumnInfo], bool] \| None = None, dtypes: DTypeMap \| None = None, - ) -> ExcelSheet: + eager: Literal[False] = ..., + ) -> ExcelTable: ... + @typing.overload + def load_table( + self, + name: str, + , + header_row: int \| None = None, + column_names: list[str] \| None = None, + skip_rows: int = 0, + n_rows: int \| None = None, + schema_sample_rows: int \| None = 1_000, + dtype_coercion: Literal["coerce", "strict"] = "coerce", + use_columns: list[str] \| list[int] \| str \| Callable[[ColumnInfo], bool] \| None = None, + dtypes: DTypeMap \| None = None, + eager: Literal[True] = ..., + ) -> pa.RecordBatch: ... + def load_table( + self, + name: str, + , + header_row: int \| None = None, + column_names: list[str] \| None = None, + skip_rows: int = 0, + n_rows: int \| None = None, + schema_sample_rows: int \| None = 1_000, + dtype_coercion: Literal["coerce", "strict"] = "coerce", + use_columns: list[str] \| list[int] \| str \| Callable[[ColumnInfo], bool] \| None = None, + dtypes: DTypeMap \| None = None, + eager: bool = False, + ) -> ExcelTable \| pa.RecordBatch: """Loads a table by name. :param name: The name of the table to load. @@ -242,19 +350,21 @@ def load_table( indicating whether the column should be used :param dtypes: An optional dict of dtypes. Keys can be column indices or names """ - return ExcelSheet( - self._reader.load_table( - name=name, - header_row=header_row, - column_names=column_names, - skip_rows=skip_rows, - n_rows=n_rows, - schema_sample_rows=schema_sample_rows, - dtype_coercion=dtype_coercion, - use_columns=use_columns, - dtypes=dtypes, - ) + output = self._reader.load_table( # type:ignore[call-overload,misc] + name=name, + header_row=header_row, + column_names=column_names, + skip_rows=skip_rows, + n_rows=n_rows, + schema_sample_rows=schema_sample_rows, + dtype_coercion=dtype_coercion, + use_columns=use_columns, + dtypes=dtypes, + eager=eager, ) + if eager: + return output + return ExcelTable(output) def load_sheet_eager( self, diff --git a/python/fastexcel/_fastexcel.pyi b/python/fastexcel/_fastexcel.pyi index 5d12bb2..eb289b0 100644 --- a/python/fastexcel/_fastexcel.pyi +++ b/python/fastexcel/_fastexcel.pyi @@ -63,6 +63,37 @@ class _ExcelSheet: def to_arrow(self) -> pa.RecordBatch: """Converts the sheet to a pyarrow `RecordBatch`""" +class _ExcelTable: + @property + def name(self) -> str: + """The name of the table""" + @property + def sheet_name(self) -> str: + """The name of the sheet this table belongs to""" + @property + def width(self) -> int: + """The table's width""" + @property + def height(self) -> int: + """The table's height""" + @property + def total_height(self) -> int: + """The table's total height""" + @property + def offset(self) -> int: + """The table's offset before data starts""" + @property + def selected_columns(self) -> list[ColumnInfo]: + """The table's selected columns""" + @property + def available_columns(self) -> list[ColumnInfo]: + """The columns available for the given table""" + @property + def specified_dtypes(self) -> DTypeMap \| None: + """The dtypes specified for the table""" + def to_arrow(self) -> pa.RecordBatch: + """Converts the table to a pyarrow `RecordBatch`""" + class _ExcelReader: """A class representing an open Excel file and allowing to read its sheets""" @@ -96,6 +127,7 @@ class _ExcelReader: dtypes: DTypeMap \| None = None, eager: Literal[True] = ..., ) -> pa.RecordBatch: ... + @typing.overload def load_table( self, name: str, @@ -108,6 +140,22 @@ class _ExcelReader: dtype_coercion: Literal["coerce", "strict"] = "coerce", use_columns: list[str] \| list[int] \| str \| Callable[[ColumnInfo], bool] \| None = None, dtypes: DTypeMap \| None = None, + eager: Literal[False] = ..., + ) -> _ExcelTable: ... + @typing.overload + def load_table( + self, + name: str, + , + header_row: int \| None = None, + column_names: list[str] \| None = None, + skip_rows: int = 0, + n_rows: int \| None = None, + schema_sample_rows: int \| None = 1_000, + dtype_coercion: Literal["coerce", "strict"] = "coerce", + use_columns: list[str] \| list[int] \| str \| Callable[[ColumnInfo], bool] \| None = None, + dtypes: DTypeMap \| None = None, + eager: Literal[True] = ..., ) -> pa.RecordBatch: ... @property def sheet_names(self) -> list[str]: ... diff --git a/src/types/python/excelsheet/sheet_data.rs b/src/data.rs similarity index 60% rename from src/types/python/excelsheet/sheet_data.rs rename to src/data.rs index 88b68d2..553d10d 100644 --- a/src/types/python/excelsheet/sheet_data.rs +++ b/src/data.rs @@ -1,11 +1,17 @@ use std::sync::Arc; -use arrow::array::Array; +use arrow::{ + array::{Array, NullArray, RecordBatch}, + datatypes::{Field, Schema}, +}; use calamine::{Data as CalData, DataRef as CalDataRef, DataType, Range}; use crate::{ - error::FastExcelResult, - types::dtype::{get_dtype_for_column, DType, DTypeCoercion}, + error::{ErrorContext, FastExcelErrorKind, FastExcelResult}, + types::{ + dtype::{get_dtype_for_column, DType, DTypeCoercion}, + python::excelsheet::column_info::ColumnInfo, + }, }; pub(crate) enum ExcelSheetData<'r> { @@ -75,7 +81,7 @@ mod array_impls { use calamine::{CellType, DataType, Range}; use chrono::NaiveDate; - pub(super) fn create_boolean_array<DT: CellType + DataType>( + pub(crate) fn create_boolean_array<DT: CellType + DataType>( data: &Range<DT>, col: usize, offset: usize, @@ -96,7 +102,7 @@ mod array_impls { }))) } - pub(super) fn create_int_array<DT: CellType + DataType>( + pub(crate) fn create_int_array<DT: CellType + DataType>( data: &Range<DT>, col: usize, offset: usize, @@ -107,7 +113,7 @@ mod array_impls { )) } - pub(super) fn create_float_array<DT: CellType + DataType>( + pub(crate) fn create_float_array<DT: CellType + DataType>( data: &Range<DT>, col: usize, offset: usize, @@ -118,7 +124,7 @@ mod array_impls { )) } - pub(super) fn create_string_array<DT: CellType + DataType>( + pub(crate) fn create_string_array<DT: CellType + DataType>( data: &Range<DT>, col: usize, offset: usize, @@ -147,7 +153,7 @@ mod array_impls { caldt.as_duration().map(\|d\| d.num_milliseconds()) } - pub(super) fn create_date_array<DT: CellType + DataType>( + pub(crate) fn create_date_array<DT: CellType + DataType>( data: &Range<DT>, col: usize, offset: usize, @@ -161,7 +167,7 @@ mod array_impls { }))) } - pub(super) fn create_datetime_array<DT: CellType + DataType>( + pub(crate) fn create_datetime_array<DT: CellType + DataType>( data: &Range<DT>, col: usize, offset: usize, @@ -176,7 +182,7 @@ mod array_impls { ))) } - pub(super) fn create_duration_array<DT: CellType + DataType>( + pub(crate) fn create_duration_array<DT: CellType + DataType>( data: &Range<DT>, col: usize, offset: usize, @@ -212,3 +218,74 @@ create_array_function!(create_float_array); create_array_function!(create_datetime_array); create_array_function!(create_date_array); create_array_function!(create_duration_array); + +pub(crate) use array_impls::create_boolean_array as create_boolean_array_from_range; +pub(crate) use array_impls::create_date_array as create_date_array_from_range; +pub(crate) use array_impls::create_datetime_array as create_datetime_array_from_range; +pub(crate) use array_impls::create_duration_array as create_duration_array_from_range; +pub(crate) use array_impls::create_float_array as create_float_array_from_range; +pub(crate) use array_impls::create_int_array as create_int_array_from_range; +pub(crate) use array_impls::create_string_array as create_string_array_from_range; + +/// Converts a list of ColumnInfo to an arrow Schema +pub(crate) fn selected_columns_to_schema(columns: &[ColumnInfo]) -> Schema { + let fields: Vec<_> = columns.iter().map(Into::<Field>::into).collect(); + Schema::new(fields) +} + +/// Creates an arrow RecordBatch from an Iterator over (column_name, column data tuples) and an arrow schema +pub(crate) fn record_batch_from_name_array_iterator< + 'a, + I: Iterator<Item = (&'a str, Arc<dyn Array>)>, +>( + iter: I, + schema: Schema, +) -> FastExcelResult<RecordBatch> { + let mut iter = iter.peekable(); + // If the iterable is empty, try_from_iter returns an Err + if iter.peek().is_none() { + Ok(RecordBatch::new_empty(Arc::new(schema))) + } else { + // We use `try_from_iter_with_nullable` because `try_from_iter` relies on `array.null_count() > 0;` + // to determine if the array is nullable. This is not the case for `NullArray` which has no nulls. + RecordBatch::try_from_iter_with_nullable(iter.map(\|(field_name, array)\| { + let nullable = array.is_nullable(); + (field_name, array, nullable) + })) + .map_err(\|err\| FastExcelErrorKind::ArrowError(err.to_string()).into()) + .with_context(\|\| "could not create RecordBatch from iterable") + } +} + +/// Creates an arrow `RecordBatch` from `ExcelSheetData`. Expects the following parameters: +/// `columns`: a slice of `ColumnInfo`, representing the columns that should be extracted from the range +/// * `data`: the sheets data, as an `ExcelSheetData` +/// * `offset`: the row index at which to start +/// * `limit`: the row index at which to stop (excluded) +pub(crate) fn record_batch_from_data_and_columns( + columns: &[ColumnInfo], + data: &ExcelSheetData, + offset: usize, + limit: usize, +) -> FastExcelResult<RecordBatch> { + let schema = selected_columns_to_schema(columns); + let iter = columns.iter().map(\|column_info\| { + let col_idx = column_info.index(); + let dtype = column_info.dtype(); + ( + column_info.name.as_str(), + match dtype { + DType::Null => Arc::new(NullArray::new(limit - offset)), + DType::Int => create_int_array(data, col_idx, offset, limit), + DType::Float => create_float_array(data, col_idx, offset, limit), + DType::String => create_string_array(data, col_idx, offset, limit), + DType::Bool => create_boolean_array(data, col_idx, offset, limit), + DType::DateTime => create_datetime_array(data, col_idx, offset, limit), + DType::Date => create_date_array(data, col_idx, offset, limit), + DType::Duration => create_duration_array(data, col_idx, offset, limit), + }, + ) + }); + + record_batch_from_name_array_iterator(iter, schema) +} diff --git a/src/lib.rs b/src/lib.rs index 569301c..4ffc8df 100644 --- a/src/lib.rs +++ b/src/lib.rs @@ -1,10 +1,13 @@ +mod data; mod error; mod types; mod utils; use error::{py_errors, ErrorContext}; use pyo3::{prelude::, types::PyString}; -use types::python::{excelsheet::column_info::ColumnInfo, ExcelReader, ExcelSheet}; +use types::python::{ + excelsheet::column_info::ColumnInfo, table::ExcelTable, ExcelReader, ExcelSheet, +}; /// Reads an excel file and returns an object allowing to access its sheets and a bit of metadata #[pyfunction] @@ -46,6 +49,7 @@ fn _fastexcel(m: &Bound<'_, PyModule>) -> PyResult<()> { m.add_class::<ColumnInfo>()?; m.add_class::<ExcelSheet>()?; m.add_class::<ExcelReader>()?; + m.add_class::<ExcelTable>()?; m.add("__version__", get_version())?; // errors diff --git a/src/types/python/excelreader.rs b/src/types/python/excelreader.rs index c0cc6d4..312ca78 100644 --- a/src/types/python/excelreader.rs +++ b/src/types/python/excelreader.rs @@ -1,5 +1,3 @@ -use crate::types::python::excelsheet::table::{extract_table_names, extract_table_range}; -use crate::utils::schema::get_schema_sample_rows; use std::{ fs::File, io::{BufReader, Cursor}, @@ -14,23 +12,25 @@ use calamine::{ }; use crate::{ + data::{record_batch_from_data_and_columns, ExcelSheetData}, error::{ py_errors::IntoPyResult, ErrorContext, FastExcelError, FastExcelErrorKind, FastExcelResult, }, types::{ dtype::{DTypeCoercion, DTypeMap}, idx_or_name::IdxOrName, + python::excelsheet::table::{extract_table_names, extract_table_range}, }, + utils::schema::get_schema_sample_rows, }; use pyo3::types::PyString; -use super::excelsheet::record_batch_from_data_and_columns; use super::excelsheet::{ column_info::{build_available_columns, build_available_columns_info}, - sheet_data::ExcelSheetData, + ExcelSheet, Header, Pagination, SelectedColumns, }; -use super::excelsheet::{ExcelSheet, Header, Pagination, SelectedColumns}; +use super::table::ExcelTable; enum ExcelSheets { File(Sheets<BufReader<File>>), @@ -155,7 +155,7 @@ impl ExcelReader { let final_columns = selected_columns.select_columns(&available_columns)?; - record_batch_from_data_and_columns(final_columns, data, offset, limit) + record_batch_from_data_and_columns(&final_columns, data, offset, limit) } #[allow(clippy::too_many_arguments)] @@ -244,21 +244,10 @@ impl ExcelReader { } }; - // TODO: Use From<Table<T>> for Range<T> once https://github.com/tafia/calamine/pull/464 is merged - let range = table.data(); - let pagination = Pagination::new(skip_rows, n_rows, range).into_pyresult()?; + let pagination = Pagination::new(skip_rows, n_rows, table.data()).into_pyresult()?; - // FIXME: We're creating sheet metadata here, but it should not be required by the future Table object - let sheet_meta = CalamineSheet { - name, - typ: calamine::SheetType::WorkSheet, - visible: calamine::SheetVisible::Visible, - }; - - let sheet = ExcelSheet::try_new( - sheet_meta, - // TODO: Remove clone when aforementioned .from() is used - ExcelSheetData::from(range.clone()), + let excel_table = ExcelTable::try_new( + table, header, pagination, schema_sample_rows, @@ -269,9 +258,9 @@ impl ExcelReader { .into_pyresult()?; if eager { - sheet.to_arrow(py) + excel_table.to_arrow(py) } else { - Ok(sheet.into_py(py)) + Ok(excel_table.into_py(py)) } } } diff --git a/src/types/python/excelsheet/column_info.rs b/src/types/python/excelsheet/column_info.rs index 1988794..a8c5b06 100644 --- a/src/types/python/excelsheet/column_info.rs +++ b/src/types/python/excelsheet/column_info.rs @@ -1,19 +1,21 @@ use std::{fmt::Display, str::FromStr}; use arrow::datatypes::Field; +use calamine::DataType; use pyo3::{pyclass, pymethods, PyResult}; use crate::{ + data::ExcelSheetData, error::{ py_errors::IntoPyResult, ErrorContext, FastExcelError, FastExcelErrorKind, FastExcelResult, }, types::{ - dtype::{DType, DTypeCoercion, DTypeMap}, + dtype::{get_dtype_for_column, DType, DTypeCoercion, DTypeMap}, idx_or_name::IdxOrName, }, }; -use super::{sheet_data::ExcelSheetData, Header, SelectedColumns}; +use super::{Header, SelectedColumns}; #[derive(Debug, Clone, PartialEq)] pub(crate) enum ColumnNameFrom { @@ -231,9 +233,9 @@ impl ColumnInfoBuilder { &self.name } - fn dtype_info( + fn dtype_info<D: CalamineDataProvider>( &self, - data: &ExcelSheetData<'_>, + data: &D, start_row: usize, end_row: usize, specified_dtypes: Option<&DTypeMap>, @@ -259,9 +261,9 @@ impl ColumnInfoBuilder { }) } - pub(super) fn finish( + pub(super) fn finish<D: CalamineDataProvider>( self, - data: &ExcelSheetData<'_>, + data: &D, start_row: usize, end_row: usize, specified_dtypes: Option<&DTypeMap>, @@ -280,8 +282,60 @@ impl ColumnInfoBuilder { } } -pub(crate) fn build_available_columns_info( - data: &ExcelSheetData<'_>, +pub(crate) trait CalamineDataProvider { + fn width(&self) -> usize; + fn get_as_string(&self, pos: (usize, usize)) -> Option<String>; + fn dtype_for_column( + &self, + start_row: usize, + end_row: usize, + col: usize, + dtype_coercion: &DTypeCoercion, + ) -> FastExcelResult<DType>; +} + +impl CalamineDataProvider for ExcelSheetData<'_> { + fn width(&self) -> usize { + self.width() + } + + fn get_as_string(&self, pos: (usize, usize)) -> Option<String> { + self.get_as_string(pos) + } + + fn dtype_for_column( + &self, + start_row: usize, + end_row: usize, + col: usize, + dtype_coercion: &DTypeCoercion, + ) -> FastExcelResult<DType> { + self.dtype_for_column(start_row, end_row, col, dtype_coercion) + } +} + +impl CalamineDataProvider for calamine::Range<calamine::Data> { + fn width(&self) -> usize { + self.width() + } + + fn get_as_string(&self, pos: (usize, usize)) -> Option<String> { + self.get(pos).and_then(\|data\| data.as_string()) + } + + fn dtype_for_column( + &self, + start_row: usize, + end_row: usize, + col: usize, + dtype_coercion: &DTypeCoercion, + ) -> FastExcelResult<DType> { + get_dtype_for_column(self, start_row, end_row, col, dtype_coercion) + } +} + +pub(crate) fn build_available_columns_info<D: CalamineDataProvider>( + data: &D, selected_columns: &SelectedColumns, header: &Header, ) -> FastExcelResult<Vec<ColumnInfoBuilder>> { @@ -397,9 +451,9 @@ fn alias_for_name(name: &str, existing_names: &[String]) -> String { rec(name, existing_names, 0) } -pub(crate) fn build_available_columns( +pub(crate) fn build_available_columns<D: CalamineDataProvider>( available_columns_info: Vec<ColumnInfoBuilder>, - data: &ExcelSheetData, + data: &D, start_row: usize, end_row: usize, specified_dtypes: Option<&DTypeMap>, diff --git a/src/types/python/excelsheet/mod.rs b/src/types/python/excelsheet/mod.rs index db91281..66aaf87 100644 --- a/src/types/python/excelsheet/mod.rs +++ b/src/types/python/excelsheet/mod.rs @@ -1,17 +1,10 @@ pub(crate) mod column_info; -pub(crate) mod sheet_data; pub(crate) mod table; use calamine::{CellType, Range, Sheet as CalamineSheet, SheetVisible as CalamineSheetVisible}; -use sheet_data::ExcelSheetData; -use std::{cmp, collections::HashSet, fmt::Debug, str::FromStr, sync::Arc}; - -use arrow::{ - array::{Array, NullArray}, - datatypes::{Field, Schema}, - pyarrow::ToPyArrow, - record_batch::RecordBatch, -}; +use std::{cmp, collections::HashSet, fmt::Debug, str::FromStr}; + +use arrow::{pyarrow::ToPyArrow, record_batch::RecordBatch}; use pyo3::{ prelude::{pyclass, pymethods, PyAnyMethods, Python}, @@ -20,21 +13,15 @@ use pyo3::{ }; use crate::{ + data::{record_batch_from_data_and_columns, ExcelSheetData}, error::{ py_errors::IntoPyResult, ErrorContext, FastExcelError, FastExcelErrorKind, FastExcelResult, }, - types::{ - dtype::{DType, DTypeMap}, - idx_or_name::IdxOrName, - }, + types::{dtype::DTypeMap, idx_or_name::IdxOrName}, }; use crate::{types::dtype::DTypeCoercion, utils::schema::get_schema_sample_rows}; use self::column_info::{build_available_columns, build_available_columns_info, ColumnInfo}; -use self::sheet_data::{ - create_boolean_array, create_date_array, create_datetime_array, create_duration_array, - create_float_array, create_int_array, create_string_array, -}; #[derive(Debug)] pub(crate) enum Header { @@ -416,11 +403,11 @@ impl ExcelSheet { &sheet.dtype_coercion, )?; + // Figure out dtype for every column let selected_columns = selected_columns.select_columns(&available_columns)?; sheet.available_columns = available_columns; sheet.selected_columns = selected_columns; - // Figure out dtype for every column Ok(sheet) } @@ -441,57 +428,6 @@ impl ExcelSheet { } } -impl From<&ExcelSheet> for Schema { - fn from(sheet: &ExcelSheet) -> Self { - let fields: Vec<_> = sheet - .selected_columns - .iter() - .map(Into::<Field>::into) - .collect(); - Schema::new(fields) - } -} - -pub(crate) fn record_batch_from_data_and_columns( - columns: Vec<ColumnInfo>, - data: &ExcelSheetData, - offset: usize, - limit: usize, -) -> FastExcelResult<RecordBatch> { - let fields = columns.iter().map(Into::<Field>::into).collect::<Vec<_>>(); - - let schema = Schema::new(fields); - - let mut iter = columns - .into_iter() - .map(\|column_info\| { - let col_idx = column_info.index(); - let dtype = *column_info.dtype(); - ( - column_info.name, - match dtype { - DType::Null => Arc::new(NullArray::new(limit - offset)), - DType::Int => create_int_array(data, col_idx, offset, limit), - DType::Float => create_float_array(data, col_idx, offset, limit), - DType::String => create_string_array(data, col_idx, offset, limit), - DType::Bool => create_boolean_array(data, col_idx, offset, limit), - DType::DateTime => create_datetime_array(data, col_idx, offset, limit), - DType::Date => create_date_array(data, col_idx, offset, limit), - DType::Duration => create_duration_array(data, col_idx, offset, limit), - }, - ) - }) - .peekable(); - // If the iterable is empty, try_from_iter returns an Err - if iter.peek().is_none() { - Ok(RecordBatch::new_empty(Arc::new(schema))) - } else { - RecordBatch::try_from_iter(iter) - .map_err(\|err\| FastExcelErrorKind::ArrowError(err.to_string()).into()) - .with_context(\|\| "could not create RecordBatch from iterable") - } -} - impl TryFrom<&ExcelSheet> for RecordBatch { type Error = FastExcelError; @@ -499,56 +435,8 @@ impl TryFrom<&ExcelSheet> for RecordBatch { let offset = sheet.offset(); let limit = sheet.limit(); - let mut iter = sheet - .selected_columns - .iter() - .map(\|column_info\| { - // At this point, we know for sure that the column is in the schema so we can - // safely unwrap - ( - column_info.name(), - match column_info.dtype() { - DType::Bool => { - create_boolean_array(sheet.data(), column_info.index(), offset, limit) - } - DType::Int => { - create_int_array(sheet.data(), column_info.index(), offset, limit) - } - DType::Float => { - create_float_array(sheet.data(), column_info.index(), offset, limit) - } - DType::String => { - create_string_array(sheet.data(), column_info.index(), offset, limit) - } - DType::DateTime => { - create_datetime_array(sheet.data(), column_info.index(), offset, limit) - } - DType::Date => { - create_date_array(sheet.data(), column_info.index(), offset, limit) - } - DType::Duration => { - create_duration_array(sheet.data(), column_info.index(), offset, limit) - } - DType::Null => Arc::new(NullArray::new(limit - offset)), - }, - ) - }) - .peekable(); - - // If the iterable is empty, try_from_iter returns an Err - if iter.peek().is_none() { - let schema: Schema = sheet.into(); - Ok(RecordBatch::new_empty(Arc::new(schema))) - } else { - // We use `try_from_iter_with_nullable` because `try_from_iter` relies on `array.null_count() > 0;` - // to determine if the array is nullable. This is not the case for `NullArray` which has no nulls. - RecordBatch::try_from_iter_with_nullable(iter.map(\|(field_name, array)\| { - let nullable = array.is_nullable(); - (field_name, array, nullable) - })) - .map_err(\|err\| FastExcelErrorKind::ArrowError(err.to_string()).into()) + record_batch_from_data_and_columns(&sheet.selected_columns, sheet.data(), offset, limit) .with_context(\|\| format!("could not convert sheet {} to RecordBatch", sheet.name())) - } } } diff --git a/src/types/python/mod.rs b/src/types/python/mod.rs index 261453f..7a1c3a3 100644 --- a/src/types/python/mod.rs +++ b/src/types/python/mod.rs @@ -1,4 +1,5 @@ pub(crate) mod excelreader; pub(crate) mod excelsheet; +pub(crate) mod table; pub(crate) use excelreader::ExcelReader; pub(crate) use excelsheet::ExcelSheet; diff --git a/src/types/python/table.rs b/src/types/python/table.rs new file mode 100644 index 0000000..b3df0dc --- /dev/null +++ b/src/types/python/table.rs @@ -0,0 +1,268 @@ +use std::sync::Arc; + +use arrow::{ + array::{NullArray, RecordBatch}, + pyarrow::ToPyArrow, +}; +use calamine::{Data, Range, Table}; +use pyo3::{pyclass, pymethods, PyObject, PyResult, Python, ToPyObject}; + +use crate::{ + data::{ + create_boolean_array_from_range, create_date_array_from_range, + create_datetime_array_from_range, create_duration_array_from_range, + create_float_array_from_range, create_int_array_from_range, create_string_array_from_range, + record_batch_from_name_array_iterator, selected_columns_to_schema, + }, + error::{ + py_errors::IntoPyResult, ErrorContext, FastExcelError, FastExcelErrorKind, FastExcelResult, + }, + types::{ + dtype::{DType, DTypeCoercion, DTypeMap}, + python::excelsheet::column_info::build_available_columns, + }, + utils::schema::get_schema_sample_rows, +}; + +use super::excelsheet::{ + column_info::{build_available_columns_info, ColumnInfo}, + Header, Pagination, SelectedColumns, +}; + +#[pyclass(name = "_ExcelTable")] +pub(crate) struct ExcelTable { + #[pyo3(get)] + name: String, + #[pyo3(get)] + sheet_name: String, + selected_columns: Vec<ColumnInfo>, + available_columns: Vec<ColumnInfo>, + table: Table<Data>, + header: Header, + pagination: Pagination, + dtypes: Option<DTypeMap>, + dtype_coercion: DTypeCoercion, + height: Option<usize>, + total_height: Option<usize>, + width: Option<usize>, +} + +impl ExcelTable { + pub(crate) fn try_new( + table: Table<Data>, + header: Header, + pagination: Pagination, + schema_sample_rows: Option<usize>, + dtype_coercion: DTypeCoercion, + selected_columns: SelectedColumns, + dtypes: Option<DTypeMap>, + ) -> FastExcelResult<Self> { + let available_columns_info = + build_available_columns_info(table.data(), &selected_columns, &header)?; + + let mut excel_table = ExcelTable { + name: table.name().to_owned(), + sheet_name: table.sheet_name().to_owned(), + // Empty vecs as they'll be replaced + available_columns: Vec::with_capacity(0), + selected_columns: Vec::with_capacity(0), + table, + header, + pagination, + dtypes, + dtype_coercion, + height: None, + total_height: None, + width: None, + }; + + let row_limit = get_schema_sample_rows( + schema_sample_rows, + excel_table.offset(), + excel_table.limit(), + ); + + // Finalizing column info + let available_columns = build_available_columns( + available_columns_info, + excel_table.data(), + excel_table.offset(), + row_limit, + excel_table.dtypes.as_ref(), + &excel_table.dtype_coercion, + )?; + + // Figure out dtype for every column + let selected_columns = selected_columns.select_columns(&available_columns)?; + excel_table.available_columns = available_columns; + excel_table.selected_columns = selected_columns; + + Ok(excel_table) + } + + pub(crate) fn data(&self) -> &Range<Data> { + self.table.data() + } +} + +impl TryFrom<&ExcelTable> for RecordBatch { + type Error = FastExcelError; + + fn try_from(table: &ExcelTable) -> FastExcelResult<Self> { + let offset = table.offset(); + let limit = table.limit(); + + let iter = table.selected_columns.iter().map(\|column_info\| { + ( + column_info.name(), + match column_info.dtype() { + DType::Bool => create_boolean_array_from_range( + table.data(), + column_info.index(), + offset, + limit, + ), + DType::Int => create_int_array_from_range( + table.data(), + column_info.index(), + offset, + limit, + ), + DType::Float => create_float_array_from_range( + table.data(), + column_info.index(), + offset, + limit, + ), + DType::String => create_string_array_from_range( + table.data(), + column_info.index(), + offset, + limit, + ), + DType::DateTime => create_datetime_array_from_range( + table.data(), + column_info.index(), + offset, + limit, + ), + DType::Date => create_date_array_from_range( + table.data(), + column_info.index(), + offset, + limit, + ), + DType::Duration => create_duration_array_from_range( + table.data(), + column_info.index(), + offset, + limit, + ), + DType::Null => Arc::new(NullArray::new(limit - offset)), + }, + ) + }); + + let schema = selected_columns_to_schema(&table.selected_columns); + + record_batch_from_name_array_iterator(iter, schema).with_context(\|\| { + format!( + "could not convert table {table} in sheet {sheet} to RecordBatch", + table = &table.name, + sheet = &table.sheet_name + ) + }) + } +} + +#[pymethods] +impl ExcelTable { + #[getter] + pub fn offset(&self) -> usize { + self.header.offset() + self.pagination.offset() + } + + #[getter] + pub(crate) fn limit(&self) -> usize { + let upper_bound = self.data().height(); + if let Some(n_rows) = self.pagination.n_rows() { + let limit = self.offset() + n_rows; + if limit < upper_bound { + return limit; + } + } + + upper_bound + } + + #[getter] + pub fn selected_columns(&self) -> Vec<ColumnInfo> { + self.selected_columns.clone() + } + + #[getter] + pub fn available_columns(&self) -> Vec<ColumnInfo> { + self.available_columns.clone() + } + + #[getter] + pub fn specified_dtypes<'p>(&'p self, py: Python<'p>) -> Option<PyObject> { + self.dtypes.as_ref().map(\|dtypes\| dtypes.to_object(py)) + } + + #[getter] + pub fn width(&mut self) -> usize { + self.width.unwrap_or_else(\|\| { + let width = self.data().width(); + self.width = Some(width); + width + }) + } + + #[getter] + pub fn height(&mut self) -> usize { + self.height.unwrap_or_else(\|\| { + let height = self.limit() - self.offset(); + self.height = Some(height); + height + }) + } + + #[getter] + pub fn total_height(&mut self) -> usize { + self.total_height.unwrap_or_else(\|\| { + let total_height = self.data().height() - self.header.offset(); + self.total_height = Some(total_height); + total_height + }) + } + + pub fn to_arrow(&self, py: Python<'_>) -> PyResult<PyObject> { + RecordBatch::try_from(self) + .with_context(\|\| { + format!( + "could not create RecordBatch from sheet \"{}\"", + self.name + ) + }) + .and_then(\|rb\| { + rb.to_pyarrow(py) + .map_err(\|err\| FastExcelErrorKind::ArrowError(err.to_string()).into()) + }) + .with_context(\|\| { + format!( + "could not convert RecordBatch to pyarrow for table \"{table}\" in sheet \"{sheet}\"", + table = self.name, sheet = self.sheet_name + ) + }) + .into_pyresult() + } + + pub fn __repr__(&self) -> String { + format!( + "ExcelTable<{sheet}/{name}>", + sheet = self.sheet_name, + name = self.name + ) + } +}	Support for Reading Excel Tables From my experience it's usually much safer to load data from an excel table than from a sheet. would be nice if one could get the table names per sheet and get the table data as arrow/pandas like with the sheets	Hi @aersam , could you please provide a bit more details on what you have in mind ? And maybe provide a few examples on how that parameter is passed in other libraries (for example pandas) ? A file with the issue you are encountering would also be great Hi there! Here's a sample file: [tables.xlsx](https://github.com/ToucanToco/fastexcel/files/14703008/tables.xlsx) I'd like to pass the table_name instead of sheet_name, table name's are unique within an excel workbook as well: ![image](https://github.com/ToucanToco/fastexcel/assets/5270024/b54cd319-4e14-4bda-9999-5cd1b6edeab8) Thanks! This seems to be doable through calamine's APIs (https://docs.rs/calamine/latest/calamine/struct.Xlsx.html#method.table_by_name) but will require quite some work, so I don't know when/if we will ship it... We might pritoritize this if a lot of people ask for this feature :slightly_smiling_face: > Thanks! This seems to be doable through calamine's APIs (https://docs.rs/calamine/latest/calamine/struct.Xlsx.html#method.table_by_name) but will require quite some work, so I don't know when/if we will ship it... We might pritoritize this if a lot of people ask for this feature 🙂 As a minor FYI, we always write "real" Excel table objects from Polars' `write_excel` method as they are generally considered quite useful, given how freeform Excel can otherwise be ;) @alexander-beedie ah good to know, thanks! How do you handle tables on read ? Is there a parameter to read a specific table ? > @alexander-beedie ah good to know, thanks! How do you handle tables on read ? Is there a parameter to read a specific table ? At the moment I don't, heh 😎 However, I was thinking about adding a `table_name` parameter, as one of our other engines (openpyxl) also supports reading table objects. If we could get an equivalent capability for fastexcel that would be really nice, as it completely eliminates the guessing game of _"where does the data start/end"_ 👌 (You may also want an option to skip the final row when reading table objects, as that can contain totals that are not part of the data - they also may or may not have a header row, so the existing parameters to determine that can be respected).	2024-09-18T15:19:30	[]	[]
ToucanToco/fastexcel	ToucanToco__fastexcel-279	c3cd0d9f5ca5f27e481b0775cd7826fb9fe3fee1	diff --git a/.gitignore b/.gitignore index 3a99b8d..5659737 100644 --- a/.gitignore +++ b/.gitignore @@ -5,6 +5,7 @@ __pycache__ .pyc .so *.dat +.DS_Store .python-version .venv diff --git a/src/types/python/excelsheet/mod.rs b/src/types/python/excelsheet/mod.rs index 9b98185..761d5c2 100644 --- a/src/types/python/excelsheet/mod.rs +++ b/src/types/python/excelsheet/mod.rs @@ -6,7 +6,7 @@ use sheet_data::ExcelSheetData; use std::{cmp, collections::HashSet, fmt::Debug, str::FromStr, sync::Arc}; use arrow::{ - array::NullArray, + array::{Array, NullArray}, datatypes::{Field, Schema}, pyarrow::ToPyArrow, record_batch::RecordBatch, @@ -521,9 +521,14 @@ impl TryFrom<&ExcelSheet> for RecordBatch { let schema: Schema = sheet.into(); Ok(RecordBatch::new_empty(Arc::new(schema))) } else { - RecordBatch::try_from_iter(iter) - .map_err(\|err\| FastExcelErrorKind::ArrowError(err.to_string()).into()) - .with_context(\|\| format!("could not convert sheet {} to RecordBatch", sheet.name)) + // We use `try_from_iter_with_nullable` because `try_from_iter` relies on `array.null_count() > 0;` + // to determine if the array is nullable. This is not the case for `NullArray` which has no nulls. + RecordBatch::try_from_iter_with_nullable(iter.map(\|(field_name, array)\| { + let nullable = array.is_nullable(); + (field_name, array, nullable) + })) + .map_err(\|err\| FastExcelErrorKind::ArrowError(err.to_string()).into()) + .with_context(\|\| format!("could not convert sheet {} to RecordBatch", sheet.name)) } } }	Sheet `to_arrow()` produces `null not null` columns With the example file [null-column.xlsx](https://github.com/user-attachments/files/16692741/null-column.xlsx): ```python >>> excel_reader = fastexcel.read_excel('null-column.xlsx') >>> sheet = excel_reader.load_sheet(0) >>> sheet.to_arrow() pyarrow.RecordBatch record_id: double not null nullonly: null not null ---- record_id: [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20] nullonly: 20 nulls ``` Apparently PyArrow's [enforcement of "nullable" is not strong](https://stackoverflow.com/questions/78848723/non-nullable-field-in-schema-does-not-prevent-null-values-in-corresponding-table), so this doesn't create serious problems for me until I try to pickle and unpickle the RecordBatch. Then I get the exception: ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "pyarrow/types.pxi", line 3532, in pyarrow.lib.field ValueError: A null type field may not be non-nullable ``` I tried tracking down the cause of this weird `null not null` column schema, but it does appear that fastexcel [attempts to define all columns as "nullable"](https://github.com/ToucanToco/fastexcel/blob/c3cd0d9f5ca5f27e481b0775cd7826fb9fe3fee1/src/types/python/excelsheet/column_info.rs#L132), so I'm not sure where this is originating from. However, if I build the RecordBatch by way of polars, I don't get this strange schema: ```python >>> sheet.to_polars().to_arrow().to_batches()[0] pyarrow.RecordBatch record_id: double nullonly: null ---- record_id: [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20] nullonly: 20 nulls ``` ... so I suppose it's at least plausible that this could be fixed within fastexcel.		2024-08-22T08:38:43	[]	[]
ToucanToco/fastexcel	ToucanToco__fastexcel-260	441a973da40b0e9710a54fd460cc4af11473a596	diff --git a/src/types/python/excelreader.rs b/src/types/python/excelreader.rs index 1837fd9..8e524a7 100644 --- a/src/types/python/excelreader.rs +++ b/src/types/python/excelreader.rs @@ -136,7 +136,9 @@ impl ExcelReader { dtype_coercion, )?; - let fields = available_columns + let final_columns = selected_columns.select_columns(&available_columns)?; + + let fields = final_columns .iter() .map(Into::<Field>::into) .collect::<Vec<_>>();	use_columns is ignored when sheets are loaded eagerly ``` In [7]: reader.load_sheet_eager(0, use_columns=['Year']) Out[7]: pyarrow.RecordBatch Month: double not null Year: double not null ---- Month: [1,2] Year: [2019,2020] ```		2024-07-19T15:36:27	[]	[]
ToucanToco/fastexcel	ToucanToco__fastexcel-251	5c7c6084e536dca60f0588077d8b769159699525	diff --git a/src/types/dtype.rs b/src/types/dtype.rs index e264949..9af844a 100644 --- a/src/types/dtype.rs +++ b/src/types/dtype.rs @@ -224,6 +224,7 @@ fn int_types() -> &'static HashSet<DType> { fn string_types() -> &'static HashSet<DType> { STRING_TYPES_CELL.get_or_init(\|\| { HashSet::from([ + DType::Bool, DType::Int, DType::Float, DType::String, diff --git a/src/types/python/excelsheet/sheet_data.rs b/src/types/python/excelsheet/sheet_data.rs index 5a4b337..88b68d2 100644 --- a/src/types/python/excelsheet/sheet_data.rs +++ b/src/types/python/excelsheet/sheet_data.rs @@ -134,6 +134,8 @@ mod array_impls { .map(\|dt\| dt.to_string()) } else if cell.is_datetime_iso() { cell.get_datetime_iso().map(str::to_string) + } else if cell.is_bool() { + cell.get_bool().map(\|v\| v.to_string()) } else { cell.as_string() }	When coercing columns to strings, boolean cells turn into null In our project, we have Excel files with mixed data types. Because of this, we need to coerce all columns to strings, and interpret the data downstream. We're currently blocked from adopting `fastexcel` because it does not coerce booleans as expected. ## How to reproduce Suppose you have an Excel file with the following data: \| Header \| \|--------\| \| `=TRUE` \| \| `=FALSE` \| \| `"some string"` \| Now lets read this Excel file into a Polars dataframe, while coercing the column to strings: `excel_reader.load_sheet(0, header_row=1, dtypes={"Header": "string"}).to_polars()` This produces the following data frame: \| Header \| \|--------\| \| `null` \| \| `null` \| \| `"some string"` \| ## Expected behavior What I would have expected fastexcel to coerce the boolean values to `"0"` and `"1"` instead of losing them. That is: \| Header \| \|--------\| \| `"1"` \| \| `"0"` \| \| `"some string"` \| ## Test case I've cloned the fastexcel repo and wrote a new unit test for this, which currently fails. Excel sheet: [sheet-bool.xlsx](https://github.com/user-attachments/files/16260997/sheet-bool.xlsx) ```python def test_bool_casting_to_string_for_polars() -> None: excel_reader = fastexcel.read_excel(path_for_fixture("sheet-bool.xlsx")) actual_polars_df = excel_reader.load_sheet( 0, header_row=None, dtypes={0: "string"}, column_names=["0"] ).to_polars() expected_polars_df = pl.DataFrame( { "0": ["1", "0", "some string"], } ) pl_assert_frame_equal(actual_polars_df, expected_polars_df) ``` Unfortunately, I'm not experienced with Rust, so I did not yet figure out where/how to make the change in the Rust code to make the test pass. ## Closing words First of all, big thanks for building `fastexcel`! I'm eager to migrate to fastexcel due to its ability to directly output Polars, which our downstream pipelines are based on, which will give us a massive performance boost. It would be much appreciated if you either gave me some pointers on where/how to make this change in Rust code (so I can open a PR), or made the fix yourself.	Hi @severinh and thanks for your kind words I'm on my phone right now so hard to help more but I would start with [this PR](https://github.com/ToucanToco/fastexcel/pull/245) You probably want to update `STRING_TYPES_CELL` to support booleans If that doesn't work we can make a quick fix soon. I'm planning on working on fastexcel this weekend and release a new version v0.11.0 soon, mostly for @lukapeschke amazing work on https://github.com/ToucanToco/fastexcel/pull/147 Btw @lukapeschke and myself tend to prefer the `"true"` / `"false"` representation (the classic `to_string()` for a boolean) Would that be ok for you @severinh ? I'll let you work on it if you want. If you prefer I can open a fix at lunch. You tell me @PrettyWood Thanks for the quick response!. > Btw @lukapeschke and myself tend to prefer the "true" / "false" representation (the classic to_string() for a boolean) Would that be ok for you @severinh ? Sure! Makes sense to be more explicit. The main thing I care about is that we don't lose legitimate data. > You probably want to update STRING_TYPES_CELL to support booleans Tried that, and unfortunately that did not seem to be enough yet. The output was still `null`. Since I won't have time this afternoon to look further into it, I would appreciate you going ahead and making the change. PS: Another problem we ran into: A cell like `=DATE(2024, 7, 17)` is coerced into `'2024-07-01 00:00:00'`. I would have expected `'2024-07-01'` for dates. For now, we're trimming `' 00:00:00'` in post-processing, which is not ideal. Is this behavior expected and intentional? If not, I can file a separate ticket for this.	2024-07-17T12:57:32	[]	[]
ToucanToco/fastexcel	ToucanToco__fastexcel-243	b93636ee1aa8674d132b9d0a97c31162dd9cb41b	diff --git a/python/fastexcel/__init__.py b/python/fastexcel/__init__.py index 60415cd..6529195 100644 --- a/python/fastexcel/__init__.py +++ b/python/fastexcel/__init__.py @@ -1,7 +1,7 @@ from __future__ import annotations import sys -from typing import TYPE_CHECKING, Literal +from typing import TYPE_CHECKING, Callable, Literal if sys.version_info < (3, 10): from typing_extensions import TypeAlias @@ -128,7 +128,7 @@ def load_sheet( skip_rows: int = 0, n_rows: int \| None = None, schema_sample_rows: int \| None = 1_000, - use_columns: list[str] \| list[int] \| str \| None = None, + use_columns: list[str] \| list[int] \| str \| Callable[[ColumnInfo], bool] \| None = None, dtypes: DTypeMap \| None = None, ) -> ExcelSheet: """Loads a sheet lazily by index or name. @@ -153,6 +153,8 @@ def load_sheet( - A string, a comma separated list of Excel column letters and column ranges (e.g. `“A:E”` or `“A,C,E:F”`, which would result in `A,B,C,D,E` and `A,C,E,F`) + - A callable, a function that takes a column and returns a boolean + indicating whether the column should be used :param dtypes: An optional dict of dtypes. Keys can be column indices or names """ return ExcelSheet( @@ -209,7 +211,7 @@ def load_sheet_by_name( skip_rows: int = 0, n_rows: int \| None = None, schema_sample_rows: int \| None = 1_000, - use_columns: list[str] \| list[int] \| str \| None = None, + use_columns: list[str] \| list[int] \| str \| Callable[[ColumnInfo], bool] \| None = None, dtypes: DTypeMap \| None = None, ) -> ExcelSheet: """Loads a sheet by name. @@ -236,7 +238,7 @@ def load_sheet_by_idx( skip_rows: int = 0, n_rows: int \| None = None, schema_sample_rows: int \| None = 1_000, - use_columns: list[str] \| list[int] \| str \| None = None, + use_columns: list[str] \| list[int] \| str \| Callable[[ColumnInfo], bool] \| None = None, dtypes: DTypeMap \| None = None, ) -> ExcelSheet: """Loads a sheet by index. diff --git a/python/fastexcel/_fastexcel.pyi b/python/fastexcel/_fastexcel.pyi index 59e892e..9efe012 100644 --- a/python/fastexcel/_fastexcel.pyi +++ b/python/fastexcel/_fastexcel.pyi @@ -1,7 +1,7 @@ from __future__ import annotations import typing -from typing import Literal +from typing import Callable, Literal import pyarrow as pa @@ -72,7 +72,7 @@ class _ExcelReader: skip_rows: int = 0, n_rows: int \| None = None, schema_sample_rows: int \| None = 1_000, - use_columns: list[str] \| list[int] \| str \| None = None, + use_columns: list[str] \| list[int] \| str \| Callable[[ColumnInfo], bool] \| None = None, dtypes: DTypeMap \| None = None, eager: Literal[False] = ..., ) -> _ExcelSheet: ... diff --git a/src/types/python/excelreader.rs b/src/types/python/excelreader.rs index 6089750..c1468b8 100644 --- a/src/types/python/excelreader.rs +++ b/src/types/python/excelreader.rs @@ -84,7 +84,7 @@ impl ExcelReader { fn build_selected_columns( use_columns: Option<&Bound<'_, PyAny>>, ) -> FastExcelResult<SelectedColumns> { - use_columns.try_into().with_context(\|\| format!("expected selected columns to be list[str] \| list[int] \| str \| None, got {use_columns:?}")) + use_columns.try_into().with_context(\|\| format!("expected selected columns to be list[str] \| list[int] \| str \| Callable[[ColumnInfo], bool] \| None, got {use_columns:?}")) } // NOTE: Not implementing TryFrom here, because we're aren't building the file from the passed diff --git a/src/types/python/excelsheet/mod.rs b/src/types/python/excelsheet/mod.rs index 543c5a1..5179d99 100644 --- a/src/types/python/excelsheet/mod.rs +++ b/src/types/python/excelsheet/mod.rs @@ -114,27 +114,53 @@ impl TryFrom<&Bound<'_, PyList>> for SelectedColumns { } } -#[derive(Debug, PartialEq)] pub(crate) enum SelectedColumns { All, Selection(Vec<IdxOrName>), + DynamicSelection(PyObject), +} + +impl std::fmt::Debug for SelectedColumns { + fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { + match self { + Self::All => write!(f, "All"), + Self::Selection(selection) => write!(f, "Selection({selection:?})"), + Self::DynamicSelection(func) => { + let addr = func as *const _ as usize; + write!(f, "DynamicSelection({addr})") + } + } + } +} + +impl PartialEq for SelectedColumns { + fn eq(&self, other: &Self) -> bool { + match (self, other) { + (Self::All, Self::All) => true, + (Self::Selection(selection), Self::Selection(other_selection)) => { + selection == other_selection + } + (Self::DynamicSelection(f1), Self::DynamicSelection(f2)) => std::ptr::eq(f1, f2), + _ => false, + } + } } impl SelectedColumns { pub(super) fn select_columns( &self, - column_info: &[ColumnInfo], + available_columns: &[ColumnInfo], ) -> FastExcelResult<Vec<ColumnInfo>> { match self { - SelectedColumns::All => Ok(column_info.to_vec()), + SelectedColumns::All => Ok(available_columns.to_vec()), SelectedColumns::Selection(selection) => selection .iter() .map(\|selected_column\| { match selected_column { - IdxOrName::Idx(index) => column_info + IdxOrName::Idx(index) => available_columns .iter() .find(\|col_info\| &col_info.index() == index), - IdxOrName::Name(name) => column_info + IdxOrName::Name(name) => available_columns .iter() .find(\|col_info\| col_info.name() == name.as_str()), } @@ -142,9 +168,28 @@ impl SelectedColumns { FastExcelErrorKind::ColumnNotFound(selected_column.clone()).into() }) .cloned() - .with_context(\|\| format!("available columns are: {column_info:?}")) + .with_context(\|\| format!("available columns are: {available_columns:?}")) }) .collect(), + SelectedColumns::DynamicSelection(use_col_func) => Python::with_gil(\|py\| { + Ok(available_columns + .iter() + .filter_map( + \|col_info\| match use_col_func.call1(py, (col_info.clone(),)) { + Err(err) => Some(Err(FastExcelErrorKind::InvalidParameters(format!( + "`use_columns` callable could not be called ({err})" + )))), + Ok(should_use_col) => match should_use_col.extract::<bool>(py) { + Err(_) => Some(Err(FastExcelErrorKind::InvalidParameters( + "`use_columns` callable should return a boolean".to_string(), + ))), + Ok(true) => Some(Ok(col_info.clone())), + Ok(false) => None, + }, + }, + ) + .collect::<Result<Vec<_>, _>>()?) + }), } } @@ -272,6 +317,8 @@ impl TryFrom<Option<&Bound<'_, PyAny>>> for SelectedColumns { .parse() } else if let Ok(py_list) = py_any.downcast::<PyList>() { py_list.try_into() + } else if let Ok(py_function) = py_any.extract::<PyObject>() { + Ok(Self::DynamicSelection(py_function)) } else { Err(FastExcelErrorKind::InvalidParameters(format!( "unsupported object type {object_type}",	feat: support callable in use_columns parameter Hi, Is it possible to support a callable in the `use_columns` parameter like in `pandas.read_excel`. ```python def load_sheet_by_name( self, name: str, *, header_row: int \| None = 0, column_names: list[str] \| None = None, skip_rows: int = 0, n_rows: int \| None = None, schema_sample_rows: int \| None = 1000, use_columns: list[str] \| list[int] \| str \| Callable[[str], bool] \| None = None, dtypes: 'DTypeMap \| None' = None ) -> ExcelSheet: ... ```		2024-06-30T15:16:41	[]	[]
ToucanToco/fastexcel	ToucanToco__fastexcel-223	21f7f222f1fac36bd56d1679ca564456e712d757	diff --git a/src/types/dtype.rs b/src/types/dtype.rs index 90e7576..fb544ba 100644 --- a/src/types/dtype.rs +++ b/src/types/dtype.rs @@ -136,7 +136,7 @@ fn get_cell_dtype(data: &Range<CalData>, row: usize, col: usize) -> FastExcelRes CalData::DurationIso(_) => Ok(DType::Duration), // Errors and nulls CalData::Error(err) => match err { - CellErrorType::NA => Ok(DType::Null), + CellErrorType::NA \| CellErrorType::Value \| CellErrorType::Null => Ok(DType::Null), _ => Err(FastExcelErrorKind::CalamineCellError(err.to_owned()).into()), }, CalData::Empty => Ok(DType::Null),	Cannot handle special symbols Hi team, I am facing one issue on reading excel sheet through Polars. It says calamine cell error: #VALUE! The sheet in interest does not get read through standard api. How to handle such special symbols through fastexcel? Thank you. I am using fastexcel==0.10.2.	Hi @durgeksh could you please provide the entire stack trace ? and maybe a file allowing to reproduce the issue ? thanks! ```py Traceback (most recent call last): File "/Users/neo/Desktop/workspace/pocs/polarsdemo.py", line 84, in <module> df = pl.read_excel("sample_data.xlsx", engine='calamine') ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/neo/Desktop/workspace/pocs/.venv/lib/python3.11/site-packages/polars/_utils/deprecation.py", line 134, in wrapper return function(args, kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/neo/Desktop/workspace/pocs/.venv/lib/python3.11/site-packages/polars/_utils/deprecation.py", line 134, in wrapper return function(args, kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/neo/Desktop/workspace/pocs/.venv/lib/python3.11/site-packages/polars/io/spreadsheet/functions.py", line 253, in read_excel return _read_spreadsheet( ^^^^^^^^^^^^^^^^^^ File "/Users/neo/Desktop/workspace/pocs/.venv/lib/python3.11/site-packages/polars/io/spreadsheet/functions.py", line 475, in _read_spreadsheet parsed_sheets = { ^ File "/Users/neo/Desktop/workspace/pocs/.venv/lib/python3.11/site-packages/polars/io/spreadsheet/functions.py", line 476, in <dictcomp> name: reader_fn( ^^^^^^^^^^ File "/Users/neo/Desktop/workspace/pocs/.venv/lib/python3.11/site-packages/polars/io/spreadsheet/functions.py", line 821, in _read_spreadsheet_calamine ws = parser.load_sheet_by_name(sheet_name, read_options) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/neo/Desktop/workspace/pocs/.venv/lib/python3.11/site-packages/fastexcel/__init__.py", line 184, in load_sheet_by_name self._reader.load_sheet( _fastexcel.CalamineCellError: calamine cell error: #VALUE! Context: 0: could not determine dtype for column Amount Process finished with exit code 1 ``` Sample file: [sample_data.xlsx](https://github.com/ToucanToco/fastexcel/files/14880107/sample_data.xlsx)	2024-04-05T08:28:02	[]	[]
ToucanToco/fastexcel	ToucanToco__fastexcel-222	8060efac0d338224e12cf3af89b4802a503e7e06	diff --git a/src/types/python/excelreader.rs b/src/types/python/excelreader.rs index 8de50de..e295e6c 100644 --- a/src/types/python/excelreader.rs +++ b/src/types/python/excelreader.rs @@ -144,14 +144,25 @@ impl ExcelReader { let name = idx_or_name .try_into() .and_then(\|idx_or_name\| match idx_or_name { - IdxOrName::Name(name) => Ok(name), + IdxOrName::Name(name) => { + if self.sheet_names.contains(&name) { + Ok(name) + } else { + Err(FastExcelErrorKind::SheetNotFound(IdxOrName::Name(name.clone())).into()).with_context(\|\| { + let available_sheets = self.sheet_names.iter().map(\|s\| format!("\"{s}\"")).collect::<Vec<_>>().join(", "); + format!( + "Sheet \"{name}\" not found in file. Available sheets: {available_sheets}." + ) + }) + } + } IdxOrName::Idx(idx) => self .sheet_names .get(idx) .ok_or_else(\|\| FastExcelErrorKind::SheetNotFound(IdxOrName::Idx(idx)).into()) .with_context(\|\| { format!( - "Sheet index {idx} is out of range. File has {} sheets", + "Sheet index {idx} is out of range. File has {} sheets.", self.sheet_names.len() ) })	load_sheet_by_name raises generic CalamineError instead of SheetNotFoundError Hi, While loading a sheet from a reader, if a missing sheet name is provided a generic `CalamineError` is raised instead of the more specific `SheetNotFoundError`. My use case is: ```python sheets = [] for reader in readers: try: sheet = reader.load_sheet_by_name("sheet_name") except SheetNotFoundError: continue # Do some other stuff with sheet before appending to list. ```		2024-04-05T07:32:41	[]	[]
ToucanToco/fastexcel	ToucanToco__fastexcel-217	026f2731211b697162fa1b8ff2d31054979c8162	diff --git a/src/types/python/excelsheet/column_info.rs b/src/types/python/excelsheet/column_info.rs index d26882e..8e502ac 100644 --- a/src/types/python/excelsheet/column_info.rs +++ b/src/types/python/excelsheet/column_info.rs @@ -182,7 +182,7 @@ impl ColumnInfo { } pub fn __repr__(&self) -> String { - format!("ColumnInfo<name=\"{name}\", index={index}, dtype=\"{dtype}\", dtype_from=\"{dtype_from}\", column_name_from=\"{column_name_from}\" >", name=self.name, index=self.index, dtype=self.dtype.to_string(), dtype_from=self.dtype_from.to_string(), column_name_from=self.column_name_from.to_string()) + format!("ColumnInfo(name=\"{name}\", index={index}, dtype=\"{dtype}\", dtype_from=\"{dtype_from}\", column_name_from=\"{column_name_from}\" )", name=self.name, index=self.index, dtype=self.dtype.to_string(), dtype_from=self.dtype_from.to_string(), column_name_from=self.column_name_from.to_string()) } pub fn __eq__(&self, other: &Self) -> bool { diff --git a/src/types/python/excelsheet/mod.rs b/src/types/python/excelsheet/mod.rs index 4351935..4792891 100644 --- a/src/types/python/excelsheet/mod.rs +++ b/src/types/python/excelsheet/mod.rs @@ -326,9 +326,6 @@ impl ExcelSheet { selected_columns: SelectedColumns, dtypes: Option<DTypeMap>, ) -> FastExcelResult<Self> { - // Ensuring dtypes are compatible with selected columns - // Self::validate_dtypes_and_selected_columns(&selected_columns, &dtypes)?; - let mut sheet = ExcelSheet { name, header, @@ -344,7 +341,7 @@ impl ExcelSheet { selected_columns: Vec::with_capacity(0), }; - let available_columns_info = sheet.get_available_columns_info(); + let available_columns_info = sheet.get_available_columns_info(&selected_columns)?; let mut aliased_available_columns = Vec::with_capacity(available_columns_info.len()); @@ -379,10 +376,13 @@ impl ExcelSheet { Ok(sheet) } - fn get_available_columns_info(&self) -> Vec<ColumnInfoBuilder> { + fn get_available_columns_info( + &self, + selected_columns: &SelectedColumns, + ) -> FastExcelResult<Vec<ColumnInfoBuilder>> { let width = self.data.width(); match &self.header { - Header::None => (0..width) + Header::None => Ok((0..width) .map(\|col_idx\| { ColumnInfoBuilder::new( format!("__UNNAMED__{col_idx}"), @@ -390,8 +390,8 @@ impl ExcelSheet { ColumnNameFrom::Generated, ) }) - .collect(), - Header::At(row_idx) => (0..width) + .collect()), + Header::At(row_idx) => Ok((0..width) .map(\|col_idx\| { self.data .get((row_idx, col_idx)) @@ -407,23 +407,73 @@ impl ExcelSheet { ) }) }) - .collect(), + .collect()), Header::With(names) => { - let nameless_start_idx = names.len(); - names - .iter() - .enumerate() - .map(\|(col_idx, name)\| { - ColumnInfoBuilder::new(name.to_owned(), col_idx, ColumnNameFrom::Provided) - }) - .chain((nameless_start_idx..width).map(\|col_idx\| { - ColumnInfoBuilder::new( - format!("__UNNAMED__{col_idx}"), - col_idx, - ColumnNameFrom::Generated, + if let SelectedColumns::Selection(column_selection) = selected_columns { + if column_selection.len() != names.len() { + return Err(FastExcelErrorKind::InvalidParameters( + "column_names and use_columns must have the same length".to_string(), ) - })) - .collect() + .into()); + } + let selected_indices = column_selection + .iter() + .map(\|idx_or_name\| { + match idx_or_name { + IdxOrName::Idx(idx) => Ok(idx), + IdxOrName::Name(name) => Err(FastExcelErrorKind::InvalidParameters( + format!("use_columns can only contain integers when used with columns_names, got \"{name}\"") + ) + .into()), + } + }) + .collect::<FastExcelResult<Vec<_>>>()?; + + Ok((0..width) + .map(\|col_idx\| { + let provided_name_opt = if let Some(pos_in_names) = + selected_indices.iter().position(\|idx\| idx == &col_idx) + { + names.get(pos_in_names).cloned() + } else { + None + }; + + match provided_name_opt { + Some(provided_name) => ColumnInfoBuilder::new( + provided_name, + col_idx, + ColumnNameFrom::Provided, + ), + None => ColumnInfoBuilder::new( + format!("__UNNAMED__{col_idx}"), + col_idx, + ColumnNameFrom::Generated, + ), + } + }) + .collect()) + } else { + let nameless_start_idx = names.len(); + Ok(names + .iter() + .enumerate() + .map(\|(col_idx, name)\| { + ColumnInfoBuilder::new( + name.to_owned(), + col_idx, + ColumnNameFrom::Provided, + ) + }) + .chain((nameless_start_idx..width).map(\|col_idx\| { + ColumnInfoBuilder::new( + format!("__UNNAMED__{col_idx}"), + col_idx, + ColumnNameFrom::Generated, + ) + })) + .collect()) + } } } }	`column_names` are taken partially when `use_columns` does not include all columns Given this worksheet data (without any empty area): \| A \| B \| C \| \| --- \| --- \| --- \| \| 21 \| 22 \| 23 \| \| 31 \| 32 \| 33 \| \| 41 \| 42 \| 43 \| The following code: ```python import fastexcel as fe file = r'<path to file>' params = {'idx_or_name': 0, 'header_row': None, 'skip_rows': 1, 'use_columns': [1, 2], 'column_names': ['Col B', 'Col C']} print(fe.read_excel(file).load_sheet(**params).to_polars()) ``` Outputs: ```python shape: (3, 2) ┌───────┬──────────────┐ │ Col C ┆ __UNNAMED__2 │ │ --- ┆ --- │ │ f64 ┆ f64 │ ╞═══════╪══════════════╡ │ 22.0 ┆ 23.0 │ │ 32.0 ┆ 33.0 │ │ 42.0 ┆ 43.0 │ └───────┴──────────────┘ ``` Expected output: ```python shape: (3, 2) ┌───────┬───────┐ │ Col B ┆ Col C │ │ --- ┆ --- │ │ f64 ┆ f64 │ ╞═══════╪═══════╡ │ 22.0 ┆ 23.0 │ │ 32.0 ┆ 33.0 │ │ 42.0 ┆ 43.0 │ └───────┴───────┘ ```	Hi @wikiped this is actually the expected behaviour: `column_names` is used to specify the name of the columns in the resulting DataFrame. Since there are three columns and only two names were provided, the third name is generated based on the column index.: https://fastexcel.toucantoco.dev/fastexcel.html#ExcelReader.load_sheet Achieving the result you want is possible with the `use_columns` parameter: ```py import fastexcel as fe file = 'repro_214.xlsx' params = {'idx_or_name': 0, 'use_columns': ['B', 'C']} print(fe.read_excel(file).load_sheet(params).to_polars()) ``` Will print ``` shape: (3, 2) ┌──────┬──────┐ │ B ┆ C │ │ --- ┆ --- │ │ f64 ┆ f64 │ ╞══════╪══════╡ │ 22.0 ┆ 23.0 │ │ 32.0 ┆ 33.0 │ │ 42.0 ┆ 43.0 │ └──────┴──────┘ ``` However there is indeed a bug when mixing `use_columns` and `column_names`, so thanks for the report! ```py import fastexcel as fe file = 'repro_214.xlsx' params = {'idx_or_name': 0, 'header_row': None, 'skip_rows': 1, 'column_names': ['Col B', 'Col C'], 'use_columns': [1, 2]} print(fe.read_excel(file).load_sheet(params).to_polars()) ``` Returns ``` shape: (3, 2) ┌───────┬──────────────┐ │ Col C ┆ __UNNAMED__2 │ │ --- ┆ --- │ │ f64 ┆ f64 │ ╞═══════╪══════════════╡ │ 22.0 ┆ 23.0 │ │ 32.0 ┆ 33.0 │ │ 42.0 ┆ 43.0 │ └───────┴──────────────┘ ``` And the expected result would be ``` shape: (3, 2) ┌───────┬───────┐ │ Col B ┆ Col C │ │ --- ┆ --- │ │ f64 ┆ f64 │ ╞═══════╪═══════╡ │ 22.0 ┆ 23.0 │ │ 32.0 ┆ 33.0 │ │ 42.0 ┆ 43.0 │ └───────┴───────┘ ``` [repro_214.xlsx](https://github.com/ToucanToco/fastexcel/files/14786952/repro_214.xlsx) Thank you for the feedback, I realized now that I forgot to paste the significant part of the parameters used: `'use_columns': [1, 2]`, which is, as you noted, part of the reason for the raising the issue.	2024-03-29T17:00:41	[]	[]
ToucanToco/fastexcel	ToucanToco__fastexcel-192	fb3a5828823c261e925ee2f8adb2d9403cd6aad1	diff --git a/.gitignore b/.gitignore index 9ed43c0..3a99b8d 100644 --- a/.gitignore +++ b/.gitignore @@ -9,5 +9,6 @@ __pycache__ .python-version .venv docs +.vscode .idea .benchmarks diff --git a/python/fastexcel/__init__.py b/python/fastexcel/__init__.py index 4c519a4..fc65be3 100644 --- a/python/fastexcel/__init__.py +++ b/python/fastexcel/__init__.py @@ -3,12 +3,11 @@ from typing import TYPE_CHECKING if TYPE_CHECKING: - from pathlib import Path - import pandas as pd import polars as pl from os.path import expanduser +from pathlib import Path import pyarrow as pa @@ -238,12 +237,14 @@ def __repr__(self) -> str: return self._reader.__repr__() -def read_excel(path: Path \| str) -> ExcelReader: +def read_excel(source: Path \| str \| bytes) -> ExcelReader: """Opens and loads an excel file. - :param path: The path to the file + :param source: The path to a file or its content as bytes """ - return ExcelReader(_read_excel(expanduser(path))) + if isinstance(source, (str, Path)): + source = expanduser(source) + return ExcelReader(_read_excel(source)) __all__ = ( diff --git a/python/fastexcel/_fastexcel.pyi b/python/fastexcel/_fastexcel.pyi index 01fd90d..0e27e59 100644 --- a/python/fastexcel/_fastexcel.pyi +++ b/python/fastexcel/_fastexcel.pyi @@ -55,7 +55,7 @@ class _ExcelReader: @property def sheet_names(self) -> list[str]: ... -def read_excel(path: str) -> _ExcelReader: +def read_excel(source: str \| bytes) -> _ExcelReader: """Reads an excel file and returns an ExcelReader""" __version__: str diff --git a/src/lib.rs b/src/lib.rs index 1537816..a6b3686 100644 --- a/src/lib.rs +++ b/src/lib.rs @@ -8,12 +8,22 @@ use types::{ExcelReader, ExcelSheet}; /// Reads an excel file and returns an object allowing to access its sheets and a bit of metadata #[pyfunction] -fn read_excel(path: &str) -> PyResult<ExcelReader> { +fn read_excel(source: &PyAny) -> PyResult<ExcelReader> { use py_errors::IntoPyResult; - ExcelReader::try_from_path(path) - .with_context(\|\| format!("could not load excel file at {path}")) - .into_pyresult() + if let Ok(path) = source.extract::<&str>() { + ExcelReader::try_from_path(path) + .with_context(\|\| format!("could not load excel file at {path}")) + .into_pyresult() + } else if let Ok(bytes) = source.extract::<&[u8]>() { + ExcelReader::try_from(bytes) + .with_context(\|\| "could not load excel file for those bytes") + .into_pyresult() + } else { + Err(py_errors::InvalidParametersError::new_err( + "source must be a string or bytes", + )) + } } // Taken from pydantic-core: diff --git a/src/types/excelreader.rs b/src/types/excelreader.rs index 6a15ce5..d1c6f20 100644 --- a/src/types/excelreader.rs +++ b/src/types/excelreader.rs @@ -1,10 +1,16 @@ -use std::{fs::File, io::BufReader}; +use std::{ + fs::File, + io::{BufReader, Cursor}, +}; -use calamine::{open_workbook_auto, Reader, Sheets}; +use calamine::{ + open_workbook_auto, open_workbook_auto_from_rs, Data, Error, Range, Reader, Sheets, +}; use pyo3::{pyclass, pymethods, PyAny, PyResult}; use crate::error::{ - py_errors::IntoPyResult, ErrorContext, FastExcelErrorKind, FastExcelResult, IdxOrName, + py_errors::IntoPyResult, ErrorContext, FastExcelError, FastExcelErrorKind, FastExcelResult, + IdxOrName, }; use super::{ @@ -12,12 +18,41 @@ use super::{ ExcelSheet, }; +enum ExcelSheets { + File(Sheets<BufReader<File>>), + Bytes(Sheets<Cursor<Vec<u8>>>), +} + +impl ExcelSheets { + fn worksheet_range(&mut self, name: &str) -> Result<Range<Data>, Error> { + match self { + Self::File(sheets) => sheets.worksheet_range(name), + Self::Bytes(sheets) => sheets.worksheet_range(name), + } + } + + fn worksheet_range_at(&mut self, idx: usize) -> Option<Result<Range<Data>, Error>> { + match self { + Self::File(sheets) => sheets.worksheet_range_at(idx), + Self::Bytes(sheets) => sheets.worksheet_range_at(idx), + } + } + + #[allow(dead_code)] + fn sheet_names(&self) -> Vec<String> { + match self { + Self::File(sheets) => sheets.sheet_names(), + Self::Bytes(sheets) => sheets.sheet_names(), + } + } +} + #[pyclass(name = "_ExcelReader")] pub(crate) struct ExcelReader { - sheets: Sheets<BufReader<File>>, + sheets: ExcelSheets, #[pyo3(get)] sheet_names: Vec<String>, - path: String, + source: String, } impl ExcelReader { @@ -29,9 +64,26 @@ impl ExcelReader { .with_context(\|\| format!("Could not open workbook at {path}"))?; let sheet_names = sheets.sheet_names().to_owned(); Ok(Self { - sheets, + sheets: ExcelSheets::File(sheets), + sheet_names, + source: path.to_owned(), + }) + } +} + +impl TryFrom<&[u8]> for ExcelReader { + type Error = FastExcelError; + + fn try_from(bytes: &[u8]) -> Result<Self, Self::Error> { + let cursor = Cursor::new(bytes.to_vec()); + let sheets = open_workbook_auto_from_rs(cursor) + .map_err(\|err\| FastExcelErrorKind::CalamineError(err).into()) + .with_context(\|\| "Could not open workbook from bytes")?; + let sheet_names = sheets.sheet_names().to_owned(); + Ok(Self { + sheets: ExcelSheets::Bytes(sheets), sheet_names, - path: path.to_owned(), + source: "bytes".to_owned(), }) } } @@ -39,7 +91,7 @@ impl ExcelReader { #[pymethods] impl ExcelReader { pub fn __repr__(&self) -> String { - format!("ExcelReader<{}>", &self.path) + format!("ExcelReader<{}>", &self.source) } #[pyo3(signature = (	Accept `bytes` rather than a file path as input format There are contexts where this would be nice to have, such as when fetching a file over the network. It would also allow a tighter integration with polars: https://github.com/pola-rs/polars/pull/14000#discussion_r1467389386		2024-02-28T00:40:19	[]	[]
ToucanToco/fastexcel	ToucanToco__fastexcel-189	6d76a1958ef98a7096916292b95f79298627c2f3	diff --git a/.github/workflows/CI.yml b/.github/workflows/CI.yml index 732fdc2..f937e1f 100644 --- a/.github/workflows/CI.yml +++ b/.github/workflows/CI.yml @@ -46,6 +46,34 @@ jobs: source .venv/bin/activate make lint + check-docs: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: "3.11" + - name: Set up rust toolchain + uses: actions-rs/toolchain@v1 + with: + profile: minimal + toolchain: stable + override: true + - run: \| + git config user.name github-actions + git config user.email [email protected] + + # venv required by maturin + python3 -m venv .venv + source .venv/bin/activate + + make install-test-requirements + make install-doc-requirements + # Required for pdoc to be able to import the sources + make dev-install + make doc + # GitHub provides only x86_64 runners, so we cannot test on arm architecture test: runs-on: ${{ matrix.os }} @@ -110,31 +138,3 @@ jobs: command: build args: "-o dist --interpreter python${{ matrix.python-version }}" target: ${{ steps.target.outputs.target }} - - check-docs: - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v4 - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: "3.11" - - name: Set up rust toolchain - uses: actions-rs/toolchain@v1 - with: - profile: minimal - toolchain: stable - override: true - - run: \| - git config user.name github-actions - git config user.email [email protected] - - # venv required by maturin - python3 -m venv .venv - source .venv/bin/activate - - make install-test-requirements - make install-doc-requirements - # Required for pdoc to be able to import the sources - make dev-install - make doc diff --git a/Cargo.lock b/Cargo.lock index 8c44037..e7c0053 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -344,6 +344,12 @@ version = "0.2.2" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "7a81dae078cea95a014a339291cec439d2f232ebe854a9d672b796c6afafa9b7" +[[package]] +name = "diff" +version = "0.1.13" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "56254986775e3233ffa9c4d7d3faaf6d36a2c09d30b20687e9f88bc8bafc16c8" + [[package]] name = "encoding_rs" version = "0.8.31" @@ -360,6 +366,7 @@ dependencies = [ "arrow", "calamine", "chrono", + "pretty_assertions", "pyo3", "rstest", ] @@ -678,6 +685,16 @@ version = "1.6.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "7170ef9988bc169ba16dd36a7fa041e5c4cbeb6a35b76d4c03daded371eae7c0" +[[package]] +name = "pretty_assertions" +version = "1.4.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "af7cee1a6c8a5b9208b3cb1061f10c0cb689087b3d8ce85fb9d2dd7a29b6ba66" +dependencies = [ + "diff", + "yansi", +] + [[package]] name = "proc-macro-hack" version = "0.5.19" @@ -1122,6 +1139,12 @@ version = "0.52.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "dff9641d1cd4be8d1a070daf9e3773c5f67e78b4d9d42263020c057706765c04" +[[package]] +name = "yansi" +version = "0.5.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "09041cd90cf85f7f8b2df60c646f853b7f535ce68f85244eb6731cf89fa498ec" + [[package]] name = "zip" version = "0.6.3" diff --git a/Cargo.toml b/Cargo.toml index c62376d..1210ccf 100644 --- a/Cargo.toml +++ b/Cargo.toml @@ -11,7 +11,8 @@ crate-type = ["cdylib"] [dependencies] calamine = { version = "0.24.0", features = ["dates"] } chrono = { version = "0.4.34", default-features = false } -pyo3 = { version = "0.20.3", features = ["extension-module", "abi3-py38"] } +# NOTE: "extension-module" is actually required, see comments on features below +pyo3 = { version = "0.20.3", features = ["abi3-py38"] } [dependencies.arrow] version = "50.0.0" @@ -20,4 +21,14 @@ default-features = false features = ["pyarrow"] [dev-dependencies] +pretty_assertions = "1.4.0" rstest = { version = "0.18.2", default-features = false } + +# NOTE: This is a hack to bypass pyo3 limitations when testing: +# https://pyo3.rs/v0.20.3/faq.html#i-cant-run-cargo-test-or-i-cant-build-in-a-cargo-workspace-im-having-linker-issues-like-symbol-not-found-or-undefined-reference-to-_pyexc_systemerror +[features] +extension-module = ["pyo3/extension-module"] +default = ["extension-module"] +# feature for tests only. This makes Python::with_gil auto-initialize Python +# interpreters, which allows us to instantiate Python objects in tests +tests = ["pyo3/auto-initialize"] diff --git a/Makefile b/Makefile index d4e2ee7..f7c29fe 100644 --- a/Makefile +++ b/Makefile @@ -9,21 +9,30 @@ pytest = pytest -v ## Rust clippy = cargo clippy fmt = cargo fmt -cargo-test = cargo test +cargo-test = cargo test --no-default-features --features tests ## Docs pdoc = pdoc -o docs python/fastexcel -lint: +lint-python: $(ruff) $(format) --check --diff $(mypy) + +lint-rust: $(clippy) -format: + +lint: lint-rust lint-python + +format-python: $(ruff) --fix $(format) + +format-rust: $(fmt) $(clippy) --fix --lib -p fastexcel --allow-dirty --allow-staged +format: format-rust format-python + install-test-requirements: pip install -U -r test-requirements.txt -r build-requirements.txt @@ -39,10 +48,14 @@ dev-install: prod-install: ./prod_install.sh -test: +test-rust: $(cargo-test) + +test-python: $(pytest) +test: test-rust test-python + doc: $(pdoc) diff --git a/python/fastexcel/__init__.py b/python/fastexcel/__init__.py index fb00d9b..b57f8f4 100644 --- a/python/fastexcel/__init__.py +++ b/python/fastexcel/__init__.py @@ -17,6 +17,7 @@ CalamineCellError, CalamineError, CannotRetrieveCellDataError, + ColumnNotFoundError, FastExcelError, InvalidParametersError, SheetNotFoundError, @@ -54,6 +55,16 @@ def total_height(self) -> int: """The sheet's total height""" return self._sheet.total_height + @property + def selected_columns(self) -> list[str] \| list[int] \| None: + """The sheet's selected columns""" + return self._sheet.selected_columns + + @property + def available_columns(self) -> list[str]: + """The columns available for the given sheet""" + return self._sheet.available_columns + def to_arrow(self) -> pa.RecordBatch: """Converts the sheet to a pyarrow `RecordBatch`""" return self._sheet.to_arrow() @@ -101,6 +112,7 @@ def load_sheet_by_name( skip_rows: int = 0, n_rows: int \| None = None, schema_sample_rows: int \| None = 1_000, + use_columns: list[str] \| list[int] \| None = None, ) -> ExcelSheet: """Loads a sheet by name. @@ -117,6 +129,9 @@ def load_sheet_by_name( :param schema_sample_rows: Specifies how many rows should be used to determine the dtype of a column. If `None`, all rows will be used. + :param use_columns: Specifies the columns to use. Can either be a list of column names, or + a list of column indices (starting at 0). + If `None`, all columns will be used. """ return ExcelSheet( self._reader.load_sheet_by_name( @@ -126,6 +141,7 @@ def load_sheet_by_name( skip_rows=skip_rows, n_rows=n_rows, schema_sample_rows=schema_sample_rows, + use_columns=use_columns, ) ) @@ -138,6 +154,7 @@ def load_sheet_by_idx( skip_rows: int = 0, n_rows: int \| None = None, schema_sample_rows: int \| None = 1_000, + use_columns: list[str] \| list[int] \| None = None, ) -> ExcelSheet: """Loads a sheet by index. @@ -154,6 +171,9 @@ def load_sheet_by_idx( :param schema_sample_rows: Specifies how many rows should be used to determine the dtype of a column. If `None`, all rows will be used. + :param use_columns: Specifies the columns to use. Can either be a list of column names, or + a list of column indices (starting at 0). + If `None`, all columns will be used. """ if idx < 0: raise ValueError(f"Expected idx to be > 0, got {idx}") @@ -165,6 +185,7 @@ def load_sheet_by_idx( skip_rows=skip_rows, n_rows=n_rows, schema_sample_rows=schema_sample_rows, + use_columns=use_columns, ) ) @@ -177,6 +198,7 @@ def load_sheet( skip_rows: int = 0, n_rows: int \| None = None, schema_sample_rows: int \| None = 1_000, + use_columns: list[str] \| list[int] \| None = None, ) -> ExcelSheet: """Loads a sheet by name if a string is passed or by index if an integer is passed. @@ -190,6 +212,7 @@ def load_sheet( skip_rows=skip_rows, n_rows=n_rows, schema_sample_rows=schema_sample_rows, + use_columns=use_columns, ) if isinstance(idx_or_name, int) else self.load_sheet_by_name( @@ -199,6 +222,7 @@ def load_sheet( skip_rows=skip_rows, n_rows=n_rows, schema_sample_rows=schema_sample_rows, + use_columns=use_columns, ) ) @@ -224,6 +248,7 @@ def read_excel(path: Path \| str) -> ExcelReader: "CalamineCellError", "CalamineError", "SheetNotFoundError", + "ColumnNotFoundError", "ArrowError", "InvalidParametersError", "UnsupportedColumnTypeCombinationError", diff --git a/python/fastexcel/_fastexcel.pyi b/python/fastexcel/_fastexcel.pyi index 865f4e7..b4e2c36 100644 --- a/python/fastexcel/_fastexcel.pyi +++ b/python/fastexcel/_fastexcel.pyi @@ -18,6 +18,12 @@ class _ExcelSheet: @property def offset(self) -> int: """The sheet's offset before data starts""" + @property + def selected_columns(self) -> list[str] \| list[int] \| None: + """The sheet's selected columns""" + @property + def available_columns(self) -> list[str]: + """The columns available for the given sheet""" def to_arrow(self) -> pa.RecordBatch: """Converts the sheet to a pyarrow `RecordBatch`""" @@ -33,6 +39,7 @@ class _ExcelReader: skip_rows: int = 0, n_rows: int \| None = None, schema_sample_rows: int \| None = 1_000, + use_columns: list[str] \| list[int] \| None = None, ) -> _ExcelSheet: ... def load_sheet_by_idx( self, @@ -43,16 +50,7 @@ class _ExcelReader: skip_rows: int = 0, n_rows: int \| None = None, schema_sample_rows: int \| None = 1_000, - ) -> _ExcelSheet: ... - def load_sheet( - self, - idx_or_name: int \| str, - , - header_row: int \| None = 0, - column_names: list[str] \| None = None, - skip_rows: int = 0, - n_rows: int \| None = None, - schema_sample_rows: int \| None = 1_000, + use_columns: list[str] \| list[int] \| None = None, ) -> _ExcelSheet: ... @property def sheet_names(self) -> list[str]: ... @@ -69,5 +67,6 @@ class CannotRetrieveCellDataError(FastExcelError): ... class CalamineCellError(FastExcelError): ... class CalamineError(FastExcelError): ... class SheetNotFoundError(FastExcelError): ... +class ColumnNotFoundError(FastExcelError): ... class ArrowError(FastExcelError): ... class InvalidParametersError(FastExcelError): ... diff --git a/src/error.rs b/src/error.rs index 4c6de09..48e6bdb 100644 --- a/src/error.rs +++ b/src/error.rs @@ -1,20 +1,28 @@ use std::{error::Error, fmt::Display}; #[derive(Debug)] -pub(crate) enum SheetIdxOrName { +pub(crate) enum IdxOrName { Idx(usize), - // Leaving this variant if someday we want to check if a name exists before calling worksheet_range - #[allow(dead_code)] Name(String), } +impl IdxOrName { + pub(super) fn format_message(&self) -> String { + match self { + Self::Idx(idx) => format!("at index {idx}"), + Self::Name(name) => format!("with name \"{name}\""), + } + } +} + #[derive(Debug)] pub(crate) enum FastExcelErrorKind { UnsupportedColumnTypeCombination(String), CannotRetrieveCellData(usize, usize), CalamineCellError(calamine::CellErrorType), CalamineError(calamine::Error), - SheetNotFound(SheetIdxOrName), + SheetNotFound(IdxOrName), + ColumnNotFound(IdxOrName), // Arrow errors can be of several different types (arrow::error::Error, PyError), and having // the actual type has not much value for us, so we just store a string context ArrowError(String), @@ -37,14 +45,13 @@ impl Display for FastExcelErrorKind { write!(f, "calamine error: {calamine_error}") } FastExcelErrorKind::SheetNotFound(idx_or_name) => { - let message = { - match idx_or_name { - SheetIdxOrName::Idx(idx) => format!("at index {idx}"), - SheetIdxOrName::Name(name) => format!("with name \"{name}\" not found"), - } - }; + let message = idx_or_name.format_message(); write!(f, "sheet {message} not found") } + FastExcelErrorKind::ColumnNotFound(idx_or_name) => { + let message = idx_or_name.format_message(); + write!(f, "column {message} not found") + } FastExcelErrorKind::ArrowError(err) => write!(f, "arrow error: {err}"), FastExcelErrorKind::InvalidParameters(err) => write!(f, "invalid parameters: {err}"), } @@ -53,7 +60,7 @@ impl Display for FastExcelErrorKind { #[derive(Debug)] pub(crate) struct FastExcelError { - kind: FastExcelErrorKind, + pub kind: FastExcelErrorKind, context: Vec<String>, } @@ -166,6 +173,13 @@ pub(crate) mod py_errors { FastExcelError, "Sheet was not found" ); + // Sheet not found + create_exception!( + _fastexcel, + ColumnNotFoundError, + FastExcelError, + "Column was not found" + ); // Arrow error create_exception!( _fastexcel, @@ -209,6 +223,9 @@ pub(crate) mod py_errors { FastExcelErrorKind::SheetNotFound(_) => { SheetNotFoundError::new_err(message) } + FastExcelErrorKind::ColumnNotFound(_) => { + ColumnNotFoundError::new_err(message) + } FastExcelErrorKind::ArrowError(_) => ArrowError::new_err(message), FastExcelErrorKind::InvalidParameters(_) => { InvalidParametersError::new_err(message) diff --git a/src/lib.rs b/src/lib.rs index 7740754..1537816 100644 --- a/src/lib.rs +++ b/src/lib.rs @@ -55,6 +55,10 @@ fn _fastexcel(py: Python, m: &PyModule) -> PyResult<()> { "SheetNotFoundError", py.get_type::<py_errors::SheetNotFoundError>(), ), + ( + "ColumnNotFoundError", + py.get_type::<py_errors::ColumnNotFoundError>(), + ), ("ArrowError", py.get_type::<py_errors::ArrowError>()), ( "InvalidParametersError", diff --git a/src/types/excelreader.rs b/src/types/excelreader.rs index 6424db1..f198061 100644 --- a/src/types/excelreader.rs +++ b/src/types/excelreader.rs @@ -1,10 +1,10 @@ use std::{fs::File, io::BufReader}; use calamine::{open_workbook_auto, Reader, Sheets}; -use pyo3::{pyclass, pymethods, PyResult}; +use pyo3::{pyclass, pymethods, types::PyList, PyResult}; use crate::error::{ - py_errors::IntoPyResult, ErrorContext, FastExcelErrorKind, FastExcelResult, SheetIdxOrName, + py_errors::IntoPyResult, ErrorContext, FastExcelErrorKind, FastExcelResult, IdxOrName, }; use super::{ @@ -50,7 +50,9 @@ impl ExcelReader { skip_rows = 0, n_rows = None, schema_sample_rows = 1_000, + use_columns = None ))] + #[allow(clippy::too_many_arguments)] pub fn load_sheet_by_name( &mut self, name: String, @@ -59,6 +61,7 @@ impl ExcelReader { skip_rows: usize, n_rows: Option<usize>, schema_sample_rows: Option<usize>, + use_columns: Option<&PyList>, ) -> PyResult<ExcelSheet> { let range = self .sheets @@ -69,13 +72,16 @@ impl ExcelReader { let header = Header::new(header_row, column_names); let pagination = Pagination::new(skip_rows, n_rows, &range).into_pyresult()?; - Ok(ExcelSheet::new( + let selected_columns = use_columns.try_into().with_context(\|\| format!("expected selected columns to be list[str] \| list[int] \| None, got {use_columns:?}")).into_pyresult()?; + ExcelSheet::try_new( name, range, header, pagination, schema_sample_rows, - )) + selected_columns, + ) + .into_pyresult() } #[pyo3(signature = ( @@ -86,7 +92,9 @@ impl ExcelReader { skip_rows = 0, n_rows = None, schema_sample_rows = 1_000, + use_columns = None ))] + #[allow(clippy::too_many_arguments)] pub fn load_sheet_by_idx( &mut self, idx: usize, @@ -95,11 +103,12 @@ impl ExcelReader { skip_rows: usize, n_rows: Option<usize>, schema_sample_rows: Option<usize>, + use_columns: Option<&PyList>, ) -> PyResult<ExcelSheet> { let name = self .sheet_names .get(idx) - .ok_or_else(\|\| FastExcelErrorKind::SheetNotFound(SheetIdxOrName::Idx(idx)).into()) + .ok_or_else(\|\| FastExcelErrorKind::SheetNotFound(IdxOrName::Idx(idx)).into()) .with_context(\|\| { format!( "Sheet index {idx} is out of range. File has {} sheets", @@ -114,7 +123,7 @@ impl ExcelReader { .worksheet_range_at(idx) // Returns Option<Result<Range<Data>, Self::Error>>, so we convert the Option into a // SheetNotFoundError and unwrap it - .ok_or_else(\|\| FastExcelErrorKind::SheetNotFound(SheetIdxOrName::Idx(idx)).into()) + .ok_or_else(\|\| FastExcelErrorKind::SheetNotFound(IdxOrName::Idx(idx)).into()) .into_pyresult()? // And here, we convert the calamine error in an owned error and unwrap it .map_err(\|err\| FastExcelErrorKind::CalamineError(err).into()) @@ -122,12 +131,15 @@ impl ExcelReader { let header = Header::new(header_row, column_names); let pagination = Pagination::new(skip_rows, n_rows, &range).into_pyresult()?; - Ok(ExcelSheet::new( + let selected_columns = use_columns.try_into().with_context(\|\| format!("expected selected columns to be list[str] \| list[int] \| None, got {use_columns:?}")).into_pyresult()?; + ExcelSheet::try_new( name, range, header, pagination, schema_sample_rows, - )) + selected_columns, + ) + .into_pyresult() } } diff --git a/src/types/excelsheet.rs b/src/types/excelsheet.rs index d950eec..5f42dff 100644 --- a/src/types/excelsheet.rs +++ b/src/types/excelsheet.rs @@ -2,6 +2,7 @@ use std::sync::Arc; use crate::error::{ py_errors::IntoPyResult, ErrorContext, FastExcelError, FastExcelErrorKind, FastExcelResult, + IdxOrName, }; use arrow::{ @@ -18,11 +19,13 @@ use chrono::NaiveDate; use pyo3::{ prelude::{pyclass, pymethods, PyObject, Python}, + types::PyList, PyResult, }; use crate::utils::arrow::arrow_schema_from_column_names_and_range; +#[derive(Debug)] pub(crate) enum Header { None, At(usize), @@ -76,6 +79,102 @@ impl Pagination { } } +#[derive(Debug, PartialEq)] +pub(crate) enum SelectedColumns { + All, + ByIndex(Vec<usize>), + ByName(Vec<String>), +} + +impl SelectedColumns { + pub(crate) fn validate_columns(&self, column_names: &[String]) -> FastExcelResult<()> { + match self { + SelectedColumns::All => Ok(()), + // If no selected indice is >= to the len of column_names, we're good + SelectedColumns::ByIndex(indices) => indices.iter().try_for_each(\|idx\| { + if idx >= &column_names.len() { + Err(FastExcelErrorKind::ColumnNotFound(IdxOrName::Idx(idx)).into()) + } else { + Ok(()) + } + }), + // Every selected column must be in the provided column_names + SelectedColumns::ByName(selected_names) => { + selected_names.iter().try_for_each(\|selected_name\| { + if column_names.contains(selected_name) { + Ok(()) + } else { + Err(FastExcelErrorKind::ColumnNotFound(IdxOrName::Name( + selected_name.to_string(), + )) + .into()) + } + }) + } + } + } + + pub(crate) fn idx_for_column( + &self, + col_names: &[String], + col_name: &str, + col_idx: usize, + ) -> Option<usize> { + match self { + SelectedColumns::All => None, + SelectedColumns::ByIndex(indices) => { + if indices.contains(&col_idx) { + Some(col_idx) + } else { + None + } + } + SelectedColumns::ByName(names) => { + // cannot use .contains() because we have &String and &str + if names.iter().any(\|name\| name == col_name) { + col_names.iter().position(\|name\| name == col_name) + } else { + None + } + } + } + } + + pub(crate) fn to_python<'p>(&self, py: Python<'p>) -> Option<&'p PyList> { + match self { + SelectedColumns::All => None, + SelectedColumns::ByIndex(idx_vec) => Some(PyList::new(py, idx_vec)), + SelectedColumns::ByName(name_vec) => Some(PyList::new(py, name_vec)), + } + } +} + +impl TryFrom<Option<&PyList>> for SelectedColumns { + type Error = FastExcelError; + + fn try_from(value: Option<&PyList>) -> FastExcelResult<Self> { + use FastExcelErrorKind::InvalidParameters; + + match value { + None => Ok(Self::All), + Some(py_list) => { + if py_list.is_empty() { + Err(InvalidParameters("list of selected columns is empty".to_string()).into()) + } else if let Ok(name_vec) = py_list.extract::<Vec<String>>() { + Ok(Self::ByName(name_vec)) + } else if let Ok(index_vec) = py_list.extract::<Vec<usize>>() { + Ok(Self::ByIndex(index_vec)) + } else { + Err(InvalidParameters(format!( + "expected list[int] \| list[str], got {py_list:?}" + )) + .into()) + } + } + } + } +} + #[pyclass(name = "_ExcelSheet")] pub(crate) struct ExcelSheet { #[pyo3(get)] @@ -87,6 +186,8 @@ pub(crate) struct ExcelSheet { total_height: Option<usize>, width: Option<usize>, schema_sample_rows: Option<usize>, + selected_columns: SelectedColumns, + available_columns: Vec<String>, } impl ExcelSheet { @@ -94,26 +195,45 @@ impl ExcelSheet { &self.data } - pub(crate) fn new( + pub(crate) fn try_new( name: String, data: Range<CalData>, header: Header, pagination: Pagination, schema_sample_rows: Option<usize>, - ) -> Self { - ExcelSheet { + selected_columns: SelectedColumns, + ) -> FastExcelResult<Self> { + let mut sheet = ExcelSheet { name, header, pagination, data, schema_sample_rows, + selected_columns, height: None, total_height: None, width: None, - } + // an empty vec as it will be replaced + available_columns: Vec::with_capacity(0), + }; + + let available_columns = sheet.get_available_columns(); + + // Ensuring selected columns are valid + sheet + .selected_columns + .validate_columns(&available_columns) + .with_context(\|\| { + format!( + "selected columns are invalid, available columns are: {available_columns:?}" + ) + })?; + + sheet.available_columns = available_columns; + Ok(sheet) } - pub(crate) fn column_names(&self) -> Vec<String> { + fn get_available_columns(&self) -> Vec<String> { let width = self.data.width(); match &self.header { Header::None => (0..width) @@ -263,10 +383,11 @@ impl TryFrom<&ExcelSheet> for Schema { arrow_schema_from_column_names_and_range( sheet.data(), - &sheet.column_names(), + &sheet.available_columns, sheet.offset(), // If sample_rows is higher than the sheet's limit, use the limit instead std::cmp::min(sample_rows, sheet.limit()), + &sheet.selected_columns, ) } } @@ -277,50 +398,71 @@ impl TryFrom<&ExcelSheet> for RecordBatch { fn try_from(sheet: &ExcelSheet) -> FastExcelResult<Self> { let offset = sheet.offset(); let limit = sheet.limit(); + let schema = Schema::try_from(sheet) - .with_context(\|\| format!("Could not build schema for sheet {}", sheet.name))?; - let mut iter = schema - .fields() + .with_context(\|\| format!("could not build schema for sheet {}", sheet.name))?; + + let mut iter = sheet + .available_columns .iter() .enumerate() - .map(\|(col_idx, field)\| { - ( - field.name(), - match field.data_type() { - ArrowDataType::Boolean => { - create_boolean_array(sheet.data(), col_idx, offset, limit) - } - ArrowDataType::Int64 => { - create_int_array(sheet.data(), col_idx, offset, limit) - } - ArrowDataType::Float64 => { - create_float_array(sheet.data(), col_idx, offset, limit) - } - ArrowDataType::Utf8 => { - create_string_array(sheet.data(), col_idx, offset, limit) - } - ArrowDataType::Timestamp(TimeUnit::Millisecond, None) => { - create_datetime_array(sheet.data(), col_idx, offset, limit) - } - ArrowDataType::Date32 => { - create_date_array(sheet.data(), col_idx, offset, limit) - } - ArrowDataType::Duration(TimeUnit::Millisecond) => { - create_duration_array(sheet.data(), col_idx, offset, limit) - } - ArrowDataType::Null => Arc::new(NullArray::new(limit - offset)), - _ => unreachable!(), - }, - ) + .filter_map(\|(idx, column_name)\| { + // checking if the current column has been selected + if let Some(col_idx) = match sheet.selected_columns { + // All columns selected, return the current index + SelectedColumns::All => Some(idx), + // Otherwise, return its index. If None is found, it means the column was not + // selected, and we will just continue + _ => sheet.selected_columns.idx_for_column( + &sheet.available_columns, + column_name, + idx, + ), + } { + // At this point, we know for sure that the column is in the schema so we can + // safely unwrap + let field = schema.field_with_name(column_name).unwrap(); + Some(( + field.name(), + match field.data_type() { + ArrowDataType::Boolean => { + create_boolean_array(sheet.data(), col_idx, offset, limit) + } + ArrowDataType::Int64 => { + create_int_array(sheet.data(), col_idx, offset, limit) + } + ArrowDataType::Float64 => { + create_float_array(sheet.data(), col_idx, offset, limit) + } + ArrowDataType::Utf8 => { + create_string_array(sheet.data(), col_idx, offset, limit) + } + ArrowDataType::Timestamp(TimeUnit::Millisecond, None) => { + create_datetime_array(sheet.data(), col_idx, offset, limit) + } + ArrowDataType::Date32 => { + create_date_array(sheet.data(), col_idx, offset, limit) + } + ArrowDataType::Duration(TimeUnit::Millisecond) => { + create_duration_array(sheet.data(), col_idx, offset, limit) + } + ArrowDataType::Null => Arc::new(NullArray::new(limit - offset)), + _ => unreachable!(), + }, + )) + } else { + None + } }) .peekable(); + // If the iterable is empty, try_from_iter returns an Err if iter.peek().is_none() { Ok(RecordBatch::new_empty(Arc::new(schema))) } else { RecordBatch::try_from_iter(iter) .map_err(\|err\| FastExcelErrorKind::ArrowError(err.to_string()).into()) - .with_context(\|\| format!("Could not convert sheet {} to RecordBatch", sheet.name)) + .with_context(\|\| format!("could not convert sheet {} to RecordBatch", sheet.name)) } } } @@ -359,16 +501,26 @@ impl ExcelSheet { self.header.offset() + self.pagination.offset() } + #[getter] + pub fn selected_columns<'p>(&'p self, py: Python<'p>) -> Option<&PyList> { + self.selected_columns.to_python(py) + } + + #[getter] + pub fn available_columns<'p>(&'p self, py: Python<'p>) -> &PyList { + PyList::new(py, &self.available_columns) + } + pub fn to_arrow(&self, py: Python<'_>) -> PyResult<PyObject> { RecordBatch::try_from(self) - .with_context(\|\| format!("Could not create RecordBatch from sheet {}", self.name)) + .with_context(\|\| format!("could not create RecordBatch from sheet \"{}\"", &self.name)) .and_then(\|rb\| { rb.to_pyarrow(py) .map_err(\|err\| FastExcelErrorKind::ArrowError(err.to_string()).into()) }) .with_context(\|\| { format!( - "Could not convert RecordBatch to pyarrow for sheet {}", + "could not convert RecordBatch to pyarrow for sheet \"{}\"", self.name ) }) @@ -379,3 +531,69 @@ impl ExcelSheet { format!("ExcelSheet<{}>", self.name) } } + +#[cfg(test)] +mod tests { + use super::*; + use pretty_assertions::assert_eq; + + #[test] + fn selected_columns_from_none() { + assert_eq!( + TryInto::<SelectedColumns>::try_into(None).unwrap(), + SelectedColumns::All + ) + } + + #[test] + fn selected_columns_from_list_of_valid_ints() { + Python::with_gil(\|py\| { + let py_list = PyList::new(py, vec![0, 1, 2]); + assert_eq!( + TryInto::<SelectedColumns>::try_into(Some(py_list)).unwrap(), + SelectedColumns::ByIndex(vec![0, 1, 2]) + ) + }); + } + + #[test] + fn selected_columns_from_list_of_valid_strings() { + Python::with_gil(\|py\| { + let py_list = PyList::new(py, vec!["foo", "bar"]); + assert_eq!( + TryInto::<SelectedColumns>::try_into(Some(py_list)).unwrap(), + SelectedColumns::ByName(vec!["foo".to_string(), "bar".to_string()]) + ) + }); + } + + #[test] + fn selected_columns_from_invalid_ints() { + Python::with_gil(\|py\| { + let py_list = PyList::new(py, vec![0, 2, -1]); + let err = TryInto::<SelectedColumns>::try_into(Some(py_list)).unwrap_err(); + + assert!(matches!(err.kind, FastExcelErrorKind::InvalidParameters(_))); + }); + } + + #[test] + fn selected_columns_from_empty_int_list() { + Python::with_gil(\|py\| { + let py_list = PyList::new(py, Vec::<usize>::new()); + let err = TryInto::<SelectedColumns>::try_into(Some(py_list)).unwrap_err(); + + assert!(matches!(err.kind, FastExcelErrorKind::InvalidParameters(_))); + }); + } + + #[test] + fn selected_columns_from_empty_string_list() { + Python::with_gil(\|py\| { + let py_list = PyList::new(py, Vec::<String>::new()); + let err = TryInto::<SelectedColumns>::try_into(Some(py_list)).unwrap_err(); + + assert!(matches!(err.kind, FastExcelErrorKind::InvalidParameters(_))); + }); + } +} diff --git a/src/utils/arrow.rs b/src/utils/arrow.rs index 33501d9..7da209b 100644 --- a/src/utils/arrow.rs +++ b/src/utils/arrow.rs @@ -3,7 +3,10 @@ use std::{collections::HashSet, sync::OnceLock}; use arrow::datatypes::{DataType as ArrowDataType, Field, Schema, TimeUnit}; use calamine::{CellErrorType, Data as CalData, DataType, Range}; -use crate::error::{FastExcelErrorKind, FastExcelResult}; +use crate::{ + error::{FastExcelErrorKind, FastExcelResult}, + types::excelsheet::SelectedColumns, +}; /// All the possible string values that should be considered as NULL const NULL_STRING_VALUES: [&str; 19] = [ @@ -136,12 +139,20 @@ pub(crate) fn arrow_schema_from_column_names_and_range( column_names: &[String], row_idx: usize, row_limit: usize, + selected_columns: &SelectedColumns, ) -> FastExcelResult<Schema> { let mut fields = Vec::with_capacity(column_names.len()); - for (col_idx, name) in column_names.iter().enumerate() { - let col_type = get_arrow_column_type(range, row_idx, row_limit, col_idx)?; - fields.push(Field::new(&alias_for_name(name, &fields), col_type, true)); + for (idx, name) in column_names.iter().enumerate() { + // If we have an index for the given column, extract it and add it to the schema. Otherwise, + // just ignore it + if let Some(col_idx) = match selected_columns { + SelectedColumns::All => Some(idx), + _ => selected_columns.idx_for_column(column_names, name, idx), + } { + let col_type = get_arrow_column_type(range, row_idx, row_limit, col_idx)?; + fields.push(Field::new(&alias_for_name(name, &fields), col_type, true)); + } } Ok(Schema::new(fields))	Be able to select a subset of columns We should be able to read an excel dataframe that starts at column `B` for example see `usecols` of https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_excel.html	@PrettyWood have you already had a look at this ? Otherwise, I'd take it :slightly_smiling_face: Go ahead! I have way too much work already :D You can take all the issues! ;) Haha I'll try :wink:	2024-02-25T16:36:09	[]	[]
ToucanToco/fastexcel	ToucanToco__fastexcel-186	5d33b75421a56bb56ca2a13a82e55fdee73094b9	diff --git a/.github/workflows/CI.yml b/.github/workflows/CI.yml index eb3a09e..732fdc2 100644 --- a/.github/workflows/CI.yml +++ b/.github/workflows/CI.yml @@ -110,3 +110,31 @@ jobs: command: build args: "-o dist --interpreter python${{ matrix.python-version }}" target: ${{ steps.target.outputs.target }} + + check-docs: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: "3.11" + - name: Set up rust toolchain + uses: actions-rs/toolchain@v1 + with: + profile: minimal + toolchain: stable + override: true + - run: \| + git config user.name github-actions + git config user.email [email protected] + + # venv required by maturin + python3 -m venv .venv + source .venv/bin/activate + + make install-test-requirements + make install-doc-requirements + # Required for pdoc to be able to import the sources + make dev-install + make doc diff --git a/Cargo.lock b/Cargo.lock index e6317d1..8c44037 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -45,12 +45,6 @@ dependencies = [ "libc", ] -[[package]] -name = "anyhow" -version = "1.0.80" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "5ad32ce52e4161730f7098c077cd2ed6229b5804ccf99e5366be1ab72a98b4e1" - [[package]] name = "arrow" version = "50.0.0" @@ -363,7 +357,6 @@ dependencies = [ name = "fastexcel" version = "0.9.1" dependencies = [ - "anyhow", "arrow", "calamine", "chrono", @@ -706,7 +699,6 @@ version = "0.20.3" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "53bdbb96d49157e65d45cc287af5f32ffadd5f4761438b527b055fb0d4bb8233" dependencies = [ - "anyhow", "cfg-if", "indoc", "libc", diff --git a/Cargo.toml b/Cargo.toml index 0e86451..c62376d 100644 --- a/Cargo.toml +++ b/Cargo.toml @@ -9,10 +9,9 @@ name = "fastexcel" crate-type = ["cdylib"] [dependencies] -anyhow = "1.0.80" calamine = { version = "0.24.0", features = ["dates"] } chrono = { version = "0.4.34", default-features = false } -pyo3 = { version = "0.20.3", features = ["extension-module", "anyhow", "abi3-py38"] } +pyo3 = { version = "0.20.3", features = ["extension-module", "abi3-py38"] } [dependencies.arrow] version = "50.0.0" diff --git a/Makefile b/Makefile index 5bee4ed..d4e2ee7 100644 --- a/Makefile +++ b/Makefile @@ -22,6 +22,7 @@ format: $(ruff) --fix $(format) $(fmt) + $(clippy) --fix --lib -p fastexcel --allow-dirty --allow-staged install-test-requirements: pip install -U -r test-requirements.txt -r build-requirements.txt diff --git a/python/fastexcel/__init__.py b/python/fastexcel/__init__.py index 46265d3..fb00d9b 100644 --- a/python/fastexcel/__init__.py +++ b/python/fastexcel/__init__.py @@ -12,7 +12,19 @@ import pyarrow as pa -from ._fastexcel import __version__, _ExcelReader, _ExcelSheet +from ._fastexcel import ( + ArrowError, + CalamineCellError, + CalamineError, + CannotRetrieveCellDataError, + FastExcelError, + InvalidParametersError, + SheetNotFoundError, + UnsupportedColumnTypeCombinationError, + __version__, + _ExcelReader, + _ExcelSheet, +) from ._fastexcel import read_excel as _read_excel @@ -202,4 +214,17 @@ def read_excel(path: Path \| str) -> ExcelReader: return ExcelReader(_read_excel(expanduser(path))) -__all__ = ("ExcelReader", "ExcelSheet", "read_excel", "__version__") +__all__ = ( + "__version__", + "read_excel", + "ExcelReader", + "ExcelSheet", + "FastExcelError", + "CannotRetrieveCellDataError", + "CalamineCellError", + "CalamineError", + "SheetNotFoundError", + "ArrowError", + "InvalidParametersError", + "UnsupportedColumnTypeCombinationError", +) diff --git a/python/fastexcel/_fastexcel.pyi b/python/fastexcel/_fastexcel.pyi index 26e1841..865f4e7 100644 --- a/python/fastexcel/_fastexcel.pyi +++ b/python/fastexcel/_fastexcel.pyi @@ -61,3 +61,13 @@ def read_excel(path: str) -> _ExcelReader: """Reads an excel file and returns an ExcelReader""" __version__: str + +# Exceptions +class FastExcelError(Exception): ... +class UnsupportedColumnTypeCombinationError(FastExcelError): ... +class CannotRetrieveCellDataError(FastExcelError): ... +class CalamineCellError(FastExcelError): ... +class CalamineError(FastExcelError): ... +class SheetNotFoundError(FastExcelError): ... +class ArrowError(FastExcelError): ... +class InvalidParametersError(FastExcelError): ... diff --git a/src/error.rs b/src/error.rs new file mode 100644 index 0000000..4c6de09 --- /dev/null +++ b/src/error.rs @@ -0,0 +1,221 @@ +use std::{error::Error, fmt::Display}; + +#[derive(Debug)] +pub(crate) enum SheetIdxOrName { + Idx(usize), + // Leaving this variant if someday we want to check if a name exists before calling worksheet_range + #[allow(dead_code)] + Name(String), +} + +#[derive(Debug)] +pub(crate) enum FastExcelErrorKind { + UnsupportedColumnTypeCombination(String), + CannotRetrieveCellData(usize, usize), + CalamineCellError(calamine::CellErrorType), + CalamineError(calamine::Error), + SheetNotFound(SheetIdxOrName), + // Arrow errors can be of several different types (arrow::error::Error, PyError), and having + // the actual type has not much value for us, so we just store a string context + ArrowError(String), + InvalidParameters(String), +} + +impl Display for FastExcelErrorKind { + fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { + match self { + FastExcelErrorKind::UnsupportedColumnTypeCombination(detail) => { + write!(f, "unsupported column type combination: {detail}") + } + FastExcelErrorKind::CannotRetrieveCellData(row, col) => { + write!(f, "cannot retrieve cell data at ({row}, {col})") + } + FastExcelErrorKind::CalamineCellError(calamine_error) => { + write!(f, "calamine cell error: {calamine_error}") + } + FastExcelErrorKind::CalamineError(calamine_error) => { + write!(f, "calamine error: {calamine_error}") + } + FastExcelErrorKind::SheetNotFound(idx_or_name) => { + let message = { + match idx_or_name { + SheetIdxOrName::Idx(idx) => format!("at index {idx}"), + SheetIdxOrName::Name(name) => format!("with name \"{name}\" not found"), + } + }; + write!(f, "sheet {message} not found") + } + FastExcelErrorKind::ArrowError(err) => write!(f, "arrow error: {err}"), + FastExcelErrorKind::InvalidParameters(err) => write!(f, "invalid parameters: {err}"), + } + } +} + +#[derive(Debug)] +pub(crate) struct FastExcelError { + kind: FastExcelErrorKind, + context: Vec<String>, +} + +pub(crate) trait ErrorContext { + fn with_context<S: ToString, F>(self, ctx_fn: F) -> Self + where + F: FnOnce() -> S; +} + +impl FastExcelError { + pub(crate) fn new(kind: FastExcelErrorKind) -> Self { + Self { + kind, + context: vec![], + } + } +} + +impl Display for FastExcelError { + fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { + write!(f, "{kind}", kind = self.kind)?; + if !self.context.is_empty() { + writeln!(f, "\nContext:")?; + + self.context + .iter() + .enumerate() + .try_for_each(\|(idx, ctx_value)\| writeln!(f, " {idx}: {ctx_value}"))?; + } + Ok(()) + } +} + +impl Error for FastExcelError {} + +impl ErrorContext for FastExcelError { + fn with_context<S: ToString, F>(mut self, ctx_fn: F) -> Self + where + F: FnOnce() -> S, + { + self.context.push(ctx_fn().to_string()); + self + } +} + +impl From<FastExcelErrorKind> for FastExcelError { + fn from(kind: FastExcelErrorKind) -> Self { + FastExcelError::new(kind) + } +} + +pub(crate) type FastExcelResult<T> = Result<T, FastExcelError>; + +impl<T> ErrorContext for FastExcelResult<T> { + fn with_context<S: ToString, F>(self, ctx_fn: F) -> Self + where + F: FnOnce() -> S, + { + match self { + Ok(_) => self, + Err(e) => Err(e.with_context(ctx_fn)), + } + } +} + +/// Contains Python versions of our custom errors +pub(crate) mod py_errors { + use super::FastExcelErrorKind; + use pyo3::{create_exception, exceptions::PyException, PyResult}; + + // Base fastexcel error + create_exception!( + _fastexcel, + FastExcelError, + PyException, + "The base class for all fastexcel errors" + ); + // Unsupported column type + create_exception!( + _fastexcel, + UnsupportedColumnTypeCombinationError, + FastExcelError, + "Column contains an unsupported type combination" + ); + // Cannot retrieve cell data + create_exception!( + _fastexcel, + CannotRetrieveCellDataError, + FastExcelError, + "Data for a given cell cannot be retrieved" + ); + // Calamine cell error + create_exception!( + _fastexcel, + CalamineCellError, + FastExcelError, + "calamine returned an error regarding the content of the cell" + ); + // Calamine error + create_exception!( + _fastexcel, + CalamineError, + FastExcelError, + "Generic calamine error" + ); + // Sheet not found + create_exception!( + _fastexcel, + SheetNotFoundError, + FastExcelError, + "Sheet was not found" + ); + // Arrow error + create_exception!( + _fastexcel, + ArrowError, + FastExcelError, + "Generic arrow error" + ); + // Invalid parameters + create_exception!( + _fastexcel, + InvalidParametersError, + FastExcelError, + "Provided parameters are invalid" + ); + + pub(crate) trait IntoPyResult { + type Inner; + + fn into_pyresult(self) -> PyResult<Self::Inner>; + } + + impl<T> IntoPyResult for super::FastExcelResult<T> { + type Inner = T; + + fn into_pyresult(self) -> PyResult<Self::Inner> { + match self { + Ok(ok) => Ok(ok), + Err(err) => { + let message = err.to_string(); + Err(match err.kind { + FastExcelErrorKind::UnsupportedColumnTypeCombination(_) => { + UnsupportedColumnTypeCombinationError::new_err(message) + } + FastExcelErrorKind::CannotRetrieveCellData(_, _) => { + CannotRetrieveCellDataError::new_err(message) + } + FastExcelErrorKind::CalamineCellError(_) => { + CalamineCellError::new_err(message) + } + FastExcelErrorKind::CalamineError(_) => CalamineError::new_err(message), + FastExcelErrorKind::SheetNotFound(_) => { + SheetNotFoundError::new_err(message) + } + FastExcelErrorKind::ArrowError(_) => ArrowError::new_err(message), + FastExcelErrorKind::InvalidParameters(_) => { + InvalidParametersError::new_err(message) + } + }) + } + } + } + } +} diff --git a/src/lib.rs b/src/lib.rs index 1f5df1e..7740754 100644 --- a/src/lib.rs +++ b/src/lib.rs @@ -1,14 +1,19 @@ +mod error; mod types; mod utils; -use anyhow::Result; +use error::{py_errors, ErrorContext}; use pyo3::prelude::*; use types::{ExcelReader, ExcelSheet}; /// Reads an excel file and returns an object allowing to access its sheets and a bit of metadata #[pyfunction] -fn read_excel(path: &str) -> Result<ExcelReader> { - Ok(ExcelReader::try_from_path(path).unwrap()) +fn read_excel(path: &str) -> PyResult<ExcelReader> { + use py_errors::IntoPyResult; + + ExcelReader::try_from_path(path) + .with_context(\|\| format!("could not load excel file at {path}")) + .into_pyresult() } // Taken from pydantic-core: @@ -24,10 +29,38 @@ fn get_version() -> String { } #[pymodule] -fn _fastexcel(_py: Python, m: &PyModule) -> PyResult<()> { +fn _fastexcel(py: Python, m: &PyModule) -> PyResult<()> { m.add_function(wrap_pyfunction!(read_excel, m)?)?; m.add_class::<ExcelSheet>()?; m.add_class::<ExcelReader>()?; m.add("__version__", get_version())?; - Ok(()) + + // errors + [ + ("FastExcelError", py.get_type::<py_errors::FastExcelError>()), + ( + "UnsupportedColumnTypeCombinationError", + py.get_type::<py_errors::UnsupportedColumnTypeCombinationError>(), + ), + ( + "CannotRetrieveCellDataError", + py.get_type::<py_errors::CannotRetrieveCellDataError>(), + ), + ( + "CalamineCellError", + py.get_type::<py_errors::CalamineCellError>(), + ), + ("CalamineError", py.get_type::<py_errors::CalamineError>()), + ( + "SheetNotFoundError", + py.get_type::<py_errors::SheetNotFoundError>(), + ), + ("ArrowError", py.get_type::<py_errors::ArrowError>()), + ( + "InvalidParametersError", + py.get_type::<py_errors::InvalidParametersError>(), + ), + ] + .into_iter() + .try_for_each(\|(exc_name, exc_type)\| m.add(exc_name, exc_type)) } diff --git a/src/types/excelreader.rs b/src/types/excelreader.rs index 0c9aadd..6424db1 100644 --- a/src/types/excelreader.rs +++ b/src/types/excelreader.rs @@ -1,8 +1,11 @@ use std::{fs::File, io::BufReader}; -use anyhow::{Context, Result}; use calamine::{open_workbook_auto, Reader, Sheets}; -use pyo3::{pyclass, pymethods}; +use pyo3::{pyclass, pymethods, PyResult}; + +use crate::error::{ + py_errors::IntoPyResult, ErrorContext, FastExcelErrorKind, FastExcelResult, SheetIdxOrName, +}; use super::{ excelsheet::{Header, Pagination}, @@ -20,8 +23,9 @@ pub(crate) struct ExcelReader { impl ExcelReader { // NOTE: Not implementing TryFrom here, because we're aren't building the file from the passed // string, but rather from the file pointed by it. Semantically, try_from_path is clearer - pub(crate) fn try_from_path(path: &str) -> Result<Self> { + pub(crate) fn try_from_path(path: &str) -> FastExcelResult<Self> { let sheets = open_workbook_auto(path) + .map_err(\|err\| FastExcelErrorKind::CalamineError(err).into()) .with_context(\|\| format!("Could not open workbook at {path}"))?; let sheet_names = sheets.sheet_names().to_owned(); Ok(Self { @@ -55,14 +59,16 @@ impl ExcelReader { skip_rows: usize, n_rows: Option<usize>, schema_sample_rows: Option<usize>, - ) -> Result<ExcelSheet> { + ) -> PyResult<ExcelSheet> { let range = self .sheets .worksheet_range(&name) - .with_context(\|\| format!("Error while loading sheet {name}"))?; + .map_err(\|err\| FastExcelErrorKind::CalamineError(err).into()) + .with_context(\|\| format!("Error while loading sheet {name}")) + .into_pyresult()?; let header = Header::new(header_row, column_names); - let pagination = Pagination::new(skip_rows, n_rows, &range)?; + let pagination = Pagination::new(skip_rows, n_rows, &range).into_pyresult()?; Ok(ExcelSheet::new( name, range, @@ -89,25 +95,33 @@ impl ExcelReader { skip_rows: usize, n_rows: Option<usize>, schema_sample_rows: Option<usize>, - ) -> Result<ExcelSheet> { + ) -> PyResult<ExcelSheet> { let name = self .sheet_names .get(idx) + .ok_or_else(\|\| FastExcelErrorKind::SheetNotFound(SheetIdxOrName::Idx(idx)).into()) .with_context(\|\| { format!( "Sheet index {idx} is out of range. File has {} sheets", self.sheet_names.len() ) - })? + }) + .into_pyresult()? .to_owned(); + let range = self .sheets .worksheet_range_at(idx) - .with_context(\|\| format!("Sheet at idx {idx} not found"))? - .with_context(\|\| format!("Error while loading sheet at idx {idx}"))?; + // Returns Option<Result<Range<Data>, Self::Error>>, so we convert the Option into a + // SheetNotFoundError and unwrap it + .ok_or_else(\|\| FastExcelErrorKind::SheetNotFound(SheetIdxOrName::Idx(idx)).into()) + .into_pyresult()? + // And here, we convert the calamine error in an owned error and unwrap it + .map_err(\|err\| FastExcelErrorKind::CalamineError(err).into()) + .into_pyresult()?; let header = Header::new(header_row, column_names); - let pagination = Pagination::new(skip_rows, n_rows, &range)?; + let pagination = Pagination::new(skip_rows, n_rows, &range).into_pyresult()?; Ok(ExcelSheet::new( name, range, diff --git a/src/types/excelsheet.rs b/src/types/excelsheet.rs index 38fcc5d..d950eec 100644 --- a/src/types/excelsheet.rs +++ b/src/types/excelsheet.rs @@ -1,6 +1,9 @@ use std::sync::Arc; -use anyhow::{anyhow, bail, Context, Result}; +use crate::error::{ + py_errors::IntoPyResult, ErrorContext, FastExcelError, FastExcelErrorKind, FastExcelResult, +}; + use arrow::{ array::{ Array, BooleanArray, Date32Array, DurationMillisecondArray, Float64Array, Int64Array, @@ -13,7 +16,10 @@ use arrow::{ use calamine::{Data as CalData, DataType, Range}; use chrono::NaiveDate; -use pyo3::prelude::{pyclass, pymethods, PyObject, Python}; +use pyo3::{ + prelude::{pyclass, pymethods, PyObject, Python}, + PyResult, +}; use crate::utils::arrow::arrow_schema_from_column_names_and_range; @@ -53,12 +59,16 @@ impl Pagination { skip_rows: usize, n_rows: Option<usize>, range: &Range<CalData>, - ) -> Result<Self> { + ) -> FastExcelResult<Self> { let max_height = range.height(); if max_height < skip_rows { - bail!("To many rows skipped. Max height is {max_height}"); + Err(FastExcelErrorKind::InvalidParameters(format!( + "Too many rows skipped. Max height is {max_height}" + )) + .into()) + } else { + Ok(Self { skip_rows, n_rows }) } - Ok(Self { skip_rows, n_rows }) } pub(crate) fn offset(&self) -> usize { @@ -244,7 +254,7 @@ fn create_duration_array( } impl TryFrom<&ExcelSheet> for Schema { - type Error = anyhow::Error; + type Error = FastExcelError; fn try_from(sheet: &ExcelSheet) -> Result<Self, Self::Error> { // Checking how many rows we want to use to determine the dtype for a column. If sample_rows is @@ -262,9 +272,9 @@ impl TryFrom<&ExcelSheet> for Schema { } impl TryFrom<&ExcelSheet> for RecordBatch { - type Error = anyhow::Error; + type Error = FastExcelError; - fn try_from(sheet: &ExcelSheet) -> Result<Self, Self::Error> { + fn try_from(sheet: &ExcelSheet) -> FastExcelResult<Self> { let offset = sheet.offset(); let limit = sheet.limit(); let schema = Schema::try_from(sheet) @@ -309,6 +319,7 @@ impl TryFrom<&ExcelSheet> for RecordBatch { Ok(RecordBatch::new_empty(Arc::new(schema))) } else { RecordBatch::try_from_iter(iter) + .map_err(\|err\| FastExcelErrorKind::ArrowError(err.to_string()).into()) .with_context(\|\| format!("Could not convert sheet {} to RecordBatch", sheet.name)) } } @@ -348,16 +359,20 @@ impl ExcelSheet { self.header.offset() + self.pagination.offset() } - pub fn to_arrow(&self, py: Python<'_>) -> Result<PyObject> { + pub fn to_arrow(&self, py: Python<'_>) -> PyResult<PyObject> { RecordBatch::try_from(self) .with_context(\|\| format!("Could not create RecordBatch from sheet {}", self.name)) - .and_then(\|rb\| match rb.to_pyarrow(py) { - Err(e) => Err(anyhow!( - "Could not convert RecordBatch to pyarrow for sheet {}: {e}", + .and_then(\|rb\| { + rb.to_pyarrow(py) + .map_err(\|err\| FastExcelErrorKind::ArrowError(err.to_string()).into()) + }) + .with_context(\|\| { + format!( + "Could not convert RecordBatch to pyarrow for sheet {}", self.name - )), - Ok(pyobj) => Ok(pyobj), + ) }) + .into_pyresult() } pub fn __repr__(&self) -> String { diff --git a/src/utils/arrow.rs b/src/utils/arrow.rs index 807d7d0..33501d9 100644 --- a/src/utils/arrow.rs +++ b/src/utils/arrow.rs @@ -1,19 +1,21 @@ use std::{collections::HashSet, sync::OnceLock}; -use anyhow::{anyhow, Context, Result}; use arrow::datatypes::{DataType as ArrowDataType, Field, Schema, TimeUnit}; use calamine::{CellErrorType, Data as CalData, DataType, Range}; +use crate::error::{FastExcelErrorKind, FastExcelResult}; + /// All the possible string values that should be considered as NULL const NULL_STRING_VALUES: [&str; 19] = [ "", "#N/A", "#N/A N/A", "#NA", "-1.#IND", "-1.#QNAN", "-NaN", "-nan", "1.#IND", "1.#QNAN", "<NA>", "N/A", "NA", "NULL", "NaN", "None", "n/a", "nan", "null", ]; -fn get_cell_type(data: &Range<CalData>, row: usize, col: usize) -> Result<ArrowDataType> { +fn get_cell_type(data: &Range<CalData>, row: usize, col: usize) -> FastExcelResult<ArrowDataType> { let cell = data .get((row, col)) - .with_context(\|\| format!("Could not retrieve data at ({row},{col})"))?; + .ok_or_else(\|\| FastExcelErrorKind::CannotRetrieveCellData(row, col))?; + match cell { CalData::Int(_) => Ok(ArrowDataType::Int64), CalData::Float(_) => Ok(ArrowDataType::Float64), @@ -42,7 +44,7 @@ fn get_cell_type(data: &Range<CalData>, row: usize, col: usize) -> Result<ArrowD // Errors and nulls CalData::Error(err) => match err { CellErrorType::NA => Ok(ArrowDataType::Null), - _ => Err(anyhow!("Error in calamine cell: {err:?}")), + _ => Err(FastExcelErrorKind::CalamineCellError(err.to_owned()).into()), }, CalData::Empty => Ok(ArrowDataType::Null), } @@ -81,10 +83,10 @@ fn get_arrow_column_type( start_row: usize, end_row: usize, col: usize, -) -> Result<ArrowDataType> { +) -> FastExcelResult<ArrowDataType> { let mut column_types = (start_row..end_row) .map(\|row\| get_cell_type(data, row, col)) - .collect::<Result<HashSet<_>>>()?; + .collect::<FastExcelResult<HashSet<_>>>()?; // All columns are nullable anyway so we're not taking Null into account here column_types.remove(&ArrowDataType::Null); @@ -106,9 +108,10 @@ fn get_arrow_column_type( Ok(ArrowDataType::Utf8) } else { // NOTE: Not being too smart about multi-types columns for now - Err(anyhow!( - "could not figure out column type for following type combination: {column_types:?}" - )) + Err( + FastExcelErrorKind::UnsupportedColumnTypeCombination(format!("{column_types:?}")) + .into(), + ) } } @@ -133,7 +136,7 @@ pub(crate) fn arrow_schema_from_column_names_and_range( column_names: &[String], row_idx: usize, row_limit: usize, -) -> Result<Schema> { +) -> FastExcelResult<Schema> { let mut fields = Vec::with_capacity(column_names.len()); for (col_idx, name) in column_names.iter().enumerate() {	Implement proper python exceptions for our different error cases For now, we only have `RuntimeError`, which is clearly sub-optimal		2024-02-23T16:44:38	[]	[]
ToucanToco/fastexcel	ToucanToco__fastexcel-180	d0ad3564ba3e49575d76112b7e11e1b707f04bb2	diff --git a/src/types/excelsheet.rs b/src/types/excelsheet.rs index e7ec2eb..bb55775 100644 --- a/src/types/excelsheet.rs +++ b/src/types/excelsheet.rs @@ -246,17 +246,17 @@ fn create_duration_array( impl TryFrom<&ExcelSheet> for Schema { type Error = anyhow::Error; - fn try_from(value: &ExcelSheet) -> Result<Self, Self::Error> { + fn try_from(sheet: &ExcelSheet) -> Result<Self, Self::Error> { // Checking how many rows we want to use to determine the dtype for a column. If sample_rows is // not provided, we sample limit rows, i.e on the entire column - let sample_rows = value.offset() + value.schema_sample_rows().unwrap_or(value.limit()); + let sample_rows = sheet.offset() + sheet.schema_sample_rows().unwrap_or(sheet.limit()); arrow_schema_from_column_names_and_range( - value.data(), - &value.column_names(), - value.offset(), + sheet.data(), + &sheet.column_names(), + sheet.offset(), // If sample_rows is higher than the sheet's limit, use the limit instead - std::cmp::min(sample_rows, value.limit()), + std::cmp::min(sample_rows, sheet.limit()), ) } } @@ -264,11 +264,11 @@ impl TryFrom<&ExcelSheet> for Schema { impl TryFrom<&ExcelSheet> for RecordBatch { type Error = anyhow::Error; - fn try_from(value: &ExcelSheet) -> Result<Self, Self::Error> { - let offset = value.offset(); - let limit = value.limit(); - let schema = Schema::try_from(value) - .with_context(\|\| format!("Could not build schema for sheet {}", value.name))?; + fn try_from(sheet: &ExcelSheet) -> Result<Self, Self::Error> { + let offset = sheet.offset(); + let limit = sheet.limit(); + let schema = Schema::try_from(sheet) + .with_context(\|\| format!("Could not build schema for sheet {}", sheet.name))?; let mut iter = schema .fields() .iter() @@ -278,25 +278,25 @@ impl TryFrom<&ExcelSheet> for RecordBatch { field.name(), match field.data_type() { ArrowDataType::Boolean => { - create_boolean_array(value.data(), col_idx, offset, limit) + create_boolean_array(sheet.data(), col_idx, offset, limit) } ArrowDataType::Int64 => { - create_int_array(value.data(), col_idx, offset, limit) + create_int_array(sheet.data(), col_idx, offset, limit) } ArrowDataType::Float64 => { - create_float_array(value.data(), col_idx, offset, limit) + create_float_array(sheet.data(), col_idx, offset, limit) } ArrowDataType::Utf8 => { - create_string_array(value.data(), col_idx, offset, limit) + create_string_array(sheet.data(), col_idx, offset, limit) } ArrowDataType::Timestamp(TimeUnit::Millisecond, None) => { - create_datetime_array(value.data(), col_idx, offset, limit) + create_datetime_array(sheet.data(), col_idx, offset, limit) } ArrowDataType::Date32 => { - create_date_array(value.data(), col_idx, offset, limit) + create_date_array(sheet.data(), col_idx, offset, limit) } ArrowDataType::Duration(TimeUnit::Millisecond) => { - create_duration_array(value.data(), col_idx, offset, limit) + create_duration_array(sheet.data(), col_idx, offset, limit) } ArrowDataType::Null => Arc::new(NullArray::new(limit - offset)), _ => unreachable!(), @@ -309,7 +309,7 @@ impl TryFrom<&ExcelSheet> for RecordBatch { Ok(RecordBatch::new_empty(Arc::new(schema))) } else { RecordBatch::try_from_iter(iter) - .with_context(\|\| format!("Could not convert sheet {} to RecordBatch", value.name)) + .with_context(\|\| format!("Could not convert sheet {} to RecordBatch", sheet.name)) } } } diff --git a/src/utils/arrow.rs b/src/utils/arrow.rs index 12b2df9..3523e03 100644 --- a/src/utils/arrow.rs +++ b/src/utils/arrow.rs @@ -2,7 +2,7 @@ use std::{collections::HashSet, sync::OnceLock}; use anyhow::{anyhow, Context, Result}; use arrow::datatypes::{DataType as ArrowDataType, Field, Schema, TimeUnit}; -use calamine::{Data as CalData, DataType, Range}; +use calamine::{CellErrorType, Data as CalData, DataType, Range}; fn get_cell_type(data: &Range<CalData>, row: usize, col: usize) -> Result<ArrowDataType> { let cell = data @@ -31,7 +31,10 @@ fn get_cell_type(data: &Range<CalData>, row: usize, col: usize) -> Result<ArrowD // A simple duration CalData::DurationIso(_) => Ok(ArrowDataType::Duration(TimeUnit::Millisecond)), // Errors and nulls - CalData::Error(err) => Err(anyhow!("Error in calamine cell: {err:?}")), + CalData::Error(err) => match err { + CellErrorType::NA => Ok(ArrowDataType::Null), + _ => Err(anyhow!("Error in calamine cell: {err:?}")), + }, CalData::Empty => Ok(ArrowDataType::Null), } }	Fastexcel fails to load an excel file with polars ```python dfs = pl.read_excel('input.xlsx', engine='calamine', sheet_id=0) for key in dfs.keys(): print(dfs[key].head()) ``` Error: ``` Traceback (most recent call last): File "/Users/Desktop/workspace/pocs/demo.py", line 27, in <module> dfs = pl.read_excel('compass_input.xlsx', engine='calamine', sheet_id=0) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/Desktop/workspace/pocs/.venv/lib/python3.11/site-packages/polars/utils/deprecation.py", line 136, in wrapper return function(args, kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/Desktop/workspace/pocs/.venv/lib/python3.11/site-packages/polars/utils/deprecation.py", line 136, in wrapper return function(args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/Desktop/workspace/pocs/.venv/lib/python3.11/site-packages/polars/io/spreadsheet/functions.py", line 259, in read_excel return _read_spreadsheet( ^^^^^^^^^^^^^^^^^^ File "/Users/Desktop/workspace/pocs/.venv/lib/python3.11/site-packages/polars/io/spreadsheet/functions.py", line 487, in _read_spreadsheet parsed_sheets = { ^ File "/Users/Desktop/workspace/pocs/.venv/lib/python3.11/site-packages/polars/io/spreadsheet/functions.py", line 488, in <dictcomp> name: reader_fn( ^^^^^^^^^^ File "/Users/Desktop/workspace/pocs/.venv/lib/python3.11/site-packages/polars/io/spreadsheet/functions.py", line 834, in _read_spreadsheet_calamine df = ws.to_polars() ^^^^^^^^^^^^^^ File "/Users/Desktop/workspace/pocs/.venv/lib/python3.11/site-packages/fastexcel/__init__.py", line 64, in to_polars df = pl.from_arrow(data=self.to_arrow()) ^^^^^^^^^^^^^^^ File "/Users/Desktop/workspace/pocs/.venv/lib/python3.11/site-packages/fastexcel/__init__.py", line 47, in to_arrow return self._sheet.to_arrow() ^^^^^^^^^^^^^^^^^^^^^^ RuntimeError: Could not create RecordBatch from sheet Hybrid_Productivity Caused by: 0: Could not build schema for sheet Hybrid_Productivity 1: Error in calamine cell: NA ``` Installed versions: ``` --------Version info--------- Polars: 0.20.7 Index type: UInt32 Platform: macOS-12.7.3-arm64-arm-64bit Python: 3.11.2 (v3.11.2:878ead1ac1, Feb 7 2023, 10:02:41) [Clang 13.0.0 (clang-1300.0.29.30)] ----Optional dependencies---- adbc_driver_manager: <not installed> cloudpickle: <not installed> connectorx: <not installed> deltalake: <not installed> fsspec: <not installed> gevent: <not installed> hvplot: <not installed> matplotlib: 3.8.2 numpy: 1.26.4 openpyxl: <not installed> pandas: 2.2.0 pyarrow: 15.0.0 pydantic: <not installed> pyiceberg: <not installed> pyxlsb: <not installed> sqlalchemy: <not installed> xlsx2csv: <not installed> xlsxwriter: <not installed> None Process finished with exit code 0 ```	Hello, could you please provide an excel file allowing to reproduce the bug ? Without this, it will be hard to investigate the issue Hi @lukapeschke, I found that if a cell contains the value "#N/A" then it is failing to load the data. I am attaching a sample below. Thank you. [sample_data.xlsx](https://github.com/ToucanToco/fastexcel/files/14277136/sample_data.xlsx) ```python df = pl.read_excel('sample_data.xlsx', engine='calamine') ```	2024-02-14T11:28:47	[]	[]
ToucanToco/fastexcel	ToucanToco__fastexcel-161	96f57be2e5dc2cfb5521ee7370f9ccc925475fd4	diff --git a/Makefile b/Makefile index a59b9e8..2cab815 100644 --- a/Makefile +++ b/Makefile @@ -5,7 +5,7 @@ ruff = ruff python/ .py format = ruff format python/ .py mypy = mypy python/ .py -pytest = pytest +pytest = pytest -v ## Rust clippy = cargo clippy fmt = cargo fmt diff --git a/src/types/excelsheet.rs b/src/types/excelsheet.rs index 59f2d03..36cf6ce 100644 --- a/src/types/excelsheet.rs +++ b/src/types/excelsheet.rs @@ -11,7 +11,7 @@ use arrow::{ record_batch::RecordBatch, }; use calamine::{DataType as CalDataType, Range}; -use chrono::{NaiveDate, Timelike}; +use chrono::NaiveDate; use pyo3::prelude::{pyclass, pymethods, PyObject, Python}; @@ -186,9 +186,7 @@ fn create_string_array( } fn duration_type_to_i64(caldt: &CalDataType) -> Option<i64> { - caldt - .as_time() - .map(\|t\| 1000 i64::from(t.num_seconds_from_midnight())) + caldt.as_duration().map(\|d\| d.num_milliseconds()) } fn create_date_array(	Some duration columns contain only null values after load For durations, we're we using calamine's `as_time` API to convert durations to milliseconds, as the `as_duration` API was not available yet. However, this does not work anymore, as `as_time` will return `None` for `Duration` values. _Originally posted by @lukapeschke in https://github.com/ToucanToco/fastexcel/issues/158#issuecomment-1914324743_		2024-01-29T16:11:01	[]	[]
icloud-photos-downloader/icloud_photos_downloader	icloud-photos-downloader__icloud_photos_downloader-956	6e9c6c69996a39d0e7f77c9fd4f9b160b4b8bcdd	diff --git a/CHANGELOG.md b/CHANGELOG.md index 4c276602d..309703f46 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -2,6 +2,8 @@ ## Unreleased +- fix: force_size should not skip subsequent sizes [ref](https://github.com/icloud-photos-downloader/icloud_photos_downloader/issues/955) + ## 1.23.4 (2024-09-02) - fix: support plain text encoding for filename in addition to base64 [ref](https://github.com/boredazfcuk/docker-icloudpd/issues/641) diff --git a/src/icloudpd/base.py b/src/icloudpd/base.py index 30f9f71ac..96620fc74 100644 --- a/src/icloudpd/base.py +++ b/src/icloudpd/base.py @@ -893,7 +893,7 @@ def download_photo_(counter: Counter, photo: PhotoAsset) -> bool: download_size.value, photo.filename, ) - return False + continue if AssetVersionSize.ORIGINAL in size: continue # that should avoid double download for original download_size = AssetVersionSize.ORIGINAL	When multiple sizes and --force-size are specified, some sizes can be skipped even if exist. ## Overview When both `--size medium` and `--size thumb` are specified with `--force-size`, but only the thumb-size asset exists for a photo, both sizes will be skipped. ## Steps to Reproduce 1. Upload a small image to iCloud Photos, such that the medium-size asset exists but thumb-size does not. (e.g. a 200*250 heic file) 2. Execute `icloudpd -d syncdir --size medium --size thumb --force-size`. ## Expected Behavior The medium-size asset should be skipped and the thumb-size asset should be downloaded. ## Actual Behavior Both medium-size and thumb-size assets are skipped. ## Context Changing this to `continue` should be a simple fix. https://github.com/icloud-photos-downloader/icloud_photos_downloader/blob/6e9c6c69996a39d0e7f77c9fd4f9b160b4b8bcdd/src/icloudpd/base.py#L896 I would be glad to issue a PR if you consider this appropriate.	Thanks for reporting the issue. Yes, you can submit PR. Please include tests. I am curious how are you using medium & thumb sized assets?	2024-09-19T07:10:43	[]	[]
googleapis/python-pubsub	googleapis__python-pubsub-1297	cdaf6e9bcd0b918c2ab50fd4a8f987f60cbd610f	diff --git a/noxfile.py b/noxfile.py index 2ccbfeae7..47611cdff 100644 --- a/noxfile.py +++ b/noxfile.py @@ -55,7 +55,9 @@ UNIT_TEST_EXTERNAL_DEPENDENCIES: List[str] = [] UNIT_TEST_LOCAL_DEPENDENCIES: List[str] = [] UNIT_TEST_DEPENDENCIES: List[str] = [] -UNIT_TEST_EXTRAS: List[str] = [] +UNIT_TEST_EXTRAS: List[str] = [ + "flaky", +] UNIT_TEST_EXTRAS_BY_PYTHON: Dict[str, List[str]] = {} SYSTEM_TEST_PYTHON_VERSIONS: List[str] = ["3.12"] diff --git a/owlbot.py b/owlbot.py index 2e4b00bc9..77eb08250 100644 --- a/owlbot.py +++ b/owlbot.py @@ -337,6 +337,7 @@ cov_level=100, versions=gcp.common.detect_versions(path="./google", default_first=True), unit_test_python_versions=["3.7", "3.8", "3.9", "3.10", "3.11", "3.12", "3.13"], + unit_test_extras=["flaky"], system_test_python_versions=["3.12"], system_test_external_dependencies=["psutil","flaky"], ) diff --git a/setup.py b/setup.py index abe06552b..fb2b94fad 100644 --- a/setup.py +++ b/setup.py @@ -50,7 +50,7 @@ "opentelemetry-sdk <= 1.22.0; python_version<='3.7'", "opentelemetry-sdk >= 1.27.0; python_version>='3.8'", ] -extras = {"libcst": "libcst >= 0.3.10"} +extras = {"libcst": "libcst >= 0.3.10,", "flaky": "flaky"} url = "https://github.com/googleapis/python-pubsub" package_root = os.path.abspath(os.path.dirname(__file__))	Fix flaky tests in test_publisher_client caused due to non-deterministic number of spans exported by OpenTelemetry Fix error: ``` =================================== FAILURES =================================== __________________ test_opentelemetry_flow_control_exception ___________________ creds = span_exporter = @pytest.mark.skipif( sys.version_info < (3, 8), reason="Open Telemetry not supported below Python version 3.8", ) def test_opentelemetry_flow_control_exception(creds, span_exporter): publisher_options = types.PublisherOptions( flow_control=types.PublishFlowControl( message_limit=10, byte_limit=150, limit_exceeded_behavior=types.LimitExceededBehavior.ERROR, ), enable_open_telemetry_tracing=True, ) client = publisher.Client(credentials=creds, publisher_options=publisher_options) mock_batch = mock.Mock(spec=client._batch_class) topic = "projects/projectID/topics/topicID" client._set_batch(topic, mock_batch) future1 = client.publish(topic, b"a" * 60) future2 = client.publish(topic, b"b" * 100) future1.result() # no error, still within flow control limits with pytest.raises(exceptions.FlowControlLimitError): future2.result() spans = span_exporter.get_finished_spans() # Span 1 = Publisher Flow Control Span of first publish # Span 2 = Publisher Batching Span of first publish # Span 3 = Publisher Flow Control Span of second publish(raises FlowControlLimitError) # Span 4 = Publish Create Span of second publish(raises FlowControlLimitError) > assert len(spans) == 4 E assert 5 == 4 E + where 5 = len((, , )) tests/unit/pubsub_v1/publisher/test_publisher_client.py:294: AssertionError - generated xml file: /tmpfs/src/github/python-pubsub/unit_3.12_sponge_log.xml - =========================== short test summary info ============================ ```		2024-11-04T22:54:15	[]	[]
googleapis/python-pubsub	googleapis__python-pubsub-1244	49c7c612ab091de63adabd59ba3b8432879fbd3e	diff --git a/google/cloud/pubsub_v1/subscriber/_protocol/streaming_pull_manager.py b/google/cloud/pubsub_v1/subscriber/_protocol/streaming_pull_manager.py index b8531db17..c01dd7f2e 100644 --- a/google/cloud/pubsub_v1/subscriber/_protocol/streaming_pull_manager.py +++ b/google/cloud/pubsub_v1/subscriber/_protocol/streaming_pull_manager.py @@ -1104,8 +1104,11 @@ def _on_response(self, response: gapic_types.StreamingPullResponse) -> None: ) with self._pause_resume_lock: - assert self._scheduler is not None - assert self._leaser is not None + if self._scheduler is None or self._leaser is None: + _LOGGER.debug( + f"self._scheduler={self._scheduler} or self._leaser={self._leaser} is None. Stopping further processing." + ) + return for received_message in received_messages: if (	Possible race condition between `_on_response` and `close` #### Environment details - OS type and version: Ubuntu 22.04 - Python version: 3.11 - pip version: 23.0.1 - `google-cloud-pubsub` version: 2.18.4 #### Steps to reproduce 1. Run code sample indefinitely 2. Sometime it shows Assertion error in `_on_response` function. #### Code example ```python def on_subscribe(subscription, until=None): """Decorator factory that provides subscribed messages to function. It handle decorated function as callback. So message should be acked/nacked inside decorated function. Args: subscription (str): Subscription ID. Should be `projects/{PROJECT_ID}/subscriptions/{SUBSCRIPTION_ID}` until (datetime.datetime): This function will subscribe messages published until this timestamp. """ def _callback_factory(func, finished, subscribe_until, kwargs): def _callback(message): """Callback function. It sends signal if subscribed all messages. """ publish_time = datetime.fromtimestamp( message.publish_time.timestamp()) if subscribe_until and publish_time <= subscribe_until: return func(message, kwargs) if subscribe_until and not finished.is_set(): logging.info('Subscribed all messages published until %s', subscribe_until) finished.set() message.nack() return _callback def _wrapper(func): @functools.wraps(func) def _inner_wrapper(kwargs): # Event variable that is triggered when all messages are subscribed all_subscribed = Event() callback = _callback_factory(func=func, finished=all_subscribed, subscribe_until=subscribe_until, kwargs) # Ensure closing subscriber for memory leak prevention. with pubsub_v1.SubscriberClient() as subscriber: future = subscriber.subscribe( subscription=subscription, callback=callback, await_callbacks_on_shutdown=True, flow_control=pubsub_v1.types.FlowControl(max_messages=5000), ) all_subscribed.wait(timeout=60) # Wait until future is finished when it's cancelled. # If it cancelled by timeout or keyboard interrupt, ignore it. try: future.cancel() future.result(timeout=60) except (KeyboardInterrupt, TimeoutError): pass except Exception as e: logging.error("Error occurs during subscription to %s: %s", subscription, str(e)) return _inner_wrapper return _wrapper @on_subscribe(subscription="SUBSCRIPTION") def callback(message): # Do something with message ``` #### Stack trace ``` Traceback (most recent call last): File "/layers/google.python.pip/pip/lib/python3.11/site-packages/google/api_core/bidi.py", line 657, in _thread_main self._on_response(response) File "/layers/google.python.pip/pip/lib/python3.11/site-packages/google/cloud/pubsub_v1/subscriber/_protocol/streaming_pull_manager.py", line 1107, in _on_response assert self._scheduler is not None ``` #### Explanation It's because `future.cancel()` executes `manager.close()` which makes `_scheduler` as `None` and it makes `_on_response` raise AssertionError. Maybe it has to be protected by threading lock somehow.	Thank you for the detailed report. I'll try to reproduce this and fix accordingly. In the meanwhile, I'm wondering why you choose to cancel first and then wait for result here ``` future.cancel() future.result(timeout=60) ``` instead of a common use case as the [sample](https://cloud.google.com/pubsub/docs/pull#client_library_code_samples) shows. This could be a mitigation for the issue you are experiencing. > Thank you for the detailed report. I'll try to reproduce this and fix accordingly. > > In the meanwhile, I'm wondering why you choose to cancel first and then wait for result here > > ``` > > future.cancel() > > future.result(timeout=60) > > ``` > > instead of a common use case as the [sample](https://cloud.google.com/pubsub/docs/pull#client_library_code_samples) shows. This could be a mitigation for the issue you are experiencing. Because I wanted to subscribe messages until certain timestamp (which turns out wrong idea 😅) and make callback to signal event if it subscribed all messages by using threading.Event object. If I call result first without timeout then it will wait indefinitely and if I set timeout then it will raise TimeoutError at second call of result. I wanted to just cancel it first to make it avoid raising error. We are also seeing this issue. Python version: 3.11 pip version: 23.3.1 google-cloud-pubsub version: 2.14.1 We are running in Google Kubernetes Engine (GKE) and listening to the following two signals in our worker for scaling: ```python import signal signal.signal(signal.SIGINT, self.exit_gracefully) signal.signal(signal.SIGTERM, self.exit_gracefully) ``` With a `exit_gracefully` implemented as follows: ```python def exit_gracefully(self) -> None: """Stop the async worker.""" self.future.cancel() # Request shutdown try: self.future.result(timeout=60 * 5) # Block until the shutdown is complete (or up to 5min). except TimeoutError: self.logger.warning("Stop timeout reached, Pod will die.") except Exception as e: self.logger.error(f"Error while shutting down worker: {e}") ``` with the following setup: ```python self.subscriber = pubsub_v1.SubscriberClient() flow_control = pubsub_v1.types.FlowControl(max_messages=self.pub_sub_flow_control_max_messages) executor = futures.ThreadPoolExecutor( max_workers=self.pub_sub_thread_pool_executor_max_workers, thread_name_prefix="TPE-PeachWorker" ) scheduler = pubsub_v1.subscriber.scheduler.ThreadScheduler(executor=executor) self.future = self.subscriber.subscribe( self.subscription_path, callback=self.pubsub_callback, flow_control=flow_control, scheduler=scheduler, await_callbacks_on_shutdown=True, ) ``` We do our main work in a separate thread from the main thread to allow python to receive signals: ```python # Execute the wait on the future for streaming pull in a different thread # because Python's main thread is the only one that will receive signals such as # SIGTERM for when a pod is requested to die so it can be replaced with a new one. thread = Thread(target=self.process, args=(), daemon=True) thread.start() ``` With the `process` method implemented as follows: ```python def process(self): with self.subscriber: try: self.future.result() except Cancelled: self.logger.info("Cancel request received.") ``` We are seeing the same scheduler assertion error that @hson2 saw: ``` AssertionError: null File "threading.py", line 982, in run self._target(self._args, self._kwargs) File "/usr/local/lib/python3.11/site-packages/google/cloud/pubsub_v1/subscriber/_protocol/streaming_pull_manager.py", line 921, in _shutdown assert self._scheduler is not None ``` However, we are also seeing another error immediately before: ``` AttributeError: 'NoneType' object has no attribute 'nack' File "threading.py", line 982, in run self._target(self._args, **self._kwargs) File "/usr/local/lib/python3.11/site-packages/google/cloud/pubsub_v1/subscriber/_protocol/streaming_pull_manager.py", line 948, in _shutdown msg.nack() ``` @liuyunnnn Could you expand on the preference to not call cancel first in a scenario like this? Thank you for your help :)!	2024-09-09T00:41:49	[]	[]
googleapis/python-pubsub	googleapis__python-pubsub-1193	0b8d7b91da7928e8f06379d19c616e7a6a411349	diff --git a/setup.py b/setup.py index a6af31207..dbb66cf7c 100644 --- a/setup.py +++ b/setup.py @@ -42,7 +42,7 @@ "google-api-core[grpc] >= 1.34.0, <3.0.0dev,!=2.0.,!=2.1.,!=2.2.,!=2.3.,!=2.4.,!=2.5.,!=2.6.,!=2.7.,!=2.8.,!=2.9.,!=2.10.*", "proto-plus >= 1.22.0, <2.0.0dev", "proto-plus >= 1.22.2, <2.0.0dev; python_version>='3.11'", - "protobuf>=3.19.5,<5.0.0dev,!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5", + "protobuf>=3.20.2,<6.0.0dev,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5", "grpc-google-iam-v1 >= 0.12.4, < 1.0.0dev", "grpcio-status >= 1.33.2", ]	Unable to use patched version of grpcio with google-cloud-pubsub There are a number of bugs (functionally and security wise) in grpcio < 1.63. However, >1.63 now requires >5 protobuf, but this library requires protobuf <5 making it no longer possible to install this library with a patched working version of grpcio. ``` Because no versions of google-cloud-pubsub match >2.21.3 and google-cloud-pubsub (2.21.3) depends on protobuf (>=3.19.5,<3.20.0 \|\| >3.20.0,<3.20.1 \|\| >3.20.1,<4.21.0 \|\| >4.21.0,<4.21.1 \|\| >4.21.1,<4.21.2 \|\| >4.21.2,<4.21.3 \|\| >4.21.3,<4.21.4 \|\| >4.21.4,<4.21.5 \|\| >4.21.5,<5.0.0dev), google-cloud-pubsub (>=2.21.3) requires protobuf (>=3.19.5,<3.20.0 \|\| >3.20.0,<3.20.1 \|\| >3.20.1,<4.21.0 \|\| >4.21.0,<4.21.1 \|\| >4.21.1,<4.21.2 \|\| >4.21.2,<4.21.3 \|\| >4.21.3,<4.21.4 \|\| >4.21.4,<4.21.5 \|\| >4.21.5,<5.0.0dev). Because grpcio (1.64.1) depends on protobuf (>=5.26.1,<6.0dev) and no versions of grpcio match >1.64.1,<2.0.0, grpcio (>=1.64.1,<2.0.0) requires protobuf (>=5.26.1,<6.0dev). Thus, google-cloud-pubsub (>=2.21.3) is incompatible with grpcio (>=1.64.1,<2.0.0). ``` Can support for a newer version of protobuf be included in this library?		2024-06-14T00:50:49	[]	[]
Solid-Energy-Systems/NewareNDA	Solid-Energy-Systems__NewareNDA-81	fe5d7d25c419fa59082092ab1282cf12e5c20092	diff --git a/CHANGELOG.md b/CHANGELOG.md index a5a8f18..b17d4c5 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -5,6 +5,18 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). +## [v2024.08.01] +### Added +- Read aux temperature data from BTS 9.1 files. + +### Fixed +- Bug fixes for BTS 9.1. + +### Changed +- setup.py converted to pyproject.toml +- Reworked logging functionality with new log_level keyword. + + ## [v2024.07.01] ### Added - Initial support for BTS 9.1 files. diff --git a/LICENSE b/LICENSE index 89c5b3d..847a993 100644 --- a/LICENSE +++ b/LICENSE @@ -1,4 +1,4 @@ -Copyright (c) 2023 SES AI Corporation, All rights reserved. +Copyright (c) 2022-2024 SES AI Corporation, All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: diff --git a/NewareNDA/NewareNDA.py b/NewareNDA/NewareNDA.py index a1c16ec..e598b6e 100644 --- a/NewareNDA/NewareNDA.py +++ b/NewareNDA/NewareNDA.py @@ -1,4 +1,4 @@ -# © 2023 Copyright SES AI +# © 2022-2024 Copyright SES AI # Author: Daniel Cogswell # Email: [email protected] @@ -13,8 +13,10 @@ multiplier_dict, state_dict from .NewareNDAx import read_ndax +logger = logging.getLogger('newarenda') -def read(file, software_cycle_number=True, cycle_mode='chg'): + +def read(file, software_cycle_number=True, cycle_mode='chg', log_level='INFO'): """ Read electrochemical data from an Neware nda or ndax binary file. @@ -26,15 +28,28 @@ def read(file, software_cycle_number=True, cycle_mode='chg'): 'chg': (Default) Sets new cycles with a Charge step following a Discharge. 'dchg': Sets new cycles with a Discharge step following a Charge. 'auto': Identifies the first non-rest state as the incremental state. + log_level (str): Sets the modules logging level. Default: 'INFO' + Options: 'CRITICAL', 'ERROR', 'WARNING', 'INFO', 'DEBUG', 'NOTSET' Returns: df (pd.DataFrame): DataFrame containing all records in the file """ + + # Set up logging + log_level = log_level.upper() + if log_level in logging._nameToLevel.keys(): + logger.setLevel(log_level) + else: + logger.warning(f"Logging level '{log_level}' not supported; Defaulting to 'INFO'. " + f"Supported options are: {', '.join(logging._nameToLevel.keys())}") + + # Identify file type and process accordingly _, ext = os.path.splitext(file) if ext == '.nda': return read_nda(file, software_cycle_number, cycle_mode) elif ext == '.ndax': return read_ndax(file, software_cycle_number, cycle_mode) else: + logger.error("File type not supported!") raise TypeError("File type not supported!") @@ -57,23 +72,24 @@ def read_nda(file, software_cycle_number, cycle_mode='chg'): mm = mmap.mmap(f.fileno(), 0, access=mmap.ACCESS_READ) if mm.read(6) != b'NEWARE': + logger.error(f"{file} does not appear to be a Neware file.") raise ValueError(f"{file} does not appear to be a Neware file.") # Get the file version [nda_version] = struct.unpack('<B', mm[14:15]) - logging.info(f"NDA version: {nda_version}") + logger.info(f"NDA version: {nda_version}") # Try to find server and client version info version_loc = mm.find(b'BTSServer') if version_loc != -1: mm.seek(version_loc) server = mm.read(50).strip(b'\x00').decode() - logging.info(f"Server: {server}") + logger.info(f"Server: {server}") mm.seek(50, 1) client = mm.read(50).strip(b'\x00').decode() - logging.info(f"Client: {client}") + logger.info(f"Client: {client}") else: - logging.info("BTS version not found!") + logger.info("BTS version not found!") # version specific settings if nda_version == 29: @@ -81,7 +97,7 @@ def read_nda(file, software_cycle_number, cycle_mode='chg'): elif nda_version == 130: output, aux = _read_nda_130(mm) else: - logging.error(f"nda version {nda_version} is not yet supported!") + logger.error(f"nda version {nda_version} is not yet supported!") raise NotImplementedError(f"nda version {nda_version} is not yet supported!") # Create DataFrame and sort by Index @@ -116,15 +132,15 @@ def _read_nda_29(mm): # Get the active mass [active_mass] = struct.unpack('<I', mm[152:156]) - logging.info(f"Active mass: {active_mass/1000} mg") + logger.info(f"Active mass: {active_mass/1000} mg") try: remarks = mm[2317:2417].decode('ASCII') # Clean null characters remarks = remarks.replace(chr(0), '').strip() - logging.info(f"Remarks: {remarks}") + logger.info(f"Remarks: {remarks}") except UnicodeDecodeError: - logging.warning(f"Converting remark bytes into ASCII failed") + logger.warning("Converting remark bytes into ASCII failed") remarks = "" # Identify the beginning of the data section @@ -132,6 +148,7 @@ def _read_nda_29(mm): identifier = b'\x00\x00\x00\x00\x55\x00' header = mm.find(identifier) if header == -1: + logger.error("File does not contain any valid records.") raise EOFError("File does not contain any valid records.") while (((mm[header + 4 + record_len] != 85) \| (not _valid_record(mm[header+4:header+4+record_len]))) @@ -168,7 +185,8 @@ def _read_nda_130(mm): record_len = 88 identifier = mm[1024:1030] if mm[1024:1025] == b'\x55': # BTS 9.1 - record_len = 56 + # Find next record and get length + record_len = mm.find(mm[1024:1026], 1026) - 1024 mm.seek(1024) # Read data records @@ -181,6 +199,8 @@ def _read_nda_130(mm): # Check for a data record if bytes[0:1] == b'\x55': output.append(_bytes_to_list_BTS91(bytes)) + if record_len == 56: + aux.append(_aux_bytes_to_list_BTS91(bytes)) elif bytes[0:6] == identifier: output.append(_bytes_to_list_BTS9(bytes[4:])) @@ -199,14 +219,14 @@ def _read_nda_130(mm): # Get the active mass [active_mass] = struct.unpack('<d', bytes[-8:]) - logging.info(f"Active mass: {active_mass} mg") + logger.info(f"Active mass: {active_mass} mg") # Get the remarks remarks = bytes[363:491].decode('ASCII') # Clean null characters remarks = remarks.replace(chr(0), '').strip() - logging.info(f"Remarks: {remarks}") + logger.info(f"Remarks: {remarks}") return output, aux @@ -222,12 +242,10 @@ def _bytes_to_list(bytes): """Helper function for interpreting a byte string""" # Extract fields from byte string - [Index, Cycle] = struct.unpack('<II', bytes[2:10]) - [Step] = struct.unpack('<I', bytes[10:14]) - [Status, Jump, Time] = struct.unpack('<BBQ', bytes[12:22]) - [Voltage, Current] = struct.unpack('<ii', bytes[22:30]) - [Charge_capacity, Discharge_capacity] = struct.unpack('<qq', bytes[38:54]) - [Charge_energy, Discharge_energy] = struct.unpack('<qq', bytes[54:70]) + [Index, Cycle, Step] = struct.unpack('<III', bytes[2:14]) + [Status, Jump, Time, Voltage, Current] = struct.unpack('<BBQii', bytes[12:30]) + [Charge_capacity, Discharge_capacity, + Charge_energy, Discharge_energy] = struct.unpack('<qqqq', bytes[38:70]) [Y, M, D, h, m, s] = struct.unpack('<HBBBBB', bytes[70:77]) [Range] = struct.unpack('<i', bytes[78:82]) @@ -259,11 +277,10 @@ def _bytes_to_list_BTS9(bytes): """Helper function to interpret byte strings from BTS9""" [Step, Status] = struct.unpack('<BB', bytes[5:7]) [Index] = struct.unpack('<I', bytes[12:16]) - [Time] = struct.unpack('<Q', bytes[24:32]) - [Voltage, Current] = struct.unpack('<ff', bytes[32:40]) - [Charge_Capacity, Charge_Energy] = struct.unpack('<ff', bytes[48:56]) - [Discharge_Capacity, Discharge_Energy] = struct.unpack('<ff', bytes[56:64]) - [Date] = struct.unpack('<Q', bytes[64:72]) + [Time, Voltage, Current] = struct.unpack('<Qff', bytes[24:40]) + [Charge_Capacity, Charge_Energy, + Discharge_Capacity, Discharge_Energy, + Date] = struct.unpack('<ffffQ', bytes[48:72]) # Create a dictionary for the record list = [ @@ -323,6 +340,12 @@ def _aux_bytes_to_list(bytes): return [Index, Aux, T/10, V/10000] +def _aux_bytes_to_list_BTS91(bytes): + [Index] = struct.unpack('<I', bytes[8:12]) + [T] = struct.unpack('<f', bytes[52:56]) + return [Index, 1, T, None] + + def _generate_cycle_number(df, cycle_mode='chg'): """ Generate a cycle number to match Neware. @@ -344,10 +367,11 @@ def _generate_cycle_number(df, cycle_mode='chg'): inkey = 'DChg' offkey = 'Chg' else: + logger.error(f"Cycle_Mode '{cycle_mode}' not recognized. Supported options are 'chg', 'dchg', and 'auto'.") raise KeyError(f"Cycle_Mode '{cycle_mode}' not recognized. Supported options are 'chg', 'dchg', and 'auto'.") # Identify the beginning of key incremental steps - inc = (df['Status'] == 'CCCV_'+inkey) \| (df['Status'] == 'CC_'+inkey) \| (df['Status'] == 'CP_'+inkey) + inc = (df['Status'] == 'CCCV_'+inkey) \| (df['Status'] == 'CC_'+inkey) \| (df['Status'] == 'CP_'+inkey) # inc series = 1 at new incremental step, 0 otherwise inc = (inc - inc.shift()).clip(0) @@ -405,7 +429,7 @@ def _id_first_state(df): _, cycle_mode = first_state.split('_', 1) except ValueError: # Status is SIM or otherwise. Set mode to chg - logging.warning("First Step not recognized. Defaulting to Cycle_Mode 'Charge'.") + logger.warning("First Step not recognized. Defaulting to Cycle_Mode 'Charge'.") cycle_mode = 'chg' return cycle_mode.lower() diff --git a/bin/NewareNDA-cli.py b/NewareNDA/NewareNDAcli.py similarity index 53% rename from bin/NewareNDA-cli.py rename to NewareNDA/NewareNDAcli.py index e5b07e6..6de8948 100755 --- a/bin/NewareNDA-cli.py +++ b/NewareNDA/NewareNDAcli.py @@ -4,21 +4,23 @@ output format is csv. Other formats may require installing additional packages. ''' import argparse +from logging import _nameToLevel import pandas as pd import NewareNDA -output_cmd = { - 'csv': lambda df, f: pd.DataFrame.to_csv(df, f, index=False), - 'excel': lambda df, f: pd.DataFrame.to_excel(df, f, index=False), - 'feather': pd.DataFrame.to_feather, - 'hdf': lambda df, f: pd.DataFrame.to_hdf(df, f, key='Index'), - 'json': pd.DataFrame.to_json, - 'parquet': pd.DataFrame.to_parquet, - 'pickle': pd.DataFrame.to_pickle, - 'stata': pd.DataFrame.to_stata -} -if __name__ == '__main__': +def main(): + output_cmd = { + 'csv': lambda df, f: pd.DataFrame.to_csv(df, f, index=False), + 'excel': lambda df, f: pd.DataFrame.to_excel(df, f, index=False), + 'feather': pd.DataFrame.to_feather, + 'hdf': lambda df, f: pd.DataFrame.to_hdf(df, f, key='Index'), + 'json': pd.DataFrame.to_json, + 'parquet': pd.DataFrame.to_parquet, + 'pickle': pd.DataFrame.to_pickle, + 'stata': pd.DataFrame.to_stata + } + parser = argparse.ArgumentParser(description=__doc__) parser.add_argument('in_file', help='input file') parser.add_argument('out_file', help='output file') @@ -28,7 +30,15 @@ help='Generate the cycle number field to match old versions of BTSDA.') parser.add_argument('-v', '--version', help='show version', action='version', version=NewareNDA.__version__) + parser.add_argument('-l', '--log_level', choices=list(_nameToLevel.keys()), default='INFO', + help='Set the logging level for NewareNDA') + parser.add_argument('-c', '--cycle_mode', choices=['chg', 'dchg', 'auto'], default='chg', + help='Selects how the cycle is incremented.') args = parser.parse_args() - df = NewareNDA.read(args.in_file, args.software_cycle_number) + df = NewareNDA.read(args.in_file, args.software_cycle_number, cycle_mode=args.cycle_mode, log_level=args.log_level) output_cmd[args.format](df, args.out_file) + + +if __name__ == '__main__': + main() diff --git a/NewareNDA/NewareNDAx.py b/NewareNDA/NewareNDAx.py index e17b4b7..a58a334 100644 --- a/NewareNDA/NewareNDAx.py +++ b/NewareNDA/NewareNDAx.py @@ -1,4 +1,4 @@ -# © 2023 Copyright SES AI +# © 2022-2024 Copyright SES AI # Author: Daniel Cogswell # Email: [email protected] @@ -16,6 +16,8 @@ from NewareNDA.dicts import rec_columns, dtype_dict, state_dict, \ multiplier_dict +logger = logging.getLogger('newarenda') + def read_ndax(file, software_cycle_number=False, cycle_mode='chg'): """ @@ -39,10 +41,10 @@ def read_ndax(file, software_cycle_number=False, cycle_mode='chg'): version_info = zf.extract('VersionInfo.xml', path=tmpdir) with open(version_info, 'r', encoding='gb2312') as f: config = ET.fromstring(f.read()).find('config/ZwjVersion') - logging.info(f"Server version: {config.attrib['SvrVer']}") - logging.info(f"Client version: {config.attrib['CurrClientVer']}") - logging.info(f"Control unit version: {config.attrib['ZwjVersion']}") - logging.info(f"Tester version: {config.attrib['MainXwjVer']}") + logger.info(f"Server version: {config.attrib['SvrVer']}") + logger.info(f"Client version: {config.attrib['CurrClientVer']}") + logger.info(f"Control unit version: {config.attrib['ZwjVersion']}") + logger.info(f"Tester version: {config.attrib['MainXwjVer']}") except Exception: pass @@ -52,7 +54,7 @@ def read_ndax(file, software_cycle_number=False, cycle_mode='chg'): with open(step, 'r', encoding='gb2312') as f: config = ET.fromstring(f.read()).find('config') active_mass = float(config.find('Head_Info/SCQ').attrib['Value']) - logging.info(f"Active mass: {active_mass/1000} mg") + logger.info(f"Active mass: {active_mass/1000} mg") except Exception: pass @@ -77,7 +79,8 @@ def read_ndax(file, software_cycle_number=False, cycle_mode='chg'): columns=rec_columns) # Fill in missing data - Neware appears to fabricate data - _data_interpolation(data_df) + if data_df.isnull().any(axis=None): + _data_interpolation(data_df) else: data_df, _ = read_ndc(data_file) @@ -107,13 +110,12 @@ def _data_interpolation(df): Some ndax from from BTS Server 8 do not seem to contain a complete dataset. This helper function fills in missing times, capacities, and energies. """ + logger.warning("IMPORTANT: This ndax has missing data. The output from " + "NewareNDA contains interpolated data!") + # Identify the valid data nan_mask = df['Time'].notnull() - if nan_mask.any(): - logging.warning("IMPORTANT: This ndax has missing data. The output " - "from NewareNDA contains interpolated data!") - # Group by step and run 'inside' interpolation on Time df['Time'] = df.groupby('Step')['Time'].transform( lambda x: pd.Series.interpolate(x, limit_area='inside')) @@ -274,7 +276,7 @@ def read_ndc(file): # Get ndc file version [ndc_version] = struct.unpack('<B', mm[2:3]) - logging.info(f"NDC version: {ndc_version}") + logger.info(f"NDC version: {ndc_version}") if ndc_version == 2: return _read_ndc_2(mm) @@ -305,7 +307,7 @@ def _read_ndc_2(mm): elif bytes[0:1] == b'\x74': aux.append(_aux_bytes_74_to_list_ndc(bytes)) else: - logging.warning("Unknown record type: "+bytes[0:1].hex()) + logger.warning("Unknown record type: "+bytes[0:1].hex()) header = mm.find(identifier, header + record_len) diff --git a/NewareNDA/dicts.py b/NewareNDA/dicts.py index f332004..c1e8fa3 100644 --- a/NewareNDA/dicts.py +++ b/NewareNDA/dicts.py @@ -70,6 +70,7 @@ 1: 1e-4, 5: 1e-4, 10: 1e-3, + 50: 1e-3, 100: 1e-2, 200: 1e-2, 1000: 1e-1, diff --git a/NewareNDA/version.py b/NewareNDA/version.py index 3fc1792..fdc0c02 100644 --- a/NewareNDA/version.py +++ b/NewareNDA/version.py @@ -1,1 +1,1 @@ -__version__ = 'v2024.07.01' +__version__ = 'v2024.08.01' diff --git a/README.md b/README.md index 426c790..ca01d98 100644 --- a/README.md +++ b/README.md @@ -3,7 +3,7 @@ [![Coverage Status](https://coveralls.io/repos/github/Solid-Energy-Systems/NewareNDA/badge.svg?branch=development)](https://coveralls.io/github/Solid-Energy-Systems/NewareNDA?branch=development) # NewareNDA -© 2024 Copyright SES AI +© 2022-2024 Copyright SES AI <br>Author: Daniel Cogswell <br>Email: [email protected] @@ -33,9 +33,33 @@ df = NewareNDA.read('filename.nda') ``` ## Command-line interface: ``` -NewareNDA-cli.py in_file.nda --format feather out_file.ftr +usage: NewareNDA-cli [-h] + [-f {csv,excel,feather,hdf,json,parquet,pickle,stata}] + [-s] [-v] + [-l {CRITICAL,FATAL,ERROR,WARN,WARNING,INFO,DEBUG,NOTSET}] + [-c {chg,dchg,auto}] + in_file out_file + +Script for converting Neware NDA files to other file formats. The default +output format is csv. Other formats may require installing additional +packages. + +positional arguments: + in_file input file + out_file output file + +options: + -h, --help show this help message and exit + -f {csv,excel,feather,hdf,json,parquet,pickle,stata}, --format {csv,excel,feather,hdf,json,parquet,pickle,stata} + -s, --software_cycle_number + Generate the cycle number field to match old versions + of BTSDA. + -v, --version show version + -l {CRITICAL,FATAL,ERROR,WARN,WARNING,INFO,DEBUG,NOTSET}, --log_level {CRITICAL,FATAL,ERROR,WARN,WARNING,INFO,DEBUG,NOTSET} + Set the logging level for NewareNDA + -c {chg,dchg,auto}, --cycle_mode {chg,dchg,auto} + Selects how the cycle is incremented. ``` -The following `--format` options are supported: `csv, excel, feather, hdf, json, parquet, pickle, stata` # Troubleshooting If you encounter a key error, it is often the case that your file has a hardware setting that we have not seen before. Usually it is a quick fix that requires comparing output from BTSDA with values extracted by NewareNDA. Please start a new Github Issue and we will help debug. diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..38b6b62 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,31 @@ +# © 2022-2024 Copyright SES AI +# Author: Daniel Cogswell +# Email: [email protected] + +[project] +name = "NewareNDA" +dynamic = ["version"] +license = { file = "LICENSE" } +readme = { file = "README.md", content-type = "text/markdown" } +authors = [{ name = "Daniel Cogswell", email = "[email protected]" }] +requires-python = ">=3.6" +classifiers = [ "Programming Language :: Python :: 3" ] +description = "Neware nda binary file reader." +dependencies = [ + "pandas", + "importlib-metadata ~= 1.0 ; python_version < '3.8'" +] + +[project.scripts] +NewareNDA-cli = "NewareNDA.NewareNDAcli:main" + +[project.urls] +homepage = "https://github.com/Solid-Energy-Systems/NewareNDA" +tracker = "https://github.com/Solid-Energy-Systems/NewareNDA/issues" + +[build-system] +requires = ["setuptools>=61.0"] +build-backend = "setuptools.build_meta" + +[project.optional-dependencies] +test = ["pytest", "pyarrow", "coveralls"] diff --git a/setup.py b/setup.py deleted file mode 100644 index 6229797..0000000 --- a/setup.py +++ /dev/null @@ -1,33 +0,0 @@ -# © 2023 Copyright SES AI -# Author: Daniel Cogswell -# Email: [email protected] - -import setuptools - -version = {} -with open('NewareNDA/version.py', 'r', encoding='utf-8') as fh: - exec(fh.read(), version) -__version__ = version['__version__'] - -with open('README.md', 'r', encoding='utf-8') as fh: - long_description = fh.read() - -setuptools.setup( - name='NewareNDA', - version=__version__, - description='Neware nda binary file reader.', - long_description=long_description, - long_description_content_type="text/markdown", - author='Daniel Cogswell', - author_email='[email protected]', - url='https://github.com/Solid-Energy-Systems/NewareNDA', - license='BSD-3-Clause', - packages=['NewareNDA'], - scripts=['bin/NewareNDA-cli.py'], - install_requires=['pandas'], - extras_require={'test': ['pytest', 'pyarrow', 'coveralls']}, - python_requires='>=3.6', - classifiers=[ - "Programming Language :: Python :: 3", - ], -)	Aux temperature data for BTS 9.1 @ferdaoues5 please help with testing this, I have very few of these files.		2024-07-17T18:33:43	[]	[]
thingsapi/things.py	thingsapi__things.py-118	85975337a4119fa80bf811e326eb863fce5f4905	diff --git a/things/api.py b/things/api.py index f6ebc4d..b3024e1 100644 --- a/things/api.py +++ b/things/api.py @@ -181,7 +181,7 @@ def tasks(uuid=None, include_items=False, kwargs): # noqa: C901 >>> things.tasks(area='hIo1FJlAYGKt1Yj38vzKc3', include_items=True) [] >>> things.tasks(status='completed', count_only=True) - 10 + 12 >>> things.tasks(status='completed', last='1w', count_only=True) 0 @@ -605,7 +605,7 @@ def completed(kwargs): Examples -------- >>> things.completed(count_only=True) - 10 + 12 >>> things.completed(type='project', count_only=True) 0 >>> things.completed(type='to-do', last='1w') diff --git a/things/database.py b/things/database.py index df66298..a62a4f4 100755 --- a/things/database.py +++ b/things/database.py @@ -962,16 +962,16 @@ def make_unixtime_filter(date_column: str, value) -> str: 'AND stopDate IS NULL' >>> make_unixtime_filter('stopDate', 'future') - "AND date(stopDate, 'unixepoch') > date('now', 'localtime')" + "AND date(stopDate, 'unixepoch', 'localtime') > date('now', 'localtime')" >>> make_unixtime_filter('creationDate', '2021-03-28') - "AND date(creationDate, 'unixepoch') == date('2021-03-28')" + "AND date(creationDate, 'unixepoch', 'localtime') == date('2021-03-28')" >>> make_unixtime_filter('creationDate', '=2021-03-28') - "AND date(creationDate, 'unixepoch') = date('2021-03-28')" + "AND date(creationDate, 'unixepoch', 'localtime') = date('2021-03-28')" >>> make_unixtime_filter('creationDate', '<=2021-03-28') - "AND date(creationDate, 'unixepoch') <= date('2021-03-28')" + "AND date(creationDate, 'unixepoch', 'localtime') <= date('2021-03-28')" >>> make_unixtime_filter('creationDate', None) '' @@ -996,7 +996,7 @@ def make_unixtime_filter(date_column: str, value) -> str: threshold = "date('now', 'localtime')" comparator = ">" if value == "future" else "<=" - date = f"date({date_column}, 'unixepoch')" + date = f"date({date_column}, 'unixepoch', 'localtime')" return f"AND {date} {comparator} {threshold}" @@ -1026,7 +1026,7 @@ def make_unixtime_range_filter(date_column: str, offset) -> str: Examples -------- >>> make_unixtime_range_filter('creationDate', '3d') - "AND datetime(creationDate, 'unixepoch') > datetime('now', '-3 days')" + "AND datetime(creationDate, 'unixepoch', 'localtime') > datetime('now', '-3 days')" >>> make_unixtime_range_filter('creationDate', None) '' @@ -1047,7 +1047,7 @@ def make_unixtime_range_filter(date_column: str, offset) -> str: # Use `typing.assert_never(suffix)` from Python 3.11 onwards. raise AssertionError("line should be unreachable.") # for static code analyzers - column_datetime = f"datetime({date_column}, 'unixepoch')" + column_datetime = f"datetime({date_column}, 'unixepoch', 'localtime')" offset_datetime = f"datetime('now', '{modifier}')" return f"AND {column_datetime} > {offset_datetime}"	timezone of completed tasks not taken into account To Reproduce I was looking for all tasks completed on 2024-02-25: ``` kwargs['status'] = None kwargs['stop_date'] = f'{DATE}' tasks = things.tasks(kwargs) ``` ...but it returns some tasks on the day before, particularly those that were completed close to midnight: Here's the stopDate for one of the returned tasks: 1708826542.13277 > GMT: Sunday, February 25, 2024 2:02:22.132 AM > Your time zone: Saturday, February 24, 2024 9:02:22.132 PM [GMT-05:00](https://www.epochconverter.com/timezones?q=1708826542.13277) Expected behavior** When I supply a `stop_date` I expect it to be reflective of my local timezone: i.e., get all tasks completed on that day _for me_.	@mikez per [our discussion](https://github.com/chrisgurney/things2md/pull/2#issuecomment-1967535472), adding your proposed solution here: Currently: - in responses, `stop_date`, `modified`, and `created` are in "localtime"; - in queries, `stop_date` expects GMT (aka UTC) dates. The database itself stores these dates as UTC. ## Suggested Changes Make both responses and queries be in localtime. What do you think? To do so, tweak these functions in [database.py](https://github.com/thingsapi/things.py/blob/main/things/database.py) of things.py: - [make_unixtime_filter](https://github.com/thingsapi/things.py/blob/854306777eda4802aaf56bf4452fad3bfb5242e4/things/database.py#L930) - optionally, `make_unixtime_range_filter` (for "created") Want to give it a shot? Hopefully, these should be easy edits and not involve much more than adding "localtime" to the SQL-query, i.e., `date(stopDate, 'unixepoch', 'localtime')` instead of `date(stopDate, 'unixepoch')`.	2024-03-10T12:48:20	[]	[]
thingsapi/things.py	thingsapi__things.py-87	f26a9d03df6a46f416e78e5e49860bb6d3f73452	diff --git a/things/api.py b/things/api.py index 0383c12..abc7a2c 100644 --- a/things/api.py +++ b/things/api.py @@ -702,8 +702,8 @@ def show(uuid): # noqa >>> tag = things.tags('Home') >>> things.show(tag['uuid']) """ - uri = link(uuid) # pragma: no cover - os.system(f"open {quote(uri)}") # pragma: no cover + uri = link(uuid) + os.system(f"open {quote(uri)}") # Helper functions	Monkey patch `# pragma: no cover` See https://github.com/thingsapi/things.py/pull/65#discussion_r615410089	@AlexanderWillner This is obsolete right? I think we removed that code. We've other instances like the following, don't we? https://github.com/thingsapi/things.py/blob/230fcd9ddcdf3737114fcd38676bccd9995c8951/things/api.py#L705 👌 You might fix it like this: https://stackoverflow.com/a/46239371	2021-06-09T19:02:59	[]	[]
etianen/logot	etianen__logot-126	78d2476f669fe284b9ff55ba5dfaab1eca7bd4d8	diff --git a/logot/_capture.py b/logot/_capture.py index 0e7bf54d..8a08e3b0 100644 --- a/logot/_capture.py +++ b/logot/_capture.py @@ -1,7 +1,5 @@ from __future__ import annotations -from logot._format import format_log - class Captured: """ @@ -55,6 +53,3 @@ def __eq__(self, other: object) -> bool: def __repr__(self) -> str: return f"Captured({self.levelname!r}, {self.msg!r}, levelno={self.levelno!r})" - - def __str__(self) -> str: - return format_log(self.levelname, self.msg) diff --git a/logot/_format.py b/logot/_format.py deleted file mode 100644 index 5a5f8de8..00000000 --- a/logot/_format.py +++ /dev/null @@ -1,18 +0,0 @@ -from __future__ import annotations - -from logot._typing import Level - - -def format_level(level: Level) -> str: - # Format `str` level. - if isinstance(level, str): - return level - # Format `int` level. - if isinstance(level, int): - return f"Level {level}" - # Handle invalid level. - raise TypeError(f"Invalid level: {level!r}") - - -def format_log(levelname: str, msg: str) -> str: - return f"[{levelname}] {msg}" diff --git a/logot/_level.py b/logot/_level.py new file mode 100644 index 00000000..3c819109 --- /dev/null +++ b/logot/_level.py @@ -0,0 +1,61 @@ +from __future__ import annotations + +from logot._capture import Captured +from logot._match import Matcher +from logot._typing import Level + + +class _LevelNameMatcher(Matcher): + __slots__ = ("_levelname",) + + def __init__(self, levelname: str) -> None: + self._levelname = levelname + + def match(self, captured: Captured) -> bool: + return captured.levelname == self._levelname + + def __eq__(self, other: object) -> bool: + return isinstance(other, _LevelNameMatcher) and other._levelname == self._levelname + + def __repr__(self) -> str: + return repr(self._levelname) + + def __str__(self) -> str: + return f"[{self._levelname}]" + + +DEBUG_MATCHER: Matcher = _LevelNameMatcher("DEBUG") +INFO_MATCHER: Matcher = _LevelNameMatcher("INFO") +WARNING_MATCHER: Matcher = _LevelNameMatcher("WARNING") +ERROR_MATCHER: Matcher = _LevelNameMatcher("ERROR") +CRITICAL_MATCHER: Matcher = _LevelNameMatcher("CRITICAL") + + +class _LevelNoMatcher(Matcher): + __slots__ = ("_levelno",) + + def __init__(self, levelno: int) -> None: + self._levelno = levelno + + def match(self, captured: Captured) -> bool: + return captured.levelno == self._levelno + + def __eq__(self, other: object) -> bool: + return isinstance(other, _LevelNoMatcher) and other._levelno == self._levelno + + def __repr__(self) -> str: + return repr(self._levelno) + + def __str__(self) -> str: + return f"[Level {self._levelno}]" + + +def level_matcher(level: Level) -> Matcher: + # Handle `str` level. + if isinstance(level, str): + return _LevelNameMatcher(level) + # Handle `int` level. + if isinstance(level, int): + return _LevelNoMatcher(level) + # Handle invalid level. + raise TypeError(f"Invalid level: {level!r}") diff --git a/logot/_logged.py b/logot/_logged.py index b9d598bf..12f1bc77 100644 --- a/logot/_logged.py +++ b/logot/_logged.py @@ -3,10 +3,10 @@ from abc import ABC, abstractmethod from logot._capture import Captured -from logot._format import format_level, format_log -from logot._msg import compile_msg_matcher +from logot._level import CRITICAL_MATCHER, DEBUG_MATCHER, ERROR_MATCHER, INFO_MATCHER, WARNING_MATCHER, level_matcher +from logot._match import Matcher +from logot._msg import msg_matcher from logot._typing import Level -from logot._validate import validate_level class Logged(ABC): @@ -78,7 +78,7 @@ def log(level: Level, msg: str) -> Logged: :param level: A log level name (e.g. ``"DEBUG"``) or numeric level (e.g. :data:`logging.DEBUG`). :param msg: A log :doc:`message pattern </log-message-matching>`. """ - return _RecordLogged(validate_level(level), msg) + return _RecordMatcher(level_matcher(level), msg_matcher(msg)) def debug(msg: str) -> Logged: @@ -88,7 +88,7 @@ def debug(msg: str) -> Logged: :param msg: A log :doc:`message pattern </log-message-matching>`. """ - return _RecordLogged("DEBUG", msg) + return _RecordMatcher(DEBUG_MATCHER, msg_matcher(msg)) def info(msg: str) -> Logged: @@ -98,7 +98,7 @@ def info(msg: str) -> Logged: :param msg: A log :doc:`message pattern </log-message-matching>`. """ - return _RecordLogged("INFO", msg) + return _RecordMatcher(INFO_MATCHER, msg_matcher(msg)) def warning(msg: str) -> Logged: @@ -108,7 +108,7 @@ def warning(msg: str) -> Logged: :param msg: A log :doc:`message pattern </log-message-matching>`. """ - return _RecordLogged("WARNING", msg) + return _RecordMatcher(WARNING_MATCHER, msg_matcher(msg)) def error(msg: str) -> Logged: @@ -118,7 +118,7 @@ def error(msg: str) -> Logged: :param msg: A log :doc:`message pattern </log-message-matching>`. """ - return _RecordLogged("ERROR", msg) + return _RecordMatcher(ERROR_MATCHER, msg_matcher(msg)) def critical(msg: str) -> Logged: @@ -128,42 +128,31 @@ def critical(msg: str) -> Logged: :param msg: A log :doc:`message pattern </log-message-matching>`. """ - return _RecordLogged("CRITICAL", msg) + return _RecordMatcher(CRITICAL_MATCHER, msg_matcher(msg)) -class _RecordLogged(Logged): - __slots__ = ("_level", "_msg", "_msg_matcher") +class _RecordMatcher(Logged): + __slots__ = ("_matchers",) - def __init__(self, level: Level, msg: str) -> None: - self._level = level - self._msg = msg - self._msg_matcher = compile_msg_matcher(msg) + def __init__(self, matchers: Matcher) -> None: + self._matchers = matchers def __eq__(self, other: object) -> bool: - return isinstance(other, _RecordLogged) and other._level == self._level and other._msg == self._msg + return isinstance(other, _RecordMatcher) and other._matchers == self._matchers def __repr__(self) -> str: - return f"log({self._level!r}, {self._msg!r})" + matchers_repr = ", ".join(map(repr, self._matchers)) + return f"log({matchers_repr})" def reduce(self, captured: Captured) -> Logged \| None: - # Match `str` level. - if isinstance(self._level, str): - if self._level != captured.levelname: - return self - # Match `int` level. - elif isinstance(self._level, int): - if self._level != captured.levelno: - return self - else: # pragma: no cover - raise TypeError(f"Invalid level: {self._level!r}") - # Match message. - if not self._msg_matcher(captured.msg): - return self - # We matched! - return None + # Handle full reduction. + if all(matcher.match(captured) for matcher in self._matchers): + return None + # Handle no reduction. + return self def _str(self, , indent: str) -> str: - return format_log(format_level(self._level), self._msg) + return " ".join(map(str, self._matchers)) class _ComposedLogged(Logged): diff --git a/logot/_match.py b/logot/_match.py new file mode 100644 index 00000000..a51658da --- /dev/null +++ b/logot/_match.py @@ -0,0 +1,25 @@ +from __future__ import annotations + +from abc import ABC, abstractmethod + +from logot._capture import Captured + + +class Matcher(ABC): + __slots__ = () + + @abstractmethod + def match(self, captured: Captured) -> bool: + raise NotImplementedError + + @abstractmethod + def __eq__(self, other: object) -> bool: + raise NotImplementedError + + @abstractmethod + def __repr__(self) -> str: + raise NotImplementedError + + @abstractmethod + def __str__(self) -> str: + raise NotImplementedError diff --git a/logot/_msg.py b/logot/_msg.py index 834076f8..3cbd72b9 100644 --- a/logot/_msg.py +++ b/logot/_msg.py @@ -1,13 +1,9 @@ from __future__ import annotations import re -from typing import Callable -from logot._typing import TypeAlias - -# Compiled matcher callable. -# The returned `object` is truthy on successful match and falsy on failed match. -MessageMatcher: TypeAlias = Callable[[str], object] +from logot._capture import Captured +from logot._match import Matcher # Regex matching a simplified conversion specifier. _RE_CONVERSION = re.compile(r"%(.\|$)") @@ -41,8 +37,38 @@ } -def compile_msg_matcher(pattern: str) -> MessageMatcher: - parts: list[str] = _RE_CONVERSION.split(pattern) +class _MessageMatcher(Matcher): + __slots__ = ("_msg",) + + def __init__(self, msg: str) -> None: + self._msg = msg + + def match(self, captured: Captured) -> bool: + return captured.msg == self._msg + + def __eq__(self, other: object) -> bool: + return isinstance(other, _MessageMatcher) and other._msg == self._msg + + def __repr__(self) -> str: + return repr(self._msg) + + def __str__(self) -> str: + return self._msg + + +class _MessagePatternMatcher(_MessageMatcher): + __slots__ = ("_pattern",) + + def __init__(self, msg: str, pattern: re.Pattern[str]) -> None: + super().__init__(msg) + self._pattern = pattern + + def match(self, captured: Captured) -> bool: + return self._pattern.fullmatch(captured.msg) is not None + + +def msg_matcher(msg: str) -> Matcher: + parts: list[str] = _RE_CONVERSION.split(msg) parts_len = len(parts) # If there is more than one part, at least one conversion specifier was found and we might need a regex matcher. if parts_len > 1: @@ -60,8 +86,9 @@ def compile_msg_matcher(pattern: str) -> MessageMatcher: # Create regex matcher. if is_regex: parts[::2] = map(re.escape, parts[::2]) - return re.compile("".join(parts), re.DOTALL).fullmatch + pattern = re.compile("".join(parts), re.DOTALL) + return _MessagePatternMatcher(msg, pattern) # Recreate the pattern with all escape sequences replaced. - pattern = "".join(parts) + msg = "".join(parts) # Create simple matcher. - return pattern.__eq__ + return _MessageMatcher(msg)	Allow matching on `extra` fields passed to `logging` calls This would need to be a recursive partial match. Support ELLIPSIS marker With using doctest you can use `...` to mean regex-like `.*` catchall. When converting to logot I stumbled upon `AssertionError: Not logged:` until realizing I had `...` inside the expected output. Support `loguru`-style message matching Originally intended as be part of #28, this has been moved to a separate issue following discussion with `loguru` devs in https://github.com/Delgan/loguru/issues/1072. 👍 ## Proposed API Support `.format()`-style placeholders like this: ``` py from logot.contrib.loguru import logged logot.wait_for(logged.info("Hello {}")) ``` Support matching structured data like this (requires #25): ``` py logot.wait_for( logged.bind(foo="bar").info("Hello {}") ) ``` ## Implementation Parsing of the format-style placeholders should be nice and easy using [`string.Formatter.parse()`](https://docs.python.org/3/library/string.html#string.Formatter.parse). Probably easier than the normal `%`-style placeholder parsing!	Just found out about https://logot.readthedocs.io/latest/log-message-matching.html which wasn't clear from the Github README alone, that solved my issue. Above idea may still be interesting. I've flirted with the idea of supporting alternate match syntax (see #31). If there was demand for this, I'd consider making a generic mechanism to match using other syntaxes, of which `...` would be a good option. I'm marking this idea as `wontfix` for now, as there are no immediate plans. If other people stumble upon this issue and think it would be worthwhile, however, feel free to comment and we can think about ways it could be implemented! ❤️ Meanwhile, I've updated the `README` with some deep links to the `logot` docs (see #125). Hopefully this will help other people find the [log message matching](https://logot.readthedocs.io/latest/log-message-matching.html) docs in future! 🙇 Matching the style of every 3rd-party log integration is going to get tedious. Let's not.	2024-02-25T13:18:32	[]	[]
etianen/logot	etianen__logot-94	02aacaafa7657ae596ea6a41a30a57fd5e241fe6	diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 1309bb24..f83073b6 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -19,6 +19,8 @@ jobs: - python-version: "3.10" - python-version: "3.11" - python-version: "3.12" + - lib-versions: "loguru~=0.6.0" + - lib-versions: "loguru~=0.7.0" - lib-versions: "pytest~=7.0" - lib-versions: "pytest~=8.0" - lib-versions: "trio~=0.22.0" diff --git a/README.md b/README.md index ddff7abf..64d0b111 100644 --- a/README.md +++ b/README.md @@ -16,6 +16,8 @@ def test_something(logot: Logot) -> None: logot.assert_logged(logged.info("Something was done")) ``` +`logot` provides first-class integrations for popular testing (e.g. [`pytest`](https://logot.readthedocs.io/latest/using-pytest.html), [`unittest`](https://logot.readthedocs.io/latest/using-unittest.html)), asynchronous (e.g. [`asyncio`](https://logot.readthedocs.io/latest/index.html#index-testing-threaded), [`trio`](https://logot.readthedocs.io/latest/integrations/trio.html)) and logging frameworks (e.g. [`logging`](https://logot.readthedocs.io/latest/log-capturing.html), [`loguru`](https://logot.readthedocs.io/latest/integrations/loguru.html)). It can be extended to support many others. 💪 + ## Documentation 📖 diff --git a/docs/api/index.rst b/docs/api/index.rst index 3f7d8718..2d64d0b1 100644 --- a/docs/api/index.rst +++ b/docs/api/index.rst @@ -13,7 +13,7 @@ Import the :mod:`logot` API in your tests: Modules ------- -Learn about the public :mod:`logot` modules with the following reference guides: +Learn about public :mod:`logot` modules with the following reference guides: .. toctree:: :maxdepth: 1 diff --git a/docs/api/logot.loguru.rst b/docs/api/logot.loguru.rst new file mode 100644 index 00000000..ebf8f998 --- /dev/null +++ b/docs/api/logot.loguru.rst @@ -0,0 +1,10 @@ +:mod:`logot.loguru` +=================== + +.. automodule:: logot.loguru + + +API reference +------------- + +.. autoclass:: LoguruCapturer diff --git a/docs/conf.py b/docs/conf.py index 794643b0..489d5e4d 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -25,9 +25,10 @@ autodoc_member_order = "bysource" intersphinx_mapping = { - "python": ("https://docs.python.org/3", None), + "python": ("https://docs.python.org/3/", None), + "loguru": ("https://loguru.readthedocs.io/en/latest/", None), "pytest": ("https://docs.pytest.org/en/latest/", None), - "trio": ("https://trio.readthedocs.io/en/latest", None), + "trio": ("https://trio.readthedocs.io/en/latest/", None), } nitpicky = True diff --git a/docs/index.rst b/docs/index.rst index 12f2c662..5da9e915 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -15,8 +15,10 @@ Log-based testing 🪵 .. note:: - These examples all show using :mod:`logot` with :mod:`pytest`. See :doc:`/using-unittest` to learn how to use - :mod:`logot` with :mod:`unittest`. + :mod:`logot` provides first-class integrations for popular testing (e.g. :doc:`pytest </using-pytest>`, + :doc:`unittest </using-unittest>`), asynchronous (e.g. :ref:`asyncio <index-testing-threaded>`, + :doc:`trio </integrations/trio>`) and logging frameworks (e.g. :doc:`logging </log-capturing>`, + :doc:`loguru </integrations/loguru>`). It can be extended to support many others. 💪 Why test logging? 🤔 @@ -100,7 +102,16 @@ Use :meth:`Logot.await_for` to pause your test until the expected logs arrive or asyncio.create_task(app.start()) await logot.await_for(logged.info("App started")) -.. include:: /include/testing-async-notes.rst +.. note:: + + Use the ``timeout`` argument to :meth:`Logot.await_for` to configure how long to wait before the test fails. This can + be configured globally with the ``timeout`` argument to :class:`Logot`, defaulting to :attr:`Logot.DEFAULT_TIMEOUT`. + +.. seealso:: + + See :doc:`/log-pattern-matching` for examples of how to wait for logs that may arrive in an unpredictable order. + + See :ref:`integrations-async` for other supported asynchronous frameworks (e.g. :doc:`trio </integrations/trio>`). Testing synchronous code diff --git a/docs/installing.rst b/docs/installing.rst index 98f5d900..677e259d 100644 --- a/docs/installing.rst +++ b/docs/installing.rst @@ -23,10 +23,11 @@ Installing package ``extras`` .. code:: bash - pip install 'logot[pytest,trio]' + pip install 'logot[loguru,pytest,trio]' Use the following package ``extras`` for supported 3rd-party integrations: +- ``loguru`` - :doc:`/integrations/loguru` - ``pytest`` - :doc:`/using-pytest` - ``trio`` - :doc:`/integrations/trio` diff --git a/docs/integrations/index.rst b/docs/integrations/index.rst index 16a731e9..2e27d61b 100644 --- a/docs/integrations/index.rst +++ b/docs/integrations/index.rst @@ -19,7 +19,7 @@ Asynchronous frameworks Integrations with 3rd-party asynchronous frameworks extend :meth:`Logot.await_for`, allowing you to :ref:`test asynchronous code <index-testing-async>` using your framework of choice. 💪 -Learn about supported 3rd-party asynchronous frameworks: +Supported frameworks: .. toctree:: :maxdepth: 1 @@ -41,11 +41,13 @@ Logging frameworks Integrations with 3rd-party logging frameworks extend :meth:`Logot.capturing`, allowing you to :doc:`capture logs </log-capturing>` using your framework of choice. 💪 -Learn about supported 3rd-party logging frameworks: +Supported frameworks: .. toctree:: :maxdepth: 1 + loguru + .. seealso:: See :doc:`/log-capturing` usage guide. diff --git a/docs/integrations/loguru.rst b/docs/integrations/loguru.rst new file mode 100644 index 00000000..09eaa852 --- /dev/null +++ b/docs/integrations/loguru.rst @@ -0,0 +1,90 @@ +Using with :mod:`loguru` +======================== + +.. currentmodule:: logot + +:mod:`logot` makes it easy to capture logs from :mod:`loguru`: + +.. code:: python + + from logot.loguru import LoguruCapturer + + with Logot(capturer=LoguruCapturer).capturing() as logot: + do_something() + logot.assert_logged(logged.info("App started")) + + +Installing +---------- + +Ensure :mod:`logot` is installed alongside a compatible :mod:`loguru` version by adding the ``loguru`` extra: + +.. code:: bash + + pip install 'logot[loguru]' + +.. seealso:: + + See :ref:`installing-extras` usage guide. + + +Enabling for :mod:`pytest` +-------------------------- + +Enable :mod:`loguru` support in your :external+pytest:doc:`pytest configuration <reference/customize>`: + +.. code:: ini + + # pytest.ini or .pytest.ini + [pytest] + logot_capturer = logot.loguru.LoguruCapturer + +.. code:: toml + + # pyproject.toml + [tool.pytest.ini_options] + logot_capturer = "logot.loguru.LoguruCapturer" + +.. seealso:: + + See :doc:`/using-pytest` usage guide. + + +Enabling for :mod:`unittest` +---------------------------- + +Enable :mod:`loguru` support in your :class:`logot.unittest.LogotTestCase`: + +.. code:: python + + from logot.loguru import LoguruCapturer + + class MyAppTest(LogotTestCase): + logot_capturer = LoguruCapturer + +.. seealso:: + + See :doc:`/using-unittest` usage guide. + + +Enabling manually +----------------- + +Enable :mod:`loguru` support for your :class:`Logot` instance: + +.. code:: python + + from logot.loguru import LoguruCapturer + + logot = Logot(capturer=LoguruCapturer) + +Enable :mod:`loguru` support for a single :meth:`Logot.capturing` call: + +.. code:: python + + with Logot().capturing(capturer=LoguruCapturer) as logot: + do_something() + +.. seealso:: + + See :class:`Logot` and :meth:`Logot.capturing` API reference. diff --git a/docs/integrations/trio.rst b/docs/integrations/trio.rst index 601fecdb..cb12bda0 100644 --- a/docs/integrations/trio.rst +++ b/docs/integrations/trio.rst @@ -14,7 +14,14 @@ Use :meth:`Logot.await_for` to pause your test until the expected logs arrive or nursery.start_soon(app.start()) await logot.await_for(logged.info("App started")) -.. include:: /include/testing-async-notes.rst +.. note:: + + Use the ``timeout`` argument to :meth:`Logot.await_for` to configure how long to wait before the test fails. This can + be configured globally with the ``timeout`` argument to :class:`Logot`, defaulting to :attr:`Logot.DEFAULT_TIMEOUT`. + +.. seealso:: + + See :doc:`/log-pattern-matching` for examples of how to wait for logs that may arrive in an unpredictable order. Installing diff --git a/docs/log-capturing.rst b/docs/log-capturing.rst index 73fecf35..300306b0 100644 --- a/docs/log-capturing.rst +++ b/docs/log-capturing.rst @@ -11,6 +11,10 @@ Log capturing do_something() logot.assert_logged(logged.info("App started")) +.. seealso:: + + See :ref:`integrations-logging` for other supported logging frameworks (e.g. :doc:`loguru </integrations/loguru>`). + Test framework integrations --------------------------- diff --git a/logot/_logging.py b/logot/_logging.py index dc30a708..557a7225 100644 --- a/logot/_logging.py +++ b/logot/_logging.py @@ -23,7 +23,7 @@ def start_capturing(self, logot: Logot, /, , level: Level, logger: Logger) -> N handler = self._handler = _Handler(level, logot) # If the logger is less verbose than the handler, force it to the necessary verboseness. self._prev_levelno = logger.level - if handler.level < logger.level: + if handler.level < logger.getEffectiveLevel(): logger.setLevel(handler.level) # Add the handler. logger.addHandler(handler) diff --git a/logot/_logot.py b/logot/_logot.py index 0243007d..f0e26924 100644 --- a/logot/_logot.py +++ b/logot/_logot.py @@ -1,9 +1,9 @@ from __future__ import annotations +from _thread import allocate_lock from abc import ABC, abstractmethod from collections import deque from contextlib import AbstractContextManager -from threading import Lock from types import TracebackType from typing import Any, Callable, ClassVar, Generic @@ -96,7 +96,7 @@ def __init__( self.capturer = capturer self.timeout = validate_timeout(timeout) self.async_waiter = async_waiter - self._lock = Lock() + self._lock = allocate_lock() self._queue: deque[Captured] = deque() self._wait: _Wait[Any] \| None = None diff --git a/logot/_loguru.py b/logot/_loguru.py new file mode 100644 index 00000000..8a1d6651 --- /dev/null +++ b/logot/_loguru.py @@ -0,0 +1,29 @@ +from __future__ import annotations + +from functools import partial + +import loguru + +from logot._capture import Captured +from logot._logot import Capturer, Logot +from logot._typing import Level, Logger + + +class LoguruCapturer(Capturer): + """ + A :class:`logot.Capturer` implementation for :mod:`loguru`. + """ + + __slots__ = ("_handler_id",) + + def start_capturing(self, logot: Logot, /, , level: Level, logger: Logger) -> None: + self._handler_id = loguru.logger.add(partial(_sink, logot=logot), level=level, filter=logger) + + def stop_capturing(self) -> None: + loguru.logger.remove(self._handler_id) + + +def _sink(msg: loguru.Message, , logot: Logot) -> None: + record = msg.record + level = record["level"] + logot.capture(Captured(level.name, record["message"], levelno=level.no)) diff --git a/logot/_wait.py b/logot/_wait.py index 3177d8c0..771d2bb7 100644 --- a/logot/_wait.py +++ b/logot/_wait.py @@ -1,7 +1,7 @@ from __future__ import annotations +from _thread import LockType, allocate_lock from abc import ABC, abstractmethod -from threading import Lock from typing import Protocol, TypeVar @@ -14,9 +14,9 @@ def release(self) -> None: W = TypeVar("W", bound=Waiter) -def create_threading_waiter() -> Lock: +def create_threading_waiter() -> LockType: # Create an already-acquired lock. This will be released by `release()`. - lock = Lock() + lock = allocate_lock() lock.acquire() return lock diff --git a/logot/loguru.py b/logot/loguru.py new file mode 100644 index 00000000..2f195e2b --- /dev/null +++ b/logot/loguru.py @@ -0,0 +1,10 @@ +""" +Integration API for :mod:`loguru`. + +.. seealso:: + + See :doc:`/integrations/loguru` usage guide. +""" +from __future__ import annotations + +from logot._loguru import LoguruCapturer as LoguruCapturer diff --git a/poetry.lock b/poetry.lock index 1652b9ab..c037c881 100644 --- a/poetry.lock +++ b/poetry.lock @@ -483,6 +483,24 @@ files = [ six = "" tornado = {version = "*", markers = "python_version > \"2.7\""} +[[package]] +name = "loguru" +version = "0.7.2" +description = "Python logging made (stupidly) simple" +optional = true +python-versions = ">=3.5" +files = [ + {file = "loguru-0.7.2-py3-none-any.whl", hash = "sha256:003d71e3d3ed35f0f8984898359d65b79e5b21943f78af86aa5491210429b8eb"}, + {file = "loguru-0.7.2.tar.gz", hash = "sha256:e671a53522515f34fd406340ee968cb9ecafbc4b36c679da03c18fd8d0bd51ac"}, +] + +[package.dependencies] +colorama = {version = ">=0.3.4", markers = "sys_platform == \"win32\""} +win32-setctime = {version = ">=1.0.0", markers = "sys_platform == \"win32\""} + +[package.extras] +dev = ["Sphinx (==7.2.5)", "colorama (==0.4.5)", "colorama (==0.4.6)", "exceptiongroup (==1.1.3)", "freezegun (==1.1.0)", "freezegun (==1.2.2)", "mypy (==v0.910)", "mypy (==v0.971)", "mypy (==v1.4.1)", "mypy (==v1.5.1)", "pre-commit (==3.4.0)", "pytest (==6.1.2)", "pytest (==7.4.0)", "pytest-cov (==2.12.1)", "pytest-cov (==4.1.0)", "pytest-mypy-plugins (==1.9.3)", "pytest-mypy-plugins (==3.0.0)", "sphinx-autobuild (==2021.3.14)", "sphinx-rtd-theme (==1.3.0)", "tox (==3.27.1)", "tox (==4.11.0)"] + [[package]] name = "markupsafe" version = "2.1.5" @@ -1065,6 +1083,20 @@ h2 = ["h2 (>=4,<5)"] socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"] zstd = ["zstandard (>=0.18.0)"] +[[package]] +name = "win32-setctime" +version = "1.1.0" +description = "A small Python utility to set file creation time on Windows" +optional = true +python-versions = ">=3.5" +files = [ + {file = "win32_setctime-1.1.0-py3-none-any.whl", hash = "sha256:231db239e959c2fe7eb1d7dc129f11172354f98361c4fa2d6d2d7e278baa8aad"}, + {file = "win32_setctime-1.1.0.tar.gz", hash = "sha256:15cf5750465118d6929ae4de4eb46e8edae9a5634350c01ba582df868e932cb2"}, +] + +[package.extras] +dev = ["black (>=19.3b0)", "pytest (>=4.6.2)"] + [[package]] name = "zipp" version = "3.17.0" @@ -1081,10 +1113,11 @@ docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.link testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"] [extras] +loguru = ["loguru"] pytest = ["pytest"] trio = ["trio"] [metadata] lock-version = "2.0" python-versions = "^3.8" -content-hash = "5ff89ddb47ec98abf8aed490978f89739e5fea0fd852da4f3c4cf53542ff3d26" +content-hash = "0ca1d41b6c4e51dd9144e15b25677c70853c62008b00ba20c4a69d394f19afe7" diff --git a/pyproject.toml b/pyproject.toml index 993c0262..2d9b7feb 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -21,11 +21,13 @@ packages = [{ include = "logot" }] [tool.poetry.dependencies] python = "^3.8" +loguru = { version = ">=0.6,<0.8", optional = true } pytest = { version = ">=7,<9", optional = true } trio = { version = ">=0.22,<0.25", optional = true } -typing-extensions = { version = ">=4.9.0", python = "<3.10" } +typing-extensions = { version = ">=4.9", python = "<3.10" } [tool.poetry.extras] +loguru = ["loguru"] pytest = ["pytest"] trio = ["trio"]	Support `loguru` log capture Supporting a basic [loguru](https://github.com/Delgan/loguru) integration would be very easy by providing a context manager via a helper func using their `LogHandler` integration. Alternatively, a `Logot` subclass called `Logurot` would be cheese and fun. That would allow the `pytest` plugin to provide a `logurot` fixture too (if `loguru` is installed).	Further API discussion and feedback from `loguru` devs in https://github.com/Delgan/loguru/issues/1072 ❤️ ## Proposed API ``` py from logot.contrib.loguru import LoguruCapture with Logot().capturing(LoguruCapture): pass ``` Any arguments given to `capturing` would be propagated to the `LoguruCapture` constructor. This would then unify capture between stdlib logging and `loguru` logging. The default capture backend would be the stdlib capture, and an `@overload` would ensure good typing. 💪	2024-02-09T19:42:22	[]	[]
etianen/logot	etianen__logot-91	e7d3f7047cbf13be9cb87c15051656062018e3a7	diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 381abb95..26d215ef 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -21,6 +21,9 @@ jobs: - python-version: "3.12" - lib-versions: "pytest~=7.0" - lib-versions: "pytest~=8.0" + - lib-versions: "trio~=0.22.0" + - lib-versions: "trio~=0.23.0" + - lib-versions: "trio~=0.24.0" fail-fast: false env: COVERAGE_FILE: .coverage.${{ matrix.python-version }}${{ matrix.lib-versions }} @@ -39,7 +42,7 @@ jobs: - name: Install dependencies run: poetry install --all-extras - name: Install lib versions - run: poetry run pip install -e .[pytest] ${{ matrix.lib-versions }} + run: poetry run pip install -e .[pytest,trio] ${{ matrix.lib-versions }} if: ${{ matrix.lib-versions }} # Run checks. - name: Check (ruff) diff --git a/docs/api/logot.trio.rst b/docs/api/logot.trio.rst new file mode 100644 index 00000000..3f7f4cd1 --- /dev/null +++ b/docs/api/logot.trio.rst @@ -0,0 +1,10 @@ +:mod:`logot.trio` +================= + +.. automodule:: logot.trio + + +API reference +------------- + +.. autoclass:: TrioWaiter diff --git a/docs/conf.py b/docs/conf.py index b5c1ecc9..794643b0 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -27,6 +27,7 @@ intersphinx_mapping = { "python": ("https://docs.python.org/3", None), "pytest": ("https://docs.pytest.org/en/latest/", None), + "trio": ("https://trio.readthedocs.io/en/latest", None), } nitpicky = True diff --git a/docs/index.rst b/docs/index.rst index 0735d8a3..12f2c662 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -100,14 +100,7 @@ Use :meth:`Logot.await_for` to pause your test until the expected logs arrive or asyncio.create_task(app.start()) await logot.await_for(logged.info("App started")) -.. note:: - - Use the ``timeout`` argument to :meth:`Logot.await_for` to configure how long to wait before the test fails. This can - be configured globally with the ``timeout`` argument to :class:`Logot`, defaulting to :attr:`Logot.DEFAULT_TIMEOUT`. - -.. seealso:: - - See :doc:`/log-pattern-matching` for examples of how to wait for logs that may arrive in an unpredictable order. +.. include:: /include/testing-async-notes.rst Testing synchronous code @@ -153,6 +146,7 @@ Learn more about :mod:`logot` with the following guides: using-pytest using-unittest installing + integrations/index api/index .. toctree:: diff --git a/docs/installing.rst b/docs/installing.rst index 6a4249db..98f5d900 100644 --- a/docs/installing.rst +++ b/docs/installing.rst @@ -18,15 +18,21 @@ Install :mod:`logot` like any other Python library: Installing package ``extras`` ----------------------------- -:mod:`logot` provides package ``extras`` to ensure compatibility with supported 3rd-party integrations. +:mod:`logot` provides package ``extras`` to ensure compatibility with supported +:doc:`3rd-party integrations </integrations/index>`. .. code:: bash - pip install 'logot[pytest]' + pip install 'logot[pytest,trio]' -Package extras for supported 3rd-party integrations: +Use the following package ``extras`` for supported 3rd-party integrations: -- ``pytest`` - :doc:`using-pytest` +- ``pytest`` - :doc:`/using-pytest` +- ``trio`` - :doc:`/integrations/trio` + +.. seealso:: + + See :doc:`/integrations/index` usage guide. Installing with Poetry diff --git a/docs/integrations/index.rst b/docs/integrations/index.rst new file mode 100644 index 00000000..16a731e9 --- /dev/null +++ b/docs/integrations/index.rst @@ -0,0 +1,53 @@ +3rd-party integrations +====================== + +.. currentmodule:: logot + +:mod:`logot` integrates with a number of 3rd-party frameworks to extend its functionality. + +.. note:: + + Adding support for additional 3rd-party frameworks is an excellent way of + `contributing <https://github.com/etianen/logot/blob/main/CONTRIBUTING.md>`_ to :mod:`logot`! 🙇 + + +.. _integrations-async: + +Asynchronous frameworks +----------------------- + +Integrations with 3rd-party asynchronous frameworks extend :meth:`Logot.await_for`, allowing you to +:ref:`test asynchronous code <index-testing-async>` using your framework of choice. 💪 + +Learn about supported 3rd-party asynchronous frameworks: + +.. toctree:: + :maxdepth: 1 + + trio + +.. seealso:: + + See :ref:`index-testing-async` usage guide. + + See :class:`AsyncWaiter` API reference for integrating with 3rd-party asynchronous frameworks. + + +.. _integrations-logging: + +Logging frameworks +------------------ + +Integrations with 3rd-party logging frameworks extend :meth:`Logot.capturing`, allowing you to +:doc:`capture logs </log-capturing>` using your framework of choice. 💪 + +Learn about supported 3rd-party logging frameworks: + +.. toctree:: + :maxdepth: 1 + +.. seealso:: + + See :doc:`/log-capturing` usage guide. + + See :class:`Capturer` API reference for integrating with 3rd-party logging frameworks. diff --git a/docs/integrations/trio.rst b/docs/integrations/trio.rst new file mode 100644 index 00000000..601fecdb --- /dev/null +++ b/docs/integrations/trio.rst @@ -0,0 +1,92 @@ +Using with :mod:`trio` +====================== + +.. currentmodule:: logot + +Use :meth:`Logot.await_for` to pause your test until the expected logs arrive or the ``timeout`` expires: + +.. code:: python + + from logot import Logot, logged + + async def test_app(logot: Logot) -> None: + async with trio.open_nursery() as nursery: + nursery.start_soon(app.start()) + await logot.await_for(logged.info("App started")) + +.. include:: /include/testing-async-notes.rst + + +Installing +---------- + +Ensure :mod:`logot` is installed alongside a compatible :mod:`trio` version by adding the ``trio`` extra: + +.. code:: bash + + pip install 'logot[trio]' + +.. seealso:: + + See :ref:`installing-extras` usage guide. + + +Enabling for :mod:`pytest` +-------------------------- + +Enable :mod:`trio` support in your :external+pytest:doc:`pytest configuration <reference/customize>`: + +.. code:: ini + + # pytest.ini or .pytest.ini + [pytest] + logot_async_waiter = logot.trio.TrioWaiter + +.. code:: toml + + # pyproject.toml + [tool.pytest.ini_options] + logot_async_waiter = "logot.trio.TrioWaiter" + +.. seealso:: + + See :doc:`/using-pytest` usage guide. + + +Enabling for :mod:`unittest` +---------------------------- + +Enable :mod:`trio` support in your :class:`logot.unittest.LogotTestCase`: + +.. code:: python + + from logot.trio import TrioWaiter + + class MyAppTest(LogotTestCase): + logot_async_waiter = TrioWaiter + +.. seealso:: + + See :doc:`/using-unittest` usage guide. + + +Enabling manually +----------------- + +Enable :mod:`trio` support for your :class:`Logot` instance: + +.. code:: python + + from logot.trio import TrioWaiter + + logot = Logot(async_waiter=TrioWaiter) + +Enable :mod:`trio` support for a single :meth:`Logot.await_for` call: + +.. code:: python + + await logot.await_for(logged.info("App started"), async_waiter=TrioWaiter) + +.. seealso:: + + See :class:`Logot` and :meth:`Logot.await_for` API reference. diff --git a/docs/log-capturing.rst b/docs/log-capturing.rst index 574c4a14..73fecf35 100644 --- a/docs/log-capturing.rst +++ b/docs/log-capturing.rst @@ -21,10 +21,8 @@ Use a supported test framework integration for automatic log capturing in tests: - :doc:`/using-unittest` -.. _log-capturing-logging: - -Capturing :mod:`logging` logs ------------------------------ +Configuring +----------- The :meth:`Logot.capturing` method defaults to capturing all records from the root logger. Customize this with the ``level`` and ``logger`` arguments to :meth:`Logot.capturing`: @@ -41,27 +39,3 @@ careful to avoid capturing duplicate logs with overlapping calls to :meth:`Logot .. seealso:: See :class:`Logot` and :meth:`Logot.capturing` API reference. - - -.. _log-capturing-3rd-party: - -Capturing 3rd-party logs ------------------------- - -Any 3rd-party logging library can be integrated with :mod:`logot` by sending :class:`Captured` logs to -:meth:`Logot.capture`: - -.. code:: python - - def on_foo_log(logot: Logot, record: FooRecord) -> None: - logot.capture(Captured(record.levelname, record.msg)) - - foo_logger.add_handler(on_foo_log) - -.. note:: - - Using a context manager to set up and tear down log capture for every test run is highly recommended! - -.. seealso:: - - See :class:`Captured` and :meth:`Logot.capture` API reference. diff --git a/logot/_capture.py b/logot/_capture.py index 2e7a8045..0e7bf54d 100644 --- a/logot/_capture.py +++ b/logot/_capture.py @@ -8,17 +8,13 @@ class Captured: A captured log record. Send :class:`Captured` logs to :meth:`Logot.capture` to integrate with - :ref:`3rd-party logging frameworks <log-capturing-3rd-party>`. + :ref:`3rd-party logging frameworks <integrations-logging>`. .. note:: - This class is for integration with :ref:`3rd-party logging frameworks <log-capturing-3rd-party>`. It is not + This class is for integration with :ref:`3rd-party logging frameworks <integrations-logging>`. It is not generally used when writing tests. - .. seealso:: - - See :ref:`log-capturing-3rd-party` usage guide. - :param levelname: See :attr:`Captured.levelname`. :param msg: See :attr:`Captured.msg`. :param levelno: See :attr:`Captured.levelno`. diff --git a/logot/_logged.py b/logot/_logged.py index fe6304be..3e10491a 100644 --- a/logot/_logged.py +++ b/logot/_logged.py @@ -16,13 +16,11 @@ class Logged(ABC): .. important:: - :class:`Logged` instances are immutable and can be reused between tests. - - .. note:: - This is an abstract class and cannot be instantiated. Use the helpers in :mod:`logot.logged` to create log patterns. + :class:`Logged` instances are immutable and can be reused between tests. + .. seealso:: See :doc:`/log-pattern-matching` usage guide. diff --git a/logot/_logot.py b/logot/_logot.py index 24a74d12..0243007d 100644 --- a/logot/_logot.py +++ b/logot/_logot.py @@ -63,7 +63,7 @@ class Logot: .. note:: - This is for integration with :ref:`3rd-party logging frameworks <log-capturing-3rd-party>`. + This is for integration with :ref:`3rd-party logging frameworks <integrations-logging>`. Defaults to :attr:`Logot.DEFAULT_CAPTURER`. """ @@ -81,7 +81,7 @@ class Logot: .. note:: - This is for integration with 3rd-party asynchronous frameworks. + This is for integration with :ref:`3rd-party asynchronous frameworks <integrations-async>`. Defaults to :attr:`Logot.DEFAULT_ASYNC_WAITER`. """ @@ -121,7 +121,7 @@ def capturing( :attr:`Logot.DEFAULT_LEVEL`. :param logger: A logger name to capture logs from. Defaults to :attr:`Logot.DEFAULT_LOGGER`. :param capturer: Protocol used to capture logs. This is for integration with - :ref:`3rd-party logging frameworks <log-capturing-3rd-party>`. Defaults to :attr:`Logot.capturer`. + :ref:`3rd-party logging frameworks <integrations-logging>`. Defaults to :attr:`Logot.capturer`. """ if capturer is None: capturer = self.capturer @@ -139,13 +139,9 @@ def capture(self, captured: Captured) -> None: .. note:: - This method is for integration with :ref:`3rd-party logging frameworks <log-capturing-3rd-party>`. It is not + This method is for integration with :ref:`3rd-party logging frameworks <integrations-logging>`. It is not generally used when writing tests. - .. seealso:: - - See :ref:`log-capturing-3rd-party` usage guide. - :param captured: The captured log. """ with self._lock: @@ -215,7 +211,7 @@ async def await_for( :param logged: The expected :doc:`log pattern </log-pattern-matching>`. :param timeout: How long to wait (in seconds) before failing the test. Defaults to :attr:`Logot.timeout`. :param async_waiter: Protocol used to pause tests until expected logs arrive. This is for integration with - 3rd-party asynchronous frameworks. Defaults to :attr:`Logot.async_waiter`. + :ref:`3rd-party asynchronous frameworks <integrations-async>`. Defaults to :attr:`Logot.async_waiter`. :raises AssertionError: If the expected ``log`` pattern does not arrive within ``timeout`` seconds. """ if async_waiter is None: @@ -286,7 +282,7 @@ class Capturer(ABC): .. note:: - This class is for integration with :ref:`3rd-party logging frameworks <log-capturing-3rd-party>`. It is not + This class is for integration with :ref:`3rd-party logging frameworks <integrations-logging>`. It is not generally used when writing tests. """ diff --git a/logot/_trio.py b/logot/_trio.py new file mode 100644 index 00000000..da78d2f5 --- /dev/null +++ b/logot/_trio.py @@ -0,0 +1,25 @@ +from __future__ import annotations + +import trio +from trio.lowlevel import current_trio_token + +from logot._wait import AsyncWaiter + + +class TrioWaiter(AsyncWaiter): + """ + A :class:`logot.AsyncWaiter` implementation for :mod:`trio`. + """ + + __slots__ = ("_token", "_event") + + def __init__(self) -> None: + self._token = current_trio_token() + self._event = trio.Event() + + def release(self) -> None: + self._token.run_sync_soon(self._event.set) + + async def wait(self, , timeout: float) -> None: + with trio.move_on_after(timeout): + await self._event.wait() diff --git a/logot/_wait.py b/logot/_wait.py index 765c4cf1..3177d8c0 100644 --- a/logot/_wait.py +++ b/logot/_wait.py @@ -27,8 +27,8 @@ class AsyncWaiter(ABC): .. note:: - This class is for integration with 3rd-party asynchronous frameworks. It is not generally used when writing - tests. + This class is for integration with :ref:`3rd-party asynchronous frameworks <integrations-async>`. It is not + generally used when writing tests. """ __slots__ = () diff --git a/logot/logging.py b/logot/logging.py index cdab473e..8064ce00 100644 --- a/logot/logging.py +++ b/logot/logging.py @@ -3,7 +3,7 @@ .. seealso:: - See :ref:`log-capturing-logging` usage guide. + See :doc:`/log-capturing` usage guide. 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"sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"}, ] +[[package]] +name = "sniffio" +version = "1.3.0" +description = "Sniff out which async library your code is running under" +optional = false +python-versions = ">=3.7" +files = [ + {file = "sniffio-1.3.0-py3-none-any.whl", hash = "sha256:eecefdce1e5bbfb7ad2eeaabf7c1eeb404d7757c379bd1f7e5cce9d8bf425384"}, + {file = "sniffio-1.3.0.tar.gz", hash = "sha256:e60305c5e5d314f5389259b7f22aaa33d8f7dee49763119234af3755c55b9101"}, +] + [[package]] name = "snowballstemmer" version = "2.2.0" @@ -865,6 +995,48 @@ files = [ {file = "tornado-6.4.tar.gz", hash = "sha256:72291fa6e6bc84e626589f1c29d90a5a6d593ef5ae68052ee2ef000dfd273dee"}, ] +[[package]] +name = "trio" +version = "0.24.0" +description = "A friendly Python library for async concurrency and I/O" +optional = false +python-versions = ">=3.8" +files = [ + {file = "trio-0.24.0-py3-none-any.whl", hash = "sha256:c3bd3a4e3e3025cd9a2241eae75637c43fe0b9e88b4c97b9161a55b9e54cd72c"}, + {file = "trio-0.24.0.tar.gz", hash = "sha256:ffa09a74a6bf81b84f8613909fb0beaee84757450183a7a2e0b47b455c0cac5d"}, +] + +[package.dependencies] +attrs = ">=20.1.0" +cffi = {version = ">=1.14", markers = "os_name == \"nt\" and implementation_name != \"pypy\""} +exceptiongroup = {version = "", markers = "python_version < \"3.11\""} +idna = "" +outcome = "" +sniffio = ">=1.3.0" +sortedcontainers = "" + +[[package]] +name = "trio-typing" +version = "0.10.0" +description = "Static type checking support for Trio and related projects" +optional = false +python-versions = "" +files = [ + {file = "trio-typing-0.10.0.tar.gz", hash = "sha256:065ee684296d52a8ab0e2374666301aec36ee5747ac0e7a61f230250f8907ac3"}, + {file = "trio_typing-0.10.0-py3-none-any.whl", hash = "sha256:6d0e7ec9d837a2fe03591031a172533fbf4a1a95baf369edebfc51d5a49f0264"}, +] + +[package.dependencies] +async-generator = "" +importlib-metadata = "" +mypy-extensions = ">=0.4.2" +packaging = "*" +trio = ">=0.16.0" +typing-extensions = ">=3.7.4" + +[package.extras] +mypy = ["mypy (>=1.0)"] + [[package]] name = "typing-extensions" version = "4.9.0" @@ -893,10 +1065,26 @@ h2 = ["h2 (>=4,<5)"] socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"] zstd = ["zstandard (>=0.18.0)"] +[[package]] +name = "zipp" +version = "3.17.0" +description = "Backport of pathlib-compatible object wrapper for zip files" +optional = false +python-versions = ">=3.8" +files = [ + {file = "zipp-3.17.0-py3-none-any.whl", hash = "sha256:0e923e726174922dce09c53c59ad483ff7bbb8e572e00c7f7c46b88556409f31"}, + {file = "zipp-3.17.0.tar.gz", hash = "sha256:84e64a1c28cf7e91ed2078bb8cc8c259cb19b76942096c8d7b84947690cabaf0"}, +] + +[package.extras] +docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"] +testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"] + [extras] pytest = ["pytest"] +trio = ["trio"] [metadata] lock-version = "2.0" python-versions = "^3.8" -content-hash = "f21c1997c5ae3160aabaca80fbea4a93281ba8676a414139bf9f8a26ded97b19" +content-hash = "5ff89ddb47ec98abf8aed490978f89739e5fea0fd852da4f3c4cf53542ff3d26" diff --git a/pyproject.toml b/pyproject.toml index 5a4e9c94..993c0262 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -21,11 +21,13 @@ packages = [{ include = "logot" }] [tool.poetry.dependencies] python = "^3.8" -pytest = { version = ">=7.0.0", optional = true } +pytest = { version = ">=7,<9", optional = true } +trio = { version = ">=0.22,<0.25", optional = true } typing-extensions = { version = ">=4.9.0", python = "<3.10" } [tool.poetry.extras] pytest = ["pytest"] +trio = ["trio"] [tool.poetry.group.coverage.dependencies] coverage = "^7.4.1" @@ -34,6 +36,7 @@ coverage = "^7.4.1" hypothesis = "^6.96.1" mypy = "^1.8.0" ruff = "^0.1.11" +trio-typing = "^0.10.0" [tool.poetry.group.docs] optional = true	Support `trio` We can support [trio](https://trio.readthedocs.io/en/stable/) by allowing the async waiter implementation to be customized in the `Logot` constructor.		2024-02-08T22:35:56	[]	[]
etianen/logot	etianen__logot-85	dc5f98669b888ce0d73c9873016eab3b1e277c6b	diff --git a/docs/api/logot.asyncio.rst b/docs/api/logot.asyncio.rst index ca72a8c0..bbf8282d 100644 --- a/docs/api/logot.asyncio.rst +++ b/docs/api/logot.asyncio.rst @@ -8,5 +8,3 @@ API reference ------------- .. autoclass:: AsyncioWaiter - :members: - :exclude-members: release,wait diff --git a/docs/api/logot.logging.rst b/docs/api/logot.logging.rst new file mode 100644 index 00000000..71a7fef1 --- /dev/null +++ b/docs/api/logot.logging.rst @@ -0,0 +1,10 @@ +:mod:`logot.logging` +==================== + +.. automodule:: logot.logging + + +API reference +------------- + +.. autoclass:: LoggingCapturer diff --git a/docs/api/logot.rst b/docs/api/logot.rst index 3daf2f32..43300559 100644 --- a/docs/api/logot.rst +++ b/docs/api/logot.rst @@ -10,10 +10,13 @@ API reference .. autoclass:: Logot :members: -.. autoclass:: Logged +.. autoclass:: Capturer + :members: .. autoclass:: Captured :members: +.. autoclass:: Logged + .. autoclass:: AsyncWaiter :members: diff --git a/docs/log-capturing.rst b/docs/log-capturing.rst index ae5fea31..574c4a14 100644 --- a/docs/log-capturing.rst +++ b/docs/log-capturing.rst @@ -21,6 +21,8 @@ Use a supported test framework integration for automatic log capturing in tests: - :doc:`/using-unittest` +.. _log-capturing-logging: + Capturing :mod:`logging` logs ----------------------------- diff --git a/logot/__init__.py b/logot/__init__.py index c1d33cfe..f45f38f6 100644 --- a/logot/__init__.py +++ b/logot/__init__.py @@ -9,5 +9,6 @@ from logot._capture import Captured as Captured from logot._logged import Logged as Logged +from logot._logot import Capturer as Capturer from logot._logot import Logot as Logot from logot._wait import AsyncWaiter as AsyncWaiter diff --git a/logot/_logging.py b/logot/_logging.py new file mode 100644 index 00000000..4fe6e285 --- /dev/null +++ b/logot/_logging.py @@ -0,0 +1,45 @@ +from __future__ import annotations + +import logging + +from logot._capture import Captured +from logot._logot import Capturer, Logot + + +class LoggingCapturer(Capturer): + """ + A :class:`logot.Capturer` implementation for :mod:`logging`. + + .. note:: + + This is the default :class:`logot.Capturer` implementation. + """ + + __slots__ = ("_logger", "_handler", "_prev_levelno") + + def start_capturing(self, logot: Logot, /, , level: str \| int, logger: str \| None) -> None: + logger = self._logger = logging.getLogger(logger) + handler = self._handler = _Handler(level, logot) + # If the logger is less verbose than the handler, force it to the necessary verboseness. + self._prev_levelno = logger.level + if handler.level < logger.level: + logger.setLevel(handler.level) + # Add the handler. + logger.addHandler(handler) + + def stop_capturing(self) -> None: + # Remove the handler and restore the previous level. + self._logger.removeHandler(self._handler) + self._logger.setLevel(self._prev_levelno) + + +class _Handler(logging.Handler): + __slots__ = ("_logot",) + + def __init__(self, level: str \| int, logot: Logot) -> None: + super().__init__(level) + self._logot = logot + + def emit(self, record: logging.LogRecord) -> None: + captured = Captured(record.levelname, record.getMessage(), levelno=record.levelno) + self._logot.capture(captured) diff --git a/logot/_logot.py b/logot/_logot.py index e26f53cf..d9546694 100644 --- a/logot/_logot.py +++ b/logot/_logot.py @@ -1,6 +1,6 @@ from __future__ import annotations -import logging +from abc import ABC, abstractmethod from collections import deque from contextlib import AbstractContextManager from threading import Lock @@ -22,11 +22,12 @@ class Logot: See :doc:`/index` usage guide. + :param capturer: See :attr:`Logot.capturer`. :param timeout: See :attr:`Logot.timeout`. :param async_waiter: See :attr:`Logot.async_waiter`. """ - __slots__ = ("timeout", "async_waiter", "_lock", "_queue", "_wait") + __slots__ = ("capturer", "timeout", "async_waiter", "_lock", "_queue", "_wait") DEFAULT_LEVEL: ClassVar[str \| int] = "DEBUG" """ @@ -40,14 +41,30 @@ class Logot: This is the root logger. """ + DEFAULT_CAPTURER: ClassVar[Callable[[], Capturer]] = LazyCallable("logot.logging", "LoggingCapturer") + """ + The default :attr:`capturer` for new :class:`Logot` instances. + """ + DEFAULT_TIMEOUT: ClassVar[float] = 3.0 """ - The default ``timeout`` (in seconds) for new :class:`Logot` instances. + The default :attr:`timeout` (in seconds) for new :class:`Logot` instances. """ DEFAULT_ASYNC_WAITER: ClassVar[Callable[[], AsyncWaiter]] = LazyCallable("logot.asyncio", "AsyncioWaiter") """ - The default ``async_waiter`` for new :class:`Logot` instances. + The default :attr:`async_waiter` for new :class:`Logot` instances. + """ + + capturer: Callable[[], Capturer] + """ + The default ``capturer`` used by :meth:`capturing`. + + .. note:: + + This is for integration with :ref:`3rd-party logging frameworks <log-capturing-3rd-party>`. + + Defaults to :attr:`Logot.DEFAULT_CAPTURER`. """ timeout: float @@ -71,9 +88,11 @@ class Logot: def __init__( self, , + capturer: Callable[[], Capturer] = DEFAULT_CAPTURER, timeout: float = DEFAULT_TIMEOUT, async_waiter: Callable[[], AsyncWaiter] = DEFAULT_ASYNC_WAITER, ) -> None: + self.capturer = capturer self.timeout = validate_timeout(timeout) self.async_waiter = async_waiter self._lock = Lock() @@ -84,7 +103,8 @@ def capturing( self, , level: str \| int = DEFAULT_LEVEL, - logger: logging.Logger \| str \| None = DEFAULT_LOGGER, + logger: str \| None = DEFAULT_LOGGER, + capturer: Callable[[], Capturer] \| None = None, ) -> AbstractContextManager[Logot]: """ Captures logs emitted at the given ``level`` by the given ``logger`` for the duration of the context. @@ -98,11 +118,16 @@ def capturing( :param level: A log level name (e.g. ``"DEBUG"``) or numeric level (e.g. :data:`logging.DEBUG`). Defaults to :attr:`Logot.DEFAULT_LEVEL`. - :param logger: A logger or logger name to capture logs from. Defaults to :attr:`Logot.DEFAULT_LOGGER`. + :param logger: A logger name to capture logs from. Defaults to :attr:`Logot.DEFAULT_LOGGER`. + :param capturer: Protocol used to capture logs. This is for integration with + :ref:`3rd-party logging frameworks <log-capturing-3rd-party>`. Defaults to :attr:`Logot.capturer`. """ + if capturer is None: + capturer = self.capturer + capturer_obj = capturer() level = validate_level(level) logger = validate_logger(logger) - return _Capturing(self, _Handler(level, self), logger=logger) + return _Capturing(self, capturer_obj, level=level, logger=logger) def capture(self, captured: Captured) -> None: """ @@ -128,7 +153,7 @@ def capture(self, captured: Captured) -> None: self._wait.logged = self._wait.logged._reduce(captured) # If the waiter has fully reduced, release the blocked caller. if self._wait.logged is None: - self._wait.waiter.release() + self._wait.waiter_obj.release() return # Otherwise, buffer the captured log. self._queue.append(captured) @@ -168,13 +193,13 @@ def wait_for( :param timeout: How long to wait (in seconds) before failing the test. Defaults to :attr:`Logot.timeout`. :raises AssertionError: If the expected ``log`` pattern does not arrive within ``timeout`` seconds. """ - waiter = self._start_waiting(logged, create_threading_waiter, timeout=timeout) - if waiter is None: + wait = self._start_waiting(logged, create_threading_waiter, timeout=timeout) + if wait is None: return try: - waiter.waiter.acquire(timeout=waiter.timeout) + wait.waiter_obj.acquire(timeout=wait.timeout) finally: - self._stop_waiting(waiter) + self._stop_waiting(wait) async def await_for( self, @@ -194,13 +219,13 @@ async def await_for( """ if async_waiter is None: async_waiter = self.async_waiter - waiter = self._start_waiting(logged, async_waiter, timeout=timeout) - if waiter is None: + wait = self._start_waiting(logged, async_waiter, timeout=timeout) + if wait is None: return try: - await waiter.waiter.wait(timeout=waiter.timeout) + await wait.waiter_obj.wait(timeout=wait.timeout) finally: - self._stop_waiting(waiter) + self._stop_waiting(wait) def clear(self) -> None: """ @@ -226,7 +251,8 @@ def _start_waiting( if logged is None: return None # All done! - wait = self._wait = _Wait(logged=logged, timeout=timeout, waiter=waiter()) + waiter_obj = waiter() + wait = self._wait = _Wait(logged=logged, timeout=timeout, waiter_obj=waiter_obj) return wait def _stop_waiting(self, wait: _Wait[Any]) -> None: @@ -253,21 +279,50 @@ def __repr__(self) -> str: return f"Logot(timeout={self.timeout!r}, async_waiter={self.async_waiter!r})" +class Capturer(ABC): + """ + Protocol used by :meth:`Logot.capturing` to capture logs. + + .. note:: + + This class is for integration with :ref:`3rd-party logging frameworks <log-capturing-3rd-party>`. It is not + generally used when writing tests. + """ + + __slots__ = () + + @abstractmethod + def start_capturing(self, logot: Logot, /, , level: str \| int, logger: str \| None) -> None: + """ + Starts capturing logs for the given :class:`Logot`. + + Captured logs should be converted to a :class:`Captured` instance and sent to :meth:`Logot.capture`. + + :param logot: The :class:`Logot` instance. + :param level: A log level name (e.g. ``"DEBUG"``) or numeric level (e.g. :data:`logging.DEBUG`). + :param logger: A logger name to capture logs from. + """ + raise NotImplementedError + + @abstractmethod + def stop_capturing(self) -> None: + """ + Stops capturing logs. + """ + raise NotImplementedError + + class _Capturing: - __slots__ = ("_logot", "_handler", "_logger", "_prev_levelno") + __slots__ = ("_logot", "_capturer_obj", "_level", "_logger") - def __init__(self, logot: Logot, handler: logging.Handler, , logger: logging.Logger) -> None: + def __init__(self, logot: Logot, capturer: Capturer, , level: str \| int, logger: str \| None) -> None: self._logot = logot - self._handler = handler + self._capturer_obj = capturer + self._level = level self._logger = logger def __enter__(self) -> Logot: - # If the logger is less verbose than the handler, force it to the necessary verboseness. - self._prev_levelno = self._logger.level - if self._handler.level < self._logger.level: - self._logger.setLevel(self._handler.level) - # Add the handler. - self._logger.addHandler(self._handler) + self._capturer_obj.start_capturing(self._logot, level=self._level, logger=self._logger) return self._logot def __exit__( @@ -276,28 +331,13 @@ def __exit__( exc_value: BaseException \| None, traceback: TracebackType \| None, ) -> None: - # Remove the handler. - self._logger.removeHandler(self._handler) - # Restore the previous level. - self._logger.setLevel(self._prev_levelno) - - -class _Handler(logging.Handler): - __slots__ = ("_logot",) - - def __init__(self, level: str \| int, logot: Logot) -> None: - super().__init__(level) - self._logot = logot - - def emit(self, record: logging.LogRecord) -> None: - captured = Captured(record.levelname, record.getMessage(), levelno=record.levelno) - self._logot.capture(captured) + self._capturer_obj.stop_capturing() class _Wait(Generic[W]): - __slots__ = ("logged", "timeout", "waiter") + __slots__ = ("logged", "timeout", "waiter_obj") - def __init__(self, , logged: Logged \| None, timeout: float, waiter: W) -> None: + def __init__(self, , logged: Logged \| None, timeout: float, waiter_obj: W) -> None: self.logged = logged self.timeout = timeout - self.waiter = waiter + self.waiter_obj = waiter_obj diff --git a/logot/_validate.py b/logot/_validate.py index 6b47a573..58997704 100644 --- a/logot/_validate.py +++ b/logot/_validate.py @@ -1,7 +1,5 @@ from __future__ import annotations -import logging - def validate_level(level: str \| int) -> str \| int: # Handle `str` or `int` level. @@ -11,12 +9,9 @@ def validate_level(level: str \| int) -> str \| int: raise TypeError(f"Invalid level: {level!r}") -def validate_logger(logger: logging.Logger \| str \| None) -> logging.Logger: +def validate_logger(logger: str \| None) -> str \| None: # Handle `None` or `str` logger. if logger is None or isinstance(logger, str): - return logging.getLogger(logger) - # Handle `Logger` logger. - if isinstance(logger, logging.Logger): return logger # Handle invalid logger. raise TypeError(f"Invalid logger: {logger!r}") diff --git a/logot/logging.py b/logot/logging.py new file mode 100644 index 00000000..cdab473e --- /dev/null +++ b/logot/logging.py @@ -0,0 +1,10 @@ +""" +Integration API for :mod:`logging`. + +.. seealso:: + + See :ref:`log-capturing-logging` usage guide. +""" +from __future__ import annotations + +from logot._logging import LoggingCapturer as LoggingCapturer	Support `loguru` log capture Supporting a basic [loguru](https://github.com/Delgan/loguru) integration would be very easy by providing a context manager via a helper func using their `LogHandler` integration. Alternatively, a `Logot` subclass called `Logurot` would be cheese and fun. That would allow the `pytest` plugin to provide a `logurot` fixture too (if `loguru` is installed). Only import `logging` if actually used Although less heavyweight than `asyncio` (see #81), `logging` is still quite chunky and part of proving our support for _any_ logging framework (see #28) is not giving special preference to `logging`.	Further API discussion and feedback from `loguru` devs in https://github.com/Delgan/loguru/issues/1072 ❤️ ## Proposed API ``` py from logot.contrib.loguru import LoguruCapture with Logot().capturing(LoguruCapture): pass ``` Any arguments given to `capturing` would be propagated to the `LoguruCapture` constructor. This would then unify capture between stdlib logging and `loguru` logging. The default capture backend would be the stdlib capture, and an `@overload` would ensure good typing. 💪	2024-02-07T20:49:42	[]	[]
etianen/logot	etianen__logot-84	2e7e25e0c99fc47917f7f14b9084b5d6093fefd9	diff --git a/logot/_logot.py b/logot/_logot.py index 240007fa..e26f53cf 100644 --- a/logot/_logot.py +++ b/logot/_logot.py @@ -250,7 +250,7 @@ def _reduce(self, logged: Logged \| None) -> Logged \| None: return logged def __repr__(self) -> str: - return f"Logot(timeout={self.timeout!r})" + return f"Logot(timeout={self.timeout!r}, async_waiter={self.async_waiter!r})" class _Capturing:	Only importing `asyncio` if actually used Closes #81		2024-02-06T15:44:25	[]	[]
etianen/logot	etianen__logot-83	ff2caa3a0935fc955c7415ddc2053b16ec367c38	diff --git a/docs/conf.py b/docs/conf.py index bf52ec02..b5c1ecc9 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -23,7 +23,6 @@ ] autodoc_member_order = "bysource" -autodoc_typehints = "both" intersphinx_mapping = { "python": ("https://docs.python.org/3", None), diff --git a/logot/_import.py b/logot/_import.py index 324ab0a4..abd506f1 100644 --- a/logot/_import.py +++ b/logot/_import.py @@ -1,7 +1,7 @@ from __future__ import annotations from importlib import import_module -from typing import Any +from typing import Any, Callable def import_any(module: str, name: str) -> Any: @@ -12,3 +12,23 @@ def import_any(module: str, name: str) -> Any: def import_any_parsed(name: str) -> Any: module, name = name.rsplit(".", 1) return import_any(module, name) + + +class LazyCallable: + __slots__ = ("_module", "_name", "_resolved") + + _resolved: Callable[..., Any] + + def __init__(self, module: str, name: str) -> None: + self._module = module + self._name = name + + def __call__(self, /, args: Any, kwargs: Any) -> Any: + try: + resolved = self._resolved + except AttributeError: + resolved = self._resolved = import_any(self._module, self._name) + return resolved(args, **kwargs) + + def __repr__(self) -> str: + return f"{self._module}.{self._name}" diff --git a/logot/_logot.py b/logot/_logot.py index 51e16ce4..240007fa 100644 --- a/logot/_logot.py +++ b/logot/_logot.py @@ -7,8 +7,8 @@ from types import TracebackType from typing import Any, Callable, ClassVar, Generic -from logot._asyncio import AsyncioWaiter from logot._capture import Captured +from logot._import import LazyCallable from logot._logged import Logged from logot._validate import validate_level, validate_logger, validate_timeout from logot._wait import AsyncWaiter, W, create_threading_waiter @@ -45,7 +45,7 @@ class Logot: The default ``timeout`` (in seconds) for new :class:`Logot` instances. """ - DEFAULT_ASYNC_WAITER: ClassVar[Callable[[], AsyncWaiter]] = AsyncioWaiter + DEFAULT_ASYNC_WAITER: ClassVar[Callable[[], AsyncWaiter]] = LazyCallable("logot.asyncio", "AsyncioWaiter") """ The default ``async_waiter`` for new :class:`Logot` instances. """	Only import `asyncio` if actually used It's a big heavy framework we don't always need to import. Since we're only importing 3rd-party libs when actually used, it would be ideal to treat heavyweight stdlib modules the same. Since `asyncio` is the default async waiter, probably a lazy import is best to keep the default async waiter zero-config.		2024-02-06T15:39:13	[]	[]
etianen/logot	etianen__logot-78	0027d37668f5ae1bdc19dd153f1d056910d83f7b	diff --git a/docs/api/logot.asyncio.rst b/docs/api/logot.asyncio.rst new file mode 100644 index 00000000..ca72a8c0 --- /dev/null +++ b/docs/api/logot.asyncio.rst @@ -0,0 +1,12 @@ +:mod:`logot.asyncio` +==================== + +.. automodule:: logot.asyncio + + +API reference +------------- + +.. autoclass:: AsyncioWaiter + :members: + :exclude-members: release,wait diff --git a/docs/api/logot.rst b/docs/api/logot.rst index b6f2f247..3daf2f32 100644 --- a/docs/api/logot.rst +++ b/docs/api/logot.rst @@ -14,3 +14,6 @@ API reference .. autoclass:: Captured :members: + +.. autoclass:: AsyncWaiter + :members: diff --git a/docs/index.rst b/docs/index.rst index c2756476..0735d8a3 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -60,6 +60,8 @@ Testing this code with :mod:`logot` is easy! logot.wait_for(logged.info("Poll finished")) +.. _index-testing-threaded: + Testing threaded code --------------------- @@ -83,6 +85,8 @@ Use :meth:`Logot.wait_for` to pause your test until the expected logs arrive or See :doc:`/log-pattern-matching` for examples of how to wait for logs that may arrive in an unpredictable order. +.. _index-testing-async: + Testing asynchronous code ------------------------- diff --git a/logot/__init__.py b/logot/__init__.py index b64dee6f..c1d33cfe 100644 --- a/logot/__init__.py +++ b/logot/__init__.py @@ -10,3 +10,4 @@ from logot._capture import Captured as Captured from logot._logged import Logged as Logged from logot._logot import Logot as Logot +from logot._wait import AsyncWaiter as AsyncWaiter diff --git a/logot/_asyncio.py b/logot/_asyncio.py index 8f99fb3d..0dcd7e03 100644 --- a/logot/_asyncio.py +++ b/logot/_asyncio.py @@ -6,14 +6,22 @@ class AsyncioWaiter(AsyncWaiter): + """ + A :class:`logot.AsyncWaiter` implementation for :mod:`asyncio`. + + .. note:: + + This is the default :class:`logot.AsyncWaiter` implementation. + """ + __slots__ = ("_loop", "_future") def __init__(self) -> None: - # Create an unresolved `asyncio.Future`. This will be resolved by `notify()`. + # Create an unresolved `asyncio.Future`. This will be resolved by `release()`. self._loop = asyncio.get_running_loop() self._future: asyncio.Future[None] = self._loop.create_future() - def notify(self) -> None: + def release(self) -> None: self._loop.call_soon_threadsafe(self._resolve) async def wait(self, , timeout: float) -> None: @@ -27,5 +35,5 @@ def _resolve(self) -> None: try: self._future.set_result(None) except asyncio.InvalidStateError: # pragma: no cover - # It's possible that the timeout and the `notify()` will both occur in the same tick of the event loop. + # It's possible that the timeout and the `release()` will both occur in the same tick of the event loop. pass diff --git a/logot/_import.py b/logot/_import.py new file mode 100644 index 00000000..324ab0a4 --- /dev/null +++ b/logot/_import.py @@ -0,0 +1,14 @@ +from __future__ import annotations + +from importlib import import_module +from typing import Any + + +def import_any(module: str, name: str) -> Any: + module_obj = import_module(module) + return getattr(module_obj, name) + + +def import_any_parsed(name: str) -> Any: + module, name = name.rsplit(".", 1) + return import_any(module, name) diff --git a/logot/_logot.py b/logot/_logot.py index d10b2e82..51e16ce4 100644 --- a/logot/_logot.py +++ b/logot/_logot.py @@ -5,13 +5,13 @@ from contextlib import AbstractContextManager from threading import Lock from types import TracebackType -from typing import ClassVar +from typing import Any, Callable, ClassVar, Generic from logot._asyncio import AsyncioWaiter from logot._capture import Captured from logot._logged import Logged from logot._validate import validate_level, validate_logger, validate_timeout -from logot._wait import AbstractWaiter, ThreadedWaiter +from logot._wait import AsyncWaiter, W, create_threading_waiter class Logot: @@ -23,9 +23,10 @@ class Logot: See :doc:`/index` usage guide. :param timeout: See :attr:`Logot.timeout`. + :param async_waiter: See :attr:`Logot.async_waiter`. """ - __slots__ = ("timeout", "_lock", "_queue", "_waiter") + __slots__ = ("timeout", "async_waiter", "_lock", "_queue", "_wait") DEFAULT_LEVEL: ClassVar[str \| int] = "DEBUG" """ @@ -41,25 +42,43 @@ class Logot: DEFAULT_TIMEOUT: ClassVar[float] = 3.0 """ - The default timeout (in seconds) for new :class:`Logot` instances. + The default ``timeout`` (in seconds) for new :class:`Logot` instances. + """ + + DEFAULT_ASYNC_WAITER: ClassVar[Callable[[], AsyncWaiter]] = AsyncioWaiter + """ + The default ``async_waiter`` for new :class:`Logot` instances. """ timeout: float """ - The default timeout (in seconds) for calls to :meth:`wait_for` and :meth:`await_for`. + The default ``timeout`` (in seconds) for calls to :meth:`wait_for` and :meth:`await_for`. Defaults to :attr:`Logot.DEFAULT_TIMEOUT`. """ + async_waiter: Callable[[], AsyncWaiter] + """ + The default ``async_waiter`` for calls to :meth:`await_for`. + + .. note:: + + This is for integration with 3rd-party asynchronous frameworks. + + Defaults to :attr:`Logot.DEFAULT_ASYNC_WAITER`. + """ + def __init__( self, , timeout: float = DEFAULT_TIMEOUT, + async_waiter: Callable[[], AsyncWaiter] = DEFAULT_ASYNC_WAITER, ) -> None: self.timeout = validate_timeout(timeout) + self.async_waiter = async_waiter self._lock = Lock() self._queue: deque[Captured] = deque() - self._waiter: AbstractWaiter \| None = None + self._wait: _Wait[Any] \| None = None def capturing( self, @@ -105,11 +124,11 @@ def capture(self, captured: Captured) -> None: """ with self._lock: # If there is a waiter that has not been fully reduced, attempt to reduce it. - if self._waiter is not None and self._waiter._logged is not None: - self._waiter._logged = self._waiter._logged._reduce(captured) - # If the waiter has fully reduced, notify the blocked caller. - if self._waiter._logged is None: - self._waiter.notify() + if self._wait is not None and self._wait.logged is not None: + self._wait.logged = self._wait.logged._reduce(captured) + # If the waiter has fully reduced, release the blocked caller. + if self._wait.logged is None: + self._wait.waiter.release() return # Otherwise, buffer the captured log. self._queue.append(captured) @@ -136,7 +155,12 @@ def assert_not_logged(self, logged: Logged) -> None: if reduced is None: raise AssertionError(f"Logged:\n\n{logged}") - def wait_for(self, logged: Logged, , timeout: float \| None = None) -> None: + def wait_for( + self, + logged: Logged, + , + timeout: float \| None = None, + ) -> None: """ Waits for the expected ``log`` pattern to arrive or the ``timeout`` to expire. @@ -144,25 +168,37 @@ def wait_for(self, logged: Logged, , timeout: float \| None = None) -> None: :param timeout: How long to wait (in seconds) before failing the test. Defaults to :attr:`Logot.timeout`. :raises AssertionError: If the expected ``log`` pattern does not arrive within ``timeout`` seconds. """ - waiter = ThreadedWaiter() - timeout = self._start_waiting(logged, waiter, timeout=timeout) + waiter = self._start_waiting(logged, create_threading_waiter, timeout=timeout) + if waiter is None: + return try: - waiter.wait(timeout=timeout) + waiter.waiter.acquire(timeout=waiter.timeout) finally: self._stop_waiting(waiter) - async def await_for(self, logged: Logged, , timeout: float \| None = None) -> None: + async def await_for( + self, + logged: Logged, + , + timeout: float \| None = None, + async_waiter: Callable[[], AsyncWaiter] \| None = None, + ) -> None: """ Waits asynchronously* for the expected ``log`` pattern to arrive or the ``timeout`` to expire. :param logged: The expected :doc:`log pattern </log-pattern-matching>`. :param timeout: How long to wait (in seconds) before failing the test. Defaults to :attr:`Logot.timeout`. + :param async_waiter: Protocol used to pause tests until expected logs arrive. This is for integration with + 3rd-party asynchronous frameworks. Defaults to :attr:`Logot.async_waiter`. :raises AssertionError: If the expected ``log`` pattern does not arrive within ``timeout`` seconds. """ - waiter = AsyncioWaiter() - timeout = self._start_waiting(logged, waiter, timeout=timeout) + if async_waiter is None: + async_waiter = self.async_waiter + waiter = self._start_waiting(logged, async_waiter, timeout=timeout) + if waiter is None: + return try: - await waiter.wait(timeout=timeout) + await waiter.waiter.wait(timeout=waiter.timeout) finally: self._stop_waiting(waiter) @@ -170,10 +206,11 @@ def clear(self) -> None: """ Clears any captured logs. """ - with self._lock: - self._queue.clear() + self._queue.clear() - def _start_waiting(self, logged: Logged \| None, waiter: AbstractWaiter, , timeout: float \| None) -> float: + def _start_waiting( + self, logged: Logged \| None, waiter: Callable[[], W], , timeout: float \| None + ) -> _Wait[W] \| None: with self._lock: # If no timeout is provided, use the default timeout. # Otherwise, validate and use the provided timeout. @@ -182,24 +219,23 @@ def _start_waiting(self, logged: Logged \| None, waiter: AbstractWaiter, , timeo else: timeout = validate_timeout(timeout) # Ensure no other waiters. - if self._waiter is not None: # pragma: no cover + if self._wait is not None: # pragma: no cover raise RuntimeError("Multiple concurrent waiters are not supported") - # Set a waiter. - waiter = self._waiter = waiter # Apply an immediate reduction. - logged = waiter._logged = self._reduce(logged) + logged = self._reduce(logged) if logged is None: - waiter.notify() + return None # All done! - return timeout + wait = self._wait = _Wait(logged=logged, timeout=timeout, waiter=waiter()) + return wait - def _stop_waiting(self, waiter: AbstractWaiter) -> None: + def _stop_waiting(self, wait: _Wait[Any]) -> None: with self._lock: # Clear the waiter. - self._waiter = None + self._wait = None # Error if the waiter logs are not fully reduced. - if waiter._logged is not None: - raise AssertionError(f"Not logged:\n\n{waiter._logged}") + if wait.logged is not None: + raise AssertionError(f"Not logged:\n\n{wait.logged}") def _reduce(self, logged: Logged \| None) -> Logged \| None: # Drain the queue until the log is fully reduced. @@ -256,3 +292,12 @@ def __init__(self, level: str \| int, logot: Logot) -> None: def emit(self, record: logging.LogRecord) -> None: captured = Captured(record.levelname, record.getMessage(), levelno=record.levelno) self._logot.capture(captured) + + +class _Wait(Generic[W]): + __slots__ = ("logged", "timeout", "waiter") + + def __init__(self, , logged: Logged \| None, timeout: float, waiter: W) -> None: + self.logged = logged + self.timeout = timeout + self.waiter = waiter diff --git a/logot/_typing.py b/logot/_typing.py index ac192bd2..85c53071 100644 --- a/logot/_typing.py +++ b/logot/_typing.py @@ -1,7 +1,7 @@ from __future__ import annotations import sys -from typing import TypeVar +from typing import Any, TypeVar if sys.version_info >= (3, 10): from typing import ParamSpec as ParamSpec @@ -12,3 +12,5 @@ P = ParamSpec("P") T = TypeVar("T") + +MISSING: Any = object() diff --git a/logot/_wait.py b/logot/_wait.py index 61b77df7..765c4cf1 100644 --- a/logot/_wait.py +++ b/logot/_wait.py @@ -2,47 +2,49 @@ from abc import ABC, abstractmethod from threading import Lock +from typing import Protocol, TypeVar -from logot._logged import Logged +class Waiter(Protocol): + @abstractmethod + def release(self) -> None: + raise NotImplementedError -class AbstractWaiter(ABC): - __slots__ = ("_logged", "_timeout") - # This protected attr is populated by `Logot._start_waiting`. - _logged: Logged \| None +W = TypeVar("W", bound=Waiter) - @abstractmethod - def notify(self) -> None: - raise NotImplementedError +def create_threading_waiter() -> Lock: + # Create an already-acquired lock. This will be released by `release()`. + lock = Lock() + lock.acquire() + return lock -class Waiter(AbstractWaiter): - __slots__ = () - @abstractmethod - def wait(self, , timeout: float) -> None: - raise NotImplementedError +class AsyncWaiter(ABC): + """ + Protocol used by :meth:`Logot.await_for` to pause tests until expected logs arrive. + + .. note:: + This class is for integration with 3rd-party asynchronous frameworks. It is not generally used when writing + tests. + """ -class AsyncWaiter(AbstractWaiter): __slots__ = () @abstractmethod - async def wait(self, , timeout: float) -> None: + def release(self) -> None: + """ + Releases the test waiting on :meth:`wait`, allowing it to resume immediately. + """ raise NotImplementedError + @abstractmethod + async def wait(self, , timeout: float) -> None: + """ + Waits asynchronously* for :meth:`release` to be called or the ``timeout`` to expire. -class ThreadedWaiter(Waiter): - __slots__ = ("_lock",) - - def __init__(self) -> None: - # Create an already-acquired lock. This will be released by `notify()`. - self._lock = Lock() - self._lock.acquire() - - def notify(self) -> None: - self._lock.release() - - def wait(self, *, timeout: float) -> None: - self._lock.acquire(timeout=timeout) + :param timeout: How long to wait (in seconds) before resuming. + """ + raise NotImplementedError diff --git a/logot/asyncio.py b/logot/asyncio.py new file mode 100644 index 00000000..25a47532 --- /dev/null +++ b/logot/asyncio.py @@ -0,0 +1,10 @@ +""" +Integration API for :mod:`asyncio`. + +.. seealso:: + + See :ref:`index-testing-async` usage guide. +""" +from __future__ import annotations + +from logot._asyncio import AsyncioWaiter as AsyncioWaiter	Support `tornado` Once we support `trio` via #78, `tornado` support should be easy.		2024-02-04T15:37:53	[]	[]
etianen/logot	etianen__logot-66	63b77e5128cac63d83028e2bd1b09435796ac0aa	diff --git a/logot/_format.py b/logot/_format.py index fd55340a..978fbe0d 100644 --- a/logot/_format.py +++ b/logot/_format.py @@ -1,7 +1,5 @@ from __future__ import annotations -import logging - def format_level(level: str \| int) -> str: # Format `str` level. @@ -9,8 +7,7 @@ def format_level(level: str \| int) -> str: return level # Format `int` level. if isinstance(level, int): - levelname: str = logging.getLevelName(level) - return levelname + return f"Level {level}" # Handle invalid level. raise TypeError(f"Invalid level: {level!r}") diff --git a/logot/_logot.py b/logot/_logot.py index c9690b0f..df4817a0 100644 --- a/logot/_logot.py +++ b/logot/_logot.py @@ -33,7 +33,7 @@ class Logot: The default ``level`` used by :meth:`capturing`. """ - DEFAULT_LOGGER: ClassVar[logging.Logger \| str \| None] = None + DEFAULT_LOGGER: ClassVar[str \| None] = None """ The default ``logger`` used by :meth:`capturing`. diff --git a/pyproject.toml b/pyproject.toml index 04f406d1..f7ae1881 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "logot" -version = "0.3.2" +version = "0.3.3" description = "Test whether your code is logging correctly 🪵" authors = ["Dave Hall <[email protected]>"] license = "MIT"	Support `loguru` log capture Supporting a basic [loguru](https://github.com/Delgan/loguru) integration would be very easy by providing a context manager via a helper func using their `LogHandler` integration. Alternatively, a `Logot` subclass called `Logurot` would be cheese and fun. That would allow the `pytest` plugin to provide a `logurot` fixture too (if `loguru` is installed).	Further API discussion and feedback from `loguru` devs in https://github.com/Delgan/loguru/issues/1072 ❤️ ## Proposed API ``` py from logot.contrib.loguru import LoguruCapture with Logot().capturing(LoguruCapture): pass ``` Any arguments given to `capturing` would be propagated to the `LoguruCapture` constructor. This would then unify capture between stdlib logging and `loguru` logging. The default capture backend would be the stdlib capture, and an `@overload` would ensure good typing. 💪	2024-02-02T21:28:09	[]	[]
etianen/logot	etianen__logot-61	0b16d95299331bb29721f628350ef127eae429f0	diff --git a/docs/api/logot.logged.rst b/docs/api/logot.logged.rst index 944b74c0..78c25466 100644 --- a/docs/api/logot.logged.rst +++ b/docs/api/logot.logged.rst @@ -7,14 +7,14 @@ API reference ------------- -.. autofunction:: logot.logged.log +.. autofunction:: log -.. autofunction:: logot.logged.debug +.. autofunction:: debug -.. autofunction:: logot.logged.info +.. autofunction:: info -.. autofunction:: logot.logged.warning +.. autofunction:: warning -.. autofunction:: logot.logged.error +.. autofunction:: error -.. autofunction:: logot.logged.critical +.. autofunction:: critical diff --git a/docs/api/logot.rst b/docs/api/logot.rst index 45b7ab0d..b6f2f247 100644 --- a/docs/api/logot.rst +++ b/docs/api/logot.rst @@ -7,10 +7,10 @@ API reference ------------- -.. autoclass:: logot.Logot +.. autoclass:: Logot :members: -.. autoclass:: logot.Logged +.. autoclass:: Logged -.. autoclass:: logot.Captured +.. autoclass:: Captured :members: diff --git a/docs/conf.py b/docs/conf.py index ce597b03..bf52ec02 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -22,7 +22,6 @@ "sphinx.ext.intersphinx", ] -autoclass_content = "both" autodoc_member_order = "bysource" autodoc_typehints = "both" diff --git a/docs/installing.rst b/docs/installing.rst index 10cff8f5..bdc291d3 100644 --- a/docs/installing.rst +++ b/docs/installing.rst @@ -24,7 +24,7 @@ Installing package ``extras`` pip install 'logot[pytest]' -Supported 3rd-party integrations: +Package extras for supported 3rd-party integrations: - ``pytest`` - :doc:`usage-pytest` diff --git a/docs/log-capturing.rst b/docs/log-capturing.rst index 5e1a4ac9..2ec74c96 100644 --- a/docs/log-capturing.rst +++ b/docs/log-capturing.rst @@ -11,14 +11,18 @@ Log capturing app.start() logot.wait_for(logged.info("App started")) -.. note:: - If using :mod:`pytest`, you can probably just use the pre-configured ``logot`` fixture included in the bundled - :doc:`pytest plugin </usage-pytest>` and skip manually configuring log capture. 💪 +Test framework integrations +--------------------------- + +Use a supported test framework integration for automatic log capturing in tests: + +- :doc:`/usage-pytest` +- :doc:`/usage-unittest` Capturing :mod:`logging` logs -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +----------------------------- The :meth:`Logot.capturing` method defaults to capturing all records from the root logger. Customize this with the ``level`` and ``logger`` arguments to :meth:`Logot.capturing`: @@ -40,7 +44,7 @@ careful to avoid capturing duplicate logs with overlapping calls to :meth:`Logot .. _log-capturing-3rd-party: Capturing 3rd-party logs -~~~~~~~~~~~~~~~~~~~~~~~~ +------------------------ Any 3rd-party logging library can be integrated with :mod:`logot` by sending :class:`Captured` logs to :meth:`Logot.capture`: diff --git a/pyproject.toml b/pyproject.toml index b0defef2..43bc0103 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "logot" -version = "0.3.1" +version = "0.3.2" description = "Log-based testing" authors = ["Dave Hall <[email protected]>"] license = "MIT"	Create a `unittest.TestCase` mixin that starts log capture on every test This would make it much easier to get started with `unittest`!		2024-02-01T23:15:03	[]	[]
etianen/logot	etianen__logot-45	59a14727ace7c64a8478e4cfb6d3c35fd7e3ea72	diff --git a/docs/api.rst b/docs/api.rst index eaa9a3de..d887474a 100644 --- a/docs/api.rst +++ b/docs/api.rst @@ -41,10 +41,12 @@ Import the :mod:`logot` API in your tests: .. autoclass:: logot.Captured - .. autoattribute:: levelno + .. autoattribute:: levelname .. autoattribute:: msg + .. autoattribute:: levelno + :mod:`logot.logged` ------------------- diff --git a/docs/captured.rst b/docs/captured.rst index 7f86568a..187bf232 100644 --- a/docs/captured.rst +++ b/docs/captured.rst @@ -48,7 +48,7 @@ Any 3rd-party logging library can be integrated with :mod:`logot` by sending :cl .. code:: python def on_foo_log(logot: Logot, record: FooRecord) -> None: - logot.capture(Captured(record.levelno, record.msg)) + logot.capture(Captured(record.levelname, record.msg, levelno=record.levelno)) foo_logger.add_handler(on_foo_log) diff --git a/logot/_captured.py b/logot/_captured.py index 21eeecd3..da2aef2c 100644 --- a/logot/_captured.py +++ b/logot/_captured.py @@ -1,10 +1,6 @@ from __future__ import annotations -import logging -from typing import Final - from logot._format import format_log -from logot._validate import validate_levelno class Captured: @@ -23,31 +19,43 @@ class Captured: See :ref:`captured-3rd-party` usage guide. - :param level: The log level (e.g. :data:`logging.DEBUG`) or string name (e.g. ``"DEBUG"``). + :param levelname: The log level name (e.g. ``"DEBUG"``). :param msg: The log message. + :param levelno: The log level number (e.g. :data:`logging.DEBUG`). """ - __slots__ = ("levelno", "msg") + __slots__ = ("levelname", "msg", "levelno") - levelno: Final[int] + levelname: str """ - The integer log level (e.g. :data:`logging.DEBUG`). + The log level name (e.g. ``"DEBUG"``). """ - msg: Final[str] + msg: str """ The log message. """ - def __init__(self, level: int \| str, msg: str) -> None: - self.levelno = validate_levelno(level) + levelno: int + """ + The log level number (e.g. :data:`logging.DEBUG`). + """ + + def __init__(self, levelname: str, msg: str, , levelno: int) -> None: + self.levelname = levelname self.msg = msg + self.levelno = levelno def __eq__(self, other: object) -> bool: - return isinstance(other, Captured) and other.levelno == self.levelno and other.msg == self.msg + return ( + isinstance(other, Captured) + and other.levelname == self.levelname + and other.msg == self.msg + and other.levelno == self.levelno + ) def __repr__(self) -> str: - return f"Captured({logging.getLevelName(self.levelno)!r}, {self.msg!r})" + return f"Captured({self.levelname!r}, {self.msg!r}, levelno={self.levelno!r})" def __str__(self) -> str: - return format_log(self.levelno, self.msg) + return format_log(self.levelname, self.msg) diff --git a/logot/_format.py b/logot/_format.py index d0ec82cb..6564284d 100644 --- a/logot/_format.py +++ b/logot/_format.py @@ -3,5 +3,14 @@ import logging -def format_log(levelno: int, msg: str) -> str: - return f"[{logging.getLevelName(levelno)}] {msg}" +def format_level(level: str \| int) -> str: + # Format `str` level. + if isinstance(level, str): + return level + # Format `int` level. + level: str = logging.getLevelName(level) + return level + + +def format_log(levelname: str, msg: str) -> str: + return f"[{levelname}] {msg}" diff --git a/logot/_logged.py b/logot/_logged.py index 6c11678b..6400537f 100644 --- a/logot/_logged.py +++ b/logot/_logged.py @@ -1,12 +1,11 @@ from __future__ import annotations -import logging from abc import ABC, abstractmethod from logot._captured import Captured -from logot._format import format_log +from logot._format import format_level, format_log from logot._match import compile_matcher -from logot._validate import validate_levelno +from logot._validate import validate_level class Logged(ABC): @@ -58,14 +57,14 @@ def _str(self, , indent: str) -> str: raise NotImplementedError -def log(level: int \| str, msg: str) -> Logged: +def log(level: str \| int, msg: str) -> Logged: """ Creates a :doc:`log pattern <logged>` representing a log record at the given ``level`` with the given ``msg``. - :param level: A log level (e.g. :data:`logging.DEBUG`) or string name (e.g. ``"DEBUG"``). + :param level: A log level name (e.g. ``"DEBUG"``) or numeric constant (e.g. :data:`logging.DEBUG`). :param msg: A log :doc:`message pattern <match>`. """ - return _RecordLogged(validate_levelno(level), msg) + return _RecordLogged(validate_level(level), msg) def debug(msg: str) -> Logged: @@ -74,7 +73,7 @@ def debug(msg: str) -> Logged: :param msg: A log :doc:`message pattern <match>`. """ - return _RecordLogged(logging.DEBUG, msg) + return _RecordLogged("DEBUG", msg) def info(msg: str) -> Logged: @@ -83,7 +82,7 @@ def info(msg: str) -> Logged: :param msg: A log :doc:`message pattern <match>`. """ - return _RecordLogged(logging.INFO, msg) + return _RecordLogged("INFO", msg) def warning(msg: str) -> Logged: @@ -92,7 +91,7 @@ def warning(msg: str) -> Logged: :param msg: A log :doc:`message pattern <match>`. """ - return _RecordLogged(logging.WARNING, msg) + return _RecordLogged("WARNING", msg) def error(msg: str) -> Logged: @@ -101,7 +100,7 @@ def error(msg: str) -> Logged: :param msg: A log :doc:`message pattern <match>`. """ - return _RecordLogged(logging.ERROR, msg) + return _RecordLogged("ERROR", msg) def critical(msg: str) -> Logged: @@ -110,30 +109,39 @@ def critical(msg: str) -> Logged: :param msg: A log :doc:`message pattern <match>`. """ - return _RecordLogged(logging.CRITICAL, msg) + return _RecordLogged("CRITICAL", msg) class _RecordLogged(Logged): - __slots__ = ("_levelno", "_msg", "_matcher") + __slots__ = ("_level", "_msg", "_matcher") - def __init__(self, levelno: int, msg: str) -> None: - self._levelno = levelno + def __init__(self, level: str \| int, msg: str) -> None: + self._level = level self._msg = msg self._matcher = compile_matcher(msg) def __eq__(self, other: object) -> bool: - return isinstance(other, _RecordLogged) and other._levelno == self._levelno and other._msg == self._msg + return isinstance(other, _RecordLogged) and other._level == self._level and other._msg == self._msg def __repr__(self) -> str: - return f"log({logging.getLevelName(self._levelno)!r}, {self._msg!r})" + return f"log({self._level!r}, {self._msg!r})" def _reduce(self, captured: Captured) -> Logged \| None: - if self._levelno == captured.levelno and self._matcher(captured.msg): - return None - return self + # Match `str` level. + if isinstance(self._level, str): + if self._level != captured.levelname: + return self + # Match `int` level. + elif self._level != captured.levelno: + return self + # Match message. + if not self._matcher(captured.msg): + return self + # We matched! + return None def _str(self, , indent: str) -> str: - return format_log(self._levelno, self._msg) + return format_log(format_level(self._level), self._msg) class _ComposedLogged(Logged): diff --git a/logot/_logot.py b/logot/_logot.py index c0441d3d..c1b802be 100644 --- a/logot/_logot.py +++ b/logot/_logot.py @@ -9,7 +9,7 @@ from logot._captured import Captured from logot._logged import Logged -from logot._validate import validate_levelno, validate_logger, validate_timeout +from logot._validate import validate_level, validate_logger, validate_timeout from logot._waiter import AsyncWaiter, SyncWaiter, Waiter W = TypeVar("W", bound=Waiter) @@ -29,11 +29,9 @@ class Logot: __slots__ = ("_timeout", "_lock", "_queue", "_waiter") - DEFAULT_LEVEL: ClassVar[int \| str] = logging.NOTSET + DEFAULT_LEVEL: ClassVar[str \| int] = "DEBUG" """ The default ``level`` used by :meth:`capturing`. - - This is :data:`logging.NOTSET`, specifying that all logs are captured. """ DEFAULT_LOGGER: ClassVar[logging.Logger \| str \| None] = None @@ -45,9 +43,7 @@ class Logot: DEFAULT_TIMEOUT: ClassVar[float] = 3.0 """ - The default ``timeout`` used by :meth:`wait_for` and :meth:`await_for`. - - This is 3 seconds. + The default ``timeout`` (in seconds) used by :meth:`wait_for` and :meth:`await_for`. """ def __init__( @@ -63,7 +59,7 @@ def __init__( def capturing( self, , - level: int \| str = DEFAULT_LEVEL, + level: str \| int = DEFAULT_LEVEL, logger: logging.Logger \| str \| None = DEFAULT_LOGGER, ) -> AbstractContextManager[Logot]: """ @@ -76,13 +72,13 @@ def capturing( See :doc:`captured` usage guide. - :param level: A log level (e.g. :data:`logging.DEBUG`) or string name (e.g. ``"DEBUG"``). Defaults to - :data:`logging.NOTSET`, specifying that all logs are captured. - :param logger: A logger or logger name to capture logs from. Defaults to the root logger. + :param level: A log level name (e.g. ``"DEBUG"``) or numeric constant (e.g. :data:`logging.DEBUG`). Defaults to + :attr:`DEFAULT_LEVEL`. + :param logger: A logger or logger name to capture logs from. Defaults to :attr:`DEFAULT_LOGGER`. """ - levelno = validate_levelno(level) + level = validate_level(level) logger = validate_logger(logger) - return _Capturing(self, _Handler(self, levelno=levelno), logger=logger) + return _Capturing(self, _Handler(level, self), logger=logger) def capture(self, captured: Captured) -> None: """ @@ -245,10 +241,10 @@ def __exit__( class _Handler(logging.Handler): __slots__ = ("_logot",) - def __init__(self, logot: Logot, *, levelno: int) -> None: - super().__init__(levelno) + def __init__(self, level: str \| int, logot: Logot) -> None: + super().__init__(level) self._logot = logot def emit(self, record: logging.LogRecord) -> None: - captured = Captured(record.levelno, record.getMessage()) + captured = Captured(record.levelname, record.getMessage(), levelno=record.levelno) self._logot.capture(captured) diff --git a/logot/_validate.py b/logot/_validate.py index ecf28c3e..6b47a573 100644 --- a/logot/_validate.py +++ b/logot/_validate.py @@ -3,16 +3,10 @@ import logging -def validate_levelno(level: int \| str) -> int: - # Handle `int` level. - if isinstance(level, int): +def validate_level(level: str \| int) -> str \| int: + # Handle `str` or `int` level. + if isinstance(level, (str, int)): return level - # Handle `str` level. - if isinstance(level, str): - levelno = logging.getLevelName(level) - if not isinstance(levelno, int): - raise ValueError(f"Unknown level: {level!r}") - return levelno # Handle invalid level. raise TypeError(f"Invalid level: {level!r}") diff --git a/pyproject.toml b/pyproject.toml index 78858450..75b875c1 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "logot" -version = "0.2.0" +version = "0.3.0" description = "Log-based testing" authors = ["Dave Hall <[email protected]>"] license = "MIT"	Support `loguru` log capture Supporting a basic [loguru](https://github.com/Delgan/loguru) integration would be very easy by providing a context manager via a helper func using their `LogHandler` integration. Alternatively, a `Logot` subclass called `Logurot` would be cheese and fun. That would allow the `pytest` plugin to provide a `logurot` fixture too (if `loguru` is installed).	Further API discussion and feedback from `loguru` devs in https://github.com/Delgan/loguru/issues/1072 ❤️ ## Proposed API ``` py from logot.contrib.loguru import LoguruCapture with Logot().capturing(LoguruCapture): pass ``` Any arguments given to `capturing` would be propagated to the `LoguruCapture` constructor. This would then unify capture between stdlib logging and `loguru` logging. The default capture backend would be the stdlib capture, and an `@overload` would ensure good typing. 💪	2024-01-29T16:35:15	[]	[]
etianen/logot	etianen__logot-40	64d0ead2f41d5cfeff1a5d562bd087ed68135ba9	diff --git a/logot/_validate.py b/logot/_validate.py index 1bdc96ca..ecf28c3e 100644 --- a/logot/_validate.py +++ b/logot/_validate.py @@ -6,8 +6,6 @@ def validate_levelno(level: int \| str) -> int: # Handle `int` level. if isinstance(level, int): - if logging.getLevelName(level).startswith("Level "): - raise ValueError(f"Unknown level: {level!r}") return level # Handle `str` level. if isinstance(level, str):	Support `loguru` log capture Supporting a basic [loguru](https://github.com/Delgan/loguru) integration would be very easy by providing a context manager via a helper func using their `LogHandler` integration. Alternatively, a `Logot` subclass called `Logurot` would be cheese and fun. That would allow the `pytest` plugin to provide a `logurot` fixture too (if `loguru` is installed).	Further API discussion and feedback from `loguru` devs in https://github.com/Delgan/loguru/issues/1072 ❤️ ## Proposed API ``` py from logot.contrib.loguru import LoguruCapture with Logot().capturing(LoguruCapture): pass ``` Any arguments given to `capturing` would be propagated to the `LoguruCapture` constructor. This would then unify capture between stdlib logging and `loguru` logging. The default capture backend would be the stdlib capture, and an `@overload` would ensure good typing. 💪	2024-01-28T22:48:20	[]	[]
etianen/logot	etianen__logot-38	b1c26621eeec94b15448bc4c74f61cdf361758c3	diff --git a/docs/api.rst b/docs/api.rst index aa70017f..eaa9a3de 100644 --- a/docs/api.rst +++ b/docs/api.rst @@ -19,6 +19,8 @@ Import the :mod:`logot` API in your tests: .. automethod:: capturing + .. automethod:: capture + .. automethod:: wait_for .. automethod:: await_for @@ -37,6 +39,12 @@ Import the :mod:`logot` API in your tests: .. autoclass:: logot.Logged +.. autoclass:: logot.Captured + + .. autoattribute:: levelno + + .. autoattribute:: msg + :mod:`logot.logged` ------------------- diff --git a/docs/captured.rst b/docs/captured.rst new file mode 100644 index 00000000..7f86568a --- /dev/null +++ b/docs/captured.rst @@ -0,0 +1,61 @@ +Log capturing +============= + +.. currentmodule:: logot + +:mod:`logot` makes it easy to capture logs from the stdlib :mod:`logging` module: + +.. code:: python + + with Logot().capturing() as logot: + app.start() + logot.wait_for(logged.info("App started")) + +.. note:: + + If using :mod:`pytest`, you can probably just use the pre-configured ``logot`` fixture included in the bundled + :doc:`pytest plugin <pytest>` and skip manually configuring log capture. 💪 + + +Capturing :mod:`logging` logs +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The :meth:`Logot.capturing` method defaults to capturing all records from the root logger. Customize this with the +``level`` and ``logger`` arguments to :meth:`Logot.capturing`: + +.. code:: python + + with Logot().capturing(level=logging.WARNING, logger="app") as logot: + app.start() + logot.wait_for(logged.info("App started")) + +For advanced use-cases, multiple :meth:`Logot.capturing` calls on the same :class:`Logot` instance are supported. Be +careful to avoid capturing duplicate logs with overlapping calls to :meth:`Logot.capturing`! + +.. seealso:: + + See :class:`Logot` and :meth:`Logot.capturing` API reference. + + +.. _captured-3rd-party: + +Capturing 3rd-party logs +~~~~~~~~~~~~~~~~~~~~~~~~ + +Any 3rd-party logging library can be integrated with :mod:`logot` by sending :class:`Captured` logs to +:meth:`Logot.capture`: + +.. code:: python + + def on_foo_log(logot: Logot, record: FooRecord) -> None: + logot.capture(Captured(record.levelno, record.msg)) + + foo_logger.add_handler(on_foo_log) + +.. note:: + + Using a context manager to set up and tear down log capture for every test run is highly recommended! + +.. seealso:: + + See :class:`Captured` and :meth:`Logot.capture` API reference. diff --git a/docs/index.rst b/docs/index.rst index bf1df2f0..2b95d97b 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -150,6 +150,7 @@ Learn more about :mod:`logot` with the following guides: match logged + captured pytest unittest api diff --git a/logot/__init__.py b/logot/__init__.py index 9b3b47a9..b63b7410 100644 --- a/logot/__init__.py +++ b/logot/__init__.py @@ -1,4 +1,5 @@ from __future__ import annotations +from logot._captured import Captured as Captured from logot._logged import Logged as Logged from logot._logot import Logot as Logot diff --git a/logot/_captured.py b/logot/_captured.py new file mode 100644 index 00000000..21eeecd3 --- /dev/null +++ b/logot/_captured.py @@ -0,0 +1,53 @@ +from __future__ import annotations + +import logging +from typing import Final + +from logot._format import format_log +from logot._validate import validate_levelno + + +class Captured: + """ + A captured log record. + + Send :class:`Captured` logs to :meth:`Logot.capture` to integrate with + :ref:`3rd-party logging frameworks <captured-3rd-party>` + + .. note:: + + This class is for integration with :ref:`3rd-party logging frameworks <captured-3rd-party>`. It is not generally + used when writing tests. + + .. seealso:: + + See :ref:`captured-3rd-party` usage guide. + + :param level: The log level (e.g. :data:`logging.DEBUG`) or string name (e.g. ``"DEBUG"``). + :param msg: The log message. + """ + + __slots__ = ("levelno", "msg") + + levelno: Final[int] + """ + The integer log level (e.g. :data:`logging.DEBUG`). + """ + + msg: Final[str] + """ + The log message. + """ + + def __init__(self, level: int \| str, msg: str) -> None: + self.levelno = validate_levelno(level) + self.msg = msg + + def __eq__(self, other: object) -> bool: + return isinstance(other, Captured) and other.levelno == self.levelno and other.msg == self.msg + + def __repr__(self) -> str: + return f"Captured({logging.getLevelName(self.levelno)!r}, {self.msg!r})" + + def __str__(self) -> str: + return format_log(self.levelno, self.msg) diff --git a/logot/_format.py b/logot/_format.py new file mode 100644 index 00000000..d0ec82cb --- /dev/null +++ b/logot/_format.py @@ -0,0 +1,7 @@ +from __future__ import annotations + +import logging + + +def format_log(levelno: int, msg: str) -> str: + return f"[{logging.getLevelName(levelno)}] {msg}" diff --git a/logot/_logged.py b/logot/_logged.py index c7fc7df8..6c11678b 100644 --- a/logot/_logged.py +++ b/logot/_logged.py @@ -3,6 +3,8 @@ import logging from abc import ABC, abstractmethod +from logot._captured import Captured +from logot._format import format_log from logot._match import compile_matcher from logot._validate import validate_levelno @@ -27,14 +29,14 @@ class Logged(ABC): __slots__ = () - def __rshift__(self, log: Logged) -> Logged: - return _OrderedAllLogged.from_compose(self, log) + def __rshift__(self, logged: Logged) -> Logged: + return _OrderedAllLogged.from_compose(self, logged) - def __and__(self, log: Logged) -> Logged: - return _UnorderedAllLogged.from_compose(self, log) + def __and__(self, logged: Logged) -> Logged: + return _UnorderedAllLogged.from_compose(self, logged) - def __or__(self, log: Logged) -> Logged: - return _AnyLogged.from_compose(self, log) + def __or__(self, logged: Logged) -> Logged: + return _AnyLogged.from_compose(self, logged) def __str__(self) -> str: return self._str(indent="") @@ -48,7 +50,7 @@ def __repr__(self) -> str: raise NotImplementedError @abstractmethod - def _reduce(self, record: logging.LogRecord) -> Logged \| None: + def _reduce(self, captured: Captured) -> Logged \| None: raise NotImplementedError @abstractmethod @@ -63,7 +65,7 @@ def log(level: int \| str, msg: str) -> Logged: :param level: A log level (e.g. :data:`logging.DEBUG`) or string name (e.g. ``"DEBUG"``). :param msg: A log :doc:`message pattern <match>`. """ - return _LogRecordLogged(validate_levelno(level), msg) + return _RecordLogged(validate_levelno(level), msg) def debug(msg: str) -> Logged: @@ -72,7 +74,7 @@ def debug(msg: str) -> Logged: :param msg: A log :doc:`message pattern <match>`. """ - return _LogRecordLogged(logging.DEBUG, msg) + return _RecordLogged(logging.DEBUG, msg) def info(msg: str) -> Logged: @@ -81,7 +83,7 @@ def info(msg: str) -> Logged: :param msg: A log :doc:`message pattern <match>`. """ - return _LogRecordLogged(logging.INFO, msg) + return _RecordLogged(logging.INFO, msg) def warning(msg: str) -> Logged: @@ -90,7 +92,7 @@ def warning(msg: str) -> Logged: :param msg: A log :doc:`message pattern <match>`. """ - return _LogRecordLogged(logging.WARNING, msg) + return _RecordLogged(logging.WARNING, msg) def error(msg: str) -> Logged: @@ -99,7 +101,7 @@ def error(msg: str) -> Logged: :param msg: A log :doc:`message pattern <match>`. """ - return _LogRecordLogged(logging.ERROR, msg) + return _RecordLogged(logging.ERROR, msg) def critical(msg: str) -> Logged: @@ -108,10 +110,10 @@ def critical(msg: str) -> Logged: :param msg: A log :doc:`message pattern <match>`. """ - return _LogRecordLogged(logging.CRITICAL, msg) + return _RecordLogged(logging.CRITICAL, msg) -class _LogRecordLogged(Logged): +class _RecordLogged(Logged): __slots__ = ("_levelno", "_msg", "_matcher") def __init__(self, levelno: int, msg: str) -> None: @@ -120,117 +122,117 @@ def __init__(self, levelno: int, msg: str) -> None: self._matcher = compile_matcher(msg) def __eq__(self, other: object) -> bool: - return isinstance(other, _LogRecordLogged) and other._levelno == self._levelno and other._msg == self._msg + return isinstance(other, _RecordLogged) and other._levelno == self._levelno and other._msg == self._msg def __repr__(self) -> str: return f"log({logging.getLevelName(self._levelno)!r}, {self._msg!r})" - def _reduce(self, record: logging.LogRecord) -> Logged \| None: - if self._levelno == record.levelno and self._matcher(record.getMessage()): + def _reduce(self, captured: Captured) -> Logged \| None: + if self._levelno == captured.levelno and self._matcher(captured.msg): return None return self def _str(self, , indent: str) -> str: - return f"[{logging.getLevelName(self._levelno)}] {self._msg}" + return format_log(self._levelno, self._msg) class _ComposedLogged(Logged): - __slots__ = ("_logs",) + __slots__ = ("_logged_items",) - def __init__(self, logs: tuple[Logged, ...]) -> None: - assert len(logs) > 1 - self._logs = logs + def __init__(self, logged_items: tuple[Logged, ...]) -> None: + assert len(logged_items) > 1 + self._logged_items = logged_items def __eq__(self, other: object) -> bool: - return isinstance(other, self.__class__) and other._logs == self._logs + return isinstance(other, self.__class__) and other._logged_items == self._logged_items @classmethod - def from_compose(cls, log_a: Logged, log_b: Logged) -> Logged: - # If possible, flatten nested logs of the same type. - if isinstance(log_a, cls): - if isinstance(log_b, cls): - return cls((log_a._logs, log_b._logs)) - return cls((log_a._logs, log_b)) - if isinstance(log_b, cls): - return cls((log_a, log_b._logs)) - # Wrap the logs without flattening. - return cls((log_a, log_b)) + def from_compose(cls, logged_a: Logged, logged_b: Logged) -> Logged: + # If possible, flatten nested logged items of the same type. + if isinstance(logged_a, cls): + if isinstance(logged_b, cls): + return cls((logged_a._logged_items, logged_b._logged_items)) + return cls((logged_a._logged_items, logged_b)) + if isinstance(logged_b, cls): + return cls((logged_a, logged_b._logged_items)) + # Wrap the logged items without flattening. + return cls((logged_a, logged_b)) @classmethod - def from_reduce(cls, logs: tuple[Logged, ...]) -> Logged \| None: - assert logs - # If there is a single log, do not wrap it. - if len(logs) == 1: - return logs[0] - # Wrap the logs. - return cls(logs) + def from_reduce(cls, logged_items: tuple[Logged, ...]) -> Logged \| None: + assert logged_items + # If there is a single logged item, do not wrap it. + if len(logged_items) == 1: + return logged_items[0] + # Wrap the logged items. + return cls(logged_items) class _OrderedAllLogged(_ComposedLogged): __slots__ = () def __repr__(self) -> str: - return f"({' >> '.join(map(repr, self._logs))})" + return f"({' >> '.join(map(repr, self._logged_items))})" - def _reduce(self, record: logging.LogRecord) -> Logged \| None: - log = self._logs[0] - reduced_log = log._reduce(record) + def _reduce(self, captured: Captured) -> Logged \| None: + logged = self._logged_items[0] + reduced = logged._reduce(captured) # Handle full reduction. - if reduced_log is None: - return _OrderedAllLogged.from_reduce(self._logs[1:]) + if reduced is None: + return _OrderedAllLogged.from_reduce(self._logged_items[1:]) # Handle partial reduction. - if reduced_log is not log: - return _OrderedAllLogged((reduced_log, self._logs[1:])) + if reduced is not logged: + return _OrderedAllLogged((reduced, self._logged_items[1:])) # Handle no reduction. return self def _str(self, , indent: str) -> str: - return f"\n{indent}".join(log._str(indent=indent) for log in self._logs) + return f"\n{indent}".join(logged._str(indent=indent) for logged in self._logged_items) class _UnorderedAllLogged(_ComposedLogged): __slots__ = () def __repr__(self) -> str: - return f"({' & '.join(map(repr, self._logs))})" + return f"({' & '.join(map(repr, self._logged_items))})" - def _reduce(self, record: logging.LogRecord) -> Logged \| None: - for n, log in enumerate(self._logs): - reduced_log = log._reduce(record) + def _reduce(self, captured: Captured) -> Logged \| None: + for n, logged in enumerate(self._logged_items): + reduced = logged._reduce(captured) # Handle full reduction. - if reduced_log is None: - return _UnorderedAllLogged.from_reduce((self._logs[:n], self._logs[n + 1 :])) + if reduced is None: + return _UnorderedAllLogged.from_reduce((self._logged_items[:n], self._logged_items[n + 1 :])) # Handle partial reduction. - if reduced_log is not log: - return _UnorderedAllLogged((self._logs[:n], reduced_log, self._logs[n + 1 :])) + if reduced is not logged: + return _UnorderedAllLogged((self._logged_items[:n], reduced, self._logged_items[n + 1 :])) # Handle no reduction. return self def _str(self, , indent: str) -> str: nested_indent = indent + " " - logs_str = "".join(f"\n{indent}- {log._str(indent=nested_indent)}" for log in self._logs) - return f"Unordered:{logs_str}" + logged_items_str = "".join(f"\n{indent}- {logged._str(indent=nested_indent)}" for logged in self._logged_items) + return f"Unordered:{logged_items_str}" class _AnyLogged(_ComposedLogged): __slots__ = () def __repr__(self) -> str: - return f"({' \| '.join(map(repr, self._logs))})" + return f"({' \| '.join(map(repr, self._logged_items))})" - def _reduce(self, record: logging.LogRecord) -> Logged \| None: - for n, log in enumerate(self._logs): - reduced_log = log._reduce(record) + def _reduce(self, captured: Captured) -> Logged \| None: + for n, logged in enumerate(self._logged_items): + reduced = logged._reduce(captured) # Handle full reduction. - if reduced_log is None: + if reduced is None: return None # Handle partial reduction. - if reduced_log is not log: - return _AnyLogged((self._logs[:n], reduced_log, self._logs[n + 1 :])) + if reduced is not logged: + return _AnyLogged((self._logged_items[:n], reduced, self._logged_items[n + 1 :])) # Handle no reduction. return self def _str(self, , indent: str) -> str: nested_indent = indent + " " - logs_str = "".join(f"\n{indent}- {log._str(indent=nested_indent)}" for log in self._logs) - return f"Any:{logs_str}" + logged_items_str = "".join(f"\n{indent}- {logged._str(indent=nested_indent)}" for logged in self._logged_items) + return f"Any:{logged_items_str}" diff --git a/logot/_logot.py b/logot/_logot.py index 531ea6ee..c0441d3d 100644 --- a/logot/_logot.py +++ b/logot/_logot.py @@ -6,8 +6,8 @@ from threading import Lock from types import TracebackType from typing import ClassVar, TypeVar -from weakref import WeakValueDictionary +from logot._captured import Captured from logot._logged import Logged from logot._validate import validate_levelno, validate_logger, validate_timeout from logot._waiter import AsyncWaiter, SyncWaiter, Waiter @@ -27,7 +27,7 @@ class Logot: :attr:`DEFAULT_TIMEOUT`. """ - __slots__ = ("_timeout", "_lock", "_seen_records", "_queue", "_waiter") + __slots__ = ("_timeout", "_lock", "_queue", "_waiter") DEFAULT_LEVEL: ClassVar[int \| str] = logging.NOTSET """ @@ -57,8 +57,7 @@ def __init__( ) -> None: self._timeout = validate_timeout(timeout) self._lock = Lock() - self._seen_records: WeakValueDictionary[int, logging.LogRecord] = WeakValueDictionary() - self._queue: deque[logging.LogRecord] = deque() + self._queue: deque[Captured] = deque() self._waiter: Waiter \| None = None def capturing( @@ -73,6 +72,10 @@ def capturing( If the given ``logger`` level is less verbose than the requested ``level``, it will be temporarily adjusted to the requested ``level`` for the duration of the context. + .. seealso:: + + See :doc:`captured` usage guide. + :param level: A log level (e.g. :data:`logging.DEBUG`) or string name (e.g. ``"DEBUG"``). Defaults to :data:`logging.NOTSET`, specifying that all logs are captured. :param logger: A logger or logger name to capture logs from. Defaults to the root logger. @@ -81,57 +84,86 @@ def capturing( logger = validate_logger(logger) return _Capturing(self, _Handler(self, levelno=levelno), logger=logger) - def wait_for(self, log: Logged, , timeout: float \| None = None) -> None: + def capture(self, captured: Captured) -> None: + """ + Adds the given captured log record to the internal capture queue. + + Any waiters blocked on :meth:`wait_for` to :meth:`await_for` will be notified and wake up if their + :doc:`log pattern <logged>` is satisfied. + + .. note:: + + This method is for integration with :ref:`3rd-party logging frameworks <captured-3rd-party>`. It is not + generally used when writing tests. + + .. seealso:: + + See :ref:`captured-3rd-party` usage guide. + + :param captured: The captured log. + """ + with self._lock: + # If there is a waiter that has not been fully reduced, attempt to reduce it. + if self._waiter is not None and self._waiter.logged is not None: + self._waiter.logged = self._waiter.logged._reduce(captured) + # If the waiter has fully reduced, notify the blocked caller. + if self._waiter.logged is None: + self._waiter.notify() + return + # Otherwise, buffer the captured log. + self._queue.append(captured) + + def wait_for(self, logged: Logged, , timeout: float \| None = None) -> None: """ Waits for the expected ``log`` pattern to arrive or the ``timeout`` to expire. - :param log: The expected :doc:`log pattern <logged>`. + :param logged: The expected :doc:`log pattern <logged>`. :param timeout: How long to wait (in seconds) before failing the test. Defaults to the ``timeout`` passed to :class:`Logot`. :raises AssertionError: If the expected ``log`` pattern does not arrive within ``timeout`` seconds. """ - waiter = self._open_waiter(log, SyncWaiter, timeout=timeout) + waiter = self._open_waiter(logged, SyncWaiter, timeout=timeout) try: waiter.wait() finally: self._close_waiter(waiter) - async def await_for(self, log: Logged, , timeout: float \| None = None) -> None: + async def await_for(self, logged: Logged, , timeout: float \| None = None) -> None: """ Waits asynchronously for the expected ``log`` pattern to arrive or the ``timeout`` to expire. - :param log: The expected :doc:`log pattern <logged>`. + :param logged: The expected :doc:`log pattern <logged>`. :param timeout: How long to wait (in seconds) before failing the test. Defaults to the ``timeout`` passed to :class:`Logot`. :raises AssertionError: If the expected ``log`` pattern does not arrive within ``timeout`` seconds. """ - waiter = self._open_waiter(log, AsyncWaiter, timeout=timeout) + waiter = self._open_waiter(logged, AsyncWaiter, timeout=timeout) try: await waiter.wait() finally: self._close_waiter(waiter) - def assert_logged(self, log: Logged) -> None: + def assert_logged(self, logged: Logged) -> None: """ Fails immediately if the expected ``log`` pattern has not arrived. - :param log: The expected :doc:`log pattern <logged>`. + :param logged: The expected :doc:`log pattern <logged>`. :raises AssertionError: If the expected ``log`` pattern has not arrived. """ - reduced_log = self._reduce(log) - if reduced_log is not None: - raise AssertionError(f"Not logged:\n\n{reduced_log}") + reduced = self._reduce(logged) + if reduced is not None: + raise AssertionError(f"Not logged:\n\n{reduced}") - def assert_not_logged(self, log: Logged) -> None: + def assert_not_logged(self, logged: Logged) -> None: """ Fails immediately if the expected ``log`` pattern has arrived. - :param log: The expected :doc:`log pattern <logged>`. + :param logged: The expected :doc:`log pattern <logged>`. :raises AssertionError: If the expected ``log`` pattern has arrived. """ - reduced_log = self._reduce(log) - if reduced_log is None: - raise AssertionError(f"Logged:\n\n{log}") + reduced = self._reduce(logged) + if reduced is None: + raise AssertionError(f"Logged:\n\n{logged}") def clear(self) -> None: """ @@ -140,25 +172,7 @@ def clear(self) -> None: with self._lock: self._queue.clear() - def _emit(self, record: logging.LogRecord) -> None: - with self._lock: - # De-duplicate log records. - # Duplicate log records are possible if we have multiple active captures. - record_id = id(record) - if record_id in self._seen_records: # pragma: no cover - return - self._seen_records[record_id] = record - # If there is a waiter that has not been fully reduced, attempt to reduce it. - if self._waiter is not None and self._waiter.log is not None: - self._waiter.log = self._waiter.log._reduce(record) - # If the waiter has fully reduced, notify the blocked caller. - if self._waiter.log is None: - self._waiter.notify() - return - # Otherwise, buffer the log record. - self._queue.append(record) - - def _open_waiter(self, log: Logged, waiter_cls: type[W], , timeout: float \| None) -> W: + def _open_waiter(self, logged: Logged, waiter_cls: type[W], , timeout: float \| None) -> W: with self._lock: # If no timeout is provided, use the default timeout. # Otherwise, validate and use the provided timeout. @@ -170,10 +184,10 @@ def _open_waiter(self, log: Logged, waiter_cls: type[W], , timeout: float \| Non if self._waiter is not None: # pragma: no cover raise RuntimeError("Multiple waiters are not supported") # Set a waiter. - waiter = self._waiter = waiter_cls(log, timeout=timeout) + waiter = self._waiter = waiter_cls(logged, timeout=timeout) # Apply an immediate reduction. - waiter.log = self._reduce(waiter.log) - if waiter.log is None: + waiter.logged = self._reduce(waiter.logged) + if waiter.logged is None: waiter.notify() # All done! return waiter @@ -183,20 +197,20 @@ def _close_waiter(self, waiter: Waiter) -> None: # Clear the waiter. self._waiter = None # Error if the waiter logs are not fully reduced. - if waiter.log is not None: - raise AssertionError(f"Not logged:\n\n{waiter.log}") + if waiter.logged is not None: + raise AssertionError(f"Not logged:\n\n{waiter.logged}") - def _reduce(self, log: Logged \| None) -> Logged \| None: + def _reduce(self, logged: Logged \| None) -> Logged \| None: # Drain the queue until the log is fully reduced. # This does not need a lock, since `deque.popleft()` is thread-safe. - while log is not None: + while logged is not None: try: - record = self._queue.popleft() + captured = self._queue.popleft() except IndexError: break - log = log._reduce(record) + logged = logged._reduce(captured) # All done! - return log + return logged class _Capturing: @@ -236,4 +250,5 @@ def __init__(self, logot: Logot, , levelno: int) -> None: self._logot = logot def emit(self, record: logging.LogRecord) -> None: - self._logot._emit(record) + captured = Captured(record.levelno, record.getMessage()) + self._logot.capture(captured) diff --git a/logot/_waiter.py b/logot/_waiter.py index 5cf950f7..66f5895e 100644 --- a/logot/_waiter.py +++ b/logot/_waiter.py @@ -8,10 +8,10 @@ class Waiter(ABC): - __slots__ = ("log", "_timeout") + __slots__ = ("logged", "_timeout") - def __init__(self, log: Logged, , timeout: float) -> None: - self.log: Logged \| None = log + def __init__(self, logged: Logged, , timeout: float) -> None: + self.logged: Logged \| None = logged self._timeout = timeout @abstractmethod @@ -26,8 +26,8 @@ def wait(self) -> object: class SyncWaiter(Waiter): __slots__ = ("_lock",) - def __init__(self, log: Logged, , timeout: float) -> None: - super().__init__(log, timeout=timeout) + def __init__(self, logged: Logged, , timeout: float) -> None: + super().__init__(logged, timeout=timeout) # Create an already-acquired lock. This will be released by `notify()`. self._lock = Lock() self._lock.acquire() @@ -42,8 +42,8 @@ def wait(self) -> None: class AsyncWaiter(Waiter): __slots__ = ("_loop", "_future") - def __init__(self, log: Logged, , timeout: float) -> None: - super().__init__(log, timeout=timeout) + def __init__(self, logged: Logged, , timeout: float) -> None: + super().__init__(logged, timeout=timeout) # Create an unresolved `asyncio.Future`. This will be resolved by `notify()`. self._loop = asyncio.get_running_loop() self._future: asyncio.Future[None] = self._loop.create_future() diff --git a/pyproject.toml b/pyproject.toml index 1ce8565b..08300a1a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "logot" -version = "0.1.1" +version = "0.2.0" description = "Log-based testing" authors = ["Dave Hall <[email protected]>"] license = "MIT"	Support `loguru` log capture Supporting a basic [loguru](https://github.com/Delgan/loguru) integration would be very easy by providing a context manager via a helper func using their `LogHandler` integration. Alternatively, a `Logot` subclass called `Logurot` would be cheese and fun. That would allow the `pytest` plugin to provide a `logurot` fixture too (if `loguru` is installed).	Further API discussion and feedback from `loguru` devs in https://github.com/Delgan/loguru/issues/1072 ❤️ ## Proposed API ``` py from logot.contrib.loguru import LoguruCapture with Logot().capturing(LoguruCapture): pass ``` Any arguments given to `capturing` would be propagated to the `LoguruCapture` constructor. This would then unify capture between stdlib logging and `loguru` logging. The default capture backend would be the stdlib capture, and an `@overload` would ensure good typing. 💪	2024-01-28T12:38:59	[]	[]
etianen/logot	etianen__logot-30	aa171e7a99b0c5958429b0c8ff89c4f701f321e0	diff --git a/pyproject.toml b/pyproject.toml index d30a63a6..417ff007 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -17,7 +17,7 @@ classifiers = [ "Topic :: System :: Logging", "Typing :: Typed", ] -packages = [{ include = "logot" }, { include = "_logot_pytest.py" }] +packages = [{ include = "logot" }] [tool.poetry.dependencies] python = "^3.8" @@ -43,7 +43,7 @@ sphinx = { version = "7.2.6", markers = "python_version >= '3.12'" } sphinx-autobuild = { version = "*", markers = "python_version >= '3.12'" } [tool.poetry.plugins.pytest11] -logot = "_logot_pytest" +logot = "logot._pytest" [tool.coverage.run] source = ["logot", "tests"]	Using `coverage` directly rather than `pytest-cov` This allows us to move the `pytest` plugin into the package namespace. Also switching to `importlib` import mode, following the [pytest recommendations](https://docs.pytest.org/en/7.1.x/explanation/goodpractices.html#choosing-a-test-layout-import-rules).		2024-01-27T14:51:07	[]	[]
blei-lab/treeffuser	blei-lab__treeffuser-106	d5c72fae3c3d5f939d7c69817ec0f98e2d5f7eef	diff --git a/.gitignore b/.gitignore index 6340d65c..e40f04d2 100644 --- a/.gitignore +++ b/.gitignore @@ -77,6 +77,7 @@ docs/_build # Data files /raw/ */data.npy +examples/m5/ # Debug files diff --git a/examples/README_example.ipynb b/examples/README_example.ipynb new file mode 100644 index 00000000..05fcf404 --- /dev/null +++ b/examples/README_example.ipynb @@ -0,0 +1,209 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Quick start: Forecasting with synthetic data\n", + "\n", + "In this notebook, we train Treeffuser on synthethic data and then visualize both the original and model-generated samples to explore how well Treeffuser captures the underlying distribution of the data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Getting started" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We first install `treeffuser` and import the relevant libraries." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install treeffuser\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "from treeffuser import Treeffuser" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We simulate a non-linear, bimodal response of $y$ given $x$, where the two modes follow two different response functions: one is a sine function and the other is a cosine function over $x$." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "seed = 0 # fixing the random seed for reproducibility\n", + "n = 5000 # number of data points\n", + "\n", + "rng = np.random.default_rng(seed=seed)\n", + "x = rng.uniform(0, 2 * np.pi, size=n) # x values in the range [0, 2π)\n", + "z = rng.integers(0, 2, size=n) # response function assignments\n", + "\n", + "y = z * np.sin(x - np.pi / 2) + (1 - z) * np.cos(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We also introduce heteroscedastic, fat-tailed noise from a Laplace distribution, meaning the variability of $y$ increases with $x$ and may result in large outliers." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "y += rng.laplace(scale=x / 30, size=n)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fitting Treffuser and producing samples" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fitting Treeffuser and generating samples is very simple, as Treeffuser adheres to the `sklearn.base.BaseEstimator` class. Fitting amounts to initializing the model and calling the `fit` method, just like any `scikit-learn` estimator. Samples are then generated using the `sample` method." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "model = Treeffuser(sde_initialize_from_data=True, seed=seed)\n", + "model.fit(x, y)\n", + "\n", + "y_samples = model.sample(x, n_samples=1, seed=seed, verbose=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plotting the samples" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We create a scatter plot to visualize both the original data and the samples produced by Treeffuser. The samples closely reflect the underlying response distributions that generated the data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.scatter(x, y, s=1, label=\"observed data\")\n", + "plt.scatter(x, y_samples[0, :], s=1, alpha=0.7, label=\"Treeffuser samples\")\n", + "\n", + "plt.xlabel(\"$x$\")\n", + "plt.ylabel(\"$y$\")\n", + "\n", + "legend = plt.legend(loc=\"upper center\", scatterpoints=1, bbox_to_anchor=(0.5, -0.125), ncol=2)\n", + "for legend_handle in legend.legend_handles:\n", + " legend_handle.set_sizes([32]) # change marker size for legend\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The samples generated by Treeffuser can be used to compute any downstream estimates of interest." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x = np.array(np.pi).reshape((1, 1))\n", + "y_samples = model.sample(x, n_samples=100, verbose=True) # y_samples.shape[0] is 100\n", + "\n", + "# Estimate downstream quantities of interest\n", + "y_mean = y_samples.mean(axis=0) # conditional mean for each x\n", + "y_std = y_samples.std(axis=0) # conditional std for each x\n", + "\n", + "print(f\"Mean of the samples: {y_mean}\")\n", + "print(f\"Standard deviation of the samples: {y_std} \")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For convenience, we also provide a class Samples that can estimate standard quantities." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from treeffuser.samples import Samples\n", + "\n", + "y_samples = Samples(y_samples)\n", + "y_mean = y_samples.sample_mean() # same as before\n", + "y_std = y_samples.sample_std() # same as before\n", + "y_quantiles = y_samples.sample_quantile(q=[0.05, 0.95]) # conditional quantiles for each x\n", + "\n", + "print(f\"Mean of the samples: {y_mean}\")\n", + "print(f\"Standard deviation of the samples: {y_std} \")\n", + "print(f\"5th and 95th quantiles of the samples: {y_quantiles.reshape(-1)}\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/m5-news-vendor-notebook.ipynb b/examples/m5-news-vendor-notebook.ipynb new file mode 100644 index 00000000..e1f8fecc --- /dev/null +++ b/examples/m5-news-vendor-notebook.ipynb @@ -0,0 +1,1754 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Forecasting Walmart sales with Treeffuser\n", + "\n", + "In this tutorial we show how to use Treeffuser to model and forecast Walmart sales using the M5 forecasting dataset from Kaggle." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Getting started\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To get started, we first install `treeffuser` and import the relevant libraries (if needed)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "ExecuteTime": { + "end_time": "2024-10-10T19:30:25.237555Z", + "start_time": "2024-10-10T19:30:23.907554Z" + } + }, + "source": [ + "!pip install treeffuser" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "from pathlib import Path\n", + "\n", + "from tqdm import tqdm\n", + "from treeffuser import Treeffuser\n", + "\n", + "# load autoreload extension\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, create a Kaggle account and download the data from https://www.kaggle.com/competitions/m5-forecasting-accuracy/data.\n", + "\n", + "If you're running this notebook in Colab, manually upload the necessary files (`calendar.csv`, `sales_train_validation.csv`, `sell_prices.csv`) to Colab by clicking the `Files` tab on the left sidebar and selecting `Upload`. Move the files into a new folder named `m5`. Once uploaded, the notebook will be able to read and process the data.\n", + "\n", + "If you're running this on your local machine, you can also use Kaggle's [command-line tool](https://www.kaggle.com/docs/api) and run the following from the command line:\n", + "\n", + "```bash\n", + "cd ./m5 # path to folder where you want to save the data\n", + "kaggle competitions download -c m5-forecasting-accuracy\n", + "```\n", + "\n", + "Use your favorite tool to unzip the archive. In Linux/macOS,\n", + "\n", + "```bash\n", + "unzip m5-forecasting-accuracy.zip\n", + "```\n", + "\n", + "We'll be using the following files: `calendar.csv`, `sales_train_validation.csv`, and `sell_prices.csv`.\n", + "\n", + "\n", + "<!-- - `calendar.csv` - Contains information about the dates on which the products are sold.\n", + "- `sales_train_validation.csv` - Contains the historical daily unit sales data per product and store `[d_1 - d_1913]`.\n", + "- `sell_prices.csv` - Contains information about the price of the products sold per store and date. -->\n", + "<!-- - `sales_train_evaluation.csv`- Includes sales [`d_1 - d_1941]` (labels used for the Public leaderboard). -->\n", + "<!-- - `sample_submission.csv` - The correct format for submissions. Reference the Evaluation tab for more info. -->" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Load the data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "data_path = Path(\"./m5\") # change with path where you extracted the data archive\n", + "\n", + "calendar_df = pd.read_csv(data_path / \"calendar.csv\")\n", + "sales_train_df = pd.read_csv(data_path / \"sales_train_validation.csv\")\n", + "sell_prices_df = pd.read_csv(data_path / \"sell_prices.csv\")\n", + "\n", + "# add explicit columns for the day, month, year for ease of processing\n", + "calendar_df[\"date\"] = pd.to_datetime(calendar_df[\"date\"])\n", + "calendar_df[\"day\"] = calendar_df[\"date\"].dt.day\n", + "calendar_df[\"month\"] = calendar_df[\"date\"].dt.month\n", + "calendar_df[\"year\"] = calendar_df[\"date\"].dt.year" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The data\n", + "\n", + "### Preprocessing\n", + "`sell_prices_df` contains the prices of each item in each store at a given time. The `wm_yr_wk` is a unique identifier for the time." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>store_id</th>\n", + " <th>item_id</th>\n", + " <th>wm_yr_wk</th>\n", + " <th>sell_price</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>CA_1</td>\n", + " <td>HOBBIES_1_001</td>\n", + " <td>11325</td>\n", + " <td>9.58</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>CA_1</td>\n", + " <td>HOBBIES_1_001</td>\n", + " <td>11326</td>\n", + " <td>9.58</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>CA_1</td>\n", + " <td>HOBBIES_1_001</td>\n", + " <td>11327</td>\n", + " <td>8.26</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>CA_1</td>\n", + " <td>HOBBIES_1_001</td>\n", + " <td>11328</td>\n", + " <td>8.26</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>CA_1</td>\n", + " <td>HOBBIES_1_001</td>\n", + " <td>11329</td>\n", + " <td>8.26</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " store_id item_id wm_yr_wk sell_price\n", + "0 CA_1 HOBBIES_1_001 11325 9.58\n", + "1 CA_1 HOBBIES_1_001 11326 9.58\n", + "2 CA_1 HOBBIES_1_001 11327 8.26\n", + "3 CA_1 HOBBIES_1_001 11328 8.26\n", + "4 CA_1 HOBBIES_1_001 11329 8.26" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sell_prices_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`calendar_df` contains information about the dates on which the products were sold." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>date</th>\n", + " <th>wm_yr_wk</th>\n", + " <th>weekday</th>\n", + " <th>wday</th>\n", + " <th>month</th>\n", + " <th>year</th>\n", + " <th>d</th>\n", + " <th>event_name_1</th>\n", + " <th>event_type_1</th>\n", + " <th>event_name_2</th>\n", + " <th>event_type_2</th>\n", + " <th>snap_CA</th>\n", + " <th>snap_TX</th>\n", + " <th>snap_WI</th>\n", + " <th>day</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>2011-01-29</td>\n", + " <td>11101</td>\n", + " <td>Saturday</td>\n", + " <td>1</td>\n", + " <td>1</td>\n", + " <td>2011</td>\n", + " <td>d_1</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>29</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>2011-01-30</td>\n", + " <td>11101</td>\n", + " <td>Sunday</td>\n", + " <td>2</td>\n", + " <td>1</td>\n", + " <td>2011</td>\n", + " <td>d_2</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>30</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>2011-01-31</td>\n", + " <td>11101</td>\n", + " <td>Monday</td>\n", + " <td>3</td>\n", + " <td>1</td>\n", + " <td>2011</td>\n", + " <td>d_3</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>31</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>2011-02-01</td>\n", + " <td>11101</td>\n", + " <td>Tuesday</td>\n", + " <td>4</td>\n", + " <td>2</td>\n", + " <td>2011</td>\n", + " <td>d_4</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>1</td>\n", + " <td>1</td>\n", + " <td>0</td>\n", + " <td>1</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>2011-02-02</td>\n", + " <td>11101</td>\n", + " <td>Wednesday</td>\n", + " <td>5</td>\n", + " <td>2</td>\n", + " <td>2011</td>\n", + " <td>d_5</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>1</td>\n", + " <td>0</td>\n", + " <td>1</td>\n", + " <td>2</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " date wm_yr_wk weekday wday month year d event_name_1 \\\n", + "0 2011-01-29 11101 Saturday 1 1 2011 d_1 NaN \n", + "1 2011-01-30 11101 Sunday 2 1 2011 d_2 NaN \n", + "2 2011-01-31 11101 Monday 3 1 2011 d_3 NaN \n", + "3 2011-02-01 11101 Tuesday 4 2 2011 d_4 NaN \n", + "4 2011-02-02 11101 Wednesday 5 2 2011 d_5 NaN \n", + "\n", + " event_type_1 event_name_2 event_type_2 snap_CA snap_TX snap_WI day \n", + "0 NaN NaN NaN 0 0 0 29 \n", + "1 NaN NaN NaN 0 0 0 30 \n", + "2 NaN NaN NaN 0 0 0 31 \n", + "3 NaN NaN NaN 1 1 0 1 \n", + "4 NaN NaN NaN 1 0 1 2 " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "calendar_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`sales_train_df` contains the number of units sold for an item in each department and store. The sales are grouped by day: for example, the `d_1907` column has the number of units sold on the 1907-th day." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>id</th>\n", + " <th>item_id</th>\n", + " <th>dept_id</th>\n", + " <th>cat_id</th>\n", + " <th>store_id</th>\n", + " <th>state_id</th>\n", + " <th>d_1</th>\n", + " <th>d_2</th>\n", + " <th>d_3</th>\n", + " <th>d_4</th>\n", + " <th>...</th>\n", + " <th>d_1904</th>\n", + " <th>d_1905</th>\n", + " <th>d_1906</th>\n", + " <th>d_1907</th>\n", + " <th>d_1908</th>\n", + " <th>d_1909</th>\n", + " <th>d_1910</th>\n", + " <th>d_1911</th>\n", + " <th>d_1912</th>\n", + " <th>d_1913</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>HOBBIES_1_001_CA_1_validation</td>\n", + " <td>HOBBIES_1_001</td>\n", + " <td>HOBBIES_1</td>\n", + " <td>HOBBIES</td>\n", + " <td>CA_1</td>\n", + " <td>CA</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>...</td>\n", + " <td>1</td>\n", + " <td>3</td>\n", + " <td>0</td>\n", + " <td>1</td>\n", + " <td>1</td>\n", + " <td>1</td>\n", + " <td>3</td>\n", + " <td>0</td>\n", + " <td>1</td>\n", + " <td>1</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>HOBBIES_1_002_CA_1_validation</td>\n", + " <td>HOBBIES_1_002</td>\n", + " <td>HOBBIES_1</td>\n", + " <td>HOBBIES</td>\n", + " <td>CA_1</td>\n", + " <td>CA</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>...</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>1</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>HOBBIES_1_003_CA_1_validation</td>\n", + " <td>HOBBIES_1_003</td>\n", + " <td>HOBBIES_1</td>\n", + " <td>HOBBIES</td>\n", + " <td>CA_1</td>\n", + " <td>CA</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>...</td>\n", + " <td>2</td>\n", + " <td>1</td>\n", + " <td>2</td>\n", + " <td>1</td>\n", + " <td>1</td>\n", + " <td>1</td>\n", + " <td>0</td>\n", + " <td>1</td>\n", + " <td>1</td>\n", + " <td>1</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>HOBBIES_1_004_CA_1_validation</td>\n", + " <td>HOBBIES_1_004</td>\n", + " <td>HOBBIES_1</td>\n", + " <td>HOBBIES</td>\n", + " <td>CA_1</td>\n", + " <td>CA</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>...</td>\n", + " <td>1</td>\n", + " <td>0</td>\n", + " <td>5</td>\n", + " <td>4</td>\n", + " <td>1</td>\n", + " <td>0</td>\n", + " <td>1</td>\n", + " <td>3</td>\n", + " <td>7</td>\n", + " <td>2</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>HOBBIES_1_005_CA_1_validation</td>\n", + " <td>HOBBIES_1_005</td>\n", + " <td>HOBBIES_1</td>\n", + " <td>HOBBIES</td>\n", + " <td>CA_1</td>\n", + " <td>CA</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>...</td>\n", + " <td>2</td>\n", + " <td>1</td>\n", + " <td>1</td>\n", + " <td>0</td>\n", + " <td>1</td>\n", + " <td>1</td>\n", + " <td>2</td>\n", + " <td>2</td>\n", + " <td>2</td>\n", + " <td>4</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<p>5 rows × 1919 columns</p>\n", + "</div>" + ], + "text/plain": [ + " id item_id dept_id cat_id store_id \\\n", + "0 HOBBIES_1_001_CA_1_validation HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 \n", + "1 HOBBIES_1_002_CA_1_validation HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 \n", + "2 HOBBIES_1_003_CA_1_validation HOBBIES_1_003 HOBBIES_1 HOBBIES CA_1 \n", + "3 HOBBIES_1_004_CA_1_validation HOBBIES_1_004 HOBBIES_1 HOBBIES CA_1 \n", + "4 HOBBIES_1_005_CA_1_validation HOBBIES_1_005 HOBBIES_1 HOBBIES CA_1 \n", + "\n", + " state_id d_1 d_2 d_3 d_4 ... d_1904 d_1905 d_1906 d_1907 d_1908 \\\n", + "0 CA 0 0 0 0 ... 1 3 0 1 1 \n", + "1 CA 0 0 0 0 ... 0 0 0 0 0 \n", + "2 CA 0 0 0 0 ... 2 1 2 1 1 \n", + "3 CA 0 0 0 0 ... 1 0 5 4 1 \n", + "4 CA 0 0 0 0 ... 2 1 1 0 1 \n", + "\n", + " d_1909 d_1910 d_1911 d_1912 d_1913 \n", + "0 1 3 0 1 1 \n", + "1 1 0 0 0 0 \n", + "2 1 0 1 1 1 \n", + "3 0 1 3 7 2 \n", + "4 1 2 2 2 4 \n", + "\n", + "[5 rows x 1919 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sales_train_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To align the sales data with the other DataFrames, we convert `sales_train_df` to a long format. We collapse the daily sales columns `d_{i}` into a single `sales` column, with an additional `day` column indicating the day corresponding to each sales entry." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>item_id</th>\n", + " <th>dept_id</th>\n", + " <th>cat_id</th>\n", + " <th>store_id</th>\n", + " <th>state_id</th>\n", + " <th>d</th>\n", + " <th>sales</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>HOBBIES_1_001</td>\n", + " <td>HOBBIES_1</td>\n", + " <td>HOBBIES</td>\n", + " <td>CA_1</td>\n", + " <td>CA</td>\n", + " <td>d_1</td>\n", + " <td>0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>HOBBIES_1_001</td>\n", + " <td>HOBBIES_1</td>\n", + " <td>HOBBIES</td>\n", + " <td>CA_1</td>\n", + " <td>CA</td>\n", + " <td>d_2</td>\n", + " <td>0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>HOBBIES_1_001</td>\n", + " <td>HOBBIES_1</td>\n", + " <td>HOBBIES</td>\n", + " <td>CA_1</td>\n", + " <td>CA</td>\n", + " <td>d_3</td>\n", + " <td>0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>HOBBIES_1_001</td>\n", + " <td>HOBBIES_1</td>\n", + " <td>HOBBIES</td>\n", + " <td>CA_1</td>\n", + " <td>CA</td>\n", + " <td>d_4</td>\n", + " <td>0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>HOBBIES_1_001</td>\n", + " <td>HOBBIES_1</td>\n", + " <td>HOBBIES</td>\n", + " <td>CA_1</td>\n", + " <td>CA</td>\n", + " <td>d_5</td>\n", + " <td>0</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " item_id dept_id cat_id store_id state_id d sales\n", + "0 HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA d_1 0\n", + "1 HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA d_2 0\n", + "2 HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA d_3 0\n", + "3 HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA d_4 0\n", + "4 HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA d_5 0" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def convert_sales_data_from_wide_to_long(sales_df_wide):\n", + " index_vars = [\"item_id\", \"dept_id\", \"cat_id\", \"store_id\", \"state_id\"]\n", + " sales_df_long = pd.wide_to_long(\n", + " sales_df_wide.iloc[:100, 1:],\n", + " i=index_vars,\n", + " j=\"day\",\n", + " stubnames=[\"d\"],\n", + " sep=\"_\",\n", + " ).reset_index()\n", + "\n", + " sales_df_long = sales_df_long.rename(columns={\"d\": \"sales\", \"day\": \"d\"})\n", + "\n", + " sales_df_long[\"d\"] = \"d_\" + sales_df_long[\"d\"].astype(\n", + " \"str\"\n", + " ) # restore \"d_{i}\" format for day\n", + " return sales_df_long\n", + "\n", + "\n", + "sales_train_df_long = convert_sales_data_from_wide_to_long(sales_train_df)\n", + "sales_train_df_long.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(\n", + " sales_train_df_long[\"sales\"],\n", + " bins=np.arange(0, 10 + 1.5) - 0.5,\n", + " range=[0, 10],\n", + " density=True,\n", + ")\n", + "plt.xticks(range(10))\n", + "plt.ylabel(\"Frequency of number of sales\")\n", + "plt.title(\"Number of sales over the entire timespan\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Train and test sets" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The dataset comprises sales data of 100 items over 1,913 days. For simplicity, we select the data from the first 365 days and discard the rest." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "n_items = 100\n", + "n_days = 1913\n" + ] + } + ], + "source": [ + "print(f\"n_items = {len(sales_train_df_long['item_id'].unique())}\")\n", + "print(f\"n_days = {len(sales_train_df_long['d'].unique())}\")\n", + "\n", + "sales_train_df_long[\"day_number\"] = sales_train_df_long[\"d\"].str.extract(\"(\\d+)\").astype(int)\n", + "data = sales_train_df_long[sales_train_df_long[\"day_number\"] <= 365].copy()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We compute the lags of the previous 30 days and merge the sales, calendar, and price data together." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>item_id</th>\n", + " <th>dept_id</th>\n", + " <th>cat_id</th>\n", + " <th>store_id</th>\n", + " <th>state_id</th>\n", + " <th>d</th>\n", + " <th>sales</th>\n", + " <th>day_number</th>\n", + " <th>sales_lag_1</th>\n", + " <th>sales_lag_2</th>\n", + " <th>...</th>\n", + " <th>year</th>\n", + " <th>event_name_1</th>\n", + " <th>event_type_1</th>\n", + " <th>event_name_2</th>\n", + " <th>event_type_2</th>\n", + " <th>snap_CA</th>\n", + " <th>snap_TX</th>\n", + " <th>snap_WI</th>\n", + " <th>day</th>\n", + " <th>sell_price</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>HOBBIES_1_002</td>\n", + " <td>HOBBIES_1</td>\n", + " <td>HOBBIES</td>\n", + " <td>CA_1</td>\n", + " <td>CA</td>\n", + " <td>d_141</td>\n", + " <td>0</td>\n", + " <td>141</td>\n", + " <td>0.0</td>\n", + " <td>0.0</td>\n", + " <td>...</td>\n", + " <td>2011</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>18</td>\n", + " <td>3.97</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>HOBBIES_1_002</td>\n", + " <td>HOBBIES_1</td>\n", + " <td>HOBBIES</td>\n", + " <td>CA_1</td>\n", + " <td>CA</td>\n", + " <td>d_142</td>\n", + " <td>0</td>\n", + " <td>142</td>\n", + " <td>0.0</td>\n", + " <td>0.0</td>\n", + " <td>...</td>\n", + " <td>2011</td>\n", + " <td>Father's day</td>\n", + " <td>Cultural</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>19</td>\n", + " <td>3.97</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>HOBBIES_1_002</td>\n", + " <td>HOBBIES_1</td>\n", + " <td>HOBBIES</td>\n", + " <td>CA_1</td>\n", + " <td>CA</td>\n", + " <td>d_143</td>\n", + " <td>0</td>\n", + " <td>143</td>\n", + " <td>0.0</td>\n", + " <td>0.0</td>\n", + " <td>...</td>\n", + " <td>2011</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>20</td>\n", + " <td>3.97</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>HOBBIES_1_002</td>\n", + " <td>HOBBIES_1</td>\n", + " <td>HOBBIES</td>\n", + " <td>CA_1</td>\n", + " <td>CA</td>\n", + " <td>d_144</td>\n", + " <td>1</td>\n", + " <td>144</td>\n", + " <td>0.0</td>\n", + " <td>0.0</td>\n", + " <td>...</td>\n", + " <td>2011</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>21</td>\n", + " <td>3.97</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>HOBBIES_1_002</td>\n", + " <td>HOBBIES_1</td>\n", + " <td>HOBBIES</td>\n", + " <td>CA_1</td>\n", + " <td>CA</td>\n", + " <td>d_145</td>\n", + " <td>0</td>\n", + " <td>145</td>\n", + " <td>1.0</td>\n", + " <td>0.0</td>\n", + " <td>...</td>\n", + " <td>2011</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>22</td>\n", + " <td>3.97</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<p>5 rows × 53 columns</p>\n", + "</div>" + ], + "text/plain": [ + " item_id dept_id cat_id store_id state_id d sales \\\n", + "0 HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 CA d_141 0 \n", + "1 HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 CA d_142 0 \n", + "2 HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 CA d_143 0 \n", + "3 HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 CA d_144 1 \n", + "4 HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 CA d_145 0 \n", + "\n", + " day_number sales_lag_1 sales_lag_2 ... year event_name_1 \\\n", + "0 141 0.0 0.0 ... 2011 NaN \n", + "1 142 0.0 0.0 ... 2011 Father's day \n", + "2 143 0.0 0.0 ... 2011 NaN \n", + "3 144 0.0 0.0 ... 2011 NaN \n", + "4 145 1.0 0.0 ... 2011 NaN \n", + "\n", + " event_type_1 event_name_2 event_type_2 snap_CA snap_TX snap_WI day \\\n", + "0 NaN NaN NaN 0 0 0 18 \n", + "1 Cultural NaN NaN 0 0 0 19 \n", + "2 NaN NaN NaN 0 0 0 20 \n", + "3 NaN NaN NaN 0 0 0 21 \n", + "4 NaN NaN NaN 0 0 0 22 \n", + "\n", + " sell_price \n", + "0 3.97 \n", + "1 3.97 \n", + "2 3.97 \n", + "3 3.97 \n", + "4 3.97 \n", + "\n", + "[5 rows x 53 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "n_lags = 30\n", + "\n", + "# sort data before computing lags\n", + "data_index_vars = [\"item_id\", \"dept_id\", \"cat_id\", \"store_id\", \"state_id\"]\n", + "data.sort_values(data_index_vars + [\"day_number\"], inplace=True)\n", + "\n", + "for lag in range(1, n_lags + 1):\n", + " data[f\"sales_lag_{lag}\"] = data.groupby(by=data_index_vars)[\"sales\"].shift(lag)\n", + "\n", + "data = data.merge(calendar_df).merge(sell_prices_df)\n", + "\n", + "data.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Treeffuser can handle categorical columns, but the dtype of those columns must be set to `category` in the DataFrame." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "categorical_columns = [\n", + " \"item_id\",\n", + " \"dept_id\",\n", + " \"cat_id\",\n", + " \"store_id\",\n", + " \"state_id\",\n", + " \"d\",\n", + " \"wm_yr_wk\",\n", + " \"weekday\",\n", + " \"event_name_1\",\n", + " \"event_type_1\",\n", + " \"event_name_2\",\n", + " \"event_type_2\",\n", + " \"snap_CA\",\n", + " \"snap_TX\",\n", + " \"snap_WI\",\n", + "]\n", + "data[categorical_columns] = data[categorical_columns].astype(\"category\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, for each item, we take the first 300 days as train data and use the remaining 65 data as test data for evaluation." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(15216, 50)\n", + "(3891, 50)\n" + ] + } + ], + "source": [ + "is_train = data[\"day_number\"] <= 300\n", + "\n", + "y_name = \"sales\"\n", + "x_names = [name for name in data.columns if name != y_name and name not in [\"day_number\", \"date\"]]\n", + "\n", + "X_train, y_train, dates_train = data[is_train][x_names], data[is_train][y_name], data[is_train][\"date\"]\n", + "X_test, y_test, dates_test = data[~is_train][x_names], data[~is_train][y_name], data[~is_train][\"date\"]\n", + "\n", + "print(X_train.shape)\n", + "print(X_test.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Probabilistic predictions with Treeffuser\n", + "\n", + "We regress the sales on the following covariates." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "item_id, dept_id, cat_id, store_id, state_id, d, sales_lag_1, sales_lag_2, sales_lag_3, sales_lag_4, sales_lag_5, sales_lag_6, sales_lag_7, sales_lag_8, sales_lag_9, sales_lag_10, sales_lag_11, sales_lag_12, sales_lag_13, sales_lag_14, sales_lag_15, sales_lag_16, sales_lag_17, sales_lag_18, sales_lag_19, sales_lag_20, sales_lag_21, sales_lag_22, sales_lag_23, sales_lag_24, sales_lag_25, sales_lag_26, sales_lag_27, sales_lag_28, sales_lag_29, sales_lag_30, wm_yr_wk, weekday, wday, month, year, event_name_1, event_type_1, event_name_2, event_type_2, snap_CA, snap_TX, snap_WI, day, sell_price\n" + ] + } + ], + "source": [ + "print(\", \".join(map(str, X_train.columns)))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN\n", + "[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN\n", + "[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN\n", + "[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN\n", + "[LightGBM] [Warning] Categorical features with more bins than the configured maximum bin number found.\n", + "[LightGBM] [Warning] For categorical features, max_bin and max_bin_by_feature may be ignored with a large number of categories.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/achille/Documents/treeffuser-ter/src/treeffuser/_base_tabular_diffusion.py:205: CastFloat32Warning: Input array is not float; it has been recast to float32.\n", + " y = _check_array(y)\n" + ] + }, + { + "data": { + "text/html": [ + 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relative;\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-parallel-item {\n", + " display: flex;\n", + " flex-direction: column;\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n", + " align-self: flex-end;\n", + " width: 50%;\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n", + " align-self: flex-start;\n", + " width: 50%;\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n", + " width: 0;\n", + "}\n", + "\n", + "/ Serial-specific style estimator block /\n", + "\n", + "#sk-container-id-1 div.sk-serial {\n", + " display: flex;\n", + " flex-direction: column;\n", + " align-items: center;\n", + " background-color: var(--sklearn-color-background);\n", + " padding-right: 1em;\n", + " padding-left: 1em;\n", + "}\n", + "\n", + "\n", + "/ Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n", + "clickable and can be expanded/collapsed.\n", + "- Pipeline 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input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", + " / fitted /\n", + " background-color: var(--sklearn-color-fitted-level-2);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n", + "#sk-container-id-1 div.sk-label label {\n", + " / The background is the default theme color /\n", + " color: var(--sklearn-color-text-on-default-background);\n", + "}\n", + "\n", + "/ On hover, darken the color of the background /\n", + "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n", + " color: var(--sklearn-color-text);\n", + " background-color: var(--sklearn-color-unfitted-level-2);\n", + "}\n", + "\n", + "/ Label box, darken color on hover, fitted /\n", + "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n", + " color: var(--sklearn-color-text);\n", + " background-color: var(--sklearn-color-fitted-level-2);\n", + "}\n", + "\n", + "/ Estimator label /\n", + "\n", + "#sk-container-id-1 div.sk-label label {\n", + " font-family: monospace;\n", + " font-weight: bold;\n", + " display: inline-block;\n", + " line-height: 1.2em;\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-label-container {\n", + " text-align: center;\n", + "}\n", + "\n", + "/ Estimator-specific /\n", + "#sk-container-id-1 div.sk-estimator {\n", + " font-family: monospace;\n", + " border: 1px dotted var(--sklearn-color-border-box);\n", + " border-radius: 0.25em;\n", + " box-sizing: border-box;\n", + " margin-bottom: 0.5em;\n", + " / unfitted /\n", + " background-color: var(--sklearn-color-unfitted-level-0);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-estimator.fitted {\n", + " / fitted /\n", + " background-color: var(--sklearn-color-fitted-level-0);\n", + "}\n", + "\n", + "/ on hover /\n", + "#sk-container-id-1 div.sk-estimator:hover {\n", + " / unfitted /\n", + " background-color: var(--sklearn-color-unfitted-level-2);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-estimator.fitted:hover {\n", + " / fitted /\n", + " background-color: var(--sklearn-color-fitted-level-2);\n", + "}\n", + "\n", + "/ Specification for estimator info (e.g. \"i\" and \"?\") /\n", + "\n", + "/ Common style for \"i\" and \"?\" /\n", + "\n", + ".sk-estimator-doc-link,\n", + "a:link.sk-estimator-doc-link,\n", + "a:visited.sk-estimator-doc-link {\n", + " float: right;\n", + " font-size: smaller;\n", + " line-height: 1em;\n", + " font-family: monospace;\n", + " background-color: var(--sklearn-color-background);\n", + " border-radius: 1em;\n", + " height: 1em;\n", + " width: 1em;\n", + " text-decoration: none !important;\n", + " margin-left: 1ex;\n", + " / unfitted /\n", + " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n", + " color: var(--sklearn-color-unfitted-level-1);\n", + "}\n", + "\n", + ".sk-estimator-doc-link.fitted,\n", + "a:link.sk-estimator-doc-link.fitted,\n", + "a:visited.sk-estimator-doc-link.fitted {\n", + " / fitted /\n", + " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n", + " color: var(--sklearn-color-fitted-level-1);\n", + "}\n", + "\n", + "/ On hover /\n", + "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n", + ".sk-estimator-doc-link:hover,\n", + "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n", + ".sk-estimator-doc-link:hover {\n", + " / unfitted /\n", + " background-color: var(--sklearn-color-unfitted-level-3);\n", + " color: var(--sklearn-color-background);\n", + " text-decoration: none;\n", + "}\n", + "\n", + "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n", + ".sk-estimator-doc-link.fitted:hover,\n", + "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n", + ".sk-estimator-doc-link.fitted:hover {\n", + " / fitted /\n", + " background-color: var(--sklearn-color-fitted-level-3);\n", + " color: var(--sklearn-color-background);\n", + " text-decoration: none;\n", + "}\n", + "\n", + "/ Span, style for the box shown on hovering the info icon /\n", + ".sk-estimator-doc-link span {\n", + " display: none;\n", + " z-index: 9999;\n", + " position: relative;\n", + " font-weight: normal;\n", + " right: .2ex;\n", + " padding: .5ex;\n", + " margin: .5ex;\n", + " width: min-content;\n", + " min-width: 20ex;\n", + " max-width: 50ex;\n", + " color: var(--sklearn-color-text);\n", + " box-shadow: 2pt 2pt 4pt #999;\n", + " / unfitted /\n", + " background: var(--sklearn-color-unfitted-level-0);\n", + " border: .5pt solid var(--sklearn-color-unfitted-level-3);\n", + "}\n", + "\n", + ".sk-estimator-doc-link.fitted span {\n", + " / fitted /\n", + " background: var(--sklearn-color-fitted-level-0);\n", + " border: var(--sklearn-color-fitted-level-3);\n", + "}\n", + "\n", + ".sk-estimator-doc-link:hover span {\n", + " display: block;\n", + "}\n", + "\n", + "/ \"?\"-specific style due to the `<a>` HTML tag /\n", + "\n", + "#sk-container-id-1 a.estimator_doc_link {\n", + " float: right;\n", + " font-size: 1rem;\n", + " line-height: 1em;\n", + " font-family: monospace;\n", + " background-color: var(--sklearn-color-background);\n", + " border-radius: 1rem;\n", + " height: 1rem;\n", + " width: 1rem;\n", + " text-decoration: none;\n", + " / unfitted /\n", + " color: var(--sklearn-color-unfitted-level-1);\n", + " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n", + "}\n", + "\n", + "#sk-container-id-1 a.estimator_doc_link.fitted {\n", + " / fitted /\n", + " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n", + " color: var(--sklearn-color-fitted-level-1);\n", + "}\n", + "\n", + "/ On hover /\n", + "#sk-container-id-1 a.estimator_doc_link:hover {\n", + " / unfitted /\n", + " background-color: var(--sklearn-color-unfitted-level-3);\n", + " color: var(--sklearn-color-background);\n", + " text-decoration: none;\n", + "}\n", + "\n", + "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n", + " / fitted /\n", + " background-color: var(--sklearn-color-fitted-level-3);\n", + "}\n", + "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Treeffuser(extra_lightgbm_params={}, seed=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow \"> Treeffuser<span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></label><div class=\"sk-toggleable__content \"><pre>Treeffuser(extra_lightgbm_params={}, seed=0)</pre></div> </div></div></div></div>" + ], + "text/plain": [ + "Treeffuser(extra_lightgbm_params={}, seed=0)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = Treeffuser(seed=0)\n", + "model.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%\|██████████\| 100/100 [12:39<00:00, 7.59s/it] \n" + ] + } + ], + "source": [ + "y_test_samples = model.sample(X_test, n_samples=100, seed=0, n_steps=50, verbose=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Newsvendor model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We illustrate the practical relevance of accurate probabilistic predictions with an application to inventory management, using the newsvendor model \\citep{arrow1951optimal}. \n", + "\n", + "Assume that every day we decide how many units $q$ of an item to buy. \n", + "We buy at a cost $c$ and sell at a price $p$. \n", + "However, the demand $y$ is random, introducing uncertainty in our decision. \n", + "The goal is to maximize the expected profit:\n", + "$$\\max_{q} p~\\mathbb{E}\\left[\\min(q, y)\\right] - c q.$$\n", + "The optimal solution to the newsvendor problem is to buy $q = F^{-1}\\left( \\frac{p-c}{p} \\right)$ units, where $F^{-1}$ is the quantile function of the distribution of $y$. \n", + "\n", + "Using Treeffuser, we can compute the quantiles from the samples and forecast the optimal quantity of units to buy.\n", + "\n", + "To compute profits, we use the observed prices, assume a margin of $50\\%$ over all products, and assume the actual number of sales of an item correspond to the demand of this item. We let Treeffuser, learn the conditional distribution of the demand of each item, estimate their quantiles, and thus determine the optimal quantity to buy. \n", + "\n", + "We use the held-out data to compute the profit made if Treeffuser was used to forecast the demand of each item and to manage the inventory of each item." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def newsvendor_utility(y_true, quantity_ordered, prices, stocking_cost):\n", + " \"\"\"\n", + " The newsvendor utility function with stock q, demand y, selling price p, stocking cost c is given by\n", + " $$ U(y, q, p, c) = p min(y, q) - c * q $$\n", + " \"\"\"\n", + " utility = prices * np.minimum(y_true, quantity_ordered) - stocking_cost * quantity_ordered\n", + " return utility\n", + "\n", + "\n", + "def newsvendor_optimal_quantity(y_samples, prices, stocking_cost):\n", + " \"\"\"\n", + " Returns the optimal quantity to order for the newsvendor problem.\n", + " \n", + " It is given theoeretically by:\n", + " $$ q* = argmax_{q} E[U(y, q, p, c)] $$ \n", + " which has a closed form solution,\n", + " $$ q* = F^{-1}( (p - c) / p) $$\n", + " where F is the CDF of the demand distribution\n", + " \"\"\"\n", + " # compute the target quantiles (p - c) / p\n", + " target_quantiles = (prices - stocking_cost) / prices\n", + " target_quantiles = np.maximum(target_quantiles, 0.0)\n", + "\n", + " # compute the empirical quantities corresponding to the target quantiles\n", + " res = []\n", + " for i in range(y_samples.shape[1]):\n", + " optimal_quantities = np.quantile(y_samples[:, i], target_quantiles[i])\n", + " res.append(optimal_quantities)\n", + " optimal_quantities = np.array(res)\n", + " return optimal_quantities\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "185.07666666666668" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# we don't know the profit margin of each item, so we assume it is 50%.\n", + "profit_margin = 0.5\n", + "\n", + "prices = X_test[\"sell_price\"].values\n", + "stocking_cost = prices / (1 + profit_margin)\n", + "\n", + "# Compute optimal quantities. \n", + "optimal_quantities = newsvendor_optimal_quantity(y_test_samples, prices, stocking_cost)\n", + "\n", + "# Treeffuser models continuous responses, hence we cast the predicted quantities into int\n", + "optimal_quantities = optimal_quantities.astype(int)\n", + "\n", + "profit = newsvendor_utility(y_test, optimal_quantities, prices, stocking_cost)\n", + "profit.sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "X_test_results = pd.DataFrame({\n", + " \"date\": dates_test,\n", + " \"profit\": profit\n", + "})\n", + "daily_profit = X_test_results.groupby(\"date\").sum().sort_values(\"date\").reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import matplotlib.dates as mdates\n", + "\n", + "\n", + "plt.plot(pd.to_datetime(daily_profit[\"date\"]), daily_profit[\"profit\"].cumsum())\n", + "plt.ylabel(\"Cumulated profit\")\n", + "plt.xlabel(\"Date\")\n", + "plt.gca().xaxis.set_major_locator(mdates.WeekdayLocator(byweekday=mdates.MONDAY))\n", + "plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))\n", + "plt.gcf().autofmt_xdate()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/pyproject.toml b/pyproject.toml index c55dda0f..1557a56d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -28,6 +28,7 @@ tqdm = "^4.66.4" lightgbm = "^4.3.0" ml-collections = "^0.1.1" scikit-learn = "^1.5.0" +pandas = "^2.0.0" [tool.poetry.dev-dependencies] pytest = "^8.2.2" diff --git a/src/treeffuser/_base_tabular_diffusion.py b/src/treeffuser/_base_tabular_diffusion.py index bca3327c..cd5fa9c2 100644 --- a/src/treeffuser/_base_tabular_diffusion.py +++ b/src/treeffuser/_base_tabular_diffusion.py @@ -6,6 +6,7 @@ import einops import numpy as np +import pandas as pd from jaxtyping import Float from numpy import ndarray from sklearn.base import BaseEstimator @@ -35,19 +36,20 @@ def _check_array(array: ndarray[float]): array = array.reshape(-1, 1) # Cast floats - try: - if not np.issubdtype(array.dtype, np.floating): + if not np.issubdtype(array.dtype, np.floating): + try: array = np.asarray(array, dtype=np.float32) warnings.warn( "Input array is not float; it has been recast to float32.", CastFloat32Warning, stacklevel=2, ) - except ValueError as e: - # Raise the ValueError preserving the original exception context, see B904 from flake8-bugbear - raise ValueError( - "Input array is not float and cannot be converted to float32." - ) from e # + except ValueError as e: + # raise the ValueError preserving the original exception context, see B904 from flake8-bugbear + raise ValueError( + "Input array is not float and cannot be converted to float32." + "Please check if you have encoded the categorical variables as numerical values." + ) from e return array @@ -57,6 +59,32 @@ def _check_array(array: ndarray[float]): ################################################### +def _ensure_numerical_columns(X: pd.DataFrame): + """ + Convert the categorical columns of a DataFrame to numerical columns. + Raise an error if the columns are not numerical or categorical. + Copy the DataFrame to avoid modifying the original, but only if necessary. + """ + X_has_been_copied = False + for c in X.columns: + if not isinstance(X[c].dtype, pd.CategoricalDtype) and not np.issubdtype( + X[c].dtype, np.number + ): + raise ValueError( + f"Dtypes of columns in `X` must be int, float, bool or category. " + f"Column {c} has dtype {X[c].dtype}." + f"Convert the column to a numerical dtype or a category, e.g.," + f"X['{c}'] = X['{c}'].astype('category')." + ) + if isinstance(X[c].dtype, pd.CategoricalDtype): + if X_has_been_copied is False: + # copy the DataFrame to avoid modifying the original + X = X.copy() + X_has_been_copied = True + X[c] = X[c].cat.codes + return X + + class BaseTabularDiffusion(BaseEstimator, abc.ABC): """ Abstract class for the tabular diffusions. Every particular @@ -72,6 +100,7 @@ def __init__( self.sde_initialize_from_data = sde_initialize_from_data self.score_model = None self._is_fitted = False + self._x_dataframe_columns = None self._x_scaler = None self._x_dim = None self._x_cat_idx = None @@ -90,26 +119,126 @@ def get_new_score_model(self) -> ScoreModel: Return the score model. """ - def _validate_data( + def _preprocess_and_validate_data( self, - X: ndarray \| None = None, - y: ndarray \| None = None, + X: ndarray \| pd.DataFrame \| None = None, + y: ndarray \| pd.Series \| pd.DataFrame \| None = None, + cat_idx: list[int] \| None = None, validate_X: bool = True, validate_y: bool = True, - ) -> tuple[Float[ndarray, "batch x_dim"] \| None, Float[ndarray, "batch y_dim"] \| None]: - """Reshape X and/or y to adhere to the (batch, n_dim) convention, and cast them as flows.""" - if validate_X and X is not None: + reset_data_schema: bool = False, + ) -> tuple[ + Float[ndarray, "batch x_dim"] \| None, Float[ndarray, "batch y_dim"] \| None, list[int] + ]: + """ + Preprocess and validate the input data by methods like `fit`, `predict`, `sample`, ... + (1) It transforms the different possible inputs (pandas, numpy) into numpy arrays. + (2) It checks and convert the shapes to be 2d arrays. (3) It detects categorical columns + in DataFrames. (4) It ensure that the input in `predict` has the same shape/columns as the + input in `fit` (see parameter `reset` below). + + Parameters + ---------- + X : np.ndarray or pd.DataFrame, optional + Input data with shape (batch, x_dim). + y : np.ndarray or pd.Series or pd.DataFrame, optional + Target data with shape (batch, y_dim) or (batch). + cat_idx : List[int], optional + If X is a np.ndarray, list of indices of categorical features in X. + If X is a DataFrame, setting `cat_idx` will raise an error. Instead, ensure that the + categorical columns have dtype `category`, and they will be automatically detected as + categorical features. E.g., X['column_name'] = X['column_name'].astype('category'). + validate_X : bool, optional + If True, validate the input data `X` and raise an error if no `X` is provided. + Default is True. + validate_y : bool, optional + If True, validate the target data `y` and raise an error if no `y` is provided. + Default is True. + reset_data_schema : bool, optional + If True, reset the cached schema of X and y (their shape, the name of the columns, + the categorical indices). + If False, check that X and y have the same shape and columns as the cached properties. + It should be set to True when `_preprocess_and_validate_data` is called from the `fit` + method and False when called from `predict` or `sample`, to ensure that the prediction + data is consistent with the training data. Default is False. + """ + if validate_X: + if X is None: + raise ValueError("Input data `X` cannot be None.") + if isinstance(X, pd.DataFrame): + if cat_idx is not None and cat_idx != []: + raise ValueError( + "Categorical indices `cat_idx` should not be provided when `X` is a DataFrame." + "Instead, ensure that the categorical columns have dtype `category`." + "E.g., X['column_name'] = X['column_name'].astype('category')." + ) + cat_idx = [ + X.columns.get_loc(col) for col in X.select_dtypes("category").columns + ] + # check dtype of columns and convert categorical columns to numerical + X = _ensure_numerical_columns(X) + # at that point, X contains only numerical columns + if reset_data_schema: + # store the columns of the DataFrame + self._x_dataframe_columns = X.columns + self._x_cat_idx = cat_idx + self._x_dim = X.shape[1] + else: + # check coherence of the input data with the training data + # ensure the columns of the DataFrame are the same as the ones used during training + if not all(col in X.columns for col in self._x_dataframe_columns): + raise ValueError( + "Column names in `X` are not present in the DataFrame but were present during training." + f"{set(self._x_dataframe_columns) - set(X.columns)}" + ) + # reorder the columns of the DataFrame to match the order of the training data + X = X[self._x_dataframe_columns] + X = X.values + + elif isinstance(X, np.ndarray): + if cat_idx is not None: + for idx in cat_idx: + if idx < 0 or idx >= X.shape[1]: + raise ValueError( + f"Invalid indices in `cat_idx`: {idx} is not in " + f"[0, {X.shape[1]}-1] (the shape of X)." + ) + else: + cat_idx = [] + + if reset_data_schema: + self._x_cat_idx = cat_idx + else: + raise TypeError( + "Input data `X` must be a numpy array or a pandas DataFrame." + f"Reveived {type(X).__name}." + ) X = _check_array(X) - if validate_y and y is not None: + if validate_y: + if y is None: + raise ValueError("Target data `y` cannot be None.") + if isinstance(y, pd.Series) or isinstance(y, pd.DataFrame): + y = y.values + elif not isinstance(y, np.ndarray): + raise TypeError( + "Target data `y` must be a numpy array, a pandas Series or a pandas DataFrame." + f"Received {type(y).__name}." + ) + + if reset_data_schema: + # store the original number of dimensions of the response + self._y_original_ndim = y.ndim + self._y_dim = y.shape[1] if y.ndim > 1 else 1 + y = _check_array(y) - return X, y + return X, y, self._x_cat_idx def fit( self, - X: Float[ndarray, "batch x_dim"], - y: Float[ndarray, "batch y_dim"], + X: Float[ndarray, "batch x_dim"] \| pd.DataFrame, + y: Float[ndarray, "batch y_dim"] \| pd.Series \| pd.DataFrame, cat_idx: list[int] \| None = None, ): """ @@ -117,12 +246,16 @@ def fit( Parameters ---------- - X : np.ndarray + X : np.ndarray or pd.DataFrame Input data with shape (batch, x_dim). - y : np.ndarray + y : np.ndarray or pd.Series or pd.DataFrame Target data with shape (batch, y_dim). cat_idx : List[int], optional - List of indices of categorical features in X. Default is None. + If X is a np.ndarray, list of indices of categorical features in X. + If X is a DataFrame, setting `cat_idx` will raise an error. Instead, ensure that the + categorical columns have dtype `category`, and they will be automatically detected as + categorical features. E.g., X['column_name'] = X['column_name'].astype('category'). + Default is None. Returns ------- @@ -134,13 +267,6 @@ def fit( The method handles 2D inputs (["batch x_dim"], ["batch y_dim"]) by convention, but also works with 1D inputs (["batch"]) for single-dimensional data. """ - if cat_idx is not None: - for idx in cat_idx: - if idx < 0 or idx >= X.shape[1]: - raise ValueError( - f"Invalid indices in `cat_idx`: {idx} is not in " - f"[0, {X.shape[1]}-1] (the shape of X)." - ) self.sde = self.get_new_sde() self.score_model = self.get_new_score_model() self._x_scaler = ScalerMixedTypes() @@ -149,14 +275,11 @@ def fit( # store the original number of dimensions of the response # before reshaping the data so as to ensure that predictions # have the same shape as the user-inputted response - self._y_original_ndim = y.ndim - X, y = self._validate_data(X=X, y=y) - - self._y_dim = y.shape[1] - x_transformed = self._x_scaler.fit_transform( - X, - cat_idx=cat_idx, + X, y, cat_idx = self._preprocess_and_validate_data( + X=X, y=y, cat_idx=cat_idx, reset_data_schema=True ) + + x_transformed = self._x_scaler.fit_transform(X, cat_idx=cat_idx) y_transformed = self._y_scaler.fit_transform(y) if self.sde_initialize_from_data: @@ -168,10 +291,10 @@ def fit( def sample( self, - X: Float[ndarray, "batch x_dim"], + X: Float[ndarray, "batch x_dim"] \| pd.DataFrame, n_samples: int, n_parallel: int = 10, - n_steps: int = 100, + n_steps: int = 50, seed=None, verbose: bool = False, ) -> Float[ndarray, "n_samples batch y_dim"]: @@ -180,7 +303,7 @@ def sample( Parameters ---------- - X : np.ndarray + X : np.ndarray or pd.DataFrame Input data with shape (batch, x_dim). n_samples : int Number of samples to draw for each input. @@ -211,10 +334,10 @@ def sample( if not self._is_fitted: raise ValueError("The model has not been fitted yet.") - X, _ = self._validate_data(X=X, validate_y=False) + X, _, _ = self._preprocess_and_validate_data(X=X, validate_y=False) y_samples = self._sample_without_validation( - X, n_samples, n_parallel, n_steps, seed, verbose + X, n_samples, n_parallel=n_parallel, n_steps=n_steps, seed=seed, verbose=verbose ) # Ensure output aligns with original shape provided by user @@ -243,7 +366,7 @@ def _sample_without_validation( y_samples = [] x_batched = None - pbar = tqdm(total=n_samples, disable=~verbose) + pbar = tqdm(total=n_samples, disable=not verbose) while n_samples_sampled < n_samples: batch_size_samples = min(n_parallel, n_samples - n_samples_sampled) y_batch = self.sde.sample_from_theoretical_prior( @@ -286,7 +409,7 @@ def _score_fn(y, t): def predict( self, - X: Float[ndarray, "batch x_dim"], + X: Float[ndarray, "batch x_dim"] \| pd.DataFrame, tol: float = 1e-3, max_samples: int = 100, verbose: bool = False, @@ -299,7 +422,7 @@ def predict( Parameters ---------- - X : Float[ndarray, "batch x_dim"] + X : Float[ndarray, "batch x_dim"] or pd.DataFrame Input data with shape (batch, x_dim). tol : float, optional Tolerance for the stopping criterion based on the relative change in the mean estimate. Default is 1e-3. @@ -324,11 +447,10 @@ def predict( The method handles 2D inputs (["batch x_dim"], ["batch y_dim"]) by convention, but also works with 1D inputs (["batch"]) for single-dimensional data. """ - X, _ = self._validate_data(X=X, validate_y=False) - if not self._is_fitted: raise ValueError("The model has not been fitted yet.") + X, _, _ = self._preprocess_and_validate_data(X=X, validate_y=False) y_preds = self._predict_from_sample(X, tol, max_samples, verbose) if self._y_original_ndim == 1: @@ -420,7 +542,7 @@ def predict_distribution( if not self._is_fitted: raise ValueError("The model has not been fitted yet.") - X, _ = self._validate_data(X=X, validate_y=False) + X, _, _ = self._preprocess_and_validate_data(X=X, validate_y=False) y_samples = self._sample_without_validation(X=X, n_samples=n_samples, verbose=verbose) @@ -473,7 +595,7 @@ def compute_nll( if not self._is_fitted: raise ValueError("The model has not been fitted yet.") - X, y = self._validate_data(X=X, y=y) + X, y, _ = self._preprocess_and_validate_data(X=X, y=y) return self._compute_nll_from_sample(X, y, n_samples, bandwidth, verbose) diff --git a/src/treeffuser/samples.py b/src/treeffuser/samples.py index 39d6dbed..216f0c48 100644 --- a/src/treeffuser/samples.py +++ b/src/treeffuser/samples.py @@ -320,6 +320,9 @@ def to_numpy(self) -> Float[np.ndarray, "n_samples batch y_dim"]: """ return self._samples + def __str__(self) -> str: + return str(self._samples) + def __getitem__(self, key): """ Prevent the user from removing the first or second dimension of the samples.	Support for pandas dataframe Support pandas Dataframe as input to Treeffuser. In particular, users won't have to manually specific the categorical columns (with `cat_idx`). Proposed in: #86 Support for pandas dataframe Support pandas Dataframe as input to Treeffuser. In particular, users won't have to manually specific the categorical columns (with `cat_idx`). Proposed in: #86		2024-10-14T19:31:35	[]	[]
blei-lab/treeffuser	blei-lab__treeffuser-50	44c0bebdf90239a7fc39183053761baa8bce2ca7	diff --git a/src/treeffuser/treeffuser.py b/src/treeffuser/treeffuser.py index dafb751a..4ce04dec 100644 --- a/src/treeffuser/treeffuser.py +++ b/src/treeffuser/treeffuser.py @@ -84,6 +84,8 @@ def fit(self, X: Float[ndarray, "batch x_dim"], y: Float[ndarray, "batch y_dim"] Returns an instance of the model for chaining. """ _check_arguments(X, y) + X = np.asarray(X, dtype=np.float32) + y = np.asarray(y, dtype=np.float32) x_transformed = self._x_preprocessor.fit_transform(X) y_transformed = self._y_preprocessor.fit_transform(y) @@ -135,7 +137,8 @@ def sample( while n_samples_sampled < n_samples: batch_size_samples = min(n_parallel, n_samples - n_samples_sampled) y_batch = self._sde.sample_from_theoretical_prior( - (batch_size_samples * batch_size_x, y_dim) + (batch_size_samples * batch_size_x, y_dim), + seed=seed, ) if x_batched is None or x_batched.shape[0] != batch_size_samples: # Reuse the same batch of x as much as possible	Achille/fix int float bug If the dtype is int, the normalized data would stay as int and be mostly -1, 0 and 1. Cast to float to avoid that!		2024-04-24T02:36:25	[]	[]
blei-lab/treeffuser	blei-lab__treeffuser-33	6d0dd64a5a1d55b85685db45784b3ead0c781d8f	diff --git a/src/treeffuser/_score_models.py b/src/treeffuser/_score_models.py index f7517e84..ff9ec0a5 100644 --- a/src/treeffuser/_score_models.py +++ b/src/treeffuser/_score_models.py @@ -79,8 +79,8 @@ def _make_training_data( Creates the training data for the score model. This functions assumes that 1. Score is parametrized as score(y, x, t) = GBT(y, x, t) / std(t) 2. The loss that we want to use is - \|\| sigma(t) * score(y_perturbed, x, t) - (mean(y, t) - y_perturbed)/sigma \|\|^2 - Which corresponds to the standard denoising objective with weights sigma(t)*2 + \|\| std(t) score(y_perturbed, x, t) - (mean(y, t) - y_perturbed)/std(t) \|\|^2 + Which corresponds to the standard denoising objective with weights std(t)2 This ends up meaning that we optimize \|\| GBT(y_perturbed, x, t) - (-z)\|\|^2 where z is the noise added to y_perturbed. diff --git a/src/treeffuser/sde/base_sde.py b/src/treeffuser/sde/base_sde.py index 1d5816c7..e42d7dc3 100644 --- a/src/treeffuser/sde/base_sde.py +++ b/src/treeffuser/sde/base_sde.py @@ -26,7 +26,7 @@ class BaseSDE(abc.ABC): @abc.abstractmethod def drift_and_diffusion( self, y: Float[ndarray, "batch y_dim"], t: Float[ndarray, "batch 1"] - ) -> (Float[ndarray, "batch y_dim"], Float[ndarray, "batch y_dim"]): + ) -> tuple[Float[ndarray, "batch y_dim"], Float[ndarray, "batch y_dim"]]: """ Computes the drift and diffusion at a given time `t` for a given state `Y=y`. @@ -76,7 +76,7 @@ def __init__( def drift_and_diffusion( self, y: Float[ndarray, "batch y_dim"], t: Float[ndarray, "batch 1"] - ) -> (Float[ndarray, "batch y_dim"], Float[ndarray, "batch y_dim"]): + ) -> tuple[Float[ndarray, "batch y_dim"], Float[ndarray, "batch y_dim"]]: drift, diffusion = self.sde.drift_and_diffusion(y, self.t_reverse_origin - t) drift = -drift + diffusion2 * self.score_fn(y, self.t_reverse_origin - t) return drift, diffusion @@ -114,7 +114,7 @@ def __init__( def drift_and_diffusion( self, y: Float[ndarray, "batch y_dim"], t: Float[ndarray, "batch 1"] - ) -> (Float[ndarray, "batch y_dim"], Float[ndarray, "batch y_dim"]): + ) -> tuple[Float[ndarray, "batch y_dim"], Float[ndarray, "batch y_dim"]]: return self.drift_fn(y, t), self.diffusion_fn(y, t) def __repr__(self): @@ -150,7 +150,7 @@ def _register(cls): return _register -def get_sde(name): +def get_sde(name: str): """ Function to retrieve a registered SDE by its name. diff --git a/src/treeffuser/sde/initialize.py b/src/treeffuser/sde/initialize.py new file mode 100644 index 00000000..1fa7775b --- /dev/null +++ b/src/treeffuser/sde/initialize.py @@ -0,0 +1,177 @@ +import warnings +from typing import Callable +from typing import Optional +from typing import Tuple +from typing import Union + +import numpy as np +from jaxtyping import Float + +from .base_sde import get_sde +from .parameter_schedule import LinearSchedule +from .sdes import VESDE +from .sdes import VPSDE +from .sdes import SubVPSDE + + +class ConvergenceWarning(Warning): + # Indicates hyperparameter initialization didn't converge. + pass + + +def initialize_sde( + name: str, + y0: Float[np.ndarray, "batch y_dim"], + T: float = 1, + KL_tol: Optional[float] = 10 (-5), + verbose: bool = False, +) -> Union[VESDE, VPSDE, SubVPSDE]: + """ + Initializes an SDE model based on the given name and initial data. + + For all SDEs, it sets `hyperparam_min` to 0.01. For VESDE, it sets `hyperparam_max` + to the maxiumum pairwise distance in y0, following [1]. For VPSDE and Sub-VPSDE, it + sets `hyperparam_max` to the smallest value that controls the KL divergence between + the perturbation kernel at T and the theorethical prior. + + Parameters + ---------- + name : str + The SDE model to initialize ('vesde', 'vpsde', 'sub-vpsde'). + y0 : np.ndarray + The data array with the training outcome. + T : float, optional + End time of the SDE, default is 1. + KL_tol : float, optional + KL divergence tolerance for initialization, default is 1e-5. This is only used + for VPSDE and Sub-VPSDE. + + Returns + ------- + An instance of the specified SDE model. + + References + ------- + [1] Song, Y. and Ermon, S., 2020. Improved techniques for training score-based + generative models. NeurIPS (2020). + """ + if name.lower() == "vesde": + hyperparam_min, hyperparam_max = _initialize_vesde(y0) + elif name.lower() == "vpsde": + hyperparam_min, hyperparam_max = _initialize_vpsde(y0, T, KL_tol) + elif name.lower() == "sub-vpsde": + hyperparam_min, hyperparam_max = _initialize_subvpsde(y0, T, KL_tol) + else: + raise NotImplementedError + + sde = get_sde(name)(hyperparam_min, hyperparam_max) + if verbose: + print(sde) + return sde + + +def _initialize_vesde(y0: Float[np.ndarray, "batch y_dim"]) -> Tuple[float, float]: + hyperparam_min = 0.01 + if y0.shape[1] == 1: + max_pairwise_difference = y0.max() - y0.min() + else: + y0_aug = y0[:, np.newaxis, :] + max_pairwise_difference = np.max(np.sqrt(np.sum((y0_aug - y0) 2, axis=-1))) + return hyperparam_min, max_pairwise_difference + + +def _initialize_vpsde( + y0: Float[np.ndarray, "batch y_dim"], T: float = 1, KL_tol: float = 10 ** (-5) +) -> Tuple[float, float]: + hyperparam_min = 0.01 + y0_max = y0.max() + + def KL_helper(hyperparam_max): + schedule = LinearSchedule(hyperparam_min, hyperparam_max) + hyperparam_integral = schedule.get_integral(T) + KL = _KL_univariate_gaussians( + y0_max * np.exp(-0.5 * hyperparam_integral), + (1 - np.exp(-hyperparam_integral)) 0.5, + 0, + 1, + ) + return KL + + hyperparam_max_ini = 100 + hyperparam_max = _bisect(KL_helper, hyperparam_min, hyperparam_max_ini, KL_tol) + + if hyperparam_max == hyperparam_max_ini: + warnings.warn( + f"Hyperparameter initialization did not converge: setting `hyperparam_max` to {hyperparam_max_ini}.", + ConvergenceWarning, + stacklevel=1, + ) + + return hyperparam_min, hyperparam_max + + +def _initialize_subvpsde( + y0: Float[np.ndarray, "batch y_dim"], T: float = 1, KL_tol: float = 10 (-5) +) -> Tuple[float, float]: + hyperparam_min = 0.01 + y0_max = y0.max() + + def KL_helper(hyperparam_max): + schedule = LinearSchedule(hyperparam_min, hyperparam_max) + hyperparam_integral = schedule.get_integral(T) + KL = _KL_univariate_gaussians( + y0_max * np.exp(-0.5 * hyperparam_integral), 1 - np.exp(-hyperparam_integral), 0, 1 + ) + return KL + + hyperparam_max_ini = 100 + hyperparam_max = _bisect(KL_helper, hyperparam_min, hyperparam_max_ini, KL_tol) + + if hyperparam_max == hyperparam_max_ini: + warnings.warn( + f"Hyperparameter initialization did not converge: setting `hyperparam_max` to {hyperparam_max_ini}.", + ConvergenceWarning, + stacklevel=1, + ) + + return hyperparam_min, hyperparam_max + + +def _KL_univariate_gaussians( + loc_1: float, scale_1: float, loc_2: float = 0, scale_2: float = 1 +) -> float: + """ + Computes the KL divergence between two univariate Gaussians. + + The KL divergence between two Gaussians `N(loc_1, scale_1)` and `N(loc_2, scale_2)` is: + `log (scale_2 / scale_1) + (scale_1^2 + (loc_1 - loc_2) ^ 2) / (2 * scale_2^2) - .5`. + """ + return ( + np.log(scale_2 / scale_1) + + (scale_12 + (loc_1 - loc_2) 2) / (2 * scale_22) + - 1 / 2 + ) + + +def _bisect( + f: Callable[[float], float], + a: float, + b: float, + tol: float = 1e-5, + x_tol: float = 1e-5, + max_iter: int = 1000, +) -> float: + """ + Return an x such that \|f(x)\| <= tol and \|f(x - x_tol)\| > tol. + + It assumes that f is continuous and decreasing in [a, b], and that \|f(a)\| > tol and \|f(b)\| <= tol. + """ + for _ in range(max_iter): + x = (a + b) / 2 + if np.abs(f(x)) > tol: + a = x + elif np.abs(f(x - x_tol)) > tol: + return x + else: + b = x + return x diff --git a/src/treeffuser/sde/sdes.py b/src/treeffuser/sde/sdes.py index c4f99bc8..43466460 100644 --- a/src/treeffuser/sde/sdes.py +++ b/src/treeffuser/sde/sdes.py @@ -1,4 +1,5 @@ import abc +from typing import Dict from typing import Optional from typing import Union @@ -15,7 +16,7 @@ class DiffusionSDE(BaseSDE): """ Abstract class representing a diffusion SDE: - `dY = (A(t) + B(t) Y) dt + \\sigma(t) dW` where `\\sigma(t)` is a time-varying + `dY = (A(t) + B(t) Y) dt + C(t) dW` where `C(t)` is a time-varying diffusion coefficient independent of `Y`, and the drift is an affine function of Y. As a result, the conditional distribution p_t(y \| y0) is Gaussian. """ @@ -25,6 +26,16 @@ def T(self) -> float: """End time of the SDE.""" return 1.0 + @abc.abstractmethod + def get_hyperparams(self) -> Dict: + """ + Return a dictionary with the hyperparameters of the SDE. + + The hyperparameters parametrize the drift and diffusion coefficients of the + SDE. + """ + raise NotImplementedError + @abc.abstractmethod def sample_from_theoretical_prior( self, shape: tuple[int, ...], seed: Optional[int] = None @@ -46,7 +57,7 @@ def get_mean_std_pt_given_y0( self, y0: Float[ndarray, "batch y_dim"], t: Float[ndarray, "batch 1"], - ) -> (Float[ndarray, "batch y_dim"], Float[ndarray, "batch y_dim"]): + ) -> tuple[Float[ndarray, "batch y_dim"], Float[ndarray, "batch y_dim"]]: """ Our diffusion SDEs have conditional distributions p_t(y \| y0) that are Gaussian. This method returns their mean and standard deviation. @@ -107,57 +118,64 @@ class VESDE(DiffusionSDE): """ Variance-exploding SDE (VESDE): `dY = 0 dt + \\sqrt{2 \\sigma(t) \\sigma'(t)} dW` - where `sigma(t)` is a time-varying diffusion coefficient. + where `sigma(t)` is a time-varying diffusion coefficient with exponential schedule: - The SDE converges to a normal distribution with variance `sigma_max2`. + `\\sigma(t) = hyperparam_min * (hyperparam_max / hyperparam_min) ^ t` + + The SDE converges to a normal distribution with variance `hyperparam_max ^ 2`. Parameters ---------- - sigma_min : float + hyperparam_min : float Minimum value of the diffusion coefficient. - sigma_max : float + hyperparam_max : float Maximum value of the diffusion coefficient. """ - def __init__(self, sigma_min=0.01, sigma_max=20): - self.sigma_min = sigma_min - self.sigma_max = sigma_max - self.sigma_schedule = ExponentialSchedule(sigma_min, sigma_max) + def __init__(self, hyperparam_min=0.01, hyperparam_max=20): + self.hyperparam_min = hyperparam_min + self.hyperparam_max = hyperparam_max + self.hyperparam_schedule = ExponentialSchedule(hyperparam_min, hyperparam_max) + + def get_hyperparams(self): + return {"hyperparam_min": self.hyperparam_min, "hyperparam_max": self.hyperparam_max} def drift_and_diffusion( self, y: Float[ndarray, "batch y_dim"], t: Float[ndarray, "batch 1"] - ) -> (Float[ndarray, "batch y_dim"], Float[ndarray, "batch y_dim"]): - sigma = self.sigma_schedule(t) - sigma_prime = self.sigma_schedule.get_derivative(t) + ) -> tuple[Float[ndarray, "batch y_dim"], Float[ndarray, "batch y_dim"]]: + hyperparam = self.hyperparam_schedule(t) + hyperparam_prime = self.hyperparam_schedule.get_derivative(t) drift = 0 - diffusion = np.sqrt(2 * sigma * sigma_prime) + diffusion = np.sqrt(2 * hyperparam * hyperparam_prime) return drift, diffusion def sample_from_theoretical_prior( self, shape: tuple[int, ...], seed: Optional[int] = None ) -> Float[ndarray, "batch x_dim y_dim"]: - # This assumes that sigma_max is large enough + # This assumes that hyperparam_max is large enough rng = np.random.default_rng(seed) - return rng.normal(0, self.sigma_max, shape) + return rng.normal(0, self.hyperparam_max, shape) def get_mean_std_pt_given_y0( self, y0: Float[ndarray, "batch y_dim"], t: Float[ndarray, "batch 1"], - ) -> (Float[ndarray, "batch y_dim"], Float[ndarray, "batch y_dim"]): + ) -> tuple[Float[ndarray, "batch y_dim"], Float[ndarray, "batch y_dim"]]: """ The conditional distribution is Gaussian with: mean: `y0` - variance: `sigma(t)2 - sigma(0)2` + variance: `hyperparam(t)2 - hyperparam(0)2` """ mean = y0 - std = (self.sigma_schedule(t) 2 - self.sigma_schedule(np.zeros_like(t)) 2) 0.5 + std = ( + self.hyperparam_schedule(t) 2 - self.hyperparam_schedule(np.zeros_like(t)) 2 + ) 0.5 std = np.broadcast_to(std, y0.shape) return mean, std def __repr__(self): - return f"VESDE(sigma_min={self.sigma_min}, sigma_max={self.sigma_max})" + return f"VESDE(hyperparam_min={self.hyperparam_min}, hyperparam_max={self.hyperparam_max})" @_register_sde(name="vpsde") @@ -165,35 +183,39 @@ class VPSDE(DiffusionSDE): """ Variance-preserving SDE (VPSDE): `dY = -0.5 \\beta(t) Y dt + \\sqrt{\\beta(t)} dW` - where `beta(t)` is a time-varying diffusion coefficient. + where `beta(t)` is a time-varying coefficient with linear schedule: + `\\beta(t) = hyperparam_min + (hyperparam_max - hyperparam_min) * t.` - The SDE converges to a standard normal distribution for large `beta(t)`. + The SDE converges to a standard normal distribution for large `hyperparam_max`. Parameters ---------- - beta_min : float - Minimum value of the diffusion coefficient. - beta_max : float - Maximum value of the diffusion coefficient. + hyperparam_min : float + Minimum value of the time-varying coefficient `\\beta(t)`. + hyperparam_max : float + Maximum value of the time-varying coefficient `\\beta(t)`. """ - def __init__(self, beta_min=0.01, beta_max=20): - self.beta_min = beta_min - self.beta_max = beta_max - self.beta_schedule = LinearSchedule(beta_min, beta_max) + def __init__(self, hyperparam_min=0.01, hyperparam_max=20): + self.hyperparam_min = hyperparam_min + self.hyperparam_max = hyperparam_max + self.hyperparam_schedule = LinearSchedule(hyperparam_min, hyperparam_max) + + def get_hyperparams(self): + return {"hyperparam_min": self.hyperparam_min, "hyperparam_max": self.hyperparam_max} def drift_and_diffusion( self, y: Float[ndarray, "batch y_dim"], t: Float[ndarray, "batch 1"] - ) -> (Float[ndarray, "batch y_dim"], Float[ndarray, "batch y_dim"]): - beta_t = self.beta_schedule(t) - drift = -0.5 * beta_t * y - diffusion = np.sqrt(beta_t) + ) -> tuple[Float[ndarray, "batch y_dim"], Float[ndarray, "batch y_dim"]]: + hyperparam_t = self.hyperparam_schedule(t) + drift = -0.5 * hyperparam_t * y + diffusion = np.sqrt(hyperparam_t) return drift, diffusion def sample_from_theoretical_prior( self, shape: tuple[int, ...], seed: Optional[int] = None ) -> Float[ndarray, "batch x_dim y_dim"]: - # Assume that beta_max is large enough so that the SDE has converged to N(0,1). + # Assume that hyperparam_max is large enough so that the SDE has converged to N(0,1). rng = np.random.default_rng(seed) return rng.normal(0, 1, shape) @@ -201,22 +223,22 @@ def get_mean_std_pt_given_y0( self, y0: Float[ndarray, "batch y_dim"], t: Float[ndarray, "batch 1"], - ) -> (Float[ndarray, "batch y_dim"], Float[ndarray, "batch y_dim"]): + ) -> tuple[Float[ndarray, "batch y_dim"], Float[ndarray, "batch y_dim"]]: """ The conditional distribution is Gaussian with: - mean: `y0 * exp(-0.5 * \\int_0^t1 beta(s) ds)` - variance: `1 - exp(-\\int_0^t1 beta(s) ds)` + mean: `y0 * exp(-0.5 * \\int_0^t1 \\beta(s) ds)` + variance: `1 - exp(-\\int_0^t1 \\beta(s) ds)` """ - beta_integral = self.beta_schedule.get_integral(t) - mean = y0 * np.exp(-0.5 * beta_integral) - std = (1 - np.exp(-beta_integral)) ** 0.5 + hyperparam_integral = self.hyperparam_schedule.get_integral(t) + mean = y0 * np.exp(-0.5 * hyperparam_integral) + std = (1 - np.exp(-hyperparam_integral)) ** 0.5 mean = np.broadcast_to(mean, y0.shape) std = np.broadcast_to(std, y0.shape) return mean, std def __repr__(self): - return f"VPSDE(beta_min={self.beta_min}, beta_max={self.beta_max})" + return f"VPSDE(hyperparam_min={self.hyperparam_min}, hyperparam_max={self.hyperparam_max})" @_register_sde(name="sub-vpsde") @@ -224,37 +246,41 @@ class SubVPSDE(DiffusionSDE): """ Sub-Variance-preserving SDE (SubVPSDE): `dY = -0.5 \\beta(t) Y dt + \\sqrt{\\beta(t) (1 - e^{-2 \\int_0^t \\beta(s) ds})} dW` - where `beta(t)` is a time-varying diffusion coefficient. + where `beta(t)` is a time-varying coefficient with linear schedule: + `\\beta(t) = hyperparam_min + (hyperparam_max - hyperparam_min) * t.` - The SDE converges to a standard normal distribution for large `beta(t)`. + The SDE converges to a standard normal distribution for large `hyperparam_max`. Parameters ---------- - beta_min : float - Minimum value of the diffusion coefficient. - beta_max : float - Maximum value of the diffusion coefficient. + hyperparam_min : float + Minimum value of the time-varying coefficient `\\beta(t)`. + hyperparam_max : float + Maximum value of the time-varying coefficient `\\beta(t)`. """ - def __init__(self, beta_min=0.01, beta_max=20): - self.beta_min = beta_min - self.beta_max = beta_max - self.beta_schedule = LinearSchedule(beta_min, beta_max) + def __init__(self, hyperparam_min=0.01, hyperparam_max=20): + self.hyperparam_min = hyperparam_min + self.hyperparam_max = hyperparam_max + self.hyperparam_schedule = LinearSchedule(hyperparam_min, hyperparam_max) + + def get_hyperparams(self): + return {"hyperparam_min": self.hyperparam_min, "hyperparam_max": self.hyperparam_max} def drift_and_diffusion( self, y: Float[ndarray, "batch y_dim"], t: Float[ndarray, "batch 1"] - ) -> (Float[ndarray, "batch y_dim"], Float[ndarray, "batch y_dim"]): - beta_t = self.beta_schedule(t) - drift = -0.5 * beta_t * y - beta_integral = self.beta_schedule.get_integral(t) - discount = 1.0 - np.exp(-2 * beta_integral) - diffusion = np.sqrt(beta_t * discount) + ) -> tuple[Float[ndarray, "batch y_dim"], Float[ndarray, "batch y_dim"]]: + hyperparam_t = self.hyperparam_schedule(t) + drift = -0.5 * hyperparam_t * y + hyperparam_integral = self.hyperparam_schedule.get_integral(t) + discount = 1.0 - np.exp(-2 * hyperparam_integral) + diffusion = np.sqrt(hyperparam_t * discount) return drift, diffusion def sample_from_theoretical_prior( self, shape: tuple[int, ...], seed: Optional[int] = None ) -> Float[ndarray, "batch x_dim y_dim"]: - # Assume that beta_max is large enough so that the SDE has converged to N(0,1). + # Assume that hyperparam_max is large enough so that the SDE has converged to N(0,1). rng = np.random.default_rng(seed) return rng.normal(0, 1, shape) @@ -262,16 +288,19 @@ def get_mean_std_pt_given_y0( self, y0: Float[ndarray, "batch y_dim"], t: Float[ndarray, "batch 1"], - ) -> (Float[ndarray, "batch y_dim"], Float[ndarray, "batch y_dim"]): + ) -> tuple[Float[ndarray, "batch y_dim"], Float[ndarray, "batch y_dim"]]: """ The conditional distribution is Gaussian with: - mean: `y0 * exp(-0.5 * \\int_0^t1 beta(s) ds)` - variance: `[1 - exp(-\\int_0^t1 beta(s) ds)]^2` + mean: `y0 * exp(-0.5 * \\int_0^t1 \\beta(s) ds)` + variance: `[1 - exp(-\\int_0^t1 \\beta(s) ds)]^2` """ - beta_integral = self.beta_schedule.get_integral(t) - mean = y0 * np.exp(-0.5 * beta_integral) - std = 1 - np.exp(-beta_integral) + hyperparam_integral = self.hyperparam_schedule.get_integral(t) + mean = y0 * np.exp(-0.5 * hyperparam_integral) + std = 1 - np.exp(-hyperparam_integral) mean = np.broadcast_to(mean, y0.shape) std = np.broadcast_to(std, y0.shape) return mean, std + + def __repr__(self): + return f"VPSDE(hyperparam_min={self.hyperparam_min}, hyperparam_max={self.hyperparam_max})" diff --git a/src/treeffuser/treeffuser.py b/src/treeffuser/treeffuser.py index 076e8a1f..2270e396 100644 --- a/src/treeffuser/treeffuser.py +++ b/src/treeffuser/treeffuser.py @@ -8,6 +8,7 @@ import numpy as np from einops import rearrange from jaxtyping import Float +from ml_collections import ConfigDict from ml_collections import FrozenConfigDict from numpy import ndarray from sklearn.base import BaseEstimator @@ -18,6 +19,7 @@ from treeffuser._score_models import Score from treeffuser.sde import get_sde from treeffuser.sde import sdeint +from treeffuser.sde.initialize import initialize_sde def _check_arguments( @@ -39,17 +41,21 @@ class Treeffuser(BaseEstimator, abc.ABC): different parameters and methods. """ - def __init__(self, sde_name: str = "vesde"): + def __init__( + self, + sde_name: str = "vesde", + sde_initialize_with_data: Optional[bool] = False, + sde_manual_hyperparams: Optional[dict] = None, + ): + self._sde_name = sde_name + self._sde_initialize_with_data = sde_initialize_with_data + self._sde_manual_hyperparams = sde_manual_hyperparams self._score_model = None self._is_fitted = False self._x_preprocessor = Preprocessor() self._y_preprocessor = Preprocessor() self._y_dim = None - # TODO: We are using the defaults but we should change this - self._sde = get_sde(sde_name)() - self._sde_name = sde_name - @property @abc.abstractmethod def score_config(self) -> FrozenConfigDict: @@ -74,6 +80,18 @@ def fit(self, X: Float[ndarray, "batch x_dim"], y: Float[ndarray, "batch y_dim"] """ _check_arguments(X, y) + if self._sde_initialize_with_data: + self._sde = initialize_sde(self._sde_name, y) + else: + sde_cls = get_sde(self._sde_name) + if self._sde_manual_hyperparams: + self._sde = sde_cls(**self._sde_manual_hyperparams) + else: + self._sde = sde_cls() + + self._score_config.update({"sde": self._sde}) + self._score_config = FrozenConfigDict(self._score_config) + x_transformed = self._x_preprocessor.fit_transform(X) y_transformed = self._y_preprocessor.fit_transform(y) @@ -152,8 +170,10 @@ def __init__( self, # Diffusion model args sde_name: Optional[str] = "vesde", - # Score estimator args + sde_initialize_with_data: Optional[bool] = False, + sde_manual_hyperparams: Optional[dict] = None, n_repeats: Optional[int] = 10, + # Score estimator args n_estimators: Optional[int] = 100, eval_percent: Optional[float] = None, early_stopping_rounds: Optional[int] = None, @@ -172,8 +192,13 @@ def __init__( """ Diffusion model args ------------------------------- + sde_name (str): The SDE name. + sde_initialize_with_data (bool): Whether to initialize the SDE hyperparameters + with data. + sde_manual_hyperparams: (dict): A dictionary for explicitly setting the SDE + hyperparameters, overriding default or data-based initializations. n_repeats (int): How many times to repeat the training dataset. i.e how - many noisy versions of a point to generate for training. + many noisy versions of a point to generate for training. LightGBM args ------------------------------- eval_percent (float): Percentage of the training data to use for validation. @@ -199,10 +224,18 @@ def __init__( n_jobs (int): Number of parallel threads. If set to -1, the number is set to the number of available cores. """ - super().__init__(sde_name=sde_name) - self._score_config = FrozenConfigDict( + if sde_initialize_with_data and sde_manual_hyperparams is not None: + raise Warning( + "Manual hypeparameter setting will override data-based initialization." + ) + + super().__init__( + sde_name=sde_name, + sde_initialize_with_data=sde_initialize_with_data, + sde_manual_hyperparams=sde_manual_hyperparams, + ) + self._score_config = ConfigDict( { - "sde": self._sde, "n_repeats": n_repeats, "n_estimators": n_estimators, "eval_percent": eval_percent,	Come up with good initialization of SDE hyper-params There are good guidelines for VESDE. There must be for the other ones and we should probably implement them.		2024-03-16T06:09:04	[]	[]
kinde-oss/kinde-python-sdk	kinde-oss__kinde-python-sdk-17	ff1907c27c565c05304237aef2576f10ab9dbd77	diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 4ad5204..a8c3ad5 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -62,4 +62,4 @@ jobs: pip install -r requirements.txt - name: Test with unittest - run: python -m unittest kinde_sdk.test + run: python -m unittest diff --git a/kinde_sdk/kinde_api_client.py b/kinde_sdk/kinde_api_client.py index 5110ef8..079571b 100644 --- a/kinde_sdk/kinde_api_client.py +++ b/kinde_sdk/kinde_api_client.py @@ -120,7 +120,8 @@ def fetch_token(self, authorization_response: Optional[str] = None) -> None: self._clear_decoded_tokens() def refresh_token(self) -> None: - if refresh_token := self.__access_token_obj.get("refresh_token"): + refresh_token = self.__access_token_obj.get("refresh_token") + if refresh_token: self.__access_token_obj = self.client.refresh_token( self.token_endpoint, refresh_token=refresh_token, @@ -165,7 +166,8 @@ def _decode_token_if_needed(self, token_name: str) -> None: "When grant_type is CLIENT_CREDENTIALS use fetch_token().\n" 'For other grant_type use "get_login_url()" or "get_register_url()".' ) - if token := self.__access_token_obj.get(token_name): + token = self.__access_token_obj.get(token_name) + if token: self.__decoded_tokens[token_name] = jwt.decode( token, options={"verify_signature": False} ) diff --git a/requirements.txt b/requirements.txt index d623a84..0412070 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,3 +2,4 @@ urllib3==1.26.14 # https://github.com/urllib3/urllib3 python-dateutil==2.8.2 # https://github.com/dateutil/dateutil Authlib==1.2.0 # https://github.com/lepture/authlib pyjwt==2.6.0 # https://github.com/jpadilla/pyjwt +requests==2.28.2 # https://github.com/psf/requests	[#184459799] fix - scope and audience		2023-02-20T14:20:09	[]	[]
ArgonneCPAC/diffstar	ArgonneCPAC__diffstar-63	099ae3d313439e803bd5b76225234439b62ff631	diff --git a/diffstar/data_loaders/load_smah_data.py b/diffstar/data_loaders/load_smah_data.py index 554b179..de3b5fa 100644 --- a/diffstar/data_loaders/load_smah_data.py +++ b/diffstar/data_loaders/load_smah_data.py @@ -1,10 +1,12 @@ """ """ + import os import warnings import h5py import numpy as np +from umachine_pyio.load_mock import load_mock_from_binaries from ..defaults import SFR_MIN from ..utils import _get_dt_array @@ -12,7 +14,10 @@ TASSO = "/Users/aphearin/work/DATA/diffmah_data" BEBOP = "/lcrc/project/halotools/diffmah_data" LAPTOP = "/Users/alarcon/Documents/diffmah_data" - +BEBOP_SMDPL = os.path.join( + "/lcrc/project/galsampler/SMDPL/", + "dr1_no_merging_upidh/sfh_binary_catalogs/a_1.000000/", +) H_BPL = 0.678 H_TNG = 0.6774 H_MDPL = H_BPL @@ -38,30 +43,107 @@ def load_fit_mah(filename, data_drn=BEBOP): logmp: ndarray of shape (n_gal, ) Base-10 logarithm of the present day peak halo mass + logmp: ndarray of shape (n_gal, ) + Base-10 logarithm of the present day peak halo mass """ - fitting_data = dict() fn = os.path.join(data_drn, filename) with h5py.File(fn, "r") as hdf: - for key in hdf.keys(): - if key == "halo_id": - fitting_data[key] = hdf[key][...] - else: - fitting_data["fit_" + key] = hdf[key][...] - - mah_fit_params = np.array( - [ - fitting_data["fit_mah_logtc"], - fitting_data["fit_mah_k"], - fitting_data["fit_early_index"], - fitting_data["fit_late_index"], - ] - ).T - logmp = fitting_data["fit_logmp_fit"] + mah_fit_params = np.array( + [ + hdf["logm0"][:], + hdf["logtc"][:], + hdf["early_index"][:], + hdf["late_index"][:], + ] + ).T + logmp = hdf["logm0"][:] return mah_fit_params, logmp +def load_fit_mah_tpeak(filename, data_drn=BEBOP): + """Load the best fit diffmah parameter data + + Parameters + ---------- + filename : string + Name of the h5 file where the diffmah best fit parameters are stored + + data_drn : string + Filepath where the Diffstar best-fit parameters are stored + + Returns + ------- + mah_fit_params: ndarray of shape (n_gal, 4) + Best fit parameters for each halo: + (logtc, k, early_index, late_index) + + logmp: ndarray of shape (n_gal, ) + Base-10 logarithm of the present day peak halo mass + + logmp: ndarray of shape (n_gal, ) + Base-10 logarithm of the present day peak halo mass + """ + + fn = os.path.join(data_drn, filename) + with h5py.File(fn, "r") as hdf: + mah_fit_params = np.array( + [ + hdf["logm0"][:], + hdf["logtc"][:], + hdf["early_index"][:], + hdf["late_index"][:], + ] + ).T + t_peak = hdf["t_peak"][:] + logmp = hdf["logm0"][:] + + return mah_fit_params, logmp, t_peak + + +def load_fit_sfh(filename, data_drn=BEBOP): + """Load the best fit diffmah parameter data + + Parameters + ---------- + filename : string + Name of the h5 file where the diffmah best fit parameters are stored + + data_drn : string + Filepath where the Diffstar best-fit parameters are stored + + Returns + ------- + sfh_fit_params: ndarray of shape (n_gal, 4) + Best fit parameters for each halo: + (logtc, k, early_index, late_index) + + """ + + fn = os.path.join(data_drn, filename) + with h5py.File(fn, "r") as hdf: + ms_fit_params = np.array( + [ + hdf["lgmcrit"][:], + hdf["lgy_at_mcrit"][:], + hdf["indx_lo"][:], + hdf["indx_hi"][:], + hdf["tau_dep"][:], + ] + ).T + q_fit_params = np.array( + [ + hdf["lg_qt"][:], + hdf["qlglgdt"][:], + hdf["lg_drop"][:], + hdf["lg_rejuv"][:], + ] + ).T + + return ms_fit_params, q_fit_params + + def load_bolshoi_data(gal_type, data_drn=BEBOP): """Load the stellar mass histories from UniverseMachine simulation applied to the Bolshoi-Planck (BPL) simulation. @@ -397,3 +479,65 @@ def load_mdpl_small_data(gal_type, data_drn=BEBOP): log_smahs = np.where(sm_cumsum == 0, 0, np.log10(sm_cumsum)) return halo_ids, log_smahs, sfrh, mdpl_t, dt + + +def load_SMDPL_data(subvols, data_drn=BEBOP_SMDPL): + """Load the stellar mass histories from UniverseMachine simulation + applied to the Bolshoi-Planck (BPL) simulation. + + The loaded stellar mass data has units of Msun assuming the h = H_BPL + from the cosmology of the underlying simulation. + + The output stellar mass data has units of Msun/h, or units of + Mstar[h=H_BPL] using the h value of the simulation. + + H_BPL is defined at the top of the module. + + Parameters + ---------- + gal_type : string + Name of the galaxy type of the file being loaded. Options are + 'cens': central galaxies + 'sats': satellite galaxies + 'orphans': orphan galaxies + data_drn : string + Filepath where the Diffstar best-fit parameters are stored. + + Returns + ------- + halo_ids: ndarray of shape (n_gal, ) + IDs of the halos in the file. + log_smahs: ndarray of shape (n_gal, n_times) + Cumulative stellar mass history in units of Msun assuming h=1. + sfrh: ndarray of shape (n_gal, n_times) + Star formation rate history in units of Msun/yr assuming h=1. + bpl_t : ndarray of shape (n_times, ) + Cosmic time of each simulated snapshot in Gyr + dt : ndarray of shape (n_times, ) + Cosmic time steps between each simulated snapshot in Gyr + """ + + galprops = ["halo_id", "sfr_history_main_prog", "mpeak_history_main_prog"] + mock = load_mock_from_binaries(subvols, root_dirname=data_drn, galprops=galprops) + + SMDPL_t = np.loadtxt(os.path.join(data_drn, "smdpl_cosmic_time.txt")) + + halo_ids = mock["halo_id"] + dt = _get_dt_array(SMDPL_t) + sfrh = mock["sfr_history_main_prog"] + sm_cumsum = np.cumsum(sfrh * dt, axis=1) * 1e9 + + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + log_smahs = np.where(sm_cumsum == 0, 0, np.log10(sm_cumsum)) + + lgmh_min = 7.0 + mh_min = 10lgmh_min + msk = mock["mpeak_history_main_prog"] < mh_min + clipped_mahs = np.where(msk, 1.0, mock["mpeak_history_main_prog"]) + log_mahs = np.log10(clipped_mahs) + log_mahs = np.maximum.accumulate(log_mahs, axis=1) + + logmp = log_mahs[:, -1] + + return halo_ids, log_smahs, sfrh, SMDPL_t, dt, log_mahs, logmp diff --git a/diffstar/fitting_helpers/fit_smah_helpers.py b/diffstar/fitting_helpers/fit_smah_helpers.py index 6a4a888..cd4f369 100644 --- a/diffstar/fitting_helpers/fit_smah_helpers.py +++ b/diffstar/fitting_helpers/fit_smah_helpers.py @@ -1,5 +1,6 @@ """ """ + import os import warnings @@ -180,7 +181,7 @@ def get_weights( t_sim, log_smah_sim, log_fstar_sim, - fstar_indx_high, + fstar_tdelay, dlogm_cut, t_fit_min, mass_fit_min, @@ -195,8 +196,9 @@ def get_weights( Base-10 log of cumulative stellar mass in Msun units. log_fstar_sim : ndarray of shape (nt, ) Base-10 log of SFH averaged over a time period in Msun/yr units. - fstar_indx_high: ndarray of shape (n_times_fstar, ) - Indices from np.searchsorted(t, t - fstar_tdelay)[index_select] + fstar_tdelay: float + Time interval in Gyr for fstar definition. + fstar = (mstar(t) - mstar(t-fstar_tdelay)) / fstar_tdelay[Gyr] dlogm_cut : float, optional Additional quantity used to place a cut on which simulated snapshots are used to define the target halo SFH. @@ -236,9 +238,9 @@ def get_weights( weight_fstar = np.ones_like(t_sim) weight_fstar[~mask] = 1e10 - weight_fstar = weight_fstar[fstar_indx_high] weight_fstar[log_fstar_sim.max() - log_fstar_sim < 0.1] = 0.5 weight_fstar[weight_fstar == -10.0] = 1e10 + weight_fstar[t_sim < fstar_tdelay + 0.01] = 1e10 return weight, weight_fstar @@ -264,8 +266,6 @@ def loss_free(params, loss_data): log_sm_target, sfr_target, fstar_target, - index_select, - fstar_indx_high, fstar_tdelay, ssfrh_floor, weight, @@ -277,15 +277,7 @@ def loss_free(params, loss_data): q_params = params[5:9] _res = calculate_sm_sfr_fstar_history_from_mah( - lgt, - dt, - dmhdt, - log_mah, - sfr_params, - q_params, - index_select, - fstar_indx_high, - fstar_tdelay, + lgt, dt, dmhdt, log_mah, sfr_params, q_params, fstar_tdelay ) mstar, sfr, fstar = _res @@ -390,10 +382,6 @@ def get_loss_data_free( Star formation history in Msun/yr. log_fstar_sim : ndarray of shape (nt, ) Base-10 log of cumulative SFH averaged over a timescale in Msun/yr. - index_select: ndarray of shape (n_times_fstar, ) - Snapshot indices used in fstar computation. - fstar_indx_high: ndarray of shape (n_times_fstar, ) - Indices of np.searchsorted(t, t - fstar_tdelay)[index_select] fstar_tdelay: float Time interval in Gyr for fstar definition. fstar = (mstar(t) - mstar(t-fstar_tdelay)) / fstar_tdelay[Gyr] @@ -409,20 +397,16 @@ def get_loss_data_free( Base-10 log of the cosmic time where SFH target history peaks. """ - fstar_indx_high = np.searchsorted(t_sim, t_sim - fstar_tdelay) - _mask = t_sim > fstar_tdelay + fstar_tdelay / 2.0 - index_select = np.arange(len(t_sim))[_mask] - fstar_indx_high = fstar_indx_high[_mask] smh = 10log_smah_sim - fstar_sim = compute_fstar(t_sim, smh, index_select, fstar_indx_high, fstar_tdelay) + fstar_sim = compute_fstar(t_sim, smh, fstar_tdelay) with warnings.catch_warnings(): warnings.simplefilter("ignore") - ssfrh = fstar_sim / smh[index_select] + ssfrh = fstar_sim / smh ssfrh = np.clip(ssfrh, ssfrh_floor, np.inf) - fstar_sim = ssfrh * smh[index_select] + fstar_sim = ssfrh * smh log_fstar_sim = np.where( fstar_sim == 0.0, np.log10(fstar_sim.max()) - 3.0, np.log10(fstar_sim) ) @@ -435,13 +419,13 @@ def get_loss_data_free( t_sim, log_smah_sim, log_fstar_sim, - fstar_indx_high, + fstar_tdelay, dlogm_cut, t_fit_min, mass_fit_min, ) - t_fstar_max = logt[index_select][np.argmax(log_fstar_sim)] + t_fstar_max = logt[np.argmax(log_fstar_sim)] default_sfr_params = np.array(DEFAULT_U_MS_PARAMS) default_sfr_params[0] = np.clip(0.3 * (logmp - 11.0) + 11.4, 11.0, 13.0) @@ -469,8 +453,6 @@ def get_loss_data_free( log_smah_sim, sfrh, log_fstar_sim, - index_select, - fstar_indx_high, fstar_tdelay, ssfrh_floor, weight, @@ -516,8 +498,6 @@ def loss_fixed_noquench(params, loss_data): log_sm_target, sfr_target, fstar_target, - index_select, - fstar_indx_high, fstar_tdelay, ssfrh_floor, weight, @@ -529,15 +509,7 @@ def loss_fixed_noquench(params, loss_data): sfr_params = params _res = calculate_sm_sfr_fstar_history_from_mah( - lgt, - dt, - dmhdt, - log_mah, - sfr_params, - q_params, - index_select, - fstar_indx_high, - fstar_tdelay, + lgt, dt, dmhdt, log_mah, sfr_params, q_params, fstar_tdelay ) mstar, sfr, fstar = _res @@ -642,10 +614,6 @@ def get_loss_data_fixed_noquench( Star formation history in Msun/yr. log_fstar_sim : ndarray of shape (nt, ) Base-10 log of cumulative SFH averaged over a timescale in Msun/yr. - index_select: ndarray of shape (n_times_fstar, ) - Snapshot indices used in fstar computation. - fstar_indx_high: ndarray of shape (n_times_fstar, ) - Indices of np.searchsorted(t, t - fstar_tdelay)[index_select] fstar_tdelay: float Time interval in Gyr for fstar definition. fstar = (mstar(t) - mstar(t-fstar_tdelay)) / fstar_tdelay[Gyr] @@ -663,20 +631,16 @@ def get_loss_data_fixed_noquench( Fixed values of the unbounded quenching parameters """ - fstar_indx_high = np.searchsorted(t_sim, t_sim - fstar_tdelay) - _mask = t_sim > fstar_tdelay + fstar_tdelay / 2.0 - index_select = np.arange(len(t_sim))[_mask] - fstar_indx_high = fstar_indx_high[_mask] smh = 10*log_smah_sim - fstar_sim = compute_fstar(t_sim, smh, index_select, fstar_indx_high, fstar_tdelay) + fstar_sim = compute_fstar(t_sim, smh, fstar_tdelay) with warnings.catch_warnings(): warnings.simplefilter("ignore") - ssfrh = fstar_sim / smh[index_select] + ssfrh = fstar_sim / smh ssfrh = np.clip(ssfrh, ssfrh_floor, np.inf) - fstar_sim = ssfrh smh[index_select] + fstar_sim = ssfrh * smh log_fstar_sim = np.where( fstar_sim == 0.0, np.log10(fstar_sim.max()) - 3.0, np.log10(fstar_sim) ) @@ -689,13 +653,13 @@ def get_loss_data_fixed_noquench( t_sim, log_smah_sim, log_fstar_sim, - fstar_indx_high, + fstar_tdelay, dlogm_cut, t_fit_min, mass_fit_min, ) - t_fstar_max = logt[index_select][np.argmax(log_fstar_sim)] + t_fstar_max = logt[np.argmax(log_fstar_sim)] default_sfr_params = np.array(DEFAULT_U_MS_PARAMS) default_sfr_params[0] = np.clip(0.3 * (logmp - 11.0) + 11.4, 11.0, 13.0) @@ -722,8 +686,6 @@ def get_loss_data_fixed_noquench( log_smah_sim, sfrh, log_fstar_sim, - index_select, - fstar_indx_high, fstar_tdelay, ssfrh_floor, weight, @@ -770,8 +732,6 @@ def loss_fixed_hi(params, loss_data): log_sm_target, sfr_target, fstar_target, - index_select, - fstar_indx_high, fstar_tdelay, ssfrh_floor, weight, @@ -784,15 +744,7 @@ def loss_fixed_hi(params, loss_data): q_params = params[4:8] _res = calculate_sm_sfr_fstar_history_from_mah( - lgt, - dt, - dmhdt, - log_mah, - sfr_params, - q_params, - index_select, - fstar_indx_high, - fstar_tdelay, + lgt, dt, dmhdt, log_mah, sfr_params, q_params, fstar_tdelay ) mstar, sfr, fstar = _res @@ -897,10 +849,6 @@ def get_loss_data_fixed_hi( Star formation history in Msun/yr. log_fstar_sim : ndarray of shape (nt, ) Base-10 log of cumulative SFH averaged over a timescale in Msun/yr. - index_select: ndarray of shape (n_times_fstar, ) - Snapshot indices used in fstar computation. - fstar_indx_high: ndarray of shape (n_times_fstar, ) - Indices of np.searchsorted(t, t - fstar_tdelay)[index_select] fstar_tdelay: float Time interval in Gyr for fstar definition. fstar = (mstar(t) - mstar(t-fstar_tdelay)) / fstar_tdelay[Gyr] @@ -918,20 +866,16 @@ def get_loss_data_fixed_hi( Fixed value of the unbounded diffstar parameter indx_hi """ - fstar_indx_high = np.searchsorted(t_sim, t_sim - fstar_tdelay) - _mask = t_sim > fstar_tdelay + fstar_tdelay / 2.0 - index_select = np.arange(len(t_sim))[_mask] - fstar_indx_high = fstar_indx_high[_mask] smh = 10*log_smah_sim - fstar_sim = compute_fstar(t_sim, smh, index_select, fstar_indx_high, fstar_tdelay) + fstar_sim = compute_fstar(t_sim, smh, fstar_tdelay) with warnings.catch_warnings(): warnings.simplefilter("ignore") - ssfrh = fstar_sim / smh[index_select] + ssfrh = fstar_sim / smh ssfrh = np.clip(ssfrh, ssfrh_floor, np.inf) - fstar_sim = ssfrh smh[index_select] + fstar_sim = ssfrh * smh log_fstar_sim = np.where( fstar_sim == 0.0, np.log10(fstar_sim.max()) - 3.0, np.log10(fstar_sim) ) @@ -944,13 +888,13 @@ def get_loss_data_fixed_hi( t_sim, log_smah_sim, log_fstar_sim, - fstar_indx_high, + fstar_tdelay, dlogm_cut, t_fit_min, mass_fit_min, ) - t_fstar_max = logt[index_select][np.argmax(log_fstar_sim)] + t_fstar_max = logt[np.argmax(log_fstar_sim)] default_sfr_params = np.array(DEFAULT_U_MS_PARAMS) default_sfr_params[0] = np.clip(0.3 * (logmp - 11.0) + 11.4, 11.0, 13.0) @@ -981,8 +925,6 @@ def get_loss_data_fixed_hi( log_smah_sim, sfrh, log_fstar_sim, - index_select, - fstar_indx_high, fstar_tdelay, ssfrh_floor, weight, @@ -1039,8 +981,6 @@ def loss_fixed_hi_rej(params, loss_data): log_sm_target, sfr_target, fstar_target, - index_select, - fstar_indx_high, fstar_tdelay, ssfrh_floor, weight, @@ -1058,15 +998,7 @@ def loss_fixed_hi_rej(params, loss_data): q_params = [params[4:7], u_lg_rejuv] _res = calculate_sm_sfr_fstar_history_from_mah( - lgt, - dt, - dmhdt, - log_mah, - sfr_params, - q_params, - index_select, - fstar_indx_high, - fstar_tdelay, + lgt, dt, dmhdt, log_mah, sfr_params, q_params, fstar_tdelay ) mstar, sfr, fstar = _res @@ -1172,10 +1104,6 @@ def get_loss_data_fixed_hi_rej( Star formation history in Msun/yr. log_fstar_sim : ndarray of shape (nt, ) Base-10 log of cumulative SFH averaged over a timescale in Msun/yr. - index_select: ndarray of shape (n_times_fstar, ) - Snapshot indices used in fstar computation. - fstar_indx_high: ndarray of shape (n_times_fstar, ) - Indices of np.searchsorted(t, t - fstar_tdelay)[index_select] fstar_tdelay: float Time interval in Gyr for fstar definition. fstar = (mstar(t) - mstar(t-fstar_tdelay)) / fstar_tdelay[Gyr] @@ -1193,20 +1121,16 @@ def get_loss_data_fixed_hi_rej( Fixed value of the unbounded diffstar parameter indx_hi """ - fstar_indx_high = np.searchsorted(t_sim, t_sim - fstar_tdelay) - _mask = t_sim > fstar_tdelay + fstar_tdelay / 2.0 - index_select = np.arange(len(t_sim))[_mask] - fstar_indx_high = fstar_indx_high[_mask] smh = 10log_smah_sim - fstar_sim = compute_fstar(t_sim, smh, index_select, fstar_indx_high, fstar_tdelay) + fstar_sim = compute_fstar(t_sim, smh, fstar_tdelay) with warnings.catch_warnings(): warnings.simplefilter("ignore") - ssfrh = fstar_sim / smh[index_select] + ssfrh = fstar_sim / smh ssfrh = np.clip(ssfrh, ssfrh_floor, np.inf) - fstar_sim = ssfrh smh[index_select] + fstar_sim = ssfrh * smh log_fstar_sim = np.where( fstar_sim == 0.0, np.log10(fstar_sim.max()) - 3.0, np.log10(fstar_sim) ) @@ -1219,13 +1143,13 @@ def get_loss_data_fixed_hi_rej( t_sim, log_smah_sim, log_fstar_sim, - fstar_indx_high, + fstar_tdelay, dlogm_cut, t_fit_min, mass_fit_min, ) - t_fstar_max = logt[index_select][np.argmax(log_fstar_sim)] + t_fstar_max = logt[np.argmax(log_fstar_sim)] default_sfr_params = np.array(DEFAULT_U_MS_PARAMS) default_sfr_params[0] = np.clip(0.3 * (logmp - 11.0) + 11.4, 11.0, 13.0) @@ -1259,8 +1183,6 @@ def get_loss_data_fixed_hi_rej( log_smah_sim, sfrh, log_fstar_sim, - index_select, - fstar_indx_high, fstar_tdelay, ssfrh_floor, weight, @@ -1321,8 +1243,6 @@ def loss_fixed_hi_depl(params, loss_data): log_sm_target, sfr_target, fstar_target, - index_select, - fstar_indx_high, fstar_tdelay, ssfrh_floor, weight, @@ -1336,15 +1256,7 @@ def loss_fixed_hi_depl(params, loss_data): q_params = params[3:7] _res = calculate_sm_sfr_fstar_history_from_mah( - lgt, - dt, - dmhdt, - log_mah, - sfr_params, - q_params, - index_select, - fstar_indx_high, - fstar_tdelay, + lgt, dt, dmhdt, log_mah, sfr_params, q_params, fstar_tdelay ) mstar, sfr, fstar = _res @@ -1450,10 +1362,6 @@ def get_loss_data_fixed_hi_depl( Star formation history in Msun/yr. log_fstar_sim : ndarray of shape (nt, ) Base-10 log of cumulative SFH averaged over a timescale in Msun/yr. - index_select: ndarray of shape (n_times_fstar, ) - Snapshot indices used in fstar computation. - fstar_indx_high: ndarray of shape (n_times_fstar, ) - Indices of np.searchsorted(t, t - fstar_tdelay)[index_select] fstar_tdelay: float Time interval in Gyr for fstar definition. fstar = (mstar(t) - mstar(t-fstar_tdelay)) / fstar_tdelay[Gyr] @@ -1473,20 +1381,16 @@ def get_loss_data_fixed_hi_depl( Fixed value of the unbounded diffstar parameter tau_dep """ - fstar_indx_high = np.searchsorted(t_sim, t_sim - fstar_tdelay) - _mask = t_sim > fstar_tdelay + fstar_tdelay / 2.0 - index_select = np.arange(len(t_sim))[_mask] - fstar_indx_high = fstar_indx_high[_mask] smh = 10*log_smah_sim - fstar_sim = compute_fstar(t_sim, smh, index_select, fstar_indx_high, fstar_tdelay) + fstar_sim = compute_fstar(t_sim, smh, fstar_tdelay) with warnings.catch_warnings(): warnings.simplefilter("ignore") - ssfrh = fstar_sim / smh[index_select] + ssfrh = fstar_sim / smh ssfrh = np.clip(ssfrh, ssfrh_floor, np.inf) - fstar_sim = ssfrh smh[index_select] + fstar_sim = ssfrh * smh log_fstar_sim = np.where( fstar_sim == 0.0, np.log10(fstar_sim.max()) - 3.0, np.log10(fstar_sim) ) @@ -1499,13 +1403,13 @@ def get_loss_data_fixed_hi_depl( t_sim, log_smah_sim, log_fstar_sim, - fstar_indx_high, + fstar_tdelay, dlogm_cut, t_fit_min, mass_fit_min, ) - t_fstar_max = logt[index_select][np.argmax(log_fstar_sim)] + t_fstar_max = logt[np.argmax(log_fstar_sim)] default_sfr_params = np.array(DEFAULT_U_MS_PARAMS) default_sfr_params[0] = np.clip(0.3 * (logmp - 11.0) + 11.4, 11.0, 13.0) @@ -1535,8 +1439,6 @@ def get_loss_data_fixed_hi_depl( log_smah_sim, sfrh, log_fstar_sim, - index_select, - fstar_indx_high, fstar_tdelay, ssfrh_floor, weight, @@ -1590,8 +1492,6 @@ def loss_fixed_depl_noquench(params, loss_data): log_sm_target, sfr_target, fstar_target, - index_select, - fstar_indx_high, fstar_tdelay, ssfrh_floor, weight, @@ -1604,15 +1504,7 @@ def loss_fixed_depl_noquench(params, loss_data): sfr_params = [params[0:4], fixed_tau] _res = calculate_sm_sfr_fstar_history_from_mah( - lgt, - dt, - dmhdt, - log_mah, - sfr_params, - q_params, - index_select, - fstar_indx_high, - fstar_tdelay, + lgt, dt, dmhdt, log_mah, sfr_params, q_params, fstar_tdelay ) mstar, sfr, fstar = _res @@ -1719,10 +1611,6 @@ def get_loss_data_fixed_depl_noquench( Star formation history in Msun/yr. log_fstar_sim : ndarray of shape (nt, ) Base-10 log of cumulative SFH averaged over a timescale in Msun/yr. - index_select: ndarray of shape (n_times_fstar, ) - Snapshot indices used in fstar computation. - fstar_indx_high: ndarray of shape (n_times_fstar, ) - Indices of np.searchsorted(t, t - fstar_tdelay)[index_select] fstar_tdelay: float Time interval in Gyr for fstar definition. fstar = (mstar(t) - mstar(t-fstar_tdelay)) / fstar_tdelay[Gyr] @@ -1742,20 +1630,16 @@ def get_loss_data_fixed_depl_noquench( Fixed value of the unbounded quenching parameters """ - fstar_indx_high = np.searchsorted(t_sim, t_sim - fstar_tdelay) - _mask = t_sim > fstar_tdelay + fstar_tdelay / 2.0 - index_select = np.arange(len(t_sim))[_mask] - fstar_indx_high = fstar_indx_high[_mask] smh = 10log_smah_sim - fstar_sim = compute_fstar(t_sim, smh, index_select, fstar_indx_high, fstar_tdelay) + fstar_sim = compute_fstar(t_sim, smh, fstar_tdelay) with warnings.catch_warnings(): warnings.simplefilter("ignore") - ssfrh = fstar_sim / smh[index_select] + ssfrh = fstar_sim / smh ssfrh = np.clip(ssfrh, ssfrh_floor, np.inf) - fstar_sim = ssfrh smh[index_select] + fstar_sim = ssfrh * smh log_fstar_sim = np.where( fstar_sim == 0.0, np.log10(fstar_sim.max()) - 3.0, np.log10(fstar_sim) ) @@ -1768,13 +1652,13 @@ def get_loss_data_fixed_depl_noquench( t_sim, log_smah_sim, log_fstar_sim, - fstar_indx_high, + fstar_tdelay, dlogm_cut, t_fit_min, mass_fit_min, ) - t_fstar_max = logt[index_select][np.argmax(log_fstar_sim)] + t_fstar_max = logt[np.argmax(log_fstar_sim)] default_sfr_params = np.array(DEFAULT_U_MS_PARAMS) default_sfr_params[0] = np.clip(0.3 * (logmp - 11.0) + 11.4, 11.0, 13.0) @@ -1802,8 +1686,6 @@ def get_loss_data_fixed_depl_noquench( log_smah_sim, sfrh, log_fstar_sim, - index_select, - fstar_indx_high, fstar_tdelay, ssfrh_floor, weight, diff --git a/diffstar/fitting_helpers/fit_smah_helpers_tpeak.py b/diffstar/fitting_helpers/fit_smah_helpers_tpeak.py new file mode 100644 index 0000000..69a0127 --- /dev/null +++ b/diffstar/fitting_helpers/fit_smah_helpers_tpeak.py @@ -0,0 +1,467 @@ +""" +""" + +import warnings + +import h5py +import numpy as np +from diffmah.defaults import LGT0 +from diffmah.diffmah_kernels import _diffmah_kern +from jax import grad +from jax import jit as jjit +from jax import numpy as jnp + +from ..defaults import ( + DEFAULT_MS_PARAMS, + DEFAULT_MS_PDICT, + DEFAULT_Q_PARAMS, + DEFAULT_Q_PDICT, +) +from ..kernels.main_sequence_kernels_tpeak import ( + _get_bounded_sfr_params, + _get_unbounded_sfr_params, +) +from ..kernels.quenching_kernels import ( + _get_bounded_q_params, + _get_bounded_qt, + _get_unbounded_q_params, +) +from ..utils import _sigmoid +from .fitting_kernels import calculate_sm_sfr_fstar_history_from_mah, compute_fstar + +T_FIT_MIN = 1.0 # Only fit snapshots above this threshold. Gyr units. +DLOGM_CUT = 3.5 # Only fit SMH within this dex of the present day stellar mass. +MIN_MASS_CUT = 7.0 # Only fit SMH above this threshold. Log10(Msun) units. +FSTAR_TIME_DELAY = 1.0 # Time period of averaged SFH (aka fstar). Gyr units. +SSFRH_FLOOR = 1e-12 # Clip SFH to this minimum sSFR value. 1/yr units. + + +def get_header(): + """ """ + colnames = ["halo_id"] + colnames.extend(list(DEFAULT_MS_PDICT.keys())) + colnames.extend(list(DEFAULT_Q_PDICT.keys())) + colnames.extend(["loss", "success"]) + header_str = "# " + " ".join(colnames) + "\n" + return header_str, colnames + + +def get_header_unbound(): + """ """ + colnames = ["halo_id"] + colnames.extend(["u_" + s for s in list(DEFAULT_MS_PDICT.keys())]) + colnames.extend(["u_" + s for s in list(DEFAULT_Q_PDICT.keys())]) + colnames.extend(["loss", "success"]) + out = "# " + " ".join(colnames) + "\n" + return out + + +def write_collated_data(outname, data, colnames): + + nrows, ncols = np.shape(data) + + assert len(colnames) == ncols, "data mismatched with header" + with h5py.File(outname, "w") as hdf: + for i, name in enumerate(colnames): + if (name == "halo_id") \| (name == "success"): + hdf[name] = data[:, i].astype(int) + else: + hdf[name] = data[:, i].astype(float) + + +@jjit +def loss_default(params, loss_data): + """ + MSE loss function for fitting individual stellar mass histories. + The parameters k, indx_hi are fixed. + + """ + ( + lgt, + dt, + dmhdt, + log_mah, + sm_target, + log_sm_target, + sfr_target, + fstar_target, + fstar_tdelay, + ssfrh_floor, + weight, + weight_fstar, + t_fstar_max, + fixed_hi, + ) = loss_data + + u_sfr_params = [params[0:3], fixed_hi, params[3]] + u_q_params = params[4:8] + + _res = calculate_sm_sfr_fstar_history_from_mah( + lgt, + dt, + dmhdt, + log_mah, + u_sfr_params, + u_q_params, + fstar_tdelay, + ) + + mstar, sfr, fstar = _res + fstar = jnp.where(fstar > 0.0, jnp.log10(fstar), mstar ssfrh_floor) + mstar = jnp.log10(mstar) + + sfr_res = 1e8 * (sfr - sfr_target) / sm_target + sfr_res = jnp.clip(sfr_res, -1.0, 1.0) + + loss = jnp.mean(((mstar - log_sm_target) / weight) 2) + loss += jnp.mean(((fstar - fstar_target) / weight_fstar) 2) + loss += jnp.mean((sfr_res / weight) ** 2) + + qt = _get_bounded_qt(u_q_params[0]) + loss += _sigmoid(qt - t_fstar_max, 0.0, 50.0, 100.0, 0.0) + sfr_params = _get_bounded_sfr_params(u_sfr_params) + ( + lgmcrit, + lgy_at_mcrit, + indx_lo, + indx_hi, + tau_dep, + ) = sfr_params + loss += _sigmoid(indx_lo, 0.0, 10.0, 1.0, 0.0) + + return loss + + +loss_grad_default = jjit(grad(loss_default, argnums=(0))) + + +def loss_grad_default_np(params, data): + return np.array(loss_grad_default(params, data)).astype(float) + + +def get_loss_data_default( + t_sim, + dt, + sfrh, + log_smah_sim, + logmp, + mah_params, + t_peak, + dlogm_cut=DLOGM_CUT, + t_fit_min=T_FIT_MIN, + mass_fit_min=MIN_MASS_CUT, + fstar_tdelay=FSTAR_TIME_DELAY, + ssfrh_floor=SSFRH_FLOOR, + lgt0=LGT0, +): + """Retrieve the target data passed to the optimizer when fitting the halo + SFH model for the case in which the parameters k, indx_hi are fixed. + + Parameters + ---------- + t_sim : ndarray of shape (nt, ) + Cosmic time of each simulated snapshot in Gyr units. + dt : ndarray of shape (nt, ) + Cosmic time steps between each simulated snapshot in Gyr units. + sfrh : ndarray of shape (nt, ) + Star formation history of simulated snapshots in Msun/yr units. + log_smah_sim : ndarray of shape (nt, ) + Base-10 log of cumulative stellar mass in Msun units. + logmp : float + Base-10 log present day halo mass in Msun units. + mah_params : ndarray of shape (4, ) + Best fit diffmah halo parameters. Includes (logtc, k, early, late). + dlogm_cut : float, optional + Additional quantity used to place a cut on which simulated snapshots + are used to define the target halo SFH. + Snapshots will not be used when log_smah_sim falls below + log_smah_sim[-1] - dlogm_cut. + Default is set as global at top of module. + t_fit_min : float, optional + Additional quantity used to place a cut on which simulated snapshots are used to + define the target halo SFH. The value of t_fit_min defines the minimum cosmic + time in Gyr used to define the target SFH. + Default is set as global at top of module. + mass_fit_min : float + Quantity used to place a cut on which simulated snapshots are used to + define the target halo SFH. + The value mass_fit_min is the base-10 log of the minimum stellar mass in the SFH + used as target data. The final mass_fit_min cut is equal to + min(log_smah_sim[-1] - 0.5, mass_fit_min). + Default is set as global at top of module. + fstar_tdelay : float + Time interval in Gyr for fstar definition. + fstar = mstar(t) - mstar(t-fstar_tdelay) + Default is set as global at top of module. + ssfrh_floor : float + Lower bound value of star formation history used in the fits. + SFH(t) = max(SFH(t), SMH(t) ssfrh_floor) + Default is set as global at top of module. + + Returns + ------- + p_init : ndarray of shape (5, ) + Initial guess at the unbounded value of the best-fit parameter. + Here we have p_init = (u_lgm, u_lgy, u_l, u_h, u_dt) + loss_data : sequence consisting of the following data + logt: ndarray of shape (nt, ) + Base-10 log of cosmic time of each simulated snapshot in Gyr. + dt : ndarray of shape (nt, ) + Cosmic time steps between each simulated snapshot in Gyr + dmhdt : ndarray of shape (nt, ) + Diffmah halo mass accretion rate in units of Msun/yr. + log_mah : ndarray of shape (nt, ) + Diffmah halo mass accretion history in units of Msun. + smh : ndarray of shape (nt, ) + Cumulative stellar mass history in Msun. + log_smah_sim : ndarray of shape (nt, ) + Base-10 log of cumulative stellar mass in Msun. + sfrh : ndarray of shape (nt, ) + Star formation history in Msun/yr. + log_fstar_sim : ndarray of shape (nt, ) + Base-10 log of cumulative SFH averaged over a timescale in Msun/yr. + fstar_tdelay: float + Time interval in Gyr for fstar definition. + fstar = (mstar(t) - mstar(t-fstar_tdelay)) / fstar_tdelay[Gyr] + ssfrh_floor : float + Lower bound value of star formation history used in the fits. + weight : ndarray of shape (nt, ) + Weight for each snapshot, to effectively remove from the fit + the SMH snapshots that fall below the threshold mass. + weight_fstar : ndarray of shape (n_times_fstar, ) + Weight for each snapshot, to effectively remove from the fit + the SFH snapshots that fall below the threshold mass. + t_fstar_max : float + Base-10 log of the cosmic time where SFH target history peaks. + fixed_hi : float + Fixed value of the unbounded diffstar parameter indx_hi + + """ + smh = 10*log_smah_sim + + fstar_sim = compute_fstar(t_sim, smh, fstar_tdelay) + + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + ssfrh = fstar_sim / smh + ssfrh = np.clip(ssfrh, ssfrh_floor, np.inf) + fstar_sim = ssfrh smh + log_fstar_sim = np.where( + fstar_sim == 0.0, np.log10(fstar_sim.max()) - 3.0, np.log10(fstar_sim) + ) + + logt = jnp.log10(t_sim) + dmhdt, log_mah = _diffmah_kern(mah_params, t_sim, t_peak, lgt0) + + weight, weight_fstar = get_weights( + t_sim, + log_smah_sim, + log_fstar_sim, + fstar_tdelay, + dlogm_cut, + t_fit_min, + mass_fit_min, + ) + + t_fstar_max = logt[np.argmax(log_fstar_sim)] + + default_sfr_params = np.array(DEFAULT_MS_PARAMS) + default_sfr_params[0] = np.clip(0.3 * (logmp - 11.0) + 11.4, 11.0, 13.0) + default_sfr_params[1] = np.clip(0.2 * (logmp - 11.0) - 0.7, -1.5, -0.2) + default_sfr_params[2] = np.clip(0.7 * (logmp - 11.0) - 0.3, 0.2, 3.0) + default_sfr_params[4] = np.clip(-8.0 * (logmp - 11.0) + 15, 2.0, 15.0) + u_default_sfr_params = np.array(_get_unbounded_sfr_params(default_sfr_params)) + + sfr_ms_params = np.zeros(4) + sfr_ms_params[0:3] = u_default_sfr_params[0:3] + sfr_ms_params[3] = u_default_sfr_params[4] + fixed_hi = u_default_sfr_params[3] + + sfr_ms_params_err = np.array([0.5, 0.5, 1.0, 3.0]) + + default_q_params = np.array(DEFAULT_Q_PARAMS) + default_q_params[0] = np.clip(-0.5 (logmp - 11.0) + 1.5, 0.7, 1.5) + default_q_params[2] = -2.0 + q_params = np.array(_get_unbounded_q_params(default_q_params)) + q_params_err = np.array([0.3, 0.5, 0.3, 0.3]) + + loss_data = ( + logt, + dt, + dmhdt, + log_mah, + smh, + log_smah_sim, + sfrh, + log_fstar_sim, + fstar_tdelay, + ssfrh_floor, + weight, + weight_fstar, + t_fstar_max, + fixed_hi, + ) + p_init = ( + np.concatenate((sfr_ms_params, q_params)), + np.concatenate((sfr_ms_params_err, q_params_err)), + ) + return p_init, loss_data + + +def get_outline_default(halo_id, loss_data, p_best, loss_best, success): + """Return the string storing fitting results that will be written to disk""" + fixed_hi = loss_data[-1] + u_sfr_params = np.zeros(5) + u_sfr_params[0:3] = p_best[0:3] + u_sfr_params[3] = fixed_hi + u_sfr_params[4] = p_best[3] + u_q_params = p_best[4:8] + + sfr_params = _get_bounded_sfr_params(u_sfr_params) + q_params = _get_bounded_q_params(u_q_params) + _d = np.concatenate((sfr_params, q_params)).astype("f4") + data_out = (_d, float(loss_best)) + out = str(halo_id) + " " + " ".join(["{:.5e}".format(x) for x in data_out]) + out = out + " " + str(success) + return out + "\n" + + +def get_weights( + t_sim, + log_smah_sim, + log_fstar_sim, + fstar_tdelay, + dlogm_cut, + t_fit_min, + mass_fit_min, +): + """Calculate weights to mask target SMH and fstar target data. + + Parameters + ---------- + t_sim : ndarray of shape (nt, ) + Cosmic time of each simulated snapshot in Gyr units. + log_smah_sim : ndarray of shape (nt, ) + Base-10 log of cumulative stellar mass in Msun units. + log_fstar_sim : ndarray of shape (nt, ) + Base-10 log of SFH averaged over a time period in Msun/yr units. + fstar_tdelay: float + Time interval in Gyr for fstar definition. + fstar = (mstar(t) - mstar(t-fstar_tdelay)) / fstar_tdelay[Gyr] + dlogm_cut : float, optional + Additional quantity used to place a cut on which simulated snapshots + are used to define the target halo SFH. + Snapshots will not be used when log_smah_sim falls below + log_smah_sim[-1] - dlogm_cut. + t_fit_min : float, optional + Additional quantity used to place a cut on which simulated snapshots are used to + define the target halo SFH. The value of t_fit_min defines the minimum cosmic + time in Gyr used to define the target SFH. + mass_fit_min : float + Quantity used to place a cut on which simulated snapshots are used to + define the target halo SFH. + The value mass_fit_min is the base-10 log of the minimum stellar mass in the SFH + used as target data. The final mass_fit_min cut is equal to + min(log_smah_sim[-1] - 0.5, mass_fit_min). + + Returns + ------- + weight : ndarray of shape (nt, ) + Weight for each snapshot, to effectively remove from the fit + the SMH snapshots that fall below the threshold mass. + weight_fstar : ndarray of shape (nt, ) + Weight for each snapshot, to effectively remove from the fit + the SFH snapshots that fall below the threshold mass. + + """ + mass_fit_min = min(log_smah_sim[-1] - 0.5, mass_fit_min) + + mask = log_smah_sim > (log_smah_sim[-1] - dlogm_cut) + mask &= log_smah_sim > mass_fit_min + mask &= t_sim >= t_fit_min + + weight = np.ones_like(t_sim) + weight[~mask] = 1e10 + weight[log_smah_sim[-1] - log_smah_sim < 0.1] = 0.5 + weight = jnp.array(weight) + + weight_fstar = np.ones_like(t_sim) + weight_fstar[~mask] = 1e10 + weight_fstar[log_fstar_sim.max() - log_fstar_sim < 0.1] = 0.5 + weight_fstar[weight_fstar == -10.0] = 1e10 + weight_fstar[t_sim < fstar_tdelay + 0.01] = 1e10 + + return weight, weight_fstar + + +@jjit +def loss_default_clipssfrh(params, loss_data): + """ + MSE loss function for fitting individual stellar mass histories. + The parameters k, indx_hi are fixed. + + """ + ( + lgt, + dt, + dmhdt, + log_mah, + sm_target, + log_sm_target, + sfr_target, + fstar_target, + fstar_tdelay, + ssfrh_floor, + weight, + weight_fstar, + t_fstar_max, + fixed_hi, + ) = loss_data + + u_sfr_params = [params[0:3], fixed_hi, params[3]] + u_q_params = params[4:8] + + _res = calculate_sm_sfr_fstar_history_from_mah( + lgt, + dt, + dmhdt, + log_mah, + u_sfr_params, + u_q_params, + fstar_tdelay, + ) + + mstar, sfr, fstar = _res + fstar = jnp.clip(fstar, mstar ssfrh_floor, jnp.inf) + fstar = jnp.where(fstar > 0.0, jnp.log10(fstar), mstar * ssfrh_floor) + + mstar = jnp.log10(mstar) + + sfr_res = 1e8 * (sfr - sfr_target) / sm_target + sfr_res = jnp.clip(sfr_res, -1.0, 1.0) + + loss = jnp.mean(((mstar - log_sm_target) / weight) 2) + loss += jnp.mean(((fstar - fstar_target) / weight_fstar) 2) + loss += jnp.mean((sfr_res / weight) ** 2) + + qt = _get_bounded_qt(u_q_params[0]) + loss += _sigmoid(qt - t_fstar_max, 0.0, 50.0, 100.0, 0.0) + + sfr_params = _get_bounded_sfr_params(u_sfr_params) + ( + lgmcrit, + lgy_at_mcrit, + indx_lo, + indx_hi, + tau_dep, + ) = sfr_params + loss += _sigmoid(indx_lo, 0.0, 10.0, 1.0, 0.0) + loss += _sigmoid(lgy_at_mcrit, 0.0, 20.0, 0.0, 1.0) + return loss + + +loss_grad_default_clipssfrh = jjit(grad(loss_default_clipssfrh, argnums=(0))) + + +def loss_grad_default_clipssfrh_np(params, data): + return np.array(loss_grad_default_clipssfrh(params, data)).astype(float) diff --git a/diffstar/fitting_helpers/fitting_kernels.py b/diffstar/fitting_helpers/fitting_kernels.py index 19d2bc3..806604e 100644 --- a/diffstar/fitting_helpers/fitting_kernels.py +++ b/diffstar/fitting_helpers/fitting_kernels.py @@ -1,5 +1,6 @@ """ """ + from diffmah.individual_halo_assembly import _calc_halo_history from jax import jit as jjit from jax import numpy as jnp @@ -13,7 +14,6 @@ _sfr_eff_plaw, ) from ..kernels.quenching_kernels import _quenching_kern_u_params -from ..utils import jax_np_interp @jjit @@ -24,8 +24,6 @@ def calculate_sm_sfr_fstar_history_from_mah( log_mah, u_ms_params, u_q_params, - index_select, - index_high, fstar_tdelay, fb=FB, ): @@ -58,12 +56,6 @@ def calculate_sm_sfr_fstar_history_from_mah( u_q_params : ndarray of shape (4, ) Quenching model unbounded parameters. Includes (u_qt, u_qs, u_drop, u_rejuv) - index_select: ndarray of shape (n_times_fstar, ) - Snapshot indices used in fstar computation - - index_high: ndarray of shape (n_times_fstar, ) - Indices of np.searchsorted(t, t - fstar_tdelay)[index_select] - fstar_tdelay: float Time interval in Gyr for fstar definition. fstar = (mstar(t) - mstar(t-fstar_tdelay)) / fstar_tdelay[Gyr] @@ -82,7 +74,7 @@ def calculate_sm_sfr_fstar_history_from_mah( """ sfr = _sfr_history_from_mah(lgt, dt, dmhdt, log_mah, u_ms_params, u_q_params, fb=fb) mstar = _integrate_sfr(sfr, dt) - fstar = compute_fstar(10lgt, mstar, index_select, index_high, fstar_tdelay) + fstar = compute_fstar(10lgt, mstar, fstar_tdelay) return mstar, sfr, fstar @@ -140,8 +132,6 @@ def calculate_histories( mah_params, u_ms_params, u_q_params, - index_select, - index_high, fstar_tdelay, fb=FB, ): @@ -173,12 +163,6 @@ def calculate_histories( Quenching model unbounded parameters. Includes (u_qt, u_qs, u_drop, u_rejuv) - index_select: ndarray of shape (n_times_fstar, ) - Snapshot indices used in fstar computation. - - index_high: ndarray of shape (n_times_fstar, ) - Indices of np.searchsorted(t, t - fstar_tdelay)[index_select] - fstar_tdelay: float Time interval in Gyr for fstar definition. fstar = (mstar(t) - mstar(t-fstar_tdelay)) / fstar_tdelay[Gyr] @@ -209,8 +193,6 @@ def calculate_histories( log_mah, u_ms_params, u_q_params, - index_select, - index_high, fstar_tdelay, fb=fb, ) @@ -218,7 +200,7 @@ def calculate_histories( calculate_histories_vmap = jjit( - vmap(calculate_histories, in_axes=(None, None, 0, 0, 0, None, None, None, None)) + vmap(calculate_histories, in_axes=(None, None, 0, 0, 0, None, None)) ) @@ -229,7 +211,7 @@ def _integrate_sfr(sfr, dt): @jjit -def compute_fstar(tarr, mstar, index_select, index_high, fstar_tdelay): +def compute_fstar(tarr, mstar, fstar_tdelay): """Time averaged SFH that has ocurred over some previous time period fstar = (mstar(t) - mstar(t-fstar_tdelay)) / fstar_tdelay @@ -242,12 +224,6 @@ def compute_fstar(tarr, mstar, index_select, index_high, fstar_tdelay): mstar : ndarray of shape (n_times, ) Stellar mass history in Msun units - index_select: ndarray of shape (n_times_fstar, ) - Snapshot indices used in fstar computation - - index_high: ndarray of shape (n_times_fstar, ) - Indices of np.searchsorted(t, t - fstar_tdelay)[index_select] - fstar_tdelay: float Time interval in Gyr units for fstar definition. fstar = (mstar(t) - mstar(t-fstar_tdelay)) / fstar_tdelay @@ -257,18 +233,17 @@ def compute_fstar(tarr, mstar, index_select, index_high, fstar_tdelay): fstar : ndarray of shape (n_times) SFH averaged over timescale fstar_tdelay in units of Msun/yr assuming h=1 + Notes + ------- + for t-fstar_tdelay < 0 t<tarr, and jnp.interp returns by default mstar[0], + so fstar will always be positive. """ - mstar_high = mstar[index_select] - mstar_low = jax_np_interp( - tarr[index_select] - fstar_tdelay, tarr, mstar, index_high - ) - fstar = (mstar_high - mstar_low) / fstar_tdelay / 1e9 + mstar_low = jnp.interp(tarr - fstar_tdelay, tarr, mstar) + fstar = (mstar - mstar_low) / fstar_tdelay / 1e9 + fstar = jnp.where(fstar > 0.0, fstar, 0.0) return fstar -compute_fstar_vmap = jjit(vmap(compute_fstar, in_axes=(None, 0, [None] * 3))) - - @jjit def _sfr_history_from_mah(lgt, dtarr, dmhdt, log_mah, sfr_params, q_params, fb=FB): """Star formation history of an individual galaxy. diff --git a/docs/source/demo_diffstar_fitter.ipynb b/docs/source/demo_diffstar_fitter.ipynb index 87a6e86..5b2e6af 100644 --- a/docs/source/demo_diffstar_fitter.ipynb +++ b/docs/source/demo_diffstar_fitter.ipynb @@ -207,7 +207,7 @@ "\n", "dmhdt_fit, log_mah_fit = loss_data[2:4]\n", "lgt = np.log10(tarr)\n", - "index_select, index_high, fstar_tdelay = loss_data[8:11]\n", + "fstar_tdelay = loss_data[8]\n", " \n", "_histories = calculate_sm_sfr_fstar_history_from_mah(\n", " lgt,\n", @@ -216,8 +216,6 @@ " log_mah_fit,\n", " u_sfr_fit_params,\n", " u_q_fit_params,\n", - " index_select,\n", - " index_high,\n", " fstar_tdelay,\n", ")\n", "smh_fit, sfh_fit, fstar_fit = _histories" @@ -331,7 +329,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.12.5" } }, "nbformat": 4, diff --git a/scripts/history_fitting_script_SMDPL_tpeak.py b/scripts/history_fitting_script_SMDPL_tpeak.py new file mode 100644 index 0000000..11f447f --- /dev/null +++ b/scripts/history_fitting_script_SMDPL_tpeak.py @@ -0,0 +1,189 @@ +import argparse +import os +import subprocess +from glob import glob +from time import time + +import numpy as np +from mpi4py import MPI + +import diffstar.fitting_helpers.fit_smah_helpers_tpeak as fitsmah +from diffstar.data_loaders.load_smah_data import load_fit_mah_tpeak, load_SMDPL_data +from diffstar.fitting_helpers.utils import minimizer_wrapper + +BEBOP_SMDPL = "/lcrc/project/galsampler/SMDPL/dr1_no_merging_upidh/sfh_binary_catalogs/a_1.000000/" +BEBOP_SMDPL_MAH = ( + "/lcrc/project/halotools/SMDPL/dr1_no_merging_upidh/diffmah_tpeak_fits/" +) + +TMP_OUTPAT = "tmp_sfh_fits_rank_{0}.dat" +NUM_SUBVOLS_SMDPL = 576 + + +if __name__ == "__main__": + comm = MPI.COMM_WORLD + rank, nranks = comm.Get_rank(), comm.Get_size() + + parser = argparse.ArgumentParser() + + parser.add_argument("outdir", help="Output directory") + parser.add_argument("-outbase", help="Basename of the output hdf5 file") + parser.add_argument( + "-indir_diffmah", help="Directory of mah parameters", default=BEBOP_SMDPL_MAH + ) + parser.add_argument("-indir", help="Input directory", default=BEBOP_SMDPL) + parser.add_argument( + "-fstar_tdelay", + help="Time interval in Gyr for fstar definition.", + type=float, + default=1.0, + ) + parser.add_argument( + "-mass_fit_min", + help="Minimum mass included in stellar mass histories.", + type=float, + default=fitsmah.MIN_MASS_CUT, + ) + parser.add_argument( + "-ssfrh_floor", + help="Clipping floor for sSFH", + type=float, + default=fitsmah.SSFRH_FLOOR, + ) + parser.add_argument("-test", help="Short test run?", type=bool, default=False) + parser.add_argument("-istart", help="First subvolume in loop", type=int, default=0) + parser.add_argument("-iend", help="Last subvolume in loop", type=int, default=-1) + parser.add_argument( + "-num_subvols_tot", help="Total # subvols", type=int, default=NUM_SUBVOLS_SMDPL + ) + + args = parser.parse_args() + + indir = args.indir + indir_diffmah = args.indir_diffmah + outbase = args.outbase + istart, iend = args.istart, args.iend + num_subvols_tot = args.num_subvols_tot # needed for string formatting + HEADER, colnames_out = fitsmah.get_header() + + kwargs = { + "fstar_tdelay": args.fstar_tdelay, + "mass_fit_min": args.mass_fit_min, + "ssfrh_floor": args.ssfrh_floor, + } + + start = time() + + all_avail_subvol_names = [ + os.path.basename(drn) for drn in glob(os.path.join(indir, "subvol_")) + ] + all_avail_subvolumes = [int(s.split("_")[1]) for s in all_avail_subvol_names] + all_avail_subvolumes = sorted(all_avail_subvolumes) + + if args.test: + subvolumes = [ + all_avail_subvolumes[0], + ] + else: + subvolumes = np.arange(istart, iend) + + for isubvol in subvolumes: + isubvol_start = time() + + nchar_subvol = len(str(num_subvols_tot)) + subvol_str = f"subvol_{isubvol:0{nchar_subvol}d}" + + subvolumes_i = [isubvol] + + subvol_data_str = indir + _data = load_SMDPL_data(subvolumes_i, subvol_data_str) + halo_ids, log_smahs, sfrhs, tarr, dt, log_mahs, logmp = _data + + subvol_diffmah_str = f"{subvol_str}_diffmah_fits.h5" + mah_fit_params, logmp_fit, t_peak_arr = load_fit_mah_tpeak( + subvol_diffmah_str, data_drn=indir_diffmah + ) + + if rank == 0: + print("Number of galaxies in mock = {}".format(len(halo_ids))) + + # Get data for rank + if args.test: + nhalos_tot = nranks 5 + else: + nhalos_tot = len(halo_ids) + + indx_all = np.arange(0, nhalos_tot).astype("i8") + indx = np.array_split(indx_all, nranks)[rank] + + halo_ids_for_rank = halo_ids[indx] + log_smahs_for_rank = log_smahs[indx] + sfrhs_for_rank = sfrhs[indx] + mah_params_for_rank = mah_fit_params[indx] + logmp_for_rank = logmp[indx] + t_peak_for_rank = t_peak_arr[indx] + + nhalos_for_rank = len(halo_ids_for_rank) + + rank_basepat = "_".join((subvol_str, outbase, TMP_OUTPAT)) + rank_outname = os.path.join(args.outdir, rank_basepat).format(rank) + + # breakpoint() + with open(rank_outname, "w") as fout: + fout.write(HEADER) + + for i in range(nhalos_for_rank): + halo_id = halo_ids_for_rank[i] + lgsmah = log_smahs_for_rank[i, :] + sfrh = sfrhs_for_rank[i, :] + mah_params = mah_params_for_rank[i] + logmp_halo = logmp_for_rank[i] + t_peak = t_peak_for_rank[i] + + p_init, loss_data = fitsmah.get_loss_data_default( + tarr, dt, sfrh, lgsmah, logmp_halo, mah_params, t_peak, *kwargs + ) + _res = minimizer_wrapper( + fitsmah.loss_default_clipssfrh, + fitsmah.loss_grad_default_clipssfrh_np, + p_init, + loss_data, + ) + p_best, loss_best, success = _res + outline = fitsmah.get_outline_default( + halo_id, loss_data, p_best, loss_best, success + ) + + fout.write(outline) + + comm.Barrier() + isubvol_end = time() + + msg = "\n\nWallclock runtime to fit {0} galaxies with {1} ranks = {2:.1f} seconds\n\n" + if rank == 0: + print("\nFinished with subvolume {}".format(isubvol)) + runtime = isubvol_end - isubvol_start + print(msg.format(nhalos_tot, nranks, runtime)) + + # collate data from ranks and rewrite to disk + pat = os.path.join(args.outdir, rank_basepat) + fit_data_fnames = [pat.format(i) for i in range(nranks)] + collector = [] + for fit_fn in fit_data_fnames: + assert os.path.isfile(fit_fn) + fit_data = np.genfromtxt(fit_fn, dtype="str") + collector.append(fit_data) + subvol_i_fit_results = np.concatenate(collector) + + subvol_str = f"subvol_{isubvol:0{nchar_subvol}d}" + outbn = "_".join((subvol_str, outbase)) + ".h5" + outfn = os.path.join(args.outdir, outbn) + + # fitsmah.write_collated_data(outfn, subvol_i_fit_results, chunk_arr=None) + fitsmah.write_collated_data(outfn, subvol_i_fit_results, colnames_out) + + # clean up ASCII data for subvol_i + bnpat = subvol_str + ".dat" + fnpat = os.path.join(args.outdir, bnpat) + command = "rm " + fnpat + subprocess.os.system(command)	Resolve some failing tests due to change of arguments to compute_fstar Still WIP: 2 test failures remain		2024-08-27T16:01:19	[]	[]
ArgonneCPAC/diffstar	ArgonneCPAC__diffstar-60	82d0a77048b0f882a553df4ec3a433f1780e3611	diff --git a/diffstar/data_loaders/load_smah_data.py b/diffstar/data_loaders/load_smah_data.py index a46ca94..de3b5fa 100644 --- a/diffstar/data_loaders/load_smah_data.py +++ b/diffstar/data_loaders/load_smah_data.py @@ -1,17 +1,16 @@ """ """ + import os import warnings import h5py import numpy as np +from umachine_pyio.load_mock import load_mock_from_binaries from ..defaults import SFR_MIN from ..utils import _get_dt_array -from umachine_pyio.load_mock import load_mock_from_binaries - - TASSO = "/Users/aphearin/work/DATA/diffmah_data" BEBOP = "/lcrc/project/halotools/diffmah_data" LAPTOP = "/Users/alarcon/Documents/diffmah_data" @@ -541,4 +540,4 @@ def load_SMDPL_data(subvols, data_drn=BEBOP_SMDPL): logmp = log_mahs[:, -1] - return halo_ids, log_smahs, sfrh, SMDPL_t, dt, log_mahs, logmp \ No newline at end of file + return halo_ids, log_smahs, sfrh, SMDPL_t, dt, log_mahs, logmp diff --git a/diffstar/fitting_helpers/fit_smah_helpers.py b/diffstar/fitting_helpers/fit_smah_helpers.py index 6a4a888..0012eba 100644 --- a/diffstar/fitting_helpers/fit_smah_helpers.py +++ b/diffstar/fitting_helpers/fit_smah_helpers.py @@ -1,5 +1,6 @@ """ """ + import os import warnings diff --git a/diffstar/fitting_helpers/fit_smah_helpers_tpeak.py b/diffstar/fitting_helpers/fit_smah_helpers_tpeak.py index 2df66d7..c7e97f2 100644 --- a/diffstar/fitting_helpers/fit_smah_helpers_tpeak.py +++ b/diffstar/fitting_helpers/fit_smah_helpers_tpeak.py @@ -1,42 +1,33 @@ """ """ -import os + import warnings import h5py import numpy as np - +from diffmah.defaults import LGT0 +from diffmah.diffmah_kernels import _diffmah_kern from jax import grad from jax import jit as jjit from jax import numpy as jnp -from diffmah.diffmah_kernels import _diffmah_kern -from diffmah.defaults import LGT0 - from ..defaults import ( - DEFAULT_MS_PDICT, - DEFAULT_Q_PDICT, - DEFAULT_U_MS_PARAMS, - DEFAULT_U_Q_PARAMS, DEFAULT_MS_PARAMS, + DEFAULT_MS_PDICT, DEFAULT_Q_PARAMS, + DEFAULT_Q_PDICT, ) from ..kernels.main_sequence_kernels_tpeak import ( - _get_bounded_sfr_params_vmap, _get_bounded_sfr_params, _get_unbounded_sfr_params, ) from ..kernels.quenching_kernels import ( - _get_bounded_lg_drop, - _get_bounded_q_params_vmap, _get_bounded_q_params, _get_bounded_qt, _get_unbounded_q_params, - _get_unbounded_qrejuv, ) -from .fitting_kernels import calculate_sm_sfr_fstar_history_from_mah, compute_fstar - from ..utils import _sigmoid +from .fitting_kernels import calculate_sm_sfr_fstar_history_from_mah, compute_fstar T_FIT_MIN = 1.0 # Only fit snapshots above this threshold. Gyr units. DLOGM_CUT = 3.5 # Only fit SMH within this dex of the present day stellar mass. @@ -52,7 +43,6 @@ def get_header(): colnames.extend(list(DEFAULT_Q_PDICT.keys())) colnames.extend(["loss", "success"]) header_str = "# " + " ".join(colnames) + "\n" - dtypes = [] return header_str, colnames @@ -130,7 +120,7 @@ def loss_default(params, loss_data): qt = _get_bounded_qt(u_q_params[0]) loss += _sigmoid(qt - t_fstar_max, 0.0, 50.0, 100.0, 0.0) sfr_params = _get_bounded_sfr_params(u_sfr_params) - ( + ( lgmcrit, lgy_at_mcrit, indx_lo, @@ -148,6 +138,7 @@ def loss_default(params, loss_data): def loss_grad_default_np(params, data): return np.array(loss_grad_default(params, data)).astype(float) + def get_loss_data_default( t_sim, dt, @@ -260,7 +251,6 @@ def get_loss_data_default( ) logt = jnp.log10(t_sim) - logtmp = np.log10(t_sim[-1]) dmhdt, log_mah = _diffmah_kern(mah_params, t_sim, t_peak, lgt0) weight, weight_fstar = get_weights( @@ -458,7 +448,7 @@ def loss_default_clipssfrh(params, loss_data): loss += _sigmoid(qt - t_fstar_max, 0.0, 50.0, 100.0, 0.0) sfr_params = _get_bounded_sfr_params(u_sfr_params) - ( + ( lgmcrit, lgy_at_mcrit, indx_lo, @@ -473,4 +463,4 @@ def loss_default_clipssfrh(params, loss_data): def loss_grad_default_clipssfrh_np(params, data): - return np.array(loss_grad_default_clipssfrh(params, data)).astype(float) \ No newline at end of file + return np.array(loss_grad_default_clipssfrh(params, data)).astype(float) diff --git a/diffstar/fitting_helpers/fitting_kernels.py b/diffstar/fitting_helpers/fitting_kernels.py index 7c216e4..806604e 100644 --- a/diffstar/fitting_helpers/fitting_kernels.py +++ b/diffstar/fitting_helpers/fitting_kernels.py @@ -1,5 +1,6 @@ """ """ + from diffmah.individual_halo_assembly import _calc_halo_history from jax import jit as jjit from jax import numpy as jnp @@ -13,7 +14,6 @@ _sfr_eff_plaw, ) from ..kernels.quenching_kernels import _quenching_kern_u_params -from ..utils import jax_np_interp @jjit @@ -235,10 +235,10 @@ def compute_fstar(tarr, mstar, fstar_tdelay): Notes ------- - for t-fstar_tdelay < 0 t<tarr, and jnp.interp returns by default mstar[0], + for t-fstar_tdelay < 0 t<tarr, and jnp.interp returns by default mstar[0], so fstar will always be positive. """ - mstar_low = jnp.interp(tarr - fstar_tdelay, tarr, mstar) + mstar_low = jnp.interp(tarr - fstar_tdelay, tarr, mstar) fstar = (mstar - mstar_low) / fstar_tdelay / 1e9 fstar = jnp.where(fstar > 0.0, fstar, 0.0) return fstar diff --git a/scripts/history_fitting_script_SMDPL_tpeak.py b/scripts/history_fitting_script_SMDPL_tpeak.py index c3fde86..11f447f 100644 --- a/scripts/history_fitting_script_SMDPL_tpeak.py +++ b/scripts/history_fitting_script_SMDPL_tpeak.py @@ -1,27 +1,20 @@ - -import os -import sys -import h5py import argparse -import warnings -import numpy as np - -from time import time +import os +import subprocess from glob import glob +from time import time -from umachine_pyio.load_mock import load_mock_from_binaries -from astropy.cosmology import Planck15 -from diffstar.utils import _get_dt_array -import diffstar.fitting_helpers.fit_smah_helpers_tpeak as fitsmah -from diffstar.fitting_helpers.utils import minimizer_wrapper -from diffstar.data_loaders.load_smah_data import load_SMDPL_data, load_fit_mah_tpeak - +import numpy as np from mpi4py import MPI -import subprocess +import diffstar.fitting_helpers.fit_smah_helpers_tpeak as fitsmah +from diffstar.data_loaders.load_smah_data import load_fit_mah_tpeak, load_SMDPL_data +from diffstar.fitting_helpers.utils import minimizer_wrapper BEBOP_SMDPL = "/lcrc/project/galsampler/SMDPL/dr1_no_merging_upidh/sfh_binary_catalogs/a_1.000000/" -BEBOP_SMDPL_MAH = "/lcrc/project/halotools/SMDPL/dr1_no_merging_upidh/diffmah_tpeak_fits/" +BEBOP_SMDPL_MAH = ( + "/lcrc/project/halotools/SMDPL/dr1_no_merging_upidh/diffmah_tpeak_fits/" +) TMP_OUTPAT = "tmp_sfh_fits_rank_{0}.dat" NUM_SUBVOLS_SMDPL = 576 @@ -30,12 +23,14 @@ if __name__ == "__main__": comm = MPI.COMM_WORLD rank, nranks = comm.Get_rank(), comm.Get_size() - + parser = argparse.ArgumentParser() parser.add_argument("outdir", help="Output directory") parser.add_argument("-outbase", help="Basename of the output hdf5 file") - parser.add_argument("-indir_diffmah", help="Directory of mah parameters", default=BEBOP_SMDPL_MAH) + parser.add_argument( + "-indir_diffmah", help="Directory of mah parameters", default=BEBOP_SMDPL_MAH + ) parser.add_argument("-indir", help="Input directory", default=BEBOP_SMDPL) parser.add_argument( "-fstar_tdelay", @@ -50,7 +45,10 @@ default=fitsmah.MIN_MASS_CUT, ) parser.add_argument( - "-ssfrh_floor", help="Clipping floor for sSFH", type=float, default=fitsmah.SSFRH_FLOOR, + "-ssfrh_floor", + help="Clipping floor for sSFH", + type=float, + default=fitsmah.SSFRH_FLOOR, ) parser.add_argument("-test", help="Short test run?", type=bool, default=False) parser.add_argument("-istart", help="First subvolume in loop", type=int, default=0) @@ -81,7 +79,7 @@ ] all_avail_subvolumes = [int(s.split("_")[1]) for s in all_avail_subvol_names] all_avail_subvolumes = sorted(all_avail_subvolumes) - + if args.test: subvolumes = [ all_avail_subvolumes[0], @@ -100,9 +98,11 @@ subvol_data_str = indir _data = load_SMDPL_data(subvolumes_i, subvol_data_str) halo_ids, log_smahs, sfrhs, tarr, dt, log_mahs, logmp = _data - + subvol_diffmah_str = f"{subvol_str}_diffmah_fits.h5" - mah_fit_params, logmp_fit, t_peak_arr = load_fit_mah_tpeak(subvol_diffmah_str, data_drn=indir_diffmah) + mah_fit_params, logmp_fit, t_peak_arr = load_fit_mah_tpeak( + subvol_diffmah_str, data_drn=indir_diffmah + ) if rank == 0: print("Number of galaxies in mock = {}".format(len(halo_ids))) @@ -112,7 +112,7 @@ nhalos_tot = nranks * 5 else: nhalos_tot = len(halo_ids) - + indx_all = np.arange(0, nhalos_tot).astype("i8") indx = np.array_split(indx_all, nranks)[rank] @@ -139,16 +139,20 @@ mah_params = mah_params_for_rank[i] logmp_halo = logmp_for_rank[i] t_peak = t_peak_for_rank[i] - p_init, loss_data = fitsmah.get_loss_data_default( tarr, dt, sfrh, lgsmah, logmp_halo, mah_params, t_peak, **kwargs ) _res = minimizer_wrapper( - fitsmah.loss_default_clipssfrh, fitsmah.loss_grad_default_clipssfrh_np, p_init, loss_data + fitsmah.loss_default_clipssfrh, + fitsmah.loss_grad_default_clipssfrh_np, + p_init, + loss_data, ) p_best, loss_best, success = _res - outline = fitsmah.get_outline_default(halo_id, loss_data, p_best, loss_best, success) + outline = fitsmah.get_outline_default( + halo_id, loss_data, p_best, loss_best, success + ) fout.write(outline)	Fix bugs in unit tests of new tpeak-based SFH model This PR resolves a failing unit test. The failure was due to a bug in the test, not the source code: within the unit test, the new SFH kernel was just being called with the arguments in the wrong order. @alexalar remember to use relative imports when importing diffstar from within diffstar (which is the only other change brought in with this PR).		2024-08-26T19:22:13	[]	[]
scholarly-python-package/scholarly	scholarly-python-package__scholarly-483	00cf1d88f06de4f753c16cdaae3a2c9414accae5	diff --git a/scholarly/_navigator.py b/scholarly/_navigator.py index 0fbda9d..8bea767 100644 --- a/scholarly/_navigator.py +++ b/scholarly/_navigator.py @@ -119,6 +119,14 @@ def _get_page(self, pagerequest: str, premium: bool = False) -> str: if resp.status_code == 200 and not has_captcha: return resp.text + elif resp.status_code == 404: + # If the scholar_id was approximate, it first appears as + # 404 (or 302), and then gets redirected to the correct profile. + # In such cases, we need to try again with the same session. + # See https://github.com/scholarly-python-package/scholarly/issues/469. + self.logger.debug("Got a 404 error. Attempting with same proxy") + tries += 1 + continue elif has_captcha: self.logger.info("Got a captcha request.") session = pm._handle_captcha2(pagerequest) diff --git a/scholarly/_proxy_generator.py b/scholarly/_proxy_generator.py index f451ec5..49d5bd5 100644 --- a/scholarly/_proxy_generator.py +++ b/scholarly/_proxy_generator.py @@ -451,7 +451,7 @@ def _handle_captcha2(self, url): return self._session def _new_session(self, **kwargs): - init_kwargs = {} + init_kwargs = {"follow_redirects": True} init_kwargs.update(kwargs) proxies = {} if self._session: @@ -610,7 +610,7 @@ def ScraperAPI(self, API_KEY, country_code=None, premium=False, render=False): # https://www.scraperapi.com/documentation/ self._TIMEOUT = 60 - prefix = "http://scraperapi" + prefix = "http://scraperapi.retry_404=true" if country_code is not None: prefix += ".country_code=" + country_code if premium: @@ -624,7 +624,7 @@ def ScraperAPI(self, API_KEY, country_code=None, premium=False, render=False): for _ in range(3): proxy_works = self._use_proxy(http=f'{prefix}:{API_KEY}@proxy-server.scraperapi.com:8001') if proxy_works: - proxies = {'http://': f"http://scraperapi:{API_KEY}@proxy-server.scraperapi.com:8001",} + proxies = {'http://': f"{prefix}:{API_KEY}@proxy-server.scraperapi.com:8001",} self.logger.info("ScraperAPI proxy setup successfully") self._new_session(verify=False, proxies=proxies) return proxy_works diff --git a/scholarly/author_parser.py b/scholarly/author_parser.py index 53dad8e..d46038e 100644 --- a/scholarly/author_parser.py +++ b/scholarly/author_parser.py @@ -440,6 +440,14 @@ def fill(self, author, sections: list = [], sortby="citedby", publication_limit: url = '{0}&pagesize={1}'.format(url_citations, _PAGESIZE) soup = self.nav._get_soup(url) + # Update scholar_id + scholar_id = re.findall(_CITATIONAUTHRE, soup.find("link", rel="canonical").get('href', ""))[0] + if scholar_id != author['scholar_id']: + self.nav.logger.warning("Changing the scholar_id following redirect from %s to %s. " + "To avoid this warning, use %s to look up this scholar.", + author['scholar_id'], scholar_id, scholar_id) + author["scholar_id"] = scholar_id + if sections == []: for i in self._sections: if i not in author['filled']: diff --git a/setup.py b/setup.py index cadec5a..b4ea378 100644 --- a/setup.py +++ b/setup.py @@ -5,7 +5,7 @@ setuptools.setup( name='scholarly', - version='1.7.10', + version='1.7.11', author='Steven A. Cholewiak, Panos Ipeirotis, Victor Silva, Arun Kannawadi', author_email='[email protected], [email protected], [email protected], [email protected]', description='Simple access to Google Scholar authors and citations',	Fill author cannot handle 302 redirects Describe the bug When calling `fill()` on an author record whose scholar_id has a 302 redirect, scholarly gets stuck in a loop on the original URL. To Reproduce ``` from scholarly import scholarly import logging logging.basicConfig(level=logging.INFO) scholar_id = 'oMaIg8sAAAAJ' author = scholarly.search_author_id(scholar_id, filled = True) ``` Results in logging such as: ``` INFO:scholarly:Getting https://scholar.google.com/citations?hl=en&user=oMaIg8sAAAAJ&pagesize=100 INFO:scholarly:Getting https://scholar.google.com/citations?hl=en&user=oMaIg8sAAAAJ&cstart=100&pagesize=100 INFO:scholarly:Getting https://scholar.google.com/citations?hl=en&user=oMaIg8sAAAAJ&cstart=200&pagesize=100 INFO:scholarly:Getting https://scholar.google.com/citations?hl=en&user=oMaIg8sAAAAJ&cstart=300&pagesize=100 INFO:scholarly:Getting https://scholar.google.com/citations?hl=en&user=oMaIg8sAAAAJ&cstart=400&pagesize=100 ... ``` Expected behavior The 302 redirect will be observed and results will be the same as for searching for `scholar_id = 'PEJ42J0AAAAJ'` Desktop (please complete the following information): - Proxy service: None - python version: 3.9.14 - OS: Linux - Version: Centos Stream 9	With no proxy and `FreeProxies`, I could not fetch this (see #465 ), but then when I tried with `ScraperAPI`, I got 404 (not found) response for `scholar_id = 'oMaIg8sAAAAJ'` and success with `scholar_id = 'PEJ42J0AAAAJ'`. It is true that the code does not handle 302 redirects, but I did not encounter this. Is this a consistent issue? Yes, I found it was a recurring issue. I had a script iterating through a list of scholar_ids and if it encountered one which effectively had a 302 redirect (when viewed in a browser), it would get stuck in the kind of loop identified in the logging referenced in my original post.	2023-01-16T21:48:55	[]	[]
scholarly-python-package/scholarly	scholarly-python-package__scholarly-478	599639ac54085c92088fec8e1a92d6b0d03f0cd1	diff --git a/scholarly/_navigator.py b/scholarly/_navigator.py index 3d85642..fcb454d 100644 --- a/scholarly/_navigator.py +++ b/scholarly/_navigator.py @@ -74,12 +74,12 @@ def use_proxy(self, pg1: ProxyGenerator, pg2: ProxyGenerator = None): self._session1 = self.pm1.get_session() self._session2 = self.pm2.get_session() - def _new_session(self, premium=True): + def _new_session(self, premium=True, kwargs): self.got_403 = False if premium: - self._session1 = self.pm1._new_session() + self._session1 = self.pm1._new_session(kwargs) else: - self._session2 = self.pm2._new_session() + self._session2 = self.pm2._new_session(kwargs) def _get_page(self, pagerequest: str, premium: bool = False) -> str: @@ -112,7 +112,8 @@ def _get_page(self, pagerequest: str, premium: bool = False) -> str: w = random.uniform(1,2) time.sleep(w) resp = session.get(pagerequest, timeout=timeout) - self.logger.debug("Session proxy config is {}".format(pm._proxies)) + if premium is False: # premium methods may contain sensitive information + self.logger.debug("Session proxy config is {}".format(pm._proxies)) has_captcha = self._requests_has_captcha(resp.text) diff --git a/scholarly/_proxy_generator.py b/scholarly/_proxy_generator.py index 01f7d77..676b28a 100644 --- a/scholarly/_proxy_generator.py +++ b/scholarly/_proxy_generator.py @@ -183,11 +183,11 @@ def _use_proxy(self, http: str, https: str = None) -> bool: :returns: whether or not the proxy was set up successfully :rtype: {bool} """ - if https is None: - https = http if http[:4] != "http": http = "http://" + http - if https[:5] != "https": + if https is None: + https = http + elif https[:5] != "https": https = "https://" + https proxies = {'http://': http, 'https://': https} @@ -205,7 +205,7 @@ def _use_proxy(self, http: str, https: str = None) -> bool: if self._proxy_works: self._proxies = proxies - self._new_session() + self._new_session(proxies=proxies) return self._proxy_works @@ -444,8 +444,9 @@ def _handle_captcha2(self, url): return self._session - def _new_session(self): + def _new_session(self, kwargs): init_kwargs = {} + init_kwargs.update(kwargs) proxies = {} if self._session: proxies = self._proxies @@ -612,8 +613,9 @@ def ScraperAPI(self, API_KEY, country_code=None, premium=False, render=False): for _ in range(3): proxy_works = self._use_proxy(http=f'{prefix}:{API_KEY}@proxy-server.scraperapi.com:8001') if proxy_works: + proxies = {'http://': f"http://scraperapi:{API_KEY}@proxy-server.scraperapi.com:8001",} self.logger.info("ScraperAPI proxy setup successfully") - self._session.verify = False + self._new_session(verify=False, proxies=proxies) return proxy_works if (r["requestCount"] >= r["requestLimit"]): diff --git a/setup.py b/setup.py index b1a3164..0a78ae0 100644 --- a/setup.py +++ b/setup.py @@ -5,7 +5,7 @@ setuptools.setup( name='scholarly', - version='1.7.7', + version='1.7.8', author='Steven A. Cholewiak, Panos Ipeirotis, Victor Silva, Arun Kannawadi', author_email='[email protected], [email protected], [email protected], [email protected]', description='Simple access to Google Scholar authors and citations',	v1.7.7 is incompatible with ScraperAPI		2022-12-28T02:23:50	[]	[]
scholarly-python-package/scholarly	scholarly-python-package__scholarly-451	ca4623a20749d826d2db4e0bc101191f1db4ff1b	diff --git a/.github/workflows/lint.yaml b/.github/workflows/lint.yaml index cca9756..4c62384 100644 --- a/.github/workflows/lint.yaml +++ b/.github/workflows/lint.yaml @@ -2,10 +2,6 @@ name: lint on: workflow_call: - push: - branches: [main, develop] - pull_request: - branches: [main, develop] jobs: lint: diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml index 1fd1152..3894ee1 100644 --- a/.github/workflows/pythonpackage.yml +++ b/.github/workflows/pythonpackage.yml @@ -10,11 +10,6 @@ on: branches: [main, develop] pull_request: branches: [main, develop] - workflow_run: - workflows: ["lint"] -# branches: [main] - types: - - completed jobs: lint: diff --git a/scholarly/_scholarly.py b/scholarly/_scholarly.py index faf6ab0..dd27c04 100644 --- a/scholarly/_scholarly.py +++ b/scholarly/_scholarly.py @@ -1,20 +1,24 @@ """scholarly.py""" import requests +import re import os import copy import csv import pprint -from typing import Dict, List +import datetime +import re +from typing import Dict, List, Union from ._navigator import Navigator from ._proxy_generator import ProxyGenerator from dotenv import find_dotenv, load_dotenv from .author_parser import AuthorParser from .publication_parser import PublicationParser, _SearchScholarIterator -from .data_types import Author, AuthorSource, Journal, Publication, PublicationSource +from .data_types import Author, AuthorSource, CitesPerYear, Journal, Publication, PublicationSource _AUTHSEARCH = '/citations?hl=en&view_op=search_authors&mauthors={0}' _KEYWORDSEARCH = '/citations?hl=en&view_op=search_authors&mauthors=label:{0}' _KEYWORDSEARCHBASE = '/citations?hl=en&view_op=search_authors&mauthors={}' +_KEYWORDSEARCH_PATTERN = "[-: #(),;]+" # Unallowed characters in the keywords. _PUBSEARCH = '/scholar?hl=en&q={0}' _CITEDBYSEARCH = '/scholar?hl=en&cites={0}' _ORGSEARCH = "/citations?view_op=view_org&hl=en&org={0}" @@ -155,11 +159,11 @@ def search_pubs(self, sort_by=sort_by, include_last_year=include_last_year, start_index=start_index) return self.__nav.search_publications(url) - def search_citedby(self, publication_id: int, kwargs): + def search_citedby(self, publication_id: Union[int, str], kwargs): """Searches by Google Scholar publication id and returns a generator of Publication objects. :param publication_id: Google Scholar publication id - :type publication_id: int + :type publication_id: int or str For the remaining parameters, see documentation of `search_pubs`. """ @@ -248,6 +252,25 @@ def bibtex(self, object: Publication)->str: self.logger.warning("Object not supported for bibtex exportation") return + @staticmethod + def _bin_citations_by_year(cites_per_year: CitesPerYear, year_end): + years = [] + y_hi, y_lo = year_end, year_end + running_count = 0 + for y in sorted(cites_per_year, reverse=True): + if running_count + cites_per_year[y] <= 1000: + running_count += cites_per_year[y] + y_lo = y + else: + running_count = cites_per_year[y] + years.append((y_hi, y_lo)) + y_hi = y + + if running_count > 0: + years.append((y_hi, y_lo)) + + return years + def citedby(self, object: Publication)->_SearchScholarIterator: """Searches Google Scholar for other articles that cite this Publication and returns a Publication generator. @@ -255,13 +278,44 @@ def citedby(self, object: Publication)->_SearchScholarIterator: :param object: The Publication object for the bibtex exportation :type object: Publication """ - if object['container_type'] == "Publication": - publication_parser = PublicationParser(self.__nav) - return publication_parser.citedby(object) - else: + + if object['container_type'] != "Publication": self.logger.warning("Object not supported for bibtex exportation") return + if object["num_citations"] <= 1000: + return PublicationParser(self.__nav).citedby(object) + + self.logger.debug("Since the paper titled %s has %d citations (>1000), " + "fetching it on an annual basis.", object["bib"]["title"], object["num_citations"]) + + year_end = int(datetime.date.today().year) + + if object["source"] == PublicationSource.AUTHOR_PUBLICATION_ENTRY: + self.fill(object) + years = self._bin_citations_by_year(object.get("cites_per_year", {}), year_end) + else: + try: + year_low = int(object["bib"]["pub_year"]) + except KeyError: + self.logger.warning("Unknown publication year for paper %s, may result in incorrect number " + "of citedby papers.", object["bib"]["title"]) + return PublicationParser(self.__nav).citedby(object) + + # Go one year at a time in decreasing order + years = zip(range(year_end, year_low-1, -1), range(year_end, year_low-1, -1)) + + # Extract cites_id. Note: There could be multiple ones, separated by commas. + m = re.search("cites=[\d+,]*", object["citedby_url"]) + pub_id = m.group()[6:] + for y_hi, y_lo in years: + sub_citations = self.search_citedby(publication_id=pub_id, year_low=y_lo, year_high=y_hi) + if sub_citations.total_results and (sub_citations.total_results > 1000): + self.logger.warn("The paper titled %s has %d citations in the year %d. " + "Due to the limitation in Google Scholar, fetching only 1000 results " + "from that year.", object["bib"]["title"], sub_citations.total_results, y_lo) + yield from sub_citations + def search_author_id(self, id: str, filled: bool = False, sortby: str = "citedby", publication_limit: int = 0)->Author: """Search by author id and return a single Author object :param sortby: select the order of the citations in the author page. Either by 'citedby' or 'year'. Defaults to 'citedby'. @@ -321,7 +375,9 @@ def search_keyword(self, keyword: str): 'source': 'SEARCH_AUTHOR_SNIPPETS', 'url_picture': 'https://scholar.google.com/citations?view_op=medium_photo&user=lHrs3Y4AAAAJ'} """ - url = _KEYWORDSEARCH.format(requests.utils.quote(keyword)) + + reg_keyword = re.sub(_KEYWORDSEARCH_PATTERN, "_", keyword) + url = _KEYWORDSEARCH.format(requests.utils.quote(reg_keyword)) return self.__nav.search_authors(url) def search_keywords(self, keywords: List[str]): @@ -355,8 +411,8 @@ def search_keywords(self, keywords: List[str]): 'url_picture': 'https://scholar.google.com/citations?view_op=medium_photo&user=_cMw1IUAAAAJ'} """ - - formated_keywords = ['label:'+requests.utils.quote(keyword) for keyword in keywords] + reg_keywords = (re.sub(_KEYWORDSEARCH_PATTERN, "_", keyword) for keyword in keywords) + formated_keywords = ['label:'+requests.utils.quote(keyword) for keyword in reg_keywords] formated_keywords = '+'.join(formated_keywords) url = _KEYWORDSEARCHBASE.format(formated_keywords) return self.__nav.search_authors(url) diff --git a/setup.py b/setup.py index de9012c..ff1ef97 100644 --- a/setup.py +++ b/setup.py @@ -5,7 +5,7 @@ setuptools.setup( name='scholarly', - version='1.7.2', + version='1.7.3', author='Steven A. Cholewiak, Panos Ipeirotis, Victor Silva, Arun Kannawadi', author_email='[email protected], [email protected], [email protected], [email protected]', description='Simple access to Google Scholar authors and citations',	Query fails to collect data for some keywords, which results in StopIteration in the next() Describe the bug Query works with some keywords but triggers StopIteration in the next(search_query) with others even if that keyword works manually on google scholar. To Reproduce ``` from scholarly import scholarly, ProxyGenerator # pg = ProxyGenerator() # success = pg.FreeProxies() # scholarly.use_proxy(pg) test = ["reinforcement", "reinforcement learning"] #it words with "reinforcement" but not with "reinforcement learning" search_query = scholarly.search_keyword(test[1]) # Retrieve the first result from the iterator first_author_result = next(search_query) #this line results in the StopIteration error scholarly.pprint(first_author_result) # Retrieve all the details for the author author = scholarly.fill(first_author_result ) scholarly.pprint(author) # Take a closer look at the first publication first_publication = author['publications'][0] first_publication_filled = scholarly.fill(first_publication) scholarly.pprint(first_publication_filled) # Print the titles of the author's publications publication_titles = [pub['bib']['title'] for pub in author['publications']] print(publication_titles) # Which papers cited that publication? citations = [citation['bib']['title'] for citation in scholarly.citedby(first_publication_filled)] print(citations) ``` Expected behavior I expected the code to work with any keyword and even with combinations of precise keywords with quotation marks as in the manual search on google scholar. Desktop (please complete the following information): - Proxy service: I am new to data crawlers, so I am not sure if I need a service or not - python version: 3.9 - OS: Windows - Version 10 Do you plan on contributing? - Not now, but maybe in the future when I learn more about crawlers	Thanks for the bug report. This is indeed a bug caused by the space between the keywords not replaced by a + internally when the URL is generated. This will be fixed in v1.7.3.	2022-10-20T18:00:50	[]	[]
scholarly-python-package/scholarly	scholarly-python-package__scholarly-409	868ab58f15e841b0a57213f992f572edd075518b	diff --git a/.github/workflows/publish-to-pypi.yml b/.github/workflows/publish-to-pypi.yml index f02572de..b593dd6c 100644 --- a/.github/workflows/publish-to-pypi.yml +++ b/.github/workflows/publish-to-pypi.yml @@ -1,5 +1,7 @@ name: Publish package to PyPI -on: push +on: + push: + branches: [main] jobs: build-n-publish: diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml index fa7d9582..4325ce80 100644 --- a/.github/workflows/pythonpackage.yml +++ b/.github/workflows/pythonpackage.yml @@ -43,7 +43,12 @@ jobs: # uses: typilus/[email protected] - name: Install Chrome uses: browser-actions/setup-chrome@latest - - name: Test with unittest + continue-on-error: true + with: + fail_ci_if_error: false + - name: Run unittests + id: unittests + continue-on-error: true env: CONNECTION_METHOD: ${{ secrets.CONNECTION_METHOD }} PASSWORD: ${{ secrets.PASSWORD }} @@ -52,20 +57,23 @@ jobs: SCRAPER_API_KEY: ${{ secrets.SCRAPER_API_KEY }} run: \| coverage run -m unittest -v test_module.TestScholarly - coverage xml - - name: Validate the repository yaml for codecov + - name: Generate coverage report (macOS only) if: - "matrix.os == 'ubuntu-latest'" - run: + "matrix.os == 'macos-latest'" + run: \| curl --data-binary @.github/.codecov.yml https://codecov.io/validate \| head -n 1 - - name: Upload code coverage + coverage xml + - name: Upload code coverage (macOS only) if: - "matrix.os != 'windows-latest'" + "matrix.os == 'macos-latest'" uses: codecov/codecov-action@v2 with: directory: ./ - fail_ci_if_error: true + fail_ci_if_error: false files: ./coverage.xml flags: unittests name: codecov-umbrella verbose: true + - name: Check if unittests failed + if: "steps.unittests.outcome == 'failure'" + run: exit 1 diff --git a/scholarly/_proxy_generator.py b/scholarly/_proxy_generator.py index 96a96d00..39059dcd 100644 --- a/scholarly/_proxy_generator.py +++ b/scholarly/_proxy_generator.py @@ -49,6 +49,7 @@ def __init__(self): self._tor_control_port = None self._tor_password = None self._session = None + self._webdriver = None self._TIMEOUT = 5 self._new_session() @@ -458,7 +459,10 @@ def _close_session(self): if self._session: self._session.close() if self._webdriver: - self._webdriver.quit() + try: + self._webdriver.quit() + except Exception as e: + self.logger.warning("Could not close webdriver cleanly: %s", e) def _fp_coroutine(self, timeout=1, wait_time=120): """A coroutine to continuosly yield free proxies	Fix test_download_mandates_csv in GHA Describe the bug The `test_download_mandates_csv` test case is throwing errors in Github Actions, despite the logic being correct. The test passes locally on my computer. To Reproduce ```python python -m unittest -v test_module.TestScholarly.test_download_mandates_csv ``` Expected behavior The test should pass without any FAIL or ERROR messages in Github Actions Screenshots <img width="937" alt="image" src="https://user-images.githubusercontent.com/5103390/156935746-7ff753a1-15ff-49b2-b7ed-26ce45f2ec76.png"> <img width="781" alt="image" src="https://user-images.githubusercontent.com/5103390/156935762-56fe1b48-cf76-406a-8fc2-71b2c816a7ab.png"> Desktop (please complete the following information): - Proxy service: [e.g. ScraperAPI] - python version: [e.g. 3.8] - OS: [e.g. macOS] - Version [e.g. 1.6]		2022-03-26T05:59:49	[]	[]
scholarly-python-package/scholarly	scholarly-python-package__scholarly-401	24e86d607e391e690b1ac6f2204275e96ac09486	diff --git a/README.md b/README.md index befc5abf..c6404cb3 100644 --- a/README.md +++ b/README.md @@ -57,9 +57,12 @@ If neither installed, `scholarly` will fetch only up to 20 co-authors. ```bash pip3 install scholarly[tor] ``` - + If you use `zsh` (which is now the default in latest macOS), you should type this as + ```zsh + pip3 install scholarly'[tor]' + ``` Note: Tor option is unavailable with conda installation. - + ## Tests To check if your installation is succesful, run the tests by executing the `test_module.py` file as: diff --git a/scholarly/_navigator.py b/scholarly/_navigator.py index 19b5c7f4..ef7c4d21 100644 --- a/scholarly/_navigator.py +++ b/scholarly/_navigator.py @@ -163,7 +163,12 @@ def _get_page(self, pagerequest: str, premium: bool = False) -> str: self.logger.info("Retrying with a new session.") tries += 1 - session, timeout = pm.get_next_proxy(num_tries = tries, old_timeout = timeout, old_proxy=session.proxies.get('http', None)) + try: + session, timeout = pm.get_next_proxy(num_tries = tries, old_timeout = timeout, old_proxy=session.proxies.get('http', None)) + except Exception: + self.logger.info("No other secondary connections possible. " + "Using the primary proxy for all requests.") + break # If secondary proxy does not work, try again primary proxy. if not premium: diff --git a/scholarly/_scholarly.py b/scholarly/_scholarly.py index bbd68ba2..a9692756 100644 --- a/scholarly/_scholarly.py +++ b/scholarly/_scholarly.py @@ -2,14 +2,15 @@ import requests import os import copy +import csv import pprint -from typing import List +from typing import Dict, List from ._navigator import Navigator from ._proxy_generator import ProxyGenerator from dotenv import find_dotenv, load_dotenv from .author_parser import AuthorParser from .publication_parser import PublicationParser, _SearchScholarIterator -from .data_types import Author, AuthorSource, Publication, PublicationSource +from .data_types import Author, AuthorSource, Journal, Publication, PublicationSource _AUTHSEARCH = '/citations?hl=en&view_op=search_authors&mauthors={0}' _KEYWORDSEARCH = '/citations?hl=en&view_op=search_authors&mauthors=label:{0}' @@ -17,6 +18,7 @@ _PUBSEARCH = '/scholar?hl=en&q={0}' _CITEDBYSEARCH = '/scholar?hl=en&cites={0}' _ORGSEARCH = "/citations?view_op=view_org&hl=en&org={0}" +_MANDATES_URL = "https://scholar.google.com/citations?view_op=mandates_leaderboard_csv" class _Scholarly: @@ -27,6 +29,13 @@ def __init__(self): self.env = os.environ.copy() self.__nav = Navigator() self.logger = self.__nav.logger + self._journal_categories = None + + @property + def journal_categories(self): + if self._journal_categories is None: + self._journal_categories = self.get_journal_categories() + return self._journal_categories def set_retries(self, num_retries: int)->None: """Sets the number of retries in case of errors @@ -465,6 +474,40 @@ def search_author_by_organization(self, organization_id: int): url = _ORGSEARCH.format(organization_id) return self.__nav.search_authors(url) + def download_mandates_csv(self, filename: str, overwrite: bool = False, + include_links: bool =True): + """ + Download the CSV file of the current mandates. + """ + if (not overwrite) and os.path.exists(filename): + raise ValueError(f"{filename} already exists. Either provide a " + "different filename or allow overwriting by " + "setting overwrite=True") + text = self.__nav._get_page(_MANDATES_URL, premium=False) + if include_links: + soup = self.__nav._get_soup("/citations?view_op=mandates_leaderboard") + text = text.replace("Funder,", "Funder,Policy,Cached,", 1) + for agency in soup.find_all("td", class_="gsc_mlt_t"): + cached = agency.find("span", class_="gs_a").a["href"] + name = agency.a.text + if name != "cached": + policy = agency.a['href'] + else: + name = agency.text[:-10] + policy = "" + + if "," in name: + text = text.replace(f'"{name}",', f'"{name}",{policy},{cached},') + else: + text = text.replace(f"{name},", f"{name},{policy},{cached},") + try: + with open(filename, 'w') as f: + f.write(text) + except IOError: + self.logger.error("Error writing mandates as %s", filename) + finally: + return text + # TODO: Make it a public method in v1.6 def _construct_url(self, baseurl: str, patents: bool = True, citations: bool = True, year_low: int = None, @@ -495,3 +538,86 @@ def _construct_url(self, baseurl: str, patents: bool = True, # improve str below return url + yr_lo + yr_hi + citations + patents + sortby + start + + def get_journal_categories(self): + """ + Get a dict of journal categories and subcategories. + """ + soup = self.__nav._get_soup("/citations?view_op=top_venues&hl=en&vq=en") + categories = {} + for category in soup.find_all("a", class_="gs_md_li"): + if not "vq=" in category['href']: + continue + vq = category['href'].split("&vq=")[1] + categories[category.text] = {} + categories[category.text][None] = vq + + for category in categories: + vq = categories[category][None] + if vq=="en": + continue + soup = self.__nav._get_soup(f"/citations?view_op=top_venues&hl=en&vq={vq}") + for subcategory in soup.find_all("a", class_="gs_md_li"): + if not f"&vq={vq}_" in subcategory['href']: + continue + categories[category][subcategory.text] = subcategory['href'].split("&vq=")[1] + + #print(categories) + return categories + + def get_journals(self, category='English', subcategory=None, include_comments: bool = False) -> Dict[int, Journal]: + try: + cat = self.journal_categories[category] + try: + subcat = cat[subcategory] + url = f"/citations?view_op=top_venues&hl=en&vq={subcat}" + soup = self.__nav._get_soup(url) + + ranks = soup.find_all("td", class_="gsc_mvt_p") + names = soup.find_all("td", class_="gsc_mvt_t") + h5indices = soup.find_all("a", class_="gs_ibl gsc_mp_anchor") + h5medians = soup.find_all("span", class_="gs_ibl") + + + #import pdb; pdb.set_trace() + result = {} + for rank, name, h5index, h5median in zip(ranks, names, h5indices, h5medians): + url_citations = h5index['href'] + comment = "" + if include_comments: + soup = self.__nav._get_soup(url_citations) + try: + for cmt in soup.find_all('ul', class_='gsc_mlhd_list')[1].find_all('li'): + comment += cmt.text+"; " + except IndexError: + pass + result[int(rank.text[:-1])] = Journal(name=name.text, + h5_index=int(h5index.text), + h5_median=int(h5median.text), + url_citations=url_citations, + comment=comment + ) + #print(result) + return result + except KeyError: + raise ValueError("Invalid subcategory: %s for %s. Choose one from %s" % (subcategory, category, cat.keys())) + except KeyError: + raise ValueError("Invalid category: %s. Choose one from %s", category, self.journal_categories.keys()) + + def save_journals_csv(self, filename, category="English", subcategory=None, include_comments=False): + """ + Save a list of journals to a file in CSV format. + """ + journals = self.get_journals(category, subcategory, include_comments) + try: + with open(filename, 'w') as f: + csv_writer = csv.writer(f) + header = ['Publication', 'h5-index', 'h5-median'] + ['Comment']include_comments + csv_writer.writerow(header) + for rank, journal in journals.items(): + row = [journal['name'], journal['h5_index'], journal['h5_median']] + [journal.get('comment', '')]include_comments + csv_writer.writerow(row) + except IOError: + self.logger.error("Error writing journals as %s", filename) + finally: + return journals \ No newline at end of file diff --git a/scholarly/author_parser.py b/scholarly/author_parser.py index 740d3e1c..f1f9bd0b 100644 --- a/scholarly/author_parser.py +++ b/scholarly/author_parser.py @@ -56,8 +56,11 @@ def get_author(self, __data)->Author: author['email_domain'] = re.sub(_EMAILAUTHORRE, r'@', email.text) int_class = self._find_tag_class_name(__data, 'a', 'one_int') - interests = __data.find_all('a', class_=int_class) - author['interests'] = [i.text.strip() for i in interests] + if int_class: + interests = __data.find_all('a', class_=int_class) + author['interests'] = [i.text.strip() for i in interests] + else: + author['interests'] = [] citedby_class = self._find_tag_class_name(__data, 'div', 'cby') citedby = __data.find('div', class_=citedby_class) diff --git a/scholarly/data_types.py b/scholarly/data_types.py index c54b20ef..85f9897a 100644 --- a/scholarly/data_types.py +++ b/scholarly/data_types.py @@ -61,6 +61,7 @@ class PublicationSource(str, Enum): ''' PUBLICATION_SEARCH_SNIPPET = "PUBLICATION_SEARCH_SNIPPET" AUTHOR_PUBLICATION_ENTRY = "AUTHOR_PUBLICATION_ENTRY" + JOURNAL_CITATION_LIST = "JOURNAL_CITATION_LIST" class AuthorSource(str, Enum): @@ -134,6 +135,27 @@ class BibEntry(TypedDict, total=False): publisher: str +class Mandate(TypedDict, total=False): + """ + :class:`Mandate <Mandate>` A funding mandate for a given year + + :param agency: name of the funding agency + :param url_policy: url of the policy for this mandate + :param url_policy_cached: url of the policy cached by Google Scholar + :param effective_date: date from which the policy is effective + :param embargo: period within which the article must be publicly available + :param acknowledgement: text in the paper acknowledging the funding + :param grant: grant ID that supported this work + """ + agency: str + url_policy: str + url_policy_cached: str + effective_date: str + embargo: str + acknowledgement: str + grant: str + + class Publication(TypedDict, total=False): """ :class:`Publication <Publication>` object used to represent a publication entry on Google Scholar. @@ -170,6 +192,7 @@ class Publication(TypedDict, total=False): values. (source: AUTHOR_PUBLICATION_ENTRY) :param public_access: Boolean corresponding to whether the article is available or not in accordance with public access mandates. + :param mandates: List of mandates with funding information and public access requirements. :param url_related_articles: the url containing link for related articles of a publication (needs fill() for AUTHOR_PUBLICATION_ENTRIES) :param url_add_sclib: (source: PUBLICATION_SEARCH_SNIPPET) :param url_scholarbib: the url containing links for @@ -189,6 +212,7 @@ class Publication(TypedDict, total=False): cites_per_year: CitesPerYear author_pub_id: str public_access: bool + mandates: List[Mandate] eprint_url: str pub_url: str url_add_sclib: str @@ -248,3 +272,23 @@ class Author(TypedDict, total=False): coauthors: List # List of authors. No self dict functionality available container_type: str source: AuthorSource + +class Journal(TypedDict, total=False): + """ + :class:`Journal <Journal>` object used to represent a journal entry on Google Scholar. + (When source is not specified, the field is present in all sources) + + + :param name: The name of the journal + :param h5-index: h5-index is the h-index for articles published in the journal during the last 5 complete years. + :param h5-median: h5-median for a publication is the median number of citations for the articles that make up its h5-index. + :param url_citations: The URL for the cached citations page of the journal + :param comment: String representing the ranking for the journal in various categories + + """ + + name: str + h5_index: int + h5_median: int + url_citations: str + comment: str diff --git a/scholarly/publication_parser.py b/scholarly/publication_parser.py index 173e06cd..04f40bb5 100644 --- a/scholarly/publication_parser.py +++ b/scholarly/publication_parser.py @@ -2,10 +2,9 @@ import bibtexparser import arrow from bibtexparser.bibdatabase import BibDatabase -from .data_types import BibEntry, Publication, PublicationSource +from .data_types import BibEntry, Mandate, Publication, PublicationSource -_HOST = 'https://scholar.google.com{0}' _SCHOLARPUBRE = r'cites=([\d,]*)' _CITATIONPUB = '/citations?hl=en&view_op=view_citation&citation_for_view={0}' _SCHOLARPUB = '/scholar?hl=en&oi=bibs&cites={0}' @@ -13,6 +12,7 @@ _BIBCITE = '/scholar?q=info:{0}:scholar.google.com/\ &output=cite&scirp={1}&hl=en' _CITEDBYLINK = '/scholar?cites={0}' +_MANDATES_URL = '/citations?view_op=view_mandate&hl=en&citation_for_view={0}' _BIB_MAPPING = { 'ENTRYTYPE': 'pub_type', @@ -48,6 +48,7 @@ class _SearchScholarIterator(object): def __init__(self, nav, url: str): self._url = url + self._pubtype = PublicationSource.PUBLICATION_SEARCH_SNIPPET if "/scholar?" in url else PublicationSource.JOURNAL_CITATION_LIST self._nav = nav self._load_url(url) self.total_results = self._get_total_results() @@ -57,7 +58,7 @@ def _load_url(self, url: str): # this is temporary until setup json file self._soup = self._nav._get_soup(url) self._pos = 0 - self._rows = self._soup.find_all('div', class_='gs_r gs_or gs_scl') + self._rows = self._soup.find_all('div', class_='gs_r gs_or gs_scl') + self._soup.find_all('div', class_='gsc_mpat_ttl') def _get_total_results(self): if self._soup.find("div", class_="gs_pda"): @@ -80,7 +81,7 @@ def __next__(self): if self._pos < len(self._rows): row = self._rows[self._pos] self._pos += 1 - res = self.pub_parser.get_publication(row, PublicationSource.PUBLICATION_SEARCH_SNIPPET) + res = self.pub_parser.get_publication(row, self._pubtype) return res elif self._soup.find(class_='gs_ico gs_ico_nav_next'): url = self._soup.find( @@ -141,6 +142,9 @@ def get_publication(self, __data, pubtype: PublicationSource)->Publication: return self._citation_pub(__data, publication) elif publication['source'] == PublicationSource.PUBLICATION_SEARCH_SNIPPET: return self._scholar_pub(__data, publication) + elif publication['source'] == PublicationSource.JOURNAL_CITATION_LIST: + return publication + # TODO: self._journal_pub(__data, publication) else: return publication @@ -346,6 +350,11 @@ def fill(self, publication: Publication)->Publication: if soup.find('div', class_='gsc_vcd_title_ggi'): publication['eprint_url'] = soup.find( 'div', class_='gsc_vcd_title_ggi').a['href'] + + if publication.get('public_access', None): + publication['mandates'] = [] + self._fill_public_access_mandates(publication) + publication['filled'] = True elif publication['source'] == PublicationSource.PUBLICATION_SEARCH_SNIPPET: bibtex_url = self._get_bibtex(publication['url_scholarbib']) @@ -400,3 +409,31 @@ def _get_bibtex(self, bib_url) -> str: if link.string.lower() == "bibtex": return link.get('href') return '' + + def _fill_public_access_mandates(self, publication: Publication) -> None: + """Fills the public access mandates""" + if publication.get('public_access', None): + soup = self.nav._get_soup(_MANDATES_URL.format(publication['author_pub_id'])) + mandates = soup.find_all('li') + for mandate in mandates: + m = Mandate() + m['agency'] = mandate.find('span', class_='gsc_md_mndt_name').text + m['url_policy'] = mandate.find('div', class_='gsc_md_mndt_title').a['href'] + m['url_policy_cached'] = mandate.find('span', class_='gs_a').a['href'] + for desc in mandate.find_all('div', class_='gsc_md_mndt_desc'): + match = re.search("Effective date: [0-9]{4}/[0-9]{1,2}", desc.text) + if match: + m['effective_date'] = re.sub(pattern="Effective date: ", repl="", + string=desc.text[match.start() : match.end()]) + + match = re.search("Embargo: ", desc.text) + if match: + m['embargo'] = re.sub(pattern="Embargo: ", repl="", string=desc.text[match.end():]) + + if "Grant: " in desc.text: + m['grant'] = desc.text.split("Grant: ")[1] + + if "Funding acknowledgment" in desc.text: + m['acknowledgement'] = desc.find('span', class_='gs_gray').text + + publication['mandates'].append(m) diff --git a/setup.py b/setup.py index 9784f44b..eeb28150 100644 --- a/setup.py +++ b/setup.py @@ -5,7 +5,7 @@ setuptools.setup( name='scholarly', - version='1.5.1', + version='1.6.0', author='Steven A. Cholewiak, Panos Ipeirotis, Victor Silva, Arun Kannawadi', author_email='[email protected], [email protected], [email protected], [email protected]', description='Simple access to Google Scholar authors and citations',	StopIteration reached when fetching free proxies Describe the bug When FreeProxies are used, StopIteration may be reached if all of the available proxies have stopped working. This may happen before `scholarly` switches to using premium proxies. To Reproduce See https://github.com/arunkannawadi/scholarly/runs/4613129195?check_suite_focus=true#step:7:85 Expected behavior When StopIteration is raised, `scholarly` needs to switch to premium proxy. If premium proxy is also FreeProxy, it should exit more graciously with a helpful message. Screenshots ![image](https://user-images.githubusercontent.com/5103390/147187722-51a691cd-77b5-495d-a1aa-aeeacfd2d53b.png) Desktop (please complete the following information): - Proxy service: FreeProxy - python version: [e.g. 3.8] - OS: ubuntu-latest in CI - Version 1.5.1 Do you plan on contributing? Your response below will clarify whether the maintainers can expect you to fix the bug you reported. - [x] Yes, I will create a Pull Request with the bugfix.		2022-03-03T19:41:52	[]	[]
scholarly-python-package/scholarly	scholarly-python-package__scholarly-400	1b223441a8bc8eb8796d2022e516e600c7a9f17e	diff --git a/README.md b/README.md index befc5abf..c6404cb3 100644 --- a/README.md +++ b/README.md @@ -57,9 +57,12 @@ If neither installed, `scholarly` will fetch only up to 20 co-authors. ```bash pip3 install scholarly[tor] ``` - + If you use `zsh` (which is now the default in latest macOS), you should type this as + ```zsh + pip3 install scholarly'[tor]' + ``` Note: Tor option is unavailable with conda installation. - + ## Tests To check if your installation is succesful, run the tests by executing the `test_module.py` file as: diff --git a/scholarly/_navigator.py b/scholarly/_navigator.py index 19b5c7f4..ef7c4d21 100644 --- a/scholarly/_navigator.py +++ b/scholarly/_navigator.py @@ -163,7 +163,12 @@ def _get_page(self, pagerequest: str, premium: bool = False) -> str: self.logger.info("Retrying with a new session.") tries += 1 - session, timeout = pm.get_next_proxy(num_tries = tries, old_timeout = timeout, old_proxy=session.proxies.get('http', None)) + try: + session, timeout = pm.get_next_proxy(num_tries = tries, old_timeout = timeout, old_proxy=session.proxies.get('http', None)) + except Exception: + self.logger.info("No other secondary connections possible. " + "Using the primary proxy for all requests.") + break # If secondary proxy does not work, try again primary proxy. if not premium: diff --git a/scholarly/_scholarly.py b/scholarly/_scholarly.py index bbd68ba2..a9692756 100644 --- a/scholarly/_scholarly.py +++ b/scholarly/_scholarly.py @@ -2,14 +2,15 @@ import requests import os import copy +import csv import pprint -from typing import List +from typing import Dict, List from ._navigator import Navigator from ._proxy_generator import ProxyGenerator from dotenv import find_dotenv, load_dotenv from .author_parser import AuthorParser from .publication_parser import PublicationParser, _SearchScholarIterator -from .data_types import Author, AuthorSource, Publication, PublicationSource +from .data_types import Author, AuthorSource, Journal, Publication, PublicationSource _AUTHSEARCH = '/citations?hl=en&view_op=search_authors&mauthors={0}' _KEYWORDSEARCH = '/citations?hl=en&view_op=search_authors&mauthors=label:{0}' @@ -17,6 +18,7 @@ _PUBSEARCH = '/scholar?hl=en&q={0}' _CITEDBYSEARCH = '/scholar?hl=en&cites={0}' _ORGSEARCH = "/citations?view_op=view_org&hl=en&org={0}" +_MANDATES_URL = "https://scholar.google.com/citations?view_op=mandates_leaderboard_csv" class _Scholarly: @@ -27,6 +29,13 @@ def __init__(self): self.env = os.environ.copy() self.__nav = Navigator() self.logger = self.__nav.logger + self._journal_categories = None + + @property + def journal_categories(self): + if self._journal_categories is None: + self._journal_categories = self.get_journal_categories() + return self._journal_categories def set_retries(self, num_retries: int)->None: """Sets the number of retries in case of errors @@ -465,6 +474,40 @@ def search_author_by_organization(self, organization_id: int): url = _ORGSEARCH.format(organization_id) return self.__nav.search_authors(url) + def download_mandates_csv(self, filename: str, overwrite: bool = False, + include_links: bool =True): + """ + Download the CSV file of the current mandates. + """ + if (not overwrite) and os.path.exists(filename): + raise ValueError(f"{filename} already exists. Either provide a " + "different filename or allow overwriting by " + "setting overwrite=True") + text = self.__nav._get_page(_MANDATES_URL, premium=False) + if include_links: + soup = self.__nav._get_soup("/citations?view_op=mandates_leaderboard") + text = text.replace("Funder,", "Funder,Policy,Cached,", 1) + for agency in soup.find_all("td", class_="gsc_mlt_t"): + cached = agency.find("span", class_="gs_a").a["href"] + name = agency.a.text + if name != "cached": + policy = agency.a['href'] + else: + name = agency.text[:-10] + policy = "" + + if "," in name: + text = text.replace(f'"{name}",', f'"{name}",{policy},{cached},') + else: + text = text.replace(f"{name},", f"{name},{policy},{cached},") + try: + with open(filename, 'w') as f: + f.write(text) + except IOError: + self.logger.error("Error writing mandates as %s", filename) + finally: + return text + # TODO: Make it a public method in v1.6 def _construct_url(self, baseurl: str, patents: bool = True, citations: bool = True, year_low: int = None, @@ -495,3 +538,86 @@ def _construct_url(self, baseurl: str, patents: bool = True, # improve str below return url + yr_lo + yr_hi + citations + patents + sortby + start + + def get_journal_categories(self): + """ + Get a dict of journal categories and subcategories. + """ + soup = self.__nav._get_soup("/citations?view_op=top_venues&hl=en&vq=en") + categories = {} + for category in soup.find_all("a", class_="gs_md_li"): + if not "vq=" in category['href']: + continue + vq = category['href'].split("&vq=")[1] + categories[category.text] = {} + categories[category.text][None] = vq + + for category in categories: + vq = categories[category][None] + if vq=="en": + continue + soup = self.__nav._get_soup(f"/citations?view_op=top_venues&hl=en&vq={vq}") + for subcategory in soup.find_all("a", class_="gs_md_li"): + if not f"&vq={vq}_" in subcategory['href']: + continue + categories[category][subcategory.text] = subcategory['href'].split("&vq=")[1] + + #print(categories) + return categories + + def get_journals(self, category='English', subcategory=None, include_comments: bool = False) -> Dict[int, Journal]: + try: + cat = self.journal_categories[category] + try: + subcat = cat[subcategory] + url = f"/citations?view_op=top_venues&hl=en&vq={subcat}" + soup = self.__nav._get_soup(url) + + ranks = soup.find_all("td", class_="gsc_mvt_p") + names = soup.find_all("td", class_="gsc_mvt_t") + h5indices = soup.find_all("a", class_="gs_ibl gsc_mp_anchor") + h5medians = soup.find_all("span", class_="gs_ibl") + + + #import pdb; pdb.set_trace() + result = {} + for rank, name, h5index, h5median in zip(ranks, names, h5indices, h5medians): + url_citations = h5index['href'] + comment = "" + if include_comments: + soup = self.__nav._get_soup(url_citations) + try: + for cmt in soup.find_all('ul', class_='gsc_mlhd_list')[1].find_all('li'): + comment += cmt.text+"; " + except IndexError: + pass + result[int(rank.text[:-1])] = Journal(name=name.text, + h5_index=int(h5index.text), + h5_median=int(h5median.text), + url_citations=url_citations, + comment=comment + ) + #print(result) + return result + except KeyError: + raise ValueError("Invalid subcategory: %s for %s. Choose one from %s" % (subcategory, category, cat.keys())) + except KeyError: + raise ValueError("Invalid category: %s. Choose one from %s", category, self.journal_categories.keys()) + + def save_journals_csv(self, filename, category="English", subcategory=None, include_comments=False): + """ + Save a list of journals to a file in CSV format. + """ + journals = self.get_journals(category, subcategory, include_comments) + try: + with open(filename, 'w') as f: + csv_writer = csv.writer(f) + header = ['Publication', 'h5-index', 'h5-median'] + ['Comment']include_comments + csv_writer.writerow(header) + for rank, journal in journals.items(): + row = [journal['name'], journal['h5_index'], journal['h5_median']] + [journal.get('comment', '')]include_comments + csv_writer.writerow(row) + except IOError: + self.logger.error("Error writing journals as %s", filename) + finally: + return journals \ No newline at end of file diff --git a/scholarly/author_parser.py b/scholarly/author_parser.py index 740d3e1c..f1f9bd0b 100644 --- a/scholarly/author_parser.py +++ b/scholarly/author_parser.py @@ -56,8 +56,11 @@ def get_author(self, __data)->Author: author['email_domain'] = re.sub(_EMAILAUTHORRE, r'@', email.text) int_class = self._find_tag_class_name(__data, 'a', 'one_int') - interests = __data.find_all('a', class_=int_class) - author['interests'] = [i.text.strip() for i in interests] + if int_class: + interests = __data.find_all('a', class_=int_class) + author['interests'] = [i.text.strip() for i in interests] + else: + author['interests'] = [] citedby_class = self._find_tag_class_name(__data, 'div', 'cby') citedby = __data.find('div', class_=citedby_class) diff --git a/scholarly/data_types.py b/scholarly/data_types.py index c54b20ef..85f9897a 100644 --- a/scholarly/data_types.py +++ b/scholarly/data_types.py @@ -61,6 +61,7 @@ class PublicationSource(str, Enum): ''' PUBLICATION_SEARCH_SNIPPET = "PUBLICATION_SEARCH_SNIPPET" AUTHOR_PUBLICATION_ENTRY = "AUTHOR_PUBLICATION_ENTRY" + JOURNAL_CITATION_LIST = "JOURNAL_CITATION_LIST" class AuthorSource(str, Enum): @@ -134,6 +135,27 @@ class BibEntry(TypedDict, total=False): publisher: str +class Mandate(TypedDict, total=False): + """ + :class:`Mandate <Mandate>` A funding mandate for a given year + + :param agency: name of the funding agency + :param url_policy: url of the policy for this mandate + :param url_policy_cached: url of the policy cached by Google Scholar + :param effective_date: date from which the policy is effective + :param embargo: period within which the article must be publicly available + :param acknowledgement: text in the paper acknowledging the funding + :param grant: grant ID that supported this work + """ + agency: str + url_policy: str + url_policy_cached: str + effective_date: str + embargo: str + acknowledgement: str + grant: str + + class Publication(TypedDict, total=False): """ :class:`Publication <Publication>` object used to represent a publication entry on Google Scholar. @@ -170,6 +192,7 @@ class Publication(TypedDict, total=False): values. (source: AUTHOR_PUBLICATION_ENTRY) :param public_access: Boolean corresponding to whether the article is available or not in accordance with public access mandates. + :param mandates: List of mandates with funding information and public access requirements. :param url_related_articles: the url containing link for related articles of a publication (needs fill() for AUTHOR_PUBLICATION_ENTRIES) :param url_add_sclib: (source: PUBLICATION_SEARCH_SNIPPET) :param url_scholarbib: the url containing links for @@ -189,6 +212,7 @@ class Publication(TypedDict, total=False): cites_per_year: CitesPerYear author_pub_id: str public_access: bool + mandates: List[Mandate] eprint_url: str pub_url: str url_add_sclib: str @@ -248,3 +272,23 @@ class Author(TypedDict, total=False): coauthors: List # List of authors. No self dict functionality available container_type: str source: AuthorSource + +class Journal(TypedDict, total=False): + """ + :class:`Journal <Journal>` object used to represent a journal entry on Google Scholar. + (When source is not specified, the field is present in all sources) + + + :param name: The name of the journal + :param h5-index: h5-index is the h-index for articles published in the journal during the last 5 complete years. + :param h5-median: h5-median for a publication is the median number of citations for the articles that make up its h5-index. + :param url_citations: The URL for the cached citations page of the journal + :param comment: String representing the ranking for the journal in various categories + + """ + + name: str + h5_index: int + h5_median: int + url_citations: str + comment: str diff --git a/scholarly/publication_parser.py b/scholarly/publication_parser.py index 173e06cd..04f40bb5 100644 --- a/scholarly/publication_parser.py +++ b/scholarly/publication_parser.py @@ -2,10 +2,9 @@ import bibtexparser import arrow from bibtexparser.bibdatabase import BibDatabase -from .data_types import BibEntry, Publication, PublicationSource +from .data_types import BibEntry, Mandate, Publication, PublicationSource -_HOST = 'https://scholar.google.com{0}' _SCHOLARPUBRE = r'cites=([\d,]*)' _CITATIONPUB = '/citations?hl=en&view_op=view_citation&citation_for_view={0}' _SCHOLARPUB = '/scholar?hl=en&oi=bibs&cites={0}' @@ -13,6 +12,7 @@ _BIBCITE = '/scholar?q=info:{0}:scholar.google.com/\ &output=cite&scirp={1}&hl=en' _CITEDBYLINK = '/scholar?cites={0}' +_MANDATES_URL = '/citations?view_op=view_mandate&hl=en&citation_for_view={0}' _BIB_MAPPING = { 'ENTRYTYPE': 'pub_type', @@ -48,6 +48,7 @@ class _SearchScholarIterator(object): def __init__(self, nav, url: str): self._url = url + self._pubtype = PublicationSource.PUBLICATION_SEARCH_SNIPPET if "/scholar?" in url else PublicationSource.JOURNAL_CITATION_LIST self._nav = nav self._load_url(url) self.total_results = self._get_total_results() @@ -57,7 +58,7 @@ def _load_url(self, url: str): # this is temporary until setup json file self._soup = self._nav._get_soup(url) self._pos = 0 - self._rows = self._soup.find_all('div', class_='gs_r gs_or gs_scl') + self._rows = self._soup.find_all('div', class_='gs_r gs_or gs_scl') + self._soup.find_all('div', class_='gsc_mpat_ttl') def _get_total_results(self): if self._soup.find("div", class_="gs_pda"): @@ -80,7 +81,7 @@ def __next__(self): if self._pos < len(self._rows): row = self._rows[self._pos] self._pos += 1 - res = self.pub_parser.get_publication(row, PublicationSource.PUBLICATION_SEARCH_SNIPPET) + res = self.pub_parser.get_publication(row, self._pubtype) return res elif self._soup.find(class_='gs_ico gs_ico_nav_next'): url = self._soup.find( @@ -141,6 +142,9 @@ def get_publication(self, __data, pubtype: PublicationSource)->Publication: return self._citation_pub(__data, publication) elif publication['source'] == PublicationSource.PUBLICATION_SEARCH_SNIPPET: return self._scholar_pub(__data, publication) + elif publication['source'] == PublicationSource.JOURNAL_CITATION_LIST: + return publication + # TODO: self._journal_pub(__data, publication) else: return publication @@ -346,6 +350,11 @@ def fill(self, publication: Publication)->Publication: if soup.find('div', class_='gsc_vcd_title_ggi'): publication['eprint_url'] = soup.find( 'div', class_='gsc_vcd_title_ggi').a['href'] + + if publication.get('public_access', None): + publication['mandates'] = [] + self._fill_public_access_mandates(publication) + publication['filled'] = True elif publication['source'] == PublicationSource.PUBLICATION_SEARCH_SNIPPET: bibtex_url = self._get_bibtex(publication['url_scholarbib']) @@ -400,3 +409,31 @@ def _get_bibtex(self, bib_url) -> str: if link.string.lower() == "bibtex": return link.get('href') return '' + + def _fill_public_access_mandates(self, publication: Publication) -> None: + """Fills the public access mandates""" + if publication.get('public_access', None): + soup = self.nav._get_soup(_MANDATES_URL.format(publication['author_pub_id'])) + mandates = soup.find_all('li') + for mandate in mandates: + m = Mandate() + m['agency'] = mandate.find('span', class_='gsc_md_mndt_name').text + m['url_policy'] = mandate.find('div', class_='gsc_md_mndt_title').a['href'] + m['url_policy_cached'] = mandate.find('span', class_='gs_a').a['href'] + for desc in mandate.find_all('div', class_='gsc_md_mndt_desc'): + match = re.search("Effective date: [0-9]{4}/[0-9]{1,2}", desc.text) + if match: + m['effective_date'] = re.sub(pattern="Effective date: ", repl="", + string=desc.text[match.start() : match.end()]) + + match = re.search("Embargo: ", desc.text) + if match: + m['embargo'] = re.sub(pattern="Embargo: ", repl="", string=desc.text[match.end():]) + + if "Grant: " in desc.text: + m['grant'] = desc.text.split("Grant: ")[1] + + if "Funding acknowledgment" in desc.text: + m['acknowledgement'] = desc.find('span', class_='gs_gray').text + + publication['mandates'].append(m)	StopIteration reached when fetching free proxies Describe the bug When FreeProxies are used, StopIteration may be reached if all of the available proxies have stopped working. This may happen before `scholarly` switches to using premium proxies. To Reproduce See https://github.com/arunkannawadi/scholarly/runs/4613129195?check_suite_focus=true#step:7:85 Expected behavior When StopIteration is raised, `scholarly` needs to switch to premium proxy. If premium proxy is also FreeProxy, it should exit more graciously with a helpful message. Screenshots ![image](https://user-images.githubusercontent.com/5103390/147187722-51a691cd-77b5-495d-a1aa-aeeacfd2d53b.png) Desktop (please complete the following information): - Proxy service: FreeProxy - python version: [e.g. 3.8] - OS: ubuntu-latest in CI - Version 1.5.1 Do you plan on contributing? Your response below will clarify whether the maintainers can expect you to fix the bug you reported. - [x] Yes, I will create a Pull Request with the bugfix.		2022-03-03T02:58:29	[]	[]
vippsas/zeroeventhub	vippsas__zeroeventhub-11	655014ea9336d37655981ca037dadd0ae4f041a8	diff --git a/README.md b/README.md index f32e736..7c5de4a 100644 --- a/README.md +++ b/README.md @@ -47,6 +47,7 @@ but the intended uses is closer to AMQP and Kafka. The third component is tooling around this protocol: * [Client/server implementations for Go](go/README.md) +* [Client implementation for Python](python/zeroeventhub/README.md) * [mssql-changefeed](https://github.com/vippsas/mssql-changefeed) offers the primitives needed to use ZeroEventHub together with an Azure SQL database @@ -77,4 +78,4 @@ the aggregate is computed on demand from the event tables. ## Further reading Head to [the specification](SPEC.md) for technical details -and discussions. \ No newline at end of file +and discussions. diff --git a/python/zeroeventhub/.flake8 b/python/zeroeventhub/.flake8 new file mode 100644 index 0000000..129982e --- /dev/null +++ b/python/zeroeventhub/.flake8 @@ -0,0 +1,6 @@ +[flake8] +ignore = F401,W503 +max-line-length = 100 +exclude = + .venv + __pycache__ diff --git a/python/zeroeventhub/.gitignore b/python/zeroeventhub/.gitignore new file mode 100644 index 0000000..617462a --- /dev/null +++ b/python/zeroeventhub/.gitignore @@ -0,0 +1,7 @@ +# vim +*/.swp + +# python +/__pycache__//* +.coverage +dist diff --git a/python/zeroeventhub/.pylintrc b/python/zeroeventhub/.pylintrc new file mode 100644 index 0000000..667e087 --- /dev/null +++ b/python/zeroeventhub/.pylintrc @@ -0,0 +1,5 @@ +[MAIN] +load-plugins=pylint.extensions.docparams + +[BASIC] +no-docstring-rgx=^$ diff --git a/python/zeroeventhub/README.md b/python/zeroeventhub/README.md new file mode 100644 index 0000000..1415542 --- /dev/null +++ b/python/zeroeventhub/README.md @@ -0,0 +1,74 @@ +# ZeroEventHub + +This README file contains information specific to the Python port of the ZeroEventHub. +Please see the [main readme file](../../README.md) for an overview of what this project is about. + +## Client + +We recommend that you store the latest checkpoint/cursor for each partition in the client's +database. Example of simple single-partition consumption. Note about the example: + +* Things starting with "my" is supplied by you +* Things starting with "their" is supplied by the service you connect to + +```python +# Step 1: Setup +their_partition_count = 1 # documented contract with server +zeh_session = requests.Session() # you can setup the authentication on the session +client = zeroeventhub.Client(their_service_url, their_partition_count, zeh_session) + +# Step 2: Load the cursors from last time we ran +cursors = my_get_cursors_from_db() +if not cursors: + # we have never run before, so we can get all events with FIRST_CURSOR + # (if we just want to receive new events from now, we would use LAST_CURSOR) + cursors = [ + zeroeventhub.Cursor(partition_id, zeroeventhub.FIRST_CURSOR) + for partition_id in range(their_partition_count) + ] + +# Step 3: Enter listening loop... +page_of_events = PageEventReceiver() +while myStillWantToReadEvents: + # Step 4: Use ZeroEventHub client to fetch the next page of events. + client.fetch_events( + cursors, + my_page_size_hint, + page_of_events + ) + + # Step 5: Write the effect of changes to our own database and the updated + # cursor value in the same transaction. + with db.begin_transaction() as tx: + my_write_effect_of_events_to_db(tx, page_of_events.events) + + my_write_cursors_to_db(tx, page_of_events.latest_checkpoints) + + tx.commit() + + cursors = page_of_events.latest_checkpoints + + page_of_events.clear() +``` + +## Development + +To run the test suite, assuming you already have Python 3.10 or later installed and on your `PATH`: +```sh +pip install poetry==1.3.1 +poetry config virtualenvs.in-project true +poetry install --sync +poetry run coverage run --branch -m pytest +poetry run coverage html +``` + +Then, you can open the `htmlcov/index.html` file in your browser to look at the code coverage report. + +Also, to pass the CI checks, you may want to run the following before pushing your changes: + +```sh +poetry run pylint ./zeroeventhub/ +poetry run flake8 +poetry run mypy --check-untyped-defs ./tests/ +poetry run mypy --disallow-untyped-defs ./zeroeventhub/ +``` diff --git a/python/zeroeventhub/poetry.lock b/python/zeroeventhub/poetry.lock new file mode 100644 index 0000000..682547a --- /dev/null +++ b/python/zeroeventhub/poetry.lock @@ -0,0 +1,1868 @@ +# This file is automatically @generated by Poetry and should not be changed by hand. + +[[package]] +name = "astroid" +version = "2.13.3" +description = "An abstract syntax tree for Python with inference support." +category = "dev" +optional = false +python-versions = ">=3.7.2" +files = [ + {file = "astroid-2.13.3-py3-none-any.whl", hash = "sha256:14c1603c41cc61aae731cad1884a073c4645e26f126d13ac8346113c95577f3b"}, + {file = "astroid-2.13.3.tar.gz", hash = "sha256:6afc22718a48a689ca24a97981ad377ba7fb78c133f40335dfd16772f29bcfb1"}, +] + +[package.dependencies] +lazy-object-proxy = ">=1.4.0" +typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.11\""} +wrapt = [ + {version = 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b/python/zeroeventhub/zeroeventhub/__init__.py @@ -0,0 +1,8 @@ +"""ZeroEventHub module""" + +from .client import Client +from .cursor import Cursor, FIRST_CURSOR, LAST_CURSOR +from .event_receiver import EventReceiver +from .errors import APIError, ErrCursorsMissing +from .constants import ALL_HEADERS +from .page_event_receiver import Event, PageEventReceiver diff --git a/python/zeroeventhub/zeroeventhub/client.py b/python/zeroeventhub/zeroeventhub/client.py new file mode 100644 index 0000000..3c6a26b --- /dev/null +++ b/python/zeroeventhub/zeroeventhub/client.py @@ -0,0 +1,161 @@ +"""Module containing client-side related code for ZeroEventHub""" + +import json +from typing import Dict, Optional, Any, Sequence +import requests + +from .cursor import Cursor +from .event_receiver import EventReceiver +from .errors import ErrCursorsMissing + + +class Client: + """ + Client-side code to query a ZeroEventHub server to fetch events and pass them to the supplied + EventReceiver instance. + """ + + def __init__( + self, + url: str, + partition_count: int, + session: Optional[requests.Session] = None, + ) -> None: + + """ + Initializes a new instance of the Client class. + + :param url: The base URL for the service. + :param partition_count: The number of partitions the ZeroEventHub server has. + :param session: An optional session under which to make the HTTP requests. + This allows one time setup of authentication etc. on the session, + and increases performance if fetching events frequently due to + connection pooling. + """ + self.url = url + self.partition_count = partition_count + self._client_owns_session = not bool(session) + self._session = session or requests.Session() + + def __del__(self) -> None: + """ + Close the session when this Client instance is destroyed if we created it + during initialization + """ + if self._client_owns_session: + self._session.close() + + @property + def session(self) -> requests.Session: + """Return the session being used by this client.""" + return self._session + + def fetch_events( + self, + cursors: Sequence[Cursor], + page_size_hint: Optional[int], + event_receiver: EventReceiver, + headers: Optional[Sequence[str]] = None, + ) -> None: + """ + Fetch events from the server using the provided context, cursors, page size hint, + event receiver, and headers. + + :param cursors: A sequence of cursors to be used in the request. + :param page_size_hint: An optional hint for the page size of the response. + :param event_receiver: An event receiver to handle the received events. + :param headers: An optional sequence containing event headers desired in the response. + :raises APIError: if cursors are missing. + :raises ValueError: if an exception occurs while the event receiver handles the response. + :raises requests.exceptions.RequestException: if unable to call the endpoint successfully. + :raises requests.HTTPError: if response status code does not indicate success. + :raises json.JSONDecodeError: if a line from the response cannot be decoded into JSON. + """ + self._validate_inputs(cursors) + req = self._build_request(cursors, page_size_hint, headers) + + with self.session.send(req, stream=True) as res: + self._process_response(res, event_receiver) + + def _validate_inputs(self, cursors: Sequence[Cursor]) -> None: + """ + Validate that the input cursors are not empty. + + :param cursors: A sequence of cursors to be used in the request. + :raises APIError: if cursors are missing. + """ + if not cursors: + raise ErrCursorsMissing + + def _build_request( + self, + cursors: Sequence[Cursor], + page_size_hint: Optional[int], + headers: Optional[Sequence[str]], + ) -> requests.PreparedRequest: + """ + Build the http request using the provided inputs. + + :param cursors: A sequence of cursors to be used in the request. + :param page_size_hint: An optional hint for the page size of the response. + :param headers: An optional sequence containing event headers desired in the response. + :return: the http request + """ + params: Dict[str, str \| int] = { + "n": self.partition_count, + } + if page_size_hint: + params["pagesizehint"] = page_size_hint + + for cursor in cursors: + params[f"cursor{cursor.partition_id}"] = cursor.cursor + + if headers: + params["headers"] = ",".join(headers) + + return requests.Request("GET", self.url, params=params).prepare() + + def _process_response(self, res: requests.Response, event_receiver: EventReceiver) -> None: + """ + Process the response from the server. + + :param res: the server response + :param event_receiver: An event receiver to handle the received events. + :raises requests.HTTPError: if response status code does not indicate success. + :raises json.JSONDecodeError: if a line from the response cannot be decoded into JSON. + :raises ValueError: error while EventReceiver handles checkpoint or event. + """ + res.raise_for_status() + + for line in res.iter_lines(): + checkpoint_or_event = json.loads(line) + self._process_checkpoint_or_event(checkpoint_or_event, event_receiver) + + def _process_checkpoint_or_event( + self, checkpoint_or_event: Dict[str, Any], event_receiver: EventReceiver + ) -> None: + """ + Process a line of response from the server. + + :param checkpoint_or_event: A dictionary containing a checkpoint or event. + :param event_receiver: An event receiver to handle the received events. + :raises ValueError: if an error occurred in the event receiver while + the event or checkpoint was being processed. + """ + + if checkpoint_or_event.get("cursor", None) is not None: + try: + event_receiver.checkpoint( + checkpoint_or_event["partition"], checkpoint_or_event["cursor"] + ) + except Exception as error: + raise ValueError("error while receiving checkpoint") from error + else: + try: + event_receiver.event( + checkpoint_or_event["partition"], + checkpoint_or_event.get("headers", None), + checkpoint_or_event["data"], + ) + except Exception as error: + raise ValueError("error while receiving event") from error diff --git a/python/zeroeventhub/zeroeventhub/constants.py b/python/zeroeventhub/zeroeventhub/constants.py new file mode 100644 index 0000000..7062506 --- /dev/null +++ b/python/zeroeventhub/zeroeventhub/constants.py @@ -0,0 +1,7 @@ +"""Module containing constants which are relevant for both Client and Server""" + +ALL_HEADERS = ("_all",) +""" +ALL_HEADERS is a special value for `headers` argument representing a request for returning +all headers available. +""" diff --git a/python/zeroeventhub/zeroeventhub/cursor.py b/python/zeroeventhub/zeroeventhub/cursor.py new file mode 100644 index 0000000..3633273 --- /dev/null +++ b/python/zeroeventhub/zeroeventhub/cursor.py @@ -0,0 +1,23 @@ +"""This module defines a Cursor dataclass for use by the client and server""" + +from dataclasses import dataclass + + +@dataclass +class Cursor: + """ + A dataclass encapsulating both the partition ID and the actual cursor within this partition. + + :param partition_id: The partition ID + :param cursor: The cursor within the partition + """ + + partition_id: int + cursor: str + + +FIRST_CURSOR = "_first" +"""FIRST_CURSOR is a special cursor: starts at the first event.""" + +LAST_CURSOR = "_last" +"""LAST_CURSOR is a special cursor: starts at the last available event.""" diff --git a/python/zeroeventhub/zeroeventhub/errors.py b/python/zeroeventhub/zeroeventhub/errors.py new file mode 100644 index 0000000..1688696 --- /dev/null +++ b/python/zeroeventhub/zeroeventhub/errors.py @@ -0,0 +1,30 @@ +""" +This module defines a custom error class, APIError, and several specific error instances that can +be used to indicate specific error conditions in an API. + +The specific error instances (e.g. `ErrCursorsMissing`) are pre-defined instances +of the APIError class, with specific error messages and codes. They can be used to indicate +specific error conditions in your API. +""" + + +class APIError(ValueError): + """ + APIError has two attributes, `message` and `code`, which are set during initialization. The + `message` attribute represents a human-readable error message, and the `code` attribute is an + HTTP status code that can be used to indicate the type of error that occurred. + """ + + def __init__(self, message: str, code: int): + self.message = message + self.code = code + + def __str__(self) -> str: + return self.message + + def status(self) -> int: + """Return the HTTP status code associated with this APIError""" + return self.code + + +ErrCursorsMissing = APIError("cursors are missing", 400) diff --git a/python/zeroeventhub/zeroeventhub/event_receiver.py b/python/zeroeventhub/zeroeventhub/event_receiver.py new file mode 100644 index 0000000..f7a0dcd --- /dev/null +++ b/python/zeroeventhub/zeroeventhub/event_receiver.py @@ -0,0 +1,28 @@ +"""Module to define the EventReceiver interface""" + +from typing import Protocol, Dict, Any, Optional + + +class EventReceiver(Protocol): + """ + EventReceiver is an interface describing an abstraction for handling either + events or checkpoints. + Checkpoint in this context is basically a cursor. + """ + + def event(self, partition_id: int, headers: Optional[Dict[str, str]], data: Any) -> None: + """ + Event method processes actual events. + + :param partition_id: the partition id + :param headers: the headers + :param data: the data + """ + + def checkpoint(self, partition_id: int, cursor: str) -> None: + """ + Checkpoint method processes cursors. + + :param partition_id: the partition id + :param cursor: the cursor + """ diff --git a/python/zeroeventhub/zeroeventhub/page_event_receiver.py b/python/zeroeventhub/zeroeventhub/page_event_receiver.py new file mode 100644 index 0000000..f0d92db --- /dev/null +++ b/python/zeroeventhub/zeroeventhub/page_event_receiver.py @@ -0,0 +1,70 @@ +"""Module to make it easy to receive a page of events""" + +from typing import Dict, Any, Sequence, Optional, List +from dataclasses import dataclass +from .event_receiver import EventReceiver +from .cursor import Cursor + + +@dataclass +class Event: + """All properties received relating to a certain event.""" + partition_id: int + headers: Optional[Dict[str, str]] + data: Any + + +class PageEventReceiver(EventReceiver): + """ + Receive a page of events + """ + + def __init__(self) -> None: + """Initialize the PageEventReceiver with empty state.""" + self._events: List[Event] = [] + self._checkpoints: List[Cursor] = [] + self._latest_checkpoints: Dict[int, str] = {} + + def clear(self) -> None: + """Clear the received events and checkpoints, ready to handle a new page.""" + self._events.clear() + self._checkpoints.clear() + self._latest_checkpoints.clear() + + @property + def events(self) -> Sequence[Event]: + """Return the page of events received.""" + return self._events + + @property + def checkpoints(self) -> Sequence[Cursor]: + """Return the page of checkpoints received.""" + return self._checkpoints + + @property + def latest_checkpoints(self) -> Sequence[Cursor]: + """Only return the latest checkpoint for each partition.""" + return [ + Cursor(partition_id, cursor) + for partition_id, cursor in self._latest_checkpoints.items() + ] + + def event(self, partition_id: int, headers: Optional[Dict[str, str]], data: Any) -> None: + """ + Add the given event to the list. + + :param partition_id: the partition id + :param headers: the headers + :param data: the data + """ + self._events.append(Event(partition_id, headers, data)) + + def checkpoint(self, partition_id: int, cursor: str) -> None: + """ + Add the given cursor to the list. + + :param partition_id: the partition id + :param cursor: the cursor + """ + self._checkpoints.append(Cursor(partition_id, cursor)) + self._latest_checkpoints[partition_id] = cursor	Create Python client library for zero event hub		2023-02-03T13:45:34	[]	[]
entropicalabs/openqaoa	entropicalabs__openqaoa-291	9053e7883b5f35a9b01fe5795bd35065d47b202c	diff --git a/CHANGELOG.md b/CHANGELOG.md index 1c41097fd..a680ea372 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,3 +1,10 @@ +## Version v0.2.3 (October 25th, 2023) + +This version brings the following changes: +* Fix QPU tests due to provider changes +* Fix `backend_qpu`/`device.backend_device` duplicate +* Compatibility of QAOA and RQAOA with Azure Quantum sessions + ## Version v0.2.2 (September 20th, 2023) This version fixes the version of `pyquil` to prevent breaking changes occuring with v4.0.0. @@ -11,7 +18,7 @@ This version fixes an issue detecting the plugins when installing `openqaoa-core This new version brings an import change to OpenQAOA, separately installable plugins to access different providers. -## What's changed +### What's changed This version brings the following new features: * Independently installable plugins @@ -27,7 +34,7 @@ This version brings the following new features: ## Version v0.1.3 (April 21st, 2023) This version brings a set of new features in OpenQAOA -## What's Changed +### What's Changed * QAOA workflows now support `evaluate_circuit` which lets compute the energy and measurement outcomes for a QAOA circuit for a specified set of parameter values. * The library design-wise is moving closer to modularization goal where each hardware provider backend can be installed independently (this feature is expected to fully functional in the next release) * OpenQAOA v0.1.3 brings in a list of new optimization problems contributed by @alemonbar, including: @@ -48,7 +55,7 @@ This release brings the following new features: * The ability to plug in custom qubit routing solutions for QAOA circuits. * AWS managed jobs are now supported through OpenQAOA -## What's Changed +### What's Changed * Refactor * The new `GateMapLabel` introduces an updated and a more consistent way to label QAOA gates in the circuit. @@ -63,7 +70,7 @@ This is OpenQAOA's first major release! V0.1.0 contains many new features and qu notice: the license has been changed from `apache 2.0` to `MIT` -## What's Changed +### What's Changed * Refactor * The code underwent a considerable refactoring effort. The most noticeable change is in the new `openqaoa-core` and other library plugins in the form `openqaoa-xyz`. @@ -85,7 +92,7 @@ This is OpenQAOA's first major release! V0.1.0 contains many new features and qu This release brings improvements to RQAOA workflow and AWS authentication, and a bugfix to TSP problem class. -## What's Changed +### What's Changed * Refactor * Authentication Refactor by @shahidee44 in https://github.com/entropicalabs/openqaoa/pull/126 * RQAOA workflow by @raulconchello in https://github.com/entropicalabs/openqaoa/pull/109 @@ -105,7 +112,7 @@ This release brings improvements to RQAOA workflow and AWS authentication, and a A release to fix two QPU-related bugs -## What's Changed +### What's Changed * Fixes * Now save_intermediate works for backends with no jobid #110 * Fix a bug that prevented the correct usage of Rigetti's QPUs when on QCS #116 diff --git a/src/openqaoa-braket/openqaoa_braket/backends/qaoa_braket_qpu.py b/src/openqaoa-braket/openqaoa_braket/backends/qaoa_braket_qpu.py index 829d76bd8..d996490c5 100644 --- a/src/openqaoa-braket/openqaoa_braket/backends/qaoa_braket_qpu.py +++ b/src/openqaoa-braket/openqaoa_braket/backends/qaoa_braket_qpu.py @@ -99,9 +99,9 @@ def __init__( "Cannot attach a bigger circuit" " to the QAOA routine" ) - if self.device.provider_connected and self.device.qpu_connected: - self.backend_qpu = self.device.backend_device - elif ( + # if self.device.provider_connected and self.device.qpu_connected: + # self.backend_qpu = self.device.backend_device + if ( self.device.provider_connected is True and self.device.qpu_connected is False ): @@ -114,7 +114,7 @@ def __init__( raise Exception( "Connection to AWS was made. A device name was not specified." ) - else: + elif not (self.device.provider_connected and self.device.qpu_connected): raise Exception("Error connecting to AWS.") if self.device.n_qubits < self.n_qubits: @@ -217,7 +217,7 @@ def get_counts(self, params: QAOAVariationalBaseParams, n_shots=None) -> dict: max_job_retries = 5 while job_state == False: - job = self.backend_qpu.run( + job = self.device.backend_device.run( circuit, (self.device.s3_bucket_name, self.device.folder_name), shots=n_shots, diff --git a/src/openqaoa-core/openqaoa/algorithms/rqaoa/rqaoa_workflow.py b/src/openqaoa-core/openqaoa/algorithms/rqaoa/rqaoa_workflow.py index 507e9ca7f..4adee4ba4 100644 --- a/src/openqaoa-core/openqaoa/algorithms/rqaoa/rqaoa_workflow.py +++ b/src/openqaoa-core/openqaoa/algorithms/rqaoa/rqaoa_workflow.py @@ -1,5 +1,6 @@ import time import numpy as np +from typing import Optional, Callable from .rqaoa_workflow_properties import RqaoaParameters from ..baseworkflow import Workflow, check_compiled @@ -11,10 +12,19 @@ ground_state_hamiltonian, exp_val_hamiltonian_termwise, generate_timestamp, + get_mixer_hamiltonian, +) +from ...qaoa_components import ( + Hamiltonian, + QAOADescriptor, + create_qaoa_variational_params, ) from ...backends.qaoa_analytical_sim import QAOABackendAnalyticalSimulator from . import rqaoa_utils from .rqaoa_result import RQAOAResult +from ...backends.wrapper import SPAMTwirlingWrapper +from ...optimizers.qaoa_optimizer import get_optimizer +from ...backends.qaoa_backend import get_qaoa_backend class RQAOA(Workflow): @@ -295,7 +305,7 @@ def compile(self, problem: QUBO = None, verbose: bool = False): ), f"Schedule is incomplete, add {np.abs(n_qubits - n_cutoff - counter) - sum(self.rqaoa_parameters.steps)} more eliminations" # Create the qaoa object with the properties - self.__q = QAOA(self.device) + self.__q = WrappedQAOA(self.device) self.__q.circuit_properties = self.circuit_properties self.__q.backend_properties = self.backend_properties self.__q.classical_optimizer = self.classical_optimizer @@ -307,6 +317,8 @@ def compile(self, problem: QUBO = None, verbose: bool = False): "algorithm" ] = "qaoa" # change the algorithm name to qaoa, since this is a qaoa object + # connect to the QPU specified + self.device.check_connection() # compile qaoa object self.__q.compile(problem, verbose=verbose) @@ -610,3 +622,150 @@ def _serializable_dict( ]["input_parameters"]["rqaoa_parameters"]["n_cutoff"] return serializable_dict + + +class WrappedQAOA(QAOA): + """ + A class implementing the QAOA object to use in RQAOA, here check_connection() is used outside of the QAOA + compilation to make sure it's only used once. + + Args: + QAOA (_type_): _description_ + """ + + def __init__(self, device=DeviceLocal("vectorized")): + super().__init__(device) + + def compile( + self, + problem: QUBO = None, + verbose: bool = False, + routing_function: Optional[Callable] = None, + ): + """ + Override of QAOA.compile(). Initialise the trainable parameters for QAOA according to the specified + strategies and by passing the problem statement. The device.compile() is called in RQAOA.compile() only. + + .. note:: + Compilation is necessary because it is the moment where the problem statement and + the QAOA instructions are used to build the actual QAOA circuit. + + .. tip:: + Set Verbose to false if you are running batch computations! + + Parameters + ---------- + problem: `Problem` + QUBO problem to be solved by QAOA + verbose: bool + Set True to have a summary of QAOA to displayed after compilation + """ + # if isinstance(routing_function,Callable): + # #assert that routing_function is supported only for Standard QAOA. + # if ( + # self.backend_properties.append_state is not None or\ + # self.backend_properties.prepend_state is not None or\ + # self.circuit_properties.mixer_hamiltonian is not 'x' or\ + + # ) + + # we compile the method of the parent class to genereate the id and + # check the problem is a QUBO object and save it + Workflow.compile(self, problem=problem) + + self.cost_hamil = Hamiltonian.classical_hamiltonian( + terms=problem.terms, coeffs=problem.weights, constant=problem.constant + ) + + self.mixer_hamil = get_mixer_hamiltonian( + n_qubits=self.cost_hamil.n_qubits, + mixer_type=self.circuit_properties.mixer_hamiltonian, + qubit_connectivity=self.circuit_properties.mixer_qubit_connectivity, + coeffs=self.circuit_properties.mixer_coeffs, + ) + + self.qaoa_descriptor = QAOADescriptor( + self.cost_hamil, + self.mixer_hamil, + p=self.circuit_properties.p, + routing_function=routing_function, + device=self.device, + ) + self.variate_params = create_qaoa_variational_params( + qaoa_descriptor=self.qaoa_descriptor, + params_type=self.circuit_properties.param_type, + init_type=self.circuit_properties.init_type, + variational_params_dict=self.circuit_properties.variational_params_dict, + linear_ramp_time=self.circuit_properties.linear_ramp_time, + q=self.circuit_properties.q, + seed=self.circuit_properties.seed, + total_annealing_time=self.circuit_properties.annealing_time, + ) + + backend_dict = self.backend_properties.__dict__.copy() + + self.backend = get_qaoa_backend( + qaoa_descriptor=self.qaoa_descriptor, + device=self.device, + backend_dict, + ) + + # Implementing SPAM Twirling error mitigation requires wrapping the backend. + # However, the BaseWrapper can have many more use cases. + if ( + self.error_mitigation_properties.error_mitigation_technique + == "spam_twirling" + ): + self.backend = SPAMTwirlingWrapper( + backend=self.backend, + n_batches=self.error_mitigation_properties.n_batches, + calibration_data_location=self.error_mitigation_properties.calibration_data_location, + ) + + self.optimizer = get_optimizer( + vqa_object=self.backend, + variational_params=self.variate_params, + optimizer_dict=self.classical_optimizer.asdict(), + ) + + # Set the header properties + self.header["target"] = self.device.device_name + self.header["cloud"] = self.device.device_location + + metadata = { + "p": self.circuit_properties.p, + "param_type": self.circuit_properties.param_type, + "init_type": self.circuit_properties.init_type, + "optimizer_method": self.classical_optimizer.method, + } + + self.set_exp_tags(tags=metadata) + + self.compiled = True + + if verbose: + print("\t \033[1m ### Summary ###\033[0m") + print("OpenQAOA has been compiled with the following properties") + print( + f"Solving QAOA with \033[1m {self.device.device_name} \033[0m on" + f"\033[1m{self.device.device_location}\033[0m" + ) + print( + f"Using p={self.circuit_properties.p} with {self.circuit_properties.param_type}" + f"parameters initialized as {self.circuit_properties.init_type}" + ) + + if hasattr(self.backend, "n_shots"): + print( + f"OpenQAOA will optimize using \033[1m{self.classical_optimizer.method}" + f"\033[0m, with up to \033[1m{self.classical_optimizer.maxiter}" + f"\033[0m maximum iterations. Each iteration will contain" + f"\033[1m{self.backend_properties.n_shots} shots\033[0m" + ) + else: + print( + f"OpenQAOA will optimize using \033[1m{self.classical_optimizer.method}\033[0m," + "with up to \033[1m{self.classical_optimizer.maxiter}\033[0m maximum iterations" + ) + + return None diff --git a/src/openqaoa-core/openqaoa/qaoa_components/variational_parameters/variational_baseparams.py b/src/openqaoa-core/openqaoa/qaoa_components/variational_parameters/variational_baseparams.py index eb628010e..6f057a462 100644 --- a/src/openqaoa-core/openqaoa/qaoa_components/variational_parameters/variational_baseparams.py +++ b/src/openqaoa-core/openqaoa/qaoa_components/variational_parameters/variational_baseparams.py @@ -62,36 +62,35 @@ def __str__(self): def mixer_1q_angles(self) -> np.ndarray: """2D array with the X-rotation angles. - 1st index goes over p and the 2nd index over the qubits to - apply X-rotations on. + 1st index goes over p and the 2nd index over the qubits, + to apply X-rotations on. """ raise NotImplementedError() @property def mixer_2q_angles(self) -> np.ndarray: - """2D array with the X-rotation angles. + """2D array with the XX and YY-rotation angles. - 1st index goes over p and the 2nd index over the qubits to - apply X-rotations on. + 1st index goes over p and the 2nd index over the qubit pairs, + to apply XX and YY-rotations on. """ raise NotImplementedError() @property def cost_1q_angles(self) -> np.ndarray: - """2D array with the ZZ-rotation angles. + """2D array with the Z-rotation angles. - 1st index goes over the p and the 2nd index over the qubit - pairs, to apply ZZ-rotations on. + 1st index goes over the p and the 2nd index over the qubits, + to apply Z-rotations on. """ raise NotImplementedError() @property def cost_2q_angles(self) -> np.ndarray: - """2D array with Z-rotation angles. + """2D array with ZZ-rotation angles. 1st index goes over the p and the 2nd index over the qubit - pairs, to apply Z-rotations on. These are needed by - ``qaoa.cost_function.make_qaoa_memory_map`` + pairs, to apply ZZ-rotations on. """ raise NotImplementedError() diff --git a/src/openqaoa-qiskit/openqaoa_qiskit/backends/qaoa_qiskit_qpu.py b/src/openqaoa-qiskit/openqaoa_qiskit/backends/qaoa_qiskit_qpu.py index 0a2e27cdb..874e1096b 100644 --- a/src/openqaoa-qiskit/openqaoa_qiskit/backends/qaoa_qiskit_qpu.py +++ b/src/openqaoa-qiskit/openqaoa_qiskit/backends/qaoa_qiskit_qpu.py @@ -105,9 +105,9 @@ def __init__( "Cannot attach a bigger circuit" "to the QAOA routine" ) - if self.device.provider_connected and self.device.qpu_connected: - self.backend_qpu = self.device.backend_device - elif self.device.provider_connected and self.device.qpu_connected in [ + # if self.device.provider_connected and self.device.qpu_connected: + # self.backend_qpu = self.device.backend_device + if self.device.provider_connected and self.device.qpu_connected in [ False, None, ]: @@ -117,7 +117,7 @@ def __init__( ) ) - else: + elif not (self.device.provider_connected and self.device.qpu_connected): raise Exception( "Error connecting to {}.".format(self.device.device_location.upper()) ) @@ -160,7 +160,7 @@ def qaoa_circuit(self, params: QAOAVariationalBaseParams) -> QuantumCircuit: if self.qaoa_descriptor.routed is True: transpiled_circuit = transpile( circuit_with_angles, - self.backend_qpu, + self.device.backend_device, initial_layout=self.initial_qubit_mapping, optimization_level=self.qiskit_optimziation_level, routing_method="none", @@ -168,7 +168,7 @@ def qaoa_circuit(self, params: QAOAVariationalBaseParams) -> QuantumCircuit: else: transpiled_circuit = transpile( circuit_with_angles, - self.backend_qpu, + self.device.backend_device, initial_layout=self.initial_qubit_mapping, optimization_level=self.qiskit_optimziation_level, ) @@ -241,7 +241,7 @@ def get_counts(self, params: QAOAVariationalBaseParams, n_shots=None) -> dict: # initial_layout only passed if not azure device # if type(self.device).__name__ == "DeviceAzure": # job = self.backend_qpu.run(circuit, input_items) - job = self.backend_qpu.run(circuit, shots=n_shots) + job = self.device.backend_device.run(circuit, shots=n_shots) api_contact = False no_of_api_retries = 0	Fix: Typo in docstring ## Description Fix docstring typo for the following in `variational_baseparams.py`: - cost_1q_angles - cost_2q_angles - mixer_2q_angles ## Checklist [//]: <> (- [ ] My code follows the style guidelines of this project) - [X] I have performed a self-review of my code. - [X] My changes generate no new warnings - [X] New and existing unit tests pass locally with my changes. [//]: <> (- [ ] Any dependent changes have been merged and published in downstream modules)		2023-10-25T03:21:19	[]	[]
entropicalabs/openqaoa	entropicalabs__openqaoa-284	750dce1cbc2614629db7a76d87b34d068357d77c	diff --git a/CHANGELOG.md b/CHANGELOG.md index 9eb1373d..1c41097f 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,3 +1,11 @@ +## Version v0.2.2 (September 20th, 2023) + +This version fixes the version of `pyquil` to prevent breaking changes occuring with v4.0.0. + +## Version v0.2.1 (September 11th, 2023) + +This version fixes an issue detecting the plugins when installing `openqaoa-core` from PyPI. + ## Version v0.2.0 (September 8th, 2023) This new version brings an import change to OpenQAOA, separately installable plugins to access diff --git a/_version.py b/_version.py index edbf19f9..b5fdc753 100644 --- a/_version.py +++ b/_version.py @@ -1,1 +1,1 @@ -__version__ = "0.2.0-rc1" +__version__ = "0.2.2" diff --git a/src/openqaoa-azure/openqaoa_azure/_version.py b/src/openqaoa-azure/openqaoa_azure/_version.py index edbf19f9..b5fdc753 100644 --- a/src/openqaoa-azure/openqaoa_azure/_version.py +++ b/src/openqaoa-azure/openqaoa_azure/_version.py @@ -1,1 +1,1 @@ -__version__ = "0.2.0-rc1" +__version__ = "0.2.2" diff --git a/src/openqaoa-braket/openqaoa_braket/_version.py b/src/openqaoa-braket/openqaoa_braket/_version.py index edbf19f9..b5fdc753 100644 --- a/src/openqaoa-braket/openqaoa_braket/_version.py +++ b/src/openqaoa-braket/openqaoa_braket/_version.py @@ -1,1 +1,1 @@ -__version__ = "0.2.0-rc1" +__version__ = "0.2.2" diff --git a/src/openqaoa-core/openqaoa/_version.py b/src/openqaoa-core/openqaoa/_version.py index edbf19f9..b5fdc753 100644 --- a/src/openqaoa-core/openqaoa/_version.py +++ b/src/openqaoa-core/openqaoa/_version.py @@ -1,1 +1,1 @@ -__version__ = "0.2.0-rc1" +__version__ = "0.2.2" diff --git a/src/openqaoa-core/openqaoa/backends/plugin_finder.py b/src/openqaoa-core/openqaoa/backends/plugin_finder.py index d0d9b7d5..fd5d1122 100644 --- a/src/openqaoa-core/openqaoa/backends/plugin_finder.py +++ b/src/openqaoa-core/openqaoa/backends/plugin_finder.py @@ -13,7 +13,7 @@ def plugin_finder_dict() -> list: if sys.version_info >= (3, 10): available_plugins = entry_points().select(group="openqaoa.plugins") else: - available_plugins = entry_points()["openqaoa.plugins"] + available_plugins = entry_points().get("openqaoa.plugins", []) output_dict = dict() for each_plugin_entry_point in available_plugins: diff --git a/src/openqaoa-core/openqaoa/utilities.py b/src/openqaoa-core/openqaoa/utilities.py index 262bee49..c42391a3 100644 --- a/src/openqaoa-core/openqaoa/utilities.py +++ b/src/openqaoa-core/openqaoa/utilities.py @@ -8,6 +8,7 @@ import numpy as np import uuid import matplotlib.pyplot as plt +from matplotlib import colormaps import networkx as nx import datetime @@ -424,7 +425,7 @@ def plot_graph(G: nx.Graph, ax=None, colormap="seismic") -> None: """ # Create plot figure - fig = plt.figure(figsize=(10, 6)) + fig, ax = plt.subplots(figsize=(10, 6), ncols=1) # Extract all graph attributes biases_and_nodes = nx.get_node_attributes(G, "weight") @@ -446,7 +447,7 @@ def plot_graph(G: nx.Graph, ax=None, colormap="seismic") -> None: cmap=cmap, norm=plt.Normalize(vmin=edge_vmin, vmax=edge_vmax) ) # Add colormap to plot - cbar = plt.colorbar(sm, pad=0.08) + cbar = plt.colorbar(sm, ax=ax, pad=0.08) cbar.ax.set_ylabel("Edge Weights", rotation=270, labelpad=15) else: weights = [1] * len(G.edges()) @@ -460,7 +461,7 @@ def plot_graph(G: nx.Graph, ax=None, colormap="seismic") -> None: vmin = min(biases) vmax = max(biases) sm2 = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=vmin, vmax=vmax)) - cbar2 = plt.colorbar(sm2, location="left") + cbar2 = plt.colorbar(sm2, ax=ax, location="left") cbar2.ax.set_ylabel("Single Qubit Biases", rotation=90) # Draw graph diff --git a/src/openqaoa-core/requirements.txt b/src/openqaoa-core/requirements.txt index 876a2b7d..2f114503 100644 --- a/src/openqaoa-core/requirements.txt +++ b/src/openqaoa-core/requirements.txt @@ -9,3 +9,4 @@ autograd>=1.4 semantic_version>=2.10 autoray>=0.3.1 requests +ipython>=8.2.0 diff --git a/src/openqaoa-pyquil/openqaoa_pyquil/_version.py b/src/openqaoa-pyquil/openqaoa_pyquil/_version.py index edbf19f9..b5fdc753 100644 --- a/src/openqaoa-pyquil/openqaoa_pyquil/_version.py +++ b/src/openqaoa-pyquil/openqaoa_pyquil/_version.py @@ -1,1 +1,1 @@ -__version__ = "0.2.0-rc1" +__version__ = "0.2.2" diff --git a/src/openqaoa-pyquil/requirements.txt b/src/openqaoa-pyquil/requirements.txt index cde21f45..19adc00d 100644 --- a/src/openqaoa-pyquil/requirements.txt +++ b/src/openqaoa-pyquil/requirements.txt @@ -1,1 +1,1 @@ -pyquil>=3.1.0 \ No newline at end of file +pyquil>=3.1.0,<4.0.0 \ No newline at end of file diff --git a/src/openqaoa-qiskit/openqaoa_qiskit/_version.py b/src/openqaoa-qiskit/openqaoa_qiskit/_version.py index edbf19f9..b5fdc753 100644 --- a/src/openqaoa-qiskit/openqaoa_qiskit/_version.py +++ b/src/openqaoa-qiskit/openqaoa_qiskit/_version.py @@ -1,1 +1,1 @@ -__version__ = "0.2.0-rc1" +__version__ = "0.2.2"	Temporary fix for pyquil breaking changes ## Description - Temporary ignore breaking changes of pyquil 4.0.0		2023-09-19T07:46:08	[]	[]
entropicalabs/openqaoa	entropicalabs__openqaoa-241	d82f2a700f00d92f384a6e1af9db7781061f37b1	diff --git a/src/openqaoa-core/qaoa_components/ansatz_constructor/operators.py b/src/openqaoa-core/qaoa_components/ansatz_constructor/operators.py index 113efb0a1..08871517b 100644 --- a/src/openqaoa-core/qaoa_components/ansatz_constructor/operators.py +++ b/src/openqaoa-core/qaoa_components/ansatz_constructor/operators.py @@ -5,6 +5,9 @@ from typing import List, Union, Tuple from sympy import Symbol import numpy as np +from scipy.sparse import kron as sparse_kron +from functools import reduce +from scipy.sparse import csr_matrix Identity = np.array(([1, 0], [0, 1]), dtype=complex) PauliX = np.array(([0, 1], [1, 0]), dtype=complex) @@ -686,3 +689,45 @@ def classical_hamiltonian( raise ValueError("Hamiltonian only supports Linear and Quadratic terms") return cls(pauli_ops, pauli_coeffs, constant) + + @property + def as_matrix(self): + """ + Build sparse matrix of Hamiltonian. Note this includes self.constant value. + + Returns + ------- + H_mat: + sparse matrix of Hamiltonian. + """ + + if self.n_qubits>20: + raise ValueError('number of qubits exceeds maximum of 20') + + p_mat_dict = {'X': csr_matrix([[0, 1], + [1, 0]]), + 'Y': csr_matrix([[0., -1j], + [1j, 0.]]), + 'Z': csr_matrix([[1, 0], + [0, -1]]), + 'I': csr_matrix([[1, 0], [0, 1]])} + + converter = lambda s: p_mat_dict[s] + + # vectorized function to map single qubit pauli strings to sparse matrix + vfunc = np.vectorize(converter) + + base = np.array(['I' for _ in range(self.n_qubits)]) + + H_mat = reduce(sparse_kron, vfunc(base)) * self.constant + for P, coeff in zip(self.terms, self.coeffs): + temp = base.copy() + + # replace single qubit pauli I in base term + # with single qubit pauli operators according to qubit_indices and pauli_strs in P + temp[np.array(P.qubit_indices)] = np.array(list(P.pauli_str)) + + # take kronecker product of sparse list of matrices + H_mat += reduce(sparse_kron, vfunc(temp)) * coeff + + return H_mat \ No newline at end of file	Implement `as_matrix` method for the `Hamiltonian` class ### Prerequisites Before raising this feature request, I made sure: - [x] It does not lead to duplication. - [x] It has not already been requested. ### Is your feature request related to a problem? Please describe. The class `Hamiltonian` in OpenQAOA defined in `src/openqaoa-src/openqaoa/qaoa_components/ansatz_constructor/operators.py` is responsible for constructing the `Cost` and `Mixer` Hamiltonians in QAOA. It fundamentally describes the Hamiltonian object via list of `PauliOp`s defined on a set of qubits and a corresponding list of their coefficients. Implicitly, the definition of the Hamiltonian is then assumed as follows: $$H = \sum_i \alpha_{i} P_i + \sum_{ij} J_{ij} P_{ij},$$ where $\ P_i, P_{ij}$ are one-qubit and two-qubit `PauliOp`s respectively. Implement a method in this class that returns the matrix representation of the `Hamiltonian` object. This will be a $2^n x 2^n$ dimensional sum over all Pauli entries as shown above. ### Describe the solution you'd like - Implement the method - Use numpy sparse matrix where relevant - Implement checks preventing users from creating matrices beyond 20 qubits to prevent memory overflow - Bonus task: If the Hamiltonian can be expressed as a sparse matrix, shift the qubit restriction to a higher number depending on the sparsity of the matrix ```{python} class Hamiltonian: def as_matrix(self): "Implements a numpy array (sparse where relevant) representation of the Hamiltonian object." #TODO return mat ```	Hello @vishal-ph! I am new to OpenQAOA and woud like to implement this functionality. Could you please assign it to me and if possible show any relavant literature to show matrix rep? @manulpatel, thanks for taking the initiative! In OpenQAOA, the Hamiltonian object is a sum over the terms of the Pauli and Identity operators set tensored over n-qubits. The goal is to represent the sum of these terms in a matrix form. To get started, you may want to start looking at the matrix representation of the Pauli operators here https://en.wikipedia.org/wiki/Pauli_matrices and think about their tensor products.	2023-05-28T15:21:55	[]	[]
entropicalabs/openqaoa	entropicalabs__openqaoa-192	ad5e943499211b61e5a03aa53685d849aee4c6f2	diff --git a/docs/source/index.rst b/docs/source/index.rst index 1eb621726..e020290b5 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -247,6 +247,7 @@ Contents notebooks/11_Mixer_example.ipynb notebooks/12_testing_azure.ipynb notebooks/13_optimizers.ipynb + notebooks/14_qaoa_benchmark.ipynb notebooks/X_dumping_data.ipynb diff --git a/examples/13_optimizers.ipynb b/examples/13_optimizers.ipynb index 3c9c5cd04..3b806e532 100644 --- a/examples/13_optimizers.ipynb +++ b/examples/13_optimizers.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# X - QAOA parameters optimization with different optimization algorithms\n", + "# 13 - QAOA parameters optimization with different optimization algorithms\n", "\n", "This notebook will provide a brief overview of all the optimizers available in OpenQAOA for optimizing the QAOA parameters. If you would like to learn more about what an optimizer is and obtain a more comprehensive explanation of all the methods implemented in the library, please refer to our documentation on this topic at https://openqaoa.entropicalabs.com/optimizers/shot-adaptive-optimizers/." ] diff --git a/examples/14_qaoa_benchmark.ipynb b/examples/14_qaoa_benchmark.ipynb new file mode 100644 index 000000000..a9a5a3330 --- /dev/null +++ b/examples/14_qaoa_benchmark.ipynb @@ -0,0 +1,744 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 14 - QAOA Benchmark\n", + "\n", + "This notebook will provide an overview of QAOA Benchmarks." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create a problem instance\n", + "We begin by creating a problem instance for a simple `MaximumCut` problem for a random graph created using the `random_instance` method of the class." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from openqaoa.problems import MaximumCut\n", + "\n", + "# Use the MinimumVertexCover class to instantiate the problem, and get the QUBO representation of the problem\n", + "prob = MaximumCut.random_instance(n_nodes=7, edge_probability=0.9, seed=10)\n", + "qubo = prob.qubo" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create a QAOA object with the device to benchmark" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then, we create the `QAOA` object with the device that we want to benchmark" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "qpu_credentials ={\n", + " \"hub\": \"ibm-q\",\n", + " \"group\": \"open\", \n", + " \"project\": \"main\"\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from openqaoa import QAOA, create_device\n", + "\n", + "# create the QAOA object\n", + "q = QAOA()\n", + "\n", + "# set device and backend properties\n", + "qiskit_cloud = create_device(location='ibmq', name='ibm_oslo', qpu_credentials, as_emulator=True)\n", + "q.set_device(qiskit_cloud)\n", + "q.set_backend_properties(n_shots=1000)\n", + "\n", + "# set properties\n", + "q.set_circuit_properties(p=1, param_type='standard')\n", + "\n", + "# compile with the problem\n", + "q.compile(qubo)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the code above, one can see that the `.set_classical_optimizer` method has not been used. This is because the benchmark process does not use the optimization loop." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Benchmark" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we create the benchmark object. This object has different methods that will allow us to benchmark the corresponding device and to plot the values taken." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from openqaoa import QAOABenchmark\n", + "\n", + "# create the QAOABenchmark object, passing the QAOA object to benchmark as an argument\n", + "benchmark = QAOABenchmark(q)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When initializing the object two attributes are set: `.qaoa` and `.reference`.\n", + "- `.qaoa` will be the QAOA object passed, the object to be benchmarked.\n", + "- `.reference` will be a QAOA object with the same circuit properties as `.qaoa` but with an `analytical_simulator` backend. It will also be compiled with the same problem. This object will be the reference for the `.qaoa` object to be compared with." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".qaoa: <class 'openqaoa.algorithms.qaoa.qaoa_workflow.QAOA'> ,\t\t .qaoa.backend: <class 'openqaoa_qiskit.backends.qaoa_qiskit_qpu.QAOAQiskitQPUBackend'>\n", + ".reference: <class 'openqaoa.algorithms.qaoa.qaoa_workflow.QAOA'> ,\t .reference.backend: <class 'openqaoa.backends.qaoa_analytical_sim.QAOABackendAnalyticalSimulator'>\n" + ] + } + ], + "source": [ + "print('.qaoa:', type(benchmark.qaoa), ',\\t\\t .qaoa.backend:', type(benchmark.qaoa.backend))\n", + "print('.reference:', type(benchmark.reference), ',\\t .reference.backend:', type(benchmark.reference.backend))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run Benchmark" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `.run` method of the `QAOABenchmark` object will run the benchmark. \n", + "\n", + "It evaluates the QAOA circuit of the benchmarked object for a given number of points and ranges per parameter. <br>\n", + "It also evaluates the QAOA circuit of the reference object with the same given points.\n", + "\n", + "It has two required positional arguments: `n_points_axis` and `ranges`. \n", + "- `n_points_axis`: The number of points per axis\n", + "- `ranges`: The sweep ranges of the parameters. The expected format is a list of tuples: (min, max) or (value,). One tuple per parameter." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting the main plot with the following parameters:\n", + "\tParameter 0: 0 to 1.57, with 33 values\n", + "\tParameter 1: 0 to 1.57, with 33 values\n" + ] + }, + { + "data": { + "image/png": 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E1m6tWXgOf0BU3BaI4okCh+z+U+I2y9pgF+bvAYBAZ9njnppP+D0mbHMgRfYry+aX1QcAVIQfUt2AOR0UztSAZPTo0VAnmGSzbNmyE+4/c+ZMzJw509hKERERWUjQoKdsgu0csom1DjOHhIiIKBHpymZIj4wuTfseZ+xzIiIiItOxh4SIiMjCOGRDREREptNhzBMy8im+8cUhGyIiIjIde0iIiIgszLiF0azdB8GAhIiIyMKMy2Vj7YDE2rUjIiKihMAeEiIiIgvToUGHEZNauXR8hxfNUvBSzRnCJZGFq0DbhUvTa1HM6HY2yJaydh0WLjPtkHXoOevlS2Ur4VLtjqPCc+ixXfIfAAKdk0XlHYeOyk4g7VcVLg8ezT7BLimyw3ulHyB5G6RL7AdT3aLyNuHS8VoUS8c7jnhF5X2nJYnK24RLzdul1y2Kp1wDyZEt+R/0x2+AgUM2RERERHHCHhIiIiILM25hNGv3QTAgISIisjBdadCNWBjNgGPEkrXDJSIiIkoI7CEhIiKyMN2gIRsujEZERERR05UNugFPyBhxjFiydu2IiIgoIbCHhIiIyMKC0BA0YFEzI44RSwxIiIiILIxDNkRERERxwh4SIiIiCwvCmOEWefKA+ErYgCTotkFzRtZB5Dkgy+fQ2NMjrk/SAb+ofNATWb6F46Tr4bgOy/JkAMfeUwk9VXb7uQ/6ROVtwtw6ABD0yOokfV/tPmGdZKk+ju3iEt4bwjbb6mS5b1SyLEfLsZ1kSUiiydMiEUxxifdRdtnnweYV3hvC3E6BNHkbghF+R4YIcy/pwuPrztgeHwDsEebXsQWjSJQTJQ7ZEBEREcVJwvaQEBERdQSJku2XAQkREZGFKWjQDZhDoiz+2K+1wyUiIiJKCOwhISIisjAO2RAREZHpdKVBlz7S18ZxrMza4RIRERElBPaQEBERWVgQNgQN6D8w4hixxICEiIjIwjhkQ0RERBQn7CEhIiKyMB026Ab0HxhxjFhK2IDE7tVhD0aWs8CbIcvF4flBnkMlkCLMPyLMGWHzy/IuSHPlAIAS7mLzyerU3FWWi8NzQJ5rwn5UllPI10WYt0iTXTdHXbPs+ABsAVkCHE2YX8efmSo7vh7FdWiSXQd/lyRReUeDLC+StD4AEEx2iveREOcsEn5nAIDdL7uXdGF+HVuE38Gh4ztlbQ4kydtsi/BSx3P0I6g0BA04oRHHiCVrh0tERESUEBK2h4SIiKgjSJRJrQxIiIiILEwpG3QDVllVFl+p1dq1IyIiooRgakCyceNGFBQUoEePHtA0DW+++eYJy7/++usYM2YMunXrhrS0NOTl5WHt2rXxqSwREZEJgtAMe1mZqQFJY2MjcnNzsXDhwojKb9y4EWPGjMHq1atRUVGBiy++GAUFBdi6dWuMa0pERGQOXf17Hkn7Xma35MRMnUMybtw4jBs3LuLyCxYsCPt5zpw5+Otf/4q3334bQ4cObXUfr9cLr9cb+rmuri6quhIREVHsdOg5JLquo76+HhkZGW2WKSsrQ3p6euiVk5MTxxoSERG1j/7jpFYjXlZm7dqdxOOPP46GhgZcc801bZaZPn06jhw5Enrt3r07jjUkIiJqHx2aYS8r67CP/b788suYNWsW/vrXvyIzM7PNcm63G263bKVVIiIiiq8OGZAsX74cN998M1577TXk5+dHdYyg2wbNGVkHkXTpa03JZw5Jl3YHhEvBu2SdYfbmoKg8AOhu2Tk8tbJl0YMe2e2q7PIOQCVcmtrRJFt2Xbrcd3P3TqLyACDtlfX4ZddadwuXLLfL/yqTp0aQLUEeSJWlIYiGzSerk6+L7A8nTfiV4UuNJh2E7Dp4amVL7Nulnx/hZ9rZILsGAOBoivDzEJB/R0YrUZaO73ABySuvvIIbb7wRy5cvx+WXX252dYiIiGLKqPkfVp9DYmpA0tDQgO3bt4d+3rlzJyorK5GRkYFevXph+vTp2LNnD1544QUAx4ZpCgsL8cQTT2DkyJGorq4GACQlJSE9Pd2UNhAREVH7mRoubd68GUOHDg09sltcXIyhQ4eipKQEALBv3z5UVVWFyj/zzDMIBAKYOnUqunfvHnrdcccdptSfiIgo1nQYsQYJJ7We0OjRo6FOMN9i2bJlYT9v2LAhthUiIiKyGGXQEzLK4gGJtQeUiIiIKCF0uEmtREREieT4kIsRx7Ey9pAQERFZmFkrtT711FMYPHgw0tLSQglt/+///b8xaiUDEiIiImrF6aefjrlz56KiogKbN2/GJZdcgiuvvBL/+Mc/YnI+DtkQERFZmFlDNgUFBWE/z549G0899RQ++eQT/OIXv2h3fX6OAQkREZGFGZWH5vgxfp71PpIUK8FgEK+99hoaGxuRl5fX7rq0hkM2RERECSQnJwfp6emhV1lZWZtlv/jiC3Tq1AlutxtTpkzBG2+8gYEDB8akXgnbQ6Lpx16RkObJsHmjyAMjzBmhHLJYUp6PR1T82D4B2U7NmR5ReWeDLO9FwBNF7g6bMF+OQ3bdpO9RNNfBWS97n4JJTlF5bxfZe2RvjqIR0q8m4f0t/fwo+a0Eh7DZNuG9YQvIvpfcQfl1kI4SBD2y9zWQkiQqL80P5P5Bli8LAPydI8spFAxEcVNEyeghm927dyMtLS20/US9I2effTYqKytx5MgRrFy5EoWFhfjwww9jEpQkbEBCRETUERgdkBx/aiYSLpcL/fv3BwAMGzYMn332GZ544gk8/fTT7a7Pz3HIhoiIiCKi6zq8Xm9Mjs0eEiIiIgsz6ymb6dOnY9y4cejVqxfq6+vx8ssvY8OGDVi7dm2769IaBiRERETUwv79+zFx4kTs27cP6enpGDx4MNauXYsxY8bE5HwMSIiIiCzMrB6SxYsXt/ucEgxIiIiILEwBBmX7tTZOaiUiIiLTsYeEiIjIwhIl2y8DEiIiIgtLlICEQzZERERkOvaQEBERWVii9JAkbEBi8+uwqcjyIihhP5L3tMhyIfyUo0mW/ybgkd1YriPC/CYueeeZ3SvLMxEUdtD5UmU5V6S5QQAgkCKrk/QcyibMWSTMcQQAmi7LsRFIjm1HqV/4ngLy3EvKLsyhIvz86M7Yf5EHhHlg7D7ZexR0y9sQdMn2cTbJvgMcwvLSnF8Qft6AyPNNSfNStUeiBCQcsiEiIiLTJWwPCRERUUeglAZlQO+GEceIJQYkREREFqZDM2RhNCOOEUscsiEiIiLTsYeEiIjIwhJlUisDEiIiIgtLlDkkHLIhIiIi07GHhIiIyMI4ZENERESm45ANERERUZwkbA+Jv5MdyhnZEtv2ZtnyxlowiiXLk2XLfWuylebRlOkSlXcI2wwAQenS19L3VbicuL9TFEuWC99X6VLW0qXgpWkLAEDvLPtYB4RLigeSRMWRfEB+LzX0kLUh6QfZOZqyZJ+35P3yNkiXgpemIZAu6y69twH58vSQFhcu7e44KmuEN0OexiPSNAEBv+weag9l0JCN1XtIEjYgISIi6ggUAGVA6pz4Zd+JDodsiIiIyHTsISEiIrIwHRq0BFg6ngEJERGRhfEpGyIiIqI4YQ8JERGRhelKg8aF0YiIiMhMShn0lI3FH7Mxdchm48aNKCgoQI8ePaBpGt58882T7rNhwwacd955cLvd6N+/P5YtWxbzehIREVFsmRqQNDY2Ijc3FwsXLoyo/M6dO3H55Zfj4osvRmVlJe68807cfPPNWLt2bYxrSkREZI7jk1qNeFmZqUM248aNw7hx4yIuv2jRIvTt2xfz5s0DAJxzzjn46KOP8Mc//hFjx45tdR+v1wuv1xv6ua6urn2VJiIiiiM+ZWNB5eXlyM/PD9s2duxYlJeXt7lPWVkZ0tPTQ6+cnJxYV5OIiIiEOtSk1urqamRlZYVty8rKQl1dHY4ePYqkpJZJNqZPn47i4uLQz3V1dcjJyYGzMQiHI8K8CNKJQMKcFIA8D0zQHds8GU2Z8jwNSbWyfB9Huwpzrnhk0b0uS98DALD5ZeU1YYoT3Skr70uL4i8a4e0nbYOrXnaCw2fI7yUl/GYKumXnsAlztEjvvWj2kb6vjubY5r4BAFeDLHeM7pCdw+aX3Xzi7z2fPAdRpAmkbP74zRDlUzanCLfbDbdbnmCJiIjICviUjQVlZ2ejpqYmbFtNTQ3S0tJa7R0hIiKijqFD9ZDk5eVh9erVYdvWrVuHvLw8k2pEREQUW8d6SIyY1GpAZWLI1B6ShoYGVFZWorKyEsCxx3orKytRVVUF4Nj8j4kTJ4bKT5kyBTt27MA999yDr7/+Gk8++SReffVV3HXXXWZUn4iIKOYS5bFfUwOSzZs3Y+jQoRg6dCgAoLi4GEOHDkVJSQkAYN++faHgBAD69u2LVatWYd26dcjNzcW8efPw3HPPtfnILxEREXUMpg7ZjB49GuoEfUitrcI6evRobN26NYa1IiIisg4F+cOebR3HyjrUHBIiIqJEw4XRiIiIiOKEPSRERERWliBjNgxIiIiIrMyoJ2Q4ZENERER0Yob1kNTU1ODpp58OPbJrdb5UB3RnZM2X5oGJplvMlyqLDZMOyJKuNGXJkqjYfKLiAAB/ijCPRUB4AmH4HHTL/xrwdpaV96XLLnbKHlmdmrvKbyZnvewcmixdCeozZOWd9bLy0QgIF2rWhPdGIEV2fADi7wF/p9h+fqS5pgDAlyrLEeSql91M/k6y4zuFx0cUHQJahNct0nJG4NLxQtXV1Zg1a5ZRhyMiIiIkzsJoEfeQ/L//9/9O+P/btm1rd2WIiIgoMUUckAwZMgSaprW6kNnx7Zpm7eiLiIiow1GaMRNST5UekoyMDDz66KO49NJLW/3/f/zjHygoKDCsYkRERJQ4c0giDkiGDRuGvXv3onfv3q3+/+HDh0+4DDwRERFRWyIOSKZMmYLGxsY2/79Xr15YunSpIZUiIiKiH3FhtHBXXXXVCf+/S5cuKCwsbHeFiIiI6N+Yy4aIiIgoTrh0PBERkdVZfLjFCAxIiIiILCxRhmwSNiBx1QXgcES49nIcrqESXgnlkI22KZusEUq2ojMAwO8RLsedLCzvERWXL00PQJetsA+bbAV/1J0tW/paOeXLfQc9wovXWZgnoF74JkUxMhz0yP4c1ALC5fJFpQF7UxRfAsJmew7Iyivh8X2p8jY4Gyz2Z7mwCTavcKl5AEF3ZG+s9P2nkxO9pYFAAA8++CD+9a9/xao+RERE9FPKwJeFiQISh8OBxx57DIFAFH96EhERURQ0A1/WJe50uuSSS/Dhhx/Goi5ERERkAWVlZTj//PORmpqKzMxMjB8/PuY568RzSMaNG4dp06bhiy++wLBhw5CSEp6X+4orrjCsckRERAnPhIXRPvzwQ0ydOhXnn38+AoEA7rvvPvz617/GP//5zxa/940iDkhuv/12AMD8+fNb/J+maQgG5ZOIiIiIqA0mBCRr1qwJ+3nZsmXIzMxERUUF/tf/+l8GVKYlcUCi6/JZ/0RERGQNdXV1YT+73W643e4T7nPkyBEAxxLtxkq7Hlxqbm42qh5ERETUGqUZ9wKQk5OD9PT00KusrOyEp9d1HXfeeScuvPBCDBo0KGbNFPeQBINBzJkzB4sWLUJNTQ2++eYb9OvXDzNmzECfPn1w0003xaKeRERECUmpYy8jjgMAu3fvRlpaWmj7yXpHpk6dii+//BIfffRR+ytxAuIektmzZ2PZsmV49NFH4XK5QtsHDRqE5557ztDKERERkbHS0tLCXicKSIqKivDOO+/ggw8+wOmnnx7TeokDkhdeeAHPPPMMrr/+etjt/14RMjc3F19//bWhlSMiIkp4JiyMppRCUVER3njjDbz//vvo27evUa1pk3jIZs+ePejfv3+L7bquw+8XrqNNREREJ/aT+R/tPk6Epk6dipdffhl//etfkZqaiurqagBAeno6kpKS2l+XVogDkoEDB+Jvf/sbevfuHbZ95cqVGDp0qGEVi7WgxwbNGWHOAk2YcyVJfuPYhE9LN2XK8pVI8y5I88wAgM0vG+TUY5xJqbmrfNA1mCx7iszWLMwp5BDWKYrvIJUkvJkCsjZonWQrNfui+TzUy26OYLqsTs4fhMdPkt9LjgZZu49myo6fvE+Y7yeKByTF+wjL6w7ZeyQtbxPmOAIiz/slzQ/W0Tz11FMAgNGjR4dtX7p0KSZNmhSTc4p/JZSUlKCwsBB79uyBrut4/fXXsW3bNrzwwgt45513YlFHIiKihKWpYy8jjhMpZcQsWiHxHJIrr7wSb7/9Nt577z2kpKSgpKQEX331Fd5++22MGTMmFnUkIiJKXAmSXC+qTvNRo0Zh3bp1RteFiIiIEpS4h6Rfv3744YcfWmw/fPgw+vXrZ0iliIiI6EcGL4xmVeIekl27drWar8br9WLPnj2GVIqIiIh+ZEIuGzNEHJC89dZboX+vXbsW6enpoZ+DwSDWr1+PPn36GFo5IiIiSgwRByTjx48HcCyjb2FhYdj/OZ1O9OnTB/PmzTO0ckRERAmPPSThjmf57du3Lz777DN07do1ZpUiIiKiHzEgad3OnTtD/25ubobH4zG0QkRERJR4xE/Z6LqOhx56CD179kSnTp2wY8cOAMCMGTOwePFicQUWLlyIPn36wOPxYOTIkfj0009PWH7BggU4++yzkZSUhJycHNx1111obm4Wn5eIiKhDSJCnbMQBycMPP2xYtt8VK1aguLgYpaWl2LJlC3JzczF27Fjs37+/1fIvv/wypk2bhtLSUnz11VdYvHgxVqxYgfvuu0/aDCIiog7h+EqtRrysTDxkczzb76WXXoopU6aEtkeT7Xf+/Pm45ZZbMHnyZADAokWLsGrVKixZsgTTpk1rUf7jjz/GhRdeiOuuuw4A0KdPH1x77bX4+9//Lm0G7M067IHIEi94M2Rvk/uIMJcIgIYeTtk56mRJIwIeYT4eWWoQAPJ8OXZhx5ZNeLd6pXljIM81Iy2veYT3RqN87UJ7F6+ofKDOdfJCP9Us/CvLJU+iIv1DTmuS5XYKdJLVyVEv/tsNdtllgLNJVt7bRfYmeX6Qfx6kv8CaM2TXwdEsO0EwSZg7Kop8M+6DkV04e0B4gemkxJ8yo7L9+nw+VFRUID8//9+VsdmQn5+P8vLyVve54IILUFFRERrW2bFjB1avXo3LLruszfN4vV7U1dWFvYiIiDoMLh3fOqOy/dbW1iIYDCIrKytse1ZWVps9Lddddx1qa2vxq1/9CkopBAIBTJky5YRDNmVlZZg1a1bE9SIiIqL461DZfjds2IA5c+bgySefxMiRI7F9+3bccccdeOihhzBjxoxW95k+fTqKi4tDP9fV1SEnJyem9SQiIiIZcUByPNvvgw8+GMr2e95554mz/Xbt2hV2ux01NTVh22tqapCdnd3qPjNmzMANN9yAm2++GQBw7rnnorGxEbfeeivuv/9+2GwtR6DcbjfcbreghURERNahwZgJqdZ+xsbEbL8ulwvDhg3D+vXrQ6vA6rqO9evXo6ioqNV9mpqaWgQddvuxSVRKWXxwjIiIKBpGPbJr8cd+owpIjmtoaAit4HpcWlpaxPsXFxejsLAQw4cPx4gRI7BgwQI0NjaGnrqZOHEievbsibKyMgBAQUEB5s+fj6FDh4aGbGbMmIGCgoJQYEJEREQdT1QrtRYVFWHDhg1hC5IppaBpWquZgNsyYcIEHDhwACUlJaiursaQIUOwZs2a0ETXqqqqsB6RBx54AJqm4YEHHsCePXvQrVs3FBQUYPbs2dJmEBERdQxcOr51v/3tb6GUwpIlS5CVlQVNa18XUFFRUZtDNBs2bAj72eFwoLS0FKWlpe06JxERUYfBgKR1n3/+OSoqKnD22WfHoj5ERESUgMQLo51//vnYvXt3LOpCREREP8Ol49vw3HPPYcqUKdizZw8GDRoEpzN8yfPBgwcbVrlYUrbIlzrXhCtf6075MFbKvshXuQUAbxfZpZO2wRbF0vG+NFm7deGK5VI2n/w6qFYeHT8RPUn4xgqXOLf55W3QdeE+wi8pJfxWs9XLJ5zrHtn7avPKrpvtqPA6u+Tf5IEU2XVwNMqO7zoiKy/9DgCAoPC7LOmg7IsjIFwK3n40ikYI+VMjS+MRCMhThESNQzatO3DgAL777rvQkzAAoGlaVJNaiYiIiIAoApIbb7wRQ4cOxSuvvGLIpFYiIiI6AfaQtO7777/HW2+91WqCPSIiIjKWUfM/rD6HRDyp9ZJLLsHnn38ei7oQERFRghL3kBQUFOCuu+7CF198gXPPPbfFpNYrrrjCsMoRERElPC4d37opU6YAAB588MEW/8dJrURERAbjHJLW/Tx3DREREVF7tSu5HhEREcVWokxqjSogaWxsxIcffoiqqir4fL6w//v9739vSMWIiIgIHLJpy9atW3HZZZehqakJjY2NyMjIQG1tLZKTk5GZmcmAhIiIiMTEj/3eddddKCgowKFDh5CUlIRPPvkE33//PYYNG4bHH388FnUkIiJKXEblsTnVekgqKyvx9NNPw2azwW63w+v1ol+/fnj00UdRWFiIq6++Ohb1NFwg2Q44I8uxoQWlyT7k9VE22eNY0rwU3lTZ8Z1Nsb9zgx5ZnZqyZXXSosjHE/MPrFN2AqVHUaFDwiRBLtnNpLll5VVyFE/eBWX3hhKW17zCPDP14r/doAmbrYQpf2zC76VIc3f9lCa8/7zpskY4moVtcMium80v//xE/l0cx0doE2TIRnyLOp1O2H5MQJaZmYmqqioAQHp6OrMAExERUVTEPSRDhw7FZ599hjPPPBMXXXQRSkpKUFtbixdffBGDBg2KRR2JiIgSF3tIWjdnzhx0794dADB79mx06dIFt912Gw4cOIBnnnnG8AoSERElMiPmjxj16HAsiXpIlFLIzMwM9YRkZmZizZo1MakYERERJQ5RD4lSCv379+dcESIiIjKUKCCx2Ww488wz8cMPP8SqPkRERPRTysCXhYnnkMydOxd33303vvzyy1jUh4iIiBKQ+CmbiRMnoqmpCbm5uXC5XEhKSgr7/4MHDxpWOSIiokTHXDZtWLBgQQyqQURERG2yeDBhBHFAUlhYGIt6EBERUQKLKtvvcc3NzS2y/aalpbWrQkRERPQTCbIwmjggaWxsxL333otXX3211adtgsEo8laYwBZQsEU4oBbrPDMAoDuFORoCsjsr+QdZpRqzhIk1IM+V4U2Xlbcflb1HQU8Unz5hG1w/yN4n/+le2Ql88gQkyh7bbx3VLGuzlixPKqQdcYrKq06ycwSk1/mQ/PMQ9Ah38MuK63ZhLpVoUq8IJx04jsY275f0u1iP4k9uuzey70pbIIov+iglyhwS8bfdPffcg/fffx9PPfUU3G43nnvuOcyaNQs9evTACy+8EIs6EhER0SlOHD++/fbbeOGFFzB69GhMnjwZo0aNQv/+/dG7d2+89NJLuP7662NRTyIiosSUIEM24h6SgwcPol+/fgCOzRc5/pjvr371K2zcuNHY2hERESW4RMllIw5I+vXrh507dwIABgwYgFdffRXAsZ6Tzp07G1o5IiIiSgzigGTy5Mn4/PPPAQDTpk3DwoUL4fF4cNddd+Huu+82vIJEREQJLUGWjhfPIbnrrrtC/87Pz8fXX3+NiooK9O/fH4MHDza0ckRERAkvQeaQRByQ6LqOxx57DG+99RZ8Ph8uvfRSlJaWonfv3ujdu3cs60hERESnuIiHbGbPno377rsPnTp1Qs+ePfHEE09g6tSpsawbERFRwuOk1p954YUX8OSTT2Lt2rV488038fbbb+Oll16CrsdvcRgiIqKEY9Icko0bN6KgoAA9evSApml48803DWhM2yIOSKqqqnDZZZeFfs7Pz4emadi7d29MKkZERETmaWxsRG5uLhYuXBiX80U8hyQQCMDjCV8L2el0wu8XrndsEc46PxyOyJaDtvlky+F7u0nXjAYcTbJzNGfI5iMHk2UPVDkb5X17gSTZss5238nL/JR0GWglX+0bunAJcq1OuMS5cNl1e2fhmwQg2CR7o5ypsuXs/UfcovIqGMWa5cLrYDsia7Nyy+7vo93lPcHJ/5J95qRLzesuWXnxsu4AvJ1lbdCEPebSNBuRLuse7fEBIOiOrM1BmzytQ9RMmtQ6btw4jBs3zoATRybiT7FSCpMmTYLb/e8vo+bmZkyZMgUpKSmhba+//rqoAgsXLsRjjz2G6upq5Obm4s9//jNGjBjRZvnDhw/j/vvvx+uvv46DBw+id+/eWLBgQVjvDRER0anC6Fw2dXV1YdvdbnfY73azRByQFBYWttj229/+tl0nX7FiBYqLi7Fo0SKMHDkSCxYswNixY7Ft2zZkZma2KO/z+TBmzBhkZmZi5cqV6NmzJ77//nsuyEZERBShnJycsJ9LS0sxc+ZMcyrzExEHJEuXLjX85PPnz8ctt9yCyZMnAwAWLVqEVatWYcmSJZg2bVqL8kuWLMHBgwfx8ccfw+k81lXep08fw+tFRERkGQYP2ezevRtpaWmhzVboHQGiWKnVKD6fDxUVFcjPz/93ZWw25Ofno7y8vNV93nrrLeTl5WHq1KnIysrCoEGDMGfOHASDbc+/8Hq9qKurC3sRERF1FEY/9puWlhb2SviApLa2FsFgEFlZWWHbs7KyUF1d3eo+O3bswMqVKxEMBrF69WrMmDED8+bNw8MPP9zmecrKypCenh56/byrioiIiMwnXjreTLquIzMzE8888wzsdjuGDRuGPXv24LHHHkNpaWmr+0yfPh3FxcWhn+vq6hiUEBFRx2HSUzYNDQ3Yvn176OedO3eisrISGRkZ6NWrlwEVCmdaQNK1a1fY7XbU1NSEba+pqUF2dnar+3Tv3h1OpxN2+78fnTznnHNQXV0Nn88Hl6vlc3BWmT1MREQUFZMCks2bN+Piiy8O/Xz8j/vCwkIsW7bMgAqFM23IxuVyYdiwYVi/fn1om67rWL9+PfLy8lrd58ILL8T27dvDVof95ptv0L1791aDESIiIorO6NGjoZRq8YpFMAKYGJAAx6KtZ599Fs8//zy++uor3HbbbWhsbAw9dTNx4kRMnz49VP62227DwYMHcccdd+Cbb77BqlWrMGfOHObUISKiU5Zm4MvKTJ1DMmHCBBw4cAAlJSWorq7GkCFDsGbNmtBE16qqKth+shpeTk4O1q5di7vuuguDBw9Gz549cccdd+Dee+81qwlERESxZdKQTbyZPqm1qKgIRUVFrf7fhg0bWmzLy8vDJ598EuNaERERUTyZHpCYJZjsgOaIrPm6S5gUJYooVHfIOtOcTbIkDQGP9S6146isvLezrHwgVZ7Iwn5Y9j55s2U5V2CT3RxaHPKFpyTJ8uV40htF5WsOpIvKA4BHmF9HT5V9frxHpPmm5KPbjX1l+ak81bLvGV/aycv8lO6Sd9g764X3q/AjF3TK6iRtgyb8eAKALRBZm6PJkxMto5eOtyrr/ZYiIiKif0uQIRtTJ7USERERAewhISIisj6L924YgQEJERGRhSXKHBIO2RAREZHp2ENCRERkZQkyqZUBCRERkYVxyIaIiIgoTthDQkREZGUcsiEiIiKzcciGiIiIKE4St4dEV8deEdCULKz0pQpz3wDw/OAXlfd3kp1Di7Ct/y4vKg4AUD5hXgqHMFzXZMf37JdfB38nYZ2CwvwgwjandhIm/AGQnFEnKu8NyL4Gmn1OUfnTMhpE5QHg7Iz9ovLbDmaKyrtcsiQnzc2yNgNAwCfMi+SV3Ut2aXnh5xMAgh7ZPs1dZMd3Cm8Nf5Lsb+jk/bLvVUCSVyyO3Q0csiEiIiLTJUhAwiEbIiIiMh17SIiIiCwsUSa1MiAhIiKyMg7ZEBEREcUHe0iIiIgsTFNK/LRnW8exMgYkREREVsYhGyIiIqL4YA8JERGRhfEpGyIiIjJfggzZJGxAEvTYoTkjW1rcUesVHbvTv5rl9XHLljl3H5YtfW0TLjUf+fLJ/yZdbt7fSTZi6JStiA7dLSsPAL504SfWLivvSZXdS16//COaniS7/5KcsuW1uycfEZV3RpGHYECnfaLyASW7l3bXdxaVPy2lSVQeAJSSfYaaTpMtT3+4PklUPvBNiqg8IP8eUDbhcvbN0t+QsuMHkuTpI1xHIvw8BILiY9OJJWxAQkRE1BFwyIaIiIjMlyBDNnzKhoiIiEzHHhIiIiIL45ANERERmY9DNkRERETxwR4SIiIii7P6cIsRGJAQERFZmVLHXkYcx8I4ZENERESmYw8JERGRhfEpGyIiIjJfgjxlk7ABiRZU0GyRXR1fhkt0bPcBeS4bZ7Msn4jull0650FZnfQk+a3hzZAlj1HCNBPedGF+HWFeDQDSVBnQ/LJRT+kQbiAgz8VR2yDLWdI346CovE+X3Rtuhyx/DwDYhd+c53SqFpVPsss+b0FhXhoAqPPJcs3YbbKcP0caZMf39pS1GQDsh2XX2haQfR6OZsne1+Rq2X2hO+XXLRhh/ptgFJ9NOrGEDUiIiIg6Ak2XJy9t6zhWxoCEiIjIyhJkyMYST9ksXLgQffr0gcfjwciRI/Hpp59GtN/y5cuhaRrGjx8f2woSERFRTJkekKxYsQLFxcUoLS3Fli1bkJubi7Fjx2L//v0n3G/Xrl34wx/+gFGjRsWppkRERPF3/CkbI15WZnpAMn/+fNxyyy2YPHkyBg4ciEWLFiE5ORlLlixpc59gMIjrr78es2bNQr9+/eJYWyIiojg7vjCaES8LMzUg8fl8qKioQH5+fmibzWZDfn4+ysvL29zvwQcfRGZmJm666aaTnsPr9aKuri7sRURERNZiakBSW1uLYDCIrKyssO1ZWVmorm79Mb6PPvoIixcvxrPPPhvROcrKypCenh565eTktLveRERE8cIhGwuqr6/HDTfcgGeffRZdu3aNaJ/p06fjyJEjodfu3btjXEsiIiIDKQNfFmbqY79du3aF3W5HTU1N2PaamhpkZ2e3KP/dd99h165dKCgoCG3T9WMPVjscDmzbtg1nnHFG2D5utxtut2zBLiIiIoovU3tIXC4Xhg0bhvXr14e26bqO9evXIy8vr0X5AQMG4IsvvkBlZWXodcUVV+Diiy9GZWUlh2OIiOiUkyhDNqYvjFZcXIzCwkIMHz4cI0aMwIIFC9DY2IjJkycDACZOnIiePXuirKwMHo8HgwYNCtu/c+fOANBiOxER0SnBqCdkLP6UjekByYQJE3DgwAGUlJSguroaQ4YMwZo1a0ITXauqqmCzGd+R42gOwhEIRla23ic6tq1JVh4AlFOWF8HR1CQ7gT8gKq4F5cNcSV7hOVSyqLyyyd6jox55HgtHo2wfb4psLWZvnex9Teosz4vUu8shUflu7gZR+QxXo6j8GZ4TrynUGqcW2WfzuCznkZgeP90h/LwBSLUdFZX/+mgPUXl/8IyTF/qJw26PqDwANDhk+XKalTDv10HZd7s0n5VTdquSyUwPSACgqKgIRUVFrf7fhg0bTrjvsmXLjK8QERGRRRg13MIhGyIiIooec9kQERERxQd7SIiIiCwsUYZs2ENCRERkZboy7iW0cOFC9OnTBx6PByNHjsSnn34agwYew4CEiIiIWlixYgWKi4tRWlqKLVu2IDc3F2PHjsX+/fIn5yLBgISIiMjKTFo6fv78+bjlllswefJkDBw4EIsWLUJycjKWLFliRKtaYEBCRESUQOrq6sJeXq+3RRmfz4eKigrk5+eHttlsNuTn56O8vDwm9WJAQkREZGEaDFo6/sfj5eTkID09PfQqKytrcc7a2loEg8HQIqXHZWVlobq6Oibt5FM2REREVmbw0vG7d+9GWlpaaLNVEtAmbEAScNuBCJdrtwuXE5cu0w4AtoOypa+V3y88gWzZdVuEy+r/lN5JthS8o1F2DpdDdh1swrcIAHSHrNMwkCRc8r+3bDlxpeTL3yc7ZKkLpEvBe3XZ18Yvk3aIygNAo3KKylcHOovKX9Lpn6LyNk2WIgAAqgPpovJOm+zz8KvM70Tl1/5rgKg8AGh22S/BYLLsffL7ZPe33Ssrrwu/MyT76FF8Nq0iLS0tLCBpTdeuXWG321FTUxO2vaamBtnZ2TGpF4dsiIiILMyMbL8ulwvDhg3D+vXrQ9t0Xcf69euRl5cXg1YmcA8JERFRh2DS0vHFxcUoLCzE8OHDMWLECCxYsACNjY2YPHmyAZVpiQEJERERtTBhwgQcOHAAJSUlqK6uxpAhQ7BmzZoWE12NwoCEiIjIwjSloBkwqTWaYxQVFaGoqKjd544EAxIiIiIr0398GXEcC+OkViIiIjIde0iIiIgszMwhm3hiQEJERGRlJj1lE28csiEiIiLTsYeEiIjIygxeOt6qGJAQERFZmHSV1RMdx8oSNiCx+3XYVWTPQIkvYhRRqAoI898EZXkvVGOT7PjS+gDQHLK8Ls5a6QlkuXI0Yc4VAEiukeWnCHpk5Y8eTBKVT+4myzMDALuOZIjKpzpaph4/Ebdddm9k2OVJhfrbZJ+hFO2AqPwQYTKxNU2yew8ADgY7icr3dB0Slf/2qGxxqnNO2y8qDwCbDvSX7SDMfaO7ZeWVTZg/Jpp0M5FWyeK/3DuihA1IiIiIOgQO2RAREZHZNP3Yy4jjWBmfsiEiIiLTsYeEiIjIyjhkQ0RERKbjwmhERERE8cEeEiIiIgtjLhsiIiIyX4LMIeGQDREREZmOPSRERERWpgAYsYaItTtIGJAQERFZGeeQnOKCLhs0Z2QjVg5pPgS7LKdLVPv4ZPlB9OZm2fGl5YGIcwMdZ+ucLirvEubvCfaSHf8Y2XXwHJB9wP0psuM3OWS5bwCg2e0Sld9mzxSVv/r0SlH5lXWDReUBwK9k71OyzScqX9Esy99zMJgiKg8ATk12vzYFZfl1ku2yNtc2y9uQnH5UVL5pv+wcNq/wy1X4+9Tmt/YvYAqXsAEJERFRh6Bg0KTW9h8ilhiQEBERWRmfsiEiIiKKD/aQEBERWZkOQDqXsa3jWJglekgWLlyIPn36wOPxYOTIkfj000/bLPvss89i1KhR6NKlC7p06YL8/PwTliciIurIjj9lY8TLykwPSFasWIHi4mKUlpZiy5YtyM3NxdixY7F///5Wy2/YsAHXXnstPvjgA5SXlyMnJwe//vWvsWfPnjjXnIiIiIxiekAyf/583HLLLZg8eTIGDhyIRYsWITk5GUuWLGm1/EsvvYTbb78dQ4YMwYABA/Dcc89B13WsX78+zjUnIiKKg+OTWo14WZipAYnP50NFRQXy8/ND22w2G/Lz81FeXh7RMZqamuD3+5GRkdHq/3u9XtTV1YW9iIiIOgwGJLFXW1uLYDCIrKyssO1ZWVmorq6O6Bj33nsvevToERbU/FRZWRnS09NDr5ycnHbXm4iIiIxl+pBNe8ydOxfLly/HG2+8AY/H02qZ6dOn48iRI6HX7t2741xLIiKidkiQHhJTH/vt2rUr7HY7ampqwrbX1NQgOzv7hPs+/vjjmDt3Lt577z0MHtz20tRutxtud8slmZ31fjgckS1Prbtky1jb7VHEeWmdZOW9sqWvNadsOXHN5RSVBwC9UbbMtL2TsM022fua9K962fEBKHuarHym7CPU5WtRcTT0lF03APB2lT3btzfYRVT+L/tHi8qf2bP1CepGctply7T3STkoKr/fK7xXAVQ3yu6lDE+TqHxzUHbvfbtXliIAAPR62feA5hN+RvfLnmW1e2W/ULUofv/q7sjqpNuMeA43QnzsN/ZcLheGDRsWNiH1+ATVvLy8Nvd79NFH8dBDD2HNmjUYPnx4PKpKREREMWT6wmjFxcUoLCzE8OHDMWLECCxYsACNjY2YPHkyAGDixIno2bMnysrKAACPPPIISkpK8PLLL6NPnz6huSadOnVCJ+lf3ERERBbHbL9xMmHCBBw4cAAlJSWorq7GkCFDsGbNmtBE16qqKth+0lX/1FNPwefz4T//8z/DjlNaWoqZM2fGs+pERESxlyC5bEwPSACgqKgIRUVFrf7fhg0bwn7etWtX7CtEREREcWWJgISIiIjaoKvoZui2dhwLY0BCRERkZQkyZNOh1yEhIiKiUwN7SIiIiCzNqEXNrN1DwoCEiIjIyjhkQ0RERBQf7CEhIiKyMl3BkOEWPmVjUZp27BUDekrL3DknYzscEJVXzbJcNvbMrrLjB2T1AQCbU5j/xim8/bw+UXE9PVl2fACeA82i8navLNfM0W6y9yg5sqTXYZyNso5P3xHZ/ervJEuIsf3g6aLyAKDssi9Oe5Oszf/09BaVd2bL8swAgL82SVR+j1/2faSEX1+OJvn3nbtBto9T+DbpwlRNHlkKInHuGwBwNEaWF0kLyPIntYvSj72MOI6FcciGiIiITJe4PSREREQdQYJMamVAQkREZGUJMoeEQzZERERkOvaQEBERWRmHbIiIiMh0CgYFJO0/RCxxyIaIiIhMxx4SIiIiK+OQDREREZlO1wEYsKiZzoXRiIiIiE6IPSRERERWxiGbU1swyQ7NYY+orDhnxJGj4voEuqbKzuHIEZUPJguTRkTRs2c/0igqH0xPEZW3ef2i8sou7wBUzsjuieMc9bL8OileWf6LJJesPgAQSJbtE3TLbnBvuux9VVH0w3ozZDs5ZLcelE34of6uk6w8AI/w0gU8wuMflP1y0e1R5O4SXrvk/bIvDum9YQvI2hzNvac7I3uf9BjlQmtVBwhIZs+ejVWrVqGyshIulwuHDx8WH4NDNkRERNQuPp8P//Vf/4Xbbrst6mMkbA8JERFRh9ABlo6fNWsWAGDZsmVRH4MBCRERkYUppUOp9j8hc/wYdXV1Ydvdbjfcbne7j99eHLIhIiJKIDk5OUhPTw+9ysrKzK4SAPaQEBERWZtSxgy3/Dipdffu3UhLSwttbqt3ZNq0aXjkkUdOeMivvvoKAwYMaH/dwICEiIjI2pRBc0h+DEjS0tLCApK2/Pd//zcmTZp0wjL9+vVrf71+xICEiIiIWujWrRu6desWt/MxICEiIrIyXQc0A5Z9N2BibFuqqqpw8OBBVFVVIRgMorKyEgDQv39/dOoU2To+DEiIiIiszOAhm1goKSnB888/H/p56NChAIAPPvgAo0ePjugYfMqGiIiI2mXZsmVQSrV4RRqMAAncQ+JLtUOPcJlw96GA6NhHT5ctAx8NX4ZsKXibXxYZO+tkS6IDQP25maLyjqOy7sOgW7bUvOdAs6g8ADR3lb2vmmwleNibZTsEpeuPR3EO3Sn7GkipkX0exMu0A+i0V3a/uo7I0gr4O8na7KyTHR8AdOG186XJ6uRolF1nW1D+17EmfLJDeq2Dbtl75Doo+0w39UwWlQcALcL3KdJyRlC6DmXAkI0Ra5nEUsIGJERERB1CBxiyMQKHbIiIiMh07CEhIiKyMl0B2qnfQ8KAhIiIyMqUAmDEY7/WDkg4ZENERESmYw8JERGRhSldQRkwZKMs3kPCgISIiMjKlA5jhmys/divJYZsFi5ciD59+sDj8WDkyJH49NNPT1j+tddew4ABA+DxeHDuuedi9erVcaopERERxYLpAcmKFStQXFyM0tJSbNmyBbm5uRg7diz279/favmPP/4Y1157LW666SZs3boV48ePx/jx4/Hll1/GueZERESxp3Rl2MvKTA9I5s+fj1tuuQWTJ0/GwIEDsWjRIiQnJ2PJkiWtln/iiSfwH//xH7j77rtxzjnn4KGHHsJ5552Hv/zlL3GuORERURwo3biXhZk6h8Tn86GiogLTp08PbbPZbMjPz0d5eXmr+5SXl6O4uDhs29ixY/Hmm2+2Wt7r9cLr9YZ+PnLkCAAg6I98CWJ7QLZUdtAWhzhPuBq3LSCLjLWAfOn4gHR17YBw6Xjh+xoIyJeOl7ZBunS8CgiXjvfLl46XniPgl30NKL/sukWzdLx0WW6b8MIFArI2a+KbG9ADsmsnvQ4QXmdLLh1vk71HNuFnOuCXfxdrEX7fBwLHfq/EY6JoAH5DFmoNQH4fx5OpAUltbS2CwSCysrLCtmdlZeHrr79udZ/q6upWy1dXV7davqysDLNmzWqxfcuq2VHWmoiIOoSK2J+ivr4e6enpMTm2y+VCdnY2Pqo2bp5kdnY2XC5Zzq54OeWfspk+fXpYj8rhw4fRu3dvVFVVxewmiqe6ujrk5ORg9+7dSEtLM7s67cK2WNep1B62xbo6UnuUUqivr0ePHj1idg6Px4OdO3fC55P3WLfF5XLB4/EYdjwjmRqQdO3aFXa7HTU1NWHba2pqkJ2d3eo+2dnZovJutxtut7vF9vT0dMvf8BJpaWmnTHvYFus6ldrDtlhXR2lPPP6o9Xg8lg0gjGbqpFaXy4Vhw4Zh/fr1oW26rmP9+vXIy8trdZ+8vLyw8gCwbt26NssTERGR9Zk+ZFNcXIzCwkIMHz4cI0aMwIIFC9DY2IjJkycDACZOnIiePXuirKwMAHDHHXfgoosuwrx583D55Zdj+fLl2Lx5M5555hkzm0FERETtYHpAMmHCBBw4cAAlJSWorq7GkCFDsGbNmtDE1aqqKth+8nTFBRdcgJdffhkPPPAA7rvvPpx55pl48803MWjQoIjO53a7UVpa2uowTkd0KrWHbbGuU6k9bIt1nWrtIRlNWX1xeyIiIjrlmb4wGhEREREDEiIiIjIdAxIiIiIyHQMSIiIiMt0pGZAsXLgQffr0gcfjwciRI/Hpp5+esPxrr72GAQMGwOPx4Nxzz8Xq1cYt02sESXueffZZjBo1Cl26dEGXLl2Qn59/0vbHk/TaHLd8+XJomobx48fHtoIC0rYcPnwYU6dORffu3eF2u3HWWWdZ6l6TtmfBggU4++yzkZSUhJycHNx1111obpbnDzLaxo0bUVBQgB49ekDTtDbzXP3Uhg0bcN5558HtdqN///5YtmxZzOsZCWlbXn/9dYwZMwbdunVDWloa8vLysHbt2vhU9iSiuS7Hbdq0CQ6HA0OGDIlZ/ch8p1xAsmLFChQXF6O0tBRbtmxBbm4uxo4di/3797da/uOPP8a1116Lm266CVu3bsX48eMxfvx4fPnll3Gueeuk7dmwYQOuvfZafPDBBygvL0dOTg5+/etfY8+ePXGueUvSthy3a9cu/OEPf8CoUaPiVNOTk7bF5/NhzJgx2LVrF1auXIlt27bh2WefRc+ePeNc89ZJ2/Pyyy9j2rRpKC0txVdffYXFixdjxYoVuO++++Jc85YaGxuRm5uLhQsXRlR+586duPzyy3HxxRejsrISd955J26++WZL/CKXtmXjxo0YM2YMVq9ejYqKClx88cUoKCjA1q1bY1zTk5O25bjDhw9j4sSJuPTSS2NUM7IMdYoZMWKEmjp1aujnYDCoevToocrKylotf80116jLL788bNvIkSPV7373u5jWM1LS9vxcIBBQqamp6vnnn49VFSMWTVsCgYC64IIL1HPPPacKCwvVlVdeGYeanpy0LU899ZTq16+f8vl88aqiiLQ9U6dOVZdccknYtuLiYnXhhRfGtJ5SANQbb7xxwjL33HOP+sUvfhG2bcKECWrs2LExrJlcJG1pzcCBA9WsWbOMr1A7SNoyYcIE9cADD6jS0lKVm5sb03qRuU6pHhKfz4eKigrk5+eHttlsNuTn56O8vLzVfcrLy8PKA8DYsWPbLB9P0bTn55qamuD3+5GRkRGrakYk2rY8+OCDyMzMxE033RSPakYkmra89dZbyMvLw9SpU5GVlYVBgwZhzpw5CAZlKeRjIZr2XHDBBaioqAgN6+zYsQOrV6/GZZddFpc6G8nK3wHtpes66uvrTf/8R2vp0qXYsWMHSktLza4KxYHpK7Uaqba2FsFgMLTK63FZWVn4+uuvW92nurq61fLV1dUxq2ekomnPz917773o0aNHiy/ceIumLR999BEWL16MysrKONQwctG0ZceOHXj//fdx/fXXY/Xq1di+fTtuv/12+P1+079so2nPddddh9raWvzqV7+CUgqBQABTpkyxxJCNVFvfAXV1dTh69CiSkpJMqln7Pf7442hoaMA111xjdlXEvv32W0ybNg1/+9vf4HCcUr+qqA2nVA8JhZs7dy6WL1+ON954o8Nli6yvr8cNN9yAZ599Fl27djW7Ou2m6zoyMzPxzDPPYNiwYZgwYQLuv/9+LFq0yOyqRWXDhg2YM2cOnnzySWzZsgWvv/46Vq1ahYceesjsqtGPXn75ZcyaNQuvvvoqMjMzza6OSDAYxHXXXYdZs2bhrLPOMrs6FCenVNjZtWtX2O121NTUhG2vqalBdnZ2q/tkZ2eLysdTNO057vHHH8fcuXPx3nvvYfDgwbGsZkSkbfnuu++wa9cuFBQUhLbpug4AcDgc2LZtG84444zYVroN0VyX7t27w+l0wm63h7adc845qK6uhs/ng8vlimmdTySa9syYMQM33HADbr75ZgDAueeei8bGRtx66624//77w/JPWV1b3wFpaWkdtndk+fLluPnmm/Haa6+Z3jsajfr6emzevBlbt25FUVERgGOff6UUHA4H3n33XVxyySUm15KM1nG+NSLgcrkwbNgwrF+/PrRN13WsX78eeXl5re6Tl5cXVh4A1q1b12b5eIqmPQDw6KOP4qGHHsKaNWswfPjweFT1pKRtGTBgAL744gtUVlaGXldccUXoSYicnJx4Vj9MNNflwgsvxPbt20NBFQB888036N69u6nBCBBde5qamloEHceDLdXB0mNZ+TsgGq+88gomT56MV155BZdffrnZ1YlKWlpai8//lClTcPbZZ6OyshIjR440u4oUCyZPqjXc8uXLldvtVsuWLVP//Oc/1a233qo6d+6sqqurlVJK3XDDDWratGmh8ps2bVIOh0M9/vjj6quvvlKlpaXK6XSqL774wqwmhJG2Z+7cucrlcqmVK1eqffv2hV719fVmNSFE2pafs9JTNtK2VFVVqdTUVFVUVKS2bdum3nnnHZWZmakefvhhs5oQRtqe0tJSlZqaql555RW1Y8cO9e6776ozzjhDXXPNNWY1IaS+vl5t3bpVbd26VQFQ8+fPV1u3blXff/+9UkqpadOmqRtuuCFUfseOHSo5OVndfffd6quvvlILFy5UdrtdrVmzxqwmhEjb8tJLLymHw6EWLlwY9vk/fPiwWU0Ikbbl5/iUzanvlAtIlFLqz3/+s+rVq5dyuVxqxIgR6pNPPgn930UXXaQKCwvDyr/66qvqrLPOUi6XS/3iF79Qq1atinONT0zSnt69eysALV6lpaXxr3grpNfmp6wUkCglb8vHH3+sRo4cqdxut+rXr5+aPXu2CgQCca512yTt8fv9aubMmeqMM85QHo9H5eTkqNtvv10dOnQo/hX/mQ8++KDVz8Dx+hcWFqqLLrqoxT5DhgxRLpdL9evXTy1dujTu9W6NtC0XXXTRCcubKZrr8lMMSE59mlIdrH+ViIiITjmn1BwSIiIi6pgYkBAREZHpGJAQERGR6RiQEBERkekYkBAREZHpGJAQERGR6RiQEBERkekYkBAREZHpGJAQERGR6RiQEMXZpEmToGkaNE2Dy+VC//798eCDDyIQCJhdtahpmoY333wzZsdXSqGkpATdu3dHUlIS8vPz8e2338bsfEQUfwxIiEzwH//xH9i3bx++/fZb/Pd//zdmzpyJxx57LKpjBYPBsCzCHZnf7291+6OPPoo//elPWLRoEf7+978jJSUFY8eORXNzc5xrSESxwoCEyARutxvZ2dno3bs3brvtNuTn5+Ott94CAMyfPx/nnnsuUlJSkJOTg9tvvx0NDQ2hfZctW4bOnTvjrbfewsCBA+F2u1FVVYXPPvsMY8aMQdeuXZGeno6LLroIW7ZsCTuvpml4+umn8Zvf/AbJyck455xzUF5eju3bt2P06NFISUnBBRdcgO+++y5sv7/+9a8477zz4PF40K9fP8yaNSvUo9OnTx8AwFVXXQVN00I/n2y/4/V56qmncMUVVyAlJQWzZ89u8V4ppbBgwQI88MADuPLKKzF48GC88MIL2Lt3b0x7ZYgovhiQEFlAUlISfD4fAMBms+FPf/oT/vGPf+D555/H+++/j3vuuSesfFNTEx555BE899xz+Mc//oHMzEzU19ejsLAQH330ET755BOceeaZuOyyy1BfXx+270MPPYSJEyeisrISAwYMwHXXXYff/e53mD59OjZv3gylFIqKikLl//a3v2HixIm444478M9//hNPP/00li1bFgoePvvsMwDA0qVLsW/fvtDPJ9vvuJkzZ+Kqq67CF198gRtvvLHFe7Nz505UV1cjPz8/tC09PR0jR45EeXl5tG85EVmNqbmGiRJQYWGhuvLKK5VSSum6rtatW6fcbrf6wx/+0Gr51157TZ122mmhn5cuXaoAqMrKyhOeJxgMqtTUVPX222+HtgFQDzzwQOjn8vJyBUAtXrw4tO2VV15RHo8n9POll16q5syZE3bsF198UXXv3j3suG+88UZYmUj3u/POO0/Yjk2bNikAau/evWHb/+u//ktdc801J9yXiDoOh5nBEFGieuedd9CpUyf4/X7ouo7rrrsOM2fOBAC89957KCsrw9dff426ujoEAgE0NzejqakJycnJAACXy4XBgweHHbOmpgYPPPAANmzYgP379yMYDKKpqQlVVVVh5X66X1ZWFgDg3HPPDdvW3NyMuro6pKWl4fPPP8emTZvCejaCwWCLOv1cpPsNHz5c+vYR0SmIAQmRCS6++GI89dRTcLlc6NGjBxyOYx/FXbt24Te/+Q1uu+02zJ49GxkZGfjoo49w0003wefzhX6JJyUlQdO0sGMWFhbihx9+wBNPPIHevXvD7XYjLy8vNBR0nNPpDP37+DFa23Z8omxDQwNmzZqFq6++ukU7PB5Pm22MdL+UlJQ2jwEA2dnZAI4FXN27dw9tr6mpwZAhQ064LxF1HAxIiEyQkpKC/v37t9heUVEBXdcxb9482GzHpni9+uqrER1z06ZNePLJJ3HZZZcBAHbv3o3a2tp21/W8887Dtm3bWq3vcU6nE8FgULxfJPr27Yvs7GysX78+FIDU1dXh73//O2677bZ2HZuIrIMBCZGF9O/fH36/H3/+859RUFCATZs2YdGiRRHte+aZZ+LFF1/E8OHDUVdXh7vvvhtJSUntrlNJSQl+85vfoFevXvjP//xP2Gw2fP755/jyyy/x8MMPAzj2pM369etx4YUXwu12o0uXLhHtFwlN03DnnXfi4Ycfxplnnom+fftixowZ6NGjB8aPH9/u9hGRNfApGyILyc3Nxfz58/HII49g0KBBeOmll1BWVhbRvosXL8ahQ4dw3nnn4YYbbsDvf/97ZGZmtrtOY8eOxTvvvIN3330X559/Pn75y1/ij3/8I3r37h0qM2/ePKxbtw45OTkYOnRoxPtF6p577sH/+T//B7feeivOP/98NDQ0YM2aNSccMiKijkVTSimzK0FERESJjT0kREREZDoGJERERGQ6BiRERERkOgYkREREZDoGJERERGQ6BiRERERkOgYkREREZDoGJERERGQ6BiRERERkOgYkREREZDoGJERERGS6/w9/9s5HH0KdHgAAAABJRU5ErkJggg==", + "text/plain": [ + "<Figure size 650x500 with 2 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "Running reference.\n", + "Point 1089 out of 1089. Expected remaining time to complete: 00:00:00, it will be finished at 03:03:28. \n", + "Plotting the reference plot with the following parameters:\n", + "\tParameter 0: 0 to 1.57, with 33 values\n", + "\tParameter 1: 0 to 1.57, with 33 values\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 650x500 with 2 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "benchmark.run(n_points_axis=25+1, ranges=[(0, 3.14/2), (0, 3.14/2)], plot=True, plot_every=100) \n", + "# with plot=True, the plot is shown every `plot_every` evaluated points and at the end of the execution" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Showing results" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After running we can plot the results and get the mean difference and the standard deviation of the difference between the benchmarked and the reference:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-0.5407383175835657" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "benchmark.difference_mean" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.3463625010931868" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "benchmark.difference_std" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting the main plot with the following parameters:\n", + "\tParameter 0: 0 to 1.57, with 33 values\n", + "\tParameter 1: 0 to 1.57, with 33 values\n", + "Plotting the reference plot with the following parameters:\n", + "\tParameter 0: 0 to 1.57, with 33 values\n", + "\tParameter 1: 0 to 1.57, with 33 values\n", + "Plotting the difference plot with the following parameters:\n", + "\tParameter 0: 0 to 1.57, with 33 values\n", + "\tParameter 1: 0 to 1.57, with 33 values\n" + ] + }, + { + "data": { + "image/png": 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aRhEREZmIpqy6R1406YPDIqzZzAkhIiK6GEXzSEizS1ZGRERE0YEjIURERCamQf8EU/nU3MhiEEJERGRiTcsTYu4LHgxCiIiITKxpGVMZhBAREZFOZz+ITk9dMzN3iERERERRiyMhQdCTAVWqrrUwE6Aw+aFNmJHVomMSVMwJWQZHx3FhdkW7LGaOqZFniFTCDKX2U8J9aOHNdAsA3pZxovL270/JdiD96SLMiqmnjq9VC9nm3dIPkLwP0syyvgTZgzatwoypFh0ZU+1VblF5zyWxovJWYYZVm/S46bg71RsXXKZbX30kM6bycgwREREZoGl5QhiEEBERkU6askDTe4sunx1DREREemlNGAkx+y265m4dERERRS2OhBAREZlY0x5gZ+6xBgYhREREJuaDBT6d+T701osUBiFEREQmxpEQIiIiMoQP+kc05NlhIuuiDUJ8TissMcFFiK4jsoQ9tR1c4vbEHqkXlfe5gkuoc4b0Li3HcVkiJOD0eyqhJchOP+cxj6i8VZg8DQB8LlmbpO+rzSNsk45HYGoO4bkh7LO1WpbcTMXJknCdriTLMqUnEZeEr4VDXEfZZJ8Hq1t4bgiT93kT5X3wBfkd6SdMrqcJt6/FhHf7AGALMoGa1acjExo1cNEGIURERM0BL8cQERGRIZi2nYiIiAyhmvAUXcW7Y4iIiEivaB4JMXfriIiIKGpxJISIiMjE+AA7IiIiMoSvCQ+w01svUhiEEBERmRhHQoiIiMgQGqzQdI5o6K0XKRdtEGJza7D5gsuM524ty/jo+k6eqdPbQpjlUpiZ0Fovy+4nzcgKAEpYxeqRtamujSzjo+uIPKOh7ZQsc62nlTA7rkV23OzVdbLtA7B6ZWlWLcIsrvXJCbLtazqOw0nZcahvFSsqbz8hy74rbQ8A+OJixHUkxJlxhd8ZAGCrl51LmjCLqzXI72D/9mNkffbGyvtsDfJQm3yAodm4aIMQIiKi5sCnLPDpjHr01osUBiFEREQmFs1zQgy9WLRp0yZkZ2ejffv2sFgsWLNmzXnLv/vuuxgxYgTatm2LxMREZGRkYMOGDZFpLBERkQHUv58do2dRTFZ2brW1tejbty8WLVoUVPlNmzZhxIgRWLduHYqLizF8+HBkZ2djx44dYW4pERGRMXywNGkxM0Mvx4wcORIjR44MuvzChQsDXs+dOxd/+tOf8P/+3/9D//79Q9w6IiIiCqdmPSdE0zTU1NSgdevW5yzjdrvhdrv9r6urqyPRNCIiopDQlP65HTpuTosoc18suoD58+fjxIkTGDNmzDnLFBQUICkpyb+kpaVFsIVERERNo3c+yJnFzMzduvNYvnw5Zs+ejbfffhvJycnnLDdjxgxUVVX5l7Kysgi2koiIqGk0WJq0mFmzvByzcuVK3HXXXXjnnXeQmZl53rJOpxNOpyzZGBERkVkwT4iJrFixAnfccQdWrlyJUaNG6d6Oz2mFJSa4gSBpxkeLkl+Ek2Y0BYQZUB2yQS9bnU9UHgA0p2wfrqOybKA+l+x0VTb5QJ8SZmS0n5RlG5VmuaxrFy8qDwDS0VdXvexYa05hpk6b/EtQnhFYlnnTmyDLvquH1SNrk6eV7MeSRfiV4UnQkwVZdhxcR2WZZW3Sz4/wMx1zQnYMAMB+MsjPg1f+HUkNGRqEnDhxAvv27fO/PnDgAEpKStC6dWt07NgRM2bMwKFDh/CHP/wBwOlLMDk5OXj++ecxaNAglJeXAwBiY2ORlJRkSB+IiIjCqSlzOzgn5Dy2bduG/v37+2+vzcvLQ//+/TFz5kwAwOHDh1FaWuovv2TJEni9XkyZMgXt2rXzL/fff78h7SciIgo3DRZ/1lTxwjkh5zZs2DCo81y6KCwsDHhdVFQU3gYRERGZjGrCBFPFIISIiIj04rNjiIiIKKq9/PLL6NOnDxITE/3PZ/vLX/4S1n1yJISIiMjEIjUx9dJLL8W8efNw+eWXQymFZcuW4eabb8aOHTtw5ZVX6tr/hTAIISIiMrFIXY7Jzs4OeP3UU0/h5Zdfxueff84ghIiI6GLUlMynZ+r98LlpF0rk6fP58M4776C2thYZGRm69h0MzgkhIiIyMd235541gpKWlhbwHLWCgoJG97Vz507Ex8fD6XRi8uTJeO+999CrV6+w9e2iHQmxaKeXYEizMVrdOrKNCjMTKrssfpRnfRUVP13HK6tUl+wSlY85Icuu6HXpyBBpFWZltcuOm/Q90nMcYmpk75MvNkZU3t1K9h7Z6vQ8xlP41SQ8v6WfHyU/lWAXdtsqPDesXtn3ktMnPw7SKwA+l+x99baIFZWXZqF1fifLygwA9S2Dy1zr8+o4KQxUVlaGxMRE/+tzjYL06NEDJSUlqKqqwurVq5GTk4O//vWvYQtELtoghIiIqDkIxZyQM3e8XIjD4cBll10GAEhPT8c//vEPPP/883jllVd07f9CGIQQERGZmJF5QjRNg9vtbtI2zodBCBERkYlFKgiZMWMGRo4ciY4dO6KmpgbLly9HUVERNmzYoGvfwWAQQkREZGIKaELa9uBVVlZi4sSJOHz4MJKSktCnTx9s2LABI0aM0LXvYDAIISIiIrz22msR3yeDECIiIhOL5mfHMAghIiIyMQYhREREZAgGIVHIWq/BqoJLfCN9bpD7kuCS3ZzNflKW4Mzrkp1YjiphAiuHPJmuzS1LJOQTJuz1JMiSakmTPwGAt4WsTdJ9KKswKZ0wiR0AWDRZEiVvXHgTJ9cL31NAnlxP2YRJsoSfHy0m/F/kXmGiL5tH9h75nPI++ByyOjEnZd8BdmF5aVJHCD9vQPAJBaWJB5simoMQpm0nIiIiQ1y0IyFERETNgVIWKJ0jGnrrRQqDECIiIhMLxVN0zYpBCBERkYlF85wQBiFEREQmFs2XYzgxlYiIiAzBkRAiIiIT4+UYIiIiMkQ0X45hEEJERGRiqgkjIQxCTKo+3gYVE1xmSVudLKufxacjU2ecLMulRZZgFSeTHaLydmGfAcAnzfgofV+FWTTr43Vk6hS+r9IMjtIMqNJsvQCgtZR9rL3CTJreWFFxxB2Rn0sn2sv6EPudbB8nU2Sft7hKeR+kGVCl2Xel2Uyl5zYgz8oqem485BmE7adknXC3lmevDjY7rrdedg5R4y7aIISIiKg5UACUzizxkUsurw+DECIiIhPTYIGFycqIiIgo0jgxlYiIiAyhKQssUXqLrqHJyjZt2oTs7Gy0b98eFosFa9asuWCdoqIiXH311XA6nbjssstQWFgY9nYSERFR6BkahNTW1qJv375YtGhRUOUPHDiAUaNGYfjw4SgpKcEDDzyAu+66Cxs2bAhzS4mIiIyhVNMWMzP0cszIkSMxcuTIoMsvXrwYXbp0wbPPPgsAuOKKK/Dpp5/iueeeQ1ZWVriaSUREZJhonhPSrJ4ds2XLFmRmZgasy8rKwpYtW85Zx+12o7q6OmAhIiJqLs4EIXoXM2tWQUh5eTlSUlIC1qWkpKC6uhqnTp1qtE5BQQGSkpL8S1paWiSaSkREFBJnnh2jdzGzqL87ZsaMGcjLy/O/rq6uRlpaGmJqfbDbg8y+J72mJsx8CMizjfqc4c3GeDJZng0w9qgsq+SpNsLMni7Zh0mTJYkFAFjrZeUtwkSaWoysvCdRxxeI8PST9sFRI9vB8W7yc0kJv5l8Ttk+rMJMoNJzT08d6ftqrwtvhlUAcJyQZSjV7LJ9WOtlJ5/4e88jz3QbbJpia73JJ1s0E80qCElNTUVFRUXAuoqKCiQmJiI2tvFc0k6nE06nPHUvERGRGTRlgiknpoZQRkYG1q1bF7Duww8/REZGhkEtIiIiCq/TQYjeiakhbkyIGTon5MSJEygpKUFJSQmA07fglpSUoLS0FMDpSykTJ070l588eTL279+PRx55BLt378ZLL72Et99+G9OmTTOi+URERGEXzRNTDR0J2bZtG4YPH+5/fWbuRk5ODgoLC3H48GF/QAIAXbp0wdq1azFt2jQ8//zzuPTSS/Hqq6/y9lwiIopaCvofRGfygRBjg5Bhw4ZBnWesqLFsqMOGDcOOHTvC2CoiIiKKhGY1J4SIiOhiE83JyhiEEBERmVkUX49hEEJERGRmTZlgypEQc/Ik2KHFBNd9aaIvPZGnJ0F2o1LsEVlWrZMpsixZVo+oOACgvoUwUZFXuAPhvVw+p/zD524pK+9Jkh3sFodkbaprIz+ZYmpk+7DI8lGhprWsfEyNrLwe3sbTBJ2TRXhueFvItg9A/D1QHx/ez480mSAAeBJkSeAcNbKTqT5etv0Y4fah4++vJcjjFmy5UIjmPCHNKm07ERERRY+LdiSEiIioOeDEVCIiIjKGsuif28EghIiIiPSK5jkhDEKIiIjMLIpv0eXEVCIiIjIER0KIiIhMjBNTiYiIyDgmv6yiF4MQIiIiE+NISBRyVHthtweZcjACx1AJj4Syy6bzKKusE0qWyBAAUO8SZqGME5Z3iYrLM7IC0GSJZWGVJa5FdQ9ZxkcVI89y6XMJD15LYXrcGuGbpGPqmc8l+9ln8QqzxIpKA7aTOr4EhN12HZGVV8LtexLkfYg5YbKf38IuWN3CDKsAfM7g3ljp+98kUTwxVXcQsnr1arz99tsoLS2FxxP4JbZ9+/YmN4yIiIgip6CgAO+++y52796N2NhYDB48GE8//TR69OgRtn3qiuV+//vfIzc3FykpKdixYwcGDhyISy65BPv378fIkSND3UYiIqKLmKWJS3D++te/YsqUKfj888/x4Ycfor6+Hj/5yU9QW1sbys4E0DUS8tJLL2HJkiUYN24cCgsL8cgjj6Br166YOXMmjh07Fuo2EhERXbwidDlm/fr1Aa8LCwuRnJyM4uJi/PjHP9bZgPPTNRJSWlqKwYMHAwBiY2NRU3P6MZkTJkzAihUrQtc6IiKii51q4gKguro6YHG73RfcbVVVFQCgdWvho7MFdAUhqamp/hGPjh074vPPPwcAHDhwAMrsOWKJiIiakzPPjtG7AEhLS0NSUpJ/KSgoOO8uNU3DAw88gCFDhuCqq64KW9d0XY65/vrr8f7776N///7Izc3FtGnTsHr1amzbtg2/+MUvQt1GIiIiaoKysjIkJib6XzudzvOWnzJlCr788kt8+umnYW2XriBkyZIl0LTTtw5OmTIFl1xyCTZv3oybbroJ9957b0gbSEREdDELxQPsEhMTA4KQ85k6dSr+/Oc/Y9OmTbj00kv17ThIuoIQq9UKq/U/V3Juu+023HbbbSFrFBEREf1bhCamKqXw3//933jvvfdQVFSELl266Nxp8HTnCamrq8P//u//orKy0j8qcsZNN93U5IYRERERAuZ26KobpClTpmD58uX405/+hISEBJSXlwMAkpKSEBsbq2//F6ArCFm/fj0mTpyIo0ePNvg/i8UCn0+epS7SfC4rLDFBZsazCDN7xspPFqvwLTuZLMuKKc3uJ81mCgDWelmoroU5X29dG/lPB1+cLEOptU6YudYubJOO7x0VKzyZvLI+WOJlqWg9ej4PNbKTw5cka1PMd8Ltx8rPJfsJWb9PJcu2H3dYmFVWnnxXXkdYXrPL3iNpeaswky4QfHZpaRbqprCo04veusF6+eWXAQDDhg0LWP/GG29g0qRJ+hpwAbrujvnv//5v3HrrrTh8+DA0TQtYmkMAQkRERIGUUo0u4QpAAJ0jIRUVFcjLy0NKSkqo20NERERni+Jnx+gaCbnllltQVFQU4qYQERFRAyHIE2JWukZCXnzxRdx6663429/+ht69eyMmJvCpmr/+9a9D0jgiIqKLXhSPhOgKQlasWIEPPvgALpcLRUVFsJw1cdNisTAIISIiChUGIYEee+wxzJ49G9OnTw/IF0JEREQULF0RhMfjwdixY0MSgCxatAidO3eGy+XCoEGDsHXr1vOWX7hwIXr06IHY2FikpaVh2rRpqKura3I7iIiITCkED7AzK11RRE5ODlatWtXkna9atQp5eXnIz8/H9u3b0bdvX2RlZaGysrLR8suXL8f06dORn5+PXbt24bXXXsOqVavwm9/8psltISIiMiVOTA3k8/nwzDPPYMOGDejTp0+DiakLFiwIajsLFizA3XffjdzcXADA4sWLsXbtWrz++uuYPn16g/KbN2/GkCFDMH78eABA586dMW7cOPz973/X0w0iIiLTi1SyMiPoCkJ27tyJ/v37AwC+/PLLgP+zBJld1OPxoLi4GDNmzPCvs1qtyMzMxJYtWxqtM3jwYLz55pvYunUrBg4ciP3792PdunWYMGHCOffjdrvhdrv9r6urqwEAtjoNNm9w6f3crWVvk7NKnrDtRPuYCxc6ex/VstSEXpcw66ssASUAeVZWm/AqmlV4trql2Ukhz2gqLW9xCc+NWvlH1NbKfeFCZ/FWO2Q7qBP+snLIU3VKf7xZTsoyCHvjZW2y18gHjW2yw4CYk7Ly7layN8n1nfzzIP0DVtdadhzsdbId+GKFGYp1ZDV1HgvuwNm8wgPcFJyYGuiTTz5p8o6PHj0Kn8/XIOFZSkoKdu/e3Wid8ePH4+jRo7j22muhlILX68XkyZPPezmmoKAAs2fPbnJ7iYiIKLSa1a0tRUVFmDt3Ll566SVs374d7777LtauXYs5c+acs86MGTNQVVXlX8rKyiLYYiIiIjoXXSMhdXV1eOGFF/DJJ580+hTd7du3X3Abbdq0gc1mQ0VFRcD6iooKpKamNlrniSeewIQJE3DXXXcBAHr37o3a2lrcc889eOyxxxq9W8fpdMLpdAbbNSIiIlOxoAlzQkLaktDTFYTceeed+OCDD3DLLbdg4MCBQc8DOZvD4UB6ejo2btyI0aNHAwA0TcPGjRsxderURuucPHmyQaBhs52+BqmUyS98ERER6dGUu1yi8e6YP//5z1i3bh2GDBnSpJ3n5eUhJycHAwYMwMCBA7Fw4ULU1tb675aZOHEiOnTogIKCAgBAdnY2FixYgP79+2PQoEHYt28fnnjiCWRnZ/uDESIioqjCiamBOnTogISEhCbvfOzYsThy5AhmzpyJ8vJy9OvXD+vXr/dPVi0tLQ0Y+Xj88cdhsVjw+OOP49ChQ2jbti2ys7Px1FNPNbktREREFFm6gpBnn30Wjz76KBYvXoxOnTo1qQFTp0495+WXHz6p1263Iz8/H/n5+U3aJxERUbPBkZBAAwYMQF1dHbp27Yq4uLgGycqOHTsWksYRERFd7Jis7AfGjRuHQ4cOYe7cuUhJSdE1MdVoyhp8ci2LMNeSFiN/P1ocrheVd7eSHTppH6w6kpV5EmX91oQ5sqSsHvlxUMLnIWmxwjdWmFTLWi/vg6YJ6wi/pJTwW81aI5+vpblk76vVLTtu1lPC4+yQf5N7W8iOg71Wtn1Hlay89DsAAHzC77LYY7IvDq8w+ZjtlI5OCNUnBJc40uuVJ6XUjSMhgTZv3owtW7agb9++oW4PERERnS2KgxBdycp69uyJU6dOhbotREREdBHRFYTMmzcPDz74IIqKivDdd9+huro6YCEiIqLQODMnRO9iZroux9x4440AgBtuuCFgvVIKFosFPl8Er5URERFFMyYrCxSKB9gRERFREKJ4ToiuIGTo0KGhbgcRERE1grfonsPJkydRWloKj8cTsL5Pnz5NahQRERFFP11ByJEjR5Cbm4u//OUvjf4/54QQERGFSBRfjtF1d8wDDzyA48eP4+9//ztiY2Oxfv16LFu2DJdffjnef//9ULeRiIjo4tWUO2NMHoToGgn5+OOP8ac//QkDBgyA1WpFp06dMGLECCQmJqKgoACjRo0KdTtDzhtnA2KCy+Ro8UlTSsrbo6yyGczS7IfuBNn2Y06G/8z1uWRtOpkqa5NFR9bXsH9gY2Q7UJqOBn0vTEXrkJ1MFqesvIrTMTLqk50bSlje4hZmM62R/16zCLuthIllrcLvpWAzRJ/NIjz/3EmyTtjrhH2wy46btV7++Qn+uziCd51wJCRQbW0tkpOTAQCtWrXCkSNHAAC9e/fG9u3bQ9c6IiKii51q4mJiuoKQHj16YM+ePQCAvn374pVXXsGhQ4ewePFitGvXLqQNJCIiouik63LM/fffj8OHDwMA8vPzceONN+LNN9+Ew+HAsmXLQtpAIiKiixlv0f2BX/7yl/5/p6en45tvvsHu3bvRsWNHtGnTJmSNIyIiouilKwjJy8trdL3FYoHL5cJll12Gm2++Ga1bt25S44iIiC56UTwxVVcQsmPHDmzfvh0+nw89evQAAOzduxc2mw09e/bESy+9hAcffBCffvopevXqFdIGExERXUyi+XKMrompN998MzIzM/Htt9+iuLgYxcXF+Ne//oURI0Zg3LhxOHToEH784x9j2rRpoW4vERERRQldQcjvfvc7zJkzB4mJif51SUlJmDVrFp555hnExcVh5syZKC4uDllDiYiILlpReHsuoDMIqaqqQmVlZYP1R44cQXV1NQCgZcuWDZ4pQ0REREJRnCdE15yQm2++GXfccQeeffZZXHPNNQCAf/zjH3jooYcwevRoAMDWrVvRvXv3kDU01KxeBWuQF8vCnc0UALQYYSZAr+zMivtO1qjaFGH6RsgzMrqTZOVtp2Tvkc+l49Mn7IPjO9n7VH+pW7YDj/x3grKF91tH1cn6bImTp661VMWIyqt42T680uP8vfzz4HMJK9TLims2YcZOPQk+hRMK7KfCm11a+l2s6fgLZ3MH911p9er4otcpmueE6ApCXnnlFUybNg233XYbvN7TH3673Y6cnBw899xzAICePXvi1VdfDV1LiYiILka8OyZQfHw8li5diueeew779+8HAHTt2hXx8fH+Mv369QtJA4mIiCg66QpCzoiPj0efPn1C1RYiIiL6AV6OISIiImPwcgwREREZgkEIERERGSGaL8foyhNCRERE0WfTpk3Izs5G+/btYbFYsGbNmrDuj0EIERGRmUUwWVltbS369u2LRYsWhajx52d4ELJo0SJ07twZLpcLgwYNwtatW89b/vjx45gyZQratWsHp9OJ7t27Y926dRFqLRERUYRFMAgZOXIknnzySfz85z8PUePPz9A5IatWrUJeXh4WL16MQYMGYeHChcjKysKePXuQnJzcoLzH48GIESOQnJyM1atXo0OHDvjmm2/QsmVL8b5jquthtweXBdHq8Ym27W4rTZUI2E/K9lHXWnbofHGyeDOmVn4h0Rsry2ZoE2b1l2Y/VPIkl9CEmTct1cLMnsJso7aW8kcf+E7K3qiYBFkW1/oqp6i88ulI1Sk8DtYqWZ+VU3Z+n2onz44Z9y/ZZ06aYVVzyMqLs5kCcLeU9cGiyd4naXbpYLOZ6t0+APicwfXZZ43cb/hQzAk580iVM5xOJ5xO2Wc5HAwdCVmwYAHuvvtu5ObmolevXli8eDHi4uLw+uuvN1r+9ddfx7Fjx7BmzRoMGTIEnTt3xtChQ9G3b98It5yIiChCQjASkpaWhqSkJP9SUFAQ4U40zrCREI/Hg+LiYsyYMcO/zmq1IjMzE1u2bGm0zvvvv4+MjAxMmTIFf/rTn9C2bVuMHz8ejz76KGy2xn9hut1uuN3/+aX3w2iQiIgo2pWVlQU8+d4MoyCAgSMhR48ehc/nQ0pKSsD6lJQUlJeXN1pn//79WL16NXw+H9atW4cnnngCzz77LJ588slz7qegoCAg+ktLSwtpP4iIiMLpzOUYvQsAJCYmBiwXfRCih6ZpSE5OxpIlS5Ceno6xY8fisccew+LFi89ZZ8aMGaiqqvIvZWVlEWwxERFRE0VwYmqkGXY5pk2bNrDZbKioqAhYX1FRgdTU1EbrtGvXDjExMQGXXq644gqUl5fD4/HA4Wg4U8ssk2+IiIh0iWDG1BMnTmDfvn3+1wcOHEBJSQlat26Njh076mzEuRk2EuJwOJCeno6NGzf612maho0bNyIjI6PROkOGDMG+ffugnTUDe+/evWjXrl2jAQgREVFzZ2niIrFt2zb0798f/fv3BwDk5eWhf//+mDlzZgh60pChl2Py8vKwdOlSLFu2DLt27cJ9992H2tpa5ObmAgAmTpwYMHH1vvvuw7Fjx3D//fdj7969WLt2LebOnYspU6YY1QUiIqKoMWzYMCilGiyFhYVh2Z+heULGjh2LI0eOYObMmSgvL0e/fv2wfv16/2TV0tJSWM+6FzstLQ0bNmzAtGnT0KdPH3To0AH3338/Hn30UaO6QEREFF58gF34TJ06FVOnTm30/4qKihqsy8jIwOeff97k/fri7LDYg+u+5hBmvdJx0DW7bNAs5qQsC4/XZfihbsB+Slbe3VJW3psgz1RkOy57n9ypsqRasMpODksEnj7VIlaWEM2VVCsqX3EkSVQeAFzCBGpaguzz466SJhSUDxrXdpElIHSVy75nPIkXLnM2zSFPGhdTIzxfhR85X4ysTdI+WIQfTwCweoPrs55EaHpF8wPszPeXiYiIiP4jikdCmtUtukRERBQ9OBJCRERkdiYf0dCLQQgREZGJcU4IERERGSOK54QwCCEiIjKxaB4J4cRUIiIiMgRHQoiIiMyMl2OIiIjICNF8OebiDUI0dXoJgkXJjqInQZhhFYDru3pR+fp42T4sQfb1P+VFxQEAyiPMfmgXfjossu27KuXHoT5e2CafMAulsM8J8cK0sgDiWleLyru9sq+BOk+MqPwlrU+IygNAj9aVovJ7jiWLyjscslSadXWyPgOA1yPMvuuWnUs2aXnh5xMAfC5ZnbpWsu3HCE+N+ljZDIK4Stn3KiDJXh3Bv+4cCSEiIiJDRHEQwompREREZAiOhBAREZkY54QQERGRMaL4cgyDECIiIhOzKCW+QeLsumbGIISIiMjMongkhBNTiYiIyBAcCSEiIjIxTkwlIiIiY0Tx5ZiLNgjxuWywxASXUdN+1C3advy/6uTtccqyezqPyzI+WoUZVoPPGvgf0iyr9fGyq4ExskSg0Jyy8gDgSRJ+Ym2y8q4E2bnkrpd/RJNiZedfbIwsq2S7uCpR+Rgd6Xd7xh8Wlfcq2blUVtNSVP6SFidF5QFAKdln6OQlsqysx2tiReW9e1uIygPy7wFlFWZxrZP+hZRt3xsrz5rsqAry8+D1ibetF0dCiIiIyBhRPBLCialERERkCI6EEBERmRgvxxAREZExovhyDIMQIiIikzP7iIZeDEKIiIjMTKnTi966JsaJqURERGQIjoQQERGZGCemRiGLT8FiDe7oeFo7RNt2HpEnK4upkyWM0pyyQxdzTNYmLVZ+arhby7KDKWEeIXeSMIGaMHESAGkuJFjqZYOJ0pFRr1eebOnoCVlSqi6tj4nKezTZueG0yxK0AYBNOJvuivhyUflYm+zz5hMmHgOAao8smZjNKkvqVnVCtn13B1mfAcB2XHasrV7Z5+FUiux9jSuXnRdajPy4+YJMcObT8dnUjRNTiYiIyAgWTZ6R+uy6ZmaKOSGLFi1C586d4XK5MGjQIGzdujWoeitXroTFYsHo0aPD20AiIiKjqCYuJmZ4ELJq1Srk5eUhPz8f27dvR9++fZGVlYXKysrz1jt48CAeeughXHfddRFqKREREYWS4UHIggULcPfddyM3Nxe9evXC4sWLERcXh9dff/2cdXw+H26//XbMnj0bXbt2jWBriYiIIuvMxFS9i5kZGoR4PB4UFxcjMzPTv85qtSIzMxNbtmw5Z73f/va3SE5Oxp133nnBfbjdblRXVwcsREREzcaZPCF6FxMzNAg5evQofD4fUlJSAtanpKSgvLzx2e6ffvopXnvtNSxdujSofRQUFCApKcm/pKWlNbndREREkcKREJOoqanBhAkTsHTpUrRp0yaoOjNmzEBVVZV/KSsrC3MriYiIQiiKJ6YaeotumzZtYLPZUFFREbC+oqICqampDcp//fXXOHjwILKzs/3rNO30/Ud2ux179uxBt27dAuo4nU44nbL8FURERBR+ho6EOBwOpKenY+PGjf51mqZh48aNyMjIaFC+Z8+e2LlzJ0pKSvzLTTfdhOHDh6OkpISXWoiIKOpE8+UYw5OV5eXlIScnBwMGDMDAgQOxcOFC1NbWIjc3FwAwceJEdOjQAQUFBXC5XLjqqqsC6rds2RIAGqy/EHudD3avL7iyNR7Rtq0nZeUBQMXIsu/ZT56U7aDeKypu8clHj2Ldwn2oOFF5ZZW9R6dc8myJ9lpZHXcLWSYgd7XsfY1tKc++26nV96LybZ0nROVbO2pF5bu5zn+7fWNiLMF9Ns9IiakK6/aT7MLPG4AE6ylR+d2n2ovK1/u6XbjQWY47XaLyAHDCLsvKWqeE2aWPyX4HS7Mmx8hOVfOK4gfYGR6EjB07FkeOHMHMmTNRXl6Ofv36Yf369f7JqqWlpbBam9XUFSIiopDhs2PCbOrUqZg6dWqj/1dUVHTeuoWFhaFvEBERkVlE8bNjOMRAREREhmAQQkREZGKRnpiq93luejAIISIiMjNNNW0R0Ps8N70YhBAREZlZBJOV6XmeW1MwCCEiIjIxC5pwOebf2/jhM9TcbneD/eh9nltTMAghIiKKcmlpaQHPUSsoKGhQRs/z3JrKFLfoEhER0TmEIFlZWVkZEhMT/avN8jiTizYI8TptQJBZSm3CLJrS7KQAYD0my/io6uuFO5BlG7UGmU32bFq8LAOqvVa2D4dddhyswrcIADS7bHDQGyvMdNtJlkVTKXnW1zi7LGOvNAOqW5N9bfwodr+oPADUqhhR+XJvS1H56+P/KSpvtcgy4wJAuTdJVD7GKvs8XJv8taj8hn/1FJUHAItN9ofPFyd7n+o9svPb5paV14TfGZI6mo7Ppl6hSFaWmJgYEIQ0Rvo8t1Dg5RgiIiIzi9DEVOnz3ELhoh0JISIiag4sSsGi83KMtN6FnucWagxCiIiICMCFn+cWagxCiIiIzEz796K3rtD5nucWagxCiIiITCySl2MijUEIERGRmUXxU3QZhBAREZlZCPKEmBVv0SUiIiJDcCSEiIjIxEKRrMysLtogxFavwaaCmzYsPog6hr+UV5hl1SfLrqhqT8q2L20PAItdlj005qh0B7KMrBZhZk8AiKuQZUH0uWTlTx2LFZWPayvLZgoAB6tai8on2Bs+yOp8nDbZudHaJk9de5lV9hlqYTkiKt9PmLJ6/UnZuQcAx3zxovIdHN+Lyn91SnbL5BWXyB/F/tmRy2QVhBlWNaesvLIKs5TqSWoabJMi+cc9ii/HXLRBCBERUXNg0U4veuuaGYMQIiIiM4vikRBOTCUiIiJDcCSEiIjIzJgnhIiIiIzAjKlERERkjCieE8IghIiIyMwU9D/AztwxCCemEhERkTEu2pEQn8MKS0xwMZhdmvDGJkvapauOR5YASqurk21fWh4IOvnbGdaWSaLyDmGCNl9H2fZPkx0H1xHZz4z6FrLtn7TLkpsBQJ3TISq/x5YsKv+LS0tE5VdX9xGVB4B6JXuf4qweUfniOlmCtmO+FqLyABBjkZ2vJ32yBGpxNlmfj9bJ+xCXdEpU/mSlbB9Wt/DLVfir3lpv8mGAIHFOCBERERlDoQlzQkLakpBjEEJERGRmnJhKREREhtCg7zk4Z+qamCkmpi5atAidO3eGy+XCoEGDsHXr1nOWXbp0Ka677jq0atUKrVq1QmZm5nnLExERkTkZHoSsWrUKeXl5yM/Px/bt29G3b19kZWWhsrLxJz4WFRVh3Lhx+OSTT7BlyxakpaXhJz/5CQ4dOhThlhMREYXfmYmpehczMzwIWbBgAe6++27k5uaiV69eWLx4MeLi4vD66683Wv6tt97Cr371K/Tr1w89e/bEq6++Ck3TsHHjxgi3nIiIKALOzAnRu5iYoUGIx+NBcXExMjMz/eusVisyMzOxZcuWoLZx8uRJ1NfXo3Xr1o3+v9vtRnV1dcBCRETUbDAICY+jR4/C5/MhJSUlYH1KSgrKy8uD2sajjz6K9u3bBwQyZysoKEBSUpJ/SUtLa3K7iYiIIoZBiDnNmzcPK1euxHvvvQeXy9VomRkzZqCqqsq/lJWVRbiVRERE1BhDb9Ft06YNbDYbKioqAtZXVFQgNTX1vHXnz5+PefPm4aOPPkKfPufOyOh0OuF0NsxEGFNTD7s9uKyMmkOWvdFm0xHbJcbLyrtlGR8tMbIsmhZHjKg8AGi1suyKtnhhn62y9zX2XzWy7QNQtkRZ+WTZR6jVblFxnOggO24A4G4juyfvW18rUfkXK4eJyl/eofFJ5qEUY5NlJ+3c4piofKVbeK4CKK+VnUutXSdF5et8snPvq29lmXEBQKuRfQ9YPMLPaKXsvlObW/ar3qJjEEBzBtcmzar3nlkdeItueDgcDqSnpwdMKj0zyTQjI+Oc9Z555hnMmTMH69evx4ABAyLRVCIiIkNE890xhicry8vLQ05ODgYMGICBAwdi4cKFqK2tRW5uLgBg4sSJ6NChAwoKCgAATz/9NGbOnInly5ejc+fO/rkj8fHxiJf+siYiIjI7ZkwNn7Fjx+LIkSOYOXMmysvL0a9fP6xfv94/WbW0tBTWs4bhX375ZXg8Htxyyy0B28nPz8esWbMi2XQiIqLw05S+a0tn6pqY4UEIAEydOhVTp05t9P+KiooCXh88eDD8DSIiIqKwM0UQQkREROfAyzFERERkjKbk+2AQQkRERHpxJISIiIgMoSnoHtEw+cTUZp0xlYiIiJqvi3ckxGI5vYSB1qJhhtYLsR73isqrOlnGVFtyG9n2vbL2AIA1RphlNUZ4+rk9ouJaUpxs+wBcR+pE5W1uWUbTU21l71FccI9QChBTK/tt4amSna/18bIUjPuOXSoqDwDKJvv1Zjsp6/M/XZ1E5WNSZdlMAaD+aKyo/KF62feREn592U/Kv++cJ2R1YoRvkyZMCOySJboVZ1gFAHttcNl3LV5Zlt4mUdrpRW9dE7t4gxAiIqLmgHNCiIiIyBBRPCeEQQgREZGZRfFICCemEhERkSE4EkJERGRmCk0YCQlpS0KOQQgREZGZRfHlGAYhREREZqZpAHTeaquZ+xZdzgkhIiIiQzAIISIiMrMzl2P0LmHy1FNPYfDgwYiLi0PLli11beOivRzji7XBYrcFVVacmbDqlLg93jYJsn3Y00TlfXHC1IQ6RvBsVbWi8r6kFqLyVne9qLyyyWNsFRPcOXGGvUaWxbWFW5ZlMdYhaw8AeONkdXxO2QnuTpK9r0rHTx13a1klu+zUg7IKP9Rfx8vKA3AJD53XJdz+MdkfF82mI0O08NjFVcq+OKTnhtUr67Oec0+LCe590sKUcbtRJp0T4vF4cOuttyIjIwOvvfaarm1ctEEIERFRs2DSZGWzZ88GABQWFureBoMQIiIiE1NKg9L5DJgz9aqrqwPWO51OOJ3y55yFGueEEBERRbm0tDQkJSX5l4KCAqObBIAjIUREROamlP7LKv+eE1JWVobExET/6nONgkyfPh1PP/30eTe5a9cu9OzZU197foBBCBERkZmpJswJ+XcQkpiYGBCEnMuDDz6ISZMmnbdM165d9bWlEQxCiIiIzEzTAIvOpGPCuSRt27ZF27Zt9e1LBwYhREREZhaCkZBwKC0txbFjx1BaWgqfz4eSkhIAwGWXXYb4+OBua2cQQkRERGIzZ87EsmXL/K/79+8PAPjkk08wbNiwoLZx0QYhngQbtCATUzm/94q2fepSWeIxPTytZcnHrPWyaDimWpaECwBqeieLyttPyYYJfU5ZcjPXkTpReQCoayN7Xy2y3GOw1ckq+KQZr3TsQ4uRfQ20qJB9HsSJwQDEfys7Xx1VskR29fGyPsdUy7YPAJrw2HkSZW2y18qOs9Un/0VsEU6GlB5rn1P2HjmOyT7TJzvEicoDgCXI9ynYcqGgNA1K5+UYvbf2BqOwsLBJOUKAizgIISIiahZMejkmFBiEEBERmZmmAAuDECIiIoo0paDrgV7+uubFjKlERERkCI6EEBERmZjSFJTOyzGKIyEXtmjRInTu3BkulwuDBg3C1q1bz1v+nXfeQc+ePeFyudC7d2+sW7cuQi0lIiKKMKU1bTExw4OQVatWIS8vD/n5+di+fTv69u2LrKwsVFZWNlp+8+bNGDduHO68807s2LEDo0ePxujRo/Hll19GuOVEREThpzTVpMXMDA9CFixYgLvvvhu5ubno1asXFi9ejLi4OLz++uuNln/++edx44034uGHH8YVV1yBOXPm4Oqrr8aLL74Y4ZYTERFFQBSPhBg6J8Tj8aC4uBgzZszwr7NarcjMzMSWLVsarbNlyxbk5eUFrMvKysKaNWsaLe92u+F2u/2vq6qqAAC++uCT3ti8suRMPmsEYjth/ierVxYNW7zyZGVeaT4nrzBZmfB99XrlycqkfZAmK1NeYbKyenmyMuk+vPWyrwFVLztuepKVSRNBWYUHzuuV9dkiPrkBzSs7dtLjAOFxNmWyMqvsPbIKP9Peevl3sSXI73uv9/TflUjMufCiXneaEC/k524kGRqEHD16FD6fDykpKQHrU1JSsHv37kbrlJeXN1q+vLy80fIFBQWYPXt2g/Xb1z6ls9VERNQsFId/FzU1NUhKSgrLth0OB1JTU/FpedPmPaampsLhkGWDjpSovztmxowZASMnx48fR6dOnVBaWhq2EyeSqqurkZaWhrKysqAe02xm7It5RVN/2Bfzak79UUqhpqYG7du3D9s+XC4XDhw4AI9HPjJ9NofDAZfLFaJWhZahQUibNm1gs9lQUVERsL6iogKpqamN1klNTRWVdzqdcDqdDdYnJSWZ/iSXSExMjJr+sC/mFU39YV/Mq7n0JxI/ZF0ul2kDiFAwdGKqw+FAeno6Nm7c6F+naRo2btyIjIyMRutkZGQElAeADz/88JzliYiIyJwMvxyTl5eHnJwcDBgwAAMHDsTChQtRW1uL3NxcAMDEiRPRoUMHFBQUAADuv/9+DB06FM8++yxGjRqFlStXYtu2bViyZImR3SAiIiIhw4OQsWPH4siRI5g5cybKy8vRr18/rF+/3j/5tLS0FNaz7ooYPHgwli9fjscffxy/+c1vcPnll2PNmjW46qqrgtqf0+lEfn5+o5domqNo6g/7Yl7R1B/2xbyirT90YRZl9pyuREREFJUMT1ZGREREFycGIURERGQIBiFERERkCAYhREREZIioDEIWLVqEzp07w+VyYdCgQdi6det5y7/zzjvo2bMnXC4XevfujXXrmpYiN9Qk/Vm6dCmuu+46tGrVCq1atUJmZuYF+x9J0mNzxsqVK2GxWDB69OjwNlBA2pfjx49jypQpaNeuHZxOJ7p3726qc03an4ULF6JHjx6IjY1FWloapk2bhro6+fN6Qm3Tpk3Izs5G+/btYbFYzvlcqbMVFRXh6quvhtPpxGWXXYbCwsKwtzMY0r68++67GDFiBNq2bYvExERkZGRgw4YNkWnsBeg5Lmd89tlnsNvt6NevX9jaR8aIuiBk1apVyMvLQ35+PrZv346+ffsiKysLlZWVjZbfvHkzxo0bhzvvvBM7duzA6NGjMXr0aHz55ZcRbnnjpP0pKirCuHHj8Mknn2DLli1IS0vDT37yExw6dCjCLW9I2pczDh48iIceegjXXXddhFp6YdK+eDwejBgxAgcPHsTq1auxZ88eLF26FB06dIhwyxsn7c/y5csxffp05OfnY9euXXjttdewatUq/OY3v4lwyxuqra1F3759sWjRoqDKHzhwAKNGjcLw4cNRUlKCBx54AHfddZcp/nhL+7Jp0yaMGDEC69atQ3FxMYYPH47s7Gzs2LEjzC29MGlfzjh+/DgmTpyIG264IUwtI0OpKDNw4EA1ZcoU/2ufz6fat2+vCgoKGi0/ZswYNWrUqIB1gwYNUvfee29Y2xksaX9+yOv1qoSEBLVs2bJwNTFoevri9XrV4MGD1auvvqpycnLUzTffHIGWXpi0Ly+//LLq2rWr8ng8kWqiiLQ/U6ZMUddff33Aury8PDVkyJCwtlMKgHrvvffOW+aRRx5RV155ZcC6sWPHqqysrDC2TC6YvjSmV69eavbs2aFvUBNI+jJ27Fj1+OOPq/z8fNW3b9+wtosiL6pGQjweD4qLi5GZmelfZ7VakZmZiS1btjRaZ8uWLQHlASArK+uc5SNJT39+6OTJk6ivr0fr1q3D1cyg6O3Lb3/7WyQnJ+POO++MRDODoqcv77//PjIyMjBlyhSkpKTgqquuwty5c+HzyR7HHg56+jN48GAUFxf7L9ns378f69atw09/+tOItDmUzPwd0FSapqGmpsbwz79eb7zxBvbv34/8/Hyjm0JhYnjG1FA6evQofD6fP9vqGSkpKdi9e3ejdcrLyxstX15eHrZ2BktPf37o0UcfRfv27Rt8yUaanr58+umneO2111BSUhKBFgZPT1/279+Pjz/+GLfffjvWrVuHffv24Ve/+hXq6+sN/4LV05/x48fj6NGjuPbaa6GUgtfrxeTJk01xOUbqXN8B1dXVOHXqFGJjYw1qWdPNnz8fJ06cwJgxY4xuithXX32F6dOn429/+xvs9qj6U0VniaqREAo0b948rFy5Eu+9916zewpjTU0NJkyYgKVLl6JNmzZGN6fJNE1DcnIylixZgvT0dIwdOxaPPfYYFi9ebHTTdCkqKsLcuXPx0ksvYfv27Xj33Xexdu1azJkzx+im0b8tX74cs2fPxttvv43k5GSjmyPi8/kwfvx4zJ49G927dze6ORRGURVetmnTBjabDRUVFQHrKyoqkJqa2mid1NRUUflI0tOfM+bPn4958+bho48+Qp8+fcLZzKBI+/L111/j4MGDyM7O9q/TNA0AYLfbsWfPHnTr1i28jT4HPcelXbt2iImJgc1m86+74oorUF5eDo/HA4fDEdY2n4+e/jzxxBOYMGEC7rrrLgBA7969UVtbi3vuuQePPfZYwPOezO5c3wGJiYnNdhRk5cqVuOuuu/DOO+8YPgqqR01NDbZt24YdO3Zg6tSpAE5//pVSsNvt+OCDD3D99dcb3EoKhebzTREEh8OB9PR0bNy40b9O0zRs3LgRGRkZjdbJyMgIKA8AH3744TnLR5Ke/gDAM888gzlz5mD9+vUYMGBAJJp6QdK+9OzZEzt37kRJSYl/uemmm/x3MKSlpUWy+QH0HJchQ4Zg3759/kAKAPbu3Yt27doZGoAA+vpz8uTJBoHGmQBLNbPHUZn5O0CPFStWIDc3FytWrMCoUaOMbo4uiYmJDT7/kydPRo8ePVBSUoJBgwYZ3UQKFYMnxobcypUrldPpVIWFheqf//ynuueee1TLli1VeXm5UkqpCRMmqOnTp/vLf/bZZ8put6v58+erXbt2qfz8fBUTE6N27txpVBcCSPszb9485XA41OrVq9Xhw4f9S01NjVFd8JP25YfMdHeMtC+lpaUqISFBTZ06Ve3Zs0f9+c9/VsnJyerJJ580qgsBpP3Jz89XCQkJasWKFWr//v3qgw8+UN26dVNjxowxqgt+NTU1aseOHWrHjh0KgFqwYIHasWOH+uabb5RSSk2fPl1NmDDBX37//v0qLi5OPfzww2rXrl1q0aJFymazqfXr1xvVBT9pX9566y1lt9vVokWLAj7/x48fN6oLftK+/BDvjolOUReEKKXUCy+8oDp27KgcDocaOHCg+vzzz/3/N3ToUJWTkxNQ/u2331bdu3dXDodDXXnllWrt2rURbvH5SfrTqVMnBaDBkp+fH/mGN0J6bM5mpiBEKXlfNm/erAYNGqScTqfq2rWreuqpp5TX641wq89N0p/6+no1a9Ys1a1bN+VyuVRaWpr61a9+pb7//vvIN/wHPvnkk0Y/A2fan5OTo4YOHdqgTr9+/ZTD4VBdu3ZVb7zxRsTb3RhpX4YOHXre8kbSc1zOxiAkOlmUamZjp0RERBQVompOCBERETUfDEKIiIjIEAxCiIiIyBAMQoiIiMgQDEKIiIjIEAxCiIiIyBAMQoiIiMgQDEKIotiwYcPwwAMPGN0MIqJGMQghonMqLCxEy5YtjW4GEUUpBiFERERkCAYhRFHO6/Vi6tSpSEpKQps2bfDEE0/4n3Trdrvx0EMPoUOHDmjRogUGDRqEoqIiAEBRURFyc3NRVVUFi8UCi8WCWbNmAQD++Mc/YsCAAUhISEBqairGjx+PyspKg3pIRM0VgxCiKLds2TLY7XZs3boVzz//PBYsWIBXX30VADB16lRs2bIFK1euxP/+7//i1ltvxY033oivvvoKgwcPxsKFC5GYmIjDhw/j8OHDeOihhwAA9fX1mDNnDr744gusWbMGBw8exKRJkwzsJRE1R3yAHVEUGzZsGCorK/F///d/sFgsAIDp06fj/fffx/r169G1a1eUlpaiffv2/jqZmZkYOHAg5s6di8LCQjzwwAM4fvz4efezbds2XHPNNaipqUF8fHw4u0REUYQjIURR7kc/+pE/AAGAjIwMfPXVV9i5cyd8Ph+6d++O+Ph4//LXv/4VX3/99Xm3WVxcjOzsbHTs2BEJCQkYOnQoAKC0tDSsfSGi6GI3ugFEZIwTJ07AZrOhuLgYNpst4P/ON5pRW1uLrKwsZGVl4a233kLbtm1RWlqKrKwseDyecDebiKIIgxCiKPf3v/894PXnn3+Oyy+/HP3794fP50NlZSWuu+66Rus6HA74fL6Adbt378Z3332HefPmIS0tDcDpyzFERFK8HEMU5UpLS5GXl4c9e/ZgxYoVeOGFF3D//feje/fuuP322zFx4kS8++67OHDgALZu3YqCggKsXbsWANC5c2ecOHECGzduxNGjR3Hy5El07NgRDocDL7zwAvbv34/3338fc+bMMbiXRNQcMQghinITJ07EqVOnMHDgQEyZMgX3338/7rnnHgDAG2+8gYkTJ+LBBx9Ejx49MHr0aPzjH/9Ax44dAQCDBw/G5MmTMXbsWLRt2xbPPPMM2rZti8LCQrzzzjvo1asX5s2bh/nz5xvZRSJqpnh3DBERERmCIyFERERkCAYhREREZAgGIURERGQIBiFERERkCAYhREREZAgGIURERGQIBiFERERkCAYhREREZAgGIURERGQIBiFERERkCAYhREREZAgGIURERGQIBiFERERkCAYhREREZAgGIURERGQIBiFERERkCAYhREREZAgGIURERGQIBiFERERkCAYhREREZAgGIURERGQIBiFERERkCAYhREREZAgGIURERGQIBiFERERkCAYhREREZAgGIURERGQIBiFERERkCAYhREREZAgGIURERGQIBiFERERkCAYhREREZAgGIURERGQIBiFEBvrjH/+Inj17IiYmBi1btjS6Oc3CwYMHYbFYUFhYaHRTiKiJGIQQGWT37t2YNGkSunXrhqVLl2LJkiVGNynqbd68GbNmzcLx48eNbgoRAbAb3QCii1VRURE0TcPzzz+Pyy67zOjmXBQ2b96M2bNnY9KkSRx5IjIBjoQQhUhtba2ofGVlJQCE9I+htA1EREZiEEKkw6xZs2CxWPDPf/4T48ePR6tWrXDttdf6///NN99Eeno6YmNj0bp1a9x2220oKyvz/3/nzp2Rn58PAGjbti0sFgtmzZrl//+//OUvuO6669CiRQskJCRg1KhR+L//+7+ANkyaNAnx8fH4+uuv8dOf/hQJCQm4/fbbAQCapmHhwoW48sor4XK5kJKSgnvvvRfff/99wDY6d+6Mn/3sZ/j0008xcOBAuFwudO3aFX/4wx8a9Pn48eOYNm0aOnfuDKfTiUsvvRQTJ07E0aNH/WXcbjfy8/Nx2WWXwel0Ii0tDY888gjcbvcF39Nhw4bhqquuQnFxMQYPHozY2Fh06dIFixcvvmBdAPj444/971nLli1x8803Y9euXf7/nzVrFh5++GEAQJcuXWCxWGCxWHDw4MGgtk9EocfLMURNcOutt+Lyyy/H3LlzoZQCADz11FN44oknMGbMGNx11104cuQIXnjhBfz4xz/Gjh070LJlSyxcuBB/+MMf8N577+Hll19GfHw8+vTpA+D0ZNWcnBxkZWXh6aefxsmTJ/Hyyy/j2muvxY4dO9C5c2f//r1eL7KysnDttddi/vz5iIuLAwDce++9KCwsRG5uLn7961/jwIEDePHFF7Fjxw589tlniImJ8W9j3759uOWWW3DnnXciJycHr7/+OiZNmoT09HRceeWVAIATJ07guuuuw65du3DHHXfg6quvxtGjR/H+++/jX//6F9q0aQNN03DTTTfh008/xT333IMrrrgCO3fuxHPPPYe9e/dizZo1F3w/v//+e/z0pz/FmDFjMG7cOLz99tu477774HA4cMcdd5yz3kcffYSRI0eia9eumDVrFk6dOoUXXngBQ4YMwfbt29G5c2f84he/wN69e7FixQo899xzaNOmDYDTQSARGUQRkVh+fr4CoMaNGxew/uDBg8pms6mnnnoqYP3OnTuV3W4PWH9mG0eOHPGvq6mpUS1btlR33313QP3y8nKVlJQUsD4nJ0cBUNOnTw8o+7e//U0BUG+99VbA+vXr1zdY36lTJwVAbdq0yb+usrJSOZ1O9eCDD/rXzZw5UwFQ7777boP3QtM0pZRSf/zjH5XValV/+9vfAv5/8eLFCoD67LPPGtQ929ChQxUA9eyzz/rXud1u1a9fP5WcnKw8Ho9SSqkDBw4oAOqNN97wlztT5rvvvvOv++KLL5TValUTJ070r/vd736nAKgDBw6cty1EFBm8HEPUBJMnTw54/e6770LTNIwZMwZHjx71L6mpqbj88svxySefnHd7H374IY4fP45x48YF1LfZbBg0aFCj9e+7776A1++88w6SkpIwYsSIgG2kp6cjPj6+wTZ69eqF6667zv+6bdu26NGjB/bv3+9f9z//8z/o27cvfv7znzfYv8Vi8e/3iiuuQM+ePQP2e/311wPABfsOAHa7Hffee6//tcPhwL333ovKykoUFxc3Wufw4cMoKSnBpEmT0Lp1a//6Pn36YMSIEVi3bt0F90tExuDlGKIm6NKlS8Drr776CkopXH755Y2WP/sySGO++uorAPD/4f6hxMTEgNd2ux2XXnppg21UVVUhOTm50W2cmRB7RseOHRuUadWqVcD8ka+//hr/9V//dcG279q165yXN36438a0b98eLVq0CFjXvXt3AKfzg/zoRz9qUOebb74BAPTo0aPB/11xxRXYsGEDamtrG2yXiIzHIISoCWJjYwNea5oGi8WCv/zlL7DZbA3Kx8fHn3d7mqYBOD0vJDU1tcH/2+2BH1mn0wmrNXBAU9M0JCcn46233mp0Hz8MEhprJwD/HJdgaZqG3r17Y8GCBY3+f1pammh7RBT9GIQQhVC3bt2glEKXLl38v+Cl9QEgOTkZmZmZutvw0UcfYciQIQ2CJL26deuGL7/88oJlvvjiC9xwww3+SzRS3377bYNRi7179wJAwITcs3Xq1AkAsGfPngb/t3v3brRp08a/Pb3tIqLw4JwQohD6xS9+AZvNhtmzZzcYSVBK4bvvvjtv/aysLCQmJmLu3Lmor69v8P9Hjhy5YBvGjBkDn8+HOXPmNPg/r9erK1vof/3Xf+GLL77Ae++91+D/zvRzzJgxOHToEJYuXdqgzKlTp4LKYeL1evHKK6/4X3s8Hrzyyito27Yt0tPTG63Trl079OvXD8uWLQvo25dffokPPvgAP/3pT/3rzgQjzJhKZA4cCSEKoW7duuHJJ5/EjBkzcPDgQYwePRoJCQk4cOAA3nvvPdxzzz146KGHzlk/MTERL7/8MiZMmICrr74at912G9q2bYvS0lKsXbsWQ4YMwYsvvnjeNgwdOhT33nsvCgoKUFJSgp/85CeIiYnBV199hXfeeQfPP/88brnlFlG/Hn74YaxevRq33nor7rjjDqSnp+PYsWN4//33sXjxYvTt2xcTJkzA22+/jcmTJ+OTTz7BkCFD4PP5sHv3brz99tvYsGEDBgwYcN79tG/fHk8//TQOHjyI7t27Y9WqVSgpKcGSJUvOO5/md7/7HUaOHImMjAzceeed/lt0k5KSAvKvnAlkHnvsMdx2222IiYlBdnY254sQGcXIW3OImqvGbq892//8z/+oa6+9VrVo0UK1aNFC9ezZU02ZMkXt2bMnqG188sknKisrSyUlJSmXy6W6deumJk2apLZt2+Yvk5OTo1q0aHHONi5ZskSlp6er2NhYlZCQoHr37q0eeeQR9e233/rLdOrUSY0aNapB3aFDh6qhQ4cGrPvuu+/U1KlTVYcOHZTD4VCXXnqpysnJUUePHvWX8Xg86umnn1ZXXnmlcjqdqlWrVio9PV3Nnj1bVVVVnbOtZ/Z55ZVXqm3btqmMjAzlcrlUp06d1IsvvhhQrrFbdJVS6qOPPlJDhgxRsbGxKjExUWVnZ6t//vOfDfYzZ84c1aFDB2W1Wnm7LpHBLEoJZ58REYXBsGHDcPTo0QvOPSGi6ME5IURERGQIBiFERERkCAYhREREZAjOCSEiIiJDcCSEiIiIDMEghIiIiAxx0SUr0zQN3377LRISEpjCmYiIdFFKoaamBu3bt2/w/KZQqqurg8fjadI2HA4HXC5XiFoUWhddEPLtt9/yQVpERBQSZWVlDZ5kHSp1dXXo0ike5ZW+Jm0nNTUVBw4cMGUgctEFIQkJCQCAgcNmwG433wEhIiLz83rrsLWowP83JRw8Hg/KK304UNwJiQn6RluqazR0Sf8GHo+HQYgZnLkEY7e7YI8x3wEhIqLmIxKX9VvEn1708Jn8/ldOTCUiIiJDXHQjIURERM2JBgUN+oY09NaLFAYhREREJqZBg9aEumbGIISIiMjEfErBpzO5ud56kcIghIiIyMSi+XIMJ6YSERGRITgSQkREZGIaFHxROhLCIKS5Mvl1vmBYmn8XAC0aOhEFrM3/EQyq+XcB4KMwwiKaL8cwCCEiIjIxTkwlIiIiQ2j/XvTWNTNOTCUiIiJDcCSEiIjIxHxNmJiqt16kMAghIiIyMZ/S/yA6sz/AjkEIERGRiUXznBAGIURERCamwQIf9N3+rOmsFymcmEpERESG4EhIOETgvmxxoi9hUi1dicSEdSzS9ync2wfkY5dhPtZmTOgWkaRa0qRXYf45pfQk4RJWUcKEa+LjEIGEbkr8ITX3r3Sz0JT+vIhmz6fIIISIiMjEfE24HKO3XqQwCCEiIjIxBiFERERkCE1ZoOm8Dqq3XqRwYioREREZgiMhREREJsbLMURERGQIH6zw6bxw4QtxW0KNQQgREZGJqSbMCVEmnxPCIISIiMjEovlyDCemEhERkSE4EhIMYVZMXVkupRlNhZk9LdLt63j0onQf4j77ZJ3WdxyEb6z0OEQgm644i6swa6U4e6ienzrSNkmzjdpkjdKT2FO6D9hkxaVt0nPqKZu5f0VfLHzKCp/SOSeEGVOJiIhILw0WaDovXGjSVPoRZujlmE2bNiE7Oxvt27eHxWLBmjVrgq772WefwW63o1+/fmFrHxERkdHOzAnRu5iZoUFIbW0t+vbti0WLFonqHT9+HBMnTsQNN9wQppYRERGZw5nLMXoXMzP0cszIkSMxcuRIcb3Jkydj/PjxsNlsotETIiIiMg9zh0iNeOONN7B//37k5+cHVd7tdqO6ujpgISIiai5OzwnRv5hZswpCvvrqK0yfPh1vvvkm7PbgBnEKCgqQlJTkX9LS0sLcSiIiotDR/p0xVc+id0JrpJi7dWfx+XwYP348Zs+eje7duwddb8aMGaiqqvIvZWVlYWwlERFRaHFOiAnU1NRg27Zt2LFjB6ZOnQoA0DQNSinY7XZ88MEHuP766xvUczqdcDqdkW4uERFRSGhNGNHgLbohkpiYiJ07d6KkpMS/TJ48GT169EBJSQkGDRpkdBOJiIiaLZ/PhyeeeAJdunRBbGwsunXrhjlz5kCFMcmioSMhJ06cwL59+/yvDxw4gJKSErRu3RodO3bEjBkzcOjQIfzhD3+A1WrFVVddFVA/OTkZLperwXrDSTOHQp4B1SrNHuoNb3bS0/uQtkmanVS4fT2pAqUZU6XvUyQypkoJU29apKk6pZlDASirMKOpXVZe2WXHQQm3D0Bnyt7gKbsJJxyKs++GqR1NEObDpotPWeDT+WZJ6j399NN4+eWXsWzZMlx55ZXYtm0bcnNzkZSUhF//+te69n8hhgYh27Ztw/Dhw/2v8/LyAAA5OTkoLCzE4cOHUVpaalTziIiIDHdmkqm+usFHVZs3b8bNN9+MUaNGAQA6d+6MFStWYOvWrbr2HQxDg5Bhw4add5insLDwvPVnzZqFWbNmhbZRREREJqIpKzSdE0y1f/+N/WF6isbmSw4ePBhLlizB3r170b17d3zxxRf49NNPsWDBAn0ND0KzmZhKRER0MQrFSMgP01Pk5+c3+BE/ffp0VFdXo2fPnrDZbPD5fHjqqadw++2369p3MBiEEBERRbmysjIkJib6Xzd21+jbb7+Nt956C8uXL8eVV16JkpISPPDAA2jfvj1ycnLC0i4GIURERCamQTbB9Id1gdN3mJ4dhDTm4YcfxvTp03HbbbcBAHr37o1vvvkGBQUFDEKIiIguRk3LExJ8vZMnT8L6gzvTbDYbNOldgwIMQoiIiEysKZlPJfWys7Px1FNPoWPHjrjyyiuxY8cOLFiwAHfccYeufQeDQQgREZGJNeVBdJJ6L7zwAp544gn86le/QmVlJdq3b497770XM2fO1LXvYDAIISIiIiQkJGDhwoVYuHBhxPbJICQI0gx60uyngPkyoFrqfaLygI6MqdJ9eIXldVzHFGeKlWZAjUTGVGlGU2mWSx0ZUKWkWVnDmVZaL+k8QmUT9tkqPW7yX9LifZjtQSDmOy10idTlGCMwCCEiIjKxpuUJYRBCREREOmnKAk3vLbpmfEDPWRiEEBERmZjWhJEQvbf2Roq5W0dERERRiyMhREREJta0B9iZe6yBQQgREZGJ+WCBT2eeEL31IoVBCBERkYlxJISIiIgM4YP+EQ15xqfIYhASDE2YGExP4iRpXi1pm6SJxHzyPkj3AWliMGHyMXHiMcB8ycekicd01BEnH7PZZOXtwvIAlF3WJhUj24cmLK8r0VeMrA+aNFmZ9D3S84NYmnBNz/kqoOu7VSpKEpw1FwxCiIiITIyXY4iIiMgQTNtOREREhlBNeIqu4t0xREREpFc0j4SYu3VEREQUtTgSQkREZGJ8gB0REREZwteEB9jprRcpDEKIiIhMjCMhREREZAgNVmg6RzT01osUBiFmIcwEGPbMgZHITGiVpmMUZoiUbd2crDq+QKQZUIX7CHc2Uz370MRtCm82U0DH+yR8m5Tw8xOJjKli0g+pJmuQRZhZmiKPQQgREZGJ+ZQFPp2XVfTWixQGIURERCbGOSFERERkCNWEZ8coJis7t02bNiE7Oxvt27eHxWLBmjVrzlv+3XffxYgRI9C2bVskJiYiIyMDGzZsiExjiYiIDOCDpUmLmRkahNTW1qJv375YtGhRUOU3bdqEESNGYN26dSguLsbw4cORnZ2NHTt2hLmlREREFGqGXo4ZOXIkRo4cGXT5hQsXBryeO3cu/vSnP+H//b//h/79+zdax+12w+12+19XV1fraisREZERNKV/bofZbxAy98WiC9A0DTU1NWjduvU5yxQUFCApKcm/pKWlRbCFRERETaP9e06I3sXMzN26C5g/fz5OnDiBMWPGnLPMjBkzUFVV5V/Kysoi2EIiIqKm0WBp0mJmzfbumOXLl2P27Nn405/+hOTk5HOWczqdcDqdEWwZERERBaNZBiErV67EXXfdhXfeeQeZmZlGN8cQyiLMHCgsr/Rk6pSeTcJrnBY9bQoz6XEQjz3q6LM4U6c082aYs5MCgCbM+qrssj5IM6AqPRlTw5wB1eQ/cIPCjKbBYbIyE1mxYgXuuOMOrFy5EqNGjTK6OURERGHVlLkdZp8TYmgQcuLECezbt8//+sCBAygpKUHr1q3RsWNHzJgxA4cOHcIf/vAHAKcvweTk5OD555/HoEGDUF5eDgCIjY1FUlKSIX0gIiIKJw1NyJhq8iEzQ0Okbdu2oX///v7ba/Py8tC/f3/MnDkTAHD48GGUlpb6yy9ZsgRerxdTpkxBu3bt/Mv9999vSPuJiIjCTTVhUqoyeRBi6EjIsGHDoM7ztNbCwsKA10VFReFtEBEREUVMs5sTQkREdDHhA+yIiIjIEJyYSkRERIbgSAgREREZoimZT3l3DBEREVEjOBJiFtKMjMJEg5owy6U0ESgAQHrt8Tx3RjVaXLZ1XZ0Qj1xKs42GuTygI6NpmLOHSrOT6tlHuLOT6rqsLv1Im/AHq0X4obNosvLizM/yb4GowMsxREREZIhoDkJ4OYaIiMjEzgQheheJQ4cO4Ze//CUuueQSxMbGonfv3ti2bVuYesaRECIiIlOL1EjI999/jyFDhmD48OH4y1/+grZt2+Krr75Cq1atdO07GAxCiIiICE8//TTS0tLwxhtv+Nd16dIlrPvk5RgiIiITU0ATnh1zWnV1dcDidrsb7Of999/HgAEDcOuttyI5ORn9+/fH0qVLw9o3BiFEREQmFoo5IWlpaUhKSvIvBQUFDfazf/9+vPzyy7j88suxYcMG3Hffffj1r3+NZcuWha1vvBxDRERkYqGYE1JWVobExET/eqfT2bCspmHAgAGYO3cuAKB///748ssvsXjxYuTk5Oja/4VwJISIiMjEQjESkpiYGLA0FoS0a9cOvXr1Clh3xRVXoLS0NGx940hIMKSJjSKRT0d65DRhH4QJryJCmgtNT7KyMCexCndiMEBHH4TnUiQSfYn3IX2bTJhITJoYTLx9YSIxAFDS5GDC98mqhbnTF2duM92GDBmCPXv2BKzbu3cvOnXqFLZ9MgghIiIysUjdojtt2jQMHjwYc+fOxZgxY7B161YsWbIES5Ys0bXvYJjw5y4RERGdoZSlSUuwrrnmGrz33ntYsWIFrrrqKsyZMwcLFy7E7bffHra+cSSEiIjIxCL5FN2f/exn+NnPfqZrX3owCCEiIjIxPjuGiIiIKMQ4EkJERGRi0rkdP6xrZgxCiIiITCyaL8cwCCEiIjIxjoQQERGRIVQTRkIYhFyE9BxzaWZMS7gzSgq3r2sfwoym4c5OerqOrLwmzYAaiT5I9yEuH+ZzTw/pPqSJQPVk3hTvQ1bB4pNtX88dntJ+hzvrKzOgRh8GIURERCamoP9xIGaP2xiEEBERmZgGCywRSlYWaQxCiIiITIwTU4mIiMgQmrLAEqW36BqaMXXTpk3Izs5G+/btYbFYsGbNmgvWKSoqwtVXXw2n04nLLrsMhYWFYW8nERERhZ6hQUhtbS369u2LRYsWBVX+wIEDGDVqFIYPH46SkhI88MADuOuuu7Bhw4Ywt5SIiMgYSjVtMTNDL8eMHDkSI0eODLr84sWL0aVLFzz77LMAgCuuuAKffvopnnvuOWRlZYWrmURERIaJ5jkhzeoBdlu2bEFmZmbAuqysLGzZsuWcddxuN6qrqwMWIiKi5uJMEKJ3MbNmFYSUl5cjJSUlYF1KSgqqq6tx6tSpRusUFBQgKSnJv6SlpUWiqURERCFx5tkxehczi/q7Y2bMmIG8vDz/6+rqanEgIj6GOrJcSmnSjKbC4mbMNqrZhZk6he0B9LRJtn15xlRZeT37EJ8bwky3kSDONqoJty8sD8gzmlp8svfVKkxDFfZspnqEOaus6TN1UfMKQlJTU1FRURGwrqKiAomJiYiNjW20jtPphNPpjETziIiIQq4pE0w5MTWEMjIysG7duoB1H374ITIyMgxqERERUXidDkL0TkwNcWNCzNA5ISdOnEBJSQlKSkoAnL4Ft6SkBKWlpQBOX0qZOHGiv/zkyZOxf/9+PPLII9i9ezdeeuklvP3225g2bZoRzSciIgq7aJ6YauhIyLZt2zB8+HD/6zNzN3JyclBYWIjDhw/7AxIA6NKlC9auXYtp06bh+eefx6WXXopXX32Vt+cSEVHUUtA/vcXkAyHGBiHDhg2DOs9YUWPZUIcNG4YdO3aEsVVEREQUCc1qTggREdHFJpqTlTEIISIiMrMovh7DIISIiMjMmjLBlCMhUUCYnEnXMZcmjBImK1PCIy1NDKanji8mzInBdJzd0jZJ9yFNPiZOPKanjjhZmXDzen6JiZOJyRolTSRm9crKn64jTCYmfl+F56pVfiCk76vpf3Y3U9GcJ6RZpW0nIiKi6MGRECIiIhPjxFQiIiIyhrLon9vBIISIiIj0iuY5IQxCiIiIzCyKb9HlxFQiIiIyBEdCiIiITIwTU4mIiMg4Jr+soheDECIiIhPjSAjJ6DjmZsuAKs0cCgCasI4vRrh9h7C8rj4Iy4c7Y6qwPBD+jKniX2Q6fsFZpBlTw50BVc/sOWEKVKvwNgYtzO9RJEiP80Uriiem6g5CVq9ejbfffhulpaXweDwB/7d9+/YmN4yIiIiim667Y37/+98jNzcXKSkp2LFjBwYOHIhLLrkE+/fvx8iRI0PdRiIioouYpYmLeekKQl566SUsWbIEL7zwAhwOBx555BF8+OGH+PWvf42qqqpQt5GIiOjipZq4mJiuIKS0tBSDBw8GAMTGxqKmpgYAMGHCBKxYsSJ0rSMiIrrYMQgJlJqaimPHjgEAOnbsiM8//xwAcODAASiz54glIiJqTs48O0bvYmK6gpDrr78e77//PgAgNzcX06ZNw4gRIzB27Fj8/Oc/D2kDiYiIKPLmzZsHi8WCBx54IGz70HV3zJIlS6D9+/6wKVOm4JJLLsHmzZtx00034d577w1pA4mIiC5mRjzA7h//+AdeeeUV9OnTR98GgqQrCLFarbBa/zOIctttt+G2224LWaOIiIjo3yKcJ+TEiRO4/fbbsXTpUjz55JM6dxwc3XlC6urq8L//+7+orKz0j4qccdNNNzW5YURERISmze34d73q6uqA1U6nE06ns9EqU6ZMwahRo5CZmWnOIGT9+vWYOHEijh492uD/LBYLfD4TpuZrgojM6xHuQ5xhVVpeR6ZOTZoNNMxZX6XZT/XUEWdMlfbZpifdaJjLRyBjqtUX3g+dNFGnnsye0sy1Yc90qyeTs9nmNF6k9z1Y1OlFb10ASEtLC1ifn5+PWbNmNSi/cuVKbN++Hf/4xz/07VBIVxDy3//937j11lsxc+ZMpKSkhLpNREREFEJlZWVITEz0v25sFKSsrAz3338/PvzwQ7hcroi0S1cQUlFRgby8PAYgRERE4RaCOSGJiYkBQUhjiouLUVlZiauvvtq/zufzYdOmTXjxxRfhdrths+kYJj8PXUHILbfcgqKiInTr1i2kjSEiIqIfCMGckGDccMMN2LlzZ8C63Nxc9OzZE48++mjIAxBAZxDy4osv4tZbb8Xf/vY39O7dGzExgRfSf/3rX4ekcURERBe9CN0dk5CQgKuuuipgXYsWLXDJJZc0WB8quoKQFStW4IMPPoDL5UJRUREsZz2y2mKxMAghIiIKlQjfohtJuoKQxx57DLNnz8b06dMD8oUQERFR9CgqKgrr9nVFEB6PB2PHjg1JALJo0SJ07twZLpcLgwYNwtatW89bfuHChejRowdiY2ORlpaGadOmoa6ursntICIiMiU+wC5QTk4OVq1a1eSdr1q1Cnl5ecjPz8f27dvRt29fZGVlobKystHyy5cvx/Tp05Gfn49du3bhtddew6pVq/Cb3/ymyW0hIiIypSh+gJ2uyzE+nw/PPPMMNmzYgD59+jSYmLpgwYKgtrNgwQLcfffdyM3NBQAsXrwYa9euxeuvv47p06c3KL9582YMGTIE48ePBwB07twZ48aNw9///nc93SAiIjK9UCQrMytdQcjOnTvRv39/AMCXX34Z8H9nT1I9H4/Hg+LiYsyYMcO/zmq1IjMzE1u2bGm0zuDBg/Hmm29i69atGDhwIPbv349169ZhwoQJ59yP2+2G2+32v/5h6tqLhSmzvoY546OePpuuTXr6EO5pWsI26co2Kt1HmM9vk/+YpGjHiamBPvnkkybv+OjRo/D5fA0SnqWkpGD37t2N1hk/fjyOHj2Ka6+9FkopeL1eTJ48+byXYwoKCjB79uwmt5eIiIhCq1nd2lJUVIS5c+fipZdewvbt2/Huu+9i7dq1mDNnzjnrzJgxA1VVVf6lrKwsgi0mIiKic9E1ElJXV4cXXngBn3zySaNP0d2+ffsFt9GmTRvYbDZUVFQErK+oqEBqamqjdZ544glMmDABd911FwCgd+/eqK2txT333IPHHnus0bt1zvekQCIiIrOzoAlzQkLaktDTFYTceeed+OCDD3DLLbdg4MCBQc8DOZvD4UB6ejo2btyI0aNHAwA0TcPGjRsxderURuucPHmyQaBxJo2sUia/8EVERKRHhNK2G0FXEPLnP/8Z69atw5AhQ5q087y8POTk5GDAgAEYOHAgFi5ciNraWv/dMhMnTkSHDh1QUFAAAMjOzsaCBQvQv39/DBo0CPv27cMTTzyB7OzssOS0JyIiMhwnpgbq0KEDEhISmrzzsWPH4siRI5g5cybKy8vRr18/rF+/3j9ZtbS0NGDk4/HHH4fFYsHjjz+OQ4cOoW3btsjOzsZTTz3V5LYQERFRZFmUjusYf/nLX/D73/8eixcvRqdOncLRrrCprq5GUlISBmfOhj3GFVSdiNxWaZNV0mJk5X3C8lrMhcs02IdD2CaHbPtamLcPyPuthGG8JiyvbPKfMWG/RVdIzy26Fp/sWFu9wu0Ly1vrZeUBwOYR7sMjO9bS7duE2wfk/bbVy/ZhFZa3+IR90DEKEOzcC299HTZ/lI+qqiokJibKdxSEM3+vOs19ClZXcH+vfkirq8M3v3ksrO1sCl0jIQMGDEBdXR26du2KuLi4BsnKjh07FpLGERERXeyYrOwHxo0bh0OHDmHu3LlISUnRNTGViCiaRWIEVULp+p422V8waRdM1nzdOCck0ObNm7Flyxb07ds31O0hIiKis0VxEKLr6nHPnj1x6tSpULeFiIiILiK6gpB58+bhwQcfRFFREb777jtUV1cHLERERBQaZ+aE6F3MTNflmBtvvBEAcMMNNwSsV0rBYrHA5/M1vWVERETEZGU/FIoH2BEREVEQonhOiK4gZOjQoaFuBxEREV1kdAUhZ5w8eRKlpaXweAKz5vTp06dJjSIiIqLTmCfkB44cOYLc3Fz85S9/afT/OSeEiIgoRKL4coyuu2MeeOABHD9+HH//+98RGxuL9evXY9myZbj88svx/vvvh7qNREREF6+m3Blj8iBE10jIxx9/jD/96U8YMGAArFYrOnXqhBEjRiAxMREFBQUYNWpUqNtpKOlwlhknI5t9SC4YEclAKawjfU6LssoOhJ7nwJjt2TG6fuoIT1hNeOCEhwHyJ2wBSvjMHGWV9UH6XCGlY4Bafn6Ht7ye5xBFBY6EBKqtrUVycjIAoFWrVjhy5AgAoHfv3ti+fXvoWkdERERRS1cQ0qNHD+zZswcA0LdvX7zyyis4dOgQFi9ejHbt2oW0gURERBc11cTFxHRdjrn//vtx+PBhAEB+fj5uvPFGvPnmm3A4HFi2bFlIG0hERHQx490xP/DLX/7S/+/09HR888032L17Nzp27Ig2bdqErHFEREQUvXQFIXl5eY2ut1gscLlcuOyyy3DzzTejdevWTWocERHRRS+KJ6bqCkJ27NiB7du3w+fzoUePHgCAvXv3wmazoWfPnnjppZfw4IMP4tNPP0WvXr1C2mAiIiKKDrompt58883IzMzEt99+i+LiYhQXF+Nf//oXRowYgXHjxuHQoUP48Y9/jGnTpoW6vURERBeVaH6Krq4g5He/+x3mzJmDxMRE/7qkpCTMmjULzzzzDOLi4jBz5kwUFxeHrKFEREQXrSi8MwbQGYRUVVWhsrKywfojR46guroaANCyZcsGz5QhIiIiId6iG+jmm2/GHXfcgWeffRbXXHMNAOAf//gHHnroIYwePRoAsHXrVnTv3j1kDW1W9Bx0aR0TnljSjKbSDJHSkFnTcXZL62gxwsyeMbLtK7v8QCubsHyYM/zqGQ62CLN7Wryy8kp4nJVH/iaFP3Oy9AMnPxAWTbYPaUZTaXlp1leLnpPPhN+tvEX3B1555RVMmzYNt912G7ze059+u92OnJwcPPfccwCAnj174tVXXw1dS4mIiCiq6ApC4uPjsXTpUjz33HPYv38/AKBr166Ij4/3l+nXr19IGkhERHRR4y26jYuPj0efPn1C1RYiIiL6AV6OISIiImNwJISIiIgMEcVBiK5bdImIiIiaiiMhREREJsY5IURERGQMXo4Jn0WLFqFz585wuVwYNGgQtm7det7yx48fx5QpU9CuXTs4nU50794d69ati1BriYiIIixCGVMLCgpwzTXXICEhAcnJyRg9ejT27NkTwo40ZOhIyKpVq5CXl4fFixdj0KBBWLhwIbKysrBnzx4kJyc3KO/xeDBixAgkJydj9erV6NChA7755hu0bNkyvA0VZhq0SDMZ6tlH2DOs6uiDtEqYM6BKM4cCgOaUvVE+YXnNISuvhOUBADZhHWl54cmn9KQO9QnrCMtb6mXlrW55H2zCOsouLC89vy16UriG93tJnGFV+B1gqZeVN6tIXY7561//iilTpuCaa66B1+vFb37zG/zkJz/BP//5T7Ro0UJfAy7A0CBkwYIFuPvuu5GbmwsAWLx4MdauXYvXX38d06dPb1D+9ddfx7Fjx7B582bExJzOf925c+dINpmIiCgqrV+/PuB1YWEhkpOTUVxcjB//+Mdh2adhl2M8Hg+Ki4uRmZn5n8ZYrcjMzMSWLVsarfP+++8jIyMDU6ZMQUpKCq666irMnTsXPt+5HyjgdrtRXV0dsBARETUbIbgc88O/g263+4K7raqqAgC0bt06pN05m2FByNGjR+Hz+ZCSkhKwPiUlBeXl5Y3W2b9/P1avXg2fz4d169bhiSeewLPPPosnn3zynPspKChAUlKSf0lLSwtpP4iIiMLpzOUYvQsApKWlBfwtLCgoOO8+NU3DAw88gCFDhuCqq64KW9+a1d0xmqYhOTkZS5Ysgc1mQ3p6Og4dOoTf/e53yM/Pb7TOjBkzkJeX539dXV3NQISIiJqPENwdU1ZWhsTERP9qp9N53mpTpkzBl19+iU8//VTnjoNjWBDSpk0b2Gw2VFRUBKyvqKhAampqo3XatWuHmJgY2Gz/mZF1xRVXoLy8HB6PBw6Ho0Edp9N5wTebiIjItEIQhCQmJgYEIeczdepU/PnPf8amTZtw6aWX6txxcAy7HONwOJCeno6NGzf612maho0bNyIjI6PROkOGDMG+ffugaf+ZUr137160a9eu0QCEiIiIgqOUwtSpU/Hee+/h448/RpcuXcK+T0PzhOTl5WHp0qVYtmwZdu3ahfvuuw+1tbX+u2UmTpyIGTNm+Mvfd999OHbsGO6//37s3bsXa9euxdy5czFlyhSjukBERBRWliYuwZoyZQrefPNNLF++HAkJCSgvL0d5eTlOnToVus78gKFzQsaOHYsjR45g5syZKC8vR79+/bB+/Xr/ZNXS0lJYrf+Jk9LS0rBhwwZMmzYNffr0QYcOHXD//ffj0UcfNaoLRERE4RWhjKkvv/wyAGDYsGEB69944w1MmjRJZwPOz/CJqVOnTsXUqVMb/b+ioqIG6zIyMvD555+HuVWBxElifMIMPAAgTOgkHcJSVtn2tXPf9XxO0sRD8h3IiisdZ7c0IZo0uZlyyd4kq8srKg8AthjZwbPbhW2yhj8PtDB3H7xeWeYub72svM8tz3yn1Qn3cVL2qbbHiIpD2eTJyqTfG/IdyIqLk5vJPz6wmDDPeaSSlSnpBy8EDA9CiIiI6Dz47BgiIiKi0OJICBERkdmZfERDLwYhREREJhapOSFGYBBCRERkZlE8J4RBCBERkYlF80gIJ6YSERGRITgSQkREZGa8HENERERGiObLMQxCgmDxyY6iRdNx1IX70HzS9KHC4hb5lTolTCqpCTM+WqR91pPBVZyVVXhuOGTZTB06MqbGOj2i8nGOetn2Y2TlbTpS6SphBmGPJjv56ryyr75at/wBmXWnZHXqXbIPhFYr67MWo+MzLcyYqizh/V6SnkpW4fcqAEBHltWw40gIERERGSKKgxBOTCUiIiJDcCSEiIjIxDgnhIiIiIwRxZdjGIQQERGZmEUpWJS+aEJvvUhhEEJERGRmUTwSwompREREZAiOhBAREZkYJ6YSERGRMaL4cgyDkCBIM6ZaPbKsmIA8y6pVmsmwXnblzeITpj8FYBGmTJVmV9SEZ6tPnuRSnJFRnPHRJqsQEyNP35jocovKt3bVisonOepE5VvYZBlcAcBulX2GvMKMqad8suykVfUuUXkA+K6uhaj88ZOxovI1Lln5eocwRTEAZZF+D8g+0xZNVt4q/DjYPMIMrgBsJvyrzZEQIiIiMkYUj4RwYioREREZgiMhREREJsbLMURERGSMKL4cwyCEiIjI5Mw+oqEXgxAiIiIzU+r0oreuiXFiKhERERmCIyFEREQmxompFzlrvSyDldUtTzBlccsTnInYhEmBYuWJjaz1wjpKdvopq2zgTouRJyryuYTJlrzCfQi/EOxWafY0IN4hS1bW1nVCVL69s0pUvk1Mjag8ALSwyvpghex9qlOyTHbfe2WJxwCg0pMgKn8orqWo/L+csvJH7PGi8gDgsThF5S3CPykWn+zzY5OdFtDq5N8BpsSJqURERGQEi6Yjm/NZdc3MFHNCFi1ahM6dO8PlcmHQoEHYunVrUPVWrlwJi8WC0aNHh7eBRERERlFNXEzM8CBk1apVyMvLQ35+PrZv346+ffsiKysLlZWV56138OBBPPTQQ7juuusi1FIiIiIKJcODkAULFuDuu+9Gbm4uevXqhcWLFyMuLg6vv/76Oev4fD7cfvvtmD17Nrp27RrB1hIREUXWmYmpehczMzQI8Xg8KC4uRmZmpn+d1WpFZmYmtmzZcs56v/3tb5GcnIw777zzgvtwu92orq4OWIiIiJqNM3lC9C4mZmgQcvToUfh8PqSkpASsT0lJQXl5eaN1Pv30U7z22mtYunRpUPsoKChAUlKSf0lLS2tyu4mIiCKFIyEmUVNTgwkTJmDp0qVo06ZNUHVmzJiBqqoq/1JWVhbmVhIREYVQFE9MNfQW3TZt2sBms6GioiJgfUVFBVJTUxuU//rrr3Hw4EFkZ2f712na6fuP7HY79uzZg27dugXUcTqdcDpl97oTERFR+Bk6EuJwOJCeno6NGzf612maho0bNyIjI6NB+Z49e2Lnzp0oKSnxLzfddBOGDx+OkpISXmohIqKoE82XYwxPVpaXl4ecnBwMGDAAAwcOxMKFC1FbW4vc3FwAwMSJE9GhQwcUFBTA5XLhqquuCqjfsmVLAGiwPpQsPmHG1FP18n2cEqYC9AozrNpk8abllDxjqsXtkpVXshEqZZW1yeeUx9jeWGFm2XpZeU0TZoi0yr9B4uweUfm2DlnG1E7Oo6LyaTHficoDwCXWk6LyTovs81Av/P1Vo8kyrAJAuTCj6X5nW1H5BLvsOyPGJs/KfEglicp7vbL31eq2icrbT8o+P8pm8r/AwYriB9gZHoSMHTsWR44cwcyZM1FeXo5+/fph/fr1/smqpaWlsArTdRMREUULPjsmzKZOnYqpU6c2+n9FRUXnrVtYWBj6BhEREZlFFD87hkMMRERE5Kf3USp6MAghIiIysUhOTNX7KBW9GIQQERGZmaaatgjoeZRKUzAIISIiMrMQJCv74eNL3O6Gd1fpfZRKUzAIISIiMjELmnA55t/bSEtLC3iESUFBQYP96HmUSlOZ4u4YIiIiCp+ysjIkJib6X5slkziDECIiIjMLQbKyxMTEgCCkMdJHqYQCg5AgWLyyjKnwyDOmqpOnwrsPJeyDjijZJs3iKrwY6IiRVfC6ZNkVAcDbQlan3iMr7xNmlPQJM6wCgFU4HT7OKsuw2tomy7Da3lYjKg8AqbJEmoi3hvdX3UklzGgMoK3tsKh8C6t8HxJuTfimAqj1yDLFHhFmWva5ZJ8HzSHMUGyTf37MKFLJys5+lMro0aMB/OdRKufK5dVUDEKIiIjMLILJyi70KJVQYxBCRERkYhalYNF5OUZa70KPUgk1BiFERETkd75HqYQagxAiIiIz0/696K1rYgxCiIiITCySl2MijUEIERGRmUXxU3QZhBAREZlZCPKEmBXTthMREZEhOBJCRERkYpFKVmYEBiHB8MmOoqXeK96FauSJhuejnaqT7cAny2ZqqdORvVG4D6tNNhBnd8hOV0esfKCvXpgx1V4rK+89Jctaecoty1gJANUel6j8CZ8s2+hJTVbereSZOn2QfYaskB0Hm0V2brh0fFXGWWSfoQSrLGtykk1WPt4uy4wLAC678DjEyG7FUHbZd6sSZkBV0TLWH8WXYxiEEBERmZhFO73orWtmDEKIiIjMLIpHQqJlsIqIiIiaGY6EEBERmRnzhBAREZERmDGViIiIjME5IUREREShxZEQIiIiM1PQ/zRccw+EMAgxDU14pggTgymvLOmQEm4fAKxW2cCa1S47/ewxwmRlTh1JspyyPmgOWfIk6fbr7LGi8gBQamslKm8VplR0a7LjUB0n70N5zHei8m1tNaLyLovs/PYJk6EBwHEtXlT+23rZcTvsaSkqf9wjPw51XtmxVj7Z+2QVlpf+QTV7ttBgcU4IERERGUOhCXNCQtqSkGMQQkREZGacmEpEREQUWqYIQhYtWoTOnTvD5XJh0KBB2Lp16znLLl26FNdddx1atWqFVq1aITMz87zliYiImjWtiYuJGR6ErFq1Cnl5ecjPz8f27dvRt29fZGVlobKystHyRUVFGDduHD755BNs2bIFaWlp+MlPfoJDhw5FuOVEREThd2Ziqt7FzAwPQhYsWIC7774bubm56NWrFxYvXoy4uDi8/vrrjZZ/66238Ktf/Qr9+vVDz5498eqrr0LTNGzcuDHCLSciIoqAM3NC9C4mZmgQ4vF4UFxcjMzMTP86q9WKzMxMbNmyJahtnDx5EvX19WjdunWj/+92u1FdXR2wEBERNRsMQsLj6NGj8Pl8SElJCVifkpKC8vLyoLbx6KOPon379gGBzNkKCgqQlJTkX9LS0prcbiIiImo6wy/HNMW8efOwcuVKvPfee3C5XI2WmTFjBqqqqvxLWVlZhFtJRETUBFE8EmJonpA2bdrAZrOhoqIiYH1FRQVSU1PPW3f+/PmYN28ePvroI/Tp0+ec5ZxOJ5xOZ9MaahNm9bPJM3XCESMqbvEIMxlKM7Iq+ZRqVS/MylpXJypvqXWIysd8Lz8OyirNjCk8bsIMkTa3/CN66mSCqPw/qxsP4M/lmyRZZs+ShEtF5QGgbewJUfnWjpOi8rFWj6i8NKssIM8se7xeltG08pTsOB+pbSEqDwBV1cI6NbLPg032FQDhYYNF+r1nVhqgI2nvf+qamKEjIQ6HA+np6QGTSs9MMs3IyDhnvWeeeQZz5szB+vXrMWDAgEg0lYiIyBDRfHeM4RlT8/LykJOTgwEDBmDgwIFYuHAhamtrkZubCwCYOHEiOnTogIKCAgDA008/jZkzZ2L58uXo3Lmzf+5IfHw84uNlz2ogIiIyvSjOmGp4EDJ27FgcOXIEM2fORHl5Ofr164f169f7J6uWlpYGPBjt5ZdfhsfjwS233BKwnfz8fMyaNSuSTSciIqImMDwIAYCpU6di6tSpjf5fUVFRwOuDBw+Gv0FERERmoSn9jwQ2+bwYUwQhREREdA68HENERETGaMqttgxCiIiISK8oHglp1snKiIiIqPniSAgREZGZaQq6L6twYmrzp8XIMm9aY+UZWi2aMK2dMCurze0WlVc+eZo9iyXMmWW9PlFxy0lZnwEgxiobHJRmZJRmQI05Ic/66vxe1gfPEdn56mkhy1x7MC5RVB4A9scJzz+HrLw1Rngu6Rgzlo6CK2E2XeWRnRsWt7wTtlpZHWetrA8xssS4iDkpe1Ot9bLtm5bSdGWx9tc1MQYhREREZhbFc0IYhBAREZlZFF+O4cRUIiIiMgRHQoiIiMyMl2OIiIjIEApNCEJC2pKQ4+UYIiIiMzszEqJ3CYODBw/izjvvRJcuXRAbG4tu3bohPz8fHo9HtB2OhBAREZmZpgHQeautNP1DkHbv3g1N0/DKK6/gsssuw5dffom7774btbW1mD9/ftDbYRBCREREIjfeeCNuvPFG/+uuXbtiz549ePnllxmEEBERRY0QTEytrq4OWO10OuF0yhNrnk9VVRVat24tqsMgJAiaU5aZ0JvoEu/D6pAdCkt8nKx8vVdWXs8QnvRDIsywqoTZTGGXZxuVstXJ3len8D2y1cmPg7Na9j75nLLyXqfsuPkcwky6ADSH7NhpMbLPjxKeGkpPxlRhty3Sj4/w1LDIksSeriM7vWHzCM9v2fQB2Nyy7dvd5s4WGrQQBCFpaWkBq/Pz8zFr1qwmNuw/9u3bhxdeeEE0CgIwCCEiIjK3ECQrKysrQ2Lifx6hcK5RkOnTp+Ppp58+7yZ37dqFnj17+l8fOnQIN954I2699VbcfffdouYxCCEiIjIxpTQonc+AOVMvMTExIAg5lwcffBCTJk06b5muXbv6//3tt99i+PDhGDx4MJYsWSJuH4MQIiIiAgC0bdsWbdu2DarsoUOHMHz4cKSnp+ONN96AVXrJHAxCiIiIzE0p/c+ACVOekEOHDmHYsGHo1KkT5s+fjyNHjvj/LzU1NejtMAghIiIyM9WEOSFhCkI+/PBD7Nu3D/v27cOll176g10Gv09mTCUiIjIzTWvaEgaTJk2CUqrRRYIjIURERGZmwpGQUOFICBERERmCIyFB8LlkmY2k5QEASQ55HaJGSBM62dyyLFY8U4kiS2kalDQ73Zm6Om/tjRQGIURERGYWxZdjGIQQERGZmabkef3PYBBCREREuikFQOdlFZMHIZyYSkRERIbgSAgREZGJKU1B6bwcI83bEWmmGAlZtGgROnfuDJfLhUGDBmHr1q3nLf/OO++gZ8+ecLlc6N27N9atWxehlhIREUWY0pq2mJjhQciqVauQl5eH/Px8bN++HX379kVWVhYqKysbLb9582aMGzcOd955J3bs2IHRo0dj9OjR+PLLLyPcciIiovBTmmrSYmaGByELFizA3XffjdzcXPTq1QuLFy9GXFwcXn/99UbLP//887jxxhvx8MMP44orrsCcOXNw9dVX48UXX4xwy4mIiCIgikdCDJ0T4vF4UFxcjBkzZvjXWa1WZGZmYsuWLY3W2bJlC/Ly8gLWZWVlYc2aNY2Wd7vdcLvd/tdVVVUAAK+3romtJyKii9WZvyGRmHPhRb3uNCFe1Ie2MSFmaBBy9OhR+Hw+pKSkBKxPSUnB7t27G61TXl7eaPny8vJGyxcUFGD27NkN1m8tKtDZaiIiotNqamqQlJQUlm07HA6kpqbi0/KmzXtMTU2Fw2HOXMdRf3fMjBkzAkZOjh8/jk6dOqG0tDRsJ04kVVdXIy0tDWVlZUhMTDS6OU3CvphXNPWHfTGv5tQfpRRqamrQvn37sO3D5XLhwIED8Hg8TdqOw+GAy+UKUatCy9AgpE2bNrDZbKioqAhYX1FRgdTU1EbrpKamiso7nU44nc4G65OSkkx/kkskJiZGTX/YF/OKpv6wL+bVXPoTiR+yLpfLtAFEKBg6MdXhcCA9PR0bN270r9M0DRs3bkRGRkajdTIyMgLKA8CHH354zvJERERkToZfjsnLy0NOTg4GDBiAgQMHYuHChaitrUVubi4AYOLEiejQoQMKCk7P4bj//vsxdOhQPPvssxg1ahRWrlyJbdu2YcmSJUZ2g4iIiIQMD0LGjh2LI0eOYObMmSgvL0e/fv2wfv16/+TT0tJSWK3/GbAZPHgwli9fjscffxy/+c1vcPnll2PNmjW46qqrgtqf0+lEfn5+o5domqNo6g/7Yl7R1B/2xbyirT90YRZl9pyuREREFJUMT1ZGREREFycGIURERGQIBiFERERkCAYhREREZIioDEIWLVqEzp07w+VyYdCgQdi6det5y7/zzjvo2bMnXC4XevfujXXrmpYiN9Qk/Vm6dCmuu+46tGrVCq1atUJmZuYF+x9J0mNzxsqVK2GxWDB69OjwNlBA2pfjx49jypQpaNeuHZxOJ7p3726qc03an4ULF6JHjx6IjY1FWloapk2bhro645/JtGnTJmRnZ6N9+/awWCznfK7U2YqKinD11VfD6XTisssuQ2FhYdjbGQxpX959912MGDECbdu2RWJiIjIyMrBhw4bINPYC9ByXMz777DPY7Xb069cvbO0jY0RdELJq1Srk5eUhPz8f27dvR9++fZGVlYXKyspGy2/evBnjxo3DnXfeiR07dmD06NEYPXo0vvzyywi3vHHS/hQVFWHcuHH45JNPsGXLFqSlpeEnP/kJDh06FOGWNyTtyxkHDx7EQw89hOuuuy5CLb0waV88Hg9GjBiBgwcPYvXq1dizZw+WLl2KDh06RLjljZP2Z/ny5Zg+fTry8/Oxa9cuvPbaa1i1ahV+85vfRLjlDdXW1qJv375YtGhRUOUPHDiAUaNGYfjw4SgpKcEDDzyAu+66yxR/vKV92bRpE0aMGIF169ahuLgYw4cPR3Z2Nnbs2BHmll6YtC9nHD9+HBMnTsQNN9wQppaRoVSUGThwoJoyZYr/tc/nU+3bt1cFBQWNlh8zZowaNWpUwLpBgwape++9N6ztDJa0Pz/k9XpVQkKCWrZsWbiaGDQ9ffF6vWrw4MHq1VdfVTk5Oermm2+OQEsvTNqXl19+WXXt2lV5PJ5INVFE2p8pU6ao66+/PmBdXl6eGjJkSFjbKQVAvffee+ct88gjj6grr7wyYN3YsWNVVlZWGFsmF0xfGtOrVy81e/bs0DeoCSR9GTt2rHr88cdVfn6+6tu3b1jbRZEXVSMhHo8HxcXFyMzM9K+zWq3IzMzEli1bGq2zZcuWgPIAkJWVdc7ykaSnPz908uRJ1NfXo3Xr1uFqZlD09uW3v/0tkpOTceedd0aimUHR05f3338fGRkZmDJlClJSUnDVVVdh7ty58Pl8kWr2Oenpz+DBg1FcXOy/ZLN//36sW7cOP/3pTyPS5lAy83dAU2mahpqaGsM//3q98cYb2L9/P/Lz841uCoWJ4RlTQ+no0aPw+Xz+bKtnpKSkYPfu3Y3WKS8vb7R8eXl52NoZLD39+aFHH30U7du3b/AlG2l6+vLpp5/itddeQ0lJSQRaGDw9fdm/fz8+/vhj3H777Vi3bh327duHX/3qV6ivrzf8C1ZPf8aPH4+jR4/i2muvhVIKXq8XkydPNsXlGKlzfQdUV1fj1KlTiI2NNahlTTd//nycOHECY8aMMbopYl999RWmT5+Ov/3tb7Dbo+pPFZ0lqkZCKNC8efOwcuVKvPfee83uKYw1NTWYMGECli5dijZt2hjdnCbTNA3JyclYsmQJ0tPTMXbsWDz22GNYvHix0U3TpaioCHPnzsVLL72E7du3491338XatWsxZ84co5tG/7Z8+XLMnj0bb7/9NpKTk41ujojP58P48eMxe/ZsdO/e3ejmUBhFVXjZpk0b2Gw2VFRUBKyvqKhAampqo3VSU1NF5SNJT3/OmD9/PubNm4ePPvoIffr0CWczgyLty9dff42DBw8iOzvbv07TNACA3W7Hnj170K1bt/A2+hz0HJd27dohJiYGNpvNv+6KK65AeXk5PB4PHA5HWNt8Pnr688QTT2DChAm46667AAC9e/dGbW0t7rnnHjz22GMBz3syu3N9ByQmJjbbUZCVK1firrvuwjvvvGP4KKgeNTU12LZtG3bs2IGpU6cCOP35V0rBbrfjgw8+wPXXX29wKykUms83RRAcDgfS09OxceNG/zpN07Bx40ZkZGQ0WicjIyOgPAB8+OGH5ywfSXr6AwDPPPMM5syZg/Xr12PAgAGRaOoFSfvSs2dP7Ny5EyUlJf7lpptu8t/BkJaWFsnmB9BzXIYMGYJ9+/b5AykA2Lt3L9q1a2doAALo68/JkycbBBpnAizVzB5HZebvAD1WrFiB3NxcrFixAqNGjTK6ObokJiY2+PxPnjwZPXr0QElJCQYNGmR0EylUDJ4YG3IrV65UTqdTFRYWqn/+85/qnnvuUS1btlTl5eVKKaUmTJigpk+f7i//2WefKbvdrubPn6927dql8vPzVUxMjNq5c6dRXQgg7c+8efOUw+FQq1evVocPH/YvNTU1RnXBT9qXHzLT3THSvpSWlqqEhAQ1depUtWfPHvXnP/9ZJScnqyeffNKoLgSQ9ic/P18lJCSoFStWqP3796sPPvhAdevWTY0ZM8aoLvjV1NSoHTt2qB07digAasGCBWrHjh3qm2++UUopNX36dDVhwgR/+f3796u4uDj18MMPq127dqlFixYpm82m1q9fb1QX/KR9eeutt5TdbleLFi0K+PwfP37cqC74SfvyQ7w7JjpFXRCilFIvvPCC6tixo3I4HGrgwIHq888/9//f0KFDVU5OTkD5t99+W3Xv3l05HA515ZVXqrVr10a4xecn6U+nTp0UgAZLfn5+5BveCOmxOZuZghCl5H3ZvHmzGjRokHI6napr167qqaeeUl6vN8KtPjdJf+rr69WsWbNUt27dlMvlUmlpaepXv/qV+v777yPf8B/45JNPGv0MnGl/Tk6OGjp0aIM6/fr1Uw6HQ3Xt2lW98cYbEW93Y6R9GTp06HnLG0nPcTkbg5DoZFGqmY2dEhERUVSIqjkhRERE1HwwCCEiIiJDMAghIiIiQzAIISIiIkMwCCEiIiJDMAghIiIiQzAIISIiIkMwCCEiIiJDMAghimLDhg3DAw88YHQziIgaxSCEiM6psLAQLVu2NLoZRBSlGIQQERGRIRiEEEU5r9eLqVOnIikpCW3atMETTzyBM4+McrvdeOihh9ChQwe0aNECgwYNQlFREQCgqKgIubm5qKqqgsVigcViwaxZswAAf/zjHzFgwAAkJCQgNTUV48ePR2VlpUE9JKLmikEIUZRbtmwZ7HY7tm7diueffx4LFizAq6++CgCYOnUqtmzZgpUrV+J///d/ceutt+LGG2/EV199hcGDB2PhwoVITEzE4cOHcfjwYTz00EMAgPr6esyZMwdffPEF1qxZg4MHD2LSpEkG9pKImiM+RZcoig0bNgyVlZX4v//7P1gsFgDA9OnT8f7772P9+vXo2rUrSktL0b59e3+dzMxMDBw4EHPnzkVhYSEeeOABHD9+/Lz72bZtG6655hrU1NQgPj4+nF0ioijCkRCiKPejH/3IH4AAQEZGBr766ivs3LkTPp8P3bt3R3x8vH/561//iq+//vq82ywuLkZ2djY6duyIhIQEDB06FABQWloa1r78//buPTyK8uwf+HcP2d2cQ8gJMBBAOSkCgqSgVNDQQH2x9H0FRAuBV7BUeEWCbZNaCJFK0CLFUhTBKrQq4BH9FUQtgocaRAJYawHBAIlAEiLmnOxmd+b3B83KkgB7Tw4z2Xw/1zWXZnLP7PPszix3nnnmHiIKLFa9G0BE+qiqqoLFYkFeXh4sFovP7y43mlFdXY3U1FSkpqbixRdfRGxsLAoKCpCamgqXy9XazSaiAMIkhCjAffrppz4/79mzB9dccw2GDBkCj8eDkpISjBo1qsltbTYbPB6Pz7rDhw/j22+/xfLly5GYmAjg/OUYIiIpXo4hCnAFBQVIT0/HkSNHsGnTJqxevRrz589Hnz59cM8992D69Ol4/fXXcfz4cezduxc5OTnYtm0bACApKQlVVVXYuXMnSktLUVNTg+7du8Nms2H16tXIz8/HW2+9haVLl+rcSyJqj5iEEAW46dOno7a2FsOHD8fcuXMxf/583HfffQCA559/HtOnT8fChQvRt29fTJw4EZ999hm6d+8OABg5ciTmzJmDKVOmIDY2Fo8//jhiY2OxYcMGvPLKKxgwYACWL1+OFStW6NlFImqneHcMERER6YIjIURERKQLJiFERESkCyYhREREpAsmIURERKQLJiFERESkCyYhREREpAsmIURERKQLJiFERESkCyYhREREpAsmIURERKQLJiFERESkCyYhREREpAsmIURERKQLJiFERESkCyYhREREpAsmIURERKQLJiFERESkCyYhREREpAsmIURERKQLJiFERESkCyYhREREpAsmIURERKQLJiFERESkCyYhREREpAsmIURERKQLJiFERESkCyYhREREpAsmIURERKQLJiFERESkCyYhREREpAsmIURERKQLJiFERESkCyYhRM20ZMkSmEwmn3VJSUmYMWOGz7qjR4/iRz/6ESIjI2EymbB161YAwGeffYaRI0ciNDQUJpMJBw8ebJuGt3NNvcdE1L5Y9W4AUUeRlpaG48eP49FHH0VUVBSGDRuG+vp6TJo0CQ6HA3/4wx8QEhKCHj166N3UgHb69GmsW7cOEydOxODBg/VuDlGHxiSEqBUcOXIEZvP3A421tbXIzc3Fww8/jHnz5nnXHz58GCdPnsT69esxa9YsPZra4Zw+fRrZ2dlISkpiEkKkM16OIWoFdrsdQUFB3p/Pnj0LAIiKivKJKykpaXJ9c1RXV7fYvoiIWhOTECKBjz/+GDfeeCMcDgd69+6NZ555psm4C+crLFmyxHuJ5Ze//CVMJpP397fccgsAYNKkSTCZTBg9erR3H4cPH8add96J6OhoOBwODBs2DG+99ZbP62zYsAEmkwkffPAB7r//fsTFxeGqq67y/v7tt9/GqFGjEBoaivDwcNx+++348ssvffYxY8YMhIWF4dSpU5g4cSLCwsIQGxuLhx56CB6PxydWURQ8+eSTGDhwIBwOB2JjYzFu3Djs27fPJ+6FF17A0KFDERwcjOjoaNx1110oLCy84vvbML/m8OHDmDx5MiIiItC5c2fMnz8fdXV1V9w+Pz8fkyZNQnR0NEJCQvCDH/wA27Zt8/5+9+7duPHGGwEAM2fOhMlkgslkwoYNG664byJqebwcQ+SnL774Aj/60Y8QGxuLJUuWwO12IysrC/Hx8Zfd7r//+78RFRWFBQsWYOrUqfjxj3+MsLAwxMfHo1u3bli2bBkeeOAB3Hjjjd59ffnll7jpppvQrVs3ZGRkIDQ0FC+//DImTpyI1157DT/96U99XuP+++9HbGwsFi9e7B0J+etf/4q0tDSkpqbiscceQ01NDZ5++mncfPPNOHDgAJKSkrzbezwepKamIjk5GStWrMDf//53PPHEE+jduzd+8YtfeOPuvfdebNiwAePHj8esWbPgdrvx0UcfYc+ePRg2bBgA4NFHH8WiRYswefJkzJo1C2fPnsXq1avxwx/+EAcOHPBr1Gfy5MlISkpCTk4O9uzZgz/+8Y/47rvv8Je//OWS2xQXF2PkyJGoqanBAw88gM6dO2Pjxo2444478Oqrr+KnP/0p+vfvj0ceeQSLFy/Gfffdh1GjRgEARo4cecU2EVErUInILxMnTlQdDod68uRJ77p///vfqsViUS8+lXr06KGmpaV5fz5+/LgKQP3973/vE7dr1y4VgPrKK6/4rL/tttvUgQMHqnV1dd51iqKoI0eOVK+55hrvuueff14FoN58882q2+32rq+srFSjoqLU2bNn++y3qKhIjYyM9FmflpamAlAfeeQRn9ghQ4aoQ4cO9f78/vvvqwDUBx54oNF7oyiKqqqqeuLECdVisaiPPvqoz++/+OIL1Wq1Nlp/saysLBWAescdd/isv//++1UA6ueff+5dd/F7/OCDD6oA1I8++sjnfejZs6ealJSkejweVVVV9bPPPlMBqM8///xl20JErY+XY4j84PF48M4772DixIno3r27d33//v2Rmpraoq917tw5vP/++5g8eTIqKytRWlqK0tJSfPvtt0hNTcXRo0dx6tQpn21mz54Ni8Xi/fm9995DWVkZpk6d6t2+tLQUFosFycnJ2LVrV6PXnTNnjs/Po0aNQn5+vvfn1157DSaTCVlZWY22bbhF+fXXX4eiKJg8ebLP6yYkJOCaa65p8nWbMnfuXJ+f/+///g8AsH379ktus337dgwfPhw333yzd11YWBjuu+8+nDhxAv/+97/9em0iaju8HEPkh7Nnz6K2thbXXHNNo9/17dv3sv84Sh07dgyqqmLRokVYtGhRkzElJSXo1q2b9+eePXv6/P7o0aMAgFtvvbXJ7SMiInx+bpjfcaFOnTrhu+++8/789ddfo2vXroiOjr5k248ePQpVVZt8nwD4TNa9nIu37927N8xmM06cOHHJbU6ePInk5ORG6/v37+/9/XXXXefX6xNR22ASQmQwiqIAAB566KFLjrJcffXVPj8HBwc3uY+//vWvSEhIaLS91ep76l84itIciqLAZDLh7bffbnKfYWFhmvZ7cTE4IgoMTEKI/BAbG4vg4GDvCMOFjhw50qKv1atXLwDnRw1SUlI07aN3794AgLi4OM37aGqf77zzDs6dO3fJ0ZDevXtDVVX07NkTffr00fxaR48e9RndOXbsGBRF8ZlMe7EePXo0+VkcPnzY+3uACQ2RkXBOCJEfLBYLUlNTsXXrVhQUFHjXHzp0CO+8806LvlZcXBxGjx6NZ555BmfOnGn0+4aaI5eTmpqKiIgILFu2DPX19Zr2cbH/+Z//gaqqyM7ObvQ7VVUBnL8TyGKxIDs727vuwphvv/3Wr9das2aNz8+rV68GAIwfP/6S2/z4xz/G3r17kZub611XXV2NdevWISkpCQMGDAAAhIaGAgDKysr8agsRtR6OhBD5KTs7Gzt27MCoUaNw//33w+12Y/Xq1bj22mvxz3/+s0Vfa82aNbj55psxcOBAzJ49G7169UJxcTFyc3PxzTff4PPPP7/s9hEREXj66acxbdo03HDDDbjrrrsQGxuLgoICbNu2DTfddBP+9Kc/ido0ZswYTJs2DX/84x9x9OhRjBs3Doqi4KOPPsKYMWMwb9489O7dG7/73e+QmZmJEydOYOLEiQgPD8fx48fxxhtv4L777sNDDz10xdc6fvw47rjjDowbNw65ubl44YUXcPfdd2PQoEGX3CYjIwObNm3C+PHj8cADDyA6OhobN27E8ePH8dprr3kr2Pbu3RtRUVFYu3YtwsPDERoaiuTk5EbzaoioDeh4Zw5Ru/PBBx+oQ4cOVW02m9qrVy917dq13ttKL9TcW3RVVVW//vprdfr06WpCQoIaFBSkduvWTf2v//ov9dVXX/XGNNyi+9lnnzXZ3l27dqmpqalqZGSk6nA41N69e6szZsxQ9+3b541JS0tTQ0NDG23bVL/cbrf6+9//Xu3Xr59qs9nU2NhYdfz48WpeXp5P3GuvvabefPPNamhoqBoaGqr269dPnTt3rnrkyJEm23nxa/773/9W77zzTjU8PFzt1KmTOm/ePLW2ttYn9uL3uOE9u/POO9WoqCjV4XCow4cPV//2t781ep0333xTHTBggGq1Wnm7LpGOTKp60ZgpEZFOlixZguzsbJw9exYxMTF6N4eIWhnnhBAREZEumIQQERGRLpiEEBERkS44J4SIiIh0wZEQIiIi0gWTECIiItJFhytWpigKTp8+jfDwcJZvJiIiTVRVRWVlJbp27eothNca6urq4HK5mrUPm80Gh8PRQi1qWR0uCTl9+jQSExP1bgYREQWAwsJCXHXVVa2y77q6OvTsEYaiEk+z9pOQkIDjx48bMhHpcElIeHg4AGDSW5MQFOrfY8UTg7+7ctAFnIr8bS13B1856ALSvNthkWXSNR6b8BUAh7nxM0oux2pWRPFBJtmJWOIMF8UDQLBF1oeooBpRfLGwTdHC/QOAR3h01Hn8Ow8amE2yueyhFqcoHgCqPXZRvEuRPQW4VtjnWHuVKB4AqtyyPkjb1NlWLYrXck73CPbvWT8NzjijxK8h4RZ+ztXC9xTw/311Vddj/bht3n9TWoPL5UJRiQfH83ogIlzbaEtFpYKeQ0/C5XIxCTGChkswQaFBsIX5d1Lag4UHsoYkxOaWfUGYIfuHwG6Rxbs1fGHZheeIPAmRvYAtSN4Hm/CJ9vYg2bEhbZM9SJYUAfIkRGnlJMRukX3OAFAv/cdDeM55hPu32+X/mLmE57S4TTbZHxZazmlHsOx9tQnPBymzMAmp1/I9Jnxf2+KyfmjY+UULj8Hvf+XEVCIiItJFhxsJISIiak8UqFCEo98XbmtkTEKIiIgMTIEC+UXN77c1MiYhREREBuZRVXg0FjfXul1bYRJCRERkYIF8OYYTU4mIiEgXHAkhIiIyMAUqPAE6EtJhk5BOthrYbf7VYKhRZPeaS4sOAUCwWXZveoWwuFmEtVYU38MhK1IEAPWq7B5+afEkaTG0a8NOieIBQFFlg4MeyGoExNraYPBRlU1Ei7NViuLDLHWi+NJ6eTGnTlZZkTZpgUCPGtqq+wcAp0e2TaiwoGCFW1Z4SlqIDwCO1cSJ4u1mt/g1JMwm2bEdHSQr6AYAiurfOe1vXEsI5MsxHTYJISIiag84MZWIiIh0ofxn0bqtkXFiKhEREemCIyFEREQG5mnGxFSt27UVJiFEREQG5lG1P4jO6A+wYxJCRERkYIE8J4RJCBERkYEpMInLAVy4rZFxYioRERHposOOhLhVC8x+FtdyCYsOdbWXidsjLfTVWViER7r/GKusgJWW16i3yN7XRJusgFqdIi8aV+SOFMWHmGQFoMJtskJfZpP8gq60uJ74cxPGRwoLjwFAYV20KF5acE3eB1mxPwBwK8Jz2lYlij/rkhWBO+cKEcUD8gJntcIChNKCX1FBsmNJ+jkD/h9LdUHy4m9aKer5Reu2RtZhkxAiIqL2wNOMyzFat2srvBxDRERkYA1JiNZF4sMPP8SECRPQtWtXmEwmbN26tXU69R9MQoiIiAxMUU3NWiSqq6sxaNAgrFmzppV644uXY4iIiAgAMH78eIwfP77NXo9JCBERkYG1xJyQiooKn/V2ux12u73ZbWsuXo4hIiIyMA/MzVoAIDExEZGRkd4lJydH516dx5EQIiIiA1M1zO24cFsAKCwsREREhHe9EUZBACYhREREhtYSl2MiIiJ8khCj4OUYIiIi0kWHHQnRcuuSv7RU6TMLH7espQqlRKjZKd4mKahUFF/gllXFLPPIKj7GWiuuHHSRxCBhVVZVVpU11OQSxX9dHyeKB4CkoLOi+DiLrFLnl66uongtHCa3KL5OlX2VBZlklT09qvzvNekzO76p7SSKt1tk71GEVX5O13pkx7e0TWWuYFG8glBRvM0saw8AnKzt7Fecq1Z2LjeHRzVrOgbPbyuLr6qqwrFjx7w/Hz9+HAcPHkR0dDS6d++uqQ2X02GTECIiovZAgQmKxgsXivAP3H379mHMmDHen9PT0wEAaWlp2LBhg6Y2XI6ul2OaU5ntH//4B6xWKwYPHtxq7SMiItJbW1ZMHT16NFRVbbS0RgIC6JyEaK3MVlZWhunTp+O2225rpZYREREZQ8PlGK2Lkel6OUZrZbY5c+bg7rvvhsViafW69kRERNQ6jJ0iNeH5559Hfn4+srKy/Ip3Op2oqKjwWYiIiNqL83NCtC9G1q6SkKNHjyIjIwMvvPACrFb/BnFycnJ8qsQlJia2ciuJiIhajtKMaqlaJ7S2FWO37gIejwd33303srOz0adPH7+3y8zMRHl5uXcpLCxsxVYSERG1LM4JMYDKykrs27cPBw4cwLx58wAAiqJAVVVYrVa8++67uPXWWxttZ5SH9BAREWmhNGNEQ3qLbltrN0lIREQEvvjiC591Tz31FN5//328+uqr6Nmzp04tIyIiIi10TUKuVJktMzMTp06dwl/+8heYzWZcd911PtvHxcXB4XA0Wu8Pt2qG2c/KpvE22WTWcEuduD2VHoco3mGqF8VHWWQVVqWVQwF5pdgfOE6J4os8shGta6yy9wgATntkk7hiLbWieLtJ9teMw3RaFA8AJZ4wUXy4WfY+JVjLRPHfCtsDAJGWalF8Tb2s2qhH+FelU5F/VQabZRU1pVWTqz2yqq9Oj7wP0TbZ90ZZvawCqlRkkOx8c2n43IL8rNZrMnvE+9bKo5rg0VjhW+t2bUXXJORKldnOnDmDgoICvZpHRESku4ZJptq25eWYS2qozHYpV6rQtmTJEixZsqRlG0VERGQgimqGonGCqXKZf2ONoN3MCSEiIuqIAnkkxNj37hAREVHA4kgIERGRgSnQPsFUadmmtDgmIURERAbWvDohxr7gwSSEiIjIwJpT+dToFVON3ToiIiIKWBwJISIiMrDmPA3X6E/R7bBJiBn+Vyh0CCtKBpnklfSuC/5GFO8QVmMMgqxNUcL9A0CRJ1QYL6uAGmt2iuLNJlkFVwDoYZWdsNLb5qT37He1+le90XebMlH8aXeQKD7UJDs26s3yCsJSPWyloni78JyuElY0BoAaRVbRNNQqO76lFVPrNFRMLaiRVaKNd1SK4ms9smOvvJUrsgJAnN2/Plg1VGTWKpAvx3TYJISIiKg9aF6dECYhREREpJGimqBovUXX4M+OMXaKRERERAGLIyFEREQGpjTjcgzrhBAREZFmzXuAHZMQIiIi0sgDEzwab7XVul1bYRJCRERkYIE8EmLs1hEREVHA6rAjISFWJ+xW/54vmF8bI9r3DyO/Ercn3xUripcWZ4qzyIoIndNQnCnBUi2Kv8oiK1RULKwBV+qRF42LscgKnAVBFn9OFRb6ktU2AwCEm2XDr0Em2XM2T7gjRfFmDc/xrFNkx8Y5T5go/ju3rLCelgKEIcKCf8XOCFF8tVtWrCwsSFYMDZAfG27hX93S20etwvZEWGtF8QDgVPz7Z9GlaDg5NfJA+2UV+ZHbtjpsEkJERNQeBPLlGCYhREREBsay7URERKQLtRkPsFMNfneMsVMkIiIiClgcCSEiIjIwXo4hIiIiXQTyA+yYhBARERmYpxnPjtG6XVsxduuIiIg6uIaREK2L1Jo1a5CUlASHw4Hk5GTs3bu3FXp1HpMQIiIiAgBs2bIF6enpyMrKwv79+zFo0CCkpqaipKSkVV6vw16OqXHb4Xb7V5WxV7CsOulxp6z6KQD0tJ8VxUeZa8SvIRFplldXjBKmtFVqvSi+uzVEFH/CLX+PvnHL4nv4WXVXq9MeWZ8BIEiR1UgsrO8sig83y6pQnvXIKoECQIFLVqU4zFIniv+uXva+ljplFVkBwGqWfQ4VrmDxa0ho+YvY5Wf10AY2s+wEciuyisN2i2z/EVbZcQEAFW7/qkVrqQSslQIzFI1jBtLtVq5cidmzZ2PmzJkAgLVr12Lbtm147rnnkJGRoakNl9NhkxAiIqL2wKOa4NE4wbRhu4qKCp/1drsddrvdZ53L5UJeXh4yMzO968xmM1JSUpCbm6vp9a+El2OIiIgMrCXmhCQmJiIyMtK75OTkNHqd0tJSeDwexMfH+6yPj49HUVFRq/SNIyFEREQGpjbj2THqf7YrLCxERMT3l0YvHgXRi64jIR9++CEmTJiArl27wmQyYevWrZeNf/311zF27FjExsYiIiICI0aMwDvvvNM2jSUiImqnIiIifJamkpCYmBhYLBYUFxf7rC8uLkZCQkKrtEvXJKS6uhqDBg3CmjVr/Ir/8MMPMXbsWGzfvh15eXkYM2YMJkyYgAMHDrRyS4mIiPThgalZi79sNhuGDh2KnTt3etcpioKdO3dixIgRrdE1fS/HjB8/HuPHj/c7ftWqVT4/L1u2DG+++Sb+3//7fxgyZEiT2zidTjid39/pcfHkHCIiIiNTVO2VTxVVFp+eno60tDQMGzYMw4cPx6pVq1BdXe29W6altes5IYqioLKyEtHR0ZeMycnJQXZ2dhu2ioiIqOUozZgTIt1uypQpOHv2LBYvXoyioiIMHjwYO3bsaDRZtaW067tjVqxYgaqqKkyePPmSMZmZmSgvL/cuhYWFbdhCIiKi5lFgatYiNW/ePJw8eRJOpxOffvopkpOTW6FX57XbkZCXXnoJ2dnZePPNNxEXF3fJuKbuhSYiIiL9tcskZPPmzZg1axZeeeUVpKSkaNpHiNUJu5/VLtviUcjViixRskBWwbFr0Hei+GKPvEIkUCWKzndf+jJaU2ItlaJ4C2yieACoVPyrltjgW0VWNdEirLLoMAlLuAKoU2Wn9UlhddJIi6wSbY3w2AaAcreseujJWlnV17ZQWic7h8KCZFWK6zz+VXxuEC7cPwDYzNWi+MLqTqL4Wj+rVjewCSumnnWFi+IBoNbP99VV7xLvW6uWKFZmVO0uCdm0aRP+93//F5s3b8btt9+ud3OIiIhaVVvOCWlruiYhVVVVOHbsmPfn48eP4+DBg4iOjkb37t2RmZmJU6dO4S9/+QuA85dg0tLS8OSTTyI5OdlbwS04OBiRkZG69IGIiKg1KdD2NNyGbY1M1xRp3759GDJkiPf22vT0dAwZMgSLFy8GAJw5cwYFBQXe+HXr1sHtdmPu3Lno0qWLd5k/f74u7SciImptajMmpaoGT0J0HQkZPXo0VPXSNzFv2LDB5+fdu3e3boOIiIiozbS7OSFEREQdyYUPotOyrZExCSEiIjIwTkwlIiIiXXAkhIiIiHShtfJpw7ZGZuxxGiIiIgpYHXYkxK2YYVH8y8HKIave6PZzvxeKtNSK4l0mWbW+c8IKqC7VIooHgDpVWMHRXCeKL6yXVcUMNcsrREqZVVkF1DIlRBSvpdroSaesAmqFW1Yl1uyQ9fmMK0oUDwDnXKGi+Eq37H1qiyFqaTVQp0f2dex0y+Kr6+UVhK0m2WcdbpOd0zEOWZVlqYIaWQVXAOgVVupXnCuoXrxvrXg5hoiIiHTBJISIiIh0wSSEiIiIdBHISQgnphIREZEuOBJCRERkYCq032p76QejGAOTECIiIgML5MsxTEKIiIgMjEkIERER6YJJSAByKUGA4l8xIauw6FWi45y4PZUeWcGoSEu1KP6oM164f1nxNAD4xiUrJhYifF+DTB5RfGF9tCgeAKIsNaL4c25ZETjp51yvoWhcQa2sQFOwRVZ06cuqbqJ4LV+Cp2oixdtIOCxuUbxNGK9FsFX2OQSZZedDhLCQGAB0ssnOh2ph0bg6j7DAoVX2nWEVfmcAgEvx759FlyIr5EZN67BJCBERUXvAkRAiIiLShaqaoGpMJrRu11aYhBARERlYID9Fl0kIERGRgQXy5RhWTCUiIiJdcCSEiIjIwDgnhIiIiHQRyJdjmIQQEREZGEdCiIiISBdqM0ZCmIQYVLStCnabf9X66vysrNqgSlgVEwDsZlm1xFJ3hCg+1lopij/rDhfFA0C9n5UGG5yok1VYlbKb5VUuD7m7iOL9ra7YoF6RVUB1q/K541X1sqqVVpOs8mOI1SWKzy+Xf84JobLj9VSlrMJq/85FoviCSnn1Xek/GhVO2fdGmE1WPbTGbRPFA/Jjo7X3X+2R9eGqkDJRPAB0DvKvGrXTJvvOpqZ12CSEiIioPVABqKr2bY2MSQgREZGBKTDBFKDFylgnhIiIyMAaJqZqXVrLo48+ipEjRyIkJARRUVGa9sEkhIiIyMAabtHVurQWl8uFSZMm4Re/+IXmfeiahHz44YeYMGECunbtCpPJhK1bt15xm927d+OGG26A3W7H1VdfjQ0bNrR6O4mIiMhXdnY2FixYgIEDB2reh65JSHV1NQYNGoQ1a9b4FX/8+HHcfvvtGDNmDA4ePIgHH3wQs2bNwjvvvNPKLSUiItKHqjZvAYCKigqfxemU3V3VWnSdmDp+/HiMHz/e7/i1a9eiZ8+eeOKJJwAA/fv3x8cff4w//OEPSE1Nba1mEhER6aYlipUlJib6rM/KysKSJUua27Rma1dzQnJzc5GSkuKzLjU1Fbm5uZfcxul0NsoAiYiI2ouWmJhaWFiI8vJy75KZmdnka2VkZMBkMl12OXz4cIv1rV3doltUVIT4+HifdfHx8aioqEBtbS2Cg4MbbZOTk4Ps7Oy2aiIREVGLUlQTTM18dkxERAQiIq5c5HLhwoWYMWPGZWN69eqlqS1NaVdJiBaZmZlIT0/3/lxRUYHExEScc4XB5vKvEqpZWNXPKayiCQBhFtn1OYewwmq5M1YU3832nSgeAPJqeojig4Tvq/RzqBVWVwSA8vrGiezllDll8VIRtrpW3T8AVAsraRaUR4niO4fWiOIBeaXY4CDZ+ZBfHiOKdyvyQePoYFm/a+pln0OVS1YZNy5EVoUWACrqZVVco2y1oniXsIJwZJDsfHCrsv0DQI3i3+fgVIxdf0Or2NhYxMbK/r1ojnaVhCQkJKC4uNhnXXFxMSIiIpocBQEAu90Ou112shIRERnFhRNMtWzbWgoKCnDu3DkUFBTA4/Hg4MGDAICrr74aYWFhfu2jXSUhI0aMwPbt233WvffeexgxYoROLSIiImpd55MQrRNTW7gxF1i8eDE2btzo/XnIkCEAgF27dmH06NF+7UPXialVVVU4ePCgN3s6fvw4Dh48iIKCAgDnL6VMnz7dGz9nzhzk5+fjV7/6FQ4fPoynnnoKL7/8MhYsWKBH84mIiFqdUSumbtiwAaqqNlr8TUAAnUdC9u3bhzFjxnh/bpi7kZaWhg0bNuDMmTPehAQAevbsiW3btmHBggV48skncdVVV+HZZ5/l7blERBSwVGh/EB0fYHcZo0ePhnqZsaKmqqGOHj0aBw4caMVWERERUVtoV3NCiIiIOpqWKFZmVExCiIiIjCyAr8cwCSEiIjKy5kww5UiIMcXZy+Gw+1esrF5Y8KZO8W+/F4q0ygobfVHZTRTfxSErV3+kJkEUDwDVblk9lrO1/t1H3iDI4hHFm03yPwHMwj8b4oJlBaAOFF0lijdHyftQXCV7X+s9suM71O4SxZ8pv3KVxotZzNJCdrL3KcgqO5YcVrcoXourwspE8cEWWYG2b50hongAiLZXi+JdwkKNnWyy7z3p/oMg+5yB7yuMtlRcSzBqnZCW0K6eHUNERESBo8OOhBAREbUHnJhKRERE+lBN2ud2MAkhIiIirQJ5TgiTECIiIiML4Ft0OTGViIiIdMGRECIiIgPjxFQiIiLSj8Evq2jFJISIiMjAOBISgIqckbAF2fyKlVbRdAqr+gGAW5FNz5FWDpRWM9XSB2kFwU4OYbVEYWXPkppwUTwg/2PjdJWsGmhceJUovl6R9VmLLuGyqq/namWVN91u+dSz4BBZNVCrsJqutAKqluq7YUFOUXxxrex4jXXIjqXOdtn5BgBuVfbZSb8DLBreV4kqt3/f8RcKt9b5FaflmNAsgCemak5CXn31Vbz88ssoKCiAy+Vbxnn//v3NbhgREREFNk13x/zxj3/EzJkzER8fjwMHDmD48OHo3Lkz8vPzMX78+JZuIxERUQdmauZiXJqSkKeeegrr1q3D6tWrYbPZ8Ktf/QrvvfceHnjgAZSXl7d0G4mIiDoutZmLgWlKQgoKCjBy5EgAQHBwMCorz19TnjZtGjZt2tRyrSMiIuromIT4SkhIwLlz5wAA3bt3x549ewAAx48fh2r0GrFERERkCJqSkFtvvRVvvfUWAGDmzJlYsGABxo4diylTpuCnP/1pizaQiIioQ2t4gJ3WxcA03R2zbt06KIoCAJg7dy46d+6MTz75BHfccQd+/vOft2gDiYiIOjI+wO4iZrMZZvP3gyh33XUX7rrrrhZrFBEREf0H64Q0VldXh3/+858oKSnxjoo0uOOOO5rdMCIiIkLzLqsE4uWYHTt2YPr06SgtLW30O5PJBI9HVr1QD5HWWtiFVRP9ZTfL91vhdojie4U0fu8vv/9gUbyWPhS7ZRUfrSbZcVJZL3uPooPlFSKlFR+Lq2R9rqkPEsV7hJV0AUARblNaI6uAGmRRrhx0gU7h8s+hximrdNk5pFoUX+6UHUsRNln10/OvITvnksLOieKL62THXltU+JRWfrYJv2c8wvPTraHicJCf30uK8PuLmqZpYur//d//YdKkSThz5gwURfFZ2kMCQkRE1F6Y1OYtRqZpJKS4uBjp6emIj49v6fYQERHRhQJ4ToimkZA777wTu3fvbuGmEBERUSO8RdfXn/70J0yaNAkfffQRBg4ciKAg3+vcDzzwQIs0joiIqMML4JEQTUnIpk2b8O6778LhcGD37t0wmb7PtEwmE5MQIiIiuiJNScjDDz+M7OxsZGRk+NQLISIiohYWwCMhmjIIl8uFKVOmtEgCsmbNGiQlJcHhcCA5ORl79+69bPyqVavQt29fBAcHIzExEQsWLEBdXV2z20FERGRIfICdr7S0NGzZsqXZL75lyxakp6cjKysL+/fvx6BBg5CamoqSkpIm41966SVkZGQgKysLhw4dwp///Gds2bIFv/nNb5rdFiIiIkPixFRfHo8Hjz/+ON555x1cf/31jSamrly50q/9rFy5ErNnz8bMmTMBAGvXrsW2bdvw3HPPISMjo1H8J598gptuugl33303ACApKQlTp07Fp59+qqUbREREhteceh+tVSfkxIkTWLp0Kd5//30UFRWha9eu+NnPfoaHH34YNpv/xQY1JSFffPEFhgwZAgD417/+5fO7CyepXo7L5UJeXh4yMzO968xmM1JSUpCbm9vkNiNHjsQLL7yAvXv3Yvjw4cjPz8f27dsxbdq0S76O0+mE0/l9tcOKigoAQKU7GE63f9Uru9rL/IprcLIuWhQPAH1DikXxX1Z1FcXXq7JBryCTrComAJiF437lLllFyWqXrIpmqNUligcAtPIfDQ5hld7SqlDxa3QKqRXFn6uWVUytdcrepGC7/HOQfm+W1cmOpSiH7D3yCM8fQF4dt7A6ShR/dbi0arJdFA/IK452D/1OFG+B7HsmKkj2uUkruAJAQW0nv+Lq6zR8vwSQw4cPQ1EUPPPMM7j66qvxr3/9C7Nnz0Z1dTVWrFjh9340JSG7du3SspmP0tJSeDyeRgXP4uPjcfjw4Sa3ufvuu1FaWoqbb74ZqqrC7XZjzpw5l70ck5OTg+zs7Ga3l4iISBctMDG14Q/wBna7HXa7PDFtMG7cOIwbN877c69evXDkyBE8/fTToiSkXd3asnv3bixbtgxPPfUU9u/fj9dffx3btm3D0qVLL7lNZmYmysvLvUthYWEbtpiIiEh/iYmJiIyM9C45OTkt/hrl5eWIjpZdCdA0ElJXV4fVq1dj165dTT5Fd//+/VfcR0xMDCwWC4qLfS9DFBcXIyEhocltFi1ahGnTpmHWrFkAgIEDB6K6uhr33XcfHn744Sbv1mlutkdERKQnE5oxJ+Q//y0sLERERIR3fUv/u3js2DGsXr1aNAoCaExC7r33Xrz77ru48847MXz4cL/ngVzIZrNh6NCh2LlzJyZOnAgAUBQFO3fuxLx585rcpqamplGiYbGcv2apqga/D4mIiEiL5tzl8p/tIiIifJKQS8nIyMBjjz122ZhDhw6hX79+3p9PnTqFcePGYdKkSZg9e7aoeZqSkL/97W/Yvn07brrpJi2be6WnpyMtLQ3Dhg3D8OHDsWrVKlRXV3vvlpk+fTq6devmHTaaMGECVq5ciSFDhiA5ORnHjh3DokWLMGHCBG8yQkRERNosXLgQM2bMuGxMr169vP9/+vRpjBkzBiNHjsS6devEr6cpCenWrRvCw8O1bOpjypQpOHv2LBYvXoyioiIMHjwYO3bs8E5WLSgo8Bn5+O1vfwuTyYTf/va3OHXqFGJjYzFhwgQ8+uijzW4LERGRIbVhxdTY2FjExsb6FXvq1CmMGTMGQ4cOxfPPP6+pgKmmJOSJJ57Ar3/9a6xduxY9evTQsguvefPmXfLyy8VP6rVarcjKykJWVlazXpOIiKjdMGDZ9lOnTmH06NHo0aMHVqxYgbNnz3p/d6l5nU3RlIQMGzYMdXV16NWrF0JCQhoVKzt37pyW3RIREdFFjFis7L333sOxY8dw7NgxXHXVVT6/k8zR1JSETJ06FadOncKyZcsQHx+vaWKq3swmBWY/C3IpwgpWwZZ6cXsOVl515aALSIv2nHPJil45NRRnirDJ2mQ1e0TxinBiVrVbVtxMy2tEOGTPLSqrdYji6+vlc508wj4oSuuev5XVsj4DQHio7H2t98iOV2lxs7jQKlE8AAQJj2+z8F+Lb2qiRPEhGor32S2y4nqnayNF8YnBsuJmZfWyz81ulrUfAGJt/n3WTpf8e14zA46EzJgx44pzR/yhKQn55JNPkJubi0GDBjW7AURERNQxaUpC+vXrh9pa2V+9REREpIEBR0JaiqaKqcuXL8fChQuxe/dufPvtt6ioqPBZiIiIqGU0zAnRuhiZppGQhnrxt912m896VVVhMpng8ciuhRIREdEltECxMqPS7QF2RERE5IcAvhyjKQm55ZZbWrodRERE1MFoSkIa1NTUoKCgAC6X761f119/fbMaRUREROcZsU5IS9GUhJw9exYzZ87E22+/3eTvOSeEiIiohQTw5RhNd8c8+OCDKCsrw6efforg4GDs2LEDGzduxDXXXIO33nqrpdtIRETUcTXnzhiDJyGaRkLef/99vPnmmxg2bBjMZjN69OiBsWPHIiIiAjk5Obj99ttbup0tLiqoBvaLys1fSr3a+k/oDfKzemsDRVjRNMYuq/h4rNK/BxhdyK3I2uT0yA6/TvYaUXxpbZgoHpBXTJWe3xazbAtVw8z24nNXflz3hSwW2bFns8mqUNqD5FUrpRThsVfjkZ3T37hllUABIMQmq6gZGiSraFqr+vf91cAmrH4KAIpH9hox9mpRvFOR7V9aAVX6PQkAZj9fQ1rhtlk4EuKruroacXFxAIBOnTp5H1wzcOBA7N+/v+VaR0RERAFLUxLSt29fHDlyBAAwaNAgPPPMMzh16hTWrl2LLl26tGgDiYiIOjS1mYuBabocM3/+fJw5cwYAkJWVhXHjxuGFF16AzWbDxo0bW7SBREREHRnvjrnIz372M+//Dx06FCdPnsThw4fRvXt3xMTEtFjjiIiIKHBpSkLS09ObXG8ymeBwOHD11VfjJz/5CaKjo5vVOCIiog4vgCemakpCDhw4gP3798Pj8aBv374AgK+++goWiwX9+vXDU089hYULF+Ljjz/GgAEDWrTBREREFBg0TUz9yU9+gpSUFJw+fRp5eXnIy8vDN998g7Fjx2Lq1Kk4deoUfvjDH2LBggUt3V4iIqIOJZCfoqspCfn973+PpUuXIiLi+3oEkZGRWLJkCR5//HGEhIRg8eLFyMvLa7GGEhERdVgBeGcMoDEJKS8vR0lJSaP1Z8+eRUVFBQAgKiqq0TNliIiISIi36Pr6yU9+gv/93//FE088gRtvvBEA8Nlnn+Ghhx7CxIkTAQB79+5Fnz59WqyhLa1escCs+Fc1UVoZT1p1EwCCLbLqik5F9tEV1HQSxcc6ZBVWAeBsnaxCaYhVVgG1pCZcFF9b36znM/rlu3OyPodG1IniXTWyipIAALfsbwvpk55cql0UbwmVV+r01Mg+O0eU7H01m2VVYivPhYriAaC+k+z4drplfbYI+xBslX3HaOESVqKNtMk+t1CL7A9bq1n+HLNaj82vOJdH/j2vFW/RvcgzzzyDBQsW4K677oLbff4Lxmq1Ii0tDX/4wx8AAP369cOzzz7bci0lIiKigKIpCQkLC8P69evxhz/8Afn5+QCAXr16ISzs+78KBw8e3CINJCIi6tB4i27TwsLCcP3117dUW4iIiOgivBxDRERE+uBICBEREekigJMQTbfoEhERETUXR0KIiIgMjHNCiIiISB+8HNN61qxZg6SkJDgcDiQnJ2Pv3r2XjS8rK8PcuXPRpUsX2O129OnTB9u3b2+j1hIREbUxVkxtHVu2bEF6ejrWrl2L5ORkrFq1CqmpqThy5Aji4uIaxbtcLowdOxZxcXF49dVX0a1bN5w8eRJRUVHi1y5xhsMW5F9lPGl10u7B58TtKXZGXDnoAuFBskqDXYJl1RXP1EaK4gHA6ZG9T9X1/r3/DWpcsuqhLmEFSgCwWYXVPctlbaqqk1WU1MJSIeu3YpcdG0EVsr9dlCD552CxyL45Pd/KKtc6w2V9hl1eebO6UFjhN8YpirdaZW1yaqggnBhVJoqv88jOh0jIvsfK6oNF8RHC70kACLf6t421DSrQNuDlmFaycuVKzJ49GzNnzgQArF27Ftu2bcNzzz2HjIyMRvHPPfcczp07h08++QRBQecP9qSkpLZsMhEREbUQ3S7HuFwu5OXlISUl5fvGmM1ISUlBbm5uk9u89dZbGDFiBObOnYv4+Hhcd911WLZsGTyeS/9F4HQ6UVFR4bMQERG1GwF8OUa3JKS0tBQejwfx8fE+6+Pj41FUVNTkNvn5+Xj11Vfh8Xiwfft2LFq0CE888QR+97vfXfJ1cnJyEBkZ6V0SExNbtB9EREStqeFyjNbFyHSfmCqhKAri4uKwbt06DB06FFOmTMHDDz+MtWvXXnKbzMxMlJeXe5fCwsI2bDEREVEzGXQk5I477kD37t3hcDjQpUsXTJs2DadPnxbtQ7ckJCYmBhaLBcXFxT7ri4uLkZCQ0OQ2Xbp0QZ8+fWCxfD+5r3///igqKoLL1fQjnu12OyIiInwWIiKidsOgSciYMWPw8ssv48iRI3jttdfw9ddf48477xTtQ7ckxGazYejQodi5c6d3naIo2LlzJ0aMGNHkNjfddBOOHTsGRfl+ZvtXX32FLl26wGaT3WlBRERE2i1YsAA/+MEP0KNHD4wcORIZGRnYs2cP6uv9v3NI18sx6enpWL9+PTZu3IhDhw7hF7/4Baqrq713y0yfPh2ZmZne+F/84hc4d+4c5s+fj6+++grbtm3DsmXLMHfuXL26QERE1KpMzVwANLpBw+mU3RJ+JefOncOLL76IkSNHeu9e9YeuSciUKVOwYsUKLF68GIMHD8bBgwexY8cO72TVgoICnDlzxhufmJiId955B5999hmuv/56PPDAA5g/f36Tt/MSEREFhBa4HJOYmOhzk0ZOTk6LNO3Xv/41QkND0blzZxQUFODNN98UbW9SVdXgc2dbVkVFBSIjI/HzD/8HtjD/srUgk6woUJhFnmFWeeyieGkBNbtZVoTrVG2UKB4AylyyQkLnakNE8S63rNCXScO08HMlwjlDbtOVYy5gqRT2Qbh/AHB8K9umXlbnSzzd3i5sDyBvkyqseCQ8peGxy4+l+gjZNsJTFEpnWbGsoGB5cS27XbaN1SwrApcUJSvsaLPIPrhoW40oHgCCzU3PL7yYs6oeT//wDZSXl7faXMOGf6+unbMMFrtD0z48zjp8ufY3KCws9Gmn3W6H3d74352MjAw89thjl93noUOH0K9fPwDn73Q9d+4cTp48iezsbERGRuJvf/sbTCb/zns+O4aIiMjIWuDZMf7emLFw4ULMmDHjsjG9evXy/n9MTAxiYmLQp08f9O/fH4mJidizZ88l53ZejEkIERERAQBiY2MRGxuraduGm0Yk802YhBARERmdwSZOfPrpp/jss89w8803o1OnTvj666+xaNEi9O7d2+9REKCdFSsjIiLqaIxYMTUkJASvv/46brvtNvTt2xf33nsvrr/+enzwwQdNzjW5FI6EEBERGVkLzAlpaQMHDsT777/f7P0wCSEiIjKw5oxo8NkxRERERE3gSAgREZGRGfByTEthEkJERGRggXw5psMmIYpqgqL6V9EtyCyr0ndd8Dfi9hyp6yKKj7TKKgHWq7JKnbUe+QMBXYrsNWqsstewCKsxFn8bKYoHIK6Aaq6V9dlSJ68eKiX96ILPyuKFhx6goSizEiR7n2ylsteQ7t9jk39u4s9aeHG8XvhB1wfLv+7rI2TbhEbUieJLasJF8V3DymX7r5OWAwZ6hMiquLYJjoQQERGRLgI4CeHEVCIiItIFR0KIiIgMjHNCiIiISB8BfDmGSQgREZGBmVQVJg0TvBu2NTImIUREREYWwCMhnJhKREREuuBICBERkYFxYioRERHpI4Avx3TYJCTcWge71b9KqP5WVm1QWB8tbk8Pe6koXloB1QxZtdFewa1/pS4ySFZd8UhZnCjeLKywCgBKsKw6ruqWvU8eu+xYcpRqqdQpiw+qkn1LmYRvq8Ul/xZUv5PFO8pkjbI4ZW2q6yQ73wDAFSn77JzCAr8mtyzeflbeB+HbhBqzbAOPR3b+hAa5RPGR9lpRPACU1Yf4Feeql7WlOTgSQkRERPoI4JEQTkwlIiIiXXAkhIiIyMB4OYaIiIj0EcCXY5iEEBERGZzRRzS0YhJCRERkZKp6ftG6rYFxYioRERHpgiMhREREBsaJqQGoXrXA7GfBryEhJ0X79mgYYAo3y4rqRFlqRPGF9Z1F8SFmpygeAK4OKRbFn3J2EsVfFV4miq9xBYniAcBZLzslaqpl8dIvBKu81hKCzwoLdwmLidm/k1XJstQKq2oBUOzCYnwuWZE5s1PWJlulTRQPAPVhsmOjLkrWZ2nhuzrZVwAAQKmQfZe5FbvsBa6qF4V/Vxcsig+2yvYPAOHB5X7FuYLk+9aME1OJiIhIDyZFXqn4wm2NzBBzQtasWYOkpCQ4HA4kJydj7969fm23efNmmEwmTJw4sXUbSERERC1O9yRky5YtSE9PR1ZWFvbv349BgwYhNTUVJSUll93uxIkTeOihhzBq1Kg2aikREZEO1GYuBqZ7ErJy5UrMnj0bM2fOxIABA7B27VqEhITgueeeu+Q2Ho8H99xzD7Kzs9GrV682bC0REVHbapiYqnUxMl2TEJfLhby8PKSkpHjXmc1mpKSkIDc395LbPfLII4iLi8O99957xddwOp2oqKjwWYiIiNqNhjohWhcD0zUJKS0thcfjQXx8vM/6+Ph4FBUVNbnNxx9/jD//+c9Yv369X6+Rk5ODyMhI75KYmNjsdhMREbUVjoQYRGVlJaZNm4b169cjJibGr20yMzNRXl7uXQoLC1u5lUREROQPXW/RjYmJgcViQXGxb32J4uJiJCQkNIr/+uuvceLECUyYMMG7TlHO339ktVpx5MgR9O7d22cbu90Ou1147zoREZFRBHCdEF1HQmw2G4YOHYqdO3d61ymKgp07d2LEiBGN4vv164cvvvgCBw8e9C533HEHxowZg4MHD/JSCxERBZxAvhyje7Gy9PR0pKWlYdiwYRg+fDhWrVqF6upqzJw5EwAwffp0dOvWDTk5OXA4HLjuuut8to+KigKARuuvpMpth8vtXxXE0/Wyyp49bKWieADoHfStKN4sTG9tQbKKkq4gWfVGAMh3xYniLXZZH+r9rHDboC5SXjH1+HfRonhTiLB66Fn5+yolraRpr5AdG9YaWaVIc41LFA8AQUXCUrGKrCKTWlUtijd3ln0HAAC6RIjCTbKPAYpV9jmrJvmxJ30NRVhYtrbcIYoPC5ZVcnYprX++tQmDP8DO6XQiOTkZn3/+OQ4cOIDBgwf7va3uSciUKVNw9uxZLF68GEVFRRg8eDB27NjhnaxaUFAAs7ldTV0hIiJqMUZ/dsyvfvUrdO3aFZ9//rl4W92TEACYN28e5s2b1+Tvdu/efdltN2zY0PINIiIioit6++238e677+K1117D22+/Ld7eEEkIERERXUILTEy9uEZWS9y0UVxcjNmzZ2Pr1q0ICQnRtA9e5yAiIjKwlpiYmpiY6FMzKycnp1ltUlUVM2bMwJw5czBs2DDN++FICBERkZEp6vlF67YACgsLERHx/WTpS42CZGRk4LHHHrvsLg8dOoR3330XlZWVyMzM1Nau/2ASQkREZGQtcDkmIiLCJwm5lIULF2LGjBmXjenVqxfef/995ObmNkpmhg0bhnvuuQcbN270q3lMQoiIiAgAEBsbi9jY2CvG/fGPf8Tvfvc778+nT59GamoqtmzZguTkZL9fj0kIERGRgZnQjFt0W7Ql3+vevbvPz2FhYQCA3r1746qrrvJ7P0xCiIiIjMzgxcqao8MmISEWF+wW/z6cSEuNaN9RFlk1RgCoV2U3KkWaZVUrw62yCpSy+pPnRZhk1QxPeSJF8ZUeWXXFrxRZBVcAsFmFZSuFf2a4omXvrP2cvOKjKv3TR/gdZfm2Srb7YGEZTQBqleyc85w9K4o3C28nNH1XceWgi9hcsnM0yC57n2p7yqq4hpYIj20AMMuOP8UmO/jcEbLvvVqXrAqyxyG/AdRu9rMKsr9xLcDoxcoAICkpCaqGhKfDJiFERETtAh9gR0RERNSyOBJCRERkYCZVhUnj3A6t27UVJiFERERGpkDbRL2GbQ2MSQgREZGBcSSEiIiI9MGJqUREREQtiyMhRERERsZiZURERKSH9lCsTKsOm4TUeWxQPP5V3wsyySrjhZpc4vZEW2TVFWPMsuqKShtMkQ4Rvk/VqqwqZlfbd6L4LsHyKpelNaGieItV9r56pBVWZUVlAQD2Mlm8ahE2Kkj2teEJa/qR4ZdjjZVVAzXXyI4lc7Rs/56iElE8AJidsgrC6CKr8BtUKfvOqO4mqzgMALYK2b9gNXGt9aSS8zwe2QwCt7ASNQAofpYc9jeuRXAkhIiIiPRgUs4vWrc1Mk5MJSIiIl1wJISIiMjIeDmGiIiIdBHAdUKYhBARERkYK6YSERGRPgL4cgwnphIREZEuOBJCRERkZCq0Pw3X2AMhHTcJCbE6Yfez0JRFWHIuxCwrIgQADpOs8E2IsFiZU5W1qU6VFR4DAAtkfYi11IriT5llxZ+uDTstigcAs/Cz3udOFMVXiaIBp1teYCqoSjbAqZotovjazp1F8WHfyIv3Wctlx5I5Jlr2Ah7ZN7qls6y4GQCoEWGieE+nEFF8bZysCJyftRl91EULjyXZoST+B9IkPD/rPdIGGRPnhBAREZE+VDRjTkiLtqTFMQkhIiIyMk5MJSIiImpZhkhC1qxZg6SkJDgcDiQnJ2Pv3r2XjF2/fj1GjRqFTp06oVOnTkhJSblsPBERUbumNHMxMN2TkC1btiA9PR1ZWVnYv38/Bg0ahNTUVJSUNP3Uyt27d2Pq1KnYtWsXcnNzkZiYiB/96Ec4depUG7eciIio9TVMTNW6GJnuScjKlSsxe/ZszJw5EwMGDMDatWsREhKC5557rsn4F198Effffz8GDx6Mfv364dlnn4WiKNi5c2cbt5yIiKgNNMwJ0boYmK5JiMvlQl5eHlJSUrzrzGYzUlJSkJub69c+ampqUF9fj+jopm/RczqdqKio8FmIiIjaDSYhraO0tBQejwfx8fE+6+Pj41FUVOTXPn7961+ja9euPonMhXJychAZGeldEhNldR2IiIiodeh+OaY5li9fjs2bN+ONN96Aw9F0UafMzEyUl5d7l8LCwjZuJRERUTME8EiIrnVCYmJiYLFYUFxc7LO+uLgYCQkJl912xYoVWL58Of7+97/j+uuvv2Sc3W6H3d64suBZZxhsQf5VHbWEyKYX1yjy0oR2q+xAqVLqRPFBJlnlwEhzsCgeAEo91aJ4YTFGDLbLKqCWeUJlLwCgT6h/I3ANgrp6RPH/ONVTFF8dKwoHAFTaZdV068pkx0bwGVk1U2ekrLInAAQnyM4hW1WkKN4TJOtDUI38FgNzveycromVfR07I2UnkJYJivWyoq9wRcveJ5NHWBnXLOuD3Sqv/Bxm8a8yc5BFXhlbMwUQFqT23dbAdB0JsdlsGDp0qM+k0oZJpiNGjLjkdo8//jiWLl2KHTt2YNiwYW3RVCIiIl0E8t0xuldMTU9PR1paGoYNG4bhw4dj1apVqK6uxsyZMwEA06dPR7du3ZCTkwMAeOyxx7B48WK89NJLSEpK8s4dCQsLQ1iYMG0nIiIyugCumKp7EjJlyhScPXsWixcvRlFREQYPHowdO3Z4J6sWFBTAbP5+wObpp5+Gy+XCnXfe6bOfrKwsLFmypC2bTkRERM2gexICAPPmzcO8efOa/N3u3bt9fj5x4kTrN4iIiMgoFBUQPkHYZ1sDM0QSQkRERJcQwJdj2vUtukRERIGvObfntl4SkpSUBJPJ5LMsX75ctA+OhBARERmZgUdCHnnkEcyePdv7c3h4uGh7JiFERESkSXh4+BXrel0OL8cQEREZmaI2bwEaPUPN6fSvKNuVLF++HJ07d8aQIUPw+9//Hm63rEBchx0JMUOF2c9rZWWeENG+KyxNl5C/nCP1srJ2lYrsNQbZZA/uqzPJKw06hFVZ61VpKT/ZsOJQxwnh/oEvnV1F8fV22Sk0roes0u3x6s6ieAAoqo4QxZfXyo6lmu6yCqjuClkFVwCorpQdS/Zzss/BJDz0XFHyv9ds5bISl6qwIqY7VHY+KBq+7RW77I1SQmQVhDslyL6XbFbZ/oPMsngAKHb5d/64XC7xvjVTlfOL1m2BRs9Na4myFg888ABuuOEGREdH45NPPkFmZibOnDmDlStX+r2PDpuEEBERtQstMCeksLAQERHfJ1hNPc4EADIyMvDYY49ddpeHDh1Cv379kJ6e7l13/fXXw2az4ec//zlycnIuuf+LMQkhIiIyMqUZd7n853JMRESETxJyKQsXLsSMGTMuG9OrV68m1ycnJ8PtduPEiRPo27evX81jEkJEREQAgNjYWMTGanhyJoCDBw/CbDYjLi7O722YhBARERmZAW/Rzc3NxaeffooxY8YgPDwcubm5WLBgAX72s5+hU6dOfu+HSQgREZGRqWhGEtKiLfGy2+3YvHkzlixZAqfTiZ49e2LBggU+80T8wSSEiIjIyAw4EnLDDTdgz549zd4PkxAiIiIjUxQAGm/RVTRu10ZYrIyIiIh0wZEQIiIiIzPg5ZiW0mGTkPAgJ2xB/g1T1SiyCpH/qO4jbs+1wd+I4uuUIFG8w1QvjJdXTD3rkT24KNZSKYov8sgqgUr7DABRlhpR/DlLmCjeqcpOuQSHrKIkAIRbZeWYy0NkFVPNMbIvtW/rQkXxABBslX12p8ojRfH1HllFVqsiLGcK+eB5qENWgbOiWva5hdjl50O4Q3YsVdbJvisjg2UVhDvZZednJ1utKB4AbGY/v/s0VGPVjEkIERER6aIFipUZFZMQIiIiA1NVBarGZ8do3a6tcGIqERER6YIjIUREREamqtovq3BOCBEREWmmNmNOCJMQIiIi0kxRAJPGuR0GnxPCJISIiMjIAngkhBNTiYiISBcddiSki70MDrt/3c+vjRXtO1RYLAoA8qp7iuItwlJI+6uTRPEldbLCYwDQN6xYFP+dO0QUH2aRva/51TGieAC4JqxEFO9RZXl8hVtWYCrCKivmpEXPkFJRfI3HJorvH14kigfkxfhiHVWi+OigalF8sVNWKA8AIoJkn128TVaY7tt6WRE4tyor0AYA9YpsmyBhAa9gs6xA26m6KFG89NgGgBKXf999iiovYKeVqihQNV6OMfotuh02CSEiImoXAvhyDJMQIiIiI1NUwMQkhIiIiNqaqkL+NKILtzUuTkwlIiIiXXAkhIiIyMBURYWq8XKMypGQK1uzZg2SkpLgcDiQnJyMvXv3Xjb+lVdeQb9+/eBwODBw4EBs3769jVpKRETUxlSleYuB6Z6EbNmyBenp6cjKysL+/fsxaNAgpKamoqSk6VslP/nkE0ydOhX33nsvDhw4gIkTJ2LixIn417/+1cYtJyIian2qojZrMTLdk5CVK1di9uzZmDlzJgYMGIC1a9ciJCQEzz33XJPxTz75JMaNG4df/vKX6N+/P5YuXYobbrgBf/rTn9q45URERG0ggEdCdJ0T4nK5kJeXh8zMTO86s9mMlJQU5ObmNrlNbm4u0tPTfdalpqZi69atTcY7nU44nd8XuSovLwcA1FW5/W9nbb3fsQBgtcritZAWK3MpssI69U5ZESEAcELWb5dbFu+0yOLra1q/D9JiZS63rPiTU8OxJH2NOpvwc/DIjiVFWMAKAJzC702X8Hh1BgmPJZf8WHJZhW0Sfg6uelm8W5V/DtJiZarwszabhZ+D8HOuM2k4f1z+beOqPh/XFnMu3KjXXCbELfxOa2u6JiGlpaXweDyIj4/3WR8fH4/Dhw83uU1RUVGT8UVFTVdlzMnJQXZ2dqP1y2/7QGOriYioPdjaBq9RWVmJyMjIVtm3zWZDQkICPi5q3rzHhIQE2GyySsdtJeDvjsnMzPQZOSkrK0OPHj1QUFDQagdOW6qoqEBiYiIKCwsRESEvLW0k7ItxBVJ/2Bfjak/9UVUVlZWV6Nq1a6u9hsPhwPHjx+HSMBJ3IZvNBodD9siItqJrEhITEwOLxYLiYt9njhQXFyMhIaHJbRISEkTxdrsddru90frIyEjDH+QSERERAdMf9sW4Aqk/7ItxtZf+tMUfsg6Hw7AJREvQdWKqzWbD0KFDsXPnTu86RVGwc+dOjBgxosltRowY4RMPAO+9994l44mIiMiYdL8ck56ejrS0NAwbNgzDhw/HqlWrUF1djZkzZwIApk+fjm7duiEnJwcAMH/+fNxyyy144okncPvtt2Pz5s3Yt28f1q1bp2c3iIiISEj3JGTKlCk4e/YsFi9ejKKiIgwePBg7duzwTj4tKCiA2fz9gM3IkSPx0ksv4be//S1+85vf4JprrsHWrVtx3XXX+fV6drsdWVlZTV6iaY8CqT/si3EFUn/YF+MKtP7QlZlUo9d0JSIiooCke7EyIiIi6piYhBAREZEumIQQERGRLpiEEBERkS4CMglZs2YNkpKS4HA4kJycjL179142/pVXXkG/fv3gcDgwcOBAbN/evBK5LU3Sn/Xr12PUqFHo1KkTOnXqhJSUlCv2vy1JP5sGmzdvhslkwsSJE1u3gQLSvpSVlWHu3Lno0qUL7HY7+vTpY6hjTdqfVatWoW/fvggODkZiYiIWLFiAurq6NmrtpX344YeYMGECunbtCpPJdMnnSl1o9+7duOGGG2C323H11Vdjw4YNrd5Of0j78vrrr2Ps2LGIjY1FREQERowYgXfeeadtGnsFWj6XBv/4xz9gtVoxePDgVmsf6SPgkpAtW7YgPT0dWVlZ2L9/PwYNGoTU1FSUlJQ0Gf/JJ59g6tSpuPfee3HgwAFMnDgREydOxL/+9a82bnnTpP3ZvXs3pk6dil27diE3NxeJiYn40Y9+hFOnTrVxyxuT9qXBiRMn8NBDD2HUqFFt1NIrk/bF5XJh7NixOHHiBF599VUcOXIE69evR7du3dq45U2T9uell15CRkYGsrKycOjQIfz5z3/Gli1b8Jvf/KaNW95YdXU1Bg0ahDVr1vgVf/z4cdx+++0YM2YMDh48iAcffBCzZs0yxD/e0r58+OGHGDt2LLZv3468vDyMGTMGEyZMwIEDB1q5pVcm7UuDsrIyTJ8+HbfddlsrtYx0pQaY4cOHq3PnzvX+7PF41K5du6o5OTlNxk+ePFm9/fbbfdYlJyerP//5z1u1nf6S9udibrdbDQ8PVzdu3NhaTfSblr643W515MiR6rPPPqumpaWpP/nJT9qgpVcm7cvTTz+t9urVS3W5XG3VRBFpf+bOnaveeuutPuvS09PVm266qVXbKQVAfeONNy4b86tf/Uq99tprfdZNmTJFTU1NbcWWyfnTl6YMGDBAzc7ObvkGNYOkL1OmTFF/+9vfqllZWeqgQYNatV3U9gJqJMTlciEvLw8pKSnedWazGSkpKcjNzW1ym9zcXJ94AEhNTb1kfFvS0p+L1dTUoL6+HtHR0a3VTL9o7csjjzyCuLg43HvvvW3RTL9o6ctbb72FESNGYO7cuYiPj8d1112HZcuWweORP169pWnpz8iRI5GXl+e9ZJOfn4/t27fjxz/+cZu0uSUZ+TuguRRFQWVlpe7nv1bPP/888vPzkZWVpXdTqJXoXjG1JZWWlsLj8XirrTaIj4/H4cOHm9ymqKioyfiioqJWa6e/tPTnYr/+9a/RtWvXRl+ybU1LXz7++GP8+c9/xsGDB9ughf7T0pf8/Hy8//77uOeee7B9+3YcO3YM999/P+rr63X/gtXSn7vvvhulpaW4+eaboaoq3G435syZY4jLMVKX+g6oqKhAbW0tgoODdWpZ861YsQJVVVWYPHmy3k0RO3r0KDIyMvDRRx/Bag2of6roAgE1EkK+li9fjs2bN+ONN95od09hrKysxLRp07B+/XrExMTo3ZxmUxQFcXFxWLduHYYOHYopU6bg4Ycfxtq1a/Vumia7d+/GsmXL8NRTT2H//v14/fXXsW3bNixdulTvptF/vPTSS8jOzsbLL7+MuLg4vZsj4vF4cPfddyM7Oxt9+vTRuznUigIqvYyJiYHFYkFxcbHP+uLiYiQkJDS5TUJCgii+LWnpT4MVK1Zg+fLl+Pvf/47rr7++NZvpF2lfvv76a5w4cQITJkzwrlMUBQBgtVpx5MgR9O7du3UbfQlaPpcuXbogKCgIFovFu65///4oKiqCy+WCzWZr1TZfjpb+LFq0CNOmTcOsWbMAAAMHDkR1dTXuu+8+PPzwwz7PezK6S30HREREtNtRkM2bN2PWrFl45ZVXdB8F1aKyshL79u3DgQMHMG/ePADnz39VVWG1WvHuu+/i1ltv1bmV1BLazzeFH2w2G4YOHYqdO3d61ymKgp07d2LEiBFNbjNixAifeAB47733LhnflrT0BwAef/xxLF26FDt27MCwYcPaoqlXJO1Lv3798MUXX+DgwYPe5Y477vDewZCYmNiWzfeh5XO56aabcOzYMW8iBQBfffUVunTpomsCAmjrT01NTaNEoyHBUtvZ46iM/B2gxaZNmzBz5kxs2rQJt99+u97N0SQiIqLR+T9nzhz07dsXBw8eRHJyst5NpJai88TYFrd582bVbrerGzZsUP/973+r9913nxoVFaUWFRWpqqqq06ZNUzMyMrzx//jHP1Sr1aquWLFCPXTokJqVlaUGBQWpX3zxhV5d8CHtz/Lly1Wbzaa++uqr6pkzZ7xLZWWlXl3wkvblYka6O0bal4KCAjU8PFydN2+eeuTIEfVvf/ubGhcXp/7ud7/Tqws+pP3JyspSw8PD1U2bNqn5+fnqu+++q/bu3VudPHmyXl3wqqysVA8cOKAeOHBABaCuXLlSPXDggHry5ElVVVU1IyNDnTZtmjc+Pz9fDQkJUX/5y1+qhw4dUtesWaNaLBZ1x44denXBS9qXF198UbVareqaNWt8zv+ysjK9uuAl7cvFeHdMYAq4JERVVXX16tVq9+7dVZvNpg4fPlzds2eP93e33HKLmpaW5hP/8ssvq3369FFtNpt67bXXqtu2bWvjFl+epD89evRQATRasrKy2r7hTZB+NhcyUhKiqvK+fPLJJ2pycrJqt9vVXr16qY8++qjqdrvbuNWXJulPfX29umTJErV3796qw+FQExMT1fvvv1/97rvv2r7hF9m1a1eT50BD+9PS0tRbbrml0TaDBw9WbTab2qtXL/X5559v83Y3RdqXW2655bLxetLyuVyISUhgMqlqOxs7JSIiooAQUHNCiIiIqP1gEkJERES6YBJCREREumASQkRERLpgEkJERES6YBJCREREumASQkRERLpgEkJERES6YBJCFMBGjx6NBx98UO9mEBE1iUkIEV3Shg0bEBUVpXcziChAMQkhIiIiXTAJIQpwbrcb8+bNQ2RkJGJiYrBo0SI0PDLK6XTioYceQrdu3RAaGork5GTs3r0bALB7927MnDkT5eXlMJlMMJlMWLJkCQDgr3/9K4YNG4bw8HAkJCTg7rvvRklJiU49JKL2ikkIUYDbuHEjrFYr9u7diyeffBIrV67Es88+CwCYN28ecnNzsXnzZvzzn//EpEmTMG7cOBw9ehQjR47EqlWrEBERgTNnzuDMmTN46KGHAAD19fVYunQpPv/8c2zduhUnTpzAjBkzdOwlEbVHfIouUQAbPXo0SkpK8OWXX8JkMgEAMjIy8NZbb2HHjh3o1asXCgoK0LVrV+82KSkpGD58OJYtW4YNGzbgwQcfRFlZ2WVfZ9++fbjxxhtRWVmJsLCw1uwSEQUQjoQQBbgf/OAH3gQEAEaMGIGjR4/iiy++gMfjQZ8+fRAWFuZdPvjgA3z99deX3WdeXh4mTJiA7t27Izw8HLfccgsAoKCgoFX7QkSBxap3A4hIH1VVVbBYLMjLy4PFYvH53eVGM6qrq5GamorU1FS8+OKLiI2NRUFBAVJTU+FyuVq72UQUQJiEEAW4Tz/91OfnPXv24JprrsGQIUPg8XhQUlKCUaNGNbmtzWaDx+PxWXf48GF8++23WL58ORITEwGcvxxDRCTFyzFEAa6goADp6ek4cuQINm3ahNWrV2P+/Pno06cP7rnnHkyfPh2vv/46jh8/jr179yInJwfbtm0DACQlJaGqqgo7d+5EaWkpampq0L17d9hsNqxevRr5+fl46623sHTpUp17SUTtEZMQogA3ffp01NbWYvjw4Zg7dy7mz5+P++67DwDw/PPPY/r06Vi4cCH69u2LiRMn4rPPPkP37t0BACNHjsScOXMwZcoUxMbG4vHHH0dsbCw2bNiAV155BQMGDMDy5cuxYsUKPbtIRO0U744hIiIiXXAkhIiIiHTBJISIiIh0wSSEiIiIdMEkhIiIiHTBJISIiIh0wSSEiIiIdMEkhIiIiHTBJISIiIh0wSSEiIiIdMEkhIiIiHTBJISIiIh08f8BsYxQVGMB044AAAAASUVORK5CYII=", + "text/plain": [ + "<Figure size 650x1500 with 6 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "benchmark.plot(main=True, reference=True, difference=True, labels=['beta', 'gamma'])\n", + "# with main=True, the plot of the benchmarked points is shown\n", + "# with reference=True, the plot of the reference points is shown\n", + "# with difference=True, the plot of the difference between the benchmarked and the reference points is shown\n", + "# with labels=['beta', 'gamma'], the labels of the axes are set to 'beta' and 'gamma', if not specified, default labels are used" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To access the raw data of the benchmark, use the following attributes:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.274, -0.074, 0.052, ..., -0.336, 0.196, -0.042],\n", + " [-0.026, -0.012, -0.48 , ..., 0.276, 0.148, -0.016],\n", + " [-0.19 , -0.192, -0.344, ..., 0.492, 0.13 , -0.096],\n", + " ...,\n", + " [-0.04 , -0.122, 0.15 , ..., -0.012, -0.044, 0.19 ],\n", + " [ 0.034, 0.05 , -0.056, ..., -0.054, -0.01 , -0.004],\n", + " [-0.202, 0.168, 0.074, ..., -0.09 , -0.214, 0.018]])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "benchmark.values # the values of the benchmarked points" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n", + " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n", + " [ 0.00000000e+00, -3.30596324e-01, -6.36201769e-01, ...,\n", + " 6.90397010e-01, 3.48489595e-01, 5.50253502e-03],\n", + " [ 0.00000000e+00, -6.07268558e-01, -1.14705888e+00, ...,\n", + " 1.33292239e+00, 6.62233861e-01, 1.02865946e-02],\n", + " ...,\n", + " [ 0.00000000e+00, -1.32919377e-02, -3.59491490e-02, ...,\n", + " -3.36256986e-03, 3.38913220e-03, 1.35164298e-04],\n", + " [ 0.00000000e+00, -2.52121083e-03, -7.71505865e-03, ...,\n", + " -3.32741998e-03, -2.75178585e-04, 1.81992750e-05],\n", + " [ 0.00000000e+00, -3.69897000e-07, -1.45593300e-06, ...,\n", + " -1.46058600e-06, -3.72279000e-07, -1.90000000e-11]])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "benchmark.values_reference # the values of the reference points" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.274 , -0.074 , 0.052 , ..., -0.336 ,\n", + " 0.196 , -0.042 ],\n", + " [-0.026 , 0.31859632, 0.15620177, ..., -0.41439701,\n", + " -0.20048959, -0.02150254],\n", + " [-0.19 , 0.41526856, 0.80305888, ..., -0.84092239,\n", + " -0.53223386, -0.10628659],\n", + " ...,\n", + " [-0.04 , -0.10870806, 0.18594915, ..., -0.00863743,\n", + " -0.04738913, 0.18986484],\n", + " [ 0.034 , 0.05252121, -0.04828494, ..., -0.05067258,\n", + " -0.00972482, -0.0040182 ],\n", + " [-0.202 , 0.16800037, 0.07400146, ..., -0.08999854,\n", + " -0.21399963, 0.018 ]])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "benchmark.difference # the difference between the benchmarked and the reference points" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run 1D Benchmark" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One can also run the benchmark fixing the value of any parameter:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running benchmark.\n", + "Point 100 out of 100. Expected remaining time to complete: 00:00:00, it will be finished at 03:03:42. \n", + "Running reference.\n", + "Point 100 out of 100. Expected remaining time to complete: 00:00:00, it will be finished at 03:03:42. \n" + ] + } + ], + "source": [ + "benchmark.run(n_points_axis=100, ranges=[(0, 3.14/2), (1,)])\n", + "# in this case the first parameter is swept from 0 to 3.14/2, while the second is fixed to 1" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting the main plot with the following parameters:\n", + "\tParameter 0: 0 to 1.57, with 100 values\n", + "\tParameter 1: 1\n", + "Plotting the reference plot with the following parameters:\n", + "\tParameter 0: 0 to 1.57, with 100 values\n", + "\tParameter 1: 1\n", + "Plotting the difference plot with the following parameters:\n", + "\tParameter 0: 0 to 1.57, with 100 values\n", + "\tParameter 1: 1\n" + ] + }, + { + "data": { + "image/png": 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x4wI+B0B6erp+vCMFKZQkEomko+Rthl9fhN/ne0eO+k4TQmXQTIjvD8GcI2Y0Q8ow8TfxCnFd2QHYvRC2fCaEWs6v4m/h32HUH+Ho6yF9bPDWcBiiKEqb0l+hxGw2e/1fURSv67R6J6fTyYcffshf//pXnnzySaZOnUp0dDSPP/44q1evbvM5nE5nkO5Bz6BnvBokEomku+F0wq7vhUDKcqcmSB8Lo89zpb76dO2a4vvB5GvFX+Uh2LoAfv8YDm2ATR+Iv37HwNE3wNDTZSH4EcSKFSuYNm0aN954o37d3r17Q7iinoMUShKJRNIWVFUIpMX/hMJt4jrFCCPPhqP/DH0mhnR5OjG9YOqN4i9nLaz+L2z7Ag6sEH8pI2HmfTBkdnAjXZJuyeDBg3nnnXf44Ycf6N+/P++++y5r166lf//+oV5at0cKJYlEIgmU7F/hx/tFOgvAGguTroDJ13V99KgtZBwl/ipyYe1rsPZ1KNwK/7sAMqbAzPsh85hQr1LSifzpT39iw4YNXHDBBSiKwkUXXcSNN97Id999F+qldXtk11sryK43iURCyV5R57PzW/F/UxhMuR6OvQXC40O6tHZRWwornoHVLwm/JoDBJ8Opj4ki8yOEljqiJIcHweh6kwlqiUQi8YfdBssehxenCpGkGGHC5XDzBjjpwZ4pkgAiEsT6b94Ik64S92v3QnE/lz0u7rdEIgGkUJJIJBLfZP8KLx8PPz0EjgYYcALc+Cuc+ayo/zkciEmHM56CP6+B/tNFdOmnh+ClY4UxpkQikUJJIpFIvLDVwNe3whuzoWg7RCTBua/CZZ9D8pBQr65zSBoEc78Q9zMyGYp3wlunw1d/EY+HRHIEI4WSRCKRaBzaIKJI694Q/x9/KcxbC2POP/w7wxRF3M95a92+TOvfEo9H7m+hXJlEElKkUJJIJBKnUxQ3v3YSlOyB6F4w90s46wVRz3MkER4Pc54R9z+6l3g8Xj8JfnkSnKEZFiuRhBIplCQSyZFNZR68ezb8eJ9w1R4+B25YAQOmh3ploWXAdPE4DD8TnHbhG/X2mcLIUiI5gpBCSSKRHLlk/wqvTIf9S8EcAXOehfPfPfKiSP6ISIDz3xGRNXMkHFgOr8wQj5tEcoQghZJEIjkyWfcGvHUGVBcIl+o/LYOJlx/+tUhtRVFErdb1v0DKCPF4vXW6MK6UNnySIwAplCQSyZGFvUF0c319q0i1jTgbrvkRkgaHemXdm8SBcPWP4vFy2uGb2+HLedBYH+qVSSSdihRKEonkyKG6EN6eI7q5UMTojvPeAktkiBfWQ7BGicdr1oOgGGDDeyK6VF0U6pVJAkBVVa677joSEhJQFIWNGzeGekk9AimUJBLJkUHJXtG9lbMawmLhkk/guNtkqq2tKIoY3XLJfAiLg9x18PosKN4T6pVJWuH777/nrbfe4uuvvyYvL49Ro0aFekk9AimUJBLJ4c/BdUIklWVBXD+45icYfFKoV9WzGTQTrlkkHs+yLJcIXRPqVR2x2Gytj53Zu3cv6enpTJs2jbS0NEwmU5vPo6oqdru9PUvssUihJJFIDm92fCuKtmtLoNd48eWeNCjUqzo8SBosHs9e46GuVKQ1t38V6lV1DFUVbuSh+GtDcfyMGTOYN28et9xyC0lJScyePZstW7Zw6qmnEhUVRWpqKpdddhnFxcUAXHHFFdx0001kZ2ejKAqZmZkAOJ1OHnnkEfr37094eDhjx45l/vz5+nmWLFmCoih89913TJw4EavVyvLlywPeb/HixUyaNImIiAimTZvGzp07ve7HV199xVFHHUVYWBhJSUmcc845+m0NDQ389a9/pXfv3kRGRjJlyhSWLFnSjie1Y7RdTkokEklPYd0bouhYdcLgk+GPb4o6G0nwiEqBK76B+VfBru/ho8vg9CfgqGtCvbL20VgL/w7RLL+/HWpTvdzbb7/NDTfcwIoVKygvL+fEE0/kmmuu4amnnqKuro677rqL888/n59++olnnnmGgQMH8sorr7B27VqMRiMAjzzyCO+99x4vvfQSgwcPZtmyZVx66aUkJyczfbrbS+zuu+/miSeeYMCAAcTHxwe837333suTTz5JcnIy119/PVdddRUrVqwA4JtvvuGcc87h3nvv5Z133sFms/Htt9/q+86bN49t27bx4Ycf0qtXLz7//HNOOeUUfv/9dwYP7rrmC0VVZX9nS1RWVhIbG0tFRQUxMTGhXo5EIgmUlc/Bwr+LyxPmwulPgVH+Nuw0HHb47g73+JeT/gXH3BzaNbVCfX09+/fvp3///oSFhYkrbTU9QijNmDGDyspKfvtNjJd56KGH+OWXX/jhhx/0bQ4ePEhGRgY7d+5kyJAhPP300zz99NNkZWUBImKTkJDAokWLmDp1qr7fNddcQ21tLR988AFLlizhhBNOYMGCBZx11llt3m/RokXMnDkTgG+//ZbTTz+duro6wsLCmDZtGgMGDOC9995rdv+ys7MZMGAA2dnZ9Orlfj5mzZrF5MmT+fe//x3Q4+TzOXYR6Pe7/NSQSCSHH0sfh58fEpePux1O/Ics2u5sjCY4/T9iBMovT8KP/4DGOph+Z8967M0RQrCE6txtYOLEifrlTZs28fPPPxMV1TxiunfvXoYMaT7Qec+ePdTW1nLSSd71ejabjfHjx3tdN2nSpHbtN2bMGP1yeno6AIWFhfTt25eNGzdy7bXX+rxvv//+Ow6Ho9m6GxoaSExM9LlPZyGFkkQiOXxQVfjpX+KLGuDEv8Pxd4R2TUcSigIz7wNzOPz0ECz5t0hlzXqg54glRekxdhGRke51VldXM2fOHB577LFm22kCpSnV1dWASIH17t3b6zar1driuQLdz2w265cV12vA6XQCEB4e7nNd2jmMRiPr16/X04QavsRgZyKFkkQiOTxQVfjhXvj1BfH/kx+GafNCu6YjlePvECNPfrgHVjwtIkunPtZzxFIPZMKECXz66adkZmYG3M02YsQIrFYr2dnZXnVFnbVfU8aMGcPixYu58sorm902fvx4HA4HhYWFHHfcce0+RzCQQkkikfR8VBW+vxtWvyT+f9oTMNl3SF/SRUy9UUSWvr4V1rwsRNIpj0qx1En8+c9/5tVXX+Wiiy7izjvvJCEhgT179vDhhx/y2muvNYvKAERHR/PXv/6VW2+9FafTybHHHktFRQUrVqwgJiaGyy+/3Oe52rtfU+6//35mzpzJwIEDufDCC7Hb7Xz77bfcddddDBkyhEsuuYS5c+fy5JNPMn78eIqKili8eDFjxozh9NNP79Dj1RakUJJIJD0bVYVF97tF0pnPieJtSeiZdCWYrLDgRvH8mKwuV28ploJNr169WLFiBXfddRcnn3wyDQ0N9OvXj1NOOQWDwb8T0L/+9S+Sk5N55JFH2LdvH3FxcUyYMIG//e1vLZ6vvft5MmPGDD755BP+9a9/8eijjxITE8Pxxx+v3/7mm2/y0EMPcfvtt5Obm0tSUhJHH300Z5xxRsDnCAay660VZNebRNLNWfKYqIUBOOMpmHRVaNcjac66N+HrW8Tl6XfDCfeEdDkaLXVESQ4PgtH1Jg0nJRJJz2XFM26RNPsRKZK6K5OuhFNcRcZLH4Vf/hPa9UgkbUAKJYlE0jNZ8yr8eJ+4fOI/RE2MpPty9PWi+w1g8YPw639DuhyJJFCkUJJIJD2PzR/Dt38Vl4/7Kxz/19CuRxIYx94qUm8giu83fxza9UgkASCFkkQi6VnsWQwLbhCXp1wvvJIkPYcZd8PRrujfghvE8ymRdGOkUJJIJD2HQxvg47ngtMOoP4i6JNlB1bNQFOFxNeqP4nn86DLIXR/qVUkkfpFCSSKR9AxK98H754GtGvpPh7P/Cy20PUu6MQaDeP4GzIDGGvG8luwN9aokEp/ITxmJRNL9qS6Ed8+FmiJIGw0XvCc8eSQ9F5NFPI/pY6G2BN49G6ryQ70qiaQZUihJJJLuja0WPjgfyvZDXD+45FMIk55mhwXWaLhkPsT3h/Js+N+F4vmWSLoRUihJJJLui9MJn18napPCE+DSzyA6NdSrkgSTqBS47DPx/B7aIJ5v19BUiaQ7IIWSRCLpvvz0T9j+FRjMcOH7kDQo1CuSdAYJA8Tza7SI53vxg6FeUY9lxowZ3HLLLQBkZmby9NNP67fl5+dz0kknERkZSVxcnN/rJN7IWW8SiaR7suE9WP6UuHzW89BvWmjXI+lc+k2DM58XEaUVT0PiQDmzr4OsXbuWyMhI/f9PPfUUeXl5bNy4kdjYWL/XSbyRQkkikXQ/9v8CX90iLh9/B4y9MKTLkXQRYy+A0r2w9DH4+lZRkzZgeqhX1WNJTk72+v/evXuZOHEigwcPbvG6tmKz2bBYLO3ev7sjU28SiaR7UbIXProUnI0w8hyYEfg0cslhwIx73B5LH18GxXu69PSqqlLbWBuSv7bOqK+pqWHu3LlERUWRnp7Ok08+6XW7Z+otMzOTTz/9lHfeeQdFUbjiiit8XgdQXl7ONddcQ3JyMjExMZx44ols2rRJP+4DDzzAuHHjeO2117yGzQa637vvvktmZiaxsbFceOGFVFVV6ds4nU7+7//+j0GDBmG1Wunbty8PP/ywfntOTg7nn38+cXFxJCQkcNZZZ5GVldWmx62tyIiSRCLpPtRXwv8ugvpy6D1JeiUdiSgKnPWC6II7uAY+vBiuWdRlnY519jqmfDClS87VlNUXrybCHBHw9nfccQdLly7liy++ICUlhb/97W/89ttvjBs3rtm2a9euZe7cucTExPDMM88QHh6OzWZrdh3AeeedR3h4ON999x2xsbG8/PLLzJw5k127dpGQkADAnj17+PTTT/nss88wGo0B77d3714WLFjA119/TVlZGeeffz6PPvqoLobuueceXn31VZ566imOPfZY8vLy2LFjBwCNjY3Mnj2bqVOn8ssvv2AymXjooYc45ZRT2Lx5c6dFtaRQkkgk3QOnEz6/Hop3QnQ6XPgBmMNDvSpJKDCHwQXvwiszxOvh8z/BBe9L0exBdXU1r7/+Ou+99x4zZ84E4O2336ZPnz4+t09OTsZqtRIeHk5aWpp+fdPrli9fzpo1aygsLMRqFV5lTzzxBAsWLGD+/Plcd911gEi3vfPOO3p6L9D9nE4nb731FtHR0QBcdtllLF68mIcffpiqqiqeeeYZnn/+eS6//HIABg4cyLHHHgvARx99hNPp5LXXXkNxOfK/+eabxMXFsWTJEk4++eQgPbreSKEkkUi6B8v+D3Z+IzqfLnhf2gAc6USnidfBm6fCzm9h6aNwQuenYcNN4ay+eHWnn8ffuQNl79692Gw2pkxxR78SEhIYOnRoh9awadMmqqurSUxM9Lq+rq6OvXvd7un9+vXzqoEKdL/MzExdJAGkp6dTWFgIwPbt22loaNCFn6+17dmzx2t/gPr6eq9zBBsplCQSSejZ8Q0seURcPuMp6DMxtOuRdA/6TIQ5z8CC60WBd+ooGHFmp55SUZQ2pb8ON6qrq0lPT2fJkiXNbvO0D/DspmvLfmaz2es2RVFwunyztNRfS2ubOHEi77//frPbmhauBxMplCQSSWgp3AGfibA8k/8E4y8N7Xok3YtxF0H+Zvj1RZGaTRwEqSNCvaqQM3DgQMxmM6tXr6Zv374AlJWVsWvXLqZPb3+n4IQJE8jPz8dkMpGZmdnp+3kyePBgwsPDWbx4Mddcc43Pc3z00UekpKQQE9N17vwy4SuRSEJHfYUo1rVVQ+ZxMPvh1veRHHmc9C8xCLmxBj66RLxujnCioqK4+uqrueOOO/jpp5/YsmULV1xxBYYO1nHNmjWLqVOncvbZZ7Nw4UKysrJYuXIl9957L+vWrQv6fp6EhYVx1113ceedd/LOO++wd+9efv31V15//XUALrnkEpKSkjjrrLP45Zdf2L9/P0uWLOHmm2/m4MGDHbrfLSEjShKJJDSoKiy4UfjmxPSB894Co7nV3SRHIEaTeH28PB1K94nXzQXviQ65I5jHH3+c6upq5syZQ3R0NLfffjsVFR0TkYqi8O2333Lvvfdy5ZVXUlRURFpaGscffzypqf7rBtu7X1P+8Y9/YDKZuO+++zh06BDp6elcf/31AERERLBs2TLuuusuzj33XKqqqujduzczZ87s1AiTorbVuOEIo7KyktjYWCoqKro01CeRHPaseBZ+/Ico3r7qe+gt65IkrZD7G7wxGxw2OOmfcMxfOnS4+vp69u/f7+UFJDm8aOk5DvT7XabeJBJJ15O1AhY9IC6f8ogUSZLA6D0BTn1MXF70gHBwl0g6GSmUJBJJ11JVAPOvBNUBo8+HSVeHekWSnsTEK2HMhaA6Yf5VUJkX6hVJDnOkUJJIJF2Hwy6+3KoLIHk4zHn6iK8zkbQRRREWEikjoaZQiG5HY6hXJTmMkUJJIpF0HT8/BAeWgyVKOC9bIlvfRyJpiiVCvH6sMZC9Chb/M9QrkhzGSKEkkUi6hj2LYPlT4vJZz0NS+6eVSyQkDhSvI4CVz8LuH9t9KNnTdPgSjOdWCiWJRNL5VObBZ38Sl4+6BkaeE9r1SA4PRpwFR10rLn/+J6g81KbdNZfo2traYK9M0k3QntumjuBtQfooSSSSzsXpgM+uhdpiSB0NJ0tTSUkQOfkhyPkV8n+HT6+BuV8K36UAMBqNxMXF6bPGIiIi9GGrkp6NqqrU1tZSWFhIXFwcRqOx3cfqUUJp2bJlPP7446xfv568vDw+//xzzj777Bb3WbJkCbfddhtbt24lIyODv//971xxxRVdsl6JRAIsewKyfgFzpDANNEu/GkkQMYfBeW/Dy8fDgRViJtyJ9wa8e1paGoAuliSHF3Fxcfpz3F56lFCqqalh7NixXHXVVZx77rmtbr9//35OP/10rr/+et5//319fkx6ejqzZ8/ughVLJEc4WcvF1HcQnUpJg0K7HsnhSeJAMTz306th2eOQeQwMmBHQroqikJ6eTkpKCo2NsnvucMJsNncokqTRY525FUVpNaJ011138c0337Blyxb9ugsvvJDy8nK+//77gM4jnbklknZSUwIvHQNVeTDuEjj7xVCvSHK48+VN8Ns7EJkCN6yEqM6bKC/p+UhnbmDVqlXMmjXL67rZs2ezatUqv/s0NDRQWVnp9SeRSNqIqsIXfxYiKWkInPZ4qFckORI45TFIHib8lb74s3gdSiQd5LAWSvn5+c2G8aWmplJZWUldXZ3PfR555BFiY2P1v4yMjK5YqkRyeLHuddj1nZjj9sc3pV+SpGuwRMAfXgejFXb/AGteDfWKJIcBh7VQag/33HMPFRUV+l9OTk6olySR9CwKt8MPrmLaWQ9C2qjQrkdyZJE2SgzMBVj4dyjYFtr1SHo8h7VQSktLo6CgwOu6goICYmJiCA8P97mP1WolJibG608ikQRIY71o0bbXw6BZMOX6UK9IciQy5U8w6CRwNIgC70bfGQSJJBAOa6E0depUFi9e7HXdjz/+yNSpU0O0IonkMGfRA1CwBSKS4Oz/guGw/oiRdFcURbz+IlOgcBv8eH+oVyTpwfSoT7Hq6mo2btzIxo0bAdH+v3HjRrKzswGRNps7d66+/fXXX8++ffu488472bFjBy+++CIff/wxt956ayiWL5Ec3uxeBKv/Ky6f/SJEpYR2PZIjm6hkIZYA1rwMu34I7XokPZYeJZTWrVvH+PHjGT9+PAC33XYb48eP57777gMgLy9PF00A/fv355tvvuHHH39k7NixPPnkk7z22mvSQ0kiCTY1xbDgBnF58p9giHyPSboBg2fB0TeKy1/8GaqLQrseSY+kx/oodRXSR0kiaQVVhY8uhR1fQ/JwuO5nMPuuAZRIupzGenj1BJGCG3o6XPi+SM1Jjnikj5JEIukaNr4vRJLBDOe+IkWSpHthDhOvS6MFdn4DG94L9YokPQwplCQSSfspy4Lv7hKXT7wX0seEdDkSiU/SRsOJfxeXv78bSveHdj2SHoUUShKJpH04HfD59WCrhr5TYdrNoV5Rj0VWQHQBU+dBv2PE6/XzP4nXr0QSAFIoSSSS9rHyWcheBZYoOOclMHR8+OSRyC+7i5j00CKe+nFXqJdyeGMwitepJRpyVsPyp0K9IkkPQQoliUTSdvI2w08Pi8unPgbxmSFdTk9l/YFSrntnPSU1Nn7aURjq5Rz+xPV1zx1c8gjkbQrteiQ9AimUJBJJ27A3iJSbsxGGnQHjLgn1inokWw9VcMWba6lrFCmgslpbiFd0hDD2Qhh+Jjjt4nVsbwj1iiTdHCmUJBJJ21jyKBRuFe7bZzwtW63bwb6iaua+voaqejsDksTA4PLaxhCv6ghBUeCMpyAyWVgG/PzvUK9I0s2RQkkikQROzlpY8bS4fMZTwv1Y0iZyy+u49LXVlNTYGNkrhrevmgxAdYOdRoczxKs7Qoh0iXxw1dqtDulyJN0bKZQkEklg2GphwfWgOmHMBTDizFCvqEdy4/u/caiingHJkbx91WR6xYXrQTkZVepChp8BYy8Sr+cF14OtJtQrknRTpFCSSCSBsfifULIHotNFAbekzdTa7GzKKQfgrSsmkxRlxWhQiA03A1BRJ+uUupRTHoWY3lC6DxY9GOrVSLopUihJJJLW2b/MPfD2zOchPD606+mhZJfWAhAbbqZvYoR+fXyEBYAyGVHqWsLj4MznxOU1L8O+pSFdjqR7IoWSRCJpmYYqMVAUYOIVYtCopF1kFQuh1M9DJAF6RKmsRkaUupxBM2HS1eLyF3+G+srQrkfS7ZBCSSKRtMyP90F5tvCgOfmhUK+mR5NdKupg+iVGel0fHyGEUnmdjCiFhJP+CXH9oCJHvN4lEg+kUJJIJP7ZtwTWvSEun/UCWKNDupyezoESV0QpwTuiFOdKvZVLL6XQYI0Sr2+A9W/C3p9Dux5Jt0IKJYlE4puGKvjiJnH5qGug//GhXc9hgCaU+iY2FUquiJKsUQod/Y+DydeJy1/eJFNwEh0plCQSiW9+vB8qXCm3WbIjKBgccKXeMpul3mQxd7dg5v0yBSdphhRKEomkOfuWwrrXxeUznxepCUmHsNmd5JbVAc2Lud0RJZl6CykyBSfxgRRKEonEm4Yq+GKeuDzpahgwPbTrOUzILa/DqUKY2UBKtNXrNneNkowohRyZgpM0QQoliUTizaIHRMotti+cJFNuweJAiavjLSESpcl8vDjNHiDIESVVVXnh5z38sDU/qMc97JEpOIkHUihJJBI3Wcth7Wvi8lnPdXqXm8Op8tj3O3h12b5OPU93QDObbFrIDe4apWBHlNYdKOPxH3Zy0wcbOFhWG9RjH9Y0TcHtXxba9UhCihRKEolEYKt1p9wmXgkDZnT6KZ9cuJP/LtnLw99uJ6c08C/yRoeTeR/8xtsrszpvcUHGnzUAeNQoBXmEycbscgBsDif/+XFXUI992NP/OLcR5Zc3yVlwRzBSKEkkEsHPD0PZfjH76qR/dvrpvt+Sx4tL9ur//3pzXsD7rj9Qxteb83jupz0tbvfwN9u45u11OJxqu9cJYkbbCz/vIb+ivt3H0FNvSZHNbtOEUn2jk/pGR7vP0ZTNuRX65c835LIjX9bbtImTHoTYDCjLgp+k2eqRihRKEokEctbCKleqYc4zEBbTqafbU1jF7R9vAmBwiuio+2rToYD3P1QuusdKaxr8iiCHU+WNFVks2l7A3qLqDq33wzU5PP7DTu78dHO7j9FSRCnKasJkEHVLwUy/bT5YDkCf+HBUFf7v+51BO/YRgTUa5jwtLv/6X8heHdLlSEKDFEoSyZFOY71rlpsKYy+GwSd16umq6hu57t311NgcHD0ggQ+uPRqTQWFbXmXAgkYTSk4VSmoafG5TWmPTRVRxle9tAmVfsVjXL7uLyHWduy04napeo9TUGgBAURQ9qhSsgu7yWpsuzp6/eAJGg8JPOwpZva8kKMc/Yhg0C8ZdCqjifdLY/qiipGcihZJEcqSz7P+geCdEpcLshzv1VE6nym0fb2JfUQ3psWE8f/EEkqOtHDMoCYCvNwWWfsstd39ZFfkRQZ7XF1V3TCgddPkfqSrMX3ewzfsXVNXTYHdiMij0jgv3uU2cbjrZXCipqsr9X2zhse93BHzO311pt36JEYzLiOPCozIAePT7Hahqx1KRbWH++oP86d111NrsXXbOoDP7IYhKg5LdsPTRUK9G0sVIoSSRHMkc2gjLnxaXT/8PRCR06uneWpnFj9sKsBgN/PfSiSRFCT+hOWN7AfDV5kMBfYnnVbijOv6EUmFV62IqUDShBPDJ+hycbax50iI7vePDMRl9f+xqFgEVPlJvBZUNvL3qAP9dspeSAEXf5oNCKI3pEwfAX2YOJtxsZEN2OQu3FejbHSqv480V+/l8Q9sFYCC8uGQPP2wt4NeeHMkKj4cznhKXVzwLhzaEdj2SLkUKJYnkSMXRCF/OA9UBI8+B4Wd0+ikXbhN+PredPIRxGXH69SePTMViNLCnsJqdBVWtHudQeetCySui1AGhpKqq7qhtMigcLKtjVRu/9LO1GW8+6pM04loYY+IpDHfmt/74AGzKKQdgbJ9YAFJiwrj62P4A/N/3O3jtl32c8+IKpj36Ew9+tY1bP9qkF5wHk8JK8diX1vRwM81hp8GoP4j3yxc3ifeP5IhACiWJ5Ehl5bOQ/7v4tXzq411ySk2wjHF9eWvEhJmZPjQZaD395ilcwH9azfP6jgil0hobda5OtHMn9Abgo7U5bTpGVonvGW+exLdgEeDZbbc9QKHUNKIEcN30AcRHmNlbVMND32xnQ3Y5igIWV5RrT2HHit6bUmuzU90gUm5lNYfBeJZTHoPwBCj4HVY8HerVSLoIKZQkkiORol2w5DFx+ZTHICq5S05bXC2+LJOjrM1uCzT9Vllvp8bmbqH3m3qrdF9f2AGhpBVvp0RbuezoTAC+35rvM0XmjwMtFHJruOe9+YooeQilvNZb/Asr68mvrMegwMhe7g7GmDAz954+AovRwOTMBB6YM4Jf75nJrBEpYp0lwTWl9HwOgu06HhKikuFU1/tm6f9BkewiPBKQQkkiOdJwOoWBnqMBBp0EY87vktM22B1U1AkRkBzdXCjNHJZCmNnAgZJatuT6FwOHmnSdaeKrKcGKKGn1SX3iwxnVO4bh6THY7E6+2JQb8DHalHrzEXnJr3QLpUC8kLRo0qCUKCKtJq/b/jixD7sePpWPr5/KFcf0JzUmjL4JItIV7NSb53NwWAglgNHnweCTwWETBq3O4PleSbonUihJJEca616HnF/BEiUKVJvMHessNEFjNirEugqXPYm0mpg5PBUQUSV/NBVKRVW+27WD1fWWqwulCBRF4fxJfYDA02+qqrpTbz7MJjXc7twtR5R2FVRjdzhbPKfmn+SZdmuJTFekK6szI0o9vUZJQ1HE+8YSDQfXuEf+SA5bpFCSSI4kyrPF0FuAWQ9AXEaXnVrzMkqKsjYbCqsxZ0w6AN9szvPbWXbIJRqiw0SkJJBi7tIaG42tiAt/aDPSeseLtv6zx/XGYjSw9VAlWzycr/1RXttIVb2o02kpouSe9+arRsktDm12J/uLW478aI7cY5vUgvlDmz+X3YYxMoHg2XlYerhElABi+7gHRi96EMoOhHY9kk5FCiWJ5EhBVeHrW8FWDX2nuudYdRGacPGVdtOYMTSFKKuJ3PI6NuSU+dxGiyiNdUVLAhFKACV+UnSt4Zl6A4iPtHDySBH5+mRd61ElrT4pNcZKmNnod7uWapS01JvVJD6yWyroVlVVT72NDjiiJCJdOaW1rUar2oJnbZgvAdijmXgl9DsGGmvgq7+I95fksEQKJYnkSGHzR7BnERitcOZzYOjat39xtTui5I8ws5GTRggR8u3v+T630YVShoiWVNbbm81H8+y2irK2HHlqDa2Y29Mo8vxJIhK3YOOhVmez6TPeWuh4A4gL920P4HSqFFSItU8bmAi0XNB9sKyO0hobZqPC8PToFs+pkRYThsVkwO5UOVQePOdpz9Rbj7cHaIrBAHOeFe+nfT/Dpv+FekWSTkIKJYnkSKC6CL6/W1yecRckDe7yJegRpRaEEsBxg4VL9+9+0lqaUBqWFqO3tRc3qUHSzhVuNuqdZkXVbRcAqqp6RJTcabNjByWRHhtGRV0ja7NKWzxGSzPePImP1CJKNq+uv9JaGzaHE0WB44eI7sQdLQgl7XEblhaD1eQ/guWJwaDoacEDpYEVdDudKpe9vppr31nnt0vRszas6f06LEgaBDNc76vv74HqwtCuR9IpSKEkkRwJfH8X1JVB2miYdnNIlqB9aSZFW1rcbkiqiILsKqjy+cWqRTx6xYXrabym0SLPNF+Kn20CobLOHZnyjCgZDAqje4uI1r6iloWFLpRasAYAd0TJ7lS97A80D6WkKKvuP7U9z3/qbZOrkHt0gPVJGm0t6D5QWssvu4v5cVuBT5NMEDYFGnanSlVD548xsdmd7a5HaxfTbhLvq/py+O7OrjuvpMuQQkkiOdzZ+T1s+RQUg0i5GZt3nHUFWtSntYjSoJQoDIqo1WnareZwqnq9Tu+4cJICEEr+xFQg5LgKuZOiLIRbvKMz/V0dbK0VVmeXBpZ6C7cY9RokT4sAreMtPTaMoWnCEym/st6vgePmnLYVcmto6zvQyv3R8DSn9DQA9aTpY17eyek3u8PJKU8v44xnl7d5zEy7MZrhzOdBMcLWz2HHt11zXkmXIYWSRHI4U18J39wmLk/9M/QaH7KluMVLWIvbhZmNenHxrnxvp+jCqnocThWTQREiyCW6mgoqrYg4pYNCyVd9kkagQikrwIgSuDvfKjwsArSOt7SYMKKsJj1FtsNHQbfTqeqdeIFaA2ho6zsQYOfb3iL3c6N1BnrS6HBS4hJzYWbxVdPZnW9F1Q3sK65hZ0GVT5uFTqPXOJg2T1z+5jaob70bUtJzkEJJIjmcWfQAVOZCfH+Y8beQLkXzUUqKajn1Bu70W9O5b1p9UlpsGEaXWIJWIkp+xFQg+KpP0ghEKNXa7Ppa+iW0HFECd+ebpzmjZ0QJYFiaeGx8FXTvL6mhqsFOmNnA4JSoVs/niR5RCtB00iuiVN48oqRFEE0Ghf5JYi2dPcbE06sp0OHBQWPGPZAwAKry4Mf7u/bckk5FCiWJ5HDlwEphLgkw5xmwtB7R6EwCsQfQGJIqvlh3NxFKuR71SZ7HalrMrfn3JEdZSYkJ8zp/W9AiJZo1gCf9kyP1bWx23zUxmi9RbLiZ2IjWU55uoeQZURL3JS1WrGFYuki/+XLo1owmR/aKxWRs28d7poeXUiBpK++IUnOhpHW8JUVZSYzUOvo6Vyh5WhB0xGS0XZjDRRccwPo3IWtF155f0mlIoSSRdHNUVaWgso0dW4318KWraHv8ZTBgesC7frw2h1s+3OD3y7891NkcelF0QEIpreWIUu8mQslfRCklpoOpN5cA6O1DKCVHWYm0GHGq/o0as4rF9ZkBpN3AXdBd0UJEaYSr5d9X6m1TjpZ2a1t9EgjxaTQo1Dc6W52Np6qqV0TJl1DyfA7iXUKptJMjSp6pvfb6ZnWI/sfBhMvF5a9uFu9DSY9HCiWJpJvz6i/7mPLvxXyxMfDZYix7HEp2Q1QqnPyvgHerszl44KutLNh4iF/3lbRjtb7RIj5Wk0H3NWqJoVrnW75355smlDTRkOxK4zUTStU+Um/tiih5m016oiiKHlXyl37TCrn7tlLIraFZBHhGlDSRnKan3kREaWd+lZc5pKqqrNkvrArGtrE+CcBsNOj3M6uV9FtRdYPuNg6+U2+FHnYQ8S2YaQYTz8ety1NvGif9E6LSoGQPLPu/0KxBElSkUJJIujmr9grBsjGnPLAd8rfAiqfF5dMeh/D4gM/1045Cal2t6a1FFdpCoUfazd/4Ek8ykyIxGxVqbA6vL2FNKDVNvTUr5q7UvqTD9G1qbA5q2tie7i7m9h0R0mpv9hdX+7xdsw4INKIUG66NMRFf+KqqNoso9U2IIMJipMHu9Grl/3lnIdvyKgkzGzhmUFJA52uKVqeU3YpFgBZNMhvFc5nro5hbS3+mxFj1IvXOLuYu94hYlXRy9Mov4XFw+hPi8opnIP/30KxDEjSkUJJIujl7XV+2AaXfnA748iZw2mHYGTD8zDad66tN7mG07XWy9kUgrtyemI0GBiRpdUpuEaJ5KOmptyh3/ZEWeXI4Vf1LMiXGSqTVRISrtb8t96mqvlHvPvOVegPo7xJA/iJKWw6JVNgIV11Ra7gjL2L9lXV26lzO36muWiuDQWFok4Jup1PlyYW7ALh8amZA6U1faKaYrUWUtNfkpH4JYp31dirrvaNFhR5djk3vV2fhKcSKQ5F60xg+R7z3nHbxfnR0vn+UpPOQQkki6cbUNzp0L5+CygC+5Fe/BId+A2sMnPaEmHQeIFX1jfy80+0s7DnQtKO0pZBbw1ed0qEK74iSZl5Z3+jUa6DKam04nCqKAgmu2hh/kaeW0KJJcRFmv+nCllJvDXYHO111RKN6B1YzpEVetKLnvMo61/VmrzlxWvpNK+j+fms+Ww9VEmU1cf30gQGdyxe6RUArEaW9rojS6D6xughq6qWkRfVSoruuRqm8O6TeNE57HKyxcGiDeF9KeixSKEkk3Zj9xTX6rE2t+8kvZVnw00Pi8kn/hJj0Np1r0fYCGjwKuIMZUWqPUBrq6nzb5RIbNQ12/YuwV5yIrkRYTM1muWlf0AkRFsyuzq/21CkdLPVfn6ThTr01F0q7C6ppdKjEhptbPIYnWmec5gGU16TjTUOb4bYjrwqHU+U/P4po0tXH9tdFSXvQLQJaGWOidbwNSo7SrROaCqUiLfUWbdUFa+fXKHWD1JtGdJq7PvCnh6B0f2jXI2k3UihJJN0Yzxbswqp6/7OyVBW+vhUaa6Hfse7Omzbw9aY8AAa4oiShTL0BDNYKuguFUMpzRZOiw0xEh7lb7Zt2tXkWcvvbJhA0awBfZpMa/V3CoqCyoVn9kzZzbXTv2IDqssAdUdIERX6T+iSN4a5U3va8ShZsyGVPYTWx4WauPq5/QOfxh1ZLdaC4tsW5bFqN0sCUSP3xaVrQ7e56C3PXKHW6j5Jn11uII0oAE+ZC5nFgr4Ovb4HDbdbdEYIUShJJN2ZvofuXfaND9f9Fs+lD2PuTmGQ+5xkx2bwNVNQ2smx3EQBXHiO+bEMfURJCaXdBNQ6n6vZQahJdaWoo6etc7RFK2he/L7NJjdgIsx4taVrXowmlQNNu0Nxw0h1R8hZKWo3SoYp6nli4E4Drpw8kJqx1r6aWyEiIQFGgqsHu97VW3WDX1zUwOUqv3/J051ZV1UuwxntElDpzMK5311uII0ogUt9zngFTGOxbAhs/CPWKJO1ACiWJpBvjGVECP3VK1UXwwz3i8oy7xETzNvLD1nwaHSrD0qKZNjAR6JyIUnIArtwaGQkRhJkNNNidZJfWenS8eYuGpiJI77byGJXSrtSb5qHUQkQJ/Dt0b9WFUmCF3OAWShV1jTidqj6+JD3G+z7HhLnTeXkV9SRFWbl8Wr+Az+OPMLORNNe5/I0y2ed6TSZFWYiLsPiMKJXVNtLoEILI0x7A5nB6DfwNNp6pt6oGO/WNnXeugEkcKFy7AX74G1QXtry9pNshhZJE0o1pLpR81Cl9fxfUlYkJ5tNubtd5vtosut3OGJOuC4+qBjt1QfpS85UOaw2jQWGQawzHzvwq8ppYA2g0S721FFFqRzF3a/VFulAq8oz+Odnuqq0a3ZaIksseQFWhsr7Rb0QJ3AXdAH8+YSARltb9qQLBXdDtu05Je00OTBbPjfb4eNYoaWI1PsKMxWQg3Ox74G8waXQ4vbydoBvUKWlMnQdpY6C+HL67M9SrkbQRKZQkkm6K06nqPjwDkrRamCZCaef3sOVTUAxw5nNiknkbKaluYKXLq+mMMb2Itpr0L7VgRJVUVXWLl6iWB+I2ZYiefqtqNr5Eo+kYk2Cl3lqa8+aJr4jSroIqbHYn0WHuIbaBYDEZiHRZGZTXNurPd3psc7GmFXT3ig3j4il9Az5Ha2gDiTVX8aa465OEUNJSb54RJXfHm3i+FUVp1tEXbLS6LkVxP9/dok4JwGgS70/FCFs/hx3fhnpFkjYghZJE0k3Jq6ynrtGB2agwsZ8wjcz3FEr1lWJSOcDUP0Ov8e06z3db8nE4VUb3jiUzKRJFUUiJ0SIwHbcIqLE5qG8U3XRaO3+gDPUYjtt0fIlG07Sa5t+T0gGhVGtz1+j481DS0IWSRwRma65o2x/VK/BCbo04D0HRUkTpwsl9mTE0mSfOG4vVZGx2e3vp6zHzzRda3dwgLaLkMuMsrrbpEUjP8SUanW0RoHk0xYab9ee+W9QpafQaB9Pmicvf3A71FSFdjiRwpFCSSLopmldNZmKk/mXtFVFa/CBU5kJ8f5jxt3afRzOZnDPWbSfQkbEfTdGOEWkxtjk9pHkp7SqoauahpKGJLy2tVtxCRKm4uiGgga9aGik6zERseMtROl8RJb3jrR0z17Q6pdzyOj2V5Eso9Y4L560rJzOtnS7c/tAjSn5Sb3uKvCNKMeEmol0WDVpUqdDHc9DZY0w0ARYfYdG7K5sOSw45M+6BhAFQdQgWPRjq1UgCRAoliaSb4lkLorky68Xc2b/C2tfE5TnPgCXw9I4nBZX1rMkS88FOH9NLv15LmQRjjEl7Ot40tNTbvqIa8sp9t8p7unP7O19ipLhsd6q6R1FLBFrIDW5hUV7bqNfftKfjTUNLUe3IEzVO0WGmgObjBYuWTCcbHU69dkmrH1MUpVn6TatR8hJKnRxR0jre4iLMJLqaBrpNjZKGOVy8XwHWvQ4HVoZ2PZKAkEJJIumm6EIpJVLvRMqvqBcTyb+8SWw0/lIYML3d51i4NR9VhQl947xEQXtqevzRHg8ljV6xYURZTdidKjaHE0VpHl1xR4ts1DTYqXL5GXmm3iwmgx7RCOQ+HQzAGkAj3GKkl2tN+4prsDuc+miRUb0C73jT0CJK2jHSYtpW19VRNNPJ0hpbs7Ek2aW1NDpUws1Gr0487bWjWQS405/ubTp7jIl23ASPiFK3qVHypP/xwl8JxPu4MXgO+JLOQQoliaSbotWCDEyO0ms9Cqvq4ZcnoHgXRKbAyQ/53f+1X/Yx6aFFevGtLzbmiMjHcYOTva7XxEdhIGNTWqEjESVFURjicugGSI0O0922NbTogcOpsss17iTMbGgWhWmL+NO+8AN11M5M0gqga9hTVE2D3UmU1aRHm9qCJpR2uLrmfKXdOpMoq4kk12PadDjuXg+jSYPBXXvVu0nnW5HH+BKNhE4ejKsdNy7CQqIretWtapQ8OelfEJUGJXtg2f+FejWSVpBCSSLppuzxSL1pUYWkmj2oy58SG5z+BITH+91/wcZciqsb+Pb3PL/bbHUNbR3ZJPKR0o52en90JKIEbnNFaO6hBGKArmb6uPWQiMIkR1ubFVG7LQJa/wWfq3e8BSaUPOuUfj/ofkw9xUSgaKk3LY3VNNXYFeijTJoIpT1NrAE0+vhJvXkKJXeReufUKGm1T/ERZhK1GqXulnrTCI8T71+A5U9D3uZQrkbSClIoSSTdkIq6Rj3yMSA5koRIC2FGJ4+ZX0Fx2sV08hFn+d1fVVW9vVurl2lKg92hR5tGNBFKwUy9dSSiBDA4xVMo+RYuWvH5Nle6yjPl03SbwCJKgdcogbdQ0sRae+qTgGbF403nvHUF/VyWBk0Lupt2vGn0dnW+aY+b5/gSDU3MdpaPknbc+EiLu0apO6beNIbPgeFnguqAL+eBw976PpKQIIWSRNIN0dyP02LCiA4zoygK88J/ZKxhH3ZLDJz2RIv7F1fbqHbV6mgRjqbsLqjG7hRDW5u13Ed7pPo6SEeFkmdEyZ9w0Y69TYso+YhetUX8BTK+xBNNKO0rrvGa8dYetIiSRmgjSt5CqWnHm4Zn6q2mwa67b6f4KOburIiS5s8UH2EhKbKbdr015bQnICwW8jbBry+EejUSP0ihJJF0Q/a6jCYHprhqXEr2cq39QwC2j7pTTCZvAc9IQH5lvU9x4Jl2a5qm0iIyxdW2gNrpW6KjqTet8w38iwZNBO3Id0WUYvwLpdY6+epsDv3xamvqLau4Rhdr7Y0oxUc2jSh1vVDKTBICccnOIr1OSVVV9rkikIOaCiWXgC2oqtf9riIsRiI96sS0Yu5Oiyh5pd7cNUqdOVuuw0Snwux/i8s//xtK9oZ2PRKfSKEkkXRDvMZEqCp89ResNLDCMZJ1Cae3un/TuWNbfKTftBTRiPTmnVmeBdIddVLuaEQpKcqif8n6Tb25jq0ZW/qKKGnir7WI0ur9wqU8NcaqF1a3RkZCBEaDQl2jg7pGBxEWoy6e2kpseOgjSjOHpzIwOZLCqgYufGUVB0pqKKxqoKrBjkFxWwhoJEVZsJoMqCpszCkHvKNJgJczd2eIFz2i5JF6sztVKuu6eUpr3CUwYAbY6+HLm8HpDPWKJE2QQkki6Ybo3UXJUfDbO5D1C42Klbvt15AfQOooq4lQ2uwj/aZFPkb6GNrqWSDdES8lVVUpdnUeJbVhIK4niqJwyZR+DEmNYnL/BJ/bNBVGvkRZoKm3H7cVAEIsBOqqbTYavEaVjOwVg7EdhdzgjrxopMd0fY1SlNXE/649moHJkRyqqOeiV37lpx1imGu/xMhmTuCeXkobdKHkLfC01FuD3UldJwyrLfMwnLSajESHiWhWcU03T78pCpzxNJgj4MBy+O3tUK9I0gQplCSSbogWURoeWQ0L/w7A2gE3kqOmBtSyr6XetNRR04Jup1PVfXpG9vKdIkoJQkF3ZZ0dm8M1vqSdqTeAv84eysJbp+udU01pKoxaSr211MnndKos2i6E0kkjUtu0xkyPKEt7026A130MNxuJCe86s0lPUmLC+N91brH0t89/B2Bgsu9ImZZ+25BdDkByk+cg0mLE4rJ2CLbppNOpUlHnTr0BHl5K3bTzzZOE/nCieJ+z8B9QkRva9Ui8kEJJIulmCPfjWkBlzKYHoaESek+kcORVgMt0shX2uzre5owVbttNU29ZJTXU2BxYTQZ94G5TgtH5prXix4SZCDMHbx5ZU5qKMF/Dd7WoU3ltIw123xGNLYcqKKhsIMJiZOqAxDatoX+Su25nlB/xGQieXW9psWFtnhUXTFKi3WJJy5Y1LeTW0Arfd2p1Yk3Eq6Ioeioz2GNMKusb0UrpNKHp9lLq5hEljSnXQ5+jwFYFX98K3bm26ghDCiWJpJuRXVqL3anyR8tqwvYtBIMZznqBlFjxRVTQSieaqqp6t9Lpo9NRFFHQ7dnBptUnDUuLxmT0/TEQaPFzSxRVudJu7axPCpSmESVfqbfYcDNmoxAd/qIMWtpt+pDkNgu7/h6RlvbMeNMwGhRiXGmjrnbl9oWnWAIYn+Hbu0uLXmqCxZdFQ0InjTHRjhdlNWExidezVqfUbb2UmmIwwpnPg9ECu3+A3+eHekUSF1IoSSTdjL2F1SRQyT+Mb4krjr8DUobrX5oFrUSUCqsaqLU5MBoUhqZF6+aAnlElzW9oRAuRj+BElFyF3B1IuwWCpzBSFPeXpCcGg6JHnvzdJ00otTXtBuiRuTCzoZkhY1vR6nlCUcjti5ToML6YdywfXnc0J/t5bPxZTHiiRZQ62iDQFM85bxqJ3XmMiT9ShsHxd4rL390J1UWhXY8E6IFC6YUXXiAzM5OwsDCmTJnCmjVr/G771ltvoSiK119YWPf44JFI/LG3qIb7ze8Qq1ZCykg49lYAfTBujc2heyT5Qut46xMfjtloYIyrXub3g5X6NlpEqakjtye6QWMHvmiKO9jxFihx4WZMruLphAhLszEnGi2Jv5zSWnbkV2E0KJwwNKXNa5iUGc/skan8ZeaQdhdya2jpo1BYA/gjymri6AGJft3GezexUmiaeoPOM53U57xFugVyUncfY+KPY2+B1FFQVyrEkiTk9Cih9NFHH3Hbbbdx//3389tvvzF27Fhmz55NYWGh331iYmLIy8vT/w4cONCFK5ZI2o5l7w+cZVyJEwOc9TyYxAd+pNVEtMuXpqU6Ja3jTZszphUW/55bDojU3DY/o0s80VyVCysDM518c8V+/v3tdhwevktFHfRQChTPaFFLoqwl8acVcU/qF69HdNqC1WTk5csmccOMgW3etylafU16gM7g3YGmnlO+Cuo7MsZkX1E1U/69iCcX7mx2m5Z68yyE19K9vkwnH/l2O8c8+lPAr+0uxWgW73vFCFs/gx3fhHpFRzw9Sij95z//4dprr+XKK69kxIgRvPTSS0RERPDGG2/43UdRFNLS0vS/1NS2h9Qlki6jrpyzc4Xr9v4hV0HvCV43p7oiDAUtfMDvd9UnaT4+Wr2M1vlWVNVAcbUNgwLD0oITUcoqruHBr7bxyrJ9fLQ2R7++ox5KbUE7R4tCqYWIUkfSbsHmT8cP4A8T+nDG6PRQLyVgUqLD9Kie9v+mJHh4KbWVN1dkUVDZwBcbDzW7zXPOm0ZipO+uN1VV+d+abHLL61iys5umtnqNh2k3ictf3wZ1ZaFdzxFOjxFKNpuN9evXM2vWLP06g8HArFmzWLVqld/9qqur6devHxkZGZx11lls3bq1xfM0NDRQWVnp9SeRdBXqD/eS6CxhnzMNx/F3Nbs91fUrvSWh5I4oieLvEekxGBQoqGygsLJeT7sNSI4i3OK/YLktNUrv/eqO1D6xcKfeql3cRTVK0DGhVFHbyOr9pUD3EEpTBiTy5Plj2xXZChVGg0K6a2ix2ag084MCzxqltkWU6hsdfLFRtMwfLKtt1rXoOb5Ew13M7f1cHyippbJepK535Fe1aR1dyoy7IXEwVOfD938L9WqOaHqMUCouLsbhcDSLCKWmppKfn+9zn6FDh/LGG2/wxRdf8N577+F0Opk2bRoHDx70e55HHnmE2NhY/S8jIyOo90Mi8cueRSgb38OpKtxtv45+ac3b07U6pfwWhZKwBsh0RZQirSa9uPj33Aqv0SUtoaVOqurt1LdgEFhnc/DxOhFFig4zUVpj49nFu4GujShpNTG+Ihka2jryKuq8rl+yqxCHU2VwSpQ+50zSdvq4huMmR1l92hq0t0bph635urhxqrjsM9z4EkpJUb5rlDYdLNcv7yzoxj+EzeFw1guAAps+gN0/hnpFRyw9Rii1h6lTpzJ37lzGjRvH9OnT+eyzz0hOTubll1/2u88999xDRUWF/peTk+N3W4kkaNRXwpd/AeBtx8nkx01o5n4MbqHkz3TS6VR1s0nPERqje7vTb4EUcgNEW01YXa3WLUWVvtyUS2W9nYyEcJ69aLy4Dyuz2FNY7Y4odYFQunByX2YMTeYPE3r73WaAy+to0fZCHvt+h15P1Z3Sbj0ZraDb3/PtHozbNqHkmc4Ft3O9RlmNK/UW2Tz1VlHXiM3uHgvi6VK/I69tEaUPVmfzz6+2dd38uL5T4OgbxOUvb4Z63wOuJZ1LjxFKSUlJGI1GCgoKvK4vKCggLa3lAaEaZrOZ8ePHs2fPHr/bWK1WYmJivP4kkk7nx39A5UFqIjL4P/sF9IrzHRXRLAL8FXPnV9bTYHdiMihe7dpaQfeW3Aq3NUB6y14/iqK06qWkqirvrBJpt0um9OOEoSnMGp6C3anyz6+3eYwv6XyhNC4jjreunMxgjyG6TTlmUCLXHT8AgP8u2cuVb62lqKqBpa5alVlSKHUI7TWX7Ceqp897a0NEKae0lpV7S1AU9BE2+5qM6PEVUYoNN+vdh57C7HcPoVRSYwvY/kJVVR7+ZhtvrNivD63uEk78B8T3h6pDuku/pGvpMULJYrEwceJEFi9erF/ndDpZvHgxU6dODegYDoeD33//nfT0nlMgKTkC2LcE1r8FwOIh/6COMNJjfXc76TVKfkwnNWuAvgkRXkaSY1wF3WuzyvS0RWsRJfAcY+L7fBtyytl6qBKLycD5k0Sa+t7TR2A2KizbVaRHbHz5GoUCRVH422nDefai8YSZDSzbVcSs/yylqsFOUpSVcX3iQr3EHs1JI1LpmxDBnLG+P2O1Yu7SJhElu8PJltwKnM7mkZpPXGndYwclcfzgJMA94kfDl1AyGBQ91adFNh1OlS2u1HOYWbw/dgZYp1RZZ6fGJlLQFXVdaDlgiXCl4BBzH/f+1HXnlgA9SCgB3Hbbbbz66qu8/fbbbN++nRtuuIGamhquvPJKAObOncs999yjb//Pf/6ThQsXsm/fPn777TcuvfRSDhw4wDXXXBOquyCReNNQDV+6uluOuobfDKMA/0aDqa2YTmpCKbPJWJIRvURBt1Zk3Ss2LKBC4dYKut91RZPmjOmlfyn1T4rkqmP669vER5j9+hqFijPH9uKzG44hIyFcf0xmDU/x6xEkCYxRvWNZducJnDXOd/ozzpUaq290Umdz1709u3g3Zzy3nL9+sskrreVwqnyyXtSUnj8pgwGuWrumER1fhpPgOcZECJs9hdXU2hxEWIxMH5IMwI78wOqUPOsCtXqpLiPzGJh8nbj85c3Q0I2L0A9DutenVytccMEFPPHEE9x3332MGzeOjRs38v333+sF3tnZ2eTl5enbl5WVce211zJ8+HBOO+00KisrWblyJSNGjAjVXZBIvFn0AJRnQ2xfmPWgXmTcmlAqrGrw+eu7qYeSRoTF5OUW3ZIjtydaYbSv1FtxdQPfbBbvt7lT+3ndNu/EQXoxbVfUJ7WHEb1i+PLPxzJ9SDJGg8IfJ/YJ9ZIOe6KtJt1CQIsC1Tc6eMfVNfnZhlwe/8Htk7R8TzF5FfXEhps5aUSq/hreV1StCypVVX0aToL7tVfi6nzTCrlH9Y5leLqIqAba+ebZAFDV1UIJYOb9ENcPKnLE4FxJlxGasdQdYN68ecybN8/nbUuWLPH6/1NPPcVTTz3VBauSSNrBvqWw9lVx+cxnwRpFnitSlOYn9ZYcbUVRwO5UKamxNRMh7kLuiGb7ju4Ty25XEeyIANJu2vnAd0Tpo7U52BxOxvaJZWxGnNdt0WFm7jplGHfM38zQFryaQk18pIW3r5pMnc3RolWCJDiIwbgWiqsbKKu10SsunK82HaK8tpEoq4nqBjsvLtlLelw4lx3dj49dRdznjO9NmNlIv8QIFEUIleJq8fqvbrDT6BCiyTP1Bu6IUrFr5qBWnzSmdyzD0kQtW6CpN09Ljqr64A71DQhrlDCifHsOrH8TRpwJA0/s+nUcgfSoiJJE0pMpr7XxzKLd5JTWitD5Fy7BP+kqGHgCgC6U/EWUzEaDXhjty0vJX+oN3J1vEFh9EvgXSg6nygerswG4bGqmz33Pm5TBgj8fw0NnjQroXKFEiqSuI8GVftM61d51RZP+fMIgbp01BID7v9jCx2tzWLhNWL9o9W9hZqPuAK7VKWlmk1aTodnzqM1707yUNrsiSmMy4nSz1V0FVV5u8v7Iq/AUSiGIKAH0Px6OulZc/uIm0S0r6XSkUJJIuoj/rcnhqUW7eP6nPSJ0XpENcX3hpH8CYLM79aLTloah+jOddDhVckpFeqBp6g3aJ5RS/HS9Ld5eQG55HfERZs4Y4785YlxGHLE+jAclRy5xHu7cG3PK2XywAovJwAVHZXDzzEFceFQGThXu/HQzjQ6VUb1jvCKgmsXDPledUpmftBu4mwhKqm3Y7E62u+wAxvaJpW9CBOFmIw12px6JbYmQR5Q0Zj0A8ZlQeVB2wXURUihJJF2EVj8UlbtUhM5BdLNYRQqgoLIeVQWL0eDzQ18jzY/p5KHyOmwOJxajgV4+ZoSN6h3LgKRIxveNazbp3R/+IkpvrcwC4PyjMggzy2iMJHA8x5i8syoLgDNGp5MQaUFRFB46exQnDE3Wt79gkrfpr2edEvie86aRpI8xaWBHfiU2h5PYcDN9EyIwGBSGpIpjBZJ+84woVdaFKKIErhTci+Lyb2/DnkWhW8sRghRKEkkXcbC8lmhqua7MVTc3+ToRSnehCZ+02DCfrsYa2rDagiamk7o1QGKEz+n1YWYji2+fzmc3TGvx+J4kewwW1YrHtx2qZOXeEowGhbl+0m4SiT80U8h9RTV87WoGuMyjGcBkNPD8xRM4ekAC/ZMiOWu8dwfdgGQRLdW8lHzNedPQI0o1Nt1ockyfWP31P9RVpxRIQXd+RTeJKIHogptyvbj8xU1QVx7S5RzuSKEkkXQRB8vq+LvpPVIpwRnfX4TQPWitPkkjzY9FgJY+8JV201AUJWCRBG6jSLtT1VMcry/fD8Cpo9ICjkxJJBpawfXH63Kw2Z2M7h3LuCbNAJFWE/+79mh+un06MWHeAkgTSlqNku6h5DP15h6Mq9cn9XGnoLU6pZ0BWATkV3aDGiVPZt4HCQOEEeUP94Z6NYc1UihJJF2Aw6kyuGIlF5iW4FQVDk1/AizegiavvGVrAA1/ppNaRMlXx1t7MXukAYuqGyisquerTWJ6+9XH9m9pV4nEJ5pQqnX5KF02tZ9P8e5P1A9ypd5ySsVwXM3l22dEycNw0h1RitNvHxZgRKm+0aFHrqDtQun91Qf0Dr6gYYl0peAU2Pge7PwuuMeX6EihJJF0AUWFh/i38RUA3nCcwq6w0c22ac0aQCPVzxiTrBY63jpCcpS7Tum9VQewOZxM6BvH+L7xQT2P5MjAM/ITF2HmzLG92rR/crSVKKsJpwrZJbW62WRTawBwR0Qb7E52Fggx5BlR0lJv2aW11Nr8i5+m77XKNqTeduZXce/nW7jz080+O1U7RL+pMPXP4vKXN0NNSXCPLwGkUJJIOh9VxfL9X0lRytnt7M3j9gvIbjL9HNwfxv7mvGl4mk56kuU6Zv8WUm/tIcUVwcoureU9lyXANccNCOo5JEcOCR6Da8+f1PZmAEVRvNJvvsaXaIRbjES6LANUVYgsLXUNIjWXFGVFVWFXQXWz/TWaNk60JaI0f707kvTrvk4QMif+A5KHQU0hfH2LuKOSoCKFkuSIYfW+El5dtq/rJn9rbPmUhKxvaVSN3Np4Aw1YyC6ta7aZ5vzr+UHuC+320hob2/MqUVUVu8Mp/JnovIjS68v3U1pjo3dcOCfL4bGSdqIJGkWBS6f0a2Vr3wxI0oRSjUeNkm8bikSPgcxjPQq5NYana8aT/uuUtB8xml1GoMXcjQ4nn284pP9/1d5OEErmMDjnZTCYYPuX8PsnwT/HEY4USpIjhnsXbOHhb7ezen9p15208hB8cxsAz9nPYYcyEBDRmaa4i7lbTr3FRZiJDhOm+qc+8wuz/rOUf369DbtTxWoytCq02kqyK6Kk+dZceUym18BdiaQtjOody+yRqfxl5mD6Jravns5tEVCjG1f6sgcA74HMY3wMPR6aKoSS5rHkCy2iNMS1bXWDPaAfXMt2FeneaACrOiOiBNBrHEy/W1z+5q9Qkds55zlCkZ92kiMCVVXJLRMRmy25FV11Uvjiz1BfQXb4MF50nMn4vnEAevRHo9HhpMj1gZrWSjG3oii8dOlEZg1PwWI0sLeohndcw2kzEyODPtg12eMXeaTFyPlHZbSwtUTSMmajgZcvm8QtLhfu9uAejlvtnvPmTyhFul+/o/s0n3E4NIBRJlpEabDLd8mpQo3HUF9/zHcN9L1gUgZGg8KBklpyy5tHk4PCsbdC74nQUCE+d2QKLmhIoSQ5IqixOahrFB9s2/K6yPZ/7Wuw9ycwhfFs9O3YMTF1YBIgIkqev0gLqxp0s8nEFswmNY4ZlMRrlx/Fun/M4qkLxjJreCrRYSbOHNe2wthA8Jwnd8FRfZu1a0skXY3upVRUTWkLNUqAPpwZYKyPiJJmEbAjv9JvlEgTSv0SIjAbxQ+R1tJvpTU2Fm0vAOCKYzJ1Z/xOSb8BGE0iBWcKg30/i88fSVDocUNxJZLteZV8uv4gYWYjseFmYsPNxISbmdgv3u+kek9n6W2HukAoFe+BH+8Tl2c9yJplyUAtR/dP4DkF6hod+lBPcFsDpMZa2xQRigkzc874Ppwzvk+w74FOSrSIcBkUkXaTSEJN/6RIFAUqPYqq4/zWKAmh1Cc+3Kfj/eDUKAwKlNU2UlTVoBu6epJX6e5IjQ4zU1pjo7LOTnrzAJXOlxtz9REsw9NjmDowkY055azaW8IfJ3bS+zVpsBiJ9N2dYkxS/+mQ3P7InUQghZKkx/Hvb7fzy+7iZtf3jgtn+V0n+PReKfToWtlbVI3N7sRi6qSAqqMRPrsGGmuh/3QcR13LoS9/AKB/ciS9YsPJLa8ju7TWLZS0+qSY7mfgOKFfHDOHpTC+bxwZCcHzaJJI2kuY2UjvuHAOutLpJoNCtNX311mfePGandjPt51FmNlIZlIk+4pq2JFf5VMoFXiYwUaHmSitsbUaUZr/m0i7/XGCEEVTByTy3yV7+XVfCaqqtsn4tU0cda3wVNr3s/gcunoRmFqPUkv8I1Nvkhapt9eTUxlko7QOohUVnz46nbPG9WKGay5Ubnmd1y9MT4o8CiobHSq7C1sfWdBuljwChzZAWByc/V8KqmzYnSpmo0JKdBgZCUIMedYpaaH99FasAUKB1WTk9SuOYt6Jg0O9FIlER6tTAlHI7U94nD2uN/8+ZzT3nDrc77GGtVCnZHc4KaxyjxfSGilasgjYnlfJltxKzEaFM8eJESyTMuMxGxVyy+v04dWdgsEAZ/8XwuMhb5P4PJJ0CCmUJC3y0K8Pcdrnp7G+YH2olwKIometjf6+OSN45sLxvHXlZGLDRdjdn6Fb06GuLXW4dIgDK+GX/4jLc56G2N76r95eceEYDQp9XVEZz863Q5o1QCuF3BKJRDDAwwbDlyu3RrjFyMVT+rb43nLXKTX/XCiutuFUwWhQSIqyEm0V52rJdPJTVxH3zGGperovwmLSR7Ws3Ns8Ih5UYtJhzrPi8vKnIGtF557vMEcKJYlf7E47i7LFZOqlOUtDvBpBXnk9ThWsJoNXN1aaH7dqjabmjJ1Sp1RfAZ/9CVBh7MUw8hwADpYJQdQnXkSSfAklPaIU5NZ+ieRwZWCyh1AKoAGiJdzDcZt/Lmg/zFKirRgNSqsRpUaHkwUbRXv+eZO8a5GmDkgEOtEmwJMRZ8K4SwEVPv+T+HyStAsplI5g7l95PzctvolGp+9fRluKt1DTKNJcm4o2deXS/KKJiz7x4V5Fz6muX4tNHXQ1tIiS9it0e2d0vn17B1RkQ1w/OPUx/WototQnTgikDB9CKdDxJRKJRDDQI/XWUkQpEEaki4jSroIq6hu92/4LKt1pN4BoV9enP6G0ZGcRxdU2kqKsHD8k2eu2owe6hNLekq4xvj31UYjPhIoc4a8kaRftEkrvvPMODQ0Nza632Wy88847HV6UpPMpry/ns92fseTgEtblr/O5zeq81frlLcVbaHQEPt+os8hxRWeaFhWnuUwRC1sRStoH17Y8/63A7eL3+bD5I1AMcO6rEBaj3+QvopTjJZS09JyMKEkkgTDASyh1LKLUJz6cpCgLjQ61mX2I/iMmRhNKWkTJ9+fhd7/nAXD2uF6YmxizTugbj8VkoLCqgX2u2YydijVafB4pRvj9Y9gsXbvbQ7uE0pVXXklFRfMwXlVVFVdeeWWHFyXpfPaU79EvLz3oO622Ot8tlGxOGztKd3T6ulojp7QWlEbiYsq9rtcHxfoRSlrqbdrAREwGhYq6Rv0DsMOUZcHXt4rLx/0V+k7xulmPKCV4C6X8ynrqGx00Opz6+mSNkkQSGKkxVn2OW0dTb4qi6PVDG7LLvW7LbxJRimkl9aYZSo51Hc+TMLORia5h0is7y0+pKRmT4fg7xOWvb4XS/V1z3sOIdgklf62NBw8eJDa2BWMJSbfBUygtyVnSLLpSZ69jY+FGAPrH9gdgY9HGNp3DqTo7skSf5JTVEZa2gMXVt/Pchuf0detCqaJ5pBPcEaU+8REMShG/RINSp+RohE+vgYZK6tMmwvQ7m22iCyVXm3JCpIVIixFVFbcVucwmzUaFpEjfPlASicQbMRxXvJc7mnoDGO8SMBuyy7yuz28SUYoJ11JvviNKJTXCANOfcexUV/rt164SSiCEUt+pYKuCT68Wn1uSgGmTUBo/fjwTJkxAURRmzpzJhAkT9L+xY8dy3HHHMWvWrM5aqySIeAql3Opc9pbv9bp9Y+FGGp2NpEakcsaAM4C21Sk9sPIBTvrkJMrqy1rfuA3klNZiCBcT7F/Z/ArPbngWVVX1DzFfXW92h5OSGiGUkqOtDHfVIwSlTunnf8PBtdQZoph1YC7L93lHWh1OlUPlmlASESVFUfTUYU5prZ52S40JC/r4EYnkcGaaS3SM6tXxH+jj/UWUKprWKLUcUSrVhFKU7x892po1P6UuwWhylQTEQu56+OmhrjnvYUKbDCfPPvtsADZu3Mjs2bOJinLniC0WC5mZmfzhD38I6gIlnYMmlMwGM43ORpYcXMKg+EH67Vp90pT0KYxLHgegR5gCYVH2IioaKthYuJET+p4QtHXnlFVh6OP+Jfba76/hUB2clHoV4FsoldbYUFXhLJ0QaWFEegyfb8jt+CiTfUtE6y3wRNiNHKxN5qtNhzh2cJK+SX5lvZeHkkbfhAh25FeRXVqrOweny7SbRNIm7jplGNccN8CvI39bGJMRh6KI1FlhVb3+ftVSb9qwaq2Y25c9gMOpUqbNnvMTURrTJ45ws5GSGhu7Cqr1jrtOJy4DznwOPp4LK56GAdNh4Ildc+4eTpuE0v333w9AZmYmF154IVarTBP0RFRV1SNIZw48k093f8rSnKVcM/oafRtNKB2dfjSjkkZhVIwU1BaQX5NPWmRai8evs9dR0SAiK9lV2UFbd02DndKGQqIUJxajldsm3sqjax7lzS1vUt1gA0ZRXN2A3eH0mm6v1f8kRYn2Xi2i1CGhVFOsWwGoEy7n/bUTACfLdhd5paYPugq2e7s8lDQ8LQIaHSJFKTveJJK2YTAoQRFJAFFWE0NSotlZUMXG7HJOHpmGqqrNUm8tRZTKasWPMkXxnw60mAxMyoznl93FrNxb3HVCCWDEWTDxSlj/Jnx+PVy/AqKSW9/vCKddNUonnngiRUVF+v/XrFnDLbfcwiuvvBK0hUk6j5L6EsobylFQuGLkFYBIq5XWlwJQaatkW+k2ACanTSbCHMGQeDEvKJA6pfyafP1yTlXwXL0PltVhsAijtr7RGVwy/BLunXIvAJ/sfp+wlIU4VW8XbnDXJ2kfqMPTxQfTgZJaqhv8u+v6RVVhwQ1QnQ9JQyk65gHqG4XYyauoZ09htdeawV2fpNE30S2UDpWLD+JeMqIkkYSU8X3jANiQUw5AeW0jDXbx3k5xdda2ZA9QUi2iSXHhZq8fa005ZpCIOr+0dK9X92uXcMojkDwcqgvE55gz+LWkhxvtEkoXX3wxP//8MwD5+fnMmjWLNWvWcO+99/LPf/4zqAuUBB8t7ZYRnUFmbCbDEoahovLLwV8AWJu/FqfqJDMmk9TIVADGJo8FYFNh63VKnkLpQOWBoK07p7RWF0r9YvoBcOGwC/n7lL8DYE5YBjibmU42FUqJUVZSXR96O9oTVVr1POxeCEYrnPcm2U3MfJfucv+IcAsl72iRp0VAfqV05ZZIugO6UHIVdGtpt4RIC2Fm0WGnRZR8pd60Wkh/9UkaF03uy5DUKAoqG7j09dV+bU06BXM4/PENMIXBnh9h5TNdd+4eSruE0pYtW5g8eTIAH3/8MaNHj2blypW8//77vPXWW8Fcn6QT2FMmhNKgOFGTNL3PdMBtE+BZn+R0qtjsTsamuIRSAAXdnRVRyilrLpQA/jjkjxgUAygOFGMNBZVNIkquCFOKR4h+RHsLurNXw6IHxOVT/g2pI72MI6GpUPL2UNLwTL1pESVZoySRhBat823zwQoczuZpN3ALpeoGO06ndzG2FlHyV5+kERtu5t2rp9A3IYIDJbVc+vpqylxF4F1C6gi3Ke7if8GBVV137h5Iu4RSY2OjXp+0aNEizjzzTACGDRtGXl5e8FYn6RS0iNLAuIEAzMiYAcCK3BXYHDav+qRHv9/BqPt/INIptt1eup16e8u/fjyFUl5NHjZHcD4AckrrMFhEIbenUDIajMRbxQecYqpqVtCt/VrzrGVoV51STQnMvxKcdhj1B5h0NeB22Namk6/ZX6q7+/pLvfWOD0dRoNbm0AdxyholiSS0DEqOItpqotbmYFdBVTMPJYAYV+pNVaHG5p1+0zrekqJa93VKjQnj/WumkBpjZVdBNVe8uaZ9pQDtZcLlMPp8UB3ic62mk+fP9WDaJZRGjhzJSy+9xC+//MKPP/7IKaecAsChQ4dITEwM6gIlwUcTSoPjxTT4EYkjSApPotZey3f7v2NfxT4UFCamTGL++oPYHE7W71VIDEvE7rSzrWRbi8fPr3ULJafqJLc6NyjrzvZIvfWN7ut1W1K4yPkrpqpmppPuiJL7w25EL00oBTgc1+kU85IqcyFxEMx5RlRs4hZKJw5LIT02jAa7k9X7Rb3XwXLfESWryajPdatziSpZoySRhBaDQWFMhrAa2JBd7jFayP3etJoMmI3ivd+0TqnE9VnTWkRJIyMhgveunkJ8hJlNByu4+q21enNHp6MocMZTkDQEqvLgs+tkvZIf2iWUHnvsMV5++WVmzJjBRRddxNixIi3z5Zdf6ik5SffEs+MtUunNnsJqDIpBT789u0FMnB6eOJyCCoP+C2ndgTJ3nVIr6TfPiBJAdmVwOt9yyipRzKJ2wDOiBN5CqcBPjZLRUsaqQyLErEWUduZX4nAG4GWy4imRzzeFwXlvi9EA2rpcQqlvQgTHDxYdJMt2FWF3OMlzpdWaRpTAewyLyaC0WtcgkUg6n/EZbuPJAh+pN0VR/BZ0u80mA38vD06N5p2rphBlNbF6fynL93RhZMcaJT7PTOGwdzEsf7Lrzt2DaJdQmjFjBsXFxRQXF/PGG2/o11933XW89NJLQVucJPgU1BZQ3ViNSTHx1w8OcebzyymsqteFUmFtISDqk1Z5OMduzClndJIQSq35KWlCSbW76nCCYBGgqioHqw6iKCphxghdGGkkhotIpsFURUFVk9SbSyh9nP1vrvvxOnaW7iQzMZJws5H6Rif7W5u5lLXcbdB22uOQNsrr5mxPoeSaJbd0VxEFVQ0eHkrNPzj7egil1JgwL/sAiUQSGjw73/J8pN7A/7w3rUYpMYDUmyej+8Qy3fXZsa+oC2bAeZI6Ak5/Qlz++d+w/5euPX8PoF1CCcBoNGK321m+fDnLly+nqKiIzMxMUlJSgrk+SZDR0m7pkX0orHRQa3Pw0/ZCju51NFaj+8t8StoUr1lE9Y1OohB1SpuKNvl1lFVVVRdK9lox+iQYEaWy2kbqlQIA+sX0bTZCJzlcfMgopiq/XW/5tWId20q2YTQoDHPZBLRYp1SVD/OvBtUJYy+C8Zd53Vzf6NCLx/smRHDsoCQMCuwprGatK/3WOy7cp+O2p1CShdwSSfdAm/m2p7CaPQWu+sEY7/dnjJ+IUmk7Ikoa/VyWIQdKulgoAYy/FMZeLD7n5l8JlYe6fg3dmHYJpZqaGq666irS09M5/vjjOf744+nVqxdXX301tbVd7AkhaRNax1u82V3js2h7IeGmcKaki2GuJoOJsUnjWL1PCKXecaK+prQ0GZPBREl9CQerD/o8flVjFbV28Rpw1AphFYzON2ENINaTGduv2e3u1Fu1V9dbTYOdWpsDDPXUOcS6siqzAI+Cbn8z3+w2+Phy4ZeUPBxOf1KvS9LQutqirSbiIszERpj1D9oPVgth5ivtBm4vJZDWABJJdyExyqr/iDlU4bsj1Z9FQHFN22qUPMlMjAQgqyRE36GnPwmpo6CmSLh3233PzTwSaZdQuu2221i6dClfffUV5eXllJeX88UXX7B06VJuv/32YK9REkS0iJLJka5ft3xPEfWNDmb2nQnAxNSJZBXbqay3E2U1ccnRQlRtyK5leMJwwH+dkmfazVkvHLyD4aXkaQ3QtJAbPISSsYrqBrvePaKl3SLD3SaQWRVZQAAWAQvvhZxfwRoDF74Plshmm2hpt4yECD3KpaXf1mSJiFLTQm6NDBlRkki6JVr6TSPVr1Bqf9dbU0IaUQKwRMAF74p5cAfXwvf3hGYd3ZB2CaVPP/2U119/nVNPPZWYmBhiYmI47bTTePXVV5k/f36w1ygJIppQqqtx29bXNzpZubeYswaexYPTHuSBqQ/o9UmT+ydw9ABR/7Muq1Qv6PZXp6QJJac9FmejEC+Hag7R2MFp1dk+zCY90WqUjGYhiDSLAC3tFhdTp2+rRZRG9xbdLav3l+jb6Wz8H6xxOc2f+wokDvS9rhJ3fZKGJpQ0/Aklz32kNYBE0n3QBuQCRFqMRFu9p325i7ndn2uNDiflteL/7YooJYkfYgfL6rqu860pCQPE8FyAda/Dxg9Cs45uRruEUm1tLampqc2uT0lJkam3boxTdbKvYh8AhcVxAGS6fsUs2l6I0WDk3MHn0ie6Dyv3ClEydUAio3rFYjUZKKttJM06FIDNRZt9nkOPKDXGodqjUZ1mnKqTQzUdy3kLDyX/QkmLKBlMoqZA61YpdBV2R0a4f6VlV2Vjd9oZ0yeWsRlx1Dc6eWXZXvfB8jbB17eIy9PvgqGn+l1XdqkQYJ5ptLF94ogNd8958pd6S4y0EGERbr/SGkAi6T5oxpMgoklNayJ9zXvThuEaFIiLaLtQSom2EmY24HCq5JbVtb5DZzFkNsxwRZO+vhUObQzdWroJ7RJKU6dO5f7776e+3l00W1dXx4MPPsjUqVODtjhJcMmtzqXOXofZYCanUHx5Xz9dREp+2l6oF2jbHU7WZok2/KkDE7GYDHrdTUN1BgC7ynbR4Giew9YjSo2xgILTJiI9HS3ozi4rx2AWg3Z9CSWtmFs11INi072UtEiRNcztl2R32smtzkVRFG6ZJbyk3v31gNi2thQ+uhTs9TD4ZJh+N99vyeOTdb7rrLJLhQDzTKMZDQrHDnZ35fmLKCmKwjjXxHKtXkoikYSe4ekxWEzi69FXWtxXREnreIuPsLSrg1VRFI86pRCl3zSOvxMGzxafgx9dJsx2j2DaJZSefvppVqxYQZ8+fZg5cyYzZ84kIyODFStW8Mwzcm5Md0XzT+oV0Q+H00BMmImzx/cmwmIkv7Kera6i5t9zK6husBMbbta/wCf3TwBg+0EDMZYYHKqDfeX7mp3DXaMUB+AWSh20CDhQIfaPMEYRZ41rdnukOZIwo/hAE+7cQiBpQslg9q5D0uqUZgxJZpwrqvTakp3wyRVQng3xmXDuKyzdU8L17/3GHfM3s6+omqZ4WgN4Mn2wO/3mL6IE8OrcSSz56ww97C6RSEKPxWRglMuUNjWmuVCK8RFR0jve2lGfpOGuUwpxZsZggHNfhvj+UJENn1wOHSyf6Mm0SyiNHj2a3bt388gjjzBu3DjGjRvHo48+yp49exg5cmSw1ygJElp9UqxJRIWGpkUTZjZyrGuS9eLtwkNplavbbUr/BP2X0aRMIZTWHyjXHb13l+9udg7NldvZGEtytBWnTRy7IxElh1OlqEG4e/eJbm4NAOLXmFanJDrftNSbEEpOQ7m4DbGvVqfkGVXKWPsQ7F8K5ki44H3ybGHc+tFG/Rya27aGqqr+hdLQZCwmA0lRFp8eShqRVhP9EqVIkki6G1pt5sDkqGa3+Uq9FbfRldsX3SaiBBAeDxf9DyxRkPULfHdnqFcUMkytb9KcRx55hNTUVK699lqv69944w2Kioq46667grI4SXDRhJLBLrrRhqQKH6FZw1NZuK2AxTsK+MuswXoh99SB7nE0E/rGYVBEBOX4yAGsZz27y3wIJb1GKZZjhibyTVbHI0r5lfWoJjFodlB8f7/bJYUnkVudi8HDS0mLKDWoIpU4LGEY20u3s79iv77f9CHJ3JW0gkurfxBXnPsK9uQR3Pzqr5TW2DAo4FTFDLeLJrs77oqqG6hvdGJQ3BYKGqkxYXx6/TTCzAafHkoSiaR78+cTBjG6dyzThyY3u81X6s0dUWq/w772oynkESWNlOHwh9fgfxfBujcgZQRMvrb1/Q4z2hVRevnllxk2bFiz67UZcJLuiZZ6q6kSUZ6haUIonTAsBUURE7MPltWyzqM+SSM6zMywNBGKNjQKa4GmQklVVQpqhCmk0x7HtIFJeuqtI15KOaW1KK5C7kwf9Ukavua9aUKp2uGKkrm8ojyFkpL1C3+qeRmAp5wXUNhnFk/+uIu1WWVEWU3862zhxL2mSURJG12SHhuu1zN4MrpPLINTo5tdL5FIuj+RVhOnjk4nwtI8nuAroqS7cncooiQi090ioqQx9FSYdb+4/N1dsG9paNcTAtollPLz80lPT292fXJyMnl5eR1elCT4OJzumqI8V8fbUNeXeHK0lbF9xHVP/bibukYHiZEWhqR4f8kflSk6QcrKhfhpKpRK60uxOW2oqoLREctR/RP01FtuVS52p7fnyJbiLdz0003ct+I+Xt38Kt/v/56txVupbfT+NZXjOQw3prmHkoanUCr0Sr05qG4sB2BymphFqKXeKN0HH8/FoNpZap3BM7YzmffBBv67RIjKx/4whnPG98ZkUMgtr9MNJsF/fZJEIjm88TXrrT1z3prSz1WrmFNaG9gMyq7imFtgzAWgOkS9Umnz+tTDmXYJJa1wuykrVqygV69eHV6UJPjkVOVgc9qwGsPIKxZpoiEe0Y5Zw8Xomc82CMftowckNksZaXVKe3NFzr6wrpCKhgr9dnchdxSpMVH0TYjArMaiOk3YVTt51d4i+ol1T7AkZwmf7/mcZzc8yx3L7uDCby7k6HdPYm9JoXvtZXW6K7evjjcNT9PJwqoGGh1OSmsaUExVqKiYDCbGpYwDhKirrMgRIeW6Mug9EcNZzwOKHjmaO7Ufp48RvyhH9xGeS55RpewSlzWAFEoSyRGFVsxdWefZ9eaqUepAMXd6TBgWk4FGh8qh8hBaBDRFUWDOs9B7ovi8/OBC8e8RQruE0rXXXsstt9zCm2++yYEDBzhw4ABvvPEGt956a7O6JUn3QJ/xFtEPMJASbSXeI0R84jDhi6WNcDvaI+2mcZRLKO041EhahIgo7irbpd/u2fGWFiuGvPZLjPLZ+ZZTlcP6gvUoKJzU62LCG6Zgr+2H6rCiGiu5bsGz+i+qrJIS3R8pkIiSwVyF3amyq6AKpwpGl61ASngK0ZZoUsKFKMxacDUU7YDodLjwA44d3kd35B3VO4Z7Tx+uH1vr+vMSSlpEKVEKJYnkSEKLKFXb7Dhdn1O6K3cHUm8Gg6L/8Oo2dUoa5jC48AOI6Q3FO4VtwBEy5qRdQumOO+7g6quv5sYbb2TAgAEMGDCAm266iZtvvpl77pG2590RTSjFGPoA7vokjeHp0V6mh1MHNBdKabFhZCSE41QhySoiO57pN8+ON2122YDkSJyNzYXSV3u/AiDcMYzPFo+hcN85mAtu4viEGwAo4Eee/FGYWu6vECNQIk2xxFj8+w1pQslsEfn9LblCIMVEiw+clAghkLRZcVlFW0RHx8UfQXQaiqLwxHljufKYTF6dOwmryagfe4oPoZTjMb5EIpEcOWg1SqoqxBK4U28d6XqDblqnpBGdBhd/DJZo0Qn3xTz3r+vDmHYJJUVReOyxxygqKuLXX39l06ZNlJaWct999wV7fZIgsbN0JwCqTXS8DW1SZKwoCjOHi6hScrSVgcm+W9aP6icEg8HmKuj2sAjw7HhLj9GEUhRqE4sAp+rky71fAlCSP5Yws4EbZgzklztP5LkzryDR0hvFVMtrmz9g+e5iDtWKQvBekRkt3sem7ty/u4SS5sqtC6UqETLeb7bAeW9D+lj9GAOTo7h/zkjSm4wUmdgvAUWBfcU1utu3rFGSSI5MwsxGLEbx9anVKWmpt450vYFn51s3FEoAaaPggnfAYILfP4afHgr1ijqddgkljaioKI466ihGjRqF1dqxF4ekc/m9+HcAaipFDdmQtObdWBcclYHFZOD8SX18ehWBu06ptFyIEq+Ikj7nLc4dUUqKbJZ6W1+wntzqXFSnFXvVCN6/Zgp3nTKM2AgzRoORv0z6EwDmhF/4y0drqLKL2qYBcZkt3kdNKDkNVYCT33OFyaQ1TBhFpkSkwG/vkJm9FoCsvhNg8KwWj6kRG25muKvrb+3+MuobHXpnnRRKEsmRh7vzrRGb3akPyO1I1xt4RpS6WerNk4EnwhyXufQvT8D6t0O7nk6mQ0JJ0jMorC2koLYAg2LgYIEQOk0jSgCjesey/Z+n8NeTh/o9ltb5tj9P7L+nfI8++sQrouSKyAxIjmo2xkSLJjVWjCE1OpoJHnOVAM4YcAapEWkYTFVUmlbphdxDEvx7KAEkhonzqDhQjHVszxNCyWASkaW0ukr46hYyG0UBZpahbSFjd51Sid79FmU1ER9hbmk3iURyGOJpEaDNeTMaFK85j+2hb3ePKGmMv1SMOgExE273j6FdTycihdIRwJbiLQBkRg+gpEpBUWBwanO3WRBvdH/RJBDCJ8JipL4mEZNioqaxRh94665R8h1ROlh9kGpbNQuzFgJgr5jIrOGpzc5nNpq5atSVAFiTlmKwig64ljretP208SaKqQqbXUzg1ly5U9a/D6qD/gNPAYRwczgdLR7TE61OafX+Uj3tlpEQ0eLjFQzya/J5dfOrlNeXd+p5JBJJ4HiaTmqu3PERlg4bzGZ6jDFxdieLABefrMvRh6Zzwt9gzIXCNuCjyyB7dWgX10lIoXQEoKXd0sOGACJV5MtELRCMBsVVCG4kySpqhnaX7cbutFNUK9yzVXusPkgyPtJCrCVJWAQ47by3/T1q7bUo9iQcdf04aUSqz/OcO/hcEsISUMxlGMOFZUFrQgm8LQI0Gp1CaKU01MKAE0g/8yUsBgs2p00XeYFwlEso7SyoYvNBEaXqm+B74G0weWnTSzy74Vnm757f6eeSSCSB4RlR0jveOmANoNE7LhyTQaHB7qSgqr71HbqQfUXV3DF/M9e9s54Gu0PYBpz5HAyaBfY6+OA8yN8S6mUGHSmUjgA0oRSuZgLe/kntYYRrUG4YooNud9luiuuKcagOVNWA4ogm2WO+2cDkGJyNQmS8s/UdAOrLxhNpMXm5f3sSZgpj7oi5Xtf1jfZvDaDhnvcmhFIfJZ8au/j1k5I8Ei58H6MlXLcZ8HTobo2kKFHkrqrw+QYxe64r6pM2FW0C3KlNiUQSejShVFlv1125O9rxBmAyGugTL36AZRV3rzqlg2XC26m6we7uADZZ4Px3IWMK1FfAu+ccdoaUUigd5jhVJ1uLtwJgq3FZA3RQKA13CSVbregi212226M+KYbk6HDMRvdLa0BSJKor/VbVKARMY/kEpg9N9mrBb8oFQy8g2uJyDw9PJsLcuihJDhdzmRRTFcmU8ZL1URpcqbGUC/8HFpH/7x8r6p2yKrICu9MuJvcX90PzOOlsoVTbWMu+CvGhU1JX0qnnkkgkgaOl3irrGt2u3B3seNNoqfNNVVVySmv57vc8/u/7Hcx9Yw3zPviN+sbAywhUVeWBL7fy0Nfb9BrTQNCGjIN7iDoAlghhs5I6CmoK4Z2zofLwmdIhhdJhTlZlFtWN1YQZwzhUJNylfXW8tYURvYRQKi4VUaLd5bubdLx5p6M8C7oBLI1DUO3xzBruO+2mEWWJ4uJhF4tjxA0IaG1a6i3MVMq7lkexmIUVQJwlBmtUmr5dZkwm4DHKJEC0OiUNrfCys9hWsg2nKmqtSuqlUJJIugueqTfdGiAIESXw3/m2el8JU/69mOP+72dueP83Xlyyl2W7ivh6cx5vrAg8Ov7jtgLeWpnFa8v3s7848KLxQo9U4M87C71FVng8XPoZxPeH8gMislRzeHxmSaF0mKMVcg9PHM7uAhE2HdZBoTQsLRpFgdIyIX6yKrL0obdqYyxpMd6/qgYkR+oz3wAqisZiNCicOCyl1XNdM/oa5o2bx18n/TWgtWlC6RjLGoYZcthpFKIuJTLNazs9otRGoTS5qVDq5IjS1pKt+mUZUZJIug+exdyl+py34AglXxElh1Plns9/p7CqAZNBYWSvGC48KoO5U0Xt5os/79WLyltCVVWeXuS2dVm5N/DPlSKPiNKBklr2NRVZ0akwdwFEpUHRdnjnTKj1HibeE5FC6TBnc5Fwt+4fNZzqBjtmo0JmB6MgERYT/RMjUe2xhBsjsat2fs37FRAdb03NGgcmuzvfzEoY9spRTOoXT1xE6x8qYaYw/jT2TwxLGBbQ2hINooi80VRHsRrDf4xnAm6zSQ09otTG1FuvuHC9fkBRROFlZ6KlTUFGlCSS7kSMR0SpWKtRCkIxN0BmUvOI0lebDrGvqIbYcDPr/34S39x8HI/+YQwPzBnJqN4xVDfYeWbRbn+H1Fm4rYBtLusUgFVtEEqeqTeAnzzTbxrxmXD5VxCZAgVb4O2eL5akUDrM0SJKMQaRuhqQFIXF1PGnXdQpKcSZRFH0bwW/AaLjLc1jFApA34RI1LqB2EqnEVF5IahWv91uHaK2lOTlTwNQaDRzke3v1LjEWGqE9/kyYzMBKKorotpW3abTaFGlXrHhQXksW2JLibuDpKaxhnp79+qCkUiOVGK8Ikpa6i34NUqqquJwqjy7WIiga4/rT6yHd5vBoHDvaSMA+GBNNnsKq5of0IWqqrqYmuZqpFm5tzhgG4KiSnE/J/UT3nc/7fAhlACSh8AVX7vE0u89PrIkhdJhTIOjgZ1lYnSJvU608ne0PklDq1MyNIpRJnZVuNIKs0lvoWQxGchIiKKh4EwOHhSRoaALpdpSePdskgrFkN5D5ih2q310V+6mQinaEq0bVLY1/TZtoEjvDUzx7UUVLCoaKvSUplERRe8yqiSRdA+8apT0Yu7gRJT6xIdjUKDW5qCoukFEk4priIswc/m0zGbbTx2YyKzhqTicKo9+t8PvcX/YKqJJUVYTT184jgiLkbLaRnbk+xdXnhS5UnsXHCW+T9ZmlVJZ3+h74+ShrshSMuT/Du+c1WPFkhRKhzE7S3did9pJCEsgp1CIl47WJ2kMTxfHqapK9rre2RhHWkxYs+0HJLnTfUNSo/RfTEGhpli8CfM2kWSJA8Cm1IFix2AWIeamqTdw1ym1xSIA4JzxvXnwzJHcd8aIjq27FbT6pIzoDJIjxOMs65Qkku6Bu0bJTml1cGuUrCYjvVxp/X1FNR7RpAH6eZty96nDMBoUFm0vdBtCeuB0qjzjOs4V0zJJiQ7To+O+tvdFoWts06TMBAYkR2J3qvyyq4V9U4bB5V9DRBLkbxZpuGo/UahujBRKhzGaf9KopFFsyC4HYGyfuKAce0S66KArLPEublbtzWuUQHS+abTW7dYmynPgjdniTRiRRMzcrzAZxC89s7kak8W/UNLSb22NKBkNCpdPy2RQJ0eUtPqkUYmj9OhXaX3P/EUmkRxuaBGlkpoGqhq0OW/Bm3mq1ZI+/9MePZqkFW77YlBKFJdMEaUQD3+zvVk6beG2Ara7oklXHyt+JLrTb63/AKtpsFNjExYEKdFWZrqacRbvKGh5x5Rh3mm4N2ZD2QGfm766bB+vLut+HkxSKB0G2Bw2n1+gmlDqHz2M3PI6FAXGZsQG5ZypMVbiI8w01rlFj+o0oToiSIlp/mExINkdQQpa2q1wB7x+MpTsgdgMuOp7lNQReufb/24YSSPlgB+h1M6C7q5Cqy8bmTRSN9KUESWJpHugCSWtkNtkUIgJb9/EA1/0c1kELN8jIjYtRZM0/jJzMNFWE1sPVfLEwp1sya2g0eFsFk2Kd0W+tDKC1ftKaHQ4Wzy21vEWaTESaTVxgksoLd1ZhKO1GqeU4XDV9xDXV5hRvjFbfH57UFhVz8Pfbufhb7d7ddd1B6RQOgy46aebOHn+yawvWO91vfZFa27MBGBISnSrb7RAURRF1Ck5w4g1i7SQ2hhHYqSVMHNzE8lhaa42/WhrcKJaB9fDm6dA1SFIGgpX/QBJgwG36WRxQz5lDcJHqWmNErhTb3vL93Z8PZ2AVsg9MnGkHlGSNUoSSfeg6WdpQqQlqHMfPbuT/dUmNSUxysqNJwwC4MUleznjueWMvP8HTnlmmR5NuuY493DxEekxxEWYqbE5+D23osVjax1v2tSFozITiLaaKKmxselgeet3KHGg+JxOHgZVeeLz+6D7O2vbIXcn3u4WCtJDgRRKPRyn6mRd/joaHA3c/cvdVDSIF3tFQwUHKkV4s6xceAhN6BcX1HMPd4mfcNcoE6ePjjeNCX3jeOwPo3ll7qQOD41kz2J4ew7UlUGvCXDldxDbW79Zi75sL9kOgMVgIdbaPJI2KmkUBsXA3oq95FV3LxfZotoiCmsLMSgGRiSOkBEliaSboUWUNILlyq2hRZRARJOirIFFq649rj93nzqMaQMTiQ4zYbM72VUgmlquPCbTy5bFYFCYOsCVftvTcp2SZjaZEi0+481GA8cPET9Kf/bX/daUmF7i87r3RPH5/fYc2P0jgJdlwZ7CtnUidzZSKPVw8mvysTlt+uX7VtyHqqp6fUvf6L5syxH58/F944N6bq3zrbFOCDG1Ma5Zx5uGoihccFRfxmXEdeyka1+D98+DxhoYMAMu/xIivefFaak3rRg6JSLF5y+9hLAExiWPA+CnnJ86tq4go619QOwAIswRMqIkkXQzwsxGLB6jmoJVyK0xPD0Go0EhMdISUDRJw2Q0cP30gXxw7dFsvv9klvx1Bs9dNJ5/nTWSeScOarZ9oHVKWjos2aO0QjMN9msT4IuIBJj7pfj8bqyBD86H1S+z7VAF4X1fJrzfS+zKr2z1MF2JFEo9HK0QOc4ah9lg5qecn/hw54dsLhZGkyMTR7E5txwQUZ1gos18Kzk0kSHhs7CVHuc3otRhHHb49k745nZQHTDmArj4Y7A27+LThJIWUUqN9F8TdWLfEwH4OfvnTlh0+9HrkxJHAsiIkkTSDfGMKgXLGkAjIyGCj647mk9vmBZwNKkpiqKQmRTJnLG9uGxqps/ZmlNddUrrDpS1OC9OT715RM5mDE1GUWDroUryK9rg8WaNgos/gXGXgOqE7+5kQvbDmCL3Y4rIYltx9yqHkEKph6Ol18aljOO2ibcB8MTaJ/gh6wcAki2DqW90EhNmYkBScLu0BiZHYTEaqKqJwlJ+Ac6GNJ/WAB2mvgL+dwGseVn8/8R/wDkvg8l3qDspTLzxK23+O940TswQQmldwTo9bdlZbC3ZytkLzg5IlOn1SUlCKCWEie5CGVGSSLoPnkIpIcgRJRBt+JlJnTtPcmByJKkxVmx2J78dKPO7XaHLbNKzWScxyl1z+v2WNpYvmCxw1gsw836xDmWFftP+ytYdxrsSKZR6OJpQyozJ5JLhlzC9z3RsTht7yvcAYK8V9UPj+8Z3vDaoCRaTQW+RX5sluu6aDsTtMCV74fXZsGcRmMLhvLfh+L+K+SF+SIpI8vq/r0JujYyYDAbHD8ahOlh2cFnQlu2L7/d/z96KvXy97+sWt/NMnY5KHAUg7QEkkm5ITLi7oDspyDVKXYWiKHr3W0vpN81sUqtR0jhjjDAdfvyHnewuaGMRtqLAcbexf+ZL7DS7j2tRtlLmMvHsDvQ4ofTCCy+QmZlJWFgYU6ZMYc2aNS1u/8knnzBs2DDCwsIYPXo03377bRettGvQhFK/mH4oisK/jvkXKeEigmJSTBwqFHVJ44OcdtPQ6pTsrvZQfzVK7WLLZ/DydDFcMTodrvwWRp7d6m5a6k2jpYgSwAkZJwDwc07npt80G4KC2pZ9Rw7VHKK8oRyTwcTQhKGAO/VW0VBBo9OPE65EIulSOjui1FVMddUprWjBeFIzm9S63jSumJbJ1AGJ1Ngc/Ond9f6dulvgV+sxvGGcpP9/dPgqSlf/r83H6Sx6lFD66KOPuO2227j//vv57bffGDt2LLNnz6aw0Hch2cqVK7nooou4+uqr2bBhA2effTZnn302W7Zs8bl9T8RTKAHEh8Xz6PGPYjFYmNprKptyxFDFCUEu5NbQ6pQ0glKjZG+Ab/4K868EWxX0nQbX/gy9JwS0e1uFklantDx3eafOUtPqyfJr8lvcTqtPGhI/BItRfPjGWmP1MSaldTKqJJF0B6Kt7ohSsIu5uxKtoHvzwQqq/AgdrZg7pYlQMhkNPH/xeHrFhrGvuIbbPtoU8Ow4jW2HKqm0uD9791pMDFx2M3x9m/g+CDE9Sij95z//4dprr+XKK69kxIgRvPTSS0RERPDGG2/43P6ZZ57hlFNO4Y477mD48OH861//YsKECTz//PNdvHIfqCqseFbU37STRkcjudW5gFsoARyVdhQL/7iQvx/1GNmltSgKjOusiFJTodTRGqXS/cJEcu2r4v/H3ibmBcWkB3wILU2l0VLqDWBEwghSI1Kps9exOm91m5ccCI2ORn1uW3FdMXan3e+2TdNuAAbFIOuUJJJuRmcWc3clfeIj6JcYgcOp6mUUnjQ6nJTWilRYU6EEolbppcsmYjEZWLS9gOd/3tOm82/Lq8BgdQc8ik1Gio0GWPe6y8k7q213KMj0GKFks9lYv349s2bN0q8zGAzMmjWLVatW+dxn1apVXtsDzJ492+/2AA0NDVRWVnr9dQrr3oAf/wEvThX1N+0gpzoHp+okzBhOXom3+VlieCJbc0U0aVBylD7pOth4CqWYMBOR7ezOQFVh/Vvw0nGQtxHC40VXxKz7wdi2Y4aZwog2u7vhWosoKYqiR5U6yyYgpzoHhyo6Shyqo8XutaaF3Bqy800i6V54mk4Gc3xJKNCiSiv2NP98Kam2oarCfTw+wrcgHNMnjofOEj/unlq0K2BvJadTZUdhLoqxHgMG4szih+3DSdeK74FDG4QljNN/R15n02OEUnFxMQ6Hg9RU7+hAamoq+fm+Uxn5+flt2h7gkUceITY2Vv/LyMjo+OJ9kTICEgZAZS689wf4Yl6bo0vZldkAOG1JnPXCSn7e6f3C/C1bdDB0VtoNIDbCTG/X8EZfM94CojwH3jsXvvqLSLVlHA1/+gWGnNzudWkF3QqK7tTdEppQWpKzBEcnvCGbjknJr/X9GnSqTraVbAPc1gAa0ktJIuleeNUo9eCIEginbcCnQ7dmNpkUZW2xKej8ozK4ZEpfVBX+8uEGKupar1fKKaulXhEdc72j+zAkTnzuLbMbxfdAxhQ47QkwNLc26Cp6jFDqKu655x4qKir0v5ycnM45Ub+pcP0KmHIDoMCGd+HFacJ12sWesj08uOpBPtj+gc9DaPVJtTVCCD3w5VYvH4wNLqHUWYXcGlqdUpvrk7Qo0otTYe9PYAqDkx8WRdtxHROoWp1SQlgCZmPr0bSJqROJtkRTWl/KpqJNbT7fd/u/4/LvLqeotsjn7U0H7xbU+C7oPlB5gJrGGsKMYQyMG+h1m4woSSTdC00oWYwGotsbTe8mDEkVUfjdBVWoqneNkV6f5GOOZ1PunzOSjIRwKuvtrD/Qej3ltkOVGKzic3Ng3ADGpwmhVKPmUBWWJsaeDJjepvsSbHqMUEpKSsJoNFJQ4P0FU1BQQFpams990tLS2rQ9gNVqJSYmxuuv07BEwKmPwhXfQHwmVB6E984l66OLuHPRPM798lzm75rPo2sepcrWvO1S+/J1NAhRcKCklldck5ftDiebcsQvgwn9Oi+iBDC6txgP0jchopUtPcjbDG+d7o4i9ZkM1y+HafOC8stB81JqLe2mYTaYmd5HvBl/ym57+u3trW/zW+FvfLPvG5+376/Y7/V/fwXdWuSpf2x/TAbvD15ZoySRdC+0koZgz3kLBYNSojAoUFbbqFsBaPgym/SHxWTQo1ObD7aeJdmeV4nBIr6nB8QOYFzqCAAMYXlilEk3eFx7jFCyWCxMnDiRxYvdERen08nixYuZOnWqz32mTp3qtT3Ajz/+6Hf7rsbumtZsyziK/Zf8j2Vjz+HvSYmcVfc73+UuRUXFbDCjorK5aHOz/bWIktOWRJhZPJUv/LyHnNJadhZUUdfoINpqYlBycI0mm3L5tH7cMXsoN8wY2PrG1UXw5c3w8vFwYIXwRjr5ITFZ2jXUNhho0ZfWCrk98axTavqLqiWcqpN9FUKgaqNHmqIJIG09/iwCDlYfBCAjunlETXopSSTdC81HqScXcmuEmY36j93dBd6z1nyZTbbEGNeP598DEErb8ir1Qu4BsQMYljAMAIOliG35Lc+f6yp6VKzwtttu4/LLL2fSpElMnjyZp59+mpqaGq688koA5s6dS+/evXnkkUcA+Mtf/sL06dN58sknOf300/nwww9Zt24dr7zySijvBgCf7fiRB5f+l8ioCqrtRai4vpijhQvr9No6/lxWznuJaXwZpvBbwXqO6X2M1zEOVLiF0qmj0imorGfl3hIe/Gob04eKupxxfeOCbjTZlLgIC38+ofkMIS8a68SctqX/Bw2uAvlRf4BZD3Y4zeaLwfGDvf4NhGN6HYPFYCGnKofnN4rOyCpbFVW2KgbHD+aqUVf53C+/Jp86ex2AXl/UFC36NyV9Cl/u/dJvROlglRBKfaL7NLtNpt4kku7FMYMSOWVkGqePCbwrtzszJDWarJJadhVUccwgt81KUbXmoRRYecVol1v35twKVFVtMdq27VAlhhSRehsQO4Ck8CSsSiwNVPBb3g4uIXg/oNtLjxJKF1xwAUVFRdx3333k5+czbtw4vv/+e71gOzs7G4PBHSSbNm0aH3zwAX//+9/529/+xuDBg1mwYAGjRo3yd4ouY+H2LJxhu6lydYmbFSuZsf0YFD+QS4ddwpiifbDwH4yrLuXLsEQ2bngNlEQYcz4YzdQ21lJYJ1S405bEoJQo/nzCQE55+hcWbS9gT6FI1QV7EG6baagSHX4rn4MaV/1O2hg49f9EnVYncdbAsxgQO4DhicMD3ifCHMHUXlNZenApr2xuLqZn9p3pZcOgoUWTALKrsqmyVRFtcXfdldeXU95QDriFUmsRJZ9CSRZzSyTdiugwMy9dNjHUywgaQ1KjWbitgF1+IkpJURb+s+4/9I3pyx+H/NHvcUa4BvoWVTVQUNngt361vNbGoaoyonuJ76v+sf0BSA8fQFbtBnaW7QDmBOGedYweJZQA5s2bx7x583zetmTJkmbXnXfeeZx33nmdvKq28/eZp/PfNSZ+3GSnqDQa1RFFfHIUU08chKM+kv2pg4i98gTGrvk/yPuKzQYHjV/ciHnpo3DsrWT3FW9OgzMKnBEMTI5kUEo0Vx/bn5eX7SOrRDOajAvNHawtFRGkX1+EOtf8oNi+MP0OMQixkzsYjAYj41LGtXm/m8bfRJgpDJPBRIwlhmhLNN/s+4bc6ly2l273KZT2lnsPcNxesp3J6ZP1/2vRpPTIdDJjMoEWUm9aRClKRpQkEknXMjhVlGnsajKKRKtRcphyeXPrmxgUA1N7TaV3VG+fxwm3GBmcEsWO/Co2HywnLdZ3XbBn2i0lIoUoizj/sIRhZNVuIK9un8/9upoeJ5QOF/rE9OLhWVfyj+kO3vv1AC/8vIe9RTXc+lHTjqupxA9fTL2hlp2xKYwqz4avbyUrLgniI7DYRORioKsO6aaZg/li4yHyXXbz4zO6MKKkqpC1HH57B7Z9AQ5XQWDCQDjudj0a1p0ZmjCUJ6Y/4XVdSV0Jn+7+lF2luzgl85Rm+3hGlECk3zyFklbInRmTqdcoFdUW4XA6MHoIRqfq1A1EW0q9lTeUN9tXIpFIOorW+bbL1fmmpcy0rjeDSUSanKqTD7Z/wB1H3eH3WGP6xLIjv4rfcys4eaRvobQ9rwqDxV2fpHFUr1F8fxDqyKHWZifCElqp0mOKuQ9XwsxGrjluAEvvPIGbTxzEiPQYeseFE6W3mhqor+oLwG8z74ZTHoXYDA4gXFJPbtzNAss/yMz6CCrziLKa+PsZIt00qncMsRGdLExUFYp3wy9PwnMT4O0z4PePhUhKHQ1/eB3mrYXxl3R7keQPbd7azrKdPm/fVy6E0pD4IUDzOqX9lS6hFJtJUngSRuX/27vv6LjKa+H/3zNVvXdLstzkggvF2DHYNNtAICQQWugklMsF3oQL5IWE3OWbkBsIP8KbXBa5XEqA3BAgCSQhtNAxxcbggm2w5W7ZsmVJVhtJI007vz/Gz5mimdHMaFS9P2t5YU85c44kZrb23s9+zHh1Ly3O0EbFFmcLfd4+zJqZssz+byx59jw0NHy6j7a+6Lt8CyFEMiYXZ2I2aTh6PRw6Um7Tdd0IlDAHMk0vbn+RLldXpMMAQX1KMRq6vzrQidke6E9S5pf722P8K98S3Gh3CEhGaZTISbNy+5nTuf3M6cZtHq+PM3+9kv3OGizZW9nQ+iVXn/YQLLiR+jeuh+bPqXJ7Oda0E16/w/+nbC7fqD2b6vMXkj/12KE5WU+ff8Xatjdh2xvQFrT03ZYFcy6C46+GiuNHxdLOwZqefyRQau0fKOm6zs4Of+ntvMnn8au1v+Kr1tBASa14q8mpwWwyU5xRTGN3I409jZRmBlblqbJbWWYZVlP/oNJispCflk9rbyuHnYf77WknhBCDYbeYqSnMYGdzN9sOOSjLTaPD6cZ1ZIW2RwsELd3ubl7a/hJXH3N1xGMZK99iNHT7RwP0zyhV51Sj6VYwuVizfxtzKxem7BqTIYHSKGYxmzi2Ko+9W2oAWHdonf8HzmRmz5GM0v/0XEtBgZNL0tdBw1po3AiNG5kLYM2E8rlQPg/Kj/X/PbcS7DnxBTC67u81at8DB7+AAxv8/236CryuwONMVph4kr+0Nut8sA/tOILhpjJFh3oO0dHXQa4917ivxdmCw+XApJk4e9LZ/Grtr9jbuTekoVv1KNXk1gBQllFGY3ejf+hk0NDwWI3cSkFaAa29rTIiQAgxJGpLs41A6ZTaYiOblJdhpcPlz2QXphVyuPcwz255lstnXt5v5hvAjPJsrGaN1m4XDe1OKvND5+y5PD62Nzmw1RwJlPICgZLFZCHXXEW7bxfrG78CJFASMcyrzOOl9RPQdAuHew+z37GfqpwqY4ZSh6uanVNPhnP+P/+Moh1v+bM8O9/zL8OvX+X/E8ySDtmlkF3eP2jSfdBzGByHoOsQ+KKMoM8qhWnLYdpZMOV0sGdHftw4kGXLYkLWBBq6GqhrrQvpP1L9SVXZVZRlllGRWcGB7gNGQ7fb52Zfp3+6u/qNqTSzFJr7N3THauRWCtMK2cEOWfkmhBgS00qzeX1zo9HQHTxsstXp/wXt4ukX88LWFzjQfYB36t/hrJqz+h3HbjEzvSybzQ2dbNrf0S9Q2tnchdvnIs3qD77UijelKmsq7Z272NmxLeXXmCgJlEa5eVV5oFvBVQn2PaxrWke2LZuOPn/d1+cqZErJkQxOVjEce7n/j8/r7x06uCGQDWr60r+fnMfp34053h2ZM4uhbM6RrNQ8/5/8mnFRVovX9Pzp/kCpLTRQUive1P/kswpncaD7gNHQ3eBowKN7SLekG1PCyzL8/Ufhs5RizVBSCtKPTOeWlW9CiCFQa6x88/cfqX3eSnLsxi9oFZkVXDrjUh794lF+/9XvIwZKAHMm5LG5oZONDR18fU7orKmvDnRisjWDppNjyzHGnyjHFM1kU+ebNPeF7mowEiRQGuVmHklf9nVVY7PvYX3TemOJuubNA91mrHgLYTJDyQz/n3nfCdzudoKj8cifg+Dq7v/c9HzILvNnjbJKwTL2p84O1vSC6by7791+fUoqozQl1z+VfFbhLN6uf9to6FZlt4k5EzFp/rUTqi+pX0YpjtKbzFISQgyl6WF7vhn7vGWnceBIyb8grYAllUv43abfsbF5IxuaNkQcxzK3Mpfn1kSe0O0fDRBo5A7vYTqpcg7P74Je0z76PF7slpFb5SuB0ihnt5iZWZ7Dl+012ApXsr5pPceXHg+Au9f/oTmlODP+A1rToWCS/4+Im2ro3tYWmgY2AqW8QKAEGA3dwY3cilrRFi2jVJUVfVJ5IrOU3tj9Bs/XPc8vl/wypGlcCCGiqSnKxGrW6HZ5aWh3GsMmi7PtbO4JBEpF6UV8Y8o3eGn7S/z+q99HDJTUPqAb97eHNHT3ur28svFAoJE7qD9JOXGCf3Nck7WTDQf2s7C6/wy74SLjAcaAuZW5+Jz+H5JdHbv4osk/a8nnKqIoy0ZehmR8hlptgb+he0f7DtxBfVuq9Kb6j1SgpBq6wxu5IfJ+b06Pk2an/7erVGWUnv7yadYeWsvb9W8P+FghhACwmk1MKvL/8r39UFdQj5LN6FFSLQBXzbwKgHfq32HNwTX0qdl5R9SWZmOzmOjs9VDf2mPc/pe1+znU2UdGpv99LHjFm5Jly8Lq8692+aR+UyovMWESKI0B8yrz0L2Z2Hz+TMTre14H/IHS5CHe8Fb4TciaQKY1E7fPbWSJ2nrbjNVnqkcpPy2f8kx/LX5r61Zj2OSknEAGL3zoJMCBrgMAZFuzybHlRD0PlVFSb1jReHwedrTvAAIDL4UQIh7TggZPqtJbTqYXl8+/2jnf7h9kPDV/KidXnIxP93Hdm9ex8NmFXPD3C7hr5V28vPNlbBYTM8v972dqnpLb6+O/3/f/gpmX538fixQoARRa/e+bm5q3DsVlxk0CpTFgXlUeAL1d/sGTDpd/NYLPVRy5P0mknEkzGWMC1OBJVXaryKwgwxpY0WGU3w5/FTGjFGnoZHAjd6wNJOMtve3t3Gv8dqfOQQgh4qH6lOoOOYxmbrvdv/F3uiU95P3ujvl3sKh8EXn2PLy6lx3tO3ht92vc89E91LXWhcxTAvjb+gYa2p0UZpnp8Ph/QYxUegOYlOPfEHevY/sQXGX8JFAaA6YUZ5FpM9N3JFBSfK7CxPqTxKCoQGlbq79PSQVKk/JC+72OKfTX1lcdXGVknIJ7lNTQSQiU3+Jp5IZA6a21txVd16M+bmtr4DcwlQETQoh4qJVvwaU3i9W/8KcgrSDksdPyp/HYmY+x8tKVvHXRWzyy9BHjl8XVB1czpzLQp+T16fz2SDbp4q9l4PH5VwSrLHy4hRPmkkYZE/Mib4EyXCRQGgPMJo3ZE3Lx9tQEbtRN6K6CwGgAMeTCtzJRW5eoFW+K8SZxYDXg3+wx+Dcw6D8iIJ4ZShB4k/LoHjpdnVEfF7w671DPIXrcPVEfK4QQwaYFZZQcvR7/jWb/uIDwZfyKpmmUZZZxSuUpnDPpHADWNK5h7pFAaXNDJ69sPMDulm7yMqzMneQPwGpyaowVweGuO+EcPrvmLX73zZ+m7NqSIYHSGDGvKg/dXYhd89d7fe4CwMxUKb0Nm/CtTFQjt1rxpqhAyav7+4+C+5OU8BEB8cxQArCZbcbE71jlt+CMEmAMKI2H0+M0NucVQhx9JhZkYDObcHn8W5fYLSacXn/pLDyjFMmJZScCsPbQWmoK00izmujq83DvK1sA+N7JkzjQ439PCh80ORpJoDRGzKvMAzTMLn8t19dXhN1ioiIvfUTP62gyNW8qGhqHew/T4mwxSm/hjYjBDd0Q2p+k9MsodcWXUYKBV77pum5kvbKs/kA63j4lp8fJFa9dwbkvncs+x764niOEGF8sZlNItaIkx260EagVb7FMz59Oti2bbnc329q3ckyFP6vU0tVHtt3CNSfVsL3N33cUrZF7NJFAaYxQ6cv2lpkAeLqnMrk4C7Pp6JmOPdIyrBnGsM91h9YZ2aBIvxGprFK0+4MzSrqux51RgoEbulucLbT2tmLSTJxSeQoQf5/Srz7/FdvbtuPVvcYbmRDi6KP6lMA/bFIFStFKb8HMJjMnlvqzSmsa1xjzlACuPmkiGTb4qOEjAGMu4GgmgdIYUZmfTmGmjb72eSww/wp320nSyD0CVEP3G3veAKA4vThkk1wlOFAKbuRWgodOHu49TK+3F5NmitrUGGygjJIqu9Xk1DCjYAYQ34iA9+rf44W6F4x/t/W2DfgcIcT4VFsa2L+zOCsooxRH6Q0wtnr6rPEz4xf9dKuZ6xZPZvXB1XS6OilMK+T4EgmURIpommb8sH241QuYZDTACFAN3Sv3rwSiL2sNCZQilN6Ch06qbFJZRhlWs3XAcxgoo6TKbtMLphtB2kClt+aeZlZ8sgIAu9kOYLwxCiGOPtPCSm/qF7N4AyXVp7S+aT1LZxZy3rwK7vv2HAoybby5900Alk1chtk0cluTxEsCpTFEzVNSDXay4m34qYZuNaMoWn19TtEcsqxZlGaURswSqYxSc0+z0WgdT9kNBs4oqWbzGQUzjCBtT+eeqOMEfLqPn3z8E9r62phRMIOLay8GJFASYjzb27k35iKP6WWBjFJJtr3fVO6BTM2bSkFaAU6Pk52dW3j4suM4/7gJuL1u3ql/ByDqZrqjjQRKY4i/oTtASm/DT2WUlPDRAEquPZfnzn2O33/99xGXvhamFRpDJzc0bwDiD5TUG1W06dyq9DYjfwaV2ZVYNAtOj7PfJrzKH776A58c+IQ0c5p/X7gj2a62Pim9CTEeOVwOLnvlMr7zyndo722P+Jiq/AzSrP73ruLsxEtvJs3E/NL5gL9PSVl9cDUOl2PMlN1AAqUxRZXelMlFklEabqUZpSFbjEQrvYG/5FaRVRHxvuChk2sPrQXiW/EGsTNKPe4e47fE2oJarCarEYBFKr/taNvBr9f9GoAfnvhDJudNNgIx6VESYnz65MAnONwOutxd/GPXPyI+xmTSjNVqE/LttPe1A/EHSgALyvx9SsGB0j/3/BOA5ROXj4myG0igNKYUZtmpzPePA5iQl066bWz8kI0nmqaFZJUGs7RVjQhQjdZxl95i9Chtb9+Ojk5RehFF6UVAoJk80sq3F7e/iNvnZsmEJUbJTe3jJKU3IcYn1WMJ8OK2F6OW5R+4aC6/unge08vN6OhoaOTZ8+J+HdXQ/UXTF/R6enF73by7711g7JTdQAKlMUf1KUl/0shRfUp59ryEfrsKp0YEKMlklMLf4FR/UnAwp/qUIq18++TAJwBcMO0CY485dU0SKAkx/nh9XmNpPsDOjp180fxFxMdOKc7iwhMqaXP53wvy7HlYTJa4X6smp4bi9GJcPhdfNH/BqoOrcLgcFKUXcVzJcYO7kGEkgdIYs3ym/8P15CkDz7IQQ0OtaKvNr425ge1AVEZJSTSj1Ofto9vdHXKf0cidP8O4Tc1xCi+9NXY3sqtjFybNZKTIIRAotfW2xdxPTggx9mw+vJnW3laybdl8Y/I3APjLtr/EfE6i/UmKpmlGVmlN45oxWXYDCZTGnG8dW8H7d57GdYtH/9j38errk77Ov53wb9y14K5BHSc4o5RpzYw7pZ1uSackvQTAWGarbG3zN3KHZJSilN5WH/TvRXdM4TEhs6Dy0/ylN7fPTZe7K65zEkKMDR/s+wCAxRWLuXT6pYC/byjW3pGJrngLpn4J+6ThE96rfw+AMyeemfBxRpIESmOMpmnUFGViMcu3bqRYTBa+N/t7xvDJZKkRAeAvuyWSnbp29rUA/HbDb+n19AL+lLqaph2p9Haw+6DxWIBVB1YB8LXyr4UcO82SRrrF3wsnDd1CjC+qP2lJ5RLmFc9jSu4Uer29vLbrtajPSTajBIF5SpsPb8bhdlCcXjymym4ggZIQI0Ytw4f4y27KJdMvoSyzjEM9h4xp2vWOepweJ2nmNCZmTzQem2/PJ8eWg45urIjz6T4jo7SoYlG/40ufkhDjT2N3I3VtdZg0E4snLEbTNC6qvQjwL+yIVmofTKBUmVVJRWZg9e9YK7uBBEpCjJjwjFIi7GY7N8+7GYDHNz2Ow+UwJnLX5teGvBFpmhYyeBJge9t2WntbSbekM694Xr/jS6AkROo4PU563D0jfRpGNmle8TyjxH7elPOwmWxsbd3KV4e/ivi8RPZ5C6dpmpFVAjizZmyV3UACJSFGTGFaIRbNv4Ik0YwS+N/gJuVOoqOvg2e+fMZo5K4t6F8SDO9TUmW3E0pPwGa29Xu8ehOV0psQg+Pyurjg7xdw4csX4vQ4R/RcPtjv709Sm2WDfzjusonLAPjL9shN3cb2JUn0KAEsLF8IMCbLbiCBkhAjxmwyU5Lhb8pOJlCymCx8/7jvA/D7r37P6gP+UlrwijdFrXzb3ekfEbDqoD9QWlTev+wGQSvfZDq3EIOyuWUzDV0N7O/az+u7Xx+x83B6nHx68FMATq08NeQ+VX57bddrETNfRjN3kuNQzq45m2uPuZafL/55xJ0KRruxd8ZCjCM3zr2RMyeeGZKaTsTS6qXMLpyN0+Nk8+HNQP9tViA0o9Tn7TOmgUfqT4JARinaxrtCiPisa1pn/P25rc+N2MiNNQfX0OftoyKzgql5U0Pum186n4k5E+nx9EQM5lRGKZnSG4DVbOWO+XdwUsVJST1/pEmgJMQIurD2Qn512q+wm+1JPV/TNH5wwg8C/0aLuBrPCJQ697C+aT193j6K04v7vWEqBfahySh5fV62tm6lrrWOvZ17aexupK23DZ/uS+nrCDFaqF9KwL8P4/qm9SNyHsFlt/AVtpqmccHUCwB4u/7tfs8dTDP3eBD/iE0hxKj0tfKvsbB8IZ8e/JTqnGoyrBn9HlOdU41JM9Ht7uYfO/9hPC/aSIKh2u/tl5/9kue2Ptfv9rnFc/nD1/8wqAGeQow2Xp+XL5r8U69nF85m8+HNPLf1OY4vHd7NYHVdNwKlU6tOjfgY1Ue0uWUzuq4b/y/2uHuM3qqjNVCSjJIQ48AP5/+Q0oxSzp96fsT7bWYbE7ImAPDG7jeA6GU3GLr93jY1bwIg25pNljULq8kKwMbmjRE37RViLNvRvgOH20GGJYOfLPoJAG/vfZumnqZhPY+6tjqaeppIt6RHLfNPz5+OzWSjva+dfY59xu0qq2wz2ci0Zg7L+Y42EigJMQ5ML5jO2xe/zfVzro/6GFV+c/lcQP9Bk8GGajyA+oB47MzHWHX5KtZdtY75pfOB0BKFEOOB+pk+tuRYjik8huNLjsejewbcMiTV1ETsr5V/LWqZ32q2MqPQvxBkY8tG4/bgqdxHa8ZXAiUhjhJqlhLA1LypFGcUR33sUOz35vF5aOltATBW+4F/RAHAukPrIj5PiLFK9SOpJfGXzbgMgD9v+zNur3tYzuHLw1/yu82/A+CM6jNiPnZu0VwgkPkF6U8CCZSEOGqojBLELrvB0Oz3dth5GJ/uw6yZQ1bPqH4NySiJVGhxtoyKQam6rhvBv/plYGn1UorTi2lxtvRrmt7YvNHI/KRKi7OFH7z7A3q9vSyZsITzJp8X8/FziuYAsKlFAqVgEigJcZRQs5Qg+vwkZSj2e1Nlt8L0wpDJ4ccWH4tZM3Og+wCN3Y0peS1xdHJ73Xz779/mopcvwuPzjOi57O/aT5OzCYvJwuyi2YC/vHVx7cVAYFTApwc/5bp/XscVr13B99/7Pp81fpaS13d5Xdz23m0c6jnEpNxJ/PKUXw64dcicYn+gtLV1Ky6vv0RvDJuUQEkIMd5NyZuC1WQlw5Jh/IYbS6r7lFSgFLzHHUCGNYOZBTMBySqJwTnce5i2vjaanc1DklX6U92f+OWaX8ZVjlZlt1mFs4xfOsA/3NGiWVjftJ5LX7mU69+8njWNa4z7NzZv7HesWP66/a/8fPXP+XD/h7h9/nKeruv8bNXP+KL5C7Jt2Tx8xsNk27IHPFZlViX59nzcPjdbW7cCg9u+ZLyQ8QBCHCUK0gr4n+X/Q5o5LeIIgUiPb+hqSFlG6VDPISC0P0k5vvR4Nh/ezNpDazl38rkpeT1x9Ono6zD+3tzTHPFnbTAeWvsQ3e5uLq69mMl5k2M+1ii7lYT+UlKcUczyict5fc/rbGndgs1k48LaCwF/lmlb27a4z+erw1+x4pMV6Oi8UPeCfzuS6mVkWbP4+86/Y9JMPHjKg0zMmTjwwfDPU5pTPIeV+1eysXkjc4vnBgKldAmUhBBHgUQmgKs+pVRnlCJ9eJ1QegK//+r30tAtBqXT1Wn8vdnZnNJj97h76HZ3A9De1z7g41V2NNLMpFuOu4WW3hZmFczimmOuoTijmJX7VyYUKPl0H7/49Bfo6EzNm0prbyutva28uP1F4zF3zr+TkyYkNg17TtGRQOnIyrfBbl8yHkigJISISM1SStV07liB0vEl/g+TnR07aettM4I0IRLR2Td0gVLwdj4Ol2PAx6q5YJE2gZ2YM5HfnfW7kNvURP09HXtweV0RN6sO9squV/ii+QvSLek8uuxRCtMLWXtoLW/seYOPGj5iWfUyrpx5ZTyXFiJ85Zs0c0ugJISIQk3nHuoeJYC8tDym5k1lR/sO1jWtY2n10pS8pji6dLgCpbeWnpaUHluNtoDQzFUkG5o2AP4xHLn23LiOX5pRSrYtG4fLwe6O3RH3bFQcLgcPff4QAP8y918ozfT/P7WwfKExYTtZs4v9jef7u/bT2tsqzdxIM7cQIgq131uqAqVYPUoQyCpJQ7dI1nBllAYKlNY2HSm7lcS/VYmmBfZpHKj89ugXj3K49zA1OTVcNeuquF8jHjm2HGOF7KbmTUaPogRKQggRRpW/Uj0eIGqgdKSXQ/qURLKCM0qpDpRanPFnlNTPcKJ7uk3LmwbEDpR2tO3g2S3PAnDXgrsGLNElQ81T+rDhQ7y6F5BASQgh+gmezj1YXa4uejw9QOTSGwSG8m1t3Wo0zQqRiOCMUspLb8GBUl/0QKnH3WMsrU8kowRQW+DPKG1v2x7xfl3XuX/N/Xh1L6dXnc7iCYsTOn68VJ+S2kg325aN1WwdktcaCyRQEkJEpAIl1aMwGCqblGXNijqaoCyzjAlZE/DqgR3XhUhEcEapyZnajWeD/z+I1cz9RfMXeHUv5ZnllGeVJ/QaA5Xe3q1/l08bP8VmsvHDE3+Y0LEToQZPqgGwR/MMJZBASQgRRXDpbbD7vQ3Un6SorJLq8RAiEcGZnlZnKz7dl7Jjx1t6W9eUXNkNAqW3aAMz/7bzbwBcMesKqrKrEj5+3OeRPy1k89yjuewGEigJIaII3u9tsKWwgfqTFGnoFoMRnFHy6J6U9ddBYJ4QxA6U6lrrgECfTyIyrBlGABRefnN73Xx68FMAzq45O+FjJ8JqsjKrcJbxbwmUhBAignRLurH1wmBXvsUbKKmM0qbmTcZeU5G4fW7uWnkXL2x9YVDnJcaX4MncEJoFGqzgY8UqvalhlMXpxUm9TrTy27qmdTg9TgrSCphRMCOpYyciONCTQEkIIaJI1X5vqvQWrZFbmZgzkYK0Alw+F18e/jLq4zY0beC13a/x6MZHB3VeYnxRmR5VNkrVyjdd1+MuvalAKc+el9RrRQuUPmr4CIDFExZj0ob+o1v1KcHRvX0JSKAkhIghVSvf4s0oaZoW6FOKUX7b79hvnFcq+1DE2OX1eY1Mz+Rc/z5szT2pCZS63F24fIEMZ6yMkspq5aXlJfVa8QRKw0GtfAPJKEmgJISIKlX7vakPrHg2KZ1XPA/wb/gZzT7HPgC8ujfmUm1x9Ohydxl/VxvWpqr0po6jMjnd7m48Pk+/x+m6HgiUkswoTcv3N3TvbN+J1+efYdTY3ciO9h2YNBMnVSS2d1uyyjPLjdVuR/uWQhIoCSGiStV+b7G2LwmnpgLXd9ZHfYzKKAG09qVmcrgY21SAkmHJoDzTvyxf/dwNlgqUKjIrjNsiZZUcbocxoDHZQKkyq5J0Szp93j7qHf7/Bz5s+BDw9w3FuyXKYGmaxlWzrmJa/jTml84fltccrSRQEkJElYr93jw+j7FPVjwZpersagDqHfVRxxLs7woKlJwSKIlA31CuPZei9CIgekapx93Dga4DcR9bzVAqySgh05oJRA6UOnr9wVq6JT3pidlmk5mpeVOBQPnto/3DW3ZTrptzHS998yXpURrpExBCjF5qv7fB9Cgddh7Gp/swa+a4eh0mZE3ApJlwepxRP+hU6Q1StxedGNtURinHlmOsOIvWzH3PR/dw7kvnGkv5B6L2eStKLyLblg1EbugebCO3Etyn5Pa6WX1wNQBLJiwZ1HFFciRQEkJElYoeJVX+KEovwmwyD/h4q9lqlE5U6SGYw+UwPpAGe25i/AjOKKnMZaRAW9d1Pj34KR7dY2zRMZDgQCnHlhPyesFSFSipPqVtbdtY37SeHk8PBWkFzCycOajjiuRIoCSEiCoVq94S6U9SJuZMBCL3KTV0NYT8WwIlAaEZJVV6a+5p7le+bXG24HD7y2bxbsCsAq7C9MK4AqXB9hGpjNL2tu3GareTK04elrEAoj/5qgshokrFHKV4ty8JpqYTR8ooBTdyD/bcxND75MAnPPT5Q7h97iF9HRUo5dpzKc7wl95cPle/gGZ3x27j7xuaN0RcvRZOBUohpbcIqy0Hu+JNUYFSQ1cDb+59Exj+/iQRIIGSECKq4NJbsvu9xTtDKZjKKO3t3NvvvuD+JHVu0by/733+VPenuF9XpN5Dnz/EU18+xWeNnw3p66iAKMeWg91sNwKa8PLbro5dxt+73d3UtQ3cp6SauQvTAhmlSM3cqcooBZcPG7oahnUsgOhPAiUhRFSp2O8tmUBJrXwLD4ogkFFSwVS0QEnXde7+8G7uXX0vB7sOJnTOInXU9z9Vwx+jMUpvdn8goxq6w0cEBAdKEF/5LWJGaQh7lCCQVQKYXTQ76QGWYvAkUBJCRBW831usPiWvz8t/rfsv/nP1f/ablJ1MoFSVc6T01tl/RIAaDaAmB0cLlDr6OozgTpX/xPDy+DxG8JDKDWojCc4oQSBQCs8oqdJbTU4NMHCg5NN9xgiKwvRCIxCLOB4gRaU3CA2UpOw2siRQEkLEpPqUVPkhnNvn5kcf/ojHNz3O83XP99t6JN593oJVZlVi0kz0eHr6va7KMs0tjh0oBQdH0sc0Mtp629DxB7pDPRg0uEcJMPqUwkcEqIzSRbUXAf7NZmOVlTv7OvHo/j6m4NLbUDZzQ2igJGMBRpYESkKImIzp3BEyAi6vizvev4PX97xu3Pb23rdDHpNMRslmtgVGBAStfPP4PEYZTW110tHXEbEhN7jkMpoDpYNdB/nRhz9i5f6VI30qKRcc5A5XRskIlNQspaCSX5ery/i5OHfyudhMNlp7W9nTuSfqcVVGKteei9VsDQRKEZq5U1l6m1M0B5NmojSjlFmFswZ9PJE8CZSEEDGp6dzh25g4PU7+z7v/h/f2vYfNZOPyGZcD8E79O8Zv6F2uLno8PUBigRIE+pSCG7oP9RzCo3uwmqxMzZ9qLJcOnqukBAdKag7OaLOjbQdXvn4lr+x6hcc3Pj7Sp5NywVPThzxQ6gstvUWazq0CoqL0IorSi5hTPAeIvQGzmipflFYUcvxYzdypCJSqc6p58swnefzMx2UswAiTr74QIiaVUQrOynS5uvjXt/+VTw58Qrolnd8u+y23z7+ddEs6h3oO8eXhL4FAsJJtzSbDmpHQ61bn9G/oVo3cE7ImYDVZjQ+kSIFQk3N0Z5Q2NG3gmjeuMb5Gg91PbzQKzigN9fegX0YpQulNld3UfoInlJ4AxO5TUj9bahuPWM3cqexRAphfNt84VzFyxkyg1NrayhVXXEFOTg55eXlcd911dHV1xXzOaaedhqZpIX9uuummYTpjIcaH8FlKuzt2c8VrV7D20FqyrFk8tvwxFpYvxG62c0rlKUCg/JbMDCUl0iwlFTSp+2LNeRrNpbeV+1dyw5s30OnqpDKrEgh8yI4nwQHsUH4P+rx99Hp7gdgZpV3t/kBpcu5kAE4oORIoNUUPlIKHTQYfPzyj1Oftw+lxApCbNjwb14rhMWYCpSuuuIIvv/ySt956i1deeYWVK1dy4403Dvi8G264gYMHDxp/HnjggWE4WyHGj+Dp3O/Uv8Nlr17Gro5dlKSX8ORZT3JsybHGY5dVLwPg7fq30XU9qf4kJdJ0bpVRqsyuDDm3sRQovb77db7/7vfp9fayeMJiHlv+GODPUISvGBzrgr/uQ1l6U2U3s2Ymy5oFRB4PoFa8qSzNvJJ5mDQTDV0NNHY3Rjx28AwlCIwf6HR1hjSBt/e2G+eQbc1OyXWJ0WFMBEpbtmzhjTfe4IknnmDhwoUsXryYhx9+mOeff54DB2LvAJ2RkUFZWZnxJycnZ5jOWojxQc1S+mDfB9z23m10u7s5vuR4XjjvhX5Npksql2A1WdnbuZed7TsHFSipHqV6R2BEgBoNoLIwsbZYGY09Srqu8/PVP8ere/nG5G/wX2f8F2WZZYB/GXqkvpexLLj01uPpoc/bNySvo7Jx2bZsNE0DAqU3p8dpjIkIL71lWjOZUTADiN6nFLzPm3oNAK/uNfrvIHTFmzoHMT6MiUBp1apV5OXlMX/+fOO2ZcuWYTKZ+PTTT2M+99lnn6WoqIjZs2fzox/9iJ6enpiP7+vro7OzM+SPEEczFSip/bGunHklT5z1hPHBESzTmmlMEH67/u1Bld4qsyvR0Oh2dxuZCVV6UxmlWJv2jsaMUrOzmU5XJybNxM9O+hlWkxWr2UqGxd+/Nd7Kb+EB6lBllTpcoaMBwP+zqL6uzT3NuL1u4+dHld4Aji85HojepxQ8bBIgzZyG1WQFQstv4eMJxPgxJgKlxsZGSkpC32gtFgsFBQU0NkZOlwJcfvnl/OEPf+C9997jRz/6Ef/7v//LlVdeGfO17rvvPnJzc40/VVVVKbkGIcYqVQJLM6dx/5L7uWvBXcYHRSRLq5cC/tVvyWyIq4SMCDjSp6RKbwP1KLm97pDb2vva49rTa6ipFXwVmRVYzYGvofpwHXeBUtgMrGizuAYrfMWbEtzQvc+xD6/uJdOaGfLzOL/U/wt4tD6l8GZuTdOMrFLw9yuVK97E6GIZyRe/++67+eUvfxnzMVu2bEn6+ME9THPmzKG8vJylS5eyc+dOpkyZEvE5P/rRj7j99tuNf3d2dkqwJI5qE3Mm8vTZT1OaUWpkcmI5reo0TJqJra1bjd/Gk8kogX9C94HuA9R31jM5d7Kx0mhC1gQg+jBMtdLJYrLg9XnR0Wnva4+YBRtOKqOhVvQpefY8DnYfjDjmYCxT4wHMmhmv7h3yjJLqH1KK0ovY27mXFmeLEdRMypkUUho7rvQ4AHa076C9t73fViHhGSXwB2Stva0hK99SOWxSjC4jGijdcccdXHvttTEfM3nyZMrKymhqCt2vx+Px0NraSllZWdyvt3DhQgB27NgRNVCy2+3Y7fa4jynE0UAto45Hflo+80vns6ZxTSBQykwuUJqYPZFPD37K3s69NHQ1AP6mWjVqQDXYhn8AG71R6SX0entp7W2ltbd1xAMllVFS/VeK+oBXH/jjgU/3GVm96pxqdnfsHrJASWWUcm2hQUrw0Em1Ki58uX1BWgGTciexu2M365vWc3r16cZ9Xp/XGNugftaAiNuYpHo0gBg9RjRQKi4upri4eMDHLVq0iPb2dtauXcsJJ/jfsN999118Pp8R/MRjw4YNAJSXlyd1vkKI+CytXsqaxjXGv5MpvUHoLKXw0QAQGIYZXnoL7o3qcnfR2tvqL6HkJ3Ua6LrO7s7dVGdXYzEl/7aprkGVMxX14TqeSm/BW39MzZvK7o7dQ9YrZmSUbP0zSuDPCqm5WpPzJhPu+JLj2d2xm7WH1oYESm19bfh0Hxqa0Q8HkWcpSelt/BoTPUozZ87k7LPP5oYbbmDNmjV8/PHH3HrrrXznO9+hoqICgIaGBmbMmMGaNf435507d3Lvvfeydu1a9uzZw8svv8zVV1/NKaecwty5c0fycoQY986oPsP4u0WzGCWyRAWvfAsfDQCRh2FC6LYpKhMwmA/pd+vf5Vt/+xYPr3846WNAUEYprPSmMiHjKVBSX+9sW7ZReh3yjFJY2Uu9bpOzqd9ogGDG4MmwPiXVn5Sflh8SIEeapSSlt/FrTARK4F+9NmPGDJYuXco555zD4sWLeeyxx4z73W43dXV1xqo2m83G22+/zZlnnsmMGTO44447uPDCC/nHP/4xUpcgxFGjLLOMuUX+X0iKMoqS3oJBBRT1nfX9VrxBIKPU7e6m19Nr3K729yrJKIk5aylenx/6HIC/7/g7Xp83qWPouh7oUQorvakP11T3KPl0H79Z9xv+sXP43/eC5w+l4nsQy0AZpeae5piB0ollJwKwuWVzyIDK8EZuJdLGuFJ6G79GtPSWiIKCAv74xz9Gvb+mpiZk+FdVVRUffPDBcJyaECKCpROXsrFlI2UZ8fcRhlMjArrcXWxq2eS/LSsQKGVbs7GYLHh8Htp62yjP8pfVVemtNKM0sHv9ID6k1fymw72H+aL5C44vPT7hYzQ7m3F6nJg0k9GMrgzVqrf1Tet5YtMTaGiUZ5Yzv2z+wE9KwD7HvojXA4EgoyCtwChbDVlGyRU5o6RWvW05vAWnx4lFs4SUbpWyzDJmF85m8+HNvFv/LpdMvwTov8+bEmljXCm9jV9jJqMkhBhbLq69mG9N+RY3zUt+2yC72W4MZNzWtg0I7VHSNC2QregLBEKq9FacUZySbIYq+4F/PlQyoo0GgKHrUdpy2L9qWEfnno/uiTrQUtd13D53Qsd2epxc+o9LufzVyyM+18gopRdSYO//PUqlqOMBjjRzqxlg1TnVUUdbLJt4ZKr83sD3N9KKNwj0KMkcpaODBEpCiCGRbcvm54t/zskTTh7UccLLVOEjCoweJGf/QCm4RynZ6dy6rocESu/sfSckex2vaKMBYOhKb1taA+NVDnQf4L5P7+v3mP2O/Vz8j4s556Vz6HHHHsgbrL6zHofbQWtvK41d/efZGWWrtMIhzyhFC1LCA5xYG8yqQOmzxs+M4yVSepOM0vglgZIQYlQLDizsZnu/D7/w6dzBe8yVZpQOOqN0uPcwvd5eNDTSLekc6D7A1tatCR8n2mgAGMKM0pFA6brZ12HSTPxj1z/4555/GvdvbN7IFa9dQV1bHY3djSGB1UAibVYcTH29C9OHPlBSAUt4RinHloPNZDP+HTyRO9zEnIlMy5+GR/fw3r73gOgZpfDxAD7dZ5yDBErjjwRKQohRLTiwqMyq7NcYHh4IOdwOY2ZOSUZJ1BEC8VLZpLLMMhZPWAwkV36LNhoAguYopTBQ6vP2savdv7fZd2Z8h+tmXwfAz1b9jEPdh3hr71t875/fC/m67OnYE/fxgzcrjhQoBfcoqe9Rl7sLl9eV8LWAP0t40csX8cyXz4TcHhykhGeUNE0z+pQgdkYJYHn1ciBQfgu+hmDh4wEcLoexobEESuOPBEpCiFGtKifQkxRpMnh4oNTU7c8m5dhySLOkDTqjFDy/SY09eGfvOwkfJ9poAAh8uDrcjpRttbK9bTte3Uu+PZ/SjFL+9dh/5ZjCY+h0dfK9f36PO96/gz5vH6dUnsL5U88HYE/nnriPHxwcDZRRyrHlYNEsIbcn6tVdr1LXVscft4Qu6ul2dxtBSvhkbgj0KUHsjBIEym+fHPiEbne30WfVL6MU1sytym4Zlox+/Wdi7JNASQgxqk3MDmRg4gqUgvqTINDD5PQ4E+rBUdSKt8rsSk6pPAWLycLOjp3GTvTxiDUaAEJLRsF9L4OhymgzC2eiaRpWk5X7ltxHmjmNekc9Ojrfmf4dfnP6b5hVOAtIMKM0QOkteDyApmnG1iDJlt8+PejfAP1A94GQzJv6e5o5Dbu5/64KiWSUpuZNpSanBrfPzcr9Kwdu5j7SJC79SeObBEpCiFEtODgKHg2ghAdKwVO5ATKsGaRb0oHkNmU1Bl1mVZJjy2FhuX83gHfr3437GLFGA4B/T7psa/+NVgdj62F/H9WMghnGbZNyJ7HipBWUZ5Zz14l38eOFP8ZislCTUwMkllEKKb11hQZKuq6HNHMDg+pTcnvdIcMgg3vEjP6kCNkkCAQ5ZZllxtY30WiaZmSVXt/9uhEARWvmdnqcuL1uWfE2zkmgJIQY1dIsacaIgEgzcAbKKEV6TCLCJ4Ivq+6/jHwgsUYDKAPNUvq44WM+a/ws7tdUwcTMgpkht39j8jd486I3uXLWlcbmsCrTst+xP64xAb2eXiMgVc8LXgnY4+kx+sRUkKFGBCQTrG5s2YjT4+x3bRD4eoU3ciuq9DYpJ3Y2SVHf3w/2++fwmTVzv0xRti0bDf/XrtPVKRmlcU4CJSHEqHfDnBs4rfI0FpQv6HdfQoGScxCB0pFs1ulVp6Oh8eXhLznYdTCuY8QaDaDECpQcLge3vnsrN799c8gE8mg8Pg91bXVAaEYpmpKMEtIt6Xh0T8gohGjUYzIsGZg0E06PMyQAUl/ndEu6kcVR34NkMkqq7KaCk+DVedEauZXTq06nNr+Wi2oviuu1ZhXOoiKzwuh7Kkwr7LeAwKSZyLJmGa/f3tsOSKA0XkmgJIQY9S6ZfgkPL33YKKEFM1a1OVv9owGcgdEAxmOSzCj1enqN46mMUmF6IceVHAfAu/viK7/FGg2gqA/ZSLOUDnQdwOPz0OvtZUf7jgFfb0/HHvq8fWRYMmIGZ4pJMxmr8eLpU1L9STW5Ncbk9eA+JRU0Ba8WM0pvfckHSqdX+TesVWVFGDijNDV/Ki9+80XOrDkzrtfSNI2lE5ca/w4vuymq1BeSUTrShyXGFwmUhBBjmtoY1+Vz0ePpiZlRSrTsc6DrAABZ1qyQbEGkKc6xxBoNoMQaERBc5qprrRvw9VTGZUbBjLj32UukTym4MV2VQ0MCpbD+JEi+R6nH3cPG5o0AXHPMNQDs7txtlOIGyiglY/nE5cbfowVKwdO5ZZ+38U0CJSHEmBbcrN3qbI0YKKkPu0QzSsEb8ap+HoCl1f6Mw7qmdXFN/I41GkCJlVEKDpTiGXapHhNP2U2pya0B4guUVCN3VXaVkWmLmFFKD2SUks3qrT20Fo/uoSKzguNKjqMgrQCf7mN723Yg+vYlgzGveJ7R2xQc7AULHhGgsmTSzD0+SaAkhBjz1IfwoZ5DRuCSih4lYzRA2Gq7iqwKpuZNxaf7jGxHNAONBlDUh2yk8QAq+AOM3qNYgkcDxMvIKCVQeqvOiZJR6o2eUUo0UFJlt4XlC9E0zWhOV8Fghyv1K85Mmomzas4CAgFkOBUoSUZp/JNASQgx5qlAaHv7dnR0LJolpD8m2WxG+Iq3YGp4YaQZQsEGGg2gxMwodQcyStvathmNxpHouh51xVssiWSU4i69BZWtkm3m/rQxEChBIEumgsGhyCgB/OD4H/DAKQ9w+YzLI94fPJ1bVr2NbxIoCSHGPPUhrPp3ijKKQnpzku1RCl/xFkyV0QYKlOIZDQCBD/qBSm/d7m4aHA1Rj9PQ1YDD5cBqsjI5L/Yk6mAqo9Ta2xpzlpPL6+Jgt3+1X0hGqTPwdVABacRm7gQCpfbediPoMwKlQn+gpBq6hyKjBP6xFF+f9PWos5eCN8aVQGl8k0BJCDHmqQ9klWUILrsF359wRulI6S3S/KZImZRI4hkNAIEPWZUhCaYySir429oWvU9JfQ2m5k3Faop/O41MayYl6f6vW6ysUkNXAz7dR7olncK0QuPr0NbXRperC4iSUToyR8nhduD2DjyrCWBN4xrjWtTgSJUl296+HY/PM2QZpYFEauaWHqXxSQIlIcSYZ5TejjT4Bo8GgMAHdltvG16fN65j6roes/QWb6AUz2gACHzIRsooqR6luUVzgdgr37Yc9gdKaluSRBjltxh9SsFlN03TyLJlGSsP1X3GPm9BPUo59hzMmhmIf0RAcH+SUpVdRaY1kz5vH7s7dg9ZRmkgapViU08Tfd4+QDJK45UESkKIMU+VddRU6fCMUp49Dw0NHT1iIBJJi7OFXm8vJs1EeWZ5v/tVoKRmHEUTz2gAdY7QfzxAt7vb2FPslMpTgNiBUjIr3pR4RgSoFW/BGTK1cbG61kgZJZNmMoKZeDN7qj9pQVlg0KhJMzE9fzrgv1aVUcq1DXOgdCSDpa7ZolnItGYO6zmI4SGBkhBizAvuhYHQHePBv5eaCkTi/ZBWZbeyjLKIvUUlGSXYTDY8usfo2YkkntEAEMiI9Hh6QkpTqj8p25ptDLqMVXobVKAUR0ZJrXgLLkcGZ9dcXpcR2IUvrU+kBNrY3cjezr2YNBPzy+aH3KeubVPLJno8/o2Oo+31NlRU6U39nOTac0NGSIjxQwIlIcSYF/6BHJ5RgsQbumOV3cCf2Rio/BbvaADwf/CqHiRVToJAf1JJRgnTC/yZlMbuxogN1y3OFpqdzWho1ObXxny9SOLKKKnRAEHXE/x1UEGQxWTp1zeUyMq31QdXA3BM4TH9jqMCpTUH/T1MGpqxpchwUeeksolSdhu/JFASQox5wYMNoX+PUvBj4p2lpAKlSI3cSqQVX8HiHQ0A/sDLWPl2ZO8wCPQnlWaWkm3LNo4Tqfym+pMm5U6KulorFpVRqu+sj9rLpa41pPR25Ouw37HfKLsVpBX0y7DEWvnW6+mlxdnCvs591LXW8W69f3uY4P4kRc2H2tmxE4AsWxZmkzm+i0yR8OBNGrnHL8tIn4AQQgxWeOktUkZJZZ3iLb0FT+WOJrw3J1y8owGUXHsu7X3toRmlI6U3FfxNz59OQ1cDdW11/TYJHkzZTZ2nzWTD5XNxoPtAvyDR7XMb27pEK71FGjapqKbv8O/Bq7te5Z6P7sGr9w/OIgVKU3KnYDFZjGzOcPcnQf9Sn2SUxi/JKAkhxjz1AazEKr0l2qMUaYaSogIEVY4KF+9oACXSyjdVeivN9AdKKgiKtJWJMZE7gUGTwcwms3GukfqUGrsa8ege7GZ7yNdYfR0aexpp7G4E+mf5IPr34C/b/oJX96KhkWHJoDCtkMqsSpZPXM4JpSf0O47VbGVa3jTj38PdnwSBHiVFNsQdvySjJIQY86xmK9m2bBwuB1nWrIhlp4QDpQF6lGDgEQHxjgZQVGYkeJaSyiipwET1KYWX3rrd3YG+nqJj4nq9SGpyatjRvoM9nXtYwpKQ+4IbuYMHehamFZJuScfpcbK5ZbNxW7hIPUpdri42NG0A4NULXjWydAOZUTDDCAxHIqNkN9uxm+3GaAApvY1fklESQowL6oM5UjYJAhmOeDaxdXqcNDubgdgZJRUA7XfsR9f1fvernqFp+dP63RdJpG1MjB4lVXo7Eijt7NgZsjruxW0v4nA5qMmpiZiFiVeslW+RVrwBaJpmBJQbmjcAoaMBFKNHKWiO0uqDq/HoHibmTIw7SILQ8uJIZJQgtE9JSm/jlwRKQohxQWUrogZKCWSUVB9OljUrZqagPKscs2am19trBFaKT/cZ2ZU5RXMGvgACWYngFW3hPUoVmRVkW7Px+Dzs6tgF+HuHfv/V7wG49phrQ7I9iYq18s2YoRQhQ1aV5Q9ydnfsBqL0KEVo5v6o4SMAFk9YnNB5Bm/4OxIZJQgtv0mgNH5JoCSEGBcGCpTUB3c84wFUKa0quyrmbByryWoMowwvv+3t3IvD7cButjM1f+rAF0D/HqU+b58R2JVllgH+7I3KKqk+pTd2v8GhnkMUpRfxjSnfiOu1oomVUVLlyEg9V+FZpvAG++Db1DXpus6HDR8CsGTCkn6Pj2V6/nQ0/N+bkSp7BWeUpPQ2fkmgJIQYF1SAVJFVEfH+RFa9xdOfpBgN3Z2hDd0qmzSzYGbce66FZ5RU2c1utod8KBt9Sm116LrO7zb/DoArZl6B3WyP67WiURmlJmcT3e7ukPuild4i3Rar9Nbp6sTtc7O9fTtNPU2kmdP6DZUcSIY1w5h2Ptz7vCnBJT/JKI1fEigJIcaFK2ddyXdnf5dLai+JeL/qUXJ6nPS4e2IeK54Vb4rKroRnlDY2bwRgTnF8ZTcI2sbEFRoolWaUhmS21BYeda11fNTwETvad5BhyeCS6ZGvPRG59lwj8xNcfvP6vDFX8fULlCKU3nJtuUZZsL233Si7nVh2YlIBnhodMDlvcsLPTQUpvR0dJFASQowLVdlV3H7C7RRnFEe8P8OSYXwYD5RVSiajFB4oJdqfBIFeG1V6Cx8NoARnlFQ26eLai1OWWTH6lILKb009Tbh9biwmC2UZZf2eE96IHSmjZDaZQ7aS+XC/v+yWaH+S8sMTf8iL33wx4bJdqkjp7egggZIQ4qigaVrcDd1GoBRHRkkFU8GBUp+3z9iPbXbR7LjPMTcttPQW3sitTMmbgkWz0NHXweeHPseiWbhy1pVxv85AjD6loIySKrtVZlVGnIJdnlmORfNPnNHQomZY1Myr/Y79xliAZAMdu9lObX7tiO2xFpxRkkBp/JJASQhx1IinT8mn+4zSW6ztSxS1Aiw4UKprrcPj85Bvz48r2FJURik8UApvULeb7UzKm2T8+5zJ5xjN3qmgMkor969k9cHVuLyuwB5vUYZnWkwWyrP8je35aflYTJHH9Kk+pdd2v5bUWIDRRGWUsqxZcfehibFHBk4KIY4axn5vMQKlra1b6fP2YTVZKcsaOPhQGaVOVycdfR3k2nPZ1LIJ8GeTEsl2qCxMn7ePXk9vvxlKwabnT2d723bAPxIglWYVzgLgq8NfccObN5BhyTAyJrGGZ1ZlV7HPsS/iijdF3ff+vveB5Mtuo4EKlCSbNL5JRkkIcdRQH9Kxhk6+tP0lAJZVL4srS5BuSack3Z/xUVklFSgl0sgNkGnNNMpX7X3tUXuUAI4tPhaAUytPjXugZbwWlC3g4TMe5ltTvkVhWiE9nh4Odh8EMFaaRaIycJEauRWVUXL5XMDYDpTUz1NRetEIn4kYSpJREkIcNQbqUepx9/DqrlcB+Hbtt+M+bmV2JU3OJuo765ldNDupRm7w91Hl2HNo7W2lo6+Dxh7/vmmRMkrfrv02GdYMTq06NaHXiPc8Tqs6jdOqTsOn+9jaupWPGj6ixdnCeVPOi/q8ybn+1WeqBBdJcLbJbrYzvzSxsQCjyaKKRVx7zLWcUnnKSJ+KGEISKAkhjhpGRinK0Mm39r5Fl7uLyqxKFpQtiPu41TnVrGtaxz7HPjr6Oow93mYXxt/IreTac2ntbaW1t5UWZwsQOVCymqwxg5ZUMWkmZhXOMspxsZw/9XzcPjfLJy6P+hiVUQL/WIA0S1pKznMk2Mw27ph/x0ifhhhiEigJIY4aasn6jvYd+HRfv60+Xtz+IgAX1l6Y0DYgwSMCVDapOrs6qR3lVZ/Sro5d+HQfFs0Ss+dnNMmwZnDNMdfEfExwoDSWy27i6CE9SkKIo8bXyr9GhiWD7W3bjaBI2dm+k/VN6zFrZr415VsJHTc4UApu5E6Gagyua60DoDijOOJy/LEquH9ppOYfCZEICZSEEEeNovQivn/89wH4f2v/n1HagkAT9ymVp0QdWhlN8IiAZPuTFDUiYFvbNiD63nVj1eTcyWRYMji2+NioowaEGE0kUBJCHFW+M/07zCqchcPl4IHPHgDA5XXx8s6XAbio9qKEj6lGBDQ7m1nXtA5IfMWbokpvO9p3AJH7k8aywvRCXr/wdR5d/uhIn4oQcZFASQhxVDGbzKxYtAKTZuL13a/zccPHvFv/Lu197ZRklHBSxUkJHzPXnmuUzBwuBxbNwoyCGUmdnzpOn7cPiDwaYKwrSCsg05o50qchRFwkUBJCHHVmFc7i8hmXA3Dv6nt5butzAFww9YKoE6UHUpUVmC5dW1Cb1Cav0H944XjLKAkx1kigJIQ4Kt163K2UZpTS0NXAuqZ1aGhcMO2CpI8XvA1Hsv1JIIGSEKONBEpCiKNSpjWTHy/8sfHvRRWLmJA1IenjBe8LN5hAKXwz2fFYehNiLJFASQhx1Dqj+gzOrjkbgKtmXTWoY4UESkk2coNklIQYbWTgpBDiqHb/kvu5/YTbY267EY+anBoAsq3Zxt+TEZxR0tAoTk9sVIEQIrUkUBJCHNXMJvOggySAecXzuGHODcwomJHQVO9wakd68K8Os5oH3phXCDF0JFASQogU0DTNGGY5GOmWdGwmGy6fS/qThBgFpEdJCCFGEU3TjD4l6U8SYuRJoCSEEKOMCpTG2/YlQoxFEigJIcQoowKlssyyET4TIYQESkIIMcpMzZsKwKyCWSN8JkIITdd1faRPYjTr7OwkNzeXjo4OcnJyBn6CEEIMktvrZp9jH5NyJ6Fp2kifjhDjUryf77LqTQghRhmr2crkvMkjfRpCCKT0JoQQQggRlQRKQgghhBBRSKAkhBBCCBGFBEpCCCGEEFFIoCSEEEIIEYUESkIIIYQQUUigJIQQQggRhQRKQgghhBBRSKAkhBBCCBGFBEpCCCGEEFFIoCSEEEIIEYUESkIIIYQQUUigJIQQQggRhQRKQgghhBBRWEb6BEY7XdcB6OzsHOEzEUIIIUSqqM919TkfjQRKA3A4HABUVVWN8JkIIYQQItUcDge5ublR79f0gUKpo5zP5+PAgQNkZ2ejaVpKj93Z2UlVVRX79u0jJycnpcceTeQ6x5+j5VrlOsefo+Va5ToHpus6DoeDiooKTKbonUiSURqAyWSisrJySF8jJydnXP8gK3Kd48/Rcq1ynePP0XKtcp2xxcokKdLMLYQQQggRhQRKQgghhBBRSKA0gux2OytWrMBut4/0qQwpuc7x52i5VrnO8edouVa5ztSRZm4hhBBCiCgkoySEEEIIEYUESkIIIYQQUUigJIQQQggRhQRKQgghhBBRSKA0xB555BFqampIS0tj4cKFrFmzJubj//znPzNjxgzS0tKYM2cOr7322jCd6eAkcp2PP/44S5YsIT8/n/z8fJYtWzbg12W0SPT7qTz//PNomsb5558/tCeYQolea3t7O7fccgvl5eXY7XZqa2vHxM9votf561//munTp5Oenk5VVRX/9m//Rm9v7zCdbXJWrlzJeeedR0VFBZqm8be//W3A57z//vscf/zx2O12pk6dytNPPz3k5zlYiV7nSy+9xPLlyykuLiYnJ4dFixbxz3/+c3hOdpCS+Z4qH3/8MRaLhWOPPXbIzi9VkrnOvr4+7rnnHiZOnIjdbqempobf/e53SZ+DBEpD6IUXXuD2229nxYoVrFu3jnnz5nHWWWfR1NQU8fGffPIJl112Gddddx3r16/n/PPP5/zzz2fz5s3DfOaJSfQ633//fS677DLee+89Vq1aRVVVFWeeeSYNDQ3DfOaJSfQ6lT179nDnnXeyZMmSYTrTwUv0Wl0uF8uXL2fPnj385S9/oa6ujscff5wJEyYM85knJtHr/OMf/8jdd9/NihUr2LJlC08++SQvvPACP/7xj4f5zBPT3d3NvHnzeOSRR+J6/O7duzn33HM5/fTT2bBhA7fddhvXX3/9qA8iEr3OlStXsnz5cl577TXWrl3L6aefznnnncf69euH+EwHL9FrVdrb27n66qtZunTpEJ1ZaiVznZdccgnvvPMOTz75JHV1dTz33HNMnz49+ZPQxZBZsGCBfssttxj/9nq9ekVFhX7fffdFfPwll1yin3vuuSG3LVy4UP+Xf/mXIT3PwUr0OsN5PB49Oztbf+aZZ4bqFFMimev0eDz6SSedpD/xxBP6Nddco3/rW98ahjMdvESv9b//+7/1yZMn6y6Xa7hOMSUSvc5bbrlFP+OMM0Juu/322/WTTz55SM8zlQD9r3/9a8zH/N//+3/1Y445JuS2Sy+9VD/rrLOG8MxSK57rjGTWrFn6T3/609Sf0BBK5FovvfRS/Sc/+Ym+YsUKfd68eUN6XqkWz3W+/vrrem5urn748OGUva5klIaIy+Vi7dq1LFu2zLjNZDKxbNkyVq1aFfE5q1atCnk8wFlnnRX18aNBMtcZrqenB7fbTUFBwVCd5qAle50/+9nPKCkp4brrrhuO00yJZK715ZdfZtGiRdxyyy2UlpYye/ZsfvGLX+D1eofrtBOWzHWedNJJrF271ijP7dq1i9dee41zzjlnWM55uIzF96JU8Pl8OByOUf1eNBhPPfUUu3btYsWKFSN9KkPm5ZdfZv78+TzwwANMmDCB2tpa7rzzTpxOZ9LHlE1xh0hLSwter5fS0tKQ20tLS9m6dWvE5zQ2NkZ8fGNj45Cd52Alc53h7rrrLioqKvq9MY8myVznRx99xJNPPsmGDRuG4QxTJ5lr3bVrF++++y5XXHEFr732Gjt27ODmm2/G7XaP2jflZK7z8ssvp6WlhcWLF6PrOh6Ph5tuumnUl94SFe29qLOzE6fTSXp6+gid2dB68MEH6erq4pJLLhnpU0m57du3c/fdd/Phhx9isYzfj/5du3bx0UcfkZaWxl//+ldaWlq4+eabOXz4ME899VRSx5SMkhhR999/P88//zx//etfSUtLG+nTSRmHw8FVV13F448/TlFR0UifzpDz+XyUlJTw2GOPccIJJ3DppZdyzz338Oijj470qaXU+++/zy9+8Qt++9vfsm7dOl566SVeffVV7r333pE+NTFIf/zjH/npT3/Kn/70J0pKSkb6dFLK6/Vy+eWX89Of/pTa2tqRPp0h5fP50DSNZ599lgULFnDOOefw0EMP8cwzzySdVRq/YeUIKyoqwmw2c+jQoZDbDx06RFlZWcTnlJWVJfT40SCZ61QefPBB7r//ft5++23mzp07lKc5aIle586dO9mzZw/nnXeecZvP5wPAYrFQV1fHlClThvakk5TM97S8vByr1YrZbDZumzlzJo2NjbhcLmw225CeczKSuc5///d/56qrruL6668HYM6cOXR3d3PjjTdyzz33YDKNj989o70X5eTkjMts0vPPP8/111/Pn//851Gd2U6Ww+Hg888/Z/369dx6662A//1I13UsFgtvvvkmZ5xxxgifZWqUl5czYcIEcnNzjdtmzpyJruvs37+fadOmJXzM8fF/9Shks9k44YQTeOedd4zbfD4f77zzDosWLYr4nEWLFoU8HuCtt96K+vjRIJnrBHjggQe49957eeONN5g/f/5wnOqgJHqdM2bMYNOmTWzYsMH4881vftNYRVRVVTWcp5+QZL6nJ598Mjt27DCCQYBt27ZRXl4+KoMkSO46e3p6+gVDKjjUx9G2mWPxvShZzz33HN/97nd57rnnOPfcc0f6dIZETk5Ov/ejm266ienTp7NhwwYWLlw40qeYMieffDIHDhygq6vLuG3btm2YTCYqKyuTO2jK2sJFP88//7xut9v1p59+Wv/qq6/0G2+8Uc/Ly9MbGxt1Xdf1q666Sr/77ruNx3/88ce6xWLRH3zwQX3Lli36ihUrdKvVqm/atGmkLiEuiV7n/fffr9tsNv0vf/mLfvDgQeOPw+EYqUuIS6LXGW4srXpL9Frr6+v17Oxs/dZbb9Xr6ur0V155RS8pKdF//vOfj9QlxCXR61yxYoWenZ2tP/fcc/quXbv0N998U58yZYp+ySWXjNQlxMXhcOjr16/X169frwP6Qw89pK9fv17fu3evruu6fvfdd+tXXXWV8fhdu3bpGRkZ+g9/+EN9y5Yt+iOPPKKbzWb9jTfeGKlLiEui1/nss8/qFotFf+SRR0Lei9rb20fqEuKW6LWGGyur3hK9TofDoVdWVuoXXXSR/uWXX+offPCBPm3aNP36669P+hwkUBpiDz/8sF5dXa3bbDZ9wYIF+urVq437Tj31VP2aa64Jefyf/vQnvba2VrfZbPoxxxyjv/rqq8N8xslJ5DonTpyoA/3+rFixYvhPPEGJfj+DjaVASdcTv9ZPPvlEX7hwoW632/XJkyfr//mf/6l7PJ5hPuvEJXKdbrdb/4//+A99ypQpelpaml5VVaXffPPNeltb2/CfeALee++9iP/PqWu75ppr9FNPPbXfc4499ljdZrPpkydP1p966qlhP+9EJXqdp556aszHj2bJfE+DjZVAKZnr3LJli75s2TI9PT1dr6ys1G+//Xa9p6cn6XPQdH0c5YuFEEIIIVJIepSEEEIIIaKQQEkIIYQQIgoJlIQQQgghopBASQghhBAiCgmUhBBCCCGikEBJCCGEECIKCZSEEEIIIaKQQEkIIYQQIgoJlIQQ485pp53GbbfdNtKnIYQYByRQEkKIME8//TR5eXkjfRpCiFFAAiUhhBBCiCgkUBJCjEsej4dbb72V3NxcioqK+Pd//3fU1pZ9fX3ceeedTJgwgczMTBYuXMj7778PwPvvv893v/tdOjo60DQNTdP4j//4DwD+93//l/nz55OdnU1ZWRmXX345TU1NI3SFQojhIIGSEGJceuaZZ7BYLKxZs4bf/OY3PPTQQzzxxBMA3HrrraxatYrnn3+ejRs3cvHFF3P22Wezfft2TjrpJH7961+Tk5PDwYMHOXjwIHfeeScAbrebe++9ly+++IK//e1v7Nmzh2uvvXYEr1IIMdQ0Xf2KJYQQ48Rpp51GU1MTX375JZqmAXD33Xfz8ssv88YbbzB58mTq6+upqKgwnrNs2TIWLFjAL37xC55++mluu+022tvbY77O559/zoknnojD4SArK2soL0kIMUIkoySEGJe+9rWvGUESwKJFi9i+fTubNm3C6/VSW1tLVlaW8eeDDz5g586dMY+5du1azjvvPKqrq8nOzubUU08FoL6+fkivRQgxciwjfQJCCDGcurq6MJvNrF27FrPZHHJfrKxQd3c3Z511FmeddRbPPvssxcXF1NfXc9ZZZ+FyuYb6tIUQI0QCJSHEuPTpp5+G/Hv16tVMmzaN4447Dq/XS1NTE0uWLIn4XJvNhtfrDblt69atHD58mPvvv5+qqirAX3oTQoxvUnoTQoxL9fX13H777dTV1fHcc8/x8MMP84Mf/IDa2lquuOIKrr76al566SV2797NmjVruO+++3j11VcBqKmpoauri3feeYeWlhZ6enqorq7GZrPx8MMPs2vXLl5++WXuvffeEb5KIcRQk0BJCDEuXX311TidThYsWMAtt9zCD37wA2688UYAnnrqKa6++mruuOMOpk+fzvnnn89nn31GdXU1ACeddBI33XQTl156KcXFxTzwwAMUFxfz9NNP8+c//5lZs2Zx//338+CDD47kJQohhoGsehNCCCGEiEIySkIIIYQQUUigJIQQQggRhQRKQgghhBBRSKAkhBBCCBGFBEpCCCGEEFFIoCSEEEIIEYUESkIIIYQQUUigJIQQQggRhQRKQgghhBBRSKAkhBBCCBGFBEpCCCGEEFH8/3bwyHLi4BbmAAAAAElFTkSuQmCC", + "text/plain": [ + "<Figure size 650x500 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "benchmark.plot(main=True, reference=True, difference=True, labels=['beta', 'cost'], title=\"comparison\", one_plot=True)\n", + "# with one_plot=True, the three plots are shown in the same figure" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Benchmark with different circuit properties" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The same procedure can be applied to an object with circuit properties that are not the standard ones, for example: p=2 and param_type='extended'.\n", + "\n", + "However, for this properties the `analytical_simulator` backend is not available, so the `vectorized` backend is used instead for the reference." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# create the QAOA object\n", + "q2 = QAOA()\n", + "\n", + "# set device and backend properties\n", + "q2.set_device(qiskit_cloud)\n", + "q2.set_backend_properties(n_shots=1000)\n", + "\n", + "# set properties\n", + "q2.set_circuit_properties(p=2, param_type='extended')\n", + "\n", + "# compile with the problem\n", + "q2.compile(MaximumCut.random_instance(n_nodes=3, edge_probability=0.9, seed=10).qubo) #use a smaller problem to speed up the example" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: vectorized simulator will be used for the reference, since the analytical simulator is not available for the circuit properties of the benchmarked QAOA object\n" + ] + } + ], + "source": [ + "# create the new QAOABenchmark object, in this case the reference will use the vectorized simulator not the analytical one\n", + "benchmark2 = QAOABenchmark(q2)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running benchmark.\n", + "Point 1089 out of 1089. Expected remaining time to complete: 00:00:00, it will be finished at 03:05:02. \n", + "Running reference.\n", + "Point 1089 out of 1089. Expected remaining time to complete: 00:00:00, it will be finished at 03:05:03. \n" + ] + } + ], + "source": [ + "benchmark2.run(n_points_axis=25+1, ranges=[(1,), (1,), (1,), (0, 3.14), (1,), (1,), (-3.14, 3.14), (1,), (1,), (1,), (1,), (1,)]) \n", + "# see that the ranges are now 12-dimensional, one tuple is needed for each parameter. \n", + "# in this case, we are fixing 10 parameters, and sweeping the other two\n", + "# one can sweep more than " + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 650x1500 with 6 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "benchmark2.plot(main=True, reference=True, difference=True, verbose=False, title=['benchmarked', 'analytical', 'difference'],) " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compare different QPUs or simulators" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this section we will show how one can compare the performance of two different QPUs or simulators.\n", + "\n", + "For that we will follow the same steps as before. Now, we will define two different QAOA objects (one for each QPU to compare) and the respective QAOABenchmark objects, and then ploting the values together.\n", + "\n", + "Here we will compare the IBM QPU with a shot-based simulator:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# we start defining the QAOA object for the ibm qpu:\n", + "\n", + "# create the QAOA object\n", + "q_ibm = QAOA()\n", + "\n", + "# set device and backend properties\n", + "qiskit_cloud = create_device(location='ibmq', name='ibm_oslo', qpu_credentials, as_emulator=True)\n", + "q_ibm.set_device(qiskit_cloud)\n", + "q_ibm.set_backend_properties(n_shots=1000)\n", + "\n", + "# set properties\n", + "q_ibm.set_circuit_properties(p=1, param_type='standard')\n", + "\n", + "# compile with the problem\n", + "q_ibm.compile(qubo)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# now, we define the QAOA object for the shot-based simulator:\n", + "\n", + "# create the QAOA object\n", + "q_simulator = QAOA()\n", + "\n", + "# set device and backend properties\n", + "qiskit_simulator = create_device(location='local', name='qiskit.shot_simulator')\n", + "q_simulator.set_device(qiskit_simulator)\n", + "q_simulator.set_backend_properties(n_shots=1000)\n", + "\n", + "# set properties\n", + "q_simulator.set_circuit_properties(p=1, param_type='standard')\n", + "\n", + "# compile with the problem\n", + "q_simulator.compile(qubo)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running benchmark.\n", + "Point 100 out of 100. Expected remaining time to complete: 00:00:00, it will be finished at 03:05:21. \n", + "Running reference.\n", + "Point 100 out of 100. Expected remaining time to complete: 00:00:00, it will be finished at 03:05:21. \n", + "Running benchmark.\n", + "Point 100 out of 100. Expected remaining time to complete: 00:00:00, it will be finished at 03:05:23. \n" + ] + } + ], + "source": [ + "# next step is to create the QAOABenchmark objects\n", + "\n", + "benchmark_ibm = QAOABenchmark(q_ibm)\n", + "benchmark_simulator = QAOABenchmark(q_simulator)\n", + "\n", + "# now we can run the benchmarks\n", + "benchmark_ibm.run(n_points_axis=100, ranges=[(3.14/4,), (0, 3.14/2)], plot=False)\n", + "benchmark_simulator.run(n_points_axis=100, ranges=[(3.14/4,), (0, 3.14/2)], plot=False, run_reference=False) #here we set reference=False because the reference is the same as in the ibm case" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# last step is to plot the results\n", + "\n", + "import matplotlib.pyplot as plt\n", + "fig, ax = plt.subplots()\n", + "benchmark_ibm.plot(main=True, reference=True, difference=False, ax=ax, labels_legend=['ibm', 'reference'], verbose=False)\n", + "benchmark_simulator.plot(main=True, reference=False, difference=False, ax=ax, labels_legend='simulator', verbose=False)\n", + "ax.legend()\n", + "ax.set_title('comparison QPU vs. simulator')\n", + "ax.set_ylabel('cost')\n", + "ax.set_xlabel('gamma')\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "OQ", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + }, + "vscode": { + "interpreter": { + "hash": "2374e833df7d90fe0779e245413a800a00448ba720136fdc7edf28b169de91eb" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/src/openqaoa-core/__init__.py b/src/openqaoa-core/__init__.py index 6eee62d87..4db56ed3a 100644 --- a/src/openqaoa-core/__init__.py +++ b/src/openqaoa-core/__init__.py @@ -1,3 +1,3 @@ -from .algorithms import QAOA, RQAOA +from .algorithms import QAOA, RQAOA, QAOABenchmark from .problems import QUBO from .backends import create_device diff --git a/src/openqaoa-core/algorithms/__init__.py b/src/openqaoa-core/algorithms/__init__.py index 3b4557928..069c4fd75 100644 --- a/src/openqaoa-core/algorithms/__init__.py +++ b/src/openqaoa-core/algorithms/__init__.py @@ -1,3 +1,3 @@ -from .qaoa import QAOA, QAOAResult +from .qaoa import QAOA, QAOAResult, QAOABenchmark from .rqaoa import RQAOA, RQAOAResult from .jobs.managed_job import AWSJobs diff --git a/src/openqaoa-core/algorithms/baseworkflow.py b/src/openqaoa-core/algorithms/baseworkflow.py index 702e0477e..15eb2dd16 100644 --- a/src/openqaoa-core/algorithms/baseworkflow.py +++ b/src/openqaoa-core/algorithms/baseworkflow.py @@ -27,12 +27,16 @@ DEVICE_ACCESS_OBJECT_MAPPER, ) + def check_compiled(func): def wrapper(self, args, kwargs): result = func(self, args, kwargs) if self.compiled: - raise ValueError("Cannot change properties of the object after compilation.") + raise ValueError( + "Cannot change properties of the object after compilation." + ) return result + return wrapper @@ -533,11 +537,17 @@ def dump( options = {options, *{"complex_to_string": True}} - project_id = self.header["project_id"] if not self.header["project_id"] is None else "None" + project_id = ( + self.header["project_id"] + if not self.header["project_id"] is None + else "None" + ) # get the full name if prepend_id == False and file_name == "": - raise ValueError("dump method missing argument: 'file_name'. Otherwise 'prepend_id' must be specified as True.") + raise ValueError( + "dump method missing argument: 'file_name'. Otherwise 'prepend_id' must be specified as True." + ) elif prepend_id == False: file = file_path + file_name elif file_name == "": diff --git a/src/openqaoa-core/algorithms/qaoa/__init__.py b/src/openqaoa-core/algorithms/qaoa/__init__.py index 435b000b4..7b5ba4677 100644 --- a/src/openqaoa-core/algorithms/qaoa/__init__.py +++ b/src/openqaoa-core/algorithms/qaoa/__init__.py @@ -1,2 +1,3 @@ from .qaoa_workflow import QAOA from .qaoa_result import QAOAResult +from .qaoa_benchmark import QAOABenchmark diff --git a/src/openqaoa-core/algorithms/qaoa/qaoa_benchmark.py b/src/openqaoa-core/algorithms/qaoa/qaoa_benchmark.py new file mode 100644 index 000000000..f99d12a42 --- /dev/null +++ b/src/openqaoa-core/algorithms/qaoa/qaoa_benchmark.py @@ -0,0 +1,656 @@ +import numpy as np +from math import log2 +from random import shuffle +from typing import List, Union +from matplotlib import pyplot as plt +from copy import deepcopy +from IPython.display import clear_output +import time + +from . import QAOA +from ...backends import create_device + + +class QAOABenchmark: + """ + Benchmark is a class that implements benchmarking for QAOA. + + Attributes + ---------- + qaoa : QAOA + The QAOA object to be benchmarked. + reference : QAOA + The reference QAOA object, which will be used to compare the results of the benchmarked QAOA object. + ranges : List[tuple] + The ranges of the variate parameters of the benchmarked QAOA object. + ranges_reference : List[tuple] + The ranges of the variate parameters of the reference QAOA object. + values : np.ndarray + The values of the benchmarked QAOA object. + values_reference : np.ndarray + The values of the reference QAOA object. + difference : np.ndarray + The difference between the values of the benchmarked QAOA object and the values of the reference QAOA object. + difference_mean : float + The mean of the difference between the values of the benchmarked QAOA object and the values of the reference QAOA object. + """ + + def __init__(self, qaoa: QAOA): + """ + Constructor for the Benchmark class. + + Parameters + ---------- + qaoa : QAOA + The QAOA object to be benchmarked. + """ + # check if the QAOA object inputted is valid and save it + assert isinstance(qaoa, QAOA), "`qaoa` must be an instance of QAOA" + assert qaoa.compiled, "`qaoa` must be compiled before benchmarking" + self.qaoa = qaoa + + # create a reference QAOA object, which will be used to compare the results of the benchmarked QAOA object + self.reference = QAOA() + self.reference.circuit_properties = ( + self.qaoa.circuit_properties + ) # copy the circuit properties of the benchmarked QAOA object + try: + self.reference.set_device( + create_device(location="local", name="analytical_simulator") + ) + self.reference.compile(self.qaoa.problem) + except: + self.reference.set_device( + create_device(location="local", name="vectorized") + ) + self.reference.compile(self.qaoa.problem) + print( + "Warning: vectorized simulator will be used for the reference, since the analytical simulator is not available for the circuit properties of the benchmarked QAOA object" + ) + + # initialize values and ranges + self.values = None + self.values_reference = None + self.ranges = None + self.ranges_reference = None + + @property + def difference(self): + "The difference between the values of the benchmarked QAOA object and the values of the reference QAOA object." + if self.values is None: + raise Exception( + "You must run the benchmark before calculating the difference" + ) + if self.values_reference is None: + raise Exception( + "You must run the reference before calculating the difference" + ) + if self.values.shape != self.values_reference.shape: + raise Exception( + "The ranges and number of points of the values and reference values must be the same" + ) + return self.values - self.values_reference + + @property + def difference_mean(self): + "The mean of the difference between the values of the benchmarked QAOA object and the values of the reference QAOA object." + return np.mean(self.difference) + + @property + def difference_std(self): + "The standard deviation of the difference between the values of the benchmarked QAOA object and the values of the reference QAOA object." + return np.std(self.difference) + + def __assert_run_inputs( + self, + n_points_axis: int, + ranges: List[tuple], + run_main: bool, + run_reference: bool, + plot: bool, + plot_difference: bool, + plot_options: dict, + verbose: bool, + ): + "Private method that checks the inputs of the run (and run_reference) method." + assert isinstance( + n_points_axis, int + ), "The number of points per axis must be an integer" + assert ( + isinstance(ranges, list) or ranges is None + ), "The ranges argument must be a list of tuples: (min, max) or (value,)" + assert len(ranges) == len( + self.qaoa.variate_params + ), "The number of ranges must be equal to the number of variate parameters, which is {}".format( + len(self.qaoa.variate_params) + ) + assert all( + [isinstance(r, tuple) or isinstance(r, list) for r in ranges] + ), "Each range must be a tuple: (min, max) or (value,)" + assert all( + [len(r) == 1 or len(r) == 2 for r in ranges] + ), "Each range must be a tuple of length 1 or 2: (min, max) or (value,)" + assert ( + len([r for r in ranges if len(r) == 2]) > 0 + ), "At least one range must be a tuple of length 2: (min, max)" + for bools in [ + "run_main", + "run_reference", + "plot", + "plot_difference", + "verbose", + ]: + assert isinstance( + eval(bools), bool + ), "The {} argument must be a boolean".format(bools) + assert ( + run_main or run_reference + ), "You must run the main or the reference or both" + assert isinstance( + plot_options, dict + ), "The plot_options argument must be a dictionary" + + def __print_expected_time(self, k, n_points): + "Private method that prints the expected remaining time to complete the benchmark." + print("\rPoint {} out of {}.".format(k + 1, n_points), end="") + if k == 0: + self.__start_time = time.time() + elif k > 0: + expected_time = (time.time() - self.__start_time) (n_points - k) / k + print( + " Expected remaining time to complete: {}, it will be finished at {}.".format( + time.strftime("%H:%M:%S", time.localtime(expected_time)), + time.strftime( + "%H:%M:%S", time.localtime(time.time() + expected_time) + ), + ), + end="", + ) + + def run( + self, + n_points_axis, + ranges: List[tuple], + run_main: bool = True, + run_reference: bool = True, + plot: bool = False, + plot_every: int = 1000, + plot_difference: bool = False, + plot_options: dict = {}, + verbose: bool = True, + ): + """ + Evaluates the QAOA circuit of the benchmarked (and the reference) QAOA object for a given number of points per axis and ranges. + + Parameters + ---------- + n_points_axis : int + The number of points per axis. It is recommended to use a number that is a power of 2 + 1: 2k+1 (where, k is an integer). + Using this number will ensure that the points evaluated are ordered such that there are k rounds, each round covers the whole range, + and each round has the double number of points (per axis) of the previous round. + ranges : List[tuple] + The sweep ranges of the parameters. The expected format is a list of tuples: (min, max) or (value,). One tuple per parameter. + If the length of the tuple is 1, the parameter will be fixed to the value of the tuple. + If the length of the tuple is 2, the parameter will be swept from the first value of the tuple to the second value of the tuple. + run_main : bool, optional + If True, the benchmarked QAOA object will be evaluated for the requested points. + The default is True. + run_reference : bool, optional + If True, the reference QAOA object will be evaluated for the same points. + The default is True. + plot : bool, optional + If True, the values will be plotted. + The default is False. + plot_every : int, optional + The number of evaluations after which the plot will be updated. + The default is 1000. + plot_difference : bool, optional + If True, the difference between the values of the benchmarked QAOA object and the values of the reference QAOA object will be plotted. + The default is False. + plot_options : dict, optional + The options for the plot. The expected format is a dictionary with the keys of the plot method of this class. + The default is {}. + verbose : bool, optional + If True, the expected remaining time to complete the benchmark will be printed. + The default is True. + """ + + # check the inputs + self.__assert_run_inputs( + n_points_axis, + ranges, + run_main, + run_reference, + plot, + plot_difference, + plot_options, + verbose, + ) + + # plot options + plot_options = {{"verbose": verbose}, plot_options} + + # save the ranges + if run_main: + self.ranges = ranges + if run_reference: + self.ranges_reference = ranges + + # get only the ranges that will be swept, and the number of parameters swept + ranges_to_use = [r for r in ranges if len(r) == 2] + n_params = len(ranges_to_use) + + # prepare the axes of the grid + axes = [ + np.linspace(r[0], r[1], n_points_axis) for r in ranges_to_use + ] # the starting and ending points are given by the ranges and the number of points is given by n_points_axis + + # function that given the values used for the parameters swept, returns the values of all the parameters in order + params = lambda new_params: [ + r[0] if len(r) == 1 else new_params.pop(0) for r in ranges + ] + + # copy the parameters object, which will be updated before each evaluation + params_obj = deepcopy(self.qaoa.variate_params) + + # initialize the array that will contain the values of the benchmarked QAOA object + if run_main: + self.values = np.zeros([n_points_axis for _ in range(n_params)]) + if run_reference: + self.values_reference = np.zeros([n_points_axis for _ in range(n_params)]) + + # initialize the arrays that will contain the difference between the values of the benchmarked QAOA object and the values of the reference QAOA object + both_qaoa = [ + qaoa + for qaoa, boolean in zip( + [self.qaoa, self.reference], [run_main, run_reference] + ) + if boolean + ] + both_values = [ + values + for values, boolean in zip( + [self.values, self.values_reference], [run_main, run_reference] + ) + if boolean + ] + both_strings = [ + string + for string, boolean in zip( + ["benchmark", "reference"], [run_main, run_reference] + ) + if boolean + ] + + # loop over the benchmarked QAOA object and the reference QAOA object (if requested) + for qaoa, values, string in zip(both_qaoa, both_values, both_strings): + + if verbose: + print(f"Running {string}.") + + # evaluate all the points in the grid, in the order provided by the function __ordered_points. We loop over the indices of the grid. + for k, i_point in enumerate(self.__ordered_points(n_params, n_points_axis)): + + if verbose: + self.__print_expected_time( + k, n_points_axisn_params + ) # print the expected remaining time, info for the user + + new_params = [ + axis[i] for axis, i in zip(axes, i_point) + ] # from the indices of the grid, get the values of the parameters that will be evaluated + params_obj.update_from_raw(params(new_params)) + values[tuple(i_point[::-1])] = qaoa.backend.expectation(params_obj) + + # plot every 'plot_every' points and at the end + if ( + ( + (k % plot_every == 0 and k > 0) + or k + 1 == n_points_axisn_params + ) + and plot + and string == "benchmark" + ): + clear_output(wait=True) + + if (not k + 1 == n_points_axisn_params) and n_params == 1: + plot_opt = {"marker": "o", "linestyle": "", "markersize": 3} + else: + plot_opt = {} + self.plot(plot_options=plot_opt, plot_options) + + if verbose: + print( + " " + ) # print a blank line, necessary because previous print: end="" + + # plot the reference if requested + if plot and run_reference: + self.plot(main=False, reference=True, difference=False, plot_options) + + # plot the difference if requested and the reference has been run + if plot_difference: + try: + self.plot(main=False, reference=False, difference=True, *plot_options) + except: + print( + "The difference cannot be plotted because the reference has not been run." + ) + + def plot( + self, + ax: plt.Axes = None, + title: Union[str, List[str]] = None, + labels: List[str] = None, + labels_legend: Union[str, List[str]] = None, + main: bool = True, + reference: bool = False, + difference: bool = False, + one_plot: bool = False, + # params_to_plot:List[int] = None, TODO: difficult because you need to specify the values of the other parameters + plot_options: dict = {}, + verbose: bool = True, + ): + """ + Plots the values of the benchmarked QAOA object and/or the values of the reference QAOA + object and/or the difference between the values of the benchmarked QAOA object and the values of the reference QAOA object. + + Parameters + ---------- + ax : plt.Axes, optional + The matplotlib Axes object to plot on. If None, a new figure will be created. + The default is None. + title : Union[str, List[str]], optional + The title of the plot. The expected format is a string or a list of strings: one for each plot. + If None, the title will be the default: `main plot`, `reference plot` or `difference plot`. + The default is None. + labels : List[str], optional + The labels of the axes. The expected format is a list of two strings: one for each axis. + If None, the labels will be the number of the parameters. + The default is None. + labels_legend : Union[str, List[str]], optional + The labels of the legend. The expected format is a string or a list of strings: one for each line. + It is only available if the sweep is over just one parameter, and if `one_plot` is True or `ax` is not None. + If None, the labels will be the default: `main`, `reference` or `difference`. + The default is None. + main : bool, optional + If True, the values of the benchmarked QAOA object will be plotted. + The default is True. + reference : bool, optional + If True, the values of the reference QAOA object will be plotted. + The default is False. + difference : bool, optional + If True, the difference between the values of the benchmarked QAOA object and the values of the reference QAOA object will be plotted. + The default is False. + one_plot : bool, optional + If True, the values of the benchmarked QAOA object, + the values of the reference QAOA object and the difference between the values of the benchmarked QAOA object + and the values of the reference QAOA object will be plotted in the same plot. + It is not available if the sweep is over more than one parameter. + The default is False. + plot_options : dict, optional + The options for the plot (plt.plot for one parameter, plt.pcolorfast for two parameters). + """ + + # check the inputs + assert ( + isinstance(ax, plt.Axes) or ax is None + ), "ax must be a matplotlib Axes or None" + assert ( + isinstance(labels, list) or labels is None + ), "labels must be a list of two strings or None" + assert ( + all([isinstance(label, str) for label in labels]) + if labels is not None + else True + ), "labels must be a list of two strings or None" + assert ( + len(labels) == 2 if labels is not None else True + ), "labels must be a list of two strings, one for each axis" + for plot in ["main", "reference", "difference"]: + assert isinstance(eval(plot), bool), plot + " must be a boolean" + assert ( + main or reference or difference + ), "You must specify at least one of the main, reference or difference plots" + assert isinstance(plot_options, dict), "plot_options must be a dictionary" + + # if labels_legend is not a list or tuple, make it a list + if not isinstance(labels_legend, (list, tuple)): + labels_legend = [labels_legend for _ in range(3)] + + # create a dictionary where the keys are the three possible plots and the values are a tuple + # with the boolean (which says if the plot is requested) and the values to plot + values = { + "main": (main, self.values if main else None), + "reference": (reference, self.values_reference if reference else None), + "difference": (difference, self.difference if difference else None), + } + + # create the figure and axes if the axis are not provided + ax_input = ax # save the input axis + nrows = 1 + if ax_input is None: + nrows = ( + 1 if one_plot else sum([1 if values[key][0] else 0 for key in values]) + ) + fig, ax = plt.subplots(nrows=nrows, ncols=1, figsize=(6.5, 5 nrows)) + + # plot the requested plots + count_sp = 0 # counter of subplots + for key in values: + if values[key][0]: # skip the plot if it is not requested + + # raise an exception if the values for a plo requested are not available + if values[key][1] is None: + raise Exception( + "You must run the benchmark before plotting the results, there are no values for the " + + key + + " plot" + ) + + # get the ranges to use, use the reference ranges if the key is "reference", otherwise use the main ranges + ranges = self.ranges if key != "reference" else self.ranges_reference + ranges_to_use = [r for r in ranges if len(r) == 2] + n_params = len(ranges_to_use) + + # create the axes, the range is provided by the ranges_to_use and the number of values is provided by the values[key][1].shape + axes = [ + np.linspace(r[0], r[1], values[key][1].shape[i]) + for i, r in enumerate(ranges_to_use) + ] + + # skip the plot if there are more than two parameters to plot, and print a warning + if n_params > 2: + print( + "Only 1 or 2 parameters can be plotted using this method. You are trying to plot " + + str(n_params) + + " parameters. You can use the argument params_to_plot to specify which parameters to plot." + ) + break + # if verbose, print the parameters used for the plot + elif verbose: + print( + "Plotting the " + key + " plot with the following parameters:" + ) + for i, r in enumerate(ranges): + if len(r) == 2: + print( + "\tParameter " + + str(i) + + ": " + + str(r[0]) + + " to " + + str(r[1]) + + ", with " + + str(values[key][1].shape[i]) + + " values" + ) + else: + print("\tParameter " + str(i) + ": " + str(r[0])) + + ## plot the values + + # if we are plotting only one plot, use the same axes, otherwise different axes for each subplot + axis = ax if nrows == 1 else ax[count_sp] + + # set the labels and plot the values + if n_params == 1: + axis.set_xlabel( + "Parameter {}".format( + [i for i, r in enumerate(ranges) if len(r) == 2][0] + ) + ) + axis.set_ylabel("Expectation value") + + label_legend = ( + labels_legend[count_sp] + if labels_legend[count_sp] is not None + else key + ) + axis.plot(axes, values[key][1], label=label_legend, plot_options) + else: + if one_plot: + raise Exception( + "For two parameters, you must specify one_plot=False" + ) + + axis.set_xlabel( + "Parameter {}".format( + [i for i, r in enumerate(ranges) if len(r) == 2][0] + ) + ) + axis.set_ylabel( + "Parameter {}".format( + [i for i, r in enumerate(ranges) if len(r) == 2][1] + ) + ) + plot = axis.pcolorfast(axes, values[key][1], plot_options) + _ = plt.colorbar(plot) + + # set the title + if not one_plot and title is None: + axis.set_title(key + " plot") + elif not title is None: + if not isinstance(title, list) and not isinstance(title, tuple): + axis.set_title(title) + else: + if len(title) < nrows: + raise Exception( + "If title is a list or tuple, it must have the same length as the number of plots" + ) + axis.set_title(title[count_sp]) + + # replace the labels if specified + if labels is not None: + axis.set_xlabel(labels[0]) + axis.set_ylabel(labels[1]) + + # increase the counter for the axes + count_sp += 1 + + # add legend if one_plot + if one_plot: + ax.legend() + + # show the figure if the axis were not provided + if ax_input is None: + if nrows > 1: + fig.subplots_adjust(hspace=0.3) # add some space between the subplots + plt.show() + plt.close(fig) + + @staticmethod + def __ordered_points(n_params, n_points_axis): + """ + This method creates a grid of points for each parameter, and then creates all the combinations of these points. + The points are ordered such that we evaluate the grid by rounds, the first round the resolution is very low, + next rounds the resolution is higher, + and also, every n_points = 2n_params one point is used at each round, and the points are shuffled. + + It works very well for n_points_axis = 2k + 1 (where k is any integer), but it also works for other values. + + Parameters + ---------- + n_params : int + The number of parameters to sweep. + n_points_axis : int + The number of points per axis. + + Returns + ------- + points : list + The list of points to evaluate in order. + """ + + assert ( + isinstance(n_params, int) and n_params > 0 + ), "The number of parameters must be an integer, and greater than 0" + assert ( + isinstance(n_points_axis, int) and n_points_axis > 3 + ), "The number of points per axis must be an integer, and greater than 3" + + ## we create a grid of points for each axis + axis_points = list(range(n_points_axis)) + + ## first we create all the points to evaluate and we order them such that every n_points = 2n_params one point is used at each round, and the points are shuffled + + # we separate the points in two lists, [0, 2, 4, ...] and [1, 3, 5, ...] + axis_points_separated = ( + [[i] for i in axis_points[::2]], + [[i] for i in axis_points[1::2]], + ) + + # we create a list of lists, where each list will tell how to combine the two lists of points to create the points for each round + zero_one = [[0], [1]] + order_list = zero_one + for _ in range(n_params - 1): + order_list = [order + y for order in order_list for y in zero_one] + + # the variable points will be a list of lists, where each list is a round of points to evaluate + points = [ + axis_points_separated[order[0]] for order in order_list + ] # we start with the first axis + + # we create the points for each round by combining the points of the previous round with the points of the next axis + for k in range(1, n_params): + for x in range(len(points)): + points[x] = [ + point_i + point_j + for point_i in points[x] + for point_j in axis_points_separated[order_list[x][k]] + ] + + # we shuffle the points at each round + for i in range(len(points)): + shuffle(points[i]) + + # the final list of points to evaluate is a concatenation of all the rounds + ordered_points = [] + for round in range(len(points)): + ordered_points += points[round] + + ## then we reorder the points such that the first round we have a grid of points with very low resolution, and then at each round we increase the resolution + values = np.zeros([n_points_axis for _ in range(n_params)]) + + used_axis_points = [] + reordered_points = [] + for round in range(1, int(log2(len(axis_points) - 1)) + 1): + new = [ + k + for k in axis_points[:: (len(axis_points) - 1) // 2round] + if k not in used_axis_points + ] + used_axis_points += new + + points_ = ordered_points.copy() + for point in ordered_points: + if ( + all([i in used_axis_points for i in point]) + and values[tuple(point)] == 0 + ): + values[tuple(point)] = round + reordered_points.append(points_.pop(points_.index(point))) + + ordered_points = points_ # there are less points now + + return reordered_points diff --git a/src/openqaoa-core/algorithms/qaoa/qaoa_workflow.py b/src/openqaoa-core/algorithms/qaoa/qaoa_workflow.py index 4d86be5d7..73a0173d4 100644 --- a/src/openqaoa-core/algorithms/qaoa/qaoa_workflow.py +++ b/src/openqaoa-core/algorithms/qaoa/qaoa_workflow.py @@ -1,8 +1,13 @@ -from typing import List, Callable, Optional -import requests +from typing import List, Callable, Optional, Union, Dict +from copy import deepcopy +import numpy as np + from .qaoa_result import QAOAResult from ..workflow_properties import CircuitProperties from ..baseworkflow import Workflow, check_compiled +from ...backends.basebackend import QAOABaseBackendStatevector +from ...backends import QAOABackendAnalyticalSimulator +from ...backends.cost_function import cost_function from ...backends.devices_core import DeviceLocal, DeviceBase from ...backends.qaoa_backend import get_qaoa_backend from ...problems import QUBO @@ -11,6 +16,9 @@ QAOADescriptor, create_qaoa_variational_params, ) +from ...qaoa_components.variational_parameters.variational_baseparams import ( + QAOAVariationalBaseParams, +) from ...utilities import get_mixer_hamiltonian, generate_timestamp from ...optimizers.qaoa_optimizer import get_optimizer @@ -305,6 +313,101 @@ def optimize(self, verbose=False): print(f"optimization completed.") return + def evaluate_circuit( + self, + params: Union[List[float], Dict[str, List[float]], QAOAVariationalBaseParams], + ): + """ + A method to evaluate the QAOA circuit at a given set of parameters + + Parameters + ---------- + params: list or dict or QAOAVariationalBaseParams or None + List of parameters or dictionary of parameters. Which will be used to evaluate the QAOA circuit. + If None, the variational parameters of the QAOA object will be used. + + Returns + ------- + result: dict + A dictionary containing the results of the evaluation: + - "expectation": the expectation value of the cost Hamiltonian + - "uncertainty": the uncertainty of the expectation value of the cost Hamiltonian + - "measurement_results": either the state of the QAOA circuit output (if the QAOA circuit is + evaluated on a state simulator) or the counts of the QAOA circuit output + (if the QAOA circuit is evaluated on a QPU or shot-based simulator) + """ + + # before evaluating the circuit we check that the QAOA object has been compiled + if self.compiled == False: + raise ValueError("Please compile the QAOA before optimizing it!") + + ## Check the type of the input parameters and save them as a QAOAVariationalBaseParams object at the variable `params_obj` + + # if the parameters are passed as a dictionary we copy and update the variational parameters of the QAOA object + if isinstance(params, dict): + params_obj = deepcopy(self.variate_params) + # we check that the dictionary contains all the parameters of the QAOA object that are not empty + for key, value in params_obj.asdict().items(): + if value.size > 0: + assert ( + key in params.keys() + ), f"The parameter `{key}` is missing from the input dictionary" + params_obj.update_from_dict(params) + + # if the parameters are passed as a list we copy and update the variational parameters of the QAOA object + elif isinstance(params, list) or isinstance(params, np.ndarray): + assert len(params) == len( + self.variate_params + ), "The number of parameters does not match the number of parameters in the QAOA circuit" + params_obj = deepcopy(self.variate_params) + params_obj.update_from_raw(params) + + # if the parameters are passed as a QAOAVariationalBaseParams object we just take it as it is + elif isinstance(params, QAOAVariationalBaseParams): + # check whether the input params object is supported for circuit evaluation + assert ( + len(self.variate_params.mixer_1q_angles) == len(params.mixer_1q_angles) + and len(self.variate_params.mixer_2q_angles) + == len(self.variate_params.mixer_2q_angles) + and len(self.variate_params.cost_1q_angles) + == len(self.variate_params.cost_1q_angles) + and len(self.variate_params.cost_2q_angles) + == len(self.variate_params.cost_2q_angles) + ), "Specify a supported params object" + params_obj = params + + # if the parameters are passed in a different format, we raise an error + else: + raise TypeError( + f"The input params must be a list or a dictionary. Instead, received {type(params)}" + ) + + ## Evaluate the QAOA circuit and return the results + output_dict = { + "cost": None, + "uncertainty": None, + "measurement_results": None, + } + # if the backend is the analytical simulator, we just return the expectation value of the cost Hamiltonian + if isinstance(self.backend, QAOABackendAnalyticalSimulator): + output_dict.update({"cost": self.backend.expectation(params_obj)[0]}) + + else: + cost, uncertainty = self.backend.expectation_w_uncertainty(params_obj) + measurement_results = ( + self.backend.measurement_outcomes + if isinstance(self.backend.measurement_outcomes, dict) + else self.backend.measurement_outcomes.tolist() + ) + output_dict.update( + { + "cost": cost, + "uncertainty": uncertainty, + "measurement_results": measurement_results, + } + ) + return output_dict + def _serializable_dict( self, complex_to_string: bool = False, intermediate_mesurements: bool = True ): diff --git a/src/openqaoa-core/backends/qaoa_device.py b/src/openqaoa-core/backends/qaoa_device.py index eb49640bb..2b27aa453 100644 --- a/src/openqaoa-core/backends/qaoa_device.py +++ b/src/openqaoa-core/backends/qaoa_device.py @@ -6,6 +6,7 @@ from openqaoa_pyquil.backends import DevicePyquil from openqaoa_azure.backends import DeviceAzure + def device_class_arg_mapper( device_class: DeviceBase, hub: str = None, @@ -56,6 +57,7 @@ def device_class_arg_mapper( } return final_device_kwargs + LOCATION_MAPPER = { "ibmq": DeviceQiskit, "qcs": DevicePyquil, @@ -64,6 +66,7 @@ def device_class_arg_mapper( "azure": DeviceAzure, } + def create_device(location: str, name: str, kwargs): """ This function returns an instance of the appropriate device class. @@ -85,8 +88,10 @@ def create_device(location: str, name: str, kwargs): """ location = location.lower() if location not in LOCATION_MAPPER: - raise ValueError(f"Invalid device location, Choose from: {list(LOCATION_MAPPER.keys())}") - + raise ValueError( + f"Invalid device location, Choose from: {list(LOCATION_MAPPER.keys())}" + ) + device_class = LOCATION_MAPPER[location] return device_class(device_name=name, kwargs) diff --git a/src/openqaoa-core/backends/qaoa_vectorized.py b/src/openqaoa-core/backends/qaoa_vectorized.py index 7ee3207bf..7cf6683a6 100644 --- a/src/openqaoa-core/backends/qaoa_vectorized.py +++ b/src/openqaoa-core/backends/qaoa_vectorized.py @@ -890,7 +890,7 @@ def expectation(self, params: Type[QAOAVariationalBaseParams]) -> float: # Reshape wavefunction wavefn_ = self.wavefn - self.measurement_outcomes = self.wavefn.flatten() + self.measurement_outcomes = wavefn_.flatten() # Compute the expectation value and its standard deviation ham_wf = self.ham_op * wavefn_ @@ -922,7 +922,7 @@ def expectation_w_uncertainty( # Reshape wavefunction wavefn_ = self.wavefn - self.measurement_outcomes = self.wavefn.flatten() + self.measurement_outcomes = wavefn_.flatten() # Compute the expectation value and its standard deviation ham_wf = self.ham_op * wavefn_ diff --git a/src/openqaoa-core/qaoa_components/variational_parameters/fourierparams.py b/src/openqaoa-core/qaoa_components/variational_parameters/fourierparams.py index 70586bfb8..69614dc94 100644 --- a/src/openqaoa-core/qaoa_components/variational_parameters/fourierparams.py +++ b/src/openqaoa-core/qaoa_components/variational_parameters/fourierparams.py @@ -57,8 +57,8 @@ def __init__( super().__init__(qaoa_descriptor) assert q is not None, f"Depth q for {type(self).__name__} must be specified" self.q = q - self.u = u self.v = v + self.u = u self.betas = dct(self.v, n=self.p) self.gammas = dst(self.u, n=self.p) diff --git a/src/openqaoa-core/qaoa_components/variational_parameters/variational_baseparams.py b/src/openqaoa-core/qaoa_components/variational_parameters/variational_baseparams.py index d1d0f925d..86278a19e 100644 --- a/src/openqaoa-core/qaoa_components/variational_parameters/variational_baseparams.py +++ b/src/openqaoa-core/qaoa_components/variational_parameters/variational_baseparams.py @@ -132,6 +132,55 @@ def raw(self) -> np.ndarray: """ raise NotImplementedError() + def update_from_dict(self, new_values: dict): + """ + Update all the parameters from a dictionary. + + The input has the same format as the output of ``self.asdict()``. + + Parameters + ---------- + new_values: `dict` + A dictionary with the new parameters. Must have the same keys as + the output of ``self.asdict()``. + + """ + + assert isinstance(new_values, dict), f"Expected dict, got {type(new_values)}" + + for key, value in new_values.items(): + if key not in self.asdict().keys(): + raise KeyError( + f"'{key}' not in {self.__class__.__name__}, expected keys: {list(self.asdict().keys())}" + ) + else: + if getattr(self, key).shape != np.array(value).shape: + raise ValueError( + f"Shape of '{key}' does not match. Expected shape {getattr(self, key).shape}, got {np.array(value).shape}." + ) + + raw_params = [] + for key, value in self.asdict().items(): + if key in new_values.keys(): + raw_params += list(np.array(new_values[key]).flatten()) + else: + raw_params += list(np.array(value).flatten()) + + self.update_from_raw(raw_params) + + def asdict(self) -> dict: + """ + Return the parameters as a dictionary. + + Returns + ------- + dict: + The parameters as a dictionary. Has the same output format as the + expected input of ``self.update_from_dict``. + + """ + return {k[2:]: v for k, v in self.__dict__.items() if k[0:2] == "__"} + @classmethod def linear_ramp_from_hamiltonian( cls, qaoa_descriptor: QAOADescriptor, time: float = None	QAOA benchmark Benchmark object for qaoa - [x] Qaoa Benchmark object - [x] decide the import - [x] Create reference object - [x] Run method for benchmarked object and reference - [x] When running the benchmark it evaluates the points such that at each round there is more resolution - [x] Plot method for results - [x] Difference property between benchmarked values and reference - [x] Mean difference property - [x] Notebook - [x] Tests For examples read notebook 14 (new), here is a simple example: ``` from openqaoa import QAOA, create_device, QAOABenchmark from openqaoa.problems import QUBO qaoa = QAOA() qaoa.set_device(create_device(name='qiskit.shot_simulator', location='local')) qaoa.compile(QUBO.random_instance(5)) benchmark = QAOABenchmark(qaoa) benchmark.run(n_points_axis=2**4, ranges=[(0,1),(0,1)], plot=True) ```		2023-02-27T12:12:58	[]	[]
trustpilot/python-trustpilot	trustpilot__python-trustpilot-17	31df744e23ae64cc76736f4abf71d7ba5950862e	diff --git a/HISTORY.md b/HISTORY.md index 8a646c9..805aed4 100644 --- a/HISTORY.md +++ b/HISTORY.md @@ -156,3 +156,10 @@ DELETED DO NOT USE\!\! ### 8.1.0 (2020-04-15) - reintroduce cli script + +### 9.0.0 (2020-04-29) + +- username/passwrd are now optional +- dot-env support in cli +- `patch` command exposed in cli +- output format can now be `raw` or `json`, (default=json) diff --git a/README.md b/README.md index 2faf6c0..b922b79 100644 --- a/README.md +++ b/README.md @@ -10,7 +10,8 @@ Python HTTP client for [Trustpilot](https://developers.trustpilot.com/). - GET, POST, PUT, DELETE, HEAD, OPTIONS and PATCH methods are exposed on module level - Implements session factory and default singleton session - Provides a simple hook system -- CLI tool with basic HTTP commands +- [CLI](#CLI) tool with basic HTTP commands + ## Installation @@ -20,158 +21,49 @@ Install the package from [PyPI](http://pypi.python.org/pypi/) using [pip](https: pip install trustpilot ``` -## Getting Started - -This client is using the [Requests](http://docs.python-requests.org/en/master/) library. Responses are standard [`requests.Response`](http://docs.python-requests.org/en/master/api/#requests.Response) objects. You can use it as a factory or as a singleton. +## Usage -### Use the singleton session - -Use the built-in `default session` to instantiate a globally accessible session. +_(for full usage documentation checkout [docs](https://github.com/trustpilot/python-trustpilot/blob/master/docs/README.md))_ ```python from trustpilot import client client.default_session.setup( - api_host="https://api.trustpilot.com", - api_version="v1", - api_key="YOUR_API_KEY", - api_secret="YOUR_API_SECRET", - username="YOUR_TRUSTPILOT_BUSINESS_USERNAME", - password="YOUR_TRUSTPILOT_BUSINESS_PASSWORD" + api_host="https://api.trustpilot.com" + api_key="YOUR_API_KEY" ) response = client.get("/foo/bar") +status_code = response.status_code ``` You can rely on environment variables for the setup of sessions so ```bash $ env -TRUSTPILOT_API_HOST=foobar.com -TRUSTPILOT_API_VERSION=v1 +TRUSTPILOT_API_HOST=https://api.trustpilot.com TRUSTPILOT_API_KEY=foo TRUSTPILOT_API_SECRET=bar -TRUSTPILOT_USERNAME=username -TRUSTPILOT_PASSWORD=password ``` -Will work with the implicit `default_session` and the `TrustpilotSession.setup` method. - -```python -from trustpilot import client -client.get("/foo/bar") -``` - -### Instantiate your own session - -You can create as many sessions as you like, as long as you pass them around yourself. - -```python -from trustpilot import client -session = client.TrustpilotSession( - api_host="https://api.trustpilot.com", - api_version="v1", - api_key="YOUR_API_KEY", - api_secret="YOUR_API_SECRET", - username="YOUR_TRUSTPILOT_BUSINESS_USERNAME", - password="YOUR_TRUSTPILOT_BUSINESS_PASSWORD" -) -response = session.get("/foo/bar") -``` - -## Async client - -Since version `3.0.0` you are able to use the `async_client` for `asyncio` usecases. - -To use the default `async_client` session, using `env-vars` for settings, import is as following: - -```python -import asyncio -from trustpilot import async_client -loop = asyncio.get_event_loop() - -async def get_response(): - response = await async_client.get('/foo/bar') - response_json = await response.json() - -loop.run_until_complete(get_response()) -``` - -Or instantiate the session yourself with: - -```python -import asyncio -from trustpilot import async_client -loop = asyncio.get_event_loop() - -session = async_client.TrustpilotAsyncSession( - api_host="https://api.trustpilot.com", - api_version="v1", - api_key="YOUR_API_KEY", - api_secret="YOUR_API_SECRET", - username="YOUR_TRUSTPILOT_BUSINESS_USERNAME", - password="YOUR_TRUSTPILOT_BUSINESS_PASSWORD" -) +### CLI -async def get_response(): - response = await session.get('/foo/bar') - response_json = await response.json() - -loop.run_until_complete(get_response()) -``` - -### Advanced async usage - -The async client uses an _asynccontextmanager_ under the hood to perform the supported request methods. -A side effect of the implementation is that it buffers up all the content before returning it to the calling scope. - -You can get around this limitation by using the _asynccontextmanager_ directly like in the next example. - -Example with stream reading the raw aiohttp response object: - -``` -import asyncio -from trustpilot import async_client -loop = asyncio.get_event_loop() - -async def get_response(): - async with session.request_context_manager('get', "/v1/foo/bar") as resp: - result = True - while True: - chunk = resp.content.read(8) - if not chunk: - break - result += chunk - return result - -loop.run_until_complete(get_response()) -``` - -## Setup User Agent - -A UserAgent header can be specified in two ways: - -1. By populating the `TRUSTPILOT_USER_AGENT` environment var -2. By creating your own (async/sync)-client instance, or calling `setup` on the `default_session`, and supplying the kwargs `user_agent=foobar` - -If no user-agent is given it will autopopulate using the function in `get_user_agent` function in [auth.py](./trustpilot/auth.py) - -## CLI - -A command line tool `trustpilot_api_client` is bundled with the module. To invoke it, use: +The `trustpilot_api_client` command is bundled with the install ```bash Usage: trustpilot_api_client [OPTIONS] COMMAND [ARGS]... Options: - --host TEXT host name - --version TEST api version - --key TEXT api key - --secret TEXT api secret - --token_issuer_host TEXT token issuer host name - --username TEXT Trustpilot username - --password TEXT Trustpilot password - -c TEXT json config file name - -v, --verbose verbosity level - --help Show this message and exit. + --host TEXT Host name + --version TEXT Api version + --key TEXT Api key + --secret TEXT Api secret + --token_issuer_host TEXT Token issuer host name + --username TEXT Trustpilot username + --password TEXT Trustpilot password + -c, --config FILENAME Json config file name + -e, --env FILENAME Dot env file + -of, --outputformat [json\|raw] Output format, default=json + -v, --verbose Verbosity level + --help Show this message and exit. Commands: create-access-token Get an access token @@ -181,15 +73,36 @@ Commands: put Send a PUT request with specified data ``` -In order to use the -c option please supply the filename of a JSON in the following format: +You can also supply the variables with: + +--config/-c : As JSON config file in the following format: ```json { "TRUSTPILOT_API_HOST": "foo", - "TRUSTPILOT_API_VERSION": "v1", "TRUSTPILOT_API_KEY": "bar", "TRUSTPILOT_API_SECRET": "baz", + "TRUSTPILOT_API_VERSION": "v1", "TRUSTPILOT_USERNAME": "username", "TRUSTPILOT_PASSWORD": "password" } ``` + +or --env/-e : As DotEnv config file in the following format: + +```ini +TRUSTPILOT_API_HOST=foo +TRUSTPILOT_API_KEY=bar +TRUSTPILOT_API_SECRET=baz +TRUSTPILOT_API_VERSION=v1 +TRUSTPILOT_USERNAME=username +TRUSTPILOT_PASSWORD=password +``` + +## Changelog + +see [HISTORY.md](https://github.com/trustpilot/python-trustpilot/blob/master/HISTORY.md) + +## Issues / DEV + +Report issues [here](https://github.com/trustpilot/python-trustpilot/issues) and we welcome collaboration through PullRequests :-) diff --git a/docs/README.md b/docs/README.md new file mode 100644 index 0000000..19c769c --- /dev/null +++ b/docs/README.md @@ -0,0 +1,208 @@ +# Usage + +Complete instructions for how to use the Trustpilot module and `trustpilot_api_client` commandline tool. + +## Installation + +Install the package from [PyPI](http://pypi.python.org/pypi/) using [pip](https://pip.pypa.io/): + +``` +pip install trustpilot +``` + +## Getting Started + +This client is using the [Requests](http://docs.python-requests.org/en/master/) library. Responses are standard [`requests.Response`](http://docs.python-requests.org/en/master/api/#requests.Response) objects. You can use it as a factory or as a singleton. + +### Use the singleton session + +Use the built-in `default session` to instantiate a globally accessible session. + +```python +from trustpilot import client +client.default_session.setup( + api_host="https://api.trustpilot.com", # optional, default: https://api.tp-staging.com + api_key="YOUR_API_KEY", # required + api_secret="YOUR_API_SECRET", # optional + api_version="v1", # optional, default: v1 + username="YOUR_TRUSTPILOT_BUSINESS_USERNAME", # optional + password="YOUR_TRUSTPILOT_BUSINESS_PASSWORD" # optional +) +response = client.get("/foo/bar") +status_code = response.status_code + +``` + +You can rely on environment variables for the setup of sessions so + +```bash +$ env +TRUSTPILOT_API_HOST=https://api.trustpilot.com +TRUSTPILOT_API_KEY=foo +TRUSTPILOT_API_SECRET=bar +``` + +> optionally supply: +> ```ini +> TRUSTPILOT_API_VERSION=v1 +> TRUSTPILOT_USERNAME=username +> TRUSTPILOT_PASSWORD=password +> ``` + +Will work with the implicit `default_session` and the `TrustpilotSession.setup` method. + +```python +from trustpilot import client +client.get("/foo/bar") +``` + +### Instantiate your own session + +You can create as many sessions as you like, as long as you pass them around yourself. + +```python +from trustpilot import client +session = client.TrustpilotSession( + api_host="https://api.trustpilot.com", + api_key="YOUR_API_KEY", + api_secret="YOUR_API_SECRET", + api_version="v1", + username="YOUR_TRUSTPILOT_BUSINESS_USERNAME", + password="YOUR_TRUSTPILOT_BUSINESS_PASSWORD" +) +response = session.get("/foo/bar") +``` + +## Async client + +Since version `3.0.0` you are able to use the `async_client` for `asyncio` usecases. +The `async_client` uses [aiohttp](https://docs.aiohttp.org/en/stable/) read here for the signarue of its [request](https://docs.aiohttp.org/en/stable/client_reference.html#aiohttp.ClientSession.request) and [response](https://docs.aiohttp.org/en/stable/client_reference.html#response-object) + +To use the default `async_client` session, using `env-vars` for settings, import is as following: + +```python +import asyncio +from trustpilot import async_client +loop = asyncio.get_event_loop() + +async def get_response(): + response = await async_client.get('/foo/bar') + response_json = await response.json() + status_code = response.status + +loop.run_until_complete(get_response()) +``` + +Or instantiate the session yourself with: + +```python +import asyncio +from trustpilot import async_client +loop = asyncio.get_event_loop() + +session = async_client.TrustpilotAsyncSession( + api_host="https://api.trustpilot.com", # optional, default: https://api.tp-staging.com + api_key="YOUR_API_KEY", # required + api_secret="YOUR_API_SECRET", # optional + api_version="v1", # optional + username="YOUR_TRUSTPILOT_BUSINESS_USERNAME", # optional + password="YOUR_TRUSTPILOT_BUSINESS_PASSWORD" # optional +) + +async def get_response(): + response = await session.get('/foo/bar') + response_json = await response.json() + +loop.run_until_complete(get_response()) +``` + +### Advanced async usage + +The async client uses an _asynccontextmanager_ under the hood to perform the supported request methods. +A side effect of the implementation is that it buffers up all the content before returning it to the calling scope. + +You can get around this limitation by using the _asynccontextmanager_ directly like in the next example. + +Example with stream reading the raw aiohttp response object: + +``` +import asyncio +from trustpilot import async_client +loop = asyncio.get_event_loop() + +async def get_response(): + async with session.request_context_manager('get', "/v1/foo/bar") as resp: + result = True + while True: + chunk = resp.content.read(8) + if not chunk: + break + result += chunk + return result + +loop.run_until_complete(get_response()) +``` + +## Setup User Agent + +A UserAgent header can be specified in two ways: + +1. By populating the `TRUSTPILOT_USER_AGENT` environment var +2. By creating your own (async/sync)-client instance, or calling `setup` on the `default_session`, and supplying the kwargs `user_agent=foobar` + +If no user-agent is given it will autopopulate using the function in `get_user_agent` function in [auth.py](./trustpilot/auth.py) + +## CLI + +The `trustpilot_api_client` command is bundled with the install + +```bash +Usage: trustpilot_api_client [OPTIONS] COMMAND [ARGS]... + +Options: + --host TEXT Host name + --version TEXT Api version + --key TEXT Api key + --secret TEXT Api secret + --token_issuer_host TEXT Token issuer host name + --username TEXT Trustpilot username + --password TEXT Trustpilot password + -c, --config FILENAME Json config file name + -e, --env FILENAME Dot env file + -of, --outputformat [json\|raw] Output format, default=json + -v, --verbose Verbosity level + --help Show this message and exit. + +Commands: + create-access-token Get an access token + delete Send a DELETE request + get Send a GET request + post Send a POST request with specified data + put Send a PUT request with specified data +``` + +You can also supply the variables through with: + +--config/-c : As JSON config file in the following format: + +```json +{ + "TRUSTPILOT_API_HOST": "foo", + "TRUSTPILOT_API_VERSION": "v1", + "TRUSTPILOT_API_KEY": "bar", + "TRUSTPILOT_API_SECRET": "baz", + "TRUSTPILOT_USERNAME": "username", + "TRUSTPILOT_PASSWORD": "password" +} +``` + +--env/-e : As DotEnv config file in the following format: + +```ini +TRUSTPILOT_API_HOST=foo +TRUSTPILOT_API_VERSION=v1 +TRUSTPILOT_API_KEY=bar +TRUSTPILOT_API_SECRET=baz +TRUSTPILOT_USERNAME=username +TRUSTPILOT_PASSWORD=password +``` \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 0b71920..72d7541 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "trustpilot" -version = "8.1.0" +version = "9.0.0" description = "trustpilot api client including cli tool" authors = [ "Johannes Valbjørn <[email protected]>", diff --git a/trustpilot/async_client.py b/trustpilot/async_client.py index 89e5046..6b4927a 100644 --- a/trustpilot/async_client.py +++ b/trustpilot/async_client.py @@ -19,8 +19,8 @@ def __init__(self, args, kwargs): def setup( self, api_host=None, - api_version=None, api_key=None, + api_version=None, api_secret=None, username=None, password=None, @@ -31,10 +31,20 @@ def setup( kwargs ): - self.api_version = api_version or environ.get("TRUSTPILOT_API_VERSION", "v1") self.api_host = api_host or environ.get( "TRUSTPILOT_API_HOST", "https://api.trustpilot.com" ) + try: + self.api_key = api_key or environ["TRUSTPILOT_API_KEY"] + self.api_secret = api_secret or environ.get("TRUSTPILOT_API_SECRET", "") + self.username = username or environ.get("TRUSTPILOT_USERNAME") + self.password = password or environ.get("TRUSTPILOT_PASSWORD") + self.access_token = access_token + except KeyError as e: + logger.debug("Not auth setup, missing env-var or setup for {}".format(e)) + + self.api_version = api_version or environ.get("TRUSTPILOT_API_VERSION", "v1") + self.token_issuer_host = token_issuer_host or self.api_host self.access_token = access_token self.token_issuer_path = token_issuer_path or environ.get( @@ -51,15 +61,6 @@ def setup( "'{}' is not a valid api_host url".format(api_host) ) - try: - self.api_key = api_key or environ["TRUSTPILOT_API_KEY"] - self.api_secret = api_secret or environ.get("TRUSTPILOT_API_SECRET", "") - self.username = username or environ.get("TRUSTPILOT_USERNAME") - self.password = password or environ.get("TRUSTPILOT_PASSWORD") - self.access_token = access_token - except KeyError as e: - logger.debug("Not auth setup, missing env-var or setup for {}".format(e)) - return self async def get_request_auth_headers(self): diff --git a/trustpilot/auth.py b/trustpilot/auth.py index 0048f5f..a2d1d4e 100644 --- a/trustpilot/auth.py +++ b/trustpilot/auth.py @@ -22,11 +22,15 @@ def create_access_token_request_params(session): api_version=session.api_version.rstrip("/"), token_issuer_path=session.token_issuer_path, ) - data = { - "grant_type": "password", - "username": session.username, - "password": session.password, - } + + data = {} + + if session.username and session.password: + data = { + "grant_type": "password", + "username": session.username, + "password": session.password, + } headers = { "Authorization": "Basic {}".format( diff --git a/trustpilot/cli.py b/trustpilot/cli.py index 5c8efda..093faa2 100644 --- a/trustpilot/cli.py +++ b/trustpilot/cli.py @@ -19,36 +19,62 @@ def get_verbosity(ctx): return ctx.meta.get("trustpilot.verbosity", 0) [email protected]_context +def get_output_format(ctx): + output_format = ctx.meta.get("trustpilot.outputformat", "json") or "json" + return output_format + + def format_response(response): content = response.text - try: - content = response.json() - except ValueError: - pass - output = OrderedDict() - output["url"] = response.url - output["status"] = response.status_code - if get_verbosity(): - headers = response.headers - output["headers"] = OrderedDict((k, headers[k]) for k in headers) - output["content"] = content - return json.dumps(output, indent=2) + output_format = get_output_format() + + if output_format == "raw": + lines = [] + lines.extend(["url", response.url, "\n"]) + lines.extend(["status", str(response.status_code), "\n"]) + if get_verbosity(): + lines.extend( + [ + e + for t in [ + (f"headers.{key}", str(value), "\n") + for key, value in response.headers.items() + ] + for e in t + ] + ) + lines.extend(["content", content]) + return "\n".join(lines) + elif output_format == "json": + try: + content = response.json() + except ValueError: + pass + output = OrderedDict() + output["url"] = response.url + output["status"] = response.status_code + if get_verbosity(): + headers = response.headers + output["headers"] = OrderedDict((k, headers[k]) for k in headers) + output["content"] = content + return json.dumps(output, indent=2) @click.group(invoke_without_command=True) @click.pass_context [email protected]("--host", type=str, help="host name", envvar="TRUSTPILOT_API_HOST") [email protected]("--host", type=str, help="Host name", envvar="TRUSTPILOT_API_HOST") @click.option( - "--version", type=str, help="api version (e.g. v1)", envvar="TRUSTPILOT_API_VERSION" + "--version", type=str, help="Api version", envvar="TRUSTPILOT_API_VERSION" ) [email protected]("--key", type=str, help="api key", envvar="TRUSTPILOT_API_KEY") [email protected]("--secret", type=str, help="api secret", envvar="TRUSTPILOT_API_SECRET") [email protected]("--key", type=str, help="Api key", envvar="TRUSTPILOT_API_KEY") [email protected]("--secret", type=str, help="Api secret", envvar="TRUSTPILOT_API_SECRET") @click.option( "--token_issuer_host", type=str, default="", - help="token issuer host name", + help="Token issuer host name", envvar="TRUSTPILOT_API_TOKEN_ISSUER_HOST", ) @click.option( @@ -65,8 +91,16 @@ def format_response(response): help="Trustpilot password", envvar="TRUSTPILOT_PASSWORD", ) [email protected]("-c", type=str, help="json config file name") [email protected]("-v", "--verbose", count=True, help="verbosity level") [email protected]("--config", "-c", type=click.File("r"), help="Json config file name") [email protected]("--env", "-e", type=click.File("r"), help="Dot env file") [email protected]( + "--outputformat", + "-of", + type=click.Choice(["json", "raw"], case_sensitive=False), + default="json", + help="Output format, default=json", +) [email protected]("-v", "--verbose", count=True, help="Verbosity level") def cli(ctx, kwargs): splash = r""" _____ _ _ _ _ @@ -88,16 +122,30 @@ def cli(ctx, kwargs): splash = click.style(splash, fg="green") + "\n" values_dict = {} - config_filename = kwargs.pop("c") + config_file = kwargs.pop("config") - if config_filename: - with open(config_filename, "r") as f: - values_dict = json.load(f) + if config_file: + values_dict.update(json.load(config_file)) + + env_file = kwargs.pop("env") + if env_file: + env_file_lines = env_file.read().split("\n") + values_dict.update( + dict( + (key, value[0]) + for key, value in (line.split("=", 1) for line in env_file_lines) + if key + ) + ) # setup verbosity level for global access verbosity = kwargs.get("verbose") ctx.meta["trustpilot.verbosity"] = verbosity + # setup output format + output_format = kwargs.get("outputformat") + ctx.meta["trustpilot.outputformat"] = output_format + # setup logging (increasing information levels) # _ : content, url, status_code # v : headers @@ -132,8 +180,10 @@ def cli(ctx, kwargs): kwargs.pop("token_issuer_host") or values_dict.get("TRUSTPILOT_API_TOKEN_ISSUER_HOST", None) ), - username=kwargs.pop("username") or values_dict["TRUSTPILOT_USERNAME"], - password=kwargs.pop("password") or values_dict["TRUSTPILOT_PASSWORD"], + username=kwargs.pop("username") + or values_dict.get("TRUSTPILOT_USERNAME", None), + password=kwargs.pop("password") + or values_dict.get("TRUSTPILOT_PASSWORD", None), ) except KeyError as key: raise SystemExit("Missing argument: {}".format(key)) @@ -209,5 +259,23 @@ def put(path, data, content_type): click.echo(format_response(response)) +@cli_command [email protected]("path") [email protected]("--data", type=str, help="json_data to post") [email protected]( + "--content-type", + type=str, + default="application/json", + help="content-type, default=application/json", +) +def patch(path, data, content_type): + """ + Send a PATCH request with specified data + """ + headers = {"content-type": content_type} + response = client.patch(url=path, data=data, headers=headers) + click.echo(format_response(response)) + + if __name__ == "__main__": cli() diff --git a/trustpilot/client.py b/trustpilot/client.py index d015083..89120e1 100644 --- a/trustpilot/client.py +++ b/trustpilot/client.py @@ -39,8 +39,8 @@ def __init__(self, kwargs): def setup( self, api_host=None, - api_version=None, api_key=None, + api_version=None, api_secret=None, username=None, password=None, @@ -51,10 +51,20 @@ def setup( **kwargs ): - self.api_version = api_version or environ.get("TRUSTPILOT_API_VERSION", "v1") self.api_host = api_host or environ.get( "TRUSTPILOT_API_HOST", "https://api.trustpilot.com" ) + + try: + self.api_key = api_key or environ["TRUSTPILOT_API_KEY"] + self.api_secret = api_secret or environ.get("TRUSTPILOT_API_SECRET", "") + self.username = username or environ.get("TRUSTPILOT_USERNAME") + self.password = password or environ.get("TRUSTPILOT_PASSWORD") + self.access_token = access_token + except KeyError as e: + logger.debug("Not auth setup, missing env-var or setup for {}".format(e)) + + self.api_version = api_version or environ.get("TRUSTPILOT_API_VERSION", "v1") self.token_issuer_host = token_issuer_host or self.api_host self.access_token = access_token self.token_issuer_path = token_issuer_path or environ.get( @@ -71,15 +81,7 @@ def setup( "'{}' is not a valid api_host url".format(api_host) ) - try: - self.api_key = api_key or environ["TRUSTPILOT_API_KEY"] - self.api_secret = api_secret or environ.get("TRUSTPILOT_API_SECRET", "") - self.username = username or environ.get("TRUSTPILOT_USERNAME") - self.password = password or environ.get("TRUSTPILOT_PASSWORD") - self.access_token = access_token - self.hooks["response"] = self._post_request_callback - except KeyError as e: - logger.debug("Not auth setup, missing env-var or setup for {}".format(e)) + self.hooks["response"] = self._post_request_callback return self	Backend application authentication We're interested in making some API calls on a schedule from our server, but it seems (also from the [docs](https://developers.trustpilot.com/authentication)) that all authentication options require a personal account password. Is this true, or is there an API-key based auth option that I'm not seeing?	we have identified the issue and we are working on it. sorry for taking so long to reply. Will keep you updated on the progress made and mention you when the issue is fixed	2020-04-29T08:52:25	[]	[]
pyapp-kit/psygnal	pyapp-kit__psygnal-293	c643ad05f78d597bc1633be3e22a52837bf9162d	diff --git a/src/psygnal/_evented_model.py b/src/psygnal/_evented_model.py index d139bb9c..60bc4222 100644 --- a/src/psygnal/_evented_model.py +++ b/src/psygnal/_evented_model.py @@ -8,6 +8,7 @@ ClassVar, Dict, Iterator, + Mapping, NamedTuple, Set, Type, @@ -23,13 +24,8 @@ from ._group_descriptor import _check_field_equality, _pick_equality_operator from ._signal import Signal -NULL = object() -ALLOW_PROPERTY_SETTERS = "allow_property_setters" -FIELD_DEPENDENCIES = "field_dependencies" -GUESS_PROPERTY_DEPENDENCIES = "guess_property_dependencies" PYDANTIC_V1 = pydantic.version.VERSION.startswith("1") - if TYPE_CHECKING: from inspect import Signature @@ -38,7 +34,7 @@ from pydantic._internal import _utils as utils from typing_extensions import dataclass_transform as dataclass_transform # py311 - from ._signal import SignalInstance + from ._signal import RecursionMode, SignalInstance EqOperator = Callable[[Any, Any], bool] else: @@ -58,6 +54,14 @@ def dataclass_transform(args, kwargs): return lambda a: a +NULL = object() +ALLOW_PROPERTY_SETTERS = "allow_property_setters" +FIELD_DEPENDENCIES = "field_dependencies" +GUESS_PROPERTY_DEPENDENCIES = "guess_property_dependencies" +RECURSION_MODE = "recursion_mode" +DEFAULT_RECURSION_MODE: "RecursionMode" = "deferred" + + @contextmanager def no_class_attributes() -> Iterator[None]: # pragma: no cover """Context in which pydantic_main.ClassAttribute just passes value 2. @@ -213,10 +217,31 @@ def __new__( model_fields = _get_fields(cls) model_config = _get_config(cls) + recursion_cfg = model_config.get(RECURSION_MODE, {}) + default_recursion: RecursionMode = DEFAULT_RECURSION_MODE + recursion_map: Mapping[str, RecursionMode] = {} + if isinstance(recursion_cfg, str): + if recursion_cfg not in {"immediate", "deferred"}: + raise ValueError( + f"Invalid recursion mode {default_recursion!r}. Must be " + "either 'immediate' or 'deferred'" + ) + default_recursion = recursion_cfg + else: + if not isinstance(recursion_cfg, Mapping) or not all( + x in {"immediate", "deferred"} for x in recursion_cfg.values() + ): + raise ValueError( + f"Invalid recursion mode {recursion_cfg!r}. Must be a mapping " + "of field names to 'immediate' or 'deferred'." + ) + recursion_map = recursion_cfg + for n, f in model_fields.items(): cls.__eq_operators__[n] = _pick_equality_operator(f.annotation) if not f.frozen: - signals[n] = Signal(f.annotation) + recursion = recursion_map.get(n, default_recursion) + signals[n] = Signal(f.annotation, recursion_mode=recursion) # If a field type has a _json_encode method, add it to the json # encoders for this model. @@ -244,7 +269,8 @@ def __new__( for key, attr in namespace.items(): if isinstance(attr, property) and attr.fset is not None: cls.__property_setters__[key] = attr - signals[key] = Signal(object) + recursion = recursion_map.get(key, default_recursion) + signals[key] = Signal(object, recursion_mode=recursion) else: for b in cls.__bases__: with suppress(AttributeError): diff --git a/src/psygnal/_signal.py b/src/psygnal/_signal.py index e53d6a19..0a063384 100644 --- a/src/psygnal/_signal.py +++ b/src/psygnal/_signal.py @@ -5,6 +5,7 @@ import threading import warnings import weakref +from collections import deque from contextlib import contextmanager, suppress from functools import lru_cache, partial, reduce from inspect import Parameter, Signature, isclass @@ -49,11 +50,13 @@ # function that takes two args tuples. it will be passed to itertools.reduce ReducerTwoArgs = Callable[[tuple, tuple], tuple] ReducerFunc = Union[ReducerOneArg, ReducerTwoArgs] + RecursionMode = Literal["immediate", "deferred"] __all__ = ["Signal", "SignalInstance", "_compiled"] _NULL = object() F = TypeVar("F", bound=Callable) RECURSION_LIMIT = sys.getrecursionlimit() +DEFAULT_RECURSION_MODE: RecursionMode = "immediate" class Signal: @@ -107,6 +110,16 @@ class attribute that is bound to the signal will be used. default None Whether to check the callback parameter types against `signature` when connecting a new callback. This can also be provided at connection time using `.connect(..., check_types=True)`. By default, False. + recursion_mode : Literal["immediate", "deferred"] + How to handle recursive emissions, when this signal is emitted by a callback + while this signal is being emitted. + + - "immediate": Nested emission events immediately begin a deeper emission loop: + calling all callbacks with the arguments of the nested emitter before + returning to continue the original emission loop. This is the default + behavior. + - "deferred": Nested emission events are deferred until the current emission + loop is complete. """ # _signature: Signature # callback signature for this signal @@ -120,11 +133,13 @@ def __init__( name: str \| None = None, check_nargs_on_connect: bool = True, check_types_on_connect: bool = False, + recursion_mode: RecursionMode = DEFAULT_RECURSION_MODE, ) -> None: self._name = name self.description = description self._check_nargs_on_connect = check_nargs_on_connect self._check_types_on_connect = check_types_on_connect + self._recursion_mode = recursion_mode self._signal_instance_class: type[SignalInstance] = SignalInstance self._signal_instance_cache: dict[int, SignalInstance] = {} @@ -220,6 +235,7 @@ def _create_signal_instance( name=name or self._name, check_nargs_on_connect=self._check_nargs_on_connect, check_types_on_connect=self._check_types_on_connect, + recursion_mode=self._recursion_mode, ) @classmethod @@ -310,6 +326,16 @@ class Emitter: Whether to check the callback parameter types against `signature` when connecting a new callback. This can also be provided at connection time using `.connect(..., check_types=True)`. By default, False. + recursion_mode : Literal["immediate", "deferred"] + How to handle recursive emissions, when this signal is emitted by a callback + while this signal is being emitted. + + - "immediate": Nested emission events immediately begin a deeper emission loop: + calling all callbacks with the arguments of the nested emitter before + returning to continue the original emission loop. This is the default + behavior. + - "deferred": Nested emission events are deferred until the current emission + loop is complete. Raises ------ @@ -330,11 +356,8 @@ def __init__( name: str \| None = None, check_nargs_on_connect: bool = True, check_types_on_connect: bool = False, + recursion_mode: RecursionMode = "immediate", ) -> None: - self._name = name - self._instance: Callable = self._instance_ref(instance) - self._args_queue: list[tuple] = [] # filled when paused - if isinstance(signature, (list, tuple)): signature = _build_signature(signature) elif not isinstance(signature, Signature): # pragma: no cover @@ -343,6 +366,15 @@ def __init__( "instance of `inspect.Signature`" ) + if recursion_mode not in ("immediate", "deferred"): # pragma: no cover + raise ValueError( + "recursion_mode must be one of 'immediate' or 'deferred', not " + f"{recursion_mode!r}" + ) + + self._name = name + self._instance: Callable = self._instance_ref(instance) + self._args_queue: list[tuple] = [] # filled when paused self._signature = signature self._check_nargs_on_connect = check_nargs_on_connect self._check_types_on_connect = check_types_on_connect @@ -350,7 +382,14 @@ def __init__( self._is_blocked: bool = False self._is_paused: bool = False self._lock = threading.RLock() - self._emit_queue: list[tuple] = [] + self._emit_queue: deque[tuple] = deque() + self._recursion_mode = recursion_mode + self._run_emit_loop_inner: Callable[[], None] + if self._recursion_mode == "immediate": + self._run_emit_loop_inner = self._run_emit_loop_immediate + else: + self._run_emit_loop_inner = self._run_emit_loop_deferred + # whether any slots in self._slots have a priority other than 0 self._priority_in_use = False @@ -954,7 +993,7 @@ def emit( checks fail. """ if self._is_blocked: - return None + return if check_nargs: try: @@ -976,7 +1015,7 @@ def emit( if self._is_paused: self._args_queue.append(args) - return None + return if SignalInstance._debug_hook is not None: from ._group import EmissionInfo @@ -984,12 +1023,11 @@ def emit( SignalInstance._debug_hook(EmissionInfo(self, args)) self._run_emit_loop(args) - return None def __call__( self, args: Any, check_nargs: bool = False, check_types: bool = False ) -> None: - """Alias for `emit()`.""" + """Alias for `emit()`. But prefer using `emit()` for clarity.""" return self.emit( args, check_nargs=check_nargs, @@ -997,33 +1035,44 @@ def __call__( ) def _run_emit_loop(self, args: tuple[Any, ...]) -> None: - # allow receiver to query sender with Signal.current_emitter() with self._lock: self._emit_queue.append(args) - if len(self._emit_queue) > 1: - return None + return try: with Signal._emitting(self): - i = 0 - while i < len(self._emit_queue): - args = self._emit_queue[i] - for caller in self._slots: - caller.cb(args) - if len(self._emit_queue) > RECURSION_LIMIT: - raise RecursionError - i += 1 + # allow receiver to query sender with Signal.current_emitter() + self._run_emit_loop_inner() except RecursionError as e: raise RecursionError( - f"RecursionError in {caller.slot_repr()} when " + f"RecursionError when " f"emitting signal {self.name!r} with args {args}" ) from e except Exception as e: - raise EmitLoopError(cb=caller, args=args, exc=e, signal=self) from e + raise EmitLoopError( + cb=self._caller, args=self._args, exc=e, signal=self + ) from e finally: self._emit_queue.clear() - return None + def _run_emit_loop_immediate(self) -> None: + self._args = args = self._emit_queue.popleft() + caller = None + for caller in self._slots: + self._caller = caller + caller.cb(args) + + def _run_emit_loop_deferred(self) -> None: + i = 0 + caller = None + while i < len(self._emit_queue): + self._args = args = self._emit_queue[i] + for caller in self._slots: + self._caller = caller + caller.cb(args) + if len(self._emit_queue) > RECURSION_LIMIT: + raise RecursionError + i += 1 def block(self, exclude: Iterable[str \| SignalInstance] = ()) -> None: """Block this signal from emitting. @@ -1172,6 +1221,7 @@ def __getstate__(self) -> dict: "_check_types_on_connect", "_emit_queue", "_priority_in_use", + "_recursion_mode", ) dd = {slot: getattr(self, slot) for slot in attrs} dd["_instance"] = self._instance() @@ -1194,6 +1244,10 @@ def __setstate__(self, state: dict) -> None: else: setattr(self, k, v) self._lock = threading.RLock() + if self._recursion_mode == "immediate": + self._run_emit_loop_inner = self._run_emit_loop_immediate + else: # pragma: no cover + self._run_emit_loop_inner = self._run_emit_loop_deferred class _SignalBlocker: diff --git a/src/psygnal/containers/_evented_set.py b/src/psygnal/containers/_evented_set.py index 4a312d35..9f68a8a2 100644 --- a/src/psygnal/containers/_evented_set.py +++ b/src/psygnal/containers/_evented_set.py @@ -218,7 +218,7 @@ class SetEvents(SignalGroup): added or removed from the set. """ - items_changed = Signal(tuple, tuple) + items_changed = Signal(tuple, tuple, recursion_mode="deferred") class EventedSet(_BaseMutableSet[_T]):	RecursionError with v0.10.0 * psygnal version: 0.10.0 * Python version: 3.11.4 * Operating System: Windows 11 ### Description I found in a specific case, psygnal<0.10 works fine but recursion error occurs in the emission loop with 0.10.0. ### What I Did This is a simple example that class `A` has an evented property `value` and class `Linker` can link the values of different `A` instances. ``` python from psygnal import SignalGroup, Signal class Events(SignalGroup): value = Signal(int) class A: def __init__(self): self.events = Events() self._value = 0 @property def value(self): return self._value @value.setter def value(self, val: int): self._value = val self.events.value.emit(val) class Linker: def __init__(self, objects: list[A]): self._objs = objects for obj in objects: obj.events.value.connect(self.set_values) self._updating = False def set_values(self, val: int): if self._updating: return # avoid recursion error self._updating = True try: for obj in self._objs: obj.value = val finally: self._updating = False a0 = A() a1 = A() linker = Linker([a0, a1]) a0.value = -1 assert a1.value == -1 ``` ``` RecursionError Traceback (most recent call last) File src\psygnal\_signal.py:1014, in _run_emit_loop() RecursionError: During handling of the above exception, another exception occurred: RecursionError Traceback (most recent call last) Cell In[1], line 42 38 a1 = A() 40 linker = Linker([a0, a1]) ---> 42 a0.value = -1 43 assert a1.value == -1 Cell In[1], line 18, in A.value(self, val) 15 @value.setter 16 def value(self, val: int): 17 self._value = val ---> 18 self.events.value.emit(val) File src\psygnal\_signal.py:986, in emit() File src\psygnal\_signal.py:1017, in _run_emit_loop() RecursionError: RecursionError in __main__.Linker.set_values when emitting signal 'value' with args (-1,) ```	Hi @hanjinliu, sorry you've hit this error. The main change that occurred in #281 was to ensure that, when a signal is emitted, all registered callbacks are called before the next emission event. This means that if one of the callbacks also triggers an emit event of the same signal (as your `value.setter` does here) that emission will wait until all callbacks have been called with the first emission. In other words, it's a bread-first callback pattern instead of a depth first callback pattern. (more on that in a moment) One solution that should work for you in both cases is to not emit unnecessary events in your value.setter if the value hasn't actually changed: ```python @value.setter def value(self, val: int) -> None: before, self._value = self._value, val if val != before: self.value_changed.emit(val) ``` ---- However, your comment prompted me to go back and check what Qt's default behavior is for calling callbacks. And I realized it does not do a breadth-first callback pattern. Emission of a signal immediately invokes all callbacks, even if it happens in the middle of another emit event. For example: ```python from qtpy import QtCore class Emitter(QtCore.QObject): sig = QtCore.Signal(int) def cb1(value: int) -> None: print("calling cb1 with", value) if value != 2: emitter.sig.emit(2) def cb2(value: int) -> None: print("calling cb2 with", value) app = QtCore.QCoreApplication([]) emitter = Emitter() emitter.sig.connect(cb1) emitter.sig.connect(cb2) emitter.sig.emit(1) ``` prints ```python calling cb1 with 1 calling cb1 with 2 calling cb2 with 2 calling cb2 with 1 ``` So, @Czaki, with #281, we actually changed the behavior of psygnal to be unlike Qt, and possibly be more surprising than the previous behavior. Can you weigh in on what prompted your PR in #281, and why you think we should be calling all callbacks first before entering the second emission round? i think (but am not sure) that the motivation came from some behavior you were observing with EventedSet in napari, is that correct? Is it possible that that specific issue could have been solved without changing the callback order of nested emission events? as a slightly deeper example: ```python from qtpy import QtCore class Emitter(QtCore.QObject): sig = QtCore.Signal(int) def cb1(value: int) -> None: print("calling cb1 with", value) if value == 1: emitter.sig.emit(2) def cb2(value: int) -> None: print("calling cb2 with", value) if value == 2: emitter.sig.emit(3) def cb3(value: int) -> None: print("calling cb3 with", value) app = QtCore.QCoreApplication([]) emitter = Emitter() emitter.sig.connect(cb1) emitter.sig.connect(cb2) emitter.sig.connect(cb3) emitter.sig.emit(1) ``` ``` calling cb1 with 1 calling cb1 with 2 calling cb2 with 2 calling cb1 with 3 calling cb2 with 3 calling cb3 with 3 calling cb3 with 2 calling cb2 with 1 calling cb3 with 1 ``` I could confirm that also for native objects the order is same as you spotted: ```python from qtpy.QtWidgets import QApplication, QListWidget app = QApplication([]) w = QListWidget() w.addItems(["a", "b", "c", "d", "e"]) w.currentRowChanged.connect(lambda x: w.setCurrentRow(2)) w.currentRowChanged.connect(lambda x: print("currentRowChanged", x)) w.show() w.setCurrentRow(1) ``` gives: ``` currentRowChanged 2 currentRowChanged 1 ``` The problem is that in such a situation you cannot depend on values emitted by signal, as the last signal contains different value than the current state in the object, that introduces inconsistency in the system. If you decide to revert this change, then there will be a need to make sender dispatch thread safe. Thanks @Czaki, your example indeed makes it very clear what the problem is with the default pattern. So, while I do think the default behavior should return to how it was, I think we need an option flag. There are three places where I can imagine having this (not mutually exclusive). ```python # defined on the signal level class Thing: sig = Signal(int, depth_first=False) t = Thing() # declared at the connection level t.sig.connect(print, depth_first=False) # declared at emission time t.sig.emit(1, depth_first=False) ``` ... option 2 (a connection parameter) would perhaps be the weirdest one, with possibly confusing results and a more difficult implementation, but it would be the only one where the receiver gets to specify how they want to be called. There's many ways we could go with this, and I think it's a worthwhile feature/switch. Thoughts? There is also a need to allow passing such selection to evented model. I prefer declaration in constructor call `Signal(int, depth_first=False)` Also qt is not consistent in order of event emmision ```python from qtpy.QtWidgets import QApplication, QListWidget from qtpy.QtCore import Qt app = QApplication([]) w = QListWidget() w.addItems(["a", "b", "c", "d", "e"]) w.currentRowChanged.connect(lambda x: w.setCurrentRow(2), Qt.QueuedConnection) w.currentRowChanged.connect(lambda x: print("currentRowChanged", x), Qt.QueuedConnection) w.show() w.setCurrentRow(1) app.exec_() ``` provides ``` currentRowChanged 1 currentRowChanged 2 ``` right, `Qt::ConnectionType` does give the connector (not the emitter) some degree of control over the order in which they will be called. so it seems that there is probably at least a user case for both declaration in the signal with `Signal(int, depth_first=False)`, as well as at connection time. I'll put together a proposal	2024-03-08T21:54:44	[]	[]
pyapp-kit/psygnal	pyapp-kit__psygnal-277	e9ee49698b53aa441e069e0c14b56bc42fbad08d	diff --git a/src/psygnal/_evented_model_v1.py b/src/psygnal/_evented_model_v1.py index f9bd6d3c..a481b8bc 100644 --- a/src/psygnal/_evented_model_v1.py +++ b/src/psygnal/_evented_model_v1.py @@ -362,10 +362,10 @@ def __setattr__(self, name: str, value: Any) -> None: deps_with_callbacks = { dep_name for dep_name in self.__field_dependents__.get(name, ()) - if len(group[dep_name]) > 1 + if len(group[dep_name]) } if ( - len(signal_instance) < 2 # the signal itself has no listeners + len(signal_instance) < 1 # the signal itself has no listeners and not deps_with_callbacks # no dependent properties with listeners and not len(group._psygnal_relay) # no listeners on the SignalGroup ): diff --git a/src/psygnal/_evented_model_v2.py b/src/psygnal/_evented_model_v2.py index 75b82375..c4413854 100644 --- a/src/psygnal/_evented_model_v2.py +++ b/src/psygnal/_evented_model_v2.py @@ -348,10 +348,10 @@ def __setattr__(self, name: str, value: Any) -> None: deps_with_callbacks = { dep_name for dep_name in self.__field_dependents__.get(name, ()) - if len(group[dep_name]) > 1 + if len(group[dep_name]) } if ( - len(signal_instance) < 2 # the signal itself has no listeners + len(signal_instance) < 1 # the signal itself has no listeners and not deps_with_callbacks # no dependent properties with listeners and not len(group._psygnal_relay) # no listeners on the SignalGroup ): diff --git a/src/psygnal/_group.py b/src/psygnal/_group.py index c79071cf..98e19c6d 100644 --- a/src/psygnal/_group.py +++ b/src/psygnal/_group.py @@ -12,12 +12,14 @@ import warnings from typing import ( + TYPE_CHECKING, Any, Callable, ClassVar, ContextManager, Iterable, Iterator, + Literal, Mapping, NamedTuple, ) @@ -26,6 +28,9 @@ from ._mypyc import mypyc_attr +if TYPE_CHECKING: + from psygnal._weak_callback import WeakCallback + __all__ = ["EmissionInfo", "SignalGroup"] @@ -63,14 +68,29 @@ def __init__( self._signals = signals self._sig_was_blocked: dict[str, bool] = {} + def _append_slot(self, slot: WeakCallback) -> None: + super()._append_slot(slot) + if len(self._slots) == 1: + self._connect_relay() + + def _connect_relay(self) -> None: # silence any warnings about failed weakrefs (will occur in compiled version) with warnings.catch_warnings(): warnings.simplefilter("ignore") - for sig in signals.values(): + for sig in self._signals.values(): sig.connect( self._slot_relay, check_nargs=False, check_types=False, unique=True ) + def _remove_slot(self, slot: int \| WeakCallback \| Literal["all"]) -> None: + super()._remove_slot(slot) + if not self._slots: + self._disconnect_relay() + + def _disconnect_relay(self) -> None: + for sig in self._signals.values(): + sig.disconnect(self._slot_relay) + def _slot_relay(self, *args: Any) -> None: if emitter := Signal.current_emitter(): info = EmissionInfo(emitter, args) diff --git a/src/psygnal/_group_descriptor.py b/src/psygnal/_group_descriptor.py index 8776e437..d751f479 100644 --- a/src/psygnal/_group_descriptor.py +++ b/src/psygnal/_group_descriptor.py @@ -268,7 +268,7 @@ def _setattr_and_emit_(self: object, name: str, value: Any) -> None: # don't emit if the signal doesn't exist or has no listeners signal: SignalInstance = group[name] - if len(signal) < 2 and not len(group._psygnal_relay): + if len(signal) < 1: return super_setattr(self, name, value) with _changes_emitted(self, name, signal): diff --git a/src/psygnal/_signal.py b/src/psygnal/_signal.py index ea3f5d9e..ee516cdc 100644 --- a/src/psygnal/_signal.py +++ b/src/psygnal/_signal.py @@ -521,14 +521,28 @@ def _wrapper( finalize=self._try_discard, on_ref_error=_on_ref_err, ) - if thread is None: - self._slots.append(cb) - else: - self._slots.append(QueuedCallback(cb, thread=thread)) + if thread is not None: + cb = QueuedCallback(cb, thread=thread) + self._append_slot(cb) return slot return _wrapper if slot is None else _wrapper(slot) + def _append_slot(self, slot: WeakCallback) -> None: + """Append a slot to the list of slots.""" + # implementing this as a method allows us to override/extend it in subclasses + self._slots.append(slot) + + def _remove_slot(self, slot: Literal["all"] \| int \| WeakCallback) -> None: + """Remove a slot from the list of slots.""" + # implementing this as a method allows us to override/extend it in subclasses + if slot == "all": + self._slots.clear() + elif isinstance(slot, int): + self._slots.pop(slot) + else: + self._slots.remove(cast("WeakCallback", slot)) + def _try_discard(self, callback: WeakCallback, missing_ok: bool = True) -> None: """Try to discard a callback from the list of slots. @@ -540,7 +554,7 @@ def _try_discard(self, callback: WeakCallback, missing_ok: bool = True) -> None: If `True`, do not raise an error if the callback is not found in the list. """ try: - self._slots.remove(callback) + self._remove_slot(callback) except ValueError: if not missing_ok: raise @@ -633,7 +647,7 @@ def connect_setattr( finalize=self._try_discard, on_ref_error=on_ref_error, ) - self._slots.append(caller) + self._append_slot(caller) return caller def disconnect_setattr( @@ -747,7 +761,7 @@ def connect_setitem( finalize=self._try_discard, on_ref_error=on_ref_error, ) - self._slots.append(caller) + self._append_slot(caller) return caller @@ -859,12 +873,12 @@ def disconnect(self, slot: Callable \| None = None, missing_ok: bool = True) -> N with self._lock: if slot is None: # NOTE: clearing an empty list is actually a RuntimeError in Qt - self._slots.clear() + self._remove_slot("all") return idx = self._slot_index(slot) if idx != -1: - self._slots.pop(idx) + self._remove_slot(idx) elif not missing_ok: raise ValueError(f"slot is not connected: {slot}")	[idea] Connect SignalRelay to signals only when have callback ### Description Currently, SignalRelay is always connected to all signals in SignalGroup. It means that emitting an event always triggers some callback. Maybe SignalRelay should connect itself to all signals in a group only if something is connected to it itself?		2024-02-21T13:07:23	[]	[]
pyapp-kit/psygnal	pyapp-kit__psygnal-255	b0d2057025e6b8b9facc0210d01222bfdc356a8a	diff --git a/src/psygnal/containers/_evented_dict.py b/src/psygnal/containers/_evented_dict.py index 2eb24773..ad504766 100644 --- a/src/psygnal/containers/_evented_dict.py +++ b/src/psygnal/containers/_evented_dict.py @@ -1,12 +1,12 @@ """Dict that emits events when altered.""" +from __future__ import annotations from typing import ( - Dict, + TYPE_CHECKING, Iterable, Iterator, Mapping, MutableMapping, - Optional, Sequence, Tuple, Type, @@ -14,6 +14,9 @@ Union, ) +if TYPE_CHECKING: + from typing import Self + from psygnal._group import SignalGroup from psygnal._signal import Signal @@ -38,13 +41,13 @@ class TypedMutableMapping(MutableMapping[_K, _V]): def __init__( self, - data: Optional[DictArg] = None, + data: DictArg \| None = None, , basetype: TypeOrSequenceOfTypes = (), kwargs: _V, ): - self._dict: Dict[_K, _V] = {} - self._basetypes: Tuple[Type[_V], ...] = ( + self._dict: dict[_K, _V] = {} + self._basetypes: tuple[type[_V], ...] = ( tuple(basetype) if isinstance(basetype, Sequence) else (basetype,) ) self.update({} if data is None else data, kwargs) @@ -76,19 +79,20 @@ def _type_check(self, value: _V) -> _V: ) return value - def __newlike__( - self, mapping: MutableMapping[_K, _V] - ) -> "TypedMutableMapping[_K, _V]": + def __newlike__(self, mapping: MutableMapping[_K, _V]) -> Self: new = self.__class__() # separating this allows subclasses to omit these from their `__init__` new._basetypes = self._basetypes new.update(mapping) return new - def copy(self) -> "TypedMutableMapping[_K, _V]": + def copy(self) -> Self: """Return a shallow copy of the dictionary.""" return self.__newlike__(self) + def __copy__(self) -> Self: + return self.copy() + class DictEvents(SignalGroup): """Events available on [EventedDict][psygnal.containers.EventedDict]. @@ -145,7 +149,7 @@ class EventedDict(TypedMutableMapping[_K, _V]): def __init__( self, - data: Optional[DictArg] = None, + data: DictArg \| None = None, , basetype: TypeOrSequenceOfTypes = (), *kwargs: _V, @@ -172,4 +176,4 @@ def __delitem__(self, key: _K) -> None: self.events.removed.emit(key, item) def __repr__(self) -> str: - return f"EventedDict({super().__repr__()})" + return f"{self.__class__.__name__}({super().__repr__()})" diff --git a/src/psygnal/containers/_evented_list.py b/src/psygnal/containers/_evented_list.py index 4cd15986..a1a7c722 100644 --- a/src/psygnal/containers/_evented_list.py +++ b/src/psygnal/containers/_evented_list.py @@ -23,7 +23,16 @@ """ from __future__ import annotations # pragma: no cover -from typing import Any, Iterable, MutableSequence, TypeVar, Union, cast, overload +from typing import ( + TYPE_CHECKING, + Any, + Iterable, + MutableSequence, + TypeVar, + Union, + cast, + overload, +) from psygnal._group import EmissionInfo, SignalGroup from psygnal._signal import Signal, SignalInstance @@ -32,6 +41,9 @@ _T = TypeVar("_T") Index = Union[int, slice] +if TYPE_CHECKING: + from typing import Self + class ListEvents(SignalGroup): """Events available on [EventedList][psygnal.containers.EventedList]. @@ -133,10 +145,10 @@ def __getitem__(self, key: int) -> _T: ... @overload - def __getitem__(self, key: slice) -> EventedList[_T]: + def __getitem__(self, key: slice) -> Self: ... - def __getitem__(self, key: Index) -> _T \| EventedList[_T]: + def __getitem__(self, key: Index) -> _T \| Self: """Return self[key].""" result = self._data[key] return self.__newlike__(result) if isinstance(result, list) else result @@ -200,21 +212,24 @@ def _pre_remove(self, index: int) -> None: if self._child_events: self._disconnect_child_emitters(self[index]) - def __newlike__(self, iterable: Iterable[_T]) -> EventedList[_T]: + def __newlike__(self, iterable: Iterable[_T]) -> Self: """Return new instance of same class.""" return self.__class__(iterable) - def copy(self) -> EventedList[_T]: + def copy(self) -> Self: """Return a shallow copy of the list.""" return self.__newlike__(self) - def __add__(self, other: Iterable[_T]) -> EventedList[_T]: + def __copy__(self) -> Self: + return self.copy() + + def __add__(self, other: Iterable[_T]) -> Self: """Add other to self, return new object.""" copy = self.copy() copy.extend(other) return copy - def __iadd__(self, other: Iterable[_T]) -> EventedList[_T]: + def __iadd__(self, other: Iterable[_T]) -> Self: """Add other to self in place (self += other).""" self.extend(other) return self diff --git a/src/psygnal/containers/_evented_set.py b/src/psygnal/containers/_evented_set.py index f03c53e0..10f53f1b 100644 --- a/src/psygnal/containers/_evented_set.py +++ b/src/psygnal/containers/_evented_set.py @@ -6,10 +6,11 @@ from psygnal import Signal, SignalGroup if TYPE_CHECKING: + from typing import Self + from typing_extensions import Final _T = TypeVar("_T") -_Cls = TypeVar("_Cls", bound="_BaseMutableSet") class BailType: @@ -89,15 +90,13 @@ def _do_discard(self, item: _T) -> None: # -------- To match set API - def __copy__(self: _Cls) -> _Cls: - inst = self.__class__.__new__(self.__class__) - inst.__dict__.update(self.__dict__) - return inst + def __copy__(self) -> Self: + return self.copy() - def copy(self: _Cls) -> _Cls: + def copy(self) -> Self: return self.__class__(self) - def difference(self: _Cls, s: Iterable[_T]) -> _Cls: + def difference(self, s: Iterable[_T]) -> Self: """Return the difference of two or more sets as a new set. (i.e. all elements that are in this set but not the others.) @@ -110,7 +109,7 @@ def difference_update(self, s: Iterable[_T]) -> None: for i in chain(s): self.discard(i) - def intersection(self: _Cls, s: Iterable[_T]) -> _Cls: + def intersection(self, s: Iterable[_T]) -> Self: """Return the intersection of two sets as a new set. (i.e. all elements that are in both sets.) @@ -133,7 +132,7 @@ def issuperset(self, __s: Iterable[Any]) -> bool: """Report whether this set contains another set.""" return set(self).issuperset(__s) - def symmetric_difference(self: _Cls, __s: Iterable[_T]) -> _Cls: + def symmetric_difference(self, __s: Iterable[_T]) -> Self: """Return the symmetric difference of two sets as a new set. (i.e. all elements that are in exactly one of the sets.) @@ -150,7 +149,7 @@ def symmetric_difference_update(self, __s: Iterable[_T]) -> None: for i in __s: self.discard(i) if i in self else self.add(i) - def union(self: _Cls, s: Iterable[_T]) -> _Cls: + def union(self, *s: Iterable[_T]) -> Self: """Return the union of sets as a new set. (i.e. all elements that are in either set.)	Copy operator of evented sets lead to sharing inner structures between copies ### Original issue https://github.com/napari/napari/issues/6620 ### Description When debugging linked issue, I noticed that `copu` of `pysygnal.containers.Selection` shares internal structures with the original one. As I understand the code of the copy operator, it does only shallow copy of internal structures. https://github.com/pyapp-kit/psygnal/blob/b0d2057025e6b8b9facc0210d01222bfdc356a8a/src/psygnal/containers/_evented_set.py#L92-L95 There is any reason to not implement copy as: ``` def __copy__(self: _Cls) -> _Cls: return self.copy() ``` Where `copy()` is implemented as: https://github.com/pyapp-kit/psygnal/blob/b0d2057025e6b8b9facc0210d01222bfdc356a8a/src/psygnal/containers/_evented_set.py#L97-L98 Current copy may also copy callbacks connected to signals.	yeah, I agree that's a tricky result, and I agree it's more surprising to retain any of the mutable characteristics of one set (including callbacks) in the other set. However, in order to mimic native set behavior as much as possible, I don't think it can simply be `self.copy()`, because the members of the set should be copied over (as they would in `set.copy()`). But, perhaps it could be ```python def __copy__(self: _Cls) -> _Cls: new = self.copy() new._data.update(self._data) return new ``` thoughts? but ```python def copy(self: _Cls) -> _Cls: return self.__class__(self) ``` Also copy elements, as it takes iterable as constructor arguments. oh right! Sorry, completely missed that. So, yep, I agree then with your original proposal	2024-01-25T20:37:38	[]	[]
pyapp-kit/psygnal	pyapp-kit__psygnal-234	12552913920ce0bfea4288a5bc40f605a4f11e6c	diff --git a/src/psygnal/_evented_model_v1.py b/src/psygnal/_evented_model_v1.py index 280600f8..ed680734 100644 --- a/src/psygnal/_evented_model_v1.py +++ b/src/psygnal/_evented_model_v1.py @@ -40,7 +40,7 @@ def dataclass_transform(args, kwargs): _NULL = object() ALLOW_PROPERTY_SETTERS = "allow_property_setters" -PROPERTY_DEPENDENCIES = "property_dependencies" +FIELD_DEPENDENCIES = "field_dependencies" GUESS_PROPERTY_DEPENDENCIES = "guess_property_dependencies" @@ -185,16 +185,26 @@ class Config: """ deps: Dict[str, Set[str]] = {} - cfg_deps = getattr(cls.__config__, PROPERTY_DEPENDENCIES, {}) # sourcery skip + cfg_deps = getattr(cls.__config__, FIELD_DEPENDENCIES, {}) # sourcery skip + if not cfg_deps: + cfg_deps = getattr(cls.__config__, "property_dependencies", {}) + if cfg_deps: + warnings.warn( + "The 'property_dependencies' configuration key is deprecated. " + "Use 'field_dependencies' instead", + DeprecationWarning, + stacklevel=2, + ) + if cfg_deps: if not isinstance(cfg_deps, dict): # pragma: no cover raise TypeError( f"Config property_dependencies must be a dict, not {cfg_deps!r}" ) for prop, fields in cfg_deps.items(): - if prop not in cls.__property_setters__: + if prop not in {cls.__fields__, cls.__property_setters__}: raise ValueError( - "Fields with dependencies must be property.setters." + "Fields with dependencies must be fields or property.setters." f"{prop!r} is not." ) for field in fields: @@ -344,21 +354,28 @@ def __setattr__(self, name: str, value: Any) -> None: # grab current value before = getattr(self, name, object()) + # if we have any dependent attributes, we need to grab their values + # before we set the new value, so that we can emit events for them + # after the new value is set (only if they have changed). + deps_before: Dict[str, Any] = { + dep: getattr(self, dep) for dep in self.__field_dependents__.get(name, ()) + } # set value using original setter signal_instance: SignalInstance = getattr(self._events, name) with signal_instance.blocked(): self._super_setattr_(name, value) - # if different we emit the event with new value + # if the value has changed we emit the event with new value after = getattr(self, name) - if not _check_field_equality(type(self), name, after, before): signal_instance.emit(after) # emit event - # emit events for any dependent computed property setters as well - for dep in self.__field_dependents__.get(name, ()): - getattr(self.events, dep).emit(getattr(self, dep)) + # also emit events for any dependent computed property setters as well + for dep, before_val in deps_before.items(): + after_val = getattr(self, dep) + if not _check_field_equality(type(self), dep, after_val, before_val): + getattr(self._events, dep).emit(after_val) # expose the private SignalGroup publically @property diff --git a/src/psygnal/_evented_model_v2.py b/src/psygnal/_evented_model_v2.py index e40bdd98..89cd9ff9 100644 --- a/src/psygnal/_evented_model_v2.py +++ b/src/psygnal/_evented_model_v2.py @@ -41,7 +41,7 @@ def dataclass_transform(args, *kwargs): _NULL = object() ALLOW_PROPERTY_SETTERS = "allow_property_setters" -PROPERTY_DEPENDENCIES = "property_dependencies" +FIELD_DEPENDENCIES = "field_dependencies" GUESS_PROPERTY_DEPENDENCIES = "guess_property_dependencies" @@ -182,16 +182,26 @@ class Config: """ deps: Dict[str, Set[str]] = {} - cfg_deps = cls.model_config.get(PROPERTY_DEPENDENCIES, {}) # sourcery skip + cfg_deps = cls.model_config.get(FIELD_DEPENDENCIES, {}) # sourcery skip + if not cfg_deps: + cfg_deps = cls.model_config.get("property_dependencies", {}) + if cfg_deps: + warnings.warn( + "The 'property_dependencies' configuration key is deprecated. " + "Use 'field_dependencies' instead", + DeprecationWarning, + stacklevel=2, + ) + if cfg_deps: if not isinstance(cfg_deps, dict): # pragma: no cover raise TypeError( f"Config property_dependencies must be a dict, not {cfg_deps!r}" ) for prop, fields in cfg_deps.items(): - if prop not in cls.__property_setters__: + if prop not in {cls.model_fields, *cls.__property_setters__}: raise ValueError( - "Fields with dependencies must be property.setters." + "Fields with dependencies must be fields or property.setters." f"{prop!r} is not." ) for field in fields: @@ -330,21 +340,28 @@ def __setattr__(self, name: str, value: Any) -> None: # grab current value before = getattr(self, name, object()) + # if we have any dependent attributes, we need to grab their values + # before we set the new value, so that we can emit events for them + # after the new value is set (only if they have changed). + deps_before: Dict[str, Any] = { + dep: getattr(self, dep) for dep in self.__field_dependents__.get(name, ()) + } # set value using original setter signal_instance: SignalInstance = getattr(self._events, name) with signal_instance.blocked(): self._super_setattr_(name, value) - # if different we emit the event with new value + # if the value has changed we emit the event with new value after = getattr(self, name) - if not _check_field_equality(type(self), name, after, before): signal_instance.emit(after) # emit event - # emit events for any dependent computed property setters as well - for dep in self.__field_dependents__.get(name, ()): - getattr(self.events, dep).emit(getattr(self, dep)) + # also emit events for any dependent attributes as well + for dep, before_val in deps_before.items(): + after_val = getattr(self, dep) + if not _check_field_equality(type(self), dep, after_val, before_val): + getattr(self._events, dep).emit(after_val) # expose the private SignalGroup publically @property	EventedModel does not emit events for all value changes in a root validator * psygnal version: `main` at the time of writing (a4a3348fb6f81c3aeefc8a1c723360c9a26db23e) * Python version: 3.7.16 * Operating System: macOS (Intel x86-64) ### Description When I define an `EventedModel` with a root validator that coerces some field values based on others, changes in derived/constrained values do not trigger events to be emitted. Coming here from [an issue on napari](https://github.com/napari/napari/pull/4819#issuecomment-1703093535) where similar problems with its similar `EventedModel` cause problems for `Dims`. No fix for that yet. ### What I Did Here's the simplest and also somewhat artificial reproducer I could write, where a model has two ints (`x` and `y`) and the root validator ensures `x >= y`. ```python from psygnal import EventedModel from pydantic import root_validator class Model(EventedModel): x: int y: int class Config: validate_assignment = True @root_validator def check(cls, values): x = values['x'] if values['y'] > x: values['y'] = x return values m = Model(x=2, y=1) m.events.x.connect(lambda x: print(f'x is now {x}')) m.events.y.connect(lambda y: print(f'y is now {y}')) print(m) # => Model(x=2, y=1) m.x = 0 # x change is printed, but not y print(m) # => Model(x=0, y=0) ``` I think this is a bug because I'd expect the change in `y` to emit an event. ### Thoughts I'm not sure this is a critical bug because I've at least had half-baked thoughts about alternatives/workarounds (e.g. define `x` and `y` as properties and then use `property_dependencies`). Taking that half-baked idea a little further, does it make any sense to allow `property_dependencies` to include `fields` as well as properties? Other ideas/workarounds are welcome!	thanks as always for the super clear issue! :) I think we should fix this (without forcing the workaround of making them properties), but it could be done in a bunch of ways, let me know which of the following sounds best to you: 1. Your idea of allowing `property_dependencies` to include fields would be the simplest and the most explicit. we would just remove [this exception](https://github.com/pyapp-kit/psygnal/blob/main/src/psygnal/_evented_model_v1.py#L195-L199), and allow: ```python class Config: validate_assignment = True property_dependencies = {"y": ["x"]} ``` It would be the most performant, and the least magical, but it would mean `events.y` will always be emitted whenever `x` is changed, regardless of whether the change in `x` actually changed the value of `y`. (for what it's worth, that's already the case with property dependencies anyway) 2. We could extend that idea to actually check whether the value of y changed before emitting. That would result in the most "expected" behavior, but it would mean that anytime `__setattr__` is called on a field, we'd need to fetch the values of all dependent fields before and after actually setting the field, then compare each of them to determine whether an emission is required. So, could be costly in terms of performance. 3. An alternative to the explicit/mandatory `property_dependencies` configuration would be to detect whether the class had a root validator, then simply check all fields in the model before and after setting any field. That's of course the worst in terms of performance, but "just does the right thing" without requiring the user to notice this special case. 4. We could do number 3, but require opt-in with a config setting thoughts? 5. as a variant/extension of number 2, we could add an additional config setting that gates the checking. allowing the user to decide whether they'd prefer more accurate event emission, or better peformance	2023-09-16T20:30:13	[]	[]
pyapp-kit/psygnal	pyapp-kit__psygnal-216	8ec0671696ff76505c2a76263f8e94a78d349f28	diff --git a/src/psygnal/_evented_model.py b/src/psygnal/_evented_model.py index c739fe62..551574db 100644 --- a/src/psygnal/_evented_model.py +++ b/src/psygnal/_evented_model.py @@ -348,13 +348,14 @@ def __setattr__(self, name: str, value: Any) -> None: before = getattr(self, name, object()) # set value using original setter - self._super_setattr_(name, value) + signal_instance: SignalInstance = getattr(self._events, name) + with signal_instance.blocked(): + self._super_setattr_(name, value) # if different we emit the event with new value after = getattr(self, name) if not _check_field_equality(type(self), name, after, before): - signal_instance: SignalInstance = getattr(self.events, name) signal_instance.emit(after) # emit event # emit events for any dependent computed property setters as well	Setting derived properties in evented models causes duplicate events * psygnal version: 0.9.1 * Python version: 3.9.6 * Operating System: macOS 13.4 (Intel) ### Description Setting a derived property when using an evented model causes the associated event to be fired twice. Coming from the same titled issue over on napari: https://github.com/napari/napari/issues/6032 Our solution (https://github.com/napari/napari/pull/6035) was to block the event in `__setattr__`, but would be curious if you had better ideas. ### What I Did ``` from psygnal import EventedModel class Model(EventedModel): a: int @property def b(self) -> int: return self.a + 1 @b.setter def b(self, b) -> None: self.a = b - 1 class Config: allow_property_setters = True property_dependencies = {"b": ["a"]} m = Model(a=0) m.events.a.connect(lambda _: print('a')) m.events.b.connect(lambda _: print('b')) m.a = 1 # prints "a" and "b" once each, as expected m.b = 3 # prints "a" once, but "b" twice ```	Thanks! I appreciate the ping. Will have a look	2023-07-06T10:52:28	[]	[]
rnag/dataclass-wizard	rnag__dataclass-wizard-111	e7a56014cd5e4d514dfc015729ec47046a90296e	diff --git a/dataclass_wizard/parsers.py b/dataclass_wizard/parsers.py index f7c338c..23cbb59 100644 --- a/dataclass_wizard/parsers.py +++ b/dataclass_wizard/parsers.py @@ -84,6 +84,14 @@ def __post_init__(self, *_): val: type(val) for val in get_args(self.base_type) } + def __contains__(self, item) -> bool: + """ + Return true if the LiteralParser is expected to handle the specified item + type. Checks that the item is incorporated in the given expected values of + the Literal. + """ + return item in self.value_to_type.keys() + def __call__(self, o: Any) -> M: """ Checks for Literal equivalence, as mentioned here:	[BUG] Incomplete Support for Union or \| Type Deserialization in Common Python Types (e.g., `int`, `Enum`, `Literal`) * Dataclass Wizard version: 0.22.1 * Python version: 3.10.4 * Operating System: Ubuntu 22.04 ### Description Using the dataclass-wizard to deserialize a YAML file fails when trying to use string literals as field options. In my particular case, a value in the config can be set manually or derived automatically depending on user preference. In this instance, the only valid string value for `x` should be `Auto`. Any other value should fail to deserialize. However, since it appears that the parser is unaware of the possible equality of str/num/bytes to a Literal this fails. ### What I Did ```python >>> from dataclass_wizard import YAMLWizard >>> from typing import Literal >>> from dataclasses import dataclass >>> @dataclass ... class Example(YAMLWizard): ... x: Literal["Auto"] \| float ... >>> config_file = """ ... x: Auto ... """ >>> Example.from_yaml(config_file) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "~/.venv/lib/python3.10/site-packages/dataclass_wizard/wizard_mixins.py", line 136, in from_yaml return fromdict(cls, o) if isinstance(o, dict) else fromlist(cls, o) File "~/.venv/lib/python3.10/site-packages/dataclass_wizard/loaders.py", line 536, in fromdict return load(d) File "~/.venv/lib/python3.10/site-packages/dataclass_wizard/loaders.py", line 619, in cls_fromdict cls_kwargs[field_name] = field_to_parser[field_name]( File "~/.venv/lib/python3.10/site-packages/dataclass_wizard/parsers.py", line 250, in __call__ raise ParseError( dataclass_wizard.errors.ParseError: Failure parsing field `x` in class `Example`. Expected a type [typing.Literal['Auto'], <class 'float'>], got str. value: 'Auto' error: Object was not in any of Union types tag_key: '__tag__' json_object: '{"x": "Auto"}' ```	I don't think this has anything to do with Literals - anything that needs to be deserialized in a union is not being deserialized. ```python >>> from dataclasses import dataclass >>> from dataclass_wizard import JSONWizard >>> @dataclass ... class B: ... thing: str ... >>> >>> @dataclass ... class A(JSONWizard): ... one_or_more: B \| list[B] ... >>> >>> result = A.from_dict({ ... 'one_or_more': { ... 'thing': 'value' ... } ... }) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/usr/local/lib/python3.10/site-packages/dataclass_wizard/loaders.py", line 536, in fromdict return load(d) File "/usr/local/lib/python3.10/site-packages/dataclass_wizard/loaders.py", line 619, in cls_fromdict cls_kwargs[field_name] = field_to_parser[field_name]( File "/usr/local/lib/python3.10/site-packages/dataclass_wizard/parsers.py", line 250, in __call__ raise ParseError( dataclass_wizard.errors.ParseError: Failure parsing field `one_or_more` in class `A`. Expected a type [<class '__main__.B'>, <class 'list'>], got dict. value: {'thing': 'value'} error: Object was not in any of Union types tag_key: '__tag__' json_object: '{"one_or_more": {"thing": "value"}}' >>> ``` Got the same issue here. Mixing primitives with dataclasses in unions seems to be the problem: ```python from dataclass_wizard import fromdict from dataclasses import dataclass @dataclass class MoreDetails: arg: str @dataclass class Config: # Unions with primitives and dataclasses entry: str \| MoreDetails \| int # Works fromdict(Config, { "entry": "a" }) # Works too fromdict(Config, { "entry": 1 }) # Fails, fails to interpret the dict as a dataclass fromdict(Config, { "entry": {"arg": "str"} }) ``` ```pytb Traceback (most recent call last): File "/mnt/c/Users/fakui/Seafile/Programming/projects/cc_crapton/./ha_modbus_mqtt/dataclasswz_bug.py", line 29, in <module> fromdict(Config2, { File "/home/fakui/.local/lib/python3.10/site-packages/dataclass_wizard/loaders.py", line 536, in fromdict return load(d) File "/home/fakui/.local/lib/python3.10/site-packages/dataclass_wizard/loaders.py", line 619, in cls_fromdict cls_kwargs[field_name] = field_to_parser[field_name]( File "/home/fakui/.local/lib/python3.10/site-packages/dataclass_wizard/parsers.py", line 250, in __call__ raise ParseError( dataclass_wizard.errors.ParseError: Failure parsing field `entry` in class `Config2`. Expected a type [<class '__main__.MoreDetails'>, <class '__main__.Config'>], got dict. value: {'arg': '1'} error: Object was not in any of Union types tag_key: '__tag__' json_object: '{"entry": {"arg": "1"}}' ``` dataclass-wizard version: 0.22.2	2024-02-21T14:20:35	[]	[]
rnag/dataclass-wizard	rnag__dataclass-wizard-105	e3fe0760422613eaf79e0236840a50f27caec064	diff --git a/.gitignore b/.gitignore index d510d86..c783fc2 100644 --- a/.gitignore +++ b/.gitignore @@ -95,3 +95,6 @@ venv/ ENV/ env.bak/ venv.bak/ + +# File created by pytest +testing.json diff --git a/dataclass_wizard/parsers.py b/dataclass_wizard/parsers.py index 2e87db5..e3c810e 100644 --- a/dataclass_wizard/parsers.py +++ b/dataclass_wizard/parsers.py @@ -401,10 +401,7 @@ def __post_init__(self, cls: Type, # Total count should be `Infinity` here, since the variadic form # accepts any number of possible arguments. self.total_count: N = float('inf') - # Check for the count of parsers which don't handle `NoneType` - this - # should exclude the parsers for `Union` types that have `None` in the - # list of args. - self.required_count = 0 if None in self.first_elem_parser[0] else 1 + self.required_count = 0 def __call__(self, o: M) -> M: """	Loading a Variadic Tuple fails for length 0 * Dataclass Wizard version: 0.22.2 * Python version: both python 3.10 and python 3.12 * Operating System: mac ### Description If you have a dataclass with: `my_tuple: tuple[int, ...]` then, trying to parse the following json fails `{my_tuple: []}` because the tuple is empty. However, if there is at least one int, then this succeeds. ### What I Did ``` from dataclasses import dataclass from dataclass_wizard import fromdict @dataclass class Test: my_ints: tuple[int, ...] fromdict(Test, {"my_ints": []}) ``` fails with ``` dataclass_wizard.errors.ParseError: Failure parsing field `my_ints` in class `Test`. Expected a type [], got list. value: [] error: Wrong number of elements. desired_count: '1 - inf' actual_count: 0 json_object: '{"my_ints": []}' ```		2024-01-14T20:41:30	[]	[]
mehta-lab/recOrder	mehta-lab__recOrder-399	858f3d41ba129544ad6df8c53d9437e02b53f7ed	diff --git a/recOrder/cli/apply_inverse_transfer_function.py b/recOrder/cli/apply_inverse_transfer_function.py index 17080e95..d28b7de6 100644 --- a/recOrder/cli/apply_inverse_transfer_function.py +++ b/recOrder/cli/apply_inverse_transfer_function.py @@ -2,6 +2,7 @@ import numpy as np import torch from iohub import open_ome_zarr +from iohub.ngff_meta import TransformationMeta from waveorder.models import ( inplane_oriented_thick_pol3d, isotropic_fluorescent_thick_3d, @@ -122,6 +123,9 @@ def apply_inverse_transfer_function_cli( 1, ) + input_dataset.data.shape[3:], # chunk by YX + transform=[ + TransformationMeta(type="scale", scale=input_dataset.scale) + ], ) else: output_array = output_dataset[0]	Passing the voxel (z,y,x) in micrometers in zattrs per position As I was playing with some datasets for mantis reconstructions using #392, I noticed we don't pass any coordinateTransforms or metadata that can be useful for the rest of the analysis pipelines. It would be useful to pass from the raw zarr the voxel size (z,y,x) dimensions in microns. In fact, these can also be applied upon creation of the zarr stores to load in napari.		2023-08-09T00:08:36	[]	[]
mehta-lab/recOrder	mehta-lab__recOrder-391	96dca0163777d7f066ce880e3f6c1bbaa687a536	diff --git a/setup.cfg b/setup.cfg index b2191912..57daeb28 100644 --- a/setup.cfg +++ b/setup.cfg @@ -34,7 +34,6 @@ packages = find: include_package_data = True python_requires = >=3.9 setup_requires = setuptools_scm -# add your package requirements here install_requires = waveorder==2.0.0rc1 pycromanager==0.27.2 @@ -43,11 +42,9 @@ install_requires = colorspacious>=1.1.2 pyqtgraph>=0.12.3 napari-ome-zarr>=0.3.2 # drag and drop convenience - napari[all];'arm64' not in platform_machine # with Qt5 and skimage - napari;'arm64' in platform_machine # without Qt and skimage - PyQt6;'arm64' in platform_machine + napari[pyqt6_experimental] importlib-metadata - iohub==0.1.0.dev3 + iohub==0.1.0.dev4 [options.extras_require] dev =	Native support for Apple Silicon Problem `recOrder` does not currently support Apple Silicon (M1, M2, etc.) platforms natively due to the incompatibility between `pip` and PyQt5 (PyQt5 does not have a wheel for `arm_64` on PyPI). Native installation would be beneficial for both performance and potential GPU-accelerated processing/ML with a Metal backend. Proposed solution Closely track [napari's progress on supporting Qt6](https://github.com/napari/napari/issues/3880), since PyQt6/PySide6 have wheels available for the Apple Silicon platform on PyPI. And we can upgrade `recOrder` accordingly when napari makes the move. (They already have initial support merged into the main branch). This may evolve into a broader set of [1.0.0](https://github.com/mehta-lab/recOrder/milestone/3) issues if napari starts supporting Qt 6 in a stable release soon enough. Alternatives - Offer a conda environment file that installs PyQt5 from `conda-forge`. This would require the removal of explicit dependency on `napari` in `setup.cfg`. #187	napari 0.4.17 has added experimental support for Qt6. `pip install napari pyqt6` now works on macOS_arm64. The only blocking factor is now the `napari[all]` line in `recOrder`'s `setup.cfg`, which really is an alias for `napari pyqt5 scikit-image`. Changing to `napari` allows for editable install of `recOrder` on Apple Silicon with `pip install -e <dir-to-recOrder>`: <img width="1260" alt="recOrder-napari_arm64" src="https://user-images.githubusercontent.com/67518483/202587161-b13242fd-745d-4f67-822e-dea330f3f35f.png"> The glitching in style sheet rendering is [a known issue](https://github.com/napari/napari/pull/3707#issuecomment-1152997251) for napari with Qt6. @ziw-liu I am trying to setup an environment for mantis project on my osx_arm64 laptop. I have been able to install all dependencies, including `nidaqmax`, `dexp`, `pyqt6`, `napari`, `pycromanager`. But, when I try `pip install -e <dir-to-recOrder>` in the main branch, pip still tries to build a pyqt5 wheel and fails. How do I bypass pyqt5 dependency when setting up recOrder? Here is the pip output. ``` Collecting PyQt5!=5.15.0,>=5.12.3 Using cached PyQt5-5.15.9.tar.gz (3.2 MB) Installing build dependencies ... done Getting requirements to build wheel ... done Preparing metadata (pyproject.toml) ... - ``` As suggested in https://github.com/mehta-lab/recOrder/issues/193#issuecomment-1319382740, for the current `recOrder` you would need to manually delete this line: https://github.com/mehta-lab/recOrder/blob/7e10ae59a1ed28cf28052b97404a2c0eba4369ff/setup.cfg#L47 before installing. And then install napari with Qt6: ```sh pip install napari pyqt6 ```	2023-08-02T18:50:05	[]	[]
mehta-lab/recOrder	mehta-lab__recOrder-384	4582bf5940ff2ee2a2d6c1563056fe5873a96559	diff --git a/recOrder/cli/apply_inverse_transfer_function.py b/recOrder/cli/apply_inverse_transfer_function.py index e9bbda37..46cac81a 100644 --- a/recOrder/cli/apply_inverse_transfer_function.py +++ b/recOrder/cli/apply_inverse_transfer_function.py @@ -109,9 +109,10 @@ def apply_inverse_transfer_function_cli( ) # Load data + tczyx_uint16_numpy = input_dataset.data.oindex[:, channel_indices] tczyx_data = torch.tensor( - input_dataset.data.oindex[:, channel_indices], dtype=torch.float32 - ) + np.int32(tczyx_uint16_numpy), dtype=torch.float32 + ) # convert to np.int32 (torch doesn't accept np.uint16), then convert to tensor float32 # Prepare background dataset if settings.birefringence is not None: @@ -164,7 +165,7 @@ def apply_inverse_transfer_function_cli( echo_headline("Reconstructing phase...") # check data shapes - if input_dataset.data.shape[1] != 1: + if tczyx_data.shape[1] != 1: raise ValueError( "You have requested a phase-only reconstruction, but the input dataset has more than one channel." ) @@ -385,10 +386,10 @@ def apply_inv_tf( """ Apply an inverse transfer function to a dataset using a configuration file. - See /examples/settings/ for example configuration files. + See /examples for example configuration files. Example usage:\n - $ recorder apply-inv-tf input.zarr/0/0/0 transfer-function.zarr -c /examples/settings/birefringence.yml -o output.zarr + $ recorder apply-inv-tf input.zarr/0/0/0 transfer-function.zarr -c /examples/birefringence.yml -o output.zarr """ apply_inverse_transfer_function_cli( input_data_path, transfer_function_path, config_path, output_path diff --git a/recOrder/cli/compute_transfer_function.py b/recOrder/cli/compute_transfer_function.py index 729531ff..80b740aa 100644 --- a/recOrder/cli/compute_transfer_function.py +++ b/recOrder/cli/compute_transfer_function.py @@ -211,9 +211,9 @@ def compute_tf(input_data_path, config_path, output_path): """ Compute a transfer function using a dataset and configuration file. - See /examples/settings/ for example configuration files. + See /examples/ for example configuration files. Example usage:\n - $ recorder compute-tf input.zarr/0/0/0 -c /examples/settings/birefringence.yml -o transfer_function.zarr + $ recorder compute-tf input.zarr/0/0/0 -c /examples/birefringence.yml -o transfer_function.zarr """ compute_transfer_function_cli(input_data_path, config_path, output_path) diff --git a/recOrder/cli/reconstruct.py b/recOrder/cli/reconstruct.py index 9844929d..8599a675 100644 --- a/recOrder/cli/reconstruct.py +++ b/recOrder/cli/reconstruct.py @@ -23,10 +23,10 @@ def reconstruct(input_data_path, config_path, output_path): convenience function for a `compute-tf` call followed by a `apply-inv-tf` call. - See /examples/settings/ for example configuration files. + See /examples for example configuration files. Example usage:\n - $ recorder reconstruct input.zarr/0/0/0 -c /examples/settings/birefringence.yml -o output.zarr + $ recorder reconstruct input.zarr/0/0/0 -c /examples/birefringence.yml -o output.zarr """ # Handle transfer function path diff --git a/setup.cfg b/setup.cfg index c7548797..d79769ee 100644 --- a/setup.cfg +++ b/setup.cfg @@ -37,7 +37,7 @@ python_requires = >=3.9 setup_requires = setuptools_scm # add your package requirements here install_requires = - waveorder==2.0.0rc0 + waveorder==2.0.0rc1 pycromanager==0.27.2 click>=8.0.1 natsort>=7.1.1	Fluorescence CLI @ziw-liu I'm requesting your review of this fairly large PR. I have tested this PR on hummingbird, and I'd like to prioritize getting this merged into `0.4.0dev` then `main` so that we can start integration testing for the `0.4.0` release. I'm particularly interested in your suggestions related to (1) the new `/examples/` folder for the new config file formats, (2) the new CLI names and simplified `recOrder reconstruct` CLI, and (3) the new `cli.settings.py` file. Summary of major changes: - new fluorescence CLI functionality - a single configuration file design instead of two configuration files (one for `compute-transfer-function` and one for `apply-inverse-transfer-function` - reworked pydantic models for configuration files (now supported birefringence, phase, birefringence + phase, and fluorescence) - reworked `examples/` folder demonstrating new CLI Minor changes: - high-level tests of CLI - new CLI names: `compute-tf` and `apply-inv-tf`. These shortened names are for the CLI only. - basic validation of CLI inputs - automatically generated example configuration files Earlier list of TODOs: - [x] finish high-level `recorder reconstruct` CLI and test - [x] integration testing at the microscope - [x] removal of example scripts with documentation (now replaced by `waveorder` scripts and CLI + example configurations) - [x] documentation of configuration file generation @edyoshikun I'm tagging you as a reviewer, but we'll likely merge before you're back. We'll make sure this is tested against your upcoming use cases before the 0.4.0 release. Run CI/CD tests on all PRs Before: Tests run only on PRs to `main` After: Tests run on all PRs Symptom: In #381 I used some local spot tests, then saw the checkmarks on GH actions and thought everything was working well. On merging #381 into `0.4.0dev`, I found that the tests weren't actually running...the checkmark was only for napari hub checks. Not a big deal; I will fix the tests in `0.4.0dev`.	Preview page for your plugin is ready here: https://preview.napari-hub.org/mehta-lab/recOrder/381 _Updated: 2023-07-08T00:00:23.839831_ I know this has been a lot of work but it's great to see it come together! I am wondering if it makes sense to have the `apply_inverse_transfer_functions` for all recons to be separate functions. We can do it for another PR post merging `0.4.0dev`. I attempted something in #380 at least for the birefringence reconstruction, which I pushed for you to have the code just in case while I was gone. Thanks @edyoshikun. Merging into `0.4.0dev` ## [Codecov](https://app.codecov.io/gh/mehta-lab/recOrder/pull/383?src=pr&el=h1&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=mehta-lab) Report > Merging [#383](https://app.codecov.io/gh/mehta-lab/recOrder/pull/383?src=pr&el=desc&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=mehta-lab) (2836e3a) into [main](https://app.codecov.io/gh/mehta-lab/recOrder/commit/6b2b3cc3a04308d22a11dbfd48ed0ea2c06c822f?el=desc&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=mehta-lab) (6b2b3cc) will not change coverage. > The diff coverage is `n/a`. ```diff @@ Coverage Diff @@ ## main #383 +/- ## ===================================== Coverage 1.95% 1.95% ===================================== Files 21 21 Lines 4851 4851 ===================================== Hits 95 95 Misses 4756 4756 ``` The napari hub preview failed in some TypeScript process...	2023-07-25T17:30:00	[]	[]
mehta-lab/recOrder	mehta-lab__recOrder-375	802cc62cac537749ab5dce5535d7c0a18137f819	diff --git a/docs/images/cli_structure.png b/docs/images/cli_structure.png new file mode 100644 index 00000000..0ddb1dd1 Binary files /dev/null and b/docs/images/cli_structure.png differ diff --git a/docs/microscope-installation-guide.md b/docs/microscope-installation-guide.md index d54a00de..b701bd1f 100644 --- a/docs/microscope-installation-guide.md +++ b/docs/microscope-installation-guide.md @@ -7,6 +7,22 @@ This guide will walk through a complete recOrder installation consisting of: Before you start you will need a machine with Windows 10, a Meadowlark DS5020 connected to a liquid crystal device, and a microscope system compatible with `Micromanager`. +## Install Meadowlark DS5020 and liquid crystals + +Start by installing the Meadowlark DS5020 and liquid crystals using the software on the USB stick provided by Meadowlark. You will need to install the USB drivers and CellDrive5000. + +Check your installation versions by opening CellDrive5000 and double clicking the Meadowlark Optics logo. We have tested `recOrder == 0.4.0` with "PC software version 1.08" and "Controller firmware version 1.04", and you will need to upgrade if your software and firmware versions are older. + +To upgrade your "PC software version" use these steps: + +- From "Add and remove programs", remove CellDrive5000 and "National Instruments Software". +- From "Device manager", open the "Meadowlark Optics" group, right click `mlousb`, click "Uninstall device", check "Delete the driver software for this device", and click "Uninstall". Uninstall `Meadowlark Optics D5020 LC Driver` following the same steps. +- Using the USB stick provided by Meadowlark, reinstall the USB drivers and CellDrive5000. +- Confirm that "PC software version" == 1.08 +- Upgrading users: you will need to reinstall the Meadowlark device to your micromanager configuration file, because the device driver's name has changed to `MeadowlarkLC`. + +To upgrade your DS5020's firmware, use Meadowlark's "Firmware Updater". + ## Install recOrder software (Optional but recommended) install [anaconda](https://www.anaconda.com/products/distribution) and create a virtual environment @@ -27,11 +43,11 @@ should launch napari (may take 15 seconds on a fresh installation) with the recO ## Install and configure `Micromanager` -Install `Micromanager 2.0` nightly build `20220920` (https://micro-manager.org/Micro-Manager_Nightly_Builds). +Download and install [`Micromanager 2.0` nightly build `20230426` (~150 MB link).](https://download.micro-manager.org/nightly/2.0/Windows/MMSetup_64bit_2.0.1_20230426.exe) -Note: We have tested recOrder with `20220920`, but most features will work with newer builds. We recommend testing a minimal installation with `20220920` before testing with a different nightly build or additional device drivers. +Note: We have tested recOrder with `20230426`, but most features will work with newer builds. We recommend testing a minimal installation with `20230426` before testing with a different nightly build or additional device drivers. -Before launching `Micromanager`, download the Meadowlark device adapters and calibration files from the [release page](https://github.com/mehta-lab/recOrder/releases/) and place these three unzipped files into your `Micromanager` folder (likely `C:\Program Files\Micro-Manager` or similar). +Before launching `Micromanager`, download the USB driver dll from the [release page](https://github.com/mehta-lab/recOrder/releases/) and place this pair of unzipped files into your `Micromanager` folder (likely `C:\Program Files\Micro-Manager` or similar). Launch `Micromanager`, open `Devices > Hardware Configuration Wizard...`, and add the `MeadowlarkLcOpenSource` device to your configuration. Confirm your installation by opening `Devices > Device Property Browser...` and confirming that `MeadowlarkLCOpenSource` properties appear. diff --git a/docs/reconstruction-guide.md b/docs/reconstruction-guide.md index bbf7990e..cbfd5924 100644 --- a/docs/reconstruction-guide.md +++ b/docs/reconstruction-guide.md @@ -1,13 +1,25 @@ # Automating reconstructions -`recOrder` is undergoing changes to the way that it handles automated reconstructions. +`recOrder` uses a configuration-file-based command-line interface (CLI) to perform all reconstructions. -`recOrder==0.2.0` had an offline mode with `config.yml` files that were used to configure reconstructions. These config files could be generated and saved via the GUI, and the reconstructions could be run via GUI or CLI. +## How can I use `recOrder`'s CLI to perform reconstructions? +`recOrder`'s CLI is summarized in the following figure: +<img src="./images/cli_structure.png" align="center"> -Although the offline mode had many valuable and convenient features, we found that it had diverged from the online mode and it was difficult to recreate results between online and offline modes. These design limitations led us to the following plan for our upcoming releases: +The main command `reconstruct` command is composed of two subcommands: `compute-tf` and `apply-inv-tf`. -`recOrder==0.3.0` (release candidate in the coming weeks) will remove the offline mode and use a set of scripts instead. We recommend modifying the reconstruction scripts in `recOrder/examples/` to recreate and automate reconstructions. +A reconstruction can be performed with a single `reconstruct` call. For example: +``` +recorder reconstruct data.zarr/0/0/0 -c config.yml -o reconstruction.zarr +``` +Equivalently, a reconstruction can be performed with a `compute-tf` call followed by an `apply-inv-tf` call. For example: +``` +recorder compute-tf data.zarr/0/0/0 -c config.yml -o tf.zarr +recorder apply-inv-tf data.zarr/0/0/0 tf.zarr -c config.yml -o reconstruction.zarr +``` +Computing the transfer function is typically the most expensive part of the reconstruction, so saving a transfer function then applying it to many datasets can save time. -Although these scripts are not as user friendly as a GUI+CLI solution, we are preparing for a much cleaner solution in `1.0.0`, and we appreciate your patience as we go through this change. We ask users who are having any difficulty with the scripts to [open an issue](https://github.com/mehta-lab/recOrder/issues/new/choose) or [send us an email](mailto:[email protected],[email protected]). +## What types of reconstructions are supported? +See `/recOrder/examples/` for a list of example configuration files. -`recOrder==1.0.0` will use a single mode to acquire and reconstruct the data. We are currently planning a refactor that will enable an "acquire once, quickly iterate your reconstruction" workflow, an "acquire now, reconstruct later" workflow, and a "live acquisition, live reconstruction" workflow, among others. +TODO: Expand this documentation...need docs for each reconstruction type and parameter. diff --git a/examples/3D-bf-to-2D-phase.py b/examples/3D-bf-to-2D-phase.py deleted file mode 100644 index c46b6552..00000000 --- a/examples/3D-bf-to-2D-phase.py +++ /dev/null @@ -1,64 +0,0 @@ -from iohub import read_micromanager, open_ome_zarr -from recOrder.compute.reconstructions import ( - initialize_reconstructor, - reconstruct_phase2D, -) -from recOrder.compute.phantoms import bf_3D_from_phantom -from datetime import datetime -import numpy as np -import napari - -timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - -## Load a dataset - -# Option 1: use random data and run this script as is. -data = bf_3D_from_phantom() # (Z, Y, X) - -# Option 2: load from file -# reader = read_micromanager("/path/to/ome-tiffs/or/zarr/store/") -# position, time, channel = 0, 0, 0 -# data = reader.get_array(position)[time, channel, ...] # read 3D volume - -## Set up a reconstructor. -Z, Y, X = data.shape -reconstructor_args = { - "image_dim": (Y, X), - "mag": 20, # magnification - "pixel_size_um": 6.5, # pixel size in um - "n_slices": Z, # number of slices in z-stack - "z_step_um": 2, # z-step size in um - "in_focus_slice": None, # integer, set to None to use the central slice - "wavelength_nm": 532, - "NA_obj": 0.4, # numerical aperture of objective - "NA_illu": 0.2, # numerical aperture of condenser - "n_obj_media": 1.0, # refractive index of objective immersion media - "mode": "2D", # phase reconstruction mode, "2D" or "3D" - "use_gpu": False, - "gpu_id": 0, -} -reconstructor = initialize_reconstructor( - pipeline="PhaseFromBF", reconstructor_args -) - -phase2D = reconstruct_phase2D( - data, reconstructor, method="Tikhonov", reg_p=1e0 -) -print(f"Shape of 2D phase data: {np.shape(phase2D)}") - -## Save to zarr -with open_ome_zarr( - "./output/reconstructions_" + timestamp + ".zarr", - layout="fov", - mode="w-", - channel_names=["Phase"], -) as dataset: - # Write to position "0", with length-one time, channel, and z dimensions - dataset["0"] = phase2D[np.newaxis, np.newaxis, np.newaxis, ...] - -# These lines open the reconstructed images -# Alternatively, drag and drop the zarr store into napari and use the recOrder-napari reader. -v = napari.Viewer() -v.add_image(data) -v.add_image(phase2D) -napari.run() diff --git a/examples/3D-bf-to-3D-phase.py b/examples/3D-bf-to-3D-phase.py deleted file mode 100644 index 8ab8b167..00000000 --- a/examples/3D-bf-to-3D-phase.py +++ /dev/null @@ -1,64 +0,0 @@ -from iohub import read_micromanager, open_ome_zarr -from recOrder.compute.reconstructions import ( - initialize_reconstructor, - reconstruct_phase3D, -) -from recOrder.compute.phantoms import bf_3D_from_phantom -from datetime import datetime -import numpy as np -import napari - -timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - -## Load a dataset - -# Option 1: use data simulated from a phantom and run this script as is. -data = bf_3D_from_phantom() # (Z, Y, X) - -# Option 2: load from file -# reader = read_micromanager('/path/to/ome-tiffs/or/zarr/store/') -# position, time, channel = 0, 0, 0 -# data = reader.get_array(position)[time, channel, ...] # read 3D volume - -## Set up a reconstructor. -Z, Y, X = data.shape -reconstructor_args = { - "image_dim": (Y, X), - "mag": 20, # magnification - "pixel_size_um": 6.5, # pixel size in um - "n_slices": Z, # number of slices in z-stack - "z_step_um": 2, # z-step size in um - "wavelength_nm": 532, - "NA_obj": 0.4, # numerical aperture of objective - "NA_illu": 0.2, # numerical aperture of condenser - "n_obj_media": 1.0, # refractive index of objective immersion media - "pad_z": 5, # slices to pad for phase reconstruction boundary artifacts - "mode": "3D", # phase reconstruction mode, "2D" or "3D" - "use_gpu": False, - "gpu_id": 0, -} -reconstructor = initialize_reconstructor( - pipeline="PhaseFromBF", reconstructor_args -) - -phase3D = reconstruct_phase3D( - data, reconstructor, method="Tikhonov", reg_re=1e-2 -) -print(f"Shape of 3D phase data: {np.shape(phase3D)}") - -## Save to zarr -with open_ome_zarr( - "./output/reconstructions_" + timestamp + ".zarr", - layout="fov", - mode="w-", - channel_names=["Phase"], -) as dataset: - # Write to position "0", with length-one time and channel dimensions - dataset["0"] = phase3D[np.newaxis, np.newaxis, ...] - -# These lines open the reconstructed images -# Alternatively, drag and drop the zarr store into napari and use the recOrder-napari reader. -v = napari.Viewer() -v.add_image(data) -v.add_image(phase3D) -napari.run() diff --git a/examples/3D-fluor-to-3D-density.py b/examples/3D-fluor-to-3D-density.py deleted file mode 100644 index b4a6665b..00000000 --- a/examples/3D-fluor-to-3D-density.py +++ /dev/null @@ -1,61 +0,0 @@ -from iohub import read_micromanager, open_ome_zarr -from recOrder.compute.reconstructions import ( - initialize_reconstructor, - reconstruct_density_from_fluorescence, -) -from recOrder.compute.phantoms import fluorescence_from_phantom -from datetime import datetime -import numpy as np -import napari - -timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - -## Load a dataset - -# Option 1: use data simulated from a phantom and run this script as is. -data = fluorescence_from_phantom() # (Z, Y, X) - -# Option 2: load from file -# reader = read_micromanager('/path/to/ome-tiffs/or/zarr/store/') -# position, time, channel = 0, 0, 0 -# data = reader.get_array(position)[time, channel, ...] # read 3D volume - -## Set up a reconstructor. -Z, Y, X = data.shape -reconstructor_args = { - "image_dim": (Y, X), - "mag": 20, # magnification - "pixel_size_um": 6.5, # pixel size in um - "n_slices": Z, # number of slices in z-stack - "z_step_um": 2, # z-step size in um - "wavelength_nm": 532, - "NA_obj": 0.4, # numerical aperture of objective - "n_obj_media": 1.0, # refractive index of objective immersion media - "pad_z": 5, # slices to pad for phase reconstruction boundary artifacts - "mode": "3D", # phase reconstruction mode, "2D" or "3D" - "use_gpu": False, - "gpu_id": 0, -} -reconstructor = initialize_reconstructor( - pipeline="fluorescence", reconstructor_args -) - -density = reconstruct_density_from_fluorescence(data, reconstructor, reg=1e-2) -print(f"Shape of 3D density data: {np.shape(density)}") - -## Save to zarr -with open_ome_zarr( - "./output/reconstructions_" + timestamp + ".zarr", - layout="fov", - mode="w-", - channel_names=["Density"], -) as dataset: - # Write to position "0", with length-one time and channel dimensions - dataset["0"] = density[np.newaxis, np.newaxis, ...] - -# These lines open the reconstructed images -# Alternatively, drag and drop the zarr store into napari and use the recOrder-napari reader. -v = napari.Viewer() -v.add_image(data) -v.add_image(density) -napari.run() diff --git a/examples/3D-pol-to-birefringence.py b/examples/3D-pol-to-birefringence.py deleted file mode 100644 index dfff646d..00000000 --- a/examples/3D-pol-to-birefringence.py +++ /dev/null @@ -1,101 +0,0 @@ -from iohub import read_micromanager, open_ome_zarr -from recOrder.io.utils import load_bg -from recOrder.compute.reconstructions import ( - initialize_reconstructor, - reconstruct_qlipp_stokes, - reconstruct_qlipp_birefringence, - reconstruct_phase3D, -) -from recOrder.compute.phantoms import pol_3D_from_phantom -from datetime import datetime -import numpy as np -import napari - -timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - -## Load a dataset - -# Option 1: use random data and run this script as is. -data, bg_data = pol_3D_from_phantom() # (C, Z, Y, X) and (C, Y, X) -C, Z, Y, X = data.shape - -# Option 2: load from file -# reader = read_micromanager('/path/to/ome-tiffs/or/zarr/store/') -# position, time = 0, 0 -# data = reader.get_array(position)[time, ...] -# C, Z, Y, X = data.shape -# bg_data = load_bg("/path/to/recorder/BG", height=Y, width=X) - -## Set up a reconstructor. -reconstructor_args = { - "image_dim": (Y, X), - "mag": 20, # magnification - "pixel_size_um": 6.5, # pixel size in um - "n_slices": Z, # number of slices in z-stack - "z_step_um": 2, # z-step size in um - "wavelength_nm": 532, - "swing": 0.1, - "calibration_scheme": "5-State", # "4-State" or "5-State" - "NA_obj": 0.4, # numerical aperture of objective - "NA_illu": 0.2, # numerical aperture of condenser - "n_obj_media": 1.0, # refractive index of objective immersion media - "pad_z": 5, # slices to pad for phase reconstruction boundary artifacts - "bg_correction": "local_fit", # BG correction method: "None", "local_fit", "global" - "mode": "3D", # phase reconstruction mode, "2D" or "3D" - "use_gpu": False, - "gpu_id": 0, -} -reconstructor = initialize_reconstructor( - pipeline="QLIPP", reconstructor_args -) - -# Reconstruct background Stokes -bg_stokes = reconstruct_qlipp_stokes(bg_data, reconstructor) -print(f"Shape of BG Stokes: {np.shape(bg_stokes)}") - -# Reconstruct data Stokes w/ background correction -stokes = reconstruct_qlipp_stokes(data, reconstructor, bg_stokes) -print(f"Shape of background corrected data Stokes: {np.shape(stokes)}") - -# Reconstruct Birefringence from Stokes -# Shape of the output birefringence will be (C, Z, Y, X) where -# Channel order = Retardance [nm], Orientation [rad], Brightfield (S0), Degree of Polarization -birefringence = reconstruct_qlipp_birefringence(stokes, reconstructor) -birefringence[0] = ( - birefringence[0] / (2 * np.pi) * reconstructor_args["wavelength_nm"] -) -print(f"Shape of birefringence data: {np.shape(birefringence)}") - -# Reconstruct Phase3D from S0 -S0 = birefringence[2] - -phase3D = reconstruct_phase3D( - S0, reconstructor, method="Tikhonov", reg_re=1e-2 -) -print(f"Shape of 3D phase data: {np.shape(phase3D)}") - -## Save to zarr -with open_ome_zarr( - "./output/reconstructions_" + timestamp + ".zarr", - layout="fov", - mode="w-", - channel_names=[ - "Retardance", - "Orientation", - "BF - computed", - "DoP", - "Phase", - ], -) as dataset: - img = dataset.create_zeros("0", (1, 5, Z, Y, X), dtype=birefringence.dtype) - img[0, 0:4] = birefringence - img[0, 4] = phase3D - -# These lines open the reconstructed images -# Alternatively, drag and drop the zarr store into napari and use the recOrder-napari reader. -v = napari.Viewer() -v.add_image(data) -v.add_image(phase3D) -v.add_image(birefringence, contrast_limits=(0, 25)) -v.dims.current_step = (0, 5, 256, 256) -napari.run() diff --git a/examples/README.md b/examples/README.md index 732ef878..adde2a8e 100644 --- a/examples/README.md +++ b/examples/README.md @@ -1,61 +1,74 @@ -# `recOrder` example scripts +# `recOrder` CLI examples -These scripts demonstrate four types of reconstruction: +`recOrder` uses a configuration-file-based command-line inferface (CLI) to +calculate transfer functions and apply these transfer functions to datasets. -1. 3D brightfield data to 3D phase in `3D-bf-to-3D-phase.py`. -2. 3D brightfield data to 2D phase in `3D-bf-to-2D-phase.py`. -3. 3D + polarization data to retardance, orientation, and 3D phase in `3D-pol-to-birefringence.py`. -4. 3D fluorescence data to 3D fluorophore density (fluorescence deconvolution) in `3D-fluor-to-3D-density.py`. +This page demonstrates `recOrder`'s CLI. ## Getting started ### 1. Check your installation -First, [install `recOrder`](../docs/software-installation-guide.md) and run any of these scripts with +First, [install `recOrder`](../docs/software-installation-guide.md) and run ```bash -python <script-name.py> +recOrder +``` +in a shell. If `recOrder` is installed correctly, you will see a usage string and +``` +recOrder: Computational Toolkit for Label-Free Imaging ``` -Running these scripts without modification will test your installation by running a reconstruction on a simulated phantom. A successful run will open a `napari` window with simulated data and a reconstruction. - -### 2. Reconstruct test data -Next, [download the test data from zenodo (47 MB)](https://zenodo.org/record/6983916/files/recOrder_test_data.zip?download=1), and modify the script to load the test data. For example, in `3D-bf-to-2D-phase.py`, replace `/path/to/ome-tiffs/or/zarr/store/` with `/path/to/recOrder_test_data/2022_08_04_recOrder_pytest_20x_04NA_BF/2T_3P_16Z_128Y_256X_Kazansky_BF_1`. - -### 3. Load and reconstruct your data -Start by loading your data (our readers currently support `.tiff`, `ome.tiff`, `NDTiff`, and `.zarr`, but any `numpy` array will work). Fill in your imaging parameters (or connect your metadata to these parameters), and prototype with this modified script. +### 2. Download and convert a test dataset +Next, [download the test data from zenodo (47 MB)](https://zenodo.org/record/6983916/files/recOrder_test_data.zip?download=1), and convert a dataset to the latest version of `.zarr` with +``` +cd /path/to/ +iohub convert -i /path/to/recOrder_test_data/2022_08_04_recOrder_pytest_20x_04NA/2T_3P_16Z_128Y_256X_Kazansky_1/ +-o ./dataset.zarr +``` -We recommend prototyping a reconstruction with a single position and time point so that you can perform initial iterations quickly. +You can view the test dataset with +``` +napari ./dataset.zarr --plugin recOrder-napari +``` -### 4. Sweep your reconstruction parameters +### 3. Run a reconstruction +Run an example reconstruction with +``` +recOrder reconstruct ./dataset.zarr/0/0/0 -c /path/to/recOrder/examples/settings/birefringence-and-phase.yml -o ./reconstruction.zarr +``` +then view the reconstruction with +``` +napari ./reconstruction.zarr --plugin recOrder-napari +``` -You may need to test several parameters to find a value that yields the best results for your application. For example, choosing a regularization parameter is commonly semi-empirical: we recommend choosing a regularization parameter that gives results that aren't too noisy or too smooth. +Try modifying the configuration file to see how the regularization parameter changes the results. -The `sweep-regularization.py` script demonstrates a `3D-bf-to-2D-phase` reconstruction with multiple regularization parameters. We recommend running and understanding this script before modifying your reconstruction script to sweep over regularization or other uncertain parameters to help you settle on a set of reconstruction parameters. +### 4. Parallelize over positions or time points -### 5. Reconstruct a multi-modal dataset in a single script +TODO: @Ed I think we'll use the same strategy as ```mantis deskew``` -You may need to perform several reconstructions on a multi-modal dataset. For example, you would like to perform a `3D-fluor-to-3D-density` reconstruction on the fluorescence channels and a `3D-bf-2D-phase` reconstruction on the brightfield channel. +## FAQ +1. Q: Which configuration file should I use? -The `multi-modal-recon.py` script demonstrates this type of reconstruction. We recommend running and understanding this script before modifying single-modality reconstructions to run a multi-modal reconstruction. + If you are acquiring: -### 6. Parallelize over positions or time points + 3D data with calibrated liquid-crystal polarizers via `recOrder` use `birefringence.yml`. -Once you've settled on a script that performs a reconstruction, the script can be applied to multiple datasets with a python `for` loop (slowest), `multiprocessing` (faster, see `parallel-reconstruct.py` for an example), or batch processing with an HPC scheduler e.g. `slurm` (fastest). + 3D fluorescence data use `fluorescence.yml`. -## FAQ -1. Q: Which script should I use? + 3D brightfield data use `phase.yml`. - If you are acquiring: + Multi-modal data, start by reconstructing the individual modaliities, each with a single config file and CLI call. Then combine the reconstructions by *TODO: @Ziwen do can you help me append to the zarrs to help me fix this? * - 3D data with calibrated liquid-crystal polarizers via `recOrder` use `3D-pol-to-birefringence.py`. +2. Q: Should I use `reconstruction_dimension` = 2 or 3? - 3D fluorescence data use `3D-fluor-to-3D-density.py`. + If your downstream processing requires 3D information or if you're unsure, then you should use `reconstruction_dimension = 3`. If your sample is very thin compared to the depth of field of the microscope, if you're in a noise-limited regime, or if your downstream processing requires 2D information, then you should use `reconstruction_dimension = 2`. Empirically, we have found that 2D reconstructions reduce the noise in our reconstructions because it uses 3D information to make a single estimate for each pixel. - 3D brightfield data use `3D-bf-to-3D-phase.py` or `3D-bf-to-2D-phase.py`, and decide if you need a 3D or 2D phase reconstruction. +3. Q: What regularization parameter should I use? - If your downstream processing requires 3D information or if you're unsure, then you should use `3D-bf-to-3D-phase.py`. If your sample is very thin compared to the depth of field of the microscope, if you're in a noise-limited regime, or if your downstream processing requires 2D phase information, then you should use `3D-bf-to-2D-phase.py`. Empirically, we have found that `3D-bf-to-2D-phase.py` reduces the noise in our reconstructions because it uses 3D information to make a single phase estimate for each pixel. + We recommend starting with the defaults then testing over a few orders of magnitude and choosing a result that isn't too noisy or too smooth. - Multi-modal data, start by reconstructing the individual modaliities, then combine the reconstructions using `multi-modal-recon.py` as a guide. +### Developers note -2. Q: What regularization parameter should I use? +These configuration files are automatically generated when the tests run. See `/tests/cli_tests/test_settings.py` - `test_generate_example_settings`. - We recommend starting with the defaults then testing over a few orders of magnitude and choosing a result that isn't too noisy or too smooth. +To keep these settings up to date, run `pytest` locally when `cli/settings.py` changes. diff --git a/examples/birefringence-and-phase.yml b/examples/birefringence-and-phase.yml new file mode 100644 index 00000000..fcf30b0c --- /dev/null +++ b/examples/birefringence-and-phase.yml @@ -0,0 +1,30 @@ +input_channel_names: +- State0 +- State1 +- State2 +- State3 +reconstruction_dimension: 3 +birefringence: + transfer_function: + swing: 0.1 + apply_inverse: + wavelength_illumination: 0.532 + background_path: '' + remove_estimated_background: false + orientation_flip: false + orientation_rotate: false +phase: + transfer_function: + wavelength_illumination: 0.532 + yx_pixel_size: 0.325 + z_pixel_size: 2.0 + z_padding: 0 + index_of_refraction_media: 1.3 + numerical_aperture_detection: 1.2 + numerical_aperture_illumination: 0.5 + axial_flip: false + apply_inverse: + reconstruction_algorithm: Tikhonov + regularization_strength: 0.001 + TV_rho_strength: 0.001 + TV_iterations: 1 diff --git a/examples/birefringence.yml b/examples/birefringence.yml new file mode 100644 index 00000000..45ffd268 --- /dev/null +++ b/examples/birefringence.yml @@ -0,0 +1,15 @@ +input_channel_names: +- State0 +- State1 +- State2 +- State3 +reconstruction_dimension: 3 +birefringence: + transfer_function: + swing: 0.1 + apply_inverse: + wavelength_illumination: 0.532 + background_path: '' + remove_estimated_background: false + orientation_flip: false + orientation_rotate: false diff --git a/examples/fluorescence.yml b/examples/fluorescence.yml new file mode 100644 index 00000000..02d47666 --- /dev/null +++ b/examples/fluorescence.yml @@ -0,0 +1,16 @@ +input_channel_names: +- GFP +reconstruction_dimension: 3 +fluorescence: + transfer_function: + yx_pixel_size: 0.325 + z_pixel_size: 2.0 + z_padding: 0 + index_of_refraction_media: 1.3 + numerical_aperture_detection: 1.2 + wavelength_emission: 0.507 + apply_inverse: + reconstruction_algorithm: Tikhonov + regularization_strength: 0.001 + TV_rho_strength: 0.001 + TV_iterations: 1 diff --git a/examples/multi-modal-recon.py b/examples/multi-modal-recon.py deleted file mode 100644 index c9aadff6..00000000 --- a/examples/multi-modal-recon.py +++ /dev/null @@ -1,97 +0,0 @@ -from iohub import read_micromanager, open_ome_zarr -from recOrder.compute.reconstructions import ( - initialize_reconstructor, - reconstruct_phase3D, - reconstruct_density_from_fluorescence, -) -from recOrder.compute.phantoms import ( - bf_3D_from_phantom, - fluorescence_from_phantom, -) -from datetime import datetime -import numpy as np -import napari - -timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - -## Load a dataset - -## Option 1: use data simulated from a phantom and run this script as is. -bf_data = bf_3D_from_phantom() # (Z, Y, X) -fluor_data = fluorescence_from_phantom() # (Z, Y, X) - -## Option 2: load from file. Uncomment all lines with a single #. -# reader = read_micromanager('/path/to/ome-tiffs/or/zarr/store/') -# position, time = 0, 0 - -## Set these by hand or use `reader.channel_names.index('BF')` to read from metadata -# bf_index = 0 -# fluor_index = 1 - -## Read -# bf_data = reader.get_array(position)[time, bf_index, ...] -# fluor_data = reader.get_array(position)[time, fluor_index, ...] - -## Set up the reconstructors. - -# Set arguments that are shared by both modalities -reconstructor_args = { - "image_dim": bf_data.shape[1:], # (Y, X) - "mag": 20, # magnification - "pixel_size_um": 6.5, # pixel size in um - "z_step_um": 2, # z-step size in um - "NA_obj": 0.4, # numerical aperture of objective - "NA_illu": 0.2, # numerical aperture of condenser - "n_obj_media": 1.0, # refractive index of objective immersion media - "pad_z": 5, # slices to pad for phase reconstruction boundary artifacts - "mode": "3D", # phase reconstruction mode, "2D" or "3D" - "use_gpu": False, - "gpu_id": 0, -} - -# Initialize reconstructors with parameters that are not shared -bf_reconstructor = initialize_reconstructor( - pipeline="PhaseFromBF", - wavelength_nm=532, - n_slices=bf_data.shape[0], - reconstructor_args -) - -fluor_reconstructor = initialize_reconstructor( - pipeline="fluorescence", - wavelength_nm=450, - n_slices=fluor_data.shape[0], - reconstructor_args -) - -# Reconstruct -phase = reconstruct_phase3D( - bf_data, bf_reconstructor, method="Tikhonov", reg_re=1e-2 -) - -density = reconstruct_density_from_fluorescence( - fluor_data, fluor_reconstructor, reg=1e-2 -) - -## Save to zarr -with open_ome_zarr( - "./output/reconstructions_" + timestamp + ".zarr", - layout="fov", - mode="w-", - channel_names=["Phase"], -) as dataset: - # Write to position "0", with length-one time dimension - dataset["0"] = phase[np.newaxis, np.newaxis, ...] - dataset.append_channel("Density") - dataset["0"][0, -1] = density - -# These lines open the reconstructed images -# Alternatively, drag and drop the zarr store into napari and use the recOrder-napari reader. -v = napari.Viewer() -v.add_image(bf_data) -v.add_image(fluor_data) -v.add_image(phase) -v.add_image(density) -v.dims.current_step = (15, 256, 256) - -napari.run() diff --git a/examples/parallel-reconstruct.py b/examples/parallel-reconstruct.py deleted file mode 100644 index 8cfc4ec3..00000000 --- a/examples/parallel-reconstruct.py +++ /dev/null @@ -1,184 +0,0 @@ -import shutil -import os -import numpy as np -import multiprocessing as mp -from wget import download -from datetime import datetime -from iohub import open_ome_zarr -from iohub.convert import TIFFConverter -from recOrder.io.utils import load_bg -from recOrder.compute.reconstructions import ( - initialize_reconstructor, - reconstruct_qlipp_stokes, - reconstruct_qlipp_birefringence, - reconstruct_phase3D, -) - -timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - -# This example demonstrates a 3D-pol-to-birfringence reconstruction applied -# to each position and time point of a micromanager dataset. The example -# uses multiprocessing to apply the reconstructions in parallel. - -# This example will download a ~50 MB micro-manager test dataset to your -# working directory, convert it to a zarr store, then reconstruct physical -# paramaters from each position and time point. - -# Choose the number of processes to parallel over. -# You should have at least this many cores available with enough RAM -# available to each process to complete the reconstruction. -N_processes = 6 - - -def precomputation(): - - # download test data and convert to zarr - # use this section for a test run, then delete it and load directly from - # a zarr store for a real reconstruction run - data_folder = os.path.join(os.getcwd(), "data_temp") - os.makedirs(data_folder, exist_ok=True) - url = "https://zenodo.org/record/6983916/files/recOrder_test_data.zip?download=1" - zip_file = "recOrder_test_Data.zip" - output = os.path.join(data_folder, zip_file) - if not os.path.exists(output): - print("Downloading test files...") - download(url, out=output) - shutil.unpack_archive(output, extract_dir=data_folder) - - temp_path = os.path.join(data_folder, timestamp + "_temp.zarr") - converter = TIFFConverter( - os.path.join( - data_folder, - "2022_08_04_recOrder_pytest_20x_04NA/2T_3P_16Z_128Y_256X_Kazansky_1", - ), - temp_path, - ) - converter.run() - - # setup input reader - reader = open_ome_zarr(temp_path, mode="r+") - T, C, Z, Y, X = reader["0/0/0"].data.shape - dtype = reader["0/0/0"].data.dtype - P = len(list(reader.positions())) - bg_data = load_bg( - os.path.join(data_folder, "2022_08_04_recOrder_pytest_20x_04NA/BG/"), - height=Y, - width=X, - ) - - # setup reconstructor. - reconstructor_args = { - "image_dim": (Y, X), - "mag": 20, # magnification - "pixel_size_um": 6.5, # pixel size in um - "n_slices": Z, # number of slices in z-stack - "z_step_um": 2, # z-step size in um - "wavelength_nm": 532, - "swing": 0.1, - "calibration_scheme": "4-State", # "4-State" or "5-State" - "NA_obj": 0.4, # numerical aperture of objective - "NA_illu": 0.2, # numerical aperture of condenser - "n_obj_media": 1.0, # refractive index of objective immersion media - "pad_z": 5, # slices to pad for phase reconstruction boundary artifacts - "bg_correction": "global", # BG correction method: "None", "local_fit", "global" - "mode": "3D", # phase reconstruction mode, "2D" or "3D" - "use_gpu": False, - "gpu_id": 0, - } - reconstructor = initialize_reconstructor( - pipeline="QLIPP", reconstructor_args - ) - - # reconstruct background Stokes - bg_stokes = reconstruct_qlipp_stokes(bg_data, reconstructor) - - # setup output zarr - writer = open_ome_zarr( - "./output/reconstructions_" + timestamp + ".zarr", - layout="hcs", - mode="w-", - channel_names=[ - "Retardance", - "Orientation", - "BF - computed", - "DoP", - "Phase", - ], - ) - for p in range(P): - position = writer.create_position("0", str(p), "0") - position.create_zeros(name="0", shape=(T, 5, Z, Y, X), dtype=dtype) - - return reader, writer, reconstructor, bg_stokes - - -# define the work to be done on a single process -# in this example a single process will loop through a set of CZYX volumes, and -# for each volume it will read the data, apply a reconstruction, then save the -# result to a hcs-format zarr -def single_process( - reader, writer, reconstructor, bg_stokes, vol_start, vol_end -): - T, C, Z, Y, X = reader["0/0/0"].data.shape - for vol in range(vol_start, vol_end + 1): - p = int(np.floor(vol / T)) - t = int(vol % T) - print(f"Reconstructing vol={vol}, pos={p}, time={t}") - - # read from input zarr - data = reader["0/" + str(p) + "/0"]["0"][t] - - # reconstruct data Stokes w/ background correction - stokes = reconstruct_qlipp_stokes(data, reconstructor, bg_stokes) - - # reconstruct birefringence from Stokes - birefringence = reconstruct_qlipp_birefringence(stokes, reconstructor) - birefringence[0] = ( - birefringence[0] / (2 * np.pi) * (reconstructor.lambda_illu / 1000) - ) - - # reconstruct phase3D from S0 - S0 = birefringence[2] - phase3D = reconstruct_phase3D( - S0, reconstructor, method="Tikhonov", reg_re=1e-2 - ) - - # write output - writer["0/" + str(p) + "/0"]["0"][t, 0:4] = birefringence - writer["0/" + str(p) + "/0"]["0"][t, 4] = phase3D - - -# prepare the processes -# we will apply the same reconstruction to each position and time point, so we -# need to split the CZYX volumes among the processes -if __name__ == "__main__": - reader, writer, reconstructor, bg_stokes = precomputation() - processes = [] - V = len(list(reader.positions())) * reader["0/0/0"].data.shape[0] - vol_per_process = int(np.ceil(V / N_processes)) - print("vol_per_process", vol_per_process) - for i in range(N_processes): - vol_start = i * vol_per_process - vol_end = np.minimum(vol_start + vol_per_process - 1, V - 1) - print(f"Preparing volumes {vol_start}-{vol_end} for process {i}") - processes.append( - mp.Process( - target=single_process, - args=( - reader, - writer, - reconstructor, - bg_stokes, - vol_start, - vol_end, - ), - ) - ) - print("Starting processes...") - # run the processes - for process in processes: - process.start() - for process in processes: - process.join() - - writer.close() diff --git a/examples/phase.yml b/examples/phase.yml new file mode 100644 index 00000000..d48c6aa6 --- /dev/null +++ b/examples/phase.yml @@ -0,0 +1,18 @@ +input_channel_names: +- BF +reconstruction_dimension: 3 +phase: + transfer_function: + wavelength_illumination: 0.532 + yx_pixel_size: 0.325 + z_pixel_size: 2.0 + z_padding: 0 + index_of_refraction_media: 1.3 + numerical_aperture_detection: 1.2 + numerical_aperture_illumination: 0.5 + axial_flip: false + apply_inverse: + reconstruction_algorithm: Tikhonov + regularization_strength: 0.001 + TV_rho_strength: 0.001 + TV_iterations: 1 diff --git a/examples/sweep-regularization.py b/examples/sweep-regularization.py deleted file mode 100644 index fca6570c..00000000 --- a/examples/sweep-regularization.py +++ /dev/null @@ -1,75 +0,0 @@ -from iohub import read_micromanager, open_ome_zarr -from recOrder.compute.reconstructions import ( - initialize_reconstructor, - reconstruct_phase3D, -) -from recOrder.compute.phantoms import bf_3D_from_phantom -from datetime import datetime -import numpy as np -import napari - -timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - -## Load a dataset - -# Option 1: use data simulated from a phantom and run this script as is. -data = bf_3D_from_phantom() # (Z, Y, X) - -# Option 2: load from file -# reader = read_micromanager('/path/to/ome-tiffs/or/zarr/store/') -# position, time, channel = 0, 0, 0 -# data = reader.get_array(position)[time, channel, ...] # read 3D volume - -## Set up a reconstructor. -Z, Y, X = data.shape -reconstructor_args = { - "image_dim": (Y, X), - "mag": 20, # magnification - "pixel_size_um": 6.5, # pixel size in um - "n_slices": Z, # number of slices in z-stack - "z_step_um": 2, # z-step size in um - "wavelength_nm": 532, - "NA_obj": 0.4, # numerical aperture of objective - "NA_illu": 0.2, # numerical aperture of condenser - "n_obj_media": 1.0, # refractive index of objective immersion media - "pad_z": 5, # slices to pad for phase reconstruction boundary artifacts - "mode": "3D", # phase reconstruction mode, "2D" or "3D" - "use_gpu": False, - "gpu_id": 0, -} -reconstructor = initialize_reconstructor( - pipeline="PhaseFromBF", reconstructor_args -) - -# Setup a single writer and viewer -reg_powers = 10.0 np.arange(-3, 3) - -dataset = open_ome_zarr( - "./output/reconstructions_" + timestamp + ".zarr", - layout="fov", - mode="w-", - channel_names=[f"{reg:.1e}" for reg in reg_powers], -) - -dataset.create_zeros( - "0", shape=(1, len(reg_powers), Z, Y, X), dtype=np.float32 -) - -v = napari.Viewer() -v.add_image(data) - -# Loop through regularizations -for i, reg in enumerate(reg_powers): - print(f"Reconstructing with 3D phase with reg = {reg}") - - phase3D = reconstruct_phase3D( - data, reconstructor, method="Tikhonov", reg_re=reg - ) - - # Save each regularization into a "position" of the output zarr - dataset["0"][0, i, ...] = phase3D - - # Add the reconstructions to the viewer - v.add_image(phase3D, name=f"reg = {reg}") - -napari.run() diff --git a/recOrder/acq/acquisition_workers.py b/recOrder/acq/acquisition_workers.py index 25dd073f..91bbe938 100644 --- a/recOrder/acq/acquisition_workers.py +++ b/recOrder/acq/acquisition_workers.py @@ -4,31 +4,25 @@ from qtpy.QtCore import Signal from iohub import open_ome_zarr from iohub.convert import TIFFConverter -from recOrder.compute.reconstructions import ( - initialize_reconstructor, - reconstruct_qlipp_birefringence, - reconstruct_qlipp_stokes, - reconstruct_phase2D, - reconstruct_phase3D, +from recOrder.cli import settings +from recOrder.cli.compute_transfer_function import ( + compute_transfer_function_cli, +) +from recOrder.cli.apply_inverse_transfer_function import ( + apply_inverse_transfer_function_cli, ) from recOrder.acq.acq_functions import ( generate_acq_settings, acquire_from_settings, ) -from recOrder.io.utils import load_bg, extract_reconstruction_parameters -from recOrder.compute.reconstructions import QLIPPBirefringenceCompute -from recOrder.io.metadata_reader import MetadataReader, get_last_metadata_file -from recOrder.io.utils import ram_message, rec_bkg_to_wo_bkg +from recOrder.io.utils import model_to_yaml +from recOrder.io.utils import ram_message from napari.qt.threading import WorkerBaseSignals, WorkerBase from napari.utils.notifications import show_warning import logging -import tifffile as tiff import numpy as np import os -import zarr import shutil -import time -import glob # type hint/check from typing import TYPE_CHECKING @@ -39,6 +33,81 @@ from recOrder.calib.Calibration import QLIPP_Calibration +def _generate_reconstruction_config_from_gui( + reconstruction_config_path, + mode, + calib_window, + input_channel_names, +): + if mode == "birefringence" or mode == "all": + if calib_window.bg_option == "None": + background_path = "" + remove_estimated_background = False + elif calib_window.bg_option == "Measured": + background_path = calib_window.acq_bg_directory + remove_estimated_background = False + elif calib_window.bg_option == "Estimated": + background_path = "" + remove_estimated_background = True + elif calib_window.bg_option == "Measured + Estimated": + background_path = calib_window.acq_bg_directory + remove_estimated_background = True + + birefringence_transfer_function_settings = ( + settings.BirefringenceTransferFunctionSettings( + swing=calib_window.swing, + ) + ) + birefringence_apply_inverse_settings = settings.BirefringenceApplyInverseSettings( + wavelength_illumination=calib_window.recon_wavelength / 1000, + background_path=background_path, + remove_estimated_background=remove_estimated_background, + orientation_flip=False, # TODO connect this to a recOrder button + orientation_rotate=False, # TODO connect this to a recOrder button + ) + birefringence_settings = settings.BirefringenceSettings( + transfer_function=birefringence_transfer_function_settings, + apply_inverse=birefringence_apply_inverse_settings, + ) + else: + birefringence_settings = None + + if mode == "phase" or mode == "all": + phase_transfer_function_settings = ( + settings.PhaseTransferFunctionSettings( + wavelength_illumination=calib_window.recon_wavelength / 1000, + yx_pixel_size=calib_window.ps / calib_window.mag, + z_pixel_size=calib_window.z_step, + z_padding=calib_window.pad_z, + index_of_refraction_media=calib_window.n_media, + numerical_aperture_detection=calib_window.obj_na, + numerical_aperture_illumination=calib_window.cond_na, + axial_flip=False, # TODO: connect this to a recOrder button + ) + ) + phase_apply_inverse_settings = settings.FourierApplyInverseSettings( + reconstruction_algorithm=calib_window.phase_regularizer, + regularization_strength=calib_window.ui.le_phase_strength.text(), + TV_rho_strength=calib_window.ui.le_rho.text(), + TV_iterations=calib_window.ui.le_itr.text(), + ) + phase_settings = settings.PhaseSettings( + transfer_function=phase_transfer_function_settings, + apply_inverse=phase_apply_inverse_settings, + ) + else: + phase_settings = None + + reconstruction_settings = settings.ReconstructionSettings( + input_channel_names=input_channel_names, + reconstruction_dimension=int(calib_window.acq_mode[0]), + birefringence=birefringence_settings, + phase=phase_settings, + ) + + model_to_yaml(reconstruction_settings, reconstruction_config_path) + + class PolarizationAcquisitionSignals(WorkerBaseSignals): """ Custom Signals class that includes napari native signals @@ -60,16 +129,6 @@ class BFAcquisitionSignals(WorkerBaseSignals): aborted = Signal() -class ListeningSignals(WorkerBaseSignals): - """ - Custom Signals class that includes napari native signals - """ - - store_emitter = Signal(object) - dim_emitter = Signal(tuple) - aborted = Signal() - - class BFAcquisitionWorker(WorkerBase): """ Class to execute a brightfield acquisition. First step is to snap the images follow by a second @@ -162,6 +221,17 @@ def work(self): channel_group = group break + # Create and validate reconstruction settings + self.config_path = os.path.join( + self.snap_dir, "reconstruction_settings.yml" + ) + _generate_reconstruction_config_from_gui( + self.config_path, + "phase", + self.calib_window, + input_channel_names=["BF"], + ) + # Acquire 3D stack logging.debug("Acquiring 3D stack") @@ -196,14 +266,14 @@ def work(self): # Reconstruct snapped images self.n_slices = stack.shape[2] - phase, meta = self._reconstruct(stack[0]) + phase = self._reconstruct() self._check_abort() - # Save images - logging.debug("Saving Images") - self._save_imgs(phase, meta) + # # Save images + # logging.debug("Saving Images") + # self._save_imgs(phase, meta) - self._check_abort() + # self._check_abort() logging.info("Finished Acquisition") logging.debug("Finished Acquisition") @@ -211,221 +281,41 @@ def work(self): # Emit the images and let thread know function is finished self.phase_image_emitter.emit(phase) - def _reconstruct(self, stack): + def _reconstruct(self): """ - Method to reconstruct, given a 2D or 3D stack. - This function also checks to see if the reconstructor needs to be updated from previous acquisitions - - Parameters - ---------- - stack: (nd-array) Dimensions are (C, Z, Y, X) - - Returns - ------- - + Method to reconstruct """ - - self.img_dim = (stack.shape[-2], stack.shape[-1], stack.shape[-3]) - self._check_abort() - - # Initialize the reconstuctor - - # if no reconstructor has been initialized before, create new reconstructor - if not self.calib_window.phase_reconstructor: - logging.debug("Computing new reconstructor") - - recon = initialize_reconstructor( - "PhaseFromBF", - image_dim=(stack.shape[-2], stack.shape[-1]), - wavelength_nm=self.calib_window.recon_wavelength, - NA_obj=self.calib_window.obj_na, - NA_illu=self.calib_window.cond_na, - mag=self.calib_window.mag, - n_slices=self.img_dim[-1], - z_step_um=self.calib_window.z_step, - pad_z=self.calib_window.pad_z, - pixel_size_um=self.calib_window.ps, - n_obj_media=self.calib_window.n_media, - mode=self.calib_window.acq_mode, - use_gpu=self.calib_window.use_gpu, - gpu_id=self.calib_window.gpu_id, - ) - - # Emit reconstructor to be saved for later reconstructions - self.phase_reconstructor_emitter.emit(recon) - - # if previous reconstructor exists - else: - self._check_abort() - - # compute new reconstructor if the old reconstructor properties have been modified - if self._reconstructor_changed(stack.shape): - logging.debug( - "Reconstruction settings changed, updating reconstructor" - ) - - recon = initialize_reconstructor( - "PhaseFromBF", - image_dim=(stack.shape[-2], stack.shape[-1]), - wavelength_nm=self.calib_window.recon_wavelength, - NA_obj=self.calib_window.obj_na, - NA_illu=self.calib_window.cond_na, - mag=self.calib_window.mag, - n_slices=self.n_slices, - z_step_um=self.calib_window.z_step, - pad_z=self.calib_window.pad_z, - pixel_size_um=self.calib_window.ps, - n_obj_media=self.calib_window.n_media, - mode=self.calib_window.acq_mode, - use_gpu=self.calib_window.use_gpu, - gpu_id=self.calib_window.gpu_id, - ) - - # Emit reconstructor to be saved for later reconstructions - self.phase_reconstructor_emitter.emit(recon) - - # use previous reconstructor - else: - logging.debug("Using previous reconstruction settings") - recon = self.calib_window.phase_reconstructor - - # Begin reconstruction with stokes (needed for birefringence or phase) - logging.debug("Reconstructing...") self._check_abort() - regularizer = self.calib_window.phase_regularizer - reg = float(self.calib_window.ui.le_phase_strength.text()) - - # Perform deconvolution - if self.dim == "2D": - phase = reconstruct_phase2D( - stack[0], - recon, - method=regularizer, - reg_p=reg, - itr=int(self.calib_window.ui.le_itr.text()), - rho=float(self.calib_window.ui.le_rho.text()), - ) - else: - phase = reconstruct_phase3D( - stack[0], - recon, - method=regularizer, - reg_re=reg, - itr=int(self.calib_window.ui.le_itr.text()), - rho=float(self.calib_window.ui.le_rho.text()), - ) - - self._check_abort() - - # Update metadata in zarr attributes with reconstruction parameters - meta = extract_reconstruction_parameters( - recon, magnification=self.calib_window.mag + # Create i/o paths + transfer_function_path = os.path.join( + self.snap_dir, "transfer_function.zarr" + ) + reconstruction_path = os.path.join( + self.snap_dir, "reconstruction.zarr" ) - meta["regularization_method"] = regularizer - meta["regularization_strength"] = reg - if regularizer == "TV": - meta["rho"] = float(self.calib_window.ui.le_rho.text()) - meta["itr"] = int(self.calib_window.ui.le_itr.text()) - - # return both variables, could contain images or could be null - return phase, meta - - def _save_imgs(self, phase, meta=None): - """ - function to save images. - Parameters - ---------- - phase: (nd-array or None) phase image or stack + input_data_path = os.path.join(self.latest_out_path, "0", "0", "0") - Returns - ------- - - """ - prefix = self.calib_window.save_name - name = ( - f"ReconstructionSnap.zarr" - if not prefix - else f"{prefix}_ReconstructionSnap.zarr" + # TODO: skip if config files match + compute_transfer_function_cli( + input_data_path, + self.config_path, + transfer_function_path, ) - with open_ome_zarr( - os.path.join(self.snap_dir, name), - layout="fov", - mode="w-", - channel_names=["Phase" + str(phase.ndim) + "D"], - ) as dataset: - dataset["0"] = phase[ - (5 - phase.ndim) * (np.newaxis,) + (Ellipsis,) - ] - dataset.zattrs["recOrder"] = meta - - def _reconstructor_changed(self, stack_shape: tuple): - """Function to check if the reconstructor has changed from the previous one in memory. - Serves to check if the worker attributes and reconstructor attributes have diverged. + apply_inverse_transfer_function_cli( + input_data_path, + transfer_function_path, + self.config_path, + reconstruction_path, + ) - Parameters - ---------- - stack_shape : tuple - shape of the stack array to reconstruct + # Read reconstruction to pass to emitters + with open_ome_zarr(reconstruction_path, mode="r") as dataset: + phase = dataset["0"][0] - Returns - ------- - bool - whether a new reconstructor should be initialized - """ - # Attributes that are directly equivalent to worker attributes - attr_list = { - "acq_mode": "phase_deconv", - "pad_z": "pad_z", - "n_media": "n_media", - "use_gpu": "use_gpu", - "gpu_id": "gpu_id", - } - # attributes that are modified upon passing them to reconstructor - attr_modified_list = { - "obj_na": "NA_obj", - "cond_na": "NA_illu", - "wavelength": "lambda_illu", - "n_slices": "N_defocus", - "swing": "chi", - "ps": "ps", - } - self._check_abort() - old_recon = self.calib_window.phase_reconstructor - # check if X/Y shape has changed - if (old_recon.N, old_recon.M) != stack_shape[-2:]: - return True - # check if equivalent attributes have diverged - for key, value in attr_list.items(): - if getattr(self.calib_window, key) != getattr(old_recon, value): - return True - # modify attributes to be equivalent and check for divergence - for key, value in attr_modified_list.items(): - if key == "wavelength": - if ( - self.calib_window.wavelength - * 1e-3 - / self.calib_window.n_media - != old_recon.lambda_illu - ): - return True - elif key == "n_slices": - if getattr(self, key) != getattr(old_recon, value): - return True - elif key == "ps": - if getattr(self.calib_window, key) / float( - self.calib_window.mag - ) != getattr(old_recon, value): - return True - else: - if getattr( - self.calib_window, key - ) / self.calib_window.n_media != getattr(old_recon, value): - return True - # not changed - return False + return phase def _cleanup_acq(self): # Get display windows @@ -459,10 +349,10 @@ def _cleanup_acq(self): if not save_prefix else f"{save_prefix}_RawBFDataSnap.zarr" ) - out_path = os.path.join(self.snap_dir, name) + self.latest_out_path = os.path.join(self.snap_dir, name) converter = TIFFConverter( os.path.join(dir_, prefix), - out_path, + self.latest_out_path, data_type="ometiff", grid_layout=False, label_positions=False, @@ -565,6 +455,17 @@ def work(self): self._check_abort() + # Create and validate reconstruction settings + self.config_path = os.path.join( + self.snap_dir, "reconstruction_settings.yml" + ) + _generate_reconstruction_config_from_gui( + self.config_path, + self.mode, + self.calib_window, + input_channel_names=channels, + ) + # Acquire 2D stack if self.dim == "2D": logging.debug("Acquiring 2D stack") @@ -615,17 +516,17 @@ def work(self): # Reconstruct snapped images self._check_abort() self.n_slices = stack.shape[2] - birefringence, phase, meta = self._reconstruct(stack[0]) + birefringence, phase = self._reconstruct() self._check_abort() if self.calib_window.orientation_offset: birefringence = self._orientation_offset(birefringence) # Save images - logging.debug("Saving Images") - self._save_imgs(birefringence, phase, meta) + # logging.debug("Saving Images") + # self._save_imgs(birefringence, phase) - self._check_abort() + # self._check_abort() logging.info("Finished Acquisition") logging.debug("Finished Acquisition") @@ -653,7 +554,16 @@ def _orientation_offset(self, birefringence: np.ndarray): "This will affect both visualization and the saved reconstruction." ) ) + + # TODO: Move these to waveorder, connect to the `orientation_flip` + # and `orientation_rotate` parameters, and connect to recOrder buttons. + + # 90 degree flip birefringence[1] = np.fmod(birefringence[1] + np.pi / 2, np.pi) + + # Reflection + birefringence[1] = np.fmod(-birefringence[1], np.pi) + np.pi + return birefringence def _check_exposure(self) -> None: @@ -700,407 +610,49 @@ def _acquire(self) -> np.ndarray: return stack - def _reconstruct(self, stack): + def _reconstruct(self): """ - Method to reconstruct, given a 2D or 3D stack. First need to initialize the reconstructor given + Method to reconstruct. First need to initialize the reconstructor given what type of acquisition it is (birefringence only skips a lot of heavy compute needed for phase). This function also checks to see if the reconstructor needs to be updated from previous acquisitions - Parameters - ---------- - stack: (nd-array) Dimensions are (C, Z, Y, X) - - Returns - ------- - """ - - # get rid of z-dimension if 2D acquisition - stack = stack[:, 0] if self.n_slices == 1 else stack - self._check_abort() - wo_background_correction = rec_bkg_to_wo_bkg( - self.calib_window.bg_option + # Create config and i/o paths + transfer_function_path = os.path.join( + self.snap_dir, "transfer_function.zarr" ) - - # Initialize the heavy reconstuctor - if self.mode == "phase" or self.mode == "all": - self._check_abort() - - # if no reconstructor has been initialized before, create new reconstructor - if not self.calib_window.phase_reconstructor: - logging.debug("Computing new reconstructor") - - recon = initialize_reconstructor( - "QLIPP", - image_dim=(stack.shape[-2], stack.shape[-1]), - wavelength_nm=self.calib_window.recon_wavelength, - swing=self.calib.swing, - calibration_scheme=self.calib.calib_scheme, - NA_obj=self.calib_window.obj_na, - NA_illu=self.calib_window.cond_na, - mag=self.calib_window.mag, - n_slices=self.n_slices, - z_step_um=self.calib_window.z_step, - pad_z=self.calib_window.pad_z, - pixel_size_um=self.calib_window.ps, - bg_correction=wo_background_correction, - n_obj_media=self.calib_window.n_media, - mode=self.calib_window.acq_mode, - use_gpu=self.calib_window.use_gpu, - gpu_id=self.calib_window.gpu_id, - ) - - # Emit reconstructor to be saved for later reconstructions - self.phase_reconstructor_emitter.emit(recon) - - # if previous reconstructor exists - else: - self._check_abort() - - # compute new reconstructor if the old reconstructor properties have been modified - if self._reconstructor_changed(): - logging.debug( - "Reconstruction settings changed, updating reconstructor" - ) - - recon = initialize_reconstructor( - "QLIPP", - image_dim=(stack.shape[-2], stack.shape[-1]), - wavelength_nm=self.calib_window.recon_wavelength, - swing=self.calib.swing, - calibration_scheme=self.calib.calib_scheme, - NA_obj=self.calib_window.obj_na, - NA_illu=self.calib_window.cond_na, - mag=self.calib_window.mag, - n_slices=self.n_slices, - z_step_um=self.calib_window.z_step, - pad_z=self.calib_window.pad_z, - pixel_size_um=self.calib_window.ps, - bg_correction=wo_background_correction, - n_obj_media=self.calib_window.n_media, - mode=self.calib_window.acq_mode, - use_gpu=self.calib_window.use_gpu, - gpu_id=self.calib_window.gpu_id, - ) - - self.phase_reconstructor_emitter.emit(recon) - - # use previous reconstructor - else: - logging.debug("Using previous reconstruction settings") - recon = self.calib_window.phase_reconstructor - - # if phase isn't desired, initialize the lighter birefringence only reconstructor - # no need to save this reconstructor for later as it is pretty quick to compute - else: - self._check_abort() - logging.debug("Creating birefringence only reconstructor") - recon = initialize_reconstructor( - "birefringence", - image_dim=(stack.shape[-2], stack.shape[-1]), - calibration_scheme=self.calib.calib_scheme, - wavelength_nm=self.calib_window.recon_wavelength, - swing=self.calib.swing, - bg_correction=wo_background_correction, - n_slices=self.n_slices, - ) - - # Prepare background corrections for waveorder - # This block mimics qlipp_pipeline.py L110-119. - if self.calib_window.bg_option in ["global", "local_fit+"]: - logging.debug("Loading BG Data") - self._check_abort() - bg_data = self._load_bg( - self.calib_window.acq_bg_directory, - stack.shape[-2], - stack.shape[-1], - ) - self._check_abort() - bg_stokes = recon.Stokes_recon(bg_data) - self._check_abort() - bg_stokes = recon.Stokes_transform(bg_stokes) - self._check_abort() - elif self.calib_window.bg_option == "local_fit": - bg_stokes = np.zeros((5, stack.shape[-2], stack.shape[-1])) - bg_stokes[ - 0, ... - ] = 1 # Set background to "identity" Stokes parameters. - else: - logging.debug("No Background Correction method chosen") - bg_stokes = None - - # Begin reconstruction with stokes (needed for birefringence or phase) - logging.debug("Reconstructing...") - self._check_abort() - stokes = reconstruct_qlipp_stokes(stack, recon, bg_stokes) - self._check_abort() - - # initialize empty variables to pass along - birefringence = None - phase = None - - regularizer = self.calib_window.phase_regularizer - - reg = float(self.calib_window.ui.le_phase_strength.text()) - - # reconstruct both phase and birefringence - if self.mode == "all": - if self.calib_window.acq_mode == "2D": - birefringence = reconstruct_qlipp_birefringence( - np.mean(stokes, axis=1), recon - ) - else: - birefringence = reconstruct_qlipp_birefringence(stokes, recon) - birefringence[0] = ( - birefringence[0] - / (2 * np.pi) - * self.calib_window.recon_wavelength - ) - self._check_abort() - - if self.calib_window.acq_mode == "2D": - phase = reconstruct_phase2D( - stokes[0], - recon, - method=regularizer, - reg_p=reg, - itr=int(self.calib_window.ui.le_itr.text()), - rho=float(self.calib_window.ui.le_rho.text()), - ) - else: - phase = reconstruct_phase3D( - stokes[0], - recon, - method=regularizer, - reg_re=reg, - itr=int(self.calib_window.ui.le_itr.text()), - rho=float(self.calib_window.ui.le_rho.text()), - ) - - self._check_abort() - - # reconstruct phase only - elif self.mode == "phase": - if self.calib_window.acq_mode == "2D": - phase = reconstruct_phase2D( - stokes[0], - recon, - method=regularizer, - reg_p=reg, - itr=int(self.calib_window.ui.le_itr.text()), - rho=float(self.calib_window.ui.le_rho.text()), - ) - else: - phase = reconstruct_phase3D( - stokes[0], - recon, - method=regularizer, - reg_re=reg, - itr=int(self.calib_window.ui.le_itr.text()), - rho=float(self.calib_window.ui.le_rho.text()), - ) - self._check_abort() - - # reconstruct birefringence only - elif self.mode == "birefringence": - birefringence = reconstruct_qlipp_birefringence(stokes, recon) - birefringence[0] = ( - birefringence[0] - / (2 * np.pi) - * self.calib_window.recon_wavelength - ) - self._check_abort() - - else: - raise ValueError("Reconstruction Mode Not Understood") - - meta = extract_reconstruction_parameters(recon, self.calib_window.mag) - meta["regularization_method"] = regularizer - meta["regularization_strength"] = reg - if regularizer == "TV": - meta["rho"] = float(self.calib_window.ui.le_rho.text()) - meta["itr"] = int(self.calib_window.ui.le_itr.text()) - - # return both variables, could contain images or could be null - return birefringence, phase, meta - - def _save_imgs(self, birefringence, phase, meta=None): - """ - function to save images. - Makes sure file names do not overlap, i.e. nothing is overwritten. - - Parameters - ---------- - birefringence: (nd-array) birefringence image(s) - phase: (nd-array or None) phase image(s) - - Returns - ------- - - """ - prefix = self.calib_window.save_name - - name = ( - f"ReconstructionSnap.zarr" - if not prefix - else f"{prefix}_ReconstructionSnap.zarr" + reconstruction_path = os.path.join( + self.snap_dir, "reconstruction.zarr" ) - with open_ome_zarr( - os.path.join(self.snap_dir, name), - layout="fov", - mode="w-", - channel_names=["Retardance", "Orientation", "BF", "Pol"], - ) as dataset: - if birefringence.ndim == 3: - birefringence = birefringence[:, np.newaxis] # CYX -> CZYX - birefringence = birefringence[np.newaxis] # CZYX -> TCZYX - dataset["0"] = birefringence - - if phase is not None: - dataset.append_channel( - "Phase" + str(phase.ndim) + "D", resize_arrays=True - ) - if phase.ndim == 2: - phase = phase[np.newaxis] # YX -> ZYX - dataset["0"][0, 4] = phase - - dataset.zattrs["recOrder"] = meta - - def _load_bg(self, path, height, width): - """ - # TODO: remove ROI for 1.0.0 - - Load background and calibration metadata. - - Parameters - ---------- - path: (str) path to the BG folder - height: (int) height of BG image - width: (int) widht of BG image - - Returns - ------- - - """ - - # TODO: Change to just accept ROI - try: - metadata_path = get_last_metadata_file(path) - metadata = MetadataReader(metadata_path) - roi = metadata.ROI - except: - roi = None - - bg_data = load_bg(path, height, width, roi) - - return bg_data + input_data_path = os.path.join(self.latest_out_path, "0", "0", "0") - def _reconstructor_changed(self): - """ - Function to check if the reconstructor has changed from the previous one in memory. - Serves to check if the worker attributes and reconstructor attributes have diverged. - - Returns - ------- + # TODO: skip if config files match + compute_transfer_function_cli( + input_data_path, + self.config_path, + transfer_function_path, + ) - """ + apply_inverse_transfer_function_cli( + input_data_path, + transfer_function_path, + self.config_path, + reconstruction_path, + ) - changed = None - - # Attributes that are directly equivalent to worker attributes - attr_list = { - "acq_mode": "phase_deconv", - "pad_z": "pad_z", - "n_media": "n_media", - "bg_option": "bg_option", - "use_gpu": "use_gpu", - "gpu_id": "gpu_id", - } - - # attributes that are modified upon passing them to reconstructor - attr_modified_list = { - "obj_na": "NA_obj", - "cond_na": "NA_illu", - "wavelength": "lambda_illu", - "n_slices": "N_defocus", - "swing": "chi", - "ps": "ps", - } + # Read reconstruction to pass to emitters + with open_ome_zarr(reconstruction_path, mode="r") as dataset: + czyx_data = dataset["0"][0] + birefringence = czyx_data[0:4] + try: + phase = czyx_data[4] + except: + phase = None - self._check_abort() - # check if equivalent attributes have diverged - for key, value in attr_list.items(): - if getattr(self.calib_window, key) != getattr( - self.calib_window.phase_reconstructor, value - ): - changed = True - break - else: - changed = False - - if not changed: - # modify attributes to be equivalent and check for divergence - for key, value in attr_modified_list.items(): - if key == "swing": - if self.calib.calib_scheme == "5-State": - if ( - self.calib.swing * 2 * np.pi - != self.calib_window.phase_reconstructor.chi - ): - changed = True - else: - changed = False - else: - if ( - self.calib.swing - != self.calib_window.phase_reconstructor.chi - ): - changed = True - else: - changed = False - - elif key == "wavelength": - if ( - self.calib_window.recon_wavelength - * 1e-3 - / self.calib_window.n_media - != self.calib_window.phase_reconstructor.lambda_illu - ): - changed = True - break - else: - changed = False - elif key == "n_slices": - if getattr(self, key) != getattr( - self.calib_window.phase_reconstructor, value - ): - changed = True - break - else: - changed = False - - elif key == "ps": - if getattr(self.calib_window, key) / float( - self.calib_window.mag - ) != getattr(self.calib_window.phase_reconstructor, value): - changed = True - break - else: - changed = False - else: - if getattr( - self.calib_window, key - ) / self.calib_window.n_media != getattr( - self.calib_window.phase_reconstructor, value - ): - changed = True - else: - changed = False - - return changed + return birefringence, phase def _cleanup_acq(self): # Get display windows @@ -1134,10 +686,10 @@ def _cleanup_acq(self): if not save_prefix else f"{save_prefix}_RawPolDataSnap.zarr" ) - out_path = os.path.join(self.snap_dir, name) + self.latest_out_path = os.path.join(self.snap_dir, name) converter = TIFFConverter( os.path.join(dir_, prefix), - out_path, + self.latest_out_path, data_type="ometiff", grid_layout=False, label_positions=False, @@ -1149,378 +701,3 @@ def _cleanup_acq(self): break else: continue - - -class ListeningWorker(WorkerBase): - """ - Class to execute a birefringence/phase acquisition. First step is to snap the images follow by a second - step of reconstructing those images. - """ - - def __init__(self, calib_window, bg_data): - super().__init__(SignalsClass=ListeningSignals) - - # Save current state of GUI window - self.calib_window = calib_window - - # Init properties - self.n_slices = None - self.n_channels = None - self.n_frames = None - self.n_pos = None - self.shape = None - self.dtype = None - self.root = None - self.prefix = None - self.store = None - self.save_directory = None - self.bg_data = bg_data - self.reconstructor = None - - def _check_abort(self): - if self.abort_requested: - self.aborted.emit() - raise TimeoutError("Stop Requested") - - def get_byte_offset(self, offsets, page): - """ - Gets the byte offset from the tiff tag metadata. - - 210 accounts for header data + page header data. - 162 accounts page header data - - Parameters - ---------- - offsets: (dict) Offset dictionary list - page: (int) Page to look at for the offset - - Returns - ------- - byte offset: (int) byte offset for the image array - - """ - - if page == 0: - array_offset = offsets[page] + 210 - else: - array_offset = offsets[page] + 162 - - return array_offset - - def listen_for_images( - self, array, file, offsets, interval, dim3, dim2, dim1, dim0, dim_order - ): - """ - - Parameters - ---------- - array: (nd-array) numpy array of size (C, Z) - file: (string) filepath corresponding to the desired tiff image - offsets: (dict) dictionary of offsets corresponding to byte offsets of pixel data in tiff image - interval: (int) time interval between timepoints in seconds - dim3: (int) outermost dimension to value begin at - dim2: (int) first-inner dimension value to begin at - dim1: (int) second-inner dimension value to begin at - dim0: (int) innermost dimension value to begin at - dim_order: (int) 1, 2, 3, or 4 corresponding to the dimensionality ordering of the acquisition (MM-provided) - - Returns - ------- - array: (nd-array) partially filled array of size (C, Z) to continue filling in next iteration - index: (int) current page number at end of function - dim3: (int) dimension values corresponding to where the next iteration should begin - dim2: (int) dimension values corresponding to where the next iteration should begin - dim1: (int) dimension values corresponding to where the next iteration should begin - dim0: (int) dimension values corresponding to where the next iteration should begin - - """ - - # Order dimensions that we will loop through in order to match the acquisition - if dim_order == 0: - dims = [ - [dim3, self.n_frames], - [dim2, self.n_pos], - [dim1, self.n_slices], - [dim0, self.n_channels], - ] - channel_dim = 0 - elif dim_order == 1: - dims = [ - [dim3, self.n_frames], - [dim2, self.n_pos], - [dim1, self.n_channels], - [dim0, self.n_slices], - ] - channel_dim = 1 - elif dim_order == 2: - dims = [ - [dim3, self.n_pos], - [dim2, self.n_frames], - [dim1, self.n_slices], - [dim0, self.n_channels], - ] - channel_dim = 0 - else: - dims = [ - [dim3, self.n_pos], - [dim2, self.n_frames], - [dim1, self.n_channels], - [dim0, self.n_slices], - ] - channel_dim = 1 - - # Loop through dimensions in the order they are acquired - idx = 0 - for dim3 in range(dims[0][0], dims[0][1]): - for dim2 in range(dims[1][0], dims[1][1]): - for dim1 in range(dims[2][0], dims[2][1]): - for dim0 in range(dims[3][0], dims[3][1]): - # GET OFFSET AND WAIT - if idx > 0: - try: - offset = self.get_byte_offset(offsets, idx) - except IndexError: - # NEED TO STOP BECAUSE RAN OUT OF PAGES OR REACHED END OF ACQ - return array, idx, dim3, dim2, dim1, dim0 - - # Checks if the offset in metadata is 0, but technically self.get_byte_offset() adds - # 162 to every offset to account for tiff file header bytes - while offset == 162: - self._check_abort() - tf = tiff.TiffFile(file) - time.sleep(interval) - offsets = tf.micromanager_metadata["IndexMap"][ - "Offset" - ] - tf.close() - offset = self.get_byte_offset(offsets, idx) - else: - offset = self.get_byte_offset(offsets, idx) - - if idx < ( - self.n_slices - * self.n_channels - * self.n_frames - * self.n_pos - ): - # Assign dimensions based off acquisition order to correctly add image to array - if dim_order == 0: - t, p, c, z = dim3, dim2, dim0, dim1 - elif dim_order == 1: - t, p, c, z = dim3, dim2, dim1, dim0 - elif dim_order == 2: - t, p, c, z = dim2, dim3, dim0, dim1 - else: - t, p, c, z = dim2, dim3, dim1, dim0 - - self._check_abort() - - # Add Image to array - img = np.memmap( - file, - dtype=self.dtype, - mode="r", - offset=offset, - shape=self.shape, - ) - array[c, z] = img - - # If Channel first, compute birefringence here - if ( - channel_dim == 0 - and dim0 == self.n_channels - 1 - ): - self._check_abort() - - # Compute birefringence - self.compute_and_save(array[:, z], p, t, z) - - idx += 1 - - # Reset Range to 0 to account for starting this function in middle of a dimension - if idx < ( - self.n_slices - * self.n_channels - * self.n_frames - * self.n_pos - ): - dims[2][0] = 0 - dims[3][0] = 0 - - # If z-first, compute the birefringence here - if channel_dim == 1 and dim1 == self.n_channels - 1: - # Assign dimensions based off acquisition order to correctly add image to array - if dim_order == 0: - t, p, c, z = dim3, dim2, dim0, dim1 - elif dim_order == 1: - t, p, c, z = dim3, dim2, dim1, dim0 - elif dim_order == 2: - t, p, c, z = dim2, dim3, dim0, dim1 - else: - t, p, c, z = dim2, dim3, dim1, dim0 - - self._check_abort() - self.compute_and_save(array, p, t, dim0) - else: - continue - - # Reset range to 0 to account for starting this function in the middle of a dimension - if idx < ( - self.n_slices - * self.n_channels - * self.n_frames - * self.n_pos - ): - dims[1][0] = 0 - - # Return at the end of the acquisition - return array, idx, dim3, dim2, dim1, dim0 - - def compute_and_save(self, array, p, t, z): - if self.n_slices == 1: - array = array[:, 0] - - birefringence = self.reconstructor.reconstruct(array) - - # If the napari layer doesn't exist yet, create the zarr store to emit to napari - if self.prefix not in self.calib_window.viewer.layers: - self.store = zarr.open( - os.path.join(self.root, self.prefix + ".zarr") - ) - self.store.zeros( - name="Birefringence", - shape=( - self.n_pos, - self.n_frames, - 2, - self.n_slices, - self.shape[0], - self.shape[1], - ), - chunks=(1, 1, 1, 1, self.shape[0], self.shape[1]), - overwrite=True, - ) - - # Add data and send out the store. Once the store has been emitted, we can add data to the store - # without needing to emit the store again (thanks to napari's handling of zarr datasets) - if len(birefringence.shape) == 4: - self.store["Birefringence"][p, t] = birefringence - else: - self.store["Birefringence"][p, t, :, z] = birefringence - - self.store_emitter.emit(self.store) - - else: - if len(birefringence.shape) == 4: - self.store["Birefringence"][p, t] = birefringence - else: - self.store["Birefringence"][p, t, :, z] = birefringence - - # Emit the current dimensions so that we can update the napari dimension slider - self.dim_emitter.emit((p, t, z)) - - def work(self): - acq_man = self.calib_window.mm.acquisitions() - - while not acq_man.isAcquisitionRunning(): - pass - - time.sleep(1) - - # Get all of the dataset dimensions, settings - s = acq_man.getAcquisitionSettings() - self.root = s.root() - self.prefix = s.prefix() - self.n_frames, self.interval = ( - (s.numFrames(), s.intervalMs() / 1000) if s.useFrames() else (1, 1) - ) - self.n_channels = s.channels().size() if s.useChannels() else 1 - self.n_slices = s.slices().size() if s.useChannels() else 1 - self.n_pos = ( - 1 - if not s.usePositionList() - else self.calib_window.mm.getPositionListManager() - .getPositionList() - .getNumberOfPositions() - ) - dim_order = s.acqOrderMode() - - # Get File Path corresponding to current dataset - path = os.path.join(self.root, self.prefix) - files = [fn for fn in glob.glob(path + "") if ".zarr" not in fn] - index = max([int(x.strip(path + "_")) for x in files]) - - self.prefix = self.prefix + f"_{index}" - full_path = os.path.join(self.root, self.prefix) - first_file_path = os.path.join( - full_path, self.prefix + "_MMStack.ome.tif" - ) - - file = tiff.TiffFile(first_file_path) - file_path = first_file_path - self.shape = ( - file.micromanager_metadata["Summary"]["Height"], - file.micromanager_metadata["Summary"]["Width"], - ) - self.dtype = file.pages[0].dtype - offsets = file.micromanager_metadata["IndexMap"]["Offset"] - file.close() - - self._check_abort() - - # Init Reconstruction Class - self.reconstructor = QLIPPBirefringenceCompute( - self.shape, - self.calib_window.calib_scheme, - self.calib_window.wavelength, - self.calib_window.swing, - self.n_slices, - self.calib_window.bg_option, - self.bg_data, - ) - - self._check_abort() - - # initialize dimensions / array for the loop - idx, dim3, dim2, dim1, dim0 = 0, 0, 0, 0, 0 - array = np.zeros( - (self.n_channels, self.n_slices, self.shape[0], self.shape[1]) - ) - file_count = 0 - total_idx = 0 - - # Run until the function has collected the totality of the data - while total_idx < ( - self.n_slices self.n_channels * self.n_frames * self.n_pos - ): - self._check_abort() - - # this will loop through reading images in a single file as it's being written - # when it has successfully loaded all of the images from the file, it'll move on to the next - array, idx, dim3, dim2, dim1, dim0 = self.listen_for_images( - array, - file_path, - offsets, - self.interval, - dim3, - dim2, - dim1, - dim0, - dim_order, - ) - - total_idx += idx - - # If acquisition is not finished, grab the next file and listen for images - if ( - total_idx - != self.n_slices * self.n_channels * self.n_frames * self.n_pos - ): - time.sleep(1) - file_count += 1 - file_path = os.path.join( - full_path, self.prefix + f"_MMStack_{file_count}.ome.tif" - ) - file = tiff.TiffFile(file_path) - offsets = file.micromanager_metadata["IndexMap"]["Offset"] - file.close() diff --git a/recOrder/calib/Calibration.py b/recOrder/calib/Calibration.py index d445d9d7..d8f50fbb 100644 --- a/recOrder/calib/Calibration.py +++ b/recOrder/calib/Calibration.py @@ -1,8 +1,7 @@ import numpy as np import matplotlib.pyplot as plt -import matplotlib.patches as patches -import tifffile as tiff import time +from iohub import open_ome_zarr from recOrder.io.core_functions import * from recOrder.calib.Optimization import BrentOptimizer, MinScalarOptimizer from mpl_toolkits.axes_grid1.axes_divider import make_axes_locatable @@ -18,7 +17,7 @@ from datetime import datetime from importlib_metadata import version -LC_DEVICE_NAME = "MeadowlarkLcOpenSource" +LC_DEVICE_NAME = "MeadowlarkLC" class QLIPP_Calibration: @@ -107,17 +106,18 @@ def __init__( # Set Mode # TODO: make sure LC or TriggerScope are loaded in the respective modes allowed_modes = ["MM-Retardance", "MM-Voltage", "DAC"] - assert ( - lc_control_mode in allowed_modes - ), f"LC control mode must be one of {allowed_modes}" + if lc_control_mode not in allowed_modes: + raise ValueError(f"LC control mode must be one of {allowed_modes}") self.mode = lc_control_mode self.LC_DAC_conversion = 4 # convert between the input range of LCs (0-20V) and the output range of the DAC (0-5V) # Initialize calibration class allowed_interp_methods = ["schnoor_fit", "linear"] - assert ( - interp_method in allowed_interp_methods - ), f"LC calibration data interpolation method must be one of {allowed_interp_methods}" + if interp_method not in allowed_interp_methods: + raise ValueError( + "LC calibration data interpolation method must be one of " + f"{allowed_interp_methods}" + ) dir_path = mmc.getDeviceAdapterSearchPaths().get( 0 ) # MM device adapter directory @@ -294,7 +294,6 @@ def define_lc_state(self, state, lca_retardance, lcb_retardance): ) def opt_lc(self, x, device_property, reference, normalize=False): - if isinstance(x, list) or isinstance(x, tuple): x = x[0] @@ -321,7 +320,6 @@ def opt_lc(self, x, device_property, reference, normalize=False): return np.abs(mean - reference) def opt_lc_cons(self, x, device_property, reference, mode): - self.set_lc(x, device_property) swing = (self.lca_ext - x) * self.ratio @@ -374,7 +372,6 @@ def opt_lc_grid(self, a_min, a_max, b_min, b_max, step): # coarse search for lca in np.arange(a_min, a_max, step): for lcb in np.arange(b_min, b_max, step): - self.set_lc(lca, "LCA") self.set_lc(lcb, "LCB") @@ -832,7 +829,6 @@ def calculate_extinction( ) def calc_inst_matrix(self): - if self.calib_scheme == "4-State": chi = self.swing inst_mat = np.array( @@ -872,7 +868,6 @@ def calc_inst_matrix(self): return inst_mat def write_metadata(self, notes=None): - inst_mat = self.calc_inst_matrix() inst_mat = np.around(inst_mat, decimals=5).tolist() @@ -990,7 +985,6 @@ def _capture_state(self, state: str, n_avg: int): return np.mean(imgs, axis=0) def _plot_bg_images(self, imgs): - img_names = ( ["Extinction", "0", "60", "120"] if len(imgs) == 4 @@ -1079,39 +1073,36 @@ def capture_bg(self, n_avg, directory): self.mmc.setAutoShutter(False) self.open_shutter() - logging.debug("Capturing Background State0") - state0 = self._capture_state("State0", n_avg) - logging.debug("Saving Background State0") - tiff.imsave(os.path.join(directory, "State0.tif"), state0) - - logging.debug("Capturing Background State1") - state1 = self._capture_state("State1", n_avg) - logging.debug("Saving Background State1") - tiff.imsave(os.path.join(directory, "State1.tif"), state1) - - logging.debug("Capturing Background State2") - state2 = self._capture_state("State2", n_avg) - logging.debug("Saving Background State2") - tiff.imsave(os.path.join(directory, "State2.tif"), state2) - - logging.debug("Capturing Background State3") - state3 = self._capture_state("State3", n_avg) - logging.debug("Saving Background State3") - tiff.imsave(os.path.join(directory, "State3.tif"), state3) - - imgs = [state0, state1, state2, state3] - - if self.calib_scheme == "5-State": - logging.debug("Capturing Background State4") - state4 = self._capture_state("State4", n_avg) - logging.debug("Saving Background State4") - tiff.imsave(os.path.join(directory, "State4.tif"), state4) - imgs.append(state4) + num_states = int(self.calib_scheme[0]) + + # Acquire background data + yx_list = [] + for channel in range(num_states): + logging.debug(f"Capturing Background State{channel}") + yx_list.append(self._capture_state(f"State{channel}", n_avg)) + logging.debug(f"Saving Background State{channel}") + cyx_data = np.array(yx_list) + + # Save to zarr + with open_ome_zarr( + os.path.join(directory, "background.zarr"), + layout="hcs", + mode="w", + channel_names=[f"State{i}" for i in range(num_states)], + ) as dataset: + position = dataset.create_position("0", "0", "0") + position.create_zeros( + name="0", + shape=(1, num_states, 1, cyx_data.shape[1], cyx_data.shape[2]), + dtype=np.float32, + chunks=(1, 1, 1, cyx_data.shape[1], cyx_data.shape[2]), + ) + position["0"][0, :, 0] = cyx_data # save to 1C1YX array # self._plot_bg_images(np.asarray(imgs)) self.reset_shutter() - return np.asarray(imgs) + return cyx_data class CalibrationData: @@ -1377,7 +1368,6 @@ def interpolate_data(self, raw_data, calib_wavelengths): self.curve = self.spline(x_range) elif self.wavelength > 630: - new_a1_y = np.interp(x_range, x_range, spline546(x_range)) new_a2_y = np.interp(x_range, x_range, spline630(x_range)) @@ -1399,7 +1389,6 @@ def interpolate_data(self, raw_data, calib_wavelengths): self.curve = self.spline(x_range) elif 490 < self.wavelength < 546: - new_a1_y = np.interp(x_range, x_range, spline490(x_range)) new_a2_y = np.interp(x_range, x_range, spline546(x_range)) @@ -1416,7 +1405,6 @@ def interpolate_data(self, raw_data, calib_wavelengths): self.curve = self.spline(x_range) elif 546 < self.wavelength < 630: - new_a1_y = np.interp(x_range, x_range, spline546(x_range)) new_a2_y = np.interp(x_range, x_range, spline630(x_range)) diff --git a/recOrder/calib/calibration_workers.py b/recOrder/calib/calibration_workers.py index 64d704ea..5ad56cca 100644 --- a/recOrder/calib/calibration_workers.py +++ b/recOrder/calib/calibration_workers.py @@ -4,15 +4,17 @@ from qtpy.QtCore import Signal from iohub import open_ome_zarr from napari.qt.threading import WorkerBaseSignals, WorkerBase, thread_worker -from recOrder.compute.reconstructions import ( - initialize_reconstructor, - reconstruct_qlipp_birefringence, - reconstruct_qlipp_stokes, -) +from recOrder.cli import settings from recOrder.io.core_functions import set_lc_state, snap_and_average -from recOrder.io.utils import MockEmitter +from recOrder.io.utils import MockEmitter, model_to_yaml from recOrder.calib.Calibration import LC_DEVICE_NAME from recOrder.io.metadata_reader import MetadataReader, get_last_metadata_file +from recOrder.cli.compute_transfer_function import ( + compute_transfer_function_cli, +) +from recOrder.cli.apply_inverse_transfer_function import ( + apply_inverse_transfer_function_cli, +) import os import shutil import numpy as np @@ -234,7 +236,6 @@ def _calibrate_4state(self): self._check_abort() def _calibrate_5state(self): - search_radius = np.min((self.calib.swing, 0.05)) self.calib.calib_scheme = "5-State" @@ -306,7 +307,6 @@ def __init__(self, calib_window, calib): super().__init__(calib_window, calib) def work(self): - # Make the background folder bg_path = os.path.join( self.calib_window.directory, @@ -315,7 +315,6 @@ def work(self): if not os.path.exists(bg_path): os.mkdir(bg_path) else: - # increment background paths idx = 1 while os.path.exists(bg_path + f"_{idx}"): @@ -334,36 +333,60 @@ def work(self): # capture and return background images imgs = self.calib.capture_bg(self.calib_window.n_avg, bg_path) - img_dim = (imgs.shape[-2], imgs.shape[-1]) - self.calib_window._dump_gui_state(bg_path) - # initialize reconstructor - recon = initialize_reconstructor( - "birefringence", - image_dim=img_dim, - calibration_scheme=self.calib.calib_scheme, - wavelength_nm=self.calib.wavelength, - swing=self.calib.swing, - bg_correction="None", + # build background-specific reconstruction settings + reconstruction_settings = settings.ReconstructionSettings( + input_channel_names=[ + f"State{i}" + for i in range(int(self.calib_window.calib_scheme[0])) + ], + reconstruction_dimension=2, + birefringence=settings.BirefringenceSettings( + transfer_function=settings.BirefringenceTransferFunctionSettings( + swing=self.calib_window.swing + ), + apply_inverse=settings.BirefringenceApplyInverseSettings( + wavelength_illumination=self.calib_window.recon_wavelength + / 1000, + background_path="", + remove_estimated_background=False, + orientation_flip=False, + ), + ), ) - self._check_abort() - - # Reconstruct birefringence from BG images - stokes = reconstruct_qlipp_stokes(imgs, recon, None) - - self._check_abort() + reconstruction_config_path = os.path.join( + bg_path, "reconstruction_settings.yml" + ) + model_to_yaml(reconstruction_settings, reconstruction_config_path) - self.birefringence = reconstruct_qlipp_birefringence(stokes, recon) + input_data_path = os.path.join( + bg_path, "background.zarr", "0", "0", "0" + ) + transfer_function_path = os.path.join( + bg_path, "transfer_function.zarr" + ) + reconstruction_path = os.path.join(bg_path, "reconstruction.zarr") - self._check_abort() + compute_transfer_function_cli( + input_data_path, + reconstruction_config_path, + transfer_function_path, + ) - # Convert retardance to nm - self.retardance = ( - self.birefringence[0] / (2 * np.pi) * self.calib.wavelength + apply_inverse_transfer_function_cli( + input_data_path, + transfer_function_path, + reconstruction_config_path, + reconstruction_path, ) + # Load reconstructions from file for layers + with open_ome_zarr(reconstruction_path, mode="r") as dataset: + self.retardance = dataset["0"][0, 0, 0] + self.birefringence = dataset["0"][0, :, 0] + # Save metadata file and emit imgs meta_file = os.path.join(bg_path, "calibration_metadata.txt") self._write_meta_file(meta_file) @@ -385,9 +408,6 @@ def work(self): self._check_abort() - self._save_bg_recon(bg_path) - self._check_abort() - # Emit background images + background birefringence self.bg_image_emitter.emit(imgs) self.bire_image_emitter.emit((self.retardance, self.birefringence[1])) @@ -395,45 +415,6 @@ def work(self): # Emit bg path self.bg_path_update_emitter.emit(bg_path) - def _save_bg_recon(self, bg_path: StrOrBytesPath): - bg_recon_path = os.path.join(bg_path, "reconstruction") - # create the reconstruction directory - if os.path.isdir(bg_recon_path): - shutil.rmtree(bg_recon_path) - elif os.path.isfile(bg_recon_path): - os.remove(bg_recon_path) - else: - os.mkdir(bg_recon_path) - # save raw reconstruction to zarr store - with open_ome_zarr( - os.path.join(bg_recon_path, "reconstruction"), - layout="fov", - mode="w-", - channel_names=[ - "Retardance", - "Orientation", - "BF - computed", - "DoP", - ], - ) as dataset: - dataset["0"] = self.birefringence[np.newaxis, :, np.newaxis, ...] - - # save intensity trace visualization - import matplotlib.pyplot as plt - - plt.imsave( - os.path.join(bg_recon_path, "retardance.png"), - self.retardance, - cmap="gray", - ) - plt.imsave( - os.path.join(bg_recon_path, "orientation.png"), - self.birefringence[1], - cmap="hsv", - vmin=0, - vmax=np.pi, - ) - @thread_worker def load_calibration(calib, metadata: MetadataReader): diff --git a/recOrder/cli/apply_inverse_transfer_function.py b/recOrder/cli/apply_inverse_transfer_function.py new file mode 100644 index 00000000..46cac81a --- /dev/null +++ b/recOrder/cli/apply_inverse_transfer_function.py @@ -0,0 +1,396 @@ +import click +import numpy as np +import torch +from iohub import open_ome_zarr +from recOrder.cli.printing import echo_headline, echo_settings +from recOrder.cli.settings import ReconstructionSettings +from recOrder.cli.parsing import ( + input_data_path_argument, + config_path_option, + output_dataset_option, +) +from recOrder.io import utils +from waveorder.models import ( + inplane_oriented_thick_pol3d, + isotropic_thin_3d, + phase_thick_3d, + isotropic_fluorescent_thick_3d, +) + + +def _check_background_consistency(background_shape, data_shape): + data_cyx_shape = (data_shape[1],) + data_shape[3:] + if background_shape != data_cyx_shape: + raise ValueError( + f"Background shape {background_shape} does not match data shape {data_cyx_shape}" + ) + + +def apply_inverse_transfer_function_cli( + input_data_path, transfer_function_path, config_path, output_path +): + echo_headline("Starting reconstruction...") + + # Load datasets + transfer_function_dataset = open_ome_zarr(transfer_function_path) + input_dataset = open_ome_zarr(input_data_path) + + # Load config file + settings = utils.yaml_to_model(config_path, ReconstructionSettings) + + # Check input channel names + if not set(settings.input_channel_names).issubset( + input_dataset.channel_names + ): + raise ValueError( + f"Each of the input_channel_names = {settings.input_channel_names} in {config_path} must appear in the dataset {input_data_path} which currently contains channel_names = {input_dataset.channel_names}." + ) + + # Find channel indices + channel_indices = [] + for input_channel_name in settings.input_channel_names: + channel_indices.append( + input_dataset.channel_names.index(input_channel_name) + ) + + # Load dataset shape + t_shape = input_dataset.data.shape[0] + + # Simplify important settings names + recon_biref = settings.birefringence is not None + recon_phase = settings.phase is not None + recon_fluo = settings.fluorescence is not None + recon_dim = settings.reconstruction_dimension + + # Prepare output dataset + channel_names = [] + if recon_biref: + channel_names.append("Retardance") + channel_names.append("Orientation") + channel_names.append("BF") + channel_names.append("Pol") + if recon_phase: + if recon_dim == 2: + channel_names.append("Phase2D") + elif recon_dim == 3: + channel_names.append("Phase3D") + if recon_fluo: + fluor_name = settings.input_channel_names[0] + if recon_dim == 2: + channel_names.append(fluor_name + "2D") + elif recon_dim == 3: + channel_names.append(fluor_name + "3D") + + if recon_dim == 2: + output_z_shape = 1 + elif recon_dim == 3: + output_z_shape = input_dataset.data.shape[2] + + output_shape = ( + t_shape, + len(channel_names), + output_z_shape, + ) + input_dataset.data.shape[3:] + + # Create output dataset + output_dataset = open_ome_zarr( + output_path, layout="fov", mode="w", channel_names=channel_names + ) + output_array = output_dataset.create_zeros( + name="0", + shape=output_shape, + dtype=np.float32, + chunks=( + 1, + 1, + 1, + ) + + input_dataset.data.shape[3:], # chunk by YX + ) + + # Load data + tczyx_uint16_numpy = input_dataset.data.oindex[:, channel_indices] + tczyx_data = torch.tensor( + np.int32(tczyx_uint16_numpy), dtype=torch.float32 + ) # convert to np.int32 (torch doesn't accept np.uint16), then convert to tensor float32 + + # Prepare background dataset + if settings.birefringence is not None: + biref_inverse_dict = settings.birefringence.apply_inverse.dict() + + # Resolve background path into array + background_path = biref_inverse_dict["background_path"] + biref_inverse_dict.pop("background_path") + if background_path != "": + cyx_no_sample_data = utils.load_background(background_path) + _check_background_consistency( + cyx_no_sample_data.shape, input_dataset.data.shape + ) + else: + cyx_no_sample_data = None + + # Main reconstruction logic + # Eight different cases [2, 3] x [biref only, phase only, biref and phase, fluorescence] + + # [biref only] [2 or 3] + if recon_biref and (not recon_phase): + echo_headline("Reconstructing birefringence with settings:") + echo_settings(settings.birefringence.apply_inverse) + echo_headline("Reconstructing birefringence...") + + # Load transfer function + intensity_to_stokes_matrix = torch.tensor( + transfer_function_dataset["intensity_to_stokes_matrix"][0, 0, 0] + ) + + for time_index in range(t_shape): + # Apply + reconstructed_parameters = ( + inplane_oriented_thick_pol3d.apply_inverse_transfer_function( + tczyx_data[time_index], + intensity_to_stokes_matrix, + cyx_no_sample_data=cyx_no_sample_data, + project_stokes_to_2d=(recon_dim == 2), + biref_inverse_dict, + ) + ) + # Save + for param_index, parameter in enumerate(reconstructed_parameters): + output_array[time_index, param_index] = parameter + + # [phase only] + if recon_phase and (not recon_biref): + echo_headline("Reconstructing phase with settings:") + echo_settings(settings.phase.apply_inverse) + echo_headline("Reconstructing phase...") + + # check data shapes + if tczyx_data.shape[1] != 1: + raise ValueError( + "You have requested a phase-only reconstruction, but the input dataset has more than one channel." + ) + + # [phase only, 2] + if recon_dim == 2: + # Load transfer functions + absorption_transfer_function = torch.tensor( + transfer_function_dataset["absorption_transfer_function"][0, 0] + ) + phase_transfer_function = torch.tensor( + transfer_function_dataset["phase_transfer_function"][0, 0] + ) + + for time_index in range(t_shape): + # Apply + ( + _, + yx_phase, + ) = isotropic_thin_3d.apply_inverse_transfer_function( + tczyx_data[time_index, 0], + absorption_transfer_function, + phase_transfer_function, + settings.phase.apply_inverse.dict(), + ) + + # Save + output_array[time_index, -1, 0] = yx_phase + + # [phase only, 3] + elif recon_dim == 3: + # Load transfer functions + real_potential_transfer_function = torch.tensor( + transfer_function_dataset["real_potential_transfer_function"][ + 0, 0 + ] + ) + imaginary_potential_transfer_function = torch.tensor( + transfer_function_dataset[ + "imaginary_potential_transfer_function" + ][0, 0] + ) + + # Apply + for time_index in range(t_shape): + zyx_phase = phase_thick_3d.apply_inverse_transfer_function( + tczyx_data[time_index, 0], + real_potential_transfer_function, + imaginary_potential_transfer_function, + z_padding=settings.phase.transfer_function.z_padding, + z_pixel_size=settings.phase.transfer_function.z_pixel_size, + wavelength_illumination=settings.phase.transfer_function.wavelength_illumination, + settings.phase.apply_inverse.dict(), + ) + # Save + output_array[time_index, -1] = zyx_phase + + # [biref and phase] + if recon_biref and recon_phase: + echo_headline("Reconstructing phase with settings:") + echo_settings(settings.phase.apply_inverse) + echo_headline("Reconstructing birefringence with settings:") + echo_settings(settings.birefringence.apply_inverse) + echo_headline("Reconstructing...") + + # Load birefringence transfer function + intensity_to_stokes_matrix = torch.tensor( + transfer_function_dataset["intensity_to_stokes_matrix"][0, 0, 0] + ) + + # [biref and phase, 2] + if recon_dim == 2: + # Load phase transfer functions + absorption_transfer_function = torch.tensor( + transfer_function_dataset["absorption_transfer_function"][0, 0] + ) + phase_transfer_function = torch.tensor( + transfer_function_dataset["phase_transfer_function"][0, 0] + ) + + for time_index in range(t_shape): + # Apply + reconstructed_parameters_2d = inplane_oriented_thick_pol3d.apply_inverse_transfer_function( + tczyx_data[time_index], + intensity_to_stokes_matrix, + cyx_no_sample_data=cyx_no_sample_data, + project_stokes_to_2d=True, + biref_inverse_dict, + ) + + reconstructed_parameters_3d = inplane_oriented_thick_pol3d.apply_inverse_transfer_function( + tczyx_data[time_index], + intensity_to_stokes_matrix, + cyx_no_sample_data=cyx_no_sample_data, + project_stokes_to_2d=False, + biref_inverse_dict, + ) + + brightfield_3d = reconstructed_parameters_3d[2] + + ( + _, + yx_phase, + ) = isotropic_thin_3d.apply_inverse_transfer_function( + brightfield_3d, + absorption_transfer_function, + phase_transfer_function, + settings.phase.apply_inverse.dict(), + ) + + # Save + for param_index, parameter in enumerate( + reconstructed_parameters_2d + ): + output_array[time_index, param_index] = parameter + output_array[time_index, -1, 0] = yx_phase + + # [biref and phase, 3] + elif recon_dim == 3: + # Load phase transfer functions + intensity_to_stokes_matrix = torch.tensor( + transfer_function_dataset["intensity_to_stokes_matrix"][ + 0, 0, 0 + ] + ) + # Load transfer functions + real_potential_transfer_function = torch.tensor( + transfer_function_dataset["real_potential_transfer_function"][ + 0, 0 + ] + ) + imaginary_potential_transfer_function = torch.tensor( + transfer_function_dataset[ + "imaginary_potential_transfer_function" + ][0, 0] + ) + + # Apply + for time_index in range(t_shape): + reconstructed_parameters_3d = inplane_oriented_thick_pol3d.apply_inverse_transfer_function( + tczyx_data[time_index], + intensity_to_stokes_matrix, + cyx_no_sample_data=cyx_no_sample_data, + project_stokes_to_2d=False, + biref_inverse_dict, + ) + + brightfield_3d = reconstructed_parameters_3d[2] + + zyx_phase = phase_thick_3d.apply_inverse_transfer_function( + brightfield_3d, + real_potential_transfer_function, + imaginary_potential_transfer_function, + z_padding=settings.phase.transfer_function.z_padding, + z_pixel_size=settings.phase.transfer_function.z_pixel_size, + wavelength_illumination=settings.phase.transfer_function.wavelength_illumination, + settings.phase.apply_inverse.dict(), + ) + # Save + for param_index, parameter in enumerate( + reconstructed_parameters_3d + ): + output_array[time_index, param_index] = parameter + output_array[time_index, -1] = zyx_phase + + # [fluo] + if recon_fluo: + echo_headline("Reconstructing fluorescence with settings:") + echo_settings(settings.fluorescence.apply_inverse) + echo_headline("Reconstructing...") + + # [fluo, 2] + if recon_dim == 2: + raise NotImplementedError + # [fluo, 3] + elif recon_dim == 3: + # Load transfer functions + optical_transfer_function = torch.tensor( + transfer_function_dataset["optical_transfer_function"][0, 0] + ) + + # Apply + for time_index in range(t_shape): + zyx_recon = isotropic_fluorescent_thick_3d.apply_inverse_transfer_function( + tczyx_data[time_index, 0], + optical_transfer_function, + settings.fluorescence.transfer_function.z_padding, + settings.fluorescence.apply_inverse.dict(), + ) + + # Save + output_array[time_index, 0] = zyx_recon + + output_dataset.zattrs["settings"] = settings.dict() + + echo_headline(f"Closing {output_path}\n") + output_dataset.close() + transfer_function_dataset.close() + input_dataset.close() + + echo_headline( + f"Recreate this reconstruction with:\n$ recorder apply-inv-tf {input_data_path} {transfer_function_path} -c {config_path} -o {output_path}" + ) + + [email protected]() [email protected]_option("-h", "--help") +@input_data_path_argument() [email protected]( + "transfer_function_path", + type=click.Path(exists=True), +) +@config_path_option() +@output_dataset_option(default="./reconstruction.zarr") +def apply_inv_tf( + input_data_path, transfer_function_path, config_path, output_path +): + """ + Apply an inverse transfer function to a dataset using a configuration file. + + See /examples for example configuration files. + + Example usage:\n + $ recorder apply-inv-tf input.zarr/0/0/0 transfer-function.zarr -c /examples/birefringence.yml -o output.zarr + """ + apply_inverse_transfer_function_cli( + input_data_path, transfer_function_path, config_path, output_path + ) diff --git a/recOrder/cli/compute_transfer_function.py b/recOrder/cli/compute_transfer_function.py new file mode 100644 index 00000000..80b740aa --- /dev/null +++ b/recOrder/cli/compute_transfer_function.py @@ -0,0 +1,219 @@ +import click +import numpy as np +from iohub import open_ome_zarr +from recOrder.cli.printing import echo_settings, echo_headline +from recOrder.cli.settings import ReconstructionSettings +from recOrder.cli.parsing import ( + input_data_path_argument, + config_path_option, + output_dataset_option, +) +from recOrder.io import utils +from waveorder.models import ( + inplane_oriented_thick_pol3d, + isotropic_thin_3d, + phase_thick_3d, + isotropic_fluorescent_thick_3d, +) + + +def generate_and_save_birefringence_transfer_function(settings, dataset): + """Generates and saves the birefringence transfer function to the dataset, based on the settings. + + Parameters + ---------- + settings: ReconstructionSettings + dataset: NGFF Node + The dataset that will be updated. + """ + echo_headline("Generating birefringence transfer function with settings:") + echo_settings(settings.birefringence.transfer_function) + + # Calculate transfer functions + intensity_to_stokes_matrix = ( + inplane_oriented_thick_pol3d.calculate_transfer_function( + scheme=str(len(settings.input_channel_names)) + "-State", + settings.birefringence.transfer_function.dict(), + ) + ) + # Save + dataset[ + "intensity_to_stokes_matrix" + ] = intensity_to_stokes_matrix.cpu().numpy()[None, None, None, ...] + + +def generate_and_save_phase_transfer_function(settings, dataset, zyx_shape): + """Generates and saves the phase transfer function to the dataset, based on the settings. + + Parameters + ---------- + settings: ReconstructionSettings + dataset: NGFF Node + The dataset that will be updated. + zyx_shape : tuple + A tuple of integers specifying the input data's shape in (Z, Y, X) order + """ + echo_headline("Generating phase transfer function with settings:") + echo_settings(settings.phase.transfer_function) + + if settings.reconstruction_dimension == 2: + # Convert zyx_shape and z_pixel_size into yx_shape and z_position_list + settings_dict = settings.phase.transfer_function.dict() + settings_dict["yx_shape"] = [zyx_shape[1], zyx_shape[2]] + settings_dict["z_position_list"] = list( + -(np.arange(zyx_shape[0]) - zyx_shape[0] // 2) + * settings_dict["z_pixel_size"] + ) + + # Remove unused parameters + settings_dict.pop("z_pixel_size") + settings_dict.pop("z_padding") + + # Calculate transfer functions + ( + absorption_transfer_function, + phase_transfer_function, + ) = isotropic_thin_3d.calculate_transfer_function( + settings_dict, + ) + + # Save + dataset[ + "absorption_transfer_function" + ] = absorption_transfer_function.cpu().numpy()[None, None, ...] + dataset[ + "phase_transfer_function" + ] = phase_transfer_function.cpu().numpy()[None, None, ...] + + elif settings.reconstruction_dimension == 3: + # Calculate transfer functions + ( + real_potential_transfer_function, + imaginary_potential_transfer_function, + ) = phase_thick_3d.calculate_transfer_function( + zyx_shape=zyx_shape, + settings.phase.transfer_function.dict(), + ) + # Save + dataset[ + "real_potential_transfer_function" + ] = real_potential_transfer_function.cpu().numpy()[None, None, ...] + dataset[ + "imaginary_potential_transfer_function" + ] = imaginary_potential_transfer_function.cpu().numpy()[ + None, None, ... + ] + + +def generate_and_save_fluorescence_transfer_function( + settings, dataset, zyx_shape +): + """Generates and saves the fluorescence transfer function to the dataset, based on the settings. + + Parameters + ---------- + settings: ReconstructionSettings + dataset: NGFF Node + The dataset that will be updated. + zyx_shape : tuple + A tuple of integers specifying the input data's shape in (Z, Y, X) order + """ + echo_headline("Generating fluorescence transfer function with settings:") + echo_settings(settings.fluorescence.transfer_function) + + if settings.reconstruction_dimension == 2: + raise NotImplementedError + elif settings.reconstruction_dimension == 3: + # Calculate transfer functions + optical_transfer_function = ( + isotropic_fluorescent_thick_3d.calculate_transfer_function( + zyx_shape=zyx_shape, + *settings.fluorescence.transfer_function.dict(), + ) + ) + # Save + dataset[ + "optical_transfer_function" + ] = optical_transfer_function.cpu().numpy()[None, None, ...] + + +def compute_transfer_function_cli(input_data_path, config_path, output_path): + """CLI command to compute the transfer function given a configuration file path + and a desired output path. + + Parameters + ---------- + input_data_path : string + Path to the input data file. + config_path : string + Path of the configuration file. + output_path : string + Path of the output file. + """ + + # Load config file + settings = utils.yaml_to_model(config_path, ReconstructionSettings) + + echo_headline( + f"Generating transfer functions and storing in {output_path}\n" + ) + + # Read shape from input dataset + input_dataset = open_ome_zarr(input_data_path, layout="fov", mode="r") + zyx_shape = input_dataset.data.shape[ + 2: + ] # only loads a single position "0" + + # Check input channel names + if not set(settings.input_channel_names).issubset( + input_dataset.channel_names + ): + raise ValueError( + f"Each of the input_channel_names = {settings.input_channel_names} in {config_path} must appear in the dataset {input_data_path} which currently contains channel_names = {input_dataset.channel_names}." + ) + + # Prepare output dataset + output_dataset = open_ome_zarr( + output_path, layout="fov", mode="w", channel_names=["None"] + ) + + # Pass settings to appropriate calculate_transfer_function and save + if settings.birefringence is not None: + generate_and_save_birefringence_transfer_function( + settings, output_dataset + ) + if settings.phase is not None: + generate_and_save_phase_transfer_function( + settings, output_dataset, zyx_shape + ) + if settings.fluorescence is not None: + generate_and_save_fluorescence_transfer_function( + settings, output_dataset, zyx_shape + ) + + # Write settings to metadata + output_dataset.zattrs["settings"] = settings.dict() + + echo_headline(f"Closing {output_path}\n") + output_dataset.close() + + echo_headline( + f"Recreate this transfer function with:\n$ recorder compute-tf {input_data_path} -c {config_path} -o {output_path}" + ) + + [email protected]() [email protected]_option("-h", "--help") +@input_data_path_argument() +@config_path_option() +@output_dataset_option(default="./transfer-function.zarr") +def compute_tf(input_data_path, config_path, output_path): + """ + Compute a transfer function using a dataset and configuration file. + + See /examples/ for example configuration files. + + Example usage:\n + $ recorder compute-tf input.zarr/0/0/0 -c /examples/birefringence.yml -o transfer_function.zarr + """ + compute_transfer_function_cli(input_data_path, config_path, output_path) diff --git a/recOrder/cli/main.py b/recOrder/cli/main.py new file mode 100644 index 00000000..ad39a932 --- /dev/null +++ b/recOrder/cli/main.py @@ -0,0 +1,16 @@ +import click +from recOrder.cli.view import view +from recOrder.cli.reconstruct import reconstruct +from recOrder.cli.compute_transfer_function import compute_tf +from recOrder.cli.apply_inverse_transfer_function import apply_inv_tf + + [email protected]() +def cli(): + """\033[92mrecOrder: Computational Toolkit for Label-Free Imaging\033[0m\n""" + + +cli.add_command(view) +cli.add_command(reconstruct) +cli.add_command(compute_tf) +cli.add_command(apply_inv_tf) diff --git a/recOrder/cli/parsing.py b/recOrder/cli/parsing.py new file mode 100644 index 00000000..298536c0 --- /dev/null +++ b/recOrder/cli/parsing.py @@ -0,0 +1,54 @@ +import click +from typing import Callable +from iohub.ngff import open_ome_zarr, Plate + + +def _validate_fov_path( + ctx: click.Context, opt: click.Option, value: str +) -> None: + dataset = open_ome_zarr(value) + if isinstance(dataset, Plate): + raise ValueError( + "Please supply a single position instead of an HCS plate. Likely fix: replace 'input.zarr' with 'input.zarr/0/0/0'" + ) + return value + + +def input_data_path_argument() -> Callable: + def decorator(f: Callable) -> Callable: + return click.argument( + "input-data-path", + type=click.Path(exists=True), + callback=_validate_fov_path, + nargs=1, + )(f) + + return decorator + + +def config_path_option() -> Callable: + def decorator(f: Callable) -> Callable: + return click.option( + "--config-path", "-c", required=True, help="Path to config.yml" + )(f) + + return decorator + + +def output_dataset_option(default) -> Callable: + click_options = [ + click.option( + "--output-path", + "-o", + default=default, + help="Path to output.zarr", + ) + ] + # good place to add chunking, overwrite flag, etc + + def decorator(f: Callable) -> Callable: + for opt in click_options: + f = opt(f) + return f + + return decorator diff --git a/recOrder/cli/printing.py b/recOrder/cli/printing.py new file mode 100644 index 00000000..8d0f6e7a --- /dev/null +++ b/recOrder/cli/printing.py @@ -0,0 +1,10 @@ +import click +import yaml + + +def echo_settings(settings): + click.echo(yaml.dump(settings.dict(), default_flow_style=False, sort_keys=False)) + + +def echo_headline(headline): + click.echo(click.style(headline, fg="green")) diff --git a/recOrder/cli/reconstruct.py b/recOrder/cli/reconstruct.py new file mode 100644 index 00000000..8599a675 --- /dev/null +++ b/recOrder/cli/reconstruct.py @@ -0,0 +1,44 @@ +import click +import os +from recOrder.cli.compute_transfer_function import ( + compute_transfer_function_cli, +) +from recOrder.cli.apply_inverse_transfer_function import ( + apply_inverse_transfer_function_cli, +) +from recOrder.cli.parsing import ( + input_data_path_argument, + config_path_option, + output_dataset_option, +) + + [email protected]() [email protected]_option("-h", "--help") +@input_data_path_argument() +@config_path_option() +@output_dataset_option(default="./reconstruction.zarr") +def reconstruct(input_data_path, config_path, output_path): + """Reconstruct a dataset using a configuration file. This is a + convenience function for a `compute-tf` call followed by a `apply-inv-tf` + call. + + See /examples for example configuration files. + + Example usage:\n + $ recorder reconstruct input.zarr/0/0/0 -c /examples/birefringence.yml -o output.zarr + """ + + # Handle transfer function path + output_directory = os.path.dirname(output_path) + transfer_function_path = os.path.join( + output_directory, "transfer_function.zarr" + ) + + # Compute transfer function and apply inverse + compute_transfer_function_cli( + input_data_path, config_path, transfer_function_path + ) + apply_inverse_transfer_function_cli( + input_data_path, transfer_function_path, config_path, output_path + ) diff --git a/recOrder/cli/settings.py b/recOrder/cli/settings.py new file mode 100644 index 00000000..051dae7a --- /dev/null +++ b/recOrder/cli/settings.py @@ -0,0 +1,178 @@ +import os +from pydantic import ( + BaseModel, + Extra, + NonNegativeInt, + NonNegativeFloat, + PositiveInt, + PositiveFloat, + root_validator, + validator, +) +from typing import Literal, List, Optional + +# This file defines the configuration settings for the CLI. + +# Example settings files in `/examples/settings/` are autmatically generated +# by the tests in `/tests/cli_tests/test_settings.py` - `test_generate_example_settings`. + +# To keep the example settings up to date, run `pytest` locally when this file changes. + + +# All settings classes inherit from MyBaseModel, which forbids extra parameters to guard against typos +class MyBaseModel(BaseModel, extra=Extra.forbid): + pass + + +# Bottom level settings +class WavelengthIllumination(MyBaseModel): + wavelength_illumination: PositiveFloat = 0.532 + + +class BirefringenceTransferFunctionSettings(MyBaseModel): + swing: float = 0.1 + + @validator("swing") + def swing_range(cls, v): + if v <= 0 or v >= 1.0: + raise ValueError(f"swing = {v} should be between 0 and 1.") + return v + + +class BirefringenceApplyInverseSettings(WavelengthIllumination): + background_path: str = "" + remove_estimated_background: bool = False + orientation_flip: bool = False + orientation_rotate: bool = False + + @validator("background_path") + def check_background_path(cls, v): + if v == "": + return v + + raw_dir = r"{}".format(v) + if not os.path.isdir(raw_dir): + raise ValueError(f"{v} is not a existing directory") + return raw_dir + + +class FourierTransferFunctionSettings(MyBaseModel): + yx_pixel_size: PositiveFloat = 6.5 / 20 + z_pixel_size: PositiveFloat = 2.0 + z_padding: NonNegativeInt = 0 + index_of_refraction_media: PositiveFloat = 1.3 + numerical_aperture_detection: PositiveFloat = 1.2 + + @validator("numerical_aperture_detection") + def na_det(cls, v, values): + n = values["index_of_refraction_media"] + if v > n: + raise ValueError( + f"numerical_aperture_detection = {v} must be less than or equal to index_of_refraction_media = {n}" + ) + return v + + @validator("z_pixel_size") + def warn_unit_consistency(cls, v, values): + yx_pixel_size = values["yx_pixel_size"] + ratio = yx_pixel_size / v + if ratio < 1.0 / 20 or ratio > 20: + raise Warning( + f"yx_pixel_size ({yx_pixel_size}) / z_pixel_size ({v}) = {ratio}. Did you use consistent units?" + ) + return v + + +class FourierApplyInverseSettings(MyBaseModel): + reconstruction_algorithm: Literal["Tikhonov", "TV"] = "Tikhonov" + regularization_strength: NonNegativeFloat = 1e-3 + TV_rho_strength: PositiveFloat = 1e-3 + TV_iterations: NonNegativeInt = 1 + + +class PhaseTransferFunctionSettings( + FourierTransferFunctionSettings, + WavelengthIllumination, +): + numerical_aperture_illumination: NonNegativeFloat = 0.5 + axial_flip: bool = False + + @validator("numerical_aperture_illumination") + def na_ill(cls, v, values): + n = values.get("index_of_refraction_media") + if v > n: + raise ValueError( + f"numerical_aperture_illumination = {v} must be less than or equal to index_of_refraction_media = {n}" + ) + return v + + +class FluorescenceTransferFunctionSettings(FourierTransferFunctionSettings): + wavelength_emission: PositiveFloat = 0.507 + + @validator("wavelength_emission") + def warn_unit_consistency(cls, v, values): + yx_pixel_size = values.get("yx_pixel_size") + ratio = yx_pixel_size / v + if ratio < 1.0 / 20 or ratio > 20: + raise Warning( + f"yx_pixel_size ({yx_pixel_size}) / wavelength_illumination ({v}) = {ratio}. Did you use consistent units?" + ) + return v + + +# Second level settings +class BirefringenceSettings(MyBaseModel): + transfer_function: BirefringenceTransferFunctionSettings = ( + BirefringenceTransferFunctionSettings() + ) + apply_inverse: BirefringenceApplyInverseSettings = ( + BirefringenceApplyInverseSettings() + ) + + +class PhaseSettings(MyBaseModel): + transfer_function: PhaseTransferFunctionSettings = ( + PhaseTransferFunctionSettings() + ) + apply_inverse: FourierApplyInverseSettings = FourierApplyInverseSettings() + + +class FluorescenceSettings(MyBaseModel): + transfer_function: FluorescenceTransferFunctionSettings = ( + FluorescenceTransferFunctionSettings() + ) + apply_inverse: FourierApplyInverseSettings = FourierApplyInverseSettings() + + +# Top level settings +class ReconstructionSettings(MyBaseModel): + input_channel_names: List[str] = [f"State{i}" for i in range(4)] + reconstruction_dimension: Literal[2, 3] = 3 + birefringence: Optional[BirefringenceSettings] + phase: Optional[PhaseSettings] + fluorescence: Optional[FluorescenceSettings] + + @root_validator(pre=False) + def validate_reconstruction_types(cls, values): + if (values.get("birefringence") or values.get("phase")) and values.get( + "fluorescence" + ) is not None: + raise ValueError( + '"fluorescence" cannot be present alongside "birefringence" or "phase". Please use one configuration file for a "fluorescence" reconstruction and another configuration file for a "birefringence" and/or "phase" reconstructions.' + ) + num_channel_names = len(values.get("input_channel_names")) + if values.get("birefringence") is None: + if ( + values.get("phase") is None + and values.get("fluorescence") is None + ): + raise ValueError( + "Provide settings for either birefringence, phase, birefringence + phase, or fluorescence." + ) + if num_channel_names != 1: + raise ValueError( + f"{num_channel_names} channels names provided. Please provide a single channel for fluorescence/phase reconstructions." + ) + + return values diff --git a/recOrder/scripts/cli.py b/recOrder/cli/view.py similarity index 97% rename from recOrder/scripts/cli.py rename to recOrder/cli/view.py index 5e54fe53..0461aa9a 100644 --- a/recOrder/scripts/cli.py +++ b/recOrder/cli/view.py @@ -1,7 +1,7 @@ import click import napari -v = napari.Viewer() # open viewer right away to use on hpc +#v = napari.Viewer() # open viewer right away to use on hpc import numpy as np from recOrder.io.utils import ret_ori_overlay from iohub.reader import print_info, _infer_format @@ -170,14 +170,7 @@ def text_overlay(): napari.run() [email protected]() -def cli(): - print( - "\033[92mrecOrder: Computational Toolkit for Label-Free Imaging\033[0m\n" - ) - - [email protected]() [email protected]() @click.help_option("-h", "--help") @click.argument("filename") @click.option( diff --git a/recOrder/compute/__init__.py b/recOrder/compute/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/recOrder/compute/phantoms.py b/recOrder/compute/phantoms.py deleted file mode 100644 index e31dcccb..00000000 --- a/recOrder/compute/phantoms.py +++ /dev/null @@ -1,157 +0,0 @@ -import numpy as np -import waveorder as wo - - -def pol_3D_from_phantom(): - # Adapted from waveorder/examples/2D_QLIPP_simulation/2D_QLIPP_forward.py - - N = 512 # number of pixel in y dimension - M = 512 # number of pixel in x dimension - mag = 20 # magnification - ps = 6.5 / mag # effective pixel size - lambda_illu = 0.532 # wavelength - n_media = 1 # refractive index in the media - NA_obj = 0.4 # objective NA - NA_illu = 0.2 # illumination NA (condenser) - N_defocus = 11 - z_defocus = ( - np.r_[:N_defocus] - N_defocus // 2 - ) 2 # a set of defocus planes - chi = 0.1 * 2 * np.pi # swing of Polscope analyzer - - # Generate phantom - star, theta, _ = wo.genStarTarget(N, M) - phase_value = 1 # average phase in radians (optical path length) - phi_s = star * (phase_value + 0.15) # slower OPL across target - phi_f = star * (phase_value - 0.15) # faster OPL across target - mu_s = np.zeros((N, M)) # absorption - mu_f = mu_s.copy() - t_eigen = np.zeros((2, N, M), complex) # complex specimen transmission - t_eigen[0] = np.exp(-mu_s + 1j * phi_s) - t_eigen[1] = np.exp(-mu_f + 1j * phi_f) - _, _, fxx, fyy = wo.gen_coordinate((N, M), ps) - sa = theta % np.pi # slow axes. - - # Generate source - Source_cont = wo.gen_Pupil(fxx, fyy, NA_illu, lambda_illu) - Source_discrete = wo.Source_subsample( - Source_cont, lambda_illu * fxx, lambda_illu * fyy, subsampled_NA=0.1 - ) - - # Generate simulator - simulator = wo.waveorder_microscopy_simulator( - (N, M), - lambda_illu, - ps, - NA_obj, - NA_illu, - z_defocus, - chi, - n_media=n_media, - illu_mode="Arbitrary", - Source=Source_discrete, - ) - - # Simulate measurements - I_meas, _ = simulator.simulate_waveorder_measurements( - t_eigen, sa, multiprocess=False - ) - - # Add background - photon_count = 300000 - ext_ratio = 10000 - const_bg = photon_count / (0.5 * (1 - np.cos(chi))) / ext_ratio - I_meas_noise = ( - np.random.poisson(I_meas / np.max(I_meas) * photon_count + const_bg) - ).astype("float64") - - # Return data and background - data = I_meas_noise.transpose(0, 3, 1, 2) - bkg = np.ones((5, N, M)) * const_bg - return data, bkg - - -def bf_3D_from_phantom(): - # Adapted from waveorder/3D_PODT_phase_simulation/3D_PODT_Phase_forward.py - - N = 512 # number of pixel in y dimension - M = 512 # number of pixel in x dimension - mag = 20 # magnification - ps = 6.5 / mag # effective pixel size - lambda_illu = 0.532 # wavelength - n_media = 1 # refractive index in the media - NA_obj = 0.4 # objective NA - NA_illu = 0.2 # illumination NA (condenser) - N_defocus = 31 - psz = 2 - z_defocus = ( - np.r_[:N_defocus] - N_defocus // 2 - ) * psz # a set of defocus planes - chi = 0.1 # not used - - # Generate sample - phantom = np.zeros((N, M, N_defocus), dtype=np.complex64) - star, _, _ = wo.genStarTarget(N, M) - n_sample = 1.50 - t_obj = np.exp(1j * 2 * np.pi * psz * (star * n_sample - n_media)) - phantom[:, :, N_defocus // 2] = t_obj - phantom += n_media - - # Generate source - _, _, fxx, fyy = wo.gen_coordinate((N, M), ps) - Source_cont = wo.gen_Pupil(fxx, fyy, NA_illu, lambda_illu) - Source_discrete = wo.Source_subsample( - Source_cont, lambda_illu * fxx, lambda_illu * fyy, subsampled_NA=0.1 - ) - - # Simulate - simulator = wo.waveorder_microscopy_simulator( - (N, M), - lambda_illu, - ps, - NA_obj, - NA_illu, - z_defocus, - chi, - n_media=n_media, - illu_mode="Arbitrary", - Source=Source_discrete, - ) - data = simulator.simulate_3D_scalar_measurements(phantom) - return data.transpose((2, 0, 1)) - - -def fluorescence_from_phantom(): - # Adapted from waveorder/examples/fluorescence_deconvolution/fluorescence_deconv.ipynb - - N = 512 # number of pixel in y dimension - M = 512 # number of pixel in x dimension - mag = 20 # magnification - ps = 6.5 / mag # effective pixel size - psz = 2 - lambda_emiss = [0.532] # wavelength - n_media = 1 # refractive index in the media - NA_obj = 0.4 # objective NA - N_defocus = 11 - - fluor_setup = wo.fluorescence_microscopy( - (N, M, N_defocus), - lambda_emiss, - ps, - psz, - NA_obj, - n_media=n_media, - deconv_mode="3D-WF", - pad_z=10, - use_gpu=False, - gpu_id=0, - ) - - star, _, _ = wo.genStarTarget(N, M) - - f = np.zeros((N, M, fluor_setup.N_defocus_3D)) - f[:, :, fluor_setup.N_defocus_3D // 2] = star - F = np.fft.fftn(f) - H = fluor_setup.OTF_WF_3D[0, ...] - g = np.abs(np.fft.ifftn(F * H)) - return g.transpose((2, 0, 1)) diff --git a/recOrder/compute/reconstructions.py b/recOrder/compute/reconstructions.py deleted file mode 100644 index 06167ddf..00000000 --- a/recOrder/compute/reconstructions.py +++ /dev/null @@ -1,492 +0,0 @@ -from waveorder.waveorder_reconstructor import ( - waveorder_microscopy, - fluorescence_microscopy, -) -import numpy as np -import time - - -def initialize_reconstructor( - pipeline, - image_dim=None, - wavelength_nm=None, - swing=None, - calibration_scheme=None, - NA_obj=1.3, - NA_illu=0.5, - mag=None, - n_slices=None, - z_step_um=None, - in_focus_slice=None, - pad_z=0, - pixel_size_um=None, - bg_correction="None", - n_obj_media=1.3, - mode="3D", - use_gpu=False, - gpu_id=0, -): - """ - Initialize the QLIPP reconstructor for downstream tasks. See tags next to parameters - for which parameters are needed for each pipeline - - Parameters - ---------- - - pipeline : string - 'birefringence', 'QLIPP', 'PhaseFromBF', 'fluorescence' - - image_dim : tuple - (height, width) of images in pixels - - wavelength_nm : int - wavelength of illumination in nm - - swing : float - swing used for calibration in waves - - calibration_scheme: str - '4-State' or '5-State' - - NA_obj : float - numerical aperture of the detection objective - - NA_illu : float - numerical aperture of the illumination condenser - - mag : float - magnification used for imaging (e.g. 20 for 20x) - - n_slices : int - number of slices in the z-stack - - z_step_um : float - z step size of the image space - - in_focus_slice : int or None - index of the in-focus z slice used for 2D-from-3D reconstructions - if None, the middle z slice is used as the in focus slice - - pad_z : float - how many padding slices to add for phase computation - - pixel_size_um : float - pixel size of the camera in um - - bg_correction : str - 'local' for estimating background with scipy uniform filter - 'local_fit' for estimating background with polynomial fit - 'None' for no BG correction - 'Global' for normal background subtraction with the provided background - - n_obj_media : float - refractive index of the objective immersion media - - mode : str - '2D' or '3D' (phase reconstruction only) - - use_gpu : bool - option to use gpu or not - - gpu_id : int - number refering to which gpu will be used - - - Returns - ------- - reconstructor : object - reconstruction object initialized with reconstruction parameters - - """ - - anisotropy_only = False - - if NA_obj > n_obj_media: - raise ValueError("Parameters are non-physical: NA_obj > n_obj_media") - if NA_illu > n_obj_media: - raise ValueError("Parameters are non-physical: NA_illu > n_obj_media") - - if pipeline == "QLIPP" or pipeline == "PhaseFromBF": - - if not NA_obj: - raise ValueError("Please specify NA_obj in function parameters") - if not NA_illu: - raise ValueError("Please specify NA_illu in function parameters") - if not mag: - raise ValueError( - "Please specify mag (magnification) in function parameters" - ) - if not wavelength_nm: - raise ValueError( - "Please specify the wavelength for reconstruction" - ) - if not n_slices: - raise ValueError("Please specify n_slices in function parameters") - if not z_step_um: - raise ValueError("Please specify z_step_um in function parameters") - if not pixel_size_um: - raise ValueError("Please specify NA_obj in function parameters") - if not n_obj_media: - raise ValueError("Please specify NA_obj in function parameters") - if not image_dim: - raise ValueError("Please specify image_dim in function parameters") - - if pipeline == "QLIPP": - if not calibration_scheme: - raise ValueError( - "Please specify qlipp_scheme (calibration scheme) for QLIPP reconstruction" - ) - if not swing: - raise ValueError("Please specify swing in function parameters") - - elif pipeline == "birefringence": - - anisotropy_only = True - - if not calibration_scheme: - raise ValueError( - "Please specify qlipp_scheme (calibration scheme) for QLIPP reconstruction" - ) - if not wavelength_nm: - raise ValueError( - "Please specify the wavelength for QLIPP reconstruction" - ) - if not swing: - raise ValueError("Please specify swing in function parameters") - elif pipeline == "fluorescence": - if not NA_obj: - raise ValueError( - "Please specify NA_obj for fluorescence reconstruction." - ) - else: - raise ValueError(f"Pipeline {pipeline} not understood") - - # Modify user inputs to fit waveorder input requirements - lambda_illu = wavelength_nm / 1000 if wavelength_nm else None - n_defocus = n_slices if n_slices else 0 - z_step_um = 0 if not z_step_um else z_step_um - - if in_focus_slice is None: - in_focus_slice = n_defocus // 2 - - z_defocus = ( - -(np.r_[:n_defocus] - in_focus_slice) * z_step_um - ) # assumes stack starts from the bottom - ps = pixel_size_um / mag if pixel_size_um else None - cali = True - NA_obj = 0 if not NA_obj else NA_obj - NA_illu = 0 if not NA_illu else NA_illu - - if calibration_scheme == "4-State": - inst_mat = np.array( - [ - [1, 0, 0, -1], - [1, np.sin(2 * np.pi * swing), 0, -np.cos(2 * np.pi * swing)], - [ - 1, - -0.5 * np.sin(2 * np.pi * swing), - np.sqrt(3) * np.cos(np.pi * swing) * np.sin(np.pi * swing), - -np.cos(2 * np.pi * swing), - ], - [ - 1, - -0.5 * np.sin(2 * np.pi * swing), - -np.sqrt(3) / 2 * np.sin(2 * np.pi * swing), - -np.cos(2 * np.pi * swing), - ], - ] - ) - n_channel = 4 - - elif calibration_scheme == "5-State": - swing = swing * 2 * np.pi - inst_mat = None - n_channel = 5 - - elif calibration_scheme == "PhaseFromBF": - inst_mat = None - n_channel = 1 - swing = 0 - else: - inst_mat = None - n_channel = 1 - swing = 0 - - print("Initializing Reconstructor...") - start_time = time.time() - if pipeline != "fluorescence": - recon = waveorder_microscopy( - img_dim=image_dim, - lambda_illu=lambda_illu, - ps=ps, - NA_obj=NA_obj, - NA_illu=NA_illu, - z_defocus=z_defocus, - chi=swing, - n_media=n_obj_media, - cali=cali, - bg_option=bg_correction, - A_matrix=inst_mat, - QLIPP_birefringence_only=anisotropy_only, - pad_z=pad_z, - phase_deconv=mode, - illu_mode="BF", - use_gpu=use_gpu, - gpu_id=gpu_id, - ) - recon.N_channel = n_channel - else: - - recon = fluorescence_microscopy( - img_dim=image_dim + (n_slices,), - lambda_emiss=[lambda_illu], - ps=ps, - psz=z_step_um, - NA_obj=NA_obj, - n_media=n_obj_media, - deconv_mode="3D-WF", - pad_z=pad_z, - use_gpu=use_gpu, - gpu_id=gpu_id, - ) - - elapsed_time = (time.time() - start_time) / 60 - print(f"Finished Initializing Reconstructor ({elapsed_time:0.2f} min)") - - return recon - - -def reconstruct_qlipp_stokes(data, recon, bg_stokes=None): - """ - From intensity data, use the waveorder.waveorder_microscopy (recon) to build a stokes array - if recon background correction flag is selected, will also perform backgroudn correction - - Parameters - ---------- - data : np.ndarray or zarr array - intensity data of shape: (C, Z, Y, X) - - recon : waveorder.waveorder_microscopy object - initialized by initialize_reconstructor - - bg_stokes : np.ndarray (5, Y, X) or (4, Y, X) - stokes array representing background data - - Returns - ------- - stokes_stack : np.ndarray - array representing stokes array - has shape: (C, Z, Y, X), where C = 5 - or (C, Y, X) if not reconstructing a z-stack - """ - - stokes_data = recon.Stokes_recon(np.copy(data)) - stokes_data = recon.Stokes_transform(stokes_data) - - # Don't do background correction if BG data isn't provided - if recon.bg_option == "None" or bg_stokes is None: - return stokes_data # C(Z)YX - - # Compute Stokes with background correction - else: - if len(np.shape(stokes_data)) == 4: - s_image = recon.Polscope_bg_correction( - np.transpose(stokes_data, (-4, -2, -1, -3)), bg_stokes - ) - s_image = np.transpose(s_image, (0, 3, 1, 2)) # Tranpose to CZYX - else: - s_image = recon.Polscope_bg_correction( - stokes_data, bg_stokes - ) # CYX - - return s_image - - -def reconstruct_qlipp_birefringence(stokes, recon): - """ - From stokes data, use waveorder.waveorder_microscopy (recon) to build a birefringence array - - Parameters - ---------- - stokes : np.ndarray or zarr array - stokes array generated by reconstruct_qlipp_stokes - dimensions: (C, Z, Y, X) or (C, Y, X) - - recon : waveorder.waveorder_microscopy object - initialized by initialize_reconstructor - - Returns - ------- - recon_data : np.ndarray - volume of shape (C, Z, Y, X) or (C, Y, X) containing reconstructed birefringence data. - """ - - if stokes.ndim != 4 and stokes.ndim != 3: - raise ValueError(f"Incompatible stokes dimension: {stokes.shape}") - - return recon.Polarization_recon(stokes) - - -def reconstruct_phase2D( - S0, recon, method="Tikhonov", reg_p=1e-4, rho=1, lambda_p=1e-4, itr=50 -): - """ - Reconstruct 2D phase from a given S0 or BF stack. - - Parameters - ---------- - S0: (nd-array) BF/S0 stack of dimensions (Z, Y, X) - recon: (waveorder_microscopy Object): initialized reconstructor object - method: (str) Regularization method 'Tikhonov' or 'TV' - reg_p: (float) Tikhonov regularization parameters - rho: (float) TV regularization parameter - lambda_p: (float) TV regularization parameter - itr: (int) TV Regularization number of iterations - - Returns - ------- - phase2D: (nd-array) Phase2D image of size (Y, X) - - """ - - S0 = np.transpose(S0, (1, 2, 0)) - _, phase2D = recon.Phase_recon( - np.copy(S0).astype("float"), - method=method, - reg_p=reg_p, - rho=rho, - lambda_p=lambda_p, - itr=itr, - verbose=False, - ) - - return phase2D - - -def reconstruct_phase3D( - S0, recon, method="Tikhonov", reg_re=1e-4, rho=1e-3, lambda_re=1e-4, itr=50 -): - """ - Reconstruct 2D phase from a given S0 or BF stack. - - Parameters - ---------- - S0: (nd-array) BF/S0 stack of dimensions (Z, Y, X) - recon: (waveorder_microscopy Object): initialized reconstructor object - method: (str) Regularization method 'Tikhonov' or 'TV' - reg_p: (float) Tikhonov regularization parameters - rho: (float) TV regularization parameter - lambda_p: (float) TV regularization parameter - itr: (int) TV Regularization number of iterations - - Returns - ------- - phase3D: (nd-array) Phase2D image of size (Z, Y, X) - - """ - - S0 = np.transpose(S0, (1, 2, 0)) - phase3D = recon.Phase_recon_3D( - np.copy(S0).astype("float"), - method=method, - reg_re=reg_re, - rho=rho, - lambda_re=lambda_re, - itr=itr, - verbose=False, - ) - - phase3D = np.transpose(phase3D, (-1, -3, -2)) - - return phase3D - - -def reconstruct_density_from_fluorescence(data3D, recon, bg_level=0, reg=1e-2): - """ - Reconstruct 3D density from fluorescence intensity - - Parameters - ---------- - data3D: (nd-array) Stack of dimensions (Z, Y, X) - recon: (fluorescence_microscopy Object): initialized reconstructor object - reg: (float) Tikhonov regularization parameters - - Returns - ------- - density: (nd-array) density image of size (Z, Y, X) - - """ - - data3D = np.transpose(data3D, (1, 2, 0)) - density = recon.deconvolve_fluor_3D( - np.copy(data3D).astype("float"), - bg_level=[bg_level], - reg=[reg], - ) - - return np.transpose(density, (-1, -3, -2)) - - -class QLIPPBirefringenceCompute: - """ - Convenience Class for computing QLIPP birefringence only. - """ - - def __init__( - self, - shape, - scheme, - wavelength, - swing, - n_slices, - bg_option, - bg_data=None, - ): - - self.shape = shape - self.scheme = scheme - self.wavelength = wavelength - self.swing = swing - self.n_slices = n_slices - self.bg_option = bg_option - - self.reconstructor = initialize_reconstructor( - pipeline="birefringence", - image_dim=self.shape, - calibration_scheme=self.scheme, - wavelength_nm=self.wavelength, - swing=self.swing, - bg_correction=self.bg_option, - n_slices=self.n_slices, - ) - - if bg_option != "None": - self.bg_stokes = reconstruct_qlipp_stokes( - bg_data, self.reconstructor - ) - else: - self.bg_stokes = None - - def reconstruct(self, array): - """ - reconstructs raw data into birefringence data - - Parameters - ---------- - array: (nd-array) of image shape (C, Z, Y, X) or (C, Y, X) with minimum C=4 - - Returns - ------- - birefringence: (nd-array) birefringence array of size (2, Z, Y, X) or (2, Y, X) - first channel is retardance [nm] second channel is orientation [0, pi] - - """ - stokes = reconstruct_qlipp_stokes( - array, self.reconstructor, self.bg_stokes - ) - birefringence = reconstruct_qlipp_birefringence( - stokes, self.reconstructor - ) - birefringence[0] = birefringence[0] / (2 * np.pi) * self.wavelength - - return birefringence[0:2] diff --git a/recOrder/io/utils.py b/recOrder/io/utils.py index 3c6731ab..e1b418b3 100644 --- a/recOrder/io/utils.py +++ b/recOrder/io/utils.py @@ -1,15 +1,21 @@ +from typing import Literal import glob import logging import os import psutil +import torch import textwrap import tifffile as tiff import numpy as np +import yaml from colorspacious import cspace_convert +from iohub import open_ome_zarr from matplotlib.colors import hsv_to_rgb from waveorder.waveorder_reconstructor import waveorder_microscopy +from recOrder.cli import settings +# TO BE DEPRECATED def extract_reconstruction_parameters(reconstructor, magnification=None): """ Function that extracts the reconstruction parameters from a waveorder reconstructor. Works for waveorder_microscopy class. @@ -53,91 +59,12 @@ def extract_reconstruction_parameters(reconstructor, magnification=None): return attr_dict -def load_bg(bg_path, height, width, ROI=None): - """ - Parameters - ---------- - bg_path : (str) path to the folder containing background images - height : (int) height of image in pixels # Remove for 1.0.0 - width : (int) width of image in pixels # Remove for 1.0.0 - ROI : (tuple) ROI of the background images to use, if None, full FOV will be used # Remove for 1.0.0 - - Returns - ------- - bg_data : (ndarray) Array of background data w/ dimensions (N_channel, Y, X) - """ - - bg_paths = glob.glob(os.path.join(bg_path, ".tif")) - bg_paths.sort() - - # Backwards compatibility warning - if ROI is not None and ROI != ( - 0, - 0, - width, - height, - ): # TODO: Remove for 1.0.0 - warning_msg = """ - Earlier versions of recOrder (0.1.2 and earlier) would have averaged over the background ROI. - This behavior is now considered a bug, and future versions of recOrder (0.2.0 and later) - will not average over the background. - """ - logging.warning(warning_msg) - - # Load background images - bg_img_list = [] - for bg_path in bg_paths: - bg_img_list.append(tiff.imread(bg_path)) - bg_img_arr = np.array(bg_img_list) # CYX - - # Error if shapes do not match - # TODO: 1.0.0 move these validation check to waveorder's Polscope_bg_correction - if bg_img_arr.shape[1:] != (height, width): - error_msg = "The background image has a different X/Y size than the acquired image." - raise ValueError(error_msg) - - return bg_img_arr # CYX - - -def create_grid_from_coordinates(xy_coords, rows, columns): - """ - Function to create a grid from XY-position coordinates. Useful for generating HCS Zarr metadata. - - Parameters - ---------- - xy_coords: (list) XY Stage position list in the order in which it was acquired: (X, Y) tuple. - rows: (int) number of rows in the grid-like acquisition - columns: (int) number of columns in the grid-like acquisition - - Returns - ------- - pos_index_grid (array) A grid-like array mimicking the shape of the acquisition where the value in the array - corresponds to the position index at that location. - """ - - coords = dict() - coords_list = [] - for idx, pos in enumerate(xy_coords): - coords[idx] = pos - coords_list.append(pos) - - # sort by X and then by Y - coords_list.sort(key=lambda x: x[0]) - coords_list.sort(key=lambda x: x[1]) - - # reshape XY coordinates into their proper 2D shape - grid = np.reshape(coords_list, (rows, columns, 2)) - pos_index_grid = np.zeros((rows, columns), "uint16") - keys = list(coords.keys()) - vals = list(coords.values()) - - for row in range(rows): - for col in range(columns): - - # append position index (key) into a final grid by indexed into the coordinate map (values) - pos_index_grid[row, col] = keys[vals.index(list(grid[row, col]))] - - return pos_index_grid +def load_background(background_path): + with open_ome_zarr( + os.path.join(background_path, "background.zarr", "0", "0", "0") + ) as dataset: + cyx_data = dataset["0"][0, :, 0] + return torch.tensor(cyx_data, dtype=torch.float32) class MockEmitter: @@ -145,48 +72,6 @@ def emit(self, value): pass -def get_unimodal_threshold(input_image): - """Determines optimal unimodal threshold - https://users.cs.cf.ac.uk/Paul.Rosin/resources/papers/unimodal2.pdf - https://www.mathworks.com/matlabcentral/fileexchange/45443-rosin-thresholding - :param np.array input_image: generate mask for this image - :return float best_threshold: optimal lower threshold for the foreground - hist - """ - - hist_counts, bin_edges = np.histogram( - input_image, - bins=256, - range=(input_image.min(), np.percentile(input_image, 99.5)), - ) - bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2 - - # assuming that background has the max count - max_idx = np.argmax(hist_counts) - int_with_max_count = bin_centers[max_idx] - p1 = [int_with_max_count, hist_counts[max_idx]] - - # find last non-empty bin - pos_counts_idx = np.where(hist_counts > 0)[0] - last_binedge = pos_counts_idx[-1] - p2 = [bin_centers[last_binedge], hist_counts[last_binedge]] - - best_threshold = -np.inf - max_dist = -np.inf - for idx in range(max_idx, last_binedge, 1): - x0 = bin_centers[idx] - y0 = hist_counts[idx] - a = [p1[0] - p2[0], p1[1] - p2[1]] - b = [x0 - p2[0], y0 - p2[1]] - cross_ab = a[0] b[1] - b[0] * a[1] - per_dist = np.linalg.norm(cross_ab) / np.linalg.norm(a) - if per_dist > max_dist: - best_threshold = x0 - max_dist = per_dist - assert best_threshold > -np.inf, "Error in unimodal thresholding" - return best_threshold - - def ram_message(): """ Determine if the system's RAM capacity is sufficient for running reconstruction. @@ -282,7 +167,9 @@ def generic_hsv_overlay( return overlay_final[0] if mode == "2D" else overlay_final -def ret_ori_overlay(retardance, orientation, ret_max=10, cmap="JCh"): +def ret_ori_overlay( + retardance, orientation, ret_max=10, cmap: Literal["JCh", "HSV"] = "JCh" +): """ This function will create an overlay of retardance and orientation with two different colormap options. HSV is the standard Hue, Saturation, Value colormap while JCh is a similar colormap but is perceptually uniform. @@ -301,14 +188,14 @@ def ret_ori_overlay(retardance, orientation, ret_max=10, cmap="JCh"): """ if retardance.shape != orientation.shape: raise ValueError( - f"Retardance and Orientation shapes do not match: {retardance.shape} vs. {orientation.shape}" + "Retardance and Orientation shapes do not match: " + f"{retardance.shape} vs. {orientation.shape}" ) # Prepare input and output arrays ret_ = np.clip(retardance, 0, ret_max) # clip and copy - ori_ = ( - np.copy(orientation) * 360 / np.pi - ) # convert 180 degree range into 360 to match periodicity of hue. + # Convert 180 degree range into 360 to match periodicity of hue. + ori_ = orientation * 360 / np.pi overlay_final = np.zeros_like(retardance) # FIX ME: this binning code leads to artifacts. @@ -350,3 +237,96 @@ def ret_ori_overlay(retardance, orientation, ret_max=10, cmap="JCh"): raise ValueError(f"Colormap {cmap} not understood") return overlay_final + + +def model_to_yaml(model, yaml_path): + """ + Save a model's dictionary representation to a YAML file. + + Parameters + ---------- + model : object + The model object to convert to YAML. + yaml_path : str + The path to the output YAML file. + + Raises + ------ + TypeError + If the `model` object does not have a `dict()` method. + + Notes + ----- + This function converts a model object into a dictionary representation + using the `dict()` method. It removes any fields with None values before + writing the dictionary to a YAML file. + + Examples + -------- + >>> from my_model import MyModel + >>> model = MyModel() + >>> model_to_yaml(model, 'model.yaml') + + """ + if not hasattr(model, "dict"): + raise TypeError("The 'model' object does not have a 'dict()' method.") + + model_dict = model.dict() + + # Remove None-valued fields + clean_model_dict = { + key: value for key, value in model_dict.items() if value is not None + } + + with open(yaml_path, "w+") as f: + yaml.dump( + clean_model_dict, f, default_flow_style=False, sort_keys=False + ) + + +def yaml_to_model(yaml_path, model): + """ + Load model settings from a YAML file and create a model instance. + + Parameters + ---------- + yaml_path : str + The path to the YAML file containing the model settings. + model : class + The model class used to create an instance with the loaded settings. + + Returns + ------- + object + An instance of the model class with the loaded settings. + + Raises + ------ + TypeError + If the provided model is not a class or does not have a callable constructor. + FileNotFoundError + If the YAML file specified by `yaml_path` does not exist. + + Notes + ----- + This function loads model settings from a YAML file using `yaml.safe_load()`. + It then creates an instance of the provided `model` class using the loaded settings. + + Examples + -------- + >>> from my_model import MyModel + >>> model = yaml_to_model('model.yaml', MyModel) + + """ + if not callable(getattr(model, "__init__", None)): + raise TypeError( + "The provided model must be a class with a callable constructor." + ) + + try: + with open(yaml_path, "r") as file: + raw_settings = yaml.safe_load(file) + except FileNotFoundError: + raise FileNotFoundError(f"The YAML file '{yaml_path}' does not exist.") + + return model(*raw_settings) diff --git a/recOrder/plugin/main_widget.py b/recOrder/plugin/main_widget.py index 985d3b2e..599e44c5 100644 --- a/recOrder/plugin/main_widget.py +++ b/recOrder/plugin/main_widget.py @@ -1,44 +1,51 @@ # TODO: remove in Python 3.11 from __future__ import annotations -from recOrder.calib.Calibration import QLIPP_Calibration, LC_DEVICE_NAME +import json +import logging +import os +import textwrap +import time +from os.path import dirname +from pathlib import Path, PurePath + +# type hint/check +from typing import TYPE_CHECKING + +import dask.array as da +import numpy as np +import yaml +from dask import delayed +from napari import Viewer +from napari.components import LayerList +from napari.qt.threading import create_worker +from napari.utils.events import Event +from napari.utils.notifications import show_info, show_warning +from numpy.typing import NDArray +from numpydoc.docscrape import NumpyDocString +from packaging import version from pycromanager import Core, Studio, zmq_bridge -from qtpy.QtCore import Slot, Signal, Qt -from qtpy.QtWidgets import QWidget, QFileDialog, QSizePolicy, QSlider -from qtpy.QtGui import QPixmap, QColor +from qtpy.QtCore import Qt, Signal, Slot +from qtpy.QtGui import QColor, QPixmap +from qtpy.QtWidgets import QFileDialog, QSizePolicy, QSlider, QWidget from superqt import QDoubleRangeSlider, QRangeSlider +from waveorder.waveorder_reconstructor import waveorder_microscopy + +from recOrder.acq.acquisition_workers import ( + BFAcquisitionWorker, + PolarizationAcquisitionWorker, +) from recOrder.calib import Calibration +from recOrder.calib.Calibration import LC_DEVICE_NAME, QLIPP_Calibration from recOrder.calib.calibration_workers import ( - CalibrationWorker, BackgroundCaptureWorker, + CalibrationWorker, load_calibration, ) -from recOrder.acq.acquisition_workers import ( - PolarizationAcquisitionWorker, - BFAcquisitionWorker, -) -from recOrder.plugin import gui from recOrder.io.core_functions import set_lc_state, snap_and_average from recOrder.io.metadata_reader import MetadataReader from recOrder.io.utils import ret_ori_overlay -from waveorder.waveorder_reconstructor import waveorder_microscopy -from pathlib import Path, PurePath -from napari import Viewer -from napari.utils.notifications import show_warning, show_info -from napari.qt.threading import create_worker -from numpydoc.docscrape import NumpyDocString -from packaging import version -import numpy as np -from numpy.typing import NDArray -import os -from os.path import dirname -import json -import logging -import textwrap -import yaml - -# type hint/check -from typing import TYPE_CHECKING +from recOrder.plugin import gui # avoid runtime import error if TYPE_CHECKING: @@ -218,6 +225,12 @@ def __init__(self, napari_viewer: Viewer): self.ui.qbutton_acq_ret_ori_phase.clicked[bool].connect( self.acq_ret_ori_phase ) + + # hook to render overlay + # acquistion updates existing layers and moves them to the top which triggers this event + self.viewer.layers.events.moved.connect(self.handle_layers_updated) + self.viewer.layers.events.inserted.connect(self.handle_layers_updated) + # Commenting for 0.3.0. Consider debugging or deleting for 1.0.0. # self.ui.cb_colormap.currentIndexChanged[int].connect( # self.enter_colormap @@ -303,7 +316,7 @@ def __init__(self, napari_viewer: Viewer): self.orientation_offset = False self.pad_z = 0 self.phase_reconstructor = None - self.acq_bg_directory = None + self.acq_bg_directory = "" self.auto_shutter = True self.lca_dac = None self.lcb_dac = None @@ -786,7 +799,7 @@ def _check_requirements_for_acq(self, mode): """ # check if a QLIPP_Calibration object has been initialized - if not self.calib: + if mode != "phase" and not self.calib: raise RuntimeError("Please run or load calibration first.") # initialize the variable to keep track of the success of the requirement check @@ -817,7 +830,10 @@ def _check_requirements_for_acq(self, mode): raise_error = True # check background path if 'Measured' or 'Measured + Estimated' is selected - if self.bg_option == "local_fit+" or self.bg_option == "global": + if ( + self.bg_option == "Measured" + or self.bg_option == "Measured + Estimated" + ): success = self._check_line_edit("bg_path") if not success: raise_error = True @@ -879,7 +895,7 @@ def connect_to_mm(self): ------- """ - RECOMMENDED_MM = "20220920" + RECOMMENDED_MM = "20230426" ZMQ_TARGET_VERSION = "4.2.0" try: self.mmc = Core(convert_camel_case=False) @@ -1127,6 +1143,7 @@ def _add_or_update_image_layer( if name in self.viewer.layers: self.viewer.layers[name].data = image if move_to_top: + logging.debug(f"Moving layer {name} to the top.") src_index = self.viewer.layers.index(name) self.viewer.layers.move(src_index, dest_index=-1) else: @@ -1146,29 +1163,62 @@ def handle_bg_bire_image_update(self, value): value[1], "Background Orientation", cmap="hsv" ) - def _draw_bire_overlay(self, overlay): - self._add_or_update_image_layer( - overlay, "BirefringenceOverlay" + self.acq_mode, cmap="rgb" + def handle_layers_updated(self, event: Event): + layers: LayerList = event.source + latest_layer_name = layers[-1].name + channels = ["Retardance", "Orientation"] + for ch in channels: + if latest_layer_name.startswith(ch): + suffix = latest_layer_name.replace(ch, "") + other_name = channels[1 - channels.index(ch)] + suffix + overlay_name = "BirefringenceOverlay" + suffix + if other_name in layers and overlay_name not in layers: + logging.info( + "Detected updated birefringence layers: " + f"'{latest_layer_name}', '{other_name}'" + ) + self._draw_bire_overlay( + [ch + suffix, other_name], overlay_name + ) + if latest_layer_name.startswith(channels[1]): + logging.info( + "Detected orientation layer in updated layer list." + "Setting its colormap to HSV." + ) + self.viewer.layers[latest_layer_name].colormap = "hsv" + + def _draw_bire_overlay( + self, source_names: list[str, str], overlay_name: str + ): + def _layer_data(name: str): + return self.viewer.layers[name].data + + def _draw(overlay): + self._add_or_update_image_layer(overlay, overlay_name, cmap="rgb") + + orientation_name, retardance_name = sorted(source_names) + retardance = _layer_data(retardance_name) + orientation = _layer_data(orientation_name) + worker = create_worker( + ret_ori_overlay, + retardance=retardance, + orientation=orientation, + ret_max=np.percentile(np.ravel(retardance), 99.99), + cmap=self.colormap, ) + worker.start() + logging.info(f"Updating the birefringence overlay layer.") + if retardance.size >= 2 2048 * 2048: + show_info("Generating large overlay. This might take a moment...") + worker.returned.connect(_draw) @Slot(object) def handle_bire_image_update(self, value: NDArray): # generate overlay in a separate thread - overlay_worker = create_worker( - ret_ori_overlay, - retardance=value[0], - orientation=value[1], - ret_max=np.percentile(value[0], 99.99), - cmap=self.colormap, - ) - overlay_worker.returned.connect(self._draw_bire_overlay) - overlay_worker.start() for i, channel in enumerate(("Retardance", "Orientation")): - name = channel + self.acq_mode + name = channel cmap = "gray" if channel != "Orientation" else "hsv" self._add_or_update_image_layer(value[i], name, cmap=cmap) - if self.acq_mode == "3D": - show_info("Generating 3D overlay. This might take a moment...") @Slot(object) def handle_phase_image_update(self, value): @@ -1521,17 +1571,17 @@ def enter_bg_correction(self): self.ui.label_bg_path.setHidden(False) self.ui.le_bg_path.setHidden(False) self.ui.qbutton_browse_bg_path.setHidden(False) - self.bg_option = "global" + self.bg_option = "Measured" elif state == 2: self.ui.label_bg_path.setHidden(True) self.ui.le_bg_path.setHidden(True) self.ui.qbutton_browse_bg_path.setHidden(True) - self.bg_option = "local_fit" + self.bg_option = "Estimated" elif state == 3: self.ui.label_bg_path.setHidden(False) self.ui.le_bg_path.setHidden(False) self.ui.qbutton_browse_bg_path.setHidden(False) - self.bg_option = "local_fit+" + self.bg_option = "Measured + Estimated" @Slot() def enter_gpu_id(self): diff --git a/setup.cfg b/setup.cfg index 8bf83809..d79769ee 100644 --- a/setup.cfg +++ b/setup.cfg @@ -37,8 +37,8 @@ python_requires = >=3.9 setup_requires = setuptools_scm # add your package requirements here install_requires = - waveorder==1.0.0rc0 - pycromanager==0.24.1 + waveorder==2.0.0rc1 + pycromanager==0.27.2 click>=8.0.1 natsort>=7.1.1 colorspacious>=1.1.2 @@ -59,14 +59,15 @@ dev = tox pre-commit black + hypothesis [options.package_data] * = *.yaml [options.entry_points] console_scripts = - recorder = recOrder.scripts.cli:cli - recOrder = recOrder.scripts.cli:cli + recorder = recOrder.cli.main:cli + recOrder = recOrder.cli.main:cli napari.manifest = recOrder = recOrder:napari.yaml	[BUG] Rare background correction failure In using `recOrder` over the last week I had >95% success rate with background corrections. Once the scope was running background corrections correctly, it would continue to do so indefinitely. But ~1/10 times I would use an incorrect image as the background correction and give an incorrect reconstruction. I was always able to "fix" this by acquiring and reconstructing again. One time I had a dramatic failure: - I took a background image beside the Kazansky target - I took a background-corrected acquisition of the Kazansky target (looked as expected) - I changed samples and took a background image (looked as expected) - I moved onto the real sample, took an acquisition, and the result was the Kazansky target. A repeat acquisition was as expected. I haven't had time to trace this further, but this is high priority bug. I suspect the recOrder side because the new waveorder doesn't carry any state. Fix 0.4.0dev tests This PR against `0.4.0dev` includes the test fixes that should have appeared in #381 (see #383 for CI/CD fix).		2023-06-07T20:45:05	[]	[]
mehta-lab/recOrder	mehta-lab__recOrder-229	6b4adbc798cef9486d46df6ab106cfed4abaa9db	diff --git a/.git-blame-ignore-revs b/.git-blame-ignore-revs new file mode 100644 index 00000000..31e202a2 --- /dev/null +++ b/.git-blame-ignore-revs @@ -0,0 +1,4 @@ +# .git-blame-ignore-revs +# created as described in: https://docs.github.com/en/repositories/working-with-files/using-files/viewing-a-file#ignore-commits-in-the-blame-view +# black-format all `.py` files except recorder_ui.py +958af2070cfb32397e7b7e8bfa7d022ec10b67af \ No newline at end of file diff --git a/docs/development-guide.md b/docs/development-guide.md index 0f1460c8..c45d9bda 100644 --- a/docs/development-guide.md +++ b/docs/development-guide.md @@ -84,4 +84,14 @@ pyuic5 -x recOrder_ui.ui -o recOrder_ui.py ``` Note: `pyuic5` is installed alongside `PyQt5`, so you can expect to find it installed in your `recOrder` conda environement. -Note: although much of the GUI is specified in the generated `recOrder_ui.py` file, the `main_widget.py` file makes extensive modifications to the GUI. \ No newline at end of file +Note: although much of the GUI is specified in the generated `recOrder_ui.py` file, the `main_widget.py` file makes extensive modifications to the GUI. + +## Make `git blame` ignore formatting commits +Note: `git --version` must be `>=2.23` to use this feature. + +If you would like `git blame` to ignore formatting commits, run this line: +```sh + git config --global blame.ignoreRevsFile .git-blame-ignore-revs +``` + +The `\.git-blame-ignore-revs` file contains a list of commit hashes corresponding to formatting commits. If you make a formatting commit, please add the commit's hash to this file. \ No newline at end of file diff --git a/recOrder/__init__.py b/recOrder/__init__.py index 6d76432a..f9ca19c7 100644 --- a/recOrder/__init__.py +++ b/recOrder/__init__.py @@ -1,4 +1,4 @@ -#todo: format overall init +# todo: format overall init # from .plugin.widget.napari_plugin_entry_point import napari_experimental_provide_dock_widget -# name = "recOrder" \ No newline at end of file +# name = "recOrder" diff --git a/recOrder/acq/acq_functions.py b/recOrder/acq/acq_functions.py index cc60e730..80219ca3 100644 --- a/recOrder/acq/acq_functions.py +++ b/recOrder/acq/acq_functions.py @@ -6,8 +6,19 @@ import time import glob -def generate_acq_settings(mm, channel_group, channels=None, zstart=None, zend=None, zstep=None, - save_dir=None, prefix=None, keep_shutter_open_channels=False, keep_shutter_open_slices=False): + +def generate_acq_settings( + mm, + channel_group, + channels=None, + zstart=None, + zend=None, + zstep=None, + save_dir=None, + prefix=None, + keep_shutter_open_channels=False, + keep_shutter_open_slices=False, +): """ This function generates a json file specific to the micromanager SequenceSettings. It has default parameters for a multi-channels z-stack acquisition but does not yet @@ -46,58 +57,67 @@ def generate_acq_settings(mm, channel_group, channels=None, zstart=None, zend=No do_z = False # Structure of the channel properties - channel_dict = {'channelGroup': channel_group, - 'config': None, - 'exposure': None, - 'zOffset': 0, - 'doZStack': do_z, - 'color': {'value': -16747854, 'falpha': 0.0}, - 'skipFactorFrame': 0, - 'useChannel': True if channels else False} + channel_dict = { + "channelGroup": channel_group, + "config": None, + "exposure": None, + "zOffset": 0, + "doZStack": do_z, + "color": {"value": -16747854, "falpha": 0.0}, + "skipFactorFrame": 0, + "useChannel": True if channels else False, + } channel_list = None if channels: # Append all the channels with their current exposure settings channel_list = [] for chan in channels: - #todo: think about how to deal with missing exposure - exposure = app.getChannelExposureTime(channel_group, chan, 10) # sets exposure to 10 if not found + # todo: think about how to deal with missing exposure + exposure = app.getChannelExposureTime( + channel_group, chan, 10 + ) # sets exposure to 10 if not found channel = channel_dict.copy() - channel['config'] = chan - channel['exposure'] = exposure + channel["config"] = chan + channel["exposure"] = exposure channel_list.append(channel) # set other parameters - original_json['numFrames'] = 1 - original_json['intervalMs'] = 0 - original_json['relativeZSlice'] = True - original_json['slicesFirst'] = True - original_json['timeFirst'] = False - original_json['keepShutterOpenSlices'] = keep_shutter_open_slices - original_json['keepShutterOpenChannels'] = keep_shutter_open_channels - original_json['useAutofocus'] = False - original_json['saveMode'] = 'MULTIPAGE_TIFF' - original_json['save'] = True if save_dir else False - original_json['root'] = save_dir if save_dir else '' - original_json['prefix'] = prefix if prefix else 'Untitled' - original_json['channels'] = channel_list - original_json['zReference'] = 0.0 - original_json['channelGroup'] = channel_group - original_json['usePositionList'] = False - original_json['shouldDisplayImages'] = True - original_json['useSlices'] = do_z - original_json['useFrames'] = False - original_json['useChannels'] = True if channels else False - original_json['slices'] = list(np.arange(float(zstart), float(zend+zstep), float(zstep))) if zstart else [] - original_json['sliceZStepUm'] = zstep - original_json['sliceZBottomUm'] = zstart - original_json['sliceZTopUm'] = zend - original_json['acqOrderMode'] = 1 + original_json["numFrames"] = 1 + original_json["intervalMs"] = 0 + original_json["relativeZSlice"] = True + original_json["slicesFirst"] = True + original_json["timeFirst"] = False + original_json["keepShutterOpenSlices"] = keep_shutter_open_slices + original_json["keepShutterOpenChannels"] = keep_shutter_open_channels + original_json["useAutofocus"] = False + original_json["saveMode"] = "MULTIPAGE_TIFF" + original_json["save"] = True if save_dir else False + original_json["root"] = save_dir if save_dir else "" + original_json["prefix"] = prefix if prefix else "Untitled" + original_json["channels"] = channel_list + original_json["zReference"] = 0.0 + original_json["channelGroup"] = channel_group + original_json["usePositionList"] = False + original_json["shouldDisplayImages"] = True + original_json["useSlices"] = do_z + original_json["useFrames"] = False + original_json["useChannels"] = True if channels else False + original_json["slices"] = ( + list(np.arange(float(zstart), float(zend + zstep), float(zstep))) + if zstart + else [] + ) + original_json["sliceZStepUm"] = zstep + original_json["sliceZBottomUm"] = zstart + original_json["sliceZTopUm"] = zend + original_json["acqOrderMode"] = 1 return original_json -def acquire_from_settings(mm, settings, grab_images = True): + +def acquire_from_settings(mm, settings, grab_images=True): """ Function to acquire an MDA acquisition with the native MM MDA Engine. Assumes single position acquisition. @@ -121,17 +141,20 @@ def acquire_from_settings(mm, settings, grab_images = True): time.sleep(3) - #TODO: speed improvements in reading the data with pycromanager acquisition? + # TODO: speed improvements in reading the data with pycromanager acquisition? if grab_images: # get the most recent acquisition if multiple - path = os.path.join(settings['root'], settings['prefix']) - files = glob.glob(path+'') - index = max([int(x.split(path + '_')[1]) for x in files]) + path = os.path.join(settings["root"], settings["prefix"]) + files = glob.glob(path + "") + index = max([int(x.split(path + "_")[1]) for x in files]) - reader = WaveorderReader(path+f'_{index}', 'ometiff', extract_data=True) + reader = WaveorderReader( + path + f"_{index}", "ometiff", extract_data=True + ) return reader.get_array(0) + def acquire_2D(mm, mmc, scheme, snap_manager=None): """ Acquire a 2D stack with pycromanager (data transfer over ZMQ) given the acquisition scheme. @@ -152,20 +175,20 @@ def acquire_2D(mm, mmc, scheme, snap_manager=None): if not snap_manager: snap_manager = mm.getSnapLiveManager() - set_lc_state(mmc, 'State0') + set_lc_state(mmc, "State0") state0 = snap_and_get_image(snap_manager) - set_lc_state(mmc, 'State1') + set_lc_state(mmc, "State1") state1 = snap_and_get_image(snap_manager) - set_lc_state(mmc, 'State2') + set_lc_state(mmc, "State2") state2 = snap_and_get_image(snap_manager) - set_lc_state(mmc, 'State3') + set_lc_state(mmc, "State3") state3 = snap_and_get_image(snap_manager) - if scheme == '5-State': - set_lc_state(mmc, 'State4') + if scheme == "5-State": + set_lc_state(mmc, "State4") state4 = snap_and_get_image(snap_manager) return np.asarray([state0, state1, state2, state3, state4]) @@ -198,12 +221,14 @@ def acquire_3D(mm, mmc, scheme, z_start, z_end, z_step, snap_manager=None): stage = mmc.getFocusDevice() current_z = mmc.getPosition(stage) - n_channels = 4 if scheme == '4-State' else 5 + n_channels = 4 if scheme == "4-State" else 5 stack = [] for c in range(n_channels): - set_lc_state(mmc, f'State{c}') + set_lc_state(mmc, f"State{c}") z_stack = [] - for z in np.arange(current_z+z_start, current_z+z_end+z_step, z_step): + for z in np.arange( + current_z + z_start, current_z + z_end + z_step, z_step + ): mmc.setPosition(stage, z) z_stack.append(snap_and_get_image(snap_manager)) diff --git a/recOrder/calib/Calibration.py b/recOrder/calib/Calibration.py index 658ea7a2..7490e831 100644 --- a/recOrder/calib/Calibration.py +++ b/recOrder/calib/Calibration.py @@ -18,14 +18,22 @@ from datetime import datetime from importlib_metadata import version -LC_DEVICE_NAME = 'MeadowlarkLcOpenSource' - - -class QLIPP_Calibration(): - - def __init__(self, mmc, mm, group='Channel', lc_control_mode='MM-Retardance', interp_method='schnoor_fit', - wavelength=532, optimization='min_scalar', print_details=True): - ''' +LC_DEVICE_NAME = "MeadowlarkLcOpenSource" + + +class QLIPP_Calibration: + def __init__( + self, + mmc, + mm, + group="Channel", + lc_control_mode="MM-Retardance", + interp_method="schnoor_fit", + wavelength=532, + optimization="min_scalar", + print_details=True, + ): + """ Parameters ---------- @@ -54,7 +62,7 @@ def __init__(self, mmc, mm, group='Channel', lc_control_mode='MM-Retardance', in LC retardance optimization method, 'min_scalar' (default) or 'brent' print_details : bool Set verbose option - ''' + """ # Micromanager API self.mm = mm @@ -62,46 +70,70 @@ def __init__(self, mmc, mm, group='Channel', lc_control_mode='MM-Retardance', in self.snap_manager = mm.getSnapLiveManager() # Meadowlark LC Device Adapter Property Names - self.PROPERTIES = {'LCA': (LC_DEVICE_NAME, 'Retardance LC-A [in waves]'), - 'LCB': (LC_DEVICE_NAME, 'Retardance LC-B [in waves]'), - 'LCA-Voltage': (LC_DEVICE_NAME, 'Voltage (V) LC-A'), - 'LCB-Voltage': (LC_DEVICE_NAME, 'Voltage (V) LC-B'), - 'LCA-DAC': ('TS_DAC01', 'Volts'), - 'LCB-DAC': ('TS_DAC02', 'Volts'), - 'State0': (LC_DEVICE_NAME, 'Pal. elem. 00; enter 0 to define; 1 to activate'), - 'State1': (LC_DEVICE_NAME, 'Pal. elem. 01; enter 0 to define; 1 to activate'), - 'State2': (LC_DEVICE_NAME, 'Pal. elem. 02; enter 0 to define; 1 to activate'), - 'State3': (LC_DEVICE_NAME, 'Pal. elem. 03; enter 0 to define; 1 to activate'), - 'State4': (LC_DEVICE_NAME, 'Pal. elem. 04; enter 0 to define; 1 to activate') - } + self.PROPERTIES = { + "LCA": (LC_DEVICE_NAME, "Retardance LC-A [in waves]"), + "LCB": (LC_DEVICE_NAME, "Retardance LC-B [in waves]"), + "LCA-Voltage": (LC_DEVICE_NAME, "Voltage (V) LC-A"), + "LCB-Voltage": (LC_DEVICE_NAME, "Voltage (V) LC-B"), + "LCA-DAC": ("TS_DAC01", "Volts"), + "LCB-DAC": ("TS_DAC02", "Volts"), + "State0": ( + LC_DEVICE_NAME, + "Pal. elem. 00; enter 0 to define; 1 to activate", + ), + "State1": ( + LC_DEVICE_NAME, + "Pal. elem. 01; enter 0 to define; 1 to activate", + ), + "State2": ( + LC_DEVICE_NAME, + "Pal. elem. 02; enter 0 to define; 1 to activate", + ), + "State3": ( + LC_DEVICE_NAME, + "Pal. elem. 03; enter 0 to define; 1 to activate", + ), + "State4": ( + LC_DEVICE_NAME, + "Pal. elem. 04; enter 0 to define; 1 to activate", + ), + } self.group = group # GUI Emitter self.intensity_emitter = MockEmitter() self.plot_sequence_emitter = MockEmitter() - #Set Mode + # Set Mode # TODO: make sure LC or TriggerScope are loaded in the respective modes - allowed_modes = ['MM-Retardance', 'MM-Voltage', 'DAC'] - assert lc_control_mode in allowed_modes, f'LC control mode must be one of {allowed_modes}' + allowed_modes = ["MM-Retardance", "MM-Voltage", "DAC"] + assert ( + lc_control_mode in allowed_modes + ), f"LC control mode must be one of {allowed_modes}" self.mode = lc_control_mode self.LC_DAC_conversion = 4 # convert between the input range of LCs (0-20V) and the output range of the DAC (0-5V) # Initialize calibration class - allowed_interp_methods = ['schnoor_fit', 'linear'] - assert interp_method in allowed_interp_methods,\ - f'LC calibration data interpolation method must be one of {allowed_interp_methods}' - dir_path = mmc.getDeviceAdapterSearchPaths().get(0) # MM device adapter directory - self.calib = CalibrationData(os.path.join(dir_path, 'mmgr_dal_MeadowlarkLC.csv'), interp_method=interp_method, - wavelength=wavelength) + allowed_interp_methods = ["schnoor_fit", "linear"] + assert ( + interp_method in allowed_interp_methods + ), f"LC calibration data interpolation method must be one of {allowed_interp_methods}" + dir_path = mmc.getDeviceAdapterSearchPaths().get( + 0 + ) # MM device adapter directory + self.calib = CalibrationData( + os.path.join(dir_path, "mmgr_dal_MeadowlarkLC.csv"), + interp_method=interp_method, + wavelength=wavelength, + ) # Optimizer - if optimization == 'min_scalar': + if optimization == "min_scalar": self.optimizer = MinScalarOptimizer(self) - elif optimization == 'brent': + elif optimization == "brent": self.optimizer = BrentOptimizer(self) else: - raise ModuleNotFoundError(f'No optimizer named {optimization}') + raise ModuleNotFoundError(f"No optimizer named {optimization}") # User / Calculated Parameters self.swing = None @@ -111,7 +143,7 @@ def __init__(self, mmc, mm, group='Channel', lc_control_mode='MM-Retardance', in self.ROI = None self.ratio = 1.793 self.print_details = print_details - self.calib_scheme = '4-State' + self.calib_scheme = "4-State" # LC States self.lca_ext = None @@ -151,8 +183,8 @@ def __init__(self, mmc, mm, group='Channel', lc_control_mode='MM-Retardance', in self._shutter_state = None def set_dacs(self, lca_dac, lcb_dac): - self.PROPERTIES['LCA-DAC'] = (f'TS_{lca_dac}', 'Volts') - self.PROPERTIES['LCB-DAC'] = (f'TS_{lcb_dac}', 'Volts') + self.PROPERTIES["LCA-DAC"] = (f"TS_{lca_dac}", "Volts") + self.PROPERTIES["LCB-DAC"] = (f"TS_{lcb_dac}", "Volts") def set_wavelength(self, wavelength): self.calib.set_wavelength(wavelength) @@ -174,15 +206,15 @@ def set_lc(self, retardance, LC: str): """ - if self.mode == 'MM-Retardance': - set_lc_waves(self.mmc, self.PROPERTIES[f'{LC}'], retardance) - elif self.mode == 'MM-Voltage': + if self.mode == "MM-Retardance": + set_lc_waves(self.mmc, self.PROPERTIES[f"{LC}"], retardance) + elif self.mode == "MM-Voltage": volts = self.calib.get_voltage(retardance) - set_lc_voltage(self.mmc, self.PROPERTIES[f'{LC}-Voltage'], volts) - elif self.mode == 'DAC': + set_lc_voltage(self.mmc, self.PROPERTIES[f"{LC}-Voltage"], volts) + elif self.mode == "DAC": volts = self.calib.get_voltage(retardance) dac_volts = volts / self.LC_DAC_conversion - set_lc_daq(self.mmc, self.PROPERTIES[f'{LC}-DAC'], dac_volts) + set_lc_daq(self.mmc, self.PROPERTIES[f"{LC}-DAC"], dac_volts) def get_lc(self, LC: str): """ @@ -198,13 +230,15 @@ def get_lc(self, LC: str): LC retardance in waves """ - if self.mode == 'MM-Retardance': - retardance = get_lc(self.mmc, self.PROPERTIES[f'{LC}']) - elif self.mode == 'MM-Voltage': - volts = get_lc(self.mmc, self.PROPERTIES[f'{LC}-Voltage']) # returned value is in volts + if self.mode == "MM-Retardance": + retardance = get_lc(self.mmc, self.PROPERTIES[f"{LC}"]) + elif self.mode == "MM-Voltage": + volts = get_lc( + self.mmc, self.PROPERTIES[f"{LC}-Voltage"] + ) # returned value is in volts retardance = self.calib.get_retardance(volts) - elif self.mode == 'DAC': - dac_volts = get_lc(self.mmc, self.PROPERTIES[f'{LC}-DAC']) + elif self.mode == "DAC": + dac_volts = get_lc(self.mmc, self.PROPERTIES[f"{LC}-DAC"]) volts = dac_volts * self.LC_DAC_conversion retardance = self.calib.get_retardance(volts) @@ -228,20 +262,37 @@ def define_lc_state(self, state, lca_retardance, lcb_retardance): """ - if self.mode == 'MM-Retardance': - self.set_lc(lca_retardance, 'LCA') - self.set_lc(lcb_retardance, 'LCB') + if self.mode == "MM-Retardance": + self.set_lc(lca_retardance, "LCA") + self.set_lc(lcb_retardance, "LCB") define_meadowlark_state(self.mmc, self.PROPERTIES[state]) - elif self.mode == 'DAC': - lca_volts = self.calib.get_voltage(lca_retardance) / self.LC_DAC_conversion - lcb_volts = self.calib.get_voltage(lcb_retardance) / self.LC_DAC_conversion - define_config_state(self.mmc, self.group, state, - [self.PROPERTIES['LCA-DAC'], self.PROPERTIES['LCB-DAC']], [lca_volts, lcb_volts]) - elif self.mode == 'MM-Voltage': + elif self.mode == "DAC": + lca_volts = ( + self.calib.get_voltage(lca_retardance) / self.LC_DAC_conversion + ) + lcb_volts = ( + self.calib.get_voltage(lcb_retardance) / self.LC_DAC_conversion + ) + define_config_state( + self.mmc, + self.group, + state, + [self.PROPERTIES["LCA-DAC"], self.PROPERTIES["LCB-DAC"]], + [lca_volts, lcb_volts], + ) + elif self.mode == "MM-Voltage": lca_volts = self.calib.get_voltage(lca_retardance) lcb_volts = self.calib.get_voltage(lcb_retardance) - define_config_state(self.mmc, self.group, state, - [self.PROPERTIES['LCA-Voltage'], self.PROPERTIES['LCB-Voltage']], [lca_volts, lcb_volts]) + define_config_state( + self.mmc, + self.group, + state, + [ + self.PROPERTIES["LCA-Voltage"], + self.PROPERTIES["LCB-Voltage"], + ], + [lca_volts, lcb_volts], + ) def opt_lc(self, x, device_property, reference, normalize=False): @@ -259,8 +310,8 @@ def opt_lc(self, x, device_property, reference, normalize=False): val = (mean - min_) / (max_ - min_) ref = (reference - min_) / (max_ - min_) - logging.debug(f'LC-Value: {x}') - logging.debug(f'F-Value:{val - ref}\n') + logging.debug(f"LC-Value: {x}") + logging.debug(f"F-Value:{val - ref}\n") return val - ref else: @@ -275,11 +326,11 @@ def opt_lc_cons(self, x, device_property, reference, mode): self.set_lc(x, device_property) swing = (self.lca_ext - x) * self.ratio - if mode == '60': - self.set_lc(self.lcb_ext + swing, 'LCB') + if mode == "60": + self.set_lc(self.lcb_ext + swing, "LCB") - if mode == '120': - self.set_lc(self.lcb_ext - swing, 'LCB') + if mode == "120": + self.set_lc(self.lcb_ext - swing, "LCB") mean = snap_and_average(self.snap_manager) logging.debug(str(mean)) @@ -325,8 +376,8 @@ def opt_lc_grid(self, a_min, a_max, b_min, b_max, step): for lca in np.arange(a_min, a_max, step): for lcb in np.arange(b_min, b_max, step): - self.set_lc(lca, 'LCA') - self.set_lc(lcb, 'LCB') + self.set_lc(lca, "LCA") + self.set_lc(lcb, "LCB") # current_int = np.mean(snap_image(calib.mmc)) current_int = snap_and_average(self.snap_manager) @@ -336,7 +387,10 @@ def opt_lc_grid(self, a_min, a_max, b_min, b_max, step): better_lca = lca better_lcb = lcb min_int = current_int - logging.debug("update (%f, %f, %f)" % (min_int, better_lca, better_lcb)) + logging.debug( + "update (%f, %f, %f)" + % (min_int, better_lca, better_lcb) + ) logging.debug("coarse search done") logging.debug("better lca = " + str(better_lca)) @@ -351,11 +405,11 @@ def opt_lc_grid(self, a_min, a_max, b_min, b_max, step): # ========== Optimization wrappers ============= # ============================================== def opt_Iext(self): - self.plot_sequence_emitter.emit('Coarse') - logging.info('Calibrating State0 (Extinction)...') - logging.debug('Calibrating State0 (Extinction)...') + self.plot_sequence_emitter.emit("Coarse") + logging.info("Calibrating State0 (Extinction)...") + logging.debug("Calibrating State0 (Extinction)...") - set_lc_state(self.mmc, self.group, 'State0') + set_lc_state(self.mmc, self.group, "State0") time.sleep(2) # Perform exhaustive search with step 0.1 over range: @@ -366,15 +420,17 @@ def opt_Iext(self): logging.debug(f"Starting first grid search, step = {step}") logging.debug(f"================================") - best_lca, best_lcb, i_ext_ = self.opt_lc_grid(0.01, 0.5, 0.25, 0.75, step) + best_lca, best_lcb, i_ext_ = self.opt_lc_grid( + 0.01, 0.5, 0.25, 0.75, step + ) logging.debug("grid search done") logging.debug("lca = " + str(best_lca)) logging.debug("lcb = " + str(best_lcb)) logging.debug("intensity = " + str(i_ext_)) - self.set_lc(best_lca, 'LCA') - self.set_lc(best_lcb, 'LCB') + self.set_lc(best_lca, "LCA") + self.set_lc(best_lcb, "LCB") logging.debug(f"================================") logging.debug(f"Starting fine search") @@ -383,29 +439,34 @@ def opt_Iext(self): # Perform brent optimization around results of 2nd grid search # threshold not very necessary here as intensity value will # vary between exposure/lamp intensities - self.plot_sequence_emitter.emit('Fine') - lca, lcb, I_ext = self.optimizer.optimize(state='ext', lca_bound=0.1, lcb_bound=0.1, - reference=self.I_Black, thresh=1, n_iter=5) + self.plot_sequence_emitter.emit("Fine") + lca, lcb, I_ext = self.optimizer.optimize( + state="ext", + lca_bound=0.1, + lcb_bound=0.1, + reference=self.I_Black, + thresh=1, + n_iter=5, + ) # Set the Extinction state to values output from optimization - self.define_lc_state('State0', lca, lcb) + self.define_lc_state("State0", lca, lcb) self.lca_ext = lca self.lcb_ext = lcb self.I_Ext = I_ext logging.debug("fine search done") - logging.info(f'LCA State0 (Extinction) = {lca:.3f}') - logging.debug(f'LCA State0 (Extinction) = {lca:.5f}') - logging.info(f'LCB State0 (Extinction) = {lcb:.3f}') - logging.debug(f'LCB State0 (Extinction) = {lcb:.5f}') - logging.info(f'Intensity (Extinction) = {I_ext:.0f}') - logging.debug(f'Intensity (Extinction) = {I_ext:.3f}') + logging.info(f"LCA State0 (Extinction) = {lca:.3f}") + logging.debug(f"LCA State0 (Extinction) = {lca:.5f}") + logging.info(f"LCB State0 (Extinction) = {lcb:.3f}") + logging.debug(f"LCB State0 (Extinction) = {lcb:.5f}") + logging.info(f"Intensity (Extinction) = {I_ext:.0f}") + logging.debug(f"Intensity (Extinction) = {I_ext:.3f}") logging.debug("--------done--------") logging.info("--------done--------") - def opt_I0(self): """ no optimization performed for this. Simply apply swing and read intensity @@ -414,25 +475,27 @@ def opt_I0(self): mean of image """ - logging.info('Calibrating State1 (I0)...') - logging.debug('Calibrating State1 (I0)...') + logging.info("Calibrating State1 (I0)...") + logging.debug("Calibrating State1 (I0)...") self.lca_0 = self.lca_ext - self.swing self.lcb_0 = self.lcb_ext - self.set_lc(self.lca_0, 'LCA') - self.set_lc(self.lcb_0, 'LCB') + self.set_lc(self.lca_0, "LCA") + self.set_lc(self.lcb_0, "LCB") - self.define_lc_state('State1', self.lca_0, self.lcb_0) + self.define_lc_state("State1", self.lca_0, self.lcb_0) intensity = snap_and_average(self.snap_manager) self.I_Elliptical = intensity - self.swing0 = np.sqrt((self.lcb_0 - self.lcb_ext) 2 + (self.lca_0 - self.lca_ext) 2) - - logging.info(f'LCA State1 (I0) = {self.lca_0:.3f}') - logging.debug(f'LCA State1 (I0) = {self.lca_0:.5f}') - logging.info(f'LCB State1 (I0) = {self.lcb_0:.3f}') - logging.debug(f'LCB State1 (I0) = {self.lcb_0:.5f}') - logging.info(f'Intensity (I0) = {intensity:.0f}') - logging.debug(f'Intensity (I0) = {intensity:.3f}') + self.swing0 = np.sqrt( + (self.lcb_0 - self.lcb_ext) 2 + (self.lca_0 - self.lca_ext) 2 + ) + + logging.info(f"LCA State1 (I0) = {self.lca_0:.3f}") + logging.debug(f"LCA State1 (I0) = {self.lca_0:.5f}") + logging.info(f"LCB State1 (I0) = {self.lcb_0:.3f}") + logging.debug(f"LCB State1 (I0) = {self.lcb_0:.5f}") + logging.info(f"Intensity (I0) = {intensity:.0f}") + logging.debug(f"Intensity (I0) = {intensity:.3f}") logging.info("--------done--------") logging.debug("--------done--------") @@ -449,25 +512,34 @@ def opt_I45(self, lca_bound, lcb_bound): intensity value at optimized state """ self.inten = [] - logging.info('Calibrating State2 (I45)...') - logging.debug('Calibrating State2 (I45)...') - - self.set_lc(self.lca_ext, 'LCA') - self.set_lc(self.lcb_ext - self.swing, 'LCB') - - self.lca_45, self.lcb_45, intensity = self.optimizer.optimize('45', lca_bound, lcb_bound, - reference=self.I_Elliptical, n_iter=5, thresh=.01) - - self.define_lc_state('State2', self.lca_45, self.lcb_45) - - self.swing45 = np.sqrt((self.lcb_45 - self.lcb_ext) 2 + (self.lca_45 - self.lca_ext) 2) - - logging.info(f'LCA State2 (I45) = {self.lca_45:.3f}') - logging.debug(f'LCA State2 (I45) = {self.lca_45:.5f}') - logging.info(f'LCB State2 (I45) = {self.lcb_45:.3f}') - logging.debug(f'LCB State2 (I45) = {self.lcb_45:.5f}') - logging.info(f'Intensity (I45) = {intensity:.0f}') - logging.debug(f'Intensity (I45) = {intensity:.3f}') + logging.info("Calibrating State2 (I45)...") + logging.debug("Calibrating State2 (I45)...") + + self.set_lc(self.lca_ext, "LCA") + self.set_lc(self.lcb_ext - self.swing, "LCB") + + self.lca_45, self.lcb_45, intensity = self.optimizer.optimize( + "45", + lca_bound, + lcb_bound, + reference=self.I_Elliptical, + n_iter=5, + thresh=0.01, + ) + + self.define_lc_state("State2", self.lca_45, self.lcb_45) + + self.swing45 = np.sqrt( + (self.lcb_45 - self.lcb_ext) 2 + + (self.lca_45 - self.lca_ext) 2 + ) + + logging.info(f"LCA State2 (I45) = {self.lca_45:.3f}") + logging.debug(f"LCA State2 (I45) = {self.lca_45:.5f}") + logging.info(f"LCB State2 (I45) = {self.lcb_45:.3f}") + logging.debug(f"LCB State2 (I45) = {self.lcb_45:.5f}") + logging.info(f"Intensity (I45) = {intensity:.0f}") + logging.debug(f"Intensity (I45) = {intensity:.3f}") logging.info("--------done--------") logging.debug("--------done--------") @@ -485,40 +557,54 @@ def opt_I60(self, lca_bound, lcb_bound): """ self.inten = [] - logging.info('Calibrating State2 (I60)...') - logging.debug('Calibrating State2 (I60)...') + logging.info("Calibrating State2 (I60)...") + logging.debug("Calibrating State2 (I60)...") # Calculate Initial Swing for initial guess to optimize around # Based on ratio calculated from ellpiticity/orientation of LC simulation - swing_ell = np.sqrt((self.lca_ext - self.lca_0) 2 + (self.lcb_ext - self.lcb_0) 2) - lca_swing = np.sqrt(swing_ell 2 / (1 + self.ratio 2)) + swing_ell = np.sqrt( + (self.lca_ext - self.lca_0) 2 + (self.lcb_ext - self.lcb_0) 2 + ) + lca_swing = np.sqrt(swing_ell2 / (1 + self.ratio2)) lcb_swing = self.ratio * lca_swing # Optimization - self.set_lc(self.lca_ext + lca_swing, 'LCA') - self.set_lc(self.lcb_ext + lcb_swing, 'LCB') + self.set_lc(self.lca_ext + lca_swing, "LCA") + self.set_lc(self.lcb_ext + lcb_swing, "LCB") - self.lca_60, self.lcb_60, intensity = self.optimizer.optimize('60', lca_bound, lcb_bound, - reference=self.I_Elliptical, - n_iter=5, thresh=.01) + self.lca_60, self.lcb_60, intensity = self.optimizer.optimize( + "60", + lca_bound, + lcb_bound, + reference=self.I_Elliptical, + n_iter=5, + thresh=0.01, + ) - self.define_lc_state('State2', self.lca_60, self.lcb_60) + self.define_lc_state("State2", self.lca_60, self.lcb_60) - self.swing60 = np.sqrt((self.lcb_60 - self.lcb_ext) 2 + (self.lca_60 - self.lca_ext) 2) + self.swing60 = np.sqrt( + (self.lcb_60 - self.lcb_ext) 2 + + (self.lca_60 - self.lca_ext) 2 + ) # Print comparison of target swing, target ratio # Ratio determines the orientation of the elliptical state # should be close to target. Swing will vary to optimize ellipticity - logging.debug(f'ratio: swing_LCB / swing_LCA = {(self.lcb_ext - self.lcb_60) / (self.lca_ext - self.lca_60):.4f} \ - \| target ratio: {-self.ratio}') - logging.debug(f'total swing = {self.swing60:.4f} \| target = {swing_ell}') + logging.debug( + f"ratio: swing_LCB / swing_LCA = {(self.lcb_ext - self.lcb_60) / (self.lca_ext - self.lca_60):.4f} \ + \| target ratio: {-self.ratio}" + ) + logging.debug( + f"total swing = {self.swing60:.4f} \| target = {swing_ell}" + ) logging.info(f"LCA State2 (I60) = {self.lca_60:.3f}") logging.debug(f"LCA State2 (I60) = {self.lca_60:.5f}") logging.info(f"LCB State2 (I60) = {self.lcb_60:.3f}") logging.debug(f"LCB State2 (I60) = {self.lcb_60:.5f}") - logging.info(f'Intensity (I60) = {intensity:.0f}') - logging.debug(f'Intensity (I60) = {intensity:.3f}') + logging.info(f"Intensity (I60) = {intensity:.0f}") + logging.debug(f"Intensity (I60) = {intensity:.3f}") logging.info("--------done--------") logging.debug("--------done--------") @@ -534,28 +620,36 @@ def opt_I90(self, lca_bound, lcb_bound): lca, lcb value at optimized state intensity value at optimized state """ - logging.info('Calibrating State3 (I90)...') - logging.debug('Calibrating State3 (I90)...') + logging.info("Calibrating State3 (I90)...") + logging.debug("Calibrating State3 (I90)...") self.inten = [] - self.set_lc(self.lca_ext + self.swing, 'LCA') - self.set_lc(self.lcb_ext, 'LCB') - - self.lca_90, self.lcb_90, intensity = self.optimizer.optimize('90', lca_bound, lcb_bound, - reference=self.I_Elliptical, - n_iter=5, thresh=.01) - - self.define_lc_state('State3', self.lca_90, self.lcb_90) - - self.swing90 = np.sqrt((self.lcb_90 - self.lcb_ext) 2 + (self.lca_90 - self.lca_ext) 2) - - logging.info(f'LCA State3 (I90) = {self.lca_90:.3f}') - logging.debug(f'LCA State3 (I90) = {self.lca_90:.5f}') - logging.info(f'LCB State3 (I90) = {self.lcb_90:.3f}') - logging.debug(f'LCB State3 (I90) = {self.lcb_90:.5f}') - logging.info(f'Intensity (I90) = {intensity:.0f}') - logging.debug(f'Intensity (I90) = {intensity:.3f}') + self.set_lc(self.lca_ext + self.swing, "LCA") + self.set_lc(self.lcb_ext, "LCB") + + self.lca_90, self.lcb_90, intensity = self.optimizer.optimize( + "90", + lca_bound, + lcb_bound, + reference=self.I_Elliptical, + n_iter=5, + thresh=0.01, + ) + + self.define_lc_state("State3", self.lca_90, self.lcb_90) + + self.swing90 = np.sqrt( + (self.lcb_90 - self.lcb_ext) 2 + + (self.lca_90 - self.lca_ext) 2 + ) + + logging.info(f"LCA State3 (I90) = {self.lca_90:.3f}") + logging.debug(f"LCA State3 (I90) = {self.lca_90:.5f}") + logging.info(f"LCB State3 (I90) = {self.lcb_90:.3f}") + logging.debug(f"LCB State3 (I90) = {self.lcb_90:.5f}") + logging.info(f"Intensity (I90) = {intensity:.0f}") + logging.debug(f"Intensity (I90) = {intensity:.3f}") logging.info("--------done--------") logging.debug("--------done--------") @@ -571,39 +665,53 @@ def opt_I120(self, lca_bound, lcb_bound): lca, lcb value at optimized state intensity value at optimized state """ - logging.info('Calibrating State3 (I120)...') - logging.debug('Calibrating State3 (I120)...') + logging.info("Calibrating State3 (I120)...") + logging.debug("Calibrating State3 (I120)...") # Calculate Initial Swing for initial guess to optimize around # Based on ratio calculated from ellpiticity/orientation of LC simulation - swing_ell = np.sqrt((self.lca_ext - self.lca_0) 2 + (self.lcb_ext - self.lcb_0) 2) - lca_swing = np.sqrt(swing_ell 2 / (1 + self.ratio 2)) + swing_ell = np.sqrt( + (self.lca_ext - self.lca_0) 2 + (self.lcb_ext - self.lcb_0) 2 + ) + lca_swing = np.sqrt(swing_ell2 / (1 + self.ratio2)) lcb_swing = self.ratio * lca_swing # Brent Optimization - self.set_lc(self.lca_ext + lca_swing, 'LCA') - self.set_lc(self.lcb_ext - lcb_swing, 'LCB') + self.set_lc(self.lca_ext + lca_swing, "LCA") + self.set_lc(self.lcb_ext - lcb_swing, "LCB") - self.lca_120, self.lcb_120, intensity = self.optimizer.optimize('120', lca_bound, lcb_bound, - reference=self.I_Elliptical, - n_iter=5, thresh=.01) + self.lca_120, self.lcb_120, intensity = self.optimizer.optimize( + "120", + lca_bound, + lcb_bound, + reference=self.I_Elliptical, + n_iter=5, + thresh=0.01, + ) - self.define_lc_state('State3', self.lca_120, self.lcb_120) + self.define_lc_state("State3", self.lca_120, self.lcb_120) - self.swing120 = np.sqrt((self.lcb_120 - self.lcb_ext) 2 + (self.lca_120 - self.lca_ext) 2) + self.swing120 = np.sqrt( + (self.lcb_120 - self.lcb_ext) 2 + + (self.lca_120 - self.lca_ext) 2 + ) # Print comparison of target swing, target ratio # Ratio determines the orientation of the elliptical state # should be close to target. Swing will vary to optimize ellipticity - logging.debug(f'ratio: swing_LCB / swing_LCA = {(self.lcb_ext - self.lcb_120) / (self.lca_ext - self.lca_120):.4f}\ - \| target ratio: {self.ratio}') - logging.debug(f'total swing = {self.swing120:.4f} \| target = {swing_ell}') + logging.debug( + f"ratio: swing_LCB / swing_LCA = {(self.lcb_ext - self.lcb_120) / (self.lca_ext - self.lca_120):.4f}\ + \| target ratio: {self.ratio}" + ) + logging.debug( + f"total swing = {self.swing120:.4f} \| target = {swing_ell}" + ) logging.info(f"LCA State3 (I120) = {self.lca_120:.3f}") logging.debug(f"LCA State3 (I120) = {self.lca_120:.5f}") logging.info(f"LCB State3 (I120) = {self.lcb_120:.3f}") logging.debug(f"LCB State3 (I120) = {self.lcb_120:.5f}") - logging.info(f'Intensity (I120) = {intensity:.0f}') - logging.debug(f'Intensity (I120) = {intensity:.3f}') + logging.info(f"Intensity (I120) = {intensity:.0f}") + logging.debug(f"Intensity (I120) = {intensity:.3f}") logging.info("--------done--------") logging.debug("--------done--------") @@ -620,33 +728,41 @@ def opt_I135(self, lca_bound, lcb_bound): intensity value at optimized state """ - logging.info('Calibrating State4 (I135)...') - logging.debug('Calibrating State4 (I135)...') + logging.info("Calibrating State4 (I135)...") + logging.debug("Calibrating State4 (I135)...") self.inten = [] - self.set_lc(self.lca_ext, 'LCA') - self.set_lc(self.lcb_ext + self.swing, 'LCB') - - self.lca_135, self.lcb_135, intensity = self.optimizer.optimize('135', lca_bound, lcb_bound, - reference=self.I_Elliptical, - n_iter=5, thresh=.01) - - self.define_lc_state('State4', self.lca_135, self.lcb_135) - - self.swing135 = np.sqrt((self.lcb_135 - self.lcb_ext) 2 + (self.lca_135 - self.lca_ext) 2) - - logging.info(f'LCA State4 (I135) = {self.lca_135:.3f}') - logging.debug(f'LCA State4 (I135) = {self.lca_135:.5f}') - logging.info(f'LCB State4 (I135) = {self.lcb_135:.3f}') - logging.debug(f'LCB State4 (I135) = {self.lcb_135:.5f}') - logging.info(f'Intensity (I135) = {intensity:.0f}') - logging.debug(f'Intensity (I135) = {intensity:.3f}') + self.set_lc(self.lca_ext, "LCA") + self.set_lc(self.lcb_ext + self.swing, "LCB") + + self.lca_135, self.lcb_135, intensity = self.optimizer.optimize( + "135", + lca_bound, + lcb_bound, + reference=self.I_Elliptical, + n_iter=5, + thresh=0.01, + ) + + self.define_lc_state("State4", self.lca_135, self.lcb_135) + + self.swing135 = np.sqrt( + (self.lcb_135 - self.lcb_ext) 2 + + (self.lca_135 - self.lca_ext) 2 + ) + + logging.info(f"LCA State4 (I135) = {self.lca_135:.3f}") + logging.debug(f"LCA State4 (I135) = {self.lca_135:.5f}") + logging.info(f"LCB State4 (I135) = {self.lcb_135:.3f}") + logging.debug(f"LCB State4 (I135) = {self.lcb_135:.5f}") + logging.info(f"Intensity (I135) = {intensity:.0f}") + logging.debug(f"Intensity (I135) = {intensity:.3f}") logging.info("--------done--------") logging.debug("--------done--------") def open_shutter(self): - if self.shutter_device == '': # no shutter - input('Please manually open the shutter and press <Enter>') + if self.shutter_device == "": # no shutter + input("Please manually open the shutter and press <Enter>") else: self.mmc.setShutterOpen(True) @@ -658,9 +774,13 @@ def reset_shutter(self): ------- """ - if self.shutter_device == '': # no shutter - input('Please reset the shutter to its original state and press <Enter>') - logging.info("This is the end of the command-line instructions. You can return to the napari window.") + if self.shutter_device == "": # no shutter + input( + "Please reset the shutter to its original state and press <Enter>" + ) + logging.info( + "This is the end of the command-line instructions. You can return to the napari window." + ) else: self.mmc.setAutoShutter(self._auto_shutter_state) self.mmc.setShutterOpen(self._shutter_state) @@ -669,8 +789,10 @@ def close_shutter_and_calc_blacklevel(self): self._auto_shutter_state = self.mmc.getAutoShutter() self._shutter_state = self.mmc.getShutterOpen() - if self.shutter_device == '': # no shutter - show_warning('No shutter found. Please follow the command-line instructions...') + if self.shutter_device == "": # no shutter + show_warning( + "No shutter found. Please follow the command-line instructions..." + ) shutter_warning_msg = """ recOrder could not find an automatic shutter configured through Micro-Manager. >>> If you would like manually enter the black level, enter an integer or float and press <Enter> @@ -678,7 +800,7 @@ def close_shutter_and_calc_blacklevel(self): """ in_string = input(shutter_warning_msg) - if in_string.isdigit(): # True if positive integer + if in_string.isdigit(): # True if positive integer self.I_Black = float(in_string) return else: @@ -699,7 +821,10 @@ def get_full_roi(self): # Get Image Parameters self.mmc.snapImage() self.mmc.getImage() - self.height, self.width = self.mmc.getImageHeight(), self.mmc.getImageWidth() + self.height, self.width = ( + self.mmc.getImageHeight(), + self.mmc.getImageWidth(), + ) self.ROI = (0, 0, self.width, self.height) def check_and_get_roi(self): @@ -711,15 +836,24 @@ def check_and_get_roi(self): for i in range(size): win = windows.get(i).toFront() time.sleep(0.05) - roi = self.mm.displays().getActiveDataViewer().getImagePlus().getRoi() + roi = ( + self.mm.displays() + .getActiveDataViewer() + .getImagePlus() + .getRoi() + ) if roi != None: boxes.append(roi) if len(boxes) == 0: - raise ValueError('No ROI Bounding Box Found, Please Draw Bounding Box on the Preview (live) Window') + raise ValueError( + "No ROI Bounding Box Found, Please Draw Bounding Box on the Preview (live) Window" + ) if len(boxes) > 1: - raise ValueError('More than one Bounding Box Found, Please Remove any box not on the preview (live) window') + raise ValueError( + "More than one Bounding Box Found, Please Remove any box not on the preview (live) window" + ) if len(boxes) == 1: rect = boxes[0].getBounds() @@ -729,50 +863,83 @@ def display_and_check_ROI(self, rect): img = snap_image(self.mmc) - print('Will Calibrate Using this ROI:') + print("Will Calibrate Using this ROI:") fig, ax = plt.subplots() - ax.imshow(np.reshape(img, (self.height, self.width)), 'gray') - box = patches.Rectangle((rect.x, rect.y), rect.width, rect.height, linewidth=2, edgecolor='r', facecolor='none') + ax.imshow(np.reshape(img, (self.height, self.width)), "gray") + box = patches.Rectangle( + (rect.x, rect.y), + rect.width, + rect.height, + linewidth=2, + edgecolor="r", + facecolor="none", + ) ax.add_patch(box) plt.show() - cont = input('Would You Like to Calibrate Using this ROI? (Yes/No): \t') + cont = input( + "Would You Like to Calibrate Using this ROI? (Yes/No): \t" + ) - if cont in ['Yes', 'Y', 'yes', 'ye', 'y', '']: + if cont in ["Yes", "Y", "yes", "ye", "y", ""]: return True - if cont in ['No', 'N', 'no', 'n']: + if cont in ["No", "N", "no", "n"]: return False else: - raise ValueError('Did not understand your answer, please check spelling') - - def calculate_extinction(self, swing, black_level, intensity_extinction, intensity_elliptical): - return np.round((1 / np.sin(np.pi * swing) ** 2) * \ - (intensity_elliptical - black_level) / (intensity_extinction - black_level), 2) + raise ValueError( + "Did not understand your answer, please check spelling" + ) + + def calculate_extinction( + self, swing, black_level, intensity_extinction, intensity_elliptical + ): + return np.round( + (1 / np.sin(np.pi * swing) ** 2) + * (intensity_elliptical - black_level) + / (intensity_extinction - black_level), + 2, + ) def calc_inst_matrix(self): - if self.calib_scheme == '4-State': + if self.calib_scheme == "4-State": chi = self.swing - inst_mat = np.array([[1, 0, 0, -1], - [1, np.sin(2 * np.pi * chi), 0, -np.cos(2 * np.pi * chi)], - [1, -0.5 * np.sin(2 * np.pi * chi), - np.sqrt(3) * np.cos(np.pi * chi) * np.sin(np.pi * chi), -np.cos(2 * np.pi * chi)], - [1, -0.5 * np.sin(2 * np.pi * chi), -np.sqrt(3) / 2 * np.sin(2 * np.pi * chi), - -np.cos(2 * np.pi * chi)]]) + inst_mat = np.array( + [ + [1, 0, 0, -1], + [1, np.sin(2 * np.pi * chi), 0, -np.cos(2 * np.pi * chi)], + [ + 1, + -0.5 * np.sin(2 * np.pi * chi), + np.sqrt(3) * np.cos(np.pi * chi) * np.sin(np.pi * chi), + -np.cos(2 * np.pi * chi), + ], + [ + 1, + -0.5 * np.sin(2 * np.pi * chi), + -np.sqrt(3) / 2 * np.sin(2 * np.pi * chi), + -np.cos(2 * np.pi * chi), + ], + ] + ) return inst_mat - if self.calib_scheme == '5-State': + if self.calib_scheme == "5-State": chi = self.swing * 2 * np.pi - inst_mat = np.array([[1, 0, 0, -1], - [1, np.sin(chi), 0, -np.cos(chi)], - [1, 0, np.sin(chi), -np.cos(chi)], - [1, -np.sin(chi), 0, -np.cos(chi)], - [1, 0, -np.sin(chi), -np.cos(chi)]]) + inst_mat = np.array( + [ + [1, 0, 0, -1], + [1, np.sin(chi), 0, -np.cos(chi)], + [1, 0, np.sin(chi), -np.cos(chi)], + [1, -np.sin(chi), 0, -np.cos(chi)], + [1, 0, -np.sin(chi), -np.cos(chi)], + ] + ) return inst_mat @@ -781,57 +948,86 @@ def write_metadata(self, notes=None): inst_mat = self.calc_inst_matrix() inst_mat = np.around(inst_mat, decimals=5).tolist() - metadata = {'Summary': { - 'Timestamp': str(datetime.now()), - 'recOrder-napari version': version('recOrder-napari'), - 'waveorder version': version('waveorder')}, - 'Calibration': { - 'Calibration scheme': self.calib_scheme, - 'Swing (waves)': self.swing, - 'Wavelength (nm)': self.wavelength, - 'Retardance to voltage interpolation method': self.calib.interp_method, - 'LC control mode': self.mode, - 'Black level': np.round(self.I_Black, 2), - 'Extinction ratio': self.extinction_ratio, - 'ROI (x, y, width, height)': self.ROI}, - 'Notes': notes - } - - if self.calib_scheme == '4-State': - metadata['Calibration'].update({ - 'Channel names': [f"State{i}" for i in range(4)], - 'LC retardance': {f'LC{i}_{j}': np.around(getattr(self, f'lc{i.lower()}_{j}'), decimals=6) - for j in ['ext', '0', '60', '120'] - for i in ['A', 'B']}, - 'LC voltage': {f'LC{i}_{j}': np.around(self.calib.get_voltage(getattr(self, f'lc{i.lower()}_{j}')), decimals=4) - for j in ['ext', '0', '60', '120'] - for i in ['A', 'B']}, - 'Swing_0': np.around(self.swing0, decimals=3), - 'Swing_60': np.around(self.swing60, decimals=3), - 'Swing_120': np.around(self.swing120, decimals=3), - 'Instrument matrix': inst_mat - }) - - elif self.calib_scheme == '5-State': - metadata['Calibration'].update({ - 'Channel names': [f"State{i}" for i in range(5)], - 'LC retardance': {f'LC{i}_{j}': np.around(getattr(self, f'lc{i.lower()}_{j}'), decimals=6) - for j in ['ext', '0', '45', '90', '135'] - for i in ['A', 'B']}, - 'LC voltage': {f'LC{i}_{j}': np.around(self.calib.get_voltage(getattr(self, f'lc{i.lower()}_{j}')), decimals=4) - for j in ['ext', '0', '45', '90', '135'] - for i in ['A', 'B']}, - 'Swing_0': np.around(self.swing0, decimals=3), - 'Swing_45': np.around(self.swing45, decimals=3), - 'Swing_90': np.around(self.swing90, decimals=3), - 'Swing_135': np.around(self.swing135, decimals=3), - 'Instrument matrix': inst_mat - }) - - if not self.meta_file.endswith('.txt'): - self.meta_file += '.txt' - - with open(self.meta_file, 'w') as metafile: + metadata = { + "Summary": { + "Timestamp": str(datetime.now()), + "recOrder-napari version": version("recOrder-napari"), + "waveorder version": version("waveorder"), + }, + "Calibration": { + "Calibration scheme": self.calib_scheme, + "Swing (waves)": self.swing, + "Wavelength (nm)": self.wavelength, + "Retardance to voltage interpolation method": self.calib.interp_method, + "LC control mode": self.mode, + "Black level": np.round(self.I_Black, 2), + "Extinction ratio": self.extinction_ratio, + "ROI (x, y, width, height)": self.ROI, + }, + "Notes": notes, + } + + if self.calib_scheme == "4-State": + metadata["Calibration"].update( + { + "Channel names": [f"State{i}" for i in range(4)], + "LC retardance": { + f"LC{i}_{j}": np.around( + getattr(self, f"lc{i.lower()}_{j}"), decimals=6 + ) + for j in ["ext", "0", "60", "120"] + for i in ["A", "B"] + }, + "LC voltage": { + f"LC{i}_{j}": np.around( + self.calib.get_voltage( + getattr(self, f"lc{i.lower()}_{j}") + ), + decimals=4, + ) + for j in ["ext", "0", "60", "120"] + for i in ["A", "B"] + }, + "Swing_0": np.around(self.swing0, decimals=3), + "Swing_60": np.around(self.swing60, decimals=3), + "Swing_120": np.around(self.swing120, decimals=3), + "Instrument matrix": inst_mat, + } + ) + + elif self.calib_scheme == "5-State": + metadata["Calibration"].update( + { + "Channel names": [f"State{i}" for i in range(5)], + "LC retardance": { + f"LC{i}_{j}": np.around( + getattr(self, f"lc{i.lower()}_{j}"), decimals=6 + ) + for j in ["ext", "0", "45", "90", "135"] + for i in ["A", "B"] + }, + "LC voltage": { + f"LC{i}_{j}": np.around( + self.calib.get_voltage( + getattr(self, f"lc{i.lower()}_{j}") + ), + decimals=4, + ) + for j in ["ext", "0", "45", "90", "135"] + for i in ["A", "B"] + }, + "Swing_0": np.around(self.swing0, decimals=3), + "Swing_45": np.around(self.swing45, decimals=3), + "Swing_90": np.around(self.swing90, decimals=3), + "Swing_135": np.around(self.swing135, decimals=3), + "Instrument matrix": inst_mat, + } + ) + + if not self.meta_file.endswith(".txt"): + self.meta_file += ".txt" + + with open(self.meta_file, "w") as metafile: json.dump(metadata, metafile, indent=1) def _add_colorbar(self, mappable): @@ -868,14 +1064,22 @@ def _capture_state(self, state: str, n_avg: int): def _plot_bg_images(self, imgs): - img_names = ['Extinction', '0', '60', '120'] if len(imgs) == 4 else ['Extinction', '0', '45', '90', 135] - fig, ax = plt.subplots(2, 2, figsize=(20, 20)) if len(imgs) == 4 else plt.subplots(3, 2, figsize=(20, 20)) + img_names = ( + ["Extinction", "0", "60", "120"] + if len(imgs) == 4 + else ["Extinction", "0", "45", "90", 135] + ) + fig, ax = ( + plt.subplots(2, 2, figsize=(20, 20)) + if len(imgs) == 4 + else plt.subplots(3, 2, figsize=(20, 20)) + ) img_idx = 0 for ax1 in range(len(ax[:, 0])): for ax2 in range(len(ax[0, :])): if img_idx < len(imgs): - im = ax[ax1, ax2].imshow(imgs[img_idx], 'gray') + im = ax[ax1, ax2].imshow(imgs[img_idx], "gray") ax[ax1, ax2].set_title(img_names[img_idx]) self._add_colorbar(im) else: @@ -904,7 +1108,9 @@ def pol_states(self): elif self.calib_scheme == "5-State": pols = ("ext", "0", "45", "90", "135") else: - raise ValueError(f"Invalid calibration state: {self.calib_scheme}.") + raise ValueError( + f"Invalid calibration state: {self.calib_scheme}." + ) return pols @property @@ -919,14 +1125,14 @@ def lc_states(self): lc_sides = ["A", "B"] return { f"LC{lc_side}": [ - self.__getattribute__("lc" + lc_side.lower() + "_" + pol) + self.__getattribute__("lc" + lc_side.lower() + "_" + pol) for pol in self.pol_states ] for lc_side in lc_sides } def capture_bg(self, n_avg, directory): - """" + """ " This function will capture an image at every state and save to specified directory This may throw errors depending on the micromanager config file-- @@ -940,38 +1146,38 @@ def capture_bg(self, n_avg, directory): if not os.path.exists(directory): os.makedirs(directory) - logging.info('Capturing Background') + logging.info("Capturing Background") self._auto_shutter_state = self.mmc.getAutoShutter() self._shutter_state = self.mmc.getShutterOpen() self.open_shutter() - logging.debug('Capturing Background State0') + logging.debug("Capturing Background State0") - state0 = self._capture_state('State0', n_avg) - logging.debug('Saving Background State0') - tiff.imsave(os.path.join(directory, 'State0.tif'), state0) + state0 = self._capture_state("State0", n_avg) + logging.debug("Saving Background State0") + tiff.imsave(os.path.join(directory, "State0.tif"), state0) - logging.debug('Capturing Background State1') - state1 = self._capture_state('State1', n_avg) - logging.debug('Saving Background State1') - tiff.imsave(os.path.join(directory, 'State1.tif'), state1) + logging.debug("Capturing Background State1") + state1 = self._capture_state("State1", n_avg) + logging.debug("Saving Background State1") + tiff.imsave(os.path.join(directory, "State1.tif"), state1) - logging.debug('Capturing Background State2') - state2 = self._capture_state('State2', n_avg) - logging.debug('Saving Background State2') - tiff.imsave(os.path.join(directory, 'State2.tif'), state2) + logging.debug("Capturing Background State2") + state2 = self._capture_state("State2", n_avg) + logging.debug("Saving Background State2") + tiff.imsave(os.path.join(directory, "State2.tif"), state2) - logging.debug('Capturing Background State3') - state3 = self._capture_state('State3', n_avg) - logging.debug('Saving Background State3') - tiff.imsave(os.path.join(directory, 'State3.tif'), state3) + logging.debug("Capturing Background State3") + state3 = self._capture_state("State3", n_avg) + logging.debug("Saving Background State3") + tiff.imsave(os.path.join(directory, "State3.tif"), state3) imgs = [state0, state1, state2, state3] - if self.calib_scheme == '5-State': - logging.debug('Capturing Background State4') - state4 = self._capture_state('State4', n_avg) - logging.debug('Saving Background State4') - tiff.imsave(os.path.join(directory, 'State4.tif'), state4) + if self.calib_scheme == "5-State": + logging.debug("Capturing Background State4") + state4 = self._capture_state("State4", n_avg) + logging.debug("Saving Background State4") + tiff.imsave(os.path.join(directory, "State4.tif"), state4) imgs.append(state4) # self._plot_bg_images(np.asarray(imgs)) @@ -985,7 +1191,7 @@ class CalibrationData: Interpolates LC calibration data between retardance (in waves), voltage (in mV), and wavelength (in nm) """ - def __init__(self, path, wavelength=532, interp_method='linear'): + def __init__(self, path, wavelength=532, interp_method="linear"): """ Parameters @@ -999,21 +1205,25 @@ def __init__(self, path, wavelength=532, interp_method='linear'): """ header, raw_data = self.read_data(path) - self.calib_wavelengths = np.array([i[:3] for i in header[1::3]]).astype('double') + self.calib_wavelengths = np.array( + [i[:3] for i in header[1::3]] + ).astype("double") self.wavelength = None self.V_min = 0.0 self.V_max = 20.0 - if interp_method in ['linear', 'schnoor_fit']: + if interp_method in ["linear", "schnoor_fit"]: self.interp_method = interp_method else: - raise ValueError('Unknown interpolation method.') + raise ValueError("Unknown interpolation method.") self.set_wavelength(wavelength) - if interp_method == 'linear': - self.interpolate_data(raw_data, self.calib_wavelengths) # calib_wavelengths is not used, values hardcoded - elif interp_method == 'schnoor_fit': + if interp_method == "linear": + self.interpolate_data( + raw_data, self.calib_wavelengths + ) # calib_wavelengths is not used, values hardcoded + elif interp_method == "schnoor_fit": self.fit_params = self.fit_data(raw_data, self.calib_wavelengths) self.ret_min = self.get_retardance(self.V_max) @@ -1055,10 +1265,10 @@ def read_data(path): Calibration data. Voltage is in millivolts and retardance is in nanometers """ - with open(path, 'r') as f: - header = f.readline().strip().split(',') + with open(path, "r") as f: + header = f.readline().strip().split(",") - raw_data = np.loadtxt(path, delimiter=',', comments='-', skiprows=3) + raw_data = np.loadtxt(path, delimiter=",", comments="-", skiprows=3) return header, raw_data @staticmethod @@ -1080,7 +1290,7 @@ def schnoor_fit(V, a, b1, b2, c, d, e, wavelength): Retardance in nanometers """ - retardance = a + (b1 + b2 / wavelength 2) / (1 + (V / c) d) e + retardance = a + (b1 + b2 / wavelength2) / (1 + (V / c) d) e return retardance @@ -1104,7 +1314,9 @@ def schnoor_fit_inv(retardance, a, b1, b2, c, d, e, wavelength): """ - voltage = c * (((b1 + b2 / wavelength 2) / (retardance - a)) (1 / e) - 1) ** (1 / d) + voltage = c * ( + ((b1 + b2 / wavelength2) / (retardance - a)) (1 / e) - 1 + ) ** (1 / d) return voltage @@ -1115,24 +1327,37 @@ def _fun(x, wavelengths, xdata, ydata): return res.flatten() def set_wavelength(self, wavelength): - if len(self.calib_wavelengths) == 1 and wavelength != self.calib_wavelengths: - raise ValueError("Calibration is not provided at this wavelength. " - "Wavelength dependence of LC retardance vs voltage cannot be extrapolated.") - - if wavelength < self.calib_wavelengths.min() or \ - wavelength > self.calib_wavelengths.max(): - warnings.warn("Specified wavelength is outside of the calibration range. " - "LC retardance vs voltage data will be extrapolated at this wavelength.") + if ( + len(self.calib_wavelengths) == 1 + and wavelength != self.calib_wavelengths + ): + raise ValueError( + "Calibration is not provided at this wavelength. " + "Wavelength dependence of LC retardance vs voltage cannot be extrapolated." + ) + + if ( + wavelength < self.calib_wavelengths.min() + or wavelength > self.calib_wavelengths.max() + ): + warnings.warn( + "Specified wavelength is outside of the calibration range. " + "LC retardance vs voltage data will be extrapolated at this wavelength." + ) self.wavelength = wavelength - if self.interp_method == 'linear': + if self.interp_method == "linear": # Interpolation of calib beyond this range produce strange results. if self.wavelength < 450: self.wavelength = 450 - warnings.warn("Wavelength is limited to 450-720 nm for this interpolation method.") + warnings.warn( + "Wavelength is limited to 450-720 nm for this interpolation method." + ) if self.wavelength > 720: self.wavelength = 720 - warnings.warn("Wavelength is limited to 450-720 nm for this interpolation method.") + warnings.warn( + "Wavelength is limited to 450-720 nm for this interpolation method." + ) def fit_data(self, raw_data, calib_wavelengths): """ @@ -1150,13 +1375,18 @@ def fit_data(self, raw_data, calib_wavelengths): ------- """ - xdata = raw_data[:, 0::3] / 1000 # convert to volts - ydata = raw_data[:, 1::3] # in nanometers + xdata = raw_data[:, 0::3] / 1000 # convert to volts + ydata = raw_data[:, 1::3] # in nanometers x0 = [10, 1000, 1e7, 1, 10, 0.1] - p = least_squares(self._fun, x0, method='trf', args=(calib_wavelengths, xdata, ydata), - bounds=((-np.inf, 0, 0, 0, 0, 0), (np.inf,)6), - x_scale=[10, 1000, 1e7, 1, 10, 0.1]) + p = least_squares( + self._fun, + x0, + method="trf", + args=(calib_wavelengths, xdata, ydata), + bounds=((-np.inf, 0, 0, 0, 0, 0), (np.inf,) 6), + x_scale=[10, 1000, 1e7, 1, 10, 0.1], + ) if not p.success: raise RuntimeError("Schnoor fit to calibration data did not work.") @@ -1166,7 +1396,9 @@ def fit_data(self, raw_data, calib_wavelengths): slope, intercept, r_value, _ = linregress(y, y_hat) r_squared = r_value2 if r_squared < 0.999: - warnings.warn(f'Schnoor fit has R2 value of {r_squared:.5f}, fit may not have worked well.') + warnings.warn( + f"Schnoor fit has R2 value of {r_squared:.5f}, fit may not have worked well." + ) return p.x @@ -1203,8 +1435,16 @@ def interpolate_data(self, raw_data, calib_wavelengths): fact1 = np.abs(490 - wavelength_new) / (546 - 490) fact2 = np.abs(546 - wavelength_new) / (546 - 490) - temp_curve = np.asarray([[i, 2 new_a1_y[i] - (fact1 * new_a1_y[i] + fact2 * new_a2_y[i])] - for i in range(len(new_a1_y))]) + temp_curve = np.asarray( + [ + [ + i, + 2 * new_a1_y[i] + - (fact1 * new_a1_y[i] + fact2 * new_a2_y[i]), + ] + for i in range(len(new_a1_y)) + ] + ) self.spline = interp1d(temp_curve[:, 0], temp_curve[:, 1]) self.curve = self.spline(x_range) @@ -1217,12 +1457,19 @@ def interpolate_data(self, raw_data, calib_wavelengths): fact1 = np.abs(630 - wavelength_new) / (630 - 546) fact2 = np.abs(546 - wavelength_new) / (630 - 546) - temp_curve = np.asarray([[i, 2 * new_a1_y[i] - (fact1 * new_a1_y[i] + fact2 * new_a2_y[i])] - for i in range(len(new_a1_y))]) + temp_curve = np.asarray( + [ + [ + i, + 2 * new_a1_y[i] + - (fact1 * new_a1_y[i] + fact2 * new_a2_y[i]), + ] + for i in range(len(new_a1_y)) + ] + ) self.spline = interp1d(temp_curve[:, 0], temp_curve[:, 1]) self.curve = self.spline(x_range) - elif 490 < self.wavelength < 546: new_a1_y = np.interp(x_range, x_range, spline490(x_range)) @@ -1231,7 +1478,12 @@ def interpolate_data(self, raw_data, calib_wavelengths): fact1 = np.abs(490 - self.wavelength) / (546 - 490) fact2 = np.abs(546 - self.wavelength) / (546 - 490) - temp_curve = np.asarray([[i, fact1 * new_a1_y[i] + fact2 * new_a2_y[i]] for i in range(len(new_a1_y))]) + temp_curve = np.asarray( + [ + [i, fact1 * new_a1_y[i] + fact2 * new_a2_y[i]] + for i in range(len(new_a1_y)) + ] + ) self.spline = interp1d(temp_curve[:, 0], temp_curve[:, 1]) self.curve = self.spline(x_range) @@ -1243,7 +1495,12 @@ def interpolate_data(self, raw_data, calib_wavelengths): fact1 = np.abs(546 - self.wavelength) / (630 - 546) fact2 = np.abs(630 - self.wavelength) / (630 - 546) - temp_curve = np.asarray([[i, fact1 * new_a1_y[i] + fact2 * new_a2_y[i]] for i in range(len(new_a1_y))]) + temp_curve = np.asarray( + [ + [i, fact1 * new_a1_y[i] + fact2 * new_a2_y[i]] + for i in range(len(new_a1_y)) + ] + ) self.spline = interp1d(temp_curve[:, 0], temp_curve[:, 1]) self.curve = self.spline(x_range) @@ -1260,7 +1517,7 @@ def interpolate_data(self, raw_data, calib_wavelengths): self.spline = spline630 else: - raise ValueError(f'Wavelength {self.wavelength} not understood') + raise ValueError(f"Wavelength {self.wavelength} not understood") def get_voltage(self, retardance): """ @@ -1277,19 +1534,21 @@ def get_voltage(self, retardance): """ - retardance = np.asarray(retardance, dtype='double') + retardance = np.asarray(retardance, dtype="double") voltage = None - ret_nanometers = retardanceself.wavelength + ret_nanometers = retardance self.wavelength if retardance < self.ret_min: voltage = self.V_max elif retardance > self.ret_max: voltage = self.V_min else: - if self.interp_method == 'linear': + if self.interp_method == "linear": voltage = np.abs(self.curve - ret_nanometers).argmin() / 1000 - elif self.interp_method == 'schnoor_fit': - voltage = self.schnoor_fit_inv(ret_nanometers, self.fit_params, self.wavelength) + elif self.interp_method == "schnoor_fit": + voltage = self.schnoor_fit_inv( + ret_nanometers, self.fit_params, self.wavelength + ) return voltage @@ -1308,21 +1567,25 @@ def get_retardance(self, volts): """ - volts = np.asarray(volts, dtype='double') + volts = np.asarray(volts, dtype="double") ret_nanometers = None if volts < self.V_min: volts = self.V_min elif volts >= self.V_max: - if self.interp_method == 'linear': - volts = self.V_max - 1e-3 # interpolation breaks down at upper boundary + if self.interp_method == "linear": + volts = ( + self.V_max - 1e-3 + ) # interpolation breaks down at upper boundary else: volts = self.V_max - if self.interp_method == 'linear': + if self.interp_method == "linear": ret_nanometers = self.spline(volts * 1000) - elif self.interp_method == 'schnoor_fit': - ret_nanometers = self.schnoor_fit(volts, self.fit_params, self.wavelength) + elif self.interp_method == "schnoor_fit": + ret_nanometers = self.schnoor_fit( + volts, self.fit_params, self.wavelength + ) retardance = ret_nanometers / self.wavelength return retardance diff --git a/recOrder/calib/Optimization.py b/recOrder/calib/Optimization.py index 88ea8933..6e6893b0 100644 --- a/recOrder/calib/Optimization.py +++ b/recOrder/calib/Optimization.py @@ -3,68 +3,104 @@ from recOrder.io.core_functions import snap_and_average import logging -class BrentOptimizer: +class BrentOptimizer: def __init__(self, calib): self.calib = calib - def _check_bounds(self, lca_bound, lcb_bound): - current_lca = self.calib.get_lc('LCA') - current_lcb = self.calib.get_lc('LCB') + current_lca = self.calib.get_lc("LCA") + current_lcb = self.calib.get_lc("LCB") # check that bounds don't exceed range of LC - lca_lower_bound = 0.01 if (current_lca - lca_bound) <= 0.01 else current_lca - lca_bound - lca_upper_bound = 1.6 if (current_lca + lca_bound) >= 1.6 else current_lca + lca_bound - - lcb_lower_bound = 0.01 if current_lcb - lcb_bound <= 0.01 else current_lcb - lcb_bound - lcb_upper_bound = 1.6 if current_lcb + lcb_bound >= 1.6 else current_lcb + lcb_bound - - return lca_lower_bound, lca_upper_bound, lcb_lower_bound, lcb_upper_bound - - def opt_lca(self, cost_function, lower_bound, upper_bound, reference, cost_function_args): - - xopt, fval, ierr, numfunc = optimize.fminbound(cost_function, - x1=lower_bound, - x2=upper_bound, - disp=0, - args=cost_function_args, - full_output=True) + lca_lower_bound = ( + 0.01 + if (current_lca - lca_bound) <= 0.01 + else current_lca - lca_bound + ) + lca_upper_bound = ( + 1.6 + if (current_lca + lca_bound) >= 1.6 + else current_lca + lca_bound + ) + + lcb_lower_bound = ( + 0.01 + if current_lcb - lcb_bound <= 0.01 + else current_lcb - lcb_bound + ) + lcb_upper_bound = ( + 1.6 if current_lcb + lcb_bound >= 1.6 else current_lcb + lcb_bound + ) + + return ( + lca_lower_bound, + lca_upper_bound, + lcb_lower_bound, + lcb_upper_bound, + ) + + def opt_lca( + self, + cost_function, + lower_bound, + upper_bound, + reference, + cost_function_args, + ): + + xopt, fval, ierr, numfunc = optimize.fminbound( + cost_function, + x1=lower_bound, + x2=upper_bound, + disp=0, + args=cost_function_args, + full_output=True, + ) lca = xopt - lcb = self.calib.get_lc(self.calib.mmc, self.calib.PROPERTIES['LCA']) + lcb = self.calib.get_lc(self.calib.mmc, self.calib.PROPERTIES["LCA"]) abs_intensity = fval + reference difference = fval / reference * 100 - logging.debug('\tOptimize lca ...') + logging.debug("\tOptimize lca ...") logging.debug(f"\tlca = {lca:.5f}") logging.debug(f"\tlcb = {lcb:.5f}") - logging.debug(f'\tIntensity = {abs_intensity}') - logging.debug(f'\tIntensity Difference = {difference:.4f}%') + logging.debug(f"\tIntensity = {abs_intensity}") + logging.debug(f"\tIntensity Difference = {difference:.4f}%") return [lca, lcb, abs_intensity, difference] - def opt_lcb(self, cost_function, lower_bound, upper_bound, reference, cost_function_args): - - xopt, fval, ierr, numfunc = optimize.fminbound(cost_function, - x1=lower_bound, - x2=upper_bound, - disp=0, - args=cost_function_args, - full_output=True) - - lca = self.calib.get_lc(self.calib.mmc, self.calib.PROPERTIES['LCA']) + def opt_lcb( + self, + cost_function, + lower_bound, + upper_bound, + reference, + cost_function_args, + ): + + xopt, fval, ierr, numfunc = optimize.fminbound( + cost_function, + x1=lower_bound, + x2=upper_bound, + disp=0, + args=cost_function_args, + full_output=True, + ) + + lca = self.calib.get_lc(self.calib.mmc, self.calib.PROPERTIES["LCA"]) lcb = xopt abs_intensity = fval + reference difference = fval / reference * 100 - logging.debug('\tOptimize lcb ...') + logging.debug("\tOptimize lcb ...") logging.debug(f"\tlca = {lca:.5f}") logging.debug(f"\tlcb = {lcb:.5f}") - logging.debug(f'\tIntensity = {abs_intensity}') - logging.debug(f'\tIntensity Difference = {difference:.4f}%') + logging.debug(f"\tIntensity = {abs_intensity}") + logging.debug(f"\tIntensity Difference = {difference:.4f}%") return [lca, lcb, abs_intensity, difference] @@ -76,40 +112,64 @@ def optimize(self, state, lca_bound, lcb_bound, reference, thresh, n_iter): optimal = [] while not converged: - logging.debug(f'iteration: {iteration}') + logging.debug(f"iteration: {iteration}") - lca_lower_bound, lca_upper_bound,\ - lcb_lower_bound, lcb_upper_bound = self._check_bounds(lca_bound, lcb_bound) + ( + lca_lower_bound, + lca_upper_bound, + lcb_lower_bound, + lcb_upper_bound, + ) = self._check_bounds(lca_bound, lcb_bound) - if state == 'ext': + if state == "ext": - results_lca = self.opt_lca(self.calib.opt_lc, lca_lower_bound, lca_upper_bound, - reference, (self.calib.PROPERTIES['LCA'], reference)) + results_lca = self.opt_lca( + self.calib.opt_lc, + lca_lower_bound, + lca_upper_bound, + reference, + (self.calib.PROPERTIES["LCA"], reference), + ) - self.calib.set_lc(self.calib.mmc, results_lca[0], 'LCA') + self.calib.set_lc(self.calib.mmc, results_lca[0], "LCA") optimal.append(results_lca) - results_lcb = self.opt_lcb(self.calib.opt_lc, lcb_lower_bound, lcb_upper_bound, - reference, (self.calib.PROPERTIES['LCB'], reference)) + results_lcb = self.opt_lcb( + self.calib.opt_lc, + lcb_lower_bound, + lcb_upper_bound, + reference, + (self.calib.PROPERTIES["LCB"], reference), + ) - self.calib.set_lc(self.calib.mmc, results_lca[1], 'LCB') + self.calib.set_lc(self.calib.mmc, results_lca[1], "LCB") optimal.append(results_lcb) results = results_lcb - if state == '45' or state == '135': + if state == "45" or state == "135": - results = self.opt_lcb(self.calib.opt_lc, lca_lower_bound, lca_upper_bound, - reference, (self.calib.PROPERTIES['LCB'], reference)) + results = self.opt_lcb( + self.calib.opt_lc, + lca_lower_bound, + lca_upper_bound, + reference, + (self.calib.PROPERTIES["LCB"], reference), + ) optimal.append(results) - if state == '60': + if state == "60": - results = self.opt_lca(self.calib.opt_lc_cons, lca_lower_bound, lca_upper_bound, - reference, (reference, '60')) + results = self.opt_lca( + self.calib.opt_lc_cons, + lca_lower_bound, + lca_upper_bound, + reference, + (reference, "60"), + ) swing = (self.calib.lca_ext - results[0]) * self.calib.ratio lca = results[0] @@ -117,16 +177,26 @@ def optimize(self, state, lca_bound, lcb_bound, reference, thresh, n_iter): optimal.append([lca, lcb, results[2], results[3]]) - if state == '90': + if state == "90": - results = self.opt_lca(self.calib.opt_lc, lca_lower_bound, lca_upper_bound, - reference, (self.calib.PROPERTIES['LCA'], reference)) + results = self.opt_lca( + self.calib.opt_lc, + lca_lower_bound, + lca_upper_bound, + reference, + (self.calib.PROPERTIES["LCA"], reference), + ) optimal.append(results) - if state == '120': - results = self.opt_lca(self.calib.opt_lc_cons, lca_lower_bound, lca_upper_bound, - reference, (reference, '120')) + if state == "120": + results = self.opt_lca( + self.calib.opt_lc_cons, + lca_lower_bound, + lca_upper_bound, + reference, + (reference, "120"), + ) swing = (self.calib.lca_ext - results[0]) * self.calib.ratio lca = results[0] @@ -143,13 +213,17 @@ def optimize(self, state, lca_bound, lcb_bound, reference, thresh, n_iter): # if loop preforms more than n_iter iterations, stop elif iteration >= n_iter: - logging.debug(f'Exceeded {n_iter} Iterations: Search discontinuing') + logging.debug( + f"Exceeded {n_iter} Iterations: Search discontinuing" + ) converged = True optimal = np.asarray(optimal) opt = np.where(optimal == np.min(np.abs(optimal[:, 0])))[0] - logging.debug(f'Lowest Inten: {optimal[opt, 0]}, lca = {optimal[opt, 1]}, lcb = {optimal[opt, 2]}') + logging.debug( + f"Lowest Inten: {optimal[opt, 0]}, lca = {optimal[opt, 1]}, lcb = {optimal[opt, 2]}" + ) return optimal[-1, 0], optimal[-1, 1], optimal[-1, 2] @@ -157,110 +231,195 @@ def optimize(self, state, lca_bound, lcb_bound, reference, thresh, n_iter): class MinScalarOptimizer: - def __init__(self, calib): self.calib = calib def _check_bounds(self, lca_bound, lcb_bound): - current_lca = self.calib.get_lc('LCA') - current_lcb = self.calib.get_lc('LCB') + current_lca = self.calib.get_lc("LCA") + current_lcb = self.calib.get_lc("LCB") - if self.calib.mode == 'voltage': + if self.calib.mode == "voltage": # check that bounds don't exceed range of LC - lca_lower_bound = 0.01 if (current_lca - lca_bound) <= 0.01 else current_lca - lca_bound - lca_upper_bound = 2.2 if (current_lca + lca_bound) >= 2.2 else current_lca + lca_bound - - lcb_lower_bound = 0.01 if current_lcb - lcb_bound <= 0.01 else current_lcb - lcb_bound - lcb_upper_bound = 2.2 if current_lcb + lcb_bound >= 2.2 else current_lcb + lcb_bound + lca_lower_bound = ( + 0.01 + if (current_lca - lca_bound) <= 0.01 + else current_lca - lca_bound + ) + lca_upper_bound = ( + 2.2 + if (current_lca + lca_bound) >= 2.2 + else current_lca + lca_bound + ) + + lcb_lower_bound = ( + 0.01 + if current_lcb - lcb_bound <= 0.01 + else current_lcb - lcb_bound + ) + lcb_upper_bound = ( + 2.2 + if current_lcb + lcb_bound >= 2.2 + else current_lcb + lcb_bound + ) else: # check that bounds don't exceed range of LC - lca_lower_bound = 0.01 if (current_lca - lca_bound) <= 0.01 else current_lca - lca_bound - lca_upper_bound = 1.6 if (current_lca + lca_bound) >= 1.6 else current_lca + lca_bound - - lcb_lower_bound = 0.01 if current_lcb - lcb_bound <= 0.01 else current_lcb - lcb_bound - lcb_upper_bound = 1.6 if current_lcb + lcb_bound >= 1.6 else current_lcb + lcb_bound - - return lca_lower_bound, lca_upper_bound, lcb_lower_bound, lcb_upper_bound - - def opt_lca(self, cost_function, lower_bound, upper_bound, reference, cost_function_args): - - res = optimize.minimize_scalar(cost_function, bounds=(lower_bound, upper_bound), - method='bounded', args=cost_function_args) + lca_lower_bound = ( + 0.01 + if (current_lca - lca_bound) <= 0.01 + else current_lca - lca_bound + ) + lca_upper_bound = ( + 1.6 + if (current_lca + lca_bound) >= 1.6 + else current_lca + lca_bound + ) + + lcb_lower_bound = ( + 0.01 + if current_lcb - lcb_bound <= 0.01 + else current_lcb - lcb_bound + ) + lcb_upper_bound = ( + 1.6 + if current_lcb + lcb_bound >= 1.6 + else current_lcb + lcb_bound + ) + + return ( + lca_lower_bound, + lca_upper_bound, + lcb_lower_bound, + lcb_upper_bound, + ) + + def opt_lca( + self, + cost_function, + lower_bound, + upper_bound, + reference, + cost_function_args, + ): + + res = optimize.minimize_scalar( + cost_function, + bounds=(lower_bound, upper_bound), + method="bounded", + args=cost_function_args, + ) lca = res.x - lcb = self.calib.get_lc('LCB') + lcb = self.calib.get_lc("LCB") abs_intensity = res.fun + reference difference = res.fun / reference * 100 - logging.debug('\tOptimize lca ...') + logging.debug("\tOptimize lca ...") logging.debug(f"\tlca = {lca:.5f}") logging.debug(f"\tlcb = {lcb:.5f}") - logging.debug(f'\tIntensity = {abs_intensity}') - logging.debug(f'\tIntensity Difference = {difference:.4f}%') + logging.debug(f"\tIntensity = {abs_intensity}") + logging.debug(f"\tIntensity Difference = {difference:.4f}%") return [lca, lcb, abs_intensity, difference] - def opt_lcb(self, cost_function, lower_bound, upper_bound, reference, cost_function_args): - - res = optimize.minimize_scalar(cost_function, bounds=(lower_bound, upper_bound), - method='bounded', args=cost_function_args) - - lca = self.calib.get_lc('LCA') + def opt_lcb( + self, + cost_function, + lower_bound, + upper_bound, + reference, + cost_function_args, + ): + + res = optimize.minimize_scalar( + cost_function, + bounds=(lower_bound, upper_bound), + method="bounded", + args=cost_function_args, + ) + + lca = self.calib.get_lc("LCA") lcb = res.x abs_intensity = res.fun + reference difference = res.fun / reference * 100 - logging.debug('\tOptimize lcb ...') + logging.debug("\tOptimize lcb ...") logging.debug(f"\tlca = {lca:.5f}") logging.debug(f"\tlcb = {lcb:.5f}") - logging.debug(f'\tIntensity = {abs_intensity}') - logging.debug(f'\tIntensity Difference = {difference:.4f}%') + logging.debug(f"\tIntensity = {abs_intensity}") + logging.debug(f"\tIntensity Difference = {difference:.4f}%") return [lca, lcb, abs_intensity, difference] - def optimize(self, state, lca_bound, lcb_bound, reference, thresh=None, n_iter=None): + def optimize( + self, state, lca_bound, lcb_bound, reference, thresh=None, n_iter=None + ): - lca_lower_bound, lca_upper_bound, lcb_lower_bound, lcb_upper_bound = self._check_bounds(lca_bound, lcb_bound) + ( + lca_lower_bound, + lca_upper_bound, + lcb_lower_bound, + lcb_upper_bound, + ) = self._check_bounds(lca_bound, lcb_bound) - if state == 'ext': + if state == "ext": optimal = [] - results_lca = self.opt_lca(self.calib.opt_lc, lca_lower_bound, lca_upper_bound, - reference, ('LCA', reference)) + results_lca = self.opt_lca( + self.calib.opt_lc, + lca_lower_bound, + lca_upper_bound, + reference, + ("LCA", reference), + ) - self.calib.set_lc(results_lca[0], 'LCA') + self.calib.set_lc(results_lca[0], "LCA") optimal.append(results_lca) - results_lcb = self.opt_lcb(self.calib.opt_lc, lcb_lower_bound, lcb_upper_bound, - reference, ('LCB', reference)) + results_lcb = self.opt_lcb( + self.calib.opt_lc, + lcb_lower_bound, + lcb_upper_bound, + reference, + ("LCB", reference), + ) - self.calib.set_lc(results_lcb[1], 'LCB') + self.calib.set_lc(results_lcb[1], "LCB") optimal.append(results_lcb) # ============BEGIN FINE SEARCH================= - logging.debug(f'\n\tBeginning Finer Search\n') - lca_lower_bound = results_lcb[0] - .01 - lca_upper_bound = results_lcb[0] + .01 - lcb_lower_bound = results_lcb[1] - .01 - lcb_upper_bound = results_lcb[1] + .01 + logging.debug(f"\n\tBeginning Finer Search\n") + lca_lower_bound = results_lcb[0] - 0.01 + lca_upper_bound = results_lcb[0] + 0.01 + lcb_lower_bound = results_lcb[1] - 0.01 + lcb_upper_bound = results_lcb[1] + 0.01 - results_lca = self.opt_lca(self.calib.opt_lc, lca_lower_bound, lca_upper_bound, - reference, ('LCA', reference)) + results_lca = self.opt_lca( + self.calib.opt_lc, + lca_lower_bound, + lca_upper_bound, + reference, + ("LCA", reference), + ) - self.calib.set_lc(results_lca[0], 'LCA') + self.calib.set_lc(results_lca[0], "LCA") optimal.append(results_lca) - results_lcb = self.opt_lcb(self.calib.opt_lc, lcb_lower_bound, lcb_upper_bound, - reference, ('LCB', reference)) + results_lcb = self.opt_lcb( + self.calib.opt_lc, + lcb_lower_bound, + lcb_upper_bound, + reference, + ("LCB", reference), + ) - self.calib.set_lc(results_lcb[1], 'LCB') + self.calib.set_lc(results_lcb[1], "LCB") optimal.append(results_lcb) @@ -273,30 +432,50 @@ def optimize(self, state, lca_bound, lcb_bound, reference, thresh=None, n_iter=N lcb = float(optimal[opt][0][1]) results = optimal[opt][0] - if state == '45' or state == '135': - results = self.opt_lcb(self.calib.opt_lc, lcb_lower_bound, lcb_upper_bound, - reference, ('LCB', reference)) + if state == "45" or state == "135": + results = self.opt_lcb( + self.calib.opt_lc, + lcb_lower_bound, + lcb_upper_bound, + reference, + ("LCB", reference), + ) lca = results[0] lcb = results[1] - if state == '60': - results = self.opt_lca(self.calib.opt_lc_cons, lca_lower_bound, lca_upper_bound, - reference, ('LCA', reference, '60')) + if state == "60": + results = self.opt_lca( + self.calib.opt_lc_cons, + lca_lower_bound, + lca_upper_bound, + reference, + ("LCA", reference, "60"), + ) swing = (self.calib.lca_ext - results[0]) * self.calib.ratio lca = results[0] lcb = self.calib.lcb_ext + swing - if state == '90': - results = self.opt_lca(self.calib.opt_lc, lca_lower_bound, lca_upper_bound, - reference, ('LCA', reference)) + if state == "90": + results = self.opt_lca( + self.calib.opt_lc, + lca_lower_bound, + lca_upper_bound, + reference, + ("LCA", reference), + ) lca = results[0] lcb = results[1] - if state == '120': - results = self.opt_lca(self.calib.opt_lc_cons, lca_lower_bound, lca_upper_bound, - reference, ('LCB', reference, '120')) + if state == "120": + results = self.opt_lca( + self.calib.opt_lc_cons, + lca_lower_bound, + lca_upper_bound, + reference, + ("LCB", reference, "120"), + ) swing = (self.calib.lca_ext - results[0]) * self.calib.ratio lca = results[0] diff --git a/recOrder/compute/__init__.py b/recOrder/compute/__init__.py index 62937c77..d4cc5512 100644 --- a/recOrder/compute/__init__.py +++ b/recOrder/compute/__init__.py @@ -1,1 +1,1 @@ -from .qlipp_compute import * \ No newline at end of file +from .qlipp_compute import * diff --git a/recOrder/compute/qlipp_compute.py b/recOrder/compute/qlipp_compute.py index ad009307..e99b0c2a 100644 --- a/recOrder/compute/qlipp_compute.py +++ b/recOrder/compute/qlipp_compute.py @@ -3,10 +3,25 @@ import time -def initialize_reconstructor(pipeline, image_dim=None, wavelength_nm=None, swing=None, calibration_scheme=None, - NA_obj=None, NA_illu=None, mag=None, n_slices=None, z_step_um=None, - pad_z=0, pixel_size_um=None, bg_correction='local_fit', n_obj_media=1.0, mode='3D', - use_gpu=False, gpu_id=0): +def initialize_reconstructor( + pipeline, + image_dim=None, + wavelength_nm=None, + swing=None, + calibration_scheme=None, + NA_obj=None, + NA_illu=None, + mag=None, + n_slices=None, + z_step_um=None, + pad_z=0, + pixel_size_um=None, + bg_correction="local_fit", + n_obj_media=1.0, + mode="3D", + use_gpu=False, + gpu_id=0, +): """ Initialize the QLIPP reconstructor for downstream tasks. See tags next to parameters for which parameters are needed for each pipeline @@ -74,76 +89,100 @@ def initialize_reconstructor(pipeline, image_dim=None, wavelength_nm=None, swing reconstructor : object reconstruction object initialized with reconstruction parameters - """ + """ anisotropy_only = False - if pipeline == 'QLIPP' or pipeline == 'PhaseFromBF': + if pipeline == "QLIPP" or pipeline == "PhaseFromBF": if not NA_obj: - raise ValueError('Please specify NA_obj in function parameters') + raise ValueError("Please specify NA_obj in function parameters") if not NA_illu: - raise ValueError('Please specify NA_illu in function parameters') + raise ValueError("Please specify NA_illu in function parameters") if not mag: - raise ValueError('Please specify mag (magnification) in function parameters') + raise ValueError( + "Please specify mag (magnification) in function parameters" + ) if not wavelength_nm: - raise ValueError('Please specify the wavelength for reconstruction') + raise ValueError( + "Please specify the wavelength for reconstruction" + ) if not n_slices: - raise ValueError('Please specify n_slices in function parameters') + raise ValueError("Please specify n_slices in function parameters") if not z_step_um: - raise ValueError('Please specify z_step_um in function parameters') + raise ValueError("Please specify z_step_um in function parameters") if not pixel_size_um: - raise ValueError('Please specify NA_obj in function parameters') + raise ValueError("Please specify NA_obj in function parameters") if not n_obj_media: - raise ValueError('Please specify NA_obj in function parameters') + raise ValueError("Please specify NA_obj in function parameters") if not image_dim: - raise ValueError('Please specify image_dim in function parameters') + raise ValueError("Please specify image_dim in function parameters") - if pipeline == 'QLIPP': + if pipeline == "QLIPP": if not calibration_scheme: - raise ValueError('Please specify qlipp_scheme (calibration scheme) for QLIPP reconstruction') + raise ValueError( + "Please specify qlipp_scheme (calibration scheme) for QLIPP reconstruction" + ) if not swing: - raise ValueError('Please specify swing in function parameters') + raise ValueError("Please specify swing in function parameters") - elif pipeline == 'birefringence': + elif pipeline == "birefringence": anisotropy_only = True if not calibration_scheme: - raise ValueError('Please specify qlipp_scheme (calibration scheme) for QLIPP reconstruction') + raise ValueError( + "Please specify qlipp_scheme (calibration scheme) for QLIPP reconstruction" + ) if not wavelength_nm: - raise ValueError('Please specify the wavelength for QLIPP reconstruction') + raise ValueError( + "Please specify the wavelength for QLIPP reconstruction" + ) if not swing: - raise ValueError('Please specify swing in function parameters') + raise ValueError("Please specify swing in function parameters") else: - raise ValueError(f'Pipeline {pipeline} not understood') + raise ValueError(f"Pipeline {pipeline} not understood") # Modify user inputs to fit waveorder input requirements lambda_illu = wavelength_nm / 1000 if wavelength_nm else None n_defocus = n_slices if n_slices else 0 z_step_um = 0 if not z_step_um else z_step_um - z_defocus = -(np.r_[:n_defocus] - n_defocus // 2) * z_step_um # assumes stack starts from the bottom + z_defocus = ( + -(np.r_[:n_defocus] - n_defocus // 2) * z_step_um + ) # assumes stack starts from the bottom ps = pixel_size_um / mag if pixel_size_um else None cali = True NA_obj = 0 if not NA_obj else NA_obj NA_illu = 0 if not NA_illu else NA_illu - if calibration_scheme == '4-State': - inst_mat = np.array([[1, 0, 0, -1], - [1, np.sin(2 * np.pi * swing), 0, -np.cos(2 * np.pi * swing)], - [1, -0.5 * np.sin(2 * np.pi * swing), np.sqrt(3) * np.cos(np.pi * swing) * np.sin(np.pi * swing), - -np.cos(2 * np.pi * swing)], - [1, -0.5 * np.sin(2 * np.pi * swing), -np.sqrt(3) / 2 * np.sin(2 * np.pi * swing), - -np.cos(2 * np.pi * swing)]]) + if calibration_scheme == "4-State": + inst_mat = np.array( + [ + [1, 0, 0, -1], + [1, np.sin(2 * np.pi * swing), 0, -np.cos(2 * np.pi * swing)], + [ + 1, + -0.5 * np.sin(2 * np.pi * swing), + np.sqrt(3) * np.cos(np.pi * swing) * np.sin(np.pi * swing), + -np.cos(2 * np.pi * swing), + ], + [ + 1, + -0.5 * np.sin(2 * np.pi * swing), + -np.sqrt(3) / 2 * np.sin(2 * np.pi * swing), + -np.cos(2 * np.pi * swing), + ], + ] + ) n_channel = 4 - elif calibration_scheme == '5-State': + elif calibration_scheme == "5-State": swing = swing * 2 * np.pi inst_mat = None n_channel = 5 - elif calibration_scheme == 'PhaseFromBF': + elif calibration_scheme == "PhaseFromBF": inst_mat = None n_channel = 1 swing = 0 @@ -152,34 +191,37 @@ def initialize_reconstructor(pipeline, image_dim=None, wavelength_nm=None, swing n_channel = 1 swing = 0 - print('Initializing Reconstructor...') + print("Initializing Reconstructor...") start_time = time.time() print(bg_correction) - recon = waveorder_microscopy(img_dim=image_dim, - lambda_illu=lambda_illu, - ps=ps, - NA_obj=NA_obj, - NA_illu=NA_illu, - z_defocus=z_defocus, - chi=swing, - n_media=n_obj_media, - cali=cali, - bg_option=bg_correction, - A_matrix=inst_mat, - QLIPP_birefringence_only=anisotropy_only, - pad_z=pad_z, - phase_deconv=mode, - illu_mode='BF', - use_gpu=use_gpu, - gpu_id=gpu_id) + recon = waveorder_microscopy( + img_dim=image_dim, + lambda_illu=lambda_illu, + ps=ps, + NA_obj=NA_obj, + NA_illu=NA_illu, + z_defocus=z_defocus, + chi=swing, + n_media=n_obj_media, + cali=cali, + bg_option=bg_correction, + A_matrix=inst_mat, + QLIPP_birefringence_only=anisotropy_only, + pad_z=pad_z, + phase_deconv=mode, + illu_mode="BF", + use_gpu=use_gpu, + gpu_id=gpu_id, + ) recon.N_channel = n_channel elapsed_time = (time.time() - start_time) / 60 - print(f'Finished Initializing Reconstructor ({elapsed_time:0.2f} min)') + print(f"Finished Initializing Reconstructor ({elapsed_time:0.2f} min)") return recon + def reconstruct_qlipp_stokes(data, recon, bg_stokes=None): """ From intensity data, use the waveorder.waveorder_microscopy (recon) to build a stokes array @@ -208,19 +250,24 @@ def reconstruct_qlipp_stokes(data, recon, bg_stokes=None): stokes_data = recon.Stokes_transform(stokes_data) # Don't do background correction if BG data isn't provided - if recon.bg_option == 'None': - return stokes_data # C(Z)YX + if recon.bg_option == "None": + return stokes_data # C(Z)YX # Compute Stokes with background correction else: if len(np.shape(stokes_data)) == 4: - s_image = recon.Polscope_bg_correction(np.transpose(stokes_data, (-4, -2, -1, -3)), bg_stokes) - s_image = np.transpose(s_image, (0, 3, 1, 2)) # Tranpose to CZYX + s_image = recon.Polscope_bg_correction( + np.transpose(stokes_data, (-4, -2, -1, -3)), bg_stokes + ) + s_image = np.transpose(s_image, (0, 3, 1, 2)) # Tranpose to CZYX else: - s_image = recon.Polscope_bg_correction(stokes_data, bg_stokes) # CYX + s_image = recon.Polscope_bg_correction( + stokes_data, bg_stokes + ) # CYX return s_image + def reconstruct_qlipp_birefringence(stokes, recon): """ From stokes data, use waveorder.waveorder_microscopy (recon) to build a birefringence array @@ -245,16 +292,21 @@ def reconstruct_qlipp_birefringence(stokes, recon): elif stokes.ndim == 3: pass else: - raise ValueError(f'Incompatible stokes dimension: {stokes.shape}') + raise ValueError(f"Incompatible stokes dimension: {stokes.shape}") birefringence = recon.Polarization_recon(np.copy(stokes)) # Return the transposed birefringence array with channel first - return np.transpose(birefringence, (-4, -1, -3, -2)) if len(birefringence.shape) == 4 else birefringence + return ( + np.transpose(birefringence, (-4, -1, -3, -2)) + if len(birefringence.shape) == 4 + else birefringence + ) -def reconstruct_phase2D(S0, recon, method='Tikhonov', reg_p=1e-4, rho=1, - lambda_p=1e-4, itr=50): +def reconstruct_phase2D( + S0, recon, method="Tikhonov", reg_p=1e-4, rho=1, lambda_p=1e-4, itr=50 +): """ Reconstruct 2D phase from a given S0 or BF stack. @@ -275,13 +327,22 @@ def reconstruct_phase2D(S0, recon, method='Tikhonov', reg_p=1e-4, rho=1, """ S0 = np.transpose(S0, (1, 2, 0)) - _, phase2D = recon.Phase_recon(np.copy(S0).astype('float'), method=method, reg_p=reg_p, rho=rho, lambda_p=lambda_p, itr=itr, verbose=False) + _, phase2D = recon.Phase_recon( + np.copy(S0).astype("float"), + method=method, + reg_p=reg_p, + rho=rho, + lambda_p=lambda_p, + itr=itr, + verbose=False, + ) return phase2D -def reconstruct_phase3D(S0, recon, method='Tikhonov', reg_re=1e-4, - rho=1e-3, lambda_re=1e-4, itr=50): +def reconstruct_phase3D( + S0, recon, method="Tikhonov", reg_re=1e-4, rho=1e-3, lambda_re=1e-4, itr=50 +): """ Reconstruct 2D phase from a given S0 or BF stack. @@ -302,19 +363,36 @@ def reconstruct_phase3D(S0, recon, method='Tikhonov', reg_re=1e-4, """ S0 = np.transpose(S0, (1, 2, 0)) - phase3D = recon.Phase_recon_3D(np.copy(S0).astype('float'), method=method, reg_re=reg_re, rho=rho, lambda_re=lambda_re, - itr=itr, verbose=False) + phase3D = recon.Phase_recon_3D( + np.copy(S0).astype("float"), + method=method, + reg_re=reg_re, + rho=rho, + lambda_re=lambda_re, + itr=itr, + verbose=False, + ) phase3D = np.transpose(phase3D, (-1, -3, -2)) return phase3D + class QLIPPBirefringenceCompute: """ Convenience Class for computing QLIPP birefringence only. """ - def __init__(self, shape, scheme, wavelength, swing, n_slices, bg_option, bg_data=None): + def __init__( + self, + shape, + scheme, + wavelength, + swing, + n_slices, + bg_option, + bg_data=None, + ): self.shape = shape self.scheme = scheme @@ -323,16 +401,20 @@ def __init__(self, shape, scheme, wavelength, swing, n_slices, bg_option, bg_dat self.n_slices = n_slices self.bg_option = bg_option - self.reconstructor = initialize_reconstructor(pipeline='birefringence', - image_dim=self.shape, - calibration_scheme=self.scheme, - wavelength_nm=self.wavelength, - swing=self.swing, - bg_correction=self.bg_option, - n_slices=self.n_slices) - - if bg_option != 'None': - self.bg_stokes = reconstruct_qlipp_stokes(bg_data, self.reconstructor) + self.reconstructor = initialize_reconstructor( + pipeline="birefringence", + image_dim=self.shape, + calibration_scheme=self.scheme, + wavelength_nm=self.wavelength, + swing=self.swing, + bg_correction=self.bg_option, + n_slices=self.n_slices, + ) + + if bg_option != "None": + self.bg_stokes = reconstruct_qlipp_stokes( + bg_data, self.reconstructor + ) else: self.bg_stokes = None @@ -350,8 +432,12 @@ def reconstruct(self, array): first channel is retardance [nm] second channel is orientation [0, pi] """ - stokes = reconstruct_qlipp_stokes(array, self.reconstructor, self.bg_stokes) - birefringence = reconstruct_qlipp_birefringence(stokes, self.reconstructor) + stokes = reconstruct_qlipp_stokes( + array, self.reconstructor, self.bg_stokes + ) + birefringence = reconstruct_qlipp_birefringence( + stokes, self.reconstructor + ) birefringence[0] = birefringence[0] / (2 * np.pi) * self.wavelength return birefringence[0:2] diff --git a/recOrder/io/__init__.py b/recOrder/io/__init__.py index 2730cc46..58c8e239 100644 --- a/recOrder/io/__init__.py +++ b/recOrder/io/__init__.py @@ -1,4 +1,4 @@ from .utils import * from .config_reader import ConfigReader from ._reader import napari_get_reader -from .metadata_reader import MetadataReader \ No newline at end of file +from .metadata_reader import MetadataReader diff --git a/recOrder/io/_reader.py b/recOrder/io/_reader.py index 27466956..6df5c4bd 100644 --- a/recOrder/io/_reader.py +++ b/recOrder/io/_reader.py @@ -2,9 +2,10 @@ import zarr from typing import Tuple, List, Dict, Union + def napari_get_reader(path): if isinstance(path, str): - if '.zarr' in path: + if ".zarr" in path: return ome_zarr_reader else: return ome_tif_reader @@ -12,29 +13,34 @@ def napari_get_reader(path): return None -def ome_zarr_reader(path: Union[str, List[str]]) -> List[Tuple[zarr.Array, Dict]]: +def ome_zarr_reader( + path: Union[str, List[str]] +) -> List[Tuple[zarr.Array, Dict]]: reader = WaveorderReader(path) results = list() - zs = zarr.open(path, 'r') + zs = zarr.open(path, "r") names = [] dict_ = zs.attrs.asdict() - wells = dict_['plate']['wells'] + wells = dict_["plate"]["wells"] for well in wells: - path = well['path'] + path = well["path"] well_dict = zs[path].attrs.asdict() - for name in well_dict['well']['images']: - names.append(name['path']) + for name in well_dict["well"]["images"]: + names.append(name["path"]) for pos in range(reader.get_num_positions()): meta = dict() name = names[pos] - meta['name'] = name + meta["name"] = name results.append((reader.get_zarr(pos), meta)) return results -def ome_tif_reader(path: Union[str, List[str]]) -> List[Tuple[zarr.Array, Dict]]: + +def ome_tif_reader( + path: Union[str, List[str]] +) -> List[Tuple[zarr.Array, Dict]]: reader = WaveorderReader(path) results = list() @@ -42,9 +48,9 @@ def ome_tif_reader(path: Union[str, List[str]]) -> List[Tuple[zarr.Array, Dict]] for pos in range(npos): meta = dict() if npos == 1: - meta['name'] = 'Pos000_000' + meta["name"] = "Pos000_000" else: - meta['name'] = reader.stage_positions[pos]['Label'][2:] + meta["name"] = reader.stage_positions[pos]["Label"][2:] results.append((reader.get_zarr(pos), meta)) - return results \ No newline at end of file + return results diff --git a/recOrder/io/config_reader.py b/recOrder/io/config_reader.py index 5dd107cd..0744f94c 100644 --- a/recOrder/io/config_reader.py +++ b/recOrder/io/config_reader.py @@ -4,72 +4,81 @@ import warnings DATASET = { - 'method': None, - 'mode': None, - 'data_dir': None, - 'save_dir': None, - 'data_type': 'zarr', - 'data_save_name': None, - 'positions': 'all', - 'timepoints': 'all', - 'background': None, - 'background_ROI': None, - 'calibration_metadata': None + "method": None, + "mode": None, + "data_dir": None, + "save_dir": None, + "data_type": "zarr", + "data_save_name": None, + "positions": "all", + "timepoints": "all", + "background": None, + "background_ROI": None, + "calibration_metadata": None, } PROCESSING = { - 'output_channels': None, - 'qlipp_birefringence_only': False, - 'background_correction': 'None', - 'use_gpu': False, - 'gpu_id': 0, - 'wavelength': None, - 'pixel_size': None, - 'magnification': None, - 'NA_objective': None, - 'NA_condenser': None, - 'n_objective_media': 1.003, - 'brightfield_channel_index': 0, - 'z_step': None, - 'focus_zidx': None, - 'reg': 1e-4, - 'phase_denoiser_2D': 'Tikhonov', - 'Tik_reg_abs_2D': 1e-4, - 'Tik_reg_ph_2D': 1e-4, - 'rho_2D': 1, - 'itr_2D': 50, - 'TV_reg_abs_2D': 1e-4, - 'TV_reg_ph_2D': 1e-4, - 'phase_denoiser_3D': 'Tikhonov', - 'rho_3D': 1e-3, - 'itr_3D': 50, - 'Tik_reg_ph_3D': 1e-4, - 'TV_reg_ph_3D': 5e-5, - 'pad_z': 0 + "output_channels": None, + "qlipp_birefringence_only": False, + "background_correction": "None", + "use_gpu": False, + "gpu_id": 0, + "wavelength": None, + "pixel_size": None, + "magnification": None, + "NA_objective": None, + "NA_condenser": None, + "n_objective_media": 1.003, + "brightfield_channel_index": 0, + "z_step": None, + "focus_zidx": None, + "reg": 1e-4, + "phase_denoiser_2D": "Tikhonov", + "Tik_reg_abs_2D": 1e-4, + "Tik_reg_ph_2D": 1e-4, + "rho_2D": 1, + "itr_2D": 50, + "TV_reg_abs_2D": 1e-4, + "TV_reg_ph_2D": 1e-4, + "phase_denoiser_3D": "Tikhonov", + "rho_3D": 1e-3, + "itr_3D": 50, + "Tik_reg_ph_3D": 1e-4, + "TV_reg_ph_3D": 5e-5, + "pad_z": 0, } -class ConfigReader(): + +class ConfigReader: """ Config Reader handles all of the requirements necessary for running the pipeline. Default values are used for those that do not need to be specified. CLI will always overried config """ - def __init__(self, cfg_path=None, data_dir=None, save_dir=None, method=None, mode=None, name=None, immutable=True): + def __init__( + self, + cfg_path=None, + data_dir=None, + save_dir=None, + method=None, + mode=None, + name=None, + immutable=True, + ): self.immutable = immutable - if data_dir is not None: - setattr(self, 'data_dir', data_dir) + setattr(self, "data_dir", data_dir) if save_dir is not None: - setattr(self, 'save_dir', save_dir) + setattr(self, "save_dir", save_dir) if method is not None: - setattr(self, 'method', method) + setattr(self, "method", method) if mode is not None: - setattr(self, 'mode', mode) + setattr(self, "mode", mode) if name is not None: - setattr(self, 'data_save_name', name) + setattr(self, "data_save_name", name) # initialize defaults for key, value in DATASET.items(): @@ -87,7 +96,7 @@ def __init__(self, cfg_path=None, data_dir=None, save_dir=None, method=None, mod self.read_config(cfg_path, data_dir, save_dir, method, mode, name) # Create yaml dict to save in save_dir - setattr(self, 'yaml_dict', self._create_yaml_dict()) + setattr(self, "yaml_dict", self._create_yaml_dict()) # self._save_yaml() # Override set attribute function to disallow changes after init @@ -100,54 +109,106 @@ def __setattr__(self, name, value): def _check_assertions(self, data_dir, save_dir, method, mode, name): # assert main fields of config - assert 'dataset' in self.config, \ - 'dataset is a required field in the config yaml file' - assert 'processing' in self.config, \ - 'processing is a required field in the config yaml file' - - if not method: assert 'method' in self.config['dataset'], \ - 'Please provide method in config file or CLI argument' - if not mode: assert 'mode' in self.config['dataset'], \ - 'Please provide mode in config file or CLI argument' - if not data_dir: assert 'data_dir' in self.config['dataset'], \ - 'Please provide data_dir in config file or CLI argument' - if not save_dir: assert 'save_dir' in self.config['dataset'], \ - 'Please provide save_dir in config file or CLI argument' - - if self.config['dataset']['positions'] != 'all' and not isinstance(self.config['dataset']['positions'], int): - assert isinstance(self.config['dataset']['positions'], list), \ - 'if not single integer value or "all", positions must be list (nested lists/tuples allowed)' - if self.config['dataset']['timepoints'] != 'all' and not isinstance(self.config['dataset']['timepoints'], int): - assert isinstance(self.config['dataset']['timepoints'], list), \ - 'if not single integer value or "all", timepoints must be list (nested lists/tuples allowed)' - - for key,value in PROCESSING.items(): - if key == 'output_channels': - if 'Phase3D' in self.config['processing'][key] or 'Phase2D' in self.config['processing'][key]: - assert 'wavelength' in self.config['processing'], 'wavelength required for phase reconstruction' - assert 'pixel_size' in self.config['processing'], 'pixel_size required for phase reconstruction' - assert 'magnification' in self.config['processing'], 'magnification required for phase reconstruction' - assert 'NA_objective' in self.config['processing'], 'NA_objective required for phase reconstruction' - assert 'NA_condenser' in self.config['processing'], 'NA_condenser required for phase reconstruction' - - if 'Phase3D' in self.config['processing'][key] and 'Phase2D' in self.config['processing'][key]: - raise KeyError(f'Both Phase3D and Phase2D cannot be specified in {key}. Please compute separately') - - if 'Phase3D' in self.config['processing'][key] and self.config['dataset']['mode'] == '2D': - raise KeyError(f'Specified mode is 2D and Phase3D was specified for reconstruction. ' - 'Only 2D reconstructions can be performed in 2D mode') - - if 'Phase2D' in self.config['processing'][key] and self.config['dataset']['mode'] == '3D': - raise KeyError(f'Specified mode is 3D and Phase2D was specified for reconstruction. ' - 'Only 3D reconstructions can be performed in 3D mode') - - elif key == 'background_correction' and self.config['dataset']['method'] == 'QLIPP': - mode = self.config['processing'][key] - if mode == 'None' or mode == 'local_fit' or mode == 'local_fit+': + assert ( + "dataset" in self.config + ), "dataset is a required field in the config yaml file" + assert ( + "processing" in self.config + ), "processing is a required field in the config yaml file" + + if not method: + assert ( + "method" in self.config["dataset"] + ), "Please provide method in config file or CLI argument" + if not mode: + assert ( + "mode" in self.config["dataset"] + ), "Please provide mode in config file or CLI argument" + if not data_dir: + assert ( + "data_dir" in self.config["dataset"] + ), "Please provide data_dir in config file or CLI argument" + if not save_dir: + assert ( + "save_dir" in self.config["dataset"] + ), "Please provide save_dir in config file or CLI argument" + + if self.config["dataset"]["positions"] != "all" and not isinstance( + self.config["dataset"]["positions"], int + ): + assert isinstance( + self.config["dataset"]["positions"], list + ), 'if not single integer value or "all", positions must be list (nested lists/tuples allowed)' + if self.config["dataset"]["timepoints"] != "all" and not isinstance( + self.config["dataset"]["timepoints"], int + ): + assert isinstance( + self.config["dataset"]["timepoints"], list + ), 'if not single integer value or "all", timepoints must be list (nested lists/tuples allowed)' + + for key, value in PROCESSING.items(): + if key == "output_channels": + if ( + "Phase3D" in self.config["processing"][key] + or "Phase2D" in self.config["processing"][key] + ): + assert ( + "wavelength" in self.config["processing"] + ), "wavelength required for phase reconstruction" + assert ( + "pixel_size" in self.config["processing"] + ), "pixel_size required for phase reconstruction" + assert ( + "magnification" in self.config["processing"] + ), "magnification required for phase reconstruction" + assert ( + "NA_objective" in self.config["processing"] + ), "NA_objective required for phase reconstruction" + assert ( + "NA_condenser" in self.config["processing"] + ), "NA_condenser required for phase reconstruction" + + if ( + "Phase3D" in self.config["processing"][key] + and "Phase2D" in self.config["processing"][key] + ): + raise KeyError( + f"Both Phase3D and Phase2D cannot be specified in {key}. Please compute separately" + ) + + if ( + "Phase3D" in self.config["processing"][key] + and self.config["dataset"]["mode"] == "2D" + ): + raise KeyError( + f"Specified mode is 2D and Phase3D was specified for reconstruction. " + "Only 2D reconstructions can be performed in 2D mode" + ) + + if ( + "Phase2D" in self.config["processing"][key] + and self.config["dataset"]["mode"] == "3D" + ): + raise KeyError( + f"Specified mode is 3D and Phase2D was specified for reconstruction. " + "Only 3D reconstructions can be performed in 3D mode" + ) + + elif ( + key == "background_correction" + and self.config["dataset"]["method"] == "QLIPP" + ): + mode = self.config["processing"][key] + if ( + mode == "None" + or mode == "local_fit" + or mode == "local_fit+" + ): pass else: - assert self.config['dataset']['background'] is not None, \ - 'path to background data must be specified for this background correction method' + assert ( + self.config["dataset"]["background"] is not None + ), "path to background data must be specified for this background correction method" def save_yaml(self, dir_=None, name=None): self.immutable = False @@ -155,84 +216,96 @@ def save_yaml(self, dir_=None, name=None): self.immutable = True dir_ = self.save_dir if dir_ is None else dir_ - name = f'config_{self.data_save_name}.yml' if name is None else name - if not name.endswith('.yml'): - name += '.yml' + name = f"config_{self.data_save_name}.yml" if name is None else name + if not name.endswith(".yml"): + name += ".yml" if not os.path.exists(dir_): os.mkdir(dir_) - with open(os.path.join(dir_, name), 'w') as file: + with open(os.path.join(dir_, name), "w") as file: yaml.dump(self.yaml_dict, file) file.close() def _create_yaml_dict(self): - yaml_dict = {'dataset': {}, - 'processing': {}} + yaml_dict = {"dataset": {}, "processing": {}} for key, value in DATASET.items(): - yaml_dict['dataset'][key] = getattr(self, key) + yaml_dict["dataset"][key] = getattr(self, key) for key, value in PROCESSING.items(): - yaml_dict['processing'][key] = getattr(self, key) + yaml_dict["processing"][key] = getattr(self, key) return yaml_dict def _use_default_name(self): path = pathlib.PurePath(self.data_dir) - super().__setattr__('data_save_name', path.name) + super().__setattr__("data_save_name", path.name) def _parse_dataset(self): - for key, value in self.config['dataset'].items(): + for key, value in self.config["dataset"].items(): if key in DATASET.keys(): # This section will prioritize the command line arguments over the # config arguments (data_dir, save_dir, data_save_name) - if key == 'name' and self.data_save_name: + if key == "name" and self.data_save_name: continue - elif key == 'data_dir' and self.data_dir: + elif key == "data_dir" and self.data_dir: continue - elif key == 'save_dir' and self.save_dir: + elif key == "save_dir" and self.save_dir: continue - elif key == 'mode' and self.mode: + elif key == "mode" and self.mode: continue - elif key == 'method' and self.method: + elif key == "method" and self.method: continue # this section appends the rest of the dataset parameters specified - elif key == 'positions' or key == 'timepoints': + elif key == "positions" or key == "timepoints": if isinstance(value, str): - if value == 'all': + if value == "all": super().__setattr__(key, [value]) else: - raise KeyError(f'{key} value {value} not understood,\ - please specify a list or "all"') + raise KeyError( + f'{key} value {value} not understood,\ + please specify a list or "all"' + ) elif isinstance(value, int): super().__setattr__(key, [value]) elif isinstance(value, tuple): if len(value) == 2: super().__setattr__(key, value) else: - raise KeyError(f'{key} value {value} is not a tuple with length of 2') + raise KeyError( + f"{key} value {value} is not a tuple with length of 2" + ) elif isinstance(value, list): super().__setattr__(key, value) else: - raise KeyError(f'{key} value {value} format not understood. \ - Must be list with nested tuple or list or "all"') + raise KeyError( + f'{key} value {value} format not understood. \ + Must be list with nested tuple or list or "all"' + ) else: super().__setattr__(key, value) else: - warnings.warn(f'yaml DATASET config field {key} is not recognized') + warnings.warn( + f"yaml DATASET config field {key} is not recognized" + ) def _parse_processing(self): - for key, value in self.config['processing'].items(): + for key, value in self.config["processing"].items(): if key in PROCESSING.keys(): super().__setattr__(key, value) else: - warnings.warn(f'yaml PROCESSING config field {key} is not recognized') + warnings.warn( + f"yaml PROCESSING config field {key} is not recognized" + ) - if 'Phase3D' not in self.output_channels and 'Phase2D' not in self.output_channels: - super().__setattr__('qlipp_birefringence_only', True) + if ( + "Phase3D" not in self.output_channels + and "Phase2D" not in self.output_channels + ): + super().__setattr__("qlipp_birefringence_only", True) def read_config(self, cfg_path, data_dir, save_dir, method, mode, name): @@ -244,6 +317,6 @@ def read_config(self, cfg_path, data_dir, save_dir, method, mode, name): self._check_assertions(data_dir, save_dir, method, mode, name) self._parse_dataset() self._parse_processing() - + if not self.data_save_name: self._use_default_name() diff --git a/recOrder/io/core_functions.py b/recOrder/io/core_functions.py index 8c432512..2c12529b 100644 --- a/recOrder/io/core_functions.py +++ b/recOrder/io/core_functions.py @@ -18,7 +18,9 @@ def snap_image(mmc): """ mmc.snapImage() - time.sleep(0.3) # sleep after snap to make sure the image we grab is the correct one + time.sleep( + 0.3 + ) # sleep after snap to make sure the image we grab is the correct one return mmc.getImage() @@ -65,12 +67,16 @@ def snap_and_get_image(snap_manager): """ snap_manager.snap(True) - time.sleep(0.3) # sleep after snap to make sure the image we grab is the correct one + time.sleep( + 0.3 + ) # sleep after snap to make sure the image we grab is the correct one # get pixels + dimensions height = snap_manager.getDisplay().getDisplayedImages().get(0).getHeight() width = snap_manager.getDisplay().getDisplayedImages().get(0).getWidth() - array = snap_manager.getDisplay().getDisplayedImages().get(0).getRawPixels() + array = ( + snap_manager.getDisplay().getDisplayedImages().get(0).getRawPixels() + ) return np.reshape(array, (height, width)) @@ -91,7 +97,9 @@ def snap_and_average(snap_manager, display=True): """ snap_manager.snap(display) - time.sleep(0.3) # sleep after snap to make sure the image we grab is the correct one + time.sleep( + 0.3 + ) # sleep after snap to make sure the image we grab is the correct one return snap_manager.getDisplay().getImagePlus().getStatistics().umean @@ -117,11 +125,13 @@ def set_lc_waves(mmc, device_property: tuple, value: float): prop_name = device_property[1] if value > 1.6 or value < 0.001: - raise ValueError(f"Requested retardance value is {value} waves. " - f"Retardance must be greater than 0.001 and less than 1.6 waves.") + raise ValueError( + f"Requested retardance value is {value} waves. " + f"Retardance must be greater than 0.001 and less than 1.6 waves." + ) mmc.setProperty(device_name, prop_name, str(value)) - time.sleep(20/1000) + time.sleep(20 / 1000) def set_lc_voltage(mmc, device_property: tuple, value: float): @@ -145,8 +155,10 @@ def set_lc_voltage(mmc, device_property: tuple, value: float): prop_name = device_property[1] if value > 20.0 or value < 0.0: - raise ValueError(f"Requested LC voltage is {value} V. " - f"LC voltage must be greater than 0.0 and less than 20.0 V.") + raise ValueError( + f"Requested LC voltage is {value} V. " + f"LC voltage must be greater than 0.0 and less than 20.0 V." + ) mmc.setProperty(device_name, prop_name, str(value)) time.sleep(20 / 1000) @@ -173,7 +185,9 @@ def set_lc_daq(mmc, device_property: tuple, value: float): prop_name = device_property[1] if value > 5.0 or value < 0.0: - raise ValueError("DAC voltage must be greater than 0.0 and less than 5.0") + raise ValueError( + "DAC voltage must be greater than 0.0 and less than 5.0" + ) mmc.setProperty(device_name, prop_name, str(value)) time.sleep(20 / 1000) @@ -229,7 +243,9 @@ def define_meadowlark_state(mmc, device_property: tuple): mmc.waitForDevice(device_name) -def define_config_state(mmc, group: str, config: str, device_properties: list, values: list): +def define_config_state( + mmc, group: str, config: str, device_properties: list, values: list +): """ Define config state by specifying the values for all device properties in this config @@ -274,4 +290,4 @@ def set_lc_state(mmc, group: str, config: str): """ mmc.setConfig(group, config) - time.sleep(20/1000) # delay for LC settle time \ No newline at end of file + time.sleep(20 / 1000) # delay for LC settle time diff --git a/recOrder/io/metadata_reader.py b/recOrder/io/metadata_reader.py index a77230d7..65fce0b0 100644 --- a/recOrder/io/metadata_reader.py +++ b/recOrder/io/metadata_reader.py @@ -4,14 +4,20 @@ def load_json(path): - with open(path, 'r') as f: + with open(path, "r") as f: data = json.load(f) return data def get_last_metadata_file(path): - last_metadata_file = natsorted([file for file in os.listdir(path) if file.startswith('calibration_metadata')])[-1] + last_metadata_file = natsorted( + [ + file + for file in os.listdir(path) + if file.startswith("calibration_metadata") + ] + )[-1] return os.path.join(path, last_metadata_file) @@ -30,38 +36,42 @@ def __init__(self, path: str): self.metadata_path = path self.json_metadata = load_json(self.metadata_path) - self.Timestamp = self.get_summary_calibration_attr('Timestamp') - self.recOrder_napari_verion = self.get_summary_calibration_attr('recOrder-napari version') - self.waveorder_version = self.get_summary_calibration_attr('waveorder version') + self.Timestamp = self.get_summary_calibration_attr("Timestamp") + self.recOrder_napari_verion = self.get_summary_calibration_attr( + "recOrder-napari version" + ) + self.waveorder_version = self.get_summary_calibration_attr( + "waveorder version" + ) self.Calibration_scheme = self.get_calibration_scheme() self.Swing = self.get_swing() - self.Wavelength = self.get_summary_calibration_attr('Wavelength (nm)') + self.Wavelength = self.get_summary_calibration_attr("Wavelength (nm)") self.Black_level = self.get_black_level() self.Extinction_ratio = self.get_extinction_ratio() - self.ROI = tuple(self.get_roi()) # JSON does not preserve tuples + self.ROI = tuple(self.get_roi()) # JSON does not preserve tuples self.Channel_names = self.get_channel_names() - self.LCA_retardance = self.get_lc_retardance('LCA') - self.LCB_retardance = self.get_lc_retardance('LCB') - self.LCA_voltage = self.get_lc_voltage('LCA') - self.LCB_voltage = self.get_lc_voltage('LCB') + self.LCA_retardance = self.get_lc_retardance("LCA") + self.LCB_retardance = self.get_lc_retardance("LCB") + self.LCA_voltage = self.get_lc_voltage("LCA") + self.LCB_voltage = self.get_lc_voltage("LCB") self.Swing_measured = self.get_swing_measured() self.Notes = self.get_notes() def get_summary_calibration_attr(self, attr): try: - val = self.json_metadata['Summary'][attr] + val = self.json_metadata["Summary"][attr] except KeyError: try: - val = self.json_metadata['Calibration'][attr] + val = self.json_metadata["Calibration"][attr] except KeyError: val = None return val def get_cal_states(self): - if self.Calibration_scheme == '4-State': - states = ['ext', '0', '60', '120'] - elif self.Calibration_scheme == '5-State': - states = ['ext', '0', '45', '90', '135'] + if self.Calibration_scheme == "4-State": + states = ["ext", "0", "60", "120"] + elif self.Calibration_scheme == "5-State": + states = ["ext", "0", "45", "90", "135"] return states def get_lc_retardance(self, lc): @@ -79,13 +89,28 @@ def get_lc_retardance(self, lc): val = None try: - val = [self.json_metadata['Calibration']['LC retardance'][f'{lc}_{state}'] for state in states] + val = [ + self.json_metadata["Calibration"]["LC retardance"][ + f"{lc}_{state}" + ] + for state in states + ] except KeyError: - states[0] = 'Ext' - if lc == 'LCA': - val = [self.json_metadata['Summary'][f'[LCA_{state}, LCB_{state}]'][0] for state in states] - elif lc == 'LCB': - val = [self.json_metadata['Summary'][f'[LCA_{state}, LCB_{state}]'][1] for state in states] + states[0] = "Ext" + if lc == "LCA": + val = [ + self.json_metadata["Summary"][ + f"[LCA_{state}, LCB_{state}]" + ][0] + for state in states + ] + elif lc == "LCB": + val = [ + self.json_metadata["Summary"][ + f"[LCA_{state}, LCB_{state}]" + ][1] + for state in states + ] return val @@ -103,68 +128,82 @@ def get_lc_voltage(self, lc): states = self.get_cal_states() val = None - if 'Calibration' in self.json_metadata: - lc_voltage = self.json_metadata['Calibration']['LC voltage'] + if "Calibration" in self.json_metadata: + lc_voltage = self.json_metadata["Calibration"]["LC voltage"] if lc_voltage: - val = [self.json_metadata['Calibration']['LC voltage'][f'{lc}_{state}'] for state in states] + val = [ + self.json_metadata["Calibration"]["LC voltage"][ + f"{lc}_{state}" + ] + for state in states + ] return val def get_swing(self): try: - val = self.json_metadata['Calibration']['Swing (waves)'] + val = self.json_metadata["Calibration"]["Swing (waves)"] except KeyError: - val = self.json_metadata['Summary']['Swing (fraction)'] + val = self.json_metadata["Summary"]["Swing (fraction)"] return val def get_swing_measured(self): states = self.get_cal_states() try: - val = [self.json_metadata['Calibration'][f'Swing_{state}'] for state in states[1:]] + val = [ + self.json_metadata["Calibration"][f"Swing_{state}"] + for state in states[1:] + ] except KeyError: - val = [self.json_metadata['Summary'][f'Swing{state}'] for state in states[1:]] + val = [ + self.json_metadata["Summary"][f"Swing{state}"] + for state in states[1:] + ] return val def get_calibration_scheme(self): try: - val = self.json_metadata['Calibration']['Calibration scheme'] + val = self.json_metadata["Calibration"]["Calibration scheme"] except KeyError: - val = self.json_metadata['Summary']['Acquired Using'] + val = self.json_metadata["Summary"]["Acquired Using"] return val def get_black_level(self): try: - val = self.json_metadata['Calibration']['Black level'] + val = self.json_metadata["Calibration"]["Black level"] except KeyError: - val = self.json_metadata['Summary']['BlackLevel'] + val = self.json_metadata["Summary"]["BlackLevel"] return val def get_extinction_ratio(self): try: - val = self.json_metadata['Calibration']['Extinction ratio'] + val = self.json_metadata["Calibration"]["Extinction ratio"] except KeyError: - val = self.json_metadata['Summary']['Extinction Ratio'] + val = self.json_metadata["Summary"]["Extinction Ratio"] return val def get_roi(self): try: - val = self.json_metadata['Calibration']['ROI (x, y, width, height)'] + val = self.json_metadata["Calibration"][ + "ROI (x, y, width, height)" + ] except KeyError: - val = self.json_metadata['Summary']['ROI Used (x, y, width, height)'] + val = self.json_metadata["Summary"][ + "ROI Used (x, y, width, height)" + ] return val def get_channel_names(self): try: - val = self.json_metadata['Calibration']['Channel names'] + val = self.json_metadata["Calibration"]["Channel names"] except KeyError: - val = self.json_metadata['Summary']['ChNames'] + val = self.json_metadata["Summary"]["ChNames"] return val def get_notes(self): try: - val = self.json_metadata['Notes'] + val = self.json_metadata["Notes"] except KeyError: val = None return val - diff --git a/recOrder/io/utils.py b/recOrder/io/utils.py index 7ab08c87..00eb91ca 100644 --- a/recOrder/io/utils.py +++ b/recOrder/io/utils.py @@ -282,7 +282,9 @@ def generic_hsv_overlay( return overlay_final[0] if mode == "2D" else overlay_final -def ret_ori_overlay(retardance, orientation, ret_max = 10, mode="2D", cmap="JCh"): +def ret_ori_overlay( + retardance, orientation, ret_max=10, mode="2D", cmap="JCh" +): """ This function will create an overlay of retardance and orientation with two different colormap options. HSV is the standard Hue, Saturation, Value colormap while JCh is a similar colormap but is perceptually uniform. diff --git a/recOrder/io/zarr_converter.py b/recOrder/io/zarr_converter.py index b030f42e..cae4c775 100644 --- a/recOrder/io/zarr_converter.py +++ b/recOrder/io/zarr_converter.py @@ -17,7 +17,14 @@ class ZarrConverter: for tiled acquisitions) """ - def __init__(self, input_dir, output_dir, data_type=None, replace_position_names=False, format_hcs=False): + def __init__( + self, + input_dir, + output_dir, + data_type=None, + replace_position_names=False, + format_hcs=False, + ): """ Parameters @@ -32,26 +39,33 @@ def __init__(self, input_dir, output_dir, data_type=None, replace_position_names format_hcs: bool """ - if not output_dir.endswith('.zarr'): - raise ValueError('Please specify .zarr at the end of your output') + if not output_dir.endswith(".zarr"): + raise ValueError("Please specify .zarr at the end of your output") # Init File IO Properties - self.version = 'recOrder converter version=0.5' + self.version = "recOrder converter version=0.5" self.data_directory = input_dir self.save_directory = os.path.dirname(output_dir) # self.files = glob.glob(os.path.join(self.data_directory, '.tif')) self.meta_file = None - print('Initializing Data...') - self.reader = WaveorderReader(self.data_directory, data_type, extract_data=False) + print("Initializing Data...") + self.reader = WaveorderReader( + self.data_directory, data_type, extract_data=False + ) self.data_type = self.reader.data_type - print('Finished initializing data') + print("Finished initializing data") - self.summary_metadata = self.reader.mm_meta['Summary'] if self.reader.mm_meta else None + self.summary_metadata = ( + self.reader.mm_meta["Summary"] if self.reader.mm_meta else None + ) self.save_name = os.path.basename(output_dir) - if self.data_type != 'upti': - self.mfile_name = os.path.join(self.save_directory, f'{self.save_name.strip(".zarr")}_ImagePlaneMetadata.txt') - self.meta_file = open(self.mfile_name, 'a') + if self.data_type != "upti": + self.mfile_name = os.path.join( + self.save_directory, + f'{self.save_name.strip(".zarr")}_ImagePlaneMetadata.txt', + ) + self.meta_file = open(self.mfile_name, "a") self.replace_position_names = replace_position_names self.format_hcs = format_hcs @@ -78,19 +92,28 @@ def __init__(self, input_dir, output_dir, data_type=None, replace_position_names self.dim = (self.p, self.t, self.c, self.z, self.y, self.x) self.focus_z = self.z // 2 self.prefix_list = [] - print(f'Found Dataset {self.save_name} w/ dimensions (P, T, C, Z, Y, X): {self.dim}') + print( + f"Found Dataset {self.save_name} w/ dimensions (P, T, C, Z, Y, X): {self.dim}" + ) # Generate coordinate set self._gen_coordset() # Initialize Metadata Dictionary self.metadata = dict() - self.metadata['recOrder_Converter_Version'] = self.version - self.metadata['Summary'] = self.summary_metadata + self.metadata["recOrder_Converter_Version"] = self.version + self.metadata["Summary"] = self.summary_metadata # initialize metadata if HCS desired, init writer - self.hcs_meta = self._generate_hcs_metadata() if self.format_hcs else None - self.writer = WaveorderWriter(self.save_directory, hcs=self.format_hcs, hcs_meta=self.hcs_meta, verbose=False) + self.hcs_meta = ( + self._generate_hcs_metadata() if self.format_hcs else None + ) + self.writer = WaveorderWriter( + self.save_directory, + hcs=self.format_hcs, + hcs_meta=self.hcs_meta, + verbose=False, + ) self.writer.create_zarr_root(self.save_name) def _gen_coordset(self): @@ -105,28 +128,38 @@ def _gen_coordset(self): """ # if acquisition information is not present, make an arbitrary dimension order - if not self.summary_metadata or 'AxisOrder' not in self.summary_metadata.keys(): + if ( + not self.summary_metadata + or "AxisOrder" not in self.summary_metadata.keys() + ): self.p_dim = 0 self.t_dim = 1 self.c_dim = 2 self.z_dim = 3 - self.dim_order = ['position', 'time', 'channel', 'z'] + self.dim_order = ["position", "time", "channel", "z"] # Assume data was collected slice first - dims = [self.reader.slices, self.reader.channels, self.reader.frames, self.reader.get_num_positions()] + dims = [ + self.reader.slices, + self.reader.channels, + self.reader.frames, + self.reader.get_num_positions(), + ] # get the order in which the data was collected to minimize i/o calls else: # 4 possible dimensions: p, c, t, z n_dim = 4 - hashmap = {'position': self.p, - 'time': self.t, - 'channel': self.c, - 'z': self.z} + hashmap = { + "position": self.p, + "time": self.t, + "channel": self.c, + "z": self.z, + } - self.dim_order = copy.copy(self.summary_metadata['AxisOrder']) + self.dim_order = copy.copy(self.summary_metadata["AxisOrder"]) dims = [] for i in range(n_dim): @@ -137,14 +170,19 @@ def _gen_coordset(self): # Reverse the dimension order and gather dimension indices self.dim_order.reverse() - self.p_dim = self.dim_order.index('position') - self.t_dim = self.dim_order.index('time') - self.c_dim = self.dim_order.index('channel') - self.z_dim = self.dim_order.index('z') + self.p_dim = self.dim_order.index("position") + self.t_dim = self.dim_order.index("time") + self.c_dim = self.dim_order.index("channel") + self.z_dim = self.dim_order.index("z") # create array of coordinate tuples with innermost dimension being the first dim acquired - self.coords = [(dim3, dim2, dim1, dim0) for dim3 in range(dims[3]) for dim2 in range(dims[2]) - for dim1 in range(dims[1]) for dim0 in range(dims[0])] + self.coords = [ + (dim3, dim2, dim1, dim0) + for dim3 in range(dims[3]) + for dim2 in range(dims[2]) + for dim1 in range(dims[1]) + for dim0 in range(dims[0]) + ] def _get_position_coords(self): @@ -152,16 +190,16 @@ def _get_position_coords(self): col_max = 0 coords_list = [] - #TODO: read rows, cols directly from XY corods - #TODO: account for non MM2gamma meta? + # TODO: read rows, cols directly from XY corods + # TODO: account for non MM2gamma meta? for idx, pos in enumerate(self.reader.stage_positions): - coords_list.append(pos['XYStage']) - row = pos['GridRow'] - col = pos['GridCol'] + coords_list.append(pos["XYStage"]) + row = pos["GridRow"] + col = pos["GridCol"] row_max = row if row > row_max else row_max col_max = col if col > col_max else col_max - return coords_list, row_max+1, col_max+1 + return coords_list, row_max + 1, col_max + 1 def _generate_hcs_metadata(self): @@ -170,20 +208,31 @@ def _generate_hcs_metadata(self): position_grid = create_grid_from_coordinates(position_list, rows, cols) # Build metadata based off of position grid - hcs_meta = {'plate': { - 'acquisitions': [{'id': 1, - 'maximumfieldcount': 1, - 'name': 'Dataset', - 'starttime': 0}], - 'columns': [{'name': f'Col_{i}'} for i in range(cols)], - - 'field_count': 1, - 'name': 'name', - 'rows': [{'name': f'Row_{i}'} for i in range(rows)], - 'version': '0.1', - 'wells': [{'path': f'Row_{i}/Col_{j}'} for i in range(rows) for j in range(cols)]}, - - 'well': [{'images': [{'path': f'Pos_{pos:03d}'}]} for pos in position_grid.flatten()] + hcs_meta = { + "plate": { + "acquisitions": [ + { + "id": 1, + "maximumfieldcount": 1, + "name": "Dataset", + "starttime": 0, + } + ], + "columns": [{"name": f"Col_{i}"} for i in range(cols)], + "field_count": 1, + "name": "name", + "rows": [{"name": f"Row_{i}"} for i in range(rows)], + "version": "0.1", + "wells": [ + {"path": f"Row_{i}/Col_{j}"} + for i in range(rows) + for j in range(cols) + ], + }, + "well": [ + {"images": [{"path": f"Pos_{pos:03d}"}]} + for pos in position_grid.flatten() + ], } return hcs_meta @@ -206,7 +255,7 @@ def _generate_plane_metadata(self, tiff_file, page): """ for tag in tiff_file.pages[page].tags.values(): - if tag.name == 'MicroManagerMetadata': + if tag.name == "MicroManagerMetadata": return tag.value else: continue @@ -227,10 +276,12 @@ def _perform_image_check(self, tiff_image, coord): """ - zarr_array = self.writer.sub_writer.current_pos_group['arr_0'] - zarr_img = zarr_array[coord[self.dim_order.index('time')], - coord[self.dim_order.index('channel')], - coord[self.dim_order.index('z')]] + zarr_array = self.writer.sub_writer.current_pos_group["arr_0"] + zarr_img = zarr_array[ + coord[self.dim_order.index("time")], + coord[self.dim_order.index("channel")], + coord[self.dim_order.index("z")], + ] return np.array_equal(zarr_img, tiff_image) @@ -259,11 +310,11 @@ def _get_position_names(self): for p in range(self.p): if self.p > 1: try: - name = self.summary_metadata['StagePositions'][p]['Label'] + name = self.summary_metadata["StagePositions"][p]["Label"] except KeyError: - name = '' + name = "" else: - name = '' + name = "" self.pos_names.append(name) def check_file_changed(self, last_file, current_file): @@ -323,8 +374,8 @@ def get_channel_clims(self, pos): for chan in range(self.c): img = self.get_image_array(pos, t=0, c=chan, z=self.focus_z) clip = 0.01 - low = np.percentile(img, clip100) - high = np.percentile(img, (1-clip)100) + low = np.percentile(img, clip 100) + high = np.percentile(img, (1 - clip) * 100) clims.append((low, high)) return clims @@ -342,24 +393,27 @@ def init_zarr_structure(self): """ - chan_names = self._get_channel_names() self._get_position_names() for pos in range(self.p): clims = self.get_channel_clims(pos) name = self.pos_names[pos] if self.replace_position_names else None - self.writer.init_array(pos, - data_shape=(self.t if self.t != 0 else 1, - self.c if self.c != 0 else 1, - self.z if self.z != 0 else 1, - self.y, - self.x), - chunk_size=(1, 1, 1, self.y, self.x), - chan_names=chan_names, - clims=clims, - dtype=self.dtype, - position_name=name) + self.writer.init_array( + pos, + data_shape=( + self.t if self.t != 0 else 1, + self.c if self.c != 0 else 1, + self.z if self.z != 0 else 1, + self.y, + self.x, + ), + chunk_size=(1, 1, 1, self.y, self.x), + chan_names=chan_names, + clims=clims, + dtype=self.dtype, + position_name=name, + ) def run_conversion(self): """ @@ -372,25 +426,27 @@ def run_conversion(self): """ # Run setup - print('Running Conversion...') - print('Setting up zarr') + print("Running Conversion...") + print("Setting up zarr") # self._gather_index_maps() self.init_zarr_structure() last_file = None - #Format bar for CLI display - bar_format = 'Status: \|{bar}\|{n_fmt}/{total_fmt} (Time Remaining: {remaining}), {rate_fmt}{postfix}]' + # Format bar for CLI display + bar_format = "Status: \|{bar}\|{n_fmt}/{total_fmt} (Time Remaining: {remaining}), {rate_fmt}{postfix}]" # Run through every coordinate and convert image + grab image metadata, statistics # loop is done in order in which the images were acquired - print('Converting Images...') + print("Converting Images...") for coord in tqdm(self.coords, bar_format=bar_format): - if self.data_type == 'ometiff': + if self.data_type == "ometiff": # re-order coordinates into zarr format - coord_reorder = (coord[self.p_dim], - coord[self.t_dim], - coord[self.c_dim], - coord[self.z_dim]) + coord_reorder = ( + coord[self.p_dim], + coord[self.t_dim], + coord[self.c_dim], + coord[self.z_dim], + ) # Only load tiff file if it has changed from previous run current_file = self.reader.reader.coord_map[coord_reorder][0] @@ -403,29 +459,49 @@ def run_conversion(self): meta = dict() plane_meta = self._generate_plane_metadata(tf, page) - meta[f'{coord_reorder}'] = plane_meta + meta[f"{coord_reorder}"] = plane_meta json.dump(meta, self.meta_file, indent=1) - elif self.data_type == 'pycromanager': + elif self.data_type == "pycromanager": # write page metadata - plane_metadata = self.reader.reader.get_image_metadata(coord[self.p_dim], - coord[self.t_dim], - coord[self.c_dim], - coord[self.z_dim]) - - json.dump({f'FrameKey-{coord[self.p_dim]}-{coord[self.t_dim]}-' - f'{coord[self.c_dim]}-{coord[self.z_dim]}': plane_metadata}, - self.meta_file, indent=1) + plane_metadata = self.reader.reader.get_image_metadata( + coord[self.p_dim], + coord[self.t_dim], + coord[self.c_dim], + coord[self.z_dim], + ) + + json.dump( + { + f"FrameKey-{coord[self.p_dim]}-{coord[self.t_dim]}-" + f"{coord[self.c_dim]}-{coord[self.z_dim]}": plane_metadata + }, + self.meta_file, + indent=1, + ) # get the memory mapped image - img_raw = self.get_image_array(coord[self.p_dim], coord[self.t_dim], coord[self.c_dim], coord[self.z_dim]) + img_raw = self.get_image_array( + coord[self.p_dim], + coord[self.t_dim], + coord[self.c_dim], + coord[self.z_dim], + ) # Write the data - self.writer.write(img_raw, coord[self.p_dim], coord[self.t_dim], coord[self.c_dim], coord[self.z_dim]) + self.writer.write( + img_raw, + coord[self.p_dim], + coord[self.t_dim], + coord[self.c_dim], + coord[self.z_dim], + ) # Perform image check if not self._perform_image_check(img_raw, coord): - raise ValueError('Converted zarr image does not match the raw data. Conversion Failed') + raise ValueError( + "Converted zarr image does not match the raw data. Conversion Failed" + ) # Put summary metadata into zarr store and cleanup self.writer.store.attrs.update(self.metadata) diff --git a/recOrder/pipelines/base.py b/recOrder/pipelines/base.py index 2c4b6264..38d89d99 100644 --- a/recOrder/pipelines/base.py +++ b/recOrder/pipelines/base.py @@ -20,5 +20,7 @@ def reconstruct_birefringence_volume(self, stokes): pass @abstractmethod - def write_data(self, p, t, pt_data, stokes, birefringence, deconvolve2D, deconvolve3D): - pass \ No newline at end of file + def write_data( + self, p, t, pt_data, stokes, birefringence, deconvolve2D, deconvolve3D + ): + pass diff --git a/recOrder/pipelines/phase_from_bf_pipeline.py b/recOrder/pipelines/phase_from_bf_pipeline.py index 19aa1b46..9124bb37 100644 --- a/recOrder/pipelines/phase_from_bf_pipeline.py +++ b/recOrder/pipelines/phase_from_bf_pipeline.py @@ -2,13 +2,24 @@ from waveorder.io.reader import WaveorderReader from waveorder.io.writer import WaveorderWriter from recOrder.io.utils import MockEmitter -from recOrder.compute.qlipp_compute import reconstruct_phase2D, reconstruct_phase3D, initialize_reconstructor +from recOrder.compute.qlipp_compute import ( + reconstruct_phase2D, + reconstruct_phase3D, + initialize_reconstructor, +) import numpy as np from recOrder.pipelines.base import PipelineInterface -class PhaseFromBF(PipelineInterface): - def __init__(self, config: ConfigReader, data: WaveorderReader, writer: WaveorderWriter, num_t: int, emitter=MockEmitter()): +class PhaseFromBF(PipelineInterface): + def __init__( + self, + config: ConfigReader, + data: WaveorderReader, + writer: WaveorderWriter, + num_t: int, + emitter=MockEmitter(), + ): # Dataset Parameters self.config = config @@ -22,37 +33,44 @@ def __init__(self, config: ConfigReader, data: WaveorderReader, writer: Waveorde self.t = num_t self.output_channels = self.config.output_channels self._check_output_channels(self.output_channels) - self.mode = '2D' if 'Phase2D' in self.output_channels else '3D' + self.mode = "2D" if "Phase2D" in self.output_channels else "3D" self.bf_chan_idx = self.config.brightfield_channel_index self.slices = self.data.slices self.focus_slice = None - if self.mode == '2D': + if self.mode == "2D": self.slices = 1 self.focus_slice = self.config.focus_zidx # Set image dimensions / writer parameters self.img_dim = (self.data.height, self.data.width, self.data.slices) - self.data_shape = (self.t, len(self.output_channels), self.slices, self.img_dim[0], self.img_dim[1]) + self.data_shape = ( + self.t, + len(self.output_channels), + self.slices, + self.img_dim[0], + self.img_dim[1], + ) self.chunk_size = (1, 1, 1, self.data_shape[-2], self.data_shape[-1]) # Initialize Reconstructor - self.reconstructor = initialize_reconstructor(pipeline='PhaseFromBF', - image_dim=(self.img_dim[0], self.img_dim[1]), - wavelength_nm=self.config.wavelength, - NA_obj=self.config.NA_objective, - NA_illu=self.config.NA_condenser, - n_obj_media=self.config.n_objective_media, - mag=self.config.magnification, - n_slices=self.data.slices, - z_step_um=self.data.z_step_size, - pad_z=self.config.pad_z, - pixel_size_um=self.config.pixel_size, - mode=self.mode, - use_gpu=self.config.use_gpu, - gpu_id=self.config.gpu_id) - + self.reconstructor = initialize_reconstructor( + pipeline="PhaseFromBF", + image_dim=(self.img_dim[0], self.img_dim[1]), + wavelength_nm=self.config.wavelength, + NA_obj=self.config.NA_objective, + NA_illu=self.config.NA_condenser, + n_obj_media=self.config.n_objective_media, + mag=self.config.magnification, + n_slices=self.data.slices, + z_step_um=self.data.z_step_size, + pad_z=self.config.pad_z, + pixel_size_um=self.config.pixel_size, + mode=self.mode, + use_gpu=self.config.use_gpu, + gpu_id=self.config.gpu_id, + ) def _check_output_channels(self, output_channels): """ @@ -69,12 +87,14 @@ def _check_output_channels(self, output_channels): """ for channel in output_channels: - if 'Phase3D' in channel: + if "Phase3D" in channel: continue - elif 'Phase2D' in channel: + elif "Phase2D" in channel: continue - elif 'Phase3D' in channel and 'Phase2D' in channel: - raise KeyError('Simultaneous 2D and 3D phase reconstruction not supported') + elif "Phase3D" in channel and "Phase2D" in channel: + raise KeyError( + "Simultaneous 2D and 3D phase reconstruction not supported" + ) else: continue @@ -124,19 +144,33 @@ def deconvolve_volume(self, bf_data): phase2D = None phase3D = None - if 'Phase3D' in self.output_channels: - phase3D = reconstruct_phase3D(bf_data, self.reconstructor, method=self.config.phase_denoiser_3D, - reg_re=self.config.Tik_reg_ph_3D, rho=self.config.rho_3D, - lambda_re=self.config.TV_reg_ph_3D, itr=self.config.itr_3D) - - if 'Phase2D' in self.output_channels: - phase2D = reconstruct_phase2D(bf_data, self.reconstructor, method=self.config.phase_denoiser_2D, - reg_p=self.config.Tik_reg_ph_2D, rho=self.config.rho_2D, - lambda_p=self.config.TV_reg_ph_2D, itr=self.config.itr_2D) + if "Phase3D" in self.output_channels: + phase3D = reconstruct_phase3D( + bf_data, + self.reconstructor, + method=self.config.phase_denoiser_3D, + reg_re=self.config.Tik_reg_ph_3D, + rho=self.config.rho_3D, + lambda_re=self.config.TV_reg_ph_3D, + itr=self.config.itr_3D, + ) + + if "Phase2D" in self.output_channels: + phase2D = reconstruct_phase2D( + bf_data, + self.reconstructor, + method=self.config.phase_denoiser_2D, + reg_p=self.config.Tik_reg_ph_2D, + rho=self.config.rho_2D, + lambda_p=self.config.TV_reg_ph_2D, + itr=self.config.itr_2D, + ) return phase2D, phase3D - def write_data(self, p, t, pt_data, stokes, birefringence, phase2D, phase3D): + def write_data( + self, p, t, pt_data, stokes, birefringence, phase2D, phase3D + ): """ This function will iteratively write the data into its proper position, time, channel, z index. Dimensions differ between data type to make compute easier with waveOrder backend. @@ -150,19 +184,19 @@ def write_data(self, p, t, pt_data, stokes, birefringence, phase2D, phase3D): birefringence: (nd-array) None or nd-array w/ dimensions (C, Z, Y, X) phase2D: (nd-array) None or nd-array w/ dimensions (Y, X) phase3D: (nd-array) None or nd-array w/ dimensions (Z, Y, X) - + Returns ------- Writes a zarr array to to given save directory. """ - z = 0 if self.mode == '2D' else None - slice_ = self.focus_slice if self.mode == '2D' else slice(None) + z = 0 if self.mode == "2D" else None + slice_ = self.focus_slice if self.mode == "2D" else slice(None) for chan in range(len(self.output_channels)): - if 'Phase3D' in self.output_channels[chan]: + if "Phase3D" in self.output_channels[chan]: self.writer.write(phase3D, p=p, t=t, c=chan, z=z) - elif 'Phase2D' in self.output_channels[chan]: + elif "Phase2D" in self.output_channels[chan]: self.writer.write(phase2D, p=p, t=t, c=chan, z=z) - self.dimension_emitter.emit((p, t, chan)) \ No newline at end of file + self.dimension_emitter.emit((p, t, chan)) diff --git a/recOrder/pipelines/pipeline_manager.py b/recOrder/pipelines/pipeline_manager.py index 4289f6e1..bd9ff77e 100644 --- a/recOrder/pipelines/pipeline_manager.py +++ b/recOrder/pipelines/pipeline_manager.py @@ -18,18 +18,25 @@ class PipelineManager: """ - def __init__(self, config: ConfigReader, overwrite: bool = False, emitter=MockEmitter()): + def __init__( + self, + config: ConfigReader, + overwrite: bool = False, + emitter=MockEmitter(), + ): self._check_ram() - + start = time.time() - print('Reading Data...') + print("Reading Data...") data = WaveorderReader(config.data_dir, extract_data=True) end = time.time() - print(f'Finished Reading Data ({(end - start) / 60 :0.1f} min)') + print(f"Finished Reading Data ({(end - start) / 60 :0.1f} min)") self.config = config self.data = data - self.use_hcs = True if isinstance(self.data.reader, ZarrReader) else False + self.use_hcs = ( + True if isinstance(self.data.reader, ZarrReader) else False + ) self._gen_coord_set() @@ -45,25 +52,47 @@ def __init__(self, config: ConfigReader, overwrite: bool = False, emitter=MockEm # Delete previous data if overwrite is true, helpful for making quick config changes since the writer # doesn't by default allow you to overwrite data if overwrite: - path = os.path.join(self.config.save_dir, self.config.data_save_name) - path = path+'.zarr' if not path.endswith('.zarr') else path + path = os.path.join( + self.config.save_dir, self.config.data_save_name + ) + path = path + ".zarr" if not path.endswith(".zarr") else path if os.path.exists(path): shutil.rmtree(path) # Writer Parameters - self.writer = WaveorderWriter(self.config.save_dir, hcs=self.use_hcs, hcs_meta=self.hcs_meta, verbose=False) + self.writer = WaveorderWriter( + self.config.save_dir, + hcs=self.use_hcs, + hcs_meta=self.hcs_meta, + verbose=False, + ) self.writer.create_zarr_root(self.config.data_save_name) # Pipeline Initiation - if self.config.method == 'QLIPP': - self.pipeline = QLIPP(self.config, self.data, self.writer, self.config.mode, self.num_t, emitter=emitter) - - elif self.config.method == 'PhaseFromBF': - self.pipeline = PhaseFromBF(self.config, self.data, self.writer, self.num_t, emitter=emitter) + if self.config.method == "QLIPP": + self.pipeline = QLIPP( + self.config, + self.data, + self.writer, + self.config.mode, + self.num_t, + emitter=emitter, + ) + + elif self.config.method == "PhaseFromBF": + self.pipeline = PhaseFromBF( + self.config, + self.data, + self.writer, + self.num_t, + emitter=emitter, + ) else: - raise NotImplementedError(f'Method {self.config.method} is not currently implemented ' - 'please specify one of the following: QLIPP or PhaseFromBF') + raise NotImplementedError( + f"Method {self.config.method} is not currently implemented " + "please specify one of the following: QLIPP or PhaseFromBF" + ) def _check_ram(self): """ @@ -99,10 +128,10 @@ def _update_hcs_meta_from_config(self, hcs_meta): for idx, p in enumerate(self.p_indices): # for every position, grab it from the original HCS metadata and place it into new list - well_meta = meta_new['well'] + well_meta = meta_new["well"] well_meta_new[idx] = well_meta[p] - wells = meta_new['plate']['wells'] + wells = meta_new["plate"]["wells"] wells_new[idx] = wells[p] # Filter out any blank entries @@ -113,18 +142,18 @@ def _update_hcs_meta_from_config(self, hcs_meta): rows_new = [] cols_new = [] for well in wells_new: - split = well['path'].split('/') - if not {'name': split[0]} in rows_new: - rows_new.append({'name': split[0]}) + split = well["path"].split("/") + if not {"name": split[0]} in rows_new: + rows_new.append({"name": split[0]}) - if not {'name': split[1]} in cols_new: - cols_new.append({'name': split[1]}) + if not {"name": split[1]} in cols_new: + cols_new.append({"name": split[1]}) # Group all of the metadata together in one dictionary - meta_new['plate']['rows'] = rows_new - meta_new['plate']['columns'] = cols_new - meta_new['plate']['wells'] = wells_new - meta_new['well'] = well_meta_new + meta_new["plate"]["rows"] = rows_new + meta_new["plate"]["columns"] = cols_new + meta_new["plate"]["wells"] = wells_new + meta_new["well"] = well_meta_new return meta_new @@ -146,7 +175,7 @@ def _gen_coord_set(self): # run through the different possible config specifications (ranges, single entries, 'all', etc.) cnt = 0 for p_entry in self.config.positions: - if p_entry == 'all': + if p_entry == "all": for p in range(self.data.get_num_positions()): p_indices.add(p) self.indices_map[p] = cnt @@ -167,11 +196,13 @@ def _gen_coord_set(self): self.indices_map[p] = cnt cnt += 1 else: - raise ValueError(f'Did not understand entry {p_entry} in config specified positions') + raise ValueError( + f"Did not understand entry {p_entry} in config specified positions" + ) # run through the different possible config specifications (ranges, single entries, 'all', etc.) for t_entry in self.config.timepoints: - if t_entry == 'all': + if t_entry == "all": for t in range(self.data.frames): t_indices.add(t) break @@ -181,10 +212,12 @@ def _gen_coord_set(self): for t in t_entry: t_indices.add(t) elif isinstance(t_entry, tuple): - for t in range(t_entry[0],t_entry[1]): + for t in range(t_entry[0], t_entry[1]): t_indices.add(t) else: - raise ValueError(f'Did not understand entry {t_entry} in config specified positions') + raise ValueError( + f"Did not understand entry {t_entry} in config specified positions" + ) self.num_t = len(t_indices) self.num_p = len(p_indices) @@ -201,48 +234,63 @@ def try_init_array(self, pt): # If not doing the full position, we still want semantic information on which positions # were reconstructed, so append the filename to the position (which would have otherwise been arbitrary) if not self.hcs_meta: - name = f'Pos_{pt[0]:03d}' + name = f"Pos_{pt[0]:03d}" else: name = None - self.pipeline.writer.init_array(self.indices_map[pt[0]], - self.pipeline.data_shape, - self.pipeline.chunk_size, - self.pipeline.output_channels, - position_name=name) + self.pipeline.writer.init_array( + self.indices_map[pt[0]], + self.pipeline.data_shape, + self.pipeline.chunk_size, + self.pipeline.output_channels, + position_name=name, + ) # assumes array exists already if there is an error thrown except: pass - #TODO: use arbol print statements - #TODO: Refactor Birefringence to Anisotropy + # TODO: use arbol print statements + # TODO: Refactor Birefringence to Anisotropy def run(self): - print(f'Beginning Reconstruction...') + print(f"Beginning Reconstruction...") for pt in sorted(self.pt_set): start_time = time.time() self.try_init_array(pt) - pt_data = self.data.get_zarr(pt[0])[pt[1]] # (C, Z, Y, X) virtual + pt_data = self.data.get_zarr(pt[0])[pt[1]] # (C, Z, Y, X) virtual # will return pt_data if the pipeline does not compute stokes stokes = self.pipeline.reconstruct_stokes_volume(pt_data) # will return None if the pipeline doesn't support birefringence reconstruction - birefringence = self.pipeline.reconstruct_birefringence_volume(stokes) + birefringence = self.pipeline.reconstruct_birefringence_volume( + stokes + ) # will return phase volumes - deconvolve2D, deconvolve3D = self.pipeline.deconvolve_volume(stokes) - - self.pipeline.write_data(self.indices_map[pt[0]], pt[1], pt_data, stokes, - birefringence, deconvolve2D, deconvolve3D) + deconvolve2D, deconvolve3D = self.pipeline.deconvolve_volume( + stokes + ) + + self.pipeline.write_data( + self.indices_map[pt[0]], + pt[1], + pt_data, + stokes, + birefringence, + deconvolve2D, + deconvolve3D, + ) end_time = time.time() - print(f'Finishing Reconstructing P = {pt[0]}, T = {pt[1]} ({(end_time-start_time)/60:0.2f}) min') + print( + f"Finishing Reconstructing P = {pt[0]}, T = {pt[1]} ({(end_time-start_time)/60:0.2f}) min" + ) existing_meta = self.writer.store.attrs.asdict().copy() - existing_meta['Config'] = self.config.yaml_dict - self.writer.store.attrs.put(existing_meta) \ No newline at end of file + existing_meta["Config"] = self.config.yaml_dict + self.writer.store.attrs.put(existing_meta) diff --git a/recOrder/pipelines/qlipp_pipeline.py b/recOrder/pipelines/qlipp_pipeline.py index 83f66eed..b3f5dd97 100644 --- a/recOrder/pipelines/qlipp_pipeline.py +++ b/recOrder/pipelines/qlipp_pipeline.py @@ -3,8 +3,13 @@ from waveorder.io.writer import WaveorderWriter from recOrder.io import MetadataReader from recOrder.io.utils import load_bg, MockEmitter, rec_bkg_to_wo_bkg -from recOrder.compute.qlipp_compute import reconstruct_qlipp_birefringence, reconstruct_qlipp_stokes, \ - reconstruct_phase2D, reconstruct_phase3D, initialize_reconstructor +from recOrder.compute.qlipp_compute import ( + reconstruct_qlipp_birefringence, + reconstruct_qlipp_stokes, + reconstruct_phase2D, + reconstruct_phase3D, + initialize_reconstructor, +) import numpy as np from recOrder.pipelines.base import PipelineInterface @@ -15,7 +20,15 @@ class QLIPP(PipelineInterface): This class contains methods to reconstruct an entire QLIPP dataset """ - def __init__(self, config: ConfigReader, data: WaveorderReader, writer: WaveorderWriter, mode: str, num_t: int, emitter=MockEmitter()): + def __init__( + self, + config: ConfigReader, + data: WaveorderReader, + writer: WaveorderWriter, + mode: str, + num_t: int, + emitter=MockEmitter(), + ): """ Parameters ---------- @@ -41,11 +54,13 @@ def __init__(self, config: ConfigReader, data: WaveorderReader, writer: Waveorde self._check_output_channels(self.output_channels) if self.data.channels < 4: - raise ValueError(f'Number of Channels is {data.channels}, cannot be less than 4') + raise ValueError( + f"Number of Channels is {data.channels}, cannot be less than 4" + ) self.slices = self.data.slices self.focus_slice = None - if self.mode == '2D': + if self.mode == "2D": self.slices = 1 self.focus_slice = self.config.focus_zidx @@ -53,68 +68,92 @@ def __init__(self, config: ConfigReader, data: WaveorderReader, writer: Waveorde # Metadata self.chan_names = self.data.channel_names - self.bg_path = self.config.background if self.config.background else None + self.bg_path = ( + self.config.background if self.config.background else None + ) if self.config.calibration_metadata: self.calib_meta = MetadataReader(self.config.calibration_metadata) self.calib_scheme = self.calib_meta.Calibration_scheme - self.bg_roi = self.calib_meta.ROI # TODO: remove for 1.0.0 + self.bg_roi = self.calib_meta.ROI # TODO: remove for 1.0.0 else: self.calib_meta = None - self.calib_scheme = '4-State' - self.bg_roi = None # TODO: remove for 1.0.0 + self.calib_scheme = "4-State" + self.bg_roi = None # TODO: remove for 1.0.0 # identify the image indicies corresponding to each polarization orientation - self.s0_idx, self.s1_idx, \ - self.s2_idx, self.s3_idx, \ - self.s4_idx, = self.parse_channel_idx(self.data.channel_names) + ( + self.s0_idx, + self.s1_idx, + self.s2_idx, + self.s3_idx, + self.s4_idx, + ) = self.parse_channel_idx(self.data.channel_names) # Writer Parameters - self.data_shape = (self.t, len(self.output_channels), self.slices, self.img_dim[0], self.img_dim[1]) + self.data_shape = ( + self.t, + len(self.output_channels), + self.slices, + self.img_dim[0], + self.img_dim[1], + ) self.chunk_size = (1, 1, 1, self.data_shape[-2], self.data_shape[-1]) - wo_background_correction = rec_bkg_to_wo_bkg(self.config.background_correction) + wo_background_correction = rec_bkg_to_wo_bkg( + self.config.background_correction + ) # Initialize Reconstructor if self.no_phase: - self.reconstructor = initialize_reconstructor(pipeline='birefringence', - image_dim=(self.img_dim[0], self.img_dim[1]), - wavelength_nm=self.config.wavelength, - swing=self.calib_meta.Swing, - calibration_scheme=self.calib_scheme, - n_slices=self.data.slices, - pad_z=self.config.pad_z, - bg_correction=wo_background_correction, - mode=self.mode, - use_gpu=self.config.use_gpu, - gpu_id=self.config.gpu_id) + self.reconstructor = initialize_reconstructor( + pipeline="birefringence", + image_dim=(self.img_dim[0], self.img_dim[1]), + wavelength_nm=self.config.wavelength, + swing=self.calib_meta.Swing, + calibration_scheme=self.calib_scheme, + n_slices=self.data.slices, + pad_z=self.config.pad_z, + bg_correction=wo_background_correction, + mode=self.mode, + use_gpu=self.config.use_gpu, + gpu_id=self.config.gpu_id, + ) else: - self.reconstructor = initialize_reconstructor(pipeline='QLIPP', - image_dim=(self.img_dim[0], self.img_dim[1]), - wavelength_nm=self.config.wavelength, - swing=self.calib_meta.Swing, - calibration_scheme=self.calib_scheme, - NA_obj=self.config.NA_objective, - NA_illu=self.config.NA_condenser, - n_obj_media=self.config.n_objective_media, - mag=self.config.magnification, - n_slices=self.data.slices, - z_step_um=self.data.z_step_size, - pad_z=self.config.pad_z, - pixel_size_um=self.config.pixel_size, - bg_correction=wo_background_correction, - mode=self.mode, - use_gpu=self.config.use_gpu, - gpu_id=self.config.gpu_id) + self.reconstructor = initialize_reconstructor( + pipeline="QLIPP", + image_dim=(self.img_dim[0], self.img_dim[1]), + wavelength_nm=self.config.wavelength, + swing=self.calib_meta.Swing, + calibration_scheme=self.calib_scheme, + NA_obj=self.config.NA_objective, + NA_illu=self.config.NA_condenser, + n_obj_media=self.config.n_objective_media, + mag=self.config.magnification, + n_slices=self.data.slices, + z_step_um=self.data.z_step_size, + pad_z=self.config.pad_z, + pixel_size_um=self.config.pixel_size, + bg_correction=wo_background_correction, + mode=self.mode, + use_gpu=self.config.use_gpu, + gpu_id=self.config.gpu_id, + ) # Prepare background corrections for waveorder - if self.config.background_correction in ['global', 'local_fit+']: - bg_data = load_bg(self.bg_path, self.img_dim[0], self.img_dim[1], self.bg_roi) # TODO: remove ROI for 1.0.0 + if self.config.background_correction in ["global", "local_fit+"]: + bg_data = load_bg( + self.bg_path, self.img_dim[0], self.img_dim[1], self.bg_roi + ) # TODO: remove ROI for 1.0.0 self.bg_stokes = self.reconstructor.Stokes_recon(bg_data) - self.bg_stokes = self.reconstructor.Stokes_transform(self.bg_stokes) - elif self.config.background_correction == 'local_fit': + self.bg_stokes = self.reconstructor.Stokes_transform( + self.bg_stokes + ) + elif self.config.background_correction == "local_fit": self.bg_stokes = np.zeros((5, self.img_dim[0], self.img_dim[1])) - self.bg_stokes[0, ...] = 1 # Set background to "identity" Stokes parameters. + self.bg_stokes[ + 0, ... + ] = 1 # Set background to "identity" Stokes parameters. else: self.bg_stokes = None @@ -122,14 +161,17 @@ def _check_output_channels(self, output_channels): self.no_birefringence = True self.no_phase = True for channel in output_channels: - if 'Retardance' in channel or 'Orientation' in channel or 'Brightfield' in channel: + if ( + "Retardance" in channel + or "Orientation" in channel + or "Brightfield" in channel + ): self.no_birefringence = False - if 'Phase3D' in channel or 'Phase2D' in channel: + if "Phase3D" in channel or "Phase2D" in channel: self.no_phase = False else: continue - def reconstruct_stokes_volume(self, data): """ This method reconstructs a stokes volume from raw data @@ -146,16 +188,20 @@ def reconstruct_stokes_volume(self, data): """ - if self.calib_scheme == '4-State': - LF_array = np.zeros([4, self.data.slices, self.data.height, self.data.width]) + if self.calib_scheme == "4-State": + LF_array = np.zeros( + [4, self.data.slices, self.data.height, self.data.width] + ) LF_array[0] = data[self.s0_idx] LF_array[1] = data[self.s1_idx] LF_array[2] = data[self.s2_idx] LF_array[3] = data[self.s3_idx] - elif self.calib_scheme == '5-State': - LF_array = np.zeros([5, self.data.slices, self.data.height, self.data.width]) + elif self.calib_scheme == "5-State": + LF_array = np.zeros( + [5, self.data.slices, self.data.height, self.data.width] + ) LF_array[0] = data[self.s0_idx] LF_array[1] = data[self.s1_idx] LF_array[2] = data[self.s2_idx] @@ -163,9 +209,13 @@ def reconstruct_stokes_volume(self, data): LF_array[4] = data[self.s4_idx] else: - raise NotImplementedError(f"calibration scheme {self.calib_scheme} not implemented") + raise NotImplementedError( + f"calibration scheme {self.calib_scheme} not implemented" + ) - stokes = reconstruct_qlipp_stokes(LF_array, self.reconstructor, self.bg_stokes) + stokes = reconstruct_qlipp_stokes( + LF_array, self.reconstructor, self.bg_stokes + ) return stokes @@ -189,15 +239,27 @@ def deconvolve_volume(self, stokes): phase2D = None phase3D = None - if 'Phase3D' in self.output_channels: - phase3D = reconstruct_phase3D(stokes[0], self.reconstructor, method=self.config.phase_denoiser_3D, - reg_re=self.config.Tik_reg_ph_3D, rho=self.config.rho_3D, - lambda_re=self.config.TV_reg_ph_3D, itr=self.config.itr_3D) - - if 'Phase2D' in self.output_channels: - phase2D = reconstruct_phase2D(stokes[0], self.reconstructor, method=self.config.phase_denoiser_2D, - reg_p=self.config.Tik_reg_ph_2D, rho=self.config.rho_2D, - lambda_p=self.config.TV_reg_ph_2D, itr=self.config.itr_2D) + if "Phase3D" in self.output_channels: + phase3D = reconstruct_phase3D( + stokes[0], + self.reconstructor, + method=self.config.phase_denoiser_3D, + reg_re=self.config.Tik_reg_ph_3D, + rho=self.config.rho_3D, + lambda_re=self.config.TV_reg_ph_3D, + itr=self.config.itr_3D, + ) + + if "Phase2D" in self.output_channels: + phase2D = reconstruct_phase2D( + stokes[0], + self.reconstructor, + method=self.config.phase_denoiser_2D, + reg_p=self.config.Tik_reg_ph_2D, + rho=self.config.rho_2D, + lambda_p=self.config.TV_reg_ph_2D, + itr=self.config.itr_2D, + ) return phase2D, phase3D @@ -221,10 +283,18 @@ def reconstruct_birefringence_volume(self, stokes): return None else: return reconstruct_qlipp_birefringence( - stokes[:, slice(None) if self.slices != 1 else self.focus_slice, :, :], - self.reconstructor) - - def write_data(self, p, t, pt_data, stokes, birefringence, phase2D, phase3D): + stokes[ + :, + slice(None) if self.slices != 1 else self.focus_slice, + :, + :, + ], + self.reconstructor, + ) + + def write_data( + self, p, t, pt_data, stokes, birefringence, phase2D, phase3D + ): """ This function will iteratively write the data into its proper position, time, channel, z index. Dimensions differ between data type to make compute easier with waveOrder backend. @@ -245,30 +315,38 @@ def write_data(self, p, t, pt_data, stokes, birefringence, phase2D, phase3D): """ - z = 0 if self.mode == '2D' else None - slice_ = self.focus_slice if self.mode == '2D' else slice(None) + z = 0 if self.mode == "2D" else None + slice_ = self.focus_slice if self.mode == "2D" else slice(None) # stokes = np.transpose(stokes, (-1, -4, -3, -2)) if len(stokes.shape) == 4 else stokes - + for chan in range(len(self.output_channels)): - if 'Retardance' in self.output_channels[chan]: + if "Retardance" in self.output_channels[chan]: ret = birefringence[0] / (2 * np.pi) * self.config.wavelength self.writer.write(ret, p=p, t=t, c=chan, z=z) - elif 'Orientation' in self.output_channels[chan]: + elif "Orientation" in self.output_channels[chan]: self.writer.write(birefringence[1], p=p, t=t, c=chan, z=z) - elif 'Brightfield' in self.output_channels[chan]: + elif "Brightfield" in self.output_channels[chan]: self.writer.write(birefringence[2], p=p, t=t, c=chan, z=z) - elif 'Phase3D' in self.output_channels[chan]: + elif "Phase3D" in self.output_channels[chan]: self.writer.write(phase3D, p=p, t=t, c=chan, z=z) - elif 'Phase2D' in self.output_channels[chan]: + elif "Phase2D" in self.output_channels[chan]: self.writer.write(phase2D, p=p, t=t, c=chan, z=z) - elif 'S0' in self.output_channels[chan]: - self.writer.write(stokes[0, slice_, :, :], p=p, t=t, c=chan, z=z) - elif 'S1' in self.output_channels[chan]: - self.writer.write(stokes[1, slice_, :, :], p=p, t=t, c=chan, z=z) - elif 'S2' in self.output_channels[chan]: - self.writer.write(stokes[2, slice_, :, :], p=p, t=t, c=chan, z=z) - elif 'S3' in self.output_channels[chan]: - self.writer.write(stokes[3, slice_, :, :], p=p, t=t, c=chan, z=z) + elif "S0" in self.output_channels[chan]: + self.writer.write( + stokes[0, slice_, :, :], p=p, t=t, c=chan, z=z + ) + elif "S1" in self.output_channels[chan]: + self.writer.write( + stokes[1, slice_, :, :], p=p, t=t, c=chan, z=z + ) + elif "S2" in self.output_channels[chan]: + self.writer.write( + stokes[2, slice_, :, :], p=p, t=t, c=chan, z=z + ) + elif "S3" in self.output_channels[chan]: + self.writer.write( + stokes[3, slice_, :, :], p=p, t=t, c=chan, z=z + ) self.dimension_emitter.emit((p, t, chan)) @@ -292,26 +370,27 @@ def parse_channel_idx(self, channel_list): s2_idx = None s3_idx = None s4_idx = None - if 'PolScope_Plugin_Version' in self.calib_meta.json_metadata['Summary']: + if ( + "PolScope_Plugin_Version" + in self.calib_meta.json_metadata["Summary"] + ): open_pol = True else: open_pol = False for channel in range(len(channel_list)): - if 'State0' in channel_list[channel]: + if "State0" in channel_list[channel]: s0_idx = channel - elif 'State1' in channel_list[channel]: + elif "State1" in channel_list[channel]: s1_idx = channel - elif 'State2' in channel_list[channel]: + elif "State2" in channel_list[channel]: s2_idx = channel - elif 'State3' in channel_list[channel]: + elif "State3" in channel_list[channel]: s3_idx = channel - elif 'State4' in channel_list[channel]: + elif "State4" in channel_list[channel]: s4_idx = channel if open_pol: s1_idx, s2_idx, s3_idx, s4_idx = s4_idx, s3_idx, s1_idx, s2_idx return s0_idx, s1_idx, s2_idx, s3_idx, s4_idx - - diff --git a/recOrder/plugin/__init__.py b/recOrder/plugin/__init__.py index ce49ae06..bd7e37d3 100644 --- a/recOrder/plugin/__init__.py +++ b/recOrder/plugin/__init__.py @@ -1,2 +1,2 @@ # data viewers that depend on napari -name = "plugin" \ No newline at end of file +name = "plugin" diff --git a/recOrder/plugin/qtdesigner/mplwidget.py b/recOrder/plugin/qtdesigner/mplwidget.py index fbfd01b5..dbe70f6f 100644 --- a/recOrder/plugin/qtdesigner/mplwidget.py +++ b/recOrder/plugin/qtdesigner/mplwidget.py @@ -1,11 +1,12 @@ # Imports from qtpy import QtWidgets import matplotlib -matplotlib.use('QT5Agg') + +matplotlib.use("QT5Agg") from matplotlib.figure import Figure from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as Canvas -matplotlib.use('QT5Agg') +matplotlib.use("QT5Agg") # Matplotlib canvas class to create figure class MplCanvas(Canvas): @@ -13,14 +14,19 @@ def __init__(self): self.fig = Figure() self.ax = self.fig.add_subplot(111) Canvas.__init__(self, self.fig) - Canvas.setSizePolicy(self, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Expanding) + Canvas.setSizePolicy( + self, + QtWidgets.QSizePolicy.Expanding, + QtWidgets.QSizePolicy.Expanding, + ) Canvas.updateGeometry(self) + # Matplotlib widget class MplWidget(QtWidgets.QWidget): def __init__(self, parent=None): - QtWidgets.QWidget.__init__(self, parent) # Inherit from QWidget - self.canvas = MplCanvas() # Create canvas object - self.vbl = QtWidgets.QVBoxLayout() # Set box for plotting + QtWidgets.QWidget.__init__(self, parent) # Inherit from QWidget + self.canvas = MplCanvas() # Create canvas object + self.vbl = QtWidgets.QVBoxLayout() # Set box for plotting self.vbl.addWidget(self.canvas) self.setLayout(self.vbl) diff --git a/recOrder/plugin/widget/loading_widget.py b/recOrder/plugin/widget/loading_widget.py index 2256b2fc..0ca69ac7 100644 --- a/recOrder/plugin/widget/loading_widget.py +++ b/recOrder/plugin/widget/loading_widget.py @@ -2,10 +2,12 @@ from qtpy.QtGui import QPalette, QPainter, QBrush, QColor, QPen from qtpy.QtWidgets import QWidget, QPushButton + class Overlay(QWidget): """ ADAPTED FROM AYDIN https://github.com/royerlab/aydin/blob/master/aydin/gui/_qt/custom_widgets/overlay.py """ + def __init__(self, parent=None): # super().__init__(self, parent) @@ -14,7 +16,6 @@ def __init__(self, parent=None): palette.setColor(palette.Background, Qt.transparent) self.setPalette(palette) - def paintEvent(self, event): painter = QPainter() painter.begin(self) @@ -58,4 +59,4 @@ def timerEvent(self, event): def hideEvent(self, event): self.killTimer(self.timer) - self.hide() \ No newline at end of file + self.hide() diff --git a/recOrder/plugin/widget/main_widget.py b/recOrder/plugin/widget/main_widget.py index 627ff15b..9f681307 100644 --- a/recOrder/plugin/widget/main_widget.py +++ b/recOrder/plugin/widget/main_widget.py @@ -5,8 +5,16 @@ from qtpy.QtGui import QPixmap, QColor from superqt import QDoubleRangeSlider, QRangeSlider from recOrder.calib import Calibration -from recOrder.plugin.workers.calibration_workers import CalibrationWorker, BackgroundCaptureWorker, load_calibration -from recOrder.plugin.workers.acquisition_workers import PolarizationAcquisitionWorker, ListeningWorker, BFAcquisitionWorker +from recOrder.plugin.workers.calibration_workers import ( + CalibrationWorker, + BackgroundCaptureWorker, + load_calibration, +) +from recOrder.plugin.workers.acquisition_workers import ( + PolarizationAcquisitionWorker, + ListeningWorker, + BFAcquisitionWorker, +) from recOrder.plugin.workers.reconstruction_workers import ReconstructionWorker from recOrder.plugin.qtdesigner import recOrder_ui from recOrder.io.core_functions import set_lc_state, snap_and_average @@ -27,6 +35,7 @@ import pathlib import textwrap + class MainWidget(QWidget): """ This is the main recOrder widget that houses all of the GUI components of recOrder. @@ -52,10 +61,10 @@ def __init__(self, napari_viewer: Viewer): # Connect to MicroManager # I'm disconnecting and hiding this button for 0.2.0, TODO: reinstate for 1.0.0 # In 1.0.0 we'll only have a single "Switch to Online/Offline" button in the "Calibration tab" - + # self.ui.qbutton_mm_connect.clicked[bool].connect(self.connect_to_mm) self.ui.qbutton_mm_connect.hide() - + # Calibration Tab # Remove QT creator calibration mode items @@ -63,35 +72,49 @@ def __init__(self, napari_viewer: Viewer): self.ui.cb_calib_mode.removeItem(0) # Populate calibration modes from docstring - cal_docs = NumpyDocString(Calibration.QLIPP_Calibration.__init__.__doc__) - mode_docs = ' '.join(cal_docs['Parameters'][3].desc).split('* ')[1:] + cal_docs = NumpyDocString( + Calibration.QLIPP_Calibration.__init__.__doc__ + ) + mode_docs = " ".join(cal_docs["Parameters"][3].desc).split("* ")[1:] for i, mode_doc in enumerate(mode_docs): - mode_name, mode_tooltip = mode_doc.split(': ') - wrapped_tooltip = '\n'.join(textwrap.wrap(mode_tooltip, width=70)) + mode_name, mode_tooltip = mode_doc.split(": ") + wrapped_tooltip = "\n".join(textwrap.wrap(mode_tooltip, width=70)) self.ui.cb_calib_mode.addItem(mode_name) - self.ui.cb_calib_mode.setItemData(i, wrapped_tooltip, Qt.ToolTipRole) + self.ui.cb_calib_mode.setItemData( + i, wrapped_tooltip, Qt.ToolTipRole + ) self.ui.qbutton_browse.clicked[bool].connect(self.browse_dir_path) self.ui.le_directory.editingFinished.connect(self.enter_dir_path) self.ui.le_directory.setText(str(Path.cwd())) self.ui.le_swing.editingFinished.connect(self.enter_swing) - self.ui.le_swing.setText('0.1') + self.ui.le_swing.setText("0.1") self.enter_swing() self.ui.le_wavelength.editingFinished.connect(self.enter_wavelength) - self.ui.le_wavelength.setText('532') + self.ui.le_wavelength.setText("532") self.enter_wavelength() - self.ui.cb_calib_scheme.currentIndexChanged[int].connect(self.enter_calib_scheme) - self.ui.cb_calib_mode.currentIndexChanged[int].connect(self.enter_calib_mode) + self.ui.cb_calib_scheme.currentIndexChanged[int].connect( + self.enter_calib_scheme + ) + self.ui.cb_calib_mode.currentIndexChanged[int].connect( + self.enter_calib_mode + ) self.ui.cb_lca.currentIndexChanged[int].connect(self.enter_dac_lca) self.ui.cb_lcb.currentIndexChanged[int].connect(self.enter_dac_lcb) - self.ui.chb_use_roi.stateChanged[int].connect(self.enter_use_cropped_roi) + self.ui.chb_use_roi.stateChanged[int].connect( + self.enter_use_cropped_roi + ) self.ui.qbutton_calibrate.clicked[bool].connect(self.run_calibration) self.ui.qbutton_load_calib.clicked[bool].connect(self.load_calibration) - self.ui.qbutton_calc_extinction.clicked[bool].connect(self.calc_extinction) - self.ui.cb_config_group.currentIndexChanged[int].connect(self.enter_config_group) + self.ui.qbutton_calc_extinction.clicked[bool].connect( + self.calc_extinction + ) + self.ui.cb_config_group.currentIndexChanged[int].connect( + self.enter_config_group + ) # Capture Background self.ui.le_bg_folder.editingFinished.connect(self.enter_bg_folder_name) @@ -99,110 +122,164 @@ def __init__(self, napari_viewer: Viewer): self.ui.qbutton_capture_bg.clicked[bool].connect(self.capture_bg) # Advanced - self.ui.cb_loglevel.currentIndexChanged[int].connect(self.enter_log_level) + self.ui.cb_loglevel.currentIndexChanged[int].connect( + self.enter_log_level + ) self.ui.qbutton_push_note.clicked[bool].connect(self.push_note) # Acquisition Tab self.ui.qbutton_gui_mode.clicked[bool].connect(self.change_gui_mode) - self.ui.qbutton_browse_save_dir.clicked[bool].connect(self.browse_save_path) + self.ui.qbutton_browse_save_dir.clicked[bool].connect( + self.browse_save_path + ) self.ui.le_save_dir.editingFinished.connect(self.enter_save_path) self.ui.le_save_dir.setText(str(Path.cwd())) self.ui.le_data_save_name.editingFinished.connect(self.enter_save_name) - self.ui.qbutton_listen.clicked[bool].connect(self.listen_and_reconstruct) + self.ui.qbutton_listen.clicked[bool].connect( + self.listen_and_reconstruct + ) self.ui.le_zstart.editingFinished.connect(self.enter_zstart) - self.ui.le_zstart.setText('-1') + self.ui.le_zstart.setText("-1") self.enter_zstart() self.ui.le_zend.editingFinished.connect(self.enter_zend) - self.ui.le_zend.setText('1') + self.ui.le_zend.setText("1") self.enter_zend() self.ui.le_zstep.editingFinished.connect(self.enter_zstep) - self.ui.le_zstep.setText('0.25') + self.ui.le_zstep.setText("0.25") self.enter_zstep() self.ui.chb_use_gpu.stateChanged[int].connect(self.enter_use_gpu) self.ui.le_gpu_id.editingFinished.connect(self.enter_gpu_id) - self.ui.le_recon_wavelength.setText('532') # This parameter seems to be wired differently than others...investigate later + self.ui.le_recon_wavelength.setText( + "532" + ) # This parameter seems to be wired differently than others...investigate later self.ui.le_obj_na.editingFinished.connect(self.enter_obj_na) - self.ui.le_obj_na.setText('1.3') + self.ui.le_obj_na.setText("1.3") self.enter_obj_na() self.ui.le_cond_na.editingFinished.connect(self.enter_cond_na) - self.ui.le_cond_na.setText('0.5') + self.ui.le_cond_na.setText("0.5") self.enter_cond_na() self.ui.le_mag.editingFinished.connect(self.enter_mag) - self.ui.le_mag.setText('60') + self.ui.le_mag.setText("60") self.enter_mag() self.ui.le_ps.editingFinished.connect(self.enter_ps) - self.ui.le_ps.setText('6.9') + self.ui.le_ps.setText("6.9") self.enter_ps() self.ui.le_n_media.editingFinished.connect(self.enter_n_media) - self.ui.le_n_media.setText('1.3') + self.ui.le_n_media.setText("1.3") self.enter_n_media() self.ui.le_pad_z.editingFinished.connect(self.enter_pad_z) - self.ui.chb_pause_updates.stateChanged[int].connect(self.enter_pause_updates) - self.ui.cb_birefringence.currentIndexChanged[int].connect(self.enter_birefringence_dim) + self.ui.chb_pause_updates.stateChanged[int].connect( + self.enter_pause_updates + ) + self.ui.cb_birefringence.currentIndexChanged[int].connect( + self.enter_birefringence_dim + ) self.ui.cb_phase.currentIndexChanged[int].connect(self.enter_phase_dim) # Populate background correction GUI element for i in range(3): self.ui.cb_bg_method.removeItem(0) - bg_options = ['None','Measured','Estimated','Measured + Estimated'] - tooltips = ['No background correction.', - 'Correct sample images with a background image acquired at an empty field of view, loaded from "Background Path".', - 'Estimate sample background by fitting a 2D surface to the sample images. Works well when structures are spatially distributed across the field of view and a clear background is unavailable.', - 'Apply "Measured" background correction and then "Estimated" background correction. Use to remove residual background after the sample retardance is corrected with measured background.'] + bg_options = ["None", "Measured", "Estimated", "Measured + Estimated"] + tooltips = [ + "No background correction.", + 'Correct sample images with a background image acquired at an empty field of view, loaded from "Background Path".', + "Estimate sample background by fitting a 2D surface to the sample images. Works well when structures are spatially distributed across the field of view and a clear background is unavailable.", + 'Apply "Measured" background correction and then "Estimated" background correction. Use to remove residual background after the sample retardance is corrected with measured background.', + ] for i, bg_option in enumerate(bg_options): - wrapped_tooltip = '\n'.join(textwrap.wrap(tooltips[i], width=70)) + wrapped_tooltip = "\n".join(textwrap.wrap(tooltips[i], width=70)) self.ui.cb_bg_method.addItem(bg_option) - self.ui.cb_bg_method.setItemData(i, wrapped_tooltip, Qt.ToolTipRole) - self.ui.cb_bg_method.currentIndexChanged[int].connect(self.enter_bg_correction) + self.ui.cb_bg_method.setItemData( + i, wrapped_tooltip, Qt.ToolTipRole + ) + self.ui.cb_bg_method.currentIndexChanged[int].connect( + self.enter_bg_correction + ) self.ui.le_bg_path.editingFinished.connect(self.enter_acq_bg_path) - self.ui.qbutton_browse_bg_path.clicked[bool].connect(self.browse_acq_bg_path) - self.ui.qbutton_acq_birefringence.clicked[bool].connect(self.acq_birefringence) + self.ui.qbutton_browse_bg_path.clicked[bool].connect( + self.browse_acq_bg_path + ) + self.ui.qbutton_acq_birefringence.clicked[bool].connect( + self.acq_birefringence + ) self.ui.qbutton_acq_phase.clicked[bool].connect(self.acq_phase) - self.ui.qbutton_acq_birefringence_phase.clicked[bool].connect(self.acq_birefringence_phase) - self.ui.cb_colormap.currentIndexChanged[int].connect(self.enter_colormap) - self.ui.chb_display_volume.stateChanged[int].connect(self.enter_use_full_volume) - self.ui.le_overlay_slice.editingFinished.connect(self.enter_display_slice) - self.ui.slider_value.sliderMoved[tuple].connect(self.handle_val_slider_move) - self.ui.slider_saturation.sliderMoved[tuple].connect(self.handle_sat_slider_move) + self.ui.qbutton_acq_birefringence_phase.clicked[bool].connect( + self.acq_birefringence_phase + ) + self.ui.cb_colormap.currentIndexChanged[int].connect( + self.enter_colormap + ) + self.ui.chb_display_volume.stateChanged[int].connect( + self.enter_use_full_volume + ) + self.ui.le_overlay_slice.editingFinished.connect( + self.enter_display_slice + ) + self.ui.slider_value.sliderMoved[tuple].connect( + self.handle_val_slider_move + ) + self.ui.slider_saturation.sliderMoved[tuple].connect( + self.handle_sat_slider_move + ) # Display Tab - self.viewer.layers.events.inserted.connect(self._add_layer_to_display_boxes) - self.viewer.layers.events.removed.connect(self._remove_layer_from_display_boxes) - self.ui.qbutton_create_overlay.clicked[bool].connect(self.create_overlay) - self.ui.cb_saturation.currentIndexChanged[int].connect(self.update_sat_scale) - self.ui.cb_value.currentIndexChanged[int].connect(self.update_value_scale) + self.viewer.layers.events.inserted.connect( + self._add_layer_to_display_boxes + ) + self.viewer.layers.events.removed.connect( + self._remove_layer_from_display_boxes + ) + self.ui.qbutton_create_overlay.clicked[bool].connect( + self.create_overlay + ) + self.ui.cb_saturation.currentIndexChanged[int].connect( + self.update_sat_scale + ) + self.ui.cb_value.currentIndexChanged[int].connect( + self.update_value_scale + ) self.ui.le_sat_max.editingFinished.connect(self.enter_sat_max) self.ui.le_sat_min.editingFinished.connect(self.enter_sat_min) self.ui.le_val_max.editingFinished.connect(self.enter_val_max) self.ui.le_val_min.editingFinished.connect(self.enter_val_min) # Reconstruction - self.ui.qbutton_browse_data_dir.clicked[bool].connect(self.browse_data_dir) - self.ui.qbutton_browse_calib_meta.clicked[bool].connect(self.browse_calib_meta) + self.ui.qbutton_browse_data_dir.clicked[bool].connect( + self.browse_data_dir + ) + self.ui.qbutton_browse_calib_meta.clicked[bool].connect( + self.browse_calib_meta + ) self.ui.qbutton_load_config.clicked[bool].connect(self.load_config) self.ui.qbutton_save_config.clicked[bool].connect(self.save_config) - self.ui.qbutton_load_default_config.clicked[bool].connect(self.load_default_config) + self.ui.qbutton_load_default_config.clicked[bool].connect( + self.load_default_config + ) self.ui.cb_method.currentIndexChanged[int].connect(self.enter_method) self.ui.cb_mode.currentIndexChanged[int].connect(self.enter_mode) - self.ui.le_calibration_metadata.editingFinished.connect(self.enter_calib_meta) + self.ui.le_calibration_metadata.editingFinished.connect( + self.enter_calib_meta + ) self.ui.qbutton_reconstruct.clicked[bool].connect(self.reconstruct) - self.ui.cb_phase_denoiser.currentIndexChanged[int].connect(self.enter_phase_denoiser) + self.ui.cb_phase_denoiser.currentIndexChanged[int].connect( + self.enter_phase_denoiser + ) # Logging log_box = QtLogger(self.ui.te_log) - log_box.setFormatter(logging.Formatter('%(levelname)s - %(message)s')) + log_box.setFormatter(logging.Formatter("%(levelname)s - %(message)s")) logging.getLogger().addHandler(log_box) logging.getLogger().setLevel(logging.INFO) @@ -210,7 +287,7 @@ def __init__(self, napari_viewer: Viewer): self.mm_status_changed.connect(self.handle_mm_status_update) # Instantiate Attributes: - self.gui_mode = 'offline' + self.gui_mode = "offline" self.bridge = None self.mm = None self.mmc = None @@ -221,21 +298,27 @@ def __init__(self, napari_viewer: Viewer): self.directory = str(Path.cwd()) # Reconstruction / Calibration Parameter Defaults - self.calib_scheme = '4-State' - self.calib_mode = 'MM-Retardance' - self.interp_method = 'schnoor_fit' - self.config_group = 'Channel' - self.calib_channels = ['State0', 'State1', 'State2', 'State3', 'State4'] + self.calib_scheme = "4-State" + self.calib_mode = "MM-Retardance" + self.interp_method = "schnoor_fit" + self.config_group = "Channel" + self.calib_channels = [ + "State0", + "State1", + "State2", + "State3", + "State4", + ] self.last_calib_meta_file = None self.use_cropped_roi = False - self.bg_folder_name = 'BG' + self.bg_folder_name = "BG" self.n_avg = 5 self.intensity_monitor = [] self.save_directory = str(Path.cwd()) self.save_name = None - self.bg_option = 'None' - self.birefringence_dim = '2D' - self.phase_dim = '2D' + self.bg_option = "None" + self.birefringence_dim = "2D" + self.phase_dim = "2D" self.gpu_id = 0 self.use_gpu = False self.pad_z = 0 @@ -245,13 +328,13 @@ def __init__(self, napari_viewer: Viewer): self.lca_dac = None self.lcb_dac = None self.pause_updates = False - self.method = 'QLIPP' - self.mode = '3D' + self.method = "QLIPP" + self.mode = "3D" self.calib_path = str(Path.cwd()) self.data_dir = str(Path.cwd()) self.config_path = str(Path.cwd()) self.save_config_path = str(Path.cwd()) - self.colormap = 'HSV' + self.colormap = "HSV" self.use_full_volume = False self.display_slice = 0 self.last_p = 0 @@ -264,26 +347,36 @@ def __init__(self, napari_viewer: Viewer): # Init Plot self.plot_item = self.ui.plot_widget.getPlotItem() self.plot_item.enableAutoRange() - self.plot_item.setLabel('left', 'Intensity') + self.plot_item.setLabel("left", "Intensity") self.ui.plot_widget.setBackground((32, 34, 40)) - self.plot_sequence = 'Coarse' + self.plot_sequence = "Coarse" # Init thread worker self.worker = None # Display/Initialiaze GUI Images (plotting legends, recOrder logo) - recorder_dir = dirname(dirname(dirname(dirname(os.path.abspath(__file__))))) - jch_legend_path = os.path.join(recorder_dir, 'docs/images/JCh_legend.png') - hsv_legend_path = os.path.join(recorder_dir, 'docs/images/HSV_legend.png') + recorder_dir = dirname( + dirname(dirname(dirname(os.path.abspath(__file__)))) + ) + jch_legend_path = os.path.join( + recorder_dir, "docs/images/JCh_legend.png" + ) + hsv_legend_path = os.path.join( + recorder_dir, "docs/images/HSV_legend.png" + ) self.jch_pixmap = QPixmap(jch_legend_path) self.hsv_pixmap = QPixmap(hsv_legend_path) self.ui.label_orientation_image.setPixmap(self.hsv_pixmap) - logo_path = os.path.join(recorder_dir, 'docs/images/recOrder_plugin_logo.png') + logo_path = os.path.join( + recorder_dir, "docs/images/recOrder_plugin_logo.png" + ) logo_pixmap = QPixmap(logo_path) self.ui.label_logo.setPixmap(logo_pixmap) # Get default config file - self.default_offline_config = os.path.join(recorder_dir, "recOrder/plugin/config_offline_default.yml") + self.default_offline_config = os.path.join( + recorder_dir, "recOrder/plugin/config_offline_default.yml" + ) # Hide initial UI elements for later implementation or for later pop-up purposes self.ui.label_lca.hide() @@ -308,7 +401,9 @@ def __init__(self, napari_viewer: Viewer): self.ui.chb_pause_updates.setHidden(True) # Hide temporarily unsupported "Overlay" functions - self.ui.tabWidget.setTabText(self.ui.tabWidget.indexOf(self.ui.Display), "Orientation Legend") + self.ui.tabWidget.setTabText( + self.ui.tabWidget.indexOf(self.ui.Display), "Orientation Legend" + ) self.ui.label_orientation_legend.setHidden(True) self.ui.DisplayOptions.setHidden(True) @@ -316,9 +411,11 @@ def __init__(self, napari_viewer: Viewer): self.ui.chb_use_roi.setHidden(True) # Set initial UI Properties - self.ui.le_gui_mode.setStyleSheet("border: 1px solid rgb(200,0,0); color: rgb(200,0,0);") - self.ui.te_log.setStyleSheet('background-color: rgb(32,34,40);') - self.ui.le_mm_status.setText('Not Connected') + self.ui.le_gui_mode.setStyleSheet( + "border: 1px solid rgb(200,0,0); color: rgb(200,0,0);" + ) + self.ui.te_log.setStyleSheet("background-color: rgb(32,34,40);") + self.ui.le_mm_status.setText("Not Connected") self.ui.le_mm_status.setStyleSheet("border: 1px solid yellow;") self.ui.le_sat_min.setStyleSheet("background-color: rgba(0, 0, 0, 0);") self.ui.le_sat_max.setStyleSheet("background-color: rgba(0, 0, 0, 0);") @@ -327,16 +424,22 @@ def __init__(self, napari_viewer: Viewer): self.setStyleSheet("QTabWidget::tab-bar {alignment: center;}") self.red_text = QColor(200, 0, 0, 255) self.original_tab_text = self.ui.tabWidget_3.tabBar().tabTextColor(0) - self.ui.tabWidget.parent().setObjectName('recOrder') # make sure the top says recOrder and not 'Form' - self.ui.tabWidget_2.setCurrentIndex(0) # set focus to "Plot" tab by default - self.ui.tabWidget_3.setCurrentIndex(0) # set focus to "General" tab by default + self.ui.tabWidget.parent().setObjectName( + "recOrder" + ) # make sure the top says recOrder and not 'Form' + self.ui.tabWidget_2.setCurrentIndex( + 0 + ) # set focus to "Plot" tab by default + self.ui.tabWidget_3.setCurrentIndex( + 0 + ) # set focus to "General" tab by default # No "Optional" text on offline mode's calibration metadata box self.ui.le_calibration_metadata.setPlaceholderText("") # disable wheel events for combo boxes for attr_name in dir(self.ui): - if 'cb_' in attr_name: + if "cb_" in attr_name: attr = getattr(self.ui, attr_name) attr.wheelEvent = lambda event: None @@ -371,15 +474,19 @@ def _demote_slider_offline(self, ui_slider, range_): # Add back the sliders as range sliders with the same properties ui_slider = QSlider(getattr(self.ui, slider_parent)) - sizePolicy.setHeightForWidth(ui_slider.sizePolicy().hasHeightForWidth()) + sizePolicy.setHeightForWidth( + ui_slider.sizePolicy().hasHeightForWidth() + ) ui_slider.setSizePolicy(sizePolicy) ui_slider.setOrientation(Qt.Horizontal) ui_slider.setObjectName(slider_name) - self.ui.gridLayout_26.addWidget(ui_slider, - slider_position[0], - slider_position[1], - slider_position[2], - slider_position[3]) + self.ui.gridLayout_26.addWidget( + ui_slider, + slider_position[0], + slider_position[1], + slider_position[2], + slider_position[3], + ) ui_slider.setRange(range_[0], range_[1]) def _promote_slider_offline(self, ui_slider, range_): @@ -411,15 +518,19 @@ def _promote_slider_offline(self, ui_slider, range_): # Add back the sliders as range sliders with the same properties ui_slider = QRangeSlider(getattr(self.ui, slider_parent)) - sizePolicy.setHeightForWidth(ui_slider.sizePolicy().hasHeightForWidth()) + sizePolicy.setHeightForWidth( + ui_slider.sizePolicy().hasHeightForWidth() + ) ui_slider.setSizePolicy(sizePolicy) ui_slider.setOrientation(Qt.Horizontal) ui_slider.setObjectName(slider_name) - self.ui.gridLayout_26.addWidget(ui_slider, - slider_position[0], - slider_position[1], - slider_position[2], - slider_position[3]) + self.ui.gridLayout_26.addWidget( + ui_slider, + slider_position[0], + slider_position[1], + slider_position[2], + slider_position[3], + ) ui_slider.setRange(range_[0], range_[1]) def _promote_slider_init(self): @@ -437,39 +548,59 @@ def _promote_slider_init(self): # Get Information from regular sliders value_slider_idx = self.ui.gridLayout_17.indexOf(self.ui.slider_value) - value_slider_position = self.ui.gridLayout_17.getItemPosition(value_slider_idx) + value_slider_position = self.ui.gridLayout_17.getItemPosition( + value_slider_idx + ) value_slider_parent = self.ui.slider_value.parent().objectName() - saturation_slider_idx = self.ui.gridLayout_17.indexOf(self.ui.slider_saturation) - saturation_slider_position = self.ui.gridLayout_17.getItemPosition(saturation_slider_idx) - saturation_slider_parent = self.ui.slider_saturation.parent().objectName() + saturation_slider_idx = self.ui.gridLayout_17.indexOf( + self.ui.slider_saturation + ) + saturation_slider_position = self.ui.gridLayout_17.getItemPosition( + saturation_slider_idx + ) + saturation_slider_parent = ( + self.ui.slider_saturation.parent().objectName() + ) # Remove regular sliders from the UI self.ui.gridLayout_17.removeWidget(self.ui.slider_value) self.ui.gridLayout_17.removeWidget(self.ui.slider_saturation) # Add back the sliders as range sliders with the same properties - self.ui.slider_saturation = QDoubleRangeSlider(getattr(self.ui, saturation_slider_parent)) - sizePolicy.setHeightForWidth(self.ui.slider_saturation.sizePolicy().hasHeightForWidth()) + self.ui.slider_saturation = QDoubleRangeSlider( + getattr(self.ui, saturation_slider_parent) + ) + sizePolicy.setHeightForWidth( + self.ui.slider_saturation.sizePolicy().hasHeightForWidth() + ) self.ui.slider_saturation.setSizePolicy(sizePolicy) self.ui.slider_saturation.setOrientation(Qt.Horizontal) self.ui.slider_saturation.setObjectName("slider_saturation") - self.ui.gridLayout_17.addWidget(self.ui.slider_saturation, - saturation_slider_position[0], - saturation_slider_position[1], - saturation_slider_position[2], - saturation_slider_position[3]) + self.ui.gridLayout_17.addWidget( + self.ui.slider_saturation, + saturation_slider_position[0], + saturation_slider_position[1], + saturation_slider_position[2], + saturation_slider_position[3], + ) self.ui.slider_saturation.setRange(0, 100) - self.ui.slider_value = QDoubleRangeSlider(getattr(self.ui, value_slider_parent)) - sizePolicy.setHeightForWidth(self.ui.slider_value.sizePolicy().hasHeightForWidth()) + self.ui.slider_value = QDoubleRangeSlider( + getattr(self.ui, value_slider_parent) + ) + sizePolicy.setHeightForWidth( + self.ui.slider_value.sizePolicy().hasHeightForWidth() + ) self.ui.slider_value.setSizePolicy(sizePolicy) self.ui.slider_value.setOrientation(Qt.Horizontal) self.ui.slider_value.setObjectName("slider_value") - self.ui.gridLayout_17.addWidget(self.ui.slider_value, - value_slider_position[0], - value_slider_position[1], - value_slider_position[2], - value_slider_position[3]) + self.ui.gridLayout_17.addWidget( + self.ui.slider_value, + value_slider_position[0], + value_slider_position[1], + value_slider_position[2], + value_slider_position[3], + ) self.ui.slider_value.setRange(0, 100) def _hide_acquisition_ui(self, val: bool): @@ -490,10 +621,12 @@ def _hide_acquisition_ui(self, val: bool): # Calibration Tab self.ui.tabWidget.setTabEnabled(0, not val) if val: - self.ui.tabWidget.setStyleSheet("QTabBar::tab::disabled {width: 0; height: 0; margin: 0; padding: 0; border: none;} ") + self.ui.tabWidget.setStyleSheet( + "QTabBar::tab::disabled {width: 0; height: 0; margin: 0; padding: 0; border: none;} " + ) else: self.ui.tabWidget.setStyleSheet("") - self.ui.le_mm_status.setText('Not Connected') + self.ui.le_mm_status.setText("Not Connected") self.ui.le_mm_status.setStyleSheet("border: 1px solid yellow;") self.mmc = None self.mm = None @@ -528,7 +661,7 @@ def _hide_offline_ui(self, val: bool): # Processing Settings self.ui.groupBox_2.setHidden(val) - + def _enable_buttons(self): """ enables the buttons that were disabled during acquisition, calibration, or reconstruction @@ -586,8 +719,10 @@ def _handle_error(self, exc): """ - self.ui.tb_calib_assessment.setText(f'Error: {str(exc)}') - self.ui.tb_calib_assessment.setStyleSheet("border: 1px solid rgb(200,0,0);") + self.ui.tb_calib_assessment.setText(f"Error: {str(exc)}") + self.ui.tb_calib_assessment.setStyleSheet( + "border: 1px solid rgb(200,0,0);" + ) # Reset ROI if it was cropped down during reconstruction if self.use_cropped_roi: @@ -619,7 +754,9 @@ def _handle_load_finished(self): ------- """ - self.ui.tb_calib_assessment.setText('Previous calibration successfully loaded') + self.ui.tb_calib_assessment.setText( + "Previous calibration successfully loaded" + ) self.ui.tb_calib_assessment.setStyleSheet("border: 1px solid green;") self.ui.progress_bar.setValue(100) @@ -642,13 +779,22 @@ def _add_layer_to_display_boxes(self, val): """ for layer in self.viewer.layers: - if 'Overlay' in layer.name: + if "Overlay" in layer.name: continue - if layer.name not in [self.ui.cb_hue.itemText(i) for i in range(self.ui.cb_hue.count())]: + if layer.name not in [ + self.ui.cb_hue.itemText(i) + for i in range(self.ui.cb_hue.count()) + ]: self.ui.cb_hue.addItem(layer.name) - if layer.name not in [self.ui.cb_saturation.itemText(i) for i in range(self.ui.cb_saturation.count())]: + if layer.name not in [ + self.ui.cb_saturation.itemText(i) + for i in range(self.ui.cb_saturation.count()) + ]: self.ui.cb_saturation.addItem(layer.name) - if layer.name not in [self.ui.cb_value.itemText(i) for i in range(self.ui.cb_value.count())]: + if layer.name not in [ + self.ui.cb_value.itemText(i) + for i in range(self.ui.cb_value.count()) + ]: self.ui.cb_value.addItem(layer.name) def _remove_layer_from_display_boxes(self, val): @@ -687,15 +833,16 @@ def _set_tab_red(self, name, state): """ # this map corresponds to the tab index in the TabWidget GUI element - name_map = {'General': 0, - 'Processing': 1} + name_map = {"General": 0, "Processing": 1} index = name_map[name] if state: self.ui.tabWidget_3.tabBar().setTabTextColor(index, self.red_text) else: - self.ui.tabWidget_3.tabBar().setTabTextColor(index, self.original_tab_text) + self.ui.tabWidget_3.tabBar().setTabTextColor( + index, self.original_tab_text + ) def _check_line_edit(self, name): """ @@ -710,10 +857,10 @@ def _check_line_edit(self, name): ------- """ - le = getattr(self.ui, f'le_{name}') + le = getattr(self.ui, f"le_{name}") text = le.text() - if text == '': + if text == "": le.setStyleSheet("border: 1px solid rgb(200,0,0);") return False else: @@ -736,59 +883,79 @@ def _check_requirements_for_acq(self, mode): """ # Initialize all tabs in their default style (not red) - self._set_tab_red('General', False) - self._set_tab_red('Processing', False) - + self._set_tab_red("General", False) + self._set_tab_red("Processing", False) + # initialize the variable to keep track of the success of the requirement check raise_error = False # define the fields required for the specific acquisition modes. Matches LineEdit object names - phase_required = {'wavelength', 'mag', 'cond_na', 'obj_na', 'n_media', - 'phase_strength', 'ps', 'zstep'} + phase_required = { + "wavelength", + "mag", + "cond_na", + "obj_na", + "n_media", + "phase_strength", + "ps", + "zstep", + } # Initalize all fields in their default style (not red). for field in phase_required: - le = getattr(self.ui, f'le_{field}') + le = getattr(self.ui, f"le_{field}") le.setStyleSheet("") - + # Check generally required fields - if mode == 'birefringence' or mode == 'phase': - success = self._check_line_edit('save_dir') + if mode == "birefringence" or mode == "phase": + success = self._check_line_edit("save_dir") if not success: raise_error = True - self._set_tab_red('General', True) + self._set_tab_red("General", True) # check background path if 'Measured' or 'Measured + Estimated' is selected - if self.bg_option == 'local_fit+' or self.bg_option == 'global': - success = self._check_line_edit('bg_path') + if self.bg_option == "local_fit+" or self.bg_option == "global": + success = self._check_line_edit("bg_path") if not success: raise_error = True - self._set_tab_red('General', True) + self._set_tab_red("General", True) # Check phase specific fields - if mode == 'phase': + if mode == "phase": # add in extra requirement is user is acquiring PhaseFromBF if self.ui.chb_phase_from_bf.isChecked(): - cont = self._check_line_edit('recon_wavelength') - tab = getattr(self.ui, f'le_recon_wavelength').parent().parent().objectName() + cont = self._check_line_edit("recon_wavelength") + tab = ( + getattr(self.ui, f"le_recon_wavelength") + .parent() + .parent() + .objectName() + ) if not cont: raise_error = True self._set_tab_red(tab, True) for field in phase_required: cont = self._check_line_edit(field) - tab = getattr(self.ui, f'le_{field}').parent().parent().objectName() + tab = ( + getattr(self.ui, f"le_{field}") + .parent() + .parent() + .objectName() + ) if not cont: raise_error = True - if field != 'zstep': + if field != "zstep": self._set_tab_red(tab, True) else: continue # Alert the user to check and enter in the missing parameters if raise_error: - raise ValueError('Please enter in all of the parameters necessary for the acquisition') + raise ValueError( + "Please enter in all of the parameters necessary for the acquisition" + ) def _check_requirements_for_reconstruction(self): """ @@ -802,8 +969,8 @@ def _check_requirements_for_reconstruction(self): """ # Initalize all tab elements and reconstruct button to default state (not red) - self._set_tab_red('General', False) - self._set_tab_red('Processing', False) + self._set_tab_red("General", False) + self._set_tab_red("Processing", False) self.ui.qbutton_reconstruct.setStyleSheet("") # initalize the success variable of the requirement check @@ -811,38 +978,73 @@ def _check_requirements_for_reconstruction(self): # gather the specified output channels (will determine which requirements are necessary to look at) output_channels = self.ui.le_output_channels.text() - output_channels = output_channels.split(',') - output_channels = [chan.replace(' ', '') for chan in output_channels] + output_channels = output_channels.split(",") + output_channels = [chan.replace(" ", "") for chan in output_channels] # intialize the reconstruction specific required fields - always_required = {'data_dir', 'save_dir', 'positions', 'timepoints', 'output_channels'} - birefringence_required = {'calibration_metadata', 'recon_wavelength'} - phase_required = {'recon_wavelength', 'mag', 'obj_na', 'cond_na', 'n_media', - 'phase_strength', 'ps'} - + always_required = { + "data_dir", + "save_dir", + "positions", + "timepoints", + "output_channels", + } + birefringence_required = {"calibration_metadata", "recon_wavelength"} + phase_required = { + "recon_wavelength", + "mag", + "obj_na", + "cond_na", + "n_media", + "phase_strength", + "ps", + } + # intialize all UI elements in the default state for field in always_required: - le = getattr(self.ui, f'le_{field}') + le = getattr(self.ui, f"le_{field}") le.setStyleSheet("") for field in phase_required: - le = getattr(self.ui, f'le_{field}') + le = getattr(self.ui, f"le_{field}") le.setStyleSheet("") for field in always_required: cont = self._check_line_edit(field) if not cont: success = False - if field == 'data_dir' or field == 'save_dir': - self._set_tab_red('General', True) - if field == 'positions' or field == 'timepoints' or field == 'output_channels': - self._set_tab_red('Processing', True) + if field == "data_dir" or field == "save_dir": + self._set_tab_red("General", True) + if ( + field == "positions" + or field == "timepoints" + or field == "output_channels" + ): + self._set_tab_red("Processing", True) else: continue - possible_channels = ['Retardance', 'Orientation', 'BF', 'S0', 'S1', 'S2', 'S3', 'Phase2D', 'Phase3D'] - bire_channels = ['Retardance', 'Orientation', 'BF', 'S0', 'S1', 'S2', 'S3'] + possible_channels = [ + "Retardance", + "Orientation", + "BF", + "S0", + "S1", + "S2", + "S3", + "Phase2D", + "Phase3D", + ] + bire_channels = [ + "Retardance", + "Orientation", + "BF", + "S0", + "S1", + "S2", + "S3", + ] qlipp_channel_present = False bire_channel_present = False - if self.method == 'QLIPP': + if self.method == "QLIPP": for channel in output_channels: if channel in possible_channels: qlipp_channel_present = True @@ -853,66 +1055,94 @@ def _check_requirements_for_reconstruction(self): if bire_channel_present: for field in birefringence_required: cont = self._check_line_edit(field) - tab = getattr(self.ui, f'le_{field}').parent().parent().objectName() + tab = ( + getattr(self.ui, f"le_{field}") + .parent() + .parent() + .objectName() + ) if not cont: - if field != 'calibration_metadata': + if field != "calibration_metadata": self._set_tab_red(tab, True) success = False else: # self._set_tab_red(tab, False) continue - if 'Phase2D' in output_channels or 'Phase3D' in output_channels: + if ( + "Phase2D" in output_channels + or "Phase3D" in output_channels + ): for field in phase_required: cont = self._check_line_edit(field) - tab = getattr(self.ui, f'le_{field}').parent().parent().objectName() + tab = ( + getattr(self.ui, f"le_{field}") + .parent() + .parent() + .objectName() + ) if not cont: self._set_tab_red(tab, True) success = False else: self._set_tab_red(tab, False) continue - if 'Phase2D' in output_channels and self.mode == '2D': - cont = self._check_line_edit('focus_zidx') + if "Phase2D" in output_channels and self.mode == "2D": + cont = self._check_line_edit("focus_zidx") if not cont: - self._set_tab_red('Processing', True) + self._set_tab_red("Processing", True) success = False else: - self._set_tab_red('Processing', True) - self.ui.le_output_channels.setStyleSheet("border: 1px solid rgb(200,0,0);") - print('User did not specify any QLIPP Specific Channels') + self._set_tab_red("Processing", True) + self.ui.le_output_channels.setStyleSheet( + "border: 1px solid rgb(200,0,0);" + ) + print("User did not specify any QLIPP Specific Channels") success = False - elif self.method == 'PhaseFromBF': - cont = self._check_line_edit('bf_chan') - tab = getattr(self.ui, f'le_bf_chan').parent().parent().objectName() + elif self.method == "PhaseFromBF": + cont = self._check_line_edit("bf_chan") + tab = ( + getattr(self.ui, f"le_bf_chan").parent().parent().objectName() + ) if not cont: self._set_tab_red(tab, True) success = False - if 'Phase2D' in output_channels or 'Phase3D' in output_channels: + if "Phase2D" in output_channels or "Phase3D" in output_channels: for field in phase_required: cont = self._check_line_edit(field) - tab = getattr(self.ui, f'le_{field}').parent().parent().objectName() + tab = ( + getattr(self.ui, f"le_{field}") + .parent() + .parent() + .objectName() + ) if not cont: self._set_tab_red(tab, True) success = False else: continue - if 'Phase2D' in output_channels and self.mode == '2D': - cont = self._check_line_edit('focus_zidx') + if "Phase2D" in output_channels and self.mode == "2D": + cont = self._check_line_edit("focus_zidx") if not cont: - self._set_tab_red('Processing', True) + self._set_tab_red("Processing", True) success = False else: - self._set_tab_red('Processing', True) - self.ui.le_output_channels.setStyleSheet("border: 1px solid rgb(200,0,0);") - print('User did not specify any PhaseFromBF Specific Channels (Phase2D, Phase3D)') + self._set_tab_red("Processing", True) + self.ui.le_output_channels.setStyleSheet( + "border: 1px solid rgb(200,0,0);" + ) + print( + "User did not specify any PhaseFromBF Specific Channels (Phase2D, Phase3D)" + ) success = False else: - print('Error in parameter checks') - self.ui.qbutton_reconstruct.setStyleSheet("border: 1px solid rgb(200,0,0);") + print("Error in parameter checks") + self.ui.qbutton_reconstruct.setStyleSheet( + "border: 1px solid rgb(200,0,0);" + ) success = False return success @@ -937,119 +1167,167 @@ def _populate_config_from_app(self): self.save_directory = self.ui.le_save_dir.text() self.config_reader.method = self.method self.config_reader.mode = self.mode - self.config_reader.data_save_name = self.ui.le_data_save_name.text() if self.ui.le_data_save_name.text() != '' \ - else pathlib.PurePath(self.data_dir).name - self.config_reader.calibration_metadata = self.ui.le_calibration_metadata.text() + self.config_reader.data_save_name = ( + self.ui.le_data_save_name.text() + if self.ui.le_data_save_name.text() != "" + else pathlib.PurePath(self.data_dir).name + ) + self.config_reader.calibration_metadata = ( + self.ui.le_calibration_metadata.text() + ) self.config_reader.background = self.ui.le_bg_path.text() self.config_reader.background_correction = self.bg_option # Assumes that positions/timepoints can either be 'all'; '[all]'; 1, 2, 3, N; (start, end) positions = self.ui.le_positions.text() - positions = positions.replace(' ', '') - if positions == 'all' or positions == "['all']" or positions == '[all]': - self.config_reader.positions = ['all'] - elif positions.startswith('[') and positions.endswith(']'): - vals = positions[1:-1].split(',') + positions = positions.replace(" ", "") + if ( + positions == "all" + or positions == "['all']" + or positions == "[all]" + ): + self.config_reader.positions = ["all"] + elif positions.startswith("[") and positions.endswith("]"): + vals = positions[1:-1].split(",") if len(vals) != 2: - self._set_tab_red('Processing', True) - self.ui.le_positions.setStyleSheet("border: 1px solid rgb(200,0,0);") + self._set_tab_red("Processing", True) + self.ui.le_positions.setStyleSheet( + "border: 1px solid rgb(200,0,0);" + ) else: - self._set_tab_red('Processing', False) + self._set_tab_red("Processing", False) self.ui.le_positions.setStyleSheet("") self.config_reader.positions = [(int(vals[0]), int(vals[1]))] - elif positions.startswith('(') and positions.endswith(')'): + elif positions.startswith("(") and positions.endswith(")"): self.config_reader.positions = [eval(positions)] else: - vals = positions.split(',') + vals = positions.split(",") vals = map(lambda x: int(x), vals) self.config_reader.positions = list(vals) timepoints = self.ui.le_timepoints.text() - timepoints = timepoints.replace(' ', '') - if timepoints == 'all' or timepoints == "['all']" or timepoints == '[all]': - self.config_reader.timepoints = ['all'] - elif timepoints.startswith('[') and timepoints.endswith(']'): - vals = timepoints[1:-1].split(',') + timepoints = timepoints.replace(" ", "") + if ( + timepoints == "all" + or timepoints == "['all']" + or timepoints == "[all]" + ): + self.config_reader.timepoints = ["all"] + elif timepoints.startswith("[") and timepoints.endswith("]"): + vals = timepoints[1:-1].split(",") if len(vals) != 2: - self._set_tab_red('Processing', True) - self.ui.le_timepoints.setStyleSheet("border: 1px solid rgb(200,0,0);") + self._set_tab_red("Processing", True) + self.ui.le_timepoints.setStyleSheet( + "border: 1px solid rgb(200,0,0);" + ) else: - self._set_tab_red('Processing', False) + self._set_tab_red("Processing", False) self.ui.le_timepoints.setStyleSheet("") self.config_reader.timepoints = [(int(vals[0]), int(vals[1]))] - elif timepoints.startswith('(') and timepoints.endswith(')'): + elif timepoints.startswith("(") and timepoints.endswith(")"): self.config_reader.timepoints = [eval(timepoints)] else: - vals = timepoints.split(',') + vals = timepoints.split(",") vals = map(lambda x: int(x), vals) self.config_reader.timepoints = list(vals) attrs = dir(self.ui) - skip = ['wavelength', 'pixel_size', 'magnification', 'NA_objective', 'NA_condenser', 'n_objective_media'] + skip = [ + "wavelength", + "pixel_size", + "magnification", + "NA_objective", + "NA_condenser", + "n_objective_media", + ] # TODO: Figure out how to catch errors in regularizer strength field for key, value in PROCESSING.items(): if key not in skip: - if key == 'background_correction': - bg_map = {0: 'None', 1: 'global', 2: 'local_fit', 3: 'local_fit+'} - setattr(self.config_reader, key, bg_map[self.ui.cb_bg_method.currentIndex()]) - - elif key == 'output_channels': + if key == "background_correction": + bg_map = { + 0: "None", + 1: "global", + 2: "local_fit", + 3: "local_fit+", + } + setattr( + self.config_reader, + key, + bg_map[self.ui.cb_bg_method.currentIndex()], + ) + + elif key == "output_channels": # Reset style sheets self.ui.le_output_channels.setStyleSheet("") self.ui.cb_mode.setStyleSheet("") - self._set_tab_red('Processing', False) + self._set_tab_red("Processing", False) # Make a list of the channels from the line edit string field_text = self.ui.le_output_channels.text() - channels = field_text.split(',') - channels = [i.replace(' ', '') for i in channels] + channels = field_text.split(",") + channels = [i.replace(" ", "") for i in channels] setattr(self.config_reader, key, channels) - if 'Phase3D' in channels and 'Phase2D' in channels: - self.ui.le_output_channels.setStyleSheet("border: 1px solid rgb(200,0,0);") - self._set_tab_red('Processing', True) + if "Phase3D" in channels and "Phase2D" in channels: + self.ui.le_output_channels.setStyleSheet( + "border: 1px solid rgb(200,0,0);" + ) + self._set_tab_red("Processing", True) + raise KeyError( + f"Both Phase3D and Phase2D cannot be specified in output_channels. Please compute " + f"separately" + ) + + if "Phase3D" in channels and self.mode == "2D": + self._set_tab_red("Processing", True) + self.ui.le_output_channels.setStyleSheet( + "border: 1px solid rgb(200,0,0);" + ) + self.ui.cb_mode.setStyleSheet( + "border: 1px solid rgb(200,0,0);" + ) raise KeyError( - f'Both Phase3D and Phase2D cannot be specified in output_channels. Please compute ' - f'separately') - - if 'Phase3D' in channels and self.mode == '2D': - self._set_tab_red('Processing', True) - self.ui.le_output_channels.setStyleSheet("border: 1px solid rgb(200,0,0);") - self.ui.cb_mode.setStyleSheet("border: 1px solid rgb(200,0,0);") - raise KeyError(f'Specified mode is 2D and Phase3D was specified for reconstruction. ' - 'Only 2D reconstructions can be performed in 2D mode') - - if 'Phase2D' in channels and self.mode == '3D': - self._set_tab_red('Processing', True) - self.ui.le_output_channels.setStyleSheet("border: 1px solid rgb(200,0,0);") - self.ui.cb_mode.setStyleSheet("border: 1px solid rgb(200,0,0);") - raise KeyError(f'Specified mode is 3D and Phase2D was specified for reconstruction. ' - 'Only 3D reconstructions can be performed in 3D mode') - - elif key == 'pad_z': + f"Specified mode is 2D and Phase3D was specified for reconstruction. " + "Only 2D reconstructions can be performed in 2D mode" + ) + + if "Phase2D" in channels and self.mode == "3D": + self._set_tab_red("Processing", True) + self.ui.le_output_channels.setStyleSheet( + "border: 1px solid rgb(200,0,0);" + ) + self.ui.cb_mode.setStyleSheet( + "border: 1px solid rgb(200,0,0);" + ) + raise KeyError( + f"Specified mode is 3D and Phase2D was specified for reconstruction. " + "Only 3D reconstructions can be performed in 3D mode" + ) + + elif key == "pad_z": val = self.ui.le_pad_z.text() setattr(self.config_reader, key, int(val)) - elif key == 'gpu_id': + elif key == "gpu_id": val = self.ui.le_gpu_id.text() setattr(self.config_reader, key, int(val)) - elif key == 'use_gpu': + elif key == "use_gpu": if self.ui.chb_use_gpu.isChecked(): setattr(self.config_reader, key, True) else: setattr(self.config_reader, key, False) - elif key == 'focus_zidx': + elif key == "focus_zidx": val = self.ui.le_focus_zidx.text() - if val == '': + if val == "": setattr(self.config_reader, key, None) else: setattr(self.config_reader, key, int(val)) else: - attr_name = f'le_{key}' + attr_name = f"le_{key}" if attr_name in attrs: le = getattr(self.ui, attr_name) try: @@ -1063,44 +1341,108 @@ def _populate_config_from_app(self): continue # Manually enter in phase regularization - if self.mode == '3D': + if self.mode == "3D": if self.ui.cb_phase_denoiser.currentIndex() == 0: - setattr(self.config_reader, 'Tik_reg_ph_3D', float(self.ui.le_phase_strength.text())) + setattr( + self.config_reader, + "Tik_reg_ph_3D", + float(self.ui.le_phase_strength.text()), + ) else: - setattr(self.config_reader, 'TV_reg_ph_3D', float(self.ui.le_phase_strength.text())) - setattr(self.config_reader, 'rho_3D', float(self.ui.le_rho.text())) - setattr(self.config_reader, 'itr_3D', int(self.ui.le_itr.text())) + setattr( + self.config_reader, + "TV_reg_ph_3D", + float(self.ui.le_phase_strength.text()), + ) + setattr( + self.config_reader, "rho_3D", float(self.ui.le_rho.text()) + ) + setattr( + self.config_reader, "itr_3D", int(self.ui.le_itr.text()) + ) else: if self.ui.cb_phase_denoiser.currentIndex() == 0: - setattr(self.config_reader, 'Tik_reg_ph_2D', float(self.ui.le_phase_strength.text())) + setattr( + self.config_reader, + "Tik_reg_ph_2D", + float(self.ui.le_phase_strength.text()), + ) else: - setattr(self.config_reader, 'TV_reg_ph_2D', float(self.ui.le_phase_strength.text())) - setattr(self.config_reader, 'rho_2D', float(self.ui.le_rho.text())) - setattr(self.config_reader, 'itr_2D', int(self.ui.le_itr.text())) - - if self.method == 'PhaseFromBF': - setattr(self.config_reader, 'wavelength', int(self.ui.le_recon_wavelength.text())) - setattr(self.config_reader, 'brightfield_channel_index', - int(self.ui.le_bf_chan.text())) + setattr( + self.config_reader, + "TV_reg_ph_2D", + float(self.ui.le_phase_strength.text()), + ) + setattr( + self.config_reader, "rho_2D", float(self.ui.le_rho.text()) + ) + setattr( + self.config_reader, "itr_2D", int(self.ui.le_itr.text()) + ) + + if self.method == "PhaseFromBF": + setattr( + self.config_reader, + "wavelength", + int(self.ui.le_recon_wavelength.text()), + ) + setattr( + self.config_reader, + "brightfield_channel_index", + int(self.ui.le_bf_chan.text()), + ) focus_zidx = self.ui.le_focus_zidx.text() - setattr(self.config_reader, 'focus_zidx', int(focus_zidx) if focus_zidx != '' else None) + setattr( + self.config_reader, + "focus_zidx", + int(focus_zidx) if focus_zidx != "" else None, + ) else: - setattr(self.config_reader, 'wavelength', int(self.ui.le_recon_wavelength.text())) + setattr( + self.config_reader, + "wavelength", + int(self.ui.le_recon_wavelength.text()), + ) # Parse name mismatch fields - setattr(self.config_reader, 'NA_objective', float(self.ui.le_obj_na.text()) if self.ui.le_obj_na.text() != '' - else None) - setattr(self.config_reader, 'NA_condenser', float(self.ui.le_cond_na.text()) if self.ui.le_cond_na.text() != '' - else None) - setattr(self.config_reader, 'pixel_size', float(self.ui.le_ps.text()) if self.ui.le_ps.text() != '' - else None) - setattr(self.config_reader, 'n_objective_media', float(self.ui.le_n_media.text()) - if self.ui.le_n_media.text() != '' else None) - setattr(self.config_reader, 'magnification', float(self.ui.le_mag.text()) if self.ui.le_mag.text() != '' - else None) + setattr( + self.config_reader, + "NA_objective", + float(self.ui.le_obj_na.text()) + if self.ui.le_obj_na.text() != "" + else None, + ) + setattr( + self.config_reader, + "NA_condenser", + float(self.ui.le_cond_na.text()) + if self.ui.le_cond_na.text() != "" + else None, + ) + setattr( + self.config_reader, + "pixel_size", + float(self.ui.le_ps.text()) + if self.ui.le_ps.text() != "" + else None, + ) + setattr( + self.config_reader, + "n_objective_media", + float(self.ui.le_n_media.text()) + if self.ui.le_n_media.text() != "" + else None, + ) + setattr( + self.config_reader, + "magnification", + float(self.ui.le_mag.text()) + if self.ui.le_mag.text() != "" + else None, + ) def _populate_from_config(self): """ @@ -1118,47 +1460,53 @@ def _populate_from_config(self): self.save_directory = self.config_reader.save_dir self.ui.le_save_dir.setText(self.config_reader.save_dir) self.ui.le_data_save_name.setText(self.config_reader.data_save_name) - self.ui.le_calibration_metadata.setText(self.config_reader.calibration_metadata) + self.ui.le_calibration_metadata.setText( + self.config_reader.calibration_metadata + ) self.ui.le_bg_path.setText(self.config_reader.background) self.mode = self.config_reader.mode - self.ui.cb_mode.setCurrentIndex(0) if self.mode == '3D' else self.ui.cb_mode.setCurrentIndex(1) + self.ui.cb_mode.setCurrentIndex( + 0 + ) if self.mode == "3D" else self.ui.cb_mode.setCurrentIndex(1) self.method = self.config_reader.method - if self.method == 'QLIPP': + if self.method == "QLIPP": self.ui.cb_method.setCurrentIndex(0) - elif self.method == 'PhaseFromBF': + elif self.method == "PhaseFromBF": self.ui.cb_method.setCurrentIndex(1) else: - print(f'Did not understand method from config: {self.method}') + print(f"Did not understand method from config: {self.method}") self.ui.cb_method.setStyleSheet("border: 1px solid rgb(200,0,0);") self.bg_option = self.config_reader.background_correction - if self.bg_option == 'None': + if self.bg_option == "None": self.ui.cb_bg_method.setCurrentIndex(0) - elif self.bg_option == 'global': + elif self.bg_option == "global": self.ui.cb_bg_method.setCurrentIndex(1) - elif self.bg_option == 'local_fit': + elif self.bg_option == "local_fit": self.ui.cb_bg_method.setCurrentIndex(2) - elif self.bg_option == 'local_fit+': + elif self.bg_option == "local_fit+": self.ui.cb_bg_method.setCurrentIndex(3) else: - print(f'Did not understand method from config: {self.method}') + print(f"Did not understand method from config: {self.method}") self.ui.cb_method.setStyleSheet("border: 1px solid rgb(200,0,0);") if isinstance(self.config_reader.positions, list): positions = self.config_reader.positions - text = '' + text = "" for idx, pos in enumerate(positions): - text += f'{pos}, ' if idx != len(positions) - 1 else f'{pos}' + text += f"{pos}, " if idx != len(positions) - 1 else f"{pos}" self.ui.le_positions.setText(text) else: self.ui.le_positions.setText(str(self.config_reader.positions)) if isinstance(self.config_reader.timepoints, list): timepoints = self.config_reader.timepoints - text = '' + text = "" for idx, time in enumerate(timepoints): - text += f'{time}, ' if idx != len(timepoints) - 1 else f'{time}' + text += ( + f"{time}, " if idx != len(timepoints) - 1 else f"{time}" + ) self.ui.le_timepoints.setText(text) else: self.ui.le_timepoints.setText(str(self.config_reader.timepoints)) @@ -1166,11 +1514,15 @@ def _populate_from_config(self): # Parse Processing name mismatch fields wavelengths = self.config_reader.wavelength if not isinstance(wavelengths, list): - self.ui.le_recon_wavelength.setText(str(int(self.config_reader.wavelength))) + self.ui.le_recon_wavelength.setText( + str(int(self.config_reader.wavelength)) + ) else: - text = '' + text = "" for idx, chan in enumerate(wavelengths): - text += f'{chan}, ' if idx != len(wavelengths) - 1 else f'{chan}' + text += ( + f"{chan}, " if idx != len(wavelengths) - 1 else f"{chan}" + ) self.ui.le_recon_wavelength.setText(text) self.ui.le_obj_na.setText(str(self.config_reader.NA_objective)) @@ -1180,72 +1532,92 @@ def _populate_from_config(self): self.ui.le_mag.setText(str(self.config_reader.magnification)) # Parse PhaseFromBF - if self.method == 'PhaseFromBF': - self.ui.le_bf_chan.setText(str(self.config_reader.brightfield_channel_index)) - + if self.method == "PhaseFromBF": + self.ui.le_bf_chan.setText( + str(self.config_reader.brightfield_channel_index) + ) + # Parse processing automatically denoiser = None for key, val in PROCESSING.items(): - if key == 'output_channels': + if key == "output_channels": channels = self.config_reader.output_channels - text = '' + text = "" for idx, chan in enumerate(channels): - text += f'{chan}, ' if idx != len(channels)-1 else f'{chan}' + text += ( + f"{chan}, " if idx != len(channels) - 1 else f"{chan}" + ) self.ui.le_output_channels.setText(text) - elif key == 'focus_zidx': + elif key == "focus_zidx": indices = self.config_reader.focus_zidx if isinstance(indices, int): self.ui.le_focus_zidx.setText(str(indices)) elif isinstance(indices, list): - text = '' + text = "" for idx, val in enumerate(indices): - text += f'{val}, ' if idx != len(indices) - 1 else f'{val}' + text += ( + f"{val}, " if idx != len(indices) - 1 else f"{val}" + ) self.ui.le_focus_zidx.setText(text) - elif key == 'use_gpu': + elif key == "use_gpu": state = getattr(self.config_reader, key) self.ui.chb_use_gpu.setChecked(state) - elif key == 'gpu_id': + elif key == "gpu_id": val = str(int(getattr(self.config_reader, key))) self.ui.le_gpu_id.setText(val) - elif key == 'pad_z': + elif key == "pad_z": val = str(int(getattr(self.config_reader, key))) self.ui.le_pad_z.setText(val) - elif key == 'focus_zidx': + elif key == "focus_zidx": val = str(int(getattr(self.config_reader, key))) self.ui.le_focus_zidx.setText(val) - elif hasattr(self.ui, f'le_{key}'): - le = getattr(self.ui, f'le_{key}') - le.setText(str(getattr(self.config_reader, key)) if not isinstance(getattr(self.config_reader, key), - str) else getattr(self.config_reader, - key)) - elif hasattr(self.ui, f'cb_{key}'): - cb = getattr(self.ui, f'cb_{key}') + elif hasattr(self.ui, f"le_{key}"): + le = getattr(self.ui, f"le_{key}") + le.setText( + str(getattr(self.config_reader, key)) + if not isinstance(getattr(self.config_reader, key), str) + else getattr(self.config_reader, key) + ) + elif hasattr(self.ui, f"cb_{key}"): + cb = getattr(self.ui, f"cb_{key}") items = [cb.itemText(i) for i in range(cb.count())] cfg_attr = getattr(self.config_reader, key) self.ui.cb_mode.setCurrentIndex(items.index(cfg_attr)) - elif key == 'phase_denoiser_2D' or key == 'phase_denoiser_3D': + elif key == "phase_denoiser_2D" or key == "phase_denoiser_3D": cb = self.ui.cb_phase_denoiser - cfg_attr = getattr(self.config_reader, f'phase_denoiser_{self.mode}') + cfg_attr = getattr( + self.config_reader, f"phase_denoiser_{self.mode}" + ) denoiser = cfg_attr - cb.setCurrentIndex(0) if cfg_attr == 'Tikhonov' else cb.setCurrentIndex(1) + cb.setCurrentIndex( + 0 + ) if cfg_attr == "Tikhonov" else cb.setCurrentIndex(1) else: - if denoiser == 'Tikhonov': - strength = getattr(self.config_reader, f'Tik_reg_ph_{self.mode}') + if denoiser == "Tikhonov": + strength = getattr( + self.config_reader, f"Tik_reg_ph_{self.mode}" + ) self.ui.le_phase_strength.setText(str(strength)) else: - strength = getattr(self.config_reader, f'TV_reg_ph_{self.mode}') + strength = getattr( + self.config_reader, f"TV_reg_ph_{self.mode}" + ) self.ui.le_phase_strength.setText(str(strength)) - self.ui.le_rho.setText(str(getattr(self.config_reader, f'rho_{self.mode}'))) - self.ui.le_itr.setText(str(getattr(self.config_reader, f'itr_{self.mode}'))) + self.ui.le_rho.setText( + str(getattr(self.config_reader, f"rho_{self.mode}")) + ) + self.ui.le_itr.setText( + str(getattr(self.config_reader, f"itr_{self.mode}")) + ) @Slot(bool) def change_gui_mode(self): @@ -1256,28 +1628,31 @@ def change_gui_mode(self): ------- """ - if self.gui_mode == 'offline': - self.ui.qbutton_gui_mode.setText('Switch to Offline') - self.ui.le_gui_mode.setText('Online') - self.ui.le_gui_mode.setStyleSheet("border: 1px solid green; color: green;") + if self.gui_mode == "offline": + self.ui.qbutton_gui_mode.setText("Switch to Offline") + self.ui.le_gui_mode.setText("Online") + self.ui.le_gui_mode.setStyleSheet( + "border: 1px solid green; color: green;" + ) self._hide_offline_ui(True) self._hide_acquisition_ui(False) - self.gui_mode = 'online' + self.gui_mode = "online" self.connect_to_mm() else: - self.ui.qbutton_gui_mode.setText('Switch to Online') - self.ui.le_gui_mode.setText('Offline') - self.ui.le_gui_mode.setStyleSheet("border: 1px solid rgb(200,0,0); color: rgb(200,0,0);") + self.ui.qbutton_gui_mode.setText("Switch to Online") + self.ui.le_gui_mode.setText("Offline") + self.ui.le_gui_mode.setStyleSheet( + "border: 1px solid rgb(200,0,0); color: rgb(200,0,0);" + ) self._hide_offline_ui(False) self._hide_acquisition_ui(True) - self.gui_mode = 'offline' + self.gui_mode = "offline" self.ui.cb_config_group.clear() - #Make sure button is still visible + # Make sure button is still visible self.ui.qbutton_mm_connect.setEnabled(True) - @Slot(bool) def connect_to_mm(self): """ @@ -1289,8 +1664,8 @@ def connect_to_mm(self): ------- """ - RECOMMENDED_MM = '20220920' - ZMQ_TARGET_VERSION = '4.2.0' + RECOMMENDED_MM = "20220920" + ZMQ_TARGET_VERSION = "4.2.0" try: # Try to open Bridge. Requires micromanager to be open with server running. # This does not fail gracefully, so I'm wrapping it in its own try-except block. @@ -1299,20 +1674,35 @@ def connect_to_mm(self): self.mmc = self.bridge.get_core() self.mm = self.bridge.get_studio() except: - print(("Could not establish pycromanager bridge.\n" - "Is micromanager open?\n" - "Is Tools > Options > Run server on port 4827 checked?\n" - f"Are you using nightly build {RECOMMENDED_MM}?")) + print( + ( + "Could not establish pycromanager bridge.\n" + "Is micromanager open?\n" + "Is Tools > Options > Run server on port 4827 checked?\n" + f"Are you using nightly build {RECOMMENDED_MM}?" + ) + ) raise EnvironmentError # Warn the user if there is a MicroManager/ZMQ version mismatch - self.bridge._main_socket.send({"command": "connect", "debug": False}) + self.bridge._main_socket.send( + {"command": "connect", "debug": False} + ) reply_json = self.bridge._main_socket.receive(timeout=500) - zmq_mm_version = reply_json['version'] + zmq_mm_version = reply_json["version"] if zmq_mm_version != ZMQ_TARGET_VERSION: - upgrade_str = 'upgrade' if version.parse(zmq_mm_version) < version.parse(ZMQ_TARGET_VERSION) else 'downgrade' - print(("WARNING: This version of Micromanager has not been tested with recOrder.\n" - f"Please {upgrade_str} to MicroManager nightly build {RECOMMENDED_MM}.")) + upgrade_str = ( + "upgrade" + if version.parse(zmq_mm_version) + < version.parse(ZMQ_TARGET_VERSION) + else "downgrade" + ) + print( + ( + "WARNING: This version of Micromanager has not been tested with recOrder.\n" + f"Please {upgrade_str} to MicroManager nightly build {RECOMMENDED_MM}." + ) + ) # Find config group containing calibration channels # calib_channels is typically ['State0', 'State1', 'State2', ...] @@ -1330,9 +1720,16 @@ def connect_to_mm(self): config_list = [] for j in range(configs.size()): config_list.append(configs.get(j)) - if np.all([np.any([ch in config for config in config_list]) for ch in self.calib_channels]): + if np.all( + [ + np.any([ch in config for config in config_list]) + for ch in self.calib_channels + ] + ): if not config_group_found: - self.config_group = group # set to first config group found + self.config_group = ( + group # set to first config group found + ) config_group_found = True self.ui.cb_config_group.addItem(group) # not entirely sure what this part does, but I left it in @@ -1341,28 +1738,32 @@ def connect_to_mm(self): if ch not in self.calib_channels: self.ui.cb_acq_channel.addItem(ch) if not config_group_found: - msg = f'No config group contains channels {self.calib_channels}. ' \ - 'Please refer to the recOrder wiki on how to set up the config properly.' - self.ui.cb_config_group.setStyleSheet("border: 1px solid rgb(200,0,0);") + msg = ( + f"No config group contains channels {self.calib_channels}. " + "Please refer to the recOrder wiki on how to set up the config properly." + ) + self.ui.cb_config_group.setStyleSheet( + "border: 1px solid rgb(200,0,0);" + ) raise KeyError(msg) - # set startup LC control mode _devices = self.mmc.getLoadedDevices() loaded_devices = [_devices.get(i) for i in range(_devices.size())] if LC_DEVICE_NAME in loaded_devices: - config_desc = self.mmc.getConfigData('Channel','State0').getVerbose() - if 'String send to' in config_desc: - self.calib_mode = 'MM-Retardance' + config_desc = self.mmc.getConfigData( + "Channel", "State0" + ).getVerbose() + if "String send to" in config_desc: + self.calib_mode = "MM-Retardance" self.ui.cb_calib_mode.setCurrentIndex(0) - if 'Voltage (V)' in config_desc: - self.calib_mode = 'MM-Voltage' + if "Voltage (V)" in config_desc: + self.calib_mode = "MM-Voltage" self.ui.cb_calib_mode.setCurrentIndex(1) else: - self.calib_mode = 'DAC' + self.calib_mode = "DAC" self.ui.cb_calib_mode.setCurrentIndex(2) - self.mm_status_changed.emit(True) except: @@ -1371,20 +1772,22 @@ def connect_to_mm(self): @Slot(bool) def handle_mm_status_update(self, value): if value: - self.ui.le_mm_status.setText('Success!') + self.ui.le_mm_status.setText("Success!") self.ui.le_mm_status.setStyleSheet("background-color: green;") - #Disabling the button + # Disabling the button self.ui.qbutton_mm_connect.setEnabled(False) else: - #Make sure button is still visible if it fails + # Make sure button is still visible if it fails self.ui.qbutton_mm_connect.setEnabled(True) - self.ui.le_mm_status.setText('Failed.') - self.ui.le_mm_status.setStyleSheet("background-color: rgb(200,0,0);") + self.ui.le_mm_status.setText("Failed.") + self.ui.le_mm_status.setStyleSheet( + "background-color: rgb(200,0,0);" + ) @Slot(tuple) def handle_progress_update(self, value): self.ui.progress_bar.setValue(value[0]) - self.ui.label_progress.setText('Progress: ' + value[1]) + self.ui.label_progress.setText("Progress: " + value[1]) @Slot(str) def handle_extinction_update(self, value): @@ -1409,12 +1812,17 @@ def handle_plot_update(self, value): self.intensity_monitor.append(value) self.ui.plot_widget.plot(self.intensity_monitor) - if self.plot_sequence[0] == 'Coarse': + if self.plot_sequence[0] == "Coarse": self.plot_item.autoRange() else: - self.plot_item.setRange(xRange=(self.plot_sequence[1], len(self.intensity_monitor)), - yRange=(0, np.max(self.intensity_monitor[self.plot_sequence[1]:])), - padding=0.1) + self.plot_item.setRange( + xRange=(self.plot_sequence[1], len(self.intensity_monitor)), + yRange=( + 0, + np.max(self.intensity_monitor[self.plot_sequence[1] :]), + ), + padding=0.1, + ) @Slot(str) def handle_calibration_assessment_update(self, value): @@ -1424,12 +1832,18 @@ def handle_calibration_assessment_update(self, value): def handle_calibration_assessment_msg_update(self, value): self.ui.tb_calib_assessment.setText(value) - if self.calib_assessment_level == 'good': - self.ui.tb_calib_assessment.setStyleSheet("border: 1px solid green;") - elif self.calib_assessment_level == 'okay': - self.ui.tb_calib_assessment.setStyleSheet("border: 1px solid rgb(252,190,3);") - elif self.calib_assessment_level == 'bad': - self.ui.tb_calib_assessment.setStyleSheet("border: 1px solid rgb(200,0,0);") + if self.calib_assessment_level == "good": + self.ui.tb_calib_assessment.setStyleSheet( + "border: 1px solid green;" + ) + elif self.calib_assessment_level == "okay": + self.ui.tb_calib_assessment.setStyleSheet( + "border: 1px solid rgb(252,190,3);" + ) + elif self.calib_assessment_level == "bad": + self.ui.tb_calib_assessment.setStyleSheet( + "border: 1px solid rgb(200,0,0);" + ) else: pass @@ -1443,99 +1857,127 @@ def handle_lc_states_emit(self, value: tuple[tuple, dict[str, list]]): 2-tuple consisting of a tuple of polarization state names and a dictionary of LC retardance values. """ pol_states, lc_values = value - - # Calculate circle - theta = np.linspace(0, 2np.pi, 100) - x_circ = self.swingnp.cos(theta) + lc_values["LCA"][0] - y_circ = self.swingnp.sin(theta) + lc_values["LCB"][0] + + # Calculate circle + theta = np.linspace(0, 2 np.pi, 100) + x_circ = self.swing * np.cos(theta) + lc_values["LCA"][0] + y_circ = self.swing * np.sin(theta) + lc_values["LCB"][0] import matplotlib.pyplot as plt - plt.close('all') - with plt.rc_context({ - "axes.spines.right": False, - "axes.spines.top": False, - }) and plt.ion(): + + plt.close("all") + with plt.rc_context( + { + "axes.spines.right": False, + "axes.spines.top": False, + } + ) and plt.ion(): plt.figure("Calibrated LC States") - plt.scatter(lc_values["LCA"], lc_values["LCB"], c='r') - plt.plot(x_circ, y_circ, 'k--', alpha=0.25) + plt.scatter(lc_values["LCA"], lc_values["LCB"], c="r") + plt.plot(x_circ, y_circ, "k--", alpha=0.25) plt.axis("equal") plt.xlabel("LCA retardance") plt.ylabel("LCB retardance") for i, pol in enumerate(pol_states): plt.annotate( - pol, + pol, xy=(lc_values["LCA"][i], lc_values["LCB"][i]), - xycoords='data', - xytext=(10,10), # annotation offset - textcoords='offset points' - ) + xycoords="data", + xytext=(10, 10), # annotation offset + textcoords="offset points", + ) @Slot(object) def handle_bg_image_update(self, value): - if 'Background Images' in self.viewer.layers: - self.viewer.layers['Background Images'].data = value + if "Background Images" in self.viewer.layers: + self.viewer.layers["Background Images"].data = value else: - self.viewer.add_image(value, name='Background Images', colormap='gray') + self.viewer.add_image( + value, name="Background Images", colormap="gray" + ) @Slot(object) def handle_bg_bire_image_update(self, value): # Separate Background Retardance and Background Orientation # Add new layer if none exists, otherwise update layer data - if 'Background Retardance' in self.viewer.layers: - self.viewer.layers['Background Retardance'].data = value[0] + if "Background Retardance" in self.viewer.layers: + self.viewer.layers["Background Retardance"].data = value[0] else: - self.viewer.add_image(value[0], name='Background Retardance', colormap='gray') + self.viewer.add_image( + value[0], name="Background Retardance", colormap="gray" + ) - if 'Background Orientation' in self.viewer.layers: - self.viewer.layers['Background Orientation'].data = value[1] + if "Background Orientation" in self.viewer.layers: + self.viewer.layers["Background Orientation"].data = value[1] else: - self.viewer.add_image(value[1], name='Background Orientation', colormap='gray') + self.viewer.add_image( + value[1], name="Background Orientation", colormap="gray" + ) @Slot(object) def handle_bire_image_update(self, value): - channel_names = {'Orientation': 1, - 'Retardance': 0, - } + channel_names = { + "Orientation": 1, + "Retardance": 0, + } # Compute Overlay if birefringence acquisition is 2D - if self.birefringence_dim == '2D': - channel_names['BirefringenceOverlay'] = None - overlay = ret_ori_overlay(retardance=value[0], - orientation=value[1], - ret_max= np.percentile(value[0], 99.99), - cmap=self.colormap) + if self.birefringence_dim == "2D": + channel_names["BirefringenceOverlay"] = None + overlay = ret_ori_overlay( + retardance=value[0], + orientation=value[1], + ret_max=np.percentile(value[0], 99.99), + cmap=self.colormap, + ) for key, chan in channel_names.items(): - if key == 'BirefringenceOverlay': - if key+self.birefringence_dim in self.viewer.layers: - self.viewer.layers[key+self.birefringence_dim].data = overlay + if key == "BirefringenceOverlay": + if key + self.birefringence_dim in self.viewer.layers: + self.viewer.layers[ + key + self.birefringence_dim + ].data = overlay else: - self.viewer.add_image(overlay, name=key+self.birefringence_dim, rgb=True) + self.viewer.add_image( + overlay, name=key + self.birefringence_dim, rgb=True + ) else: - if key+self.birefringence_dim in self.viewer.layers: - self.viewer.layers[key+self.birefringence_dim].data = value[chan] + if key + self.birefringence_dim in self.viewer.layers: + self.viewer.layers[ + key + self.birefringence_dim + ].data = value[chan] else: - cmap = 'gray' if key != 'Orientation' else 'hsv' - self.viewer.add_image(value[chan], name=key+self.birefringence_dim, colormap=cmap) + cmap = "gray" if key != "Orientation" else "hsv" + self.viewer.add_image( + value[chan], + name=key + self.birefringence_dim, + colormap=cmap, + ) @Slot(object) def handle_phase_image_update(self, value): - name = 'Phase2D' if self.phase_dim == '2D' else 'Phase3D' + name = "Phase2D" if self.phase_dim == "2D" else "Phase3D" # Add new layer if none exists, otherwise update layer data if name in self.viewer.layers: self.viewer.layers[name].data = value else: - self.viewer.add_image(value, name=name, colormap='gray') - - if 'Phase' not in [self.ui.cb_saturation.itemText(i) for i in range(self.ui.cb_saturation.count())]: - self.ui.cb_saturation.addItem('Retardance') - if 'Phase' not in [self.ui.cb_value.itemText(i) for i in range(self.ui.cb_value.count())]: - self.ui.cb_value.addItem('Retardance') + self.viewer.add_image(value, name=name, colormap="gray") + + if "Phase" not in [ + self.ui.cb_saturation.itemText(i) + for i in range(self.ui.cb_saturation.count()) + ]: + self.ui.cb_saturation.addItem("Retardance") + if "Phase" not in [ + self.ui.cb_value.itemText(i) + for i in range(self.ui.cb_value.count()) + ]: + self.ui.cb_value.addItem("Retardance") @Slot(object) def handle_qlipp_reconstructor_update(self, value): @@ -1593,19 +2035,27 @@ def handle_reconstruction_dim_update(self, value): layer_name = self.worker.manager.config.data_save_name if p == 0 and t == 0 and c == 0: - self.reconstruction_data = WaveorderReader(self.reconstruction_data_path, 'zarr') - self.viewer.add_image(self.reconstruction_data.get_zarr(p), name=layer_name + f'_Pos_{p:03d}') - - self.viewer.dims.set_axis_label(0, 'T') - self.viewer.dims.set_axis_label(1, 'C') - self.viewer.dims.set_axis_label(2, 'Z') + self.reconstruction_data = WaveorderReader( + self.reconstruction_data_path, "zarr" + ) + self.viewer.add_image( + self.reconstruction_data.get_zarr(p), + name=layer_name + f"_Pos_{p:03d}", + ) + self.viewer.dims.set_axis_label(0, "T") + self.viewer.dims.set_axis_label(1, "C") + self.viewer.dims.set_axis_label(2, "Z") # Add each new position as a new layer in napari - name = layer_name + f'_Pos_{p:03d}' + name = layer_name + f"_Pos_{p:03d}" if name not in self.viewer.layers: - self.reconstruction_data = WaveorderReader(self.reconstruction_data_path, 'zarr') - self.viewer.add_image(self.reconstruction_data.get_zarr(p), name=name) + self.reconstruction_data = WaveorderReader( + self.reconstruction_data_path, "zarr" + ) + self.viewer.add_image( + self.reconstruction_data.get_zarr(p), name=name + ) # update the napari dimension slider position if the user hasn't specified to pause updates if not self.pause_updates: @@ -1616,7 +2066,7 @@ def handle_reconstruction_dim_update(self, value): @Slot(bool) def browse_dir_path(self): - result = self._open_file_dialog(self.current_dir_path, 'dir') + result = self._open_file_dialog(self.current_dir_path, "dir") self.directory = result self.current_dir_path = result self.ui.le_directory.setText(result) @@ -1625,20 +2075,20 @@ def browse_dir_path(self): @Slot(bool) def browse_save_path(self): - result = self._open_file_dialog(self.current_save_path, 'dir') + result = self._open_file_dialog(self.current_save_path, "dir") self.save_directory = result self.current_save_path = result self.ui.le_save_dir.setText(result) @Slot(bool) def browse_data_dir(self): - path = self._open_file_dialog(self.data_dir, 'dir') + path = self._open_file_dialog(self.data_dir, "dir") self.data_dir = path self.ui.le_data_dir.setText(self.data_dir) @Slot(bool) def browse_calib_meta(self): - path = self._open_file_dialog(self.calib_path, 'file') + path = self._open_file_dialog(self.calib_path, "file") self.calib_path = path self.ui.le_calibration_metadata.setText(self.calib_path) @@ -1650,7 +2100,7 @@ def enter_dir_path(self): self.save_directory = path self.ui.le_save_dir.setText(path) else: - self.ui.le_directory.setText('Path Does Not Exist') + self.ui.le_directory.setText("Path Does Not Exist") @Slot() def enter_swing(self): @@ -1664,27 +2114,27 @@ def enter_wavelength(self): def enter_calib_scheme(self): index = self.ui.cb_calib_scheme.currentIndex() if index == 0: - self.calib_scheme = '4-State' + self.calib_scheme = "4-State" else: - self.calib_scheme = '5-State' + self.calib_scheme = "5-State" @Slot() def enter_calib_mode(self): index = self.ui.cb_calib_mode.currentIndex() if index == 0: - self.calib_mode = 'MM-Retardance' + self.calib_mode = "MM-Retardance" self.ui.label_lca.hide() self.ui.label_lcb.hide() self.ui.cb_lca.hide() self.ui.cb_lcb.hide() elif index == 1: - self.calib_mode = 'MM-Voltage' + self.calib_mode = "MM-Voltage" self.ui.label_lca.hide() self.ui.label_lcb.hide() self.ui.cb_lca.hide() self.ui.cb_lcb.hide() elif index == 2: - self.calib_mode = 'DAC' + self.calib_mode = "DAC" self.ui.cb_lca.clear() self.ui.cb_lcb.clear() self.ui.cb_lca.show() @@ -1692,18 +2142,18 @@ def enter_calib_mode(self): self.ui.label_lca.show() self.ui.label_lcb.show() - cfg = self.mmc.getConfigData(self.config_group, 'State0') + cfg = self.mmc.getConfigData(self.config_group, "State0") # Update the DAC combo boxes with available DAC's from the config. Necessary for the user # to specify which DAC output corresponds to which LC for voltage-space calibration memory = set() for i in range(cfg.size()): prop = cfg.getSetting(i) - if 'TS_DAC' in prop.getDeviceLabel(): + if "TS_DAC" in prop.getDeviceLabel(): dac = prop.getDeviceLabel()[-2:] if dac not in memory: - self.ui.cb_lca.addItem('DAC'+dac) - self.ui.cb_lcb.addItem('DAC'+dac) + self.ui.cb_lca.addItem("DAC" + dac) + self.ui.cb_lcb.addItem("DAC" + dac) memory.add(dac) else: continue @@ -1731,7 +2181,7 @@ def enter_config_group(self): ------- """ - #if/else takes care of the clearing of config + # if/else takes care of the clearing of config if self.ui.cb_config_group.count() != 0: self.mmc = self.bridge.get_core() self.mm = self.bridge.get_studio() @@ -1740,12 +2190,12 @@ def enter_config_group(self): self.config_group = self.ui.cb_config_group.currentText() config = self.mmc.getAvailableConfigs(self.config_group) - channels = [] + channels = [] for i in range(config.size()): channels.append(config.get(i)) # Check to see if any states are missing - states = ['State0', 'State1', 'State2', 'State3', 'State4'] + states = ["State0", "State1", "State2", "State3", "State4"] missing = [] for state in states: if state not in channels: @@ -1753,14 +2203,18 @@ def enter_config_group(self): # if states are missing, set the combo box red and alert the user if len(missing) != 0: - msg = f'The chosen config group ({self.config_group}) is missing states: {missing}. '\ - 'Please refer to the recOrder wiki on how to set up the config properly.' - - self.ui.cb_config_group.setStyleSheet("border: 1px solid rgb(200,0,0);") + msg = ( + f"The chosen config group ({self.config_group}) is missing states: {missing}. " + "Please refer to the recOrder wiki on how to set up the config properly." + ) + + self.ui.cb_config_group.setStyleSheet( + "border: 1px solid rgb(200,0,0);" + ) raise KeyError(msg) else: self.ui.cb_config_group.setStyleSheet("") - + @Slot() def enter_use_cropped_roi(self): state = self.ui.chb_use_roi.checkState() @@ -1792,7 +2246,7 @@ def enter_save_path(self): self.save_directory = path self.current_save_path = path else: - self.ui.le_save_dir.setText('Path Does Not Exist') + self.ui.le_save_dir.setText("Path Does Not Exist") @Slot() def enter_save_name(self): @@ -1815,17 +2269,17 @@ def enter_zstep(self): def enter_birefringence_dim(self): state = self.ui.cb_birefringence.currentIndex() if state == 0: - self.birefringence_dim = '2D' + self.birefringence_dim = "2D" elif state == 1: - self.birefringence_dim = '3D' + self.birefringence_dim = "3D" @Slot() def enter_phase_dim(self): state = self.ui.cb_phase.currentIndex() if state == 0: - self.phase_dim = '2D' + self.phase_dim = "2D" elif state == 1: - self.phase_dim = '3D' + self.phase_dim = "3D" @Slot() def enter_phase_denoiser(self): @@ -1849,7 +2303,7 @@ def enter_acq_bg_path(self): self.acq_bg_directory = path self.current_bg_path = path else: - self.ui.le_bg_path.setText('Path Does Not Exist') + self.ui.le_bg_path.setText("Path Does Not Exist") @Slot(str) def handle_bg_path_update(self, value: str): @@ -1876,7 +2330,7 @@ def handle_bg_path_update(self, value: str): @Slot(bool) def browse_acq_bg_path(self): - result = self._open_file_dialog(self.current_bg_path, 'dir') + result = self._open_file_dialog(self.current_bg_path, "dir") self.acq_bg_directory = result self.current_bg_path = result self.ui.le_bg_path.setText(result) @@ -1888,22 +2342,22 @@ def enter_bg_correction(self): self.ui.label_bg_path.setHidden(True) self.ui.le_bg_path.setHidden(True) self.ui.qbutton_browse_bg_path.setHidden(True) - self.bg_option = 'None' + self.bg_option = "None" elif state == 1: self.ui.label_bg_path.setHidden(False) self.ui.le_bg_path.setHidden(False) self.ui.qbutton_browse_bg_path.setHidden(False) - self.bg_option = 'global' + self.bg_option = "global" elif state == 2: self.ui.label_bg_path.setHidden(True) self.ui.le_bg_path.setHidden(True) self.ui.qbutton_browse_bg_path.setHidden(True) - self.bg_option = 'local_fit' + self.bg_option = "local_fit" elif state == 3: self.ui.label_bg_path.setHidden(False) self.ui.le_bg_path.setHidden(False) self.ui.qbutton_browse_bg_path.setHidden(False) - self.bg_option = 'local_fit+' + self.bg_option = "local_fit+" @Slot() def enter_gpu_id(self): @@ -1969,29 +2423,31 @@ def enter_method(self): idx = self.ui.cb_method.currentIndex() if idx == 0: - self.method = 'QLIPP' + self.method = "QLIPP" self.ui.label_bf_chan.hide() self.ui.le_bf_chan.hide() - self.ui.label_chan_desc.setText('Retardance, Orientation, BF, Phase3D, Phase2D, S0, S1, S2, S3') + self.ui.label_chan_desc.setText( + "Retardance, Orientation, BF, Phase3D, Phase2D, S0, S1, S2, S3" + ) elif idx == 1: - self.method = 'PhaseFromBF' + self.method = "PhaseFromBF" self.ui.label_bf_chan.show() self.ui.le_bf_chan.show() - self.ui.label_bf_chan.setText('Brightfield Channel Index') - self.ui.le_bf_chan.setPlaceholderText('int') - self.ui.label_chan_desc.setText('Phase3D, Phase2D') + self.ui.label_bf_chan.setText("Brightfield Channel Index") + self.ui.le_bf_chan.setPlaceholderText("int") + self.ui.label_chan_desc.setText("Phase3D, Phase2D") @Slot(int) def enter_mode(self): idx = self.ui.cb_mode.currentIndex() if idx == 0: - self.mode = '3D' + self.mode = "3D" self.ui.label_focus_zidx.hide() self.ui.le_focus_zidx.hide() else: - self.mode = '2D' + self.mode = "2D" self.ui.label_focus_zidx.show() self.ui.le_focus_zidx.show() @@ -1999,8 +2455,10 @@ def enter_mode(self): def enter_data_dir(self): entry = self.ui.le_data_dir.text() if not os.path.exists(entry): - self.ui.le_data_dir.setStyleSheet("border: 1px solid rgb(200,0,0);") - self.ui.le_data_dir.setText('Path Does Not Exist') + self.ui.le_data_dir.setStyleSheet( + "border: 1px solid rgb(200,0,0);" + ) + self.ui.le_data_dir.setText("Path Does Not Exist") else: self.ui.le_data_dir.setStyleSheet("") self.data_dir = entry @@ -2009,8 +2467,10 @@ def enter_data_dir(self): def enter_calib_meta(self): entry = self.ui.le_calibration_metadata.text() if not os.path.exists(entry): - self.ui.le_calibration_metadata.setStyleSheet("border: 1px solid rgb(200,0,0);") - self.ui.le_calibration_metadata.setText('Path Does Not Exist') + self.ui.le_calibration_metadata.setStyleSheet( + "border: 1px solid rgb(200,0,0);" + ) + self.ui.le_calibration_metadata.setText("Path Does Not Exist") else: self.ui.le_calibration_metadata.setStyleSheet("") self.calib_path = entry @@ -2030,23 +2490,30 @@ def enter_colormap(self): state = self.ui.cb_colormap.currentIndex() if state == 0: self.ui.label_orientation_image.setPixmap(self.jch_pixmap) - self.colormap = 'JCh' + self.colormap = "JCh" else: self.ui.label_orientation_image.setPixmap(self.hsv_pixmap) - self.colormap = 'HSV' + self.colormap = "HSV" # Update the birefringence overlay to new colormap if the colormap has changed if prev_cmap != self.colormap: - #TODO: Handle case where there are multiple snaps - if 'BirefringenceOverlay2D' in self.viewer.layers: - if 'Retardance2D' in self.viewer.layers and 'Orientation2D' in self.viewer.layers: - - overlay = ret_ori_overlay(retardance=self.viewer.layers['Retardance2D'].data, - orientation=self.viewer.layers['Orientation2D'].data, - ret_max= np.percentile(self.viewer.layers['Retardance2D'].data, 99.99), - cmap=self.colormap) - - self.viewer.layers['BirefringenceOverlay2D'].data = overlay + # TODO: Handle case where there are multiple snaps + if "BirefringenceOverlay2D" in self.viewer.layers: + if ( + "Retardance2D" in self.viewer.layers + and "Orientation2D" in self.viewer.layers + ): + + overlay = ret_ori_overlay( + retardance=self.viewer.layers["Retardance2D"].data, + orientation=self.viewer.layers["Orientation2D"].data, + ret_max=np.percentile( + self.viewer.layers["Retardance2D"].data, 99.99 + ), + cmap=self.colormap, + ) + + self.viewer.layers["BirefringenceOverlay2D"].data = overlay @Slot(int) def enter_use_full_volume(self): @@ -2101,24 +2568,26 @@ def push_note(self): # make sure the user has performed a calibration in this session (or loaded a previous one) if not self.last_calib_meta_file: - raise ValueError('No calibration has been performed yet so there is no previous metadata file') + raise ValueError( + "No calibration has been performed yet so there is no previous metadata file" + ) else: note = self.ui.le_notes_field.text() # Open the existing calibration metadata file and append the notes - with open(self.last_calib_meta_file, 'r') as file: + with open(self.last_calib_meta_file, "r") as file: current_json = json.load(file) # Append note to the end of the old note (so we don't overwrite previous notes) or write a new # note in the blank notes field - old_note = current_json['Notes'] - if old_note is None or old_note == '' or old_note == note: - current_json['Notes'] = note + old_note = current_json["Notes"] + if old_note is None or old_note == "" or old_note == note: + current_json["Notes"] = note else: - current_json['Notes'] = old_note + ', ' + note + current_json["Notes"] = old_note + ", " + note # dump the contents into the metadata file - with open(self.last_calib_meta_file, 'w') as file: + with open(self.last_calib_meta_file, "w") as file: json.dump(current_json, file, indent=1) @Slot(bool) @@ -2134,13 +2603,15 @@ def calc_extinction(self): """ # Snap images from the extinction state and first elliptical state - set_lc_state(self.mmc, self.config_group, 'State0') + set_lc_state(self.mmc, self.config_group, "State0") extinction = snap_and_average(self.calib.snap_manager) - set_lc_state(self.mmc, self.config_group, 'State1') + set_lc_state(self.mmc, self.config_group, "State1") state1 = snap_and_average(self.calib.snap_manager) # Calculate extinction based off captured intensities - extinction = self.calib.calculate_extinction(self.swing, self.calib.I_Black, extinction, state1) + extinction = self.calib.calculate_extinction( + self.swing, self.calib.I_Black, extinction, state1 + ) self.ui.le_extinction.setText(str(extinction)) @property @@ -2149,19 +2620,20 @@ def _microscope_params(self): A dictionary containing microscope parameters from the current GUI Unused in 0.2.0 --- candidate for deletion """ + def _param_value(param_name: str, ui=self.ui): # refer to main widget class ui attribute names ui_attr_name = "le_" + param_name param_text = ui.__getattribute__(ui_attr_name).text() # handle blank string - return float(param_text) if param_text != '' else None + return float(param_text) if param_text != "" else None return { - 'n_objective_media': _param_value("n_media"), - 'objective_NA': _param_value("obj_na"), - 'condenser_NA': _param_value("cond_na"), - 'magnification': _param_value("mag"), - 'pixel_size': _param_value("ps") + "n_objective_media": _param_value("n_media"), + "objective_NA": _param_value("obj_na"), + "condenser_NA": _param_value("cond_na"), + "magnification": _param_value("mag"), + "pixel_size": _param_value("ps"), } @Slot(bool) @@ -2170,7 +2642,7 @@ def load_calibration(self): Uses previous JSON calibration metadata to load previous calibration """ - metadata_path = self._open_file_dialog(self.current_dir_path, 'file') + metadata_path = self._open_file_dialog(self.current_dir_path, "file") metadata = MetadataReader(metadata_path) # Update Properties @@ -2178,15 +2650,21 @@ def load_calibration(self): self.swing = metadata.Swing # Initialize calibration class - self.calib = QLIPP_Calibration(self.mmc, self.mm, group=self.config_group, lc_control_mode=self.calib_mode, - interp_method=self.interp_method, wavelength=self.wavelength) + self.calib = QLIPP_Calibration( + self.mmc, + self.mm, + group=self.config_group, + lc_control_mode=self.calib_mode, + interp_method=self.interp_method, + wavelength=self.wavelength, + ) self.calib.swing = self.swing self.ui.le_swing.setText(str(self.swing)) self.calib.wavelength = self.wavelength self.ui.le_wavelength.setText(str(self.wavelength)) # Update Calibration Scheme Combo Box - if metadata.Calibration_scheme == '4-State': + if metadata.Calibration_scheme == "4-State": self.ui.cb_calib_scheme.setCurrentIndex(0) else: self.ui.cb_calib_scheme.setCurrentIndex(1) @@ -2218,21 +2696,27 @@ def run_calibration(self): """ self._check_MM_config_setup() - - self.calib = QLIPP_Calibration(self.mmc, self.mm, group=self.config_group, lc_control_mode=self.calib_mode, - interp_method=self.interp_method, wavelength=self.wavelength) - if self.calib_mode == 'DAC': + self.calib = QLIPP_Calibration( + self.mmc, + self.mm, + group=self.config_group, + lc_control_mode=self.calib_mode, + interp_method=self.interp_method, + wavelength=self.wavelength, + ) + + if self.calib_mode == "DAC": self.calib.set_dacs(self.lca_dac, self.lcb_dac) # Reset Styling - self.ui.tb_calib_assessment.setText('') + self.ui.tb_calib_assessment.setText("") self.ui.tb_calib_assessment.setStyleSheet("") # Save initial autoshutter state for when we set it back later self.auto_shutter = self.mmc.getAutoShutter() - logging.info('Starting Calibration') + logging.info("Starting Calibration") # Initialize displays + parameters for calibration self.ui.progress_bar.setValue(0) @@ -2240,7 +2724,9 @@ def run_calibration(self): self.intensity_monitor = [] self.calib.swing = self.swing self.calib.wavelength = self.wavelength - self.calib.meta_file = os.path.join(self.directory, 'calibration_metadata.txt') + self.calib.meta_file = os.path.join( + self.directory, "calibration_metadata.txt" + ) # Make sure Live Mode is off if self.calib.snap_manager.getIsLiveModeOn(): @@ -2253,10 +2739,16 @@ def run_calibration(self): self.worker.progress_update.connect(self.handle_progress_update) self.worker.extinction_update.connect(self.handle_extinction_update) self.worker.intensity_update.connect(self.handle_plot_update) - self.worker.calib_assessment.connect(self.handle_calibration_assessment_update) - self.worker.calib_assessment_msg.connect(self.handle_calibration_assessment_msg_update) + self.worker.calib_assessment.connect( + self.handle_calibration_assessment_update + ) + self.worker.calib_assessment_msg.connect( + self.handle_calibration_assessment_msg_update + ) self.worker.calib_file_emit.connect(self.handle_calib_file_update) - self.worker.plot_sequence_emit.connect(self.handle_plot_sequence_update) + self.worker.plot_sequence_emit.connect( + self.handle_plot_sequence_update + ) self.worker.lc_states.connect(self.handle_lc_states_emit) self.worker.started.connect(self._disable_buttons) self.worker.finished.connect(self._enable_buttons) @@ -2268,38 +2760,59 @@ def run_calibration(self): @property def _channel_descriptions(self): return [ - self.mmc.getConfigData(self.config_group, calib_channel).getVerbose() + self.mmc.getConfigData( + self.config_group, calib_channel + ).getVerbose() for calib_channel in self.calib_channels ] def _check_MM_config_setup(self): # Warns the user if the MM configuration is not correctly set up. desc = self._channel_descriptions - if self.calib_mode == 'MM-Retardance': - if all('String send to' in s for s in desc) and not any('Voltage (V)' in s for s in desc): + if self.calib_mode == "MM-Retardance": + if all("String send to" in s for s in desc) and not any( + "Voltage (V)" in s for s in desc + ): return else: - msg = ' \n'.join(textwrap.wrap("In \'MM-Retardance\' mode each preset must include the " \ - "\'String send to\' property, and no \'Voltage\' properties.", width=40)) + msg = " \n".join( + textwrap.wrap( + "In 'MM-Retardance' mode each preset must include the " + "'String send to' property, and no 'Voltage' properties.", + width=40, + ) + ) show_warning(msg) - - elif self.calib_mode == 'MM-Voltage': - if all('Voltage (V) LC-A' in s for s in desc) and all('Voltage (V) LC-B' in s for s in desc) and not any('String send to' in s for s in desc): + + elif self.calib_mode == "MM-Voltage": + if ( + all("Voltage (V) LC-A" in s for s in desc) + and all("Voltage (V) LC-B" in s for s in desc) + and not any("String send to" in s for s in desc) + ): return else: - msg = ' \n'.join(textwrap.wrap("In \'MM-Voltage\' mode each preset must include the \'Voltage (V) LC-A\' " \ - "property, the \'Voltage (V) LC-B\' property, and no \'String send to\' properties.", width=40)) + msg = " \n".join( + textwrap.wrap( + "In 'MM-Voltage' mode each preset must include the 'Voltage (V) LC-A' " + "property, the 'Voltage (V) LC-B' property, and no 'String send to' properties.", + width=40, + ) + ) show_warning(msg) - elif self.calib_mode == 'DAC': + elif self.calib_mode == "DAC": _devices = self.mmc.getLoadedDevices() loaded_devices = [_devices.get(i) for i in range(_devices.size())] if LC_DEVICE_NAME in loaded_devices: - show_warning("In \'DAC\' mode the MeadowLarkLC device adapter must not be loaded in MM.") - + show_warning( + "In 'DAC' mode the MeadowLarkLC device adapter must not be loaded in MM." + ) + else: - raise ValueError(f'self.calib_mode = {self.calib_mode} is an unrecognized state.') - + raise ValueError( + f"self.calib_mode = {self.calib_mode} is an unrecognized state." + ) @Slot(bool) def capture_bg(self): @@ -2316,20 +2829,22 @@ def capture_bg(self): no_calibration_message = """Capturing a background requires calibrated liquid crystals. \ Please either run a calibration or load a calibration from file.""" raise RuntimeError(no_calibration_message) - + # Init worker and thread self.worker = BackgroundCaptureWorker(self, self.calib) # Connect Handlers self.worker.bg_image_emitter.connect(self.handle_bg_image_update) - self.worker.bire_image_emitter.connect(self.handle_bg_bire_image_update) + self.worker.bire_image_emitter.connect( + self.handle_bg_bire_image_update + ) self.worker.started.connect(self._disable_buttons) self.worker.finished.connect(self._enable_buttons) self.worker.errored.connect(self._handle_error) self.ui.qbutton_stop_calib.clicked.connect(self.worker.quit) self.worker.aborted.connect(self._handle_calib_abort) - + # Connect to BG Correction Path self.worker.bg_path_update_emitter.connect(self.handle_bg_path_update) @@ -2345,10 +2860,12 @@ def acq_birefringence(self): """ - self._check_requirements_for_acq('birefringence') + self._check_requirements_for_acq("birefringence") # Init Worker and thread - self.worker = PolarizationAcquisitionWorker(self, self.calib, 'birefringence') + self.worker = PolarizationAcquisitionWorker( + self, self.calib, "birefringence" + ) # Connect Handlers self.worker.bire_image_emitter.connect(self.handle_bire_image_update) @@ -2366,17 +2883,21 @@ def acq_phase(self): Wrapper function to acquire phase stack and plot in napari """ - self._check_requirements_for_acq('phase') + self._check_requirements_for_acq("phase") # Init worker and thread if self.ui.chb_phase_from_bf.isChecked(): self.worker = BFAcquisitionWorker(self) else: - self.worker = PolarizationAcquisitionWorker(self, self.calib, 'phase') + self.worker = PolarizationAcquisitionWorker( + self, self.calib, "phase" + ) # Connect Handlers self.worker.phase_image_emitter.connect(self.handle_phase_image_update) - self.worker.phase_reconstructor_emitter.connect(self.handle_qlipp_reconstructor_update) + self.worker.phase_reconstructor_emitter.connect( + self.handle_qlipp_reconstructor_update + ) self.worker.meta_emitter.connect(self.handle_meta_update) self.worker.started.connect(self._disable_buttons) self.worker.finished.connect(self._enable_buttons) @@ -2391,15 +2912,17 @@ def acq_birefringence_phase(self): Wrapper function to acquire both birefringence and phase stack and plot in napari """ - self._check_requirements_for_acq('phase') + self._check_requirements_for_acq("phase") # Init worker # Init worker and thread - self.worker = PolarizationAcquisitionWorker(self, self.calib, 'all') + self.worker = PolarizationAcquisitionWorker(self, self.calib, "all") # connect handlers self.worker.phase_image_emitter.connect(self.handle_phase_image_update) - self.worker.phase_reconstructor_emitter.connect(self.handle_qlipp_reconstructor_update) + self.worker.phase_reconstructor_emitter.connect( + self.handle_qlipp_reconstructor_update + ) self.worker.bire_image_emitter.connect(self.handle_bire_image_update) self.worker.meta_emitter.connect(self.handle_meta_update) self.worker.started.connect(self._disable_buttons) @@ -2422,12 +2945,14 @@ def listen_and_reconstruct(self): """ # Init reconstructor - if self.bg_option != 'None': + if self.bg_option != "None": metadata_file = get_last_metadata_file(self.current_bg_path) metadata = MetadataReader(metadata_file) roi = metadata.ROI height, width = roi[2], roi[3] - bg_data = load_bg(self.current_bg_path, height, width, roi) # TODO: remove ROI for 1.0.0 + bg_data = load_bg( + self.current_bg_path, height, width, roi + ) # TODO: remove ROI for 1.0.0 else: bg_data = None @@ -2454,14 +2979,20 @@ def reconstruct(self): success = self._check_requirements_for_reconstruction() if not success: - raise ValueError('Please make sure all necessary parameters are set before reconstruction') + raise ValueError( + "Please make sure all necessary parameters are set before reconstruction" + ) self._populate_config_from_app() self.worker = ReconstructionWorker(self, self.config_reader) # connect handlers - self.worker.dimension_emitter.connect(self.handle_reconstruction_dim_update) - self.worker.store_emitter.connect(self.handle_reconstruction_store_update) + self.worker.dimension_emitter.connect( + self.handle_reconstruction_dim_update + ) + self.worker.store_emitter.connect( + self.handle_reconstruction_store_update + ) self.worker.started.connect(self._disable_buttons) self.worker.finished.connect(self._enable_buttons) self.worker.errored.connect(self._handle_acq_error) @@ -2471,7 +3002,7 @@ def reconstruct(self): @Slot(bool) def save_config(self): - path = self._open_file_dialog(self.save_config_path, 'save') + path = self._open_file_dialog(self.save_config_path, "save") self.save_config_path = path name = PurePath(self.save_config_path).name dir_ = self.save_config_path.strip(name) @@ -2479,10 +3010,12 @@ def save_config(self): if isinstance(self.config_reader.positions, tuple): pos = self.config_reader.positions - self.config_reader.positions = f'[!!python/tuple [{pos[0]},{pos[1]}]]' + self.config_reader.positions = ( + f"[!!python/tuple [{pos[0]},{pos[1]}]]" + ) if isinstance(self.config_reader.timepoints, tuple): t = self.config_reader.timepoints - self.config_reader.timepoints = f'[!!python/tuple [{t[0]},{t[1]}]]' + self.config_reader.timepoints = f"[!!python/tuple [{t[0]},{t[1]}]]" self.config_reader.save_yaml(dir_=dir_, name=name) @@ -2495,8 +3028,8 @@ def load_config(self): ------- """ - path = self._open_file_dialog(self.save_config_path, 'file') - if path == '': + path = self._open_file_dialog(self.save_config_path, "file") + if path == "": pass else: self.config_path = path @@ -2517,7 +3050,7 @@ def update_sat_scale(self): min_, max_ = np.min(data), np.max(data) self.ui.slider_saturation.setMinimum(min_) self.ui.slider_saturation.setMaximum(max_) - self.ui.slider_saturation.setSingleStep((max_ - min_)/250) + self.ui.slider_saturation.setSingleStep((max_ - min_) / 250) self.ui.slider_saturation.setValue((min_, max_)) self.ui.le_sat_max.setText(str(np.round(max_, 3))) self.ui.le_sat_min.setText(str(np.round(min_, 3))) @@ -2531,7 +3064,7 @@ def update_value_scale(self): min_, max_ = np.min(data), np.max(data) self.ui.slider_value.setMinimum(min_) self.ui.slider_value.setMaximum(max_) - self.ui.slider_value.setSingleStep((max_ - min_)/250) + self.ui.slider_value.setSingleStep((max_ - min_) / 250) self.ui.slider_value.setValue((min_, max_)) self.ui.le_val_max.setText(str(np.round(max_, 3))) self.ui.le_val_min.setText(str(np.round(min_, 3))) @@ -2547,25 +3080,50 @@ def create_overlay(self): """ - if self.ui.cb_hue.count() == 0 or self.ui.cb_saturation.count() == 0 or self.ui.cb_value == 0: - raise ValueError('Cannot create overlay until all 3 combo boxes are populated') + if ( + self.ui.cb_hue.count() == 0 + or self.ui.cb_saturation.count() == 0 + or self.ui.cb_value == 0 + ): + raise ValueError( + "Cannot create overlay until all 3 combo boxes are populated" + ) # Gather channel data - H = self.viewer.layers[self.ui.cb_hue.itemText(self.ui.cb_hue.currentIndex())].data - S = self.viewer.layers[self.ui.cb_saturation.itemText(self.ui.cb_saturation.currentIndex())].data - V = self.viewer.layers[self.ui.cb_value.itemText(self.ui.cb_value.currentIndex())].data - + H = self.viewer.layers[ + self.ui.cb_hue.itemText(self.ui.cb_hue.currentIndex()) + ].data + S = self.viewer.layers[ + self.ui.cb_saturation.itemText( + self.ui.cb_saturation.currentIndex() + ) + ].data + V = self.viewer.layers[ + self.ui.cb_value.itemText(self.ui.cb_value.currentIndex()) + ].data - #TODO: this is a temp fix which handles on data with n-dimensions of 4, 3, or 2 which automatically + # TODO: this is a temp fix which handles on data with n-dimensions of 4, 3, or 2 which automatically # chooses the first timepoint if H.ndim > 2 or S.ndim > 2 or V.ndim > 2: if H.ndim == 4: # assumes this is a (T, Z, Y, X) array read from napari-ome-zarr - H = H[0, self.display_slice] if not self.use_full_volume else H[0] + H = ( + H[0, self.display_slice] + if not self.use_full_volume + else H[0] + ) if S.ndim == 4: - S = S[0, self.display_slice] if not self.use_full_volume else S[0] + S = ( + S[0, self.display_slice] + if not self.use_full_volume + else S[0] + ) if V.ndim == 4: - V = V[0, self.display_slice] if not self.use_full_volume else V[0] + V = ( + V[0, self.display_slice] + if not self.use_full_volume + else V[0] + ) if H.ndim == 3: # assumes this is a (Z, Y, X) array collected from acquisition module @@ -2577,31 +3135,37 @@ def create_overlay(self): if S.ndim == 3: S = S[self.display_slice] if not self.use_full_volume else S - mode = '2D' if not self.use_full_volume else '3D' + mode = "2D" if not self.use_full_volume else "3D" H_name = self.ui.cb_hue.itemText(self.ui.cb_hue.currentIndex()) - H_scale = (np.min(H), np.max(H)) if 'Orientation' not in H_name else (0, np.pi) + H_scale = ( + (np.min(H), np.max(H)) + if "Orientation" not in H_name + else (0, np.pi) + ) S_scale = self.ui.slider_saturation.value() V_scale = self.ui.slider_value.value() - hsv_image = generic_hsv_overlay(H, S, V, H_scale, S_scale, V_scale, mode=mode) + hsv_image = generic_hsv_overlay( + H, S, V, H_scale, S_scale, V_scale, mode=mode + ) # Create overlay layer name idx = 0 - while f'HSV_Overlay_{idx}' in self.viewer.layers: + while f"HSV_Overlay_{idx}" in self.viewer.layers: idx += 1 # add overlay image to napari - self.viewer.add_image(hsv_image, name=f'HSV_Overlay_{idx}', rgb=True) + self.viewer.add_image(hsv_image, name=f"HSV_Overlay_{idx}", rgb=True) @Slot(object) def add_listener_data(self, store): - self.viewer.add_image(store['Birefringence'], name=self.worker.prefix) - self.viewer.dims.set_axis_label(0, 'P') - self.viewer.dims.set_axis_label(1, 'T') - self.viewer.dims.set_axis_label(2, 'C') - self.viewer.dims.set_axis_label(3, 'Z') + self.viewer.add_image(store["Birefringence"], name=self.worker.prefix) + self.viewer.dims.set_axis_label(0, "P") + self.viewer.dims.set_axis_label(1, "T") + self.viewer.dims.set_axis_label(2, "C") + self.viewer.dims.set_axis_label(3, "Z") @Slot(tuple) def update_dims(self, dims): @@ -2619,9 +3183,7 @@ def _reset_listening(self): def _open_file_dialog(self, default_path, type): - return self._open_dialog("select a directory", - str(default_path), - type) + return self._open_dialog("select a directory", str(default_path), type) def _open_dialog(self, title, ref, type): """ @@ -2641,23 +3203,20 @@ def _open_dialog(self, title, ref, type): options = QFileDialog.Options() options \|= QFileDialog.DontUseNativeDialog - if type == 'dir': - path = QFileDialog.getExistingDirectory(None, - title, - ref, - options=options) - elif type == 'file': - path = QFileDialog.getOpenFileName(None, - title, - ref, - options=options)[0] - elif type == 'save': - path = QFileDialog.getSaveFileName(None, - 'Choose a save name', - ref, - options=options)[0] + if type == "dir": + path = QFileDialog.getExistingDirectory( + None, title, ref, options=options + ) + elif type == "file": + path = QFileDialog.getOpenFileName( + None, title, ref, options=options + )[0] + elif type == "save": + path = QFileDialog.getSaveFileName( + None, "Choose a save name", ref, options=options + )[0] else: - raise ValueError('Did not understand file dialogue type') + raise ValueError("Did not understand file dialogue type") return path diff --git a/recOrder/plugin/widget/napari_plugin_entry_point.py b/recOrder/plugin/widget/napari_plugin_entry_point.py index 23ee4576..f7fdcbf7 100644 --- a/recOrder/plugin/widget/napari_plugin_entry_point.py +++ b/recOrder/plugin/widget/napari_plugin_entry_point.py @@ -1,1 +1,1 @@ -from recOrder.plugin.widget.main_widget import MainWidget \ No newline at end of file +from recOrder.plugin.widget.main_widget import MainWidget diff --git a/recOrder/plugin/workers/acquisition_workers.py b/recOrder/plugin/workers/acquisition_workers.py index 4887d214..0dcb585f 100644 --- a/recOrder/plugin/workers/acquisition_workers.py +++ b/recOrder/plugin/workers/acquisition_workers.py @@ -1,7 +1,15 @@ from qtpy.QtCore import Signal -from recOrder.compute.qlipp_compute import initialize_reconstructor, \ - reconstruct_qlipp_birefringence, reconstruct_qlipp_stokes, reconstruct_phase2D, reconstruct_phase3D -from recOrder.acq.acq_functions import generate_acq_settings, acquire_from_settings +from recOrder.compute.qlipp_compute import ( + initialize_reconstructor, + reconstruct_qlipp_birefringence, + reconstruct_qlipp_stokes, + reconstruct_phase2D, + reconstruct_phase3D, +) +from recOrder.acq.acq_functions import ( + generate_acq_settings, + acquire_from_settings, +) from recOrder.io.utils import load_bg, extract_reconstruction_parameters from recOrder.compute import QLIPPBirefringenceCompute from recOrder.io.zarr_converter import ZarrConverter @@ -42,6 +50,7 @@ class BFAcquisitionSignals(WorkerBaseSignals): meta_emitter = Signal(dict) aborted = Signal() + class ListeningSignals(WorkerBaseSignals): """ Custom Signals class that includes napari native signals @@ -65,23 +74,39 @@ def __init__(self, calib_window): self.calib_window = calib_window # Init Properties - self.prefix = 'recOrderPluginSnap' + self.prefix = "recOrderPluginSnap" self.dm = self.calib_window.mm.displays() - self.dim = '2D' if self.calib_window.ui.cb_phase.currentIndex() == 0 else '3D' + self.dim = ( + "2D" if self.calib_window.ui.cb_phase.currentIndex() == 0 else "3D" + ) self.img_dim = None - save_dir = self.calib_window.save_directory if self.calib_window.save_directory else self.calib_window.directory + save_dir = ( + self.calib_window.save_directory + if self.calib_window.save_directory + else self.calib_window.directory + ) if save_dir is None: - raise ValueError('save directory is empty, please specify a directory in the plugin') + raise ValueError( + "save directory is empty, please specify a directory in the plugin" + ) # increment filename one more than last found saved snap i = 0 prefix = self.calib_window.save_name - snap_dir = f'recOrderPluginSnap_{i}' if not prefix else f'{prefix}_recOrderPluginSnap_{i}' + snap_dir = ( + f"recOrderPluginSnap_{i}" + if not prefix + else f"{prefix}_recOrderPluginSnap_{i}" + ) while os.path.exists(os.path.join(save_dir, snap_dir)): i += 1 - snap_dir = f'recOrderPluginSnap_{i}' if not prefix else f'{prefix}_recOrderPluginSnap_{i}' + snap_dir = ( + f"recOrderPluginSnap_{i}" + if not prefix + else f"{prefix}_recOrderPluginSnap_{i}" + ) self.snap_dir = os.path.join(save_dir, snap_dir) os.mkdir(self.snap_dir) @@ -89,7 +114,7 @@ def __init__(self, calib_window): def _check_abort(self): if self.abort_requested: self.aborted.emit() - raise TimeoutError('Stop Requested') + raise TimeoutError("Stop Requested") def _check_ram(self): """ @@ -106,7 +131,7 @@ def work(self): Function that runs the 2D or 3D acquisition and reconstructs the data """ self._check_ram() - logging.info('Running Acquisition...') + logging.info("Running Acquisition...") self._check_abort() channel_idx = self.calib_window.ui.cb_acq_channel.currentIndex() @@ -126,23 +151,27 @@ def work(self): break # Acquire 3D stack - logging.debug('Acquiring 3D stack') + logging.debug("Acquiring 3D stack") # Generate MDA Settings - settings = generate_acq_settings(self.calib_window.mm, - channel_group=channel_group, - channels=[channel], - zstart=self.calib_window.z_start, - zend=self.calib_window.z_end, - zstep=self.calib_window.z_step, - save_dir=self.snap_dir, - prefix=self.prefix) + settings = generate_acq_settings( + self.calib_window.mm, + channel_group=channel_group, + channels=[channel], + zstart=self.calib_window.z_start, + zend=self.calib_window.z_end, + zstep=self.calib_window.z_step, + save_dir=self.snap_dir, + prefix=self.prefix, + ) self._check_abort() # Acquire from MDA settings uses MM MDA GUI # Returns (1, 4/5, Z, Y, X) array - stack = acquire_from_settings(self.calib_window.mm, settings, grab_images=True) + stack = acquire_from_settings( + self.calib_window.mm, settings, grab_images=True + ) self._check_abort() # Cleanup acquisition by closing window, converting to zarr, and deleting temp directory @@ -155,13 +184,13 @@ def work(self): self._check_abort() # Save images - logging.debug('Saving Images') + logging.debug("Saving Images") self._save_imgs(phase, meta) self._check_abort() - logging.info('Finished Acquisition') - logging.debug('Finished Acquisition') + logging.info("Finished Acquisition") + logging.debug("Finished Acquisition") # Emit the images and let thread know function is finished self.phase_image_emitter.emit(phase) @@ -188,22 +217,26 @@ def _reconstruct(self, stack): # if no reconstructor has been initialized before, create new reconstructor if not self.calib_window.phase_reconstructor: - logging.debug('Computing new reconstructor') - - recon = initialize_reconstructor('PhaseFromBF', - image_dim=(stack.shape[-2], stack.shape[-1]), - wavelength_nm=int(self.calib_window.ui.le_recon_wavelength.text()), - NA_obj=self.calib_window.obj_na, - NA_illu=self.calib_window.cond_na, - mag=self.calib_window.mag, - n_slices=self.img_dim[-1], - z_step_um=self.calib_window.z_step, - pad_z=self.calib_window.pad_z, - pixel_size_um=self.calib_window.ps, - n_obj_media=self.calib_window.n_media, - mode=self.calib_window.phase_dim, - use_gpu=self.calib_window.use_gpu, - gpu_id=self.calib_window.gpu_id) + logging.debug("Computing new reconstructor") + + recon = initialize_reconstructor( + "PhaseFromBF", + image_dim=(stack.shape[-2], stack.shape[-1]), + wavelength_nm=int( + self.calib_window.ui.le_recon_wavelength.text() + ), + NA_obj=self.calib_window.obj_na, + NA_illu=self.calib_window.cond_na, + mag=self.calib_window.mag, + n_slices=self.img_dim[-1], + z_step_um=self.calib_window.z_step, + pad_z=self.calib_window.pad_z, + pixel_size_um=self.calib_window.ps, + n_obj_media=self.calib_window.n_media, + mode=self.calib_window.phase_dim, + use_gpu=self.calib_window.use_gpu, + gpu_id=self.calib_window.gpu_id, + ) # Emit reconstructor to be saved for later reconstructions self.phase_reconstructor_emitter.emit(recon) @@ -214,65 +247,81 @@ def _reconstruct(self, stack): # compute new reconstructor if the old reconstructor properties have been modified if self._reconstructor_changed(): - logging.debug('Reconstruction settings changed, updating reconstructor') - - recon = initialize_reconstructor('PhaseFromBF', - image_dim=(stack.shape[-2], stack.shape[-1]), - wavelength_nm=int(self.calib_window.ui.le_recon_wavelength.text()), - NA_obj=self.calib_window.obj_na, - NA_illu=self.calib_window.cond_na, - mag=self.calib_window.mag, - n_slices=self.n_slices, - z_step_um=self.calib_window.z_step, - pad_z=self.calib_window.pad_z, - pixel_size_um=self.calib_window.ps, - n_obj_media=self.calib_window.n_media, - mode=self.calib_window.phase_dim, - use_gpu=self.calib_window.use_gpu, - gpu_id=self.calib_window.gpu_id) + logging.debug( + "Reconstruction settings changed, updating reconstructor" + ) + + recon = initialize_reconstructor( + "PhaseFromBF", + image_dim=(stack.shape[-2], stack.shape[-1]), + wavelength_nm=int( + self.calib_window.ui.le_recon_wavelength.text() + ), + NA_obj=self.calib_window.obj_na, + NA_illu=self.calib_window.cond_na, + mag=self.calib_window.mag, + n_slices=self.n_slices, + z_step_um=self.calib_window.z_step, + pad_z=self.calib_window.pad_z, + pixel_size_um=self.calib_window.ps, + n_obj_media=self.calib_window.n_media, + mode=self.calib_window.phase_dim, + use_gpu=self.calib_window.use_gpu, + gpu_id=self.calib_window.gpu_id, + ) # Emit reconstructor to be saved for later reconstructions self.phase_reconstructor_emitter.emit(recon) # use previous reconstructor else: - logging.debug('Using previous reconstruction settings') + logging.debug("Using previous reconstruction settings") recon = self.calib_window.phase_reconstructor # Begin reconstruction with stokes (needed for birefringence or phase) - logging.debug('Reconstructing...') + logging.debug("Reconstructing...") self._check_abort() - regularizer = 'Tikhonov' if self.calib_window.ui.cb_phase_denoiser.currentIndex() == 0 else 'TV' + regularizer = ( + "Tikhonov" + if self.calib_window.ui.cb_phase_denoiser.currentIndex() == 0 + else "TV" + ) reg = float(self.calib_window.ui.le_phase_strength.text()) # Perform deconvolution - if self.dim == '2D': - - phase = reconstruct_phase2D(stack[0], - recon, - method=regularizer, - reg_p=reg, - itr=int(self.calib_window.ui.le_itr.text()), - rho=float(self.calib_window.ui.le_rho.text())) + if self.dim == "2D": + + phase = reconstruct_phase2D( + stack[0], + recon, + method=regularizer, + reg_p=reg, + itr=int(self.calib_window.ui.le_itr.text()), + rho=float(self.calib_window.ui.le_rho.text()), + ) else: - phase = reconstruct_phase3D(stack[0], - recon, - method=regularizer, - reg_re=reg, - itr=int(self.calib_window.ui.le_itr.text()), - rho=float(self.calib_window.ui.le_rho.text())) + phase = reconstruct_phase3D( + stack[0], + recon, + method=regularizer, + reg_re=reg, + itr=int(self.calib_window.ui.le_itr.text()), + rho=float(self.calib_window.ui.le_rho.text()), + ) self._check_abort() # Update metadata in zarr attributes with reconstruction parameters - meta = extract_reconstruction_parameters(recon, magnification=self.calib_window.mag) - meta['regularization_method'] = regularizer - meta['regularization_strength'] = reg - if regularizer == 'TV': - meta['rho'] = float(self.calib_window.ui.le_rho.text()) - meta['itr'] = int(self.calib_window.ui.le_itr.text()) + meta = extract_reconstruction_parameters( + recon, magnification=self.calib_window.mag + ) + meta["regularization_method"] = regularizer + meta["regularization_strength"] = reg + if regularizer == "TV": + meta["rho"] = float(self.calib_window.ui.le_rho.text()) + meta["itr"] = int(self.calib_window.ui.le_itr.text()) # return both variables, could contain images or could be null return phase, meta @@ -295,29 +344,37 @@ def _save_imgs(self, phase, meta=None): # initialize chunk_size = (1, 1, 1, phase.shape[-2], phase.shape[-1]) prefix = self.calib_window.save_name - name = f'PhaseSnap.zarr' if not prefix else f'{prefix}_PhaseSnap.zarr' + name = f"PhaseSnap.zarr" if not prefix else f"{prefix}_PhaseSnap.zarr" # create zarr root and position group writer.create_zarr_root(name) # Check if 2D if len(phase.shape) == 2: - writer.init_array(0, (1, 1, 1, phase.shape[-2], phase.shape[-1]), chunk_size, ['Phase2D']) + writer.init_array( + 0, + (1, 1, 1, phase.shape[-2], phase.shape[-1]), + chunk_size, + ["Phase2D"], + ) z = 0 # Check if 3D else: - writer.init_array(0, (1, 1, phase.shape[-3], phase.shape[-2], phase.shape[-1]), chunk_size, - ['Phase3D']) + writer.init_array( + 0, + (1, 1, phase.shape[-3], phase.shape[-2], phase.shape[-1]), + chunk_size, + ["Phase3D"], + ) z = slice(0, phase.shape[-3]) - # Write data to disk writer.write(phase, p=0, t=0, c=0, z=z) current_meta = writer.store.attrs.asdict() - current_meta['recOrder'] = meta + current_meta["recOrder"] = meta writer.store.attrs.put(current_meta) def _reconstructor_changed(self): @@ -333,26 +390,30 @@ def _reconstructor_changed(self): changed = None # Attributes that are directly equivalent to worker attributes - attr_list = {'phase_dim': 'phase_deconv', - 'pad_z': 'pad_z', - 'n_media': 'n_media', - 'use_gpu': 'use_gpu', - 'gpu_id': 'gpu_id' - } + attr_list = { + "phase_dim": "phase_deconv", + "pad_z": "pad_z", + "n_media": "n_media", + "use_gpu": "use_gpu", + "gpu_id": "gpu_id", + } # attributes that are modified upon passing them to reconstructor - attr_modified_list = {'obj_na': 'NA_obj', - 'cond_na': 'NA_illu', - 'wavelength': 'lambda_illu', - 'n_slices': 'N_defocus', - 'swing': 'chi', - 'ps': 'ps' - } + attr_modified_list = { + "obj_na": "NA_obj", + "cond_na": "NA_illu", + "wavelength": "lambda_illu", + "n_slices": "N_defocus", + "swing": "chi", + "ps": "ps", + } self._check_abort() # check if equivalent attributes have diverged for key, value in attr_list.items(): - if getattr(self.calib_window, key) != getattr(self.calib_window.phase_reconstructor, value): + if getattr(self.calib_window, key) != getattr( + self.calib_window.phase_reconstructor, value + ): changed = True break else: @@ -361,29 +422,40 @@ def _reconstructor_changed(self): if not changed: # modify attributes to be equivalent and check for divergence for key, value in attr_modified_list.items(): - if key == 'wavelength': - if self.calib_window.wavelength * 1e-3 / self.calib_window.n_media != \ - self.calib_window.phase_reconstructor.lambda_illu: + if key == "wavelength": + if ( + self.calib_window.wavelength + * 1e-3 + / self.calib_window.n_media + != self.calib_window.phase_reconstructor.lambda_illu + ): changed = True break else: changed = False - elif key == 'n_slices': - if getattr(self, key) != getattr(self.calib_window.phase_reconstructor, value): + elif key == "n_slices": + if getattr(self, key) != getattr( + self.calib_window.phase_reconstructor, value + ): changed = True break else: changed = False - elif key == 'ps': - if getattr(self.calib_window, key) / float(self.calib_window.mag) != getattr(self.calib_window.phase_reconstructor, value): + elif key == "ps": + if getattr(self.calib_window, key) / float( + self.calib_window.mag + ) != getattr(self.calib_window.phase_reconstructor, value): changed = True break else: changed = False else: - if getattr(self.calib_window, key)/self.calib_window.n_media != \ - getattr(self.calib_window.phase_reconstructor, value): + if getattr( + self.calib_window, key + ) / self.calib_window.n_media != getattr( + self.calib_window.phase_reconstructor, value + ): changed = True else: changed = False @@ -410,14 +482,27 @@ def _cleanup_acq(self): closed = disp.isClosed() dp.close() - # Try to delete the data, sometime it isn't cleaned up quickly enough and will # return an error. In this case, catch the error and then try to close again (seems to work). try: - save_prefix = self.calib_window.save_name if self.calib_window.save_name else None - name = f'RawBFDataSnap.zarr' if not save_prefix else f'{save_prefix}_RawBFDataSnap.zarr' + save_prefix = ( + self.calib_window.save_name + if self.calib_window.save_name + else None + ) + name = ( + f"RawBFDataSnap.zarr" + if not save_prefix + else f"{save_prefix}_RawBFDataSnap.zarr" + ) out_path = os.path.join(self.snap_dir, name) - converter = ZarrConverter(os.path.join(dir_, prefix), out_path, 'ometiff', False, False) + converter = ZarrConverter( + os.path.join(dir_, prefix), + out_path, + "ometiff", + False, + False, + ) converter.run_conversion() shutil.rmtree(os.path.join(dir_, prefix)) except PermissionError as ex: @@ -426,6 +511,7 @@ def _cleanup_acq(self): else: continue + # TODO: Cache common OTF's on local computers and use those for reconstruction class PolarizationAcquisitionWorker(WorkerBase): """ @@ -443,28 +529,45 @@ def __init__(self, calib_window, calib, mode): self.calib = calib self.mode = mode self.n_slices = None - self.prefix = 'recOrderPluginSnap' + self.prefix = "recOrderPluginSnap" self.dm = self.calib_window.mm.displays() self.channel_group = self.calib_window.config_group # Determine whether 2D or 3D acquisition is needed - if self.mode == 'birefringence' and self.calib_window.birefringence_dim == '2D': - self.dim = '2D' + if ( + self.mode == "birefringence" + and self.calib_window.birefringence_dim == "2D" + ): + self.dim = "2D" else: - self.dim = '3D' + self.dim = "3D" - save_dir = self.calib_window.save_directory if self.calib_window.save_directory else self.calib_window.directory + save_dir = ( + self.calib_window.save_directory + if self.calib_window.save_directory + else self.calib_window.directory + ) if save_dir is None: - raise ValueError('save directory is empty, please specify a directory in the plugin') + raise ValueError( + "save directory is empty, please specify a directory in the plugin" + ) # increment filename one more than last found saved snap i = 0 prefix = self.calib_window.save_name - snap_dir = f'recOrderPluginSnap_{i}' if not prefix else f'{prefix}_recOrderPluginSnap_{i}' + snap_dir = ( + f"recOrderPluginSnap_{i}" + if not prefix + else f"{prefix}_recOrderPluginSnap_{i}" + ) while os.path.exists(os.path.join(save_dir, snap_dir)): i += 1 - snap_dir = f'recOrderPluginSnap_{i}' if not prefix else f'{prefix}_recOrderPluginSnap_{i}' + snap_dir = ( + f"recOrderPluginSnap_{i}" + if not prefix + else f"{prefix}_recOrderPluginSnap_{i}" + ) self.snap_dir = os.path.join(save_dir, snap_dir) os.mkdir(self.snap_dir) @@ -472,7 +575,7 @@ def __init__(self, calib_window, calib, mode): def _check_abort(self): if self.abort_requested: self.aborted.emit() - raise TimeoutError('Stop Requested') + raise TimeoutError("Stop Requested") def _check_ram(self): """ @@ -489,51 +592,55 @@ def work(self): Function that runs the 2D or 3D acquisition and reconstructs the data """ self._check_ram() - logging.info('Running Acquisition...') + logging.info("Running Acquisition...") # List the Channels to acquire, if 5-state then append 5th channel - channels = ['State0', 'State1', 'State2', 'State3'] - if self.calib_window.calib_scheme == '5-State': - channels.append('State4') + channels = ["State0", "State1", "State2", "State3"] + if self.calib_window.calib_scheme == "5-State": + channels.append("State4") self._check_abort() # Acquire 2D stack - if self.dim == '2D': - logging.debug('Acquiring 2D stack') + if self.dim == "2D": + logging.debug("Acquiring 2D stack") # Generate MDA Settings - self.settings = generate_acq_settings(self.calib_window.mm, - channel_group=self.channel_group, - channels=channels, - save_dir=self.snap_dir, - prefix=self.prefix, - keep_shutter_open_channels=True) + self.settings = generate_acq_settings( + self.calib_window.mm, + channel_group=self.channel_group, + channels=channels, + save_dir=self.snap_dir, + prefix=self.prefix, + keep_shutter_open_channels=True, + ) self._check_abort() # acquire images stack = self._acquire() # Acquire 3D stack else: - logging.debug('Acquiring 3D stack') + logging.debug("Acquiring 3D stack") # Generate MDA Settings - self.settings = generate_acq_settings(self.calib_window.mm, - channel_group=self.channel_group, - channels=channels, - zstart=self.calib_window.z_start, - zend=self.calib_window.z_end, - zstep=self.calib_window.z_step, - save_dir=self.snap_dir, - prefix=self.prefix, - keep_shutter_open_channels=True, - keep_shutter_open_slices=True) + self.settings = generate_acq_settings( + self.calib_window.mm, + channel_group=self.channel_group, + channels=channels, + zstart=self.calib_window.z_start, + zend=self.calib_window.z_end, + zstep=self.calib_window.z_step, + save_dir=self.snap_dir, + prefix=self.prefix, + keep_shutter_open_channels=True, + keep_shutter_open_slices=True, + ) self._check_abort() # set acquisition order to channel-first - self.settings['slicesFirst'] = False - self.settings['acqOrderMode'] = 0 # TIME_POS_SLICE_CHANNEL + self.settings["slicesFirst"] = False + self.settings["acqOrderMode"] = 0 # TIME_POS_SLICE_CHANNEL # acquire images stack = self._acquire() @@ -548,13 +655,13 @@ def work(self): self._check_abort() # Save images - logging.debug('Saving Images') + logging.debug("Saving Images") self._save_imgs(birefringence, phase, meta) self._check_abort() - logging.info('Finished Acquisition') - logging.debug('Finished Acquisition') + logging.info("Finished Acquisition") + logging.debug("Finished Acquisition") # Emit the images and let thread know function is finished self.bire_image_emitter.emit(birefringence) @@ -565,19 +672,21 @@ def _check_exposure(self) -> None: """ Check that all LF channels have the same exposure settings. If not, abort Acquisition. """ - logging.debug('Verifying exposure times...') + logging.debug("Verifying exposure times...") # parse exposure times channel_exposures = [] - for channel in self.settings['channels']: - channel_exposures.append(channel['exposure']) + for channel in self.settings["channels"]: + channel_exposures.append(channel["exposure"]) channel_exposures = np.array(channel_exposures) # check if exposure times are equal if not np.all(channel_exposures == channel_exposures[0]): - error_exposure_msg = f'The MDA exposure times are not equal! Aborting Acquisition.\n' \ - f'Please manually set the exposure times to the same value from the MDA menu.' + error_exposure_msg = ( + f"The MDA exposure times are not equal! Aborting Acquisition.\n" + f"Please manually set the exposure times to the same value from the MDA menu." + ) - raise ValueError(error_exposure_msg) + raise ValueError(error_exposure_msg) self._check_abort() @@ -594,7 +703,9 @@ def _acquire(self) -> np.ndarray: # Acquire from MDA settings uses MM MDA GUI # Returns (1, 4/5, Z, Y, X) array - stack = acquire_from_settings(self.calib_window.mm, self.settings, grab_images=True) + stack = acquire_from_settings( + self.calib_window.mm, self.settings, grab_images=True + ) self._check_abort() return stack @@ -619,35 +730,38 @@ def _reconstruct(self, stack): self._check_abort() - wo_background_correction = rec_bkg_to_wo_bkg(self.calib_window.bg_option) + wo_background_correction = rec_bkg_to_wo_bkg( + self.calib_window.bg_option + ) # Initialize the heavy reconstuctor - if self.mode == 'phase' or self.mode == 'all': + if self.mode == "phase" or self.mode == "all": self._check_abort() # if no reconstructor has been initialized before, create new reconstructor if not self.calib_window.phase_reconstructor: - logging.debug('Computing new reconstructor') - - - recon = initialize_reconstructor('QLIPP', - image_dim=(stack.shape[-2], stack.shape[-1]), - wavelength_nm=self.calib_window.wavelength, - swing=self.calib_window.swing, - calibration_scheme=self.calib_window.calib_scheme, - NA_obj=self.calib_window.obj_na, - NA_illu=self.calib_window.cond_na, - mag=self.calib_window.mag, - n_slices=self.n_slices, - z_step_um=self.calib_window.z_step, - pad_z=self.calib_window.pad_z, - pixel_size_um=self.calib_window.ps, - bg_correction=wo_background_correction, - n_obj_media=self.calib_window.n_media, - mode=self.calib_window.phase_dim, - use_gpu=self.calib_window.use_gpu, - gpu_id=self.calib_window.gpu_id) + logging.debug("Computing new reconstructor") + + recon = initialize_reconstructor( + "QLIPP", + image_dim=(stack.shape[-2], stack.shape[-1]), + wavelength_nm=self.calib_window.wavelength, + swing=self.calib_window.swing, + calibration_scheme=self.calib_window.calib_scheme, + NA_obj=self.calib_window.obj_na, + NA_illu=self.calib_window.cond_na, + mag=self.calib_window.mag, + n_slices=self.n_slices, + z_step_um=self.calib_window.z_step, + pad_z=self.calib_window.pad_z, + pixel_size_um=self.calib_window.ps, + bg_correction=wo_background_correction, + n_obj_media=self.calib_window.n_media, + mode=self.calib_window.phase_dim, + use_gpu=self.calib_window.use_gpu, + gpu_id=self.calib_window.gpu_id, + ) # Emit reconstructor to be saved for later reconstructions self.phase_reconstructor_emitter.emit(recon) @@ -658,66 +772,78 @@ def _reconstruct(self, stack): # compute new reconstructor if the old reconstructor properties have been modified if self._reconstructor_changed(): - logging.debug('Reconstruction settings changed, updating reconstructor') - - recon = initialize_reconstructor('QLIPP', - image_dim=(stack.shape[-2], stack.shape[-1]), - wavelength_nm=self.calib_window.wavelength, - swing=self.calib_window.swing, - calibration_scheme=self.calib_window.calib_scheme, - NA_obj=self.calib_window.obj_na, - NA_illu=self.calib_window.cond_na, - mag=self.calib_window.mag, - n_slices=self.n_slices, - z_step_um=self.calib_window.z_step, - pad_z=self.calib_window.pad_z, - pixel_size_um=self.calib_window.ps, - bg_correction=wo_background_correction, - n_obj_media=self.calib_window.n_media, - mode=self.calib_window.phase_dim, - use_gpu=self.calib_window.use_gpu, - gpu_id=self.calib_window.gpu_id) + logging.debug( + "Reconstruction settings changed, updating reconstructor" + ) + + recon = initialize_reconstructor( + "QLIPP", + image_dim=(stack.shape[-2], stack.shape[-1]), + wavelength_nm=self.calib_window.wavelength, + swing=self.calib_window.swing, + calibration_scheme=self.calib_window.calib_scheme, + NA_obj=self.calib_window.obj_na, + NA_illu=self.calib_window.cond_na, + mag=self.calib_window.mag, + n_slices=self.n_slices, + z_step_um=self.calib_window.z_step, + pad_z=self.calib_window.pad_z, + pixel_size_um=self.calib_window.ps, + bg_correction=wo_background_correction, + n_obj_media=self.calib_window.n_media, + mode=self.calib_window.phase_dim, + use_gpu=self.calib_window.use_gpu, + gpu_id=self.calib_window.gpu_id, + ) self.phase_reconstructor_emitter.emit(recon) # use previous reconstructor else: - logging.debug('Using previous reconstruction settings') + logging.debug("Using previous reconstruction settings") recon = self.calib_window.phase_reconstructor # if phase isn't desired, initialize the lighter birefringence only reconstructor # no need to save this reconstructor for later as it is pretty quick to compute else: self._check_abort() - logging.debug('Creating birefringence only reconstructor') - recon = initialize_reconstructor('birefringence', - image_dim=(stack.shape[-2], stack.shape[-1]), - calibration_scheme=self.calib_window.calib_scheme, - wavelength_nm=self.calib_window.wavelength, - swing=self.calib_window.swing, - bg_correction=wo_background_correction, - n_slices=self.n_slices) + logging.debug("Creating birefringence only reconstructor") + recon = initialize_reconstructor( + "birefringence", + image_dim=(stack.shape[-2], stack.shape[-1]), + calibration_scheme=self.calib_window.calib_scheme, + wavelength_nm=self.calib_window.wavelength, + swing=self.calib_window.swing, + bg_correction=wo_background_correction, + n_slices=self.n_slices, + ) # Prepare background corrections for waveorder # This block mimics qlipp_pipeline.py L110-119. - if self.calib_window.bg_option in ['global', 'local_fit+']: - logging.debug('Loading BG Data') + if self.calib_window.bg_option in ["global", "local_fit+"]: + logging.debug("Loading BG Data") self._check_abort() - bg_data = self._load_bg(self.calib_window.acq_bg_directory, stack.shape[-2], stack.shape[-1]) + bg_data = self._load_bg( + self.calib_window.acq_bg_directory, + stack.shape[-2], + stack.shape[-1], + ) self._check_abort() bg_stokes = recon.Stokes_recon(bg_data) self._check_abort() bg_stokes = recon.Stokes_transform(bg_stokes) self._check_abort() - elif self.calib_window.bg_option == 'local_fit': + elif self.calib_window.bg_option == "local_fit": bg_stokes = np.zeros((5, stack.shape[-2], stack.shape[-1])) - bg_stokes[0, ...] = 1 # Set background to "identity" Stokes parameters. + bg_stokes[ + 0, ... + ] = 1 # Set background to "identity" Stokes parameters. else: - logging.debug('No Background Correction method chosen') + logging.debug("No Background Correction method chosen") bg_stokes = None # Begin reconstruction with stokes (needed for birefringence or phase) - logging.debug('Reconstructing...') + logging.debug("Reconstructing...") self._check_abort() stokes = reconstruct_qlipp_stokes(stack, recon, bg_stokes) self._check_abort() @@ -726,68 +852,86 @@ def _reconstruct(self, stack): birefringence = None phase = None - regularizer = 'Tikhonov' if self.calib_window.ui.cb_phase_denoiser.currentIndex() == 0 else 'TV' + regularizer = ( + "Tikhonov" + if self.calib_window.ui.cb_phase_denoiser.currentIndex() == 0 + else "TV" + ) reg = float(self.calib_window.ui.le_phase_strength.text()) # reconstruct both phase and birefringence - if self.mode == 'all': - if self.calib_window.birefringence_dim == '2D': - birefringence = reconstruct_qlipp_birefringence(stokes[:, stokes.shape[1]//2, :, :], recon) + if self.mode == "all": + if self.calib_window.birefringence_dim == "2D": + birefringence = reconstruct_qlipp_birefringence( + stokes[:, stokes.shape[1] // 2, :, :], recon + ) else: birefringence = reconstruct_qlipp_birefringence(stokes, recon) - birefringence[0] = birefringence[0] / (2 * np.pi) * self.calib_window.wavelength + birefringence[0] = ( + birefringence[0] / (2 * np.pi) * self.calib_window.wavelength + ) self._check_abort() - if self.calib_window.phase_dim == '2D': - phase = reconstruct_phase2D(stokes[0], - recon, - method=regularizer, - reg_p=reg, - itr=int(self.calib_window.ui.le_itr.text()), - rho=float(self.calib_window.ui.le_rho.text())) + if self.calib_window.phase_dim == "2D": + phase = reconstruct_phase2D( + stokes[0], + recon, + method=regularizer, + reg_p=reg, + itr=int(self.calib_window.ui.le_itr.text()), + rho=float(self.calib_window.ui.le_rho.text()), + ) else: - phase = reconstruct_phase3D(stokes[0], - recon, - method=regularizer, - reg_re=reg, - itr=int(self.calib_window.ui.le_itr.text()), - rho=float(self.calib_window.ui.le_rho.text())) + phase = reconstruct_phase3D( + stokes[0], + recon, + method=regularizer, + reg_re=reg, + itr=int(self.calib_window.ui.le_itr.text()), + rho=float(self.calib_window.ui.le_rho.text()), + ) self._check_abort() # reconstruct phase only - elif self.mode == 'phase': - if self.calib_window.phase_dim == '2D': - phase = reconstruct_phase2D(stokes[0], - recon, - method=regularizer, - reg_p=reg, - itr=int(self.calib_window.ui.le_itr.text()), - rho=float(self.calib_window.ui.le_rho.text())) + elif self.mode == "phase": + if self.calib_window.phase_dim == "2D": + phase = reconstruct_phase2D( + stokes[0], + recon, + method=regularizer, + reg_p=reg, + itr=int(self.calib_window.ui.le_itr.text()), + rho=float(self.calib_window.ui.le_rho.text()), + ) else: - phase = reconstruct_phase3D(stokes[0], - recon, - method=regularizer, - reg_re=reg, - itr=int(self.calib_window.ui.le_itr.text()), - rho=float(self.calib_window.ui.le_rho.text())) + phase = reconstruct_phase3D( + stokes[0], + recon, + method=regularizer, + reg_re=reg, + itr=int(self.calib_window.ui.le_itr.text()), + rho=float(self.calib_window.ui.le_rho.text()), + ) self._check_abort() # reconstruct birefringence only - elif self.mode == 'birefringence': + elif self.mode == "birefringence": birefringence = reconstruct_qlipp_birefringence(stokes, recon) - birefringence[0] = birefringence[0] / (2 * np.pi) * self.calib_window.wavelength + birefringence[0] = ( + birefringence[0] / (2 * np.pi) * self.calib_window.wavelength + ) self._check_abort() else: - raise ValueError('Reconstruction Mode Not Understood') + raise ValueError("Reconstruction Mode Not Understood") meta = extract_reconstruction_parameters(recon, self.calib_window.mag) - meta['regularization_method'] = regularizer - meta['regularization_strength'] = reg - if regularizer == 'TV': - meta['rho'] = float(self.calib_window.ui.le_rho.text()) - meta['itr'] = int(self.calib_window.ui.le_itr.text()) + meta["regularization_method"] = regularizer + meta["regularization_strength"] = reg + if regularizer == "TV": + meta["rho"] = float(self.calib_window.ui.le_rho.text()) + meta["itr"] = int(self.calib_window.ui.le_itr.text()) # return both variables, could contain images or could be null return birefringence, phase, meta @@ -811,32 +955,62 @@ def _save_imgs(self, birefringence, phase, meta=None): if birefringence is not None: # initialize - chunk_size = (1, 1, 1, birefringence.shape[-2],birefringence.shape[-1]) + chunk_size = ( + 1, + 1, + 1, + birefringence.shape[-2], + birefringence.shape[-1], + ) # increment filename one more than last found saved snap prefix = self.calib_window.save_name - name = f'BirefringenceSnap.zarr' if not prefix else f'{prefix}_BirefringenceSnap.zarr' + name = ( + f"BirefringenceSnap.zarr" + if not prefix + else f"{prefix}_BirefringenceSnap.zarr" + ) # create zarr root and position group writer.create_zarr_root(name) # Check if 2D if len(birefringence.shape) == 3: - writer.init_array(0, (1, 4, 1, birefringence.shape[-2], birefringence.shape[-1]), - chunk_size, ['Retardance', 'Orientation', 'BF', 'Pol']) + writer.init_array( + 0, + ( + 1, + 4, + 1, + birefringence.shape[-2], + birefringence.shape[-1], + ), + chunk_size, + ["Retardance", "Orientation", "BF", "Pol"], + ) z = 0 # Check if 3D else: - writer.init_array(0, (1, 4, birefringence.shape[-3], birefringence.shape[-2], birefringence.shape[-1]), - chunk_size, ['Retardance', 'Orientation', 'BF', 'Pol']) + writer.init_array( + 0, + ( + 1, + 4, + birefringence.shape[-3], + birefringence.shape[-2], + birefringence.shape[-1], + ), + chunk_size, + ["Retardance", "Orientation", "BF", "Pol"], + ) z = slice(0, birefringence.shape[-3]) # Write the data to disk writer.write(birefringence, p=0, t=0, c=slice(0, 4), z=z) current_meta = writer.store.attrs.asdict() - current_meta['recOrder'] = meta + current_meta["recOrder"] = meta writer.store.attrs.put(current_meta) if phase is not None: @@ -846,26 +1020,38 @@ def _save_imgs(self, birefringence, phase, meta=None): # increment filename one more than last found saved snap prefix = self.calib_window.save_name - name = f'PhaseSnap.zarr' if not prefix else f'{prefix}_PhaseSnap.zarr' + name = ( + f"PhaseSnap.zarr" if not prefix else f"{prefix}_PhaseSnap.zarr" + ) # create zarr root and position group writer.create_zarr_root(name) # Check if 2D if len(phase.shape) == 2: - writer.init_array(0, (1, 1, 1, phase.shape[-2], phase.shape[-1]), chunk_size, ['Phase2D']) + writer.init_array( + 0, + (1, 1, 1, phase.shape[-2], phase.shape[-1]), + chunk_size, + ["Phase2D"], + ) z = 0 # Check if 3D else: - writer.init_array(0, (1, 1, phase.shape[-3], phase.shape[-2], phase.shape[-1]), chunk_size, ['Phase3D']) + writer.init_array( + 0, + (1, 1, phase.shape[-3], phase.shape[-2], phase.shape[-1]), + chunk_size, + ["Phase3D"], + ) z = slice(0, phase.shape[-3]) # Write data to disk writer.write(phase, p=0, t=0, c=0, z=z) current_meta = writer.store.attrs.asdict() - current_meta['recOrder'] = meta + current_meta["recOrder"] = meta writer.store.attrs.put(current_meta) def _load_bg(self, path, height, width): @@ -885,7 +1071,7 @@ def _load_bg(self, path, height, width): """ - #TODO: Change to just accept ROI + # TODO: Change to just accept ROI try: metadata_path = get_last_metadata_file(path) metadata = MetadataReader(metadata_path) @@ -910,27 +1096,31 @@ def _reconstructor_changed(self): changed = None # Attributes that are directly equivalent to worker attributes - attr_list = {'phase_dim': 'phase_deconv', - 'pad_z': 'pad_z', - 'n_media': 'n_media', - 'bg_option': 'bg_option', - 'use_gpu': 'use_gpu', - 'gpu_id': 'gpu_id' - } + attr_list = { + "phase_dim": "phase_deconv", + "pad_z": "pad_z", + "n_media": "n_media", + "bg_option": "bg_option", + "use_gpu": "use_gpu", + "gpu_id": "gpu_id", + } # attributes that are modified upon passing them to reconstructor - attr_modified_list = {'obj_na': 'NA_obj', - 'cond_na': 'NA_illu', - 'wavelength': 'lambda_illu', - 'n_slices': 'N_defocus', - 'swing': 'chi', - 'ps': 'ps' - } + attr_modified_list = { + "obj_na": "NA_obj", + "cond_na": "NA_illu", + "wavelength": "lambda_illu", + "n_slices": "N_defocus", + "swing": "chi", + "ps": "ps", + } self._check_abort() # check if equivalent attributes have diverged for key, value in attr_list.items(): - if getattr(self.calib_window, key) != getattr(self.calib_window.phase_reconstructor, value): + if getattr(self.calib_window, key) != getattr( + self.calib_window.phase_reconstructor, value + ): changed = True break else: @@ -939,41 +1129,58 @@ def _reconstructor_changed(self): if not changed: # modify attributes to be equivalent and check for divergence for key, value in attr_modified_list.items(): - if key == 'swing': - if self.calib_window.calib_scheme == '5-State': - if self.calib_window.swing * 2 * np.pi != self.calib_window.phase_reconstructor.chi: + if key == "swing": + if self.calib_window.calib_scheme == "5-State": + if ( + self.calib_window.swing * 2 * np.pi + != self.calib_window.phase_reconstructor.chi + ): changed = True else: changed = False else: - if self.calib_window.swing != self.calib_window.phase_reconstructor.chi: + if ( + self.calib_window.swing + != self.calib_window.phase_reconstructor.chi + ): changed = True else: changed = False - elif key == 'wavelength': - if self.calib_window.wavelength * 1e-3 / self.calib_window.n_media != \ - self.calib_window.phase_reconstructor.lambda_illu: + elif key == "wavelength": + if ( + self.calib_window.wavelength + * 1e-3 + / self.calib_window.n_media + != self.calib_window.phase_reconstructor.lambda_illu + ): changed = True break else: changed = False - elif key == 'n_slices': - if getattr(self, key) != getattr(self.calib_window.phase_reconstructor, value): + elif key == "n_slices": + if getattr(self, key) != getattr( + self.calib_window.phase_reconstructor, value + ): changed = True break else: changed = False - elif key == 'ps': - if getattr(self.calib_window, key) / float(self.calib_window.mag) != getattr(self.calib_window.phase_reconstructor, value): + elif key == "ps": + if getattr(self.calib_window, key) / float( + self.calib_window.mag + ) != getattr(self.calib_window.phase_reconstructor, value): changed = True break else: changed = False else: - if getattr(self.calib_window, key)/self.calib_window.n_media != \ - getattr(self.calib_window.phase_reconstructor, value): + if getattr( + self.calib_window, key + ) / self.calib_window.n_media != getattr( + self.calib_window.phase_reconstructor, value + ): changed = True else: changed = False @@ -1000,14 +1207,27 @@ def _cleanup_acq(self): closed = disp.isClosed() dp.close() - # Try to delete the data, sometime it isn't cleaned up quickly enough and will # return an error. In this case, catch the error and then try to close again (seems to work). try: - save_prefix = self.calib_window.save_name if self.calib_window.save_name else None - name = f'RawPolDataSnap.zarr' if not save_prefix else f'{save_prefix}_RawPolDataSnap.zarr' + save_prefix = ( + self.calib_window.save_name + if self.calib_window.save_name + else None + ) + name = ( + f"RawPolDataSnap.zarr" + if not save_prefix + else f"{save_prefix}_RawPolDataSnap.zarr" + ) out_path = os.path.join(self.snap_dir, name) - converter = ZarrConverter(os.path.join(dir_, prefix), out_path, 'ometiff', False, False) + converter = ZarrConverter( + os.path.join(dir_, prefix), + out_path, + "ometiff", + False, + False, + ) converter.run_conversion() shutil.rmtree(os.path.join(dir_, prefix)) except PermissionError as ex: @@ -1046,7 +1266,7 @@ def __init__(self, calib_window, bg_data): def _check_abort(self): if self.abort_requested: self.aborted.emit() - raise TimeoutError('Stop Requested') + raise TimeoutError("Stop Requested") def get_byte_offset(self, offsets, page): """ @@ -1073,7 +1293,9 @@ def get_byte_offset(self, offsets, page): return array_offset - def listen_for_images(self, array, file, offsets, interval, dim3, dim2, dim1, dim0, dim_order): + def listen_for_images( + self, array, file, offsets, interval, dim3, dim2, dim1, dim0, dim_order + ): """ Parameters @@ -1101,16 +1323,36 @@ def listen_for_images(self, array, file, offsets, interval, dim3, dim2, dim1, di # Order dimensions that we will loop through in order to match the acquisition if dim_order == 0: - dims = [[dim3, self.n_frames], [dim2, self.n_pos], [dim1, self.n_slices], [dim0, self.n_channels]] + dims = [ + [dim3, self.n_frames], + [dim2, self.n_pos], + [dim1, self.n_slices], + [dim0, self.n_channels], + ] channel_dim = 0 elif dim_order == 1: - dims = [[dim3, self.n_frames], [dim2, self.n_pos], [dim1, self.n_channels], [dim0, self.n_slices]] + dims = [ + [dim3, self.n_frames], + [dim2, self.n_pos], + [dim1, self.n_channels], + [dim0, self.n_slices], + ] channel_dim = 1 elif dim_order == 2: - dims = [[dim3, self.n_pos], [dim2, self.n_frames], [dim1, self.n_slices], [dim0, self.n_channels]] + dims = [ + [dim3, self.n_pos], + [dim2, self.n_frames], + [dim1, self.n_slices], + [dim0, self.n_channels], + ] channel_dim = 0 else: - dims = [[dim3, self.n_pos], [dim2, self.n_frames], [dim1, self.n_channels], [dim0, self.n_slices]] + dims = [ + [dim3, self.n_pos], + [dim2, self.n_frames], + [dim1, self.n_channels], + [dim0, self.n_slices], + ] channel_dim = 1 # Loop through dimensions in the order they are acquired @@ -1134,13 +1376,20 @@ def listen_for_images(self, array, file, offsets, interval, dim3, dim2, dim1, di self._check_abort() tf = tiff.TiffFile(file) time.sleep(interval) - offsets = tf.micromanager_metadata['IndexMap']['Offset'] + offsets = tf.micromanager_metadata["IndexMap"][ + "Offset" + ] tf.close() offset = self.get_byte_offset(offsets, idx) else: offset = self.get_byte_offset(offsets, idx) - if idx < (self.n_slices * self.n_channels * self.n_frames * self.n_pos): + if idx < ( + self.n_slices + * self.n_channels + * self.n_frames + * self.n_pos + ): # Assign dimensions based off acquisition order to correctly add image to array if dim_order == 0: @@ -1155,11 +1404,20 @@ def listen_for_images(self, array, file, offsets, interval, dim3, dim2, dim1, di self._check_abort() # Add Image to array - img = np.memmap(file, dtype=self.dtype, mode='r', offset=offset, shape=self.shape) + img = np.memmap( + file, + dtype=self.dtype, + mode="r", + offset=offset, + shape=self.shape, + ) array[c, z] = img # If Channel first, compute birefringence here - if channel_dim == 0 and dim0 == self.n_channels - 1: + if ( + channel_dim == 0 + and dim0 == self.n_channels - 1 + ): self._check_abort() # Compute birefringence @@ -1168,7 +1426,12 @@ def listen_for_images(self, array, file, offsets, interval, dim3, dim2, dim1, di idx += 1 # Reset Range to 0 to account for starting this function in middle of a dimension - if idx < (self.n_slices * self.n_channels * self.n_frames * self.n_pos): + if idx < ( + self.n_slices + * self.n_channels + * self.n_frames + * self.n_pos + ): dims[2][0] = 0 dims[3][0] = 0 @@ -1191,7 +1454,12 @@ def listen_for_images(self, array, file, offsets, interval, dim3, dim2, dim1, di continue # Reset range to 0 to account for starting this function in the middle of a dimension - if idx < (self.n_slices * self.n_channels * self.n_frames * self.n_pos): + if idx < ( + self.n_slices + * self.n_channels + * self.n_frames + * self.n_pos + ): dims[1][0] = 0 # Return at the end of the acquisition @@ -1206,31 +1474,37 @@ def compute_and_save(self, array, p, t, z): # If the napari layer doesn't exist yet, create the zarr store to emit to napari if self.prefix not in self.calib_window.viewer.layers: - self.store = zarr.open(os.path.join(self.root, self.prefix+'.zarr')) - self.store.zeros(name='Birefringence', - shape=(self.n_pos, - self.n_frames, - 2, - self.n_slices, - self.shape[0], - self.shape[1]), - chunks=(1, 1, 1, 1, self.shape[0], self.shape[1]), - overwrite=True) + self.store = zarr.open( + os.path.join(self.root, self.prefix + ".zarr") + ) + self.store.zeros( + name="Birefringence", + shape=( + self.n_pos, + self.n_frames, + 2, + self.n_slices, + self.shape[0], + self.shape[1], + ), + chunks=(1, 1, 1, 1, self.shape[0], self.shape[1]), + overwrite=True, + ) # Add data and send out the store. Once the store has been emitted, we can add data to the store # without needing to emit the store again (thanks to napari's handling of zarr datasets) if len(birefringence.shape) == 4: - self.store['Birefringence'][p, t] = birefringence + self.store["Birefringence"][p, t] = birefringence else: - self.store['Birefringence'][p, t, :, z] = birefringence + self.store["Birefringence"][p, t, :, z] = birefringence self.store_emitter.emit(self.store) else: if len(birefringence.shape) == 4: - self.store['Birefringence'][p, t] = birefringence + self.store["Birefringence"][p, t] = birefringence else: - self.store['Birefringence'][p, t, :, z] = birefringence + self.store["Birefringence"][p, t, :, z] = birefringence # Emit the current dimensions so that we can update the napari dimension slider self.dim_emitter.emit((p, t, z)) @@ -1247,68 +1521,97 @@ def work(self): s = acq_man.getAcquisitionSettings() self.root = s.root() self.prefix = s.prefix() - self.n_frames, self.interval = (s.numFrames(), s.intervalMs() / 1000) if s.useFrames() else (1, 1) + self.n_frames, self.interval = ( + (s.numFrames(), s.intervalMs() / 1000) if s.useFrames() else (1, 1) + ) self.n_channels = s.channels().size() if s.useChannels() else 1 self.n_slices = s.slices().size() if s.useChannels() else 1 - self.n_pos = 1 if not s.usePositionList() else \ - self.calib_window.mm.getPositionListManager().getPositionList().getNumberOfPositions() + self.n_pos = ( + 1 + if not s.usePositionList() + else self.calib_window.mm.getPositionListManager() + .getPositionList() + .getNumberOfPositions() + ) dim_order = s.acqOrderMode() # Get File Path corresponding to current dataset path = os.path.join(self.root, self.prefix) - files = [fn for fn in glob.glob(path + '') if '.zarr' not in fn] - index = max([int(x.strip(path + '_')) for x in files]) + files = [fn for fn in glob.glob(path + "") if ".zarr" not in fn] + index = max([int(x.strip(path + "_")) for x in files]) - self.prefix = self.prefix + f'_{index}' + self.prefix = self.prefix + f"_{index}" full_path = os.path.join(self.root, self.prefix) - first_file_path = os.path.join(full_path, self.prefix + '_MMStack.ome.tif') + first_file_path = os.path.join( + full_path, self.prefix + "_MMStack.ome.tif" + ) file = tiff.TiffFile(first_file_path) file_path = first_file_path - self.shape = (file.micromanager_metadata['Summary']['Height'], file.micromanager_metadata['Summary']['Width']) + self.shape = ( + file.micromanager_metadata["Summary"]["Height"], + file.micromanager_metadata["Summary"]["Width"], + ) self.dtype = file.pages[0].dtype - offsets = file.micromanager_metadata['IndexMap']['Offset'] + offsets = file.micromanager_metadata["IndexMap"]["Offset"] file.close() self._check_abort() # Init Reconstruction Class - self.reconstructor = QLIPPBirefringenceCompute(self.shape, - self.calib_window.calib_scheme, - self.calib_window.wavelength, - self.calib_window.swing, - self.n_slices, - self.calib_window.bg_option, - self.bg_data) + self.reconstructor = QLIPPBirefringenceCompute( + self.shape, + self.calib_window.calib_scheme, + self.calib_window.wavelength, + self.calib_window.swing, + self.n_slices, + self.calib_window.bg_option, + self.bg_data, + ) self._check_abort() # initialize dimensions / array for the loop idx, dim3, dim2, dim1, dim0 = 0, 0, 0, 0, 0 - array = np.zeros((self.n_channels, self.n_slices, self.shape[0], self.shape[1])) + array = np.zeros( + (self.n_channels, self.n_slices, self.shape[0], self.shape[1]) + ) file_count = 0 total_idx = 0 # Run until the function has collected the totality of the data - while total_idx < (self.n_slices * self.n_channels * self.n_frames * self.n_pos): + while total_idx < ( + self.n_slices * self.n_channels * self.n_frames * self.n_pos + ): self._check_abort() # this will loop through reading images in a single file as it's being written # when it has successfully loaded all of the images from the file, it'll move on to the next - array, idx, dim3, dim2, dim1, dim0 = self.listen_for_images(array, - file_path, - offsets, - self.interval, - dim3, dim2, dim1, dim0, dim_order) + array, idx, dim3, dim2, dim1, dim0 = self.listen_for_images( + array, + file_path, + offsets, + self.interval, + dim3, + dim2, + dim1, + dim0, + dim_order, + ) total_idx += idx # If acquisition is not finished, grab the next file and listen for images - if total_idx != self.n_slices * self.n_channels * self.n_frames * self.n_pos: + if ( + total_idx + != self.n_slices * self.n_channels * self.n_frames * self.n_pos + ): time.sleep(1) file_count += 1 - file_path = os.path.join(full_path, self.prefix + f'_MMStack_{file_count}.ome.tif') + file_path = os.path.join( + full_path, self.prefix + f"_MMStack_{file_count}.ome.tif" + ) file = tiff.TiffFile(file_path) - offsets = file.micromanager_metadata['IndexMap']['Offset'] + offsets = file.micromanager_metadata["IndexMap"]["Offset"] file.close() diff --git a/recOrder/plugin/workers/calibration_workers.py b/recOrder/plugin/workers/calibration_workers.py index 24911a47..6183729a 100644 --- a/recOrder/plugin/workers/calibration_workers.py +++ b/recOrder/plugin/workers/calibration_workers.py @@ -3,8 +3,11 @@ from qtpy.QtCore import Signal from napari.qt.threading import WorkerBaseSignals, WorkerBase, thread_worker -from recOrder.compute.qlipp_compute import initialize_reconstructor, \ - reconstruct_qlipp_birefringence, reconstruct_qlipp_stokes +from recOrder.compute.qlipp_compute import ( + initialize_reconstructor, + reconstruct_qlipp_birefringence, + reconstruct_qlipp_stokes, +) from recOrder.io.core_functions import set_lc_state, snap_and_average from recOrder.io.utils import MockEmitter from recOrder.calib.Calibration import LC_DEVICE_NAME @@ -19,6 +22,7 @@ # type hint/check from typing import TYPE_CHECKING + # avoid runtime import error if TYPE_CHECKING: from _typeshed import StrOrBytesPath @@ -68,7 +72,7 @@ def __init_subclass__(cls, signals: WorkerBaseSignals): """ super().__init_subclass__() cls.signals = signals - + def __init__(self, calib_window: MainWidget, calib: QLIPP_Calibration): """Initialize the worker object. @@ -90,17 +94,20 @@ def _check_abort(self): """ if self.abort_requested: self.aborted.emit() - raise TimeoutError('Stop Requested.') - + raise TimeoutError("Stop Requested.") + def _write_meta_file(self, meta_file: str): self.calib.meta_file = meta_file - self.calib.write_metadata(notes=self.calib_window.ui.le_notes_field.text()) + self.calib.write_metadata( + notes=self.calib_window.ui.le_notes_field.text() + ) class CalibrationWorker(CalibrationWorkerBase, signals=CalibrationSignals): """ Class to execute calibration """ + def __init__(self, calib_window, calib): super().__init__(calib_window, calib) @@ -109,44 +116,48 @@ def work(self): Runs the full calibration algorithm and emits necessary signals. """ - self.plot_sequence_emit.emit('Coarse') + self.plot_sequence_emit.emit("Coarse") self.calib.intensity_emitter = self.intensity_update self.calib.plot_sequence_emitter = self.plot_sequence_emit self.calib.get_full_roi() - self.progress_update.emit((1, 'Calculating Blacklevel...')) + self.progress_update.emit((1, "Calculating Blacklevel...")) self._check_abort() # Check if change of ROI is needed if self.calib_window.use_cropped_roi: rect = self.calib.check_and_get_roi() - self.calib_window.mmc.setROI(rect.x, rect.y, rect.width, rect.height) + self.calib_window.mmc.setROI( + rect.x, rect.y, rect.width, rect.height + ) self.calib.ROI = (rect.x, rect.y, rect.width, rect.height) self._check_abort() - logging.info('Calculating Black Level ...') - logging.debug('Calculating Black Level ...') + logging.info("Calculating Black Level ...") + logging.debug("Calculating Black Level ...") self.calib.close_shutter_and_calc_blacklevel() # Calculate Blacklevel - logging.info(f'Black Level: {self.calib.I_Black:.0f}\n') - logging.debug(f'Black Level: {self.calib.I_Black:.0f}\n') + logging.info(f"Black Level: {self.calib.I_Black:.0f}\n") + logging.debug(f"Black Level: {self.calib.I_Black:.0f}\n") self._check_abort() - self.progress_update.emit((10, 'Calibrating Extinction State...')) + self.progress_update.emit((10, "Calibrating Extinction State...")) # Open shutter self.calib.open_shutter() # Set LC Wavelength: self.calib.set_wavelength(int(self.calib_window.wavelength)) - if self.calib_window.calib_mode == 'MM-Retardance': - self.calib_window.mmc.setProperty(LC_DEVICE_NAME, 'Wavelength', self.calib_window.wavelength) + if self.calib_window.calib_mode == "MM-Retardance": + self.calib_window.mmc.setProperty( + LC_DEVICE_NAME, "Wavelength", self.calib_window.wavelength + ) self._check_abort() # Optimize States - self._calibrate_4state() if self.calib_window.calib_scheme == '4-State' else self._calibrate_5state() + self._calibrate_4state() if self.calib_window.calib_scheme == "4-State" else self._calibrate_5state() # Reset shutter autoshutter self.calib.reset_shutter() @@ -158,8 +169,12 @@ def work(self): self._check_abort() # Calculate Extinction - extinction_ratio = self.calib.calculate_extinction(self.calib.swing, self.calib.I_Black, self.calib.I_Ext, - self.calib.I_Elliptical) + extinction_ratio = self.calib.calculate_extinction( + self.calib.swing, + self.calib.I_Black, + self.calib.I_Ext, + self.calib.I_Elliptical, + ) self._check_abort() # Update main GUI with extinction ratio @@ -183,7 +198,7 @@ def _meta_path(metadata_idx: str): # Write Metadata self._write_meta_file(meta_file) self.calib_file_emit.emit(self.calib.meta_file) - self.progress_update.emit((100, 'Finished')) + self.progress_update.emit((100, "Finished")) self._check_abort() @@ -205,9 +220,9 @@ def _calibrate_4state(self): Run through the 4-state calibration algorithm """ - search_radius = np.min((self.calib.swing/self.calib.ratio, 0.05)) + search_radius = np.min((self.calib.swing / self.calib.ratio, 0.05)) - self.calib.calib_scheme = '4-State' + self.calib.calib_scheme = "4-State" self._check_abort() @@ -215,23 +230,23 @@ def _calibrate_4state(self): self.calib.opt_Iext() self._check_abort() - self.progress_update.emit((60, 'Calibrating State 1...')) + self.progress_update.emit((60, "Calibrating State 1...")) # Optimize first elliptical (reference) state self.calib.opt_I0() - self.progress_update.emit((65, 'Calibrating State 2...')) + self.progress_update.emit((65, "Calibrating State 2...")) self._check_abort() # Optimize 60 deg state self.calib.opt_I60(search_radius, search_radius) - self.progress_update.emit((75, 'Calibrating State 3...')) + self.progress_update.emit((75, "Calibrating State 3...")) self._check_abort() # Optimize 120 deg state self.calib.opt_I120(search_radius, search_radius) - self.progress_update.emit((85, 'Writing Metadata...')) + self.progress_update.emit((85, "Writing Metadata...")) self._check_abort() @@ -239,35 +254,35 @@ def _calibrate_5state(self): search_radius = np.min((self.calib.swing, 0.05)) - self.calib.calib_scheme = '5-State' + self.calib.calib_scheme = "5-State" # Optimize Extinction State self.calib.opt_Iext() - self.progress_update.emit((50, 'Calibrating State 1...')) + self.progress_update.emit((50, "Calibrating State 1...")) self._check_abort() # Optimize First elliptical state self.calib.opt_I0() - self.progress_update.emit((55, 'Calibrating State 2...')) + self.progress_update.emit((55, "Calibrating State 2...")) self._check_abort() # Optimize 45 deg state self.calib.opt_I45(search_radius, search_radius) - self.progress_update.emit((65, 'Calibrating State 3...')) + self.progress_update.emit((65, "Calibrating State 3...")) self._check_abort() # Optimize 90 deg state self.calib.opt_I90(search_radius, search_radius) - self.progress_update.emit((75, 'Calibrating State 4...')) + self.progress_update.emit((75, "Calibrating State 4...")) self._check_abort() # Optimize 135 deg state self.calib.opt_I135(search_radius, search_radius) - self.progress_update.emit((85, 'Writing Metadata...')) + self.progress_update.emit((85, "Writing Metadata...")) self._check_abort() @@ -278,22 +293,28 @@ def _assess_calibration(self): """ if self.calib.extinction_ratio >= 100: - self.calib_assessment.emit('good') - self.calib_assessment_msg.emit('Successful Calibration') + self.calib_assessment.emit("good") + self.calib_assessment_msg.emit("Successful Calibration") elif 80 <= self.calib.extinction_ratio < 100: - self.calib_assessment.emit('okay') - self.calib_assessment_msg.emit('Successful Calibration, Okay Extinction Ratio') + self.calib_assessment.emit("okay") + self.calib_assessment_msg.emit( + "Successful Calibration, Okay Extinction Ratio" + ) else: - self.calib_assessment.emit('bad') - message = ("Possibilities are: a) linear polarizer and LC are not oriented properly, " - "b) circular analyzer has wrong handedness, " - "c) the condenser is not setup for Kohler illumination, " - "d) a component, such as autofocus dichroic or sample chamber, distorts the polarization state") + self.calib_assessment.emit("bad") + message = ( + "Possibilities are: a) linear polarizer and LC are not oriented properly, " + "b) circular analyzer has wrong handedness, " + "c) the condenser is not setup for Kohler illumination, " + "d) a component, such as autofocus dichroic or sample chamber, distorts the polarization state" + ) - self.calib_assessment_msg.emit('Poor Extinction. '+message) + self.calib_assessment_msg.emit("Poor Extinction. " + message) -class BackgroundCaptureWorker(CalibrationWorkerBase, signals=BackgroundSignals): +class BackgroundCaptureWorker( + CalibrationWorkerBase, signals=BackgroundSignals +): """ Class to execute background capture. """ @@ -304,22 +325,27 @@ def __init__(self, calib_window, calib): def work(self): # Make the background folder - bg_path = os.path.join(self.calib_window.directory, self.calib_window.ui.le_bg_folder.text()) + bg_path = os.path.join( + self.calib_window.directory, + self.calib_window.ui.le_bg_folder.text(), + ) if not os.path.exists(bg_path): os.mkdir(bg_path) else: # increment background paths idx = 1 - while os.path.exists(bg_path+f'_{idx}'): + while os.path.exists(bg_path + f"_{idx}"): idx += 1 - bg_path = bg_path+f'_{idx}' - for state_file in glob.glob(os.path.join(bg_path, 'State')): + bg_path = bg_path + f"_{idx}" + for state_file in glob.glob(os.path.join(bg_path, "State")): os.remove(state_file) - if os.path.exists(os.path.join(bg_path, 'calibration_metadata.txt')): - os.remove(os.path.join(bg_path, 'calibration_metadata.txt')) + if os.path.exists( + os.path.join(bg_path, "calibration_metadata.txt") + ): + os.remove(os.path.join(bg_path, "calibration_metadata.txt")) self._check_abort() @@ -328,12 +354,14 @@ def work(self): img_dim = (imgs.shape[-2], imgs.shape[-1]) # initialize reconstructor - recon = initialize_reconstructor('birefringence', - image_dim=img_dim, - calibration_scheme=self.calib_window.calib_scheme, - wavelength_nm=self.calib_window.wavelength, - swing=self.calib_window.swing, - bg_correction='None') + recon = initialize_reconstructor( + "birefringence", + image_dim=img_dim, + calibration_scheme=self.calib_window.calib_scheme, + wavelength_nm=self.calib_window.wavelength, + swing=self.calib_window.swing, + bg_correction="None", + ) self._check_abort() @@ -347,25 +375,27 @@ def work(self): self._check_abort() # Convert retardance to nm - self.retardance = self.birefringence[0] / (2 * np.pi) * self.calib_window.wavelength + self.retardance = ( + self.birefringence[0] / (2 * np.pi) * self.calib_window.wavelength + ) # Save metadata file and emit imgs - meta_file = os.path.join(bg_path, 'calibration_metadata.txt') + meta_file = os.path.join(bg_path, "calibration_metadata.txt") self._write_meta_file(meta_file) # Update last calibration file note = self.calib_window.ui.le_notes_field.text() - with open(self.calib_window.last_calib_meta_file, 'r') as file: + with open(self.calib_window.last_calib_meta_file, "r") as file: current_json = json.load(file) - old_note = current_json['Notes'] - if old_note is None or old_note == '' or old_note == note: - current_json['Notes'] = note + old_note = current_json["Notes"] + if old_note is None or old_note == "" or old_note == note: + current_json["Notes"] = note else: - current_json['Notes'] = old_note + ', ' + note + current_json["Notes"] = old_note + ", " + note - with open(self.calib_window.last_calib_meta_file, 'w') as file: + with open(self.calib_window.last_calib_meta_file, "w") as file: json.dump(current_json, file, indent=1) self._check_abort() @@ -397,26 +427,25 @@ def _save_bg_recon(self, bg_path: StrOrBytesPath): position=0, data_shape=(1, 2, 1, rows, columns), chunk_size=(1, 1, 1, rows, columns), - chan_names=['Retardance', 'Orientation'] + chan_names=["Retardance", "Orientation"], ) writer.write(self.birefringence[:2], p=0, t=0, z=0) # save intensity trace visualization import matplotlib.pyplot as plt + plt.imsave( - os.path.join(bg_recon_path, - "retardance.png"), + os.path.join(bg_recon_path, "retardance.png"), self.retardance, - cmap="gray" + cmap="gray", ) plt.imsave( - os.path.join(bg_recon_path, - "orientation.png"), - self.birefringence[1], + os.path.join(bg_recon_path, "orientation.png"), + self.birefringence[1], cmap="hsv", vmin=0, - vmax=np.pi - ) - + vmax=np.pi, + ) + @thread_worker def load_calibration(calib, metadata: MetadataReader): @@ -438,13 +467,17 @@ def load_calibration(calib, metadata: MetadataReader): def _set_calib_attrs(calib, metadata): """Set the retardance attributes in the recOrder Calibration object""" if calib.calib_scheme == "4-State": - lc_states = ['ext', '0', '60', '120'] + lc_states = ["ext", "0", "60", "120"] elif calib.calib_scheme == "5-State": - lc_states = ['ext', '0', '45', '90', '135'] + lc_states = ["ext", "0", "45", "90", "135"] else: - raise ValueError("Invalid calibration scheme in metadata: {calib.calib_scheme}") + raise ValueError( + "Invalid calibration scheme in metadata: {calib.calib_scheme}" + ) for side in ("A", "B"): - retardance_values = metadata.__getattribute__("LC" + side + "_retardance") + retardance_values = metadata.__getattribute__( + "LC" + side + "_retardance" + ) for i, state in enumerate(lc_states): # set the retardance value attribute (e.g. 'lca_0') retardance_name = "lc" + side.lower() + "_" + state @@ -456,19 +489,27 @@ def _set_calib_attrs(calib, metadata): _set_calib_attrs(calib, metadata) - for state, lca, lcb in zip([f'State{i}' for i in range(5)], metadata.LCA_retardance, metadata.LCB_retardance): + for state, lca, lcb in zip( + [f"State{i}" for i in range(5)], + metadata.LCA_retardance, + metadata.LCB_retardance, + ): calib.define_lc_state(state, lca, lcb) # Calculate black level after loading these properties calib.intensity_emitter = MockEmitter() calib.close_shutter_and_calc_blacklevel() calib.open_shutter() - set_lc_state(calib.mmc, calib.group, 'State0') + set_lc_state(calib.mmc, calib.group, "State0") calib.I_Ext = snap_and_average(calib.snap_manager) - set_lc_state(calib.mmc, calib.group, 'State1') + set_lc_state(calib.mmc, calib.group, "State1") calib.I_Elliptical = snap_and_average(calib.snap_manager) calib.reset_shutter() - yield str(calib.calculate_extinction(calib.swing, calib.I_Black, calib.I_Ext, calib.I_Elliptical)) + yield str( + calib.calculate_extinction( + calib.swing, calib.I_Black, calib.I_Ext, calib.I_Elliptical + ) + ) return calib diff --git a/recOrder/plugin/workers/reconstruction_workers.py b/recOrder/plugin/workers/reconstruction_workers.py index 3832b7cf..e6976bf6 100644 --- a/recOrder/plugin/workers/reconstruction_workers.py +++ b/recOrder/plugin/workers/reconstruction_workers.py @@ -4,6 +4,7 @@ import time import os + class ReconstructionSignals(WorkerBaseSignals): """ Custom Signals class that includes napari native signals @@ -15,7 +16,6 @@ class ReconstructionSignals(WorkerBaseSignals): class ReconstructionWorker(WorkerBase): - def __init__(self, calib_window, config): super().__init__(SignalsClass=ReconstructionSignals) @@ -26,17 +26,25 @@ def __init__(self, calib_window, config): def _check_abort(self): if self.abort_requested: self.aborted.emit() - raise TimeoutError('Stop Requested') + raise TimeoutError("Stop Requested") def work(self): - self.manager = PipelineManager(self.config, emitter=self.dimension_emitter) - - store_path = os.path.join(self.manager.config.save_dir, self.manager.config.data_save_name) - store_path = store_path + '.zarr' if not store_path.endswith('.zarr') else store_path + self.manager = PipelineManager( + self.config, emitter=self.dimension_emitter + ) + + store_path = os.path.join( + self.manager.config.save_dir, self.manager.config.data_save_name + ) + store_path = ( + store_path + ".zarr" + if not store_path.endswith(".zarr") + else store_path + ) self.store_emitter.emit(store_path) - print(f'Beginning Reconstruction...') + print(f"Beginning Reconstruction...") for pt in sorted(self.manager.pt_set): start_time = time.time() @@ -45,7 +53,9 @@ def work(self): self._check_abort() - pt_data = self.manager.data.get_zarr(pt[0])[pt[1]] # (C, Z, Y, X) virtual + pt_data = self.manager.data.get_zarr(pt[0])[ + pt[1] + ] # (C, Z, Y, X) virtual self._check_abort() @@ -53,25 +63,39 @@ def work(self): self._check_abort() - birefringence = self.manager.pipeline.reconstruct_birefringence_volume(stokes) + birefringence = ( + self.manager.pipeline.reconstruct_birefringence_volume(stokes) + ) self._check_abort() # will return phase volumes - deconvolve2D, deconvolve3D = self.manager.pipeline.deconvolve_volume(stokes) + ( + deconvolve2D, + deconvolve3D, + ) = self.manager.pipeline.deconvolve_volume(stokes) self._check_abort() - self.manager.pipeline.write_data(self.manager.indices_map[pt[0]], pt[1], pt_data, stokes, - birefringence, deconvolve2D, deconvolve3D) + self.manager.pipeline.write_data( + self.manager.indices_map[pt[0]], + pt[1], + pt_data, + stokes, + birefringence, + deconvolve2D, + deconvolve3D, + ) self._check_abort() end_time = time.time() - print(f'Finishing Reconstructing P = {pt[0]}, T = {pt[1]} ({(end_time - start_time) / 60:0.2f}) min') + print( + f"Finishing Reconstructing P = {pt[0]}, T = {pt[1]} ({(end_time - start_time) / 60:0.2f}) min" + ) existing_meta = self.manager.writer.store.attrs.asdict().copy() - existing_meta['Config'] = self.manager.config.yaml_dict + existing_meta["Config"] = self.manager.config.yaml_dict self.manager.writer.store.attrs.put(existing_meta) self._check_abort() diff --git a/recOrder/scripts/cli.py b/recOrder/scripts/cli.py index 0bddc2ee..506dc5c2 100644 --- a/recOrder/scripts/cli.py +++ b/recOrder/scripts/cli.py @@ -10,18 +10,20 @@ class ShowUsageOnMissingError(click.Command): def __call__(self, args, kwargs): try: return super(ShowUsageOnMissingError, self).__call__( - args, standalone_mode=False, *kwargs) + args, standalone_mode=False, *kwargs + ) except click.MissingParameter as exc: exc.ctx = None exc.show(file=sys.stdout) click.echo() try: - super(ShowUsageOnMissingError, self).__call__(['--help']) + super(ShowUsageOnMissingError, self).__call__(["--help"]) except SystemExit: sys.exit(exc.exit_code) + @click.command(cls=ShowUsageOnMissingError) [email protected]_option('-h', '--help') [email protected]_option("-h", "--help") def help(): """\033[92mrecOrder: Computational Toolkit for Label-Free Imaging\033[0m @@ -40,41 +42,106 @@ def help(): """ print(help.__doc__) + @click.command(cls=ShowUsageOnMissingError) [email protected]_option('-h', '--help') [email protected]('--method', required=False, type=str, help='mode of reconstruction: \ - QLIPP, IPS, UPTI') [email protected]('--mode', required=False, type=str, help='mode of reconstruction: \ - 2D, 3D, Stokes') [email protected]('--data_dir', required=False, type=click.Path(exists=True), help='path to the data') [email protected]('--save_dir', required=False, type=click.Path(), help='path to the save directory') [email protected]('--name', required=False, type=str, help='name to use for saving the data') [email protected]('--config', required=True, type=click.Path(exists=True), help='path to config yml file') [email protected]('--overwrite', required=False, type=bool, - help='whether or not to overwrite any previous data under this name') -def reconstruct(method, mode, data_dir, save_dir, name, config, overwrite=False): [email protected]_option("-h", "--help") [email protected]( + "--method", + required=False, + type=str, + help="mode of reconstruction: \ + QLIPP, IPS, UPTI", +) [email protected]( + "--mode", + required=False, + type=str, + help="mode of reconstruction: \ + 2D, 3D, Stokes", +) [email protected]( + "--data_dir", + required=False, + type=click.Path(exists=True), + help="path to the data", +) [email protected]( + "--save_dir", + required=False, + type=click.Path(), + help="path to the save directory", +) [email protected]( + "--name", required=False, type=str, help="name to use for saving the data" +) [email protected]( + "--config", + required=True, + type=click.Path(exists=True), + help="path to config yml file", +) [email protected]( + "--overwrite", + required=False, + type=bool, + help="whether or not to overwrite any previous data under this name", +) +def reconstruct( + method, mode, data_dir, save_dir, name, config, overwrite=False +): if config: if not os.path.exists(config): - raise ValueError('Specified config path does not exist') + raise ValueError("Specified config path does not exist") else: - config = ConfigReader(config, data_dir, save_dir, method, mode, name, - immutable=False) + config = ConfigReader( + config, data_dir, save_dir, method, mode, name, immutable=False + ) config.save_yaml() else: - config = ConfigReader(None, data_dir, save_dir, method, mode, name, immutable=False) + config = ConfigReader( + None, data_dir, save_dir, method, mode, name, immutable=False + ) config.save_yaml() manager = PipelineManager(config, overwrite) manager.run() + @click.command(cls=ShowUsageOnMissingError) [email protected]_option('-h', '--help') [email protected]('--input', required=True, type=click.Path(exists=True), help='path to the raw data folder containing ome.tifs') [email protected]('--output', required=True, type=str, help='full path to save the zarr store (../../Experiment.zarr') [email protected]('--data_type', required=False, type=str, help='Data type, "ometiff", "upti", "zarr"') [email protected]('--replace_pos_name', required=False, type=bool, help='whether or not to append position name to data') [email protected]('--format_hcs', required=False, type=bool, help='whether or not to format the data as an HCS "well-plate"') [email protected]_option("-h", "--help") [email protected]( + "--input", + required=True, + type=click.Path(exists=True), + help="path to the raw data folder containing ome.tifs", +) [email protected]( + "--output", + required=True, + type=str, + help="full path to save the zarr store (../../Experiment.zarr", +) [email protected]( + "--data_type", + required=False, + type=str, + help='Data type, "ometiff", "upti", "zarr"', +) [email protected]( + "--replace_pos_name", + required=False, + type=bool, + help="whether or not to append position name to data", +) [email protected]( + "--format_hcs", + required=False, + type=bool, + help='whether or not to format the data as an HCS "well-plate"', +) def convert(input, output, data_type, replace_pos_name, format_hcs): - converter = ZarrConverter(input, output, data_type, replace_pos_name, format_hcs) - converter.run_conversion() \ No newline at end of file + converter = ZarrConverter( + input, output, data_type, replace_pos_name, format_hcs + ) + converter.run_conversion() diff --git a/recOrder/scripts/launch_napari.py b/recOrder/scripts/launch_napari.py index c9c6cd61..40043651 100644 --- a/recOrder/scripts/launch_napari.py +++ b/recOrder/scripts/launch_napari.py @@ -9,4 +9,4 @@ def main(): if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/recOrder/scripts/md5_check_sum.py b/recOrder/scripts/md5_check_sum.py index a3c39d0f..14ba4648 100644 --- a/recOrder/scripts/md5_check_sum.py +++ b/recOrder/scripts/md5_check_sum.py @@ -5,19 +5,28 @@ import pathlib as path import click + @click.command() [email protected]('--zarr_path', required=True, type=str, help='path to the save directory') [email protected]('--raw_stats_path', required=True, type=str, help='path to the statistics file of the raw data') [email protected]( + "--zarr_path", required=True, type=str, help="path to the save directory" +) [email protected]( + "--raw_stats_path", + required=True, + type=str, + help="path to the statistics file of the raw data", +) def parse_args(zarr_path, raw_stats_path): """parse command line arguments and return class with the arguments""" - class Args(): + class Args: def __init__(self, zarr_path, raw_stats_path): self.zarr_path = zarr_path self.raw_stats_path = raw_stats_path return Args(zarr_path, raw_stats_path) + def md5(fname): hash_md5 = hashlib.md5() with open(fname, "rb") as f: @@ -25,15 +34,16 @@ def md5(fname): hash_md5.update(chunk) return hash_md5.hexdigest() + def gen_stats_file(zarr_path, save_path): name = path.PurePath(zarr_path).name - if name.endswith('.zarr'): + if name.endswith(".zarr"): name = name[:-5] - file_name = os.path.join(save_path, name+'_ZarrStatistics.txt') - file = open(file_name, 'w') + file_name = os.path.join(save_path, name + "_ZarrStatistics.txt") + file = open(file_name, "w") - zstore = zarr.open(zarr_path, 'r')['array'] + zstore = zarr.open(zarr_path, "r")["array"] shape = zstore.shape for p in range(shape[0]): @@ -45,7 +55,9 @@ def gen_stats_file(zarr_path, save_path): mean = np.mean(image) median = np.median(image) std = np.std(image) - file.write(f'Coord: {(p, t, c, z)}, Mean: {mean}, Median: {median}, Std: {std}\n') + file.write( + f"Coord: {(p, t, c, z)}, Mean: {mean}, Median: {median}, Std: {std}\n" + ) file.close() return file_name @@ -62,11 +74,6 @@ def gen_stats_file(zarr_path, save_path): converted_md5 = md5(conv_stats_path) if raw_md5 != converted_md5: - print('MD5 check sum failed. Potential Error in Conversion') + print("MD5 check sum failed. Potential Error in Conversion") else: - print('MD5 check sum passed. Conversion successful') - - - - - + print("MD5 check sum passed. Conversion successful") diff --git a/recOrder/scripts/repeat-cal-acq-rec.py b/recOrder/scripts/repeat-cal-acq-rec.py index 9e63beeb..912f5679 100644 --- a/recOrder/scripts/repeat-cal-acq-rec.py +++ b/recOrder/scripts/repeat-cal-acq-rec.py @@ -71,11 +71,16 @@ def measure_fov(mmc: Core): """ pixel_size = float(mmc.getPixelSizeUm()) if pixel_size == 0: - float(input("Pixel size is not calibrated. Please provide an estimate (in microns):")) + float( + input( + "Pixel size is not calibrated. Please provide an estimate (in microns):" + ) + ) fov_x = pixel_size float(mmc.getImageWidth()) fov_y = pixel_size * float(mmc.getImageHeight()) return fov_x, fov_y + def rand_shift(length: float): """Randomly signed shift of a certain length. @@ -92,12 +97,13 @@ def rand_shift(length: float): sign = random.randint(0, 1) * 2 - 1 return sign * length + def main(): viewer = napari.Viewer() app = MainWidget(viewer) viewer.window.add_dock_widget(app) app.ui.qbutton_gui_mode.click() - app.calib_scheme = '5-State' + app.calib_scheme = "5-State" app.directory = SAVE_DIR app.save_directory = SAVE_DIR @@ -112,7 +118,7 @@ def main(): with stage_detour(app, dx, dy) as app: print(f"Calibration repeat # {cal_repeat}") app.swing = SWING - + print(f"Calibrating with swing = {SWING}") app.run_calibration() time.sleep(90) @@ -124,12 +130,15 @@ def main(): app.last_calib_meta_file = app.calib.meta_file app.capture_bg() time.sleep(20) - app.ui.cb_bg_method.setCurrentIndex(1) # Set to "Measured" bg correction + app.ui.cb_bg_method.setCurrentIndex( + 1 + ) # Set to "Measured" bg correction app.enter_bg_correction() app.save_name = f"cal-{cal_repeat}-bkg-{bkg_repeat}" app.enter_acq_bg_path() app.acq_birefringence() time.sleep(15) + if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/recOrder/scripts/repeat-calibration.py b/recOrder/scripts/repeat-calibration.py index 56d04df7..a2a61283 100644 --- a/recOrder/scripts/repeat-calibration.py +++ b/recOrder/scripts/repeat-calibration.py @@ -8,12 +8,13 @@ SWINGS = [0.1, 0.03, 0.01, 0.005] REPEATS = 5 + def main(): viewer = napari.Viewer() recorder = MainWidget(viewer) viewer.window.add_dock_widget(recorder) recorder.ui.qbutton_gui_mode.click() - recorder.calib_scheme = '5-State' + recorder.calib_scheme = "5-State" for repeat in range(REPEATS): for swing in SWINGS: @@ -22,6 +23,7 @@ def main(): recorder.directory = SAVE_DIR recorder.run_calibration() time.sleep(100) - + + if __name__ == "__main__": - main() \ No newline at end of file + main()	Format with black Automatic formatting of `.py` documents with `black`. Did not modify the auto-generated `./recOrder/plugin/qtdesigner/recOrder_ui.py`. Enforce consistent code formatting in development Problem Although we depend on the `black` formatter in the `[dev]` requirements, it is not currently consistently used, and there are unconventional formatting (wrt PEP 8) widespread in the code base. Proposed solution - [x] We should arrange a PR that formats the existing code. (Done: #229) - [x] The contributors should be required to format their code with the `black` formatter we specify in the `[dev]` requirement before pushing to the repository. (#185) - [ ] (Optionally) we can also [incorporate formatting formatting in our GItHub Actions workflow](https://black.readthedocs.io/en/stable/integrations/github_actions.html).. Example `black` can be executed from the command line if the user has the `[dev]` environment activated. If you are using a modern IDE, formatting can also be done by selecting the `black` installed in their virtual environment as the formatter backend (VS Code: `^/⌘ ⇧ P` and type 'format document with...').	# [Codecov](https://codecov.io/gh/mehta-lab/recOrder/pull/179?src=pr&el=h1&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=mehta-lab) Report > Merging [#179](https://codecov.io/gh/mehta-lab/recOrder/pull/179?src=pr&el=desc&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=mehta-lab) (543f06a) into [main](https://codecov.io/gh/mehta-lab/recOrder/commit/2c6eb2de379ab123ac6eeb8409a9c3d4c3bb3eb1?el=desc&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=mehta-lab) (2c6eb2d) will decrease coverage by `0.04%`. > The diff coverage is `21.49%`. ```diff @@ Coverage Diff @@ ## main #179 +/- ## ========================================== - Coverage 11.21% 11.16% -0.05% ========================================== Files 45 45 Lines 5984 6010 +26 ========================================== Hits 671 671 - Misses 5313 5339 +26 ``` \| [Impacted Files](https://codecov.io/gh/mehta-lab/recOrder/pull/179?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=mehta-lab) \| Coverage Δ \| \| \|---\|---\|---\| \| [recOrder/acq/acq\_functions.py](https://codecov.io/gh/mehta-lab/recOrder/pull/179/diff?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=mehta-lab#diff-cmVjT3JkZXIvYWNxL2FjcV9mdW5jdGlvbnMucHk=) \| `0.00% <0.00%> (ø)` \| \| \| [recOrder/calib/Calibration.py](https://codecov.io/gh/mehta-lab/recOrder/pull/179/diff?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=mehta-lab#diff-cmVjT3JkZXIvY2FsaWIvQ2FsaWJyYXRpb24ucHk=) \| `0.00% <0.00%> (ø)` \| \| \| [recOrder/calib/Optimization.py](https://codecov.io/gh/mehta-lab/recOrder/pull/179/diff?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=mehta-lab#diff-cmVjT3JkZXIvY2FsaWIvT3B0aW1pemF0aW9uLnB5) \| `0.00% <0.00%> (ø)` \| \| \| [recOrder/compute/fluorescence\_compute.py](https://codecov.io/gh/mehta-lab/recOrder/pull/179/diff?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=mehta-lab#diff-cmVjT3JkZXIvY29tcHV0ZS9mbHVvcmVzY2VuY2VfY29tcHV0ZS5weQ==) \| `0.00% <0.00%> (ø)` \| \| \| [recOrder/compute/qlipp\_compute.py](https://codecov.io/gh/mehta-lab/recOrder/pull/179/diff?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=mehta-lab#diff-cmVjT3JkZXIvY29tcHV0ZS9xbGlwcF9jb21wdXRlLnB5) \| `0.00% <0.00%> (ø)` \| \| \| [recOrder/io/\_\_init\_\_.py](https://codecov.io/gh/mehta-lab/recOrder/pull/179/diff?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=mehta-lab#diff-cmVjT3JkZXIvaW8vX19pbml0X18ucHk=) \| `0.00% <0.00%> (ø)` \| \| \| [recOrder/io/\_reader.py](https://codecov.io/gh/mehta-lab/recOrder/pull/179/diff?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=mehta-lab#diff-cmVjT3JkZXIvaW8vX3JlYWRlci5weQ==) \| `0.00% <0.00%> (ø)` \| \| \| [recOrder/io/config\_reader.py](https://codecov.io/gh/mehta-lab/recOrder/pull/179/diff?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=mehta-lab#diff-cmVjT3JkZXIvaW8vY29uZmlnX3JlYWRlci5weQ==) \| `0.00% <0.00%> (ø)` \| \| \| [recOrder/io/core\_functions.py](https://codecov.io/gh/mehta-lab/recOrder/pull/179/diff?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=mehta-lab#diff-cmVjT3JkZXIvaW8vY29yZV9mdW5jdGlvbnMucHk=) \| `0.00% <0.00%> (ø)` \| \| \| [recOrder/io/metadata\_reader.py](https://codecov.io/gh/mehta-lab/recOrder/pull/179/diff?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=mehta-lab#diff-cmVjT3JkZXIvaW8vbWV0YWRhdGFfcmVhZGVyLnB5) \| `0.00% <0.00%> (ø)` \| \| \| ... and [31 more](https://codecov.io/gh/mehta-lab/recOrder/pull/179/diff?src=pr&el=tree-more&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=mehta-lab) \| \| Help us with your feedback. Take ten seconds to tell us [how you rate us](https://about.codecov.io/nps?utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=mehta-lab). Have a feature suggestion? [Share it here.](https://app.codecov.io/gh/feedback/?utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=mehta-lab) Preview page for your plugin is ready here: https://preview.napari-hub.org/mehta-lab/recOrder/179 _Updated: 2022-09-24T00:17:29.595624_ 'Initial formatting' is done manually here to break up the process into commits each modifying a single submodule for easier bug-fix traceback. This should not be necessary given the reliability of `black`, and is only performed out of an abundance of precaution. We should consider [enforcing formatting in our GItHub Actions workflow](https://black.readthedocs.io/en/stable/integrations/github_actions.html). This PR will start to address #176. @talonchandler I think we should try to merge this relatively soon to avoid more conflicts. Roger...I just took a quick flip through and highlighted some of the points that are really bizarre. Sorry I let this slip. Can you merge main into this branch, reformat, then I'll test Monday morning? Fixed two typos I spotted in my sift through the changes. This PR is getting increasingly cluttered with conflicts now. I'm tempted to close it and open a new one following [instructions here](https://black.readthedocs.io/en/stable/guides/introducing_black_to_your_project.html#avoiding-ruining-git-blame). Any suggestions @talonchandler? I'm very in favor of preserving git-blame info if possible (I use this info for context quite often). How about we do this on Wednesday (I just sent an invite for hummingbird)? [GitHub's doc on formatter & `git blame`](https://docs.github.com/en/repositories/working-with-files/using-files/viewing-a-file#ignore-commits-in-the-blame-view) @ziw-liu and I are closing together. We will revisit black formatting after release. Another issue to also think about is enforcing consistent Markdown documents with a linter, e.g. this one that is available as both a [GitHub Action](https://github.com/marketplace/actions/markdownlint-cli2-action) and an [editor plugin](https://marketplace.visualstudio.com/items?itemName=DavidAnson.vscode-markdownlint).	2022-10-08T00:49:40	[]	[]
GiadaLalli/ISN-tractor	GiadaLalli__ISN-tractor-49	7f6558cbc84828e2ca178366d413be9c18fc7d91	diff --git a/benchmark/dense_isn_offline.py b/benchmark/dense_isn_offline.py index 36de2ea..ba4f8b2 100644 --- a/benchmark/dense_isn_offline.py +++ b/benchmark/dense_isn_offline.py @@ -1,22 +1,17 @@ """Implementation of the offline LionessR algorithm using an offline Pearson metric.""" import torch as t -from numpy import float32 +from numpy import float64 def dense_isn_offline(data, device=None): """Lionessr using offline pearson metric.""" - xt = t.tensor(data.T.to_numpy(dtype=float32, copy=False), device=device) + xt = t.tensor(data.T.to_numpy(dtype=float64, copy=False), device=device) nrsamples = xt.size(1) net = t.corrcoef(xt) agg = net.flatten() - # output = t.empty((net.size(0) * net.size(1), nrsamples)) - for i in range(nrsamples): ss = t.corrcoef(t.cat((xt[:, :i], xt[:, i + 1 :]), dim=1)).flatten() yield nrsamples * (agg - ss) + ss - # output[:, i] = nrsamples * (agg - ss) + ss - - # return output.T diff --git a/isn_tractor/ibisn.py b/isn_tractor/ibisn.py index 11aec67..c3d1beb 100644 --- a/isn_tractor/ibisn.py +++ b/isn_tractor/ibisn.py @@ -510,8 +510,8 @@ def dense_isn( """ Network computation based on the Lioness algorithm """ - num_samples = t.tensor(data.shape[0], dtype=t.float32) - orig = t.from_numpy(data.to_numpy(dtype=np.float32, copy=False)).to(device) + num_samples = t.tensor(data.shape[0], dtype=t.float64) + orig = t.from_numpy(data.to_numpy(dtype=np.float64, copy=False)).to(device) orig_transpose = t.transpose(orig, 0, 1) dot_prod = t.matmul(orig_transpose, orig) mean_vect = t.sum(orig, dim=0)	Discrepency between online/offline `dense_isn` computation As reported by @marouenbg: This code, ```python import pandas as pd import numpy as np import isn_tractor.ibisn as it import time def dataframe(size, values): n_rows, n_cols = size mapped = "mapped" if "mapped" in values else "unmapped" if "discrete" in values: data = np.random.randint(0, 3, size=(n_rows, n_cols)) elif "continuous" in values: data = np.random.uniform(0, 100, size=(n_rows, n_cols)) else: raise ValueError("Values must contain either 'discrete' or 'continuous'") col_names = [mapped + "_" + "feature" + "_" + str(i) for i in range(n_cols)] index_names = ["sample_" + str(i) for i in range(n_rows)] df = pd.DataFrame(data, index=index_names, columns=col_names) return df dfs = [] sizes = [ (200, 1000), (200, 2000), (200, 3000), (500, 1000), (500, 2000), (500, 3000), (1000, 1000), (1000, 2000), (2000, 1000), ] for i, size in enumerate(sizes): df_name = f"m_df{i+1}" df = dataframe(size, ["mapped", "continuous"]) locals()[df_name] = df dfs.append(df) diffs=[] for df in dfs: st = it.dense_isn(df) a = list(st) se = it.dense_isn_offline(df) b = list(se) diffs.append(np.max(list(np.abs(a[0] - b[0])))) ``` Produces: `diffs == [0.00072455406, 0.00076186657, 0.0007597208, 0.0010197759, 0.0010986328, 0.001281023, 0.0018423535, 0.0021594092,0.0035106242]`	Thanks James, the fix could be as simple as setting the inputs to double maybe.	2024-10-11T10:21:15	[]	[]
resgroup/wind-up	resgroup__wind-up-39	980f6db15b2f4f33642c807ffef19780c46fc03a	diff --git a/examples/smarteole_example.ipynb b/examples/smarteole_example.ipynb index 16d7d11..e294b1b 100644 --- a/examples/smarteole_example.ipynb +++ b/examples/smarteole_example.ipynb @@ -1275,7 +1275,7 @@ { "data": { "text/markdown": [ - "filter_stuck_data set 0 rows [0.0%] to NA" + "filter_stuck_data set 7443 rows [7.6%] to NA" ], "text/plain": [ "<IPython.core.display.Markdown object>" @@ -2561,7 +2561,7 @@ }, { "data": { - "image/png": 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WLarff//dPtalSxcdOXJEGzZssM9/69atDocyb/SzXaZMGfn4+GTJ9dNPP+mhhx5SSEiIgoODFR4erscff1zS/18/mdmuze7l5aWYmBiHsRvZNrAur+tPAhQePj4+ql27tmrXrq3bb79dPXv21Keffprtv9br1q2r2NhYDRw4UAkJCTnugch09YnvXbt21dy5c+Xp6elwXo0klS5dWpIcCoR0Za9aWFjYdf8iKmzLc7XDhw/rhx9+UO/evbM9dy83V+99kK6c72Oz2bRkyRJ5enpmmb5IkSI3NP+b0blzZ61fv17PP/+8qlevriJFiigjI0OtWrVyKF21atWSn5+fvv/+e0VFRalEiRK6/fbb1bBhQ02ZMkUpKSn64Ycf7OcCZsrcK5fT7VnWrFljP58qU3brQlKu9x3Mq8xl+uijjxQREZHl+av3+Fyra9eumjp1qoKCgtSlS5ccbxOTkZGhu+66S+PGjcv2+cjISElXClGzZs1UqVIljRs3TpGRkfLx8dE333yj8ePHZym9+bleMuX2D6FM999/vwICAvTJJ5+ofv36+uSTT+Th4aFOnTrZp7nRz/a1PxuSdPbsWTVu3FjBwcEaMWKEYmNj5efnp23btunFF1+8qfsv5nXbwNoodnBbmVeo/fnnnzlO8+ijj+q1115T5cqVVb169Vzn5+vrq4cfflgffvihEhMT9emnn+q+++7L8hdkzZo1JUl//PGHw3hqaqpOnjyp8PDwm1ga1y3P1ebNmydjzA0fhs1ObGysjDEqV65crod+88uZM2e0cuVKDR8+XEOHDrWPZ+7VupqPj4/9SsOoqCj7IcGGDRsqJSVFc+bMUWJiosOFE5l7Q7t06aKHH344yzz79++vOXPmZCl21xMdHa2MjAwlJCSoQoUK9vEDBw5c97WxsbGSrlxdndNNrXPStWtXDR06VH/++ac++uijXN9j586datasWa5F6csvv1RKSoq++OILh71xzj4cGBsbq2XLlun06dM57rXLXKf79+9X5cqV7eOJiYk6e/asoqOj7WOBgYFq166dPv30U40bN04LFixQw4YN7f+gy3zPW/1sr169WqdOndLnn3/u8LlKSEjIkl26sv2v/ixdvnxZhw4dUtWqVR1y5WXbwNo4FItCb9WqVdn+qz3zvJmKFSvm+NqnnnpKr776qt5+++08vddjjz2mtLQ09enTRydOnMi24DRp0sR+DtWlS5fs47Nnz1Z6erqaN2/uVstztblz5yoqKsrhPLKb1aFDB3l6emr48OFZltcY43DLkfyQuSfl2veeMGFCttM3bNhQGzdu1KpVq+zFrnjx4qpcubLGjBljnybTwoULdfHiRcXHx+vhhx/O8qddu3b67LPPbvg2E5m3r5kyZYrD+LXfSpLTa4ODg/XGG29ke6ub3G6gHRsbqwkTJmjUqFGqU6dOjtN17txZf/zxh2bMmJHlub/++st+nmJ26//cuXOaNWvWdZfjRnTs2FHGmCxXFl/93m3atJGUddtn7tm69urfLl266OjRo/rPf/6jnTt3Zrmi1Bmf7ezWT2pqapbtXqtWLYWFhWnGjBkO33wzZ86cLEcH8rptYG3ssUOh169fPyUnJ+uhhx5SpUqVlJqaqvXr12vBggWKiYnJ9qTkTNHR0Xm+J5okNW7cWGXLltXixYtzvN2Hr6+v3nzzTXXv3l2NGjVSt27ddPjwYU2cOFENGza87q06CtvyZNqzZ4927dqll156ySn/2o+NjdVrr72ml19+WYcOHVL79u0VFBSkhIQELVy4UL1799Zzzz13y++Tk+DgYDVq1Ehjx45VWlqaypQpo+XLl2fZI5KpYcOGev3113XkyBGHAteoUSO99957iomJUdmyZe3jc+bMUVhYmOrXr5/t/B544AHNmDFDX3/99Q3dNqZmzZrq2LGjJkyYoFOnTumee+7RmjVr9Ouvv0rK/XBicHCwpk6dqm7duqlGjRp65JFHFB4ersOHD+vrr7/Wvffeq3fffTfH1w8YMOC6+bp166ZPPvlEzzzzjFatWqV7771X6enp+uWXX/TJJ59o2bJlqlWrllq0aCEfHx/df//96tOnjy5cuKAZM2aoRIkSue6VvlFNmzZVt27d9M4772j//v32w+w//PCDmjZtqr59+6patWrq3r27pk+fbj8EumnTJn3wwQdq3759lr2qbdq0UVBQkJ577jl5enqqY8eODs8747Ndv359hYaGqnv37urfv79sNps++uijLEXRx8dHw4YNU79+/XTfffepc+fOOnTokGbPnq3Y2FiHz0Netw0sriAvwQVuxpIlS0yvXr1MpUqVTJEiRYyPj48pX7686devX7bf1NC2bdtc55fd7UGu9vzzzxtJ173dx7x580y1atWMr6+vKVmypOnbt69JSkpy2+XJvBXKrl27rrsMV7verUo+++wz06BBAxMYGGgCAwNNpUqVTHx8vMMtV271difGXLmtxB133OEw9r///c889NBDpmjRoiYkJMR06tTJHD16NMttQ4wxJikpyXh6epqgoCBz+fJl+/jHH39sJJlu3brZxxITE42Xl5fD2LWSk5NNQECAeeihh3Jdxuy+PeLixYsmPj7eFCtWzBQpUsS0b9/e7Nu3z0gyo0ePzvW1xhizatUq07JlSxMSEmL8/PxMbGys6dGjh9myZYt9mryuc2XzzROpqalmzJgx5o477jC+vr4mNDTU1KxZ0wwfPtycO3fOPt0XX3xhqlatavz8/ExMTIwZM2aMmTlzZpbMOX3OGzdu7HA7j5xcvnzZvPnmm6ZSpUrGx8fHhIeHm9atW5utW7fap0lLSzPDhw835cqVM97e3iYyMtK8/PLL9tuKXOuxxx4zkkxcXFyO75uXz3Z2n8tM69atM/fcc4/x9/c3pUuXtt/6SJJZtWqVw7TvvPOOiY6ONr6+vqZOnTpm3bp1pmbNmqZVq1YO0+V128C6bMY48cxUAEC+2LFjh+6++259/PHHTjkHEu4tIyND4eHh6tChQ7aHXvH3xTl2AFDI/PXXX1nGJkyYIA8PjyzffAHru3TpUpZDtB9++KFOnz6d5SvFAM6xA1DoXLhwwX4j6JyEh4fneIsMdzd27Fht3bpVTZs2lZeXl5YsWaIlS5aod+/e3LLib+jHH3/UoEGD1KlTJ4WFhWnbtm16//33deeddzrchgWQJA7FAih0hg0blu1VjldLSEjIcoNWq1ixYoWGDx+uvXv36sKFC4qKilK3bt30r3/9K9d70cGaDh06pP79+2vTpk3227q0adNGo0ePVokSJVwdD4UMxQ5AofPbb79d96ukGjRoID8/vwJKBADugWIHAABgEVw8AQAAYBGWP1kjIyNDR48eVVBQEF+xAgAA3I4xRufPn1fp0qVz/B7nTJYvdkePHuUqMgAA4PaOHDni8A042bF8sQsKCpJ0ZWUEBwe7OM2NSUtL0/Lly9WiRQt5e3u7Ok6O3CWnRNb84C45JbLmB3fJKZE1P7hLTsm9sl4rKSlJkZGR9k6TG8sXu8zDr8HBwW5Z7AICAhQcHFyoP4TuklMia35wl5wSWfODu+SUyJof3CWn5F5Zc5KXU8q4eAIAAMAiKHYAAAAWQbEDAACwCMufYwcAAApWenq60tLSXB3DQVpamry8vHTp0iWlp6e7Oo4Db29vp333NcUOAAA4hTFGx44d09mzZ10dJQtjjCIiInTkyJFCeV/bokWLKiIi4pazUewAAIBTZJa6EiVKKCAgoFAVqIyMDF24cEFFihS57k1+C5IxRsnJyTp+/LgkqVSpUrc0P4odAAC4Zenp6fZSFxYW5uo4WWRkZCg1NVV+fn6FqthJkr+/vyTp+PHjKlGixC0dli1cSwYAANxS5jl1AQEBLk7injLX262em0ixAwAATlOYDr+6E2etN4odAACARVDsAAAALIKLJwAAQL6KeenrAnuvQ6PbFth7FUbssQMAALgBqampro6QI4odAAD4W2vSpIn69u2rvn37KiQkRMWLF9eQIUNkjJEkxcTEaOTIkXriiScUHBys3r17S5LWrl2rhg0byt/fX5GRkerfv78uXrzoykWh2AEAAHzwwQfy8vLSpk2bNHHiRI0bN07/+c9/7M+/9dZbqlatmrZv364hQ4bo4MGDatWqlTp27Khdu3ZpwYIFWrt2rfr27evCpeAcOwAAAEVGRmr8+PGy2WyqWLGidu/erfHjx+vpp5+WJN1333365z//aZ/+qaee0mOPPaaBAwdKkipUqKB33nlHjRs31tSpU+Xn5+eKxaDYAcDfyqpRzp2f8ZBUSfphnGTLcO68m77s3PkBubjnnnsc7iVXr149vf3220pPT5ck1apVy2H6nTt3ateuXZozZ459zBijjIwMJSQkqHLlygUT/BoUOwAAgOsIDAx0eHzhwgX16dNH/fv3zzJtVFRUQcXKgmIHAAD+9jZu3Ojw+Mcff1SFChVy/N7WGjVqaO/evSpfvnxBxMszLp4AAAB/e4cPH9bgwYO1b98+zZs3T5MmTdKAAQNynP7FF1/U+vXr1bdvX+3YsUP79+/X4sWLuXgCAFBwJqz81anzs3l4KaZmJU1ZfUAm47JT5z2wqVNnB+TqiSee0F9//aU6derI09NTAwYMsN/WJDtVq1bVmjVr9K9//UsNGzaUMUaxsbHq0qVLAabOimIHAADylTt8G4S3t7cmTJigqVOnZnnu0KFD2b6mdu3aWr58eT4nuzEcigUAALAIih0AAIBFcCgWAAD8ra1evdrVEZyGPXYAAAAWQbEDAACwCIodAACARVDsAAAALIJiBwAAYBEUOwAAAIug2AEAAFgE97EDAAD5a9Wognuvpi8X3HvlwerVq9W0aVOdOXNGRYsWzff3Y48dAACARVDsAADA31qTJk3Ut29f9e3bVyEhISpevLiGDBkiY4wk6cyZM3riiScUGhqqgIAAtW7dWvv377e//vfff9f999+v0NBQBQYG6o477tA333yjQ4cOqWnTppKk0NBQ2Ww29ejRI1+XhWIHAAD+9j744AN5eXlp06ZNmjhxosaNG6f//Oc/kqQePXpoy5Yt+uKLL7RhwwYZY9SmTRulpaVJkuLj45WSkqLvv/9eu3fv1pgxY1SkSBFFRkbqs88+kyTt27dPf/75pyZOnJivy8E5dgAA4G8vMjJS48ePl81mU8WKFbV7926NHz9eTZo00RdffKF169apfv36kqQ5c+YoMjJSixYtUqdOnXT48GF17NhRd911lyTptttus8+3WLFikqQSJUpwjh0AAEBBuOeee2Sz2eyP69Wrp/3792vv3r3y8vJS3bp17c+FhYWpYsWK+vnnnyVJ/fv312uvvaZ7771Xr776qnbt2lXg+TNR7AAAAG7BU089pd9++03dunXT7t27VatWLU2aNMklWSh2AADgb2/jxo0Oj3/88UdVqFBBVapU0eXLlx2eP3XqlPbt26cqVarYxyIjI/XMM8/o888/1z//+U/NmDFDkuTj4yNJSk9PL4CloNgBAADo8OHDGjx4sPbt26d58+Zp0qRJGjBggCpUqKAHH3xQTz/9tNauXaudO3fq8ccfV5kyZfTggw9KkgYOHKhly5YpISFB27Zt06pVq1S5cmVJUnR0tGw2m7766iudOHFCFy5cyNfloNgBAIC/vSeeeEJ//fWX6tSpo/j4eA0YMEC9e/eWJM2aNUs1a9ZUu3btVK9ePRlj9M0338jb21vSlb1x8fHxqly5slq1aqXbb79dU6ZMkSSVKVNGw4cP10svvaSSJUuqb9+++bocXBULAADyVyH7NojseHt7a8KECZo6dWqW50JDQ/Xhhx/m+NrrnU83ZMgQDRky5JYz5gV77AAAACyCYgcAAGARHIoFAAB/a6tXr3Z1BKdhjx0AAIBFUOwAAAAsgmIHAACcJiMjw9UR3JKz1hvn2AEAgFvm4+MjDw8PHT16VOHh4fLx8XH47lVXy8jIUGpqqi5duiQPj8KzX8sYo9TUVJ04cUIeHh72b6q4WRQ7AABwyzw8PFSuXDn9+eefOnr0qKvjZGGM0V9//SV/f/9CVTgzBQQEKCoq6pZLJ8UOAAA4hY+Pj6KionT58uUC+27UvEpLS9P333+vRo0a2b8xorDw9PSUl5eXUwonxQ4AADiNzWaTt7d3oSxPly9flp+fX6HL5kyF5yAzAAAAbgnFDgAAwCIodgAAABZBsQMAALAIih0AAIBFUOwAAAAsgmIHAABgERQ7AAAAi6DYAQAAWATFDgAAwCIodgAAABZBsQMAALAIih0AAIBFUOwAAAAswqXFLj09XUOGDFG5cuXk7++v2NhYjRw5UsYY+zTGGA0dOlSlSpWSv7+/4uLitH//fhemBgAAKJxcWuzGjBmjqVOn6t1339XPP/+sMWPGaOzYsZo0aZJ9mrFjx+qdd97RtGnTtHHjRgUGBqply5a6dOmSC5MDAAAUPl6ufPP169frwQcfVNu2bSVJMTExmjdvnjZt2iTpyt66CRMm6N///rcefPBBSdKHH36okiVLatGiRXrkkUdclh0AAKCwcekeu/r162vlypX69ddfJUk7d+7U2rVr1bp1a0lSQkKCjh07pri4OPtrQkJCVLduXW3YsMElmQEAAAorl+6xe+mll5SUlKRKlSrJ09NT6enpev311/XYY49Jko4dOyZJKlmypMPrSpYsaX/uWikpKUpJSbE/TkpKkiSlpaUpLS0tPxYj32TmLey53SWnRNb84C45JbJKks3Dub/2M+fn7PlKzl92tr/zuUtOyb2yXutGMtvM1VcqFLD58+fr+eef15tvvqk77rhDO3bs0MCBAzVu3Dh1795d69ev17333qujR4+qVKlS9td17txZNptNCxYsyDLPYcOGafjw4VnG586dq4CAgHxdHgAAAGdLTk5W165dde7cOQUHB+c6rUuLXWRkpF566SXFx8fbx1577TV9/PHH+uWXX/Tbb78pNjZW27dvV/Xq1e3TNG7cWNWrV9fEiROzzDO7PXaRkZE6efLkdVdGYZOWlqYVK1aoefPm8vb2dnWcHLlLToms+cFdckpklaQpI5912rykK3vqou9upd+3L5XJuOzUeT87ZIpT58f2dz53ySm5V9ZrJSUlqXjx4nkqdi49FJucnCwPD8fT/Dw9PZWRkSFJKleunCIiIrRy5Up7sUtKStLGjRv1j3/8I9t5+vr6ytfXN8u4t7e3223ITO6S3V1ySmTND+6SU/p7Z3V2+bp6vs6ed35to7/z9s8v7pJTcq+smW4kr0uL3f3336/XX39dUVFRuuOOO7R9+3aNGzdOvXr1kiTZbDYNHDhQr732mipUqKBy5cppyJAhKl26tNq3b+/K6AAAAIWOS4vdpEmTNGTIED377LM6fvy4SpcurT59+mjo0KH2aV544QVdvHhRvXv31tmzZ9WgQQMtXbpUfn5+LkwOAABQ+Li02AUFBWnChAmaMGFCjtPYbDaNGDFCI0aMKLhgAAAAbojvigUAALAIih0AAIBFUOwAAAAsgmIHAABgERQ7AAAAi6DYAQAAWATFDgAAwCIodgAAABZBsQMAALAIih0AAIBFUOwAAAAsgmIHAABgERQ7AAAAi6DYAQAAWATFDgAAwCIodgAAABZBsQMAALAIih0AAIBFUOwAAAAsgmIHAABgERQ7AAAAi6DYAQAAWATFDgAAwCIodgAAABZBsQMAALAIih0AAIBFUOwAAAAsgmIHAABgERQ7AAAAi6DYAQAAWATFDgAAwCIodgAAABZBsQMAALAIih0AAIBFUOwAAAAsgmIHAABgERQ7AAAAi6DYAQAAWATFDgAAwCIodgAAABZBsQMAALAIih0AAIBFUOwAAAAsgmIHAABgERQ7AAAAi6DYAQAAWATFDgAAwCIodgAAABZBsQMAALAIih0AAIBFUOwAAAAsgmIHAABgERQ7AAAAi6DYAQAAWATFDgAAwCIodgAAABZBsQMAALAIih0AAIBFUOwAAAAsgmIHAABgERQ7AAAAi6DYAQAAWATFDgAAwCIodgAAABZBsQMAALAIih0AAIBFUOwAAAAswuXF7o8//tDjjz+usLAw+fv766677tKWLVvszxtjNHToUJUqVUr+/v6Ki4vT/v37XZgYAACgcHJpsTtz5ozuvfdeeXt7a8mSJdq7d6/efvtthYaG2qcZO3as3nnnHU2bNk0bN25UYGCgWrZsqUuXLrkwOQAAQOHj5co3HzNmjCIjIzVr1iz7WLly5ez/b4zRhAkT9O9//1sPPvigJOnDDz9UyZIltWjRIj3yyCMFnhkAAKCwcmmx++KLL9SyZUt16tRJa9asUZkyZfTss8/q6aefliQlJCTo2LFjiouLs78mJCREdevW1YYNG7ItdikpKUpJSbE/TkpKkiSlpaUpLS0tn5fIuTLzFvbc7pJTImt+cJecElklyebh3F/7mfNz9nwl5y8729/53CWn5F5Zr3UjmW3GGJOPWXLl5+cnSRo8eLA6deqkzZs3a8CAAZo2bZq6d++u9evX695779XRo0dVqlQp++s6d+4sm82mBQsWZJnnsGHDNHz48Czjc+fOVUBAQP4tDAAAQD5ITk5W165dde7cOQUHB+c6rUuLnY+Pj2rVqqX169fbx/r376/Nmzdrw4YNN1XssttjFxkZqZMnT153ZRQ2aWlpWrFihZo3by5vb29Xx8mRu+SUyJof3CWnRFZJmjLyWafNS7qypy767lb6fftSmYzLTp33s0OmOHV+bH/nc5eckntlvVZSUpKKFy+ep2Ln0kOxpUqVUpUqVRzGKleurM8++0ySFBERIUlKTEx0KHaJiYmqXr16tvP09fWVr69vlnFvb2+325CZ3CW7u+SUyJof3CWn9PfO6uzydfV8nT3v/NpGf+ftn1/cJafkXlkz3Uhel14Ve++992rfvn0OY7/++quio6MlXbmQIiIiQitXrrQ/n5SUpI0bN6pevXoFmhUAAKCwc+keu0GDBql+/fp644031LlzZ23atEnTp0/X9OnTJUk2m00DBw7Ua6+9pgoVKqhcuXIaMmSISpcurfbt27syOgAAQKHj0mJXu3ZtLVy4UC+//LJGjBihcuXKacKECXrsscfs07zwwgu6ePGievfurbNnz6pBgwZaunSp/cILAAAAXOHSYidJ7dq1U7t27XJ83mazacSIERoxYkQBpgIAAHA/Lv9KMQAAADgHxQ4AAMAiKHYAAAAWQbEDAACwCIodAACARVDsAAAALIJiBwAAYBEUOwAAAIug2AEAAFgExQ4AAMAiKHYAAAAWQbEDAACwCIodAACARVDsAAAALIJiBwAAYBEUOwAAAIug2AEAAFiEU4pdenq6duzYoTNnzjhjdgAAALgJN1XsBg4cqPfff1/SlVLXuHFj1ahRQ5GRkVq9erUz8wEAACCPbqrY/fe//1W1atUkSV9++aUSEhL0yy+/aNCgQfrXv/7l1IAAAADIm5sqdidPnlRERIQk6ZtvvlGnTp10++23q1evXtq9e7dTAwIAACBvbqrYlSxZUnv37lV6erqWLl2q5s2bS5KSk5Pl6enp1IAAAADIG6+beVHPnj3VuXNnlSpVSjabTXFxcZKkjRs3qlKlSk4NCAAAgLy5qWI3bNgw3XXXXTp8+LA6deokX19fSZKnp6deeuklpwYEAABA3txwsUtLS1OrVq00bdo0dezY0eG57t27Oy0YAAAAbswNn2Pn7e2tXbt25UcWAAAA3IKbunji8ccft9/HDgAAAIXDTZ1jd/nyZc2cOVPffvutatasqcDAQIfnx40b55RwAAAAyLubKnZ79uxRjRo1JEm//vqrw3M2m+3WUwEAAOCG3VSxW7VqlbNzAAAA4Bbd1Dl2AAAAKHxuao9d06ZNcz3k+t133910IAAAANycmyp21atXd3iclpamHTt2aM+ePdzLDgAAwEVuqtiNHz8+2/Fhw4bpwoULtxQIAAAAN8ep59g9/vjjmjlzpjNnCQAAgDxyarHbsGGD/Pz8nDlLAAAA5NFNHYrt0KGDw2NjjP78809t2bJFQ4YMcUowAAAA3JibKnYhISEOjz08PFSxYkWNGDFCLVq0cEowAAAA3JibKnazZs1ydg4AAADcopsqdpm2bt2qn3/+WZJ0xx136O6773ZKKAAAANy4myp2x48f1yOPPKLVq1eraNGikqSzZ8+qadOmmj9/vsLDw52ZEQAAAHlwU1fF9uvXT+fPn9dPP/2k06dP6/Tp09qzZ4+SkpLUv39/Z2cEAABAHtzUHrulS5fq22+/VeXKle1jVapU0eTJk7l4AgAAwEVuao9dRkaGvL29s4x7e3srIyPjlkMBAADgxt1Usbvvvvs0YMAAHT161D72xx9/aNCgQWrWrJnTwgEAACDvbqrYvfvuu0pKSlJMTIxiY2MVGxurmJgYJSUladKkSc7OCAAAgDy4qXPsIiMjtW3bNq1cudJ+u5PKlSsrLi7OqeEAAACQdzd9H7vvvvtO3333nY4fP66MjAxt375dc+fOlSTNnDnTaQEBAACQNzdV7IYPH64RI0aoVq1aKlWqlGw2m7NzAQAA4AbdVLGbNm2aZs+erW7dujk7DwAAAG7STV08kZqaqvr16zs7CwAAAG7BTRW7p556yn4+HQAAAAqHPB+KHTx4sP3/MzIyNH36dH377beqWrVqlpsVjxs3znkJAQAAkCd5Lnbbt293eFy9enVJ0p49exzGuZACAADANfJc7FatWpWfOQAAAHCLbuocOwAAABQ+FDsAAACLoNgBAABYBMUOAADAIih2AAAAFkGxAwAAsAiKHQAAgEVQ7AAAACyCYgcAAGARFDsAAACLoNgBAABYRKEpdqNHj5bNZtPAgQPtY5cuXVJ8fLzCwsJUpEgRdezYUYmJia4LCQAAUIgVimK3efNmvffee6patarD+KBBg/Tll1/q008/1Zo1a3T06FF16NDBRSkBAAAKN5cXuwsXLuixxx7TjBkzFBoaah8/d+6c3n//fY0bN0733XefatasqVmzZmn9+vX68ccfXZgYAACgcHJ5sYuPj1fbtm0VFxfnML5161alpaU5jFeqVElRUVHasGFDQccEAAAo9Lxc+ebz58/Xtm3btHnz5izPHTt2TD4+PipatKjDeMmSJXXs2LEc55mSkqKUlBT746SkJElSWlqa0tLSnBO8gGTmLey53SWnRNb84C45JbJKks3Dub/2M+fn7PlKzl92tr/zuUtOyb2yXutGMtuMMSYfs+ToyJEjqlWrllasWGE/t65JkyaqXr26JkyYoLlz56pnz54OJU2S6tSpo6ZNm2rMmDHZznfYsGEaPnx4lvG5c+cqICDA+QsCAACQj5KTk9W1a1edO3dOwcHBuU7rsmK3aNEiPfTQQ/L09LSPpaeny2azycPDQ8uWLVNcXJzOnDnjsNcuOjpaAwcO1KBBg7Kdb3Z77CIjI3Xy5MnrrozCJi0tTStWrFDz5s3l7e3t6jg5cpecElnzg7vklMgqSVNGPuu0eUlX9tRF391Kv29fKpNx2anzfnbIFKfOj+3vfO6SU3KvrNdKSkpS8eLF81TsXHYotlmzZtq9e7fDWM+ePVWpUiW9+OKLioyMlLe3t1auXKmOHTtKkvbt26fDhw+rXr16Oc7X19dXvr6+Wca9vb3dbkNmcpfs7pJTImt+cJec0t87q7PL19Xzdfa882sb/Z23f35xl5ySe2XNdCN5XVbsgoKCdOeddzqMBQYGKiwszD7+5JNPavDgwSpWrJiCg4PVr18/1atXT/fcc48rIgMAABRqLr144nrGjx8vDw8PdezYUSkpKWrZsqWmTHHurnkAAACrKFTFbvXq1Q6P/fz8NHnyZE2ePNk1gQAAANyIy+9jBwAAAOeg2AEAAFgExQ4AAMAiKHYAAAAWQbEDAACwCIodAACARVDsAAAALIJiBwAAYBEUOwAAAIug2AEAAFgExQ4AAMAiKHYAAAAWQbEDAACwCIodAACARVDsAAAALIJiBwAAYBEUOwAAAIug2AEAAFgExQ4AAMAiKHYAAAAWQbEDAACwCIodAACARVDsAAAALIJiBwAAYBEUOwAAAIug2AEAAFgExQ4AAMAiKHYAAAAWQbEDAACwCIodAACARVDsAAAALIJiBwAAYBEUOwAAAIug2AEAAFgExQ4AAMAiKHYAAAAWQbEDAACwCIodAACARVDsAAAALIJiBwAAYBEUOwAAAIug2AEAAFgExQ4AAMAiKHYAAAAWQbEDAACwCIodAACARVDsAAAALIJiBwAAYBEUOwAAAIug2AEAAFgExQ4AAMAiKHYAAAAW4eXqAAAAZCfmpa+dOj9fT6OxdaQ7hy1TSrrNafM9NLqt0+YF3Cr22AEAAFgExQ4AAMAiKHYAAAAWQbEDAACwCIodAACARVDsAAAALIJiBwAAYBEUOwAAAIug2AEAAFgExQ4AAMAiKHYAAAAWQbEDAACwCC9XBwAAt7dqlPPnaTwkVZJ+GCfZMpw/fzcw0Ou/Tp2fzcNLUjs967lYxnbZiXNu68R5AbeGPXYAAAAW4dJiN2rUKNWuXVtBQUEqUaKE2rdvr3379jlMc+nSJcXHxyssLExFihRRx44dlZiY6KLEAAAAhZdLi92aNWsUHx+vH3/8UStWrFBaWppatGihixcv2qcZNGiQvvzyS3366adas2aNjh49qg4dOrgwNQAAQOHk0nPsli5d6vB49uzZKlGihLZu3apGjRrp3Llzev/99zV37lzdd999kqRZs2apcuXK+vHHH3XPPfe4IjYAOJiw8lenz9Pm4aWYmpU0ZfUBmQxnng8GwMoK1cUT586dkyQVK1ZMkrR161alpaUpLi7OPk2lSpUUFRWlDRs2ZFvsUlJSlJKSYn+clJQkSUpLS1NaWlp+xne6zLyFPbe75JTImh/cJaeUf1mvnJTvXJnzzI95O5O75JTyL2t+fPbd5efKXXJK7pX1WjeS2WaMMfmYJc8yMjL0wAMP6OzZs1q7dq0kae7cuerZs6dDUZOkOnXqqGnTphozZkyW+QwbNkzDhw/PMj537lwFBATkT3gAAIB8kpycrK5du+rcuXMKDg7OddpC80+s+Ph47dmzx17qbtbLL7+swYMH2x8nJSUpMjJSLVq0uO7KKGzS0tK0YsUKNW/eXN7e3q6OkyN3ySmRNT+4S04p/7JOGfms0+aVyebhpei7W+n37UsL9aFYd8kp5V/WZ4dMcdq8MrnLz5W75JTcK+u1Mo8+5kWhKHZ9+/bVV199pe+//15ly5a1j0dERCg1NVVnz55V0aJF7eOJiYmKiIjIdl6+vr7y9fXNMu7t7e12GzKTu2R3l5wSWfODu+SUnJ81PwuNybhc6AuT5D45Jednzc/Pvbv8XLlLTsm9sma6kbwuvSrWGKO+fftq4cKF+u6771SuXDmH52vWrClvb2+tXLnSPrZv3z4dPnxY9erVK+i4AAAAhZpL99jFx8dr7ty5Wrx4sYKCgnTs2DFJUkhIiPz9/RUSEqInn3xSgwcPVrFixRQcHKx+/fqpXr16XBELAABwDZcWu6lTp0qSmjRp4jA+a9Ys9ejRQ5I0fvx4eXh4qGPHjkpJSVHLli01ZYrzz2cAAABwdy4tdnm5INfPz0+TJ0/W5MmTCyARAACA++K7YgEAACyCYgcAAGARFDsAAACLoNgBAABYBMUOAADAIih2AAAAFkGxAwAAsAiKHQAAgEVQ7AAAACyCYgcAAGARFDsAAACLoNgBAABYBMUOAADAIih2AAAAFkGxAwAAsAiKHQAAgEVQ7AAAACyCYgcAAGARFDsAAACLoNgBAABYBMUOAADAIih2AAAAFkGxAwAAsAiKHQAAgEVQ7AAAACyCYgcAAGARFDsAAACLoNgBAABYBMUOAADAIih2AAAAFkGxAwAAsAiKHQAAgEVQ7AAAACyCYgcAAGARFDsAAACLoNgBAABYBMUOAADAIih2AAAAFkGxAwAAsAiKHQAAgEVQ7AAAACyCYgcAAGARFDsAAACLoNgBAABYBMUOAADAIih2AAAAFkGxAwAAsAiKHQAAgEVQ7AAAACyCYgcAAGARFDsAAACLoNgBAABYBMUOAADAIih2AAAAFkGxAwAAsAiKHQAAgEVQ7AAAACyCYgcAAGARFDsAAACLoNgBAABYBMUOAADAIih2AAAAFkGxAwAAsAiKHQAAgEVQ7AAAACyCYgcAAGARblHsJk+erJiYGPn5+alu3bratGmTqyMBAAAUOoW+2C1YsECDBw/Wq6++qm3btqlatWpq2bKljh8/7upoAAAAhUqhL3bjxo3T008/rZ49e6pKlSqaNm2aAgICNHPmTFdHAwAAKFQKdbFLTU3V1q1bFRcXZx/z8PBQXFycNmzY4MJkAAAAhY+XqwPk5uTJk0pPT1fJkiUdxkuWLKlffvkl29ekpKQoJSXF/vjcuXOSpNOnTystLS3/wuaDtLQ0JScn69SpU/L29nZ1nBy5S06JrPnBXXJK+Zc1JS3DafPKZPPIUHJyslLSMmQynD9/Z3GXnFL+ZT116pTT5pXJXX6u3CWn5F5Zr3X+/HlJkjHmutMW6mJ3M0aNGqXhw4dnGS9XrpwL0gDArXCXU07cJaeUH1lfGvuh0+cJZOf8+fMKCQnJdZpCXeyKFy8uT09PJSYmOownJiYqIiIi29e8/PLLGjx4sP1xRkaGTp8+rbCwMNlstnzN62xJSUmKjIzUkSNHFBwc7Oo4OXKXnBJZ84O75JTImh/cJadE1vzgLjkl98p6LWOMzp8/r9KlS1932kJd7Hx8fFSzZk2tXLlS7du3l3SlqK1cuVJ9+/bN9jW+vr7y9fV1GCtatGg+J81fwcHBbvEhdJecElnzg7vklMiaH9wlp0TW/OAuOSX3ynq16+2py1Soi50kDR48WN27d1etWrVUp04dTZgwQRcvXlTPnj1dHQ0AAKBQKfTFrkuXLjpx4oSGDh2qY8eOqXr16lq6dGmWCyoAAAD+7gp9sZOkvn375njo1cp8fX316quvZjm0XNi4S06JrPnBXXJKZM0P7pJTImt+cJeckntlvRU2k5drZwEAAFDoFeobFAMAACDvKHYAAAAWQbEDAACwCIpdIfD999/r/vvvV+nSpWWz2bRo0SKH53v06CGbzebwp1WrVq4J+39Gjx4tm82mgQMH2seaNGmSJeczzzzjknwxMTFZsthsNsXHx7s06/W2tTFGQ4cOValSpeTv76+4uDjt37//uss2evToAs86bNgwVapUSYGBgQoNDVVcXJw2btxY4Fmvl1OSfv75Zz3wwAMKCQlRYGCgateurcOHD9ufL6jPw/WyJiYmqkePHipdurQCAgLUqlWrLNu/ILKOGjVKtWvXVlBQkEqUKKH27dtr37599udPnz6tfv36qWLFivL391dUVJT69+9v/wrHTNn9DM6fP79As0p5W2eFJeuxY8fUrVs3RUREKDAwUDVq1NBnn33mME1+/1xNnTpVVatWtd/vrV69elqyZIn9+enTp6tJkyYKDg6WzWbT2bNns8yjoH5PXS9rJmOMWrdune3PXUFs+4JEsSsELl68qGrVqmny5Mk5TtOqVSv9+eef9j/z5s0rwISONm/erPfee09Vq1bN8tzTTz/tkHPs2LEuSHgl49U5VqxYIUnq1KmTS7Neb1uPHTtW77zzjqZNm6aNGzcqMDBQLVu21KVLlxymGzFihEP2fv36FXjW22+/Xe+++652796ttWvXKiYmRi1atNCJEycKNOv1ch48eFANGjRQpUqVtHr1au3atUtDhgyRn5+fw3QF8XnILasxRu3bt9dvv/2mxYsXa/v27YqOjlZcXJwuXrxYoFnXrFmj+Ph4/fjjj1qxYoXS0tLUokULe46jR4/q6NGjeuutt7Rnzx7Nnj1bS5cu1ZNPPpllXrNmzXLImnmz+YLKmikv66wwZH3iiSe0b98+ffHFF9q9e7c6dOigzp07a/v27Q7zys+fq7Jly2r06NHaunWrtmzZovvuu08PPvigfvrpJ0lScnKyWrVqpVdeeSXX+RTE76nrZc00YcKEXL99Kr+3fYEyKFQkmYULFzqMde/e3Tz44IMuyXOt8+fPmwoVKpgVK1aYxo0bmwEDBtifu/ZxYTJgwAATGxtrMjIyjDGFI+u12zojI8NERESYN9980z529uxZ4+vra+bNm2cfi46ONuPHjy/ApNl/Lq917tw5I8l8++239rGCzppdzi5dupjHH38819e54vNwbdZ9+/YZSWbPnj32sfT0dBMeHm5mzJhhH3NF1uPHjxtJZs2aNTlO88knnxgfHx+TlpZmH8vL58bZssual3VWWLIGBgaaDz/80GG6YsWKOXwGXPE7IDQ01PznP/9xGFu1apWRZM6cOZNleldkzHRt1u3bt5syZcqYP//8M9vt7Iptn5/YY+cmVq9erRIlSqhixYr6xz/+oVOnTrkkR3x8vNq2bau4uLhsn58zZ46KFy+uO++8Uy+//LKSk5MLOGFWqamp+vjjj9WrVy+Hf7EVtqwJCQk6duyYw7oNCQlR3bp1tWHDBodpR48erbCwMN1999168803dfny5YKO6yA1NVXTp09XSEiIqlWr5vCcK7NmZGTo66+/1u23366WLVuqRIkSqlu3braHa139eUhJSZEkhz2JHh4e8vX11dq1a12aNfMQa7FixXKdJjg4WF5ejrdHjY+PV/HixVWnTh3NnDlTJp/vsJVT1ryss8KQtX79+lqwYIFOnz6tjIwMzZ8/X5cuXVKTJk0cXltQP1fp6emaP3++Ll68qHr16t3Qawv6Zz+7rMnJyeratasmT56c43fMSwW/7fOVi4slrqFs/uUwb948s3jxYrNr1y6zcOFCU7lyZVO7dm1z+fLlAs02b948c+edd5q//vrLGJP1X8HvvfeeWbp0qdm1a5f5+OOPTZkyZcxDDz1UoBmzs2DBAuPp6Wn++OMP+1hhyHrttl63bp2RZI4ePeowXadOnUznzp3tj99++22zatUqs3PnTjN16lRTtGhRM2jQoALNmunLL780gYGBxmazmdKlS5tNmzY5PF/QWa/Nmfkv9ICAADNu3Dizfft2M2rUKGOz2czq1avt07ni83Bt1tTUVBMVFWU6depkTp8+bVJSUszo0aONJNOiRQuXZU1PTzdt27Y19957b47TnDhxwkRFRZlXXnnFYXzEiBFm7dq1Ztu2bWb06NHG19fXTJw4scCz5mWdFZasZ86cMS1atDCSjJeXlwkODjbLli1zmKYgfq527dplAgMDjaenpwkJCTFff/11lmly22NXkD/7uWXt3bu3efLJJ+2Ps/tdVtDbPr9R7AqZnP4CvdrBgwezHPLKb4cPHzYlSpQwO3futI9d7/DGypUrjSRz4MCBAkiYsxYtWph27drlOo0rst5ssbvW+++/b7y8vMylS5fyK2qOn8sLFy6Y/fv3mw0bNphevXqZmJgYk5iY6LKs1+b8448/jCTz6KOPOkx3//33m0ceeSTH+RTE5yG7dbplyxZTrVo1I8l4enqali1bmtatW5tWrVq5LOszzzxjoqOjzZEjR7J9/ty5c6ZOnTqmVatWJjU1Ndd5DRkyxJQtWzY/Yhpjrp81U17Wmauy9u3b19SpU8d8++23ZseOHWbYsGEmJCTE7Nq1K8d55cfPVUpKitm/f7/ZsmWLeemll0zx4sXNTz/95DBNbsWuIDJeL+vixYtN+fLlzfnz5+3T5uXv2Pze9vmNYlfI5OVDZ4wxxYsXN9OmTcv/QP9n4cKF9r9sMv9IMjabzXh6ema79/DChQtGklm6dGmB5bzWoUOHjIeHh1m0aFGu07ki67XbOrOwb9++3WG6Ro0amf79++c4nz179hhJ5pdffsmnpHn/XJYvX9688cYbOT6f31mvzZmSkmK8vLzMyJEjHaZ74YUXTP369XOcT0F8HnJbp2fPnjXHjx83xhhTp04d8+yzz+Y4n/zMGh8fb8qWLWt+++23bJ9PSkoy9erVM82aNbPvyc/NV199ZSTly1/u18t6tbysM1dkPXDgQJbzLI0xplmzZqZPnz45zq8gfgc0a9bM9O7d22HsRopdQWTMlJl1wIAB9r+jrv57y8PDwzRu3DjH1+fnti8IbvFdsXD0v//9T6dOnVKpUqUK7D2bNWum3bt3O4z17NlTlSpV0osvvihPT88sr9mxY4ckFWjOa82aNUslSpRQ27Ztc52uMGQtV66cIiIitHLlSlWvXl2SlJSUpI0bN+of//hHjq/bsWOHPDw8VKJEiQJKmrOMjAz7uWLZKeisPj4+ql27dpZbSvz666+Kjo7O8XWu/jyEhIRIkvbv368tW7Zo5MiROU6bH1mNMerXr58WLlyo1atXq1y5clmmSUpKUsuWLeXr66svvvgiy1XGOWUNDQ116nd15iVrdjmk3NeZK7Jmnvfn4eF4+runp6cyMjJyzZrfP1fX+9m+noL82c/MOnz4cD311FMOz911110aP3687r///hxfnx/bviBR7AqBCxcu6MCBA/bHCQkJ2rFjh4oVK6ZixYpp+PDh6tixoyIiInTw4EG98MILKl++vFq2bFlgGYOCgnTnnXc6jAUGBiosLEx33nmnDh48qLlz56pNmzYKCwvTrl27NGjQIDVq1Cjb26IUhIyMDM2aNUvdu3d3OKHblVlz29ZRUVEaOHCgXnvtNVWoUEHlypXTkCFDVLp0aful9xs2bNDGjRvVtGlTBQUFacOGDRo0aJAef/xxhYaGFljWsLAwvf7663rggQdUqlQpnTx5UpMnT9Yff/xhv6VMQWW93jp9/vnn1aVLFzVq1EhNmzbV0qVL9eWXX2r16tWSCvbzcL2sn376qcLDwxUVFaXdu3drwIABat++vVq0aFGgWePj4zV37lwtXrxYQUFBOnbsmKQrhdPf319JSUlq0aKFkpOT9fHHHyspKUlJSUmSpPDwcHl6eurLL79UYmKi7rnnHvn5+WnFihV644039NxzzzktZ16y5mWdFZaslSpVUvny5dWnTx+99dZbCgsL06JFi7RixQp99dVXkgrm5+rll19W69atFRUVpfPnz2vu3LlavXq1li1bJunKvfaOHTtm/yzv3r1bQUFBioqKUrFixQr091RuWSMiIrK9YCIqKspeqgtq2xcoF+8xhPn/u7Ov/dO9e3eTnJxsWrRoYcLDw423t7eJjo42Tz/9tDl27JirYzucY3f48GHTqFEjU6xYMePr62vKly9vnn/+eXPu3DmX5Vu2bJmRZPbt2+cw7sqsuW1rY67c8mTIkCGmZMmSxtfX1zRr1swh/9atW03dunVNSEiI8fPzM5UrVzZvvPFGvhwyyC3rX3/9ZR566CFTunRp4+PjY0qVKmUeeOABh4snCirr9dapMVfO7ylfvrzx8/Mz1apVczg0X5Cfh+tlnThxoilbtqzx9vY2UVFR5t///rdJSUkp8KzZZZRkZs2aletySDIJCQnGGGOWLFliqlevbooUKWICAwNNtWrVzLRp00x6enqBZs3LOissWY0x5tdffzUdOnQwJUqUMAEBAaZq1aoOtz8piJ+rXr16mejoaOPj42PCw8NNs2bNzPLly+3Pv/rqq7kuR0H+nrpe1mvpmlMgCmrbFySbMe58TS8AAAAycR87AAAAi6DYAQAAWATFDgAAwCIodgAAABZBsQMAALAIih0AAIBFUOwAAAAsgmIHAABgERQ7AC5jjFHv3r1VrFgx2Ww2+3d4/p0cOnTob7vsAJyPYgfAZZYuXarZs2frq6++0p9//pnl+4gzGWMUFxeX7fcjT5kyRUWLFtX//ve/fMnYsmVLeXp6avPmzfky/5vRo0cP2Ww2PfPMM1mei4+Pl81mU48ePQo+GACXo9gByBepqanXnebgwYMqVaqU6tevr4iICHl5eWU7nc1m06xZs7Rx40a999579vGEhAS98MILmjRpksqWLeu07JkOHz6s9evXq2/fvpo5c6bT538rIiMjNX/+fP3111/2sUuXLmnu3LmKiopyYTIArkSxA+AUTZo0Ud++fTVw4EAVL15cLVu21J49e9S6dWsVKVJEJUuWVLdu3XTy5ElJV/Y69evXT4cPH5bNZlNMTEyu84+MjNTEiRP13HPPKSEhQcYYPfnkk2rRooW6du2qJ598UuXKlZO/v78qVqyoiRMn2l+7Z88eeXh46MSJE5Kk06dPy8PDQ4888oh9mtdee00NGjRweM9Zs2apXbt2+sc//qF58+Y5lKjMZe7fv79eeOEFFStWTBERERo2bJjDNL/88osaNGggPz8/ValSRd9++61sNpsWLVqU47Lmtt4y1ahRQ5GRkfr888/tY59//rmioqJ09913O0ybkZGhUaNG2ddPtWrV9N///tf+fHp6eq7rT7qyvdq3b6+33npLpUqVUlhYmOLj45WWlpbjcgAoeBQ7AE7zwQcfyMfHR+vWrdPo0aN133336e6779aWLVu0dOlSJSYmqnPnzpKkiRMnasSIESpbtqz+/PPPPB3q7N69u5o1a6ZevXrp3Xff1Z49e/Tee+8pIyNDZcuW1aeffqq9e/dq6NCheuWVV/TJJ59Iku644w6FhYVpzZo1kqQffvjB4bEkrVmzRk2aNLE/NsZo1qxZevzxx1WpUiWVL1/eoQxdvcyBgYHauHGjxo4dqxEjRmjFihWSrhSm9u3bKyAgQBs3btT06dP1r3/9K9dlPHv2bK7r7Wq9evXSrFmz7I9nzpypnj17Zplu1KhR+vDDDzVt2jT99NNPGjRokB5//HH78l9v/WVatWqVDh48qFWrVumDDz7Q7NmzNXv27FyXB0ABMwDgBI0bNzZ33323/fHIkSNNixYtHKY5cuSIkWT27dtnjDFm/PjxJjo6+obeJzEx0RQvXtx4eHiYhQsX5jhdfHy86dixo/1xhw4dTHx8vDHGmIEDB5rnn3/ehIaGmp9//tmkpqaagIAAs3z5cvv0y5cvN+Hh4SYtLc2etXHjxlmWuUGDBg5jtWvXNi+++KIxxpglS5YYLy8v8+eff9qfX7FihZFkz56QkGAkme3btxtj8rbeunfvbh588EFz/Phx4+vraw4dOmQOHTpk/Pz8zIkTJ8yDDz5ounfvbowx5tKlSyYgIMCsX7/eYZ5PPvmkefTRR/O8/rp3726io6PN5cuX7WOdOnUyXbp0yXEeAApe9ie0AMBNqFmzpv3/d+7cqVWrVqlIkSJZpjt48KBuv/32m3qPEiVKqE+fPlq0aJHat29vH588ebJmzpypw4cP66+//lJqaqqqV69uf75x48aaPn26pCt759544w39+uuvWr16tU6fPq20tDTde++99ulnzpypLl262M/7e/TRR/X888/r4MGDio2NtU9XtWpVh3ylSpXS8ePHJUn79u1TZGSkIiIi7M/XqVMn1+W7kfUWHh6utm3bavbs2TLGqG3btipevLjDaw4cOKDk5GQ1b97cYTw1NdXhkO311p90Zc+np6enw7Lu3r071+UBULAodgCcJjAw0P7/Fy5c0P33368xY8Zkma5UqVK39D5eXl4OF1rMnz9fzz33nN5++23Vq1dPQUFBevPNN7Vx40b7NE2aNNHAgQO1f/9+7d27Vw0aNNAvv/yi1atX68yZM6pVq5YCAgIkXTkHb+HChUpLS9PUqVPt80hPT9fMmTP1+uuv28e8vb0dstlsNmVkZNz0st3oeuvVq5f69u0r6Uo5y25+kvT111+rTJkyDs/5+vpKytv6k5y/rACcj2IHIF/UqFFDn332mWJiYnK82tVZ1q1bp/r16+vZZ5+1jx08eNBhmrvuukuhoaF67bXXVL16dRUpUkRNmjTRmDFjdObMGYfz6+bMmaOyZctmucBh+fLlevvttzVixAiHPVc5qVixoo4cOaLExESVLFlSkq57LuGNrrdWrVopNTVVNpst29vBVKlSRb6+vjp8+LAaN26c7Tzysv4AuAcungCQL+Lj43X69Gk9+uij2rx5sw4ePKhly5apZ8+eSk9Pd+p7VahQQVu2bNGyZcv066+/asiQIVkKlM1mU6NGjTRnzhx7iatatapSUlK0cuVKh9Lz/vvv6+GHH9add97p8OfJJ5/UyZMntXTp0jzlat68uWJjY9W9e3ft2rVL69at07///W97nuzc6Hrz9PTUzz//rL1792ZbNoOCgvTcc89p0KBB+uCDD3Tw4EFt27ZNkyZN0gcffJDn9QfAPVDsAOSL0qVLa926dUpPT1eLFi101113aeDAgSpatKg8PJz7q6dPnz7q0KGDunTporp16+rUqVMOe58yNW7cWOnp6fZi5+HhoUaNGslms9nPr9u6dat27typjh07Znl9SEiImjVrpvfffz9PuTw9PbVo0SJduHBBtWvX1lNPPWW/KtbPzy/b19zMegsODlZwcHCOOUaOHKkhQ4Zo1KhRqly5slq1aqWvv/5a5cqVk5T39Qeg8LMZY4yrQwDA38W6devUoEEDHThwwOEiDABwBoodAOSjhQsXqkiRIqpQoYIOHDigAQMGKDQ0VGvXrnV1NAAWxMUTAAqFw4cPq0qVKjk+v3fvXrf8qqzz58/rxRdf1OHDh1W8eHHFxcXp7bffdnUsABbFHjsAhcLly5d16NChHJ8viKtrAcDdUewAAAAsgqtiAQAALIJiBwAAYBEUOwAAAIug2AEAAFgExQ4AAMAiKHYAAAAWQbEDAACwCIodAACARfw/NCy9hHPqoTMAAAAASUVORK5CYII=", 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -2581,7 +2581,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -2786,7 +2786,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8a3fe823eaff4180889d356dd1d4cab4", + "model_id": "a2457ea9318744fd80c413a42676fcd0", "version_major": 2, "version_minor": 0 }, @@ -2908,7 +2908,7 @@ { "data": { "text/markdown": [ - "{'ref': 'SMV7', 'ref_ws_col': 'ref_ws_est_blend', 'distance_m': 314.4465998943834, 'bearing_deg': 173.69483366777283, 'ref_max_northing_error_v_reanalysis': np.float64(2.6590754551807976), 'ref_max_northing_error_v_wf': np.float64(2.842170943040401e-14), 'ref_max_ws_drift': np.float64(0.08697706942338845), 'ref_max_ws_drift_pp_period': np.float64(0.08697706942338845), 'ref_powercurve_shift': np.float64(0.003117456887993697), 'ref_rpm_shift': np.float64(0.0015313319638985412), 'ref_pitch_shift': np.float64(-0.05548555519736481), 'detrend_pre_r2_improvement': np.float64(0.004942384513915488), 'detrend_post_r2_improvement': np.float64(0.0011487228730558963), 'mean_power_pre': np.float64(1149.2323289820358), 'mean_power_post': np.float64(1148.4157066585956), 'mean_test_yaw_offset_pre': np.float64(-0.022511156888877885), 'mean_test_yaw_offset_post': np.float64(3.8213264238880478), 'mean_test_yaw_offset_command_pre': np.float64(0.0002638323353293413), 'mean_test_yaw_offset_command_post': np.float64(6.636967675544794), 'mean_ref_yaw_offset_command_pre': np.float64(0.0), 'test_ref_warning_counts': 0, 'time_calculated': Timestamp('2024-09-26 13:14:03.984554+0000', tz='UTC'), 'uplift_frc': np.float64(-0.010361770845398625), 'unc_one_sigma_frc': np.float64(0.0057851151530948705), 't_value_one_sigma': np.float64(1.000630119597717), 'missing_bins_unc_scale_factor': 1, 'pp_valid_hours_pre': np.float64(132.5), 'pp_valid_hours_post': np.float64(136.0), 'pp_valid_hours': np.float64(268.5), 'pp_data_coverage': np.float64(0.11496467565831728), 'pp_invalid_bin_count': np.int64(16), 'uplift_noadj_frc': np.float64(-0.011505660672016103), 'unc_one_sigma_noadj_frc': np.float64(0.0057851151530948705), 'poweronly_uplift_frc': np.float64(-0.012003308408347353), 'reversed_uplift_frc': np.float64(-0.009715528755112396), 'reversal_error': np.float64(0.0022877796532349576), 'unc_one_sigma_lowerbound_frc': np.float64(0.0011438898266174788), 'unc_one_sigma_bootstrap_frc': np.float64(0.0049406532714673), 'uplift_p5_frc': np.float64(-0.000846103203498606), 'uplift_p95_frc': np.float64(-0.019877438487298643), 'wind_up_version': '0.1.9', 'test_wtg': 'SMV6', 'test_pw_col': 'test_pw_clipped', 'lt_wtg_hours_raw': 0, 'lt_wtg_hours_filt': 0, 'test_max_ws_drift': np.float64(0.03653354205095605), 'test_max_ws_drift_pp_period': np.float64(0.03653354205095605), 'test_powercurve_shift': np.float64(0.0010615707256107498), 'test_rpm_shift': np.float64(0.0011316163321652972), 'test_pitch_shift': np.float64(-0.037158903030505286), 'preprocess_warning_counts': 0, 'test_warning_counts': 0}" + "{'ref': 'SMV7', 'ref_ws_col': 'ref_ws_est_blend', 'distance_m': 314.4465998943834, 'bearing_deg': 173.69483366777283, 'ref_max_northing_error_v_reanalysis': np.float64(2.6590754551807976), 'ref_max_northing_error_v_wf': np.float64(2.842170943040401e-14), 'ref_max_ws_drift': np.float64(0.08697706942338845), 'ref_max_ws_drift_pp_period': np.float64(0.08697706942338845), 'ref_powercurve_shift': np.float64(0.003117456887993697), 'ref_rpm_shift': np.float64(0.0015313319638985412), 'ref_pitch_shift': np.float64(-0.05548555519736481), 'detrend_pre_r2_improvement': np.float64(0.004942384513915488), 'detrend_post_r2_improvement': np.float64(0.0011487228730558963), 'mean_power_pre': np.float64(1149.2323289820358), 'mean_power_post': np.float64(1148.4157066585956), 'mean_test_yaw_offset_pre': np.float64(-0.022511156888877885), 'mean_test_yaw_offset_post': np.float64(3.8213264238880478), 'mean_test_yaw_offset_command_pre': np.float64(0.0002638323353293413), 'mean_test_yaw_offset_command_post': np.float64(6.636967675544794), 'mean_ref_yaw_offset_command_pre': np.float64(0.0), 'test_ref_warning_counts': 0, 'time_calculated': Timestamp('2024-11-04 14:53:53.646317+0000', tz='UTC'), 'uplift_frc': np.float64(-0.010361770845398625), 'unc_one_sigma_frc': np.float64(0.0057851151530948705), 't_value_one_sigma': np.float64(1.000630119597717), 'missing_bins_unc_scale_factor': 1, 'pp_valid_hours_pre': np.float64(132.5), 'pp_valid_hours_post': np.float64(136.0), 'pp_valid_hours': np.float64(268.5), 'pp_data_coverage': np.float64(0.11496467565831728), 'pp_invalid_bin_count': np.int64(16), 'uplift_noadj_frc': np.float64(-0.011505660672016103), 'unc_one_sigma_noadj_frc': np.float64(0.0057851151530948705), 'poweronly_uplift_frc': np.float64(-0.012003308408347353), 'reversed_uplift_frc': np.float64(-0.009715528755112396), 'reversal_error': np.float64(0.0022877796532349576), 'unc_one_sigma_lowerbound_frc': np.float64(0.0011438898266174788), 'unc_one_sigma_bootstrap_frc': np.float64(0.0049406532714673), 'uplift_p5_frc': np.float64(-0.000846103203498606), 'uplift_p95_frc': np.float64(-0.019877438487298643), 'wind_up_version': '0.1.9', 'test_wtg': 'SMV6', 'test_pw_col': 'test_pw_clipped', 'lt_wtg_hours_raw': 0, 'lt_wtg_hours_filt': 0, 'test_max_ws_drift': np.float64(0.03653354205095605), 'test_max_ws_drift_pp_period': np.float64(0.03653354205095605), 'test_powercurve_shift': np.float64(0.0010615707256107498), 'test_rpm_shift': np.float64(0.0011316163321652972), 'test_pitch_shift': np.float64(-0.037158903030505286), 'preprocess_warning_counts': 0, 'test_warning_counts': 0}" ], "text/plain": [ "<IPython.core.display.Markdown object>" @@ -3523,7 +3523,7 @@ }, { "data": { - "image/png": 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ExKh///6Ki4vL9ETwNNef+N65c2fNnTtXnp6eDufV3ExERISka2XCXdfn8OHD+uWXX9SzZ88Mz927GX9/f4fHqampstlsWrRokTw9PdPNnydPntta/p3o0KGD1q5dqxdffFEVKlRQnjx5lJqaqmbNmjmUrsqVK8vPz08///yzIiMjFRYWpvvuu0+1a9fWlClTlJiYqF9++cV+LmCatHPpMrs9y6pVq+znU6XJaFtIuul9B7MqbZ1mz56t8PDwdNOvv13PjTp37qypU6cqb9686tixY6a3iUlNTdUDDzygcePGZTg97efg4MGDatiwoUqVKqVx48YpIiJCPj4++uGHHzR+/Ph0pTc7t0uam/1HKE2rVq0UEBCgL774QjVq1NAXX3whDw8PtW/f3j7P7b63b/zZkKRz586pbt26CgoK0vDhwxUTEyM/Pz9t3bpVL7/88h3dfzGr+wbWRrGD20q7Qu3YsWOZzvP4449r5MiRKl26tCpUqHDT5fn6+uqxxx7TJ598ohMnTujLL79UgwYNMvwHMiN//PGHpGtXdt6J3LA+8+bNkzEmS0cpbyUmJkbGGBUrVsx+xCAnnT17VsuXL9ewYcM0ePBg+3jaUa3r+fj42K80jIyMVO3atSVdO5KXmJioOXPm6MSJEw4XTqQdDe3YsaMee+yxdMvs27ev5syZk67Y3UpUVJRSU1MVFxenEiVK2McPHDhwy+fGxMRIunZ1dWY3tc5M586dNXjwYB07dkyzZ8++6Wvs2LFDDRs2vGlR+vbbb5WYmKhvvvnG4WjcihUrbivXrcTExGjJkiU6c+ZMpkft0rbp/v37Vbp0afv4iRMndO7cOUVFRdnHAgMD1bJlS3355ZcaN26cPv/8c9WuXVtFihRxeM27fW+vXLlSp0+f1tdff+3wvoqLi0uXXbq2/69/L129elWHDh1SuXLlHHJlZd/A2vgoFrneihUrMvxf+w8//CBJKlmyZKbPfeaZZzRkyBC98847WXqtLl26KDk5Wb169dKpU6cyLDinTp1KN3bhwgVNmDBBBQsWVKVKlW76Grltfa43d+5cRUZGOtyu5E61bdtWnp6eGjZsWLr1NcY43HIkO6QdSbnxtSdMmJDh/LVr19aGDRu0YsUKe7ErWLCgSpcurTFjxtjnSTN//nxdunRJsbGxeuyxx9L9admypb766qvbvs1E2u1rpkyZ4jB+47eSZPbcoKAgvfnmmxne6iaj926amJgYTZgwQaNGjVLVqlUzna9Dhw7666+/9MEHH6SbdvnyZV26dElSxtv//Pnzmjlz5i3X43a0a9dOxph0VxZf/9oPP/ywpPT7Pu3I1o1X/3bs2FFHjx7Vhx9+qB07dqS7otQZ7+2Mtk9SUlK6/V65cmUVKFBAH3zwgf2bb6RrR4tv/Jg6q/sG1sYRO+R6ffr0UUJCgtq0aaNSpUopKSlJa9eu1eeff67o6Gh179490+dGRUVl+Z5oklS3bl0VLVpUCxcuzPR2H5MnT9aCBQvUqlUrRUZG6tixY5oxY4YOHz6s2bNnZ3guTW5enzS7d+/Wzp079corrzjlf/sxMTEaOXKkXn31VR06dEitW7dW3rx5FRcXp/nz56tnz5564YUX7vp1MhMUFKQ6depo7NixSk5O1j333KOlS5emOyKSpnbt2nrjjTd05MgRhwJXp04dvf/++4qOjlbRokXt43PmzFGBAgVUo0aNDJf3yCOP6IMPPtD3339/W7eNqVSpktq1a6cJEybo9OnTeuihh7Rq1Srt27dP0s0/TgwKCtLUqVP15JNPqmLFiurUqZNCQ0N1+PBhff/996pZs6bee++9TJ/fr1+/W+Z78skn9cUXX+i5557TihUrVLNmTaWkpOj333/XF198oSVLlqhy5cpq0qSJfHx81KpVK/Xq1UsXL17UBx98oLCwsJselb5d9evX15NPPql3331X+/fvt3/M/ssvv6h+/frq3bu3ypcvr65du2r69On2j0A3btyojz/+WK1bt053VPXhhx9W3rx59cILL8jT01Pt2rVzmO6M93aNGjUUEhKirl27qm/fvrLZbJo9e3a6oujj46OhQ4eqT58+atCggTp06KBDhw5p1qxZiomJcXg/ZHXfwOJy+GIN4LYtWrTI9OjRw5QqVcrkyZPH+Pj4mOLFi5s+ffpk+E0NaVeRZiazq0jTvPjii0ZSprf7WLp0qWncuLEJDw833t7eJl++fKZJkyZm+fLlbrk+adJuhbJz584srUeaW13R+tVXX5latWqZwMBAExgYaEqVKmViY2Mdbrlyt1fFGnPt6sP777/fYex///ufadOmjcmXL58JDg427du3N0ePHk13dakx165K9PT0NHnz5jVXr161j6ddpfjkk0/ax06cOGG8vLwcxm6UkJBgAgIC7Fd1ZraOGX17xKVLl0xsbKzJnz+/yZMnj2ndurXZu3evkWRGjx590+caY8yKFStM06ZNTXBwsPHz8zMxMTGmW7duZvPmzfZ5srrNlcE3TyQlJZkxY8aY+++/3/j6+pqQkBBTqVIlM2zYMPvVvcYY880335hy5coZPz8/Ex0dbcaMGWNmzJiRLnNm7/O6des6XPWZmatXr5q33nrLlCpVyvj4+JjQ0FDTvHlzs2XLFvs8ycnJZtiwYaZYsWLG29vbREREmFdffdV+W5EbdenSxUhyuML1Rll5b2f0vkyzZs0a89BDDxl/f39TpEgR+62PJJkVK1Y4zPvuu++aqKgo4+vra6pWrWrWrFljKlWqZJo1a+YwX1b3DazLZowTz0wFAGSL7du368EHH9Snn37qlHMg4d5SU1MVGhqqtm3bZvjRK/65OMcOAHKZy5cvpxubMGGCPDw80n3zBazvypUr6T6i/eSTT3TmzJl0XykGcI4dgFzn4sWLunjx4k3nCQ0NzfQWGe5u7Nix2rJli+rXry8vLy8tWrRIixYtUs+ePbllxT/Q+vXrNWDAALVv314FChTQ1q1b9dFHH6ls2bIOt2EBJImPYgHkOkOHDs3wKsfrxcXFpbtBq1UsW7ZMw4YN0549e3Tx4kVFRkbqySef1Ouvv37Te9HBmg4dOqS+fftq48aN9tu6PPzwwxo9erTCwsJcHQ+5DMUOQK7zxx9/2O8LmJlatWrJz88vhxIBgHug2AEAAFgEF08AAABYhOVP1khNTdXRo0eVN29evmIFAAC4HWOMLly4oCJFimT6Pc5pLF/sjh49ylVkAADA7R05csThG3AyYvlilzdvXknXNkZQUJCL09ye5ORkLV26VE2aNJG3t7er42TKXXJKZM0O7pJTImt2cJecElmzg7vklNwr643i4+MVERFh7zQ3Y/lil/bxa1BQkFsWu4CAAAUFBeXqN6G75JTImh3cJadE1uzgLjklsmYHd8kpuVfWzGTllDIungAAALAIih0AAIBFUOwAAAAswvLn2AEAgJyVkpKi5ORkV8dwkJycLC8vL125ckUpKSmujuPA29vbad99TbEDAABOYYzR8ePHde7cOVdHSccYo/DwcB05ciRX3tc2X758Cg8Pv+tsFDsAAOAUaaUuLCxMAQEBuapApaam6uLFi8qTJ88tb/Kbk4wxSkhI0MmTJyVJhQsXvqvlUewAAMBdS0lJsZe6AgUKuDpOOqmpqUpKSpKfn1+uKnaS5O/vL0k6efKkwsLC7upj2dy1ZgAAwC2lnVMXEBDg4iTuKW273e25iRQ7AADgNLnp41d34qztRrEDAACwCIodAACARXDxBAAAyFbRr3yfY691aHSLHHut3IgjdgAAALchKSnJ1REyRbEDAAD/aPXq1VPv3r3Vu3dvBQcHq2DBgho0aJCMMZKk6OhojRgxQk899ZSCgoLUs2dPSdLq1atVu3Zt+fv7KyIiQn379tWlS5dcuSoUOwAAgI8//lheXl7auHGjJk6cqHHjxunDDz+0T3/77bdVvnx5bdu2TYMGDdLBgwfVrFkztWvXTjt37tTnn3+u1atXq3fv3i5cC86xAwAAUEREhMaPHy+bzaaSJUtq165dGj9+vJ599llJUoMGDfTvf//bPv8zzzyjLl26qH///pKkEiVK6N1331XdunU1depU+fn5uWI1KHYA8I+yYpRzl2c8JJWSfhkn2VKdu+z6rzp3ecBNPPTQQw73kqtevbreeecdpaSkSJIqV67sMP+OHTu0c+dOzZkzxz5mjFFqaqri4uJUunTpnAl+A4odAADALQQGBjo8vnjxonr16qW+ffummzcyMjKnYqVDsQMAAP94GzZscHi8fv16lShRItPvba1YsaL27Nmj4sWL50S8LOPiCQAA8I93+PBhDRw4UHv37tW8efM0adIk9evXL9P5X375Za1du1a9e/fW9u3btX//fi1cuJCLJwAAOWfC8n1OXZ7Nw0vRlUppysoDMqlXnbrs/vWdujjgpp566ildvnxZVatWlaenp/r162e/rUlGypUrp1WrVun1119X7dq1ZYxRTEyMOnbsmIOp06PYAQCAbOUO3wbh7e2tCRMmaOrUqemmHTp0KMPnVKlSRUuXLs3mZLeHj2IBAAAsgmIHAABgEXwUCwAA/tFWrlzp6ghOwxE7AAAAi6DYAQAAWATFDgAAwCIodgAAABZBsQMAALAIih0AAIBFUOwAAAAsgvvYAQCA7LViVM69Vv1Xc+61smDlypWqX7++zp49q3z58mX763HEDgAAwCIodgAA4B+tXr166t27t3r37q3g4GAVLFhQgwYNkjFGknT27Fk99dRTCgkJUUBAgJo3b679+/fbn//nn3+qVatWCgkJUWBgoO6//3798MMPOnTokOrXry9JCgkJkc1mU7du3bJ1XSh2AADgH+/jjz+Wl5eXNm7cqIkTJ2rcuHH68MMPJUndunXT5s2b9c0332jdunUyxujhhx9WcnKyJCk2NlaJiYn6+eeftWvXLo0ZM0Z58uRRRESEvvrqK0nS3r17dezYMU2cODFb14Nz7AAAwD9eRESExo8fL5vNppIlS2rXrl0aP3686tWrp2+++UZr1qxRjRo1JElz5sxRRESEFixYoPbt2+vw4cNq166dHnjgAUnSvffea19u/vz5JUlhYWGcYwcAAJATHnroIdlsNvvj6tWra//+/dqzZ4+8vLxUrVo1+7QCBQqoZMmS+u233yRJffv21ciRI1WzZk0NGTJEO3fuzPH8aSh2AAAAd+GZZ57RH3/8oSeffFK7du1S5cqVNWnSJJdkodgBAIB/vA0bNjg8Xr9+vUqUKKEyZcro6tWrDtNPnz6tvXv3qkyZMvaxiIgIPffcc/r666/173//Wx988IEkycfHR5KUkpKSA2tBsQMAANDhw4c1cOBA7d27V/PmzdOkSZPUr18/lShRQo8++qieffZZrV69Wjt27NATTzyhe+65R48++qgkqX///lqyZIni4uK0detWrVixQqVLl5YkRUVFyWaz6bvvvtOpU6d08eLFbF0Pih0AAPjHe+qpp3T58mVVrVpVsbGx6tevn3r27ClJmjlzpipVqqSWLVuqevXqMsbohx9+kLe3t6RrR+NiY2NVunRpNWvWTPfdd5+mTJkiSbrnnns0bNgwvfLKKypUqJB69+6drevBVbEAACB75bJvg8iIt7e3JkyYoKlTp6abFhISok8++STT597qfLpBgwZp0KBBd50xKzhiBwAAYBEUOwAAAIvgo1gAAPCPtnLlSldHcBqO2AEAAFgExQ4AAMAiKHYAAMBpUlNTXR3BLTlru3GOHQAAuGs+Pj7y8PDQ0aNHFRoaKh8fH4fvXnW11NRUJSUl6cqVK/LwyD3HtYwxSkpK0qlTp+Th4WH/poo7RbEDAAB3zcPDQ8WKFdOxY8d09OhRV8dJxxijy5cvy9/fP1cVzjQBAQGKjIy869JJsQMAAE7h4+OjyMhIXb16Nce+GzWrkpOT9fPPP6tOnTr2b4zILTw9PeXl5eWUwkmxAwAATmOz2eTt7Z0ry9PVq1fl5+eX67I5U+75kBkAAAB3hWIHAABgERQ7AAAAi6DYAQAAWATFDgAAwCIodgAAABZBsQMAALAIih0AAIBFUOwAAAAsgmIHAABgERQ7AAAAi6DYAQAAWATFDgAAwCIodgAAABbh0mKXkpKiQYMGqVixYvL391dMTIxGjBghY4x9HmOMBg8erMKFC8vf31+NGjXS/v37XZgaAAAgd3JpsRszZoymTp2q9957T7/99pvGjBmjsWPHatKkSfZ5xo4dq3fffVfTpk3Thg0bFBgYqKZNm+rKlSsuTA4AAJD7eLnyxdeuXatHH31ULVq0kCRFR0dr3rx52rhxo6RrR+smTJig//znP3r00UclSZ988okKFSqkBQsWqFOnTi7LDgAAkNu49IhdjRo1tHz5cu3bt0+StGPHDq1evVrNmzeXJMXFxen48eNq1KiR/TnBwcGqVq2a1q1b55LMAAAAuZVLj9i98sorio+PV6lSpeTp6amUlBS98cYb6tKliyTp+PHjkqRChQo5PK9QoUL2aTdKTExUYmKi/XF8fLwkKTk5WcnJydmxGtkmLW9uz+0uOSWyZgd3ySmRVZJsHs79tZ+2PGcvV3L+urP/nc9dckrulfVGt5PZZq6/UiGHffbZZ3rxxRf11ltv6f7779f27dvVv39/jRs3Tl27dtXatWtVs2ZNHT16VIULF7Y/r0OHDrLZbPr888/TLXPo0KEaNmxYuvG5c+cqICAgW9cHAADA2RISEtS5c2edP39eQUFBN53XpcUuIiJCr7zyimJjY+1jI0eO1Keffqrff/9df/zxh2JiYrRt2zZVqFDBPk/dunVVoUIFTZw4Md0yMzpiFxERob///vuWGyO3SU5O1rJly9S4cWN5e3u7Ok6m3CWnRNbs4C45JbJK0pQRzzttWdK1I3VRDzbTn9sWy6Redeqynx80xanLY/87n7vklNwr643i4+NVsGDBLBU7l34Um5CQIA8Px9P8PD09lZqaKkkqVqyYwsPDtXz5cnuxi4+P14YNG/Svf/0rw2X6+vrK19c33bi3t7fb7cg07pLdXXJKZM0O7pJT+mdndXb5un65zl52du2jf/L+zy7uklNyr6xpbievS4tdq1at9MYbbygyMlL333+/tm3bpnHjxqlHjx6SJJvNpv79+2vkyJEqUaKEihUrpkGDBqlIkSJq3bq1K6MDAADkOi4tdpMmTdKgQYP0/PPP6+TJkypSpIh69eqlwYMH2+d56aWXdOnSJfXs2VPnzp1TrVq1tHjxYvn5+bkwOQAAQO7j0mKXN29eTZgwQRMmTMh0HpvNpuHDh2v48OE5FwwAAMAN8V2xAAAAFkGxAwAAsAiKHQAAgEVQ7AAAACyCYgcAAGARFDsAAACLoNgBAABYBMUOAADAIih2AAAAFkGxAwAAsAiKHQAAgEVQ7AAAACyCYgcAAGARFDsAAACLoNgBAABYBMUOAADAIih2AAAAFkGxAwAAsAiKHQAAgEVQ7AAAACyCYgcAAGARFDsAAACLoNgBAABYBMUOAADAIih2AAAAFkGxAwAAsAiKHQAAgEVQ7AAAACyCYgcAAGARFDsAAACLoNgBAABYBMUOAADAIih2AAAAFkGxAwAAsAiKHQAAgEVQ7AAAACyCYgcAAGARFDsAAACLoNgBAABYBMUOAADAIih2AAAAFkGxAwAAsAiKHQAAgEVQ7AAAACyCYgcAAGARFDsAAACLoNgBAABYBMUOAADAIih2AAAAFkGxAwAAsAiKHQAAgEVQ7AAAACyCYgcAAGARFDsAAACLoNgBAABYBMUOAADAIih2AAAAFkGxAwAAsAiKHQAAgEVQ7AAAACyCYgcAAGARFDsAAACLoNgBAABYBMUOAADAIih2AAAAFkGxAwAAsAiKHQAAgEW4vNj99ddfeuKJJ1SgQAH5+/vrgQce0ObNm+3TjTEaPHiwChcuLH9/fzVq1Ej79+93YWIAAIDcyaXF7uzZs6pZs6a8vb21aNEi7dmzR++8845CQkLs84wdO1bvvvuupk2bpg0bNigwMFBNmzbVlStXXJgcAAAg9/Fy5YuPGTNGERERmjlzpn2sWLFi9r8bYzRhwgT95z//0aOPPipJ+uSTT1SoUCEtWLBAnTp1yvHMAAAAuZVLi90333yjpk2bqn379lq1apXuuecePf/883r22WclSXFxcTp+/LgaNWpkf05wcLCqVaumdevWZVjsEhMTlZiYaH8cHx8vSUpOTlZycnI2r5FzpeXN7bndJadE1uzgLjklskqSzcO5v/bTlufs5UrOX3f2v/O5S07JvbLe6HYy24wxJhuz3JSfn58kaeDAgWrfvr02bdqkfv36adq0aeratavWrl2rmjVr6ujRoypcuLD9eR06dJDNZtPnn3+ebplDhw7VsGHD0o3PnTtXAQEB2bcyAAAA2SAhIUGdO3fW+fPnFRQUdNN5XVrsfHx8VLlyZa1du9Y+1rdvX23atEnr1q27o2KX0RG7iIgI/f3337fcGLlNcnKyli1bpsaNG8vb29vVcTLlLjklsmYHd8kpkVWSpox43mnLkq4dqYt6sJn+3LZYJvWqU5f9/KApTl0e+9/53CWn5F5ZbxQfH6+CBQtmqdi59KPYwoULq0yZMg5jpUuX1ldffSVJCg8PlySdOHHCodidOHFCFSpUyHCZvr6+8vX1TTfu7e3tdjsyjbtkd5ecElmzg7vklP7ZWZ1dvq5frrOXnV376J+8/7OLu+SU3CtrmtvJ69KrYmvWrKm9e/c6jO3bt09RUVGSrl1IER4eruXLl9unx8fHa8OGDapevXqOZgUAAMjtXHrEbsCAAapRo4befPNNdejQQRs3btT06dM1ffp0SZLNZlP//v01cuRIlShRQsWKFdOgQYNUpEgRtW7d2pXRAQAAch2XFrsqVapo/vz5evXVVzV8+HAVK1ZMEyZMUJcuXezzvPTSS7p06ZJ69uypc+fOqVatWlq8eLH9wgsAAABc49JiJ0ktW7ZUy5YtM51us9k0fPhwDR8+PAdTAQAAuB+Xf6UYAAAAnINiBwAAYBEUOwAAAIug2AEAAFgExQ4AAMAiKHYAAAAWQbEDAACwCIodAACARVDsAAAALIJiBwAAYBEUOwAAAIug2AEAAFgExQ4AAMAiKHYAAAAWQbEDAACwCIodAACARVDsAAAALMIpxS4lJUXbt2/X2bNnnbE4AAAA3IE7Knb9+/fXRx99JOlaqatbt64qVqyoiIgIrVy50pn5AAAAkEV3VOz++9//qnz58pKkb7/9VnFxcfr99981YMAAvf76604NCAAAgKy5o2L3999/Kzw8XJL0ww8/qH379rrvvvvUo0cP7dq1y6kBAQAAkDV3VOwKFSqkPXv2KCUlRYsXL1bjxo0lSQkJCfL09HRqQAAAAGSN1508qXv37urQoYMKFy4sm82mRo0aSZI2bNigUqVKOTUgAAAAsuaOit3QoUP1wAMP6PDhw2rfvr18fX0lSZ6ennrllVecGhAAAABZc9vFLjk5Wc2aNdO0adPUrl07h2ldu3Z1WjAAAADcnts+x87b21s7d+7MjiwAAAC4C3d08cQTTzxhv48dAAAAcoc7Osfu6tWrmjFjhn788UdVqlRJgYGBDtPHjRvnlHAAAADIujsqdrt371bFihUlSfv27XOYZrPZ7j4VAAAAbtsdFbsVK1Y4OwcAAADu0h2dYwcAAIDc546O2NWvX/+mH7n+9NNPdxwIAAAAd+aOil2FChUcHicnJ2v79u3avXs397IDAABwkTsqduPHj89wfOjQobp48eJdBQIAAMCdceo5dk888YRmzJjhzEUCAAAgi5xa7NatWyc/Pz9nLhIAAABZdEcfxbZt29bhsTFGx44d0+bNmzVo0CCnBAMAAMDtuaNiFxwc7PDYw8NDJUuW1PDhw9WkSROnBAMAAMDtuaNiN3PmTGfnAAAAwF26o2KXZsuWLfrtt98kSffff78efPBBp4QCAADA7bujYnfy5El16tRJK1euVL58+SRJ586dU/369fXZZ58pNDTUmRkBAACQBXd0VWyfPn104cIF/frrrzpz5ozOnDmj3bt3Kz4+Xn379nV2RgAAAGTBHR2xW7x4sX788UeVLl3aPlamTBlNnjyZiycAAABc5I6O2KWmpsrb2zvduLe3t1JTU+86FAAAAG7fHRW7Bg0aqF+/fjp69Kh97K+//tKAAQPUsGFDp4UDAABA1t1RsXvvvfcUHx+v6OhoxcTEKCYmRtHR0YqPj9ekSZOcnREAAABZcEfn2EVERGjr1q1avny5/XYnpUuXVqNGjZwaDgAAAFl3x/ex++mnn/TTTz/p5MmTSk1N1bZt2zR37lxJ0owZM5wWEAAAAFlzR8Vu2LBhGj58uCpXrqzChQvLZrM5OxcAAABu0x0Vu2nTpmnWrFl68sknnZ0HAAAAd+iOLp5ISkpSjRo1nJ0FAAAAd+GOit0zzzxjP58OAAAAuUOWP4odOHCg/e+pqamaPn26fvzxR5UrVy7dzYrHjRvnvIQAAADIkiwXu23btjk8rlChgiRp9+7dDuNcSAEAAOAaWS52K1asyM4cAAAAuEt3dI4dAAAAch+KHQAAgEVQ7AAAACyCYgcAAGARFDsAAACLoNgBAABYBMUOAADAIih2AAAAFkGxAwAAsAiKHQAAgEVQ7AAAACwi1xS70aNHy2azqX///vaxK1euKDY2VgUKFFCePHnUrl07nThxwnUhAQAAcrFcUew2bdqk999/X+XKlXMYHzBggL799lt9+eWXWrVqlY4ePaq2bdu6KCUAAEDu5vJid/HiRXXp0kUffPCBQkJC7OPnz5/XRx99pHHjxqlBgwaqVKmSZs6cqbVr12r9+vUuTAwAAJA7ubzYxcbGqkWLFmrUqJHD+JYtW5ScnOwwXqpUKUVGRmrdunU5HRMAACDX83Lli3/22WfaunWrNm3alG7a8ePH5ePjo3z58jmMFypUSMePH890mYmJiUpMTLQ/jo+PlyQlJycrOTnZOcFzSFre3J7bXXJKZM0O7pJTIqsk2Tyc+2s/bXnOXq7k/HVn/zufu+SU3CvrjW4ns80YY7IxS6aOHDmiypUra9myZfZz6+rVq6cKFSpowoQJmjt3rrp37+5Q0iSpatWqql+/vsaMGZPhcocOHaphw4alG587d64CAgKcvyIAAADZKCEhQZ07d9b58+cVFBR003ldVuwWLFigNm3ayNPT0z6WkpIim80mDw8PLVmyRI0aNdLZs2cdjtpFRUWpf//+GjBgQIbLzeiIXUREhP7+++9bbozcJjk5WcuWLVPjxo3l7e3t6jiZcpecElmzg7vklMgqSVNGPO+0ZUnXjtRFPdhMf25bLJN61anLfn7QFKcuj/3vfO6SU3KvrDeKj49XwYIFs1TsXPZRbMOGDbVr1y6Hse7du6tUqVJ6+eWXFRERIW9vby1fvlzt2rWTJO3du1eHDx9W9erVM12ur6+vfH190417e3u73Y5M4y7Z3SWnRNbs4C45pX92VmeXr+uX6+xlZ9c++ifv/+ziLjkl98qa5nbyuqzY5c2bV2XLlnUYCwwMVIECBezjTz/9tAYOHKj8+fMrKChIffr0UfXq1fXQQw+5IjIAAECu5tKLJ25l/Pjx8vDwULt27ZSYmKimTZtqyhTnHpoHAACwilxV7FauXOnw2M/PT5MnT9bkyZNdEwgAAMCNuPw+dgAAAHAOih0AAIBFUOwAAAAsgmIHAABgERQ7AAAAi6DYAQAAWATFDgAAwCIodgAAABZBsQMAALAIih0AAIBFUOwAAAAsgmIHAABgERQ7AAAAi6DYAQAAWATFDgAAwCIodgAAABZBsQMAALAIih0AAIBFUOwAAAAsgmIHAABgERQ7AAAAi6DYAQAAWATFDgAAwCIodgAAABZBsQMAALAIih0AAIBFUOwAAAAsgmIHAABgERQ7AAAAi6DYAQAAWATFDgAAwCIodgAAABZBsQMAALAIih0AAIBFUOwAAAAsgmIHAABgERQ7AAAAi6DYAQAAWATFDgAAwCIodgAAABZBsQMAALAIih0AAIBFUOwAAAAsgmIHAABgERQ7AAAAi6DYAQAAWATFDgAAwCIodgAAABZBsQMAALAIih0AAIBFUOwAAAAsgmIHAABgEV6uDgAAQEaiX/neqcvz9TQaW1UqO3SJElNsTlvuodEtnLYs4G5xxA4AAMAiKHYAAAAWQbEDAACwCIodAACARVDsAAAALIJiBwAAYBEUOwAAAIug2AEAAFgExQ4AAMAiKHYAAAAWQbEDAACwCIodAACARXi5OgAAuL0Vo5y/TOMhqZT0yzjJlur85buB/l7/derybB5eklrqec+FMrarTlxyCycuC7g7HLEDAACwCJcWu1GjRqlKlSrKmzevwsLC1Lp1a+3du9dhnitXrig2NlYFChRQnjx51K5dO504ccJFiQEAAHIvlxa7VatWKTY2VuvXr9eyZcuUnJysJk2a6NKlS/Z5BgwYoG+//VZffvmlVq1apaNHj6pt27YuTA0AAJA7ufQcu8WLFzs8njVrlsLCwrRlyxbVqVNH58+f10cffaS5c+eqQYMGkqSZM2eqdOnSWr9+vR566CFXxAYABxOW73P6Mm0eXoquVEpTVh6QSXXm+WAArCxXXTxx/vx5SVL+/PklSVu2bFFycrIaNWpkn6dUqVKKjIzUunXrMix2iYmJSkxMtD+Oj4+XJCUnJys5OTk74ztdWt7cnttdckpkzQ7uklPKvqzXTsp3rrRlZseyncldckrZlzU73vvu8nPlLjkl98p6o9vJbDPGmGzMkmWpqal65JFHdO7cOa1evVqSNHfuXHXv3t2hqElS1apVVb9+fY0ZMybdcoYOHaphw4alG587d64CAgKyJzwAAEA2SUhIUOfOnXX+/HkFBQXddN5c81+s2NhY7d69217q7tSrr76qgQMH2h/Hx8crIiJCTZo0ueXGyG2Sk5O1bNkyNW7cWN7e3q6Okyl3ySmRNTu4S04p+7JOGfG805aVxubhpagHm+nPbYtz9Uex7pJTyr6szw+a4rRlpXGXnyt3ySm5V9YbpX36mBW5otj17t1b3333nX7++WcVLVrUPh4eHq6kpCSdO3dO+fLls4+fOHFC4eHhGS7L19dXvr6+6ca9vb3dbkemcZfs7pJTImt2cJeckvOzZmehMalXc31hktwnp+T8rNn5vneXnyt3ySm5V9Y0t5PXpVfFGmPUu3dvzZ8/Xz/99JOKFSvmML1SpUry9vbW8uXL7WN79+7V4cOHVb169ZyOCwAAkKu59IhdbGys5s6dq4ULFypv3rw6fvy4JCk4OFj+/v4KDg7W008/rYEDByp//vwKCgpSnz59VL16da6IBQAAuIFLi93UqVMlSfXq1XMYnzlzprp16yZJGj9+vDw8PNSuXTslJiaqadOmmjLF+eczAAAAuDuXFrusXJDr5+enyZMna/LkyTmQCAAAwH3xXbEAAAAWQbEDAACwCIodAACARVDsAAAALIJiBwAAYBEUOwAAAIug2AEAAFgExQ4AAMAiKHYAAAAWQbEDAACwCIodAACARVDsAAAALIJiBwAAYBEUOwAAAIug2AEAAFgExQ4AAMAiKHYAAAAWQbEDAACwCIodAACARVDsAAAALIJiBwAAYBEUOwAAAIug2AEAAFgExQ4AAMAiKHYAAAAWQbEDAACwCIodAACARVDsAAAALIJiBwAAYBEUOwAAAIug2AEAAFgExQ4AAMAiKHYAAAAWQbEDAACwCIodAACARVDsAAAALIJiBwAAYBEUOwAAAIug2AEAAFgExQ4AAMAiKHYAAAAWQbEDAACwCIodAACARVDsAAAALIJiBwAAYBEUOwAAAIug2AEAAFgExQ4AAMAiKHYAAAAWQbEDAACwCIodAACARVDsAAAALIJiBwAAYBEUOwAAAIug2AEAAFgExQ4AAMAiKHYAAAAWQbEDAACwCIodAACARVDsAAAALIJiBwAAYBEUOwAAAIug2AEAAFgExQ4AAMAiKHYAAAAWQbEDAACwCLcodpMnT1Z0dLT8/PxUrVo1bdy40dWRAAAAcp1cX+w+//xzDRw4UEOGDNHWrVtVvnx5NW3aVCdPnnR1NAAAgFwl1xe7cePG6dlnn1X37t1VpkwZTZs2TQEBAZoxY4arowEAAOQqubrYJSUlacuWLWrUqJF9zMPDQ40aNdK6detcmAwAACD38XJ1gJv5+++/lZKSokKFCjmMFypUSL///nuGz0lMTFRiYqL98fnz5yVJZ86cUXJycvaFzQbJyclKSEjQ6dOn5e3t7eo4mXKXnBJZs4O75JSyL2ticqrTlpXG5pGqhIQEJSanyqQ6f/nO4i45pezLevr0aactK427/Fy5S07JvbLe6MKFC5IkY8wt583Vxe5OjBo1SsOGDUs3XqxYMRekAYC74S6nnLhLTik7sr4y9hOnLxPIyIULFxQcHHzTeXJ1sStYsKA8PT114sQJh/ETJ04oPDw8w+e8+uqrGjhwoP1xamqqzpw5owIFCshms2VrXmeLj49XRESEjhw5oqCgIFfHyZS75JTImh3cJadE1uzgLjklsmYHd8kpuVfWGxljdOHCBRUpUuSW8+bqYufj46NKlSpp+fLlat26taRrRW358uXq3bt3hs/x9fWVr6+vw1i+fPmyOWn2CgoKcos3obvklMiaHdwlp0TW7OAuOSWyZgd3ySm5V9br3epIXZpcXewkaeDAgeratasqV66sqlWrasKECbp06ZK6d+/u6mgAAAC5Sq4vdh07dtSpU6c0ePBgHT9+XBUqVNDixYvTXVABAADwT5fri50k9e7dO9OPXq3M19dXQ4YMSffRcm7jLjklsmYHd8kpkTU7uEtOiazZwV1ySu6V9W7YTFaunQUAAECul6tvUAwAAICso9gBAABYBMUOAADAIih2ucDPP/+sVq1aqUiRIrLZbFqwYIHD9G7duslmszn8adasmWvC/n+jR4+WzWZT//797WP16tVLl/O5555zSb7o6Oh0WWw2m2JjY12a9Vb72hijwYMHq3DhwvL391ejRo20f//+W67b6NGjczzr0KFDVapUKQUGBiokJESNGjXShg0bcjzrrXJK0m+//aZHHnlEwcHBCgwMVJUqVXT48GH79Jx6P9wq64kTJ9StWzcVKVJEAQEBatasWbr9nxNZR40apSpVqihv3rwKCwtT69attXfvXvv0M2fOqE+fPipZsqT8/f0VGRmpvn372r/CMU1GP4OfffZZjmaVsrbNckvW48eP68knn1R4eLgCAwNVsWJFffXVVw7zZPfP1dSpU1WuXDn7/d6qV6+uRYsW2adPnz5d9erVU1BQkGw2m86dO5duGTn1e+pWWdMYY9S8efMMf+5yYt/nJIpdLnDp0iWVL19ekydPznSeZs2a6dixY/Y/8+bNy8GEjjZt2qT3339f5cqVSzft2Wefdcg5duxYFyS8lvH6HMuWLZMktW/f3qVZb7Wvx44dq3fffVfTpk3Thg0bFBgYqKZNm+rKlSsO8w0fPtwhe58+fXI863333af33ntPu3bt0urVqxUdHa0mTZro1KlTOZr1VjkPHjyoWrVqqVSpUlq5cqV27typQYMGyc/Pz2G+nHg/3CyrMUatW7fWH3/8oYULF2rbtm2KiopSo0aNdOnSpRzNumrVKsXGxmr9+vVatmyZkpOT1aRJE3uOo0eP6ujRo3r77be1e/duzZo1S4sXL9bTTz+dblkzZ850yJp2s/mcypomK9ssN2R96qmntHfvXn3zzTfatWuX2rZtqw4dOmjbtm0Oy8rOn6uiRYtq9OjR2rJlizZv3qwGDRro0Ucf1a+//ipJSkhIULNmzfTaa6/ddDk58XvqVlnTTJgw4abfPpXd+z5HGeQqksz8+fMdxrp27WoeffRRl+S50YULF0yJEiXMsmXLTN26dU2/fv3s0258nJv069fPxMTEmNTUVGNM7sh6475OTU014eHh5q233rKPnTt3zvj6+pp58+bZx6Kiosz48eNzMGnG78sbnT9/3kgyP/74o30sp7NmlLNjx47miSeeuOnzXPF+uDHr3r17jSSze/du+1hKSooJDQ01H3zwgX3MFVlPnjxpJJlVq1ZlOs8XX3xhfHx8THJysn0sK+8bZ8soa1a2WW7JGhgYaD755BOH+fLnz+/wHnDF74CQkBDz4YcfOoytWLHCSDJnz55NN78rMqa5Meu2bdvMPffcY44dO5bhfnbFvs9OHLFzEytXrlRYWJhKliypf/3rXzp9+rRLcsTGxqpFixZq1KhRhtPnzJmjggULqmzZsnr11VeVkJCQwwnTS0pK0qeffqoePXo4/I8tt2WNi4vT8ePHHbZtcHCwqlWrpnXr1jnMO3r0aBUoUEAPPvig3nrrLV29ejWn4zpISkrS9OnTFRwcrPLlyztMc2XW1NRUff/997rvvvvUtGlThYWFqVq1ahl+XOvq90NiYqIkORxJ9PDwkK+vr1avXu3SrGkfsebPn/+m8wQFBcnLy/H2qLGxsSpYsKCqVq2qGTNmyGTzHbYyy5qVbZYbstaoUUOff/65zpw5o9TUVH322We6cuWK6tWr5/DcnPq5SklJ0WeffaZLly6pevXqt/XcnP7ZzyhrQkKCOnfurMmTJ2f6HfNSzu/7bOXiYokbKIP/OcybN88sXLjQ7Ny508yfP9+ULl3aVKlSxVy9ejVHs82bN8+ULVvWXL582RiT/n/B77//vlm8eLHZuXOn+fTTT80999xj2rRpk6MZM/L5558bT09P89dff9nHckPWG/f1mjVrjCRz9OhRh/nat29vOnToYH/8zjvvmBUrVpgdO3aYqVOnmnz58pkBAwbkaNY03377rQkMDDQ2m80UKVLEbNy40WF6Tme9MWfa/9ADAgLMuHHjzLZt28yoUaOMzWYzK1eutM/nivfDjVmTkpJMZGSkad++vTlz5oxJTEw0o0ePNpJMkyZNXJY1JSXFtGjRwtSsWTPTeU6dOmUiIyPNa6+95jA+fPhws3r1arN161YzevRo4+vrayZOnJjjWbOyzXJL1rNnz5omTZoYScbLy8sEBQWZJUuWOMyTEz9XO3fuNIGBgcbT09MEBweb77//Pt08Nztil5M/+zfL2rNnT/P000/bH2f0uyyn9312o9jlMpn9A3q9gwcPpvvIK7sdPnzYhIWFmR07dtjHbvXxxvLly40kc+DAgRxImLkmTZqYli1b3nQeV2S902J3o48++sh4eXmZK1euZFfUTN+XFy9eNPv37zfr1q0zPXr0MNHR0ebEiRMuy3pjzr/++stIMo8//rjDfK1atTKdOnXKdDk58X7IaJtu3rzZlC9f3kgynp6epmnTpqZ58+amWbNmLsv63HPPmaioKHPkyJEMp58/f95UrVrVNGvWzCQlJd10WYMGDTJFixbNjpjGmFtnTZOVbeaqrL179zZVq1Y1P/74o9m+fbsZOnSoCQ4ONjt37sx0Wdnxc5WYmGj2799vNm/ebF555RVTsGBB8+uvvzrMc7NilxMZb5V14cKFpnjx4ubChQv2ebPyb2x27/vsRrHLZbLypjPGmIIFC5pp06Zlf6D/b/78+fZ/bNL+SDI2m814enpmePTw4sWLRpJZvHhxjuW80aFDh4yHh4dZsGDBTedzRdYb93VaYd+2bZvDfHXq1DF9+/bNdDm7d+82kszvv/+eTUmz/r4sXry4efPNNzOdnt1Zb8yZmJhovLy8zIgRIxzme+mll0yNGjUyXU5OvB9utk3PnTtnTp48aYwxpmrVqub555/PdDnZmTU2NtYULVrU/PHHHxlOj4+PN9WrVzcNGza0H8m/me+++85IypZ/3G+V9XpZ2WauyHrgwIF051kaY0zDhg1Nr169Ml1eTvwOaNiwoenZs6fD2O0Uu5zImCYta79+/ez/Rl3/75aHh4epW7dups/Pzn2fE9ziu2Lh6H//+59Onz6twoUL59hrNmzYULt27XIY6969u0qVKqWXX35Znp6e6Z6zfft2ScrRnDeaOXOmwsLC1KJFi5vOlxuyFitWTOHh4Vq+fLkqVKggSYqPj9eGDRv0r3/9K9Pnbd++XR4eHgoLC8uhpJlLTU21nyuWkZzO6uPjoypVqqS7pcS+ffsUFRWV6fNc/X4IDg6WJO3fv1+bN2/WiBEjMp03O7IaY9SnTx/Nnz9fK1euVLFixdLNEx8fr6ZNm8rX11fffPNNuquMM8saEhLi1O/qzErWjHJIN99mrsiadt6fh4fj6e+enp5KTU29adbs/rm61c/2reTkz35a1mHDhumZZ55xmPbAAw9o/PjxatWqVabPz459n5ModrnAxYsXdeDAAfvjuLg4bd++Xfnz51f+/Pk1bNgwtWvXTuHh4Tp48KBeeuklFS9eXE2bNs2xjHnz5lXZsmUdxgIDA1WgQAGVLVtWBw8e1Ny5c/Xwww+rQIEC2rlzpwYMGKA6depkeFuUnJCamqqZM2eqa9euDid0uzLrzfZ1ZGSk+vfvr5EjR6pEiRIqVqyYBg0apCJFitgvvV+3bp02bNig+vXrK2/evFq3bp0GDBigJ554QiEhITmWtUCBAnrjjTf0yCOPqHDhwvr77781efJk/fXXX/ZbyuRU1ltt0xdffFEdO3ZUnTp1VL9+fS1evFjffvutVq5cKSln3w+3yvrll18qNDRUkZGR2rVrl/r166fWrVurSZMmOZo1NjZWc+fO1cKFC5U3b14dP35c0rXC6e/vr/j4eDVp0kQJCQn69NNPFR8fr/j4eElSaGioPD099e233+rEiRN66KGH5Ofnp2XLlunNN9/UCy+84LScWcmalW2WW7KWKlVKxYsXV69evfT222+rQIECWrBggZYtW6bvvvtOUs78XL366qtq3ry5IiMjdeHCBc2dO1crV67UkiVLJF27197x48ft7+Vdu3Ypb968ioyMVP78+XP099TNsoaHh2d4wURkZKS9VOfUvs9RLj5iCPN/h7Nv/NO1a1eTkJBgmjRpYkJDQ423t7eJiooyzz77rDl+/LirYzucY3f48GFTp04dkz9/fuPr62uKFy9uXnzxRXP+/HmX5VuyZImRZPbu3esw7sqsN9vXxly75cmgQYNMoUKFjK+vr2nYsKFD/i1btphq1aqZ4OBg4+fnZ0qXLm3efPPNbPnI4GZZL1++bNq0aWOKFClifHx8TOHChc0jjzzicPFETmW91TY15tr5PcWLFzd+fn6mfPnyDh/N5+T74VZZJ06caIoWLWq8vb1NZGSk+c9//mMSExNzPGtGGSWZmTNn3nQ9JJm4uDhjjDGLFi0yFSpUMHny5DGBgYGmfPnyZtq0aSYlJSVHs2Zlm+WWrMYYs2/fPtO2bVsTFhZmAgICTLly5Rxuf5ITP1c9evQwUVFRxsfHx4SGhpqGDRuapUuX2qcPGTLkpuuRk7+nbpX1RrrhFIic2vc5yWaMO1/TCwAAgDTcxw4AAMAiKHYAAAAWQbEDAACwCIodAACARVDsAAAALIJiBwAAYBEUOwAAAIug2AEAAFgExQ6Ayxhj1LNnT+XPn182m83+HZ7/JIcOHfrHrjsA56PYAXCZxYsXa9asWfruu+907NixdN9HnMYYo0aNGmX4/chTpkxRvnz59L///S9bMjZt2lSenp7atGlTtiz/TnTr1k02m03PPfdcummxsbGy2Wzq1q1bzgcD4HIUOwDZIikp6ZbzHDx4UIULF1aNGjUUHh4uLy+vDOez2WyaOXOmNmzYoPfff98+HhcXp5deekmTJk1S0aJFnZY9zeHDh7V27Vr17t1bM2bMcPry70ZERIQ+++wzXb582T525coVzZ07V5GRkS5MBsCVKHYAnKJevXrq3bu3+vfvr4IFC6pp06bavXu3mjdvrjx58qhQoUJ68skn9ffff0u6dtSpT58+Onz4sGw2m6Kjo2+6/IiICE2cOFEvvPCC4uLiZIzR008/rSZNmqhz5856+umnVaxYMfn7+6tkyZKaOHGi/bm7d++Wh4eHTp06JUk6c+aMPDw81KlTJ/s8I0eOVK1atRxec+bMmWrZsqX+9a9/ad68eQ4lKm2d+/btq5deekn58+dXeHi4hg4d6jDP77//rlq1asnPz09lypTRjz/+KJvNpgULFmS6rjfbbmkqVqyoiIgIff311/axr7/+WpGRkXrwwQcd5k1NTdWoUaPs26d8+fL673//a5+ekpJy0+0nXdtfrVu31ttvv63ChQurQIECio2NVXJycqbrASDnUewAOM3HH38sHx8frVmzRqNHj1aDBg304IMPavPmzVq8eLFOnDihDh06SJImTpyo4cOHq2jRojp27FiWPurs2rWrGjZsqB49eui9997T7t279f777ys1NVVFixbVl19+qT179mjw4MF67bXX9MUXX0iS7r//fhUoUECrVq2SJP3yyy8OjyVp1apVqlevnv2xMUYzZ87UE088oVKlSql48eIOZej6dQ4MDNSGDRs0duxYDR8+XMuWLZN0rTC1bt1aAQEB2rBhg6ZPn67XX3/9put47ty5m2636/Xo0UMzZ860P54xY4a6d++ebr5Ro0bpk08+0bRp0/Trr79qwIABeuKJJ+zrf6vtl2bFihU6ePCgVqxYoY8//lizZs3SrFmzbro+AHKYAQAnqFu3rnnwwQftj0eMGGGaNGniMM+RI0eMJLN3715jjDHjx483UVFRt/U6J06cMAULFjQeHh5m/vz5mc4XGxtr2rVrZ3/ctm1bExsba4wxpn///ubFF180ISEh5rfffjNJSUkmICDALF261D7/0qVLTWhoqElOTrZnrVu3brp1rlWrlsNYlSpVzMsvv2yMMWbRokXGy8vLHDt2zD592bJlRpI9e1xcnJFktm3bZozJ2nbr2rWrefTRR83JkyeNr6+vOXTokDl06JDx8/Mzp06dMo8++qjp2rWrMcaYK1eumICAALN27VqHZT799NPm8ccfz/L269q1q4mKijJXr161j7Vv39507Ngx02UAyHkZn9ACAHegUqVK9r/v2LFDK1asUJ48edLNd/DgQd1333139BphYWHq1auXFixYoNatW9vHJ0+erBkzZujw4cO6fPmykpKSVKFCBfv0unXravr06ZKuHZ178803tW/fPq1cuVJnzpxRcnKyatasaZ9/xowZ6tixo/28v8cff1wvvviiDh48qJiYGPt85cqVc8hXuHBhnTx5UpK0d+9eRUREKDw83D69atWqN12/29luoaGhatGihWbNmiVjjFq0aKGCBQs6POfAgQNKSEhQ48aNHcaTkpIcPrK91faTrh359PT0dFjXXbt23XR9AOQsih0ApwkMDLT//eLFi2rVqpXGjBmTbr7ChQvf1et4eXk5XGjx2Wef6YUXXtA777yj6tWrK2/evHrrrbe0YcMG+zz16tVT//79tX//fu3Zs0e1atXS77//rpUrV+rs2bOqXLmyAgICJF07B2/+/PlKTk7W1KlT7ctISUnRjBkz9MYbb9jHvL29HbLZbDalpqbe8brd7nbr0aOHevfuLelaOctoeZL0/fff65577nGY5uvrKylr209y/roCcD6KHYBsUbFiRX311VeKjo7O9GpXZ1mzZo1q1Kih559/3j528OBBh3keeOABhYSEaOTIkapQoYLy5MmjevXqacyYMTp79qzD+XVz5sxR0aJF013gsHTpUr3zzjsaPny4w5GrzJQsWVJHjhzRiRMnVKhQIUm65bmEt7vdmjVrpqSkJNlstgxvB1OmTBn5+vrq8OHDqlu3bobLyMr2A+AeuHgCQLaIjY3VmTNn9Pjjj2vTpk06ePCglixZou7duyslJcWpr1WiRAlt3rxZS5Ys0b59+zRo0KB0Bcpms6lOnTqaM2eOvcSVK1dOiYmJWr58uUPp+eijj/TYY4+pbNmyDn+efvpp/f3331q8eHGWcjVu3FgxMTHq2rWrdu7cqTVr1ug///mPPU9Gbne7eXp66rffftOePXsyLJt58+bVCy+8oAEDBujjjz/WwYMHtXXrVk2aNEkff/xxlrcfAPdAsQOQLYoUKaI1a9YoJSVFTZo00QMPPKD+/fsrX7588vBw7q+eXr16qW3bturYsaOqVaum06dPOxx9SlO3bl2lpKTYi52Hh4fq1Kkjm81mP79uy5Yt2rFjh9q1a5fu+cHBwWrYsKE++uijLOXy9PTUggULdPHiRVWpUkXPPPOM/apYPz+/DJ9zJ9stKChIQUFBmeYYMWKEBg0apFGjRql06dJq1qyZvv/+exUrVkxS1rcfgNzPZowxrg4BAP8Ua9asUa1atXTgwAGHizAAwBkodgCQjebPn688efKoRIkSOnDggPr166eQkBCtXr3a1dEAWBAXTwDIFQ4fPqwyZcpkOn3Pnj1u+VVZFy5c0Msvv6zDhw+rYMGCatSokd555x1XxwJgURyxA5ArXL16VYcOHcp0ek5cXQsA7o5iBwAAYBFcFQsAAGARFDsAAACLoNgBAABYBMUOAADAIih2AAAAFkGxAwAAsAiKHQAAgEVQ7AAAACzi/wHNoTxxrWVY+QAAAABJRU5ErkJggg==", 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -3543,7 +3543,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -3748,7 +3748,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2168cd5405004d16b8116014d42d5fe5", + "model_id": "df804baf4f2041b18c2dc67189a83f67", "version_major": 2, "version_minor": 0 }, @@ -3870,7 +3870,7 @@ { "data": { "text/markdown": [ - "{'ref': 'SMV7', 'ref_ws_col': 'ref_ws_est_blend', 'distance_m': 591.1178519927024, 'bearing_deg': 190.23567745705736, 'ref_max_northing_error_v_reanalysis': np.float64(2.6590754551807976), 'ref_max_northing_error_v_wf': np.float64(2.842170943040401e-14), 'ref_max_ws_drift': np.float64(0.08697706942338845), 'ref_max_ws_drift_pp_period': np.float64(0.08697706942338845), 'ref_powercurve_shift': np.float64(0.003117456887993697), 'ref_rpm_shift': np.float64(0.0015313319638985412), 'ref_pitch_shift': np.float64(-0.05548555519736481), 'detrend_pre_r2_improvement': np.float64(0.09621188863947527), 'detrend_post_r2_improvement': np.float64(0.11890364717818414), 'mean_power_pre': np.float64(955.493497245509), 'mean_power_post': np.float64(993.6911992736077), 'mean_test_yaw_offset_pre': np.float64(-2.2725466102034675), 'mean_test_yaw_offset_post': np.float64(-2.876981850327039), 'mean_test_yaw_offset_command_pre': np.float64(0.0), 'mean_test_yaw_offset_command_post': np.float64(0.0), 'mean_ref_yaw_offset_command_pre': np.float64(0.0), 'test_ref_warning_counts': 0, 'time_calculated': Timestamp('2024-09-26 13:15:57.997400+0000', tz='UTC'), 'uplift_frc': np.float64(0.030879346731271313), 'unc_one_sigma_frc': np.float64(0.01167847006525424), 't_value_one_sigma': np.float64(1.0006277462668354), 'missing_bins_unc_scale_factor': 1, 'pp_valid_hours_pre': np.float64(133.0), 'pp_valid_hours_post': np.float64(137.16666666666669), 'pp_valid_hours': np.float64(270.1666666666667), 'pp_data_coverage': np.float64(0.11567829872261472), 'pp_invalid_bin_count': np.int64(16), 'uplift_noadj_frc': np.float64(0.030509447623790466), 'unc_one_sigma_noadj_frc': np.float64(0.01004760180633488), 'poweronly_uplift_frc': np.float64(0.029990866525649328), 'reversed_uplift_frc': np.float64(0.03073066474061102), 'reversal_error': np.float64(0.0007397982149616941), 'unc_one_sigma_lowerbound_frc': np.float64(0.00036989910748084706), 'unc_one_sigma_bootstrap_frc': np.float64(0.01167847006525424), 'uplift_p5_frc': np.float64(0.050088720575348945), 'uplift_p95_frc': np.float64(0.01166997288719368), 'wind_up_version': '0.1.9', 'test_wtg': 'SMV5', 'test_pw_col': 'test_pw_clipped', 'lt_wtg_hours_raw': 0, 'lt_wtg_hours_filt': 0, 'test_max_ws_drift': np.float64(0.10726609831004863), 'test_max_ws_drift_pp_period': np.float64(0.10726609831004863), 'test_powercurve_shift': np.float64(-0.005678000921447213), 'test_rpm_shift': np.float64(0.0013951853610039144), 'test_pitch_shift': np.float64(-0.02783487184623068), 'preprocess_warning_counts': 0, 'test_warning_counts': 0}" + "{'ref': 'SMV7', 'ref_ws_col': 'ref_ws_est_blend', 'distance_m': 591.1178519927024, 'bearing_deg': 190.23567745705736, 'ref_max_northing_error_v_reanalysis': np.float64(2.6590754551807976), 'ref_max_northing_error_v_wf': np.float64(2.842170943040401e-14), 'ref_max_ws_drift': np.float64(0.08697706942338845), 'ref_max_ws_drift_pp_period': np.float64(0.08697706942338845), 'ref_powercurve_shift': np.float64(0.003117456887993697), 'ref_rpm_shift': np.float64(0.0015313319638985412), 'ref_pitch_shift': np.float64(-0.05548555519736481), 'detrend_pre_r2_improvement': np.float64(0.09621188863947527), 'detrend_post_r2_improvement': np.float64(0.11890364717818414), 'mean_power_pre': np.float64(955.493497245509), 'mean_power_post': np.float64(993.6911992736077), 'mean_test_yaw_offset_pre': np.float64(-2.2725466102034675), 'mean_test_yaw_offset_post': np.float64(-2.876981850327039), 'mean_test_yaw_offset_command_pre': np.float64(0.0), 'mean_test_yaw_offset_command_post': np.float64(0.0), 'mean_ref_yaw_offset_command_pre': np.float64(0.0), 'test_ref_warning_counts': 0, 'time_calculated': Timestamp('2024-11-04 14:55:21.795722+0000', tz='UTC'), 'uplift_frc': np.float64(0.030879346731271313), 'unc_one_sigma_frc': np.float64(0.01167847006525424), 't_value_one_sigma': np.float64(1.0006277462668354), 'missing_bins_unc_scale_factor': 1, 'pp_valid_hours_pre': np.float64(133.0), 'pp_valid_hours_post': np.float64(137.16666666666669), 'pp_valid_hours': np.float64(270.1666666666667), 'pp_data_coverage': np.float64(0.11567829872261472), 'pp_invalid_bin_count': np.int64(16), 'uplift_noadj_frc': np.float64(0.030509447623790466), 'unc_one_sigma_noadj_frc': np.float64(0.01004760180633488), 'poweronly_uplift_frc': np.float64(0.029990866525649328), 'reversed_uplift_frc': np.float64(0.03073066474061102), 'reversal_error': np.float64(0.0007397982149616941), 'unc_one_sigma_lowerbound_frc': np.float64(0.00036989910748084706), 'unc_one_sigma_bootstrap_frc': np.float64(0.01167847006525424), 'uplift_p5_frc': np.float64(0.050088720575348945), 'uplift_p95_frc': np.float64(0.01166997288719368), 'wind_up_version': '0.1.9', 'test_wtg': 'SMV5', 'test_pw_col': 'test_pw_clipped', 'lt_wtg_hours_raw': 0, 'lt_wtg_hours_filt': 0, 'test_max_ws_drift': np.float64(0.10726609831004863), 'test_max_ws_drift_pp_period': np.float64(0.10726609831004863), 'test_powercurve_shift': np.float64(-0.005678000921447213), 'test_rpm_shift': np.float64(0.0013951853610039144), 'test_pitch_shift': np.float64(-0.02783487184623068), 'preprocess_warning_counts': 0, 'test_warning_counts': 0}" ], "text/plain": [ "<IPython.core.display.Markdown object>" @@ -3999,7 +3999,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.4" + "version": "3.12.5" } }, "nbformat": 4, diff --git a/pyproject.toml b/pyproject.toml index 2e83e4a..14076a0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "res-wind-up" -version = "0.1.9" +version = "0.1.10" authors = [ { name = "Alex Clerc", email = "[email protected]" } ] diff --git a/wind_up/scada_funcs.py b/wind_up/scada_funcs.py index 187afa4..48e4802 100644 --- a/wind_up/scada_funcs.py +++ b/wind_up/scada_funcs.py @@ -28,7 +28,15 @@ def filter_stuck_data(df: pd.DataFrame) -> pd.DataFrame: - diffdf = df.groupby("TurbineName", observed=False).ffill().fillna(0).diff() + """ + Filter out rows where all columns (except timestamp columns) are the same as the previous row + (only where the wind is greater than a low wind threshold). + + :param df: time series where the index is a MultiIndex with levels "TurbineName" and "<your timestamp index name>" + :return: dataframe with stuck data rows set to NA + """ + timestamp_columns = [col for col, data_type in df.dtypes.to_dict().items() if "date" in data_type.name] + diffdf = df.drop(columns=timestamp_columns).groupby("TurbineName", observed=False).ffill().fillna(0).diff() stuck_data = (diffdf == 0).all(axis=1) very_low_wind_threshold = 1.5 very_low_wind = df["WindSpeedMean"] < very_low_wind_threshold	Timestamp column can be converted to type "object" which potentially causes the `.diff()` to fail https://github.com/resgroup/wind-up/blob/980f6db15b2f4f33642c807ffef19780c46fc03a/wind_up/scada_funcs.py#L31 Example error when the `.diff()` is run: `TypeError: unsupported operand type(s) for -: 'int' and 'Timestamp'`		2024-11-04T13:39:55	[]	[]
AMYPAD/AmyPET	AMYPAD__AmyPET-16	1af0de595ba4e46bcc4669402ab7562a441620c7	diff --git a/.gitignore b/.gitignore index 3eacabe1..19d59b27 100644 --- a/.gitignore +++ b/.gitignore @@ -1,11 +1,12 @@ -.py[co] +.py[cod] __pycache__/ # build -/amypad/_dist_ver.py +/amypet/_dist_ver.py /build/ /dist/ /.egg/ /.coverage* /coverage.xml +/.pytest_cache/ diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 62ffebe4..b5f649e4 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -31,9 +31,11 @@ repos: - id: flake8 args: [-j8] additional_dependencies: + - flake8-broken-line - flake8-bugbear - flake8-comprehensions - flake8-debugger + - flake8-isort - flake8-string-format - repo: https://github.com/google/yapf rev: v0.31.0 @@ -41,10 +43,10 @@ repos: - id: yapf args: [-i] - repo: https://github.com/PyCQA/isort - rev: 5.9.3 + rev: 5.10.1 hooks: - id: isort - hooks: - id: mbeautify repo: https://github.com/AMYPAD/miutil - rev: v0.9.0 + rev: v0.9.1 diff --git a/README.rst b/README.rst index 29fdb60a..5cbfab0e 100644 --- a/README.rst +++ b/README.rst @@ -1,6 +1,6 @@ \|Logo\| -AMYPAD +AMYPET ====== Amyloid imaging to prevent Alzheimer's Disease. @@ -13,8 +13,8 @@ Choose one of the following: .. code:: sh - pip install amypad # command line interface (CLI) version - pip install amypad[gui] # CLI and graphical user interface (GUI) version + pip install amypet # command line interface (CLI) version + pip install amypet[gui] # CLI and graphical user interface (GUI) version Usage @@ -22,8 +22,8 @@ Usage .. code:: sh - amypad --help # CLI version - amypad.gui # GUI version + amypet --help # CLI version + amypet.gui # GUI version .. \|Logo\| image:: https://amypad.eu/wp-content/themes/AMYPAD/images/AMYPAD_Logo.jpg diff --git a/amypad/__init__.py b/amypet/__init__.py similarity index 90% rename from amypad/__init__.py rename to amypet/__init__.py index 7b1b7acb..f7b99085 100644 --- a/amypad/__init__.py +++ b/amypet/__init__.py @@ -14,7 +14,7 @@ __version__ = "UNKNOWN" try: - __licence__ = get_distribution("amypad").get_metadata("LICENCE.md") + __licence__ = get_distribution("amypet").get_metadata("LICENCE.md") except (DistributionNotFound, FileNotFoundError): try: __licence__ = (Path(__file__).parent.parent / "LICENCE.md").read_text() diff --git a/amypad/__main__.py b/amypet/__main__.py similarity index 100% rename from amypad/__main__.py rename to amypet/__main__.py diff --git a/amypad/centiloid/__init__.py b/amypet/centiloid/__init__.py similarity index 100% rename from amypad/centiloid/__init__.py rename to amypet/centiloid/__init__.py diff --git a/amypad/centiloid/f_1CorregisterEstimate.m b/amypet/centiloid/f_1CorregisterEstimate.m similarity index 100% rename from amypad/centiloid/f_1CorregisterEstimate.m rename to amypet/centiloid/f_1CorregisterEstimate.m diff --git a/amypad/centiloid/f_2CorregisterEstimate.m b/amypet/centiloid/f_2CorregisterEstimate.m similarity index 100% rename from amypad/centiloid/f_2CorregisterEstimate.m rename to amypet/centiloid/f_2CorregisterEstimate.m diff --git a/amypad/centiloid/f_3Segment.m b/amypet/centiloid/f_3Segment.m similarity index 100% rename from amypad/centiloid/f_3Segment.m rename to amypet/centiloid/f_3Segment.m diff --git a/amypad/centiloid/f_4Normalise.m b/amypet/centiloid/f_4Normalise.m similarity index 100% rename from amypad/centiloid/f_4Normalise.m rename to amypet/centiloid/f_4Normalise.m diff --git a/amypad/centiloid/f_Quant_centiloid.m b/amypet/centiloid/f_Quant_centiloid.m similarity index 100% rename from amypad/centiloid/f_Quant_centiloid.m rename to amypet/centiloid/f_Quant_centiloid.m diff --git a/amypad/centiloid/f_acpcReorientation.m b/amypet/centiloid/f_acpcReorientation.m similarity index 100% rename from amypad/centiloid/f_acpcReorientation.m rename to amypet/centiloid/f_acpcReorientation.m diff --git a/amypad/centiloid/f_noNaN.m b/amypet/centiloid/f_noNaN.m similarity index 100% rename from amypad/centiloid/f_noNaN.m rename to amypet/centiloid/f_noNaN.m diff --git a/amypad/cli.py b/amypet/cli.py similarity index 100% rename from amypad/cli.py rename to amypet/cli.py diff --git a/amypad/config_icon.png b/amypet/config_icon.png similarity index 100% rename from amypad/config_icon.png rename to amypet/config_icon.png diff --git a/amypad/gui.py b/amypet/gui.py similarity index 94% rename from amypad/gui.py rename to amypet/gui.py index d2c89c64..bec07df3 100755 --- a/amypad/gui.py +++ b/amypet/gui.py @@ -232,15 +232,15 @@ def print_not_none(value, *kwargs): # progress_expr="float(percent or 0)", # hide_progress_msg=True, # richtext_controls=True, -@Gooey(default_size=(768, 768), program_name="amypad", sidebar_title="pipeline", +@Gooey(default_size=(768, 768), program_name="amypet", sidebar_title="pipeline", image_dir=resource_filename(__name__, ""), show_restart_button=False, header_bg_color="#ffffff", sidebar_bg_color="#a3b5cd", body_bg_color="#a3b5cd", footer_bg_color="#2a569f", terminal_font_family="monospace", menu=[{ "name": "Help", "items": [{ "type": "Link", "menuTitle": "🌐 View source (online)", - "url": "https://github.com/AMYPAD/amypad"}, { - "type": "AboutDialog", "menuTitle": "🔍 About", "name": "AMYPAD Pipeline", - "description": "GUI to run AMYPAD tools", "version": __version__, + "url": "https://github.com/AMYPAD/amypet"}, { + "type": "AboutDialog", "menuTitle": "🔍 About", "name": "AMYPET Pipeline", + "description": "GUI to run AMYPET tools", "version": __version__, "copyright": "2021", "website": "https://amypad.eu", "developer": "https://github.com/AMYPAD", "license": __licence__}]}]) def main(args=None, gui_mode=True): @@ -249,9 +249,9 @@ def main(args=None, gui_mode=True): import niftypad.api import niftypad.models - from amypad import centiloid, imscroll, imtrimup + from amypet import centiloid, imscroll, imtrimup - parser = fix_subparser(MyParser(prog=None if gui_mode else "amypad"), gui_mode=gui_mode) + parser = fix_subparser(MyParser(prog=None if gui_mode else "amypet"), gui_mode=gui_mode) sub_kwargs = {} if sys.version_info[:2] >= (3, 7): sub_kwargs["required"] = True @@ -290,11 +290,11 @@ def argparser(prog, description=None, epilog=None, formatter_class=None): <src> : Input file/folder [default: FileChooser] Options: - -d PATH, --dst PATH : Output file/folder (default: input folder) - [default: FileChooser] - -m MODEL, --model MODEL : model [default: srtmb_basis] - -p FILE, --params FILE : config file hint (relative to `--input`) - [default: FileChooser] + --dst PATH : Output file/folder (default: input folder) + [default: FileChooser] + --model MODEL : model [default: srtmb_basis] + --params FILE : config file hint (relative to `src` input). + Default: search for {config,params}.{yaml,json}. --w W : (default: None) --r1 R1 : [default: 0.905:float] --k2p K2P : [default: 0.000250:float] @@ -333,7 +333,7 @@ def argparser(prog, description=None, epilog=None, formatter_class=None): args = [i for i in args if i not in ("--dry-run",)] # strip args if gui_mode: - print(" ".join([Path(sys.executable).name, "-m amypad"] + args)) + print(" ".join([Path(sys.executable).name, "-m amypet"] + args)) if opts.dry_run: pass elif hasattr(opts, "main__"): # Cmd diff --git a/amypad/imscroll.py b/amypet/imscroll.py similarity index 100% rename from amypad/imscroll.py rename to amypet/imscroll.py diff --git a/amypad/imtrimup.py b/amypet/imtrimup.py similarity index 100% rename from amypad/imtrimup.py rename to amypet/imtrimup.py diff --git a/amypad/program_icon.png b/amypet/program_icon.png similarity index 100% rename from amypad/program_icon.png rename to amypet/program_icon.png diff --git a/amypad/utils.py b/amypet/utils.py similarity index 100% rename from amypad/utils.py rename to amypet/utils.py diff --git a/pyproject.toml b/pyproject.toml index a77e1bab..f49fc768 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -2,5 +2,5 @@ requires = ["setuptools>=42", "wheel", "setuptools_scm[toml]>=3.4"] [tool.setuptools_scm] -write_to = "amypad/_dist_ver.py" +write_to = "amypet/_dist_ver.py" write_to_template = "__version__ = '{version}'\n" diff --git a/setup.cfg b/setup.cfg index 27218c72..2a100733 100644 --- a/setup.cfg +++ b/setup.cfg @@ -1,5 +1,5 @@ [metadata] -name=amypad +name=amypet description=Amyloid imaging to prevent Alzheimer's Disease long_description=file: README.rst long_description_content_type=text/x-rst @@ -7,14 +7,14 @@ license=MPL 2.0 license_file=LICENCE.md url=https://amypad.eu project_urls= - Repository=https://github.com/AMYPAD/amypad - Changelog=https://github.com/AMYPAD/amypad/releases - Documentation=https://github.com/AMYPAD/amypad/#amypad + Repository=https://github.com/AMYPAD/amypet + Changelog=https://github.com/AMYPAD/amypet/releases + Documentation=https://github.com/AMYPAD/amypet/#amypet maintainer=Casper da Costa-Luis [email protected] keywords=pet, alzheimers platforms=any -provides=amypad +provides=amypet classifiers= Development Status :: 3 - Alpha Intended Audience :: Developers @@ -61,8 +61,8 @@ gui=Gooey>=1.0.8 niftypet=niftypet>=0.0.1 [options.entry_points] console_scripts= - amypad=amypad.cli:main - amypad.gui=amypad.gui:main + amypet=amypet.cli:main + amypet.gui=amypet.gui:main [options.packages.find] exclude=tests [options.package_data] @@ -87,12 +87,12 @@ split_before_closing_bracket=False [isort] profile=black line_length=99 -known_first_party=amypad,tests +known_first_party=amypet,tests [tool:pytest] timeout=10 log_level=INFO python_files=tests/test_.py -addopts=-v --tb=short -rxs -W=error -n=auto --durations=0 --durations-min=1 --cov=amypad --cov-report=term-missing --cov-report=xml +addopts=-v --tb=short -rxs -W=error -n=auto --durations=0 --durations-min=1 --cov=amypet --cov-report=term-missing --cov-report=xml filterwarnings= ignore:numpy.ufunc size changed.*:RuntimeWarning	rename to amypet - AMYPAD = org - amypet = package		2021-12-01T08:33:28	[]	[]
AMYPAD/AmyPET	AMYPAD__AmyPET-3	5314ca01168ec3eeaa3c953e45addabba7edeceb	diff --git a/.github/workflows/comment-bot.yml b/.github/workflows/comment-bot.yml new file mode 100644 index 00000000..b44ee7ba --- /dev/null +++ b/.github/workflows/comment-bot.yml @@ -0,0 +1,52 @@ +name: Comment Bot +on: + issue_comment: + types: [created] + pull_request_review_comment: + types: [created] +jobs: + tag: # /tag <tagname> <commit> + if: startsWith(github.event.comment.body, '/tag ') + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v2 + - name: React Seen + uses: actions/github-script@v2 + with: + script: \| + const perm = await github.repos.getCollaboratorPermissionLevel({ + owner: context.repo.owner, repo: context.repo.repo, + username: context.payload.comment.user.login}) + post = (context.eventName == "issue_comment" + ? github.reactions.createForIssueComment + : github.reactions.createForPullRequestReviewComment) + if (!["admin", "write"].includes(perm.data.permission)){ + post({ + owner: context.repo.owner, repo: context.repo.repo, + comment_id: context.payload.comment.id, content: "laugh"}) + throw "Permission denied for user " + context.payload.comment.user.login + } + post({ + owner: context.repo.owner, repo: context.repo.repo, + comment_id: context.payload.comment.id, content: "eyes"}) + github-token: ${{ secrets.GH_TOKEN }} + - name: Tag Commit + run: \| + git clone https://${GITHUB_TOKEN}@github.com/${GITHUB_REPOSITORY} repo + git -C repo tag $(echo "$BODY" \| awk '{print $2" "$3}') + git -C repo push --tags + rm -rf repo + env: + BODY: ${{ github.event.comment.body }} + GITHUB_TOKEN: ${{ secrets.GH_TOKEN }} + - name: React Success + uses: actions/github-script@v2 + with: + script: \| + post = (context.eventName == "issue_comment" + ? github.reactions.createForIssueComment + : github.reactions.createForPullRequestReviewComment) + post({ + owner: context.repo.owner, repo: context.repo.repo, + comment_id: context.payload.comment.id, content: "rocket"}) + github-token: ${{ secrets.GH_TOKEN }} diff --git a/.gitignore b/.gitignore index 753afde1..3eacabe1 100644 --- a/.gitignore +++ b/.gitignore @@ -1,9 +1,11 @@ -.py[cod] +.py[co] __pycache__/ + +# build /amypad/_dist_ver.py -/.eggs/ -/.egg-info/ /build/ /dist/ +/.egg/ + /.coverage /coverage.xml diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 00000000..afba6eee --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,46 @@ +default_language_version: + python: python3 +repos: +- repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.0.1 + hooks: + - id: check-added-large-files + - id: check-case-conflict + - id: check-docstring-first + - id: check-executables-have-shebangs + - id: check-toml + - id: check-merge-conflict + - id: check-yaml + - id: debug-statements + - id: end-of-file-fixer + - id: mixed-line-ending + - id: sort-simple-yaml + - id: trailing-whitespace +- repo: local + hooks: + - id: todo + name: Check TODO + language: pygrep + args: [-i] + entry: TODO + types: [text] + exclude: ^(.pre-commit-config.yaml\|.github/workflows/test.yml)$ +- repo: https://gitlab.com/pycqa/flake8 + rev: 3.9.2 + hooks: + - id: flake8 + args: [-j8] + additional_dependencies: + - flake8-bugbear + - flake8-comprehensions + - flake8-debugger + - flake8-string-format +- repo: https://github.com/google/yapf + rev: v0.31.0 + hooks: + - id: yapf + args: [-i] +- repo: https://github.com/PyCQA/isort + rev: 5.9.3 + hooks: + - id: isort diff --git a/LICENCE.md b/LICENCE.md index 957e9cbb..53871088 100644 --- a/LICENCE.md +++ b/LICENCE.md @@ -1,13 +1,7 @@ -Copyright 2020 AMYPAD +Copyright (c) 2021 AMYPAD -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at +Mozilla Public License Version 2.0 (MPL-2.0) - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. +This Source Code Form is subject to the terms of the Mozilla Public +License, v. 2.0. If a copy of the MPL was not distributed with this +file, You can obtain one at http://mozilla.org/MPL/2.0/. diff --git a/README.rst b/README.rst index b6b87b51..29fdb60a 100644 --- a/README.rst +++ b/README.rst @@ -3,7 +3,28 @@ AMYPAD ====== -Amyloid imaging to prevent Alzheimer's Disease +Amyloid imaging to prevent Alzheimer's Disease. + + +Install +------- + +Choose one of the following: + +.. code:: sh + + pip install amypad # command line interface (CLI) version + pip install amypad[gui] # CLI and graphical user interface (GUI) version + + +Usage +----- + +.. code:: sh + + amypad --help # CLI version + amypad.gui # GUI version + .. \|Logo\| image:: https://amypad.eu/wp-content/themes/AMYPAD/images/AMYPAD_Logo.jpg :target: https://amypad.eu diff --git a/amypad/__init__.py b/amypad/__init__.py index 812265af..7b1b7acb 100644 --- a/amypad/__init__.py +++ b/amypad/__init__.py @@ -1,3 +1,7 @@ +from pathlib import Path + +from pkg_resources import DistributionNotFound, get_distribution + # version detector. Precedence: installed dist, git, 'UNKNOWN' try: from ._dist_ver import __version__ @@ -8,3 +12,11 @@ __version__ = get_version(root="..", relative_to=__file__) except (ImportError, LookupError): __version__ = "UNKNOWN" + +try: + __licence__ = get_distribution("amypad").get_metadata("LICENCE.md") +except (DistributionNotFound, FileNotFoundError): + try: + __licence__ = (Path(__file__).parent.parent / "LICENCE.md").read_text() + except FileNotFoundError: + __licence__ = "MPL-2.0" diff --git a/amypad/__main__.py b/amypad/__main__.py new file mode 100755 index 00000000..3bb6d4b2 --- /dev/null +++ b/amypad/__main__.py @@ -0,0 +1,4 @@ +#!/usr/bin/env python3 +from .cli import main + +main() diff --git a/amypad/cli.py b/amypad/cli.py new file mode 100755 index 00000000..7a3c47b1 --- /dev/null +++ b/amypad/cli.py @@ -0,0 +1,12 @@ +#!/usr/bin/env python3 +import sys +from functools import partial + +if "--ignore-gooey" not in sys.argv: + sys.argv.insert(1, "--ignore-gooey") # must be before gooey import +from .gui import main as gui_main + +main = partial(gui_main, gui_mode=False) + +if __name__ == "__main__": # pragma: no cover + main() diff --git a/amypad/config_icon.png b/amypad/config_icon.png new file mode 100644 index 00000000..5e8a23a5 Binary files /dev/null and b/amypad/config_icon.png differ diff --git a/amypad/gui.py b/amypad/gui.py new file mode 100755 index 00000000..d819d8c1 --- /dev/null +++ b/amypad/gui.py @@ -0,0 +1,352 @@ +#!/usr/bin/env python3 +import logging +import re +import sys +from argparse import SUPPRESS, ArgumentParser, RawDescriptionHelpFormatter +from functools import partial +from pathlib import Path +from subprocess import PIPE, Popen +from textwrap import dedent +from weakref import WeakSet + +import shtab +from argopt import argopt +from pkg_resources import resource_filename + +try: + from . import __licence__, __version__ +except ImportError: + __version__, __licence__ = "", "MPL-2.0" + +log = logging.getLogger(__name__) +WIDGETS = ( + "FileChooser", + "MultiFileChooser", + "DirChooser", + "FileSaver", + "MultiFileSaver", + "Slider", +) +RE_DEFAULT = re.compile(f"\\[default: (None:.?\|{'\|'.join(WIDGETS)})\\]", flags=re.M) +RE_PRECOLON = re.compile(r"^\s:\s", flags=re.M) +ENCODING = sys.getfilesystemencoding() + + +def patch_argument_kwargs(kwargs, gooey=True): + kwargs = kwargs.copy() + if "help" in kwargs: + kwargs["help"] = RE_PRECOLON.sub("", RE_DEFAULT.sub("", kwargs["help"])) + + default = kwargs.get("default", None) + if default in WIDGETS: + if gooey: + kwargs["widget"] = default + kwargs["default"] = None + elif gooey: + typ = kwargs.get("type", None) + if typ == open: + nargs = kwargs.get("nargs", 1) + if nargs and (nargs > 1 if isinstance(nargs, int) else nargs in "+"): + kwargs['widget'] = "MultiFileChooser" + else: + kwargs['widget'] = "FileChooser" + elif typ == int: + kwargs['widget'] = "IntegerField" + kwargs['gooey_options'] = {'min': 0, 'max': 1_000} + elif typ == float: + kwargs['widget'] = "DecimalField" + kwargs['gooey_options'] = {'min': -1, 'max': 1e3, 'increment': 1e-6} + + return kwargs + + +try: + from gooey import Gooey + from gooey import GooeyParser as BaseParser + + patch_argument_kwargs = partial(patch_argument_kwargs, gooey=True) +except ImportError: + BaseParser = ArgumentParser + patch_argument_kwargs = partial(patch_argument_kwargs, gooey=False) + + def Gooey(*_): + def wrapper(func): + return func + + return wrapper + + +class MyParser(BaseParser): + def add_argument(self, args, *kwargs): + kwargs = patch_argument_kwargs(kwargs) + log.debug("%r, %r", args, kwargs) + return super(MyParser, self).add_argument(args, *kwargs) + + +class CmdException(Exception): + def __init__(self, code, cmd, stdout, stderr): + super(CmdException, self).__init__( + dedent("""\ + Code {:d}: + === command === + {} + === stderr === + {}=== stdout === + {}===""").format(code, cmd, stderr, stdout)) + + +class Base(object): + _instances = WeakSet() + + def __init__(self, python_deps=None, matlab_deps=None, version=__version__): + self.python_deps = python_deps or [] + self.matlab_deps = matlab_deps or [] + self.version = version + + def __new__(cls, _, __): + self = object.__new__(cls) + cls._instances.add(self) + return self + + def __str__(self): + pydeps = "" + if self.python_deps: + pydeps = "\n - " + "\n - ".join(self.python_deps) + mdeps = "" + if self.matlab_deps: + mdeps = "\n - " + "\n - ".join(self.matlab_deps) + + return dedent("""\ + . + version: {} + python_deps:{} + matlab_deps:{}""")[2:].format(self.version, pydeps, mdeps) + + +class Cmd(Base): + def __init__( + self, + cmd, + doc, + version=None, + argparser=MyParser, + formatter_class=RawDescriptionHelpFormatter, + kwargs, + ): + """ + Args: + cmd (list): e.g. `[sys.executable, "-m", "miutil.cuinfo"]` + doc (str): an `argopt`-compatible docstring for `cmd` + version (str): optional + """ + super(Cmd, self).__init__(kwargs) + self.parser = argopt( + dedent(doc), + argparser=argparser, + formatter_class=formatter_class, + version=version, + ) + self.parser.set_defaults(main__=self.main) + self.cmd = cmd + + def __str__(self): + return dedent("""\ + {} + {}""").format(self.parser.prog, + super(Cmd, self).__str__()) + + def main(self, args, verify_args=True): + """ + Args: + args (list): list of arguments (e.g. `sys.argv[1:]`) + verify_args (bool): whether to parse args to ensure no input errors + """ + try: + if verify_args: + self.parser.parse_args(args=args) + except SystemExit as exc: + if exc.code: + raise + else: + # return check_output(self.cmd + args, stderr=STDOUT).decode("U8") + out = Popen(self.cmd + args, stdout=PIPE, stderr=PIPE) + stdout, stderr = out.communicate() + if out.returncode != 0: + raise CmdException( + out.returncode, + str(self), + stdout.decode(ENCODING), + stderr.decode(ENCODING), + ) + return stdout.decode(ENCODING) + + +class Func(Base): + def __init__( + self, + func, + doc, + version=None, + argparser=MyParser, + formatter_class=RawDescriptionHelpFormatter, + kwargs, + ): + """ + Args: + func (callable): e.g. `miutil.hasext` + doc (str): an `argopt`-compatible docstring for `func` + version (str): optional + """ + super(Func, self).__init__(kwargs) + self.parser = argopt( + dedent(doc), + argparser=argparser, + formatter_class=formatter_class, + version=version, + ) + self.parser.set_defaults(run__=func) + # self.func = func + + def __str__(self): + return dedent("""\ + {} + {}""").format(self.parser.prog, + super(Func, self).__str__()) + + +def fix_subparser(subparser, gui_mode=True): + subparser.add_argument( + "--dry-run", + action="store_true", + help="don't run command (implies print_command)" if gui_mode else SUPPRESS, + ) + return subparser + + +def print_not_none(value, kwargs): + if value is not None: + print(value, *kwargs) + + +# progress_regex="^\s(?P<percent>\d[.\d])%\|", +# progress_expr="float(percent or 0)", +# hide_progress_msg=True, +# richtext_controls=True, +@Gooey(default_size=(768, 768), program_name="amypad", sidebar_title="pipeline", + image_dir=resource_filename(__name__, ""), show_restart_button=False, + header_bg_color="#ffffff", sidebar_bg_color="#a3b5cd", body_bg_color="#a3b5cd", + footer_bg_color="#2a569f", terminal_font_family="monospace", menu=[{ + "name": "Help", "items": [{ + "type": "Link", "menuTitle": "🌐 View source (online)", + "url": "https://github.com/AMYPAD/amypad"}, { + "type": "AboutDialog", "menuTitle": "🔍 About", "name": "AMYPAD Pipeline", + "description": "GUI to run AMYPAD tools", "version": __version__, + "copyright": "2021", "website": "https://amypad.eu", + "developer": "https://github.com/AMYPAD", "license": __licence__}]}]) +def main(args=None, gui_mode=True): + logging.basicConfig(level=logging.INFO) + import miutil.cuinfo + import niftypad.api + import niftypad.models + + parser = fix_subparser(MyParser(prog=None if gui_mode else "amypad"), gui_mode=gui_mode) + sub_kwargs = {} + if sys.version_info[:2] >= (3, 7): + sub_kwargs["required"] = True + subparsers = parser.add_subparsers(help="pipeline to run", sub_kwargs) + if not gui_mode: + subparser = subparsers.add_parser("completion", help="Print tab completion scripts") + shtab.add_argument_to(subparser, "shell", parent=parser) + + def argparser(prog, description=None, epilog=None, formatter_class=None): + """handle (prog, description, epilog) => (title, help)""" + return fix_subparser( + subparsers.add_parser( + {"miutil.cuinfo": "cuinfo"}.get(prog, prog), # override + help="\n".join([description or "", epilog or ""]).strip(), + ), + gui_mode=gui_mode, + ) + + kinetic_model = Func( + niftypad.api.kinetic_model, + """\ + Kinetic modelling + + Usage: + kinetic_model [options] <src> + + Arguments: + <src> : Input file/folder [default: FileChooser] + + Options: + -d PATH, --dst PATH : Output file/folder (default: input folder) + [default: FileChooser] + -m MODEL, --model MODEL : model [default: srtmb_basis] + -p FILE, --params FILE : config file hint (relative to `--input`) + [default: FileChooser] + --w W : (default: None) + --r1 R1 : [default: 0.905:float] + --k2p K2P : [default: 0.000250:float] + --beta_lim BETA_LIM : (default: None) + --n_beta N_BETA : [default: 40:int] + --linear_phase_start LINEAR_PHASE_START : [default: 500:int] + --linear_phase_end LINEAR_PHASE_END : (default: None) + --km_outputs KM_OUTPUTS : (default: None) + --thr THR : [default: 0.1:float] + """, + version=niftypad.__version__, + python_deps=["niftypad>=1.1.0"], + argparser=argparser, + ) + opts = kinetic_model.parser._get_optional_actions() + model = next(i for i in opts if i.dest == "model") + model.choices = niftypad.models.NAMES + + # example of how to wrap any CLI command using an `argopt`-style docstring + Cmd( + [sys.executable, "-m", "miutil.cuinfo"], + miutil.cuinfo.__doc__, + version=miutil.__version__, + python_deps=["miutil[cuda]>=0.8.0"], + argparser=argparser, + ) + + # example of how to wrap any callable using an `argopt`-style docstring + Func( + miutil.hasext, + """\ + Check if a given filename has a given extension + + Usage: + hasext <fname> <ext> + + Arguments: + <fname> : path to file [default: FileChooser] + <ext> : extension (with or without `.` prefix) + """, + version=miutil.__version__, + python_deps=["miutil>=0.8.0"], + argparser=argparser, + ) + + if args is None: + args = sys.argv[1:] + args = [i for i in args if i not in ("--ignore-gooey",)] + opts = parser.parse_args(args=args) + args = [i for i in args if i not in ("--dry-run",)] # strip args + + if gui_mode: + print(" ".join([Path(sys.executable).name, "-m amypad"] + args)) + if opts.dry_run: + pass + elif hasattr(opts, "main__"): # Cmd + print_not_none(opts.main__(args[1:], verify_args=False), end="") + elif hasattr(opts, "run__"): # Func + kwargs = {k: v + for (k, v) in opts._get_kwargs() if k not in ("dry_run", "run__")} # strip opts + print_not_none(opts.run__(opts._get_args(), *kwargs)) + + +if __name__ == "__main__": # pragma: no cover + main() diff --git a/amypad/program_icon.png b/amypad/program_icon.png new file mode 100644 index 00000000..43d28359 Binary files /dev/null and b/amypad/program_icon.png differ diff --git a/setup.cfg b/setup.cfg index 770da816..e7035614 100644 --- a/setup.cfg +++ b/setup.cfg @@ -1,61 +1,93 @@ [metadata] -name = amypad -description = Amyloid imaging to prevent Alzheimer's Disease -long_description = file: README.rst -long_description_content_type = text/x-rst -license = Apache 2.0 -license_file = LICENCE.md -url = https://amypad.eu -project_urls = - Repository = https://github.com/AMYPAD/amypad - Changelog = https://github.com/AMYPAD/amypad/releases - Documentation = https://github.com/AMYPAD/amypad/#amypad -maintainer = Casper da Costa-Luis -maintainer_email = [email protected] -keywords = pet, alzheimers -platforms = any -provides = amypad -classifiers = +name=amypad +description=Amyloid imaging to prevent Alzheimer's Disease +long_description=file: README.rst +long_description_content_type=text/x-rst +license=MPL 2.0 +license_file=LICENCE.md +url=https://amypad.eu +project_urls= + Repository=https://github.com/AMYPAD/amypad + Changelog=https://github.com/AMYPAD/amypad/releases + Documentation=https://github.com/AMYPAD/amypad/#amypad +maintainer=Casper da Costa-Luis [email protected] +keywords=pet, alzheimers +platforms=any +provides=amypad +classifiers= Development Status :: 3 - Alpha + Intended Audience :: Developers Intended Audience :: Education Intended Audience :: Healthcare Industry Intended Audience :: Science/Research - License :: OSI Approved :: Apache Software License + License :: OSI Approved :: Mozilla Public License 2.0 (MPL 2.0) Operating System :: Microsoft :: Windows Operating System :: POSIX :: Linux Programming Language :: Python :: 3 - Programming Language :: Python :: 3.5 Programming Language :: Python :: 3.6 Programming Language :: Python :: 3.7 Programming Language :: Python :: 3.8 Programming Language :: Python :: 3.9 + Programming Language :: Python :: 3 :: Only Topic :: Scientific/Engineering :: Medical Science Apps. + Topic :: Software Development :: Libraries + Topic :: Software Development :: Libraries :: Python Modules + Topic :: Software Development :: User Interfaces + Topic :: System :: Installation/Setup + Topic :: Utilities [options] -zip_safe = False -setup_requires = setuptools>=42; setuptools_scm[toml]>=3.4 -install_requires = - argparse; python_version == "2.6" -include_package_data = True -packages = find: +setup_requires=setuptools>=42; setuptools_scm[toml]>=3.4 +install_requires= + argopt + miutil[cuda]>=0.8.0 + niftypad>=1.1.0 + setuptools + shtab>1.3.2 +packages=find: +python_requires=>=3.6 [options.extras_require] -dev = - black; python_version >= "3.0" - flake8 - twine - wheel +dev= + pre-commit pytest pytest-cov pytest-timeout pytest-xdist - codecov -niftypet = niftypet>=0.0.1 +gui=Gooey>=1.0.8 +niftypet=niftypet>=0.0.1 +niftypad= +[options.entry_points] +console_scripts= + amypad=amypad.cli:main + amypad.gui=amypad.gui:main +[options.packages.find] +exclude=tests [options.package_data] - = .md, .rst, .m +=.md, .rst, .m, .png [flake8] -max_line_length = 88 -extend-ignore = E203 -exclude = .git,__pycache__,build,dist,.eggs +max_line_length=99 +extend-ignore=E261,P1 +exclude=.git,__pycache__,build,dist,.eggs -[bdist_wheel] -universal = 1 +[yapf] +spaces_before_comment=15, 20 +arithmetic_precedence_indication=true +allow_split_before_dict_value=false +coalesce_brackets=True +column_limit=99 +each_dict_entry_on_separate_line=False +space_between_ending_comma_and_closing_bracket=False +split_before_named_assigns=False +split_before_closing_bracket=False + +[isort] +profile=black +line_length=99 +known_first_party=amypad,tests + +[tool:pytest] +timeout=10 +log_level=INFO +python_files=tests/test_*.py +addopts=-v --tb=short -rxs -W=error -n=auto --durations=0 --durations-min=1 --cov=amypad --cov-report=term-missing --cov-report=xml diff --git a/setup.py b/setup.py index 60684932..d5d43d7c 100644 --- a/setup.py +++ b/setup.py @@ -1,3 +1,3 @@ from setuptools import setup -setup() +setup(use_scm_version=True)	GUI launch error ```python Traceback (most recent call last): ... File "\AppData\Local\Continuum\anaconda3\lib\site-packages\amypad\gui.py", line 278, in main import miutil.cuinfo File "\AppData\Local\Continuum\anaconda3\lib\site-packages\miutil\__init__.py", line 1, in <module> from .fdio import * # NOQA File "\AppData\Local\Continuum\anaconda3\lib\site-packages\miutil\fdio.py", line 22, in <module> from tqdm.utils import CallbackIOWrapper ImportError: cannot import name 'CallbackIOWrapper' from 'tqdm.utils' (\AppData\Local\Continuum\anaconda3\lib\site-packages\tqdm\utils.py) ``` - [x] https://github.com/AMYPAD/miutil needs to specify [tqdm>=4.40.0](https://tqdm.github.io/releases/#v4400-2019-12-01) - [ ] bump `miutil>=0.8.0`		2020-11-28T05:27:35	[]	[]
jessepollak/mixpanel-python-async	jessepollak__mixpanel-python-async-20	c7b7e52b34d6b871c0c5bd1d5aa8cd48e787fd0b	diff --git a/CHANGELOG.md b/CHANGELOG.md index 319a580..12f3ae9 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,17 @@ project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). ## [Unreleased] +### Added + +* New parameters for the `AsyncBufferedConsumer` constructor: `import_url`, + `request_timeout`, `groups_url`, `api_host`, `retry_limit`, `retry_backoff_factor`, + `verify_cert`. + +### Changed + +* Extra arguments (`args`, `kwargs`) are no longer accepted in the + `AsyncBufferedConsumer` constructor. + ## [0.2.0] - 2019-07-11 ### Added diff --git a/README.md b/README.md index 103fe53..0b901d2 100644 --- a/README.md +++ b/README.md @@ -34,14 +34,17 @@ For most users, the default configuration should work perfectly. For more specif `flush_after (datetime.timedelta)` - defaults to 10 seconds - the time period after which the AsyncBufferedConsumer will flush the events upon receiving a new event (no matter what the event queue size is) - * `flush_first (bool)` - defaults to True - whether the consumer should always flush the first event. - * `max_size (int)` - defaults to 20 - how big a given event queue can get before it is flushed by the consumer - * `events_url (str)` - defaults to standard Mixpanel API URL - the Mixpanel API URL that track events will be sent to - * `people_url (str)` - defaults to standard Mixpanel API URL - the Mixpanel API URL that people events will be sent to +* `import_url (str)` - defaults to standard Mixpanel API URL - the Mixpanel API URL that import events will be sent to +* `request_timeout (int)` - defaults to `None` (no timeout) - Connection timeout in seconds. +* `groups_url (str)` - defaults to standard Mixpanel API URL - the Mixpanel API URL that groups events will be sent to +* `api_host (str)` - defaults to api.mixpanel.com - Mixpanel API domain for all requests unless overridden by above URLs +* `retry_limit (int)` - defaults to 4 - Number of times to retry each request in case of an error, 0 to fail after first attempt. +* `retry_backoff_factor` - defaults to 0.25 - Factor which controls sleep duration between retries: `sleep_seconds = backoff_factor * (2 ^ (retry_count - 1))` +* `verify_cert` - defaults to `True`- Whether to verify the server certificate. `True` is recommended. ### Usage diff --git a/mixpanel_async/async_buffered_consumer.py b/mixpanel_async/async_buffered_consumer.py index 2b5d1d3..c03fc81 100644 --- a/mixpanel_async/async_buffered_consumer.py +++ b/mixpanel_async/async_buffered_consumer.py @@ -53,8 +53,21 @@ class AsyncBufferedConsumer(SynchronousBufferedConsumer): ALL = "ALL" ENDPOINT = "ENDPOINT" - def __init__(self, flush_after=timedelta(0, 10), flush_first=True, max_size=20, - events_url=None, people_url=None, args, kwargs): + def __init__( + self, + flush_after=timedelta(0, 10), + flush_first=True, + max_size=20, + events_url=None, + people_url=None, + import_url=None, + request_timeout=None, + groups_url=None, + api_host="api.mixpanel.com", + retry_limit=4, + retry_backoff_factor=0.25, + verify_cert=True, + ): ''' Create a new instance of a AsyncBufferedConsumer class. @@ -65,13 +78,30 @@ def __init__(self, flush_after=timedelta(0, 10), flush_first=True, max_size=20, the consumer receives :param max_size (int): the number of events in queue that will trigger the queue to be flushed asynchronously - :param events_url: the Mixpanel API URL that track events will be sent to - :param people_url: the Mixpanel API URL that people events will be sent to + :param events_url: Mixpanel API URL that track events will be sent to + :param people_url: Mixpanel API URL that people events will be sent to + :param import_url: Mixpanel API URL that import events will be sent to + :param request_timeout: Connection timeout in seconds + :param groups_url: Mixpanel API URL that groups events will be sent to + :param api_host: Mixpanel API domain for all requests unless overriden by above URLs + :param retry_limit: Number of times to retry each request in case of an error, 0 + to fail after first attempt. + :param retry_backoff_factor: Factor which controls sleep duration between retries: + sleep_seconds = backoff_factor (2 ^ (retry_count - 1)) + :param verify_cert: Whether to verify the server certificate. "True" is + recommended. ''' super(AsyncBufferedConsumer, self).__init__( max_size=max_size, events_url=events_url, - people_url=people_url + people_url=people_url, + import_url=import_url, + request_timeout=request_timeout, + groups_url=groups_url, + api_host=api_host, + retry_limit=retry_limit, + retry_backoff_factor=retry_backoff_factor, + verify_cert=verify_cert, ) # remove the minimum max size that the SynchronousBufferedConsumer	Parameter api_host is not passed to the BufferedConsumer Hello! The parameter `api_host` is used when we want to use mixpanel's EU servers: `consumer = AsyncBufferedConsumer(api_host="api-eu.mixpanel.com")` That parameter is not passed to the mixpanel consumer when using AsyncBufferedConsumer. There is also some other parameters not passed, like `request_timeout`, `verify_cert`, etc.	@bbc2 Any chance you have time to look into this?	2021-02-17T18:02:56	[]	[]
h4l/sortedcontainers-stubs	h4l__sortedcontainers-stubs-4	edae30a8abdce8f0997dd1d0f5b65217879559df	diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 70c4769..c39982d 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -17,7 +17,6 @@ jobs: outputs: test_targets: ${{ steps.expand_bake_matrix.outputs.test_targets }} lint_targets: ${{ steps.expand_bake_matrix.outputs.lint_targets }} - mypy_targets: ${{ steps.expand_bake_matrix.outputs.mypy_targets }} steps: - name: Checkout @@ -30,7 +29,7 @@ jobs: id: expand_bake_matrix run: \| docker buildx bake --print \ - \| jq -cer '{test: .group.test.targets, lint: .group.lint.targets, mypy: .group.mypy.targets} + \| jq -cer '{test: .group.test.targets, lint: .group.lint.targets} \| to_entries[] \| {name: .key, targets: .value} \| "\(.name)_targets=\(.targets \| tojson)"' \ @@ -85,28 +84,3 @@ jobs: set: \| .cache-from=type=gha .cache-to=type=gha,mode=max - - mypy: - name: Run mypy under several Python versions - runs-on: ubuntu-latest - needs: - - expand_bake_matrix - strategy: - matrix: - target: ${{ fromJSON(needs.expand_bake_matrix.outputs.mypy_targets) }} - - steps: - - name: Checkout - uses: actions/checkout@c85c95e3d7251135ab7dc9ce3241c5835cc595a9 - - - name: Set up Docker Buildx - uses: docker/setup-buildx-action@4c0219f9ac95b02789c1075625400b2acbff50b1 - - - name: Run Lint Matrix - uses: docker/bake-action@f32f8b8d70bc284af19f8148dd14ad1d2fbc6c28 - with: - targets: ${{ matrix.target }} - files: docker-bake.hcl - set: \| - .cache-from=type=gha - .cache-to=type=gha,mode=max diff --git a/CHANGELOG.md b/CHANGELOG.md index 4c26d78..1729640 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -11,7 +11,28 @@ minor version; the patch version increments independently to release fixes. ## [Unreleased] -- Nothing yet +### Fixed + +- Using the SortedList (and the other) types as a function that creates an + instance of the type works in pyright now. + ([#3](https://github.com/h4l/sortedcontainers-stubs/issues/3)) + +### Changed + +- Because of the fix to allow pyright to allow using the types as functions, the + constructors with no arguments had to be widened to permit assignment to + non-comparable/hashable types. The result is that it's unfortunately not a + type error to do something like `sl: SortedList[type] = SortedList()` even + though subsequently adding multiple values to this list will fail at runtime. + + It doesn't seem to be possible to type the signature of `__new__` to constrain + `SortedList()` to only allow comparable element types in a standard way (mypy + supported the previous method which worked, but other checkers don't). + +- The signature definition of SortedDict.setdefault was restructured to satisfy + pyright, but the effect of the signature itself remains the same. + +- pyright runs in CI now ## [2.4.1] — 2023-10-10 diff --git a/Dockerfile b/Dockerfile index 5b92546..9ba48cd 100644 --- a/Dockerfile +++ b/Dockerfile @@ -4,24 +4,28 @@ FROM python:${PYTHON_VER:?} AS python-base FROM python-base AS poetry -RUN pip install poetry +RUN --mount=type=cache,target=/root/.cache pip install poetry +RUN python -m venv /venv +ENV VIRTUAL_ENV=/venv \ + PATH="/venv/bin:$PATH" +RUN poetry config virtualenvs.create false WORKDIR /workspace COPY pyproject.toml poetry.lock /workspace/ # Poetry needs these to exist to setup the editable install RUN mkdir sortedcontainers-stubs && touch README.md -RUN poetry install +RUN --mount=type=cache,target=/root/.cache poetry install FROM poetry AS test RUN --mount=source=.,target=/workspace,rw \ --mount=type=cache,uid=1000,target=.pytest_cache \ - poetry run pytest + pytest FROM poetry AS lint-flake8 RUN --mount=source=.,target=/workspace,rw \ - poetry run flake8 + flake8 FROM poetry AS lint-black @@ -38,3 +42,15 @@ FROM poetry AS lint-mypy RUN --mount=source=.,target=/workspace,rw \ --mount=type=cache,target=.mypy_cache \ poetry run mypy . + + +FROM python-base AS pyright +RUN apt-get update && apt-get install -y --no-install-recommends nodejs npm +RUN npm install -g pyright +WORKDIR /workspace +COPY --link --from=poetry /venv /venv + + +FROM pyright AS lint-pyright +RUN --mount=source=.,target=/workspace,rw \ + pyright --pythonpath /venv/bin/python . diff --git a/docker-bake.hcl b/docker-bake.hcl index 73ccb4b..11a5ccd 100644 --- a/docker-bake.hcl +++ b/docker-bake.hcl @@ -1,5 +1,5 @@ group "default" { - targets = ["test", "lint", "mypy"] + targets = ["test", "lint"] } target "test" { @@ -15,23 +15,10 @@ target "test" { output = ["type=cacheonly"] } -target "mypy" { - name = "mypy_python-${replace(py, ".", "-")}" - matrix = { - py = ["3.8", "3.9", "latest"], - } - args = { - PYTHON_VER = py == "latest" ? "slim" : "${py}-slim" - } - target = "lint-mypy" - no-cache-filter = ["lint-mypy"] - output = ["type=cacheonly"] -} - target "lint" { name = "lint-${lint_type}" matrix = { - lint_type = ["flake8", "black", "isort"], + lint_type = ["flake8", "black", "isort", "mypy", "pyright"], } args = { PYTHON_VER = "slim" diff --git a/pyproject.toml b/pyproject.toml index e3b05de..54ed13e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -56,3 +56,7 @@ extra_standard_library = ["typing_extensions"] [tool.mypy] strict = true + +[tool.pyright] +include = ["sortedcontainers-stubs", "test"] +strict = ["sortedcontainers-stubs", "test"] diff --git a/sortedcontainers-stubs/_typing.pyi b/sortedcontainers-stubs/_typing.pyi index 74a5d1f..f7be9d4 100644 --- a/sortedcontainers-stubs/_typing.pyi +++ b/sortedcontainers-stubs/_typing.pyi @@ -1,8 +1,10 @@ from collections.abc import Hashable -from typing import Any, Protocol +from typing import Any, Protocol, TypeVar from typing_extensions import TypeAlias -from _typeshed import SupportsDunderGT, SupportsDunderLT, _T_contra # noqa: F401 +from _typeshed import SupportsDunderGT, SupportsDunderLT + +_T_contra = TypeVar("_T_contra", contravariant=True) class SupportsHashableAndDunderGT( Hashable, SupportsDunderGT[_T_contra], Protocol[_T_contra] diff --git a/sortedcontainers-stubs/sorteddict.pyi b/sortedcontainers-stubs/sorteddict.pyi index a08587a..09f6390 100644 --- a/sortedcontainers-stubs/sorteddict.pyi +++ b/sortedcontainers-stubs/sorteddict.pyi @@ -39,10 +39,15 @@ _OrderT = TypeVar( KeyFunc: TypeAlias = Callable[[_KT], _OrderT] class SortedDict(MutableMapping[_KT, _VT]): + # mypy 1.5.1 complains about overlapping overloaded function signatures with + # incompatible return types. This seems to be because the signatures using + # kwargs can would overlap when no kwargs are present, but there's no way to + # type a non-empty kwargs param. + + # Should return SortedDict[_OrderT, _VT] but we can't reliably express that. + # See comment on SortedList.__new__. @overload - def __new__( - cls, - ) -> SortedDict[_OrderT, _VT]: ... + def __new__(cls) -> Self: ... # type: ignore[misc] @overload def __new__( cls, @@ -69,17 +74,6 @@ class SortedDict(MutableMapping[_KT, _VT]): /, kwargs: _VT, ) -> SortedKeyDict[str, _VT, _OrderT]: ... - # mypy 1.5.1 complains: "Overloaded function signatures 5 and 6 overlap with - # incompatible return types [misc]". If no kwargs are passed, they would - # indeed overlap, but there's no way to specify non-empty kwargs. We need - # this overload to prevent kwargs adding str keys to non-str __entries. - # - # Type-enforcement seems to work correct despite the error we ignore here... - # Alternatively, we would need to not type calls with both entries and - # kwargs. - # - # Ideally str would be "str or a superclass of str", but I can't find a way - # to type that. @overload def __new__( # type: ignore[misc] cls, @@ -125,7 +119,10 @@ class SortedDict(MutableMapping[_KT, _VT]): def pop(self, key: _KT, /, default: _T) -> _VT \| _T: ... def popitem(self, index: int = ...) -> tuple[_KT, _VT]: ... def peekitem(self, index: int = ...) -> tuple[_KT, _VT]: ... - def setdefault(self, key: _KT, default: _VT = ...) -> _VT: ... + @overload + def setdefault(self: MutableMapping[_KT, _T \| None], key: _KT) -> _T \| None: ... + @overload + def setdefault(self, key: _KT, default: _VT) -> _VT: ... # Our override of update is strictly incompatible in that we only allow # kwargs when _KT is str. This is consistent with dict()'s constructor # typing, and allowing kwargs with non-str _KT causes TypeErrors at runtime. @@ -146,7 +143,7 @@ class SortedDict(MutableMapping[_KT, _VT]): @overload def update(self, __iterable: Iterable[tuple[_KT, _VT]]) -> None: ... @overload - def update(self: SortedDict[str, _VT], kwargs: _VT) -> None: ... + def update(self: SortedDict[str, _VT], **kwargs: _VT) -> None: ... # type: ignore[override,unused-ignore] def __reduce__( self, ) -> tuple[ diff --git a/sortedcontainers-stubs/sortedlist.pyi b/sortedcontainers-stubs/sortedlist.pyi index f3fe0be..1cf8883 100644 --- a/sortedcontainers-stubs/sortedlist.pyi +++ b/sortedcontainers-stubs/sortedlist.pyi @@ -19,8 +19,15 @@ class SortedList(MutableSequence[_T]): DEFAULT_LOAD_FACTOR: Final[int] = ... @overload def __new__(cls, key: KeyFunc[_T, _OrderT]) -> SortedKeyList[_T, _OrderT]: ... + # Returning SortedList[Any] is a compromise. + # The signature should be something like: + # (cls: type[SortedList[_OrderT]], ...) -> SortedList[_OrderT] + # but neither mypy nor pyright support typing cls like this. If we leave cls + # bare then mypy correctly constrains the return type to only allow _OrderT + # elements, but pylance doesn't like the solo type argument, and does not + # allow the type to be used as () -> SortedList[...]. @overload - def __new__(cls, iterable: None = ..., key: None = ...) -> SortedList[_OrderT]: ... + def __new__(cls, iterable: None = ..., key: None = ...) -> Self: ... @overload def __new__( cls, iterable: Iterable[_OrderT], key: None = ... diff --git a/sortedcontainers-stubs/sortedset.pyi b/sortedcontainers-stubs/sortedset.pyi index 0bf4085..27c9538 100644 --- a/sortedcontainers-stubs/sortedset.pyi +++ b/sortedcontainers-stubs/sortedset.pyi @@ -21,8 +21,10 @@ class SortedSet(MutableSet[_T], Sequence[_T]): def __new__(cls, iterable: Iterable[_OrderT]) -> SortedSet[_OrderT]: ... @overload def __new__(cls, key: KeyFunc[_T, _OrderT]) -> SortedKeySet[_T, _OrderT]: ... + # Should return SortedSet[_OrderT] but we can't reliably express that. + # See comment on SortedList.__new__. @overload - def __new__(cls, iterable: None = ..., key: None = ...) -> SortedSet[_OrderT]: ... + def __new__(cls, iterable: None = ..., key: None = ...) -> Self: ... @overload def __new__(cls, iterable: Iterable[_OrderT], key: None) -> SortedSet[_OrderT]: ... @overload	Problem using typed sortedcontainer with defaultfactory so with dataclass, it is supposed to use default_factory to create stuff, I can't really find a way to make annotation without problems sample code: ``` from dataclasses import dataclass, field from sortedcontainers import SortedList @dataclass class Example(): samples: SortedList[str] = field(default_factory=SortedList) ``` error: ``` Argument of type "type[SortedList[_T@SortedList]]" cannot be assigned to parameter "default_factory" of type "() -> _T@field" in function "field" No overloaded function matches type "() -> SortedList[str]" ```	Thanks for the report. It's probably because of the way `SortedList.__new__` has several overloads but no explicit empty argument overload. I'll see if I can tweak the overloads to make this work.	2023-10-28T07:16:38	[]	[]
Mityuha/fresh-bakery	Mityuha__fresh-bakery-44	c58d3fea7d953c7d9a4324be5536f82096f78999	diff --git a/pyproject.toml b/pyproject.toml index 18cec60..4727209 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -33,7 +33,7 @@ profile = "black" [tool.poetry] name = "fresh-bakery" -version = "0.3.5" +version = "0.3.6" description = "Bake your dependencies stupidly simple!" readme = "README.md" license = "MIT" @@ -166,7 +166,8 @@ deps = py{36,37}: pylint==2.13.9 py{38,39,310,311,312}: pylint==3.1.0 - pytest==7.0.1 + py{36,37}: pytest==7.0.1 + py{38,39,310,311,312}: pytest>=8.3.2 pytest-mock==3.6.1 typing_extensions==4.1.1 pytest-trio==0.7.0 @@ -175,6 +176,7 @@ deps = py36: mypy==0.971 py37: mypy==1.4.1 py{38,39,310,311,312}: mypy==1.10.0 + commands = black --check bakery/ tests/ isort --check bakery tests/	Patch cakes with the Cake object only Now it's possible to patch attributes this way (from the docs): ```python async def test_example_3(bakery_mock: BakeryMock) -> None: bakery_mock.settings = lambda dsn: "any value" async with bakery_mock(MyBakery): assert MyBakery().settings == "any value" ``` As the practice show such a behaviour is an error prone and leads to confusion. There is a suggestion to get rid of any implicit behaviour in favour of explicit Cake objects' behaviour, like this ```python async def test_example_3(bakery_mock: BakeryMock) -> None: bakery_mock.settings = Cake(lambda dsn: "any value") async with bakery_mock(MyBakery): assert MyBakery().settings == "any value" ```		2024-08-24T10:45:40	[]	[]
Mityuha/fresh-bakery	Mityuha__fresh-bakery-33	9df814817391ad3044edb0539a81e2c1396fbc08	diff --git a/README.md b/README.md index 4af1d5f..e8ededb 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ <p align="center"> - <a href="https://fresh-bakery.readthedocs.io/en/latest/"><img width="300px" src="https://user-images.githubusercontent.com/17745407/187294435-a3bc6b26-b7df-43e5-abd3-0d7a7f92b71e.png" alt='fresh-bakery'></a> + <a href="https://fresh-bakery.readthedocs.io/en/latest/"><img width="300px" src="https://github.com/Mityuha/fresh-bakery/assets/17745407/9ad83683-03dc-43af-b66f-f8a010bde264" alt='fresh-bakery'></a> </p> <p align="center"> <em>🍰 The little DI framework that tastes like a cake. 🍰</em> @@ -25,6 +25,7 @@ It is [nearly] production-ready, and gives you the following: * Zero dependencies. * `Mypy` compatible (no probably need for `# type: ignore`). * `FastAPI` fully compatible. +* `Litestar` compatible. * `Pytest` fully compatible (Fresh Bakery encourages the use of `pytest`). * Ease of testing. * Easily extended (contribution is welcome). diff --git a/bakery/bakery.py b/bakery/bakery.py index 91f9051..acb4209 100644 --- a/bakery/bakery.py +++ b/bakery/bakery.py @@ -71,15 +71,13 @@ async def aopen(cls: Type[T]) -> T: cls.__bakery_visitors__ += 1 - cakes: Dict[str, Any] = {} exception: Optional[Union[Exception, BaseException]] = None # let's bake all your cakes - name: str cake: Cakeable[Any] - for name, cake in cls.__bakery_items__.items(): + for cake in cls.__bakery_items__.values(): try: - cakes[name] = await bake(cake) + await bake(cake) except (Exception, BaseException) as exc: # pylint: disable=broad-except exception = exc logger.error(f'{cake} cannot be baked: {exc}') diff --git a/bakery/baking.py b/bakery/baking.py index f5a69fc..bd5bbd3 100644 --- a/bakery/baking.py +++ b/bakery/baking.py @@ -8,7 +8,7 @@ "determine_baking_method", "unbake", ] - +from copy import deepcopy from inspect import isawaitable, iscoroutinefunction from typing import Any, AsyncContextManager, ContextManager, Optional, TypeVar @@ -104,6 +104,8 @@ class Ingredients: Cake internal state. """ + # pylint: disable=too-many-instance-attributes + def __init__( self, recipe: Any, @@ -133,6 +135,19 @@ def __init__( self.result = self.recipe self.is_baked = True + self.__name__: Final = getattr(self.recipe, "__name__", None) or self.__call__.__name__ + self.__code__: Final = getattr(self.recipe, "__code__", None) or self.__call__.__code__ + self.__defaults__: Final = ( + getattr(self.recipe, "__defaults__", None) or self.__call__.__defaults__ + ) + self.__kwdefaults__: Final = ( + getattr(self.recipe, "__kwdefaults__", None) or self.__call__.__kwdefaults__ + ) + self.__annotations__: Final = ( + getattr(self.recipe, "__annotations__", None) or self.__call__.__annotations__ + ) + self._is_coroutine: Final = getattr(self.recipe, "_is_coroutine", False) + def __repr__(self) -> str: """Repr.""" name: str = self.name or "<anon>" @@ -149,6 +164,17 @@ def __copy__(self) -> "Ingredients": ingr_copy.name = self.name return ingr_copy + def __deepcopy__(self, memo: Any) -> "Ingredients": + """Deep copy all ingredients and technologies.""" + ingr_copy: Ingredients = Ingredients( + self.recipe, + deepcopy(self.recipe_args), + cake_baking_method=self.cake_baking_method, + deepcopy(self.recipe_kwargs), + ) + ingr_copy.name = self.name + return ingr_copy + def __call__(self) -> Any: """Just return result.""" return self.result diff --git a/bakery/cake.py b/bakery/cake.py index 57ece83..ece4a81 100644 --- a/bakery/cake.py +++ b/bakery/cake.py @@ -12,6 +12,7 @@ Callable, ContextManager, Optional, + Set, TypeVar, cast, overload, @@ -28,6 +29,7 @@ IngredientsProto, assert_baked, cake_ingredients, + is_baked, is_cake, ) @@ -38,11 +40,22 @@ class Pastry(CakeRecipe): Your item ingredients and cooking method stored here. """ + __CAKE_ATTRIBUTE_ERROR_NAMES__: Final[Set[str]] = { + "func", # partial + "__wrapped__", # functools special attribute + } + def __init__( self, ingredients: Ingredients, ): self.__ingredients: Final[Ingredients] = ingredients + self.__name__: Final = ingredients.__name__ + self.__code__: Final = ingredients.__code__ + self.__defaults__: Final = ingredients.__defaults__ + self.__kwdefaults__: Final = ingredients.__kwdefaults__ + self.__annotations__: Final = ingredients.__annotations__ + self._is_coroutine: Final = ingredients._is_coroutine def __set_name__(self, _: Any, name: str) -> None: self.__ingredients.name = name @@ -54,19 +67,49 @@ def __repr__(self) -> str: def __copy__(self) -> "Cakeable[Any]": """Copy itself with all ingredients and technologies. + If cake is not baked then such a cake's copy is cake itself. + 'Copy cake' just sounds like 'cupcake'. And I like cupcakes ;) """ - assert_baked(cast(Cakeable[Any], self)) # we only copy proven recipes! + if not is_baked(self): + return self + ingr_copy: Ingredients = self.__ingredients.__copy__() return cast(Cakeable[Any], Pastry(ingr_copy)) + def __deepcopy__(self, memo: Any) -> "Cakeable[Any]": + """Deep copy with all ingredients and technologies. + + If cake is not baked then such a cake's deep copy is cake itself. + """ + if not is_baked(self): + return self + + ingr_copy: Ingredients = self.__ingredients.__deepcopy__(memo) + return cast(Cakeable[Any], Pastry(ingr_copy)) + def __call__(self) -> Any: """Just return whole cake.""" assert_baked(cast(Cakeable[Any], self)) return self.__ingredients() def __getattr__(self, piece_name: str) -> PieceOfCake: - """Cut a piece of cake.""" + """Cut a piece of cake. + + With __CAKE_ATTRIBUTE_ERROR_NAMES__ you can no longer write + the things like: + class MyBakery(Bakery): + ctrl: Any = Cake(Controller) + controller_func: Any = Cake(some_func, ctrl.func) # or __wrapped__ + + There is the place where AttribureError exception will occured. + You can make a simple refactoring: + class MyBakery(Bakery): + ctrl: Any = Cake(Controller) + controller_func: Any = Cake(some_func, Cake(getattr, ctrl, "func")) + """ + if piece_name in self.__CAKE_ATTRIBUTE_ERROR_NAMES__: + raise AttributeError(piece_name) # explicit __getattr__ call to avoid collisions return PieceOfCake(self).__getattr__(piece_name) diff --git a/docs/index.md b/docs/index.md index 8fec128..1e33319 100644 --- a/docs/index.md +++ b/docs/index.md @@ -1,9 +1,10 @@ # Fresh Bakery Fresh Bakery is a lightweight [Dependency Injection](dependency_injection.md) framework. -Fresh Bakery can only be used within async applications, so if you have a sync one you probably should look for some alternatives. +Actually, the only two asyncronious things about Fresh Bakery are its opening and its closing. +It doesn't add up how you open and close your bakery: just make it asynchronously. That's it. -Fresh Bakery is suitable for integrating against any async Web framework, such as [Starlette][starlette], [aiohttp][aiohttp], [FastAPI][fastapi] and so forth. +Fresh Bakery is suitable for integrating against any async Web framework, such as [Starlette][starlette], [aiohttp][aiohttp], [FastAPI][fastapi], [Litestar][litestar] and so forth. Requirements*: Python 3.6+ @@ -115,3 +116,4 @@ async def main() -> None: [starlette]: https://github.com/encode/starlette [aiohttp]: https://github.com/aio-libs/aiohttp [fastapi]: https://github.com/tiangolo/fastapi +[litestar]: https://github.com/litestar-org/litestar diff --git a/pyproject.toml b/pyproject.toml index 042717c..f5bd85d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -32,7 +32,7 @@ use_parentheses= true [tool.poetry] name = "fresh-bakery" -version = "0.2.1" +version = "0.3.1" description = "Bake your dependencies stupidly simple!" readme = "README.md" license = "MIT"	Delete unused dict on bakery open Delete [unused dict](https://github.com/Mityuha/fresh-bakery/blob/main/bakery/bakery.py#L74) in Bakery.aopen method		2023-07-09T16:45:16	[]	[]
ucla-mobility/OpenCDA	ucla-mobility__OpenCDA-200	8e0b167c26c3b631aa17ec4d0c9ac98514f26240	diff --git a/README.md b/README.md index e8072e21..8fdb2bd4 100644 --- a/README.md +++ b/README.md @@ -21,12 +21,12 @@ The key features of OpenCDA are: Users could refer to [OpenCDA documentation](https://opencda-documentation.readthedocs.io/en/latest/) for more details. ## What's New -### March 2022 +### March 2023 * OpenCDA now supports Docker Installation! Many thanks to @GoodarzMehr! * OpenCDA has make the configuration system better! We provide a `default.yaml` as a template for all scenarios. Users now can only change the parts that are different from the default parameters, which makes the configuration file much cleaner. -### Jan 2022 +### Jan 2023 * Our paper [The OpenCDA Open-source Ecosystem for Cooperative Driving Automation Research](https://ieeexplore.ieee.org/document/10045043) has been accepted by IEEE Transactions on Intelligent Vehicles. We extend the scope of the original OpenCDA simulation framework to a ecosystem, which contains a model zoo, a suite of driving simulators at various resolutions, large-scale real-world and simulated datasets, complete devel-opment toolkits for benchmark training/testing, and a scenario database/generator. @@ -102,7 +102,7 @@ The arxiv link to the paper: https://arxiv.org/abs/2107.06260 Also, under this LICENSE, OpenCDA is for non-commercial research only. Researchers can modify the source code for their own research only. Contracted work that generates corporate revenues and other general commercial use are prohibited under this LICENSE. See the LICENSE file for details and possible opportunities for commercial use. ## Contributors -OpenCDA is supported by the [UCLA Mobility Lab](https://mobility-lab.seas.ucla.edu/). <br> +OpenCDA is mainly supported by the [UCLA Mobility Lab](https://mobility-lab.seas.ucla.edu/). <br> ### Lab Principal Investigator: - Dr. Jiaqi Ma ([linkedin](https://www.linkedin.com/in/jiaqi-ma-17037838/), @@ -118,3 +118,6 @@ OpenCDA is supported by the [UCLA Mobility Lab](https://mobility-lab.seas.ucla.e - Zonglin Meng([linkedin](https://www.linkedin.com/in/zonglin-meng-a393b31ab/) - Dr. Xin Xia ([linkedin](https://www.linkedin.com/in/yi-guo-4008baaa/)) +### External Contributor Acknowledgements +- We would like to acknowledge the great contributions from UC Davis Professor [Junshan Zhang's](https://faculty.engineering.ucdavis.edu/jzhang/) group, particularly to Dr. [Wei Shao](https://scholar.google.com.au/citations?user=zbqNhWwAAAAJ&hl=en), who played a crucial role in this collaboration. Their expertise enabled the seamless integration of openScenario with OpenCDA. +- We would like to thank @GoodarzMehr for his outstanding contribution in creating the OpenCDA Dockerfile. diff --git a/docs/md_files/coperception/setup_scenario_runner.md b/docs/md_files/coperception/setup_scenario_runner.md new file mode 100644 index 00000000..70564372 --- /dev/null +++ b/docs/md_files/coperception/setup_scenario_runner.md @@ -0,0 +1,30 @@ +## Scenario Runner + +To enable the scenario runner, follow the official docs: +https://carla-scenariorunner.readthedocs.io/en/latest/ to set up the environments. + +### Details for each step: +1. Clone the Scenario Runner repo. Find the match version with your installed Carla. We'll use `0.9.12` as the following example +2. The `SCENARIO_RUNNER_ROOT="/to/scenario_runner/installation/path"`. In our example, we use `${HOME}/scenario_runner-0.9.12` +3. Set up the environment in `.bashrc` like +``` +export SCENARIO_RUNNER_ROOT=${HOME}/scenario_runner-0.9.12 +export PYTHONPATH=$SCENARIO_RUNNER_ROOT:$PYTHONPATH +export PYTHONPATH=$PYTHONPATH:${CARLA_ROOT}/PythonAPI/carla/dist/carla-0.9.12-py3.7-linux-x86_64.egg +export PYTHONPATH=$PYTHONPATH:${CARLA_ROOT}/PythonAPI/carla +``` +4. source your environment file as `source .bashrc` +5. create an empty `__init__.py` under the scenario runner repo so that the folder is treated as a package + +### Verify the installation +1. verify with the import +``` +python -c 'import scenario_runner' +``` +2. Verify with python prompt +``` +>> import scenario_runner as sr +>> dir(sr) +['CarlaDataProvider', 'LooseVersion', 'OpenScenario', 'OpenScenarioConfiguration', 'RawTextHelpFormatter', 'RouteParser', 'RouteScenario', 'ScenarioConfigurationParser', 'ScenarioManager', 'ScenarioRunner', 'VERSION', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__spec__', 'argparse', 'carla', 'datetime', 'glob', 'importlib', 'inspect', 'json', 'main', 'os', 'pkg_resources', 'print_function', 'signal', 'sys', 'time', 'traceback'] +``` + diff --git a/docs/md_files/installation.md b/docs/md_files/installation.md index 0004168d..eb025c0e 100644 --- a/docs/md_files/installation.md +++ b/docs/md_files/installation.md @@ -144,9 +144,16 @@ export SUMO_HOME=/usr/share/sumo ``` --- +### 5. Install OpenScenario (Optional) +If you want to use OpenScenario to conduct scenario testing, e.g. `python opencda.py -t openscenario_carla -v 0.9.12`, you need to install OpenScenario first. <br> + +Please follow the [openscenario installation](coperception/setup_scenario_runner.md) to install OpenScenario. + ## Docker Installation OpenCDA provides docker image for users to run directly. +Note: Openscenario is not supported in docker image yet. + ### 1. Prerequisite First, make sure that you have installed `docker` in your ubuntu system. If you have nvidia gpu, it is recommended to install `nvidia-docker` as well. diff --git a/opencda/core/common/misc.py b/opencda/core/common/misc.py index 6fb0bd15..7beab1bc 100644 --- a/opencda/core/common/misc.py +++ b/opencda/core/common/misc.py @@ -57,6 +57,30 @@ def draw_trajetory_points(world, waypoints, z=0.25, life_time=lt) +def draw_prediction_points(world, points, z=0.5, lt=0.06, size=0.05): + """ + This function draws prediction points on the carla server. + + Parameters: + world (carla.World): The world object where points will be drawn. + points (list): A list of points to be drawn. Each point is a tuple (x, y, yaw). + z (float, optional): The z-coordinate for the points. Default is 0.5. + lt (float, optional): The lifetime of the points in seconds. Default is 0.06. + size (float, optional): The size of the points. Default is 0.05. + + Returns: + None + """ + for point in points: + color = carla.Color(255, 103, 0) + x, y, yaw = point + world.debug.draw_point(carla.Location(x, y, z), size=size, color=color, life_time=lt) + + +def draw_prediction_bbx(world, x, y, color=carla.Color(255, 0, 0), z=2.0, lt=0.06, size=0.05): + """ Draw the bounding box of the predicted vehicle.""" + world.debug.draw_point(carla.Location(x, y, z), size=size, color=color, life_time=lt) + def draw_waypoints(world, waypoints, z=0.5): """ Draw a list of waypoints at a certain height given in z. diff --git a/opencda/core/plan/behavior_agent.py b/opencda/core/plan/behavior_agent.py index 847d9b5c..1e9a1644 100644 --- a/opencda/core/plan/behavior_agent.py +++ b/opencda/core/plan/behavior_agent.py @@ -14,12 +14,14 @@ import numpy as np import carla +from opencda.core.common.misc import draw_prediction_points from opencda.core.common.misc import get_speed, positive, cal_distance_angle from opencda.core.plan.collision_check import CollisionChecker from opencda.core.plan.local_planner_behavior import LocalPlanner from opencda.core.plan.global_route_planner import GlobalRoutePlanner from opencda.core.plan.global_route_planner_dao import GlobalRoutePlannerDAO from opencda.core.plan.planer_debug_helper import PlanDebugHelper +from opencda.core.sensing.prediction.physics_model import PredictionManager class BehaviorAgent(object): @@ -90,6 +92,7 @@ def __init__(self, vehicle, carla_map, config_yaml): self._ego_pos = None self._ego_speed = 0.0 self._map = carla_map + self._cav_world = self.vehicle.get_world() # speed related, check yaml file to see the meaning self.max_speed = config_yaml['max_speed'] @@ -104,7 +107,8 @@ def __init__(self, vehicle, carla_map, config_yaml): self.ttc = 1000 # collision checker self._collision_check = CollisionChecker( - time_ahead=config_yaml['collision_time_ahead']) + time_ahead=config_yaml['collision_time_ahead'], + cav_world=self._cav_world) self.ignore_traffic_light = config_yaml['ignore_traffic_light'] self.overtake_allowed = config_yaml['overtake_allowed'] self.overtake_allowed_origin = config_yaml['overtake_allowed'] @@ -145,6 +149,23 @@ def __init__(self, vehicle, carla_map, config_yaml): # print message in debug mode self.debug = False if 'debug' not in \ config_yaml else config_yaml['debug'] + # prediction + self.enable_prediction = False + self.prediction_scan_window = 0 + if 'local_planner' in config_yaml and 'enable_prediction' in config_yaml['local_planner'] and \ + config_yaml['local_planner']['enable_prediction']: + print("Prediction is enabled") + print(f"Prediction model used: {config_yaml['local_planner']['prediction_model']}") + local_planner_config = config_yaml['local_planner'] + dt = local_planner_config['dt'] + self.enable_prediction = local_planner_config['enable_prediction'] + self.prediction_scan_window = config_yaml['local_planner']['prediction_scan_window'] # override zero + self.prediction_manager = PredictionManager( + observed_length=int(local_planner_config['observation_seconds'] // dt), + predict_length=int(local_planner_config['observation_seconds'] // dt), + dt=dt, + model=config_yaml['local_planner']['prediction_model'] + ) def update_information(self, ego_pos, ego_speed, objects): """ @@ -168,6 +189,9 @@ def update_information(self, ego_pos, ego_speed, objects): self.break_distance = self._ego_speed / 3.6 * self.emergency_param # update the localization info to trajectory planner self.get_local_planner().update_information(ego_pos, ego_speed) + # prediction + if self.enable_prediction: + self.prediction_manager.update_information(objects) self.objects = objects # current version only consider about vehicles @@ -403,7 +427,10 @@ def traffic_light_manager(self, waypoint): self.light_id_to_ignore = -1 return 0 - def collision_manager(self, rx, ry, ryaw, waypoint, adjacent_check=False): + def collision_manager(self, rx, ry, ryaw, + waypoint, + adjacent_check=False, + obstacle_vehicle_predictions={}): """ This module is in charge of warning in case of a collision. @@ -423,6 +450,9 @@ def collision_manager(self, rx, ry, ryaw, waypoint, adjacent_check=False): adjacent_check : boolean Whether it is a check for adjacent lane. + + obstacle_vehicle_predictions: dict + dict of vehicle and its predicted future paths """ def dist(v): @@ -432,24 +462,57 @@ def dist(v): min_distance = 100000 target_vehicle = None - for vehicle in self.obstacle_vehicles: - collision_free = self._collision_check.collision_circle_check( - rx, ry, ryaw, vehicle, self._ego_speed / 3.6, self._map, - adjacent_check=adjacent_check) - if not collision_free: - vehicle_state = True + if self.enable_prediction: + for v_id, predictions in obstacle_vehicle_predictions.items(): + vehicle = predictions['vehicle'] + # not collide with prediction points + collision_free_prediction = self._collision_check.collision_circle_check_enable_prediction( + rx, ry, ryaw, + predictions['vehicle'], + predictions['points'], + self._ego_speed / 3.6, + self.prediction_scan_window, + adjacent_check=adjacent_check + ) + + # not collide with original check + collision_free = self._collision_check.collision_circle_check( + rx, ry, ryaw, vehicle, self._ego_speed / 3.6, self._map, + adjacent_check=adjacent_check) + + # either collide with original check or the prediction + if not collision_free or not collision_free_prediction: + vehicle_state = True + + # the vehicle length is typical 3 meters, + # so we need to consider that when calculating the distance + distance = positive(dist(vehicle) - 3) + + # updating the minimal distance and the closet vehicle + if distance < min_distance: + min_distance = distance + target_vehicle = vehicle + return vehicle_state, target_vehicle, min_distance + + else: + for vehicle in self.obstacle_vehicles: + collision_free = self._collision_check.collision_circle_check( + rx, ry, ryaw, vehicle, self._ego_speed / 3.6, self._map, + adjacent_check=adjacent_check) + if not collision_free: + vehicle_state = True - # the vehicle length is typical 3 meters, - # so we need to consider that when calculating the distance - distance = positive(dist(vehicle) - 3) + # the vehicle length is typical 3 meters, + # so we need to consider that when calculating the distance + distance = positive(dist(vehicle) - 3) - if distance < min_distance: - min_distance = distance - target_vehicle = vehicle + if distance < min_distance: + min_distance = distance + target_vehicle = vehicle - return vehicle_state, target_vehicle, min_distance + return vehicle_state, target_vehicle, min_distance - def overtake_management(self, obstacle_vehicle): + def overtake_management(self, obstacle_vehicle, obstacle_vehicle_predictions): """ Overtake behavior. @@ -489,7 +552,9 @@ def overtake_management(self, obstacle_vehicle): overtake=True, world=self.vehicle.get_world()) vehicle_state, _, _ = self.collision_manager( rx, ry, ryaw, self._map.get_waypoint( - self._ego_pos.location), True) + self._ego_pos.location), + True, + obstacle_vehicle_predictions) if not vehicle_state: print("left overtake is operated") self.overtake_counter = 100 @@ -690,10 +755,10 @@ def check_lane_change_permission(self, lane_change_allowed, collision_detector_e # * overtake hasn't happened : if previously we have been doing an overtake, then lane change should not be allowed. # * destination is not pushed : if we have been doing destination pushed, then lane change should not be allowed. lane_change_enabled_flag = collision_detector_enabled and \ - self.get_local_planner().lane_id_change and \ - self.get_local_planner().lane_lateral_change and \ - self.overtake_counter <= 0 and \ - not self.destination_push_flag + self.get_local_planner().lane_id_change and \ + self.get_local_planner().lane_lateral_change and \ + self.overtake_counter <= 0 and \ + not self.destination_push_flag if lane_change_enabled_flag: lane_change_allowed = lane_change_allowed and self.lane_change_management() if not lane_change_allowed: @@ -769,6 +834,13 @@ def run_step( ego_vehicle_loc = self._ego_pos.location ego_vehicle_wp = self._map.get_waypoint(ego_vehicle_loc) waipoint_buffer = self.get_local_planner().get_waypoint_buffer() + # prediction + obstacle_vehicle_predictions = {} + if self.enable_prediction: + obstacle_vehicle_predictions = self.prediction_manager.predict() + for v_id, predictions in obstacle_vehicle_predictions.items(): + draw_prediction_points(self._cav_world, predictions['points']) + # ttc reset to 1000 at the beginning self.ttc = 1000 # when overtake_counter > 0, another overtake/lane change is forbidden @@ -817,13 +889,16 @@ def run_step( rx, ry, rk, ryaw = self._local_planner.generate_path() # check whether lane change is allowed - self.lane_change_allowed = self.check_lane_change_permission(lane_change_allowed, collision_detector_enabled, rk) + self.lane_change_allowed = self.check_lane_change_permission(lane_change_allowed, collision_detector_enabled, + rk) # 3. Collision check is_hazard = False if collision_detector_enabled: is_hazard, obstacle_vehicle, distance = self.collision_manager( - rx, ry, ryaw, ego_vehicle_wp) + rx, ry, ryaw, ego_vehicle_wp, + False, + obstacle_vehicle_predictions) car_following_flag = False if not is_hazard: @@ -872,7 +947,7 @@ def run_step( # front obstacle if self._ego_speed >= obstacle_speed - 5: - car_following_flag = self.overtake_management(obstacle_vehicle) + car_following_flag = self.overtake_management(obstacle_vehicle, obstacle_vehicle_predictions) else: car_following_flag = True @@ -890,6 +965,4 @@ def run_step( target_speed, target_loc = self._local_planner.run_step( rx, ry, rk, target_speed=self.max_speed - self.speed_lim_dist if not target_speed else target_speed) - return target_speed, target_loc - - + return target_speed, target_loc \ No newline at end of file diff --git a/opencda/core/plan/collision_check.py b/opencda/core/plan/collision_check.py index 22adf811..37f4cbe2 100644 --- a/opencda/core/plan/collision_check.py +++ b/opencda/core/plan/collision_check.py @@ -10,7 +10,7 @@ import carla import numpy as np -from opencda.core.common.misc import cal_distance_angle, draw_trajetory_points +from opencda.core.common.misc import cal_distance_angle, draw_prediction_bbx from opencda.core.plan.spline import Spline2D @@ -21,15 +21,20 @@ class CollisionChecker: Parameters ---------- time_ahead : float - how many seconds we look ahead in advance for collision check. + How many seconds we look ahead in advance for collision check. + cav_world : carla.world + The simulation world. circle_radius : float The radius of the collision checking circle. circle_offsets : float The offset between collision checking circle and the trajectory point. """ - def __init__(self, time_ahead=1.2, circle_radius=1.0, circle_offsets=None): - + def __init__(self, time_ahead=3, + cav_world=None, + circle_radius=1.0, + circle_offsets=None): + self._cav_world = cav_world self.time_ahead = time_ahead self._circle_offsets = [-1.0, 0, @@ -144,14 +149,14 @@ def adjacent_lane_collision_check( target_wpt_previous = target_wpt.previous(diff_s) target_wpt_previous = target_wpt_previous[0] - target_wpt_middle = target_wpt_previous.next(diff_s/2)[0] + target_wpt_middle = target_wpt_previous.next(diff_s / 2)[0] x, y = [target_wpt_next.transform.location.x, target_wpt_middle.transform.location.x, target_wpt_previous.transform.location.x], \ - [target_wpt_next.transform.location.y, - target_wpt_middle.transform.location.y, - target_wpt_previous.transform.location.y] + [target_wpt_next.transform.location.y, + target_wpt_middle.transform.location.y, + target_wpt_previous.transform.location.y] ds = 0.1 sp = Spline2D(x, y) @@ -188,7 +193,6 @@ def collision_circle_check( """ Use circled collision check to see whether potential hazard on the forwarding path. - Args: -adjacent_check (boolean): Indicator of whether do adjacent check. Note: always give full path for adjacent lane check. @@ -205,10 +209,12 @@ def collision_circle_check( collision_free = True # detect x second ahead. in case the speed is very slow, # there is some minimum threshold for the check distance + ### Look ahead how many points in the path list ### distance_check = min(max(int(self.time_ahead * speed / 0.1), 90), len(path_x)) \ if not adjacent_check else len(path_x) + ### Get the 3D location of the vehicle being checked ### obstacle_vehicle_loc = obstacle_vehicle.get_location() obstacle_vehicle_yaw = \ carla_map.get_waypoint(obstacle_vehicle_loc).transform.rotation.yaw @@ -248,7 +254,6 @@ def collision_circle_check( corrected_extent_x, obstacle_vehicle_loc.y + corrected_extent_y]]) - # compute whether the distance between the four corners of the # vehicle to the trajectory point collision_dists = spatial.distance.cdist( @@ -261,3 +266,91 @@ def collision_circle_check( break return collision_free + + def collision_circle_check_enable_prediction( + self, + path_x, + path_y, + path_yaw, + obstacle_vehicle, + vehicle_predictions, + speed, + prediction_scan_window, + adjacent_check=False): + """ + This function checks for potential collisions between a vehicle and obstacles along a given path. + + Parameters: + path_x (list): The x-coordinates of the path. + path_y (list): The y-coordinates of the path. + path_yaw (list): The yaw angles of the path. + obstacle_vehicle (Vehicle): The obstacle vehicle to check for collisions. + vehicle_predictions (list): The predicted positions of the vehicle. + speed (float): The speed of the vehicle. + prediction_scan_window (int): The time window for prediction scan. + adjacent_check (bool, optional): Whether to check for adjacent collisions. Default is False. + + Returns: + collision_free (bool): True if the path is collision-free, False otherwise. + """ + collision_free = True + dt = 0.05 + max_time_on_path = min(int((len(path_x) * 0.1 / max(speed, 2.0) / dt)), + len(vehicle_predictions)) + time_check = min(max(int(self.time_ahead / dt), int(2 / dt)), + max_time_on_path) \ + if not adjacent_check else max_time_on_path + # decide to align on the time axis, need to convert the points to distances + # 1. the distance between every two point is 0.1m + # 2. the distance traveled is speed * time + # 3. find the corresponding index in the original array + # check every 1 sec -> 1 / 0.05 = 20. every 20 points for the prediction points + interval = int(0.5 / dt) + + # align on the time axis + for time in range(0, time_check, interval): + # calculating for corresponding ego index + distance_traveled = time * dt * speed + ego_idx = int(distance_traveled // 0.1) + # ego circle (3 points) + ptx, pty, yaw = path_x[ego_idx], path_y[ego_idx], path_yaw[ego_idx] + circle_locations = np.zeros((len(self._circle_offsets), 2)) + circle_offsets = np.array(self._circle_offsets) + circle_locations[:, 0] = ptx + circle_offsets * cos(yaw) + circle_locations[:, 1] = pty + circle_offsets * sin(yaw) + draw_prediction_bbx(self._cav_world, ptx, pty, carla.Color(0, 255, 0)) + + # if enable the scan window, examine through + # [vehicle_predictions[i - window], vehicle_predictions[i + window]] + # calculating surrounding vehicle + scan_range = [time - prediction_scan_window, time + prediction_scan_window] + for idx in scan_range: + x, y, obstacle_vehicle_yaw = vehicle_predictions[idx] + # obstacle_vehicle_yaw = math.radians(obstacle_vehicle_yaw) + draw_prediction_bbx(self._cav_world, x, y) + dx, dy = obstacle_vehicle.bounding_box.extent.x, obstacle_vehicle.bounding_box.extent.y + vehicle_extent = np.array([dx, dy]) + corner_points = np.array([ + [-dx, -dy], + [dx, -dy], + [-dx, dy], + [dx, dy] + ]) + rotation_matrix = np.array([ + [np.cos(obstacle_vehicle_yaw), -np.sin(obstacle_vehicle_yaw)], + [np.sin(obstacle_vehicle_yaw), np.cos(obstacle_vehicle_yaw)] + ]) + rotated_points = corner_points @ rotation_matrix.T + obstacle_vehicle_bbx_array = rotated_points + np.array([x, y]) + for pt in obstacle_vehicle_bbx_array.tolist(): + draw_prediction_bbx(self._cav_world, pt[0], pt[1], carla.Color(0, 0, 255), z=3.0) + + collision_dists = spatial.distance.cdist( + obstacle_vehicle_bbx_array, circle_locations) + + collision_dists = np.subtract(collision_dists, self._circle_radius) + collision_free = collision_free and not np.any(collision_dists < 0) + + if not collision_free: + break + return collision_free diff --git a/opencda/core/sensing/perception/obstacle_vehicle.py b/opencda/core/sensing/perception/obstacle_vehicle.py index 83ca5996..c016fe09 100644 --- a/opencda/core/sensing/perception/obstacle_vehicle.py +++ b/opencda/core/sensing/perception/obstacle_vehicle.py @@ -133,6 +133,12 @@ def get_velocity(self): """ return self.velocity + def get_carla_id(self): + """ + Return the carla id + """ + return self.carla_id + def set_carla_id(self, id): """ Set carla id according to the carla.vehicle. diff --git a/opencda/core/sensing/prediction/physics_model.py b/opencda/core/sensing/prediction/physics_model.py new file mode 100644 index 00000000..f933b7e2 --- /dev/null +++ b/opencda/core/sensing/prediction/physics_model.py @@ -0,0 +1,282 @@ +# -- coding: utf-8 -- +""" +Physics-based trajectory prediction model +""" +import math +import numpy as np +from collections import deque + +def check_positive_integer(value, name): + assert isinstance(value, int) and value > 0, f"{name} must be int and greater than 0" + +def check_positive_real_number(value, name): + assert (isinstance(value, int) or isinstance(value, float)) and value > 0, f"{name} must be real number and greater than 0" + +def angle_diff(x, y): + """ + Get the smallest angle difference between 2 angles: the angle from y to x. + Parameters + ---------- + x : float + To angle. + + y : float + From angle. + + Returns + ------- + diff : float + Angle difference from y to x. + + """ + # calculate angle difference, modulo to [0, 2pi] + period = 2 np.pi + diff = (x - y + period / 2) % period - period / 2 + if diff > np.pi: + diff = diff - (2 * np.pi) # shift (pi, 2pi] to (-pi, 0] + return diff + + +class TrajectoryData: + def __init__(self, l): + self.observed_traj = deque(maxlen=l) + self.observed_velocity = deque(maxlen=l) + self.observed_yaw = deque(maxlen=l) + self.yaw_rate = 0 + self.acc = [0, 0] + + def add(self, pose, v, time_diff, current_yaw): + # use current yaw from system to avoid getting 0.0 + # yaw when velocity is very slow. + # current_yaw = np.arctan2(v[1], v[0]) + if len(self.observed_velocity): + # update acceleration + past_v = self.observed_velocity[-1] + self.acc = [(v[i] - past_v[i]) / time_diff for i in range(2)] + # update yaw rate + past_yaw = self.observed_yaw[-1] + self.yaw_rate = angle_diff(current_yaw, past_yaw) / time_diff + + self.observed_traj.append(pose) + self.observed_velocity.append(v) + self.observed_yaw.append(current_yaw) + + +def get_kinematics(trajectory_data, observed_length): + observed_traj = list(trajectory_data.observed_traj)[-observed_length:] + velocity = list(trajectory_data.observed_velocity)[-1] + yaw = trajectory_data.observed_yaw[-1] + yaw_rate = trajectory_data.yaw_rate + acc = trajectory_data.acc + return observed_traj, velocity, acc, yaw, yaw_rate + + + +class PredictionManager: + def __init__(self, observed_length, predict_length, dt, model="ConstantVelocityHeading"): + check_positive_integer(observed_length, "observed_length") + check_positive_integer(predict_length, "predict_length") + check_positive_real_number(dt, "dt") + + model_classes = { + "ConstantVelocityHeading": ConstantVelocityHeading, + "ConstantAccelerationHeading": ConstantAccelerationHeading, + "ConstantSpeedYawRate": ConstantSpeedYawRate, + "ConstantMagnitudeAccelAndYawRate": ConstantMagnitudeAccelAndYawRate, + "PhysicsOracle": PhysicsOracle + } + + self.observed_length = observed_length + self.predict_length = predict_length + self.dt = dt + self.model = model_classes[model](self.observed_length, self.predict_length, self.dt) + self.vehicle_trajectory_data = {} + self.objects = None + self.vehicles = {} + + def update_information(self, objects): + # only keep track of the vehicles that is visible in current frame + self.vehicle_trajectory_data = {v.get_carla_id(): self.vehicle_trajectory_data.get(v.get_carla_id(), TrajectoryData(self.observed_length)) \ + for v in objects['vehicles']} + self.objects = objects + self.vehicles = {v.get_carla_id(): v for v in objects['vehicles']} + for vehicle in objects['vehicles']: + location = vehicle.get_location() + x, y = location.x, location.y + v = vehicle.get_velocity() + yaw = math.radians(vehicle.get_transform().rotation.yaw) + self.vehicle_trajectory_data[vehicle.get_carla_id()].add([x, y], [v.x, v.y], self.dt, yaw) + + def predict(self): + predictions = {} + for vehicle_id, vehicle in self.vehicles.items(): + kinematics_data = get_kinematics(self.vehicle_trajectory_data[vehicle_id], self.observed_length) + predictions.update({ + vehicle_id: { + 'vehicle': vehicle, + 'points': self.model(kinematics_data) + } + }) + return predictions + + +class Baseline: + """ + Baseline class for physics-based trajectory prediction model. + Parameters + ---------- + observed_length : int + Observed trajectory length. + + predict_length : int + Predicted trajectory length. + + dt : float + Time discretization interval. + + """ + + def __init__(self, observed_length, predict_length, dt): + check_positive_integer(observed_length, "observed_length") + check_positive_integer(predict_length, "predict_length") + check_positive_real_number(dt, "dt") + self.observed_length = observed_length + self.predict_length = predict_length + self.dt = dt + + +class ConstantVelocityHeading(Baseline): + def __call__(self, kinematics_data): + observed_traj, velocity, acc, yaw, yaw_rate = kinematics_data + x = observed_traj[-1][0] + y = observed_traj[-1][1] + vx, vy = velocity + pred_traj = constant_velocity_heading(x, y, vx, vy, yaw, self.predict_length, self.dt) + return pred_traj + + +class ConstantAccelerationHeading(Baseline): + def __call__(self, kinematics_data): + observed_traj, velocity, acc, yaw, yaw_rate = kinematics_data + x = observed_traj[-1][0] + y = observed_traj[-1][1] + vx, vy = velocity + ax, ay = acc + pred_traj = constant_acceleration_and_heading(x, y, vx, vy, yaw, ax, ay, self.predict_length, self.dt) + return pred_traj + + +class ConstantSpeedYawRate(Baseline): + def __call__(self, kinematics_data): + observed_traj, velocity, acc, yaw, yaw_rate = kinematics_data + x = observed_traj[-1][0] + y = observed_traj[-1][1] + vx, vy = velocity + pred_traj = constant_speed_and_yaw_rate(x, y, vx, vy, yaw, yaw_rate, self.predict_length, self.dt) + return pred_traj + + +class ConstantMagnitudeAccelAndYawRate(Baseline): + def __call__(self, kinematics_data): + observed_traj, velocity, acc, yaw, yaw_rate = kinematics_data + x = observed_traj[-1][0] + y = observed_traj[-1][1] + vx, vy = velocity + ax, ay = acc + pred_traj = constant_magnitude_accel_and_yaw_rate(x, y, vx, vy, ax, ay, + yaw, yaw_rate, self.predict_length, self.dt) + return pred_traj + + +class PhysicsOracle(Baseline): + def __call__(self, kinematics_data, ground_truth): + assert len(ground_truth.shape) == 2 and ground_truth.shape[0] == self.predict_length \ + and ground_truth.shape[1] == 2 + models = [ + ConstantVelocityHeading, + ConstantAccelerationHeading, + ConstantSpeedYawRate, + ConstantMagnitudeAccelAndYawRate + ] + models = [model(self.observed_length, self.predict_length, self.dt) for model in models] + all_preds = [model(kinematics_data) for model in models] + oracles = sorted(all_preds, + key=lambda x: np.linalg.norm(np.array(x) - ground_truth, ord="fro")) + oracle = oracles[0] + return oracle + + +def constant_velocity_heading(x, y, vx, vy, yaw, predict_length, dt): + """ + Predict trajectories based on constant velocity. + Parameters + ---------- + x : float + Vehicle's current x-axis position. + + y : float + Vehicle's current x-axis position. + + vx : float + Vehicle's current x-axis velocity. + + vy : float + Vehicle's current y-axis velocity. + + yaw : float + Vehicle's current yaw angle + + predict_length : int + Predicted trajectory length. + + dt : float + Time discretization interval. + + Returns + ------- + preds : np.array + Predicted trajectory with shape (predict_length, 2) + + """ + preds = [] + for i in range(1, predict_length + 1): + t = i dt + preds.append((x + vx * t, y + vy * t, yaw)) + return np.array(preds) + + +def constant_acceleration_and_heading(x, y, vx, vy, yaw, ax, ay, predict_length, dt): + preds = [] + for i in range(1, predict_length + 1): + t = i * dt + half_time_squared = 1 / 2 * t * t + preds.append((x + vx * t + half_time_squared * ax, y + vy * t + half_time_squared * ay, yaw)) + return np.array(preds) + + +def constant_speed_and_yaw_rate(x, y, vx, vy, yaw, yaw_rate, predict_length, dt): + preds = [] + distance_step = np.sqrt(vx 2 + vy 2) * dt + yaw_step = yaw_rate * dt + for i in range(1, predict_length + 1): + x += distance_step * np.cos(yaw) + y += distance_step * np.sin(yaw) + yaw += yaw_step + preds.append((x, y, yaw)) + return np.array(preds) + + +def constant_magnitude_accel_and_yaw_rate(x, y, vx, vy, ax, ay, yaw, yaw_rate, predict_length, dt): + preds = [] + a_value = np.sqrt(ax 2 + ay 2) + v_value = np.sqrt(vx 2 + vy 2) + speed_step = a_value * dt + yaw_step = yaw_rate * dt + for i in range(1, predict_length + 1): + distance_step = dt * v_value + x += distance_step * np.cos(yaw) + y += distance_step * np.sin(yaw) + v_value += speed_step + yaw += yaw_step + preds.append((x, y, yaw)) + return np.array(preds) diff --git a/requirements.txt b/requirements.txt index c91e774e..91e2354d 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,6 +1,6 @@ matplotlib==3.4.2 networkx==2.5.1 -numpy==1.2.0 +numpy==1.20.0 pillow pyparsing==2.4.7 six==1.16.0 @@ -13,3 +13,4 @@ scipy==1.6.3 seaborn lxml shapely==1.8.4 +omegaconf	Coperception - basic docs for setting up the scenario runner ![image](https://user-images.githubusercontent.com/8717276/232376028-2e881157-6fc4-4837-99be-6426a1d3757b.png)		2023-04-09T04:49:13	[]	[]
ucla-mobility/OpenCDA	ucla-mobility__OpenCDA-196	a29544a93564c88e1f1dcda5b26731cf361fefdc	diff --git a/opencda/core/common/cav_world.py b/opencda/core/common/cav_world.py index 794bab8f..db492a85 100644 --- a/opencda/core/common/cav_world.py +++ b/opencda/core/common/cav_world.py @@ -55,6 +55,12 @@ def __init__(self, apply_ml=False): # this is used only when co-simulation activated. self.sumo2carla_ids = {} + def destroy(self): + for vehicle_manager in self._vehicle_manager_dict.values(): + vehicle_manager.destroy() + for rsu_manager in self._rsu_manager_dict.values(): + rsu_manager.destory() + def update_vehicle_manager(self, vehicle_manager): """ Update created CAV manager to the world. diff --git a/opencda/core/map/map_manager.py b/opencda/core/map/map_manager.py index 92e7f645..70f70aed 100644 --- a/opencda/core/map/map_manager.py +++ b/opencda/core/map/map_manager.py @@ -289,7 +289,10 @@ def generate_lane_cross_info(self): while nxt.road_id == waypoint.road_id \ and nxt.lane_id == waypoint.lane_id: waypoints.append(nxt) - nxt = nxt.next(self.lane_sample_resolution)[0] + nxt_list = nxt.next(self.lane_sample_resolution) + if len(nxt_list) == 0: + break + nxt = nxt_list[0] # waypoint is the centerline, we need to calculate left lane mark left_marking = [lateral_shift(w.transform, -w.lane_width * 0.5) for	Develop This PR mainly include the docker build for OpenCDA.		2023-03-23T01:32:21	[]	[]
ucla-mobility/OpenCDA	ucla-mobility__OpenCDA-191	e95071dbd640c2c732e98d053da40d5ef785a185	diff --git a/.gitignore b/.gitignore index 65583e2c..67e1370c 100644 --- a/.gitignore +++ b/.gitignore @@ -148,4 +148,4 @@ evaluation_outputs/ docs/tmp.py video_dumping data_dumping - +ready* diff --git a/docs/md_files/yaml_define.md b/docs/md_files/yaml_define.md index 6cc442d6..77267000 100644 --- a/docs/md_files/yaml_define.md +++ b/docs/md_files/yaml_define.md @@ -1,16 +1,26 @@ ## Yaml Rule -To construct a scenario testing in OpenCDA, users have to first write a yml file to define the simulation parameters. To help reuse the parameters across different modules within the system, we adopt the `name anchor`. To know more details about the `named anchor` feature in yaml file, [read this blog](https://anil.io/blog/symfony/yaml/using-variables-in-yaml-files/). +To create a scenario test in OpenCDA, you need to start by writing a yml file to define the simulation parameters. This can be a bit tricky, so we've provided a helpful starting point in the form of a yaml file called default.yaml. -Below we demonstrate an example in `platoon_2lanefree_carla` scenario. Our example yaml files for various scenes are stored in the path`opencda/scenario_testing/config_yaml`. +The default.yaml file provides default parameters for a scenario, which you can modify as needed to create your own scenario. Instead of starting from scratch, you can use this file as a template and only change the parts that are different from the default parameters. +If you're not sure where to start, we've included example yaml files for various scenarios in the opencda/scenario_testing/config_yaml directory. You can use these as a guide or a starting point for your own scenario. + +Below show an concrete example: ```yaml -# platoon_2lanefree_carla.yaml -world: # define the CARLA server setting - sync_mode: true # whether to use sync mode - client_port: 2000 # client port to connect to the server - fixed_delta_seconds: &delta 0.05 # fixed time step - weather: # set weather parameters +# default.yaml +description: \|- + Copyright 2021 <UCLA Mobility Lab> + Author: Runsheng Xu <[email protected]> + Content: This is the template scenario testing configuration file that other scenarios could directly refer + +# define carla simulation setting +world: + sync_mode: true + client_port: 2000 + fixed_delta_seconds: 0.05 + seed: 11 # seed for numpy and random + weather: sun_altitude_angle: 15 # 90 is the midday and -90 is the midnight cloudiness: 0 # 0 is the clean sky and 100 is the thickest cloud precipitation: 0 # rain, 100 is the heaviest rain @@ -22,58 +32,277 @@ world: # define the CARLA server setting wetness: 0 -vehicle_base: &vehicle_base # define cav default parameters - sensing: &base_sensing # define sensing parameters - perception: &base_perception # define perception related settings - localization: &base_localize # define localization related settings - map_manager: &base_map_manager # define HDMap manager - behavior: &base_behavior # define planning related parameters - controller: &base_controller # define controller - type: pid_controller # define the type of controller - args: ... # define the detailed parmaters of the controller - v2x: &base_v2x # define v2x configuration - -carla_traffic_manager: # define the background traffic controled by carla.TrafficManager - global_speed_perc: -100 # define the default speed of traffic flow - auto_lane_change: false # whether lane change is allowed in the traffic flow - ...: # some other parameters - random: true # whether the car models of traffic flow are random selected - vehicle_list: # define all human drive vehicles' position and individual speed - - spawn_position: [x,y,z,pitch,yaw,roll] - vehicle_speed_perc: -200 # this speed will overwrite the default traffic flow speed - # this is another we to define carla traffic flow by giving the number of - # spawn vehicles and the spawn range. - # vehicle_list : 10 - # range: [x1, y1, x2, y2] - -platoon_base: &platoon_base # define the platoon default characteristics +# Define the basic parameters of the rsu +rsu_base: + sensing: + perception: + activate: false # when not activated, objects positions will be retrieved from server directly + camera: + visualize: 4 # how many camera images need to be visualized. 0 means no visualization for camera + num: 4 # how many cameras are mounted on the vehicle. Maximum 3(frontal, left and right cameras) + # relative positions (x,y,z,yaw) of the camera. len(positions) should be equal to camera num + positions: + - [2.5, 0, 1.0, 0] + - [0.0, 0.3, 1.8, 100] + - [0.0, -0.3, 1.8, -100] + - [-2.0, 0.0, 1.5, 180] + lidar: # lidar sensor configuration, check CARLA sensor reference for more details + visualize: true + channels: 32 + range: 120 + points_per_second: 1000000 + rotation_frequency: 20 # the simulation is 20 fps + upper_fov: 2 + lower_fov: -25 + dropoff_general_rate: 0.3 + dropoff_intensity_limit: 0.7 + dropoff_zero_intensity: 0.4 + noise_stddev: 0.02 + localization: + activate: true # when not activated, ego position will be retrieved from server directly + dt: ${world.fixed_delta_seconds} # used for kalman filter + gnss: # gnss sensor configuration + noise_alt_stddev: 0.05 + noise_lat_stddev: 3e-6 + noise_lon_stddev: 3e-6 + +# Basic parameters of the vehicles +vehicle_base: + sensing: # include perception and localization + perception: + activate: false # when not activated, objects positions will be retrieved from server directly + camera: + visualize: 1 # how many camera images need to be visualized. 0 means no visualization for camera + num: 1 # how many cameras are mounted on the vehicle. + positions: # relative positions (x,y,z,yaw) of the camera. len(positions) should be equal to camera num + - [2.5, 0, 1.0, 0] + lidar: # lidar sensor configuration, check CARLA sensor reference for more details + visualize: true + channels: 32 + range: 50 + points_per_second: 100000 + rotation_frequency: 20 # the simulation is 20 fps + upper_fov: 10.0 + lower_fov: -30.0 + dropoff_general_rate: 0.0 + dropoff_intensity_limit: 1.0 + dropoff_zero_intensity: 0.0 + noise_stddev: 0.0 + + localization: + activate: false # when not activated, ego position will be retrieved from server directly + dt: ${world.fixed_delta_seconds} # used for kalman filter + gnss: # gnss sensor configuration + noise_alt_stddev: 0.001 + noise_lat_stddev: 1.0e-6 + noise_lon_stddev: 1.0e-6 + heading_direction_stddev: 0.1 # degree + speed_stddev: 0.2 + debug_helper: + show_animation: false # whether to show real-time trajectory plotting + x_scale: 1.0 # used to multiply with the x coordinate to make the error on x axis clearer + y_scale: 100.0 # used to multiply with the y coordinate to make the error on y axis clearer + + map_manager: + pixels_per_meter: 2 # rasterization map resolution + raster_size: [224, 224] # the rasterize map size (pixel) + lane_sample_resolution: 0.1 # for every 0.1m, we draw a point of lane + visualize: true # whether to visualize the rasteraization map + activate: true # whether activate the map manager + + safety_manager: # used to watch the safety status of the cav + print_message: true # whether to print the message if hazard happens + collision_sensor: + history_size: 30 + col_thresh: 1 + stuck_dector: + len_thresh: 500 + speed_thresh: 0.5 + offroad_dector: [ ] + traffic_light_detector: # whether the vehicle violate the traffic light + light_dist_thresh: 20 + + behavior: + max_speed: 111 # maximum speed, km/h + tailgate_speed: 121 # when a vehicles needs to be close to another vehicle asap + speed_lim_dist: 3 # max_speed - speed_lim_dist = target speed + speed_decrease: 15 # used in car following mode to decrease speed for distance keeping + safety_time: 4 # ttc safety thresholding for decreasing speed + emergency_param: 0.4 # used to identify whether a emergency stop needed + ignore_traffic_light: true # whether to ignore traffic light + overtake_allowed: true # whether overtake allowed, typically false for platoon leader + collision_time_ahead: 1.5 # used for collision checking + overtake_counter_recover: 35 # the vehicle can not do another overtake during next certain steps + sample_resolution: 4.5 # the unit distance between two adjacent waypoints in meter + local_planner: # trajectory planning related + buffer_size: 12 # waypoint buffer size + trajectory_update_freq: 15 # used to control trajectory points updating frequency + waypoint_update_freq: 9 # used to control waypoint updating frequency + min_dist: 3 # used to pop out the waypoints too close to current location + trajectory_dt: 0.20 # for every dt seconds, we sample a trajectory point from the trajectory path as next goal state + debug: false # whether to draw future/history waypoints + debug_trajectory: false # whether to draw the trajectory points and path + + controller: + type: pid_controller # this has to be exactly the same name as the controller py file + args: + lat: + k_p: 0.75 + k_d: 0.02 + k_i: 0.4 + lon: + k_p: 0.37 + k_d: 0.024 + k_i: 0.032 + dynamic: false # whether use dynamic pid setting + dt: ${world.fixed_delta_seconds} # this should be equal to your simulation time-step + max_brake: 1.0 + max_throttle: 1.0 + max_steering: 0.3 + v2x: # communication related + enabled: true + communication_range: 35 + + +# define the background traffic control by carla +carla_traffic_manager: + sync_mode: true # has to be same as the world setting + global_distance: 5 # the minimum distance in meters that vehicles have to keep with the rest + # Sets the difference the vehicle's intended speed and its current speed limit. + # Carla default speed is 30 km/h, so -100 represents 60 km/h, + # and 20 represents 24 km/h + global_speed_perc: -100 + set_osm_mode: true # Enables or disables the OSM mode. + auto_lane_change: false + ignore_lights_percentage: 0 # whether set the traffic ignore traffic lights + random: false # whether to random select vehicles' color and model + vehicle_list: [] # define in each scenario. If set to ~, then the vehicles be spawned in a certain range + # Used only when vehicle_list is ~ + # x_min, x_max, y_min, y_max, x_step, y_step, vehicle_num + range: [] + +# define the platoon basic characteristics +platoon_base: + max_capacity: 10 + inter_gap: 0.6 # desired time gap + open_gap: 1.2 # open gap + warm_up_speed: 55 # required speed before cooperative merging + change_leader_speed: true # whether to assign leader multiple speed to follow + leader_speeds_profile: [ 85, 95 ] # different speed for leader to follow + stage_duration: 10 # how long should the leader keeps in the current velocity stage + +# define tne scenario in each specific scenario +scenario: + single_cav_list: [] + platoon_list: [] +``` +The above yaml file is the `default.yaml`. If the users wants to create a platoon joining scenario in highway, +here is how we create `platoon_joining_2lanefree_carla.yaml`: + +```yaml +# platoon_joining_2lanefree_carla.yaml +vehicle_base: + sensing: + perception: + camera: + visualize: 0 # how many camera images need to be visualized. 0 means no visualization for camera + num: 0 # how many cameras are mounted on the vehicle. Maximum 3(frontal, left and right cameras) + # relative positions (x,y,z,yaw) of the camera. len(positions) should be equal to camera num + positions: [] + lidar: + visualize: false + map_manager: + visualize: false + activate: false + behavior: + max_speed: 95 # maximum speed, km/h + tailgate_speed: 105 # when a vehicles needs to be close to another vehicle asap + overtake_allowed: false # whether overtake allowed, typically false for platoon leader + collision_time_ahead: 1.3 # used for collision checking + overtake_counter_recover: 35 # the vehicle can not do another overtake during next certain steps + local_planner: + trajectory_dt: 0.25 # for every dt seconds, we sample a trajectory point from the trajectory path as next goal state + +# define the platoon basic characteristics +platoon_base: max_capacity: 10 inter_gap: 0.6 # desired time gap - open_gap: 1.5 # desired open gap during cut-in join - warm_up_speed: 55 # required minimum speed before cooperative merging - - -scenario: # define each cav's spawn position, driving task, and parameters - platoon_list: # define the platoons - - <<: platoon_base # the first platoon will take the default platoon parameters - destination: [x, y, z] # platoon destination - members: # define the first platoon's members - - <<: vehicle_base # the platoon leader(a cav) will take the default cav parameters - spawn_position: ... - behavior: - <<: base_behavior - max_speed: 100 # overwrite the default target speed defined in &vehicle_base - platoon: platoon_base # add a new category 'platoon' to the origin vehicle parameters - - <<: vehicle_base # the second platoon mameber(a cav) - ... - single_cav_list: # define the cavs that are not in any platoon and aim to search and join one. - - <<: vehicle_base - spawn_position: [x,y,z,pitch,yaw,roll] - destination: [x, y, z] - sensing: &base_sensing # point to default sensing setting - ...: ... # overwrite the default sensing parameters for this cav - + open_gap: 1.5 # open gap + warm_up_speed: 55 # required speed before cooperative merging + +# define the background traffic control by carla +carla_traffic_manager: + global_distance: 4.0 # the minimum distance in meters that vehicles have to keep with the rest + # Sets the difference the vehicle's intended speed and its current speed limit. + # Carla default speed is 30 km/h, so -100 represents 60 km/h, + # and 20 represents 24 km/h + global_speed_perc: -300 + vehicle_list: + - spawn_position: [-285, 8.3, 0.3, 0, 0, 0] + - spawn_position: [-310, 8.3, 0.3, 0, 0, 0] + - spawn_position: [-390, 8.3, 0.3, 0, 0, 0] + - spawn_position: [-320, 4.8, 0.3, 0, 0, 0] + vehicle_speed_perc: -200 + - spawn_position: [-335, 4.8, 0.3, 0, 0, 0] + - spawn_position: [-360, 4.8, 0.3, 0, 0, 0] + - spawn_position: [-400, 4.8, 0.3, 0, 0, 0] + - spawn_position: [-410, 4.8, 0.3, 0, 0, 0] + +# define scenario. In this scenario, a 4-vehicle platoon already exists. +scenario: + platoon_list: + - name: platoon1 + destination: [1000.372955, 8.3, 0.3] + members: # the first one is regarded as leader by default + - name: cav1 + spawn_position: [-350, 8.3, 0.3, 0, 0, 0] # x, y, z, roll, yaw, pitch + perception: + camera: + visualize: 1 # how many camera images need to be visualized. 0 means no visualization for camera + num: 1 # how many cameras are mounted on the vehicle. Maximum 3(frontal, left and right cameras) + # relative positions (x,y,z,yaw) of the camera. len(positions) should be equal to camera num + positions: + - [2.5, 0, 1.0, 0] + lidar: + visualize: true + behavior: + local_planner: + debug_trajectory: true + debug: false + - name: cav2 + spawn_position: [-360, 8.3, 0.3, 0, 0, 0] + - name: cav3 + spawn_position: [-370, 8.3, 0.3, 0, 0, 0] + - name: cav4 + spawn_position: [-380, 8.3, 0.3, 0, 0, 0] + single_cav_list: # this is for merging vehicle or single cav without v2x + - name: single_cav + spawn_position: [-380, 4.8, 0.3, 0, 0, 0] + # when this is defined, the above parameter will be ignored, and a special map function will + # be used to define the spawn position based on the argument + spawn_special: [0.625] + destination: [300, 12.0, 0] + sensing: + perception: + camera: + visualize: 1 # how many camera images need to be visualized. 0 means no visualization for camera + num: 1 # how many cameras are mounted on the vehicle. Maximum 3(frontal, left and right cameras) + # relative positions (x,y,z,yaw) of the camera. len(positions) should be equal to camera num + positions: + - [2.5, 0, 1.0, 0] + lidar: + visualize: true + v2x: + communication_range: 35 + behavior: + overtake_allowed: true + local_planner: + debug_trajectory: true + debug: false ``` +As you can see, the `platoon_joining_2lanefree_carla.yaml` only contains the part that `default.yaml` does not have or has different +parameters. + ### Detailed Explanation --- @@ -174,14 +403,16 @@ carla_traffic_manager: - spawn_position: [100, 100, 0.3, 0 , 20, 0] - spawn_position: [122, 666, 0.3, 0 , 0, 0] ``` -* Set the parameter `vehicle_list` under `carla_traffic_manager` as an integer. The CARLA server will then spawn -the same number of vehicles. If `vehicle_list` is an integer, an additional parameter `range` needs to be set to -give the server the spawn area. In the example shown below, 5 vehicles will be randomly spawn in the restricted -rectangle area `0<x<100, 22<y<334`. +* Set the parameter `vehicle_list` under `carla_traffic_manager` as `~`. The CARLA server will then spawn +the vehicles randomly in a certain rectangle range given by the additional parameter `range`. ```yaml carla_traffic_manager: - vehicle_list : 5 - range: [0, 100, 22, 334] + vehicle_list: ~ # a number or a list + # Used only when vehicle_list is a number. + # x_min, x_max, y_min, y_max, x_step, y_step, veh_num + range: + - [ 2, 10, 0, 200, 3.5, 25, 30] + ``` Other important parameters: * `sync_mode` : bool type, it should be consistent with server's sync setting. diff --git a/opencda.py b/opencda.py index 8eca93c1..20ef5199 100644 --- a/opencda.py +++ b/opencda.py @@ -10,19 +10,22 @@ import importlib import os import sys +from omegaconf import OmegaConf from opencda.version import __version__ def arg_parse(): + # create an argument parser parser = argparse.ArgumentParser(description="OpenCDA scenario runner.") + # add arguments to the parser parser.add_argument('-t', "--test_scenario", required=True, type=str, help='Define the name of the scenario you want to test. The given name must' 'match one of the testing scripts(e.g. single_2lanefree_carla) in ' 'opencda/scenario_testing/ folder' ' as well as the corresponding yaml file in opencda/scenario_testing/config_yaml.') - parser.add_argument("--record", action='store_true', help='whether to record and save the simulation process to' - '.log file') + parser.add_argument("--record", action='store_true', + help='whether to record and save the simulation process to .log file') parser.add_argument("--apply_ml", action='store_true', help='whether ml/dl framework such as sklearn/pytorch is needed in the testing. ' @@ -30,25 +33,41 @@ def arg_parse(): parser.add_argument('-v', "--version", type=str, default='0.9.11', help='Specify the CARLA simulator version, default' 'is 0.9.11, 0.9.12 is also supported.') - + # parse the arguments and return the result opt = parser.parse_args() return opt def main(): + # parse the arguments opt = arg_parse() + # print the version of OpenCDA print("OpenCDA Version: %s" % __version__) - - testing_scenario = importlib.import_module("opencda.scenario_testing.%s" % opt.test_scenario) - + # set the default yaml file + default_yaml = config_yaml = os.path.join( + os.path.dirname(os.path.realpath(__file__)), + 'opencda/scenario_testing/config_yaml/default.yaml') + # set the yaml file for the specific testing scenario config_yaml = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'opencda/scenario_testing/config_yaml/%s.yaml' % opt.test_scenario) + # load the default yaml file and the scenario yaml file as dictionaries + default_dict = OmegaConf.load(default_yaml) + scene_dict = OmegaConf.load(config_yaml) + # merge the dictionaries + scene_dict = OmegaConf.merge(default_dict, scene_dict) + + # import the testing script + testing_scenario = importlib.import_module( + "opencda.scenario_testing.%s" % opt.test_scenario) + # check if the yaml file for the specific testing scenario exists if not os.path.isfile(config_yaml): - sys.exit("opencda/scenario_testing/config_yaml/%s.yaml not found!" % opt.test_cenario) + sys.exit( + "opencda/scenario_testing/config_yaml/%s.yaml not found!" % opt.test_cenario) + # get the function for running the scenario from the testing script scenario_runner = getattr(testing_scenario, 'run_scenario') - # run scenario testing - scenario_runner(opt, config_yaml) + # run the scenario testing + scenario_runner(opt, scene_dict) if __name__ == '__main__': diff --git a/opencda/core/common/data_dumper.py b/opencda/core/common/data_dumper.py index b347f9f0..78b1b3b9 100644 --- a/opencda/core/common/data_dumper.py +++ b/opencda/core/common/data_dumper.py @@ -100,13 +100,14 @@ def run_step(self, if self.count % 2 != 0: return - self.save_rgb_image() - self.save_lidar_points() + self.save_rgb_image(self.count) + # self.save_lidar_points() self.save_yaml_file(perception_manager, localization_manager, - behavior_agent) + behavior_agent, + self.count) - def save_rgb_image(self): + def save_rgb_image(self, count): """ Save camera rgb images to disk. """ @@ -115,7 +116,7 @@ def save_rgb_image(self): frame = camera.frame image = camera.image - image_name = '%06d' % frame + '_' + 'camera%d' % i + '.png' + image_name = '%06d' % count + '_' + 'camera%d' % i + '.png' cv2.imwrite(os.path.join(self.save_parent_folder, image_name), image) @@ -149,7 +150,8 @@ def save_lidar_points(self): def save_yaml_file(self, perception_manager, localization_manager, - behavior_agent): + behavior_agent, + count): """ Save objects positions/spped, true ego position, predicted ego position, sensor transformations. @@ -165,7 +167,7 @@ def save_yaml_file(self, behavior_agent : opencda object OpenCDA behavior agent. """ - frame = self.lidar.frame + frame = count dump_yml = {} vehicle_dict = {} diff --git a/opencda/core/common/vehicle_manager.py b/opencda/core/common/vehicle_manager.py index 99a15afa..5135f082 100644 --- a/opencda/core/common/vehicle_manager.py +++ b/opencda/core/common/vehicle_manager.py @@ -17,6 +17,7 @@ import LocalizationManager from opencda.core.sensing.perception.perception_manager \ import PerceptionManager +from opencda.core.safety.safety_manager import SafetyManager from opencda.core.plan.behavior_agent \ import BehaviorAgent from opencda.core.map.map_manager import MapManager @@ -107,7 +108,9 @@ def __init__( self.map_manager = MapManager(vehicle, carla_map, map_config) - + # safety manager + self.safety_manager = SafetyManager(vehicle=vehicle, + params=config_yaml['safety_manager']) # behavior agent self.agent = None if 'platooning' in application: @@ -181,6 +184,15 @@ def update_info(self): # update the ego pose for map manager self.map_manager.update_information(ego_pos) + # this is required by safety manager + safety_input = {'ego_pos': ego_pos, + 'ego_speed': ego_spd, + 'objects': objects, + 'carla_map': self.carla_map, + 'world': self.vehicle.get_world(), + 'static_bev': self.map_manager.static_bev} + self.safety_manager.update_info(safety_input) + # update ego position and speed to v2x manager, # and then v2x manager will search the nearby cavs self.v2x_manager.update_info(ego_pos, ego_spd) diff --git a/opencda/core/map/map_manager.py b/opencda/core/map/map_manager.py index 4e0a2cdc..92e7f645 100644 --- a/opencda/core/map/map_manager.py +++ b/opencda/core/map/map_manager.py @@ -26,8 +26,6 @@ class MapManager(object): """ This class is used to manage HD Map. We emulate the style of Lyft dataset. - todo: Currently mainly used for map rasterization. - todo: everything is groundtruth loaded from server directly. Parameters ---------- @@ -65,7 +63,6 @@ class MapManager(object): crosswalk_info : dict A dictionary that contains all crosswalk information. - todo: will be implemented in the next version. traffic_light_info : dict A dictionary that contains all traffic light information. @@ -127,15 +124,9 @@ def __init__(self, vehicle, carla_map, config): self.generate_lane_cross_info() # bev maps - self.dynamic_bev = 255 * np.zeros( - shape=(self.raster_size[1], self.raster_size[0], 3), - dtype=np.uint8) - self.static_bev = 255 * np.ones( - shape=(self.raster_size[1], self.raster_size[0], 3), - dtype=np.uint8) - self.vis_bev = 255 * np.ones( - shape=(self.raster_size[1], self.raster_size[0], 3), - dtype=np.uint8) + self.dynamic_bev = None + self.static_bev = None + self.vis_bev = None def update_information(self, ego_pose): """ diff --git a/opencda/core/plan/behavior_agent.py b/opencda/core/plan/behavior_agent.py index d45bb38a..847d9b5c 100644 --- a/opencda/core/plan/behavior_agent.py +++ b/opencda/core/plan/behavior_agent.py @@ -142,6 +142,9 @@ def __init__(self, vehicle, carla_map, config_yaml): # debug helper self.debug_helper = PlanDebugHelper(self.vehicle.id) + # print message in debug mode + self.debug = False if 'debug' not in \ + config_yaml else config_yaml['debug'] def update_information(self, ego_pos, ego_speed, objects): """ @@ -308,14 +311,15 @@ def reroute(self, spawn_points): spawn_points : list List of possible destinations for the agent. """ - - print("Target almost reached, setting new destination...") + if self.debug: + print("Target almost reached, setting new destination...") random.shuffle(spawn_points) new_start = \ self._local_planner.waypoints_queue[-1][0].transform.location destination = spawn_points[0].location if \ spawn_points[0].location != new_start else spawn_points[1].location - print("New destination: " + str(destination)) + if self.debug: + print("New destination: " + str(destination)) self.set_destination(new_start, destination) @@ -390,7 +394,6 @@ def traffic_light_manager(self, waypoint): # indicate no need to stop return 0 - if not waypoint.is_junction and ( self.light_id_to_ignore != light_id or light_id == -1): return 1 @@ -728,13 +731,13 @@ def get_push_destination(self, ego_vehicle_wp, is_intersection): reset_target = \ ego_vehicle_wp.next(max(self._ego_speed / 3.6 * 3, 10.0))[0] - - print( - 'Vehicle id: %d :destination pushed forward because of ' - 'potential collision, reset destination :%f. %f, %f' % - (self.vehicle.id, reset_target.transform.location.x, - reset_target.transform.location.y, - reset_target.transform.location.z)) + if self.debug: + print( + 'Vehicle id: %d :destination pushed forward because of ' + 'potential collision, reset destination :%f. %f, %f' % + (self.vehicle.id, reset_target.transform.location.x, + reset_target.transform.location.y, + reset_target.transform.location.z)) return reset_target def run_step( @@ -791,7 +794,8 @@ def run_step( # 2. when the temporary route is finished, we return to the global route if len(self.get_local_planner().get_waypoints_queue()) == 0 \ and len(self.get_local_planner().get_waypoint_buffer()) <= 2: - print('Destination Reset!') + if self.debug: + print('Destination Reset!') # in case the vehicle is disabled overtaking function # at the beginning self.overtake_allowed = True and self.overtake_allowed_origin @@ -812,7 +816,6 @@ def run_step( # Path generation based on the global route rx, ry, rk, ryaw = self._local_planner.generate_path() - # check whether lane change is allowed self.lane_change_allowed = self.check_lane_change_permission(lane_change_allowed, collision_detector_enabled, rk) diff --git a/opencda/core/plan/local_planner_behavior.py b/opencda/core/plan/local_planner_behavior.py index ddbb0a5e..04d80392 100644 --- a/opencda/core/plan/local_planner_behavior.py +++ b/opencda/core/plan/local_planner_behavior.py @@ -469,7 +469,7 @@ def buffer_filter(self): self._ego_pos.location, self._ego_pos.rotation.yaw) if angle > 90: - print('delete waypoint!') + # print('delete waypoint!') del self._waypoint_buffer[j] continue diff --git a/opencda/core/safety/__init__.py b/opencda/core/safety/__init__.py new file mode 100644 index 00000000..38d0f116 --- /dev/null +++ b/opencda/core/safety/__init__.py @@ -0,0 +1,1 @@ +"""Safety/Human in the loop system related""" \ No newline at end of file diff --git a/opencda/core/safety/safety_manager.py b/opencda/core/safety/safety_manager.py new file mode 100644 index 00000000..ac3069d4 --- /dev/null +++ b/opencda/core/safety/safety_manager.py @@ -0,0 +1,51 @@ +""" +The safety manager is used to collect the AV's hazard status and give the +control back to human if necessary +""" +import numpy as np +import carla + +from opencda.core.safety.sensors import CollisionSensor, \ + TrafficLightDector, StuckDetector, OffRoadDetector + + +class SafetyManager: + """ + A class that manages the safety of a given vehicle in a simulation environment. + + Parameters + ---------- + vehicle: carla.Actor + The vehicle that the SafetyManager is responsible for. + params: dict + A dictionary of parameters that are used to configure the SafetyManager. + """ + def __init__(self, vehicle, params): + self.vehicle = vehicle + self.print_message = params['print_message'] + self.sensors = [CollisionSensor(vehicle, params['collision_sensor']), + StuckDetector(params['stuck_dector']), + OffRoadDetector(params['offroad_dector']), + TrafficLightDector(params['traffic_light_detector'], + vehicle)] + + def update_info(self, data_dict) -> dict: + status_dict = {} + for sensor in self.sensors: + sensor.tick(data_dict) + status_dict.update(sensor.return_status()) + if self.print_message: + print_flag = False + # only print message when it has hazard + for key, val in status_dict.items(): + if val == True: + print_flag = True + break + if print_flag: + print("Safety Warning from the safety manager:") + print(status_dict) + + + def destroy(self): + for sensor in self.sensors: + sensor.destroy() diff --git a/opencda/core/safety/sensors.py b/opencda/core/safety/sensors.py new file mode 100644 index 00000000..fa77fe3f --- /dev/null +++ b/opencda/core/safety/sensors.py @@ -0,0 +1,355 @@ +""" +Sensors related to safety status check +""" +import math +import numpy as np +import carla +import weakref +import shapely +from collections import deque +from typing import List + + +class CollisionSensor(object): + """ + Collision detection sensor. + + Parameters + ---------- + vehicle : carla.Vehicle + The carla.Vehicle, this is for cav. + params : dict + The dictionary containing sensor configurations. + + Attributes + ---------- + image : np.ndarray + Current received rgb image. + sensor : carla.sensor + The carla sensor that mounts at the vehicle. + """ + + def __init__(self, vehicle, params): + world = vehicle.get_world() + + blueprint = world.get_blueprint_library().find('sensor.other.collision') + self.sensor = world.spawn_actor(blueprint, carla.Transform(), + attach_to=vehicle) + + # We need to pass the lambda a weak reference to self to avoid circular + # reference. + weak_self = weakref.ref(self) + self.sensor.listen( + lambda event: CollisionSensor._on_collision(weak_self, event)) + + self.collided = False + self.collided_frame = -1 + self._history = deque(maxlen=params['history_size']) + self._threshold = params['col_thresh'] + + @staticmethod + def _on_collision(weak_self, event) -> None: + self = weak_self() + if not self: + return + impulse = event.normal_impulse + intensity = math.sqrt(impulse.x 2 + impulse.y 2 + impulse.z 2) + self._history.append((event.frame, intensity)) + if intensity > self._threshold: + self.collided = True + self.collided_frame = event.frame + + def return_status(self): + return {'collision': self.collided} + + def tick(self, data_dict): + pass + + def destroy(self) -> None: + """ + Clear collision sensor in Carla world. + """ + self._history.clear() + if self.sensor.is_alive: + self.sensor.stop() + self.sensor.destroy() + + +class StuckDetector(object): + """ + Stuck detector used to detect vehicle stuck in simulator. + It takes speed as input in each tick. + + Parameters + ---------- + params : dict + The dictionary containing sensor configurations. + """ + + def __init__(self, params): + self._speed_queue = deque(maxlen=params['len_thresh']) + self._len_thresh = params['len_thresh'] + self._speed_thresh = params['speed_thresh'] + + self.stuck = False + + def tick(self, data_dict) -> None: + """ + Update one tick + + Parameters + ---------- + data_dict : dict + The data dictionary provided by the upsteam modules. + """ + speed = data_dict['ego_speed'] + self._speed_queue.append(speed) + if len(self._speed_queue) >= self._len_thresh: + if np.average(self._speed_queue) < self._speed_thresh: + self.stuck = True + return + self.stuck = False + + def return_status(self): + return {'stuck': self.stuck} + + def destroy(self): + """ + Clear speed history + """ + self._speed_queue.clear() + + +class OffRoadDetector(object): + """ + A detector to monitor whether + + Parameters + ---------- + params : dict + The dictionary containing sensor configurations. + """ + + def __init__(self, params): + self.off_road = False + + def tick(self, data_dict) -> None: + """ + Update one tick + + Parameters + ---------- + data_dict : dict + The data dictionary provided by the upsteam modules. + """ + # static bev map that indicate where is the road + static_map = data_dict['static_bev'] + if static_map is None: + return + h, w = static_map.shape[0], static_map.shape[1] + # the ego is always at the center of the bev map. If the pixel is + # black, that means the vehicle is off road. + if np.mean(static_map[h // 2, w // 2]) == 255: + self.off_road = True + else: + self.off_road = False + + def return_status(self): + return {'offroad': self.off_road} + + def destroy(self): + pass + + +class TrafficLightDector(object): + """ + Interface of traffic light detector and recorder. It detects next traffic light state, + calculates distance from hero vehicle to the end of this road, and if hero vehicle crosses + this line when correlated light is red, it will record running a red light + """ + + def __init__(self, params, vehicle): + self.ran_light = False + self._map = None + self.veh_extent = vehicle.bounding_box.extent.x + + self._light_dis_thresh = params['light_dist_thresh'] + self._active_light = None + self._last_light = None + + self.total_lights_ran = 0 + self.total_lights = 0 + self.ran_light = False + self.active_light_state = carla.TrafficLightState.Off + self.active_light_dis = 200 + + def tick(self, data_dict): + # Reset the "ran light" flag + self.ran_light = False + + # Extract the active traffic lights, vehicle transform, world, and map from data_dict + active_lights = data_dict['objects']['traffic_lights'] + vehicle_transform = data_dict['ego_pos'] + world = data_dict['world'] + self._map = data_dict['carla_map'] + + # Get the location of the first active traffic light + self._active_light = active_lights[0] if len(active_lights) > 0 \ + else None + vehicle_location = vehicle_transform.location + + # If there is an active traffic light, + # compute the distance between the vehicle and the traffic light + if self._active_light is not None: + light_trigger_location = self._active_light.get_location() + self.active_light_state = self._active_light.get_state() + delta = vehicle_location - light_trigger_location + distance = np.sqrt(sum([delta.x 2, delta.y 2, delta.z 2])) + + # Set the active light distance to the minimum of the + # computed distance and a maximum threshold + self.active_light_dis = min(200, distance) + + # If the vehicle is close enough to the traffic light, + # and the traffic light has changed since the last tick, + # increment the total number of traffic lights seen and set the + # last light to the current light + if self.active_light_dis < self._light_dis_thresh: + if self._last_light is None or self._active_light.actor.id != self._last_light.id: + self.total_lights += 1 + self._last_light = self._active_light.actor + else: + # If there is no active traffic light, set the active light state + # to "Off" and set the active light distance to a default value + self.active_light_state = carla.TrafficLightState.Off + self.active_light_dis = 200 + + # If there is a last light (i.e., a traffic light that was active + # in the previous tick), check if it is currently red + if self._last_light is not None: + if self._last_light.state != carla.TrafficLightState.Red: + return + + # Compute the endpoints of a line segment representing the + # vehicle's position and direction + veh_extent = self.veh_extent + tail_close_pt = self._rotate_point( + carla.Vector3D(-0.8 * veh_extent, 0.0, vehicle_location.z), + vehicle_transform.rotation.yaw + ) + tail_close_pt = vehicle_location + carla.Location(tail_close_pt) + tail_far_pt = self._rotate_point( + carla.Vector3D(-veh_extent - 1, 0.0, vehicle_location.z), + vehicle_transform.rotation.yaw + ) + tail_far_pt = vehicle_location + carla.Location(tail_far_pt) + + # Get the trigger waypoints for the last traffic light + trigger_waypoints = self._get_traffic_light_trigger_waypoints( + self._last_light) + + # For each trigger waypoint, + # check if the vehicle has crossed the stop line + for wp in trigger_waypoints: + tail_wp = self._map.get_waypoint(tail_far_pt) + + # Calculate the dot product (Might be unscaled, + # as only its sign is important) + ve_dir = vehicle_transform.get_forward_vector() + wp_dir = wp.transform.get_forward_vector() + dot_ve_wp = ve_dir.x * wp_dir.x + ve_dir.y * wp_dir.y + ve_dir.z * wp_dir.z + + # Check the lane until all the "tail" has passed + if tail_wp.road_id == wp.road_id and tail_wp.lane_id == wp.lane_id and dot_ve_wp > 0: + # This light is red and is affecting our lane + yaw_wp = wp.transform.rotation.yaw + lane_width = wp.lane_width + location_wp = wp.transform.location + + lft_lane_wp = self._rotate_point( + carla.Vector3D(0.4 * lane_width, 0.0, location_wp.z), + yaw_wp + 90) + lft_lane_wp = location_wp + carla.Location(lft_lane_wp) + rgt_lane_wp = self._rotate_point( + carla.Vector3D(0.4 * lane_width, 0.0, location_wp.z), + yaw_wp - 90) + rgt_lane_wp = location_wp + carla.Location(rgt_lane_wp) + + # Is the vehicle traversing the stop line? + if self._is_vehicle_crossing_line( + (tail_close_pt, tail_far_pt), + (lft_lane_wp, rgt_lane_wp)): + self.ran_light = True + self.total_lights_ran += 1 + self._last_light = None + + def _is_vehicle_crossing_line(self, seg1: List, seg2: List) -> bool: + """ + check if vehicle crosses a line segment + """ + line1 = shapely.geometry.LineString( + [(seg1[0].x, seg1[0].y), (seg1[1].x, seg1[1].y)]) + line2 = shapely.geometry.LineString( + [(seg2[0].x, seg2[0].y), (seg2[1].x, seg2[1].y)]) + inter = line1.intersection(line2) + + return not inter.is_empty + + def _rotate_point(self, point: carla.Vector3D, + angle: float) -> carla.Vector3D: + """ + rotate a given point by a given angle + """ + x_ = math.cos(math.radians(angle)) * point.x - math.sin( + math.radians(angle)) * point.y + y_ = math.sin(math.radians(angle)) * point.x + math.cos( + math.radians(angle)) * point.y + return carla.Vector3D(x_, y_, point.z) + + def _get_traffic_light_trigger_waypoints(self, + traffic_light: carla.Actor) -> \ + List[carla.Waypoint]: + # Get the transform information for the traffic light + base_transform = traffic_light.get_transform() + base_rot = base_transform.rotation.yaw + area_loc = base_transform.transform( + traffic_light.trigger_volume.location) + + # Get the extent of the trigger volume + area_ext = traffic_light.trigger_volume.extent + # Discretize the trigger box into points along the x-axis + x_values = np.arange(-0.9 * area_ext.x, 0.9 * area_ext.x, + 1.0) # 0.9 to avoid crossing to adjacent lanes + + # Create a list of discretized points + area = [] + for x in x_values: + point = self._rotate_point(carla.Vector3D(x, 0, area_ext.z), + base_rot) + point_location = area_loc + carla.Location(x=point.x, y=point.y) + area.append(point_location) + + # Get the waypoints of these points, removing duplicates + ini_wps = [] + for pt in area: + wpx = self._map.get_waypoint(pt) + # As x_values are arranged in order, only the last one has to be checked + if not ini_wps or ini_wps[-1].road_id != wpx.road_id or ini_wps[ + -1].lane_id != wpx.lane_id: + ini_wps.append(wpx) + + # Advance the waypoints until the intersection + wps = [] + for wpx in ini_wps: + while not wpx.is_intersection: + next_wp = wpx.next(0.5)[0] + if next_wp and not next_wp.is_intersection: + wpx = next_wp + else: + break + wps.append(wpx) + + return wps + + def return_status(self): + return {'ran_light': self.ran_light} diff --git a/opencda/core/sensing/perception/perception_manager.py b/opencda/core/sensing/perception/perception_manager.py index ecc777a9..49a7cca8 100644 --- a/opencda/core/sensing/perception/perception_manager.py +++ b/opencda/core/sensing/perception/perception_manager.py @@ -87,7 +87,10 @@ def spawn_point_estimation(relative_position, global_position): pitch = 0 carla_location = carla.Location(x=0, y=0, z=0) + x, y, z, yaw = relative_position + # this is for rsu. It utilizes global position instead of relative + # position to the vehicle if global_position is not None: carla_location = carla.Location( x=global_position[0], @@ -95,28 +98,9 @@ def spawn_point_estimation(relative_position, global_position): z=global_position[2]) pitch = -35 - if relative_position == 'front': - carla_location = carla.Location(x=carla_location.x + 2.5, - y=carla_location.y, - z=carla_location.z + 1.0) - yaw = 0 - - elif relative_position == 'right': - carla_location = carla.Location(x=carla_location.x + 0.0, - y=carla_location.y + 0.3, - z=carla_location.z + 1.8) - yaw = 100 - - elif relative_position == 'left': - carla_location = carla.Location(x=carla_location.x + 0.0, - y=carla_location.y - 0.3, - z=carla_location.z + 1.8) - yaw = -100 - else: - carla_location = carla.Location(x=carla_location.x - 2.0, - y=carla_location.y, - z=carla_location.z + 1.5) - yaw = 180 + carla_location = carla.Location(x=carla_location.x + x, + y=carla_location.y + y, + z=carla_location.z + z) carla_rotation = carla.Rotation(roll=0, yaw=yaw, pitch=pitch) spawn_point = carla.Transform(carla_location, carla_rotation) @@ -379,12 +363,13 @@ def __init__(self, vehicle, config_yaml, cav_world, self.vehicle = vehicle self.carla_world = carla_world if carla_world is not None \ else self.vehicle.get_world() + self._map = self.carla_world.get_map() self.id = infra_id if infra_id is not None else vehicle.id self.activate = config_yaml['activate'] - self.camera_visualize = config_yaml['camera_visualize'] - self.camera_num = min(config_yaml['camera_num'], 4) - self.lidar_visualize = config_yaml['lidar_visualize'] + self.camera_visualize = config_yaml['camera']['visualize'] + self.camera_num = config_yaml['camera']['num'] + self.lidar_visualize = config_yaml['lidar']['visualize'] self.global_position = config_yaml['global_position'] \ if 'global_position' in config_yaml else None @@ -406,7 +391,11 @@ def __init__(self, vehicle, config_yaml, cav_world, # camera visualization is needed if self.activate or self.camera_visualize: self.rgb_camera = [] - mount_position = ['front', 'right', 'left', 'back'] + mount_position = config_yaml['camera']['positions'] + assert len(mount_position) == self.camera_num, \ + "The camera number has to be the same as the length of the" \ + "relative positions list" + for i in range(self.camera_num): self.rgb_camera.append( CameraSensor( @@ -443,6 +432,9 @@ def __init__(self, vehicle, config_yaml, cav_world, # the dictionary contains all objects self.objects = {} + # traffic light detection related + self.traffic_thresh = config_yaml['traffic_light_thresh'] \ + if 'traffic_light_thresh' in config_yaml else 50 def dist(self, a): """ @@ -544,7 +536,6 @@ def activate_mode(self, objects): self.speed_retrieve(objects) if self.camera_visualize: - names = ['front', 'right', 'left', 'back'] for (i, rgb_image) in enumerate(rgb_draw_images): if i > self.camera_num - 1 or i > self.camera_visualize - 1: break @@ -552,8 +543,8 @@ def activate_mode(self, objects): yolo_detection, rgb_image, i) rgb_image = cv2.resize(rgb_image, (0, 0), fx=0.4, fy=0.4) cv2.imshow( - '%s camera of actor %d, perception activated' % - (names[i], self.id), rgb_image) + '%s-th camera of actor %d, perception activated' % + (str(i), self.id), rgb_image) cv2.waitKey(1) if self.lidar_visualize: @@ -590,6 +581,7 @@ def deactivate_mode(self, objects): world = self.carla_world vehicle_list = world.get_actors().filter("vehicle") + # todo: hard coded thresh = 50 if not self.data_dump else 120 vehicle_list = [v for v in vehicle_list if self.dist(v) < thresh and @@ -798,16 +790,49 @@ def retrieve_traffic_lights(self, objects): world = self.carla_world tl_list = world.get_actors().filter('traffic.traffic_light') + vehicle_location = self.ego_pos.location + vehicle_waypoint = self._map.get_waypoint(vehicle_location) + + activate_tl, light_trigger_location = \ + self._get_active_light(tl_list, vehicle_location, vehicle_waypoint) + objects.update({'traffic_lights': []}) - for tl in tl_list: - distance = self.dist(tl) - if distance < 50: - traffic_light = TrafficLight(tl.get_location(), - tl.get_state()) - objects['traffic_lights'].append(traffic_light) + if activate_tl is not None: + traffic_light = TrafficLight(activate_tl, + light_trigger_location, + activate_tl.get_state()) + objects['traffic_lights'].append(traffic_light) return objects + def _get_active_light(self, tl_list, vehicle_location, vehicle_waypoint): + for tl in tl_list: + object_location = \ + TrafficLight.get_trafficlight_trigger_location(tl) + object_waypoint = self._map.get_waypoint(object_location) + + if object_waypoint.road_id != vehicle_waypoint.road_id: + continue + + ve_dir = vehicle_waypoint.transform.get_forward_vector() + wp_dir = object_waypoint.transform.get_forward_vector() + dot_ve_wp = ve_dir.x wp_dir.x +\ + ve_dir.y * wp_dir.y + \ + ve_dir.z * wp_dir.z + + if dot_ve_wp < 0: + continue + while not object_waypoint.is_intersection: + next_waypoint = object_waypoint.next(0.5)[0] + if next_waypoint and not next_waypoint.is_intersection: + object_waypoint = next_waypoint + else: + break + + return tl, object_waypoint.transform.location + + return None, None + def destroy(self): """ Destroy sensors. diff --git a/opencda/core/sensing/perception/static_obstacle.py b/opencda/core/sensing/perception/static_obstacle.py index 01e64c93..d3f105cb 100644 --- a/opencda/core/sensing/perception/static_obstacle.py +++ b/opencda/core/sensing/perception/static_obstacle.py @@ -6,6 +6,7 @@ # Author: Runsheng Xu <[email protected]> # License: TDG-Attribution-NonCommercial-NoDistrib import sys +import math import numpy as np import carla @@ -71,6 +72,12 @@ class TrafficLight(object): Parameters --------- + tl : carla.Actor + The CARLA traffic actor + + trigger_location : carla.Vector3D + The trigger location of te traffic light. + pos : carla.Location The location of this traffic light. @@ -79,12 +86,42 @@ class TrafficLight(object): """ - def __init__(self, pos, light_state): - self._location = pos + def __init__(self, tl, trigger_location, light_state): + self._location = trigger_location self.state = light_state + self.actor = tl def get_location(self): return self._location def get_state(self): - return self.state \ No newline at end of file + return self.state + + @staticmethod + def get_trafficlight_trigger_location(traffic_light: carla.Actor) \ + -> carla.Vector3D: # pylint: disable=invalid-name + """ + Calculates the yaw of the waypoint that represents the trigger + volume of the traffic light + """ + + def rotate_point(point, angle): + """ + rotate a given point by a given angle + """ + x_ = math.cos(math.radians(angle)) * point.x - math.sin(math.radians(angle)) * point.y + y_ = math.sin(math.radians(angle)) * point.x - math.cos(math.radians(angle)) * point.y + + return carla.Vector3D(x_, y_, point.z) + + base_transform = traffic_light.get_transform() + base_rot = base_transform.rotation.yaw + area_loc = base_transform.transform(traffic_light.trigger_volume.location) + area_ext = traffic_light.trigger_volume.extent + + point = rotate_point(carla.Vector3D(0, 0, area_ext.z), base_rot) + point_location = area_loc + carla.Location(x=point.x, y=point.y) + + return carla.Location(point_location.x, + point_location.y, + point_location.z) diff --git a/opencda/version.py b/opencda/version.py index 51720796..823854a8 100644 --- a/opencda/version.py +++ b/opencda/version.py @@ -1,3 +1,3 @@ """Specifies the current version number of OpenCDA.""" -__version__ = "0.1.2" +__version__ = "0.1.3"	Feature/new config system OpenCDA now has a better configuration system! We have a default.yaml for all scenarios with default parameters, and users just need to change the part that is specific in each scenario yaml.		2023-03-03T17:20:54	[]	[]
ucla-mobility/OpenCDA	ucla-mobility__OpenCDA-90	391f82a5520c75a0456db1936cc42e6fc0ea606a	diff --git a/opencda/assets/2lane_freeway_simplified/2lane_freeway_simplified.net.xml b/opencda/assets/2lane_freeway_simplified/2lane_freeway_simplified.net.xml index 5cbecdfa..b41857e5 100644 --- a/opencda/assets/2lane_freeway_simplified/2lane_freeway_simplified.net.xml +++ b/opencda/assets/2lane_freeway_simplified/2lane_freeway_simplified.net.xml @@ -1,14 +1,14 @@ <?xml version='1.0' encoding='UTF-8'?> -<!-- generated on 2021-07-19 15:29:09 by Eclipse SUMO netconvert Version 1.9.2 +<!-- generated on 2021-07-24 21:24:41 by Eclipse SUMO netconvert Version 1.9.2 <configuration xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:noNamespaceSchemaLocation="http://sumo.dlr.de/xsd/netconvertConfiguration.xsd"> <input> - <type-files value="/home/runshengxu/carla/Co-Simulation/Sumo/util/data/opendrive_netconvert.typ.xml"/> - <opendrive-files value="../examples/2lane_freeway_simplified.xodr"/> + <type-files value="/home/runshengxu/project/OpenCDA/scripts/data/opendrive_netconvert.typ.xml"/> + <opendrive-files value="opencda/assets/2lane_freeway_simplified/2lane_freeway_simplified.xodr"/> </input> <output> - <output-file value="/tmp/tmpxc0su5ae/2lane_freeway_simplified.net.xml"/> + <output-file value="/tmp/tmp5_huiap9/2lane_freeway_simplified.net.xml"/> <output.original-names value="true"/> </output> @@ -39,116 +39,87 @@ </configuration> --> <net xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" version="1.9" junctionCornerDetail="5" rectangularLaneCut="true" limitTurnSpeed="5.50" xsi:noNamespaceSchemaLocation="http://sumo.dlr.de/xsd/net_file.xsd"> - <location netOffset="1204.72,456.54" convBoundary="0.00,0.00,2831.71,453.54" origBoundary="-1204.72,-456.54,1626.99,-3.00" projParameter="!"/> - <type id="bidirectional" priority="1" speed="1.39" disallow="pedestrian tram rail_urban rail rail_electric rail_fast ship" width="3.65"/> - <type id="biking" priority="-1" speed="5.56" allow="bicycle" width="1.50"/> - <type id="border" priority="0" speed="1.39" disallow="all" discard="1" width="0.10"/> - <type id="driving" priority="1" speed="13.89" disallow="pedestrian tram rail_urban rail rail_electric rail_fast ship" width="3.65"/> - <type id="entry" priority="1" speed="22.22" disallow="pedestrian tram rail_urban rail rail_electric rail_fast ship" width="3.65"/> - <type id="exit" priority="1" speed="22.22" disallow="pedestrian tram rail_urban rail rail_electric rail_fast ship" width="3.65"/> - <type id="median" priority="0" speed="1.39" disallow="all" discard="1" width="0.10"/> - <type id="mwyEntry" priority="1" speed="22.22" allow="private emergency authority army vip passenger hov taxi bus coach delivery truck trailer motorcycle evehicle custom1 custom2" width="3.65"/> - <type id="mwyExit" priority="1" speed="22.22" allow="private emergency authority army vip passenger hov taxi bus coach delivery truck trailer motorcycle evehicle custom1 custom2" width="3.65"/> - <type id="none" priority="0" speed="1.39" disallow="all" discard="1" width="1.00"/> - <type id="offRamp" priority="1" speed="22.22" allow="private emergency authority army vip passenger hov taxi bus coach delivery truck trailer motorcycle evehicle custom1 custom2" width="3.65"/> - <type id="onRamp" priority="1" speed="22.22" allow="private emergency authority army vip passenger hov taxi bus coach delivery truck trailer motorcycle evehicle custom1 custom2" width="3.65"/> - <type id="parking" priority="1" speed="1.39" disallow="pedestrian tram rail_urban rail rail_electric rail_fast ship" width="2.50"/> - <type id="rail" priority="3" speed="33.33" allow="rail_urban rail rail_electric rail_fast" width="3.65"/> - <type id="restricted" priority="0" speed="13.89" disallow="all" width="3.65"/> - <type id="roadWorks" priority="0" speed="1.39" allow="authority" width="3.65"/> - <type id="shoulder" priority="0" speed="1.39" disallow="all" discard="1" width="1.00"/> - <type id="sidewalk" priority="-2" speed="2.78" allow="pedestrian" width="2.50"/> - <type id="special1" priority="1" speed="22.22" allow="custom1" discard="1" width="3.65"/> - <type id="special2" priority="1" speed="22.22" allow="custom2" discard="1" width="3.65"/> - <type id="special3" priority="1" speed="22.22" allow="custom1 custom2" discard="1" width="3.65"/> - <type id="stop" priority="1" speed="13.89" disallow="pedestrian tram rail_urban rail rail_electric rail_fast ship" width="3.65"/> - <type id="tram" priority="2" speed="13.89" allow="tram" width="3.65"/> + <location netOffset="1204.72,456.54" convBoundary="0.00,0.00,2836.67,453.54" origBoundary="-1204.72,-456.54,1631.95,-3.00" projParameter="!"/> <edge id=":1_0" function="internal"> - <lane id=":1_0_0" index="0" disallow="pedestrian tram rail_urban rail rail_electric rail_fast ship" speed="31.31" length="16.98" width="3.66" shape="1188.16,441.22 1191.54,442.45 1195.04,443.46 1199.24,444.14 1204.72,444.39"> + <lane id=":1_0_0" index="0" speed="31.31" length="16.98" width="3.66" shape="1188.16,441.22 1191.54,442.45 1195.04,443.46 1199.24,444.14 1204.72,444.39"> <param key="origId" value="67_-1"/> </lane> </edge> <edge id=":1_1" function="internal"> - <lane id=":1_1_0" index="0" disallow="pedestrian tram rail_urban rail rail_electric rail_fast ship" speed="33.53" length="9.90" width="3.66" shape="1194.82,448.05 1204.72,448.05"> + <lane id=":1_1_0" index="0" speed="33.53" length="9.90" width="3.66" shape="1194.82,448.05 1204.72,448.05"> <param key="origId" value="68_-2"/> </lane> - <lane id=":1_1_1" index="1" disallow="pedestrian tram rail_urban rail rail_electric rail_fast ship" speed="33.53" length="9.90" width="3.66" shape="1194.82,451.71 1204.72,451.71"> + <lane id=":1_1_1" index="1" speed="33.53" length="9.90" width="3.66" shape="1194.82,451.71 1204.72,451.71"> <param key="origId" value="68_-1"/> </lane> </edge> <edge id=":2_0" function="internal"> - <lane id=":2_0_0" index="0" disallow="pedestrian tram rail_urban rail rail_electric rail_fast ship" speed="6.02" length="5.58" width="3.66" shape="1623.10,444.39 1625.25,444.32 1626.85,444.10 1627.89,443.73 1628.37,443.21"> + <lane id=":2_0_0" index="0" speed="6.02" length="5.58" width="3.66" shape="1623.10,444.39 1625.25,444.32 1626.85,444.10 1627.89,443.73 1628.37,443.21"> <param key="origId" value="65_-1"/> </lane> </edge> - <edge id=":2_1" function="internal"> - <lane id=":2_1_0" index="0" disallow="pedestrian tram rail_urban rail rail_electric rail_fast ship" speed="33.53" length="9.06" width="3.66" shape="1623.10,448.05 1632.16,448.05"> - <param key="origId" value="66_-2"/> - </lane> - <lane id=":2_1_1" index="1" disallow="pedestrian tram rail_urban rail rail_electric rail_fast ship" speed="33.53" length="9.06" width="3.66" shape="1623.10,451.71 1632.16,451.71"> - <param key="origId" value="66_-1"/> - </lane> - </edge> <edge id="-60.0.00" from="1" to="2" priority="1" type="driving" shape="1204.72,453.54 1623.10,453.54"> - <lane id="-60.0.00_0" index="0" disallow="pedestrian tram rail_urban rail rail_electric rail_fast ship" speed="33.53" length="418.37" width="3.66" shape="1204.72,444.39 1623.10,444.39" type="driving"> + <lane id="-60.0.00_0" index="0" speed="33.53" length="418.37" width="3.66" shape="1204.72,444.39 1623.10,444.39" type="driving"> <param key="origId" value="60_-3"/> </lane> - <lane id="-60.0.00_1" index="1" disallow="pedestrian tram rail_urban rail rail_electric rail_fast ship" speed="33.53" length="418.37" width="3.66" shape="1204.72,448.05 1623.10,448.05" type="driving"> + <lane id="-60.0.00_1" index="1" speed="33.53" length="418.37" width="3.66" shape="1204.72,448.05 1623.10,448.05" type="driving"> <param key="origId" value="60_-2"/> </lane> - <lane id="-60.0.00_2" index="2" disallow="pedestrian tram rail_urban rail rail_electric rail_fast ship" speed="33.53" length="418.37" width="3.66" shape="1204.72,451.71 1623.10,451.71" type="driving"> + <lane id="-60.0.00_2" index="2" speed="33.53" length="418.37" width="3.66" shape="1204.72,451.71 1623.10,451.71" type="driving"> <param key="origId" value="60_-1"/> </lane> </edge> <edge id="-61.0.00" from="61.begin" to="1" priority="1" type="driving" shape="2.00,0.00 1187.52,442.93"> - <lane id="-61.0.00_0" index="0" disallow="pedestrian tram rail_urban rail rail_electric rail_fast ship" speed="31.29" length="1265.57" width="3.66" shape="2.64,-1.71 1188.16,441.22" type="driving"> + <lane id="-61.0.00_0" index="0" speed="31.29" length="1265.57" width="3.66" shape="2.64,-1.71 1188.16,441.22" type="driving"> <param key="origId" value="61_-1"/> </lane> </edge> - <edge id="-62.0.00" from="2" to="62.end" priority="1" type="driving" shape="1631.71,453.54 2831.71,453.54"> - <lane id="-62.0.00_0" index="0" disallow="pedestrian tram rail_urban rail rail_electric rail_fast ship" speed="33.53" length="1199.56" width="3.66" shape="1632.16,448.05 2831.71,448.05" type="driving"> + <edge id="-62.0.00" from="4" to="3" priority="1" type="driving"> + <lane id="-62.0.00_0" index="0" speed="33.53" length="1200.00" width="3.66" shape="1631.69,448.05 2831.69,448.05" type="driving"> <param key="origId" value="62_-2"/> </lane> - <lane id="-62.0.00_1" index="1" disallow="pedestrian tram rail_urban rail rail_electric rail_fast ship" speed="33.53" length="1199.56" width="3.66" shape="1632.16,451.71 2831.71,451.71" type="driving"> + <lane id="-62.0.00_1" index="1" speed="33.53" length="1200.00" width="3.66" shape="1631.69,451.71 2831.69,451.71" type="driving"> <param key="origId" value="62_-1"/> </lane> </edge> <edge id="-63.0.00" from="63.begin" to="1" priority="1" type="driving" shape="0.00,453.54 1200.00,453.54"> - <lane id="-63.0.00_0" index="0" disallow="pedestrian tram rail_urban rail rail_electric rail_fast ship" speed="33.53" length="1194.82" width="3.66" shape="0.00,448.05 1194.82,448.05" type="driving"> + <lane id="-63.0.00_0" index="0" speed="33.53" length="1194.82" width="3.66" shape="0.00,448.05 1194.82,448.05" type="driving"> <param key="origId" value="63_-2"/> </lane> - <lane id="-63.0.00_1" index="1" disallow="pedestrian tram rail_urban rail rail_electric rail_fast ship" speed="33.53" length="1194.82" width="3.66" shape="0.00,451.71 1194.82,451.71" type="driving"> + <lane id="-63.0.00_1" index="1" speed="33.53" length="1194.82" width="3.66" shape="0.00,451.71 1194.82,451.71" type="driving"> <param key="origId" value="63_-1"/> </lane> </edge> <edge id="-64.0.00" from="2" to="64.end" priority="1" type="driving" shape="1629.16,446.58 1629.23,446.39"> - <lane id="-64.0.00_0" index="0" disallow="pedestrian tram rail_urban rail rail_electric rail_fast ship" speed="39.44" length="0.20" width="3.66" shape="1628.37,443.21 1628.43,443.02" type="driving"> + <lane id="-64.0.00_0" index="0" speed="39.44" length="0.20" width="3.66" shape="1628.37,443.21 1628.43,443.02" type="driving"> <param key="origId" value="64_-1"/> </lane> </edge> + <edge id="-69.0.00" from="2" to="69.end" priority="1" type="driving" shape="2831.67,453.54 2836.67,453.54"> + <lane id="-69.0.00_0" index="0" speed="33.53" length="2.13" width="3.66" shape="2834.55,448.05 2836.67,448.05" type="driving"> + <param key="origId" value="69_-2"/> + </lane> + <lane id="-69.0.00_1" index="1" speed="33.53" length="2.13" width="3.66" shape="2834.55,451.71 2836.67,451.71" type="driving"> + <param key="origId" value="69_-1"/> + </lane> + </edge> <junction id="1" type="priority" x="1196.05" y="448.21" incLanes="-61.0.00_0 -63.0.00_0 -63.0.00_1" intLanes=":1_0_0 :1_1_0 :1_1_1" shape="1204.72,453.54 1204.72,442.56 1201.26,442.53 1198.68,442.40 1196.57,442.10 1194.50,441.56 1192.06,440.72 1188.80,439.51 1187.52,442.93 1190.17,443.94 1192.25,444.76 1193.75,445.40 1194.68,445.86 1195.04,446.13 1194.82,446.22 1194.82,453.54"> <request index="0" response="000" foes="000" cont="0"/> <request index="1" response="000" foes="000" cont="0"/> <request index="2" response="000" foes="000" cont="0"/> </junction> - <junction id="2" type="priority" x="1627.41" y="450.06" incLanes="-60.0.00_0 -60.0.00_1 -60.0.00_2" intLanes=":2_0_0 :2_1_0 :2_1_1" shape="1632.16,453.54 1632.16,446.22 1631.04,441.01 1627.58,439.84 1623.10,442.56 1623.10,453.54"> - <request index="0" response="000" foes="000" cont="0"/> - <request index="1" response="000" foes="000" cont="0"/> - <request index="2" response="000" foes="000" cont="0"/> + <junction id="2" type="priority" x="1627.40" y="450.06" incLanes="-60.0.00_0 -60.0.00_1 -60.0.00_2" intLanes=":2_0_0" shape="2834.55,453.54 2834.55,446.22 1631.04,441.01 1627.58,439.84 1623.10,442.56 1623.10,453.54"> + <request index="0" response="0" foes="0" cont="0"/> </junction> + <junction id="3" type="dead_end" x="2831.69" y="453.54" incLanes="-62.0.00_0 -62.0.00_1" intLanes="" shape="2831.69,446.22 2831.69,453.54"/> + <junction id="4" type="dead_end" x="1631.69" y="453.54" incLanes="" intLanes="" shape="1631.69,453.54 1631.69,446.22"/> <junction id="61.begin" type="dead_end" x="2.00" y="0.00" incLanes="" intLanes="" shape="2.00,-0.00 3.28,-3.43"/> - <junction id="62.end" type="dead_end" x="2831.71" y="453.54" incLanes="-62.0.00_0 -62.0.00_1" intLanes="" shape="2831.71,446.22 2831.71,453.54"/> <junction id="63.begin" type="dead_end" x="0.00" y="453.54" incLanes="" intLanes="" shape="0.00,453.54 0.00,446.22"/> <junction id="64.end" type="dead_end" x="1629.23" y="446.39" incLanes="-64.0.00_0" intLanes="" shape="1625.76,445.22 1629.23,446.39"/> + <junction id="69.end" type="dead_end" x="2836.67" y="453.54" incLanes="-69.0.00_0 -69.0.00_1" intLanes="" shape="2836.67,446.22 2836.67,453.54"/> <connection from="-60.0.00" to="-64.0.00" fromLane="0" toLane="0" via=":2_0_0" dir="r" state="M"> <param key="origId" value="65_-1"/> </connection> - <connection from="-60.0.00" to="-62.0.00" fromLane="1" toLane="0" via=":2_1_0" dir="s" state="M"> - <param key="origId" value="66_-2"/> - </connection> - <connection from="-60.0.00" to="-62.0.00" fromLane="2" toLane="1" via=":2_1_1" dir="s" state="M"> - <param key="origId" value="66_-1"/> - </connection> <connection from="-61.0.00" to="-60.0.00" fromLane="0" toLane="0" via=":1_0_0" dir="s" state="M"> <param key="origId" value="67_-1"/> </connection> @@ -162,6 +133,4 @@ <connection from=":1_1" to="-60.0.00" fromLane="0" toLane="1" dir="s" state="M"/> <connection from=":1_1" to="-60.0.00" fromLane="1" toLane="2" dir="s" state="M"/> <connection from=":2_0" to="-64.0.00" fromLane="0" toLane="0" dir="r" state="M"/> - <connection from=":2_1" to="-62.0.00" fromLane="0" toLane="0" dir="s" state="M"/> - <connection from=":2_1" to="-62.0.00" fromLane="1" toLane="1" dir="s" state="M"/> </net> diff --git a/opencda/assets/2lane_freeway_simplified/2lane_freeway_simplified.rou.xml b/opencda/assets/2lane_freeway_simplified/2lane_freeway_simplified.rou.xml index 6e0ef779..b66f4f9b 100644 --- a/opencda/assets/2lane_freeway_simplified/2lane_freeway_simplified.rou.xml +++ b/opencda/assets/2lane_freeway_simplified/2lane_freeway_simplified.rou.xml @@ -4,7 +4,7 @@ <routes> <vType id="vType_0" minGap="2.00" speedFactor="normc(1.00,0.00)" vClass="passenger" carFollowModel="IDMM" tau="0.6"/> <vType id="DEFAULT_VEHTYPE" minGap="2.50" tau="1.0" color="255,255,255" Class="passenger" accel="0.5"/> - <flow id="flow_0" begin="0.00" departLane="1" departSpeed="15" departPos="-320" from="-63.0.00" to="-62.0.00" via="-60.0.00" end="4800.00" vehsPerHour="1000.00" type="DEFAULT_VEHTYPE"/> - <flow id="flow_1" begin="0.2" departLane="0" departSpeed="15" departPos="-320" from="-63.0.00" to="-62.0.00" via="-60.0.00" end="4800.00" vehsPerHour="1000.00" type="DEFAULT_VEHTYPE"/> + <flow id="flow_0" begin="0.00" departLane="1" departSpeed="15" departPos="-320" from="-63.0.00" to="-60.0.00" via="-60.0.00" end="4800.00" vehsPerHour="1000.00" type="DEFAULT_VEHTYPE"/> + <flow id="flow_1" begin="0.2" departLane="0" departSpeed="15" departPos="-320" from="-63.0.00" to="-60.0.00" via="-60.0.00" end="4800.00" vehsPerHour="1000.00" type="DEFAULT_VEHTYPE"/> </routes> \ No newline at end of file diff --git a/opencda/assets/2lane_freeway_simplified/2lane_freeway_simplified.xodr b/opencda/assets/2lane_freeway_simplified/2lane_freeway_simplified.xodr index 3cc9f6f8..041a27c8 100644 --- a/opencda/assets/2lane_freeway_simplified/2lane_freeway_simplified.xodr +++ b/opencda/assets/2lane_freeway_simplified/2lane_freeway_simplified.xodr @@ -88,11 +88,12 @@ </road> <road name="gneE5" length="1200" id="62" junction="-1"> <link> - <predecessor elementType="junction" elementId="2"/> + <predecessor elementType="junction" elementId="4"/> + <successor elementType="junction" elementId="3"/> </link> <type s="0" type="town"/> <planView> - <geometry s="0.00000000" x="426.99172801" y="-3.0" hdg="0.00000000" length="1200"> + <geometry s="0.00000000" x="426.97" y="-3.0" hdg="0.00000000" length="1200"> <line/> </geometry> </planView> @@ -105,7 +106,7 @@ <center> <lane id="0" type="none" level="true"> <link/> - <roadMark sOffset="0" type="solid" weight="standard" color="standard" width="0.13" laneChange="none"/> + <roadMark sOffset="0" type="solid" weight="standard" color="standard" width="0.13" laneChange="both"/> </lane> </center> <right> @@ -118,7 +119,7 @@ <lane id="-2" type="driving" level="true"> <link/> <width sOffset="0" a="3.6576" b="0" c="0" d="0"/> - <roadMark sOffset="0" type="solid" weight="standard" color="standard" width="0.13" laneChange="none"/> + <roadMark sOffset="0" type="solid" weight="standard" color="standard" width="0.13" laneChange="both"/> <speed sOffset="0" max="33.53"/> </lane> </right> @@ -203,6 +204,47 @@ <objects/> <signals/> </road> + <road name="gneE6" length="5.0" id="69" junction="-1"> + <link> + <predecessor elementType="junction" elementId="2"/> + </link> + <type s="0" type="town"/> + <planView> + <geometry s="0.00000000" x="1626.95" y="-3.0" hdg="0.00000000" length="5.0"> + <line/> + </geometry> + </planView> + <elevationProfile> + <elevation s="0" a="0.00" b="0" c="0" d="0"/> + </elevationProfile> + <lateralProfile/> + <lanes> + <laneSection s="0"> + <center> + <lane id="0" type="none" level="true"> + <link/> + <roadMark sOffset="0" type="solid" weight="standard" color="standard" width="0.13" laneChange="both"/> + </lane> + </center> + <right> + <lane id="-1" type="driving" level="true"> + <link/> + <width sOffset="0" a="3.6576" b="0" c="0" d="0"/> + <roadMark sOffset="0" type="broken" weight="standard" color="standard" width="0.13" laneChange="both"/> + <speed sOffset="0" max="33.53"/> + </lane> + <lane id="-2" type="driving" level="true"> + <link/> + <width sOffset="0" a="3.6576" b="0" c="0" d="0"/> + <roadMark sOffset="0" type="solid" weight="standard" color="standard" width="0.13" laneChange="both"/> + <speed sOffset="0" max="33.53"/> + </lane> + </right> + </laneSection> + </lanes> + <objects/> + <signals/> + </road> <road name="gneJ6_0" length="6.28738509" id="65" junction="2"> <link> @@ -250,7 +292,7 @@ </link> <type s="0" type="town"/> <planView> - <geometry s="0.00000000" x="418.3729844599999" y="-3.0" hdg="0.00000000" length="8.61874355"> + <geometry s="0.00000000" x="418.372" y="-3.0" hdg="0.00000000" length="8.61874355"> <line/> </geometry> </planView> @@ -263,7 +305,7 @@ <center> <lane id="0" type="none" level="true"> <link/> - <roadMark sOffset="0" type="none" weight="standard" color="standard" width="0.00" laneChange="none"/> + <roadMark sOffset="0" type="none" weight="standard" color="standard" width="0.00" laneChange="both"/> </lane> </center> <right> @@ -388,6 +430,14 @@ <laneLink from="-2" to="-2"/> </connection> </junction> + + <junction name="8433034436" id="4"> + <connection id="2" incomingRoad="66" connectingRoad="62" contactPoint="start"> + <laneLink from="-1" to="-1"/> + <laneLink from="-2" to="-2"/> + </connection> + </junction> + <junction name="8433034435" id="1"> <connection id="0" incomingRoad="61" connectingRoad="67" contactPoint="start"> <laneLink from="-1" to="-1"/> @@ -397,4 +447,12 @@ <laneLink from="-2" to="-2"/> </connection> </junction> + + <junction name="8433034434" id="3"> + <connection id="0" incomingRoad="62" connectingRoad="69" contactPoint="start"> + <laneLink from="-1" to="-1"/> + <laneLink from="-2" to="-2"/> + </connection> + </junction> + </OpenDRIVE>	Develop The development branch is nearly ready for the formal version v0.1 except for some part of the documentation still needs to be proofread.		2021-07-25T04:23:22	[]	[]
flekschas/jupyter-scatter	flekschas__jupyter-scatter-168	939b9cb58acace667a9463b344d3e4cadea871e8	diff --git a/CHANGELOG.md b/CHANGELOG.md index 30719af..af02e45 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,3 +1,7 @@ +## v0.19.1 + +- Fix: revert back to `pd.api.types.is_string_dtype` from `pd.StringDType` because the two function, albeit looking semantically identical, do not do the same thing. The former detects object and string types while the latter only strictly detects string types. This is confusing as `pd.Series(['a', 'b'])` is of type `object`. So `pd.StringDtype.is_dtype(pd.Series(['a', 'b'])) == False` but `pd.api.types.is_string_dtype(pd.Series(['a', 'b'])) == True`. [Wat](https://www.destroyallsoftware.com/talks/wat)?! + ## v0.19.0 - Feat: add contour line annotations [#163](https://github.com/flekschas/jupyter-scatter/issues/163) diff --git a/jscatter/utils.py b/jscatter/utils.py index ddc173f..4e1020f 100644 --- a/jscatter/utils.py +++ b/jscatter/utils.py @@ -1,9 +1,9 @@ import ipywidgets as widgets +import pandas as pd import warnings from matplotlib.colors import LogNorm, PowerNorm, Normalize from numpy import histogram, isnan, sum -from pandas import CategoricalDtype, StringDtype from urllib.parse import urlparse from typing import List, Union @@ -78,13 +78,13 @@ def to_scale_type(norm = None): return 'categorical' def get_scale_type_from_df(data): - if isinstance(data.dtype, CategoricalDtype) or isinstance(data.dtype, StringDtype): + if pd.CategoricalDtype.is_dtype(data) or pd.api.types.is_string_dtype(data): return 'categorical' return 'linear' def get_domain_from_df(data): - if isinstance(data.dtype, CategoricalDtype) or isinstance(data.dtype, StringDtype): + if pd.CategoricalDtype.is_dtype(data) or pd.api.types.is_string_dtype(data): # We need to recreate the categorization in case the data is just a # filtered view, in which case it might contain "missing" indices _data = data.copy().astype(str).astype('category') @@ -120,7 +120,7 @@ def create_labeling(partial_labeling, column: Union[str, None] = None) -> Labeli return labeling def get_histogram_from_df(data, bins=20, range=None): - if isinstance(data.dtype, CategoricalDtype) or isinstance(data.dtype, StringDtype): + if pd.CategoricalDtype.is_dtype(data) or pd.api.types.is_string_dtype(data): # We need to recreate the categorization in case the data is just a # filtered view, in which case it might contain "missing" indices value_counts = data.copy().astype(str).astype('category').cat.codes.value_counts() diff --git a/notebooks/Untitled4.ipynb b/notebooks/Untitled4.ipynb new file mode 100644 index 0000000..920b5ec --- /dev/null +++ b/notebooks/Untitled4.ipynb @@ -0,0 +1,152 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "c8d67f28-de9c-4cd4-b60b-c5bf92db023b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "env: ANYWIDGET_HMR=1\n" + ] + } + ], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "%env ANYWIDGET_HMR=1" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "63452dc6-5726-4242-81a0-fc79b68e64a7", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "473f9edbd47840f5ab4720fc1abf3479", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(VBox(children=(Button(button_style='primary', icon='arrows', layout=Layout(width='36px'), style…" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import jscatter\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "data1 = pd.DataFrame({\n", + " \"x\": np.random.rand(1000),\n", + " \"y\": np.random.rand(1000),\n", + " \"z\": ['a','b']*500,\n", + "})\n", + "scatter = jscatter.Scatter(data=data1, x=\"x\", y=\"y\", width=500, height=500)\n", + "scatter.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "3d052d61-79e7-4f7a-9839-92f8e34a5bbf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<jscatter.jscatter.Scatter at 0x12cc5d640>" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scatter.tooltip(True, properties = ['x','y', 'z']) " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "3b01ac2a-c9bf-48cc-bb8e-34a646528db3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.StringDtype.is_dtype(pd.Series(['a', 'b']))" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "c8d03133-e4e2-43ad-9292-6ea39dcb3bfb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.api.types.is_string_dtype(pd.Series(['a', 'b']))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "069b99ef-74f9-401e-b26e-851589f0328a", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}	tooltip stopped supporting categorical columns set as properties in 0.18.1 ``` import jscatter import numpy as np import pandas as pd data1 = pd.DataFrame({ "x": np.random.rand(1000), "y": np.random.rand(1000), "z": ['a','b']*500, }) scatter = jscatter.Scatter(data=data1, x="x", y="y", width=500, height=500) scatter.show() ``` The following works in 0.16.1: `scatter.tooltip(True, properties = ['x','y', 'z']) ` But in 0.18.1, I get a `TypeError: ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''`		2024-09-26T01:25:49	[]	[]
flekschas/jupyter-scatter	flekschas__jupyter-scatter-151	750d1511d8397b3a21de758b7097d510502ddb1a	diff --git a/CHANGELOG.md b/CHANGELOG.md index 0e55d5d..cc1acb2 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,6 +1,7 @@ ## v0.18.0 -- Feat: The exported and saved images now include a background color instead of a transparent background. You can still still export or save images with a transparent background by holding down the ALT key while clicking on the download or camera button. +- Feat: add support for line-based annotations via `scatter.annotations()` +- Feat: the exported and saved images now include a background color instead of a transparent background. You can still still export or save images with a transparent background by holding down the ALT key while clicking on the download or camera button. - Refactor: When saving the current view as an image via the camera button on the left side bar, the image gets saved in `scatter.widget.view_data` as a 3D Numpy array (shape: `[height, width, 4]`) instead of a 1D Numpy array. Since the shape is now encoded by the 3D numpy array, `scatter.widget.view_shape` is no longer needed and is removed. - Fix: hide button for activating rotate mouse mode as the rotation did not work (which is easily fixable) and should not be available when axes are shown as the axes are not rotateable. However rotating the plot without rotating the axis leads to incorrect tick marks. - Fix: VSCode integration by updating regl-scatterplot to v1.10.4 ([#37](https://github.com/flekschas/jupyter-scatter/issues/37)) @@ -15,7 +16,7 @@ ## v0.17.0 -- Feat: Add `scatter.show_tooltip(point_idx)` +- Feat: add `scatter.show_tooltip(point_idx)` - Fix: reset scale & norm ranges upon updating the data via `scatter.data()` - Fix: ensure `scatter.axes(labels=['x_label', 'y_label'])` works properly ([#137](https://github.com/flekschas/jupyter-scatter/issues/137])) diff --git a/docs/.vitepress/config.ts b/docs/.vitepress/config.ts index da1870a..a50cb30 100644 --- a/docs/.vitepress/config.ts +++ b/docs/.vitepress/config.ts @@ -71,6 +71,7 @@ export default defineConfig({ { text: 'Selections', link: '/selections' }, { text: 'Link Multiple Scatter Plots', link: '/link-multiple-plots' }, { text: 'Axes & Legends', link: '/axes-legends' }, + { text: 'Annotations', link: '/annotations' }, { text: 'Tooltip', link: '/tooltip' }, { text: 'Scales', link: '/scales' }, { text: 'Connected Scatterplots', link: '/connected-scatterplots' }, diff --git a/docs/annotations.md b/docs/annotations.md new file mode 100644 index 0000000..b841bff --- /dev/null +++ b/docs/annotations.md @@ -0,0 +1,96 @@ +# Annotations + +To help navigating and relating points and clusters, Jupyter Scatter offers +annotations such a lines and rectangles. + +::: info +Currently, Jupyter Scatter supports line-based annotations only. In the future, +we plan to add support for text annotations as well. +::: + +## Line, HLine, VLine, and Rect + +To draw annotations, create instances of `Line`, `HLine`, `VLine`, or `Rect`. +You can then either pass the annotations into the constructor, as shown below, +or call `scatter.annotations()`. + +```py{9-17,22} +import jscatter +import numpy as np + +x1, y1 = np.random.normal(-1, 0.2, 1000), np.random.normal(+1, 0.05, 1000) +x2, y2 = np.random.normal(+1, 0.2, 1000), np.random.normal(+1, 0.05, 1000) +x3, y3 = np.random.normal(+1, 0.2, 1000), np.random.normal(-1, 0.05, 1000) +x4, y4 = np.random.normal(-1, 0.2, 1000), np.random.normal(-1, 0.05, 1000) + +y0 = jscatter.HLine(0) +x0 = jscatter.VLine(0) +c1 = jscatter.Rect(x_start=-1.5, x_end=-0.5, y_start=+0.75, y_end=+1.25) +c2 = jscatter.Rect(x_start=+0.5, x_end=+1.5, y_start=+0.75, y_end=+1.25) +c3 = jscatter.Rect(x_start=+0.5, x_end=+1.5, y_start=-1.25, y_end=-0.75) +c4 = jscatter.Rect(x_start=-1.5, x_end=-0.5, y_start=-1.25, y_end=-0.75) +l = jscatter.Line([ + (-2, -2), (-1.75, -1), (-1.25, -0.5), (1.25, 0.5), (1.75, 1), (2, 2) +]) + +scatter = jscatter.Scatter( + x=np.concatenate((x1, x2, x3, x4)), x_scale=(-2, 2), + y=np.concatenate((y1, y2, y3, y4)), y_scale=(-2, 2), + annotations=[x0, y0, c1, c2, c3, c4, l], + width=400, + height=400, +) +scatter.show() +``` + +<div class="img simple"><div /></div> + +## Line Color & Width + +You can customize the line color and width of `Line`, `HLine`, `VLine`, or +`Rect` via the `line_color` and `line_width` attributes. + +```py +y0 = jscatter.HLine(0, line_color=(0, 0, 0, 0.1)) +x0 = jscatter.VLine(0, line_color=(0, 0, 0, 0.1)) +c1 = jscatter.Rect(x_start=-1.5, x_end=-0.5, y_start=+0.75, y_end=+1.25, line_color="#56B4E9", line_width=2) +c2 = jscatter.Rect(x_start=+0.5, x_end=+1.5, y_start=+0.75, y_end=+1.25, line_color="#56B4E9", line_width=2) +c3 = jscatter.Rect(x_start=+0.5, x_end=+1.5, y_start=-1.25, y_end=-0.75, line_color="#56B4E9", line_width=2) +c4 = jscatter.Rect(x_start=-1.5, x_end=-0.5, y_start=-1.25, y_end=-0.75, line_color="#56B4E9", line_width=2) +l = jscatter.Line( + [(-2, -2), (-1.75, -1), (-1.25, -0.5), (1.25, 0.5), (1.75, 1), (2, 2)], + line_color="red", + line_width=3 +) +``` + +<div class="img styles"><div /></div> + +<style scoped> + .img { + max-width: 100%; + background-position: center; + background-repeat: no-repeat; + background-size: cover; + } + + .img.simple { + width: 316px; + background-image: url(/images/annotations-simple-light.png) + } + .img.simple div { padding-top: 83.2278481% } + + :root.dark .img.simple { + background-image: url(/images/annotations-simple-dark.png) + } + + .img.styles { + width: 314px; + background-image: url(/images/annotations-styles-light.png) + } + .img.styles div { padding-top: 83.75796178% } + + :root.dark .img.styles { + background-image: url(/images/annotations-styles-dark.png) + } +</style> diff --git a/docs/api.md b/docs/api.md index fb446e3..9c28a53 100644 --- a/docs/api.md +++ b/docs/api.md @@ -6,7 +6,7 @@ - [selection()](#scatter.selection) and [filter()](#scatter.filter) - [color()](#scatter.color), [opacity()](#scatter.opacity), and [size()](#scatter.size) - [connect()](#scatter.connect), [connection_color()](#scatter.connection_color), [connection_opacity()](#scatter.connection_opacity), and [connection_size()](#scatter.connection_size) - - [axes()](#scatter.axes) and [legend()](#scatter.legend) + - [axes()](#scatter.axes), [legend()](#scatter.legend), and [annotations()](#scatter.annotations) - [tooltip()](#scatter.tooltip) and [show_tooltip()](#scatter.show_tooltip) - [zoom()](#scatter.zoom) and [camera()](#scatter.camera) - [lasso()](#scatter.lasso), [reticle()](#scatter.reticle), and [mouse()](#scatter.mouse), @@ -357,6 +357,21 @@ Set or get the legend settings. scatter.legend(True, 'top-right', 'small') ``` +### scatter.annotations(_annotations=Undefined_) {#scatter.annotations} + +Set or get annotations. + +Arguments: +- `annotations` is a list of annotations (`Line`, `HLine`, `VLine`, or `Rect`) + +Returns: either the annotation properties when all arguments are `Undefined` or `self`. + +Example: + +```python +from jscatter import HLine, VLine +scatter.annotations([HLine(42), VLine(42)]) +``` ### scatter.tooltip(_enable=Undefined_, _properties=Undefined_, _histograms=Undefined_, _histograms_bins=Undefined_, _histograms_ranges=Undefined_, _histograms_size=Undefined_, _preview=Undefined_, _preview_type=Undefined_, _preview_text_lines=Undefined_, _preview_image_background_color=Undefined_, _preview_image_position=Undefined_, _preview_image_size=Undefined_, _preview_audio_length=Undefined_, _preview_audio_loop=Undefined_, _preview_audio_controls=Undefined_, _size=Undefined_) {#scatter.tooltip} diff --git a/docs/public/images/annotations-simple-dark.png b/docs/public/images/annotations-simple-dark.png new file mode 100644 index 0000000..0c9e065 --- /dev/null +++ b/docs/public/images/annotations-simple-dark.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d01dc4bade1496dff2c9643a870b5805da6c53b0093c916669e0a7103ed0d029 +size 18822 diff --git a/docs/public/images/annotations-simple-light.png b/docs/public/images/annotations-simple-light.png new file mode 100644 index 0000000..61fe341 --- /dev/null +++ b/docs/public/images/annotations-simple-light.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dcdf68d391800cab5365eb7083f9d100f3e9256a5f8082dc2e11f40bf1760736 +size 18348 diff --git a/docs/public/images/annotations-styles-dark.png b/docs/public/images/annotations-styles-dark.png new file mode 100644 index 0000000..6c540de --- /dev/null +++ b/docs/public/images/annotations-styles-dark.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bcbaaf43b01f4270579257aa152f8d8973ddde125891d58514e100e5a28f807d +size 18657 diff --git a/docs/public/images/annotations-styles-light.png b/docs/public/images/annotations-styles-light.png new file mode 100644 index 0000000..9cc06dd --- /dev/null +++ b/docs/public/images/annotations-styles-light.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8b293f4c7d75f60f335b6f6941ffb7488d3093dbb26dacd04076574b5c72dbf8 +size 18173 diff --git a/js/src/codecs.js b/js/src/codecs.js index 1cdd253..0c9affd 100644 --- a/js/src/codecs.js +++ b/js/src/codecs.js @@ -1,3 +1,5 @@ +import { camelToSnake, snakeToCamel } from './utils'; + const DTYPES = { uint8: Uint8Array, int8: Int8Array, @@ -120,3 +122,43 @@ export function Numpy1D(dtype) { } } } + +export function Annotations() { + const pyToJsKey = { x_start: 'x1', x_end: 'x2', y_start: 'y1', y_end: 'y2' }; + const jsToPyKey = { x1: 'x_start', x2: 'x_end', y1: 'y_start', y2: 'y_end' }; + return { + /** + * @param {string[] \| null} annotationStrs + * @returns {object[]} + / + deserialize: (annotationStrs) => { + if (annotationStrs === null) { + return null; + } + return annotationStrs.map((annotationStr) => { + return Object.entries(JSON.parse(annotationStr)).reduce((acc, [key, value]) => { + const jsKey = key in pyToJsKey ? pyToJsKey[key] : snakeToCamel(key); + acc[jsKey] = value; + return acc; + }, {}) + }); + }, + /* + * @param {object[] \| null} annotations + * @returns {string[]} + / + serialize: (annotations) => { + if (annotations === null) { + return null; + } + return annotations.map((annotation) => { + const pyAnnotation = Object.entries(annotation).reduce((acc, [key, value]) => { + const pyKey = key in jsToPyKey ? jsToPyKey[key] : camelToSnake(key); + acc[pyKey] = value; + return acc; + }, {}); + return JSON.stringify(pyAnnotation); + }) + }, + } +} diff --git a/js/src/index.js b/js/src/index.js index c840e45..fe6000f 100644 --- a/js/src/index.js +++ b/js/src/index.js @@ -7,7 +7,7 @@ import { format } from 'd3-format'; import { scaleLinear } from 'd3-scale'; import { select } from 'd3-selection'; -import { Numpy1D, Numpy2D, NumpyImage } from "./codecs"; +import { Annotations, Numpy1D, Numpy2D, NumpyImage } from "./codecs"; import { createHistogram } from "./histogram"; import { createLegend } from "./legend"; import { @@ -59,6 +59,7 @@ const TOOLTIP_OFFSET_REM = 0.5; different. E.g., size (Python) vs pointSize (JavaScript) / const properties = { + annotations: 'annotations', backgroundColor: 'backgroundColor', backgroundImage: 'backgroundImage', cameraDistance: 'cameraDistance', @@ -371,6 +372,9 @@ class JupyterScatterView { this.scatterplot .draw(this.points, options) .then(() => { + if (this.annotations) { + this.scatterplot.drawAnnotations(this.annotations); + } if (this.filter?.length && this.model.get('zoom_on_filter')) { this.zoomToHandler(this.filter); } @@ -1766,6 +1770,10 @@ class JupyterScatterView { } } + annotationsHandler(annotations) { + this.scatterplot.drawAnnotations(annotations \|\| []); + } + // Event handlers for JS-triggered events pointoverHandler(pointIndex) { this.hoveringChangedByJs = true; @@ -2426,6 +2434,7 @@ async function render({ model, el }) { filter: Numpy1D('uint32'), view_data: NumpyImage(), zoom_to: Numpy1D('uint32'), + annotations: Annotations(), }), }); view.render(); diff --git a/js/src/utils.js b/js/src/utils.js index f56c727..94ea0fe 100644 --- a/js/src/utils.js +++ b/js/src/utils.js @@ -5,6 +5,10 @@ export function camelToSnake(string) { return string.replace(/[\w]([A-Z])/g, (m) => m[0] + "_" + m[1]).toLowerCase(); } +export function snakeToCamel(string) { + return string.toLowerCase().replace(/[-_][a-z]/g, (group) => group.slice(-1).toUpperCase()); +} + export function toCapitalCase(string) { if (string.length === 0) return string; return string.at(0).toUpperCase() + string.slice(1); diff --git a/jscatter/__init__.py b/jscatter/__init__.py index 8035c25..768f23e 100644 --- a/jscatter/__init__.py +++ b/jscatter/__init__.py @@ -9,5 +9,6 @@ __version__ = "uninstalled" from .jscatter import Scatter, plot +from .annotations import Line, HLine, VLine, Rect from .compose import compose, link from .color_maps import okabe_ito, glasbey_light, glasbey_dark diff --git a/jscatter/annotations.py b/jscatter/annotations.py new file mode 100644 index 0000000..62f597d --- /dev/null +++ b/jscatter/annotations.py @@ -0,0 +1,55 @@ +from __future__ import annotations + +from dataclasses import dataclass +from matplotlib.colors import to_rgba +from typing import List, Tuple, Optional + +from .types import Color + +DEFAULT_1D_LINE_START = None +DEFAULT_1D_LINE_END = None +DEFAULT_LINE_COLOR = '#000000' +DEFAULT_LINE_WIDTH = 1 + +@dataclass +class HLine(): + y: float + x_start: Optional[float] = DEFAULT_1D_LINE_START + x_end: Optional[float] = DEFAULT_1D_LINE_END + line_color: Color = DEFAULT_LINE_COLOR + line_width: int = DEFAULT_LINE_WIDTH + + def __post_init__(self): + self.line_color = to_rgba(self.line_color) + +@dataclass +class VLine(): + x: float + y_start: Optional[float] = DEFAULT_1D_LINE_START + y_end: Optional[float] = DEFAULT_1D_LINE_END + line_color: Color = DEFAULT_LINE_COLOR + line_width: int = DEFAULT_LINE_WIDTH + + def __post_init__(self): + self.line_color = to_rgba(self.line_color) + +@dataclass +class Rect(): + x_start: float + x_end: float + y_start: float + y_end: float + line_color: Color = DEFAULT_LINE_COLOR + line_width: int = DEFAULT_LINE_WIDTH + + def __post_init__(self): + self.line_color = to_rgba(self.line_color) + +@dataclass +class Line(): + vertices: List[Tuple[float]] + line_color: Color = DEFAULT_LINE_COLOR + line_width: int = DEFAULT_LINE_WIDTH + + def __post_init__(self): + self.line_color = to_rgba(self.line_color) diff --git a/jscatter/annotations_traits.py b/jscatter/annotations_traits.py new file mode 100644 index 0000000..777d849 --- /dev/null +++ b/jscatter/annotations_traits.py @@ -0,0 +1,85 @@ +from __future__ import annotations + +import json + +from dataclasses import asdict +from traitlets import TraitType +from typing import Any + +from .annotations import ( + HLine as AHLine, + VLine as AVLine, + Line as ALine, + Rect as ARect, +) + +class Line(TraitType): + info_text = 'line annotation' + + def validate(self, obj: Any, value: Any): + if isinstance(value, ALine): + return value + self.error(obj, value) + +class HLine(TraitType): + info_text = 'horizontal line annotation' + + def validate(self, obj: Any, value: Any): + if isinstance(value, AHLine): + return value + self.error(obj, value) + +class VLine(TraitType): + info_text = 'vertical line annotation' + + def validate(self, obj: Any, value: Any): + if isinstance(value, AVLine): + return value + self.error(obj, value) + +class Rect(TraitType): + info_text = 'rectangle annotation' + + def validate(self, obj: Any, value: Any): + if isinstance(value, ARect): + return value + self.error(obj, value) + +def to_json(value, args, *kwargs): + d = None if value is None else asdict(value) + return json.dumps(d, allow_nan=False) + +def annotations_to_json(value, args, *kwargs): + if value is None: + return None + return [to_json(v) for v in value] + +def from_json(value, args, kwargs): + d = json.loads(value) + + if 'y' in d: + return AHLine(d) + + if 'x' in d: + return AVLine(d) + + if 'x_start' in d and 'x_end' in d and 'y_start' in d and 'y_end' in d: + return ALine(d) + + if 'vertices' in d: + return ARect(*d) + + return None + +def annotations_from_json(value): + value = json.loads(value) + + if value is None: + return None + + return [from_json(v) for v in value] + +serialization = dict( + to_json=annotations_to_json, + from_json=annotations_from_json, +) diff --git a/jscatter/jscatter.py b/jscatter/jscatter.py index 780daf7..3c2f7d9 100644 --- a/jscatter/jscatter.py +++ b/jscatter/jscatter.py @@ -5,8 +5,9 @@ import pandas as pd from matplotlib.colors import to_rgba, Normalize, LogNorm, PowerNorm, LinearSegmentedColormap, ListedColormap -from typing import Dict, Optional, Union, List, Tuple, Literal +from typing import Dict, Optional, Union, List, Tuple +from .annotations import Line, HLine, VLine, Rect from .encodings import Encodings from .widget import JupyterScatter, SELECTION_DTYPE from .color_maps import okabe_ito, glasbey_light, glasbey_dark @@ -119,6 +120,54 @@ def get_histogram_range(ranges, property): return None +def normalize_annotation(annotation, x_scale, y_scale): + def to_ndc(value, norm): + return (norm(value) 2) - 1 + + if isinstance(annotation, HLine): + return HLine( + y=to_ndc(annotation.y, y_scale), + x_start=None if annotation.x_start is None else to_ndc(annotation.x_start, x_scale), + x_end=None if annotation.x_end is None else to_ndc(annotation.x_end, x_scale), + line_color=annotation.line_color, + line_width=annotation.line_width, + ) + + if isinstance(annotation, VLine): + return VLine( + x=to_ndc(annotation.x, x_scale), + y_start=None if annotation.y_start is None else to_ndc(annotation.y_start, y_scale), + y_end=None if annotation.y_end is None else to_ndc(annotation.y_end, y_scale), + line_color=annotation.line_color, + line_width=annotation.line_width, + ) + + + if isinstance(annotation, Line): + return Line( + vertices=[ + (to_ndc(v[0], x_scale), to_ndc(v[1], y_scale)) for v in annotation.vertices + ], + line_color=annotation.line_color, + line_width=annotation.line_width, + ) + + if isinstance(annotation, Rect): + return Rect( + x_start=to_ndc(annotation.x_start, x_scale), + x_end=to_ndc(annotation.x_end, x_scale), + y_start=to_ndc(annotation.y_start, y_scale), + y_end=to_ndc(annotation.y_end, y_scale), + line_color=annotation.line_color, + line_width=annotation.line_width, + ) + +def normalize_annotations(annotations, x_scale, y_scale): + if annotations is None: + return None + return [normalize_annotation(a, x_scale, y_scale) for a in annotations] + + class Scatter(): def __init__( @@ -198,6 +247,7 @@ def __init__( kwargs.get('background_image', UNDEF), ) + self._annotations = None self._lasso_color = (0, 0.666666667, 1, 1) self._lasso_on_long_press = True self._lasso_initiator = False @@ -430,6 +480,9 @@ def __init__( kwargs.get('zoom_on_selection', UNDEF), kwargs.get('zoom_on_filter', UNDEF), ) + self.annotations( + kwargs.get('annotations', UNDEF) + ) self.options(kwargs.get('options', UNDEF)) def get_point_list(self): @@ -649,6 +702,16 @@ def x( if 'skip_widget_update' not in kwargs: self.update_widget('x_scale', to_scale_type(self._x_scale)) + if self._annotations is not None: + self.update_widget( + 'annotations', + normalize_annotations( + self._annotations, + self._x_scale, + self._y_scale + ) + ) + if x is not UNDEF: self._x = x if isinstance(x, str): @@ -765,6 +828,16 @@ def y( if 'skip_widget_update' not in kwargs: self.update_widget('y_scale', to_scale_type(self._y_scale)) + if self._annotations is not None: + self.update_widget( + 'annotations', + normalize_annotations( + self._annotations, + self._x_scale, + self._y_scale + ) + ) + if y is not UNDEF: self._y = y if isinstance(y, str): @@ -884,6 +957,17 @@ def xy( self.update_widget('y_title', self._y_by) self.update_widget('axes_labels', self.get_axes_labels()) self.update_widget('points', self.get_point_list()) + + if self._annotations is not None: + self.update_widget( + 'annotations', + normalize_annotations( + self._annotations, + self._x_scale, + self._y_scale + ) + ) + return self return dict( @@ -3658,6 +3742,51 @@ def tooltip( size = self._tooltip_size, ) + def annotations( + self, + annotations: Optional[Union[List[Union[Line, HLine, VLine, Rect]], Undefined]] = UNDEF, + ): + """ + Draw line-based annotatons + + Parameters + ---------- + annotations : list of `Line`, `HLine`, `VLine`, `Rect`, optional + A list of annotations (`Line`, `HLine`, `VLine`, `Rect`) or `None`. + When set to `None` or `[]` all annotations are removed. + + Returns + ------- + self or dict + If no parameters are provided the current annotation settings are + returned as a dictionary. Otherwise, `self` is returned. + + Examples + -------- + >>> scatter.annotations([HLine(0), VLine(0)]) + <jscatter.jscatter.Scatter> + + >>> scatter.annotations() + {'annotations': [HLine(0), VLine(0)]} + """ + + if annotations is not UNDEF: + self._annotations = annotations + self.update_widget( + 'annotations', + normalize_annotations( + self._annotations, + self._x_scale, + self._y_scale + ) + ) + + if any_not([annotations], UNDEF): + return self + + return dict( + annotations = self._annotations + ) def zoom( self, @@ -3937,6 +4066,11 @@ def widget(self): self._widget = JupyterScatter( data=self._data, + annotations=normalize_annotations( + self._annotations, + self._x_scale, + self._y_scale + ), axes=self._axes, axes_color=self.get_axes_color(), axes_grid=self._axes_grid, diff --git a/jscatter/widget.py b/jscatter/widget.py index 8fd5a23..fd4f8fa 100644 --- a/jscatter/widget.py +++ b/jscatter/widget.py @@ -10,7 +10,13 @@ from traitlets import Bool, Dict, Enum, Float, Int, List, Unicode, Union from traittypes import Array -from .utils import to_hex, with_left_label +from .annotations_traits import ( + Line, + HLine, + VLine, + Rect, + serialization as annotation_serialization, +) SELECTION_DTYPE = 'uint32' EVENT_TYPES = { @@ -107,6 +113,13 @@ class JupyterScatter(anywidget.AnyWidget): opacity_histogram_range = List(None, allow_none=True, minlen=2, maxlen=2).tag(sync=True) size_histogram_range = List(None, allow_none=True, minlen=2, maxlen=2).tag(sync=True) + # Annotations + annotations = List( + trait=Union([Line(), HLine(), VLine(), Rect()]), + default_value=None, + allow_none=True, + ).tag(sync=True, **annotation_serialization) + # View properties camera_target = List([0, 0]).tag(sync=True) camera_distance = Float(1).tag(sync=True) diff --git a/notebooks/annotations.ipynb b/notebooks/annotations.ipynb new file mode 100644 index 0000000..3fd5380 --- /dev/null +++ b/notebooks/annotations.ipynb @@ -0,0 +1,85 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "60d8190f-2372-42d9-8816-ba34bedb544c", + "metadata": {}, + "source": [ + "# Annotations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d5d03018-14f2-4a79-a296-340fbd8e296a", + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "%env ANYWIDGET_HMR=1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3785ca6e-c2f8-45a9-afcf-d7284852ad9e", + "metadata": {}, + "outputs": [], + "source": [ + "import jscatter\n", + "import numpy as np\n", + "\n", + "x1, y1 = np.random.normal(-1, 0.2, 1000), np.random.normal(+1, 0.05, 1000)\n", + "x2, y2 = np.random.normal(+1, 0.2, 1000), np.random.normal(+1, 0.05, 1000)\n", + "x3, y3 = np.random.normal(+1, 0.2, 1000), np.random.normal(-1, 0.05, 1000)\n", + "x4, y4 = np.random.normal(-1, 0.2, 1000), np.random.normal(-1, 0.05, 1000)\n", + "\n", + "y0 = jscatter.HLine(0)\n", + "x0 = jscatter.VLine(0)\n", + "c1 = jscatter.Rect(x_start=-1.5, x_end=-0.5, y_start=+0.75, y_end=+1.25)\n", + "c2 = jscatter.Rect(x_start=+0.5, x_end=+1.5, y_start=+0.75, y_end=+1.25)\n", + "c3 = jscatter.Rect(x_start=+0.5, x_end=+1.5, y_start=-1.25, y_end=-0.75)\n", + "c4 = jscatter.Rect(x_start=-1.5, x_end=-0.5, y_start=-1.25, y_end=-0.75)\n", + "\n", + "scatter = jscatter.Scatter(\n", + " x=np.concatenate((x1, x2, x3, x4)), x_scale=(-2, 2),\n", + " y=np.concatenate((y1, y2, y3, y4)), y_scale=(-2, 2),\n", + " annotations=[x0, y0, c1, c2, c3, c4],\n", + " width=400,\n", + " height=400,\n", + ")\n", + "scatter.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7de74320-6941-4a26-9728-1ec870b6f477", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}	axvline and axhline Is there a possibility to show horizontal or vertical (non-selectable) lines in the plot, similar to matplotlibs axvline and axhline?	Unfortunately such a feature is not available yet. The closest thing you can get is drawing grid lines where the axis ticks are. While not the same as a dedicated x/y line the grid lines redraw as you zoom in and should help locating points. You can activate them via `scatter.axes(grid=True)`	2024-08-28T09:07:15	[]	[]
flekschas/jupyter-scatter	flekschas__jupyter-scatter-132	fd6fad3c749801f73bc2f5ffdbac3e8e7b9470e4	diff --git a/CHANGELOG.md b/CHANGELOG.md index 5555240..39adac1 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,3 +1,7 @@ +## v0.16.2 + +- Fix: reset scale & norm ranges upon updating the data via `scatter.data()` + ## v0.16.1 - Fix: preserve filter state upon changing visual encoding ([#134](https://github.com/flekschas/jupyter-scatter/issues/134)) diff --git a/js/package-lock.json b/js/package-lock.json index fae5aad..f1e1052 100644 --- a/js/package-lock.json +++ b/js/package-lock.json @@ -22,7 +22,7 @@ "gl-matrix": "~3.3.0", "pub-sub-es": "~3.0.0", "regl": "~2.1.0", - "regl-scatterplot": "~1.9.4" + "regl-scatterplot": "~1.9.6" }, "devDependencies": { "esbuild": "^0.19.5", @@ -2348,9 +2348,9 @@ } }, "node_modules/regl-scatterplot": { - "version": "1.9.4", - "resolved": "https://registry.npmjs.org/regl-scatterplot/-/regl-scatterplot-1.9.4.tgz", - "integrity": "sha512-hS/NtmYcJfCJxYBr9w9tHM0j4OTVKRZMwSxajz0T/kTWusOqC6VN5Uo64t6ZvwnymXJbBQTwhDC+9gTVwww2KA==", + "version": "1.9.6", + "resolved": "https://registry.npmjs.org/regl-scatterplot/-/regl-scatterplot-1.9.6.tgz", + "integrity": "sha512-hvroIbBmvLkPoTRgX99DKc3eg2Q6Ex7BwMb6Ih16wfBdJ651NQKu4n6TC/USUc+m89MjBZuA2csySi0Q8z/T3w==", "dependencies": { "@flekschas/utils": "^0.31.0", "dom-2d-camera": "~2.2.5", diff --git a/js/package.json b/js/package.json index cf9304b..7eaee70 100644 --- a/js/package.json +++ b/js/package.json @@ -30,7 +30,7 @@ "gl-matrix": "~3.3.0", "pub-sub-es": "~3.0.0", "regl": "~2.1.0", - "regl-scatterplot": "~1.9.4" + "regl-scatterplot": "~1.9.6" }, "devDependencies": { "esbuild": "^0.19.5", diff --git a/js/src/index.js b/js/src/index.js index 9284b64..98a2568 100644 --- a/js/src/index.js +++ b/js/src/index.js @@ -32,7 +32,6 @@ const AXES_PADDING_X = 60; const AXES_PADDING_X_WITH_LABEL = AXES_PADDING_X + AXES_LABEL_SIZE; const AXES_PADDING_Y = 20; const AXES_PADDING_Y_WITH_LABEL = AXES_PADDING_Y + AXES_LABEL_SIZE; -const TOOLTIP_EVENT_TYPE = 'tooltip'; const TOOLTIP_DEBOUNCE_TIME = 250; const TOOLTIP_MANDATORY_VISUAL_PROPERTIES = (/** @type {const} / ({ x: 'X', y: 'Y' })); const TOOLTIP_OPTIONAL_VISUAL_PROPERTIES = (/* @type {const} / ({ color: 'Col', opacity: 'Opa', size: 'Size' })); @@ -97,7 +96,6 @@ const properties = { connectionSize: 'pointConnectionSize', connectionSizeBy: 'pointConnectionSizeBy', viewDownload: 'viewDownload', - viewReset: 'viewReset', viewSync: 'viewSync', hovering: 'hovering', axes: 'axes', @@ -207,6 +205,7 @@ class JupyterScatterView { constructor({ el, model }) { this.el = el; this.model = model; + this.eventTypes = model.get('event_types'); } render() { @@ -385,10 +384,31 @@ class JupyterScatterView { } customEventHandler(event) { - if (event.type === TOOLTIP_EVENT_TYPE) { + if (event.type === this.eventTypes.TOOLTIP) { if (event.index !== this.tooltipPointIdx) return; this.tooltipDataHandlers(event) } + if (event.type === this.eventTypes.VIEW_RESET) { + if (!this.scatterplot) return; + if (event.area) { + this.scatterplot.zoomToArea( + event.area, + { + transition: event.animation > 0, + transitionDuration: event.animation, + transitionEasing: 'quadInOut', + } + ); + } else { + this.scatterplot.zoomToOrigin( + { + transition: event.animation > 0, + transitionDuration: event.animation, + transitionEasing: 'quadInOut', + } + ); + } + } } getOuterDimensions() { @@ -1501,7 +1521,7 @@ class JupyterScatterView { this.tooltipContentsUpdater = (pointIdx) => { this.model.send({ - type: TOOLTIP_EVENT_TYPE, + type: this.events.TOOLTIP, index: pointIdx, properties: this.tooltipPropertiesNonVisual, preview: this.tooltipPreview, @@ -2009,7 +2029,7 @@ class JupyterScatterView { if (newPoints.length === this.scatterplot.get('points').length) { // We assume point correspondence this.scatterplot.draw(newPoints, { - transition: true, + transition: this.model.get('transition_points'), transitionDuration: 3000, transitionEasing: 'quadInOut', preventFilterReset: this.model.get('prevent_filter_reset'), @@ -2337,14 +2357,6 @@ class JupyterScatterView { }); } - viewResetHandler() { - this.scatterplot.reset(); - setTimeout(() => { - this.model.set('view_reset', false); - this.model.save_changes(); - }, 0); - } - optionsHandler(newOptions) { this.scatterplot.set(newOptions); } @@ -2385,7 +2397,7 @@ function modelWithSerializers(model, serializers) { } } -export async function render({ model, el }) { +async function render({ model, el }) { const view = new JupyterScatterView({ el: el, model: modelWithSerializers(model, { @@ -2399,3 +2411,5 @@ export async function render({ model, el }) { view.render(); return () => view.destroy(); } + +export default { render }; diff --git a/jscatter/jscatter.py b/jscatter/jscatter.py index e3bb9f8..295c7b2 100644 --- a/jscatter/jscatter.py +++ b/jscatter/jscatter.py @@ -5,7 +5,7 @@ import pandas as pd from matplotlib.colors import to_rgba, Normalize, LogNorm, PowerNorm, LinearSegmentedColormap, ListedColormap -from typing import Dict, Optional, Union, List, Tuple +from typing import Dict, Optional, Union, List, Tuple, Literal from .encodings import Encodings from .widget import JupyterScatter, SELECTION_DTYPE @@ -452,6 +452,9 @@ def data( self, data: Optional[pd.DataFrame] = None, use_index: Optional[Union[bool, Undefined]] = UNDEF, + reset_scales: Optional[bool] = False, + reset_view: Optional[bool] = False, + animate: Optional[bool] = False, kwargs ) -> Union[Scatter, dict]: """ @@ -460,13 +463,22 @@ def data( Parameters ---------- data : pd.DataFrame, optional - The data frame that holds the x and y coordinates as well as other - possible dimensions that can be used for color, size, or opacity - encoding. + The new data frame that holds the x and y coordinates as well as + other possible dimensions that can be used for color, size, or + opacity encoding. label_index : bool, optional If `True` and if the data frame's index is not an instance of `RangeIndex` then the selection and filter methods will reference points by the data frame's label index instead of the row index. + reset_scales : bool, optional + If `True`, all scales (and norms) will be reset to the extend of the + the new data. + reset_view : bool, optional + If `True`, the view will be reset to the origin. + animate : bool, optional + If `True`, if the number of points remain the same, and if + `reset_scales` is `False`, the points will transition smoothly. If + `reset_view` is `True`, the view will also transition smoothly. Returns ------- @@ -493,6 +505,8 @@ def data( {'data': <pandas.DataFrame>, 'use_label_index': False} """ if data is not None: + old_n = self._n + try: self._n = len(data) self._data = data @@ -504,6 +518,35 @@ def data( self._points = np.zeros((self._n, 6)) + same_n = old_n == self._n + + if reset_scales == True: + self._x_scale.vmin = None + self._x_scale.vmax = None + self._y_scale.vmin = None + self._y_scale.vmax = None + self._color_norm.vmin = None + self._color_norm.vmax = None + self._opacity_norm.vmin = None + self._opacity_norm.vmax = None + self._size_norm.vmin = None + self._size_norm.vmax = None + self._connection_color_norm.vmin = None + self._connection_color_norm.vmax = None + self._connection_opacity_norm.vmin = None + self._connection_opacity_norm.vmax = None + self._connection_size_norm.vmin = None + self._connection_size_norm.vmax = None + + if 'skip_widget_update' not in kwargs: + # We have to update the following widget props now to ensure + # that the update propagated to the widget prior to updating the + # points below. This seems to be a bug in Jupyter Lab but I + # noticed that the order in which updates are registered on the + # front-end does not* correspond to the call order in Python! + self.update_widget('transition_points', animate and same_n) + self.update_widget('prevent_filter_reset', False) + self.x(skip_widget_update=True, self.x()) self.y(skip_widget_update=True, self.y()) self.color(skip_widget_update=True, self.color()) @@ -515,13 +558,26 @@ def data( self.connection_size(skip_widget_update=True, self.connection_size()) if 'skip_widget_update' not in kwargs: - self.update_widget('prevent_filter_reset', False) self.update_widget('points', self.get_point_list()) + self.update_widget('transition_points', animate == True) + self.update_widget('x_domain', self._x_domain) + self.update_widget('x_scale_domain', self._x_scale_domain) + self.update_widget('y_domain', self._y_domain) + self.update_widget('y_scale_domain', self._y_scale_domain) + + if reset_view: + if reset_scales: + self.widget.reset_view() + else: + animation = 3000 if animate and same_n else 0 + self.widget.reset_view( + animation=animation, + data_extent=True, + ) if use_index is not UNDEF: self._data_use_index = use_index - if any_not([data, use_index], UNDEF): return self @@ -576,18 +632,17 @@ def x( if scale is None or scale == 'linear': self._x_scale = create_default_norm() elif scale == 'time': - self._x_scale = create_default_norm(True) + self._x_scale = create_default_norm(is_time=True) elif scale == 'log': - self._x_scale = LogNorm(clip=True) + self._x_scale = LogNorm() elif scale == 'pow': - self._x_scale = PowerNorm(2, clip=True) - elif isinstance(scale, LogNorm) or isinstance(scale, PowerNorm): + self._x_scale = PowerNorm(2) + elif isinstance(scale, LogNorm) or isinstance(scale, PowerNorm) or isinstance(scale, Normalize): self._x_scale = scale - self._x_scale.clip = True else: try: vmin, vmax = scale - self._x_scale = Normalize(vmin, vmax, clip=True) + self._x_scale = Normalize(vmin, vmax) except: pass @@ -604,7 +659,8 @@ def x( self._x_by = 'Custom X Data' if x is not UNDEF or scale is not UNDEF: - self.update_widget('prevent_filter_reset', True) + if 'skip_widget_update' not in kwargs: + self.update_widget('prevent_filter_reset', True) self._points[:, 0] = zerofy_missing_values(self.x_data.values, 'X') self._x_min = np.min(self._points[:, 0]) @@ -692,18 +748,17 @@ def y( if scale is None or scale == 'linear': self._y_scale = create_default_norm() elif scale == 'time': - self._y_scale = create_default_norm(True) + self._y_scale = create_default_norm(is_time=True) elif scale == 'log': - self._y_scale = LogNorm(clip=True) + self._y_scale = LogNorm() elif scale == 'pow': - self._y_scale = PowerNorm(2, clip=True) - elif isinstance(scale, LogNorm) or isinstance(scale, PowerNorm): + self._y_scale = PowerNorm(2) + elif isinstance(scale, LogNorm) or isinstance(scale, PowerNorm) or isinstance(scale, Normalize): self._y_scale = scale - self._y_scale.clip = True else: try: vmin, vmax = scale - self._y_scale = Normalize(vmin, vmax, clip=True) + self._y_scale = Normalize(vmin, vmax) except: pass @@ -1077,13 +1132,12 @@ def color( if callable(norm): try: self._color_norm = norm - self._color_norm.clip = True except: pass else: try: vmin, vmax = norm - self._color_norm = Normalize(vmin, vmax, clip=True) + self._color_norm = Normalize(vmin, vmax) except: if norm is None: self._color_norm = create_default_norm() @@ -1340,13 +1394,12 @@ def opacity( if callable(norm): try: self._opacity_norm = norm - self._opacity_norm.clip = True except: pass else: try: vmin, vmax = norm - self._opacity_norm = Normalize(vmin, vmax, clip=True) + self._opacity_norm = Normalize(vmin, vmax) except: if norm is None: # Reset to default value @@ -1574,13 +1627,12 @@ def size( if callable(norm): try: self._size_norm = norm - self._size_norm.clip = True except: pass else: try: vmin, vmax = norm - self._size_norm = Normalize(vmin, vmax, clip=True) + self._size_norm = Normalize(vmin, vmax) except: if norm is not None: self._size_norm = create_default_norm() @@ -1954,13 +2006,12 @@ def connection_color( if callable(norm): try: self._connection_color_norm = norm - self._connection_color_norm.clip = True except: pass else: try: vmin, vmax = norm - self._connection_color_norm = Normalize(vmin, vmax, clip=True) + self._connection_color_norm = Normalize(vmin, vmax) except: if norm is None: self._connection_color_norm = create_default_norm() @@ -2206,13 +2257,12 @@ def connection_opacity( if callable(norm): try: self._connection_opacity_norm.clip = norm - self._connection_opacity_norm.clip = True except: pass else: try: vmin, vmax = norm - self._connection_opacity_norm = Normalize(vmin, vmax, clip=True) + self._connection_opacity_norm = Normalize(vmin, vmax) except: if norm is None: self._connection_opacity_norm = create_default_norm() @@ -2437,13 +2487,12 @@ def connection_size( if callable(norm): try: self._connection_size_norm = norm - self._connection_size_norm.clip = True except: pass else: try: vmin, vmax = norm - self._connection_size_norm = Normalize(vmin, vmax, clip=True) + self._connection_size_norm = Normalize(vmin, vmax) except: if norm is None: self._connection_size_norm = create_default_norm() diff --git a/jscatter/utils.py b/jscatter/utils.py index 72009b3..913584f 100644 --- a/jscatter/utils.py +++ b/jscatter/utils.py @@ -56,8 +56,8 @@ class TimeNormalize(Normalize): def create_default_norm(is_time=False): if is_time: - return TimeNormalize(clip=True) - return Normalize(clip=True) + return TimeNormalize() + return Normalize() def to_ndc(X, norm): return (norm(X).data * 2) - 1 diff --git a/jscatter/widget.py b/jscatter/widget.py index 922c90a..c79623c 100644 --- a/jscatter/widget.py +++ b/jscatter/widget.py @@ -13,7 +13,10 @@ from .utils import to_hex, with_left_label SELECTION_DTYPE = 'uint32' -TOOLTIP_EVENT_TYPE = 'tooltip'; +EVENT_TYPES = { + "TOOLTIP": "tooltip", + "VIEW_RESET": "view_reset", +} def component_idx_to_name(idx): if idx == 2: @@ -57,6 +60,7 @@ class JupyterScatter(anywidget.AnyWidget): # Data points = Array(default_value=None).tag(sync=True, ndarray_serialization) + transition_points = Bool(False).tag(sync=True) prevent_filter_reset = Bool(False).tag(sync=True) selection = Array(default_value=None, allow_none=True).tag(sync=True, ndarray_serialization) filter = Array(default_value=None, allow_none=True).tag(sync=True, ndarray_serialization) @@ -213,7 +217,6 @@ class JupyterScatter(anywidget.AnyWidget): # be overwritten by the short-hand options other_options = Dict(dict()).tag(sync=True) - view_reset = Bool(False).tag(sync=True) # Used for triggering a view reset view_download = Unicode(None, allow_none=True).tag(sync=True) # Used for triggering a download view_data = Array(default_value=None, allow_none=True, read_only=True).tag(sync=True, ndarray_serialization) view_shape = List(None, allow_none=True, read_only=True).tag(sync=True) @@ -221,6 +224,8 @@ class JupyterScatter(anywidget.AnyWidget): # For synchronyzing view changes across scatter plot instances view_sync = Unicode(None, allow_none=True).tag(sync=True) + event_types = Dict(EVENT_TYPES, read_only=True).tag(sync=True) + def __init__(self, data, args, kwargs): super().__init__(args, *kwargs) @@ -228,10 +233,10 @@ def __init__(self, data, args, kwargs): self.on_msg(self._handle_custom_msg) def _handle_custom_msg(self, event: dict, buffers): - if event["type"] == TOOLTIP_EVENT_TYPE and isinstance(self.data, pd.DataFrame): + if event["type"] == EVENT_TYPES["TOOLTIP"] and isinstance(self.data, pd.DataFrame): data = self.data.iloc[event["index"]] self.send({ - "type": TOOLTIP_EVENT_TYPE, + "type": EVENT_TYPES["TOOLTIP"], "index": event["index"], "preview": data[event["preview"]] if event["preview"] is not None else None, "properties": data[event["properties"]].to_dict() @@ -267,20 +272,33 @@ def click_handler(b): button.on_click(click_handler) return button - def reset_view(self): - self.view_reset = True + def reset_view(self, animation: int = 0, data_extent: bool = False): + if data_extent: + self.send({ + "type": EVENT_TYPES["VIEW_RESET"], + "area": { + "x": self.points[:, 0].min(), + "width": self.points[:, 0].max() - self.points[:, 0].min(), + "y": self.points[:, 1].min(), + "height": self.points[:, 1].max() - self.points[:, 1].min(), + }, + "animation": animation + }) + else: + self.send({ + "type": EVENT_TYPES["VIEW_RESET"], + "animation": animation + }) def create_reset_view_button(self, icon_only=False, width=None): - button = widgets.Button( + button = Button( description='' if icon_only else 'Reset View', - icon='refresh' + icon='refresh', + width=width, ) - if width is not None: - button.layout.width = f'{width}px' - - def click_handler(b): - self.reset_view() + def click_handler(event): + self.reset_view(500, event["alt_key"]) button.on_click(click_handler) return button @@ -363,3 +381,91 @@ def show(self): ) return widgets.HBox([buttons, plots]) + +class Button(anywidget.AnyWidget): + _esm = """ + function render({ model, el }) { + const button = document.createElement('button'); + + button.classList.add('jupyter-widgets'); + button.classList.add('jupyter-button'); + button.classList.add('widget-button'); + + const update = () => { + const description = model.get('description'); + const icon = model.get('icon'); + const width = model.get('width'); + + button.textContent = ''; + + if (icon) { + const i = document.createElement('i'); + i.classList.add('fa', `fa-${icon}`); + + if (!description) { + i.classList.add('center'); + } + + button.appendChild(i); + } + + if (description) { + button.appendChild(document.createTextNode(description)); + } + + if (width) { + button.style.width = `${width}px`; + } + } + + const createEventHandler = (eventType) => (event) => { + model.send({ + type: eventType, + alt_key: event.altKey, + shift_key: event.shiftKey, + meta_key: event.metaKey, + }); + } + + const clickHandler = createEventHandler('click'); + const dblclickHandler = createEventHandler('dblclick'); + + button.addEventListener('click', clickHandler); + button.addEventListener('dblclick', dblclickHandler); + + model.on('change:description', update); + model.on('change:icon', update); + model.on('change:width', update); + + update(); + + el.appendChild(button); + + return () => { + button.removeEventListener('click', clickHandler); + button.removeEventListener('dblclick', dblclickHandler); + }; + } + export default { render } + """ + + description = Unicode().tag(sync=True) + icon = Unicode().tag(sync=True) + width = Int(allow_none=True).tag(sync=True) + + def __init__(self, kwargs): + super().__init__(**kwargs) + self._click_handler = None + self.on_msg(self._handle_custom_msg) + + def _handle_custom_msg(self, event: dict, buffers): + if event["type"] == "click" and self._click_handler is not None: + self._click_handler(event) + if event["type"] == "dblclick" and self._dblclick_handler is not None: + self._dblclick_handler(event) + + def on_click(self, callback): + self._click_handler = callback + + def on_dblclick(self, callback): + self._dblclick_handler = callback diff --git a/pyproject.toml b/pyproject.toml index 170ba2b..6d126bf 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -15,11 +15,11 @@ classifiers = [ "Intended Audience :: Developers", "Intended Audience :: Science/Research", "Topic :: Multimedia :: Graphics", - "Programming Language :: Python :: 3.7", "Programming Language :: Python :: 3.8", "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", ] keywords = [ "scatter", @@ -32,7 +32,7 @@ keywords = [ "widgets", ] dependencies = [ - "anywidget>=0.2.0", + "anywidget>=0.9.0", "ipywidgets>=7.6,<9", "ipython", "matplotlib", @@ -81,7 +81,7 @@ path = "js" [tool.hatch.envs.default] features = ["dev"] -python = "3.11" +python = "3.12" [tool.hatch.envs.default.scripts] test = "pytest ."	When updating the source data, xy point coordinates are clipped to original data range - [x] Check for duplicate issues. - [x] Use the latest versions of Jupyter Scatter. - [x] Describe the bug: What did you do to trigger it? What was your expected behavior? And what did you experience instead? * Create a scatter with an initial dataframe. * Replace the dataframe with `scatter.data(df_new)` * Points try to migrate to their new locations but are "blocked" by the bounding box of the first dataframe * Doesn't depend on current camera location <img width="620" alt="image" src="https://github.com/flekschas/jupyter-scatter/assets/1270651/4d62afd0-0245-4057-8b02-4d1cd0f969dc"> - [x] Provide an [minimal, reproducible example](https://stackoverflow.com/help/minimal-reproducible-example) of the bug. ```python data1 = pd.DataFrame({ "x": np.random.rand(1000), "y": np.random.rand(1000), }) data2 = pd.DataFrame({ "x": 2np.random.rand(1000), "y": 2np.random.rand(1000), }) scatter = Scatter(data=data1, x="x", y="y", width=500, height=500) scatter.show() ``` ```python scatter.data(data=data2) ```		2024-06-10T01:29:15	[]	[]
prophyle/prophyle	prophyle__prophyle-352	5abb7d78ddb77b062c6b451f0859bd3f5c789ee8	diff --git a/prophyle/__commit.py b/prophyle/__commit.py index bcb05a8f..8849b1dc 100644 --- a/prophyle/__commit.py +++ b/prophyle/__commit.py @@ -1,4 +1,4 @@ -BRANCH = "mof" -SHORTHASH = "01de02c" -REVCOUNT = 1734 +BRANCH = "fix-mimic-kraken" +SHORTHASH = "fe13d9d" +REVCOUNT = 1737 LATESTTAG = "0.3.1.0" diff --git a/prophyle/prophyle_assignment.py b/prophyle/prophyle_assignment.py index 6acd6dbf..73546730 100755 --- a/prophyle/prophyle_assignment.py +++ b/prophyle/prophyle_assignment.py @@ -108,7 +108,7 @@ def process_read(self, krakline, form, measure): if CONFIG['DIAGNOSTICS']: self.diagnostics() - self.select_best_assignments(measure) + self.select_best_assignments(measure, self.tie_lca) if CONFIG['DIAGNOSTICS']: self.diagnostics() @@ -146,7 +146,7 @@ def blocks_to_masks(self, kmer_blocks, kmer_lca): covmask_1block = self.bitarray_block(covmask_len, count + self.k - 1, pos) if kmer_lca: - node_names = [self.tree_index.lca(node_names)] + node_names = [self.tree_index.lca(node_names)] for node_name in node_names: hitmasks_dict[node_name] \|= hitmask_1block @@ -183,8 +183,6 @@ def evaluate_single_assignment(self, nodename): hitmask = bitarray(self.hitmasks_dict[nodename]) covmask = bitarray(self.covmasks_dict[nodename]) - node = self.tree_index.nodename_to_node[nodename] - ########################## # B) Update from ancestors ########################## @@ -199,6 +197,7 @@ def evaluate_single_assignment(self, nodename): hit = hitmask.count() cov = covmask.count() readlen = self.krakline_parser.readlen + kcount = self.tree_index.nodename_to_kmercount[nodename] assignment = { # 1. hit count @@ -206,14 +205,14 @@ def evaluate_single_assignment(self, nodename): #'hitcigar': self.cigar_from_bitmask(hitmask), 'h1': [hit], 'hf': [hit / (readlen - self.k + 1)], - 'h2': [hit / self.tree_index.nodename_to_kmercount[nodename]], + 'h2': [hit / kcount if kcount > 0 else 0], # 2. coverage 'covmask': covmask, #'covcigar': self.cigar_from_bitmask(covmask), 'c1': [cov], 'cf': [cov / readlen], - 'c2': [cov / self.tree_index.nodename_to_kmercount[nodename]], + 'c2': [cov / kcount if kcount > 0 else 0], # 3. other values 'ln': readlen, @@ -221,18 +220,18 @@ def evaluate_single_assignment(self, nodename): return assignment - def select_best_assignments(self, measure): + def select_best_assignments(self, measure, tie_lca=False): """Find the best assignments and save it to self.max_nodenames (max value: self.max_val). Args: measure (str): Measure (h1/c1/h2/c2). + tie_lca (bool): Compute LCA in case of tie. """ self.max_val = 0 self.max_nodenames = [] - for nodename in self.ass_dict: - ass = self.ass_dict[nodename] + for nodename, ass in self.ass_dict.items(): if ass[measure][0] > self.max_val: self.max_val = ass[measure][0] @@ -241,6 +240,9 @@ def select_best_assignments(self, measure): elif ass[measure][0] == self.max_val: self.max_nodenames.append(nodename) + if tie_lca: + self.make_lca_from_winners() + if CONFIG['SORT_NODES']: self.max_nodenames.sort() @@ -252,11 +254,10 @@ def make_lca_from_winners(self): """ # all characteristics are already computed - if len(self.max_nodenames) == 1: + if len(self.max_nodenames) <= 1: return - first_winner = self.ass_dict[max_nodenames[0]] - lca = self.tree_index.lca(winners) + lca = self.tree_index.lca(self.max_nodenames) ass = { 'covmask': None, @@ -267,14 +268,12 @@ def make_lca_from_winners(self): } for tag in ['h1', 'hf', 'h2', 'c1', 'cf', 'c2']: - ass[tag] = [self.ass_dict[nodename][tag] for nodename in self.max_nodenames], + # ass[tag] = [self.ass_dict[nodename][tag] for nodename in self.max_nodenames] + ass[tag] = [0] self.max_nodenames = [lca] self.ass_dict[lca] = ass - asg['covmask'] = None - asg['hitmask'] = None - def print_selected_assignments(self, form): """Print the best assignments. @@ -332,7 +331,6 @@ def print_sam_line(self, node_name, suffix): suffix (str): Suffix with additional tags. """ - tags = [] qname = self.krakline_parser.readname if node_name is not None: @@ -438,7 +436,7 @@ def print_kraken_line(self, nodenames): #else: # pass else: - nodename = self.tree_index.lca(nodenames) + nodename = self.tree_index.lca(nodenames) nodenames_lca_seq.extend(count [nodename]) c = [] runs = itertools.groupby(nodenames_lca_seq) @@ -539,10 +537,10 @@ def __init__(self, tree_newick_fn, k): node = node.up self.nodename_to_upnodenames[nodename].add(node.name) - def lca(self, node_names): + def lca(self, node_names): """Return LCA for a given list of nodes. - node_names (list of str): List of node names. + node_names (list of str): List of node names. Returns: str: Name of the LCA. @@ -550,12 +548,12 @@ def lca(self, *node_names): assert len(node_names) > 0 if len(node_names) == 1: return node_names[0] - nodes = list(map(lambda x: self.nodename_to_node[x], node_names)) + nodes = [self.nodename_to_node[n] for n in node_names] lca = nodes[0].get_common_ancestor(nodes) if lca.is_root() and len(lca.children) == 1: lca = lca.children[0] - assert lca.name != "" #, [x.name for x in lca.children] + assert lca.name != "" # , [x.name for x in lca.children] return lca.name	No LCA computation in case of tie In the current version of `prophyle_assignment.py`, the function `make_lca_from_winners` is never called, even though the option `-L` (tie lca) is active. The list of nodes with the highest score is output instead.		2021-08-07T03:44:05	[]	[]
adamcharnock/django-hordak	adamcharnock__django-hordak-131	41eea6ba7a5a33b0527f5e73657084c5501c4df0	diff --git a/hordak/forms/transactions.py b/hordak/forms/transactions.py index 1454984..f811b18 100644 --- a/hordak/forms/transactions.py +++ b/hordak/forms/transactions.py @@ -21,17 +21,17 @@ class SimpleTransactionForm(forms.ModelForm): * :meth:`hordak.models.Account.transfer_to()`. """ - from_account = TreeNodeChoiceField( + debit_account = TreeNodeChoiceField( queryset=Account.objects.all(), to_field_name="uuid" ) - to_account = TreeNodeChoiceField( + credit_account = TreeNodeChoiceField( queryset=Account.objects.all(), to_field_name="uuid" ) amount = MoneyField(max_digits=MAX_DIGITS, decimal_places=DECIMAL_PLACES) class Meta: model = Transaction - fields = ["amount", "from_account", "to_account", "date", "description"] + fields = ["amount", "debit_account", "credit_account", "date", "description"] def __init__(self, args, kwargs): super().__init__(args, *kwargs) @@ -49,12 +49,12 @@ def __init__(self, args, kwargs): self.fields["amount"].initial[1] = default_currency def save(self, commit=True): - from_account = self.cleaned_data.get("from_account") - to_account = self.cleaned_data.get("to_account") + debit_account = self.cleaned_data.get("debit_account") + credit_account = self.cleaned_data.get("credit_account") amount = self.cleaned_data.get("amount") - return from_account.transfer_to( - to_account=to_account, + return debit_account.transfer_to( + to_account=credit_account, amount=amount, description=self.cleaned_data.get("description"), date=self.cleaned_data.get("date"), diff --git a/hordak/models/core.py b/hordak/models/core.py index cd07868..f24cf67 100644 --- a/hordak/models/core.py +++ b/hordak/models/core.py @@ -44,7 +44,6 @@ ) from hordak.utilities.currency import Balance from hordak.utilities.db_functions import GetBalance -from hordak.utilities.dreprecation import deprecated #: Debit @@ -328,73 +327,9 @@ def _zero_balance(self): """Get a balance for this account with all currencies set to zero""" return Balance([Money("0", currency) for currency in self.currencies]) - @deprecated( - "transfer_to() has been deprecated. This method does not adhere to expected transfers based on the " - "accounting equation, see notes. Use .accounting_transfer_to() instead. " - "This method will raise an error in v2.0.0." - ) @db_transaction.atomic() def transfer_to(self, to_account, amount, transaction_kwargs): - """Deprecated Please use `.accounting_transfer_to()` instead. Will raise an error in Hordak 2.x. - - Create a transaction which transfers amount to to_account - - This is a shortcut utility method which simplifies the process of - transferring between accounts. - - This method attempts to perform the transaction in an intuitive manner. - For example: - - * Transferring income -> income will result in the former decreasing and the latter increasing - * Transferring asset (i.e. bank) -> income will result in the balance of both increasing - * Transferring asset -> asset will result in the former decreasing and the latter increasing - - .. note:: - - Transfers in any direction between ``{asset \| expense} <-> {income \| liability \| equity}`` - will always result in both balances increasing. This may change in future if it is - found to be unhelpful. - - Transfers to trading accounts will always behave as normal. - - Args: - - to_account (Account): The destination account. - amount (Money): The amount to be transferred. - transaction_kwargs: Passed through to transaction creation. Useful for setting the - transaction `description` field. - """ - if not isinstance(amount, Money): - raise TypeError("amount must be of type Money") - - if to_account.sign == 1 and to_account.type != AccountType.trading: - # Transferring from two positive-signed accounts implies that - # the caller wants to reduce the first account and increase the second - # (which is opposite to the implicit behaviour) - direction = -1 - elif ( - self.type == AccountType.liability - and to_account.type == AccountType.expense - ): - # Transfers from liability -> asset accounts should reduce both. - # For example, moving money from Rent Payable (liability) to your Rent (expense) account - # should use the funds you've built up in the liability account to pay off the expense account. - direction = -1 - else: - direction = 1 - - transaction = Transaction.objects.create(*transaction_kwargs) - Leg.objects.create( - transaction=transaction, account=self, amount=+amount direction - ) - Leg.objects.create( - transaction=transaction, account=to_account, amount=-amount * direction - ) - return transaction - - @db_transaction.atomic() - def accounting_transfer_to(self, to_account, amount, **transaction_kwargs): - """Create a transaction which transfers amount to to_account using double entry accounting rules + """Create a transaction which credits self and debits `to_account`. See https://en.wikipedia.org/wiki/Double-entry_bookkeeping. @@ -610,7 +545,8 @@ def is_credit(self): def account_balance_after(self): """Get the balance of the account associated with this leg following the transaction""" # TODO: Consider moving to annotation, - # particularly once we can count on Django 1.11's subquery support + # particularly once we can count on Django 1.11's subquery support + # Or use the new LegView. transaction_date = self.transaction.date return self.account.balance( leg_query=( @@ -625,7 +561,8 @@ def account_balance_after(self): def account_balance_before(self): """Get the balance of the account associated with this leg before the transaction""" # TODO: Consider moving to annotation, - # particularly once we can count on Django 1.11's subquery support + # particularly once we can count on Django 1.11's subquery support. + # Or use the new LegView. transaction_date = self.transaction.date return self.account.balance( leg_query=(	Replace `transfer_to()` with `accounting_transfer_to()` We've been warning this would happen in Hordak 2.0.0 for a while.		2024-06-30T20:42:46	[]	[]
adamcharnock/django-hordak	adamcharnock__django-hordak-90	8c5453ad74f9f9a1c09ae7c23d2cfe8995be7658	diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index a5962088..878445ef 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -70,13 +70,15 @@ jobs: pip install Django==${{ matrix.DJANGO_VERSION }} pip install codecov - - name: Testing + - name: Check Migrations run: \| pip install "py-moneyed<3" --upgrade PYTHONPATH=`pwd` ./manage.py makemigrations --check hordak pip install "py-moneyed>=3" --upgrade # Different version of py-moneyed should not create different migrations PYTHONPATH=`pwd` ./manage.py makemigrations --check hordak + - name: Testing + run: \| PYTHONPATH=`pwd` python -Wall -W error::DeprecationWarning -m coverage run ./manage.py test hordak pip install -e .[subqueries] PYTHONPATH=`pwd` python -Wall -W error::DeprecationWarning -m coverage run --append ./manage.py test hordak # Test with subquery diff --git a/CHANGES.txt b/CHANGES.txt index bc8e6b4c..8cce58da 100644 --- a/CHANGES.txt +++ b/CHANGES.txt @@ -6,6 +6,14 @@ Version 1.14.0, This function will raise an error in Hordak 2.0. * Now using Django's locale to `Balance.__str__`'s `format_money` allowing better formatting and specs. +* Changed `Account.currencies` from `ArrayField => JSONField` to allow database agnostic work. + * Breaking Change + * `Account.currencies` input must be valid JSON now. + * This is mostly important in 2 areas + * `Forms`, but also if you performed a `", ".join(currencies_str_arr)`. + * `Fixtures` + * Previous: `EUR, USD` => `["EUR", "USD"]` + * Form complains if it is not valid JSON and returns an explanation. Version 1.13.0, Fri 17 Feb 2023 =============================== diff --git a/hordak/defaults.py b/hordak/defaults.py index dccd0e8e..8adbc391 100644 --- a/hordak/defaults.py +++ b/hordak/defaults.py @@ -8,18 +8,22 @@ DEFAULT_CURRENCY = getattr(settings, "DEFAULT_CURRENCY", "EUR") + +def default_currency(): + return DEFAULT_CURRENCY + + CURRENCIES = getattr(settings, "HORDAK_CURRENCIES", getattr(settings, "CURRENCIES", [])) -def default_currencies(): - default_currs = getattr( - settings, "HORDAK_CURRENCIES", getattr(settings, "CURRENCIES", []) - ) +# Expected to be an array of currencies ["EUR", "USD", "GBP"] +def project_currencies() -> list: + project_currs = CURRENCIES - if callable(default_currs): - return default_currs() + if callable(project_currs): + return project_currs() - return default_currs + return project_currs DECIMAL_PLACES = getattr(settings, "HORDAK_DECIMAL_PLACES", 2) diff --git a/hordak/forms/accounts.py b/hordak/forms/accounts.py index c5dd522c..b0fd7529 100644 --- a/hordak/forms/accounts.py +++ b/hordak/forms/accounts.py @@ -1,4 +1,7 @@ +import json + from django import forms +from djmoney.settings import CURRENCY_CHOICES from hordak.models import Account @@ -16,6 +19,8 @@ class Meta: model = Account exclude = ["full_code"] + currencies = forms.JSONField() + def __init__(self, args, kwargs): self.is_updating = bool(kwargs.get("instance")) and kwargs["instance"].pk @@ -33,11 +38,40 @@ def __init__(self, args, *kwargs): super(AccountForm, self).__init__(args, *kwargs) + try: + self.tmp_currencies = args[0].get("currencies") + except IndexError: + self.tmp_currencies = kwargs.get("data", {}).get("currencies") + if self.is_updating: del self.fields["type"] del self.fields["currencies"] del self.fields["is_bank_account"] + def _check_currencies_json(self): + currencies = self.tmp_currencies + + if isinstance(self.tmp_currencies, str): + try: + currencies = json.loads(self.tmp_currencies) + except json.JSONDecodeError: + if "currencies" in self.fields: + self.add_error( + "currencies", + 'Currencies needs to be valid JSON (i.e. ["USD", "EUR"] or ["USD"])' + + f" - {self.tmp_currencies} is not valid JSON.", + ) + + return + + for currency in currencies: + if currency not in [choice[0] for choice in CURRENCY_CHOICES]: + if "currencies" in self.fields: + self.add_error( + "currencies", + f"Select a valid choice. {currency} is not one of the available choices.", + ) + def clean(self): cleaned_data = super(AccountForm, self).clean() is_bank_account = ( @@ -60,4 +94,6 @@ def clean(self): ): raise forms.ValidationError("Bank accounts may only have one currency.") + self._check_currencies_json() + return cleaned_data diff --git a/hordak/migrations/0030_alter_leg_amount_currency.py b/hordak/migrations/0030_alter_leg_amount_currency.py index b830ed9b..bd1793b3 100644 --- a/hordak/migrations/0030_alter_leg_amount_currency.py +++ b/hordak/migrations/0030_alter_leg_amount_currency.py @@ -2,6 +2,8 @@ import djmoney.models.fields from django.db import migrations +import hordak.models.core +import hordak.defaults class Migration(migrations.Migration): @@ -14,318 +16,8 @@ class Migration(migrations.Migration): model_name="leg", name="amount_currency", field=djmoney.models.fields.CurrencyField( - choices=[ - ("XUA", "ADB Unit of Account"), - ("AFN", "Afghan Afghani"), - ("AFA", "Afghan Afghani (1927–2002)"), - ("ALL", "Albanian Lek"), - ("ALK", "Albanian Lek (1946–1965)"), - ("DZD", "Algerian Dinar"), - ("ADP", "Andorran Peseta"), - ("AOA", "Angolan Kwanza"), - ("AOK", "Angolan Kwanza (1977–1991)"), - ("AON", "Angolan New Kwanza (1990–2000)"), - ("AOR", "Angolan Readjusted Kwanza (1995–1999)"), - ("ARA", "Argentine Austral"), - ("ARS", "Argentine Peso"), - ("ARM", "Argentine Peso (1881–1970)"), - ("ARP", "Argentine Peso (1983–1985)"), - ("ARL", "Argentine Peso Ley (1970–1983)"), - ("AMD", "Armenian Dram"), - ("AWG", "Aruban Florin"), - ("AUD", "Australian Dollar"), - ("ATS", "Austrian Schilling"), - ("AZN", "Azerbaijani Manat"), - ("AZM", "Azerbaijani Manat (1993–2006)"), - ("BSD", "Bahamian Dollar"), - ("BHD", "Bahraini Dinar"), - ("BDT", "Bangladeshi Taka"), - ("BBD", "Barbadian Dollar"), - ("BYN", "Belarusian Ruble"), - ("BYB", "Belarusian Ruble (1994–1999)"), - ("BYR", "Belarusian Ruble (2000–2016)"), - ("BEF", "Belgian Franc"), - ("BEC", "Belgian Franc (convertible)"), - ("BEL", "Belgian Franc (financial)"), - ("BZD", "Belize Dollar"), - ("BMD", "Bermudan Dollar"), - ("BTN", "Bhutanese Ngultrum"), - ("BOB", "Bolivian Boliviano"), - ("BOL", "Bolivian Boliviano (1863–1963)"), - ("BOV", "Bolivian Mvdol"), - ("BOP", "Bolivian Peso"), - ("BAM", "Bosnia-Herzegovina Convertible Mark"), - ("BAD", "Bosnia-Herzegovina Dinar (1992–1994)"), - ("BAN", "Bosnia-Herzegovina New Dinar (1994–1997)"), - ("BWP", "Botswanan Pula"), - ("BRC", "Brazilian Cruzado (1986–1989)"), - ("BRZ", "Brazilian Cruzeiro (1942–1967)"), - ("BRE", "Brazilian Cruzeiro (1990–1993)"), - ("BRR", "Brazilian Cruzeiro (1993–1994)"), - ("BRN", "Brazilian New Cruzado (1989–1990)"), - ("BRB", "Brazilian New Cruzeiro (1967–1986)"), - ("BRL", "Brazilian Real"), - ("GBP", "British Pound"), - ("BND", "Brunei Dollar"), - ("BGL", "Bulgarian Hard Lev"), - ("BGN", "Bulgarian Lev"), - ("BGO", "Bulgarian Lev (1879–1952)"), - ("BGM", "Bulgarian Socialist Lev"), - ("BUK", "Burmese Kyat"), - ("BIF", "Burundian Franc"), - ("XPF", "CFP Franc"), - ("KHR", "Cambodian Riel"), - ("CAD", "Canadian Dollar"), - ("CVE", "Cape Verdean Escudo"), - ("KYD", "Cayman Islands Dollar"), - ("XAF", "Central African CFA Franc"), - ("CLE", "Chilean Escudo"), - ("CLP", "Chilean Peso"), - ("CLF", "Chilean Unit of Account (UF)"), - ("CNX", "Chinese People’s Bank Dollar"), - ("CNY", "Chinese Yuan"), - ("CNH", "Chinese Yuan (offshore)"), - ("COP", "Colombian Peso"), - ("COU", "Colombian Real Value Unit"), - ("KMF", "Comorian Franc"), - ("CDF", "Congolese Franc"), - ("CRC", "Costa Rican Colón"), - ("HRD", "Croatian Dinar"), - ("HRK", "Croatian Kuna"), - ("CUC", "Cuban Convertible Peso"), - ("CUP", "Cuban Peso"), - ("CYP", "Cypriot Pound"), - ("CZK", "Czech Koruna"), - ("CSK", "Czechoslovak Hard Koruna"), - ("DKK", "Danish Krone"), - ("DJF", "Djiboutian Franc"), - ("DOP", "Dominican Peso"), - ("NLG", "Dutch Guilder"), - ("XCD", "East Caribbean Dollar"), - ("DDM", "East German Mark"), - ("ECS", "Ecuadorian Sucre"), - ("ECV", "Ecuadorian Unit of Constant Value"), - ("EGP", "Egyptian Pound"), - ("GQE", "Equatorial Guinean Ekwele"), - ("ERN", "Eritrean Nakfa"), - ("EEK", "Estonian Kroon"), - ("ETB", "Ethiopian Birr"), - ("EUR", "Euro"), - ("XBA", "European Composite Unit"), - ("XEU", "European Currency Unit"), - ("XBB", "European Monetary Unit"), - ("XBC", "European Unit of Account (XBC)"), - ("XBD", "European Unit of Account (XBD)"), - ("FKP", "Falkland Islands Pound"), - ("FJD", "Fijian Dollar"), - ("FIM", "Finnish Markka"), - ("FRF", "French Franc"), - ("XFO", "French Gold Franc"), - ("XFU", "French UIC-Franc"), - ("GMD", "Gambian Dalasi"), - ("GEK", "Georgian Kupon Larit"), - ("GEL", "Georgian Lari"), - ("DEM", "German Mark"), - ("GHS", "Ghanaian Cedi"), - ("GHC", "Ghanaian Cedi (1979–2007)"), - ("GIP", "Gibraltar Pound"), - ("XAU", "Gold"), - ("GRD", "Greek Drachma"), - ("GTQ", "Guatemalan Quetzal"), - ("GWP", "Guinea-Bissau Peso"), - ("GNF", "Guinean Franc"), - ("GNS", "Guinean Syli"), - ("GYD", "Guyanaese Dollar"), - ("HTG", "Haitian Gourde"), - ("HNL", "Honduran Lempira"), - ("HKD", "Hong Kong Dollar"), - ("HUF", "Hungarian Forint"), - ("IMP", "IMP"), - ("ISK", "Icelandic Króna"), - ("ISJ", "Icelandic Króna (1918–1981)"), - ("INR", "Indian Rupee"), - ("IDR", "Indonesian Rupiah"), - ("IRR", "Iranian Rial"), - ("IQD", "Iraqi Dinar"), - ("IEP", "Irish Pound"), - ("ILS", "Israeli New Shekel"), - ("ILP", "Israeli Pound"), - ("ILR", "Israeli Shekel (1980–1985)"), - ("ITL", "Italian Lira"), - ("JMD", "Jamaican Dollar"), - ("JPY", "Japanese Yen"), - ("JOD", "Jordanian Dinar"), - ("KZT", "Kazakhstani Tenge"), - ("KES", "Kenyan Shilling"), - ("KWD", "Kuwaiti Dinar"), - ("KGS", "Kyrgystani Som"), - ("LAK", "Laotian Kip"), - ("LVL", "Latvian Lats"), - ("LVR", "Latvian Ruble"), - ("LBP", "Lebanese Pound"), - ("LSL", "Lesotho Loti"), - ("LRD", "Liberian Dollar"), - ("LYD", "Libyan Dinar"), - ("LTL", "Lithuanian Litas"), - ("LTT", "Lithuanian Talonas"), - ("LUL", "Luxembourg Financial Franc"), - ("LUC", "Luxembourgian Convertible Franc"), - ("LUF", "Luxembourgian Franc"), - ("MOP", "Macanese Pataca"), - ("MKD", "Macedonian Denar"), - ("MKN", "Macedonian Denar (1992–1993)"), - ("MGA", "Malagasy Ariary"), - ("MGF", "Malagasy Franc"), - ("MWK", "Malawian Kwacha"), - ("MYR", "Malaysian Ringgit"), - ("MVR", "Maldivian Rufiyaa"), - ("MVP", "Maldivian Rupee (1947–1981)"), - ("MLF", "Malian Franc"), - ("MTL", "Maltese Lira"), - ("MTP", "Maltese Pound"), - ("MRU", "Mauritanian Ouguiya"), - ("MRO", "Mauritanian Ouguiya (1973–2017)"), - ("MUR", "Mauritian Rupee"), - ("MXV", "Mexican Investment Unit"), - ("MXN", "Mexican Peso"), - ("MXP", "Mexican Silver Peso (1861–1992)"), - ("MDC", "Moldovan Cupon"), - ("MDL", "Moldovan Leu"), - ("MCF", "Monegasque Franc"), - ("MNT", "Mongolian Tugrik"), - ("MAD", "Moroccan Dirham"), - ("MAF", "Moroccan Franc"), - ("MZE", "Mozambican Escudo"), - ("MZN", "Mozambican Metical"), - ("MZM", "Mozambican Metical (1980–2006)"), - ("MMK", "Myanmar Kyat"), - ("NAD", "Namibian Dollar"), - ("NPR", "Nepalese Rupee"), - ("ANG", "Netherlands Antillean Guilder"), - ("TWD", "New Taiwan Dollar"), - ("NZD", "New Zealand Dollar"), - ("NIO", "Nicaraguan Córdoba"), - ("NIC", "Nicaraguan Córdoba (1988–1991)"), - ("NGN", "Nigerian Naira"), - ("KPW", "North Korean Won"), - ("NOK", "Norwegian Krone"), - ("OMR", "Omani Rial"), - ("PKR", "Pakistani Rupee"), - ("XPD", "Palladium"), - ("PAB", "Panamanian Balboa"), - ("PGK", "Papua New Guinean Kina"), - ("PYG", "Paraguayan Guarani"), - ("PEI", "Peruvian Inti"), - ("PEN", "Peruvian Sol"), - ("PES", "Peruvian Sol (1863–1965)"), - ("PHP", "Philippine Peso"), - ("XPT", "Platinum"), - ("PLN", "Polish Zloty"), - ("PLZ", "Polish Zloty (1950–1995)"), - ("PTE", "Portuguese Escudo"), - ("GWE", "Portuguese Guinea Escudo"), - ("QAR", "Qatari Rial"), - ("XRE", "RINET Funds"), - ("RHD", "Rhodesian Dollar"), - ("RON", "Romanian Leu"), - ("ROL", "Romanian Leu (1952–2006)"), - ("RUB", "Russian Ruble"), - ("RUR", "Russian Ruble (1991–1998)"), - ("RWF", "Rwandan Franc"), - ("SVC", "Salvadoran Colón"), - ("WST", "Samoan Tala"), - ("SAR", "Saudi Riyal"), - ("RSD", "Serbian Dinar"), - ("CSD", "Serbian Dinar (2002–2006)"), - ("SCR", "Seychellois Rupee"), - ("SLL", "Sierra Leonean Leone"), - ("XAG", "Silver"), - ("SGD", "Singapore Dollar"), - ("SKK", "Slovak Koruna"), - ("SIT", "Slovenian Tolar"), - ("SBD", "Solomon Islands Dollar"), - ("SOS", "Somali Shilling"), - ("ZAR", "South African Rand"), - ("ZAL", "South African Rand (financial)"), - ("KRH", "South Korean Hwan (1953–1962)"), - ("KRW", "South Korean Won"), - ("KRO", "South Korean Won (1945–1953)"), - ("SSP", "South Sudanese Pound"), - ("SUR", "Soviet Rouble"), - ("ESP", "Spanish Peseta"), - ("ESA", "Spanish Peseta (A account)"), - ("ESB", "Spanish Peseta (convertible account)"), - ("XDR", "Special Drawing Rights"), - ("LKR", "Sri Lankan Rupee"), - ("SHP", "St. Helena Pound"), - ("XSU", "Sucre"), - ("SDD", "Sudanese Dinar (1992–2007)"), - ("SDG", "Sudanese Pound"), - ("SDP", "Sudanese Pound (1957–1998)"), - ("SRD", "Surinamese Dollar"), - ("SRG", "Surinamese Guilder"), - ("SZL", "Swazi Lilangeni"), - ("SEK", "Swedish Krona"), - ("CHF", "Swiss Franc"), - ("SYP", "Syrian Pound"), - ("STN", "São Tomé & Príncipe Dobra"), - ("STD", "São Tomé & Príncipe Dobra (1977–2017)"), - ("TVD", "TVD"), - ("TJR", "Tajikistani Ruble"), - ("TJS", "Tajikistani Somoni"), - ("TZS", "Tanzanian Shilling"), - ("XTS", "Testing Currency Code"), - ("THB", "Thai Baht"), - ( - "XXX", - "The codes assigned for transactions where no currency is involved", - ), - ("TPE", "Timorese Escudo"), - ("TOP", "Tongan Paʻanga"), - ("TTD", "Trinidad & Tobago Dollar"), - ("TND", "Tunisian Dinar"), - ("TRY", "Turkish Lira"), - ("TRL", "Turkish Lira (1922–2005)"), - ("TMT", "Turkmenistani Manat"), - ("TMM", "Turkmenistani Manat (1993–2009)"), - ("USD", "US Dollar"), - ("USN", "US Dollar (Next day)"), - ("USS", "US Dollar (Same day)"), - ("UGX", "Ugandan Shilling"), - ("UGS", "Ugandan Shilling (1966–1987)"), - ("UAH", "Ukrainian Hryvnia"), - ("UAK", "Ukrainian Karbovanets"), - ("AED", "United Arab Emirates Dirham"), - ("UYW", "Uruguayan Nominal Wage Index Unit"), - ("UYU", "Uruguayan Peso"), - ("UYP", "Uruguayan Peso (1975–1993)"), - ("UYI", "Uruguayan Peso (Indexed Units)"), - ("UZS", "Uzbekistani Som"), - ("VUV", "Vanuatu Vatu"), - ("VES", "Venezuelan Bolívar"), - ("VEB", "Venezuelan Bolívar (1871–2008)"), - ("VEF", "Venezuelan Bolívar (2008–2018)"), - ("VND", "Vietnamese Dong"), - ("VNN", "Vietnamese Dong (1978–1985)"), - ("CHE", "WIR Euro"), - ("CHW", "WIR Franc"), - ("XOF", "West African CFA Franc"), - ("YDD", "Yemeni Dinar"), - ("YER", "Yemeni Rial"), - ("YUN", "Yugoslavian Convertible Dinar (1990–1992)"), - ("YUD", "Yugoslavian Hard Dinar (1966–1990)"), - ("YUM", "Yugoslavian New Dinar (1994–2002)"), - ("YUR", "Yugoslavian Reformed Dinar (1992–1993)"), - ("ZWN", "ZWN"), - ("ZRN", "Zairean New Zaire (1993–1998)"), - ("ZRZ", "Zairean Zaire (1971–1993)"), - ("ZMW", "Zambian Kwacha"), - ("ZMK", "Zambian Kwacha (1968–2012)"), - ("ZWD", "Zimbabwean Dollar (1980–2008)"), - ("ZWR", "Zimbabwean Dollar (2008)"), - ("ZWL", "Zimbabwean Dollar (2009)"), - ], - default="EUR", + choices=hordak.models.core.get_currency_choices(), + default=hordak.defaults.get_internal_currency, editable=False, max_length=3, ), diff --git a/hordak/migrations/0034_alter_account_currencies_alter_leg_amount_currency.py b/hordak/migrations/0034_alter_account_currencies_alter_leg_amount_currency.py index 90d343c2..f5a15453 100644 --- a/hordak/migrations/0034_alter_account_currencies_alter_leg_amount_currency.py +++ b/hordak/migrations/0034_alter_account_currencies_alter_leg_amount_currency.py @@ -4,6 +4,7 @@ import djmoney.models.fields from django.db import migrations, models import hordak.models.core +import hordak.defaults class Migration(migrations.Migration): @@ -21,7 +22,7 @@ class Migration(migrations.Migration): max_length=3, ), db_index=True, - default=hordak.models.core.default_currencies, + default=hordak.defaults.DEFAULT_CURRENCY, size=None, verbose_name="currencies", ), @@ -31,7 +32,7 @@ class Migration(migrations.Migration): name="amount_currency", field=djmoney.models.fields.CurrencyField( choices=hordak.models.core.get_currency_choices(), - default=hordak.models.core.get_internal_currency, + default=hordak.defaults.get_internal_currency, editable=False, max_length=3, ), diff --git a/hordak/migrations/0035_account_currencies_json.py b/hordak/migrations/0035_account_currencies_json.py new file mode 100644 index 00000000..2fc51859 --- /dev/null +++ b/hordak/migrations/0035_account_currencies_json.py @@ -0,0 +1,36 @@ +# Generated by Django 4.2 on 2023-05-16 01:31 + +from django.db import migrations, models + +import hordak + + +def copy_currencies_data(apps, schema_editor): + MyModel = apps.get_model( + "hordak", "Account" + ) # replace 'myapp' and 'MyModel' with your actual app and model names + table_name = MyModel._meta.db_table + with schema_editor.connection.cursor() as cursor: + cursor.execute( + f""" + UPDATE {table_name} SET currencies_json = array_to_json(currencies)::jsonb; + """ + ) + + +class Migration(migrations.Migration): + dependencies = [ + ("hordak", "0034_alter_account_currencies_alter_leg_amount_currency"), + ] + + operations = [ + migrations.AddField( + model_name="account", + name="currencies_json", + field=models.JSONField( + db_index=True, + default=hordak.defaults.project_currencies, + ), + ), + migrations.RunPython(copy_currencies_data), + ] diff --git a/hordak/migrations/0036_remove_currencies_and_rename_account_currencies_json_to_currencies.py b/hordak/migrations/0036_remove_currencies_and_rename_account_currencies_json_to_currencies.py new file mode 100644 index 00000000..d9b5558c --- /dev/null +++ b/hordak/migrations/0036_remove_currencies_and_rename_account_currencies_json_to_currencies.py @@ -0,0 +1,32 @@ +# Generated by Django 4.2 on 2023-05-16 01:36 + +from django.db import migrations, models + +import hordak + + +class Migration(migrations.Migration): + dependencies = [ + ("hordak", "0035_account_currencies_json"), + ] + + operations = [ + migrations.RemoveField( + model_name="account", + name="currencies", + ), + migrations.RenameField( + model_name="account", + old_name="currencies_json", + new_name="currencies", + ), + migrations.AlterField( + model_name="account", + name="currencies", + field=models.JSONField( + db_index=True, + default=hordak.models.core.project_currencies, + verbose_name="currencies", + ), + ), + ] diff --git a/hordak/migrations/0037_auto_20230516_0142.py b/hordak/migrations/0037_auto_20230516_0142.py new file mode 100644 index 00000000..dcf46d3f --- /dev/null +++ b/hordak/migrations/0037_auto_20230516_0142.py @@ -0,0 +1,43 @@ +# Generated by Django 4.2 on 2023-05-16 01:42 + +from django.db import migrations + + +class Migration(migrations.Migration): + dependencies = [ + ( + "hordak", + "0036_remove_currencies_and_rename_account_currencies_json_to_currencies", + ), + ] + + operations = [ + migrations.RunSQL( + """ + CREATE OR REPLACE FUNCTION check_leg_and_account_currency_match() + RETURNS trigger AS + $$ + DECLARE + account RECORD; + BEGIN + + IF (TG_OP = 'DELETE') THEN + RETURN OLD; + END IF; + + PERFORM FROM hordak_account WHERE id = NEW.account_id AND currencies::jsonb @> to_jsonb(ARRAY[NEW.amount_currency]::text[]); + + IF NOT FOUND THEN + SELECT * INTO account FROM hordak_account WHERE id = NEW.account_id; + + RAISE EXCEPTION 'Destination Account#% does not support currency %. Account currencies: %', account.id, NEW.amount_currency, account.currencies; + END IF; + + RETURN NEW; + END; + $$ + LANGUAGE plpgsql + """, + "DROP FUNCTION check_leg_and_account_currency_match()", + ), + ] diff --git a/hordak/models/core.py b/hordak/models/core.py index 03b5e22a..f8a75fd5 100644 --- a/hordak/models/core.py +++ b/hordak/models/core.py @@ -21,7 +21,6 @@ create a transaction for the statement line. """ -from django.contrib.postgres.fields.array import ArrayField from django.db import models from django.db import transaction as db_transaction from django.db.models import JSONField @@ -38,8 +37,8 @@ from hordak.defaults import ( DECIMAL_PLACES, MAX_DIGITS, - default_currencies, get_internal_currency, + project_currencies, ) from hordak.utilities.currency import Balance from hordak.utilities.dreprecation import deprecated @@ -142,10 +141,9 @@ class Account(MPTTModel): "statements into it and that it only supports a single currency", verbose_name=_("is bank account"), ) - currencies = ArrayField( - models.CharField(max_length=3, choices=get_currency_choices()), + currencies = JSONField( db_index=True, - default=default_currencies, + default=project_currencies, verbose_name=_("currencies"), )	Mark accounts as 'placeholder' This idea is from GnuCash. Accounts marked as placeholders cannot have transactions associated with them. Typically this is useful for parent accounts, as we normally only wish transactions to be associated with leaf accounts.		2023-05-16T02:55:26	[]	[]
adamcharnock/django-hordak	adamcharnock__django-hordak-65	ab9249c46c83e61bf7e70b1a69fffc08f92a01a0	diff --git a/hordak/views/accounts.py b/hordak/views/accounts.py index 64413c7b..6d752d86 100644 --- a/hordak/views/accounts.py +++ b/hordak/views/accounts.py @@ -1,6 +1,5 @@ from django.contrib.auth.mixins import LoginRequiredMixin from django.urls.base import reverse_lazy -from django.views import View from django.views.generic.detail import SingleObjectMixin from django.views.generic.edit import CreateView, UpdateView from django.views.generic.list import ListView @@ -78,7 +77,8 @@ class AccountUpdateView(LoginRequiredMixin, UpdateView): success_url = reverse_lazy("hordak:accounts_list") -class AccountTransactionsView(LoginRequiredMixin, SingleObjectMixin, View): +# TODO: remove ignore comment once https://github.com/typeddjango/django-stubs/issues/873 is fixed +class AccountTransactionsView(LoginRequiredMixin, SingleObjectMixin, ListView): # type: ignore template_name = "hordak/accounts/account_transactions.html" model = Leg slug_field = "uuid"	class AccountTransactionsView(LoginRequiredMixin, SingleObjectMixin, View): ->class AccountTransactionsView(LoginRequiredMixin, SingleObjectMixin, ListView): Hi! after migrating from django-hordak==1.10.0 to django-hordak==1.10.1 there is error when trying to view account transactions: 'super' object has no attribute 'get' /usr/local/lib/python3.9/site-packages/hordak/views/accounts.py, line 89, in get return super(AccountTransactionsView, self).get(request, args, kwargs) Please verify if instead of View in class AccountTransactionsView(LoginRequiredMixin, SingleObjectMixin, View): should be ListView, i.e.: class AccountTransactionsView(LoginRequiredMixin, SingleObjectMixin, ListView*): like it was in previous version. Best regards, Pawel		2022-03-15T10:30:35	[]	[]
tmux-python/tmuxp	tmux-python__tmuxp-897	5daa1ae89b3a5295bea716be7d2860fb12880eda	diff --git a/CHANGES b/CHANGES index 74d7187c6f..df1dbc7baf 100644 --- a/CHANGES +++ b/CHANGES @@ -24,6 +24,7 @@ $ pipx install --suffix=@next 'tmuxp' --pip-args '\--pre' --force - libtmux: 0.24.1 -> 0.25.0, maintenance release (#896) Improve styling via pydocstyle. +- `config_reader`: Move to `tmuxp._internal` (#897) ## tmuxp 1.33.0 (2023-12-21) diff --git a/docs/api.md b/docs/api.md index 7e1ac090a0..eb8c67a595 100644 --- a/docs/api.md +++ b/docs/api.md @@ -110,7 +110,7 @@ If you need an internal API stabilized please [file an issue](https://github.com ## Configuration reader ```{eval-rst} -.. automodule:: tmuxp.config_reader +.. automodule:: tmuxp._internal.config_reader ``` ## Workspace Builder diff --git a/src/tmuxp/_internal/__init__.py b/src/tmuxp/_internal/__init__.py new file mode 100644 index 0000000000..01dccbcfcb --- /dev/null +++ b/src/tmuxp/_internal/__init__.py @@ -0,0 +1,1 @@ +"""Internal APIs for tmuxp.""" diff --git a/src/tmuxp/config_reader.py b/src/tmuxp/_internal/config_reader.py similarity index 95% rename from src/tmuxp/config_reader.py rename to src/tmuxp/_internal/config_reader.py index 6943aff30d..74f1989acd 100644 --- a/src/tmuxp/config_reader.py +++ b/src/tmuxp/_internal/config_reader.py @@ -55,13 +55,13 @@ def load(cls, format: "FormatLiteral", content: str) -> "ConfigReader": >>> cfg = ConfigReader.load("json", '{ "session_name": "my session" }') >>> cfg - <tmuxp.config_reader.ConfigReader object at ...> + <tmuxp._internal.config_reader.ConfigReader object at ...> >>> cfg.content {'session_name': 'my session'} >>> cfg = ConfigReader.load("yaml", 'session_name: my session') >>> cfg - <tmuxp.config_reader.ConfigReader object at ...> + <tmuxp._internal.config_reader.ConfigReader object at ...> >>> cfg.content {'session_name': 'my session'} """ @@ -133,7 +133,7 @@ def from_file(cls, path: pathlib.Path) -> "ConfigReader": >>> cfg = ConfigReader.from_file(yaml_file) >>> cfg - <tmuxp.config_reader.ConfigReader object at ...> + <tmuxp._internal.config_reader.ConfigReader object at ...> >>> cfg.content {'session_name': 'my session'} @@ -150,7 +150,7 @@ def from_file(cls, path: pathlib.Path) -> "ConfigReader": >>> cfg = ConfigReader.from_file(json_file) >>> cfg - <tmuxp.config_reader.ConfigReader object at ...> + <tmuxp._internal.config_reader.ConfigReader object at ...> >>> cfg.content {'session_name': 'my session'} diff --git a/src/tmuxp/cli/convert.py b/src/tmuxp/cli/convert.py index 88d995d67d..75a09ab98d 100644 --- a/src/tmuxp/cli/convert.py +++ b/src/tmuxp/cli/convert.py @@ -4,7 +4,7 @@ import pathlib import typing as t -from tmuxp.config_reader import ConfigReader +from tmuxp._internal.config_reader import ConfigReader from tmuxp.workspace.finders import find_workspace_file, get_workspace_dir from .. import exc diff --git a/src/tmuxp/cli/freeze.py b/src/tmuxp/cli/freeze.py index f82dbb1032..e36795fa18 100644 --- a/src/tmuxp/cli/freeze.py +++ b/src/tmuxp/cli/freeze.py @@ -7,7 +7,7 @@ from libtmux.server import Server -from tmuxp.config_reader import ConfigReader +from tmuxp._internal.config_reader import ConfigReader from tmuxp.exc import TmuxpException from tmuxp.workspace.finders import get_workspace_dir diff --git a/src/tmuxp/cli/import_config.py b/src/tmuxp/cli/import_config.py index 70f4bf71f5..8bab8d918a 100644 --- a/src/tmuxp/cli/import_config.py +++ b/src/tmuxp/cli/import_config.py @@ -5,7 +5,7 @@ import sys import typing as t -from tmuxp.config_reader import ConfigReader +from tmuxp._internal.config_reader import ConfigReader from tmuxp.workspace.finders import find_workspace_file from ..workspace import importers diff --git a/src/tmuxp/cli/load.py b/src/tmuxp/cli/load.py index 4311e87efc..7f426938e4 100644 --- a/src/tmuxp/cli/load.py +++ b/src/tmuxp/cli/load.py @@ -14,7 +14,8 @@ from tmuxp.types import StrPath -from .. import config_reader, exc, log, util +from .. import exc, log, util +from .._internal import config_reader from ..workspace import loader from ..workspace.builder import WorkspaceBuilder from ..workspace.finders import find_workspace_file, get_workspace_dir diff --git a/src/tmuxp/workspace/builder.py b/src/tmuxp/workspace/builder.py index 4fb6f7c60d..0efa22f150 100644 --- a/src/tmuxp/workspace/builder.py +++ b/src/tmuxp/workspace/builder.py @@ -97,7 +97,7 @@ class WorkspaceBuilder: 1. Load JSON / YAML file via via :class:`pathlib.Path`:: - from tmuxp import config_reader + from tmuxp._internal import config_reader session_config = config_reader.ConfigReader._load(raw_yaml) The reader automatically detects the file type from :attr:`pathlib.suffix`. @@ -105,7 +105,7 @@ class WorkspaceBuilder: We can also parse raw file:: import pathlib - from tmuxp import config_reader + from tmuxp._internal import config_reader session_config = config_reader.ConfigReader._from_file( pathlib.Path('path/to/config.yaml')	Move `config_reader` to `_internal` `config_reader` is a generic utility / helper.		2023-12-21T18:59:15	[]	[]
tmux-python/tmuxp	tmux-python__tmuxp-859	41d027ada3836bd0f3a572652d1900f942f58907	diff --git a/CHANGES b/CHANGES index bc92b1c36f..da2ec72306 100644 --- a/CHANGES +++ b/CHANGES @@ -25,6 +25,12 @@ $ pipx install --suffix=@next 'tmuxp' --pip-args '\--pre' --force ### Development +- Improved typings + + Now [`mypy --strict`] compliant (#859) + + [`mypy --strict`]: https://mypy.readthedocs.io/en/stable/command_line.html#cmdoption-mypy-strict + - Poetry 1.5.1 -> 1.6.1 (#885) ## tmuxp 1.30.1 (2023-09-09) @@ -35,7 +41,7 @@ _Maintenance only, no bug fixes or new features_ - Cut last python 3.7 release (EOL was June 27th, 2023) - For security updates, a 1.30.x branch can be maintained for a limited time, + For security updates, a 1.30.x branch can be maintained for a limited time, if necessary. ## tmuxp 1.30.0 (2023-09-04) @@ -49,6 +55,7 @@ _Maintenance only, no bug fixes or new features_ This includes fixes made by hand alongside ruff's automated fixes. The more stringent rules include import sorting, and still runs almost instantly against the whole codebase. + - CI: `black . --check` now runs on pushes and pull requests ### Packaging diff --git a/docs/_ext/aafig.py b/docs/_ext/aafig.py index 3770613d68..d3885405e2 100644 --- a/docs/_ext/aafig.py +++ b/docs/_ext/aafig.py @@ -21,6 +21,10 @@ from sphinx.errors import SphinxError from sphinx.util.osutil import ensuredir, relative_uri +if t.TYPE_CHECKING: + from sphinx.application import Sphinx + + try: import aafigure except ImportError: @@ -32,14 +36,18 @@ DEFAULT_FORMATS = {"html": "svg", "latex": "pdf", "text": None} -def merge_dict(dst, src): +def merge_dict( + dst: t.Dict[str, t.Optional[str]], src: t.Dict[str, t.Optional[str]] +) -> t.Dict[str, t.Optional[str]]: for k, v in src.items(): if k not in dst: dst[k] = v return dst -def get_basename(text, options, prefix="aafig"): +def get_basename( + text: str, options: t.Dict[str, str], prefix: t.Optional[str] = "aafig" +) -> str: options = options.copy() if "format" in options: del options["format"] @@ -52,7 +60,7 @@ class AafigError(SphinxError): category = "aafig error" -class AafigDirective(images.Image): +class AafigDirective(images.Image): # type:ignore """ Directive to insert an ASCII art figure to be rendered by aafigure. """ @@ -71,7 +79,7 @@ class AafigDirective(images.Image): option_spec = images.Image.option_spec.copy() option_spec.update(own_option_spec) - def run(self): + def run(self) -> t.List[nodes.Node]: aafig_options = {} own_options_keys = [self.own_option_spec.keys(), "scale"] for k, v in self.options.items(): @@ -93,7 +101,7 @@ def run(self): return [image_node] -def render_aafig_images(app, doctree): +def render_aafig_images(app: "Sphinx", doctree: nodes.Node) -> None: format_map = app.builder.config.aafig_format merge_dict(format_map, DEFAULT_FORMATS) if aafigure is None: @@ -144,7 +152,9 @@ def __init__(self, args: object, kwargs: object) -> None: return super().__init__("aafigure module not installed", args, *kwargs) -def render_aafigure(app, text, options): +def render_aafigure( + app: "Sphinx", text: str, options: t.Dict[str, str] +) -> t.Tuple[str, str, t.Optional[str], t.Optional[str]]: """ Render an ASCII art figure into the requested format output file. """ @@ -186,7 +196,7 @@ def render_aafigure(app, text, options): finally: if f is not None: f.close() - return relfn, outfn, id, extra + return relfn, outfn, None, extra except AafigError: pass @@ -204,10 +214,10 @@ def render_aafigure(app, text, options): with open(metadata_fname, "w") as f: f.write(extra) - return relfn, outfn, id, extra + return relfn, outfn, None, extra -def setup(app): +def setup(app: "Sphinx") -> None: app.add_directive("aafig", AafigDirective) app.connect("doctree-read", render_aafig_images) app.add_config_value("aafig_format", DEFAULT_FORMATS, "html") diff --git a/docs/conf.py b/docs/conf.py index fea1c2e785..f7c5dd4b7a 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -11,7 +11,6 @@ if t.TYPE_CHECKING: from sphinx.application import Sphinx - # Get the project root dir, which is the parent dir of this cwd = pathlib.Path(__file__).parent project_root = cwd.parent @@ -177,7 +176,7 @@ aafig_default_options = {"scale": 0.75, "aspect": 0.5, "proportional": True} -def linkcode_resolve(domain, info): +def linkcode_resolve(domain: str, info: t.Dict[str, str]) -> t.Union[None, str]: """ Determine the URL corresponding to Python object @@ -210,7 +209,8 @@ def linkcode_resolve(domain, info): except AttributeError: pass else: - obj = unwrap(obj) + if callable(obj): + obj = unwrap(obj) try: fn = inspect.getsourcefile(obj) diff --git a/pyproject.toml b/pyproject.toml index 72ea545623..d3ece281a0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -138,10 +138,12 @@ exclude_lines = [ ] [tool.mypy] +strict = true files = [ "src/", "tests/", ] +enable_incomplete_feature = ["Unpack"] [[tool.mypy.overrides]] module = [ diff --git a/src/tmuxp/_compat.py b/src/tmuxp/_compat.py index 805d6c1cc5..1b0009e3a1 100644 --- a/src/tmuxp/_compat.py +++ b/src/tmuxp/_compat.py @@ -12,7 +12,7 @@ else: import pdb - breakpoint = pdb.set_trace # type: ignore + breakpoint = pdb.set_trace console_encoding = sys.__stdout__.encoding diff --git a/src/tmuxp/_types.py b/src/tmuxp/_types.py new file mode 100644 index 0000000000..79eac5c9c2 --- /dev/null +++ b/src/tmuxp/_types.py @@ -0,0 +1,27 @@ +"""Internal, :const:`typing.TYPE_CHECKING` guarded :term:`type annotations <annotation>` + +These are _not_ to be imported at runtime as `typing_extensions` is not +bundled with tmuxp. Usage example: + +>>> import typing as t + +>>> if t.TYPE_CHECKING: +... from tmuxp._types import PluginConfigSchema +... +""" +import typing as t + +from typing_extensions import NotRequired, TypedDict + + +class PluginConfigSchema(TypedDict): + plugin_name: NotRequired[str] + tmux_min_version: NotRequired[str] + tmux_max_version: NotRequired[str] + tmux_version_incompatible: NotRequired[t.List[str]] + libtmux_min_version: NotRequired[str] + libtmux_max_version: NotRequired[str] + libtmux_version_incompatible: NotRequired[t.List[str]] + tmuxp_min_version: NotRequired[str] + tmuxp_max_version: NotRequired[str] + tmuxp_version_incompatible: NotRequired[t.List[str]] diff --git a/src/tmuxp/cli/debug_info.py b/src/tmuxp/cli/debug_info.py index d72e3fc1ac..df6a65e19d 100644 --- a/src/tmuxp/cli/debug_info.py +++ b/src/tmuxp/cli/debug_info.py @@ -29,19 +29,19 @@ def command_debug_info( Print debug info to submit with Issues. """ - def prepend_tab(strings): + def prepend_tab(strings: t.List[str]) -> t.List[str]: """ Prepend tab to strings in list. """ return ["\t%s" % x for x in strings] - def output_break(): + def output_break() -> str: """ Generate output break. """ return "-" 25 - def format_tmux_resp(std_resp): + def format_tmux_resp(std_resp: tmux_cmd) -> str: """ Format tmux command response for tmuxp stdout. """ diff --git a/src/tmuxp/cli/edit.py b/src/tmuxp/cli/edit.py index 184077c3cc..31b263dd36 100644 --- a/src/tmuxp/cli/edit.py +++ b/src/tmuxp/cli/edit.py @@ -22,7 +22,7 @@ def create_edit_subparser( def command_edit( workspace_file: t.Union[str, pathlib.Path], parser: t.Optional[argparse.ArgumentParser] = None, -): +) -> None: workspace_file = find_workspace_file(workspace_file) sys_editor = os.environ.get("EDITOR", "vim") diff --git a/src/tmuxp/cli/freeze.py b/src/tmuxp/cli/freeze.py index 3a8f050215..88a2f9751b 100644 --- a/src/tmuxp/cli/freeze.py +++ b/src/tmuxp/cli/freeze.py @@ -31,12 +31,6 @@ class CLIFreezeNamespace(argparse.Namespace): force: t.Optional[bool] -def session_completion(ctx, params, incomplete): - server = Server() - choices = [session.name for session in server.sessions] - return sorted(str(c) for c in choices if str(c).startswith(incomplete)) - - def create_freeze_subparser( parser: argparse.ArgumentParser, ) -> argparse.ArgumentParser: @@ -177,12 +171,14 @@ def extract_workspace_format( workspace_format = extract_workspace_format(dest) if not is_valid_ext(workspace_format): - workspace_format = prompt_choices( + _workspace_format = prompt_choices( "Couldn't ascertain one of [%s] from file name. Convert to" % ", ".join(valid_workspace_formats), - choices=valid_workspace_formats, + choices=t.cast(t.List[str], valid_workspace_formats), default="yaml", ) + assert is_valid_ext(_workspace_format) + workspace_format = _workspace_format if workspace_format == "yaml": workspace = configparser.dump( diff --git a/src/tmuxp/cli/import_config.py b/src/tmuxp/cli/import_config.py index 301f0dd8cf..9048331fa9 100644 --- a/src/tmuxp/cli/import_config.py +++ b/src/tmuxp/cli/import_config.py @@ -59,7 +59,7 @@ def command_import( workspace_file: str, print_list: str, parser: argparse.ArgumentParser, -): +) -> None: """Import a teamocil/tmuxinator config.""" @@ -116,9 +116,14 @@ def create_import_subparser( return parser +class ImportConfigFn(t.Protocol): + def __call__(self, workspace_dict: t.Dict[str, t.Any]) -> t.Dict[str, t.Any]: + ... + + def import_config( workspace_file: str, - importfunc: t.Callable, + importfunc: ImportConfigFn, parser: t.Optional[argparse.ArgumentParser] = None, ) -> None: existing_workspace_file = ConfigReader._from_file(pathlib.Path(workspace_file)) diff --git a/src/tmuxp/cli/load.py b/src/tmuxp/cli/load.py index 2873d764f7..e965299f90 100644 --- a/src/tmuxp/cli/load.py +++ b/src/tmuxp/cli/load.py @@ -153,7 +153,7 @@ def load_plugins(session_config: t.Dict[str, t.Any]) -> t.List[t.Any]: return plugins -def _reattach(builder: WorkspaceBuilder): +def _reattach(builder: WorkspaceBuilder) -> None: """ Reattach session (depending on env being inside tmux already or not) diff --git a/src/tmuxp/cli/utils.py b/src/tmuxp/cli/utils.py index 91f2ef165c..482f20d042 100644 --- a/src/tmuxp/cli/utils.py +++ b/src/tmuxp/cli/utils.py @@ -34,9 +34,9 @@ def tmuxp_echo( def prompt( name: str, - default: t.Any = None, + default: t.Optional[str] = None, value_proc: t.Optional[t.Callable[[str], str]] = None, -) -> t.Any: +) -> str: """Return user input from command line. :meth:`~prompt`, :meth:`~prompt_bool` and :meth:`prompt_choices` are from `flask-script`_. See the `flask-script license`_. @@ -107,15 +107,12 @@ def prompt_yes_no(name: str, default: bool = True) -> bool: return prompt_bool(name, default=default) -_C = t.TypeVar("_C") - - def prompt_choices( name: str, - choices: t.Union[t.List[_C], t.Tuple[str, _C]], - default: t.Optional[_C] = None, + choices: t.Union[t.List[str], t.Tuple[str, str]], + default: t.Optional[str] = None, no_choice: t.Sequence[str] = ("none",), -) -> t.Optional[_C]: +) -> t.Optional[str]: """Return user input from command line from set of provided choices. :param name: prompt text :param choices: list or tuple of available choices. Choices may be @@ -125,8 +122,8 @@ def prompt_choices( :rtype: str """ - _choices = [] - options = [] + _choices: t.List[str] = [] + options: t.List[str] = [] for choice in choices: if isinstance(choice, str): diff --git a/src/tmuxp/config_reader.py b/src/tmuxp/config_reader.py index 3217cbc442..6c0913d2c3 100644 --- a/src/tmuxp/config_reader.py +++ b/src/tmuxp/config_reader.py @@ -36,12 +36,15 @@ def _load(format: "FormatLiteral", content: str) -> t.Dict[str, t.Any]: {'session_name': 'my session'} """ if format == "yaml": - return yaml.load( - content, - Loader=yaml.SafeLoader, + return t.cast( + t.Dict[str, t.Any], + yaml.load( + content, + Loader=yaml.SafeLoader, + ), ) elif format == "json": - return json.loads(content) + return t.cast(t.Dict[str, t.Any], json.loads(content)) else: raise NotImplementedError(f"{format} not supported in configuration") diff --git a/src/tmuxp/exc.py b/src/tmuxp/exc.py index 5fb0d9f722..3d04c63735 100644 --- a/src/tmuxp/exc.py +++ b/src/tmuxp/exc.py @@ -80,10 +80,10 @@ class TmuxpPluginException(TmuxpException): class BeforeLoadScriptNotExists(OSError): - def __init__(self, args, kwargs) -> None: + def __init__(self, args: object, *kwargs: object) -> None: super().__init__(args, *kwargs) - self.strerror = "before_script file '%s' doesn't exist." % self.strerror + self.strerror = f"before_script file '{self.strerror}' doesn't exist." @implements_to_string @@ -106,5 +106,5 @@ def __init__( f"{self.output}" ) - def __str__(self): + def __str__(self) -> str: return self.message diff --git a/src/tmuxp/log.py b/src/tmuxp/log.py index 3a18b67916..b05b5ac7ef 100644 --- a/src/tmuxp/log.py +++ b/src/tmuxp/log.py @@ -115,8 +115,8 @@ def template( return levelname + asctime + name - def __init__(self, color: bool = True, args, *kwargs) -> None: - logging.Formatter.__init__(self, args, kwargs) + def __init__(self, color: bool = True, kwargs: t.Any) -> None: + logging.Formatter.__init__(self, kwargs) def format(self, record: logging.LogRecord) -> str: try: @@ -125,7 +125,7 @@ def format(self, record: logging.LogRecord) -> str: record.message = f"Bad message ({e!r}): {record.__dict__!r}" date_format = "%H:%m:%S" - formatting = self.converter(record.created) # type:ignore + formatting = self.converter(record.created) record.asctime = time.strftime(date_format, formatting) prefix = self.template(record) % record.__dict__ diff --git a/src/tmuxp/plugin.py b/src/tmuxp/plugin.py index 7d56316630..f6b49432d2 100644 --- a/src/tmuxp/plugin.py +++ b/src/tmuxp/plugin.py @@ -27,7 +27,11 @@ if t.TYPE_CHECKING: - from typing_extensions import TypedDict + from libtmux.session import Session + from libtmux.window import Window + from typing_extensions import TypedDict, TypeGuard, Unpack + + from ._types import PluginConfigSchema class VersionConstraints(TypedDict): version: t.Union[Version, str] @@ -41,20 +45,52 @@ class TmuxpPluginVersionConstraints(TypedDict): libtmux: VersionConstraints +class Config(t.TypedDict): + plugin_name: str + tmux_min_version: str + tmux_max_version: t.Optional[str] + tmux_version_incompatible: t.Optional[t.List[str]] + libtmux_min_version: str + libtmux_max_version: t.Optional[str] + libtmux_version_incompatible: t.Optional[t.List[str]] + tmuxp_min_version: str + tmuxp_max_version: t.Optional[str] + tmuxp_version_incompatible: t.Optional[t.List[str]] + + +DEFAULT_CONFIG: "Config" = { + "plugin_name": "tmuxp-plugin", + "tmux_min_version": TMUX_MIN_VERSION, + "tmux_max_version": TMUX_MAX_VERSION, + "tmux_version_incompatible": None, + "libtmux_min_version": LIBTMUX_MIN_VERSION, + "libtmux_max_version": LIBTMUX_MAX_VERSION, + "libtmux_version_incompatible": None, + "tmuxp_min_version": TMUXP_MIN_VERSION, + "tmuxp_max_version": TMUXP_MAX_VERSION, + "tmuxp_version_incompatible": None, +} + + +def validate_plugin_config(config: "PluginConfigSchema") -> "TypeGuard[Config]": + return isinstance(config, dict) + + +def setup_plugin_config( + config: "PluginConfigSchema", default_config: "Config" = DEFAULT_CONFIG +) -> "Config": + new_config = config.copy() + for default_key, default_value in default_config.items(): + if default_key not in new_config: + new_config[default_key] = default_value # type:ignore + + assert validate_plugin_config(new_config) + + return new_config + + class TmuxpPlugin: - def __init__( - self, - plugin_name: str = "tmuxp-plugin", - tmux_min_version: str = TMUX_MIN_VERSION, - tmux_max_version: t.Optional[str] = TMUX_MAX_VERSION, - tmux_version_incompatible: t.Optional[t.List[str]] = None, - libtmux_min_version: str = LIBTMUX_MIN_VERSION, - libtmux_max_version: t.Optional[str] = LIBTMUX_MAX_VERSION, - libtmux_version_incompatible: t.Optional[t.List[str]] = None, - tmuxp_min_version: str = TMUXP_MIN_VERSION, - tmuxp_max_version: t.Optional[str] = TMUXP_MAX_VERSION, - tmuxp_version_incompatible: t.Optional[t.List[str]] = None, - ) -> None: + def __init__(self, kwargs: "Unpack[PluginConfigSchema]") -> None: """ Initialize plugin. @@ -95,7 +131,8 @@ def __init__( Versions of tmuxp that are incompatible with the plugin """ - self.plugin_name = plugin_name + config = setup_plugin_config(config=kwargs) + self.plugin_name = config["plugin_name"] # Dependency versions self.tmux_version = get_version() @@ -105,26 +142,26 @@ def __init__( self.version_constraints: "TmuxpPluginVersionConstraints" = { "tmux": { "version": self.tmux_version, - "vmin": tmux_min_version, - "vmax": tmux_max_version, - "incompatible": tmux_version_incompatible - if tmux_version_incompatible + "vmin": config["tmux_min_version"], + "vmax": config["tmux_max_version"], + "incompatible": config["tmux_version_incompatible"] + if config["tmux_version_incompatible"] else [], }, "libtmux": { "version": self.libtmux_version, - "vmin": libtmux_min_version, - "vmax": libtmux_max_version, - "incompatible": libtmux_version_incompatible - if libtmux_version_incompatible + "vmin": config["libtmux_min_version"], + "vmax": config["libtmux_max_version"], + "incompatible": config["libtmux_version_incompatible"] + if config["libtmux_version_incompatible"] else [], }, "tmuxp": { "version": self.tmuxp_version, - "vmin": tmuxp_min_version, - "vmax": tmuxp_max_version, - "incompatible": tmuxp_version_incompatible - if tmuxp_version_incompatible + "vmin": config["tmuxp_min_version"], + "vmax": config["tmuxp_max_version"], + "incompatible": config["tmuxp_version_incompatible"] + if config["tmuxp_version_incompatible"] else [], }, } @@ -167,7 +204,7 @@ def _pass_version_check( return True - def before_workspace_builder(self, session): + def before_workspace_builder(self, session: "Session") -> None: """ Provide a session hook previous to creating the workspace. @@ -180,7 +217,7 @@ def before_workspace_builder(self, session): session to hook into """ - def on_window_create(self, window): + def on_window_create(self, window: "Window") -> None: """ Provide a window hook previous to doing anything with a window. @@ -192,7 +229,7 @@ def on_window_create(self, window): window to hook into """ - def after_window_finished(self, window): + def after_window_finished(self, window: "Window") -> None: """ Provide a window hook after creating the window. @@ -206,7 +243,7 @@ def after_window_finished(self, window): window to hook into """ - def before_script(self, session): + def before_script(self, session: "Session") -> None: """ Provide a session hook after the workspace has been built. @@ -234,7 +271,7 @@ def before_script(self, session): session to hook into """ - def reattach(self, session): + def reattach(self, session: "Session") -> None: """ Provide a session hook before reattaching to the session. diff --git a/src/tmuxp/shell.py b/src/tmuxp/shell.py index 1955d2bc32..bb06fb4b64 100644 --- a/src/tmuxp/shell.py +++ b/src/tmuxp/shell.py @@ -12,12 +12,35 @@ logger = logging.getLogger(__name__) if t.TYPE_CHECKING: - from typing_extensions import TypeAlias + from types import ModuleType + + from libtmux.pane import Pane + from libtmux.server import Server + from libtmux.session import Session + from libtmux.window import Window + from typing_extensions import NotRequired, TypeAlias, TypedDict, Unpack CLIShellLiteral: TypeAlias = t.Literal[ "best", "pdb", "code", "ptipython", "ptpython", "ipython", "bpython" ] + class LaunchOptionalImports(TypedDict): + server: NotRequired["Server"] + session: NotRequired["Session"] + window: NotRequired["Window"] + pane: NotRequired["Pane"] + + class LaunchImports(t.TypedDict): + libtmux: ModuleType + Server: t.Type[Server] + Session: t.Type[Session] + Window: t.Type[Window] + Pane: t.Type[Pane] + server: t.Optional["Server"] + session: t.Optional["Session"] + window: t.Optional["Window"] + pane: t.Optional["Pane"] + def has_ipython() -> bool: try: @@ -77,13 +100,15 @@ def detect_best_shell() -> "CLIShellLiteral": return "code" -def get_bpython(options, extra_args=None): +def get_bpython( + options: "LaunchOptionalImports", extra_args: t.Optional[t.Dict[str, t.Any]] = None +) -> t.Callable[[], None]: if extra_args is None: extra_args = {} from bpython import embed # F841 - def launch_bpython(): + def launch_bpython() -> None: imported_objects = get_launch_args(options) kwargs = {} if extra_args: @@ -93,16 +118,18 @@ def launch_bpython(): return launch_bpython -def get_ipython_arguments(): +def get_ipython_arguments() -> t.List[str]: ipython_args = "IPYTHON_ARGUMENTS" return os.environ.get(ipython_args, "").split() -def get_ipython(options, extra_args): +def get_ipython( + options: "LaunchOptionalImports", extra_args: t.Dict[str, t.Any] +) -> t.Any: try: from IPython import start_ipython - def launch_ipython(): + def launch_ipython() -> None: imported_objects = get_launch_args(options) ipython_arguments = extra_args or get_ipython_arguments() start_ipython(argv=ipython_arguments, user_ns=imported_objects) @@ -115,7 +142,7 @@ def launch_ipython(): # Notebook not supported for IPython < 0.11. from IPython.Shell import IPShell - def launch_ipython(): + def launch_ipython() -> None: imported_objects = get_launch_args(options) shell = IPShell(argv=[], user_ns=imported_objects) shell.mainloop() @@ -123,13 +150,13 @@ def launch_ipython(): return launch_ipython -def get_ptpython(options, vi_mode=False): +def get_ptpython(options: "LaunchOptionalImports", vi_mode: bool = False) -> t.Any: try: from ptpython.repl import embed, run_config except ImportError: from prompt_toolkit.contrib.repl import embed, run_config - def launch_ptpython(): + def launch_ptpython() -> None: imported_objects = get_launch_args(options) history_filename = str(pathlib.Path("~/.ptpython_history").expanduser()) embed( @@ -142,7 +169,7 @@ def launch_ptpython(): return launch_ptpython -def get_ptipython(options, vi_mode=False): +def get_ptipython(options: "LaunchOptionalImports", vi_mode: bool = False) -> t.Any: """Based on django-extensions Run renamed to launch, get_imported_objects renamed to get_launch_args @@ -155,7 +182,7 @@ def get_ptipython(options, vi_mode=False): from prompt_toolkit.contrib.ipython import embed from prompt_toolkit.contrib.repl import run_config - def launch_ptipython(): + def launch_ptipython() -> None: imported_objects = get_launch_args(options) history_filename = str(pathlib.Path("~/.ptpython_history").expanduser()) embed( @@ -168,7 +195,7 @@ def launch_ptipython(): return launch_ptipython -def get_launch_args(kwargs): +def get_launch_args(kwargs: "Unpack[LaunchOptionalImports]") -> "LaunchImports": import libtmux from libtmux.pane import Pane from libtmux.server import Server @@ -188,7 +215,7 @@ def get_launch_args(kwargs): } -def get_code(use_pythonrc, imported_objects): +def get_code(use_pythonrc: bool, imported_objects: "LaunchImports") -> t.Any: import code try: @@ -201,7 +228,11 @@ def get_code(use_pythonrc, imported_objects): # we already know 'readline' was imported successfully. import rlcompleter - readline.set_completer(rlcompleter.Completer(imported_objects).complete) + readline.set_completer( + rlcompleter.Completer( + imported_objects, # type:ignore + ).complete + ) # Enable tab completion on systems using libedit (e.g. macOS). # These lines are copied from Lib/site.py on Python 3.4. readline_doc = getattr(readline, "__doc__", "") @@ -226,9 +257,12 @@ def get_code(use_pythonrc, imported_objects): pythonrc_code = handle.read() # Match the behavior of the cpython shell where an error in # PYTHONSTARTUP prints an exception and continues. - exec(compile(pythonrc_code, pythonrc, "exec"), imported_objects) + exec( + compile(pythonrc_code, pythonrc, "exec"), + imported_objects, # type:ignore + ) - def launch_code(): + def launch_code() -> None: code.interact(local=imported_objects) return launch_code @@ -238,7 +272,7 @@ def launch( shell: t.Optional["CLIShellLiteral"] = "best", use_pythonrc: bool = False, use_vi_mode: bool = False, - kwargs + kwargs: "Unpack[LaunchOptionalImports]" ) -> None: # Also allowing passing shell='code' to force using code.interact imported_objects = get_launch_args(**kwargs) diff --git a/src/tmuxp/workspace/builder.py b/src/tmuxp/workspace/builder.py index b73a250fec..f149d87d4d 100644 --- a/src/tmuxp/workspace/builder.py +++ b/src/tmuxp/workspace/builder.py @@ -188,7 +188,7 @@ def __init__( pass @property - def session(self): + def session(self) -> Session: if self._session is None: raise exc.SessionMissingWorkspaceException() return self._session diff --git a/src/tmuxp/workspace/freezer.py b/src/tmuxp/workspace/freezer.py index 8f38e7be4b..0d1d14e98f 100644 --- a/src/tmuxp/workspace/freezer.py +++ b/src/tmuxp/workspace/freezer.py @@ -7,7 +7,7 @@ from libtmux.window import Window -def inline(workspace_dict): +def inline(workspace_dict: t.Dict[str, t.Any]) -> t.Any: """Return workspace with inlined shorthands. Opposite of :meth:`loader.expand`. Parameters @@ -28,7 +28,7 @@ def inline(workspace_dict): workspace_dict["shell_command"] = workspace_dict["shell_command"][0] if len(workspace_dict.keys()) == 1: - workspace_dict = workspace_dict["shell_command"] + return workspace_dict["shell_command"] if ( "shell_command_before" in workspace_dict and isinstance(workspace_dict["shell_command_before"], list) diff --git a/src/tmuxp/workspace/loader.py b/src/tmuxp/workspace/loader.py index 1668a55454..83da8deb10 100644 --- a/src/tmuxp/workspace/loader.py +++ b/src/tmuxp/workspace/loader.py @@ -7,7 +7,7 @@ import logging import os import pathlib -from typing import Dict +import typing as t logger = logging.getLogger(__name__) @@ -30,7 +30,7 @@ def expandshell(value: str) -> str: return os.path.expandvars(os.path.expanduser(value)) # NOQA: PTH111 -def expand_cmd(p: Dict) -> Dict: +def expand_cmd(p: t.Dict[str, t.Any]) -> t.Dict[str, t.Any]: if isinstance(p, str): p = {"shell_command": [p]} elif isinstance(p, list): @@ -66,7 +66,11 @@ def expand_cmd(p: Dict) -> Dict: return p -def expand(workspace_dict, cwd=None, parent=None): +def expand( + workspace_dict: t.Dict[str, t.Any], + cwd: t.Optional[t.Union[pathlib.Path, str]] = None, + parent: t.Optional[t.Any] = None, +) -> t.Dict[str, t.Any]: """Return workspace with shorthand and inline properties expanded. This is necessary to keep the code in the :class:`WorkspaceBuilder` clean @@ -185,7 +189,7 @@ def expand(workspace_dict, cwd=None, parent=None): return workspace_dict -def trickle(workspace_dict): +def trickle(workspace_dict: t.Dict[str, t.Any]) -> t.Dict[str, t.Any]: """Return a dict with "trickled down" / inherited workspace values. This will only work if workspace has been expanded to full form with	mypy: More annotations Tougher mypy typings.	# [Codecov](https://codecov.io/gh/tmux-python/tmuxp/pull/796?src=pr&el=h1&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=tmux-python) Report > Merging [#796](https://codecov.io/gh/tmux-python/tmuxp/pull/796?src=pr&el=desc&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=tmux-python) (b14fcb4) into [master](https://codecov.io/gh/tmux-python/tmuxp/commit/b14fcb4f4737b520104c5e1a4803d3cdebf5bc88?el=desc&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=tmux-python) (b14fcb4) will not change coverage. > The diff coverage is `n/a`. > :exclamation: Current head b14fcb4 differs from pull request most recent head 86b044f. Consider uploading reports for the commit 86b044f to get more accurate results ```diff @@ Coverage Diff @@ ## master #796 +/- ## ======================================= Coverage 71.64% 71.64% ======================================= Files 25 25 Lines 1774 1774 Branches 398 398 ======================================= Hits 1271 1271 Misses 391 391 Partials 112 112 ``` :mega: We’re building smart automated test selection to slash your CI/CD build times. [Learn more](https://about.codecov.io/iterative-testing/?utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=tmux-python)	2022-12-31T00:45:57	[]	[]
tmux-python/tmuxp	tmux-python__tmuxp-858	bf0c47b25e2502cdcfaa7eb209d817673c45f445	diff --git a/CHANGES b/CHANGES index 6327f36f51..728719430a 100644 --- a/CHANGES +++ b/CHANGES @@ -19,6 +19,12 @@ $ pipx install --suffix=@next 'tmuxp' --pip-args '\--pre' --force <!-- Maintainers, insert changes / features for the next release here --> +### Internal improvements + +- CLI Tests: Refactor `tests/cli` (#858) + + - Fix resolution of direcotries + ## tmuxp 1.23.0 (_yanked_, 2022-12-28) _Yanked release: `tmuxp load` issues, see #856_	WorkspaceBuilder: Harden typings - feat(WorkspaceBuilder): Require server in constructor Related to #856		2022-12-29T20:01:14	[]	[]
tmux-python/tmuxp	tmux-python__tmuxp-857	a7ea98c23bdd75e9115978322ddc62e9cdaba79e	diff --git a/CHANGES b/CHANGES index 3b252a3b68..a85eff48f2 100644 --- a/CHANGES +++ b/CHANGES @@ -29,6 +29,8 @@ $ pipx install --suffix=@next 'tmuxp' --pip-args '\--pre' --force - Fix resolution of direcotries +- WorkspaceBuilder: Require `Server` in constructor (#857) + ## tmuxp 1.23.0 (_yanked_, 2022-12-28) _Yanked release: `tmuxp load` issues, see #856_ diff --git a/src/tmuxp/cli/load.py b/src/tmuxp/cli/load.py index 41fbf4a869..46862bb423 100644 --- a/src/tmuxp/cli/load.py +++ b/src/tmuxp/cli/load.py @@ -107,13 +107,13 @@ def set_layout_hook(session: Session, hook_name: str) -> None: session.cmd(cmd) -def load_plugins(sconf: t.Dict[str, t.Any]) -> t.List[t.Any]: +def load_plugins(session_config: t.Dict[str, t.Any]) -> t.List[t.Any]: """ Load and return plugins in workspace """ plugins = [] - if "plugins" in sconf: - for plugin in sconf["plugins"]: + if "plugins" in session_config: + for plugin in session_config["plugins"]: try: module_name = plugin.split(".") module_name = ".".join(module_name[:-1]) @@ -396,7 +396,9 @@ def load_workspace( try: # load WorkspaceBuilder object for tmuxp workspace / tmux server builder = WorkspaceBuilder( - sconf=expanded_workspace, plugins=load_plugins(expanded_workspace), server=t + session_config=expanded_workspace, + plugins=load_plugins(expanded_workspace), + server=t, ) except exc.EmptyWorkspaceException: tmuxp_echo("%s is empty or parsed no workspace data" % workspace_file) diff --git a/src/tmuxp/workspace/builder.py b/src/tmuxp/workspace/builder.py index 619e67f86d..1af6026d6d 100644 --- a/src/tmuxp/workspace/builder.py +++ b/src/tmuxp/workspace/builder.py @@ -10,7 +10,6 @@ from libtmux._internal.query_list import ObjectDoesNotExist from libtmux.common import has_lt_version -from libtmux.exc import TmuxSessionExists from libtmux.pane import Pane from libtmux.server import Server from libtmux.session import Session @@ -27,12 +26,10 @@ class WorkspaceBuilder: + """Load workspace from workspace :py:obj:`dict` object. - """ - Load workspace from session :py:obj:`dict`. - - Build tmux workspace from a configuration. Creates and names windows, sets - options, splits windows into panes. + Build tmux workspace from a configuration. Creates and names windows, sets options, + splits windows into panes. Examples -------- @@ -62,7 +59,7 @@ class WorkspaceBuilder: ... - cmd: htop ... ''', Loader=yaml.Loader) - >>> builder = WorkspaceBuilder(sconf=session_config, server=server) + >>> builder = WorkspaceBuilder(session_config=session_config, server=server) New session:* @@ -102,7 +99,7 @@ class WorkspaceBuilder: 1. Load JSON / YAML file via via :class:`pathlib.Path`:: from tmuxp import config_reader - sconf = config_reader.ConfigReader._load(raw_yaml) + session_config = config_reader.ConfigReader._load(raw_yaml) The reader automatically detects the file type from :attr:`pathlib.suffix`. @@ -111,46 +108,46 @@ class WorkspaceBuilder: import pathlib from tmuxp import config_reader - sconf = config_reader.ConfigReader._from_file( + session_config = config_reader.ConfigReader._from_file( pathlib.Path('path/to/config.yaml') ) - 2. :meth:`config.expand` sconf inline shorthand:: + 2. :meth:`config.expand` session_config inline shorthand:: from tmuxp import config - sconf = config.expand(sconf) + session_config = config.expand(session_config) 3. :meth:`config.trickle` passes down default values from session -> window -> pane if applicable:: - sconf = config.trickle(sconf) + session_config = config.trickle(session_config) 4. (You are here) We will create a :class:`libtmux.Session` (a real ``tmux(1)`` session) and iterate through the list of windows, and their panes, returning full :class:`libtmux.Window` and :class:`libtmux.Pane` objects each step of the way:: - workspace = WorkspaceBuilder(sconf=sconf) + workspace = WorkspaceBuilder(session_config=session_config, server=server) It handles the magic of cases where the user may want to start a session inside tmux (when `$TMUX` is in the env variables). """ - server: t.Optional["Server"] - session: t.Optional["Session"] + server: "Server" + _session: t.Optional["Session"] + session_name: str def __init__( self, - sconf: t.Dict[str, t.Any], + session_config: t.Dict[str, t.Any], + server: Server, plugins: t.List[t.Any] = [], - server: t.Optional[Server] = None, ) -> None: - """ - Initialize workspace loading. + """Initialize workspace loading. Parameters ---------- - sconf : dict + session_config : dict session config, includes a :py:obj:`list` of ``windows``. plugins : list @@ -165,18 +162,38 @@ def __init__( ``self.session``. """ - if not sconf: - raise exc.EmptyWorkspaceException("session configuration is empty.") + if not session_config: + raise exc.EmptyWorkspaceException("Session configuration is empty.") - # validation.validate_schema(sconf) + # validation.validate_schema(session_config) - if isinstance(server, Server): - self.server = server - - self.sconf = sconf + assert isinstance(server, Server) + self.server = server + self.session_config = session_config self.plugins = plugins + if self.server is not None and self.session_exists( + session_name=self.session_config["session_name"] + ): + try: + session = self.server.sessions.get( + session_name=self.session_config["session_name"] + ) + assert session is not None + self._session = session + except ObjectDoesNotExist: + pass + + @property + def session(self): + if self._session is None: + raise ObjectDoesNotExist( + "No session object exists for WorkspaceBuilder. " + "Tip: Add session_name in constructor or run WorkspaceBuilder.build()" + ) + return self._session + def session_exists(self, session_name: str) -> bool: assert session_name is not None assert isinstance(session_name, str) @@ -192,14 +209,12 @@ def session_exists(self, session_name: str) -> bool: return True def build(self, session: t.Optional[Session] = None, append: bool = False) -> None: - """ - Build tmux workspace in session. + """Build tmux workspace in session. Optionally accepts ``session`` to build with only session object. - Without ``session``, it will use :class:`libmtux.Server` at - ``self.server`` passed in on initialization to create a new Session - object. + Without ``session``, it will use :class:`libmtux.Server` at ``self.server`` + passed in on initialization to create a new Session object. Parameters ---------- @@ -215,38 +230,25 @@ def build(self, session: t.Optional[Session] = None, append: bool = False) -> No "WorkspaceBuilder.build requires server to be passed " + "on initialization, or pass in session object to here." ) - - if self.server.has_session(self.sconf["session_name"]): - try: - self.session = self.server.sessions.get( - session_name=self.sconf["session_name"] - ) - - raise TmuxSessionExists( - "Session name %s is already running." - % self.sconf["session_name"] - ) - except ObjectDoesNotExist: - pass else: new_session_kwargs = {} - if "start_directory" in self.sconf: - new_session_kwargs["start_directory"] = self.sconf[ + if "start_directory" in self.session_config: + new_session_kwargs["start_directory"] = self.session_config[ "start_directory" ] session = self.server.new_session( - session_name=self.sconf["session_name"], + session_name=self.session_config["session_name"], **new_session_kwargs, ) assert session is not None - assert self.sconf["session_name"] == session.name - assert len(self.sconf["session_name"]) > 0 + assert self.session_config["session_name"] == session.name + assert len(self.session_config["session_name"]) > 0 assert session is not None assert session.name is not None - self.session: "Session" = session + self._session = session assert session.server is not None @@ -262,52 +264,55 @@ def build(self, session: t.Optional[Session] = None, append: bool = False) -> No focus = None - if "before_script" in self.sconf: + if "before_script" in self.session_config: try: cwd = None # we want to run the before_script file cwd'd from the # session start directory, if it exists. - if "start_directory" in self.sconf: - cwd = self.sconf["start_directory"] - run_before_script(self.sconf["before_script"], cwd=cwd) + if "start_directory" in self.session_config: + cwd = self.session_config["start_directory"] + run_before_script(self.session_config["before_script"], cwd=cwd) except Exception as e: self.session.kill_session() raise e - if "options" in self.sconf: - for option, value in self.sconf["options"].items(): + + if "options" in self.session_config: + for option, value in self.session_config["options"].items(): self.session.set_option(option, value) - if "global_options" in self.sconf: - for option, value in self.sconf["global_options"].items(): + + if "global_options" in self.session_config: + for option, value in self.session_config["global_options"].items(): self.session.set_option(option, value, _global=True) - if "environment" in self.sconf: - for option, value in self.sconf["environment"].items(): + + if "environment" in self.session_config: + for option, value in self.session_config["environment"].items(): self.session.set_environment(option, value) - for w, wconf in self.iter_create_windows(session, append): - assert isinstance(w, Window) + for window, window_config in self.iter_create_windows(session, append): + assert isinstance(window, Window) for plugin in self.plugins: - plugin.on_window_create(w) + plugin.on_window_create(window) focus_pane = None - for p, pconf in self.iter_create_panes(w, wconf): - assert isinstance(p, Pane) - p = p + for pane, pane_config in self.iter_create_panes(window, window_config): + assert isinstance(pane, Pane) + pane = pane - if "layout" in wconf: - w.select_layout(wconf["layout"]) + if "layout" in window_config: + window.select_layout(window_config["layout"]) - if "focus" in pconf and pconf["focus"]: - focus_pane = p + if "focus" in pane_config and pane_config["focus"]: + focus_pane = pane - if "focus" in wconf and wconf["focus"]: - focus = w + if "focus" in window_config and window_config["focus"]: + focus = window - self.config_after_window(w, wconf) + self.config_after_window(window, window_config) for plugin in self.plugins: - plugin.after_window_finished(w) + plugin.after_window_finished(window) if focus_pane: focus_pane.select_pane() @@ -318,11 +323,10 @@ def build(self, session: t.Optional[Session] = None, append: bool = False) -> No def iter_create_windows( self, session: Session, append: bool = False ) -> t.Iterator[t.Any]: - """ - Return :class:`libtmux.Window` iterating through session config dict. + """Return :class:`libtmux.Window` iterating through session config dict. Generator yielding :class:`libtmux.Window` by iterating through - ``sconf['windows']``. + ``session_config['windows']``. Applies ``window_options`` to window. @@ -335,172 +339,182 @@ def iter_create_windows( Returns ------- - tuple of (:class:`libtmux.Window`, ``wconf``) + tuple of (:class:`libtmux.Window`, ``window_config``) Newly created window, and the section from the tmuxp configuration that was used to create the window. """ - for i, wconf in enumerate(self.sconf["windows"], start=1): - if "window_name" not in wconf: + for window_iterator, window_config in enumerate( + self.session_config["windows"], start=1 + ): + if "window_name" not in window_config: window_name = None else: - window_name = wconf["window_name"] + window_name = window_config["window_name"] - is_first_window_pass = self.first_window_pass(i, session, append) + is_first_window_pass = self.first_window_pass( + window_iterator, session, append + ) w1 = None if is_first_window_pass: # if first window, use window 1 w1 = session.attached_window w1.move_window("99") - if "start_directory" in wconf: - sd = wconf["start_directory"] + if "start_directory" in window_config: + start_directory = window_config["start_directory"] else: - sd = None + start_directory = None # If the first pane specifies a start_directory, use that instead. - panes = wconf["panes"] + panes = window_config["panes"] if panes and "start_directory" in panes[0]: - sd = panes[0]["start_directory"] + start_directory = panes[0]["start_directory"] - if "window_shell" in wconf: - ws = wconf["window_shell"] + if "window_shell" in window_config: + window_shell = window_config["window_shell"] else: - ws = None + window_shell = None # If the first pane specifies a shell, use that instead. try: - if wconf["panes"][0]["shell"] != "": - ws = wconf["panes"][0]["shell"] + if window_config["panes"][0]["shell"] != "": + window_shell = window_config["panes"][0]["shell"] except (KeyError, IndexError): pass - environment = panes[0].get("environment", wconf.get("environment")) + environment = panes[0].get("environment", window_config.get("environment")) if environment and has_lt_version("3.0"): # Falling back to use the environment of the first pane for the window # creation is nice but yields misleading error messages. pane_env = panes[0].get("environment") - win_env = wconf.get("environment") + win_env = window_config.get("environment") if pane_env and win_env: target = "panes and windows" elif pane_env: target = "panes" else: target = "windows" - logging.warning( + logger.warning( f"Cannot set environment for new {target}. " "You need tmux 3.0 or newer for this." ) environment = None - w = session.new_window( + window = session.new_window( window_name=window_name, - start_directory=sd, + start_directory=start_directory, attach=False, # do not move to the new window - window_index=wconf.get("window_index", ""), - window_shell=ws, + window_index=window_config.get("window_index", ""), + window_shell=window_shell, environment=environment, ) + assert isinstance(window, Window) if is_first_window_pass: # if first window, use window 1 session.attached_window.kill_window() - assert isinstance(w, Window) - if "options" in wconf and isinstance(wconf["options"], dict): - for key, val in wconf["options"].items(): - w.set_window_option(key, val) + if "options" in window_config and isinstance( + window_config["options"], dict + ): + for key, val in window_config["options"].items(): + window.set_window_option(key, val) - if "focus" in wconf and wconf["focus"]: - w.select_window() + if "focus" in window_config and window_config["focus"]: + window.select_window() - yield w, wconf + yield window, window_config def iter_create_panes( - self, w: Window, wconf: t.Dict[str, t.Any] + self, window: Window, window_config: t.Dict[str, t.Any] ) -> t.Iterator[t.Any]: - """ - Return :class:`libtmux.Pane` iterating through window config dict. + """Return :class:`libtmux.Pane` iterating through window config dict. Run ``shell_command`` with ``$ tmux send-keys``. Parameters ---------- - w : :class:`libtmux.Window` + window : :class:`libtmux.Window` window to create panes for - wconf : dict + window_config : dict config section for window Returns ------- - tuple of (:class:`libtmux.Pane`, ``pconf``) + tuple of (:class:`libtmux.Pane`, ``pane_config``) Newly created pane, and the section from the tmuxp configuration that was used to create the pane. """ - assert isinstance(w, Window) + assert isinstance(window, Window) - pane_base_index_str = w.show_window_option("pane-base-index", g=True) + pane_base_index_str = window.show_window_option("pane-base-index", g=True) assert pane_base_index_str is not None pane_base_index = int(pane_base_index_str) - p = None + pane = None - for pindex, pconf in enumerate(wconf["panes"], start=pane_base_index): - if pindex == int(pane_base_index): - p = w.attached_pane + for pane_index, pane_config in enumerate( + window_config["panes"], start=pane_base_index + ): + if pane_index == int(pane_base_index): + pane = window.attached_pane else: def get_pane_start_directory(): - if "start_directory" in pconf: - return pconf["start_directory"] - elif "start_directory" in wconf: - return wconf["start_directory"] + if "start_directory" in pane_config: + return pane_config["start_directory"] + elif "start_directory" in window_config: + return window_config["start_directory"] else: return None def get_pane_shell(): - if "shell" in pconf: - return pconf["shell"] - elif "window_shell" in wconf: - return wconf["window_shell"] + if "shell" in pane_config: + return pane_config["shell"] + elif "window_shell" in window_config: + return window_config["window_shell"] else: return None - environment = pconf.get("environment", wconf.get("environment")) + environment = pane_config.get( + "environment", window_config.get("environment") + ) if environment and has_lt_version("3.0"): # Just issue a warning when the environment comes from the pane # configuration as a warning for the window was already issued when # the window was created. - if pconf.get("environment"): - logging.warning( + if pane_config.get("environment"): + logger.warning( "Cannot set environment for new panes. " "You need tmux 3.0 or newer for this." ) environment = None - assert p is not None + assert pane is not None - p = w.split_window( + pane = window.split_window( attach=True, start_directory=get_pane_start_directory(), shell=get_pane_shell(), - target=p.id, + target=pane.id, environment=environment, ) - assert isinstance(p, Pane) - if "layout" in wconf: - w.select_layout(wconf["layout"]) + assert isinstance(pane, Pane) - if "suppress_history" in pconf: - suppress = pconf["suppress_history"] - elif "suppress_history" in wconf: - suppress = wconf["suppress_history"] + if "layout" in window_config: + window.select_layout(window_config["layout"]) + + if "suppress_history" in pane_config: + suppress = pane_config["suppress_history"] + elif "suppress_history" in window_config: + suppress = window_config["suppress_history"] else: suppress = True - enter = pconf.get("enter", True) - sleep_before = pconf.get("sleep_before", None) - sleep_after = pconf.get("sleep_after", None) - for cmd in pconf["shell_command"]: + enter = pane_config.get("enter", True) + sleep_before = pane_config.get("sleep_before", None) + sleep_after = pane_config.get("sleep_after", None) + for cmd in pane_config["shell_command"]: enter = cmd.get("enter", enter) sleep_before = cmd.get("sleep_before", sleep_before) sleep_after = cmd.get("sleep_after", sleep_after) @@ -508,18 +522,20 @@ def get_pane_shell(): if sleep_before is not None: time.sleep(sleep_before) - p.send_keys(cmd["cmd"], suppress_history=suppress, enter=enter) + pane.send_keys(cmd["cmd"], suppress_history=suppress, enter=enter) if sleep_after is not None: time.sleep(sleep_after) - if "focus" in pconf and pconf["focus"]: - assert p.pane_id is not None - w.select_pane(p.pane_id) + if "focus" in pane_config and pane_config["focus"]: + assert pane.pane_id is not None + window.select_pane(pane.pane_id) - yield p, pconf + yield pane, pane_config - def config_after_window(self, w: Window, wconf: t.Dict[str, t.Any]) -> None: + def config_after_window( + self, window: Window, window_config: t.Dict[str, t.Any] + ) -> None: """Actions to apply to window after window and pane finished. When building a tmux session, sometimes its easier to postpone things @@ -528,14 +544,16 @@ def config_after_window(self, w: Window, wconf: t.Dict[str, t.Any]) -> None: Parameters ---------- - w : :class:`libtmux.Window` + window : :class:`libtmux.Window` window to create panes for - wconf : dict + window_config : dict config section for window """ - if "options_after" in wconf and isinstance(wconf["options_after"], dict): - for key, val in wconf["options_after"].items(): - w.set_window_option(key, val) + if "options_after" in window_config and isinstance( + window_config["options_after"], dict + ): + for key, val in window_config["options_after"].items(): + window.set_window_option(key, val) def find_current_attached_session(self) -> Session: assert self.server is not None diff --git a/src/tmuxp/workspace/freezer.py b/src/tmuxp/workspace/freezer.py index aed0bf4438..b9b4ab9ddb 100644 --- a/src/tmuxp/workspace/freezer.py +++ b/src/tmuxp/workspace/freezer.py @@ -1,3 +1,8 @@ +from libtmux.pane import Pane +from libtmux.session import Session +import typing as t + + def inline(workspace_dict): """Return workspace with inlined shorthands. Opposite of :meth:`loader.expand`. @@ -40,7 +45,7 @@ def inline(workspace_dict): return workspace_dict -def freeze(session): +def freeze(session: Session) -> t.Dict[str, t.Any]: """Freeze live tmux session into a tmuxp workspacee. Parameters @@ -53,53 +58,61 @@ def freeze(session): dict tmuxp compatible workspace """ - sconf = {"session_name": session.session_name, "windows": []} - - for w in session.windows: - wconf = { - "options": w.show_window_options(), - "window_name": w.name, - "layout": w.window_layout, + session_config: t.Dict[str, t.Any] = { + "session_name": session.session_name, + "windows": [], + } + + for window in session.windows: + window_config: t.Dict[str, t.Any] = { + "options": window.show_window_options(), + "window_name": window.name, + "layout": window.window_layout, "panes": [], } - if getattr(w, "window_active", "0") == "1": - wconf["focus"] = "true" + + if getattr(window, "window_active", "0") == "1": + window_config["focus"] = "true" # If all panes have same path, set 'start_directory' instead # of using 'cd' shell commands. - def pane_has_same_path(p): - return w.panes[0].pane_current_path == p.pane_current_path + def pane_has_same_path(pane: Pane) -> bool: + return window.panes[0].pane_current_path == pane.pane_current_path - if all(pane_has_same_path(p) for p in w.panes): - wconf["start_directory"] = w.panes[0].pane_current_path + if all(pane_has_same_path(pane=pane) for pane in window.panes): + window_config["start_directory"] = window.panes[0].pane_current_path - for p in w.panes: - pconf = {"shell_command": []} + for pane in window.panes: + pane_config: t.Union[str, t.Dict[str, t.Any]] = {"shell_command": []} + assert isinstance(pane_config, dict) - if "start_directory" not in wconf: - pconf["shell_command"].append("cd " + p.pane_current_path) + if "start_directory" not in window_config and pane.pane_current_path: + pane_config["shell_command"].append("cd " + pane.pane_current_path) - if getattr(p, "pane_active", "0") == "1": - pconf["focus"] = "true" + if getattr(pane, "pane_active", "0") == "1": + pane_config["focus"] = "true" - current_cmd = p.pane_current_command + current_cmd = pane.pane_current_command - def filter_interpretters_and_shells(): - return current_cmd.startswith("-") or any( - current_cmd.endswith(cmd) for cmd in ["python", "ruby", "node"] + def filter_interpretters_and_shells() -> bool: + return current_cmd is not None and ( + current_cmd.startswith("-") + or any( + current_cmd.endswith(cmd) for cmd in ["python", "ruby", "node"] + ) ) if filter_interpretters_and_shells(): current_cmd = None if current_cmd: - pconf["shell_command"].append(current_cmd) + pane_config["shell_command"].append(current_cmd) else: - if not len(pconf["shell_command"]): - pconf = "pane" + if not len(pane_config["shell_command"]): + pane_config = "pane" - wconf["panes"].append(pconf) + window_config["panes"].append(pane_config) - sconf["windows"].append(wconf) + session_config["windows"].append(window_config) - return sconf + return session_config	`Builder`: Race conditions after #796 / strict mypy strict mypy #796 makes certain uncovered loading scenarios error - `tmuxp load` when session already exists - Launching a new tmux server with default socket path and socket name <details> ``` eduflow is already running. Attach? [Y/n] y Traceback (most recent call last): File "/home/d/.cache/pypoetry/virtualenvs/tmuxp-m3jEKDEh-py3.11/bin/tmuxp", line 6, in <module> sys.exit(cli.cli()) ^^^^^^^^^ File "~/projects/python/tmuxp/src/tmuxp/cli/__init__.py", line 134, in cli command_load( File "~/projects/python/tmuxp/src/tmuxp/cli/load.py", line 640, in command_load load_workspace( File "~/projects/python/tmuxp/src/tmuxp/cli/load.py", line 416, in load_workspace _reattach(builder) File "~/projects/python/tmuxp/src/tmuxp/cli/load.py", line 161, in _reattach assert builder.session is not None ^^^^^^^^^^^^^^^ AttributeError: 'WorkspaceBuilder' object has no attribute 'session' ``` </details> Declone to loading existing session <details> ``` ❯ tmuxp load ~/work/python/libvcs [Loading] ~/projects/python/libvcs/.tmuxp.yaml Already inside TMUX, switch to session? yes/no Or (a)ppend windows in the current active session? [y/n/a] - (y, n, a): n Traceback (most recent call last): File "/home/d/.cache/pypoetry/virtualenvs/tmuxp-m3jEKDEh-py3.11/bin/tmuxp", line 6, in <module> sys.exit(cli.cli()) ^^^^^^^^^ File "~/projects/python/tmuxp/src/tmuxp/cli/__init__.py", line 134, in cli command_load( File "~/projects/python/tmuxp/src/tmuxp/cli/load.py", line 640, in command_load load_workspace( File "~/projects/python/tmuxp/src/tmuxp/cli/load.py", line 450, in load_workspace _load_detached(builder) File "~/projects/python/tmuxp/src/tmuxp/cli/load.py", line 217, in _load_detached builder.build() File "~/projects/python/tmuxp/src/tmuxp/workspace/builder.py", line 293, in build for w, wconf in self.iter_create_windows(session, append): File "~/projects/python/tmuxp/src/tmuxp/workspace/builder.py", line 416, in iter_create_windows w.set_window_option(key, val) File "~/projects/python/libtmux/src/libtmux/window.py", line 346, in set_window_option self.refresh() File "~/projects/python/libtmux/src/libtmux/window.py", line 84, in refresh return super()._refresh( ^^^^^^^^^^^^^^^^^ File "~/projects/python/libtmux/src/libtmux/neo.py", line 174, in _refresh obj = fetch_obj( ^^^^^^^^^^ File "~/projects/python/libtmux/src/libtmux/neo.py", line 242, in fetch_obj assert obj is not None AssertionError ``` </details> Intermittently during `tmuxp load` <details> ``` Done in 1.87s. Traceback (most recent call last): File "~/.local/bin/tmuxp", line 8, in <module> sys.exit(cli.cli()) ^^^^^^^^^ File "~/projects/tmuxp/src/tmuxp/cli/__init__.py", line 134, in cli command_load( File "~/projects/tmuxp/src/tmuxp/cli/load.py", line 640, in command_load load_workspace( File "~/projects/tmuxp/src/tmuxp/cli/load.py", line 452, in load_workspace _load_attached(builder, detached) File "~/projects/tmuxp/src/tmuxp/cli/load.py", line 184, in _load_attached builder.build() File "~/projects/tmuxp/src/tmuxp/workspace/builder.py", line 289, in build for w, wconf in self.iter_create_windows(session, append): File "~/projects/tmuxp/src/tmuxp/workspace/builder.py", line 411, in iter_create_windows w.set_window_option(key, val) File "~/.local/lib/python3.11/site-packages/libtmux/window.py", line 346, in set_window_option self.refresh() File "~/.local/lib/python3.11/site-packages/libtmux/window.py", line 84, in refresh return super()._refresh( ^^^^^^^^^^^^^^^^^ File "~/.local/lib/python3.11/site-packages/libtmux/neo.py", line 174, in _refresh obj = fetch_obj( ^^^^^^^^^^ File "~/.local/lib/python3.11/site-packages/libtmux/neo.py", line 242, in fetch_obj assert obj is not None AssertionError ``` </details>		2022-12-29T01:44:27	[]	[]
tmux-python/tmuxp	tmux-python__tmuxp-845	b7e7a799f3cf9179f80be767dae279909c238017	diff --git a/CHANGES b/CHANGES index 3261ec59af..78c152ec0e 100644 --- a/CHANGES +++ b/CHANGES @@ -17,7 +17,28 @@ $ pipx install --suffix=@next 'tmuxp' --pip-args '\--pre' --force ## tmuxp 1.19.x (unreleased) -- Notes on upcoming releases will be added here +### What's new + +- #845: allow to configure window and pane specific environment variables + + Having a setup like: + ```yaml + session_name: env-demo + environment: + DATABASE_URL: "sqlite3:///default.db" + windows: + - window_name: dev + environment: + DATABASE_URL: "sqlite3:///dev-1.db" + panes: + - pane + - environment: + DATABASE_URL: "sqlite3:///dev-2.db" + ``` + will result in a window with two panes. In the first pane `$DATABASE_URL` is + `sqlite3:///dev-1.db`, while in the second pane it is `sqlite3://dev-2.db`. + Any freshly created window gets `sqlite3:///default.db` as this is what was + defined for the session. <!-- Maintainers, insert changes / features for the next release here --> diff --git a/docs/configuration/examples.md b/docs/configuration/examples.md index 6677c6d64f..33c8505796 100644 --- a/docs/configuration/examples.md +++ b/docs/configuration/examples.md @@ -262,7 +262,9 @@ please make a ticket on the [issue tracker][issue tracker]. ## Environment variables -tmuxp will set session environment variables. +tmuxp will set session, window and pane environment variables. Note that +setting environment variables for windows and panes requires tmuxp 1.19 or +newer. ````{tab} YAML diff --git a/examples/session-environment.json b/examples/session-environment.json index d4c5c17cf9..3c31e12ec3 100644 --- a/examples/session-environment.json +++ b/examples/session-environment.json @@ -2,15 +2,32 @@ "environment": { "EDITOR": "/usr/bin/vim", "DJANGO_SETTINGS_MODULE": "my_app.settings.local", - "SERVER_PORT": "8009", - }, + "SERVER_PORT": "8009" + }, "windows": [ { "panes": [ - "./manage.py runserver 0.0.0.0:${SERVER_PORT}" - ], + "./manage.py runserver 0.0.0.0:${SERVER_PORT}" + ], "window_name": "Django project" - }, - ], + }, + { + "environment": { + "DJANGO_SETTINGS_MODULE": "my_app.settings.local", + "SERVER_PORT": "8010" + }, + "panes": [ + "./manage.py runserver 0.0.0.0:${SERVER_PORT}", + { + "environment": { + "DJANGO_SETTINGS_MODULE": "my_app.settings.local-testing", + "SERVER_PORT": "8011" + }, + "shell_command": "./manage.py runserver 0.0.0.0:${SERVER_PORT}" + } + ], + "window_name": "Another Django project" + } + ], "session_name": "Environment variables test" -} +} \ No newline at end of file diff --git a/examples/session-environment.yaml b/examples/session-environment.yaml index 1c766b56e4..a1091e7a18 100644 --- a/examples/session-environment.yaml +++ b/examples/session-environment.yaml @@ -7,3 +7,13 @@ windows: - window_name: Django project panes: - ./manage.py runserver 0.0.0.0:${SERVER_PORT} + - window_name: Another Django project + environment: + DJANGO_SETTINGS_MODULE: my_app.settings.local + SERVER_PORT: "8010" + panes: + - ./manage.py runserver 0.0.0.0:${SERVER_PORT} + - environment: + DJANGO_SETTINGS_MODULE: my_app.settings.local-testing + SERVER_PORT: "8011" + shell_command: ./manage.py runserver 0.0.0.0:${SERVER_PORT} diff --git a/src/tmuxp/workspace/builder.py b/src/tmuxp/workspace/builder.py index 677ad9f923..b13bf4caa0 100644 --- a/src/tmuxp/workspace/builder.py +++ b/src/tmuxp/workspace/builder.py @@ -7,6 +7,7 @@ import logging import time +from libtmux.common import has_lt_version from libtmux.exc import TmuxSessionExists from libtmux.pane import Pane from libtmux.server import Server @@ -346,12 +347,31 @@ def iter_create_windows(self, session, append=False): except (KeyError, IndexError): pass + environment = panes[0].get("environment", wconf.get("environment")) + if environment and has_lt_version("3.0"): + # Falling back to use the environment of the first pane for the window + # creation is nice but yields misleading error messages. + pane_env = panes[0].get("environment") + win_env = wconf.get("environment") + if pane_env and win_env: + target = "panes and windows" + elif pane_env: + target = "panes" + else: + target = "windows" + logging.warning( + f"Cannot set environment for new {target}. " + "You need tmux 3.0 or newer for this." + ) + environment = None + w = session.new_window( window_name=window_name, start_directory=sd, attach=False, # do not move to the new window window_index=wconf.get("window_index", ""), window_shell=ws, + environment=environment, ) if is_first_window_pass: # if first window, use window 1 @@ -418,11 +438,24 @@ def get_pane_shell(): else: return None + environment = pconf.get("environment", wconf.get("environment")) + if environment and has_lt_version("3.0"): + # Just issue a warning when the environment comes from the pane + # configuration as a warning for the window was already issued when + # the window was created. + if pconf.get("environment"): + logging.warning( + "Cannot set environment for new panes. " + "You need tmux 3.0 or newer for this." + ) + environment = None + p = w.split_window( attach=True, start_directory=get_pane_start_directory(), shell=get_pane_shell(), target=p.id, + environment=environment, ) assert isinstance(p, Pane)	Per-window environment variables It would be nice if I could set different `environment` values in different windows within a session. Using `tmuxp 1.15.0, libtmux 0.15.4`, I can set environment variables for a session, e.g.: ```yaml session_name: foo environment: DATABASE_URL: "..." ``` However, what I really want to do is set different env vars in different windows, e.g.: ```yaml session_name: foo environment: DATABASE_URL: "sqlite3:///default.db" windows: - window_name: deprecated environment: DATABASE_URL: "... one test DB ..." - window_name: not_ready_yet environment: DATABASE_URL: "... another test DB ..." ``` Thanks!	@tony I could work on this issue if accepted. I could also generalize it to allow setting up pane-specific environments as well (if you agree on that). @zappolowski Go for it Just one question on the desired/expected behavior. Given this (currently hypothetical) configuration: ```yaml session_name: env-test environment: ENV_VAR: "session" windows: - panes: - "echo $ENV_VAR == window" - environment: ENV_VAR: "pane" shell_command: "echo $ENV_VAR == pane" - "echo $ENV_VAR == ???" environment: ENV_VAR: "window" - panes: - "echo $ENV_VAR == session" ``` What should be the value of `???`? Should it be `window`, i.e. pane environment falls back to window environment before using session environment, or should it be `session`, which means that it always ignores the window environment in that case?	2022-11-07T16:33:46	[]	[]
YosefLab/Cassiopeia	YosefLab__Cassiopeia-189	feec0207170f427afa2eee5d583cb7cd5160b3af	diff --git a/cassiopeia/preprocess/cassiopeia_preprocess.py b/cassiopeia/preprocess/cassiopeia_preprocess.py index 0b6e22ec..abcc6dc6 100755 --- a/cassiopeia/preprocess/cassiopeia_preprocess.py +++ b/cassiopeia/preprocess/cassiopeia_preprocess.py @@ -12,7 +12,6 @@ import os import argparse -import configparser import logging import pandas as pd from typing import Any, Dict diff --git a/cassiopeia/preprocess/pipeline.py b/cassiopeia/preprocess/pipeline.py index 726e6d37..23e1c24d 100755 --- a/cassiopeia/preprocess/pipeline.py +++ b/cassiopeia/preprocess/pipeline.py @@ -364,7 +364,7 @@ def resolve_umi_sequence( bins=range(1, equivClass_group["grpFlag"].max()), ) plt.title("Unique Seqs per cellBC+UMI") - plt.yscale("log", basey=10) + plt.yscale("log", base=10) plt.xlabel("Number of Unique Seqs") plt.ylabel("Count (Log)") plt.savefig(os.path.join(output_directory, "seqs_per_equivClass.png")) @@ -975,8 +975,8 @@ def filter_molecule_table( plt.legend() plt.ylabel("Number of UMIs") plt.xlabel("Rank Order") - plt.xscale("log", basex=10) - plt.yscale("log", basey=10) + plt.xscale("log", base=10) + plt.yscale("log", base=10) plt.title("UMIs per CellBC") plt.savefig(os.path.join(output_directory, "umis_per_cellbc.png")) plt.close() diff --git a/cassiopeia/preprocess/utilities.py b/cassiopeia/preprocess/utilities.py index 83d0319d..9517e086 100755 --- a/cassiopeia/preprocess/utilities.py +++ b/cassiopeia/preprocess/utilities.py @@ -10,8 +10,6 @@ import warnings from collections import defaultdict, OrderedDict -import matplotlib -import matplotlib.pyplot as plt import ngs_tools as ngs import numpy as np import pandas as pd	Error in resolve_umi_sequence Hello, I was following the tutorial for the preprocessing, and when I get to the resolve_umi_sequence I get an error thrown. `__init__() got an unexpected keyword argument 'basey'` I believe it's a deprecation of the params in the new matplotlib version. Best, Chang	Hi @cnk113 , Thanks for bringing this to our attention - we'll mark this as a bug and fix this as soon as we can. Thanks, Matt @mattjones315 Hi, I met the same error, have you fix the bug?	2023-03-06T18:51:02	[]	[]
YosefLab/Cassiopeia	YosefLab__Cassiopeia-170	ff56dcc0ce27b38808eee51901cb203b3a29888d	diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 00000000..b6115e6e --- /dev/null +++ b/.gitattributes @@ -0,0 +1,2 @@ +.py linguist-language=python +.ipynb linguist-documentation diff --git a/.gitignore b/.gitignore index 0b04937f..4cb7d7a2 100755 --- a/.gitignore +++ b/.gitignore @@ -163,4 +163,5 @@ build .c stdout.log notebooks/.ipynb_checkpoints -cassiopeia/tools/branch_length_estimator/_iid_exponential_bayesian.cpp \ No newline at end of file +cassiopeia/tools/branch_length_estimator/_iid_exponential_bayesian.cpp +docs/api/reference/* \ No newline at end of file diff --git a/.readthedocs.yml b/.readthedocs.yml index b513e835..dec7d862 100755 --- a/.readthedocs.yml +++ b/.readthedocs.yml @@ -4,10 +4,9 @@ build: sphinx: configuration: docs/conf.py python: - version: 3.6 + version: 3.7 install: - method: pip path: . extra_requirements: - - docs - - requirements: docs/requirements.txt + - docs \ No newline at end of file diff --git a/Makefile b/Makefile index f416d215..aab3a5bd 100644 --- a/Makefile +++ b/Makefile @@ -18,23 +18,11 @@ clean_sdist: clean: clean_develop clean_pypi install: - - $(python) -m pip install --user . - - $(python) setup.py build - - $(python) setup.py build_ext --inplace + - $(python) -m pip install . check_build_reqs: @$(python) -c 'import pytest' \ \|\| ( printf "$(redpip)Build requirements are missing. Run 'make prepare' to install them.$(normal)" ; false ) test: check_build_reqs - $(python) -m pytest -vv $(tests) - - -pypi: clean clean_sdist - set -x \ - && $(python) setup.py sdist bdist_wheel \ - && twine check dist/* \ - && twine upload --repository-url https://test.pypi.org/legacy/ dist/* - -clean_pypi: - - rm -rf build/ + $(python) -m pytest -vv $(tests) \ No newline at end of file diff --git a/README.md b/README.md index e406f583..1c858967 100755 --- a/README.md +++ b/README.md @@ -31,20 +31,21 @@ Installation 1. Clone the package as so: ``git clone https://github.com/YosefLab/Cassiopeia.git`` -2. Ensure you are using python>=3.7. (Python 3.6 may still work, but is not guaranteed.) +2. Ensure that you have Python >= 3.7 installed. (Python 3.6 may still work, but is not guaranteed.) We prefer using [miniconda](https://docs.conda.io/en/latest/miniconda.html). -3. Make sure that Gurobi is installed. You can follow the instructions listed [here](http://www.gurobi.com/academia/for-universities). To verify that it's working correctly, use the following tests: +3. [Optional] Make sure that Gurobi is installed. You can follow the instructions listed [here](http://www.gurobi.com/academia/for-universities). To verify that it's working correctly, use the following tests: * Run the command ``gurobi.sh`` from a terminal window * From the Gurobi installation directory (where there is a setup.py file), use ``python setup.py install --user`` - -4. Install Cassiopeia by first making sure you have stable version of `Cython` and `pytest`. Then, you can install Cassiopeia by running `make install` from the directory where you have Cassiopeia downloaded. - -To verify that it installed correctly, try running our tests with `make test`. + +4. Install Cassiopeia by first changing into the Cassiopeia directory and then `pip3 install .`. To install dev and docs requirements, you can run `pip3 install .[dev,docs]`. + +To verify that it installed correctly, try running our tests with `pytest`. Reference ---------------------- If you've found Cassiopeia useful for your research, please consider citing our paper published in Genome Biology: - +``` Matthew G Jones, Alex Khodaverdian, Jeffrey J Quinn, Michelle M Chan, Jeffrey A Hussmann, Robert Wang, Chenling Xu, Jonathan S Weissman, Nir Yosef. (2020), [Inference of single-cell phylogenies from lineage tracing data using Cassiopeia](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02000-8), Genome Biology +``` \ No newline at end of file diff --git a/build.py b/build.py new file mode 100644 index 00000000..c714b629 --- /dev/null +++ b/build.py @@ -0,0 +1,61 @@ +import os +import shutil +from distutils.command.build_ext import build_ext +from distutils.core import Distribution, Extension + +import numpy +from Cython.Build import cythonize + +# https://github.com/mdgoldberg/poetry-cython-example + + +def build(): + extensions = [ + Extension( + "cassiopeia.preprocess.collapse_cython", + ["cassiopeia/preprocess/collapse_cython.pyx"], + ), + Extension( + "cassiopeia.solver.ilp_solver_utilities", + ["cassiopeia/solver/ilp_solver_utilities.pyx"], + include_dirs=[numpy.get_include()], + ), + Extension( + "cassiopeia.tools.branch_length_estimator._iid_exponential_bayesian", + sources=[ + "cassiopeia/tools/branch_length_estimator/_iid_exponential_bayesian.pyx", + "cassiopeia/tools/branch_length_estimator/_iid_exponential_bayesian_cpp.cpp", + ], + extra_compile_args=[ + "-std=c++17", + "-Wall", + "-Wextra", + "-pedantic", + "-O3", + ], + language="c++", + ), + ] + ext_modules = cythonize( + extensions, + compiler_directives={"language_level": 3}, + ) + + distribution = Distribution({"name": "extended", "ext_modules": ext_modules}) + distribution.package_dir = "extended" + + cmd = build_ext(distribution) + cmd.ensure_finalized() + cmd.run() + + # Copy built extensions back to the project + for output in cmd.get_outputs(): + relative_extension = os.path.relpath(output, cmd.build_lib) + shutil.copyfile(output, relative_extension) + mode = os.stat(relative_extension).st_mode + mode \|= (mode & 0o444) >> 2 + os.chmod(relative_extension, mode) + + +if __name__ == "__main__": + build() \ No newline at end of file diff --git a/cassiopeia/__init__.py b/cassiopeia/__init__.py index e01f7388..18a134d8 100755 --- a/cassiopeia/__init__.py +++ b/cassiopeia/__init__.py @@ -2,15 +2,26 @@ """Top-level for Cassiopeia development.""" -package_name = "cassiopeia" -__author__ = "Matt Jones, Alex Khodaveridan, Richard Zhang, Sebastian Prillo" -__email__ = "[email protected]" -__version__ = "0.0.1" - -from . import pp +from . import preprocess as pp from . import solver -from . import pl +from . import plotting as pl from . import data from . import critique -from . import sim -from . import tl +from . import simulator as sim +from . import tools as tl + +# https://github.com/python-poetry/poetry/pull/2366#issuecomment-652418094 +# https://github.com/python-poetry/poetry/issues/144#issuecomment-623927302 +try: + import importlib.metadata as importlib_metadata +except ModuleNotFoundError: + import importlib_metadata +package_name = "cassiopeia" +__version__ = importlib_metadata.version(package_name) + +import sys + +sys.modules.update({f"{__name__}.{m}": globals()[m] for m in ["tl", "pp", "pl", "sim"]}) +del sys + +__all__ = ["pp", "solver", "pl", "data", "critique", "sim", "tl"] diff --git a/cassiopeia/critique/__init__.py b/cassiopeia/critique/__init__.py index 5fd2a7cb..f281f832 100644 --- a/cassiopeia/critique/__init__.py +++ b/cassiopeia/critique/__init__.py @@ -1,7 +1,3 @@ """Top level for the cassiopeia critique module.""" -__author__ = "Matt Jones, Richard Zhang" -__email__ = "[email protected]" -__version__ = "0.0.1" - from .compare import robinson_foulds, triplets_correct diff --git a/cassiopeia/pl.py b/cassiopeia/pl.py deleted file mode 100644 index 098fa923..00000000 --- a/cassiopeia/pl.py +++ /dev/null @@ -1,1 +0,0 @@ -from cassiopeia.plotting import diff --git a/cassiopeia/plotting/__init__.py b/cassiopeia/plotting/__init__.py index 476ba074..70418056 100644 --- a/cassiopeia/plotting/__init__.py +++ b/cassiopeia/plotting/__init__.py @@ -4,3 +4,6 @@ from .itol_utilities import upload_and_export_itol from .local import plot_matplotlib, plot_plotly + + +__all__ = ["upload_and_export_itol", "plot_matplotlib", "plot_plotly"] diff --git a/cassiopeia/pp.py b/cassiopeia/pp.py deleted file mode 100644 index 3cc480e9..00000000 --- a/cassiopeia/pp.py +++ /dev/null @@ -1,1 +0,0 @@ -from cassiopeia.preprocess import * diff --git a/cassiopeia/preprocess/__init__.py b/cassiopeia/preprocess/__init__.py index d09bef8c..b1cda397 100755 --- a/cassiopeia/preprocess/__init__.py +++ b/cassiopeia/preprocess/__init__.py @@ -24,3 +24,25 @@ filter_umis, ) from .setup_utilities import setup + + +__all__ = [ + "align_sequences", + "call_alleles", + "collapse_umis", + "convert_fastqs_to_unmapped_bam", + "error_correct_cellbcs_to_whitelist", + "error_correct_intbcs_to_whitelist", + "error_correct_umis", + "filter_bam", + "resolve_umi_sequence", + "filter_molecule_table", + "call_lineage_groups", + "compute_empirical_indel_priors", + "convert_alleletable_to_character_matrix", + "convert_alleletable_to_lineage_profile", + "convert_lineage_profile_to_character_matrix", + "filter_cells", + "filter_umis", + "setup", +] diff --git a/cassiopeia/sim.py b/cassiopeia/sim.py deleted file mode 100644 index 47a1106b..00000000 --- a/cassiopeia/sim.py +++ /dev/null @@ -1,1 +0,0 @@ -from cassiopeia.simulator import * diff --git a/cassiopeia/simulator/__init__.py b/cassiopeia/simulator/__init__.py index 116639ad..a7118473 100755 --- a/cassiopeia/simulator/__init__.py +++ b/cassiopeia/simulator/__init__.py @@ -11,3 +11,18 @@ from .SupercellularSampler import SupercellularSampler from .TreeSimulator import TreeSimulator from .UniformLeafSubsampler import UniformLeafSubsampler + + +__all__ = [ + "BirthDeathFitnessSimulator", + "BrownianSpatialDataSimulator", + "Cas9LineageTracingDataSimulator", + "CompleteBinarySimulator", + "DataSimulator", + "LeafSubsampler", + "LineageTracingDataSimulator", + "SimpleFitSubcloneSimulator", + "SupercellularSampler", + "TreeSimulator", + "UniformLeafSubsampler", +] diff --git a/cassiopeia/solver/HybridSolver.py b/cassiopeia/solver/HybridSolver.py index 68a0d792..9a78c748 100644 --- a/cassiopeia/solver/HybridSolver.py +++ b/cassiopeia/solver/HybridSolver.py @@ -226,6 +226,9 @@ def apply_top_solver( [subtree-root, subtree-samples]. """ + if len(samples) == 1: + return samples[0], [samples], tree + clades = list( self.top_solver.perform_split( character_matrix, samples, weights, missing_state_indicator @@ -233,41 +236,37 @@ def apply_top_solver( ) root = next(node_name_generator) - tree.add_node(root) + + for clade in clades: + if len(clade) == 0: + clades.remove(clade) + if len(clades) == 1: for clade in clades[0]: tree.add_edge(root, clade) return root, [], tree - new_clades = [] subproblems = [] for clade in clades: - if len(clade) == 0: - continue - if self.assess_cutoff( clade, character_matrix, missing_state_indicator ): subproblems += [(root, clade)] - continue - - new_clades.append(clade) - - for clade in new_clades: - child, new_subproblems, tree = self.apply_top_solver( - character_matrix, - clade, - tree, - node_name_generator, - weights, - missing_state_indicator, - root, - ) - tree.add_edge(root, child) + else: + child, new_subproblems, tree = self.apply_top_solver( + character_matrix, + clade, + tree, + node_name_generator, + weights, + missing_state_indicator, + root, + ) + tree.add_edge(root, child) - subproblems += new_subproblems + subproblems += new_subproblems return root, subproblems, tree diff --git a/cassiopeia/solver/__init__.py b/cassiopeia/solver/__init__.py index 1e62d9da..d6c0146b 100755 --- a/cassiopeia/solver/__init__.py +++ b/cassiopeia/solver/__init__.py @@ -1,9 +1,5 @@ """Top level for Tree Solver development.""" -__author__ = "Matt Jones, Alex Khodaverdian, Richard Zhang, Sebastian Prillo" -__email__ = "[email protected]" -__version__ = "0.0.1" - from .HybridSolver import HybridSolver from .ILPSolver import ILPSolver from .MaxCutGreedySolver import MaxCutGreedySolver diff --git a/cassiopeia/tl.py b/cassiopeia/tl.py deleted file mode 100644 index 882c1147..00000000 --- a/cassiopeia/tl.py +++ /dev/null @@ -1,1 +0,0 @@ -from cassiopeia.tools import * diff --git a/cassiopeia/tools/__init__.py b/cassiopeia/tools/__init__.py index ece00aea..821f0771 100644 --- a/cassiopeia/tools/__init__.py +++ b/cassiopeia/tools/__init__.py @@ -14,3 +14,19 @@ calculate_likelihood_discrete, calculate_parsimony, ) + + +__all__ = [ + "calculate_likelihood_continuous", + "calculate_likelihood_discrete", + "calculate_parsimony", + "compute_morans_i", + "compute_evolutionary_coupling", + "estimate_missing_data_rates", + "estimate_mutation_rate", + "fitch_count", + "fitch_hartigan", + "score_small_parsimony", + "compute_cophenetic_correlation", + "compute_expansion_pvalues", +] diff --git a/docs/api/critique.rst b/docs/api/critique.rst index 46694706..46b46d7f 100644 --- a/docs/api/critique.rst +++ b/docs/api/critique.rst @@ -1,7 +1,6 @@ =========== Critique =========== -.. module:: cassiopeia.critique .. currentmodule:: cassiopeia Critique @@ -11,6 +10,6 @@ We support functionality for comparing trees to one another, for example when be .. autosummary:: :toctree: reference/ - + critique.robinson_foulds critique.triplets_correct \ No newline at end of file diff --git a/docs/api/data.rst b/docs/api/data.rst index 6d03519a..48a8e908 100644 --- a/docs/api/data.rst +++ b/docs/api/data.rst @@ -1,6 +1,7 @@ =========== Data =========== + .. module:: cassiopeia.data .. currentmodule:: cassiopeia @@ -11,7 +12,7 @@ The main data structure that Cassiopeia uses for all tree-based analyses is the .. autosummary:: :toctree: reference/ - + data.CassiopeiaTree Utilities diff --git a/docs/api/plotting.rst b/docs/api/plotting.rst index a938aaa3..a4258fa6 100644 --- a/docs/api/plotting.rst +++ b/docs/api/plotting.rst @@ -2,7 +2,6 @@ Plotting ========== -.. module:: cassiopeia.pl .. currentmodule:: cassiopeia Plotting diff --git a/docs/api/preprocess.rst b/docs/api/preprocess.rst index f79eb921..1b7e4432 100644 --- a/docs/api/preprocess.rst +++ b/docs/api/preprocess.rst @@ -1,7 +1,6 @@ =========== Preprocess =========== -.. module:: cassiopeia.pp .. currentmodule:: cassiopeia Data Preprocessing @@ -11,7 +10,7 @@ We have several functions that are part of our pipeline for processing sequencin .. autosummary:: :toctree: reference/ - + pp.align_sequences pp.call_alleles pp.call_lineage_groups @@ -25,8 +24,8 @@ We have several functions that are part of our pipeline for processing sequencin pp.filter_cells pp.filter_umis pp.resolve_umi_sequence - - + + Data Utilities @@ -36,7 +35,7 @@ We also have several functions that are useful for converting between data forma .. autosummary:: :toctree: reference/ - + pp.compute_empirical_indel_priors pp.convert_alleletable_to_character_matrix pp.convert_alleletable_to_lineage_profile diff --git a/docs/api/simulator.rst b/docs/api/simulator.rst index 45d16f96..dfb6b6e9 100644 --- a/docs/api/simulator.rst +++ b/docs/api/simulator.rst @@ -1,7 +1,6 @@ =========== Simulator =========== -.. module:: cassiopeia.sim .. currentmodule:: cassiopeia @@ -14,7 +13,7 @@ We have several frameworks available for simulating topologies: .. autosummary:: :toctree: reference/ - + sim.BirthDeathFitnessSimulator sim.CompleteBinarySimulator sim.SimpleFitSubcloneSimulator diff --git a/docs/api/solver.rst b/docs/api/solver.rst index 1215dac1..059a9075 100644 --- a/docs/api/solver.rst +++ b/docs/api/solver.rst @@ -1,7 +1,6 @@ =========== Solver =========== -.. module:: cassiopeia.solver .. currentmodule:: cassiopeia CassiopeiaSolvers @@ -11,7 +10,7 @@ We have several algorithms available for solving phylogenies: .. autosummary:: :toctree: reference/ - + solver.HybridSolver solver.ILPSolver solver.MaxCutSolver @@ -32,7 +31,7 @@ For use in our distance-based solver and for comparing character states, we also .. autosummary:: :toctree: reference/ - + solver.dissimilarity_functions.cluster_dissimilarity solver.dissimilarity_functions.hamming_distance solver.dissimilarity_functions.hamming_similarity_normalized_over_missing diff --git a/docs/api/tools.rst b/docs/api/tools.rst index 579cf988..10286a63 100644 --- a/docs/api/tools.rst +++ b/docs/api/tools.rst @@ -2,7 +2,6 @@ Tools ========== -.. module:: cassiopeia.tl .. currentmodule:: cassiopeia This library stores code for post-reconstruction analysis of trees. We are @@ -22,7 +21,7 @@ Branch Length Estimation (BLE) .. autosummary:: :toctree: reference/ - + tl.IIDExponentialBayesian tl.IIDExponentialMLE @@ -31,14 +30,14 @@ Coupling .. autosummary:: :toctree: reference/ - + tl.compute_evolutionary_coupling Metrics ~~~~~~~~ .. autosummary:: :toctree: reference/ - + tl.calculate_likelihood_continuous tl.calculate_likelihood_discrete tl.calculate_parsimony @@ -48,17 +47,17 @@ Parameter Estimation .. autosummary:: :toctree: reference/ - + tl.estimate_missing_data_rates tl.estimate_mutation_rate -Small-Parsimony +Small-Parsimony ~~~~~~~~~~~~~~~~~~~ .. autosummary:: :toctree: reference/ - + tl.fitch_count tl.fitch_hartigan tl.score_small_parsimony @@ -67,5 +66,5 @@ Topology ~~~~~~~~~~~~~~~~~~~ .. autosummary:: :toctree: reference/ - + tl.compute_expansion_pvalues \ No newline at end of file diff --git a/docs/conf.py b/docs/conf.py index ae662a95..fd361fdf 100755 --- a/docs/conf.py +++ b/docs/conf.py @@ -18,20 +18,14 @@ # relative to the documentation root, use os.path.abspath to make it # absolute, like shown here. # -import os import sys -import warnings from pathlib import Path HERE = Path(__file__).parent -# sys.path[:0] = [str(HERE.parent), str(HERE / "extensions")] - +sys.path[:0] = [str(HERE.parent), str(HERE / "extensions")] import cassiopeia # noqa -on_rtd = os.environ.get("READTHEDOCS") == "True" - -autodoc_mock_imports = ["gurobipy"] # -- General configuration --------------------------------------------- @@ -44,11 +38,10 @@ extensions = [ "sphinx.ext.autodoc", "sphinx.ext.intersphinx", - "sphinx.ext.mathjax", "sphinx.ext.viewcode", "nbsphinx", "nbsphinx_link", - "sphinx_gallery.load_style", + "sphinx.ext.mathjax", "sphinx.ext.napoleon", "sphinx_autodoc_typehints", # needs to be after napoleon "sphinx.ext.autosummary", @@ -56,13 +49,15 @@ "scanpydoc.definition_list_typed_field", "scanpydoc.autosummary_generate_imported", [p.stem for p in (HERE / "extensions").glob(".py")], - [p.stem for p in (HERE / "extensions").glob(".pyx")], + "sphinx_gallery.load_style", ] # nbsphinx specific settings exclude_patterns = ["_build", "*.ipynb_checkpoints"] nbsphinx_execute = "never" +autodoc_mock_imports = ["gurobipy"] + # Add any paths that contain templates here, relative to this directory. templates_path = ["_templates"] @@ -76,7 +71,7 @@ autosummary_generate = True autodoc_member_order = "bysource" napoleon_google_docstring = True # for pytorch lightning -napoleon_numpy_docstring = True +napoleon_numpy_docstring = False napoleon_include_init_with_doc = False napoleon_use_rtype = True # having a separate entry generally helps readability napoleon_use_param = True @@ -97,9 +92,9 @@ ) # General information about the project. -project = u"cassiopeia" -copyright = u"2021, Yosef Lab, UC Berkeley" -author = u"Matthew G Jones, Richard Zhang, Sebastian Prillo, Joseph Min, Jeffrey J Quinn, Alex Khodaverdian" +project = "cassiopeia" +copyright = "2022, Yosef Lab, UC Berkeley" +author = "Matthew G Jones, Richard Zhang, Sebastian Prillo, Joseph Min, Jeffrey J Quinn, Alex Khodaverdian" # The version info for the project you're documenting, acts as replacement # for \|version\| and \|release\|, also used in various other places throughout @@ -120,7 +115,7 @@ # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This patterns also effect to html_static_path and html_extra_path -exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"] +exclude_patterns = ["_build", "Thumbs.db", ".DS_Store", ".pyx"] # The name of the Pygments (syntax highlighting) style to use. pygments_style = "sphinx" @@ -172,10 +167,6 @@ } -def setup(app): - app.warningiserror = on_rtd - - # -- Options for HTMLHelp output --------------------------------------- # Output file base name for HTML help builder. @@ -191,56 +182,6 @@ def setup(app): }, } -# -- Options for LaTeX output ------------------------------------------ - -latex_elements = { - # The paper size ('letterpaper' or 'a4paper'). - # - # 'papersize': 'letterpaper', - # The font size ('10pt', '11pt' or '12pt'). - # - # 'pointsize': '10pt', - # Additional stuff for the LaTeX preamble. - # - # 'preamble': '', - # Latex figure (float) alignment - # - # 'figure_align': 'htbp', -} - -# Grouping the document tree into LaTeX files. List of tuples -# (source start file, target name, title, author, documentclass -# [howto, manual, or own class]). -# latex_documents = [ -# (master_doc, "cassiopeia.tex", u"Cassiopeia Documentation", u"Matthew Jones", "manual") -# ] - - -# -- Options for manual page output ------------------------------------ - -# One entry per manual page. List of tuples -# (source start file, name, description, authors, manual section). -man_pages = [ - (master_doc, "Cassiopeia", u"Cassiopeia Documentation", [author], 1) -] - - -# -- Options for Texinfo output ---------------------------------------- - -# Grouping the document tree into Texinfo files. List of tuples -# (source start file, target name, title, author, -# dir menu entry, description, category) -texinfo_documents = [ - ( - master_doc, - "cassiopeia", - u"Cassiopeia Documentation", - author, - "Cassiopeia", - "One line description of project.", - "Miscellaneous", - ) -] from sphinx.ext.autosummary import Autosummary from sphinx.ext.autosummary import get_documenter @@ -255,6 +196,7 @@ class AutoAutoSummary(Autosummary): option_spec = { "methods": directives.unchanged, "attributes": directives.unchanged, + "toctree": directives.unchanged, } required_arguments = 1 @@ -271,9 +213,7 @@ def get_members(obj, typ, include_public=None): continue if documenter.objtype == typ: items.append(name) - public = [ - x for x in items if x in include_public or not x.startswith("_") - ] + public = [x for x in items if x in include_public or not x.startswith("_")] return public, items def run(self): diff --git a/docs/extensions/typed_returns.py b/docs/extensions/typed_returns.py new file mode 100644 index 00000000..d4e1d267 --- /dev/null +++ b/docs/extensions/typed_returns.py @@ -0,0 +1,28 @@ +# code from https://github.com/theislab/scanpy/blob/master/docs/extensions/typed_returns.py +# with some minor adjustment +import re + +from sphinx.application import Sphinx +from sphinx.ext.napoleon import NumpyDocstring + + +def process_return(lines): + for line in lines: + m = re.fullmatch(r"(?P<param>\w+)\s+:\s+(?P<type>[\w.]+)", line) + if m: + # Once this is in scanpydoc, we can use the fancy hover stuff + yield f'-{m["param"]} (:class:`~{m["type"]}`)' + else: + yield line + + +def scanpy_parse_returns_section(self, section): + lines_raw = list(process_return(self._dedent(self._consume_to_next_section()))) + lines = self._format_block(":returns: ", lines_raw) + if lines and lines[-1]: + lines.append("") + return lines + + +def setup(app: Sphinx): + NumpyDocstring._parse_returns_section = scanpy_parse_returns_section diff --git a/docs/requirements.txt b/docs/requirements.txt deleted file mode 100755 index 5546eebb..00000000 --- a/docs/requirements.txt +++ /dev/null @@ -1,14 +0,0 @@ -sphinx_rtd_theme==0.5.0 -sphinx==4.0.0 -nbsphinx -sphinx-autodoc-typehints<1.12.0 -nbsphinx-link -sphinx_gallery -pandas -numpy>=1.9 -networkx >= 2.0 -Cython >= 0.29.2 -scanpydoc>=0.4.5 -pydata-sphinx-theme>=0.4.0 -pathlib -typing_extensions; python_version < '3.8' diff --git a/pyproject.toml b/pyproject.toml index 9420f4d3..1fa96b90 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,3 +1,102 @@ +[tool.poetry] +authors = ["Matthew Jones <[email protected]>", "Alex Khodaverdian", "Richard Zhang", "Sebastian Prillo", "Joseph Min"] +classifiers = [ + "Development Status :: 4 - Beta", + "Intended Audience :: Science/Research", + "Natural Language :: English", + "Programming Language :: Python :: 3.7", + "Programming Language :: Python :: 3.8", + "Programming Language :: Python :: 3.9", + "Operating System :: MacOS :: MacOS X", + "Operating System :: Microsoft :: Windows", + "Operating System :: POSIX :: Linux", + "Topic :: Scientific/Engineering :: Bio-Informatics", +] +description = "Single Cell Lineage Reconstruction with Cas9-Enabled Lineage Recorders" +documentation = "https://cassiopeia-lineage.readthedocs.io/" +homepage = "https://github.com/YosefLab/Cassiopeia" +keywords = ['scLT'] +license = "MIT" +name = "cassiopeia" +readme = 'README.md' +repository = "https://github.com/YosefLab/Cassiopeia" +version = "2.0.0" + +include = [ + {path = "cassiopeia/preprocess/.so", format = "wheel"}, + {path = "cassiopeia/preprocess/.pyx", format = "wheel"}, + {path = "cassiopeia/solver/.so", format = "wheel"}, + {path = "cassiopeia/solver/.pyx", format = "wheel"}, + {path = "cassiopeia/tools/branch_length_estimator/.so", format = "wheel"}, + {path = "cassiopeia/tools/branch_length_estimator/.pyx", format = "wheel"}, +] +packages = [ + {include = "cassiopeia"}, +] + +[tool.poetry.dependencies] +Biopython = ">=1.71" +Cython = ">=0.29.2" +PyYAML = ">=3.12" +black = {version = ">=20.8b1", optional = true} +bokeh = ">=0.12.15" +codecov = {version = ">=2.0.8", optional = true} +cvxpy = "" +ete3 = ">=3.1.1" +hits = "" +importlib-metadata = {version = "^1.0", python = "<3.8"} +ipython = {version = ">=7.20", optional = true, python = ">=3.7"} +isort = {version = ">=5.7", optional = true} +itolapi = "" +jupyter = {version = ">=1.0", optional = true} +matplotlib = ">=2.2.2" +nbconvert = ">=5.4.0" +nbformat = ">=4.4.0" +nbsphinx = {version = "", optional = true} +nbsphinx-link = {version = "", optional = true} +networkx = ">=2.5" +ngs-tools = ">=1.5.6" +numba = ">=0.51.0" +numpy = ">=1.19.5,<1.22" +pandas = ">=1.1.4" +parameterized = "" +plotly = ">=5.0.0" +pre-commit = {version = ">=2.7.1", optional = true} +pydata-sphinx-theme = {version = ">=0.4.3", optional = true} +pysam = ">=0.14.1" +pyseq-align = ">=1.0.2" +pytest = {version = ">=4.4", optional = true} +python = ">=3.7,<4.0" +scanpydoc = {version = ">=0.5", optional = true} +scipy = ">=1.2.0" +sphinx = {version = ">=3.4", optional = true} +sphinx-autodoc-typehints = {version = "", optional = true} +sphinx-gallery = {version = ">0.6", optional = true} +tqdm = ">=4" +typing-extensions = ">=3.7.4" +typing_extensions = {version = "", python = "<3.8", optional = true} + +[tool.poetry.build] +generate-setup-file = false +script = "build.py" + [build-system] -requires = ["setuptools", "wheel", "Cython", "oldest-supported-numpy"] -build-backend = "setuptools.build_meta" +build-backend = "poetry.core.masonry.api" +requires = ["poetry-core>=1.0.7", "Cython", "numpy>=1.19.5,<1.22", "setuptools", "pip>=22.0.0"] + +[tool.poetry.scripts] +cassiopeia-preprocess = 'cassiopeia.preprocess.cassiopeia_preprocess:main' + +[tool.poetry.extras] +dev = ["black", "pytest", "flake8", "codecov", "jupyter", "pre-commit", "isort"] +docs = [ + "sphinx", + "scanpydoc", + "nbsphinx", + "nbsphinx-link", + "ipython", + "pydata-sphinx-theme", + "typing_extensions", + "sphinx-autodoc-typehints", + "sphinx_gallery", +] diff --git a/setup.py b/setup.py index 7730c87b..dbb2a9e3 100755 --- a/setup.py +++ b/setup.py @@ -1,113 +1,8 @@ -#!/usr/bin/env python3.6 -# -- coding: utf-8 -*- +#!/usr/bin/env python -from setuptools import setup, Extension, Distribution, find_packages -from setuptools import find_packages -from Cython.Build import cythonize -from Cython.Distutils import build_ext -import numpy +# This is a shim to hopefully allow Github to detect the package, build is done with poetry -with open("README.md") as readme_file: - readme = readme_file.read() +import setuptools -requirements = [ - "Biopython>=1.71", - "bokeh>=0.12.15", - "cython>=0.29.2", - "ete3>=3.1.1", - "hits", - "itolapi", - "matplotlib>=2.2.2", - "nbconvert>=5.4.0", - "nbformat>=4.4.0", - "networkx>=2.5", - "ngs-tools>=1.5.6", - "numba>=0.51.0", - "numpy>=1.19.5<1.22", - "pandas>=1.1.4", - "plotly>=5.0.0", - "pysam>=0.14.1", - "pyseq-align>=1.0.2", - "PyYAML>=3.12", - "scipy>=1.2.0", - "typing-extensions>=3.7.4", - "tqdm>=4", - "cvxpy", - "parameterized", -] - - -author = "Matthew Jones, Alex Khodaverdian, Richard Zhang, Sebastian Prillo, Joseph Min" - -cmdclass = {"build_ext": build_ext} - -# files to wrap with cython -to_cythonize = [ - Extension( - "cassiopeia.preprocess.collapse_cython", - ["cassiopeia/preprocess/collapse_cython.pyx"], - ), - Extension( - "cassiopeia.solver.ilp_solver_utilities", - ["cassiopeia/solver/ilp_solver_utilities.pyx"], - include_dirs=[numpy.get_include()], - ), -] - -extension_modules_with_custom_compile_args = [ - Extension( - "cassiopeia.tools.branch_length_estimator._iid_exponential_bayesian", - sources=[ - "cassiopeia/tools/branch_length_estimator/_iid_exponential_bayesian.pyx", - "cassiopeia/tools/branch_length_estimator/_iid_exponential_bayesian_cpp.cpp", - ], - extra_compile_args=[ - "-std=c++17", - "-Wall", - "-Wextra", - "-pedantic", - "-O3", - ], - language="c++", - ), -] - -setup( - name="cassiopeia-lineage", - python_requires=">=3.6", - ext_modules=cythonize( - to_cythonize + extension_modules_with_custom_compile_args, - compiler_directives={"language_level": "3"} - ), - # ext_modules=to_cythonize, - setup_requires=["cython", "numpy"], - cmdclass=cmdclass, - entry_points={ - "console_scripts": [ - "cassiopeia-preprocess = cassiopeia.preprocess.cassiopeia_preprocess:main" - ] - }, - author_email="[email protected]", - classifiers=[ - "Development Status :: 4 - Beta", - "Intended Audience :: Science/Research", - "License :: OSI Approved :: MIT License", - "Programming Language :: Python :: 3", - "Operating System :: MacOS :: MacOS X", - "Operating System :: Microsoft :: Windows", - "Operating System :: POSIX :: Linux", - "Topic :: Scientific/Engineering :: Bio-Informatics", - ], - long_description=readme + "\n\n", - description="Single Cell Lineage Reconstruction with Cas9-Enabled Lineage Recorders", - install_requires=requirements, - license="MIT license", - include_package_data=True, - packages=find_packages(), - keywords="scLT", - url="https://github.com/YosefLab/Cassiopeia", - version="2.0.0", - zip_safe=False, - test_suite="nose.collector", - tests_require=["nose"], -) +if __name__ == "__main__": + setuptools.setup(name="cassiopeia")	Installation instructions in readme https://github.com/YosefLab/Cassiopeia/blob/4a4ba1ac5bac1158f8ebc652ab7850c05ff95d59/README.md?plain=1#L34 This isn't required and you can't install python from pip?!		2022-02-03T18:49:43	[]	[]
YosefLab/Cassiopeia	YosefLab__Cassiopeia-154	d13d3e72acbeabd807af01356acb7a9783346aca	diff --git a/cassiopeia/data/__init__.py b/cassiopeia/data/__init__.py index c48b9e2b..026cfd77 100644 --- a/cassiopeia/data/__init__.py +++ b/cassiopeia/data/__init__.py @@ -3,8 +3,10 @@ from .CassiopeiaTree import CassiopeiaTree from .utilities import ( compute_dissimilarity_map, + compute_inter_cluster_distances, compute_phylogenetic_weight_matrix, get_lca_characters, + net_relatedness_index, sample_bootstrap_allele_tables, sample_bootstrap_character_matrices, to_newick, diff --git a/cassiopeia/data/utilities.py b/cassiopeia/data/utilities.py index eaa06562..77e4c127 100755 --- a/cassiopeia/data/utilities.py +++ b/cassiopeia/data/utilities.py @@ -14,6 +14,7 @@ from cassiopeia.data import CassiopeiaTree from cassiopeia.mixins import CassiopeiaTreeWarning, is_ambiguous_state +from cassiopeia.mixins.errors import CassiopeiaError from cassiopeia.preprocess import utilities as preprocessing_utilities @@ -412,3 +413,97 @@ def compute_phylogenetic_weight_matrix( np.fill_diagonal(W.values, 0) return W + [email protected](nopython=True) +def net_relatedness_index( + dissimilarity_map: np.array, indices_1: np.array, indices_2: np.array +) -> float: + """Computes the net relatedness index between indices. + + Using the dissimilarity map specified and the indices of samples, compute + the net relatedness index, defined as: + + sum(distances over i,j in indices_1,indices_2) / (\|indices_1\| x \|indices_2\|) + + Args: + dissimilarity_map: Dissimilarity map between all samples. + indices_1: Indices corresponding to the first group. + indices_2: Indices corresponding to the second group. + + Returns: + The Net Relatedness Index (NRI) + """ + + nri = 0 + for i in indices_1: + for j in indices_2: + nri += dissimilarity_map[i, j] + + return nri / (len(indices_1) * len(indices_2)) + +def compute_inter_cluster_distances( + tree: CassiopeiaTree, + meta_item: Optional[str] = None, + meta_data: Optional[pd.DataFrame] = None, + dissimilarity_map: Optional[pd.DataFrame] = None, + distance_function: Callable = net_relatedness_index, + *kwargs, +) -> pd.DataFrame: + """Computes mean distance between clusters. + + Compute the mean distance between categories in a categorical variable. By + default, the phylogenetic weight matrix will be computed and used for this + distance calculation, but a user can optionally provide a dissimilarity + map instead. + + This function performs the computation in O(K^2)O(distance_function) time + for a variable with K categories. + + Args: + tree: CassiopeiaTree + meta_item: Column in the cell meta data of the tree. If `meta_data` is + specified, this is ignored. + meta_data: Meta data to use for this calculation. This argument takes + priority over meta_item. + dissimilarity_map: Dissimilarity map to use for distances. If this is + specified, the phylogenetic weight matrix is not computed. + number_of_neighbors: Number of nearest neighbors to use for computing + the mean distances. If this is not specified, then all cells are + used. + kwargs: Arguments to pass to the distance function. + + Returns: + A K x K distance matrix. + """ + meta_data = tree.cell_meta[meta_item] if (meta_data is None) else meta_data + + # ensure that the meta data is categorical + if not pd.api.types.is_string_dtype(meta_data): + raise CassiopeiaError("Meta data must be categorical or a string.") + + D = ( + compute_phylogenetic_weight_matrix(tree) + if (dissimilarity_map is None) + else dissimilarity_map + ) + + unique_states = meta_data.unique() + K = len(unique_states) + inter_cluster_distances = pd.DataFrame( + np.zeros((K, K)), index=unique_states, columns=unique_states + ) + + # align distance matrix and meta_data + D = D.loc[meta_data.index.values, meta_data.index.values] + + for state1 in unique_states: + indices_1 = np.where(np.array(meta_data) == state1)[0] + for state2 in unique_states: + indices_2 = np.where(np.array(meta_data) == state2)[0] + + distance = distance_function( + D.values, indices_1, indices_2, kwargs + ) + inter_cluster_distances.loc[state1, state2] = distance + + return inter_cluster_distances diff --git a/cassiopeia/tools/__init__.py b/cassiopeia/tools/__init__.py index 792a38ce..de98134f 100644 --- a/cassiopeia/tools/__init__.py +++ b/cassiopeia/tools/__init__.py @@ -2,5 +2,6 @@ from .autocorrelation import compute_morans_i from .branch_length_estimator import IIDExponentialBayesian, IIDExponentialMLE +from .coupling import compute_evolutionary_coupling from .small_parsimony import fitch_count, fitch_hartigan, score_small_parsimony from .topology import compute_cophenetic_correlation, compute_expansion_pvalues diff --git a/cassiopeia/tools/coupling.py b/cassiopeia/tools/coupling.py new file mode 100644 index 00000000..2ba982c7 --- /dev/null +++ b/cassiopeia/tools/coupling.py @@ -0,0 +1,125 @@ +""" +File storing functionality for computing coupling statistics between meta +variables on a tree. +""" +from typing import Callable, Optional + +from collections import defaultdict +import numpy as np +import pandas as pd +from tqdm import tqdm + +from cassiopeia.data import CassiopeiaTree +from cassiopeia.data import utilities as data_utilities + + +def compute_evolutionary_coupling( + tree: CassiopeiaTree, + meta_variable: str, + minimum_proportion: float = 0.05, + number_of_shuffles: int = 500, + random_state: Optional[np.random.RandomState] = None, + dissimilarity_map: Optional[pd.DataFrame] = None, + cluster_comparison_function: Callable = data_utilities.net_relatedness_index, + *comparison_kwargs, +) -> pd.DataFrame: + """Computes Evolutionary Coupling of categorical variables. + + Using the methodology described in Yang, Jones et al, BioRxiv (2021), this + function will compute the "evolutionary coupling" statistic between values + that a categorical variable can take on with the tree. For example, this + categorical variable can be a "cell type", and this function will compute + the evolutionary couplings between all types of cell types. This indicates + how closely related these cell types are to one another. + + Briefly, this statistic is the Z-normalized mean distance between categories + in the specified categorical variable. Note that empirical nulls that have a + standard deviation of 0 lead to NaNs in the resulting evolutionary coupling + matrix. + + The computational complexity of this function is + O(n^2 log n + (B+1)(K^2 O(distance_function)) for a tree with n leaves, a + variable with K categories, and B random shuffles. + + Args: + tree: CassiopeiaTree + meta_variable: Column in `tree.cell_meta` that stores a categorical + variable with K categories. + minimum_proportion: Minimum proportion of cells that a category needs + to appear in to be considered. + number_of_shuffles: Number of times to shuffle the data to compute the + empirical Z score. + random_state: Numpy random state to parameterize the shuffling. + dissimilarity_map: A precomputed dissimilarity map between all leaves. + cluster_comparison_function: A function for comparing the mean distance + between groups. By default, this is the Net Relatedness Index. + *comparison_kwargs: Extra arguments to pass to the cluster comparison + function. + + Returns: + A K x K evolutionary coupling dataframe. + """ + + W = ( + data_utilities.compute_phylogenetic_weight_matrix(tree) + if (dissimilarity_map is None) + else dissimilarity_map + ) + + meta_data = tree.cell_meta[meta_variable] + + # subset meta data by minimum proportion + if minimum_proportion > 0: + filter_threshold = int(len(tree.leaves) minimum_proportion) + category_frequencies = meta_data.value_counts() + passing_categories = category_frequencies[ + category_frequencies > filter_threshold + ].index.values + meta_data = meta_data[meta_data.isin(passing_categories)] + W = W.loc[meta_data.index.values, meta_data.index.values] + + # compute inter-cluster distances + inter_cluster_distances = data_utilities.compute_inter_cluster_distances( + tree, + meta_data=meta_data, + dissimilarity_map=W, + distance_function=cluster_comparison_function, + comparison_kwargs, + ) + + # compute background for Z-scoring + background = defaultdict(list) + for _ in tqdm( + range(number_of_shuffles), desc="Creating empirical background" + ): + permuted_assignments = meta_data.copy() + if random_state: + permuted_assignments.index = random_state.permutation( + meta_data.index.values + ) + else: + permuted_assignments.index = np.random.permutation( + meta_data.index.values + ) + background_distances = data_utilities.compute_inter_cluster_distances( + tree, + meta_data=permuted_assignments, + dissimilarity_map=W, + distance_function=cluster_comparison_function, + comparison_kwargs, + ) + for s1 in background_distances.index: + for s2 in background_distances.columns: + background[(s1, s2)].append(background_distances.loc[s1, s2]) + + Z_scores = inter_cluster_distances.copy() + for s1 in Z_scores.index: + for s2 in Z_scores.columns: + mean = np.mean(background[(s1, s2)]) + sd = np.std(background[(s1, s2)]) + + Z_scores.loc[s1, s2] = ( + inter_cluster_distances.loc[s1, s2] - mean + ) / sd + + return Z_scores diff --git a/cassiopeia/tools/topology.py b/cassiopeia/tools/topology.py index 39f5fc9f..33af2250 100644 --- a/cassiopeia/tools/topology.py +++ b/cassiopeia/tools/topology.py @@ -9,7 +9,6 @@ import pandas as pd from scipy import spatial, stats - from cassiopeia.data import CassiopeiaTree, compute_phylogenetic_weight_matrix from cassiopeia.mixins import CassiopeiaError from cassiopeia.solver import dissimilarity_functions diff --git a/docs/api/plotting.rst b/docs/api/plotting.rst index c738dba3..bc065672 100644 --- a/docs/api/plotting.rst +++ b/docs/api/plotting.rst @@ -13,6 +13,4 @@ Currently, our plotting functionality is linked to the rich iTOL framework: .. autosummary:: :toctree: reference/ - pl.upload_and_export_itol - - \ No newline at end of file + pl.upload_and_export_itol \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.data.CassiopeiaTree.rst b/docs/api/reference/cassiopeia.data.CassiopeiaTree.rst deleted file mode 100644 index d0d90838..00000000 --- a/docs/api/reference/cassiopeia.data.CassiopeiaTree.rst +++ /dev/null @@ -1,13 +0,0 @@ -cassiopeia.data.CassiopeiaTree -============================== - -.. currentmodule:: cassiopeia.data - -.. autoclass:: CassiopeiaTree - :members: - :undoc-members: - - .. rubric:: Methods - - .. autoautosummary:: CassiopeiaTree - :methods: \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.data.sample_bootstrap_allele_tables.rst b/docs/api/reference/cassiopeia.data.sample_bootstrap_allele_tables.rst deleted file mode 100644 index 1639e437..00000000 --- a/docs/api/reference/cassiopeia.data.sample_bootstrap_allele_tables.rst +++ /dev/null @@ -1,6 +0,0 @@ -cassiopeia.data.sample\_bootstrap\_allele\_tables -================================================= - -.. currentmodule:: cassiopeia.data - -.. autofunction:: sample_bootstrap_allele_tables \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.data.sample_bootstrap_character_matrices.rst b/docs/api/reference/cassiopeia.data.sample_bootstrap_character_matrices.rst deleted file mode 100644 index 45ba28e1..00000000 --- a/docs/api/reference/cassiopeia.data.sample_bootstrap_character_matrices.rst +++ /dev/null @@ -1,6 +0,0 @@ -cassiopeia.data.sample\_bootstrap\_character\_matrices -====================================================== - -.. currentmodule:: cassiopeia.data - -.. autofunction:: sample_bootstrap_character_matrices \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.data.to_newick.rst b/docs/api/reference/cassiopeia.data.to_newick.rst deleted file mode 100644 index 6b0ff7e3..00000000 --- a/docs/api/reference/cassiopeia.data.to_newick.rst +++ /dev/null @@ -1,6 +0,0 @@ -cassiopeia.data.to\_newick -========================== - -.. currentmodule:: cassiopeia.data - -.. autofunction:: to_newick \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.pl.upload_and_export_itol.rst b/docs/api/reference/cassiopeia.pl.upload_and_export_itol.rst deleted file mode 100644 index a8e70af8..00000000 --- a/docs/api/reference/cassiopeia.pl.upload_and_export_itol.rst +++ /dev/null @@ -1,6 +0,0 @@ -cassiopeia.pl.upload\_and\_export\_itol -======================================= - -.. currentmodule:: cassiopeia.pl - -.. autofunction:: upload_and_export_itol \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.pp.align_sequences.rst b/docs/api/reference/cassiopeia.pp.align_sequences.rst deleted file mode 100644 index 11927b42..00000000 --- a/docs/api/reference/cassiopeia.pp.align_sequences.rst +++ /dev/null @@ -1,6 +0,0 @@ -cassiopeia.pp.align\_sequences -============================== - -.. currentmodule:: cassiopeia.pp - -.. autofunction:: align_sequences \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.pp.call_alleles.rst b/docs/api/reference/cassiopeia.pp.call_alleles.rst deleted file mode 100644 index 9bea85ee..00000000 --- a/docs/api/reference/cassiopeia.pp.call_alleles.rst +++ /dev/null @@ -1,6 +0,0 @@ -cassiopeia.pp.call\_alleles -=========================== - -.. currentmodule:: cassiopeia.pp - -.. autofunction:: call_alleles \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.pp.call_lineage_groups.rst b/docs/api/reference/cassiopeia.pp.call_lineage_groups.rst deleted file mode 100644 index 8df5914d..00000000 --- a/docs/api/reference/cassiopeia.pp.call_lineage_groups.rst +++ /dev/null @@ -1,6 +0,0 @@ -cassiopeia.pp.call\_lineage\_groups -=================================== - -.. currentmodule:: cassiopeia.pp - -.. autofunction:: call_lineage_groups \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.pp.collapse_umis.rst b/docs/api/reference/cassiopeia.pp.collapse_umis.rst deleted file mode 100644 index 020d2ee7..00000000 --- a/docs/api/reference/cassiopeia.pp.collapse_umis.rst +++ /dev/null @@ -1,6 +0,0 @@ -cassiopeia.pp.collapse\_umis -============================ - -.. currentmodule:: cassiopeia.pp - -.. autofunction:: collapse_umis \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.pp.compute_empirical_indel_priors.rst b/docs/api/reference/cassiopeia.pp.compute_empirical_indel_priors.rst deleted file mode 100644 index 1b0f4e5c..00000000 --- a/docs/api/reference/cassiopeia.pp.compute_empirical_indel_priors.rst +++ /dev/null @@ -1,6 +0,0 @@ -cassiopeia.pp.compute\_empirical\_indel\_priors -=============================================== - -.. currentmodule:: cassiopeia.pp - -.. autofunction:: compute_empirical_indel_priors \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.pp.convert_alleletable_to_character_matrix.rst b/docs/api/reference/cassiopeia.pp.convert_alleletable_to_character_matrix.rst deleted file mode 100644 index 5e11c83b..00000000 --- a/docs/api/reference/cassiopeia.pp.convert_alleletable_to_character_matrix.rst +++ /dev/null @@ -1,6 +0,0 @@ -cassiopeia.pp.convert\_alleletable\_to\_character\_matrix -========================================================= - -.. currentmodule:: cassiopeia.pp - -.. autofunction:: convert_alleletable_to_character_matrix \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.pp.convert_alleletable_to_lineage_profile.rst b/docs/api/reference/cassiopeia.pp.convert_alleletable_to_lineage_profile.rst deleted file mode 100644 index 370d0832..00000000 --- a/docs/api/reference/cassiopeia.pp.convert_alleletable_to_lineage_profile.rst +++ /dev/null @@ -1,6 +0,0 @@ -cassiopeia.pp.convert\_alleletable\_to\_lineage\_profile -======================================================== - -.. currentmodule:: cassiopeia.pp - -.. autofunction:: convert_alleletable_to_lineage_profile \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.pp.convert_lineage_profile_to_character_matrix.rst b/docs/api/reference/cassiopeia.pp.convert_lineage_profile_to_character_matrix.rst deleted file mode 100644 index 893a4f42..00000000 --- a/docs/api/reference/cassiopeia.pp.convert_lineage_profile_to_character_matrix.rst +++ /dev/null @@ -1,6 +0,0 @@ -cassiopeia.pp.convert\_lineage\_profile\_to\_character\_matrix -============================================================== - -.. currentmodule:: cassiopeia.pp - -.. autofunction:: convert_lineage_profile_to_character_matrix \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.pp.error_correct_umis.rst b/docs/api/reference/cassiopeia.pp.error_correct_umis.rst deleted file mode 100644 index 95101d0d..00000000 --- a/docs/api/reference/cassiopeia.pp.error_correct_umis.rst +++ /dev/null @@ -1,6 +0,0 @@ -cassiopeia.pp.error\_correct\_umis -================================== - -.. currentmodule:: cassiopeia.pp - -.. autofunction:: error_correct_umis \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.pp.filter_cells.rst b/docs/api/reference/cassiopeia.pp.filter_cells.rst deleted file mode 100644 index db1c5704..00000000 --- a/docs/api/reference/cassiopeia.pp.filter_cells.rst +++ /dev/null @@ -1,6 +0,0 @@ -cassiopeia.pp.filter\_cells -=========================== - -.. currentmodule:: cassiopeia.pp - -.. autofunction:: filter_cells \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.pp.filter_molecule_table.rst b/docs/api/reference/cassiopeia.pp.filter_molecule_table.rst deleted file mode 100644 index d062d345..00000000 --- a/docs/api/reference/cassiopeia.pp.filter_molecule_table.rst +++ /dev/null @@ -1,6 +0,0 @@ -cassiopeia.pp.filter\_molecule\_table -===================================== - -.. currentmodule:: cassiopeia.pp - -.. autofunction:: filter_molecule_table \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.pp.filter_umis.rst b/docs/api/reference/cassiopeia.pp.filter_umis.rst deleted file mode 100644 index 962eb79c..00000000 --- a/docs/api/reference/cassiopeia.pp.filter_umis.rst +++ /dev/null @@ -1,6 +0,0 @@ -cassiopeia.pp.filter\_umis -========================== - -.. currentmodule:: cassiopeia.pp - -.. autofunction:: filter_umis \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.pp.resolve_umi_sequence.rst b/docs/api/reference/cassiopeia.pp.resolve_umi_sequence.rst deleted file mode 100644 index a1a1ddc6..00000000 --- a/docs/api/reference/cassiopeia.pp.resolve_umi_sequence.rst +++ /dev/null @@ -1,6 +0,0 @@ -cassiopeia.pp.resolve\_umi\_sequence -==================================== - -.. currentmodule:: cassiopeia.pp - -.. autofunction:: resolve_umi_sequence \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.solver.HybridSolver.rst b/docs/api/reference/cassiopeia.solver.HybridSolver.rst deleted file mode 100644 index 9646d98f..00000000 --- a/docs/api/reference/cassiopeia.solver.HybridSolver.rst +++ /dev/null @@ -1,13 +0,0 @@ -cassiopeia.solver.HybridSolver -============================== - -.. currentmodule:: cassiopeia.solver - -.. autoclass:: HybridSolver - :members: - :undoc-members: - - .. rubric:: Methods - - .. autoautosummary:: HybridSolver - :methods: \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.solver.ILPSolver.rst b/docs/api/reference/cassiopeia.solver.ILPSolver.rst deleted file mode 100644 index eebcd985..00000000 --- a/docs/api/reference/cassiopeia.solver.ILPSolver.rst +++ /dev/null @@ -1,13 +0,0 @@ -cassiopeia.solver.ILPSolver -=========================== - -.. currentmodule:: cassiopeia.solver - -.. autoclass:: ILPSolver - :members: - :undoc-members: - - .. rubric:: Methods - - .. autoautosummary:: ILPSolver - :methods: \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.solver.MaxCutGreedySolver.rst b/docs/api/reference/cassiopeia.solver.MaxCutGreedySolver.rst deleted file mode 100644 index 726bb6a1..00000000 --- a/docs/api/reference/cassiopeia.solver.MaxCutGreedySolver.rst +++ /dev/null @@ -1,13 +0,0 @@ -cassiopeia.solver.MaxCutGreedySolver -==================================== - -.. currentmodule:: cassiopeia.solver - -.. autoclass:: MaxCutGreedySolver - :members: - :undoc-members: - - .. rubric:: Methods - - .. autoautosummary:: MaxCutGreedySolver - :methods: \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.solver.MaxCutSolver.rst b/docs/api/reference/cassiopeia.solver.MaxCutSolver.rst deleted file mode 100644 index 218c45fe..00000000 --- a/docs/api/reference/cassiopeia.solver.MaxCutSolver.rst +++ /dev/null @@ -1,13 +0,0 @@ -cassiopeia.solver.MaxCutSolver -============================== - -.. currentmodule:: cassiopeia.solver - -.. autoclass:: MaxCutSolver - :members: - :undoc-members: - - .. rubric:: Methods - - .. autoautosummary:: MaxCutSolver - :methods: \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.solver.NeighborJoiningSolver.rst b/docs/api/reference/cassiopeia.solver.NeighborJoiningSolver.rst deleted file mode 100644 index 17c83adf..00000000 --- a/docs/api/reference/cassiopeia.solver.NeighborJoiningSolver.rst +++ /dev/null @@ -1,13 +0,0 @@ -cassiopeia.solver.NeighborJoiningSolver -======================================= - -.. currentmodule:: cassiopeia.solver - -.. autoclass:: NeighborJoiningSolver - :members: - :undoc-members: - - .. rubric:: Methods - - .. autoautosummary:: NeighborJoiningSolver - :methods: \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.solver.PercolationSolver.rst b/docs/api/reference/cassiopeia.solver.PercolationSolver.rst deleted file mode 100644 index 8ddcc3c7..00000000 --- a/docs/api/reference/cassiopeia.solver.PercolationSolver.rst +++ /dev/null @@ -1,13 +0,0 @@ -cassiopeia.solver.PercolationSolver -=================================== - -.. currentmodule:: cassiopeia.solver - -.. autoclass:: PercolationSolver - :members: - :undoc-members: - - .. rubric:: Methods - - .. autoautosummary:: PercolationSolver - :methods: \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.solver.SharedMutationJoiningSolver.rst b/docs/api/reference/cassiopeia.solver.SharedMutationJoiningSolver.rst deleted file mode 100644 index 2b8081c3..00000000 --- a/docs/api/reference/cassiopeia.solver.SharedMutationJoiningSolver.rst +++ /dev/null @@ -1,13 +0,0 @@ -cassiopeia.solver.SharedMutationJoiningSolver -============================================= - -.. currentmodule:: cassiopeia.solver - -.. autoclass:: SharedMutationJoiningSolver - :members: - :undoc-members: - - .. rubric:: Methods - - .. autoautosummary:: SharedMutationJoiningSolver - :methods: \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.solver.SpectralGreedySolver.rst b/docs/api/reference/cassiopeia.solver.SpectralGreedySolver.rst deleted file mode 100644 index f9f4cfb6..00000000 --- a/docs/api/reference/cassiopeia.solver.SpectralGreedySolver.rst +++ /dev/null @@ -1,13 +0,0 @@ -cassiopeia.solver.SpectralGreedySolver -====================================== - -.. currentmodule:: cassiopeia.solver - -.. autoclass:: SpectralGreedySolver - :members: - :undoc-members: - - .. rubric:: Methods - - .. autoautosummary:: SpectralGreedySolver - :methods: \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.solver.SpectralSolver.rst b/docs/api/reference/cassiopeia.solver.SpectralSolver.rst deleted file mode 100644 index 7d152bf9..00000000 --- a/docs/api/reference/cassiopeia.solver.SpectralSolver.rst +++ /dev/null @@ -1,13 +0,0 @@ -cassiopeia.solver.SpectralSolver -================================ - -.. currentmodule:: cassiopeia.solver - -.. autoclass:: SpectralSolver - :members: - :undoc-members: - - .. rubric:: Methods - - .. autoautosummary:: SpectralSolver - :methods: \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.solver.UPGMASolver.rst b/docs/api/reference/cassiopeia.solver.UPGMASolver.rst deleted file mode 100644 index 691ac333..00000000 --- a/docs/api/reference/cassiopeia.solver.UPGMASolver.rst +++ /dev/null @@ -1,13 +0,0 @@ -cassiopeia.solver.UPGMASolver -============================= - -.. currentmodule:: cassiopeia.solver - -.. autoclass:: UPGMASolver - :members: - :undoc-members: - - .. rubric:: Methods - - .. autoautosummary:: UPGMASolver - :methods: \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.solver.VanillaGreedySolver.rst b/docs/api/reference/cassiopeia.solver.VanillaGreedySolver.rst deleted file mode 100644 index 7e761f6f..00000000 --- a/docs/api/reference/cassiopeia.solver.VanillaGreedySolver.rst +++ /dev/null @@ -1,13 +0,0 @@ -cassiopeia.solver.VanillaGreedySolver -===================================== - -.. currentmodule:: cassiopeia.solver - -.. autoclass:: VanillaGreedySolver - :members: - :undoc-members: - - .. rubric:: Methods - - .. autoautosummary:: VanillaGreedySolver - :methods: \ No newline at end of file diff --git a/docs/api/tools.rst b/docs/api/tools.rst index 11f6b212..0ded5a5e 100644 --- a/docs/api/tools.rst +++ b/docs/api/tools.rst @@ -14,10 +14,39 @@ Small-Parsimony .. autosummary:: :toctree: reference/ - - tl.compute_cophenetic_correlation - tl.compute_expansion_pvalues - tl.compute_morans_i + tl.fitch_count tl.fitch_hartigan tl.score_small_parsimony + +Branch Length Estimation (BLE) +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autosummary:: + :toctree: reference/ + + tl.IIDExponentialBayesian + tl.IIDExponentialMLE + +Autocorrelation +~~~~~~~~~~~~~~~~~~~ + +.. autosummary:: + :toctree: reference/ + + tl.compute_morans_i + +Coupling +~~~~~~~~~~~ + +.. autosummary:: + :toctree: reference/ + + tl.compute_evolutionary_coupling + +Topology +~~~~~~~~~~~~~~~~~~~ +.. autosummary:: + :toctree: reference/ + + tl.compute_expansion_pvalues \ No newline at end of file	Implement Utilities from KP-Tracer MS Implement utilities from recent [KP-Tracer manuscript](https://www.biorxiv.org/content/10.1101/2021.10.12.464111v1) into the Cassiopeia codebase. Specifically: - Expansion index - Phylogenetic coupling - Phylotime		2021-10-27T00:04:06	[]	[]
YosefLab/Cassiopeia	YosefLab__Cassiopeia-153	3002617a00e1281ef073a6f17b6dd55c3337bdad	diff --git a/cassiopeia/data/utilities.py b/cassiopeia/data/utilities.py index ba503d94..eaa06562 100755 --- a/cassiopeia/data/utilities.py +++ b/cassiopeia/data/utilities.py @@ -163,8 +163,7 @@ def compute_dissimilarity_map( # in a partial, which raises a TypeError when trying to numbaize. except TypeError: warnings.warn( - "Failed to numbaize dissimilarity function. " - "Falling back to Python.", + "Failed to numbaize dissimilarity function. Falling back to Python.", CassiopeiaTreeWarning, ) numbaize = False @@ -309,10 +308,8 @@ def sample_bootstrap_allele_tables( allele_table ) - lineage_profile = ( - preprocessing_utilities.convert_alleletable_to_lineage_profile( - allele_table, cut_sites - ) + lineage_profile = preprocessing_utilities.convert_alleletable_to_lineage_profile( + allele_table, cut_sites ) intbcs = allele_table["intBC"].unique() @@ -375,6 +372,7 @@ def resolve_most_abundant(state: Tuple[int, ...]) -> int: [state for state, count in most_common if count == most_common[0][1]] ) + def compute_phylogenetic_weight_matrix( tree: CassiopeiaTree, inverse: bool = False, @@ -399,10 +397,10 @@ def compute_phylogenetic_weight_matrix( An NxN phylogenetic weight matrix """ N = tree.n_cell - W = pd.DataFrame(np.zeros((N , N)), index=tree.leaves, columns=tree.leaves) + W = pd.DataFrame(np.zeros((N, N)), index=tree.leaves, columns=tree.leaves) for leaf1 in tree.leaves: - + distances = tree.get_distances(leaf1, leaves_only=True) for leaf2, _d in distances.items(): @@ -412,5 +410,5 @@ def compute_phylogenetic_weight_matrix( W.loc[leaf1, leaf2] = W.loc[leaf2, leaf1] = _d np.fill_diagonal(W.values, 0) - - return W \ No newline at end of file + + return W diff --git a/cassiopeia/tools/__init__.py b/cassiopeia/tools/__init__.py index 4c39eab6..792a38ce 100644 --- a/cassiopeia/tools/__init__.py +++ b/cassiopeia/tools/__init__.py @@ -3,4 +3,4 @@ from .autocorrelation import compute_morans_i from .branch_length_estimator import IIDExponentialBayesian, IIDExponentialMLE from .small_parsimony import fitch_count, fitch_hartigan, score_small_parsimony -from .topology import compute_expansion_pvalues \ No newline at end of file +from .topology import compute_cophenetic_correlation, compute_expansion_pvalues diff --git a/cassiopeia/tools/topology.py b/cassiopeia/tools/topology.py index 0549559e..39f5fc9f 100644 --- a/cassiopeia/tools/topology.py +++ b/cassiopeia/tools/topology.py @@ -2,14 +2,17 @@ Utilities to assess topological properties of a phylogeny, such as balance and expansion. """ -from typing import Union +from typing import Callable, Dict, Optional, Tuple, Union import math import numpy as np import pandas as pd +from scipy import spatial, stats -from cassiopeia.data import CassiopeiaTree + +from cassiopeia.data import CassiopeiaTree, compute_phylogenetic_weight_matrix from cassiopeia.mixins import CassiopeiaError +from cassiopeia.solver import dissimilarity_functions def compute_expansion_pvalues( @@ -55,14 +58,14 @@ def compute_expansion_pvalues( tree = tree.copy() if copy else tree # instantiate attributes - _depths = {} + _depths = {} for node in tree.depth_first_traverse_nodes(postorder=False): tree.set_attribute(node, "expansion_pvalue", 1.0) if tree.is_root(node): _depths[node] = 0 else: - _depths[node] = (_depths[tree.parent(node)] + 1) + _depths[node] = _depths[tree.parent(node)] + 1 for node in tree.depth_first_traverse_nodes(postorder=False): @@ -73,7 +76,7 @@ def compute_expansion_pvalues( if len(tree.leaves_in_subtree(c)) < min_clade_size: continue - + depth = _depths[c] if depth < min_depth: continue @@ -83,13 +86,79 @@ def compute_expansion_pvalues( # this value below is a simplification of the quantity: # sum[simple_coalescent_probability(n, b2, k) for \ # b2 in range(b, n - k + 2)] - p = nCk(n-b, k-1) / nCk(n-1, k-1) + p = nCk(n - b, k - 1) / nCk(n - 1, k - 1) tree.set_attribute(c, "expansion_pvalue", p) return tree if copy else None +def compute_cophenetic_correlation( + tree: CassiopeiaTree, + weights: Optional[pd.DataFrame] = None, + dissimilarity_map: Optional[pd.DataFrame] = None, + dissimilarity_function: Optional[ + Callable[[np.array, np.array, int, Dict[int, Dict[int, float]]], float] + ] = dissimilarity_functions.weighted_hamming_distance, +) -> Tuple[float, float]: + """Computes the cophenetic correlation of a lineage. + + Computes the cophenetic correlation of a lineage, which is defined as the + Pearson correlation between the phylogenetic distance and dissimilarity + between characters. + + If neither weight matrix nor the dissimilarity map are precomputed, then + this function will run in O(mn^2 + n^2logn + n^2) time, as the dissimilarity + map will take O(mn^2) time, the phylogenetic distance will take O(n^2 logn) + time, and the Pearson correlation will take O(n^2) time since it must + compare n^2 entries (n = number of leaves; m = number of characters). + + Args: + tree: CassiopeiaTree + weights: Phylogenetic weights matrix. If this is not specified, invokes + `cas.data.compute_phylogenetic_weight_matrix` + dissimilarity_map: Dissimilarity matrix between samples. If this is not + specified, then `tree.compute_dissimilarity_map` will be called. + dissimilarity_function: Dissimilarity function to use. If dissimilarity + map is not passed in, and one does not already exist in the + CassiopeiaTree, then this function will be used to compute the + dissimilarities between samples. + + Returns: + The cophenetic correlation value and significance for the tree. + """ + + # set phylogenetic weight matrix + W = ( + compute_phylogenetic_weight_matrix(tree) + if (weights is None) + else weights + ) + + # set dissimilarity map + D = ( + tree.get_dissimilarity_map() + if (dissimilarity_map is None) + else dissimilarity_map + ) + if D is None: + tree.compute_dissimilarity_map( + dissimilarity_function=dissimilarity_function + ) + D = tree.get_dissimilarity_map() + + # align matrices + cells = tree.leaves + W = W.loc[cells, cells] + D = D.loc[cells, cells] + + # convert to condensed distance matrices + Wp = spatial.distance.squareform(W) + Dp = spatial.distance.squareform(D) + + return stats.pearsonr(Wp, Dp) + + def simple_coalescent_probability(n: int, b: int, k: int) -> float: """Simple coalescent probability of imbalance. diff --git a/docs/api/tools.rst b/docs/api/tools.rst index 7352008d..11f6b212 100644 --- a/docs/api/tools.rst +++ b/docs/api/tools.rst @@ -15,6 +15,7 @@ Small-Parsimony .. autosummary:: :toctree: reference/ + tl.compute_cophenetic_correlation tl.compute_expansion_pvalues tl.compute_morans_i tl.fitch_count	Cophenetic Distance Implement utility to compute cophenetic distance for a tree. The cophenetic distance is the correlation between phylogenetic distance and some dissimilarity map between samples' character information.		2021-10-26T18:21:08	[]	[]
YosefLab/Cassiopeia	YosefLab__Cassiopeia-150	15dc005666b0c9da627145df07a30b162136e3a7	diff --git a/cassiopeia/tools/__init__.py b/cassiopeia/tools/__init__.py index e4549f00..4c39eab6 100644 --- a/cassiopeia/tools/__init__.py +++ b/cassiopeia/tools/__init__.py @@ -3,3 +3,4 @@ from .autocorrelation import compute_morans_i from .branch_length_estimator import IIDExponentialBayesian, IIDExponentialMLE from .small_parsimony import fitch_count, fitch_hartigan, score_small_parsimony +from .topology import compute_expansion_pvalues \ No newline at end of file diff --git a/cassiopeia/tools/topology.py b/cassiopeia/tools/topology.py new file mode 100644 index 00000000..0549559e --- /dev/null +++ b/cassiopeia/tools/topology.py @@ -0,0 +1,126 @@ +""" +Utilities to assess topological properties of a phylogeny, such as balance +and expansion. +""" +from typing import Union + +import math +import numpy as np +import pandas as pd + +from cassiopeia.data import CassiopeiaTree +from cassiopeia.mixins import CassiopeiaError + + +def compute_expansion_pvalues( + tree: CassiopeiaTree, + min_clade_size: int = 10, + min_depth: int = 1, + copy: bool = False, +) -> Union[CassiopeiaTree, None]: + """Call expansion pvalues on a tree. + + Uses the methodology described in Yang, Jones et al, BioRxiv (2021) to + assess the expansion probability of a given subclade of a phylogeny. + Mathematical treatment of the coalescent probability is described in + Griffiths and Tavare, Stochastic Models (1998). + + The probability computed corresponds to the probability that, under a simple + neutral coalescent model, a given subclade contains the observed number of + cells; in other words, a one-sided p-value. Often, if the probability is + less than some threshold (e.g., 0.05), this might indicate that there exists + some subclade under this node that to which this expansion probability can + be attributed (i.e. the null hypothesis that the subclade is undergoing + neutral drift can be rejected). + + This function will add an attribute "expansion_pvalue" to the tree, and + return None unless :param:`copy` is set to True. + + On a typical balanced tree, this function will perform in O(n log n) time, + but can be up to O(n^3) on highly unbalanced trees. A future endeavor may + be to impelement the function in O(n) time. + + Args: + tree: CassiopeiaTree + min_clade_size: Minimum number of leaves in a subtree to be considered. + min_depth: Minimum depth of clade to be considered. Depth is measured + in number of nodes from the root, not branch lengths. + copy: Return copy. + + Returns: + If copy is set to False, returns the tree with attributes added + in place. Else, returns a new CassiopeiaTree. + """ + + tree = tree.copy() if copy else tree + + # instantiate attributes + _depths = {} + for node in tree.depth_first_traverse_nodes(postorder=False): + tree.set_attribute(node, "expansion_pvalue", 1.0) + + if tree.is_root(node): + _depths[node] = 0 + else: + _depths[node] = (_depths[tree.parent(node)] + 1) + + for node in tree.depth_first_traverse_nodes(postorder=False): + + n = len(tree.leaves_in_subtree(node)) + + k = len(tree.children(node)) + for c in tree.children(node): + + if len(tree.leaves_in_subtree(c)) < min_clade_size: + continue + + depth = _depths[c] + if depth < min_depth: + continue + + b = len(tree.leaves_in_subtree(c)) + + # this value below is a simplification of the quantity: + # sum[simple_coalescent_probability(n, b2, k) for \ + # b2 in range(b, n - k + 2)] + p = nCk(n-b, k-1) / nCk(n-1, k-1) + + tree.set_attribute(c, "expansion_pvalue", p) + + return tree if copy else None + + +def simple_coalescent_probability(n: int, b: int, k: int) -> float: + """Simple coalescent probability of imbalance. + + Assuming a simple coalescent model, compute the probability that a given + lineage has exactly b samples, given there are n cells and k lineages + overall. + + Args: + n: Number of leaves in subtree + b: Number of leaves in one lineage + k: Number of lineages + Returns: + Probability of observing b leaves on one lineage in a tree of n total + leaves + """ + return nCk(n - b - 1, k - 2) / nCk(n - 1, k - 1) + + +def nCk(n: int, k: int) -> float: + """Compute the quantity n choose k. + + Args: + n: Number of items total. + k: Number of items to choose. + + Returns: + The number of ways to choose k items from n. + """ + + if k > n: + raise CassiopeiaError("Argument k cannot be larger than n.") + + f = math.factorial + return f(n) // f(k) // f(n - k) diff --git a/docs/api/tools.rst b/docs/api/tools.rst index 3347e481..7352008d 100644 --- a/docs/api/tools.rst +++ b/docs/api/tools.rst @@ -15,6 +15,7 @@ Small-Parsimony .. autosummary:: :toctree: reference/ + tl.compute_expansion_pvalues tl.compute_morans_i tl.fitch_count tl.fitch_hartigan	Implement Utilities from KP-Tracer MS Implement utilities from recent [KP-Tracer manuscript](https://www.biorxiv.org/content/10.1101/2021.10.12.464111v1) into the Cassiopeia codebase. Specifically: - Expansion index - Phylogenetic coupling - Phylotime		2021-10-25T22:05:36	[]	[]
YosefLab/Cassiopeia	YosefLab__Cassiopeia-133	670dd12d949bf466f67c1117e5f6e912ae3c860d	diff --git a/cassiopeia/preprocess/lineage_utils.py b/cassiopeia/preprocess/lineage_utils.py index e5f9ffb4..b0739155 100755 --- a/cassiopeia/preprocess/lineage_utils.py +++ b/cassiopeia/preprocess/lineage_utils.py @@ -388,9 +388,11 @@ def filtered_lineage_group_to_allele_table( Returns: final_df: A final processed DataFrame with indel information """ - final_df = pd.concat(filtered_lgs, sort=True) + if "lineageGrp" not in final_df.columns: + final_df["lineageGrp"] = 1 + grouping = [] for i in final_df.columns: if bool(re.search(r"r\d", i)): @@ -401,10 +403,6 @@ def filtered_lineage_group_to_allele_table( {"UMI": "count", "readCount": "sum"} ) - final_df["Sample"] = final_df.apply( - lambda x: x.cellBC.split(".")[0], axis=1 - ) - return final_df diff --git a/cassiopeia/preprocess/pipeline.py b/cassiopeia/preprocess/pipeline.py index d6ce0798..45f9b7c8 100755 --- a/cassiopeia/preprocess/pipeline.py +++ b/cassiopeia/preprocess/pipeline.py @@ -3,6 +3,7 @@ data into character matrices ready for phylogenetic inference. This file is mainly invoked by cassiopeia_preprocess.py. """ +import warnings from functools import partial import os @@ -21,6 +22,7 @@ from typing_extensions import Literal from cassiopeia.mixins import logger, PreprocessError +from cassiopeia.mixins.warnings import PreprocessWarning from cassiopeia.preprocess import ( alignment_utilities, constants, @@ -487,8 +489,10 @@ def align_sequences( try: from skbio import alignment except ModuleNotFoundError: - raise PreprocessError("Scikit-bio is not installed. Try pip-installing " - " first and then re-running this function.") + raise PreprocessError( + "Scikit-bio is not installed. Try pip-installing " + " first and then re-running this function." + ) if (ref is None) == (ref_filepath is None): raise PreprocessError( @@ -635,6 +639,17 @@ def call_alleles( alignments.reset_index(inplace=True) + # check cut-sites and raise a warning if any missing data is detected + cutsites = utilities.get_default_cut_site_columns(alignments) + if np.any((alignments[cutsites] == "").sum(axis=0) > 0): + warnings.warn( + "Detected missing data in alleles. You might" + " consider re-running align_sequences with a" + " lower gap-open penalty, or using a separate" + " alignment strategy.", + PreprocessWarning, + ) + return alignments diff --git a/cassiopeia/preprocess/utilities.py b/cassiopeia/preprocess/utilities.py index ddf726da..39a50d76 100755 --- a/cassiopeia/preprocess/utilities.py +++ b/cassiopeia/preprocess/utilities.py @@ -384,6 +384,7 @@ def convert_alleletable_to_character_matrix( alleletable: pd.DataFrame, ignore_intbcs: List[str] = [], allele_rep_thresh: float = 1.0, + missing_data_allele: Optional[str] = None, missing_data_state: int = -1, mutation_priors: Optional[pd.DataFrame] = None, cut_sites: Optional[List[str]] = None, @@ -404,6 +405,9 @@ def convert_alleletable_to_character_matrix( ignore_intbcs: A set of intBCs to ignore allele_rep_thresh: A threshold for removing target sites that have an allele represented by this proportion + missing_data_allele: Value in the allele table that indicates that the + cut-site is missing. This will be converted into + ``missing_data_state`` missing_data_state: A state to use for missing data. mutation_priors: A table storing the prior probability of a mutation occurring. This table is used to create a character matrix-specific @@ -416,8 +420,8 @@ def convert_alleletable_to_character_matrix( effect if there are no allele conflicts. Defaults to True. Returns: - A character matrix, a probability dictionary, and a dictionary mapping - states to the original mutation. + A character matrix, a probability dictionary, and a dictionary mapping + states to the original mutation. """ if cut_sites is None: cut_sites = get_default_cut_site_columns(alleletable) @@ -489,6 +493,11 @@ def convert_alleletable_to_character_matrix( if state == "NONE" or "None" in state: transformed_states.append(0) + elif ( + missing_data_allele is not None + and state == missing_data_allele + ): + transformed_states.append(missing_data_state) else: if state in allele_counter[c]: transformed_states.append(allele_counter[c][state]) @@ -614,6 +623,7 @@ def convert_alleletable_to_lineage_profile( def convert_lineage_profile_to_character_matrix( lineage_profile: pd.DataFrame, indel_priors: Optional[pd.DataFrame] = None, + missing_allele_indicator: Optional[str] = None, missing_state_indicator: int = -1, ) -> Tuple[ pd.DataFrame, Dict[int, Dict[int, float]], Dict[int, Dict[int, str]] @@ -633,6 +643,8 @@ def convert_lineage_profile_to_character_matrix( Args: lineage_profile: Lineage profile indel_priors: Dataframe mapping indels to prior probabilities + missing_allele_indicator: An allele that is being used to represent + missing data. missing_state_indicator: State to indicate missing data Returns: @@ -643,7 +655,13 @@ def convert_lineage_profile_to_character_matrix( prior_probs = defaultdict(dict) indel_to_charstate = defaultdict(dict) + lineage_profile = lineage_profile.copy() + lineage_profile = lineage_profile.fillna("Missing").copy() + if missing_allele_indicator: + lineage_profile.replace( + {missing_allele_indicator: "Missing"}, inplace=True + ) samples = [] diff --git a/docs/api/tools.rst b/docs/api/tools.rst index 257618fc..c162ecf5 100644 --- a/docs/api/tools.rst +++ b/docs/api/tools.rst @@ -15,5 +15,6 @@ Small-Parsimony .. autosummary:: :toctree: reference/ + tl.fitch_count tl.fitch_hartigan tl.score_small_parsimony	Global Alignment in Cassiopeia Preprocessing We've found that the local alignment strategy used in our Cassiopeia preprocessing pipeline is not ideal for some technologies like GESTALT. We'd like to implement a global alignment option in `cas.pp.align_sequences` as was described in the original GESTALT paper ([McKenna et al, 2016](https://science.sciencemag.org/content/353/6298/aaf7907)).		2021-08-06T22:21:30	[]	[]
VNG-Realisatie/vng-api-common	VNG-Realisatie__vng-api-common-187	484d1fce7fd300dd336edce8ac88817f1e0f45a5	diff --git a/vng_api_common/notifications/api/serializers.py b/vng_api_common/notifications/api/serializers.py index dd8a12ea..0772ed8d 100644 --- a/vng_api_common/notifications/api/serializers.py +++ b/vng_api_common/notifications/api/serializers.py @@ -50,6 +50,7 @@ class NotificatieSerializer(serializers.Serializer): label=_("kenmerk"), max_length=1000, help_text=_("Een waarde behorende bij de sleutel."), + allow_blank=True, ), help_text=_( "Mapping van kenmerken (sleutel/waarde) van de notificatie. De "	Fix/lege waarde kenmerken notificatie Kenmerken in notificaties can sometimes have values that are empty (i.e., ""). Currently this isn't allowed but it should be.	# [Codecov](https://codecov.io/gh/VNG-Realisatie/vng-api-common/pull/186?src=pr&el=h1&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=VNG+Realisatie) Report > Merging [#186](https://codecov.io/gh/VNG-Realisatie/vng-api-common/pull/186?src=pr&el=desc&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=VNG+Realisatie) (c16087d) into [stable/1.0.x](https://codecov.io/gh/VNG-Realisatie/vng-api-common/commit/8c4999cc01f52baa5bed3f933d2b05192c27d82a?el=desc&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=VNG+Realisatie) (8c4999c) will not change coverage. > The diff coverage is `n/a`. [![Impacted file tree graph](https://codecov.io/gh/VNG-Realisatie/vng-api-common/pull/186/graphs/tree.svg?width=650&height=150&src=pr&token=yMrjUJVnq9&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=VNG+Realisatie)](https://codecov.io/gh/VNG-Realisatie/vng-api-common/pull/186?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=VNG+Realisatie) ```diff @@ Coverage Diff @@ ## stable/1.0.x #186 +/- ## ============================================== Coverage 41.94% 41.94% ============================================== Files 79 79 Lines 3350 3350 Branches 451 613 +162 ============================================== Hits 1405 1405 Misses 1886 1886 Partials 59 59 ``` \| [Impacted Files](https://codecov.io/gh/VNG-Realisatie/vng-api-common/pull/186?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=VNG+Realisatie) \| Coverage Δ \| \| \|---\|---\|---\| \| [vng\_api\_common/notifications/api/serializers.py](https://codecov.io/gh/VNG-Realisatie/vng-api-common/pull/186/diff?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=VNG+Realisatie#diff-dm5nX2FwaV9jb21tb24vbm90aWZpY2F0aW9ucy9hcGkvc2VyaWFsaXplcnMucHk=) \| `100.00% <ø> (ø)` \| \| ------ [Continue to review full report at Codecov](https://codecov.io/gh/VNG-Realisatie/vng-api-common/pull/186?src=pr&el=continue&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=VNG+Realisatie). > Legend - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta?utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=VNG+Realisatie) > `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data` > Powered by [Codecov](https://codecov.io/gh/VNG-Realisatie/vng-api-common/pull/186?src=pr&el=footer&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=VNG+Realisatie). Last update [8c4999c...c16087d](https://codecov.io/gh/VNG-Realisatie/vng-api-common/pull/186?src=pr&el=lastupdated&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=VNG+Realisatie). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments?utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=VNG+Realisatie).	2021-12-01T15:13:14	[]	[]
VNG-Realisatie/vng-api-common	VNG-Realisatie__vng-api-common-180	d7969a1a4ee52ef3573e5be624b8c8d198ea6a33	diff --git a/setup.cfg b/setup.cfg index 56c7bcfe..7ccc7030 100644 --- a/setup.cfg +++ b/setup.cfg @@ -43,7 +43,7 @@ install_requires = django-rest-framework-condition drf-yasg>=1.20.0 drf-nested-routers>=0.93.3 - gemma-zds-client>=0.13.0 + gemma-zds-client>=0.14.0 iso-639 isodate oyaml	Support PyJWT 2.0.0+ There's a breaking change . `encode` and `decode` no longer operate on bytestrings, but on regular strings now. It's been dealt with in gemma-zds-client already.		2021-08-30T20:50:27	[]	[]

discsim/frank

discsim__frank-173

0f9f073a0f4ca947ac7b43c891c838488fff67ee

diff --git a/frank/radial_fitters.py b/frank/radial_fitters.py index e54bd503..d5bd58da 100644 --- a/frank/radial_fitters.py +++ b/frank/radial_fitters.py @@ -320,7 +320,7 @@ def predict(self, u, v, I=None, geometry=None, block_size=10**5): geometry = self._geometry if geometry is not None: - u, v = self._geometry.deproject(u, v) + u, v = geometry.deproject(u, v) q = np.hypot(u, v) V = self._predict(q, I, block_size)

Predict using a different geometry Hi! I think that there is a small error when trying to predict the visibilities using a different geometry. Right now the code is: ``` if geometry is not None: u, v = self._geometry.deproject(u, v) ``` but I think that the correct one should be: ``` if geometry is not None: u, v = geometry.deproject(u, v) ``` Thanks!!

Hi @anibalsierram Thanks! I agree this a bug, we will fix it ASAP.

2021-11-29T17:02:59

0.0

[]

discsim/frank

discsim__frank-161

670ebdd823060a6bd26790d8cc81d3d9c16ff34d

diff --git a/.circleci/config.yml b/.circleci/config.yml deleted file mode 100644 index 2e710daa..00000000 --- a/.circleci/config.yml +++ /dev/null @@ -1,125 +0,0 @@ -# Python CircleCI 2.0 configuration file -# -# Check https://circleci.com/docs/2.0/language-python/ for more details -# -version: 2.1 - -jobs: - test: - parameters: - python-version: - type: string - docker: - # specify the version you desire here - - image: circleci/python:<<parameters.python-version>> - - # Specify service dependencies here if necessary - # CircleCI maintains a library of pre-built images - # documented at https://circleci.com/docs/2.0/circleci-images/ - # - image: circleci/postgres:9.4 - - working_directory: ~/repo - - steps: - - checkout - - # Download and cache dependencies - - restore_cache: - keys: - - v1-dependencies-{{ checksum "requirements.txt" }} - # fallback to using the latest cache if no exact match is found - - v1-dependencies- - - run: - name: install package - command: | - python3 -m venv venv - . venv/bin/activate - pip install .[test] - - save_cache: - paths: - - ./venv - key: v1-dependencies-{{ checksum "requirements.txt" }} - # run tests - - run: - name: run tests - command: | - . venv/bin/activate - mkdir -p test-reports - py.test --junitxml=test-reports/junit.xml frank/tests.py - - store_test_results: - path: test-reports - destination: test-reports - - docs-build: - docker: - - image: circleci/python:3.7 - steps: - - checkout - - run: - name: Install package - command: | - python3 -m venv venv - . venv/bin/activate - sudo apt-get install pandoc - pip install jupyter_client - pip install ipykernel - python3 -m ipykernel install --user - pip install .[docs-build] .[test] - - run: - name: Build docs - command: | - . venv/bin/activate - cd docs/ && make html - - run: - name: Coverage - command: | - . venv/bin/activate - coverage run --source=frank -m pytest frank/tests.py - coverage report - mkdir -p docs/_build/html/coverage - coverage html -d docs/_build/html/coverage - coverage-badge -f -o docs/_build/html/coverage/badge.svg - - - persist_to_workspace: - root: docs/_build - paths: html - - docs-deploy: - docker: - - image: node:8.10.0 - steps: - - checkout - - attach_workspace: - at: docs/_build - - run: - name: Disable jekyll builds - command: touch docs/_build/html/.nojekyll - - run: - name: Install and configure dependencies - command: | - npm install -g --silent [email protected] - git config user.email "[email protected]" - git config user.name "ci-build" - - add_ssh_keys: - fingerprints: - - "6a:8d:f2:c3:f1:0a:97:9c:86:3b:02:e7:fd:ed:ea:a6" - - run: - name: Deploy docs to gh-pages branch - command: gh-pages --dotfiles --message "[skip ci] Updates" --dist docs/_build/html - -workflows: - version: 2.1 - build: - jobs: - - test: - matrix: - parameters: - python-version: ["3.6", "3.7", "3.8"] - - docs-build - - docs-deploy: - requires: - - test - - docs-build - filters: - branches: - only: master diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md index b7c63512..7539f0d5 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.md +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -1,30 +1,27 @@ --- name: Bug report about: Create a report to help us improve -title: "[BUG] " -labels: bug [e.g.: 'bug', 'enhancement', 'question'] -assignees: '' [Leave blank unless you want to suggest specifically @rbooth200, @jeffjennings and/or @mtazzari.] +title: '' +labels: bug +assignees: '' --- -**Describe the bug** -A clear and concise description of what the bug is. +**Describe the bug -** +1. A clear and concise description of what the bug is. +2. Include the full Traceback of the error. -**To Reproduce** -Steps to reproduce the behavior: -1. provide input parameters (or attach parameters file) -2. report the failing command [e.g., `python -m frank.fit uvtdata.npz`] +**To reproduce -** +1. Include a simple, standalone code snippet that demonstrates the issue. +2. If relevant, provide the input parameters for the frank fit (or attach the .json parameter file). -**Expected behavior** +**Expected behavior -** A clear and concise description of what you expected to happen. -**Screenshots/Logs** -If applicable, add screenshots to help explain your problem. -Even better, paste the full Traceback of the error. +**Your setup -** + - frank version (check with `python -c 'import frank; print(frank.__version__)'`): + - Python version: + - Operating system: -**Desktop (please complete the following information):** - - OS: [e.g. MacOS] - - frank version [run `python -c 'import frank; print(frank.__version__)'` and paste here the output] - -**Additional context** -Add any other context about the problem here (e.g., any peculiarity of the data or of the problem you're trying to solve?) +**Additional context -** +Add any other context about the problem (e.g., any peculiarity of the data or of the problem you're trying to solve?). diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md new file mode 100644 index 00000000..8170920f --- /dev/null +++ b/.github/ISSUE_TEMPLATE/feature_request.md @@ -0,0 +1,20 @@ +--- +name: Feature request +about: Suggest an idea for frank +title: '' +labels: enhancement +assignees: '' + +--- + +**Is your feature request related to a problem? Please describe -** +A clear and concise description of what the problem is. + +**Describe the solution you'd like -** +A description of what you want to happen. + +**Describe alternatives you've considered -** +A description of any alternative solutions or features you've considered. + +**Additional context -** +Add any other context or screenshots about the feature request. diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml new file mode 100644 index 00000000..5d69d36d --- /dev/null +++ b/.github/workflows/docs.yml @@ -0,0 +1,105 @@ +name: docs + +# Run this workflow when a review is requested on a PR that targets the master +# branch, or the PR is closed +on: + pull_request: + types: [review_requested, closed] + branches: [master] + +# Prevent multiple PRs from building/deploying the docs at the same time +concurrency: + group: ${{ github.workflow }} + +jobs: + docs-build: + name: Build docs + runs-on: ubuntu-latest + + steps: + - name: Checkout repo + uses: actions/checkout@v2 + + - name: Set up Python + uses: actions/setup-python@v2 + with: + python-version: 3.7 + + - name: Display Python version + run: python -c "import sys; print(sys.version)" + + - name: Install docs dependencies + run: | + sudo apt-get install pandoc + pip install setuptools --upgrade + pip install .[test,docs-build] + + - name: Make docs + run: | + pip install . + cd docs + make html + + # check code coverage, store results in the built docs. + # coverage.py creates a .gitignore with '*' where it's run; remove it + # to keep the cooverage report and badge on gh-pages + - name: Coverage + # only continue if the PR is not just closed, but also merged + if: github.event.pull_request.merged == true + run: | + coverage run --source=frank -m pytest frank/tests.py + coverage report + mkdir -p docs/_build/html/coverage + coverage html -d docs/_build/html/coverage + rm docs/_build/html/coverage/.gitignore + coverage-badge -f -o docs/_build/html/coverage/badge.svg + + # upload the built docs as an artifact so the files can be accessed + # by a subsequent job in the workflow. + # only store the artifact for 'retention-days' + - name: Upload docs artifact + if: github.event.pull_request.merged == true + uses: actions/upload-artifact@v3 + with: + name: built_docs + path: docs/_build/html + retention-days: 1 + + docs-deploy: + name: Deploy docs + needs: docs-build + if: github.event.pull_request.merged == true + runs-on: ubuntu-latest + + steps: + - name: Checkout repo + uses: actions/checkout@v2 + + # download the previously uploaded 'built_docs' artifact + - name: Download docs artifact + uses: actions/download-artifact@v3 + id: download + with: + name: built_docs + path: docs/_build/html + + # - name: Echo download path + # run: echo ${{steps.download.outputs.download-path}} + + - name: Disable jekyll builds + run: touch docs/_build/html/.nojekyll + + # - name: Display docs file structure + # run: ls -aR + # working-directory: docs/_build/html + + - name: Install and configure dependencies + run: | + npm install -g --silent [email protected] + + - name: Deploy docs to gh-pages branch + run: | + git remote set-url origin https://git:${GITHUB_TOKEN}@github.com/${GITHUB_REPOSITORY}.git + npx gh-pages --dotfiles --dist docs/_build/html --user "github-actions-bot <[email protected]>" --message "Update docs [skip ci]" + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} diff --git a/LICENSE.txt b/LICENSE.txt index b059d641..65c5ca88 100644 --- a/LICENSE.txt +++ b/LICENSE.txt @@ -1,621 +1,165 @@ - GNU GENERAL PUBLIC LICENSE - Version 3, 29 June 2007 - - Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/> - Everyone is permitted to copy and distribute verbatim copies - of this license document, but changing it is not allowed. - - Preamble - - The GNU General Public License is a free, copyleft license for -software and other kinds of works. - - The licenses for most software and other practical works are designed -to take away your freedom to share and change the works. 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Such new +versions will be similar in spirit to the present version, but may +differ in detail to address new problems or concerns. + + Each version is given a distinguishing version number. If the +Library as you received it specifies that a certain numbered version +of the GNU Lesser General Public License "or any later version" +applies to it, you have the option of following the terms and +conditions either of that published version or of any later version +published by the Free Software Foundation. If the Library as you +received it does not specify a version number of the GNU Lesser +General Public License, you may choose any version of the GNU Lesser +General Public License ever published by the Free Software Foundation. + + If the Library as you received it specifies that a proxy can decide +whether future versions of the GNU Lesser General Public License shall +apply, that proxy's public statement of acceptance of any version is +permanent authorization for you to choose that version for the +Library. diff --git a/MANIFEST.in b/MANIFEST.in index 543949de..6e62abde 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1,4 +1,4 @@ -include README.md AUTHORS.rst HISTORY.rst LICENSE.txt requirements.txt +include README.md AUTHORS.rst HISTORY.rst LICENSE.txt global-include default_parameters.json parameter_descriptions.json frank.mplstyle prune docs prune .ci diff --git a/README.md b/README.md index 12b53b55..24ad5021 100644 --- a/README.md +++ b/README.md @@ -3,43 +3,50 @@ </p> <p align="center"> +  <a href="https://github.com/discsim/frank/releases"> <img src="https://img.shields.io/github/release/discsim/frank/all.svg"> </a> +  <a href="https://pypi.python.org/pypi/frank"> <img src="https://img.shields.io/pypi/v/frank.svg"> - </a> - - <a href="https://discsim.github.io/frank/blob/master/HISTORY.rst"> - <img src="https://img.shields.io/badge/Changelog-detailed-blue"/> - - <br/> - <a href="https://discsim.github.io/frank/"> - <img src="https://img.shields.io/badge/docs-Read%20em!-blue.svg?style=flat"/> <br/> - <a href="https://academic.oup.com/mnras/advance-article/doi/10.1093/mnras/staa1365/5838058?guestAccessKey=7f163a1f-c12f-4771-8e54-928636794a5b"> - <img src="https://img.shields.io/badge/paper-MNRAS-blue.svg"> +  + <a href="https://github.com/discsim/frank/blob/master/HISTORY.rst"> + <img src="https://img.shields.io/badge/changelog-detailed-blue"/> </a> +  <a href="https://doi.org/10.5281/zenodo.3832064"> <img src="https://zenodo.org/badge/DOI/10.5281/zenodo.3832064.svg" alt="DOI"> - </a> <br/> - <a href="https://circleci.com/gh/discsim/frank"> - <img src="https://circleci.com/gh/discsim/frank.svg?style=shield"> +  + <a href="https://github.com/discsim/frank/actions/workflows/tests.yml"> + <img src="https://github.com/discsim/frank/actions/workflows/tests.yml/badge.svg"> </a> +  + <a href="https://github.com/discsim/frank/actions/workflows/docs.yml"> + <img src="https://github.com/discsim/frank/actions/workflows/docs.yml/badge.svg"/> + </a> + +  <a href="https://discsim.github.io/frank/coverage/index.html"> <img src="https://discsim.github.io/frank/coverage/badge.svg"> - </a> <br/> +  + <a href="https://academic.oup.com/mnras/advance-article/doi/10.1093/mnras/staa1365/5838058?guestAccessKey=7f163a1f-c12f-4771-8e54-928636794a5b"> + <img src="https://img.shields.io/badge/paper-MNRAS-blue.svg"> + </a> + + <br/> +  <a href="https://www.gnu.org/licenses/lgpl-3.0"> - <img src="https://img.shields.io/badge/License-LGPL%20v3-blue.svg" - [![License: LGPL v3](https://img.shields.io/badge/License-LGPL%20v3-blue.svg"> + <img src="https://img.shields.io/badge/license-LGPL%20v3-blue.svg"> </a> </p> diff --git a/frank/utilities.py b/frank/utilities.py index 831d540a..84427da2 100644 --- a/frank/utilities.py +++ b/frank/utilities.py @@ -456,7 +456,7 @@ def estimate_weights(u, v=None, V=None, nbins=300, log=True, use_median=False): In each case the variance of V in the uv-bins is used to estimate the weights. The first call will use q = np.hypot(u, v) in the uv-bins. The second and third calls are equivalent to the first with u=0. - + """ logging.info(' Estimating visibility weights') @@ -594,6 +594,7 @@ def sweep_profile(r, I, project=False, phase_shift=False, geom=None, axis=0, ----- Sign convention: a negative geom.dRA shifts the source to the right in the image + """ if project: inc, pa, dra, ddec = geom.inc, geom.PA, geom.dRA, geom.dDec diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index 5406d671..00000000 --- a/requirements.txt +++ /dev/null @@ -1,3 +0,0 @@ -numpy>=1.12.0 -matplotlib>=3.1.0 -scipy>=0.18.0 diff --git a/setup.cfg b/setup.cfg new file mode 100644 index 00000000..361f2040 --- /dev/null +++ b/setup.cfg @@ -0,0 +1,57 @@ +# to set version dynamically: https://github.com/pypa/setuptools/issues/1724#issuecomment-627241822 + +[metadata] +name = frank +version = attr: frank.__version__ +author = Richard Booth, Jeff Jennings, Marco Tazzari +author_email = [email protected] +description = Frankenstein, the flux reconstructor +long_description = file: README.md +long_description_content_type = text/markdown +license = LGPLv3 +license_file = LICENSE.txt +include_package_data = True +url = https://github.com/discsim/frank +project_urls = + Bug Tracker = https://github.com/discsim/frank/issues +classifiers = + Development Status :: 5 - Production/Stable + License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3) + Intended Audience :: Developers + Intended Audience :: Science/Research + Operating System :: MacOS :: MacOS X + Operating System :: POSIX :: Linux + Programming Language :: Python :: 3.6 + Programming Language :: Python :: 3.7 + Programming Language :: Python :: 3.8 + Programming Language :: Python :: 3.9 +keywords = + science + astronomy + interferometry + +[options] +packages = frank + +# python_requires docs: https://packaging.python.org/guides/distributing-packages-using-setuptools/#python-requires +python_requires = >=3.6 + +# PEP 440 - pinning package versions: https://www.python.org/dev/peps/pep-0440/#compatible-release +install_requires = + numpy>=1.12 + matplotlib>=3.1.0 + scipy>=0.18.0 + +# extras_require syntax: +# https://setuptools.readthedocs.io/en/latest/userguide/declarative_config.html?highlight=options.extras_require#configuring-setup-using-setup-cfg-files +[options.extras_require] +test = pytest + coverage==6.2 + coverage-badge==1.1.0 + +docs-build = sphinx + sphinxcontrib-fulltoc + sphinx_rtd_theme + nbsphinx + jupyter_client + ipykernel diff --git a/setup.py b/setup.py index cc0a49a1..2ca29466 100644 --- a/setup.py +++ b/setup.py @@ -1,37 +1,6 @@ #!/usr/bin/env python # coding=utf-8 -from setuptools import setup, find_packages +from setuptools import setup -with open("frank/__init__.py", "r") as f: - for line in f: - if line.startswith('__version__'): - version = line.split('=')[1].strip().strip('"\'') - -setup( - name="frank", - version=version, - packages=find_packages(), - include_package_data=True, - author="Richard Booth, Jeff Jennings, Marco Tazzari", - author_email="[email protected]", - description="Frankenstein, the flux reconstructor", - long_description_content_type="text/markdown", - long_description=open('README.md').read(), - python_requires='>=3', - install_requires=[line.rstrip() for line in open("requirements.txt", "r").readlines()], - extras_require={ - 'test' : ['pytest', 'coverage', 'coverage-badge'], - 'docs-build' : ['sphinx', 'sphinxcontrib-fulltoc', 'sphinx_rtd_theme', 'nbsphinx'], - }, - license="GPLv3", - url="https://github.com/discsim/frank", - keywords=["science", "astronomy", "interferometry"], - classifiers=[ - "Development Status :: 5 - Production/Stable", - "Intended Audience :: Developers", - "Intended Audience :: Science/Research", - "Operating System :: OS Independent", - "License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)", - "Programming Language :: Python :: 3", - ] -) +# setup configuration is in `setup.cfg` +setup()

circleci tests queuing The circleci tests for any given push are still constantly failing because the tests are being queued (not running) with the notice, "This job was waitlisted due to your concurrency limits". The tests are delayed from starting by 5 - 9 minutes, then time out at ~10 minutes and so 'fail'. You have to re-run tests multiple times to get all of them to pass, which can take 30 minutes - > 1 hr for a single push. The circleci account plan says we can have "1 concurrent job with access to Linux and Windows builds on Small and Medium sizes". I googled and don't know why this queuing started happening seemingly randomly. I found this - https://support.circleci.com/hc/en-us/articles/360055741771-Build-Not-Running-due-to-concurrency-limit-but-no-other-job-is-running - but I don't see any background jobs that are running which would cause the tests to queue. Unless someone sees a quick fix, we should open a support ticket on circleci.

Hey, I was just about to open an issue to migrate to Github Actions. I just did it for both uvplot and galario. It's just great, tests are run immediately and it's completely free for public projects. I'll open an issue asap. Opened issue #157 Awesome, thank you! Sounds much faster and easier I'll have a look in the next days and will submit a PR.

2021-03-17T20:36:36

0.0

[]

ToucanToco/fastexcel

ToucanToco__fastexcel-304

152ff50011da173be5c59e09ba3bd4d7b0f20cad

diff --git a/python/fastexcel/__init__.py b/python/fastexcel/__init__.py index 50320e6..d1e6380 100644 --- a/python/fastexcel/__init__.py +++ b/python/fastexcel/__init__.py @@ -233,7 +233,8 @@ def load_sheet( - if `skip_rows` is a number, it skips the specified number of rows from the start of the sheet. :param schema_sample_rows: Specifies how many rows should be used to determine - the dtype of a column. + the dtype of a column. Cannot be 0. A specific dtype can be + enforced for some or all columns through the `dtypes` parameter. If `None`, all rows will be used. :param dtype_coercion: Specifies how type coercion should behave. `coerce` (the default) will try to coerce different dtypes in a column to the same one, @@ -336,7 +337,8 @@ def load_table( If `header_row` is `None`, it skips the number of rows from the start of the sheet. :param schema_sample_rows: Specifies how many rows should be used to determine - the dtype of a column. + the dtype of a column. Cannot be 0. A specific dtype can be + enforced for some or all columns through the `dtypes` parameter. If `None`, all rows will be used. :param dtype_coercion: Specifies how type coercion should behave. `coerce` (the default) will try to coerce different dtypes in a column to the same one, diff --git a/src/types/python/excelreader.rs b/src/types/python/excelreader.rs index 0be6ca8..f6c8549 100644 --- a/src/types/python/excelreader.rs +++ b/src/types/python/excelreader.rs @@ -350,6 +350,14 @@ impl ExcelReader { eager: bool, py: Python<'_>, ) -> PyResult<PyObject> { + // Cannot use NonZeroUsize in the parameters, as it is not supported by pyo3 + if let Some(0) = schema_sample_rows { + return Err(FastExcelErrorKind::InvalidParameters( + "schema_sample_rows cannot be 0, as it would prevent dtype inferring".to_string(), + ) + .into()) + .into_pyresult(); + } let sheet = idx_or_name .try_into() .and_then(|idx_or_name| match idx_or_name { @@ -420,6 +428,14 @@ impl ExcelReader { eager: bool, py: Python<'_>, ) -> PyResult<PyObject> { + // Cannot use NonZeroUsize in the parameters, as it is not supported by pyo3 + if let Some(0) = schema_sample_rows { + return Err(FastExcelErrorKind::InvalidParameters( + "schema_sample_rows cannot be 0, as it would prevent dtype inferring".to_string(), + ) + .into()) + .into_pyresult(); + } self.build_table( name.to_string(), header_row,

schema_sample_rows=0 results in a table filled with null values When reading a excel file and setting the schema_sample_rows to 0 results in a table of the correct height, but all values are set to null. If the schema_sample_rows is set to n and the first n values in a column are empty, then the remainder of the values in this column are also filled with null values. This means that the values that are present in this column, are replaced with null values. This is causing a loss of data, while this can be fixed by defaulting to a column dtype of type string. For example, xlsx2csv has this also as default.

I don't really understand. `schema_sample_rows` sets the number of rows used to infer the type of the columns. If you set it to 0, we can't infer anything because no data is read hence the `null` type being generated I understand that the type cannot be inferred, but why would you replace all the values with null then? I have a table filled with values, but I do not want to have any types inferred as I want to assign them by myself. However, the columns are all filled with null values instead of the values that are present in the column. I would be fine with the fact that the column dtype is null, but then all the values are kept as strings for example. In that way I would be able to cast the columns by myself, to the values that I want them to be and prevent the extra overhead of inferring the dtype. For example the following excel on the left with schema_sample_rows = 0 results in the table on the right after reading it with all dtypes being null. ![image](https://github.com/ToucanToco/fastexcel/assets/44165272/9b18dac5-4fcb-42f6-8472-a305789e729c) Another example is the following excel on the left with schema_sample_rows = 1 results in the table on the right after reading it with the dtypes of the first three columns being string and the last column being of dtype null. ![image](https://github.com/ToucanToco/fastexcel/assets/44165272/51296571-450e-48d0-8833-691dd135b5f8) I do not see why this is wanted behavior to remove all data from the column if the dtype cannot be inferred. Could you please elaborate why this is the default behavior and how to prevent the replacement in way that is not casting all 16384 columns with the dtypes parameter to type string. I ran into this as well. I have very inconsistent excel files and the requirement is to load all data as strings. Since I don't know how many columns there are, I am currently doing a 3-step process: load the first row, build a dtype map based on the width of the row, then load the sheet using the map. The time hit for the double load is noticeable for larger worksheets. I am certainly open to better solutions. I have tried some combinations of `Polars` and `fastexcel` and always run into trouble... dropped columns/null data at one end and `unsupported column type combination: {DateTime, String}` at the other. I was also going to open a feature request to pass a single `dtype` to apply to all columns. ```python ws = wb.load_sheet_by_name(name=sheet_name, header_row=None, n_rows=1, schema_sample_rows=0) type_map = {i: "string" for i in range(ws.width + 1)} ws = wb.load_sheet_by_name(name=sheet_name, header_row=None, dtypes=type_map) ``` The solution that I have used with polars is by creating the `type_map` for all 16384 columns as this is the limit of an excel file. Of course I am not creating it for the all columns but for my purpose with polars I have set it for the first 100 columns. This returns the correct dataframe. The extra columns in the `type_map` are not created at all, so therefore you are able to create a `type_map` without knowing the width of the worksheet in the first place. @Tim-Kracht @niekrongen we made some improvements by supporting most mix types. Does it solve your problems? I don't want to add another option but if a way to have all columns in string is really needed, we'll work on a solution (like https://github.com/ToucanToco/fastexcel/issues/257) @lukapeschke I reckon we should forbid `0` as value. It makes no sense. And then we can close this issue. WDYT? @PrettyWood I agree, having `schema_sample_rows=0` should result in an `InvalidParameters` error

2024-10-15T15:13:22

0.0

[]

ToucanToco/fastexcel

ToucanToco__fastexcel-297

8c9559fe753f27b38f6b952613ea65d82ce74745

diff --git a/python/fastexcel/__init__.py b/python/fastexcel/__init__.py index 382eda6..c406883 100644 --- a/python/fastexcel/__init__.py +++ b/python/fastexcel/__init__.py @@ -208,7 +208,7 @@ def load_sheet( *, header_row: int | None = 0, column_names: list[str] | None = None, - skip_rows: int = 0, + skip_rows: int | None = None, n_rows: int | None = None, schema_sample_rows: int | None = 1_000, dtype_coercion: Literal["coerce", "strict"] = "coerce", @@ -227,8 +227,11 @@ def load_sheet( If `None`, all rows are loaded :param skip_rows: Specifies how many rows should be skipped after the `header_row`. Any rows before the `header_row` are automatically skipped. - If `header_row` is `None`, it skips the number of rows from the - start of the sheet. + If `header_row` is `None`: + - if `skip_rows` is `None` (default): it skips all empty rows + at the beginning of the sheet. + - if `skip_rows` is a number, it skips the specified number + of rows from the start of the sheet. :param schema_sample_rows: Specifies how many rows should be used to determine the dtype of a column. If `None`, all rows will be used. @@ -372,7 +375,7 @@ def load_sheet_eager( *, header_row: int | None = 0, column_names: list[str] | None = None, - skip_rows: int = 0, + skip_rows: int | None = None, n_rows: int | None = None, schema_sample_rows: int | None = 1_000, dtype_coercion: Literal["coerce", "strict"] = "coerce", @@ -405,7 +408,7 @@ def load_sheet_by_name( *, header_row: int | None = 0, column_names: list[str] | None = None, - skip_rows: int = 0, + skip_rows: int | None = None, n_rows: int | None = None, schema_sample_rows: int | None = 1_000, dtype_coercion: Literal["coerce", "strict"] = "coerce", @@ -434,7 +437,7 @@ def load_sheet_by_idx( *, header_row: int | None = 0, column_names: list[str] | None = None, - skip_rows: int = 0, + skip_rows: int | None = None, n_rows: int | None = None, schema_sample_rows: int | None = 1_000, dtype_coercion: Literal["coerce", "strict"] = "coerce", diff --git a/python/fastexcel/_fastexcel.pyi b/python/fastexcel/_fastexcel.pyi index 33ba39f..41a2b2e 100644 --- a/python/fastexcel/_fastexcel.pyi +++ b/python/fastexcel/_fastexcel.pyi @@ -104,7 +104,7 @@ class _ExcelReader: *, header_row: int | None = 0, column_names: list[str] | None = None, - skip_rows: int = 0, + skip_rows: int | None = None, n_rows: int | None = None, schema_sample_rows: int | None = 1_000, dtype_coercion: Literal["coerce", "strict"] = "coerce", @@ -119,7 +119,7 @@ class _ExcelReader: *, header_row: int | None = 0, column_names: list[str] | None = None, - skip_rows: int = 0, + skip_rows: int | None = None, n_rows: int | None = None, schema_sample_rows: int | None = 1_000, dtype_coercion: Literal["coerce", "strict"] = "coerce", diff --git a/src/types/python/excelreader.rs b/src/types/python/excelreader.rs index d36f9aa..d0b0b69 100644 --- a/src/types/python/excelreader.rs +++ b/src/types/python/excelreader.rs @@ -7,8 +7,8 @@ use arrow::{pyarrow::ToPyArrow, record_batch::RecordBatch}; use pyo3::{prelude::PyObject, pyclass, pymethods, Bound, IntoPy, PyAny, PyResult, Python}; use calamine::{ - open_workbook_auto, open_workbook_auto_from_rs, Data, DataRef, Range, Reader, ReaderRef, - Sheet as CalamineSheet, Sheets, Table, + open_workbook_auto, open_workbook_auto_from_rs, Data, DataRef, HeaderRow, Range, Reader, + ReaderRef, Sheet as CalamineSheet, Sheets, Table, }; use crate::{ @@ -73,6 +73,19 @@ impl ExcelSheets { ) } + fn with_header_row(&mut self, header_row: HeaderRow) -> &mut Self { + match self { + Self::File(sheets) => { + sheets.with_header_row(header_row); + self + } + Self::Bytes(ref mut sheets) => { + sheets.with_header_row(header_row); + self + } + } + } + fn worksheet_range_ref(&mut self, name: &str) -> FastExcelResult<Range<DataRef>> { match self { ExcelSheets::File(Sheets::Xlsx(sheets)) => Ok(sheets.worksheet_range_ref(name)?), @@ -164,7 +177,7 @@ impl ExcelReader { sheet_meta: CalamineSheet, header_row: Option<usize>, column_names: Option<Vec<String>>, - skip_rows: usize, + skip_rows: Option<usize>, n_rows: Option<usize>, schema_sample_rows: Option<usize>, dtype_coercion: DTypeCoercion, @@ -173,14 +186,28 @@ impl ExcelReader { eager: bool, py: Python<'_>, ) -> PyResult<PyObject> { - let header = Header::new(header_row, column_names); + // calamine `header_row` is the first row of the range to be read. + // For us `header_row` can be `None` (meaning there is no header and we should start reading + // the data at the beginning) + let calamine_header_row = match (header_row, skip_rows) { + (None, None) | (Some(0), None) => HeaderRow::FirstNonEmptyRow, + (None, Some(_)) => HeaderRow::Row(0), + (Some(row), _) => HeaderRow::Row(row as u32), + }; + // And our header row is simply the first row of the data if defined. + let data_header_row = header_row.and(Some(0)); + + let header = Header::new(data_header_row, column_names); let selected_columns = Self::build_selected_columns(use_columns).into_pyresult()?; + if eager && self.sheets.supports_by_ref() { let range = self .sheets + .with_header_row(calamine_header_row) .worksheet_range_ref(&sheet_meta.name) .into_pyresult()?; - let pagination = Pagination::new(skip_rows, n_rows, &range).into_pyresult()?; + let pagination = + Pagination::new(skip_rows.unwrap_or(0), n_rows, &range).into_pyresult()?; Self::load_sheet_eager( &range.into(), pagination, @@ -195,9 +222,11 @@ impl ExcelReader { } else { let range = self .sheets + .with_header_row(calamine_header_row) .worksheet_range(&sheet_meta.name) .into_pyresult()?; - let pagination = Pagination::new(skip_rows, n_rows, &range).into_pyresult()?; + let pagination = + Pagination::new(skip_rows.unwrap_or(0), n_rows, &range).into_pyresult()?; let sheet = ExcelSheet::try_new( sheet_meta, range.into(), @@ -298,7 +327,7 @@ impl ExcelReader { *, header_row = 0, column_names = None, - skip_rows = 0, + skip_rows = None, n_rows = None, schema_sample_rows = 1_000, dtype_coercion = DTypeCoercion::Coerce, @@ -312,7 +341,7 @@ impl ExcelReader { idx_or_name: &Bound<'_, PyAny>, header_row: Option<usize>, column_names: Option<Vec<String>>, - skip_rows: usize, + skip_rows: Option<usize>, n_rows: Option<usize>, schema_sample_rows: Option<usize>, dtype_coercion: DTypeCoercion, @@ -337,14 +366,14 @@ impl ExcelReader { } } IdxOrName::Idx(idx) => self - .sheet_metadata .get(idx) + .sheet_metadata + .get(idx) .ok_or_else(|| FastExcelErrorKind::SheetNotFound(IdxOrName::Idx(idx)).into()) - .with_context(|| { format!( - "Sheet index {idx} is out of range. File has {} sheets.", - self.sheet_metadata.len() - ) - }) - , + .with_context(|| format!( + "Sheet index {idx} is out of range. File has {} sheets.", + self.sheet_metadata.len() + ) + ), }) .into_pyresult()?.to_owned();

header_row does not work as expected if there are blank rows If I put expect the header on e.g. row index 15, but there are blank rows (at least at the beginning of the sheet), I cannot do `header_row=15`. It looks like they are ignored, so I have to put in a lower index.

Hello! can you please provide an excel sheet example? I will try to create a basic sheet to reproduce. It seems the issue is not actually with blank rows, but there is some other reason I am having to put in a much lower index than I expect. It looks like it does have to do with empty rows in some case.. not exactly clear to me, but it's very easy to reproduce with this file: [TestFastExcelRead.xlsx](https://github.com/ToucanToco/fastexcel/files/15015950/TestFastExcelRead.xlsx) I have the header row on row 8 (0-based; 9 in excel) but have to specify it as 6. You can also remove that "Note 1" cell to change things. ```python def test_row_issue(): reader = read_excel("...TestFastExcelRead.xlsx") ws = reader.load_sheet("Sheet1", header_row=6) df = ws.to_pandas() ``` I'll try to dig into it this weekend seem related to #209 see https://github.com/ToucanToco/fastexcel/issues/209#issuecomment-2239211891

2024-10-11T22:38:02

0.0

[]

ToucanToco/fastexcel

ToucanToco__fastexcel-292

5145063f439f8b2801c73e9800a0ec0bf8e3693a

diff --git a/src/data.rs b/src/data.rs index 553d10d..8da9ada 100644 --- a/src/data.rs +++ b/src/data.rs @@ -81,6 +81,8 @@ mod array_impls { use calamine::{CellType, DataType, Range}; use chrono::NaiveDate; + use crate::types::dtype::excel_float_to_string; + pub(crate) fn create_boolean_array<DT: CellType + DataType>( data: &Range<DT>, col: usize, @@ -142,6 +144,8 @@ mod array_impls { cell.get_datetime_iso().map(str::to_string) } else if cell.is_bool() { cell.get_bool().map(|v| v.to_string()) + } else if cell.is_float() { + cell.get_float().map(excel_float_to_string) } else { cell.as_string() } diff --git a/src/types/dtype.rs b/src/types/dtype.rs index 9af844a..32b3dd9 100644 --- a/src/types/dtype.rs +++ b/src/types/dtype.rs @@ -280,6 +280,31 @@ pub(crate) fn get_dtype_for_column<DT: CellType + Debug + DataType>( } } +/// Convert a float to a nice string to mimic Excel behaviour. +/// +/// Excel can store a float like 29.02 set by the user as "29.020000000000003" in the XML. +/// But in fact, the user will see "29.02" in the cell. +/// Excel indeed displays decimal numbers with 8 digits in a standard cell width +/// and 10 digits in a wide cell. Like this: +/// +/// Format = 0.000000000 | Unformatted, wide cell | Unformatted, standard width +/// ---------------------|--------------------------|---------------------------- +/// 1.123456789 | 1.123456789 | 1.123457 +/// 12.123456789 | 12.12345679 | 12.12346 +/// ... | ... | ... +/// 123456.123456789 | 123456.1235 | 123456.1 +/// +/// Excel also trims trailing zeros and the decimal point if there is no fractional part. +/// +/// We do not distinguish between wide cells and standard cells here, so we retain at most +/// nine digits after the decimal point and trim any trailing zeros. +pub(crate) fn excel_float_to_string(x: f64) -> String { + format!("{x:.9}") + .trim_end_matches('0') + .trim_end_matches('.') + .to_string() +} + #[cfg(test)] mod tests { use calamine::{Cell, Data as CalData}; @@ -394,4 +419,12 @@ mod tests { FastExcelErrorKind::UnsupportedColumnTypeCombination(_) )); } + + #[rstest] + #[case(29.020000000000003, "29.02")] + #[case(10000_f64, "10000")] + #[case(23.0, "23")] + fn test_excel_float_to_string(#[case] x: f64, #[case] expected: &str) { + assert_eq!(excel_float_to_string(x), expected.to_string()); + } }

29.02 cell cast to 29.020000000000003 string ## Steps to reproduce See the Excel file [decimal-numbers.xlsx](https://github.com/user-attachments/files/17162995/decimal-numbers.xlsx). It contains a column with two decimals: | Decimals | |--------| | 28.14 | | 29.02 | Read the Excel file using fastexcel: ```python read_excel("decimal-numbers.xlsx").load_sheet_by_name("Sheet1").to_polars() ``` ``` shape: (2, 1) ┌──────────┐ │ Decimals │ │ --- │ │ f64 │ ╞══════════╡ │ 28.14 │ │ 29.02 │ └──────────┘ ``` Looks fine. Then read the Excel file while casting to strings: ```python read_excel("decimal-numbers.xlsx").load_sheet_by_name("Sheet1", dtypes={0: 'string'}).to_polars() ``` ``` shape: (2, 1) ┌────────────────────┐ │ Decimals │ │ --- │ │ str │ ╞════════════════════╡ │ 28.14 │ │ 29.020000000000003 │ └────────────────────┘ ``` The expected result was that the strings would be the same as shown to the user in the Excel file: ``` shape: (2, 1) ┌──────────┐ │ Decimals │ │ --- │ │ str │ ╞══════════╡ │ 28.14 │ │ 29.02 │ └──────────┘ ``` # Wrap-up I understand this looks like an issue due to floating point precision, and I'm not sure if this: 1. could be fixed in fastexcel 2. could be fixed in the underlying calamine. 3. cannot be fixed at all, since it's a fundamental property of the Excel file format or parsing process. What's the motivation for filing this bug: In our system, we have highly heterogeneous data, so we have to read all Excel values as strings. However, if users see 29.02 in their Excel files, but `29.020000000000003` in our system, that's highly confusing and surprising to users. What do you think?

Hello @severinh Always great to have a clear bug issue with file and clear explanation thank you! I checked quickly and indeed it feels hard to fix on `fastexcel` side In `src/data.rs`, in `create_string_array` function we simply do ```rs cell.as_string() ``` Even with ```rs cell.get_float().map(|v| v.to_string()) ``` or ```rs cell.as_f64() ``` we get the same behavior with this floating precision issue and AFAIK there is no way for us to get more info on the original input. I'll try to dig on calamine side tonight EDIT: quick check on calamine ```rs inner: [ SharedString( "Decimals", ), Float( 28.14, ), Float( 29.020000000000003, ), ], ``` when reading the xml content we get ```rs BytesText { content: Borrowed("29.020000000000003") } ``` so I guess it goes even further directly in the xml content 😞 The floating point number in the Excel file is 29.020000000000003: ```xml <row r="2" spans="1:1" x14ac:dyDescent="0.2"> <c r="A2" s="1"> <v>28.14</v> </c> </row> <row r="3" spans="1:1" x14ac:dyDescent="0.2"> <c r="A3" s="1"> <v>29.020000000000003</v> </c> </row> ``` For the display (and storage) of floating point numbers Excel uses something very similar to the `printf()` format option `%.16G`: ```python $ python >>> print("%.16G" % 29.020000000000003) 29.02 ``` That format option isn't supported by Rust in [std::fmt](https://doc.rust-lang.org/std/fmt/) however.

2024-10-02T16:50:08

0.0

[]

ToucanToco/fastexcel

ToucanToco__fastexcel-287

79a7f17b8654438c691872522fca70b9ec7d9898

diff --git a/python/fastexcel/__init__.py b/python/fastexcel/__init__.py index 0899cea..8a8e33a 100644 --- a/python/fastexcel/__init__.py +++ b/python/fastexcel/__init__.py @@ -1,6 +1,7 @@ from __future__ import annotations import sys +import typing from typing import TYPE_CHECKING, Callable, Literal if sys.version_info < (3, 10): @@ -31,6 +32,7 @@ __version__, _ExcelReader, _ExcelSheet, + _ExcelTable, ) from ._fastexcel import read_excel as _read_excel @@ -41,6 +43,14 @@ SheetVisible: TypeAlias = Literal["visible", "hidden", "veryhidden"] +def _recordbatch_to_polars(rb: pa.RecordBatch) -> pl.DataFrame: + import polars as pl + + df = pl.from_arrow(data=rb) + assert isinstance(df, pl.DataFrame) + return df + + class ExcelSheet: """A class representing a single sheet in an Excel File""" @@ -104,16 +114,83 @@ def to_polars(self) -> "pl.DataFrame": Requires the `polars` extra to be installed. """ - import polars as pl - - df = pl.from_arrow(data=self.to_arrow()) - assert isinstance(df, pl.DataFrame) - return df + return _recordbatch_to_polars(self.to_arrow()) def __repr__(self) -> str: return self._sheet.__repr__() +class ExcelTable: + """A class representing a single table in an Excel file""" + + def __init__(self, table: _ExcelTable) -> None: + self._table = table + + @property + def name(self) -> str: + """The name of the table""" + return self._table.name + + @property + def sheet_name(self) -> str: + """The name of the sheet this table belongs to""" + return self._table.sheet_name + + @property + def width(self) -> int: + """The table's width""" + return self._table.width + + @property + def height(self) -> int: + """The table's height""" + return self._table.height + + @property + def total_height(self) -> int: + """The table's total height""" + return self._table.total_height + + @property + def offset(self) -> int: + """The table's offset before data starts""" + return self._table.offset + + @property + def selected_columns(self) -> list[ColumnInfo]: + """The table's selected columns""" + return self._table.selected_columns + + @property + def available_columns(self) -> list[ColumnInfo]: + """The columns available for the given table""" + return self._table.available_columns + + @property + def specified_dtypes(self) -> DTypeMap | None: + """The dtypes specified for the table""" + return self._table.specified_dtypes + + def to_arrow(self) -> pa.RecordBatch: + """Converts the table to a pyarrow `RecordBatch`""" + return self._table.to_arrow() + + def to_pandas(self) -> "pd.DataFrame": + """Converts the table to a Pandas `DataFrame`. + + Requires the `pandas` extra to be installed. + """ + # We know for sure that the table will yield exactly one RecordBatch + return self.to_arrow().to_pandas() + + def to_polars(self) -> "pl.DataFrame": + """Converts the table to a Polars `DataFrame`. + + Requires the `polars` extra to be installed. + """ + return _recordbatch_to_polars(self.to_arrow()) + + class ExcelReader: """A class representing an open Excel file and allowing to read its sheets""" @@ -196,6 +273,7 @@ def table_names(self, sheet_idx_or_name: str | int | None = None) -> list[str]: """ return self._reader.table_names(sheet_idx_or_name) + @typing.overload def load_table( self, name: str, @@ -208,7 +286,37 @@ def load_table( dtype_coercion: Literal["coerce", "strict"] = "coerce", use_columns: list[str] | list[int] | str | Callable[[ColumnInfo], bool] | None = None, dtypes: DTypeMap | None = None, - ) -> ExcelSheet: + eager: Literal[False] = ..., + ) -> ExcelTable: ... + @typing.overload + def load_table( + self, + name: str, + *, + header_row: int | None = None, + column_names: list[str] | None = None, + skip_rows: int = 0, + n_rows: int | None = None, + schema_sample_rows: int | None = 1_000, + dtype_coercion: Literal["coerce", "strict"] = "coerce", + use_columns: list[str] | list[int] | str | Callable[[ColumnInfo], bool] | None = None, + dtypes: DTypeMap | None = None, + eager: Literal[True] = ..., + ) -> pa.RecordBatch: ... + def load_table( + self, + name: str, + *, + header_row: int | None = None, + column_names: list[str] | None = None, + skip_rows: int = 0, + n_rows: int | None = None, + schema_sample_rows: int | None = 1_000, + dtype_coercion: Literal["coerce", "strict"] = "coerce", + use_columns: list[str] | list[int] | str | Callable[[ColumnInfo], bool] | None = None, + dtypes: DTypeMap | None = None, + eager: bool = False, + ) -> ExcelTable | pa.RecordBatch: """Loads a table by name. :param name: The name of the table to load. @@ -242,19 +350,21 @@ def load_table( indicating whether the column should be used :param dtypes: An optional dict of dtypes. Keys can be column indices or names """ - return ExcelSheet( - self._reader.load_table( - name=name, - header_row=header_row, - column_names=column_names, - skip_rows=skip_rows, - n_rows=n_rows, - schema_sample_rows=schema_sample_rows, - dtype_coercion=dtype_coercion, - use_columns=use_columns, - dtypes=dtypes, - ) + output = self._reader.load_table( # type:ignore[call-overload,misc] + name=name, + header_row=header_row, + column_names=column_names, + skip_rows=skip_rows, + n_rows=n_rows, + schema_sample_rows=schema_sample_rows, + dtype_coercion=dtype_coercion, + use_columns=use_columns, + dtypes=dtypes, + eager=eager, ) + if eager: + return output + return ExcelTable(output) def load_sheet_eager( self, diff --git a/python/fastexcel/_fastexcel.pyi b/python/fastexcel/_fastexcel.pyi index 5d12bb2..eb289b0 100644 --- a/python/fastexcel/_fastexcel.pyi +++ b/python/fastexcel/_fastexcel.pyi @@ -63,6 +63,37 @@ class _ExcelSheet: def to_arrow(self) -> pa.RecordBatch: """Converts the sheet to a pyarrow `RecordBatch`""" +class _ExcelTable: + @property + def name(self) -> str: + """The name of the table""" + @property + def sheet_name(self) -> str: + """The name of the sheet this table belongs to""" + @property + def width(self) -> int: + """The table's width""" + @property + def height(self) -> int: + """The table's height""" + @property + def total_height(self) -> int: + """The table's total height""" + @property + def offset(self) -> int: + """The table's offset before data starts""" + @property + def selected_columns(self) -> list[ColumnInfo]: + """The table's selected columns""" + @property + def available_columns(self) -> list[ColumnInfo]: + """The columns available for the given table""" + @property + def specified_dtypes(self) -> DTypeMap | None: + """The dtypes specified for the table""" + def to_arrow(self) -> pa.RecordBatch: + """Converts the table to a pyarrow `RecordBatch`""" + class _ExcelReader: """A class representing an open Excel file and allowing to read its sheets""" @@ -96,6 +127,7 @@ class _ExcelReader: dtypes: DTypeMap | None = None, eager: Literal[True] = ..., ) -> pa.RecordBatch: ... + @typing.overload def load_table( self, name: str, @@ -108,6 +140,22 @@ class _ExcelReader: dtype_coercion: Literal["coerce", "strict"] = "coerce", use_columns: list[str] | list[int] | str | Callable[[ColumnInfo], bool] | None = None, dtypes: DTypeMap | None = None, + eager: Literal[False] = ..., + ) -> _ExcelTable: ... + @typing.overload + def load_table( + self, + name: str, + *, + header_row: int | None = None, + column_names: list[str] | None = None, + skip_rows: int = 0, + n_rows: int | None = None, + schema_sample_rows: int | None = 1_000, + dtype_coercion: Literal["coerce", "strict"] = "coerce", + use_columns: list[str] | list[int] | str | Callable[[ColumnInfo], bool] | None = None, + dtypes: DTypeMap | None = None, + eager: Literal[True] = ..., ) -> pa.RecordBatch: ... @property def sheet_names(self) -> list[str]: ... diff --git a/src/types/python/excelsheet/sheet_data.rs b/src/data.rs similarity index 60% rename from src/types/python/excelsheet/sheet_data.rs rename to src/data.rs index 88b68d2..553d10d 100644 --- a/src/types/python/excelsheet/sheet_data.rs +++ b/src/data.rs @@ -1,11 +1,17 @@ use std::sync::Arc; -use arrow::array::Array; +use arrow::{ + array::{Array, NullArray, RecordBatch}, + datatypes::{Field, Schema}, +}; use calamine::{Data as CalData, DataRef as CalDataRef, DataType, Range}; use crate::{ - error::FastExcelResult, - types::dtype::{get_dtype_for_column, DType, DTypeCoercion}, + error::{ErrorContext, FastExcelErrorKind, FastExcelResult}, + types::{ + dtype::{get_dtype_for_column, DType, DTypeCoercion}, + python::excelsheet::column_info::ColumnInfo, + }, }; pub(crate) enum ExcelSheetData<'r> { @@ -75,7 +81,7 @@ mod array_impls { use calamine::{CellType, DataType, Range}; use chrono::NaiveDate; - pub(super) fn create_boolean_array<DT: CellType + DataType>( + pub(crate) fn create_boolean_array<DT: CellType + DataType>( data: &Range<DT>, col: usize, offset: usize, @@ -96,7 +102,7 @@ mod array_impls { }))) } - pub(super) fn create_int_array<DT: CellType + DataType>( + pub(crate) fn create_int_array<DT: CellType + DataType>( data: &Range<DT>, col: usize, offset: usize, @@ -107,7 +113,7 @@ mod array_impls { )) } - pub(super) fn create_float_array<DT: CellType + DataType>( + pub(crate) fn create_float_array<DT: CellType + DataType>( data: &Range<DT>, col: usize, offset: usize, @@ -118,7 +124,7 @@ mod array_impls { )) } - pub(super) fn create_string_array<DT: CellType + DataType>( + pub(crate) fn create_string_array<DT: CellType + DataType>( data: &Range<DT>, col: usize, offset: usize, @@ -147,7 +153,7 @@ mod array_impls { caldt.as_duration().map(|d| d.num_milliseconds()) } - pub(super) fn create_date_array<DT: CellType + DataType>( + pub(crate) fn create_date_array<DT: CellType + DataType>( data: &Range<DT>, col: usize, offset: usize, @@ -161,7 +167,7 @@ mod array_impls { }))) } - pub(super) fn create_datetime_array<DT: CellType + DataType>( + pub(crate) fn create_datetime_array<DT: CellType + DataType>( data: &Range<DT>, col: usize, offset: usize, @@ -176,7 +182,7 @@ mod array_impls { ))) } - pub(super) fn create_duration_array<DT: CellType + DataType>( + pub(crate) fn create_duration_array<DT: CellType + DataType>( data: &Range<DT>, col: usize, offset: usize, @@ -212,3 +218,74 @@ create_array_function!(create_float_array); create_array_function!(create_datetime_array); create_array_function!(create_date_array); create_array_function!(create_duration_array); + +pub(crate) use array_impls::create_boolean_array as create_boolean_array_from_range; +pub(crate) use array_impls::create_date_array as create_date_array_from_range; +pub(crate) use array_impls::create_datetime_array as create_datetime_array_from_range; +pub(crate) use array_impls::create_duration_array as create_duration_array_from_range; +pub(crate) use array_impls::create_float_array as create_float_array_from_range; +pub(crate) use array_impls::create_int_array as create_int_array_from_range; +pub(crate) use array_impls::create_string_array as create_string_array_from_range; + +/// Converts a list of ColumnInfo to an arrow Schema +pub(crate) fn selected_columns_to_schema(columns: &[ColumnInfo]) -> Schema { + let fields: Vec<_> = columns.iter().map(Into::<Field>::into).collect(); + Schema::new(fields) +} + +/// Creates an arrow RecordBatch from an Iterator over (column_name, column data tuples) and an arrow schema +pub(crate) fn record_batch_from_name_array_iterator< + 'a, + I: Iterator<Item = (&'a str, Arc<dyn Array>)>, +>( + iter: I, + schema: Schema, +) -> FastExcelResult<RecordBatch> { + let mut iter = iter.peekable(); + // If the iterable is empty, try_from_iter returns an Err + if iter.peek().is_none() { + Ok(RecordBatch::new_empty(Arc::new(schema))) + } else { + // We use `try_from_iter_with_nullable` because `try_from_iter` relies on `array.null_count() > 0;` + // to determine if the array is nullable. This is not the case for `NullArray` which has no nulls. + RecordBatch::try_from_iter_with_nullable(iter.map(|(field_name, array)| { + let nullable = array.is_nullable(); + (field_name, array, nullable) + })) + .map_err(|err| FastExcelErrorKind::ArrowError(err.to_string()).into()) + .with_context(|| "could not create RecordBatch from iterable") + } +} + +/// Creates an arrow `RecordBatch` from `ExcelSheetData`. Expects the following parameters: +/// * `columns`: a slice of `ColumnInfo`, representing the columns that should be extracted from the range +/// * `data`: the sheets data, as an `ExcelSheetData` +/// * `offset`: the row index at which to start +/// * `limit`: the row index at which to stop (excluded) +pub(crate) fn record_batch_from_data_and_columns( + columns: &[ColumnInfo], + data: &ExcelSheetData, + offset: usize, + limit: usize, +) -> FastExcelResult<RecordBatch> { + let schema = selected_columns_to_schema(columns); + let iter = columns.iter().map(|column_info| { + let col_idx = column_info.index(); + let dtype = *column_info.dtype(); + ( + column_info.name.as_str(), + match dtype { + DType::Null => Arc::new(NullArray::new(limit - offset)), + DType::Int => create_int_array(data, col_idx, offset, limit), + DType::Float => create_float_array(data, col_idx, offset, limit), + DType::String => create_string_array(data, col_idx, offset, limit), + DType::Bool => create_boolean_array(data, col_idx, offset, limit), + DType::DateTime => create_datetime_array(data, col_idx, offset, limit), + DType::Date => create_date_array(data, col_idx, offset, limit), + DType::Duration => create_duration_array(data, col_idx, offset, limit), + }, + ) + }); + + record_batch_from_name_array_iterator(iter, schema) +} diff --git a/src/lib.rs b/src/lib.rs index 569301c..4ffc8df 100644 --- a/src/lib.rs +++ b/src/lib.rs @@ -1,10 +1,13 @@ +mod data; mod error; mod types; mod utils; use error::{py_errors, ErrorContext}; use pyo3::{prelude::*, types::PyString}; -use types::python::{excelsheet::column_info::ColumnInfo, ExcelReader, ExcelSheet}; +use types::python::{ + excelsheet::column_info::ColumnInfo, table::ExcelTable, ExcelReader, ExcelSheet, +}; /// Reads an excel file and returns an object allowing to access its sheets and a bit of metadata #[pyfunction] @@ -46,6 +49,7 @@ fn _fastexcel(m: &Bound<'_, PyModule>) -> PyResult<()> { m.add_class::<ColumnInfo>()?; m.add_class::<ExcelSheet>()?; m.add_class::<ExcelReader>()?; + m.add_class::<ExcelTable>()?; m.add("__version__", get_version())?; // errors diff --git a/src/types/python/excelreader.rs b/src/types/python/excelreader.rs index c0cc6d4..312ca78 100644 --- a/src/types/python/excelreader.rs +++ b/src/types/python/excelreader.rs @@ -1,5 +1,3 @@ -use crate::types::python::excelsheet::table::{extract_table_names, extract_table_range}; -use crate::utils::schema::get_schema_sample_rows; use std::{ fs::File, io::{BufReader, Cursor}, @@ -14,23 +12,25 @@ use calamine::{ }; use crate::{ + data::{record_batch_from_data_and_columns, ExcelSheetData}, error::{ py_errors::IntoPyResult, ErrorContext, FastExcelError, FastExcelErrorKind, FastExcelResult, }, types::{ dtype::{DTypeCoercion, DTypeMap}, idx_or_name::IdxOrName, + python::excelsheet::table::{extract_table_names, extract_table_range}, }, + utils::schema::get_schema_sample_rows, }; use pyo3::types::PyString; -use super::excelsheet::record_batch_from_data_and_columns; use super::excelsheet::{ column_info::{build_available_columns, build_available_columns_info}, - sheet_data::ExcelSheetData, + ExcelSheet, Header, Pagination, SelectedColumns, }; -use super::excelsheet::{ExcelSheet, Header, Pagination, SelectedColumns}; +use super::table::ExcelTable; enum ExcelSheets { File(Sheets<BufReader<File>>), @@ -155,7 +155,7 @@ impl ExcelReader { let final_columns = selected_columns.select_columns(&available_columns)?; - record_batch_from_data_and_columns(final_columns, data, offset, limit) + record_batch_from_data_and_columns(&final_columns, data, offset, limit) } #[allow(clippy::too_many_arguments)] @@ -244,21 +244,10 @@ impl ExcelReader { } }; - // TODO: Use From<Table<T>> for Range<T> once https://github.com/tafia/calamine/pull/464 is merged - let range = table.data(); - let pagination = Pagination::new(skip_rows, n_rows, range).into_pyresult()?; + let pagination = Pagination::new(skip_rows, n_rows, table.data()).into_pyresult()?; - // FIXME: We're creating sheet metadata here, but it should not be required by the future Table object - let sheet_meta = CalamineSheet { - name, - typ: calamine::SheetType::WorkSheet, - visible: calamine::SheetVisible::Visible, - }; - - let sheet = ExcelSheet::try_new( - sheet_meta, - // TODO: Remove clone when aforementioned .from() is used - ExcelSheetData::from(range.clone()), + let excel_table = ExcelTable::try_new( + table, header, pagination, schema_sample_rows, @@ -269,9 +258,9 @@ impl ExcelReader { .into_pyresult()?; if eager { - sheet.to_arrow(py) + excel_table.to_arrow(py) } else { - Ok(sheet.into_py(py)) + Ok(excel_table.into_py(py)) } } } diff --git a/src/types/python/excelsheet/column_info.rs b/src/types/python/excelsheet/column_info.rs index 1988794..a8c5b06 100644 --- a/src/types/python/excelsheet/column_info.rs +++ b/src/types/python/excelsheet/column_info.rs @@ -1,19 +1,21 @@ use std::{fmt::Display, str::FromStr}; use arrow::datatypes::Field; +use calamine::DataType; use pyo3::{pyclass, pymethods, PyResult}; use crate::{ + data::ExcelSheetData, error::{ py_errors::IntoPyResult, ErrorContext, FastExcelError, FastExcelErrorKind, FastExcelResult, }, types::{ - dtype::{DType, DTypeCoercion, DTypeMap}, + dtype::{get_dtype_for_column, DType, DTypeCoercion, DTypeMap}, idx_or_name::IdxOrName, }, }; -use super::{sheet_data::ExcelSheetData, Header, SelectedColumns}; +use super::{Header, SelectedColumns}; #[derive(Debug, Clone, PartialEq)] pub(crate) enum ColumnNameFrom { @@ -231,9 +233,9 @@ impl ColumnInfoBuilder { &self.name } - fn dtype_info( + fn dtype_info<D: CalamineDataProvider>( &self, - data: &ExcelSheetData<'_>, + data: &D, start_row: usize, end_row: usize, specified_dtypes: Option<&DTypeMap>, @@ -259,9 +261,9 @@ impl ColumnInfoBuilder { }) } - pub(super) fn finish( + pub(super) fn finish<D: CalamineDataProvider>( self, - data: &ExcelSheetData<'_>, + data: &D, start_row: usize, end_row: usize, specified_dtypes: Option<&DTypeMap>, @@ -280,8 +282,60 @@ impl ColumnInfoBuilder { } } -pub(crate) fn build_available_columns_info( - data: &ExcelSheetData<'_>, +pub(crate) trait CalamineDataProvider { + fn width(&self) -> usize; + fn get_as_string(&self, pos: (usize, usize)) -> Option<String>; + fn dtype_for_column( + &self, + start_row: usize, + end_row: usize, + col: usize, + dtype_coercion: &DTypeCoercion, + ) -> FastExcelResult<DType>; +} + +impl CalamineDataProvider for ExcelSheetData<'_> { + fn width(&self) -> usize { + self.width() + } + + fn get_as_string(&self, pos: (usize, usize)) -> Option<String> { + self.get_as_string(pos) + } + + fn dtype_for_column( + &self, + start_row: usize, + end_row: usize, + col: usize, + dtype_coercion: &DTypeCoercion, + ) -> FastExcelResult<DType> { + self.dtype_for_column(start_row, end_row, col, dtype_coercion) + } +} + +impl CalamineDataProvider for calamine::Range<calamine::Data> { + fn width(&self) -> usize { + self.width() + } + + fn get_as_string(&self, pos: (usize, usize)) -> Option<String> { + self.get(pos).and_then(|data| data.as_string()) + } + + fn dtype_for_column( + &self, + start_row: usize, + end_row: usize, + col: usize, + dtype_coercion: &DTypeCoercion, + ) -> FastExcelResult<DType> { + get_dtype_for_column(self, start_row, end_row, col, dtype_coercion) + } +} + +pub(crate) fn build_available_columns_info<D: CalamineDataProvider>( + data: &D, selected_columns: &SelectedColumns, header: &Header, ) -> FastExcelResult<Vec<ColumnInfoBuilder>> { @@ -397,9 +451,9 @@ fn alias_for_name(name: &str, existing_names: &[String]) -> String { rec(name, existing_names, 0) } -pub(crate) fn build_available_columns( +pub(crate) fn build_available_columns<D: CalamineDataProvider>( available_columns_info: Vec<ColumnInfoBuilder>, - data: &ExcelSheetData, + data: &D, start_row: usize, end_row: usize, specified_dtypes: Option<&DTypeMap>, diff --git a/src/types/python/excelsheet/mod.rs b/src/types/python/excelsheet/mod.rs index db91281..66aaf87 100644 --- a/src/types/python/excelsheet/mod.rs +++ b/src/types/python/excelsheet/mod.rs @@ -1,17 +1,10 @@ pub(crate) mod column_info; -pub(crate) mod sheet_data; pub(crate) mod table; use calamine::{CellType, Range, Sheet as CalamineSheet, SheetVisible as CalamineSheetVisible}; -use sheet_data::ExcelSheetData; -use std::{cmp, collections::HashSet, fmt::Debug, str::FromStr, sync::Arc}; - -use arrow::{ - array::{Array, NullArray}, - datatypes::{Field, Schema}, - pyarrow::ToPyArrow, - record_batch::RecordBatch, -}; +use std::{cmp, collections::HashSet, fmt::Debug, str::FromStr}; + +use arrow::{pyarrow::ToPyArrow, record_batch::RecordBatch}; use pyo3::{ prelude::{pyclass, pymethods, PyAnyMethods, Python}, @@ -20,21 +13,15 @@ use pyo3::{ }; use crate::{ + data::{record_batch_from_data_and_columns, ExcelSheetData}, error::{ py_errors::IntoPyResult, ErrorContext, FastExcelError, FastExcelErrorKind, FastExcelResult, }, - types::{ - dtype::{DType, DTypeMap}, - idx_or_name::IdxOrName, - }, + types::{dtype::DTypeMap, idx_or_name::IdxOrName}, }; use crate::{types::dtype::DTypeCoercion, utils::schema::get_schema_sample_rows}; use self::column_info::{build_available_columns, build_available_columns_info, ColumnInfo}; -use self::sheet_data::{ - create_boolean_array, create_date_array, create_datetime_array, create_duration_array, - create_float_array, create_int_array, create_string_array, -}; #[derive(Debug)] pub(crate) enum Header { @@ -416,11 +403,11 @@ impl ExcelSheet { &sheet.dtype_coercion, )?; + // Figure out dtype for every column let selected_columns = selected_columns.select_columns(&available_columns)?; sheet.available_columns = available_columns; sheet.selected_columns = selected_columns; - // Figure out dtype for every column Ok(sheet) } @@ -441,57 +428,6 @@ impl ExcelSheet { } } -impl From<&ExcelSheet> for Schema { - fn from(sheet: &ExcelSheet) -> Self { - let fields: Vec<_> = sheet - .selected_columns - .iter() - .map(Into::<Field>::into) - .collect(); - Schema::new(fields) - } -} - -pub(crate) fn record_batch_from_data_and_columns( - columns: Vec<ColumnInfo>, - data: &ExcelSheetData, - offset: usize, - limit: usize, -) -> FastExcelResult<RecordBatch> { - let fields = columns.iter().map(Into::<Field>::into).collect::<Vec<_>>(); - - let schema = Schema::new(fields); - - let mut iter = columns - .into_iter() - .map(|column_info| { - let col_idx = column_info.index(); - let dtype = *column_info.dtype(); - ( - column_info.name, - match dtype { - DType::Null => Arc::new(NullArray::new(limit - offset)), - DType::Int => create_int_array(data, col_idx, offset, limit), - DType::Float => create_float_array(data, col_idx, offset, limit), - DType::String => create_string_array(data, col_idx, offset, limit), - DType::Bool => create_boolean_array(data, col_idx, offset, limit), - DType::DateTime => create_datetime_array(data, col_idx, offset, limit), - DType::Date => create_date_array(data, col_idx, offset, limit), - DType::Duration => create_duration_array(data, col_idx, offset, limit), - }, - ) - }) - .peekable(); - // If the iterable is empty, try_from_iter returns an Err - if iter.peek().is_none() { - Ok(RecordBatch::new_empty(Arc::new(schema))) - } else { - RecordBatch::try_from_iter(iter) - .map_err(|err| FastExcelErrorKind::ArrowError(err.to_string()).into()) - .with_context(|| "could not create RecordBatch from iterable") - } -} - impl TryFrom<&ExcelSheet> for RecordBatch { type Error = FastExcelError; @@ -499,56 +435,8 @@ impl TryFrom<&ExcelSheet> for RecordBatch { let offset = sheet.offset(); let limit = sheet.limit(); - let mut iter = sheet - .selected_columns - .iter() - .map(|column_info| { - // At this point, we know for sure that the column is in the schema so we can - // safely unwrap - ( - column_info.name(), - match column_info.dtype() { - DType::Bool => { - create_boolean_array(sheet.data(), column_info.index(), offset, limit) - } - DType::Int => { - create_int_array(sheet.data(), column_info.index(), offset, limit) - } - DType::Float => { - create_float_array(sheet.data(), column_info.index(), offset, limit) - } - DType::String => { - create_string_array(sheet.data(), column_info.index(), offset, limit) - } - DType::DateTime => { - create_datetime_array(sheet.data(), column_info.index(), offset, limit) - } - DType::Date => { - create_date_array(sheet.data(), column_info.index(), offset, limit) - } - DType::Duration => { - create_duration_array(sheet.data(), column_info.index(), offset, limit) - } - DType::Null => Arc::new(NullArray::new(limit - offset)), - }, - ) - }) - .peekable(); - - // If the iterable is empty, try_from_iter returns an Err - if iter.peek().is_none() { - let schema: Schema = sheet.into(); - Ok(RecordBatch::new_empty(Arc::new(schema))) - } else { - // We use `try_from_iter_with_nullable` because `try_from_iter` relies on `array.null_count() > 0;` - // to determine if the array is nullable. This is not the case for `NullArray` which has no nulls. - RecordBatch::try_from_iter_with_nullable(iter.map(|(field_name, array)| { - let nullable = array.is_nullable(); - (field_name, array, nullable) - })) - .map_err(|err| FastExcelErrorKind::ArrowError(err.to_string()).into()) + record_batch_from_data_and_columns(&sheet.selected_columns, sheet.data(), offset, limit) .with_context(|| format!("could not convert sheet {} to RecordBatch", sheet.name())) - } } } diff --git a/src/types/python/mod.rs b/src/types/python/mod.rs index 261453f..7a1c3a3 100644 --- a/src/types/python/mod.rs +++ b/src/types/python/mod.rs @@ -1,4 +1,5 @@ pub(crate) mod excelreader; pub(crate) mod excelsheet; +pub(crate) mod table; pub(crate) use excelreader::ExcelReader; pub(crate) use excelsheet::ExcelSheet; diff --git a/src/types/python/table.rs b/src/types/python/table.rs new file mode 100644 index 0000000..b3df0dc --- /dev/null +++ b/src/types/python/table.rs @@ -0,0 +1,268 @@ +use std::sync::Arc; + +use arrow::{ + array::{NullArray, RecordBatch}, + pyarrow::ToPyArrow, +}; +use calamine::{Data, Range, Table}; +use pyo3::{pyclass, pymethods, PyObject, PyResult, Python, ToPyObject}; + +use crate::{ + data::{ + create_boolean_array_from_range, create_date_array_from_range, + create_datetime_array_from_range, create_duration_array_from_range, + create_float_array_from_range, create_int_array_from_range, create_string_array_from_range, + record_batch_from_name_array_iterator, selected_columns_to_schema, + }, + error::{ + py_errors::IntoPyResult, ErrorContext, FastExcelError, FastExcelErrorKind, FastExcelResult, + }, + types::{ + dtype::{DType, DTypeCoercion, DTypeMap}, + python::excelsheet::column_info::build_available_columns, + }, + utils::schema::get_schema_sample_rows, +}; + +use super::excelsheet::{ + column_info::{build_available_columns_info, ColumnInfo}, + Header, Pagination, SelectedColumns, +}; + +#[pyclass(name = "_ExcelTable")] +pub(crate) struct ExcelTable { + #[pyo3(get)] + name: String, + #[pyo3(get)] + sheet_name: String, + selected_columns: Vec<ColumnInfo>, + available_columns: Vec<ColumnInfo>, + table: Table<Data>, + header: Header, + pagination: Pagination, + dtypes: Option<DTypeMap>, + dtype_coercion: DTypeCoercion, + height: Option<usize>, + total_height: Option<usize>, + width: Option<usize>, +} + +impl ExcelTable { + pub(crate) fn try_new( + table: Table<Data>, + header: Header, + pagination: Pagination, + schema_sample_rows: Option<usize>, + dtype_coercion: DTypeCoercion, + selected_columns: SelectedColumns, + dtypes: Option<DTypeMap>, + ) -> FastExcelResult<Self> { + let available_columns_info = + build_available_columns_info(table.data(), &selected_columns, &header)?; + + let mut excel_table = ExcelTable { + name: table.name().to_owned(), + sheet_name: table.sheet_name().to_owned(), + // Empty vecs as they'll be replaced + available_columns: Vec::with_capacity(0), + selected_columns: Vec::with_capacity(0), + table, + header, + pagination, + dtypes, + dtype_coercion, + height: None, + total_height: None, + width: None, + }; + + let row_limit = get_schema_sample_rows( + schema_sample_rows, + excel_table.offset(), + excel_table.limit(), + ); + + // Finalizing column info + let available_columns = build_available_columns( + available_columns_info, + excel_table.data(), + excel_table.offset(), + row_limit, + excel_table.dtypes.as_ref(), + &excel_table.dtype_coercion, + )?; + + // Figure out dtype for every column + let selected_columns = selected_columns.select_columns(&available_columns)?; + excel_table.available_columns = available_columns; + excel_table.selected_columns = selected_columns; + + Ok(excel_table) + } + + pub(crate) fn data(&self) -> &Range<Data> { + self.table.data() + } +} + +impl TryFrom<&ExcelTable> for RecordBatch { + type Error = FastExcelError; + + fn try_from(table: &ExcelTable) -> FastExcelResult<Self> { + let offset = table.offset(); + let limit = table.limit(); + + let iter = table.selected_columns.iter().map(|column_info| { + ( + column_info.name(), + match column_info.dtype() { + DType::Bool => create_boolean_array_from_range( + table.data(), + column_info.index(), + offset, + limit, + ), + DType::Int => create_int_array_from_range( + table.data(), + column_info.index(), + offset, + limit, + ), + DType::Float => create_float_array_from_range( + table.data(), + column_info.index(), + offset, + limit, + ), + DType::String => create_string_array_from_range( + table.data(), + column_info.index(), + offset, + limit, + ), + DType::DateTime => create_datetime_array_from_range( + table.data(), + column_info.index(), + offset, + limit, + ), + DType::Date => create_date_array_from_range( + table.data(), + column_info.index(), + offset, + limit, + ), + DType::Duration => create_duration_array_from_range( + table.data(), + column_info.index(), + offset, + limit, + ), + DType::Null => Arc::new(NullArray::new(limit - offset)), + }, + ) + }); + + let schema = selected_columns_to_schema(&table.selected_columns); + + record_batch_from_name_array_iterator(iter, schema).with_context(|| { + format!( + "could not convert table {table} in sheet {sheet} to RecordBatch", + table = &table.name, + sheet = &table.sheet_name + ) + }) + } +} + +#[pymethods] +impl ExcelTable { + #[getter] + pub fn offset(&self) -> usize { + self.header.offset() + self.pagination.offset() + } + + #[getter] + pub(crate) fn limit(&self) -> usize { + let upper_bound = self.data().height(); + if let Some(n_rows) = self.pagination.n_rows() { + let limit = self.offset() + n_rows; + if limit < upper_bound { + return limit; + } + } + + upper_bound + } + + #[getter] + pub fn selected_columns(&self) -> Vec<ColumnInfo> { + self.selected_columns.clone() + } + + #[getter] + pub fn available_columns(&self) -> Vec<ColumnInfo> { + self.available_columns.clone() + } + + #[getter] + pub fn specified_dtypes<'p>(&'p self, py: Python<'p>) -> Option<PyObject> { + self.dtypes.as_ref().map(|dtypes| dtypes.to_object(py)) + } + + #[getter] + pub fn width(&mut self) -> usize { + self.width.unwrap_or_else(|| { + let width = self.data().width(); + self.width = Some(width); + width + }) + } + + #[getter] + pub fn height(&mut self) -> usize { + self.height.unwrap_or_else(|| { + let height = self.limit() - self.offset(); + self.height = Some(height); + height + }) + } + + #[getter] + pub fn total_height(&mut self) -> usize { + self.total_height.unwrap_or_else(|| { + let total_height = self.data().height() - self.header.offset(); + self.total_height = Some(total_height); + total_height + }) + } + + pub fn to_arrow(&self, py: Python<'_>) -> PyResult<PyObject> { + RecordBatch::try_from(self) + .with_context(|| { + format!( + "could not create RecordBatch from sheet \"{}\"", + self.name + ) + }) + .and_then(|rb| { + rb.to_pyarrow(py) + .map_err(|err| FastExcelErrorKind::ArrowError(err.to_string()).into()) + }) + .with_context(|| { + format!( + "could not convert RecordBatch to pyarrow for table \"{table}\" in sheet \"{sheet}\"", + table = self.name, sheet = self.sheet_name + ) + }) + .into_pyresult() + } + + pub fn __repr__(&self) -> String { + format!( + "ExcelTable<{sheet}/{name}>", + sheet = self.sheet_name, + name = self.name + ) + } +}

Support for Reading Excel Tables From my experience it's usually much safer to load data from an excel table than from a sheet. would be nice if one could get the table names per sheet and get the table data as arrow/pandas like with the sheets

Hi @aersam , could you please provide a bit more details on what you have in mind ? And maybe provide a few examples on how that parameter is passed in other libraries (for example pandas) ? A file with the issue you are encountering would also be great Hi there! Here's a sample file: [tables.xlsx](https://github.com/ToucanToco/fastexcel/files/14703008/tables.xlsx) I'd like to pass the table_name instead of sheet_name, table name's are unique within an excel workbook as well: ![image](https://github.com/ToucanToco/fastexcel/assets/5270024/b54cd319-4e14-4bda-9999-5cd1b6edeab8) Thanks! This seems to be doable through calamine's APIs (https://docs.rs/calamine/latest/calamine/struct.Xlsx.html#method.table_by_name) but will require quite some work, so I don't know when/if we will ship it... We might pritoritize this if a lot of people ask for this feature :slightly_smiling_face: > Thanks! This seems to be doable through calamine's APIs (https://docs.rs/calamine/latest/calamine/struct.Xlsx.html#method.table_by_name) but will require quite some work, so I don't know when/if we will ship it... We might pritoritize this if a lot of people ask for this feature 🙂 As a minor FYI, we always write "real" Excel table objects from Polars' `write_excel` method as they are generally considered quite useful, given how freeform Excel can otherwise be ;) @alexander-beedie ah good to know, thanks! How do you handle tables on read ? Is there a parameter to read a specific table ? > @alexander-beedie ah good to know, thanks! How do you handle tables on read ? Is there a parameter to read a specific table ? At the moment I don't, heh 😎 However, I was thinking about adding a `table_name` parameter, as one of our other engines (openpyxl) also supports reading table objects. If we could get an equivalent capability for fastexcel that would be really nice, as it completely eliminates the guessing game of _"where does the data start/end"_ 👌 (You may also want an option to skip the final row when reading table objects, as that can contain totals that are not part of the data - they also may or may not have a header row, so the existing parameters to determine that can be respected).

2024-09-18T15:19:30

0.0

[]

ToucanToco/fastexcel

ToucanToco__fastexcel-279

c3cd0d9f5ca5f27e481b0775cd7826fb9fe3fee1

diff --git a/.gitignore b/.gitignore index 3a99b8d..5659737 100644 --- a/.gitignore +++ b/.gitignore @@ -5,6 +5,7 @@ __pycache__ *.pyc *.so *.dat +.DS_Store .python-version .venv diff --git a/src/types/python/excelsheet/mod.rs b/src/types/python/excelsheet/mod.rs index 9b98185..761d5c2 100644 --- a/src/types/python/excelsheet/mod.rs +++ b/src/types/python/excelsheet/mod.rs @@ -6,7 +6,7 @@ use sheet_data::ExcelSheetData; use std::{cmp, collections::HashSet, fmt::Debug, str::FromStr, sync::Arc}; use arrow::{ - array::NullArray, + array::{Array, NullArray}, datatypes::{Field, Schema}, pyarrow::ToPyArrow, record_batch::RecordBatch, @@ -521,9 +521,14 @@ impl TryFrom<&ExcelSheet> for RecordBatch { let schema: Schema = sheet.into(); Ok(RecordBatch::new_empty(Arc::new(schema))) } else { - RecordBatch::try_from_iter(iter) - .map_err(|err| FastExcelErrorKind::ArrowError(err.to_string()).into()) - .with_context(|| format!("could not convert sheet {} to RecordBatch", sheet.name)) + // We use `try_from_iter_with_nullable` because `try_from_iter` relies on `array.null_count() > 0;` + // to determine if the array is nullable. This is not the case for `NullArray` which has no nulls. + RecordBatch::try_from_iter_with_nullable(iter.map(|(field_name, array)| { + let nullable = array.is_nullable(); + (field_name, array, nullable) + })) + .map_err(|err| FastExcelErrorKind::ArrowError(err.to_string()).into()) + .with_context(|| format!("could not convert sheet {} to RecordBatch", sheet.name)) } } }

Sheet `to_arrow()` produces `null not null` columns With the example file [null-column.xlsx](https://github.com/user-attachments/files/16692741/null-column.xlsx): ```python >>> excel_reader = fastexcel.read_excel('null-column.xlsx') >>> sheet = excel_reader.load_sheet(0) >>> sheet.to_arrow() pyarrow.RecordBatch record_id: double not null nullonly: null not null ---- record_id: [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20] nullonly: 20 nulls ``` Apparently PyArrow's [enforcement of "nullable" is not strong](https://stackoverflow.com/questions/78848723/non-nullable-field-in-schema-does-not-prevent-null-values-in-corresponding-table), so this doesn't create serious problems for me until I try to pickle and unpickle the RecordBatch. Then I get the exception: ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "pyarrow/types.pxi", line 3532, in pyarrow.lib.field ValueError: A null type field may not be non-nullable ``` I tried tracking down the cause of this weird `null not null` column schema, but it does appear that fastexcel [attempts to define all columns as "nullable"](https://github.com/ToucanToco/fastexcel/blob/c3cd0d9f5ca5f27e481b0775cd7826fb9fe3fee1/src/types/python/excelsheet/column_info.rs#L132), so I'm not sure where this is originating from. However, if I build the RecordBatch by way of polars, I don't get this strange schema: ```python >>> sheet.to_polars().to_arrow().to_batches()[0] pyarrow.RecordBatch record_id: double nullonly: null ---- record_id: [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20] nullonly: 20 nulls ``` ... so I suppose it's at least plausible that this could be fixed within fastexcel.

2024-08-22T08:38:43

0.0

[]

ToucanToco/fastexcel

ToucanToco__fastexcel-260

441a973da40b0e9710a54fd460cc4af11473a596

diff --git a/src/types/python/excelreader.rs b/src/types/python/excelreader.rs index 1837fd9..8e524a7 100644 --- a/src/types/python/excelreader.rs +++ b/src/types/python/excelreader.rs @@ -136,7 +136,9 @@ impl ExcelReader { dtype_coercion, )?; - let fields = available_columns + let final_columns = selected_columns.select_columns(&available_columns)?; + + let fields = final_columns .iter() .map(Into::<Field>::into) .collect::<Vec<_>>();

use_columns is ignored when sheets are loaded eagerly ``` In [7]: reader.load_sheet_eager(0, use_columns=['Year']) Out[7]: pyarrow.RecordBatch Month: double not null Year: double not null ---- Month: [1,2] Year: [2019,2020] ```

2024-07-19T15:36:27

0.0

[]

ToucanToco/fastexcel

ToucanToco__fastexcel-251

5c7c6084e536dca60f0588077d8b769159699525

diff --git a/src/types/dtype.rs b/src/types/dtype.rs index e264949..9af844a 100644 --- a/src/types/dtype.rs +++ b/src/types/dtype.rs @@ -224,6 +224,7 @@ fn int_types() -> &'static HashSet<DType> { fn string_types() -> &'static HashSet<DType> { STRING_TYPES_CELL.get_or_init(|| { HashSet::from([ + DType::Bool, DType::Int, DType::Float, DType::String, diff --git a/src/types/python/excelsheet/sheet_data.rs b/src/types/python/excelsheet/sheet_data.rs index 5a4b337..88b68d2 100644 --- a/src/types/python/excelsheet/sheet_data.rs +++ b/src/types/python/excelsheet/sheet_data.rs @@ -134,6 +134,8 @@ mod array_impls { .map(|dt| dt.to_string()) } else if cell.is_datetime_iso() { cell.get_datetime_iso().map(str::to_string) + } else if cell.is_bool() { + cell.get_bool().map(|v| v.to_string()) } else { cell.as_string() }

When coercing columns to strings, boolean cells turn into null In our project, we have Excel files with mixed data types. Because of this, we need to coerce all columns to strings, and interpret the data downstream. We're currently blocked from adopting `fastexcel` because it does not coerce booleans as expected. ## How to reproduce Suppose you have an Excel file with the following data: | Header | |--------| | `=TRUE` | | `=FALSE` | | `"some string"` | Now lets read this Excel file into a Polars dataframe, while coercing the column to strings: `excel_reader.load_sheet(0, header_row=1, dtypes={"Header": "string"}).to_polars()` This produces the following data frame: | Header | |--------| | `null` | | `null` | | `"some string"` | ## Expected behavior What I would have expected fastexcel to coerce the boolean values to `"0"` and `"1"` instead of losing them. That is: | Header | |--------| | `"1"` | | `"0"` | | `"some string"` | ## Test case I've cloned the fastexcel repo and wrote a new unit test for this, which currently fails. Excel sheet: [sheet-bool.xlsx](https://github.com/user-attachments/files/16260997/sheet-bool.xlsx) ```python def test_bool_casting_to_string_for_polars() -> None: excel_reader = fastexcel.read_excel(path_for_fixture("sheet-bool.xlsx")) actual_polars_df = excel_reader.load_sheet( 0, header_row=None, dtypes={0: "string"}, column_names=["0"] ).to_polars() expected_polars_df = pl.DataFrame( { "0": ["1", "0", "some string"], } ) pl_assert_frame_equal(actual_polars_df, expected_polars_df) ``` Unfortunately, I'm not experienced with Rust, so I did not yet figure out where/how to make the change in the Rust code to make the test pass. ## Closing words First of all, big thanks for building `fastexcel`! I'm eager to migrate to fastexcel due to its ability to directly output Polars, which our downstream pipelines are based on, which will give us a massive performance boost. It would be much appreciated if you either gave me some pointers on where/how to make this change in Rust code (so I can open a PR), or made the fix yourself.

Hi @severinh and thanks for your kind words I'm on my phone right now so hard to help more but I would start with [this PR](https://github.com/ToucanToco/fastexcel/pull/245) You probably want to update `STRING_TYPES_CELL` to support booleans If that doesn't work we can make a quick fix soon. I'm planning on working on fastexcel this weekend and release a new version v0.11.0 soon, mostly for @lukapeschke amazing work on https://github.com/ToucanToco/fastexcel/pull/147 Btw @lukapeschke and myself tend to prefer the `"true"` / `"false"` representation (the classic `to_string()` for a boolean) Would that be ok for you @severinh ? I'll let you work on it if you want. If you prefer I can open a fix at lunch. You tell me @PrettyWood Thanks for the quick response!. > Btw @lukapeschke and myself tend to prefer the "true" / "false" representation (the classic to_string() for a boolean) Would that be ok for you @severinh ? Sure! Makes sense to be more explicit. The main thing I care about is that we don't lose legitimate data. > You probably want to update STRING_TYPES_CELL to support booleans Tried that, and unfortunately that did not seem to be enough yet. The output was still `null`. Since I won't have time this afternoon to look further into it, I would appreciate you going ahead and making the change. PS: Another problem we ran into: A cell like `=DATE(2024, 7, 17)` is coerced into `'2024-07-01 00:00:00'`. I would have expected `'2024-07-01'` for dates. For now, we're trimming `' 00:00:00'` in post-processing, which is not ideal. Is this behavior expected and intentional? If not, I can file a separate ticket for this.

2024-07-17T12:57:32

0.0

[]

ToucanToco/fastexcel

ToucanToco__fastexcel-243

b93636ee1aa8674d132b9d0a97c31162dd9cb41b

diff --git a/python/fastexcel/__init__.py b/python/fastexcel/__init__.py index 60415cd..6529195 100644 --- a/python/fastexcel/__init__.py +++ b/python/fastexcel/__init__.py @@ -1,7 +1,7 @@ from __future__ import annotations import sys -from typing import TYPE_CHECKING, Literal +from typing import TYPE_CHECKING, Callable, Literal if sys.version_info < (3, 10): from typing_extensions import TypeAlias @@ -128,7 +128,7 @@ def load_sheet( skip_rows: int = 0, n_rows: int | None = None, schema_sample_rows: int | None = 1_000, - use_columns: list[str] | list[int] | str | None = None, + use_columns: list[str] | list[int] | str | Callable[[ColumnInfo], bool] | None = None, dtypes: DTypeMap | None = None, ) -> ExcelSheet: """Loads a sheet lazily by index or name. @@ -153,6 +153,8 @@ def load_sheet( - A string, a comma separated list of Excel column letters and column ranges (e.g. `“A:E”` or `“A,C,E:F”`, which would result in `A,B,C,D,E` and `A,C,E,F`) + - A callable, a function that takes a column and returns a boolean + indicating whether the column should be used :param dtypes: An optional dict of dtypes. Keys can be column indices or names """ return ExcelSheet( @@ -209,7 +211,7 @@ def load_sheet_by_name( skip_rows: int = 0, n_rows: int | None = None, schema_sample_rows: int | None = 1_000, - use_columns: list[str] | list[int] | str | None = None, + use_columns: list[str] | list[int] | str | Callable[[ColumnInfo], bool] | None = None, dtypes: DTypeMap | None = None, ) -> ExcelSheet: """Loads a sheet by name. @@ -236,7 +238,7 @@ def load_sheet_by_idx( skip_rows: int = 0, n_rows: int | None = None, schema_sample_rows: int | None = 1_000, - use_columns: list[str] | list[int] | str | None = None, + use_columns: list[str] | list[int] | str | Callable[[ColumnInfo], bool] | None = None, dtypes: DTypeMap | None = None, ) -> ExcelSheet: """Loads a sheet by index. diff --git a/python/fastexcel/_fastexcel.pyi b/python/fastexcel/_fastexcel.pyi index 59e892e..9efe012 100644 --- a/python/fastexcel/_fastexcel.pyi +++ b/python/fastexcel/_fastexcel.pyi @@ -1,7 +1,7 @@ from __future__ import annotations import typing -from typing import Literal +from typing import Callable, Literal import pyarrow as pa @@ -72,7 +72,7 @@ class _ExcelReader: skip_rows: int = 0, n_rows: int | None = None, schema_sample_rows: int | None = 1_000, - use_columns: list[str] | list[int] | str | None = None, + use_columns: list[str] | list[int] | str | Callable[[ColumnInfo], bool] | None = None, dtypes: DTypeMap | None = None, eager: Literal[False] = ..., ) -> _ExcelSheet: ... diff --git a/src/types/python/excelreader.rs b/src/types/python/excelreader.rs index 6089750..c1468b8 100644 --- a/src/types/python/excelreader.rs +++ b/src/types/python/excelreader.rs @@ -84,7 +84,7 @@ impl ExcelReader { fn build_selected_columns( use_columns: Option<&Bound<'_, PyAny>>, ) -> FastExcelResult<SelectedColumns> { - use_columns.try_into().with_context(|| format!("expected selected columns to be list[str] | list[int] | str | None, got {use_columns:?}")) + use_columns.try_into().with_context(|| format!("expected selected columns to be list[str] | list[int] | str | Callable[[ColumnInfo], bool] | None, got {use_columns:?}")) } // NOTE: Not implementing TryFrom here, because we're aren't building the file from the passed diff --git a/src/types/python/excelsheet/mod.rs b/src/types/python/excelsheet/mod.rs index 543c5a1..5179d99 100644 --- a/src/types/python/excelsheet/mod.rs +++ b/src/types/python/excelsheet/mod.rs @@ -114,27 +114,53 @@ impl TryFrom<&Bound<'_, PyList>> for SelectedColumns { } } -#[derive(Debug, PartialEq)] pub(crate) enum SelectedColumns { All, Selection(Vec<IdxOrName>), + DynamicSelection(PyObject), +} + +impl std::fmt::Debug for SelectedColumns { + fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { + match self { + Self::All => write!(f, "All"), + Self::Selection(selection) => write!(f, "Selection({selection:?})"), + Self::DynamicSelection(func) => { + let addr = func as *const _ as usize; + write!(f, "DynamicSelection({addr})") + } + } + } +} + +impl PartialEq for SelectedColumns { + fn eq(&self, other: &Self) -> bool { + match (self, other) { + (Self::All, Self::All) => true, + (Self::Selection(selection), Self::Selection(other_selection)) => { + selection == other_selection + } + (Self::DynamicSelection(f1), Self::DynamicSelection(f2)) => std::ptr::eq(f1, f2), + _ => false, + } + } } impl SelectedColumns { pub(super) fn select_columns( &self, - column_info: &[ColumnInfo], + available_columns: &[ColumnInfo], ) -> FastExcelResult<Vec<ColumnInfo>> { match self { - SelectedColumns::All => Ok(column_info.to_vec()), + SelectedColumns::All => Ok(available_columns.to_vec()), SelectedColumns::Selection(selection) => selection .iter() .map(|selected_column| { match selected_column { - IdxOrName::Idx(index) => column_info + IdxOrName::Idx(index) => available_columns .iter() .find(|col_info| &col_info.index() == index), - IdxOrName::Name(name) => column_info + IdxOrName::Name(name) => available_columns .iter() .find(|col_info| col_info.name() == name.as_str()), } @@ -142,9 +168,28 @@ impl SelectedColumns { FastExcelErrorKind::ColumnNotFound(selected_column.clone()).into() }) .cloned() - .with_context(|| format!("available columns are: {column_info:?}")) + .with_context(|| format!("available columns are: {available_columns:?}")) }) .collect(), + SelectedColumns::DynamicSelection(use_col_func) => Python::with_gil(|py| { + Ok(available_columns + .iter() + .filter_map( + |col_info| match use_col_func.call1(py, (col_info.clone(),)) { + Err(err) => Some(Err(FastExcelErrorKind::InvalidParameters(format!( + "`use_columns` callable could not be called ({err})" + )))), + Ok(should_use_col) => match should_use_col.extract::<bool>(py) { + Err(_) => Some(Err(FastExcelErrorKind::InvalidParameters( + "`use_columns` callable should return a boolean".to_string(), + ))), + Ok(true) => Some(Ok(col_info.clone())), + Ok(false) => None, + }, + }, + ) + .collect::<Result<Vec<_>, _>>()?) + }), } } @@ -272,6 +317,8 @@ impl TryFrom<Option<&Bound<'_, PyAny>>> for SelectedColumns { .parse() } else if let Ok(py_list) = py_any.downcast::<PyList>() { py_list.try_into() + } else if let Ok(py_function) = py_any.extract::<PyObject>() { + Ok(Self::DynamicSelection(py_function)) } else { Err(FastExcelErrorKind::InvalidParameters(format!( "unsupported object type {object_type}",

feat: support callable in use_columns parameter Hi, Is it possible to support a callable in the `use_columns` parameter like in `pandas.read_excel`. ```python def load_sheet_by_name( self, name: str, *, header_row: int | None = 0, column_names: list[str] | None = None, skip_rows: int = 0, n_rows: int | None = None, schema_sample_rows: int | None = 1000, use_columns: list[str] | list[int] | str | Callable[[str], bool] | None = None, dtypes: 'DTypeMap | None' = None ) -> ExcelSheet: ... ```

2024-06-30T15:16:41

0.0

[]

ToucanToco/fastexcel

ToucanToco__fastexcel-223

21f7f222f1fac36bd56d1679ca564456e712d757

diff --git a/src/types/dtype.rs b/src/types/dtype.rs index 90e7576..fb544ba 100644 --- a/src/types/dtype.rs +++ b/src/types/dtype.rs @@ -136,7 +136,7 @@ fn get_cell_dtype(data: &Range<CalData>, row: usize, col: usize) -> FastExcelRes CalData::DurationIso(_) => Ok(DType::Duration), // Errors and nulls CalData::Error(err) => match err { - CellErrorType::NA => Ok(DType::Null), + CellErrorType::NA | CellErrorType::Value | CellErrorType::Null => Ok(DType::Null), _ => Err(FastExcelErrorKind::CalamineCellError(err.to_owned()).into()), }, CalData::Empty => Ok(DType::Null),

Cannot handle special symbols Hi team, I am facing one issue on reading excel sheet through Polars. It says **calamine cell error: #VALUE!** The sheet in interest does not get read through standard api. How to handle such special symbols through fastexcel? Thank you. I am using fastexcel==0.10.2.

Hi @durgeksh could you please provide the entire stack trace ? and maybe a file allowing to reproduce the issue ? thanks! ```py Traceback (most recent call last): File "/Users/neo/Desktop/workspace/pocs/polarsdemo.py", line 84, in <module> df = pl.read_excel("sample_data.xlsx", engine='calamine') ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/neo/Desktop/workspace/pocs/.venv/lib/python3.11/site-packages/polars/_utils/deprecation.py", line 134, in wrapper return function(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/neo/Desktop/workspace/pocs/.venv/lib/python3.11/site-packages/polars/_utils/deprecation.py", line 134, in wrapper return function(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/neo/Desktop/workspace/pocs/.venv/lib/python3.11/site-packages/polars/io/spreadsheet/functions.py", line 253, in read_excel return _read_spreadsheet( ^^^^^^^^^^^^^^^^^^ File "/Users/neo/Desktop/workspace/pocs/.venv/lib/python3.11/site-packages/polars/io/spreadsheet/functions.py", line 475, in _read_spreadsheet parsed_sheets = { ^ File "/Users/neo/Desktop/workspace/pocs/.venv/lib/python3.11/site-packages/polars/io/spreadsheet/functions.py", line 476, in <dictcomp> name: reader_fn( ^^^^^^^^^^ File "/Users/neo/Desktop/workspace/pocs/.venv/lib/python3.11/site-packages/polars/io/spreadsheet/functions.py", line 821, in _read_spreadsheet_calamine ws = parser.load_sheet_by_name(sheet_name, **read_options) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/neo/Desktop/workspace/pocs/.venv/lib/python3.11/site-packages/fastexcel/__init__.py", line 184, in load_sheet_by_name self._reader.load_sheet( _fastexcel.CalamineCellError: calamine cell error: #VALUE! Context: 0: could not determine dtype for column Amount Process finished with exit code 1 ``` Sample file: [sample_data.xlsx](https://github.com/ToucanToco/fastexcel/files/14880107/sample_data.xlsx)

2024-04-05T08:28:02

0.0

[]

ToucanToco/fastexcel

ToucanToco__fastexcel-222

8060efac0d338224e12cf3af89b4802a503e7e06

diff --git a/src/types/python/excelreader.rs b/src/types/python/excelreader.rs index 8de50de..e295e6c 100644 --- a/src/types/python/excelreader.rs +++ b/src/types/python/excelreader.rs @@ -144,14 +144,25 @@ impl ExcelReader { let name = idx_or_name .try_into() .and_then(|idx_or_name| match idx_or_name { - IdxOrName::Name(name) => Ok(name), + IdxOrName::Name(name) => { + if self.sheet_names.contains(&name) { + Ok(name) + } else { + Err(FastExcelErrorKind::SheetNotFound(IdxOrName::Name(name.clone())).into()).with_context(|| { + let available_sheets = self.sheet_names.iter().map(|s| format!("\"{s}\"")).collect::<Vec<_>>().join(", "); + format!( + "Sheet \"{name}\" not found in file. Available sheets: {available_sheets}." + ) + }) + } + } IdxOrName::Idx(idx) => self .sheet_names .get(idx) .ok_or_else(|| FastExcelErrorKind::SheetNotFound(IdxOrName::Idx(idx)).into()) .with_context(|| { format!( - "Sheet index {idx} is out of range. File has {} sheets", + "Sheet index {idx} is out of range. File has {} sheets.", self.sheet_names.len() ) })

load_sheet_by_name raises generic CalamineError instead of SheetNotFoundError Hi, While loading a sheet from a reader, if a missing sheet name is provided a generic `CalamineError` is raised instead of the more specific `SheetNotFoundError`. My use case is: ```python sheets = [] for reader in readers: try: sheet = reader.load_sheet_by_name("sheet_name") except SheetNotFoundError: continue # Do some other stuff with sheet before appending to list. ```

2024-04-05T07:32:41

0.0

[]

ToucanToco/fastexcel

ToucanToco__fastexcel-217

026f2731211b697162fa1b8ff2d31054979c8162

diff --git a/src/types/python/excelsheet/column_info.rs b/src/types/python/excelsheet/column_info.rs index d26882e..8e502ac 100644 --- a/src/types/python/excelsheet/column_info.rs +++ b/src/types/python/excelsheet/column_info.rs @@ -182,7 +182,7 @@ impl ColumnInfo { } pub fn __repr__(&self) -> String { - format!("ColumnInfo<name=\"{name}\", index={index}, dtype=\"{dtype}\", dtype_from=\"{dtype_from}\", column_name_from=\"{column_name_from}\" >", name=self.name, index=self.index, dtype=self.dtype.to_string(), dtype_from=self.dtype_from.to_string(), column_name_from=self.column_name_from.to_string()) + format!("ColumnInfo(name=\"{name}\", index={index}, dtype=\"{dtype}\", dtype_from=\"{dtype_from}\", column_name_from=\"{column_name_from}\" )", name=self.name, index=self.index, dtype=self.dtype.to_string(), dtype_from=self.dtype_from.to_string(), column_name_from=self.column_name_from.to_string()) } pub fn __eq__(&self, other: &Self) -> bool { diff --git a/src/types/python/excelsheet/mod.rs b/src/types/python/excelsheet/mod.rs index 4351935..4792891 100644 --- a/src/types/python/excelsheet/mod.rs +++ b/src/types/python/excelsheet/mod.rs @@ -326,9 +326,6 @@ impl ExcelSheet { selected_columns: SelectedColumns, dtypes: Option<DTypeMap>, ) -> FastExcelResult<Self> { - // Ensuring dtypes are compatible with selected columns - // Self::validate_dtypes_and_selected_columns(&selected_columns, &dtypes)?; - let mut sheet = ExcelSheet { name, header, @@ -344,7 +341,7 @@ impl ExcelSheet { selected_columns: Vec::with_capacity(0), }; - let available_columns_info = sheet.get_available_columns_info(); + let available_columns_info = sheet.get_available_columns_info(&selected_columns)?; let mut aliased_available_columns = Vec::with_capacity(available_columns_info.len()); @@ -379,10 +376,13 @@ impl ExcelSheet { Ok(sheet) } - fn get_available_columns_info(&self) -> Vec<ColumnInfoBuilder> { + fn get_available_columns_info( + &self, + selected_columns: &SelectedColumns, + ) -> FastExcelResult<Vec<ColumnInfoBuilder>> { let width = self.data.width(); match &self.header { - Header::None => (0..width) + Header::None => Ok((0..width) .map(|col_idx| { ColumnInfoBuilder::new( format!("__UNNAMED__{col_idx}"), @@ -390,8 +390,8 @@ impl ExcelSheet { ColumnNameFrom::Generated, ) }) - .collect(), - Header::At(row_idx) => (0..width) + .collect()), + Header::At(row_idx) => Ok((0..width) .map(|col_idx| { self.data .get((*row_idx, col_idx)) @@ -407,23 +407,73 @@ impl ExcelSheet { ) }) }) - .collect(), + .collect()), Header::With(names) => { - let nameless_start_idx = names.len(); - names - .iter() - .enumerate() - .map(|(col_idx, name)| { - ColumnInfoBuilder::new(name.to_owned(), col_idx, ColumnNameFrom::Provided) - }) - .chain((nameless_start_idx..width).map(|col_idx| { - ColumnInfoBuilder::new( - format!("__UNNAMED__{col_idx}"), - col_idx, - ColumnNameFrom::Generated, + if let SelectedColumns::Selection(column_selection) = selected_columns { + if column_selection.len() != names.len() { + return Err(FastExcelErrorKind::InvalidParameters( + "column_names and use_columns must have the same length".to_string(), ) - })) - .collect() + .into()); + } + let selected_indices = column_selection + .iter() + .map(|idx_or_name| { + match idx_or_name { + IdxOrName::Idx(idx) => Ok(*idx), + IdxOrName::Name(name) => Err(FastExcelErrorKind::InvalidParameters( + format!("use_columns can only contain integers when used with columns_names, got \"{name}\"") + ) + .into()), + } + }) + .collect::<FastExcelResult<Vec<_>>>()?; + + Ok((0..width) + .map(|col_idx| { + let provided_name_opt = if let Some(pos_in_names) = + selected_indices.iter().position(|idx| idx == &col_idx) + { + names.get(pos_in_names).cloned() + } else { + None + }; + + match provided_name_opt { + Some(provided_name) => ColumnInfoBuilder::new( + provided_name, + col_idx, + ColumnNameFrom::Provided, + ), + None => ColumnInfoBuilder::new( + format!("__UNNAMED__{col_idx}"), + col_idx, + ColumnNameFrom::Generated, + ), + } + }) + .collect()) + } else { + let nameless_start_idx = names.len(); + Ok(names + .iter() + .enumerate() + .map(|(col_idx, name)| { + ColumnInfoBuilder::new( + name.to_owned(), + col_idx, + ColumnNameFrom::Provided, + ) + }) + .chain((nameless_start_idx..width).map(|col_idx| { + ColumnInfoBuilder::new( + format!("__UNNAMED__{col_idx}"), + col_idx, + ColumnNameFrom::Generated, + ) + })) + .collect()) + } } } }

`column_names` are taken partially when `use_columns` does not include all columns Given this worksheet data (without any empty area): | A | B | C | | --- | --- | --- | | 21 | 22 | 23 | | 31 | 32 | 33 | | 41 | 42 | 43 | The following code: ```python import fastexcel as fe file = r'<path to file>' params = {'idx_or_name': 0, 'header_row': None, 'skip_rows': 1, 'use_columns': [1, 2], 'column_names': ['Col B', 'Col C']} print(fe.read_excel(file).load_sheet(**params).to_polars()) ``` Outputs: ```python shape: (3, 2) ┌───────┬──────────────┐ │ Col C ┆ __UNNAMED__2 │ │ --- ┆ --- │ │ f64 ┆ f64 │ ╞═══════╪══════════════╡ │ 22.0 ┆ 23.0 │ │ 32.0 ┆ 33.0 │ │ 42.0 ┆ 43.0 │ └───────┴──────────────┘ ``` Expected output: ```python shape: (3, 2) ┌───────┬───────┐ │ Col B ┆ Col C │ │ --- ┆ --- │ │ f64 ┆ f64 │ ╞═══════╪═══════╡ │ 22.0 ┆ 23.0 │ │ 32.0 ┆ 33.0 │ │ 42.0 ┆ 43.0 │ └───────┴───────┘ ```

Hi @wikiped this is actually the expected behaviour: `column_names` is used to specify the name of the columns in the resulting DataFrame. Since there are three columns and only two names were provided, the third name is generated based on the column index.: https://fastexcel.toucantoco.dev/fastexcel.html#ExcelReader.load_sheet Achieving the result you want is possible with the `use_columns` parameter: ```py import fastexcel as fe file = 'repro_214.xlsx' params = {'idx_or_name': 0, 'use_columns': ['B', 'C']} print(fe.read_excel(file).load_sheet(**params).to_polars()) ``` Will print ``` shape: (3, 2) ┌──────┬──────┐ │ B ┆ C │ │ --- ┆ --- │ │ f64 ┆ f64 │ ╞══════╪══════╡ │ 22.0 ┆ 23.0 │ │ 32.0 ┆ 33.0 │ │ 42.0 ┆ 43.0 │ └──────┴──────┘ ``` However there is indeed a bug when mixing `use_columns` and `column_names`, so thanks for the report! ```py import fastexcel as fe file = 'repro_214.xlsx' params = {'idx_or_name': 0, 'header_row': None, 'skip_rows': 1, 'column_names': ['Col B', 'Col C'], 'use_columns': [1, 2]} print(fe.read_excel(file).load_sheet(**params).to_polars()) ``` Returns ``` shape: (3, 2) ┌───────┬──────────────┐ │ Col C ┆ __UNNAMED__2 │ │ --- ┆ --- │ │ f64 ┆ f64 │ ╞═══════╪══════════════╡ │ 22.0 ┆ 23.0 │ │ 32.0 ┆ 33.0 │ │ 42.0 ┆ 43.0 │ └───────┴──────────────┘ ``` And the expected result would be ``` shape: (3, 2) ┌───────┬───────┐ │ Col B ┆ Col C │ │ --- ┆ --- │ │ f64 ┆ f64 │ ╞═══════╪═══════╡ │ 22.0 ┆ 23.0 │ │ 32.0 ┆ 33.0 │ │ 42.0 ┆ 43.0 │ └───────┴───────┘ ``` [repro_214.xlsx](https://github.com/ToucanToco/fastexcel/files/14786952/repro_214.xlsx) Thank you for the feedback, I realized now that I forgot to paste the significant part of the parameters used: `'use_columns': [1, 2]`, which is, as you noted, part of the reason for the raising the issue.

2024-03-29T17:00:41

0.0

[]

ToucanToco/fastexcel

ToucanToco__fastexcel-192

fb3a5828823c261e925ee2f8adb2d9403cd6aad1

diff --git a/.gitignore b/.gitignore index 9ed43c0..3a99b8d 100644 --- a/.gitignore +++ b/.gitignore @@ -9,5 +9,6 @@ __pycache__ .python-version .venv docs +.vscode .idea .benchmarks diff --git a/python/fastexcel/__init__.py b/python/fastexcel/__init__.py index 4c519a4..fc65be3 100644 --- a/python/fastexcel/__init__.py +++ b/python/fastexcel/__init__.py @@ -3,12 +3,11 @@ from typing import TYPE_CHECKING if TYPE_CHECKING: - from pathlib import Path - import pandas as pd import polars as pl from os.path import expanduser +from pathlib import Path import pyarrow as pa @@ -238,12 +237,14 @@ def __repr__(self) -> str: return self._reader.__repr__() -def read_excel(path: Path | str) -> ExcelReader: +def read_excel(source: Path | str | bytes) -> ExcelReader: """Opens and loads an excel file. - :param path: The path to the file + :param source: The path to a file or its content as bytes """ - return ExcelReader(_read_excel(expanduser(path))) + if isinstance(source, (str, Path)): + source = expanduser(source) + return ExcelReader(_read_excel(source)) __all__ = ( diff --git a/python/fastexcel/_fastexcel.pyi b/python/fastexcel/_fastexcel.pyi index 01fd90d..0e27e59 100644 --- a/python/fastexcel/_fastexcel.pyi +++ b/python/fastexcel/_fastexcel.pyi @@ -55,7 +55,7 @@ class _ExcelReader: @property def sheet_names(self) -> list[str]: ... -def read_excel(path: str) -> _ExcelReader: +def read_excel(source: str | bytes) -> _ExcelReader: """Reads an excel file and returns an ExcelReader""" __version__: str diff --git a/src/lib.rs b/src/lib.rs index 1537816..a6b3686 100644 --- a/src/lib.rs +++ b/src/lib.rs @@ -8,12 +8,22 @@ use types::{ExcelReader, ExcelSheet}; /// Reads an excel file and returns an object allowing to access its sheets and a bit of metadata #[pyfunction] -fn read_excel(path: &str) -> PyResult<ExcelReader> { +fn read_excel(source: &PyAny) -> PyResult<ExcelReader> { use py_errors::IntoPyResult; - ExcelReader::try_from_path(path) - .with_context(|| format!("could not load excel file at {path}")) - .into_pyresult() + if let Ok(path) = source.extract::<&str>() { + ExcelReader::try_from_path(path) + .with_context(|| format!("could not load excel file at {path}")) + .into_pyresult() + } else if let Ok(bytes) = source.extract::<&[u8]>() { + ExcelReader::try_from(bytes) + .with_context(|| "could not load excel file for those bytes") + .into_pyresult() + } else { + Err(py_errors::InvalidParametersError::new_err( + "source must be a string or bytes", + )) + } } // Taken from pydantic-core: diff --git a/src/types/excelreader.rs b/src/types/excelreader.rs index 6a15ce5..d1c6f20 100644 --- a/src/types/excelreader.rs +++ b/src/types/excelreader.rs @@ -1,10 +1,16 @@ -use std::{fs::File, io::BufReader}; +use std::{ + fs::File, + io::{BufReader, Cursor}, +}; -use calamine::{open_workbook_auto, Reader, Sheets}; +use calamine::{ + open_workbook_auto, open_workbook_auto_from_rs, Data, Error, Range, Reader, Sheets, +}; use pyo3::{pyclass, pymethods, PyAny, PyResult}; use crate::error::{ - py_errors::IntoPyResult, ErrorContext, FastExcelErrorKind, FastExcelResult, IdxOrName, + py_errors::IntoPyResult, ErrorContext, FastExcelError, FastExcelErrorKind, FastExcelResult, + IdxOrName, }; use super::{ @@ -12,12 +18,41 @@ use super::{ ExcelSheet, }; +enum ExcelSheets { + File(Sheets<BufReader<File>>), + Bytes(Sheets<Cursor<Vec<u8>>>), +} + +impl ExcelSheets { + fn worksheet_range(&mut self, name: &str) -> Result<Range<Data>, Error> { + match self { + Self::File(sheets) => sheets.worksheet_range(name), + Self::Bytes(sheets) => sheets.worksheet_range(name), + } + } + + fn worksheet_range_at(&mut self, idx: usize) -> Option<Result<Range<Data>, Error>> { + match self { + Self::File(sheets) => sheets.worksheet_range_at(idx), + Self::Bytes(sheets) => sheets.worksheet_range_at(idx), + } + } + + #[allow(dead_code)] + fn sheet_names(&self) -> Vec<String> { + match self { + Self::File(sheets) => sheets.sheet_names(), + Self::Bytes(sheets) => sheets.sheet_names(), + } + } +} + #[pyclass(name = "_ExcelReader")] pub(crate) struct ExcelReader { - sheets: Sheets<BufReader<File>>, + sheets: ExcelSheets, #[pyo3(get)] sheet_names: Vec<String>, - path: String, + source: String, } impl ExcelReader { @@ -29,9 +64,26 @@ impl ExcelReader { .with_context(|| format!("Could not open workbook at {path}"))?; let sheet_names = sheets.sheet_names().to_owned(); Ok(Self { - sheets, + sheets: ExcelSheets::File(sheets), + sheet_names, + source: path.to_owned(), + }) + } +} + +impl TryFrom<&[u8]> for ExcelReader { + type Error = FastExcelError; + + fn try_from(bytes: &[u8]) -> Result<Self, Self::Error> { + let cursor = Cursor::new(bytes.to_vec()); + let sheets = open_workbook_auto_from_rs(cursor) + .map_err(|err| FastExcelErrorKind::CalamineError(err).into()) + .with_context(|| "Could not open workbook from bytes")?; + let sheet_names = sheets.sheet_names().to_owned(); + Ok(Self { + sheets: ExcelSheets::Bytes(sheets), sheet_names, - path: path.to_owned(), + source: "bytes".to_owned(), }) } } @@ -39,7 +91,7 @@ impl ExcelReader { #[pymethods] impl ExcelReader { pub fn __repr__(&self) -> String { - format!("ExcelReader<{}>", &self.path) + format!("ExcelReader<{}>", &self.source) } #[pyo3(signature = (

Accept `bytes` rather than a file path as input format There are contexts where this would be nice to have, such as when fetching a file over the network. It would also allow a tighter integration with polars: https://github.com/pola-rs/polars/pull/14000#discussion_r1467389386

2024-02-28T00:40:19

0.0

[]

ToucanToco/fastexcel

ToucanToco__fastexcel-189

6d76a1958ef98a7096916292b95f79298627c2f3

diff --git a/.github/workflows/CI.yml b/.github/workflows/CI.yml index 732fdc2..f937e1f 100644 --- a/.github/workflows/CI.yml +++ b/.github/workflows/CI.yml @@ -46,6 +46,34 @@ jobs: source .venv/bin/activate make lint + check-docs: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: "3.11" + - name: Set up rust toolchain + uses: actions-rs/toolchain@v1 + with: + profile: minimal + toolchain: stable + override: true + - run: | + git config user.name github-actions + git config user.email [email protected] + + # venv required by maturin + python3 -m venv .venv + source .venv/bin/activate + + make install-test-requirements + make install-doc-requirements + # Required for pdoc to be able to import the sources + make dev-install + make doc + # GitHub provides only x86_64 runners, so we cannot test on arm architecture test: runs-on: ${{ matrix.os }} @@ -110,31 +138,3 @@ jobs: command: build args: "-o dist --interpreter python${{ matrix.python-version }}" target: ${{ steps.target.outputs.target }} - - check-docs: - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v4 - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: "3.11" - - name: Set up rust toolchain - uses: actions-rs/toolchain@v1 - with: - profile: minimal - toolchain: stable - override: true - - run: | - git config user.name github-actions - git config user.email [email protected] - - # venv required by maturin - python3 -m venv .venv - source .venv/bin/activate - - make install-test-requirements - make install-doc-requirements - # Required for pdoc to be able to import the sources - make dev-install - make doc diff --git a/Cargo.lock b/Cargo.lock index 8c44037..e7c0053 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -344,6 +344,12 @@ version = "0.2.2" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "7a81dae078cea95a014a339291cec439d2f232ebe854a9d672b796c6afafa9b7" +[[package]] +name = "diff" +version = "0.1.13" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "56254986775e3233ffa9c4d7d3faaf6d36a2c09d30b20687e9f88bc8bafc16c8" + [[package]] name = "encoding_rs" version = "0.8.31" @@ -360,6 +366,7 @@ dependencies = [ "arrow", "calamine", "chrono", + "pretty_assertions", "pyo3", "rstest", ] @@ -678,6 +685,16 @@ version = "1.6.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "7170ef9988bc169ba16dd36a7fa041e5c4cbeb6a35b76d4c03daded371eae7c0" +[[package]] +name = "pretty_assertions" +version = "1.4.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "af7cee1a6c8a5b9208b3cb1061f10c0cb689087b3d8ce85fb9d2dd7a29b6ba66" +dependencies = [ + "diff", + "yansi", +] + [[package]] name = "proc-macro-hack" version = "0.5.19" @@ -1122,6 +1139,12 @@ version = "0.52.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "dff9641d1cd4be8d1a070daf9e3773c5f67e78b4d9d42263020c057706765c04" +[[package]] +name = "yansi" +version = "0.5.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "09041cd90cf85f7f8b2df60c646f853b7f535ce68f85244eb6731cf89fa498ec" + [[package]] name = "zip" version = "0.6.3" diff --git a/Cargo.toml b/Cargo.toml index c62376d..1210ccf 100644 --- a/Cargo.toml +++ b/Cargo.toml @@ -11,7 +11,8 @@ crate-type = ["cdylib"] [dependencies] calamine = { version = "0.24.0", features = ["dates"] } chrono = { version = "0.4.34", default-features = false } -pyo3 = { version = "0.20.3", features = ["extension-module", "abi3-py38"] } +# NOTE: "extension-module" is actually required, see comments on features below +pyo3 = { version = "0.20.3", features = ["abi3-py38"] } [dependencies.arrow] version = "50.0.0" @@ -20,4 +21,14 @@ default-features = false features = ["pyarrow"] [dev-dependencies] +pretty_assertions = "1.4.0" rstest = { version = "0.18.2", default-features = false } + +# NOTE: This is a hack to bypass pyo3 limitations when testing: +# https://pyo3.rs/v0.20.3/faq.html#i-cant-run-cargo-test-or-i-cant-build-in-a-cargo-workspace-im-having-linker-issues-like-symbol-not-found-or-undefined-reference-to-_pyexc_systemerror +[features] +extension-module = ["pyo3/extension-module"] +default = ["extension-module"] +# feature for tests only. This makes Python::with_gil auto-initialize Python +# interpreters, which allows us to instantiate Python objects in tests +tests = ["pyo3/auto-initialize"] diff --git a/Makefile b/Makefile index d4e2ee7..f7c29fe 100644 --- a/Makefile +++ b/Makefile @@ -9,21 +9,30 @@ pytest = pytest -v ## Rust clippy = cargo clippy fmt = cargo fmt -cargo-test = cargo test +cargo-test = cargo test --no-default-features --features tests ## Docs pdoc = pdoc -o docs python/fastexcel -lint: +lint-python: $(ruff) $(format) --check --diff $(mypy) + +lint-rust: $(clippy) -format: + +lint: lint-rust lint-python + +format-python: $(ruff) --fix $(format) + +format-rust: $(fmt) $(clippy) --fix --lib -p fastexcel --allow-dirty --allow-staged +format: format-rust format-python + install-test-requirements: pip install -U -r test-requirements.txt -r build-requirements.txt @@ -39,10 +48,14 @@ dev-install: prod-install: ./prod_install.sh -test: +test-rust: $(cargo-test) + +test-python: $(pytest) +test: test-rust test-python + doc: $(pdoc) diff --git a/python/fastexcel/__init__.py b/python/fastexcel/__init__.py index fb00d9b..b57f8f4 100644 --- a/python/fastexcel/__init__.py +++ b/python/fastexcel/__init__.py @@ -17,6 +17,7 @@ CalamineCellError, CalamineError, CannotRetrieveCellDataError, + ColumnNotFoundError, FastExcelError, InvalidParametersError, SheetNotFoundError, @@ -54,6 +55,16 @@ def total_height(self) -> int: """The sheet's total height""" return self._sheet.total_height + @property + def selected_columns(self) -> list[str] | list[int] | None: + """The sheet's selected columns""" + return self._sheet.selected_columns + + @property + def available_columns(self) -> list[str]: + """The columns available for the given sheet""" + return self._sheet.available_columns + def to_arrow(self) -> pa.RecordBatch: """Converts the sheet to a pyarrow `RecordBatch`""" return self._sheet.to_arrow() @@ -101,6 +112,7 @@ def load_sheet_by_name( skip_rows: int = 0, n_rows: int | None = None, schema_sample_rows: int | None = 1_000, + use_columns: list[str] | list[int] | None = None, ) -> ExcelSheet: """Loads a sheet by name. @@ -117,6 +129,9 @@ def load_sheet_by_name( :param schema_sample_rows: Specifies how many rows should be used to determine the dtype of a column. If `None`, all rows will be used. + :param use_columns: Specifies the columns to use. Can either be a list of column names, or + a list of column indices (starting at 0). + If `None`, all columns will be used. """ return ExcelSheet( self._reader.load_sheet_by_name( @@ -126,6 +141,7 @@ def load_sheet_by_name( skip_rows=skip_rows, n_rows=n_rows, schema_sample_rows=schema_sample_rows, + use_columns=use_columns, ) ) @@ -138,6 +154,7 @@ def load_sheet_by_idx( skip_rows: int = 0, n_rows: int | None = None, schema_sample_rows: int | None = 1_000, + use_columns: list[str] | list[int] | None = None, ) -> ExcelSheet: """Loads a sheet by index. @@ -154,6 +171,9 @@ def load_sheet_by_idx( :param schema_sample_rows: Specifies how many rows should be used to determine the dtype of a column. If `None`, all rows will be used. + :param use_columns: Specifies the columns to use. Can either be a list of column names, or + a list of column indices (starting at 0). + If `None`, all columns will be used. """ if idx < 0: raise ValueError(f"Expected idx to be > 0, got {idx}") @@ -165,6 +185,7 @@ def load_sheet_by_idx( skip_rows=skip_rows, n_rows=n_rows, schema_sample_rows=schema_sample_rows, + use_columns=use_columns, ) ) @@ -177,6 +198,7 @@ def load_sheet( skip_rows: int = 0, n_rows: int | None = None, schema_sample_rows: int | None = 1_000, + use_columns: list[str] | list[int] | None = None, ) -> ExcelSheet: """Loads a sheet by name if a string is passed or by index if an integer is passed. @@ -190,6 +212,7 @@ def load_sheet( skip_rows=skip_rows, n_rows=n_rows, schema_sample_rows=schema_sample_rows, + use_columns=use_columns, ) if isinstance(idx_or_name, int) else self.load_sheet_by_name( @@ -199,6 +222,7 @@ def load_sheet( skip_rows=skip_rows, n_rows=n_rows, schema_sample_rows=schema_sample_rows, + use_columns=use_columns, ) ) @@ -224,6 +248,7 @@ def read_excel(path: Path | str) -> ExcelReader: "CalamineCellError", "CalamineError", "SheetNotFoundError", + "ColumnNotFoundError", "ArrowError", "InvalidParametersError", "UnsupportedColumnTypeCombinationError", diff --git a/python/fastexcel/_fastexcel.pyi b/python/fastexcel/_fastexcel.pyi index 865f4e7..b4e2c36 100644 --- a/python/fastexcel/_fastexcel.pyi +++ b/python/fastexcel/_fastexcel.pyi @@ -18,6 +18,12 @@ class _ExcelSheet: @property def offset(self) -> int: """The sheet's offset before data starts""" + @property + def selected_columns(self) -> list[str] | list[int] | None: + """The sheet's selected columns""" + @property + def available_columns(self) -> list[str]: + """The columns available for the given sheet""" def to_arrow(self) -> pa.RecordBatch: """Converts the sheet to a pyarrow `RecordBatch`""" @@ -33,6 +39,7 @@ class _ExcelReader: skip_rows: int = 0, n_rows: int | None = None, schema_sample_rows: int | None = 1_000, + use_columns: list[str] | list[int] | None = None, ) -> _ExcelSheet: ... def load_sheet_by_idx( self, @@ -43,16 +50,7 @@ class _ExcelReader: skip_rows: int = 0, n_rows: int | None = None, schema_sample_rows: int | None = 1_000, - ) -> _ExcelSheet: ... - def load_sheet( - self, - idx_or_name: int | str, - *, - header_row: int | None = 0, - column_names: list[str] | None = None, - skip_rows: int = 0, - n_rows: int | None = None, - schema_sample_rows: int | None = 1_000, + use_columns: list[str] | list[int] | None = None, ) -> _ExcelSheet: ... @property def sheet_names(self) -> list[str]: ... @@ -69,5 +67,6 @@ class CannotRetrieveCellDataError(FastExcelError): ... class CalamineCellError(FastExcelError): ... class CalamineError(FastExcelError): ... class SheetNotFoundError(FastExcelError): ... +class ColumnNotFoundError(FastExcelError): ... class ArrowError(FastExcelError): ... class InvalidParametersError(FastExcelError): ... diff --git a/src/error.rs b/src/error.rs index 4c6de09..48e6bdb 100644 --- a/src/error.rs +++ b/src/error.rs @@ -1,20 +1,28 @@ use std::{error::Error, fmt::Display}; #[derive(Debug)] -pub(crate) enum SheetIdxOrName { +pub(crate) enum IdxOrName { Idx(usize), - // Leaving this variant if someday we want to check if a name exists before calling worksheet_range - #[allow(dead_code)] Name(String), } +impl IdxOrName { + pub(super) fn format_message(&self) -> String { + match self { + Self::Idx(idx) => format!("at index {idx}"), + Self::Name(name) => format!("with name \"{name}\""), + } + } +} + #[derive(Debug)] pub(crate) enum FastExcelErrorKind { UnsupportedColumnTypeCombination(String), CannotRetrieveCellData(usize, usize), CalamineCellError(calamine::CellErrorType), CalamineError(calamine::Error), - SheetNotFound(SheetIdxOrName), + SheetNotFound(IdxOrName), + ColumnNotFound(IdxOrName), // Arrow errors can be of several different types (arrow::error::Error, PyError), and having // the actual type has not much value for us, so we just store a string context ArrowError(String), @@ -37,14 +45,13 @@ impl Display for FastExcelErrorKind { write!(f, "calamine error: {calamine_error}") } FastExcelErrorKind::SheetNotFound(idx_or_name) => { - let message = { - match idx_or_name { - SheetIdxOrName::Idx(idx) => format!("at index {idx}"), - SheetIdxOrName::Name(name) => format!("with name \"{name}\" not found"), - } - }; + let message = idx_or_name.format_message(); write!(f, "sheet {message} not found") } + FastExcelErrorKind::ColumnNotFound(idx_or_name) => { + let message = idx_or_name.format_message(); + write!(f, "column {message} not found") + } FastExcelErrorKind::ArrowError(err) => write!(f, "arrow error: {err}"), FastExcelErrorKind::InvalidParameters(err) => write!(f, "invalid parameters: {err}"), } @@ -53,7 +60,7 @@ impl Display for FastExcelErrorKind { #[derive(Debug)] pub(crate) struct FastExcelError { - kind: FastExcelErrorKind, + pub kind: FastExcelErrorKind, context: Vec<String>, } @@ -166,6 +173,13 @@ pub(crate) mod py_errors { FastExcelError, "Sheet was not found" ); + // Sheet not found + create_exception!( + _fastexcel, + ColumnNotFoundError, + FastExcelError, + "Column was not found" + ); // Arrow error create_exception!( _fastexcel, @@ -209,6 +223,9 @@ pub(crate) mod py_errors { FastExcelErrorKind::SheetNotFound(_) => { SheetNotFoundError::new_err(message) } + FastExcelErrorKind::ColumnNotFound(_) => { + ColumnNotFoundError::new_err(message) + } FastExcelErrorKind::ArrowError(_) => ArrowError::new_err(message), FastExcelErrorKind::InvalidParameters(_) => { InvalidParametersError::new_err(message) diff --git a/src/lib.rs b/src/lib.rs index 7740754..1537816 100644 --- a/src/lib.rs +++ b/src/lib.rs @@ -55,6 +55,10 @@ fn _fastexcel(py: Python, m: &PyModule) -> PyResult<()> { "SheetNotFoundError", py.get_type::<py_errors::SheetNotFoundError>(), ), + ( + "ColumnNotFoundError", + py.get_type::<py_errors::ColumnNotFoundError>(), + ), ("ArrowError", py.get_type::<py_errors::ArrowError>()), ( "InvalidParametersError", diff --git a/src/types/excelreader.rs b/src/types/excelreader.rs index 6424db1..f198061 100644 --- a/src/types/excelreader.rs +++ b/src/types/excelreader.rs @@ -1,10 +1,10 @@ use std::{fs::File, io::BufReader}; use calamine::{open_workbook_auto, Reader, Sheets}; -use pyo3::{pyclass, pymethods, PyResult}; +use pyo3::{pyclass, pymethods, types::PyList, PyResult}; use crate::error::{ - py_errors::IntoPyResult, ErrorContext, FastExcelErrorKind, FastExcelResult, SheetIdxOrName, + py_errors::IntoPyResult, ErrorContext, FastExcelErrorKind, FastExcelResult, IdxOrName, }; use super::{ @@ -50,7 +50,9 @@ impl ExcelReader { skip_rows = 0, n_rows = None, schema_sample_rows = 1_000, + use_columns = None ))] + #[allow(clippy::too_many_arguments)] pub fn load_sheet_by_name( &mut self, name: String, @@ -59,6 +61,7 @@ impl ExcelReader { skip_rows: usize, n_rows: Option<usize>, schema_sample_rows: Option<usize>, + use_columns: Option<&PyList>, ) -> PyResult<ExcelSheet> { let range = self .sheets @@ -69,13 +72,16 @@ impl ExcelReader { let header = Header::new(header_row, column_names); let pagination = Pagination::new(skip_rows, n_rows, &range).into_pyresult()?; - Ok(ExcelSheet::new( + let selected_columns = use_columns.try_into().with_context(|| format!("expected selected columns to be list[str] | list[int] | None, got {use_columns:?}")).into_pyresult()?; + ExcelSheet::try_new( name, range, header, pagination, schema_sample_rows, - )) + selected_columns, + ) + .into_pyresult() } #[pyo3(signature = ( @@ -86,7 +92,9 @@ impl ExcelReader { skip_rows = 0, n_rows = None, schema_sample_rows = 1_000, + use_columns = None ))] + #[allow(clippy::too_many_arguments)] pub fn load_sheet_by_idx( &mut self, idx: usize, @@ -95,11 +103,12 @@ impl ExcelReader { skip_rows: usize, n_rows: Option<usize>, schema_sample_rows: Option<usize>, + use_columns: Option<&PyList>, ) -> PyResult<ExcelSheet> { let name = self .sheet_names .get(idx) - .ok_or_else(|| FastExcelErrorKind::SheetNotFound(SheetIdxOrName::Idx(idx)).into()) + .ok_or_else(|| FastExcelErrorKind::SheetNotFound(IdxOrName::Idx(idx)).into()) .with_context(|| { format!( "Sheet index {idx} is out of range. File has {} sheets", @@ -114,7 +123,7 @@ impl ExcelReader { .worksheet_range_at(idx) // Returns Option<Result<Range<Data>, Self::Error>>, so we convert the Option into a // SheetNotFoundError and unwrap it - .ok_or_else(|| FastExcelErrorKind::SheetNotFound(SheetIdxOrName::Idx(idx)).into()) + .ok_or_else(|| FastExcelErrorKind::SheetNotFound(IdxOrName::Idx(idx)).into()) .into_pyresult()? // And here, we convert the calamine error in an owned error and unwrap it .map_err(|err| FastExcelErrorKind::CalamineError(err).into()) @@ -122,12 +131,15 @@ impl ExcelReader { let header = Header::new(header_row, column_names); let pagination = Pagination::new(skip_rows, n_rows, &range).into_pyresult()?; - Ok(ExcelSheet::new( + let selected_columns = use_columns.try_into().with_context(|| format!("expected selected columns to be list[str] | list[int] | None, got {use_columns:?}")).into_pyresult()?; + ExcelSheet::try_new( name, range, header, pagination, schema_sample_rows, - )) + selected_columns, + ) + .into_pyresult() } } diff --git a/src/types/excelsheet.rs b/src/types/excelsheet.rs index d950eec..5f42dff 100644 --- a/src/types/excelsheet.rs +++ b/src/types/excelsheet.rs @@ -2,6 +2,7 @@ use std::sync::Arc; use crate::error::{ py_errors::IntoPyResult, ErrorContext, FastExcelError, FastExcelErrorKind, FastExcelResult, + IdxOrName, }; use arrow::{ @@ -18,11 +19,13 @@ use chrono::NaiveDate; use pyo3::{ prelude::{pyclass, pymethods, PyObject, Python}, + types::PyList, PyResult, }; use crate::utils::arrow::arrow_schema_from_column_names_and_range; +#[derive(Debug)] pub(crate) enum Header { None, At(usize), @@ -76,6 +79,102 @@ impl Pagination { } } +#[derive(Debug, PartialEq)] +pub(crate) enum SelectedColumns { + All, + ByIndex(Vec<usize>), + ByName(Vec<String>), +} + +impl SelectedColumns { + pub(crate) fn validate_columns(&self, column_names: &[String]) -> FastExcelResult<()> { + match self { + SelectedColumns::All => Ok(()), + // If no selected indice is >= to the len of column_names, we're good + SelectedColumns::ByIndex(indices) => indices.iter().try_for_each(|idx| { + if idx >= &column_names.len() { + Err(FastExcelErrorKind::ColumnNotFound(IdxOrName::Idx(*idx)).into()) + } else { + Ok(()) + } + }), + // Every selected column must be in the provided column_names + SelectedColumns::ByName(selected_names) => { + selected_names.iter().try_for_each(|selected_name| { + if column_names.contains(selected_name) { + Ok(()) + } else { + Err(FastExcelErrorKind::ColumnNotFound(IdxOrName::Name( + selected_name.to_string(), + )) + .into()) + } + }) + } + } + } + + pub(crate) fn idx_for_column( + &self, + col_names: &[String], + col_name: &str, + col_idx: usize, + ) -> Option<usize> { + match self { + SelectedColumns::All => None, + SelectedColumns::ByIndex(indices) => { + if indices.contains(&col_idx) { + Some(col_idx) + } else { + None + } + } + SelectedColumns::ByName(names) => { + // cannot use .contains() because we have &String and &str + if names.iter().any(|name| name == col_name) { + col_names.iter().position(|name| name == col_name) + } else { + None + } + } + } + } + + pub(crate) fn to_python<'p>(&self, py: Python<'p>) -> Option<&'p PyList> { + match self { + SelectedColumns::All => None, + SelectedColumns::ByIndex(idx_vec) => Some(PyList::new(py, idx_vec)), + SelectedColumns::ByName(name_vec) => Some(PyList::new(py, name_vec)), + } + } +} + +impl TryFrom<Option<&PyList>> for SelectedColumns { + type Error = FastExcelError; + + fn try_from(value: Option<&PyList>) -> FastExcelResult<Self> { + use FastExcelErrorKind::InvalidParameters; + + match value { + None => Ok(Self::All), + Some(py_list) => { + if py_list.is_empty() { + Err(InvalidParameters("list of selected columns is empty".to_string()).into()) + } else if let Ok(name_vec) = py_list.extract::<Vec<String>>() { + Ok(Self::ByName(name_vec)) + } else if let Ok(index_vec) = py_list.extract::<Vec<usize>>() { + Ok(Self::ByIndex(index_vec)) + } else { + Err(InvalidParameters(format!( + "expected list[int] | list[str], got {py_list:?}" + )) + .into()) + } + } + } + } +} + #[pyclass(name = "_ExcelSheet")] pub(crate) struct ExcelSheet { #[pyo3(get)] @@ -87,6 +186,8 @@ pub(crate) struct ExcelSheet { total_height: Option<usize>, width: Option<usize>, schema_sample_rows: Option<usize>, + selected_columns: SelectedColumns, + available_columns: Vec<String>, } impl ExcelSheet { @@ -94,26 +195,45 @@ impl ExcelSheet { &self.data } - pub(crate) fn new( + pub(crate) fn try_new( name: String, data: Range<CalData>, header: Header, pagination: Pagination, schema_sample_rows: Option<usize>, - ) -> Self { - ExcelSheet { + selected_columns: SelectedColumns, + ) -> FastExcelResult<Self> { + let mut sheet = ExcelSheet { name, header, pagination, data, schema_sample_rows, + selected_columns, height: None, total_height: None, width: None, - } + // an empty vec as it will be replaced + available_columns: Vec::with_capacity(0), + }; + + let available_columns = sheet.get_available_columns(); + + // Ensuring selected columns are valid + sheet + .selected_columns + .validate_columns(&available_columns) + .with_context(|| { + format!( + "selected columns are invalid, available columns are: {available_columns:?}" + ) + })?; + + sheet.available_columns = available_columns; + Ok(sheet) } - pub(crate) fn column_names(&self) -> Vec<String> { + fn get_available_columns(&self) -> Vec<String> { let width = self.data.width(); match &self.header { Header::None => (0..width) @@ -263,10 +383,11 @@ impl TryFrom<&ExcelSheet> for Schema { arrow_schema_from_column_names_and_range( sheet.data(), - &sheet.column_names(), + &sheet.available_columns, sheet.offset(), // If sample_rows is higher than the sheet's limit, use the limit instead std::cmp::min(sample_rows, sheet.limit()), + &sheet.selected_columns, ) } } @@ -277,50 +398,71 @@ impl TryFrom<&ExcelSheet> for RecordBatch { fn try_from(sheet: &ExcelSheet) -> FastExcelResult<Self> { let offset = sheet.offset(); let limit = sheet.limit(); + let schema = Schema::try_from(sheet) - .with_context(|| format!("Could not build schema for sheet {}", sheet.name))?; - let mut iter = schema - .fields() + .with_context(|| format!("could not build schema for sheet {}", sheet.name))?; + + let mut iter = sheet + .available_columns .iter() .enumerate() - .map(|(col_idx, field)| { - ( - field.name(), - match field.data_type() { - ArrowDataType::Boolean => { - create_boolean_array(sheet.data(), col_idx, offset, limit) - } - ArrowDataType::Int64 => { - create_int_array(sheet.data(), col_idx, offset, limit) - } - ArrowDataType::Float64 => { - create_float_array(sheet.data(), col_idx, offset, limit) - } - ArrowDataType::Utf8 => { - create_string_array(sheet.data(), col_idx, offset, limit) - } - ArrowDataType::Timestamp(TimeUnit::Millisecond, None) => { - create_datetime_array(sheet.data(), col_idx, offset, limit) - } - ArrowDataType::Date32 => { - create_date_array(sheet.data(), col_idx, offset, limit) - } - ArrowDataType::Duration(TimeUnit::Millisecond) => { - create_duration_array(sheet.data(), col_idx, offset, limit) - } - ArrowDataType::Null => Arc::new(NullArray::new(limit - offset)), - _ => unreachable!(), - }, - ) + .filter_map(|(idx, column_name)| { + // checking if the current column has been selected + if let Some(col_idx) = match sheet.selected_columns { + // All columns selected, return the current index + SelectedColumns::All => Some(idx), + // Otherwise, return its index. If None is found, it means the column was not + // selected, and we will just continue + _ => sheet.selected_columns.idx_for_column( + &sheet.available_columns, + column_name, + idx, + ), + } { + // At this point, we know for sure that the column is in the schema so we can + // safely unwrap + let field = schema.field_with_name(column_name).unwrap(); + Some(( + field.name(), + match field.data_type() { + ArrowDataType::Boolean => { + create_boolean_array(sheet.data(), col_idx, offset, limit) + } + ArrowDataType::Int64 => { + create_int_array(sheet.data(), col_idx, offset, limit) + } + ArrowDataType::Float64 => { + create_float_array(sheet.data(), col_idx, offset, limit) + } + ArrowDataType::Utf8 => { + create_string_array(sheet.data(), col_idx, offset, limit) + } + ArrowDataType::Timestamp(TimeUnit::Millisecond, None) => { + create_datetime_array(sheet.data(), col_idx, offset, limit) + } + ArrowDataType::Date32 => { + create_date_array(sheet.data(), col_idx, offset, limit) + } + ArrowDataType::Duration(TimeUnit::Millisecond) => { + create_duration_array(sheet.data(), col_idx, offset, limit) + } + ArrowDataType::Null => Arc::new(NullArray::new(limit - offset)), + _ => unreachable!(), + }, + )) + } else { + None + } }) .peekable(); + // If the iterable is empty, try_from_iter returns an Err if iter.peek().is_none() { Ok(RecordBatch::new_empty(Arc::new(schema))) } else { RecordBatch::try_from_iter(iter) .map_err(|err| FastExcelErrorKind::ArrowError(err.to_string()).into()) - .with_context(|| format!("Could not convert sheet {} to RecordBatch", sheet.name)) + .with_context(|| format!("could not convert sheet {} to RecordBatch", sheet.name)) } } } @@ -359,16 +501,26 @@ impl ExcelSheet { self.header.offset() + self.pagination.offset() } + #[getter] + pub fn selected_columns<'p>(&'p self, py: Python<'p>) -> Option<&PyList> { + self.selected_columns.to_python(py) + } + + #[getter] + pub fn available_columns<'p>(&'p self, py: Python<'p>) -> &PyList { + PyList::new(py, &self.available_columns) + } + pub fn to_arrow(&self, py: Python<'_>) -> PyResult<PyObject> { RecordBatch::try_from(self) - .with_context(|| format!("Could not create RecordBatch from sheet {}", self.name)) + .with_context(|| format!("could not create RecordBatch from sheet \"{}\"", &self.name)) .and_then(|rb| { rb.to_pyarrow(py) .map_err(|err| FastExcelErrorKind::ArrowError(err.to_string()).into()) }) .with_context(|| { format!( - "Could not convert RecordBatch to pyarrow for sheet {}", + "could not convert RecordBatch to pyarrow for sheet \"{}\"", self.name ) }) @@ -379,3 +531,69 @@ impl ExcelSheet { format!("ExcelSheet<{}>", self.name) } } + +#[cfg(test)] +mod tests { + use super::*; + use pretty_assertions::assert_eq; + + #[test] + fn selected_columns_from_none() { + assert_eq!( + TryInto::<SelectedColumns>::try_into(None).unwrap(), + SelectedColumns::All + ) + } + + #[test] + fn selected_columns_from_list_of_valid_ints() { + Python::with_gil(|py| { + let py_list = PyList::new(py, vec![0, 1, 2]); + assert_eq!( + TryInto::<SelectedColumns>::try_into(Some(py_list)).unwrap(), + SelectedColumns::ByIndex(vec![0, 1, 2]) + ) + }); + } + + #[test] + fn selected_columns_from_list_of_valid_strings() { + Python::with_gil(|py| { + let py_list = PyList::new(py, vec!["foo", "bar"]); + assert_eq!( + TryInto::<SelectedColumns>::try_into(Some(py_list)).unwrap(), + SelectedColumns::ByName(vec!["foo".to_string(), "bar".to_string()]) + ) + }); + } + + #[test] + fn selected_columns_from_invalid_ints() { + Python::with_gil(|py| { + let py_list = PyList::new(py, vec![0, 2, -1]); + let err = TryInto::<SelectedColumns>::try_into(Some(py_list)).unwrap_err(); + + assert!(matches!(err.kind, FastExcelErrorKind::InvalidParameters(_))); + }); + } + + #[test] + fn selected_columns_from_empty_int_list() { + Python::with_gil(|py| { + let py_list = PyList::new(py, Vec::<usize>::new()); + let err = TryInto::<SelectedColumns>::try_into(Some(py_list)).unwrap_err(); + + assert!(matches!(err.kind, FastExcelErrorKind::InvalidParameters(_))); + }); + } + + #[test] + fn selected_columns_from_empty_string_list() { + Python::with_gil(|py| { + let py_list = PyList::new(py, Vec::<String>::new()); + let err = TryInto::<SelectedColumns>::try_into(Some(py_list)).unwrap_err(); + + assert!(matches!(err.kind, FastExcelErrorKind::InvalidParameters(_))); + }); + } +} diff --git a/src/utils/arrow.rs b/src/utils/arrow.rs index 33501d9..7da209b 100644 --- a/src/utils/arrow.rs +++ b/src/utils/arrow.rs @@ -3,7 +3,10 @@ use std::{collections::HashSet, sync::OnceLock}; use arrow::datatypes::{DataType as ArrowDataType, Field, Schema, TimeUnit}; use calamine::{CellErrorType, Data as CalData, DataType, Range}; -use crate::error::{FastExcelErrorKind, FastExcelResult}; +use crate::{ + error::{FastExcelErrorKind, FastExcelResult}, + types::excelsheet::SelectedColumns, +}; /// All the possible string values that should be considered as NULL const NULL_STRING_VALUES: [&str; 19] = [ @@ -136,12 +139,20 @@ pub(crate) fn arrow_schema_from_column_names_and_range( column_names: &[String], row_idx: usize, row_limit: usize, + selected_columns: &SelectedColumns, ) -> FastExcelResult<Schema> { let mut fields = Vec::with_capacity(column_names.len()); - for (col_idx, name) in column_names.iter().enumerate() { - let col_type = get_arrow_column_type(range, row_idx, row_limit, col_idx)?; - fields.push(Field::new(&alias_for_name(name, &fields), col_type, true)); + for (idx, name) in column_names.iter().enumerate() { + // If we have an index for the given column, extract it and add it to the schema. Otherwise, + // just ignore it + if let Some(col_idx) = match selected_columns { + SelectedColumns::All => Some(idx), + _ => selected_columns.idx_for_column(column_names, name, idx), + } { + let col_type = get_arrow_column_type(range, row_idx, row_limit, col_idx)?; + fields.push(Field::new(&alias_for_name(name, &fields), col_type, true)); + } } Ok(Schema::new(fields))

Be able to select a subset of columns We should be able to read an excel dataframe that starts at column `B` for example see `usecols` of https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_excel.html

@PrettyWood have you already had a look at this ? Otherwise, I'd take it :slightly_smiling_face: Go ahead! I have way too much work already :D You can take all the issues! ;) Haha I'll try :wink:

2024-02-25T16:36:09

0.0

[]

ToucanToco/fastexcel

ToucanToco__fastexcel-186

5d33b75421a56bb56ca2a13a82e55fdee73094b9

diff --git a/.github/workflows/CI.yml b/.github/workflows/CI.yml index eb3a09e..732fdc2 100644 --- a/.github/workflows/CI.yml +++ b/.github/workflows/CI.yml @@ -110,3 +110,31 @@ jobs: command: build args: "-o dist --interpreter python${{ matrix.python-version }}" target: ${{ steps.target.outputs.target }} + + check-docs: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: "3.11" + - name: Set up rust toolchain + uses: actions-rs/toolchain@v1 + with: + profile: minimal + toolchain: stable + override: true + - run: | + git config user.name github-actions + git config user.email [email protected] + + # venv required by maturin + python3 -m venv .venv + source .venv/bin/activate + + make install-test-requirements + make install-doc-requirements + # Required for pdoc to be able to import the sources + make dev-install + make doc diff --git a/Cargo.lock b/Cargo.lock index e6317d1..8c44037 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -45,12 +45,6 @@ dependencies = [ "libc", ] -[[package]] -name = "anyhow" -version = "1.0.80" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "5ad32ce52e4161730f7098c077cd2ed6229b5804ccf99e5366be1ab72a98b4e1" - [[package]] name = "arrow" version = "50.0.0" @@ -363,7 +357,6 @@ dependencies = [ name = "fastexcel" version = "0.9.1" dependencies = [ - "anyhow", "arrow", "calamine", "chrono", @@ -706,7 +699,6 @@ version = "0.20.3" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "53bdbb96d49157e65d45cc287af5f32ffadd5f4761438b527b055fb0d4bb8233" dependencies = [ - "anyhow", "cfg-if", "indoc", "libc", diff --git a/Cargo.toml b/Cargo.toml index 0e86451..c62376d 100644 --- a/Cargo.toml +++ b/Cargo.toml @@ -9,10 +9,9 @@ name = "fastexcel" crate-type = ["cdylib"] [dependencies] -anyhow = "1.0.80" calamine = { version = "0.24.0", features = ["dates"] } chrono = { version = "0.4.34", default-features = false } -pyo3 = { version = "0.20.3", features = ["extension-module", "anyhow", "abi3-py38"] } +pyo3 = { version = "0.20.3", features = ["extension-module", "abi3-py38"] } [dependencies.arrow] version = "50.0.0" diff --git a/Makefile b/Makefile index 5bee4ed..d4e2ee7 100644 --- a/Makefile +++ b/Makefile @@ -22,6 +22,7 @@ format: $(ruff) --fix $(format) $(fmt) + $(clippy) --fix --lib -p fastexcel --allow-dirty --allow-staged install-test-requirements: pip install -U -r test-requirements.txt -r build-requirements.txt diff --git a/python/fastexcel/__init__.py b/python/fastexcel/__init__.py index 46265d3..fb00d9b 100644 --- a/python/fastexcel/__init__.py +++ b/python/fastexcel/__init__.py @@ -12,7 +12,19 @@ import pyarrow as pa -from ._fastexcel import __version__, _ExcelReader, _ExcelSheet +from ._fastexcel import ( + ArrowError, + CalamineCellError, + CalamineError, + CannotRetrieveCellDataError, + FastExcelError, + InvalidParametersError, + SheetNotFoundError, + UnsupportedColumnTypeCombinationError, + __version__, + _ExcelReader, + _ExcelSheet, +) from ._fastexcel import read_excel as _read_excel @@ -202,4 +214,17 @@ def read_excel(path: Path | str) -> ExcelReader: return ExcelReader(_read_excel(expanduser(path))) -__all__ = ("ExcelReader", "ExcelSheet", "read_excel", "__version__") +__all__ = ( + "__version__", + "read_excel", + "ExcelReader", + "ExcelSheet", + "FastExcelError", + "CannotRetrieveCellDataError", + "CalamineCellError", + "CalamineError", + "SheetNotFoundError", + "ArrowError", + "InvalidParametersError", + "UnsupportedColumnTypeCombinationError", +) diff --git a/python/fastexcel/_fastexcel.pyi b/python/fastexcel/_fastexcel.pyi index 26e1841..865f4e7 100644 --- a/python/fastexcel/_fastexcel.pyi +++ b/python/fastexcel/_fastexcel.pyi @@ -61,3 +61,13 @@ def read_excel(path: str) -> _ExcelReader: """Reads an excel file and returns an ExcelReader""" __version__: str + +# Exceptions +class FastExcelError(Exception): ... +class UnsupportedColumnTypeCombinationError(FastExcelError): ... +class CannotRetrieveCellDataError(FastExcelError): ... +class CalamineCellError(FastExcelError): ... +class CalamineError(FastExcelError): ... +class SheetNotFoundError(FastExcelError): ... +class ArrowError(FastExcelError): ... +class InvalidParametersError(FastExcelError): ... diff --git a/src/error.rs b/src/error.rs new file mode 100644 index 0000000..4c6de09 --- /dev/null +++ b/src/error.rs @@ -0,0 +1,221 @@ +use std::{error::Error, fmt::Display}; + +#[derive(Debug)] +pub(crate) enum SheetIdxOrName { + Idx(usize), + // Leaving this variant if someday we want to check if a name exists before calling worksheet_range + #[allow(dead_code)] + Name(String), +} + +#[derive(Debug)] +pub(crate) enum FastExcelErrorKind { + UnsupportedColumnTypeCombination(String), + CannotRetrieveCellData(usize, usize), + CalamineCellError(calamine::CellErrorType), + CalamineError(calamine::Error), + SheetNotFound(SheetIdxOrName), + // Arrow errors can be of several different types (arrow::error::Error, PyError), and having + // the actual type has not much value for us, so we just store a string context + ArrowError(String), + InvalidParameters(String), +} + +impl Display for FastExcelErrorKind { + fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { + match self { + FastExcelErrorKind::UnsupportedColumnTypeCombination(detail) => { + write!(f, "unsupported column type combination: {detail}") + } + FastExcelErrorKind::CannotRetrieveCellData(row, col) => { + write!(f, "cannot retrieve cell data at ({row}, {col})") + } + FastExcelErrorKind::CalamineCellError(calamine_error) => { + write!(f, "calamine cell error: {calamine_error}") + } + FastExcelErrorKind::CalamineError(calamine_error) => { + write!(f, "calamine error: {calamine_error}") + } + FastExcelErrorKind::SheetNotFound(idx_or_name) => { + let message = { + match idx_or_name { + SheetIdxOrName::Idx(idx) => format!("at index {idx}"), + SheetIdxOrName::Name(name) => format!("with name \"{name}\" not found"), + } + }; + write!(f, "sheet {message} not found") + } + FastExcelErrorKind::ArrowError(err) => write!(f, "arrow error: {err}"), + FastExcelErrorKind::InvalidParameters(err) => write!(f, "invalid parameters: {err}"), + } + } +} + +#[derive(Debug)] +pub(crate) struct FastExcelError { + kind: FastExcelErrorKind, + context: Vec<String>, +} + +pub(crate) trait ErrorContext { + fn with_context<S: ToString, F>(self, ctx_fn: F) -> Self + where + F: FnOnce() -> S; +} + +impl FastExcelError { + pub(crate) fn new(kind: FastExcelErrorKind) -> Self { + Self { + kind, + context: vec![], + } + } +} + +impl Display for FastExcelError { + fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { + write!(f, "{kind}", kind = self.kind)?; + if !self.context.is_empty() { + writeln!(f, "\nContext:")?; + + self.context + .iter() + .enumerate() + .try_for_each(|(idx, ctx_value)| writeln!(f, " {idx}: {ctx_value}"))?; + } + Ok(()) + } +} + +impl Error for FastExcelError {} + +impl ErrorContext for FastExcelError { + fn with_context<S: ToString, F>(mut self, ctx_fn: F) -> Self + where + F: FnOnce() -> S, + { + self.context.push(ctx_fn().to_string()); + self + } +} + +impl From<FastExcelErrorKind> for FastExcelError { + fn from(kind: FastExcelErrorKind) -> Self { + FastExcelError::new(kind) + } +} + +pub(crate) type FastExcelResult<T> = Result<T, FastExcelError>; + +impl<T> ErrorContext for FastExcelResult<T> { + fn with_context<S: ToString, F>(self, ctx_fn: F) -> Self + where + F: FnOnce() -> S, + { + match self { + Ok(_) => self, + Err(e) => Err(e.with_context(ctx_fn)), + } + } +} + +/// Contains Python versions of our custom errors +pub(crate) mod py_errors { + use super::FastExcelErrorKind; + use pyo3::{create_exception, exceptions::PyException, PyResult}; + + // Base fastexcel error + create_exception!( + _fastexcel, + FastExcelError, + PyException, + "The base class for all fastexcel errors" + ); + // Unsupported column type + create_exception!( + _fastexcel, + UnsupportedColumnTypeCombinationError, + FastExcelError, + "Column contains an unsupported type combination" + ); + // Cannot retrieve cell data + create_exception!( + _fastexcel, + CannotRetrieveCellDataError, + FastExcelError, + "Data for a given cell cannot be retrieved" + ); + // Calamine cell error + create_exception!( + _fastexcel, + CalamineCellError, + FastExcelError, + "calamine returned an error regarding the content of the cell" + ); + // Calamine error + create_exception!( + _fastexcel, + CalamineError, + FastExcelError, + "Generic calamine error" + ); + // Sheet not found + create_exception!( + _fastexcel, + SheetNotFoundError, + FastExcelError, + "Sheet was not found" + ); + // Arrow error + create_exception!( + _fastexcel, + ArrowError, + FastExcelError, + "Generic arrow error" + ); + // Invalid parameters + create_exception!( + _fastexcel, + InvalidParametersError, + FastExcelError, + "Provided parameters are invalid" + ); + + pub(crate) trait IntoPyResult { + type Inner; + + fn into_pyresult(self) -> PyResult<Self::Inner>; + } + + impl<T> IntoPyResult for super::FastExcelResult<T> { + type Inner = T; + + fn into_pyresult(self) -> PyResult<Self::Inner> { + match self { + Ok(ok) => Ok(ok), + Err(err) => { + let message = err.to_string(); + Err(match err.kind { + FastExcelErrorKind::UnsupportedColumnTypeCombination(_) => { + UnsupportedColumnTypeCombinationError::new_err(message) + } + FastExcelErrorKind::CannotRetrieveCellData(_, _) => { + CannotRetrieveCellDataError::new_err(message) + } + FastExcelErrorKind::CalamineCellError(_) => { + CalamineCellError::new_err(message) + } + FastExcelErrorKind::CalamineError(_) => CalamineError::new_err(message), + FastExcelErrorKind::SheetNotFound(_) => { + SheetNotFoundError::new_err(message) + } + FastExcelErrorKind::ArrowError(_) => ArrowError::new_err(message), + FastExcelErrorKind::InvalidParameters(_) => { + InvalidParametersError::new_err(message) + } + }) + } + } + } + } +} diff --git a/src/lib.rs b/src/lib.rs index 1f5df1e..7740754 100644 --- a/src/lib.rs +++ b/src/lib.rs @@ -1,14 +1,19 @@ +mod error; mod types; mod utils; -use anyhow::Result; +use error::{py_errors, ErrorContext}; use pyo3::prelude::*; use types::{ExcelReader, ExcelSheet}; /// Reads an excel file and returns an object allowing to access its sheets and a bit of metadata #[pyfunction] -fn read_excel(path: &str) -> Result<ExcelReader> { - Ok(ExcelReader::try_from_path(path).unwrap()) +fn read_excel(path: &str) -> PyResult<ExcelReader> { + use py_errors::IntoPyResult; + + ExcelReader::try_from_path(path) + .with_context(|| format!("could not load excel file at {path}")) + .into_pyresult() } // Taken from pydantic-core: @@ -24,10 +29,38 @@ fn get_version() -> String { } #[pymodule] -fn _fastexcel(_py: Python, m: &PyModule) -> PyResult<()> { +fn _fastexcel(py: Python, m: &PyModule) -> PyResult<()> { m.add_function(wrap_pyfunction!(read_excel, m)?)?; m.add_class::<ExcelSheet>()?; m.add_class::<ExcelReader>()?; m.add("__version__", get_version())?; - Ok(()) + + // errors + [ + ("FastExcelError", py.get_type::<py_errors::FastExcelError>()), + ( + "UnsupportedColumnTypeCombinationError", + py.get_type::<py_errors::UnsupportedColumnTypeCombinationError>(), + ), + ( + "CannotRetrieveCellDataError", + py.get_type::<py_errors::CannotRetrieveCellDataError>(), + ), + ( + "CalamineCellError", + py.get_type::<py_errors::CalamineCellError>(), + ), + ("CalamineError", py.get_type::<py_errors::CalamineError>()), + ( + "SheetNotFoundError", + py.get_type::<py_errors::SheetNotFoundError>(), + ), + ("ArrowError", py.get_type::<py_errors::ArrowError>()), + ( + "InvalidParametersError", + py.get_type::<py_errors::InvalidParametersError>(), + ), + ] + .into_iter() + .try_for_each(|(exc_name, exc_type)| m.add(exc_name, exc_type)) } diff --git a/src/types/excelreader.rs b/src/types/excelreader.rs index 0c9aadd..6424db1 100644 --- a/src/types/excelreader.rs +++ b/src/types/excelreader.rs @@ -1,8 +1,11 @@ use std::{fs::File, io::BufReader}; -use anyhow::{Context, Result}; use calamine::{open_workbook_auto, Reader, Sheets}; -use pyo3::{pyclass, pymethods}; +use pyo3::{pyclass, pymethods, PyResult}; + +use crate::error::{ + py_errors::IntoPyResult, ErrorContext, FastExcelErrorKind, FastExcelResult, SheetIdxOrName, +}; use super::{ excelsheet::{Header, Pagination}, @@ -20,8 +23,9 @@ pub(crate) struct ExcelReader { impl ExcelReader { // NOTE: Not implementing TryFrom here, because we're aren't building the file from the passed // string, but rather from the file pointed by it. Semantically, try_from_path is clearer - pub(crate) fn try_from_path(path: &str) -> Result<Self> { + pub(crate) fn try_from_path(path: &str) -> FastExcelResult<Self> { let sheets = open_workbook_auto(path) + .map_err(|err| FastExcelErrorKind::CalamineError(err).into()) .with_context(|| format!("Could not open workbook at {path}"))?; let sheet_names = sheets.sheet_names().to_owned(); Ok(Self { @@ -55,14 +59,16 @@ impl ExcelReader { skip_rows: usize, n_rows: Option<usize>, schema_sample_rows: Option<usize>, - ) -> Result<ExcelSheet> { + ) -> PyResult<ExcelSheet> { let range = self .sheets .worksheet_range(&name) - .with_context(|| format!("Error while loading sheet {name}"))?; + .map_err(|err| FastExcelErrorKind::CalamineError(err).into()) + .with_context(|| format!("Error while loading sheet {name}")) + .into_pyresult()?; let header = Header::new(header_row, column_names); - let pagination = Pagination::new(skip_rows, n_rows, &range)?; + let pagination = Pagination::new(skip_rows, n_rows, &range).into_pyresult()?; Ok(ExcelSheet::new( name, range, @@ -89,25 +95,33 @@ impl ExcelReader { skip_rows: usize, n_rows: Option<usize>, schema_sample_rows: Option<usize>, - ) -> Result<ExcelSheet> { + ) -> PyResult<ExcelSheet> { let name = self .sheet_names .get(idx) + .ok_or_else(|| FastExcelErrorKind::SheetNotFound(SheetIdxOrName::Idx(idx)).into()) .with_context(|| { format!( "Sheet index {idx} is out of range. File has {} sheets", self.sheet_names.len() ) - })? + }) + .into_pyresult()? .to_owned(); + let range = self .sheets .worksheet_range_at(idx) - .with_context(|| format!("Sheet at idx {idx} not found"))? - .with_context(|| format!("Error while loading sheet at idx {idx}"))?; + // Returns Option<Result<Range<Data>, Self::Error>>, so we convert the Option into a + // SheetNotFoundError and unwrap it + .ok_or_else(|| FastExcelErrorKind::SheetNotFound(SheetIdxOrName::Idx(idx)).into()) + .into_pyresult()? + // And here, we convert the calamine error in an owned error and unwrap it + .map_err(|err| FastExcelErrorKind::CalamineError(err).into()) + .into_pyresult()?; let header = Header::new(header_row, column_names); - let pagination = Pagination::new(skip_rows, n_rows, &range)?; + let pagination = Pagination::new(skip_rows, n_rows, &range).into_pyresult()?; Ok(ExcelSheet::new( name, range, diff --git a/src/types/excelsheet.rs b/src/types/excelsheet.rs index 38fcc5d..d950eec 100644 --- a/src/types/excelsheet.rs +++ b/src/types/excelsheet.rs @@ -1,6 +1,9 @@ use std::sync::Arc; -use anyhow::{anyhow, bail, Context, Result}; +use crate::error::{ + py_errors::IntoPyResult, ErrorContext, FastExcelError, FastExcelErrorKind, FastExcelResult, +}; + use arrow::{ array::{ Array, BooleanArray, Date32Array, DurationMillisecondArray, Float64Array, Int64Array, @@ -13,7 +16,10 @@ use arrow::{ use calamine::{Data as CalData, DataType, Range}; use chrono::NaiveDate; -use pyo3::prelude::{pyclass, pymethods, PyObject, Python}; +use pyo3::{ + prelude::{pyclass, pymethods, PyObject, Python}, + PyResult, +}; use crate::utils::arrow::arrow_schema_from_column_names_and_range; @@ -53,12 +59,16 @@ impl Pagination { skip_rows: usize, n_rows: Option<usize>, range: &Range<CalData>, - ) -> Result<Self> { + ) -> FastExcelResult<Self> { let max_height = range.height(); if max_height < skip_rows { - bail!("To many rows skipped. Max height is {max_height}"); + Err(FastExcelErrorKind::InvalidParameters(format!( + "Too many rows skipped. Max height is {max_height}" + )) + .into()) + } else { + Ok(Self { skip_rows, n_rows }) } - Ok(Self { skip_rows, n_rows }) } pub(crate) fn offset(&self) -> usize { @@ -244,7 +254,7 @@ fn create_duration_array( } impl TryFrom<&ExcelSheet> for Schema { - type Error = anyhow::Error; + type Error = FastExcelError; fn try_from(sheet: &ExcelSheet) -> Result<Self, Self::Error> { // Checking how many rows we want to use to determine the dtype for a column. If sample_rows is @@ -262,9 +272,9 @@ impl TryFrom<&ExcelSheet> for Schema { } impl TryFrom<&ExcelSheet> for RecordBatch { - type Error = anyhow::Error; + type Error = FastExcelError; - fn try_from(sheet: &ExcelSheet) -> Result<Self, Self::Error> { + fn try_from(sheet: &ExcelSheet) -> FastExcelResult<Self> { let offset = sheet.offset(); let limit = sheet.limit(); let schema = Schema::try_from(sheet) @@ -309,6 +319,7 @@ impl TryFrom<&ExcelSheet> for RecordBatch { Ok(RecordBatch::new_empty(Arc::new(schema))) } else { RecordBatch::try_from_iter(iter) + .map_err(|err| FastExcelErrorKind::ArrowError(err.to_string()).into()) .with_context(|| format!("Could not convert sheet {} to RecordBatch", sheet.name)) } } @@ -348,16 +359,20 @@ impl ExcelSheet { self.header.offset() + self.pagination.offset() } - pub fn to_arrow(&self, py: Python<'_>) -> Result<PyObject> { + pub fn to_arrow(&self, py: Python<'_>) -> PyResult<PyObject> { RecordBatch::try_from(self) .with_context(|| format!("Could not create RecordBatch from sheet {}", self.name)) - .and_then(|rb| match rb.to_pyarrow(py) { - Err(e) => Err(anyhow!( - "Could not convert RecordBatch to pyarrow for sheet {}: {e}", + .and_then(|rb| { + rb.to_pyarrow(py) + .map_err(|err| FastExcelErrorKind::ArrowError(err.to_string()).into()) + }) + .with_context(|| { + format!( + "Could not convert RecordBatch to pyarrow for sheet {}", self.name - )), - Ok(pyobj) => Ok(pyobj), + ) }) + .into_pyresult() } pub fn __repr__(&self) -> String { diff --git a/src/utils/arrow.rs b/src/utils/arrow.rs index 807d7d0..33501d9 100644 --- a/src/utils/arrow.rs +++ b/src/utils/arrow.rs @@ -1,19 +1,21 @@ use std::{collections::HashSet, sync::OnceLock}; -use anyhow::{anyhow, Context, Result}; use arrow::datatypes::{DataType as ArrowDataType, Field, Schema, TimeUnit}; use calamine::{CellErrorType, Data as CalData, DataType, Range}; +use crate::error::{FastExcelErrorKind, FastExcelResult}; + /// All the possible string values that should be considered as NULL const NULL_STRING_VALUES: [&str; 19] = [ "", "#N/A", "#N/A N/A", "#NA", "-1.#IND", "-1.#QNAN", "-NaN", "-nan", "1.#IND", "1.#QNAN", "<NA>", "N/A", "NA", "NULL", "NaN", "None", "n/a", "nan", "null", ]; -fn get_cell_type(data: &Range<CalData>, row: usize, col: usize) -> Result<ArrowDataType> { +fn get_cell_type(data: &Range<CalData>, row: usize, col: usize) -> FastExcelResult<ArrowDataType> { let cell = data .get((row, col)) - .with_context(|| format!("Could not retrieve data at ({row},{col})"))?; + .ok_or_else(|| FastExcelErrorKind::CannotRetrieveCellData(row, col))?; + match cell { CalData::Int(_) => Ok(ArrowDataType::Int64), CalData::Float(_) => Ok(ArrowDataType::Float64), @@ -42,7 +44,7 @@ fn get_cell_type(data: &Range<CalData>, row: usize, col: usize) -> Result<ArrowD // Errors and nulls CalData::Error(err) => match err { CellErrorType::NA => Ok(ArrowDataType::Null), - _ => Err(anyhow!("Error in calamine cell: {err:?}")), + _ => Err(FastExcelErrorKind::CalamineCellError(err.to_owned()).into()), }, CalData::Empty => Ok(ArrowDataType::Null), } @@ -81,10 +83,10 @@ fn get_arrow_column_type( start_row: usize, end_row: usize, col: usize, -) -> Result<ArrowDataType> { +) -> FastExcelResult<ArrowDataType> { let mut column_types = (start_row..end_row) .map(|row| get_cell_type(data, row, col)) - .collect::<Result<HashSet<_>>>()?; + .collect::<FastExcelResult<HashSet<_>>>()?; // All columns are nullable anyway so we're not taking Null into account here column_types.remove(&ArrowDataType::Null); @@ -106,9 +108,10 @@ fn get_arrow_column_type( Ok(ArrowDataType::Utf8) } else { // NOTE: Not being too smart about multi-types columns for now - Err(anyhow!( - "could not figure out column type for following type combination: {column_types:?}" - )) + Err( + FastExcelErrorKind::UnsupportedColumnTypeCombination(format!("{column_types:?}")) + .into(), + ) } } @@ -133,7 +136,7 @@ pub(crate) fn arrow_schema_from_column_names_and_range( column_names: &[String], row_idx: usize, row_limit: usize, -) -> Result<Schema> { +) -> FastExcelResult<Schema> { let mut fields = Vec::with_capacity(column_names.len()); for (col_idx, name) in column_names.iter().enumerate() {

Implement proper python exceptions for our different error cases For now, we only have `RuntimeError`, which is clearly sub-optimal

2024-02-23T16:44:38

0.0

[]

ToucanToco/fastexcel

ToucanToco__fastexcel-180

d0ad3564ba3e49575d76112b7e11e1b707f04bb2

diff --git a/src/types/excelsheet.rs b/src/types/excelsheet.rs index e7ec2eb..bb55775 100644 --- a/src/types/excelsheet.rs +++ b/src/types/excelsheet.rs @@ -246,17 +246,17 @@ fn create_duration_array( impl TryFrom<&ExcelSheet> for Schema { type Error = anyhow::Error; - fn try_from(value: &ExcelSheet) -> Result<Self, Self::Error> { + fn try_from(sheet: &ExcelSheet) -> Result<Self, Self::Error> { // Checking how many rows we want to use to determine the dtype for a column. If sample_rows is // not provided, we sample limit rows, i.e on the entire column - let sample_rows = value.offset() + value.schema_sample_rows().unwrap_or(value.limit()); + let sample_rows = sheet.offset() + sheet.schema_sample_rows().unwrap_or(sheet.limit()); arrow_schema_from_column_names_and_range( - value.data(), - &value.column_names(), - value.offset(), + sheet.data(), + &sheet.column_names(), + sheet.offset(), // If sample_rows is higher than the sheet's limit, use the limit instead - std::cmp::min(sample_rows, value.limit()), + std::cmp::min(sample_rows, sheet.limit()), ) } } @@ -264,11 +264,11 @@ impl TryFrom<&ExcelSheet> for Schema { impl TryFrom<&ExcelSheet> for RecordBatch { type Error = anyhow::Error; - fn try_from(value: &ExcelSheet) -> Result<Self, Self::Error> { - let offset = value.offset(); - let limit = value.limit(); - let schema = Schema::try_from(value) - .with_context(|| format!("Could not build schema for sheet {}", value.name))?; + fn try_from(sheet: &ExcelSheet) -> Result<Self, Self::Error> { + let offset = sheet.offset(); + let limit = sheet.limit(); + let schema = Schema::try_from(sheet) + .with_context(|| format!("Could not build schema for sheet {}", sheet.name))?; let mut iter = schema .fields() .iter() @@ -278,25 +278,25 @@ impl TryFrom<&ExcelSheet> for RecordBatch { field.name(), match field.data_type() { ArrowDataType::Boolean => { - create_boolean_array(value.data(), col_idx, offset, limit) + create_boolean_array(sheet.data(), col_idx, offset, limit) } ArrowDataType::Int64 => { - create_int_array(value.data(), col_idx, offset, limit) + create_int_array(sheet.data(), col_idx, offset, limit) } ArrowDataType::Float64 => { - create_float_array(value.data(), col_idx, offset, limit) + create_float_array(sheet.data(), col_idx, offset, limit) } ArrowDataType::Utf8 => { - create_string_array(value.data(), col_idx, offset, limit) + create_string_array(sheet.data(), col_idx, offset, limit) } ArrowDataType::Timestamp(TimeUnit::Millisecond, None) => { - create_datetime_array(value.data(), col_idx, offset, limit) + create_datetime_array(sheet.data(), col_idx, offset, limit) } ArrowDataType::Date32 => { - create_date_array(value.data(), col_idx, offset, limit) + create_date_array(sheet.data(), col_idx, offset, limit) } ArrowDataType::Duration(TimeUnit::Millisecond) => { - create_duration_array(value.data(), col_idx, offset, limit) + create_duration_array(sheet.data(), col_idx, offset, limit) } ArrowDataType::Null => Arc::new(NullArray::new(limit - offset)), _ => unreachable!(), @@ -309,7 +309,7 @@ impl TryFrom<&ExcelSheet> for RecordBatch { Ok(RecordBatch::new_empty(Arc::new(schema))) } else { RecordBatch::try_from_iter(iter) - .with_context(|| format!("Could not convert sheet {} to RecordBatch", value.name)) + .with_context(|| format!("Could not convert sheet {} to RecordBatch", sheet.name)) } } } diff --git a/src/utils/arrow.rs b/src/utils/arrow.rs index 12b2df9..3523e03 100644 --- a/src/utils/arrow.rs +++ b/src/utils/arrow.rs @@ -2,7 +2,7 @@ use std::{collections::HashSet, sync::OnceLock}; use anyhow::{anyhow, Context, Result}; use arrow::datatypes::{DataType as ArrowDataType, Field, Schema, TimeUnit}; -use calamine::{Data as CalData, DataType, Range}; +use calamine::{CellErrorType, Data as CalData, DataType, Range}; fn get_cell_type(data: &Range<CalData>, row: usize, col: usize) -> Result<ArrowDataType> { let cell = data @@ -31,7 +31,10 @@ fn get_cell_type(data: &Range<CalData>, row: usize, col: usize) -> Result<ArrowD // A simple duration CalData::DurationIso(_) => Ok(ArrowDataType::Duration(TimeUnit::Millisecond)), // Errors and nulls - CalData::Error(err) => Err(anyhow!("Error in calamine cell: {err:?}")), + CalData::Error(err) => match err { + CellErrorType::NA => Ok(ArrowDataType::Null), + _ => Err(anyhow!("Error in calamine cell: {err:?}")), + }, CalData::Empty => Ok(ArrowDataType::Null), } }

Fastexcel fails to load an excel file with polars ```python dfs = pl.read_excel('input.xlsx', engine='calamine', sheet_id=0) for key in dfs.keys(): print(dfs[key].head()) ``` Error: ``` Traceback (most recent call last): File "/Users/Desktop/workspace/pocs/demo.py", line 27, in <module> dfs = pl.read_excel('compass_input.xlsx', engine='calamine', sheet_id=0) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/Desktop/workspace/pocs/.venv/lib/python3.11/site-packages/polars/utils/deprecation.py", line 136, in wrapper return function(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/Desktop/workspace/pocs/.venv/lib/python3.11/site-packages/polars/utils/deprecation.py", line 136, in wrapper return function(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/Desktop/workspace/pocs/.venv/lib/python3.11/site-packages/polars/io/spreadsheet/functions.py", line 259, in read_excel return _read_spreadsheet( ^^^^^^^^^^^^^^^^^^ File "/Users/Desktop/workspace/pocs/.venv/lib/python3.11/site-packages/polars/io/spreadsheet/functions.py", line 487, in _read_spreadsheet parsed_sheets = { ^ File "/Users/Desktop/workspace/pocs/.venv/lib/python3.11/site-packages/polars/io/spreadsheet/functions.py", line 488, in <dictcomp> name: reader_fn( ^^^^^^^^^^ File "/Users/Desktop/workspace/pocs/.venv/lib/python3.11/site-packages/polars/io/spreadsheet/functions.py", line 834, in _read_spreadsheet_calamine df = ws.to_polars() ^^^^^^^^^^^^^^ File "/Users/Desktop/workspace/pocs/.venv/lib/python3.11/site-packages/fastexcel/__init__.py", line 64, in to_polars df = pl.from_arrow(data=self.to_arrow()) ^^^^^^^^^^^^^^^ File "/Users/Desktop/workspace/pocs/.venv/lib/python3.11/site-packages/fastexcel/__init__.py", line 47, in to_arrow return self._sheet.to_arrow() ^^^^^^^^^^^^^^^^^^^^^^ RuntimeError: Could not create RecordBatch from sheet Hybrid_Productivity Caused by: 0: Could not build schema for sheet Hybrid_Productivity 1: Error in calamine cell: NA ``` Installed versions: ``` --------Version info--------- Polars: 0.20.7 Index type: UInt32 Platform: macOS-12.7.3-arm64-arm-64bit Python: 3.11.2 (v3.11.2:878ead1ac1, Feb 7 2023, 10:02:41) [Clang 13.0.0 (clang-1300.0.29.30)] ----Optional dependencies---- adbc_driver_manager: <not installed> cloudpickle: <not installed> connectorx: <not installed> deltalake: <not installed> fsspec: <not installed> gevent: <not installed> hvplot: <not installed> matplotlib: 3.8.2 numpy: 1.26.4 openpyxl: <not installed> pandas: 2.2.0 pyarrow: 15.0.0 pydantic: <not installed> pyiceberg: <not installed> pyxlsb: <not installed> sqlalchemy: <not installed> xlsx2csv: <not installed> xlsxwriter: <not installed> None Process finished with exit code 0 ```

Hello, could you please provide an excel file allowing to reproduce the bug ? Without this, it will be hard to investigate the issue Hi @lukapeschke, I found that if a cell contains the value "#N/A" then it is failing to load the data. I am attaching a sample below. Thank you. [sample_data.xlsx](https://github.com/ToucanToco/fastexcel/files/14277136/sample_data.xlsx) ```python df = pl.read_excel('sample_data.xlsx', engine='calamine') ```

2024-02-14T11:28:47

0.0

[]

ToucanToco/fastexcel

ToucanToco__fastexcel-161

96f57be2e5dc2cfb5521ee7370f9ccc925475fd4

diff --git a/Makefile b/Makefile index a59b9e8..2cab815 100644 --- a/Makefile +++ b/Makefile @@ -5,7 +5,7 @@ ruff = ruff python/ *.py format = ruff format python/ *.py mypy = mypy python/ *.py -pytest = pytest +pytest = pytest -v ## Rust clippy = cargo clippy fmt = cargo fmt diff --git a/src/types/excelsheet.rs b/src/types/excelsheet.rs index 59f2d03..36cf6ce 100644 --- a/src/types/excelsheet.rs +++ b/src/types/excelsheet.rs @@ -11,7 +11,7 @@ use arrow::{ record_batch::RecordBatch, }; use calamine::{DataType as CalDataType, Range}; -use chrono::{NaiveDate, Timelike}; +use chrono::NaiveDate; use pyo3::prelude::{pyclass, pymethods, PyObject, Python}; @@ -186,9 +186,7 @@ fn create_string_array( } fn duration_type_to_i64(caldt: &CalDataType) -> Option<i64> { - caldt - .as_time() - .map(|t| 1000 * i64::from(t.num_seconds_from_midnight())) + caldt.as_duration().map(|d| d.num_milliseconds()) } fn create_date_array(

Some duration columns contain only null values after load For durations, we're we using calamine's `as_time` API to convert durations to milliseconds, as the `as_duration` API was not available yet. However, this does not work anymore, as `as_time` will return `None` for `Duration` values. _Originally posted by @lukapeschke in https://github.com/ToucanToco/fastexcel/issues/158#issuecomment-1914324743_

2024-01-29T16:11:01

0.0

[]

icloud-photos-downloader/icloud_photos_downloader

icloud-photos-downloader__icloud_photos_downloader-956

6e9c6c69996a39d0e7f77c9fd4f9b160b4b8bcdd

diff --git a/CHANGELOG.md b/CHANGELOG.md index 4c276602d..309703f46 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -2,6 +2,8 @@ ## Unreleased +- fix: force_size should not skip subsequent sizes [ref](https://github.com/icloud-photos-downloader/icloud_photos_downloader/issues/955) + ## 1.23.4 (2024-09-02) - fix: support plain text encoding for filename in addition to base64 [ref](https://github.com/boredazfcuk/docker-icloudpd/issues/641) diff --git a/src/icloudpd/base.py b/src/icloudpd/base.py index 30f9f71ac..96620fc74 100644 --- a/src/icloudpd/base.py +++ b/src/icloudpd/base.py @@ -893,7 +893,7 @@ def download_photo_(counter: Counter, photo: PhotoAsset) -> bool: download_size.value, photo.filename, ) - return False + continue if AssetVersionSize.ORIGINAL in size: continue # that should avoid double download for original download_size = AssetVersionSize.ORIGINAL

When multiple sizes and --force-size are specified, some sizes can be skipped even if exist. ## Overview When both `--size medium` and `--size thumb` are specified with `--force-size`, but only the thumb-size asset exists for a photo, both sizes will be skipped. ## Steps to Reproduce 1. Upload a small image to iCloud Photos, such that the medium-size asset exists but thumb-size does not. (e.g. a 200*250 heic file) 2. Execute `icloudpd -d syncdir --size medium --size thumb --force-size`. ## Expected Behavior The medium-size asset should be skipped and the thumb-size asset should be downloaded. ## Actual Behavior Both medium-size and thumb-size assets are skipped. ## Context Changing this to `continue` should be a simple fix. https://github.com/icloud-photos-downloader/icloud_photos_downloader/blob/6e9c6c69996a39d0e7f77c9fd4f9b160b4b8bcdd/src/icloudpd/base.py#L896 I would be glad to issue a PR if you consider this appropriate.

Thanks for reporting the issue. Yes, you can submit PR. Please include tests. I am curious how are you using medium & thumb sized assets?

2024-09-19T07:10:43

0.0

[]

googleapis/python-pubsub

googleapis__python-pubsub-1297

cdaf6e9bcd0b918c2ab50fd4a8f987f60cbd610f

diff --git a/noxfile.py b/noxfile.py index 2ccbfeae7..47611cdff 100644 --- a/noxfile.py +++ b/noxfile.py @@ -55,7 +55,9 @@ UNIT_TEST_EXTERNAL_DEPENDENCIES: List[str] = [] UNIT_TEST_LOCAL_DEPENDENCIES: List[str] = [] UNIT_TEST_DEPENDENCIES: List[str] = [] -UNIT_TEST_EXTRAS: List[str] = [] +UNIT_TEST_EXTRAS: List[str] = [ + "flaky", +] UNIT_TEST_EXTRAS_BY_PYTHON: Dict[str, List[str]] = {} SYSTEM_TEST_PYTHON_VERSIONS: List[str] = ["3.12"] diff --git a/owlbot.py b/owlbot.py index 2e4b00bc9..77eb08250 100644 --- a/owlbot.py +++ b/owlbot.py @@ -337,6 +337,7 @@ cov_level=100, versions=gcp.common.detect_versions(path="./google", default_first=True), unit_test_python_versions=["3.7", "3.8", "3.9", "3.10", "3.11", "3.12", "3.13"], + unit_test_extras=["flaky"], system_test_python_versions=["3.12"], system_test_external_dependencies=["psutil","flaky"], ) diff --git a/setup.py b/setup.py index abe06552b..fb2b94fad 100644 --- a/setup.py +++ b/setup.py @@ -50,7 +50,7 @@ "opentelemetry-sdk <= 1.22.0; python_version<='3.7'", "opentelemetry-sdk >= 1.27.0; python_version>='3.8'", ] -extras = {"libcst": "libcst >= 0.3.10"} +extras = {"libcst": "libcst >= 0.3.10,", "flaky": "flaky"} url = "https://github.com/googleapis/python-pubsub" package_root = os.path.abspath(os.path.dirname(__file__))

Fix flaky tests in test_publisher_client caused due to non-deterministic number of spans exported by OpenTelemetry Fix error: ``` =================================== FAILURES =================================== __________________ test_opentelemetry_flow_control_exception ___________________ creds = span_exporter = @pytest.mark.skipif( sys.version_info < (3, 8), reason="Open Telemetry not supported below Python version 3.8", ) def test_opentelemetry_flow_control_exception(creds, span_exporter): publisher_options = types.PublisherOptions( flow_control=types.PublishFlowControl( message_limit=10, byte_limit=150, limit_exceeded_behavior=types.LimitExceededBehavior.ERROR, ), enable_open_telemetry_tracing=True, ) client = publisher.Client(credentials=creds, publisher_options=publisher_options) mock_batch = mock.Mock(spec=client._batch_class) topic = "projects/projectID/topics/topicID" client._set_batch(topic, mock_batch) future1 = client.publish(topic, b"a" * 60) future2 = client.publish(topic, b"b" * 100) future1.result() # no error, still within flow control limits with pytest.raises(exceptions.FlowControlLimitError): future2.result() spans = span_exporter.get_finished_spans() # Span 1 = Publisher Flow Control Span of first publish # Span 2 = Publisher Batching Span of first publish # Span 3 = Publisher Flow Control Span of second publish(raises FlowControlLimitError) # Span 4 = Publish Create Span of second publish(raises FlowControlLimitError) > assert len(spans) == 4 E assert 5 == 4 E + where 5 = len((, , )) tests/unit/pubsub_v1/publisher/test_publisher_client.py:294: AssertionError - generated xml file: /tmpfs/src/github/python-pubsub/unit_3.12_sponge_log.xml - =========================== short test summary info ============================ ```

2024-11-04T22:54:15

0.0

[]

googleapis/python-pubsub

googleapis__python-pubsub-1244

49c7c612ab091de63adabd59ba3b8432879fbd3e

diff --git a/google/cloud/pubsub_v1/subscriber/_protocol/streaming_pull_manager.py b/google/cloud/pubsub_v1/subscriber/_protocol/streaming_pull_manager.py index b8531db17..c01dd7f2e 100644 --- a/google/cloud/pubsub_v1/subscriber/_protocol/streaming_pull_manager.py +++ b/google/cloud/pubsub_v1/subscriber/_protocol/streaming_pull_manager.py @@ -1104,8 +1104,11 @@ def _on_response(self, response: gapic_types.StreamingPullResponse) -> None: ) with self._pause_resume_lock: - assert self._scheduler is not None - assert self._leaser is not None + if self._scheduler is None or self._leaser is None: + _LOGGER.debug( + f"self._scheduler={self._scheduler} or self._leaser={self._leaser} is None. Stopping further processing." + ) + return for received_message in received_messages: if (

Possible race condition between `_on_response` and `close` #### Environment details - OS type and version: Ubuntu 22.04 - Python version: 3.11 - pip version: 23.0.1 - `google-cloud-pubsub` version: 2.18.4 #### Steps to reproduce 1. Run code sample indefinitely 2. Sometime it shows Assertion error in `_on_response` function. #### Code example ```python def on_subscribe(subscription, until=None): """Decorator factory that provides subscribed messages to function. It handle decorated function as callback. So message should be acked/nacked inside decorated function. Args: subscription (str): Subscription ID. Should be `projects/{PROJECT_ID}/subscriptions/{SUBSCRIPTION_ID}` until (datetime.datetime): This function will subscribe messages published until this timestamp. """ def _callback_factory(func, finished, subscribe_until, **kwargs): def _callback(message): """Callback function. It sends signal if subscribed all messages. """ publish_time = datetime.fromtimestamp( message.publish_time.timestamp()) if subscribe_until and publish_time <= subscribe_until: return func(message, **kwargs) if subscribe_until and not finished.is_set(): logging.info('Subscribed all messages published until %s', subscribe_until) finished.set() message.nack() return _callback def _wrapper(func): @functools.wraps(func) def _inner_wrapper(**kwargs): # Event variable that is triggered when all messages are subscribed all_subscribed = Event() callback = _callback_factory(func=func, finished=all_subscribed, subscribe_until=subscribe_until, **kwargs) # Ensure closing subscriber for memory leak prevention. with pubsub_v1.SubscriberClient() as subscriber: future = subscriber.subscribe( subscription=subscription, callback=callback, await_callbacks_on_shutdown=True, flow_control=pubsub_v1.types.FlowControl(max_messages=5000), ) all_subscribed.wait(timeout=60) # Wait until future is finished when it's cancelled. # If it cancelled by timeout or keyboard interrupt, ignore it. try: future.cancel() future.result(timeout=60) except (KeyboardInterrupt, TimeoutError): pass except Exception as e: logging.error("Error occurs during subscription to %s: %s", subscription, str(e)) return _inner_wrapper return _wrapper @on_subscribe(subscription="SUBSCRIPTION") def callback(message): # Do something with message ``` #### Stack trace ``` Traceback (most recent call last): File "/layers/google.python.pip/pip/lib/python3.11/site-packages/google/api_core/bidi.py", line 657, in _thread_main self._on_response(response) File "/layers/google.python.pip/pip/lib/python3.11/site-packages/google/cloud/pubsub_v1/subscriber/_protocol/streaming_pull_manager.py", line 1107, in _on_response assert self._scheduler is not None ``` #### Explanation It's because `future.cancel()` executes `manager.close()` which makes `_scheduler` as `None` and it makes `_on_response` raise AssertionError. Maybe it has to be protected by threading lock somehow.

Thank you for the detailed report. I'll try to reproduce this and fix accordingly. In the meanwhile, I'm wondering why you choose to cancel first and then wait for result here ``` future.cancel() future.result(timeout=60) ``` instead of a common use case as the [sample](https://cloud.google.com/pubsub/docs/pull#client_library_code_samples) shows. This could be a mitigation for the issue you are experiencing. > Thank you for the detailed report. I'll try to reproduce this and fix accordingly. > > In the meanwhile, I'm wondering why you choose to cancel first and then wait for result here > > ``` > > future.cancel() > > future.result(timeout=60) > > ``` > > instead of a common use case as the [sample](https://cloud.google.com/pubsub/docs/pull#client_library_code_samples) shows. This could be a mitigation for the issue you are experiencing. Because I wanted to subscribe messages until certain timestamp (which turns out wrong idea 😅) and make callback to signal event if it subscribed all messages by using threading.Event object. If I call result first without timeout then it will wait indefinitely and if I set timeout then it will raise TimeoutError at second call of result. I wanted to just cancel it first to make it avoid raising error. We are also seeing this issue. Python version: 3.11 pip version: 23.3.1 google-cloud-pubsub version: 2.14.1 We are running in Google Kubernetes Engine (GKE) and listening to the following two signals in our worker for scaling: ```python import signal signal.signal(signal.SIGINT, self.exit_gracefully) signal.signal(signal.SIGTERM, self.exit_gracefully) ``` With a `exit_gracefully` implemented as follows: ```python def exit_gracefully(self) -> None: """Stop the async worker.""" self.future.cancel() # Request shutdown try: self.future.result(timeout=60 * 5) # Block until the shutdown is complete (or up to 5min). except TimeoutError: self.logger.warning("Stop timeout reached, Pod will die.") except Exception as e: self.logger.error(f"Error while shutting down worker: {e}") ``` with the following setup: ```python self.subscriber = pubsub_v1.SubscriberClient() flow_control = pubsub_v1.types.FlowControl(max_messages=self.pub_sub_flow_control_max_messages) executor = futures.ThreadPoolExecutor( max_workers=self.pub_sub_thread_pool_executor_max_workers, thread_name_prefix="TPE-PeachWorker" ) scheduler = pubsub_v1.subscriber.scheduler.ThreadScheduler(executor=executor) self.future = self.subscriber.subscribe( self.subscription_path, callback=self.pubsub_callback, flow_control=flow_control, scheduler=scheduler, await_callbacks_on_shutdown=True, ) ``` We do our main work in a separate thread from the main thread to allow python to receive signals: ```python # Execute the wait on the future for streaming pull in a different thread # because Python's main thread is the only one that will receive signals such as # SIGTERM for when a pod is requested to die so it can be replaced with a new one. thread = Thread(target=self.process, args=(), daemon=True) thread.start() ``` With the `process` method implemented as follows: ```python def process(self): with self.subscriber: try: self.future.result() except Cancelled: self.logger.info("Cancel request received.") ``` We are seeing the same scheduler assertion error that @hson2 saw: ``` AssertionError: null File "threading.py", line 982, in run self._target(*self._args, **self._kwargs) File "/usr/local/lib/python3.11/site-packages/google/cloud/pubsub_v1/subscriber/_protocol/streaming_pull_manager.py", line 921, in _shutdown assert self._scheduler is not None ``` However, we are also seeing another error immediately before: ``` AttributeError: 'NoneType' object has no attribute 'nack' File "threading.py", line 982, in run self._target(*self._args, **self._kwargs) File "/usr/local/lib/python3.11/site-packages/google/cloud/pubsub_v1/subscriber/_protocol/streaming_pull_manager.py", line 948, in _shutdown msg.nack() ``` @liuyunnnn Could you expand on the preference to not call cancel first in a scenario like this? Thank you for your help :)!

2024-09-09T00:41:49

0.0

[]

googleapis/python-pubsub

googleapis__python-pubsub-1193

0b8d7b91da7928e8f06379d19c616e7a6a411349

diff --git a/setup.py b/setup.py index a6af31207..dbb66cf7c 100644 --- a/setup.py +++ b/setup.py @@ -42,7 +42,7 @@ "google-api-core[grpc] >= 1.34.0, <3.0.0dev,!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,!=2.9.*,!=2.10.*", "proto-plus >= 1.22.0, <2.0.0dev", "proto-plus >= 1.22.2, <2.0.0dev; python_version>='3.11'", - "protobuf>=3.19.5,<5.0.0dev,!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5", + "protobuf>=3.20.2,<6.0.0dev,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5", "grpc-google-iam-v1 >= 0.12.4, < 1.0.0dev", "grpcio-status >= 1.33.2", ]

Unable to use patched version of grpcio with google-cloud-pubsub There are a number of bugs (functionally and security wise) in grpcio < 1.63. However, >1.63 now requires >5 protobuf, but this library requires protobuf <5 making it no longer possible to install this library with a patched working version of grpcio. ``` Because no versions of google-cloud-pubsub match >2.21.3 and google-cloud-pubsub (2.21.3) depends on protobuf (>=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev), google-cloud-pubsub (>=2.21.3) requires protobuf (>=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev). Because grpcio (1.64.1) depends on protobuf (>=5.26.1,<6.0dev) and no versions of grpcio match >1.64.1,<2.0.0, grpcio (>=1.64.1,<2.0.0) requires protobuf (>=5.26.1,<6.0dev). Thus, google-cloud-pubsub (>=2.21.3) is incompatible with grpcio (>=1.64.1,<2.0.0). ``` Can support for a newer version of protobuf be included in this library?

2024-06-14T00:50:49

0.0

[]

Solid-Energy-Systems/NewareNDA

Solid-Energy-Systems__NewareNDA-81

fe5d7d25c419fa59082092ab1282cf12e5c20092

diff --git a/CHANGELOG.md b/CHANGELOG.md index a5a8f18..b17d4c5 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -5,6 +5,18 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). +## [v2024.08.01] +### Added +- Read aux temperature data from BTS 9.1 files. + +### Fixed +- Bug fixes for BTS 9.1. + +### Changed +- setup.py converted to pyproject.toml +- Reworked logging functionality with new log_level keyword. + + ## [v2024.07.01] ### Added - Initial support for BTS 9.1 files. diff --git a/LICENSE b/LICENSE index 89c5b3d..847a993 100644 --- a/LICENSE +++ b/LICENSE @@ -1,4 +1,4 @@ -Copyright (c) 2023 SES AI Corporation, All rights reserved. +Copyright (c) 2022-2024 SES AI Corporation, All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: diff --git a/NewareNDA/NewareNDA.py b/NewareNDA/NewareNDA.py index a1c16ec..e598b6e 100644 --- a/NewareNDA/NewareNDA.py +++ b/NewareNDA/NewareNDA.py @@ -1,4 +1,4 @@ -# © 2023 Copyright SES AI +# © 2022-2024 Copyright SES AI # Author: Daniel Cogswell # Email: [email protected] @@ -13,8 +13,10 @@ multiplier_dict, state_dict from .NewareNDAx import read_ndax +logger = logging.getLogger('newarenda') -def read(file, software_cycle_number=True, cycle_mode='chg'): + +def read(file, software_cycle_number=True, cycle_mode='chg', log_level='INFO'): """ Read electrochemical data from an Neware nda or ndax binary file. @@ -26,15 +28,28 @@ def read(file, software_cycle_number=True, cycle_mode='chg'): 'chg': (Default) Sets new cycles with a Charge step following a Discharge. 'dchg': Sets new cycles with a Discharge step following a Charge. 'auto': Identifies the first non-rest state as the incremental state. + log_level (str): Sets the modules logging level. Default: 'INFO' + Options: 'CRITICAL', 'ERROR', 'WARNING', 'INFO', 'DEBUG', 'NOTSET' Returns: df (pd.DataFrame): DataFrame containing all records in the file """ + + # Set up logging + log_level = log_level.upper() + if log_level in logging._nameToLevel.keys(): + logger.setLevel(log_level) + else: + logger.warning(f"Logging level '{log_level}' not supported; Defaulting to 'INFO'. " + f"Supported options are: {', '.join(logging._nameToLevel.keys())}") + + # Identify file type and process accordingly _, ext = os.path.splitext(file) if ext == '.nda': return read_nda(file, software_cycle_number, cycle_mode) elif ext == '.ndax': return read_ndax(file, software_cycle_number, cycle_mode) else: + logger.error("File type not supported!") raise TypeError("File type not supported!") @@ -57,23 +72,24 @@ def read_nda(file, software_cycle_number, cycle_mode='chg'): mm = mmap.mmap(f.fileno(), 0, access=mmap.ACCESS_READ) if mm.read(6) != b'NEWARE': + logger.error(f"{file} does not appear to be a Neware file.") raise ValueError(f"{file} does not appear to be a Neware file.") # Get the file version [nda_version] = struct.unpack('<B', mm[14:15]) - logging.info(f"NDA version: {nda_version}") + logger.info(f"NDA version: {nda_version}") # Try to find server and client version info version_loc = mm.find(b'BTSServer') if version_loc != -1: mm.seek(version_loc) server = mm.read(50).strip(b'\x00').decode() - logging.info(f"Server: {server}") + logger.info(f"Server: {server}") mm.seek(50, 1) client = mm.read(50).strip(b'\x00').decode() - logging.info(f"Client: {client}") + logger.info(f"Client: {client}") else: - logging.info("BTS version not found!") + logger.info("BTS version not found!") # version specific settings if nda_version == 29: @@ -81,7 +97,7 @@ def read_nda(file, software_cycle_number, cycle_mode='chg'): elif nda_version == 130: output, aux = _read_nda_130(mm) else: - logging.error(f"nda version {nda_version} is not yet supported!") + logger.error(f"nda version {nda_version} is not yet supported!") raise NotImplementedError(f"nda version {nda_version} is not yet supported!") # Create DataFrame and sort by Index @@ -116,15 +132,15 @@ def _read_nda_29(mm): # Get the active mass [active_mass] = struct.unpack('<I', mm[152:156]) - logging.info(f"Active mass: {active_mass/1000} mg") + logger.info(f"Active mass: {active_mass/1000} mg") try: remarks = mm[2317:2417].decode('ASCII') # Clean null characters remarks = remarks.replace(chr(0), '').strip() - logging.info(f"Remarks: {remarks}") + logger.info(f"Remarks: {remarks}") except UnicodeDecodeError: - logging.warning(f"Converting remark bytes into ASCII failed") + logger.warning("Converting remark bytes into ASCII failed") remarks = "" # Identify the beginning of the data section @@ -132,6 +148,7 @@ def _read_nda_29(mm): identifier = b'\x00\x00\x00\x00\x55\x00' header = mm.find(identifier) if header == -1: + logger.error("File does not contain any valid records.") raise EOFError("File does not contain any valid records.") while (((mm[header + 4 + record_len] != 85) | (not _valid_record(mm[header+4:header+4+record_len]))) @@ -168,7 +185,8 @@ def _read_nda_130(mm): record_len = 88 identifier = mm[1024:1030] if mm[1024:1025] == b'\x55': # BTS 9.1 - record_len = 56 + # Find next record and get length + record_len = mm.find(mm[1024:1026], 1026) - 1024 mm.seek(1024) # Read data records @@ -181,6 +199,8 @@ def _read_nda_130(mm): # Check for a data record if bytes[0:1] == b'\x55': output.append(_bytes_to_list_BTS91(bytes)) + if record_len == 56: + aux.append(_aux_bytes_to_list_BTS91(bytes)) elif bytes[0:6] == identifier: output.append(_bytes_to_list_BTS9(bytes[4:])) @@ -199,14 +219,14 @@ def _read_nda_130(mm): # Get the active mass [active_mass] = struct.unpack('<d', bytes[-8:]) - logging.info(f"Active mass: {active_mass} mg") + logger.info(f"Active mass: {active_mass} mg") # Get the remarks remarks = bytes[363:491].decode('ASCII') # Clean null characters remarks = remarks.replace(chr(0), '').strip() - logging.info(f"Remarks: {remarks}") + logger.info(f"Remarks: {remarks}") return output, aux @@ -222,12 +242,10 @@ def _bytes_to_list(bytes): """Helper function for interpreting a byte string""" # Extract fields from byte string - [Index, Cycle] = struct.unpack('<II', bytes[2:10]) - [Step] = struct.unpack('<I', bytes[10:14]) - [Status, Jump, Time] = struct.unpack('<BBQ', bytes[12:22]) - [Voltage, Current] = struct.unpack('<ii', bytes[22:30]) - [Charge_capacity, Discharge_capacity] = struct.unpack('<qq', bytes[38:54]) - [Charge_energy, Discharge_energy] = struct.unpack('<qq', bytes[54:70]) + [Index, Cycle, Step] = struct.unpack('<III', bytes[2:14]) + [Status, Jump, Time, Voltage, Current] = struct.unpack('<BBQii', bytes[12:30]) + [Charge_capacity, Discharge_capacity, + Charge_energy, Discharge_energy] = struct.unpack('<qqqq', bytes[38:70]) [Y, M, D, h, m, s] = struct.unpack('<HBBBBB', bytes[70:77]) [Range] = struct.unpack('<i', bytes[78:82]) @@ -259,11 +277,10 @@ def _bytes_to_list_BTS9(bytes): """Helper function to interpret byte strings from BTS9""" [Step, Status] = struct.unpack('<BB', bytes[5:7]) [Index] = struct.unpack('<I', bytes[12:16]) - [Time] = struct.unpack('<Q', bytes[24:32]) - [Voltage, Current] = struct.unpack('<ff', bytes[32:40]) - [Charge_Capacity, Charge_Energy] = struct.unpack('<ff', bytes[48:56]) - [Discharge_Capacity, Discharge_Energy] = struct.unpack('<ff', bytes[56:64]) - [Date] = struct.unpack('<Q', bytes[64:72]) + [Time, Voltage, Current] = struct.unpack('<Qff', bytes[24:40]) + [Charge_Capacity, Charge_Energy, + Discharge_Capacity, Discharge_Energy, + Date] = struct.unpack('<ffffQ', bytes[48:72]) # Create a dictionary for the record list = [ @@ -323,6 +340,12 @@ def _aux_bytes_to_list(bytes): return [Index, Aux, T/10, V/10000] +def _aux_bytes_to_list_BTS91(bytes): + [Index] = struct.unpack('<I', bytes[8:12]) + [T] = struct.unpack('<f', bytes[52:56]) + return [Index, 1, T, None] + + def _generate_cycle_number(df, cycle_mode='chg'): """ Generate a cycle number to match Neware. @@ -344,10 +367,11 @@ def _generate_cycle_number(df, cycle_mode='chg'): inkey = 'DChg' offkey = 'Chg' else: + logger.error(f"Cycle_Mode '{cycle_mode}' not recognized. Supported options are 'chg', 'dchg', and 'auto'.") raise KeyError(f"Cycle_Mode '{cycle_mode}' not recognized. Supported options are 'chg', 'dchg', and 'auto'.") # Identify the beginning of key incremental steps - inc = (df['Status'] == 'CCCV_'+inkey) | (df['Status'] == 'CC_'+inkey) | (df['Status'] == 'CP_'+inkey) + inc = (df['Status'] == 'CCCV_'+inkey) | (df['Status'] == 'CC_'+inkey) | (df['Status'] == 'CP_'+inkey) # inc series = 1 at new incremental step, 0 otherwise inc = (inc - inc.shift()).clip(0) @@ -405,7 +429,7 @@ def _id_first_state(df): _, cycle_mode = first_state.split('_', 1) except ValueError: # Status is SIM or otherwise. Set mode to chg - logging.warning("First Step not recognized. Defaulting to Cycle_Mode 'Charge'.") + logger.warning("First Step not recognized. Defaulting to Cycle_Mode 'Charge'.") cycle_mode = 'chg' return cycle_mode.lower() diff --git a/bin/NewareNDA-cli.py b/NewareNDA/NewareNDAcli.py similarity index 53% rename from bin/NewareNDA-cli.py rename to NewareNDA/NewareNDAcli.py index e5b07e6..6de8948 100755 --- a/bin/NewareNDA-cli.py +++ b/NewareNDA/NewareNDAcli.py @@ -4,21 +4,23 @@ output format is csv. Other formats may require installing additional packages. ''' import argparse +from logging import _nameToLevel import pandas as pd import NewareNDA -output_cmd = { - 'csv': lambda df, f: pd.DataFrame.to_csv(df, f, index=False), - 'excel': lambda df, f: pd.DataFrame.to_excel(df, f, index=False), - 'feather': pd.DataFrame.to_feather, - 'hdf': lambda df, f: pd.DataFrame.to_hdf(df, f, key='Index'), - 'json': pd.DataFrame.to_json, - 'parquet': pd.DataFrame.to_parquet, - 'pickle': pd.DataFrame.to_pickle, - 'stata': pd.DataFrame.to_stata -} -if __name__ == '__main__': +def main(): + output_cmd = { + 'csv': lambda df, f: pd.DataFrame.to_csv(df, f, index=False), + 'excel': lambda df, f: pd.DataFrame.to_excel(df, f, index=False), + 'feather': pd.DataFrame.to_feather, + 'hdf': lambda df, f: pd.DataFrame.to_hdf(df, f, key='Index'), + 'json': pd.DataFrame.to_json, + 'parquet': pd.DataFrame.to_parquet, + 'pickle': pd.DataFrame.to_pickle, + 'stata': pd.DataFrame.to_stata + } + parser = argparse.ArgumentParser(description=__doc__) parser.add_argument('in_file', help='input file') parser.add_argument('out_file', help='output file') @@ -28,7 +30,15 @@ help='Generate the cycle number field to match old versions of BTSDA.') parser.add_argument('-v', '--version', help='show version', action='version', version=NewareNDA.__version__) + parser.add_argument('-l', '--log_level', choices=list(_nameToLevel.keys()), default='INFO', + help='Set the logging level for NewareNDA') + parser.add_argument('-c', '--cycle_mode', choices=['chg', 'dchg', 'auto'], default='chg', + help='Selects how the cycle is incremented.') args = parser.parse_args() - df = NewareNDA.read(args.in_file, args.software_cycle_number) + df = NewareNDA.read(args.in_file, args.software_cycle_number, cycle_mode=args.cycle_mode, log_level=args.log_level) output_cmd[args.format](df, args.out_file) + + +if __name__ == '__main__': + main() diff --git a/NewareNDA/NewareNDAx.py b/NewareNDA/NewareNDAx.py index e17b4b7..a58a334 100644 --- a/NewareNDA/NewareNDAx.py +++ b/NewareNDA/NewareNDAx.py @@ -1,4 +1,4 @@ -# © 2023 Copyright SES AI +# © 2022-2024 Copyright SES AI # Author: Daniel Cogswell # Email: [email protected] @@ -16,6 +16,8 @@ from NewareNDA.dicts import rec_columns, dtype_dict, state_dict, \ multiplier_dict +logger = logging.getLogger('newarenda') + def read_ndax(file, software_cycle_number=False, cycle_mode='chg'): """ @@ -39,10 +41,10 @@ def read_ndax(file, software_cycle_number=False, cycle_mode='chg'): version_info = zf.extract('VersionInfo.xml', path=tmpdir) with open(version_info, 'r', encoding='gb2312') as f: config = ET.fromstring(f.read()).find('config/ZwjVersion') - logging.info(f"Server version: {config.attrib['SvrVer']}") - logging.info(f"Client version: {config.attrib['CurrClientVer']}") - logging.info(f"Control unit version: {config.attrib['ZwjVersion']}") - logging.info(f"Tester version: {config.attrib['MainXwjVer']}") + logger.info(f"Server version: {config.attrib['SvrVer']}") + logger.info(f"Client version: {config.attrib['CurrClientVer']}") + logger.info(f"Control unit version: {config.attrib['ZwjVersion']}") + logger.info(f"Tester version: {config.attrib['MainXwjVer']}") except Exception: pass @@ -52,7 +54,7 @@ def read_ndax(file, software_cycle_number=False, cycle_mode='chg'): with open(step, 'r', encoding='gb2312') as f: config = ET.fromstring(f.read()).find('config') active_mass = float(config.find('Head_Info/SCQ').attrib['Value']) - logging.info(f"Active mass: {active_mass/1000} mg") + logger.info(f"Active mass: {active_mass/1000} mg") except Exception: pass @@ -77,7 +79,8 @@ def read_ndax(file, software_cycle_number=False, cycle_mode='chg'): columns=rec_columns) # Fill in missing data - Neware appears to fabricate data - _data_interpolation(data_df) + if data_df.isnull().any(axis=None): + _data_interpolation(data_df) else: data_df, _ = read_ndc(data_file) @@ -107,13 +110,12 @@ def _data_interpolation(df): Some ndax from from BTS Server 8 do not seem to contain a complete dataset. This helper function fills in missing times, capacities, and energies. """ + logger.warning("IMPORTANT: This ndax has missing data. The output from " + "NewareNDA contains interpolated data!") + # Identify the valid data nan_mask = df['Time'].notnull() - if nan_mask.any(): - logging.warning("IMPORTANT: This ndax has missing data. The output " - "from NewareNDA contains interpolated data!") - # Group by step and run 'inside' interpolation on Time df['Time'] = df.groupby('Step')['Time'].transform( lambda x: pd.Series.interpolate(x, limit_area='inside')) @@ -274,7 +276,7 @@ def read_ndc(file): # Get ndc file version [ndc_version] = struct.unpack('<B', mm[2:3]) - logging.info(f"NDC version: {ndc_version}") + logger.info(f"NDC version: {ndc_version}") if ndc_version == 2: return _read_ndc_2(mm) @@ -305,7 +307,7 @@ def _read_ndc_2(mm): elif bytes[0:1] == b'\x74': aux.append(_aux_bytes_74_to_list_ndc(bytes)) else: - logging.warning("Unknown record type: "+bytes[0:1].hex()) + logger.warning("Unknown record type: "+bytes[0:1].hex()) header = mm.find(identifier, header + record_len) diff --git a/NewareNDA/dicts.py b/NewareNDA/dicts.py index f332004..c1e8fa3 100644 --- a/NewareNDA/dicts.py +++ b/NewareNDA/dicts.py @@ -70,6 +70,7 @@ 1: 1e-4, 5: 1e-4, 10: 1e-3, + 50: 1e-3, 100: 1e-2, 200: 1e-2, 1000: 1e-1, diff --git a/NewareNDA/version.py b/NewareNDA/version.py index 3fc1792..fdc0c02 100644 --- a/NewareNDA/version.py +++ b/NewareNDA/version.py @@ -1,1 +1,1 @@ -__version__ = 'v2024.07.01' +__version__ = 'v2024.08.01' diff --git a/README.md b/README.md index 426c790..ca01d98 100644 --- a/README.md +++ b/README.md @@ -3,7 +3,7 @@ [![Coverage Status](https://coveralls.io/repos/github/Solid-Energy-Systems/NewareNDA/badge.svg?branch=development)](https://coveralls.io/github/Solid-Energy-Systems/NewareNDA?branch=development) # NewareNDA -© 2024 Copyright SES AI +© 2022-2024 Copyright SES AI <br>Author: Daniel Cogswell <br>Email: [email protected] @@ -33,9 +33,33 @@ df = NewareNDA.read('filename.nda') ``` ## Command-line interface: ``` -NewareNDA-cli.py in_file.nda --format feather out_file.ftr +usage: NewareNDA-cli [-h] + [-f {csv,excel,feather,hdf,json,parquet,pickle,stata}] + [-s] [-v] + [-l {CRITICAL,FATAL,ERROR,WARN,WARNING,INFO,DEBUG,NOTSET}] + [-c {chg,dchg,auto}] + in_file out_file + +Script for converting Neware NDA files to other file formats. The default +output format is csv. Other formats may require installing additional +packages. + +positional arguments: + in_file input file + out_file output file + +options: + -h, --help show this help message and exit + -f {csv,excel,feather,hdf,json,parquet,pickle,stata}, --format {csv,excel,feather,hdf,json,parquet,pickle,stata} + -s, --software_cycle_number + Generate the cycle number field to match old versions + of BTSDA. + -v, --version show version + -l {CRITICAL,FATAL,ERROR,WARN,WARNING,INFO,DEBUG,NOTSET}, --log_level {CRITICAL,FATAL,ERROR,WARN,WARNING,INFO,DEBUG,NOTSET} + Set the logging level for NewareNDA + -c {chg,dchg,auto}, --cycle_mode {chg,dchg,auto} + Selects how the cycle is incremented. ``` -The following `--format` options are supported: `csv, excel, feather, hdf, json, parquet, pickle, stata` # Troubleshooting If you encounter a key error, it is often the case that your file has a hardware setting that we have not seen before. Usually it is a quick fix that requires comparing output from BTSDA with values extracted by NewareNDA. Please start a new Github Issue and we will help debug. diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..38b6b62 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,31 @@ +# © 2022-2024 Copyright SES AI +# Author: Daniel Cogswell +# Email: [email protected] + +[project] +name = "NewareNDA" +dynamic = ["version"] +license = { file = "LICENSE" } +readme = { file = "README.md", content-type = "text/markdown" } +authors = [{ name = "Daniel Cogswell", email = "[email protected]" }] +requires-python = ">=3.6" +classifiers = [ "Programming Language :: Python :: 3" ] +description = "Neware nda binary file reader." +dependencies = [ + "pandas", + "importlib-metadata ~= 1.0 ; python_version < '3.8'" +] + +[project.scripts] +NewareNDA-cli = "NewareNDA.NewareNDAcli:main" + +[project.urls] +homepage = "https://github.com/Solid-Energy-Systems/NewareNDA" +tracker = "https://github.com/Solid-Energy-Systems/NewareNDA/issues" + +[build-system] +requires = ["setuptools>=61.0"] +build-backend = "setuptools.build_meta" + +[project.optional-dependencies] +test = ["pytest", "pyarrow", "coveralls"] diff --git a/setup.py b/setup.py deleted file mode 100644 index 6229797..0000000 --- a/setup.py +++ /dev/null @@ -1,33 +0,0 @@ -# © 2023 Copyright SES AI -# Author: Daniel Cogswell -# Email: [email protected] - -import setuptools - -version = {} -with open('NewareNDA/version.py', 'r', encoding='utf-8') as fh: - exec(fh.read(), version) -__version__ = version['__version__'] - -with open('README.md', 'r', encoding='utf-8') as fh: - long_description = fh.read() - -setuptools.setup( - name='NewareNDA', - version=__version__, - description='Neware nda binary file reader.', - long_description=long_description, - long_description_content_type="text/markdown", - author='Daniel Cogswell', - author_email='[email protected]', - url='https://github.com/Solid-Energy-Systems/NewareNDA', - license='BSD-3-Clause', - packages=['NewareNDA'], - scripts=['bin/NewareNDA-cli.py'], - install_requires=['pandas'], - extras_require={'test': ['pytest', 'pyarrow', 'coveralls']}, - python_requires='>=3.6', - classifiers=[ - "Programming Language :: Python :: 3", - ], -)

Aux temperature data for BTS 9.1 @ferdaoues5 please help with testing this, I have very few of these files.

2024-07-17T18:33:43

0.0

[]

thingsapi/things.py

thingsapi__things.py-118

85975337a4119fa80bf811e326eb863fce5f4905

diff --git a/things/api.py b/things/api.py index f6ebc4d..b3024e1 100644 --- a/things/api.py +++ b/things/api.py @@ -181,7 +181,7 @@ def tasks(uuid=None, include_items=False, **kwargs): # noqa: C901 >>> things.tasks(area='hIo1FJlAYGKt1Yj38vzKc3', include_items=True) [] >>> things.tasks(status='completed', count_only=True) - 10 + 12 >>> things.tasks(status='completed', last='1w', count_only=True) 0 @@ -605,7 +605,7 @@ def completed(**kwargs): Examples -------- >>> things.completed(count_only=True) - 10 + 12 >>> things.completed(type='project', count_only=True) 0 >>> things.completed(type='to-do', last='1w') diff --git a/things/database.py b/things/database.py index df66298..a62a4f4 100755 --- a/things/database.py +++ b/things/database.py @@ -962,16 +962,16 @@ def make_unixtime_filter(date_column: str, value) -> str: 'AND stopDate IS NULL' >>> make_unixtime_filter('stopDate', 'future') - "AND date(stopDate, 'unixepoch') > date('now', 'localtime')" + "AND date(stopDate, 'unixepoch', 'localtime') > date('now', 'localtime')" >>> make_unixtime_filter('creationDate', '2021-03-28') - "AND date(creationDate, 'unixepoch') == date('2021-03-28')" + "AND date(creationDate, 'unixepoch', 'localtime') == date('2021-03-28')" >>> make_unixtime_filter('creationDate', '=2021-03-28') - "AND date(creationDate, 'unixepoch') = date('2021-03-28')" + "AND date(creationDate, 'unixepoch', 'localtime') = date('2021-03-28')" >>> make_unixtime_filter('creationDate', '<=2021-03-28') - "AND date(creationDate, 'unixepoch') <= date('2021-03-28')" + "AND date(creationDate, 'unixepoch', 'localtime') <= date('2021-03-28')" >>> make_unixtime_filter('creationDate', None) '' @@ -996,7 +996,7 @@ def make_unixtime_filter(date_column: str, value) -> str: threshold = "date('now', 'localtime')" comparator = ">" if value == "future" else "<=" - date = f"date({date_column}, 'unixepoch')" + date = f"date({date_column}, 'unixepoch', 'localtime')" return f"AND {date} {comparator} {threshold}" @@ -1026,7 +1026,7 @@ def make_unixtime_range_filter(date_column: str, offset) -> str: Examples -------- >>> make_unixtime_range_filter('creationDate', '3d') - "AND datetime(creationDate, 'unixepoch') > datetime('now', '-3 days')" + "AND datetime(creationDate, 'unixepoch', 'localtime') > datetime('now', '-3 days')" >>> make_unixtime_range_filter('creationDate', None) '' @@ -1047,7 +1047,7 @@ def make_unixtime_range_filter(date_column: str, offset) -> str: # Use `typing.assert_never(suffix)` from Python 3.11 onwards. raise AssertionError("line should be unreachable.") # for static code analyzers - column_datetime = f"datetime({date_column}, 'unixepoch')" + column_datetime = f"datetime({date_column}, 'unixepoch', 'localtime')" offset_datetime = f"datetime('now', '{modifier}')" return f"AND {column_datetime} > {offset_datetime}"

timezone of completed tasks not taken into account **To Reproduce** I was looking for all tasks completed on 2024-02-25: ``` kwargs['status'] = None kwargs['stop_date'] = f'{DATE}' tasks = things.tasks(**kwargs) ``` ...but it returns some tasks on the day before, particularly those that were completed close to midnight: Here's the stopDate for one of the returned tasks: 1708826542.13277 > GMT: Sunday, February 25, 2024 2:02:22.132 AM > Your time zone: Saturday, February 24, 2024 9:02:22.132 PM [GMT-05:00](https://www.epochconverter.com/timezones?q=1708826542.13277) **Expected behavior** When I supply a `stop_date` I expect it to be reflective of my local timezone: i.e., get all tasks completed on that day _for me_.

@mikez per [our discussion](https://github.com/chrisgurney/things2md/pull/2#issuecomment-1967535472), adding your proposed solution here: Currently: - in responses, `stop_date`, `modified`, and `created` are in "localtime"; - in queries, `stop_date` expects GMT (aka UTC) dates. The database itself stores these dates as UTC. ## Suggested Changes Make both responses and queries be in localtime. What do you think? To do so, tweak these functions in [database.py](https://github.com/thingsapi/things.py/blob/main/things/database.py) of things.py: - [make_unixtime_filter](https://github.com/thingsapi/things.py/blob/854306777eda4802aaf56bf4452fad3bfb5242e4/things/database.py#L930) - optionally, `make_unixtime_range_filter` (for "created") Want to give it a shot? Hopefully, these should be easy edits and not involve much more than adding "localtime" to the SQL-query, i.e., `date(stopDate, 'unixepoch', 'localtime')` instead of `date(stopDate, 'unixepoch')`.

2024-03-10T12:48:20

0.0

[]

thingsapi/things.py

thingsapi__things.py-87

f26a9d03df6a46f416e78e5e49860bb6d3f73452

diff --git a/things/api.py b/things/api.py index 0383c12..abc7a2c 100644 --- a/things/api.py +++ b/things/api.py @@ -702,8 +702,8 @@ def show(uuid): # noqa >>> tag = things.tags('Home') >>> things.show(tag['uuid']) """ - uri = link(uuid) # pragma: no cover - os.system(f"open {quote(uri)}") # pragma: no cover + uri = link(uuid) + os.system(f"open {quote(uri)}") # Helper functions

Monkey patch `# pragma: no cover` See https://github.com/thingsapi/things.py/pull/65#discussion_r615410089

@AlexanderWillner This is obsolete right? I think we removed that code. We've other instances like the following, don't we? https://github.com/thingsapi/things.py/blob/230fcd9ddcdf3737114fcd38676bccd9995c8951/things/api.py#L705 👌 You might fix it like this: https://stackoverflow.com/a/46239371

2021-06-09T19:02:59

0.0

[]

etianen/logot

etianen__logot-126

78d2476f669fe284b9ff55ba5dfaab1eca7bd4d8

diff --git a/logot/_capture.py b/logot/_capture.py index 0e7bf54d..8a08e3b0 100644 --- a/logot/_capture.py +++ b/logot/_capture.py @@ -1,7 +1,5 @@ from __future__ import annotations -from logot._format import format_log - class Captured: """ @@ -55,6 +53,3 @@ def __eq__(self, other: object) -> bool: def __repr__(self) -> str: return f"Captured({self.levelname!r}, {self.msg!r}, levelno={self.levelno!r})" - - def __str__(self) -> str: - return format_log(self.levelname, self.msg) diff --git a/logot/_format.py b/logot/_format.py deleted file mode 100644 index 5a5f8de8..00000000 --- a/logot/_format.py +++ /dev/null @@ -1,18 +0,0 @@ -from __future__ import annotations - -from logot._typing import Level - - -def format_level(level: Level) -> str: - # Format `str` level. - if isinstance(level, str): - return level - # Format `int` level. - if isinstance(level, int): - return f"Level {level}" - # Handle invalid level. - raise TypeError(f"Invalid level: {level!r}") - - -def format_log(levelname: str, msg: str) -> str: - return f"[{levelname}] {msg}" diff --git a/logot/_level.py b/logot/_level.py new file mode 100644 index 00000000..3c819109 --- /dev/null +++ b/logot/_level.py @@ -0,0 +1,61 @@ +from __future__ import annotations + +from logot._capture import Captured +from logot._match import Matcher +from logot._typing import Level + + +class _LevelNameMatcher(Matcher): + __slots__ = ("_levelname",) + + def __init__(self, levelname: str) -> None: + self._levelname = levelname + + def match(self, captured: Captured) -> bool: + return captured.levelname == self._levelname + + def __eq__(self, other: object) -> bool: + return isinstance(other, _LevelNameMatcher) and other._levelname == self._levelname + + def __repr__(self) -> str: + return repr(self._levelname) + + def __str__(self) -> str: + return f"[{self._levelname}]" + + +DEBUG_MATCHER: Matcher = _LevelNameMatcher("DEBUG") +INFO_MATCHER: Matcher = _LevelNameMatcher("INFO") +WARNING_MATCHER: Matcher = _LevelNameMatcher("WARNING") +ERROR_MATCHER: Matcher = _LevelNameMatcher("ERROR") +CRITICAL_MATCHER: Matcher = _LevelNameMatcher("CRITICAL") + + +class _LevelNoMatcher(Matcher): + __slots__ = ("_levelno",) + + def __init__(self, levelno: int) -> None: + self._levelno = levelno + + def match(self, captured: Captured) -> bool: + return captured.levelno == self._levelno + + def __eq__(self, other: object) -> bool: + return isinstance(other, _LevelNoMatcher) and other._levelno == self._levelno + + def __repr__(self) -> str: + return repr(self._levelno) + + def __str__(self) -> str: + return f"[Level {self._levelno}]" + + +def level_matcher(level: Level) -> Matcher: + # Handle `str` level. + if isinstance(level, str): + return _LevelNameMatcher(level) + # Handle `int` level. + if isinstance(level, int): + return _LevelNoMatcher(level) + # Handle invalid level. + raise TypeError(f"Invalid level: {level!r}") diff --git a/logot/_logged.py b/logot/_logged.py index b9d598bf..12f1bc77 100644 --- a/logot/_logged.py +++ b/logot/_logged.py @@ -3,10 +3,10 @@ from abc import ABC, abstractmethod from logot._capture import Captured -from logot._format import format_level, format_log -from logot._msg import compile_msg_matcher +from logot._level import CRITICAL_MATCHER, DEBUG_MATCHER, ERROR_MATCHER, INFO_MATCHER, WARNING_MATCHER, level_matcher +from logot._match import Matcher +from logot._msg import msg_matcher from logot._typing import Level -from logot._validate import validate_level class Logged(ABC): @@ -78,7 +78,7 @@ def log(level: Level, msg: str) -> Logged: :param level: A log level name (e.g. ``"DEBUG"``) or numeric level (e.g. :data:`logging.DEBUG`). :param msg: A log :doc:`message pattern </log-message-matching>`. """ - return _RecordLogged(validate_level(level), msg) + return _RecordMatcher(level_matcher(level), msg_matcher(msg)) def debug(msg: str) -> Logged: @@ -88,7 +88,7 @@ def debug(msg: str) -> Logged: :param msg: A log :doc:`message pattern </log-message-matching>`. """ - return _RecordLogged("DEBUG", msg) + return _RecordMatcher(DEBUG_MATCHER, msg_matcher(msg)) def info(msg: str) -> Logged: @@ -98,7 +98,7 @@ def info(msg: str) -> Logged: :param msg: A log :doc:`message pattern </log-message-matching>`. """ - return _RecordLogged("INFO", msg) + return _RecordMatcher(INFO_MATCHER, msg_matcher(msg)) def warning(msg: str) -> Logged: @@ -108,7 +108,7 @@ def warning(msg: str) -> Logged: :param msg: A log :doc:`message pattern </log-message-matching>`. """ - return _RecordLogged("WARNING", msg) + return _RecordMatcher(WARNING_MATCHER, msg_matcher(msg)) def error(msg: str) -> Logged: @@ -118,7 +118,7 @@ def error(msg: str) -> Logged: :param msg: A log :doc:`message pattern </log-message-matching>`. """ - return _RecordLogged("ERROR", msg) + return _RecordMatcher(ERROR_MATCHER, msg_matcher(msg)) def critical(msg: str) -> Logged: @@ -128,42 +128,31 @@ def critical(msg: str) -> Logged: :param msg: A log :doc:`message pattern </log-message-matching>`. """ - return _RecordLogged("CRITICAL", msg) + return _RecordMatcher(CRITICAL_MATCHER, msg_matcher(msg)) -class _RecordLogged(Logged): - __slots__ = ("_level", "_msg", "_msg_matcher") +class _RecordMatcher(Logged): + __slots__ = ("_matchers",) - def __init__(self, level: Level, msg: str) -> None: - self._level = level - self._msg = msg - self._msg_matcher = compile_msg_matcher(msg) + def __init__(self, *matchers: Matcher) -> None: + self._matchers = matchers def __eq__(self, other: object) -> bool: - return isinstance(other, _RecordLogged) and other._level == self._level and other._msg == self._msg + return isinstance(other, _RecordMatcher) and other._matchers == self._matchers def __repr__(self) -> str: - return f"log({self._level!r}, {self._msg!r})" + matchers_repr = ", ".join(map(repr, self._matchers)) + return f"log({matchers_repr})" def reduce(self, captured: Captured) -> Logged | None: - # Match `str` level. - if isinstance(self._level, str): - if self._level != captured.levelname: - return self - # Match `int` level. - elif isinstance(self._level, int): - if self._level != captured.levelno: - return self - else: # pragma: no cover - raise TypeError(f"Invalid level: {self._level!r}") - # Match message. - if not self._msg_matcher(captured.msg): - return self - # We matched! - return None + # Handle full reduction. + if all(matcher.match(captured) for matcher in self._matchers): + return None + # Handle no reduction. + return self def _str(self, *, indent: str) -> str: - return format_log(format_level(self._level), self._msg) + return " ".join(map(str, self._matchers)) class _ComposedLogged(Logged): diff --git a/logot/_match.py b/logot/_match.py new file mode 100644 index 00000000..a51658da --- /dev/null +++ b/logot/_match.py @@ -0,0 +1,25 @@ +from __future__ import annotations + +from abc import ABC, abstractmethod + +from logot._capture import Captured + + +class Matcher(ABC): + __slots__ = () + + @abstractmethod + def match(self, captured: Captured) -> bool: + raise NotImplementedError + + @abstractmethod + def __eq__(self, other: object) -> bool: + raise NotImplementedError + + @abstractmethod + def __repr__(self) -> str: + raise NotImplementedError + + @abstractmethod + def __str__(self) -> str: + raise NotImplementedError diff --git a/logot/_msg.py b/logot/_msg.py index 834076f8..3cbd72b9 100644 --- a/logot/_msg.py +++ b/logot/_msg.py @@ -1,13 +1,9 @@ from __future__ import annotations import re -from typing import Callable -from logot._typing import TypeAlias - -# Compiled matcher callable. -# The returned `object` is truthy on successful match and falsy on failed match. -MessageMatcher: TypeAlias = Callable[[str], object] +from logot._capture import Captured +from logot._match import Matcher # Regex matching a simplified conversion specifier. _RE_CONVERSION = re.compile(r"%(.|$)") @@ -41,8 +37,38 @@ } -def compile_msg_matcher(pattern: str) -> MessageMatcher: - parts: list[str] = _RE_CONVERSION.split(pattern) +class _MessageMatcher(Matcher): + __slots__ = ("_msg",) + + def __init__(self, msg: str) -> None: + self._msg = msg + + def match(self, captured: Captured) -> bool: + return captured.msg == self._msg + + def __eq__(self, other: object) -> bool: + return isinstance(other, _MessageMatcher) and other._msg == self._msg + + def __repr__(self) -> str: + return repr(self._msg) + + def __str__(self) -> str: + return self._msg + + +class _MessagePatternMatcher(_MessageMatcher): + __slots__ = ("_pattern",) + + def __init__(self, msg: str, pattern: re.Pattern[str]) -> None: + super().__init__(msg) + self._pattern = pattern + + def match(self, captured: Captured) -> bool: + return self._pattern.fullmatch(captured.msg) is not None + + +def msg_matcher(msg: str) -> Matcher: + parts: list[str] = _RE_CONVERSION.split(msg) parts_len = len(parts) # If there is more than one part, at least one conversion specifier was found and we might need a regex matcher. if parts_len > 1: @@ -60,8 +86,9 @@ def compile_msg_matcher(pattern: str) -> MessageMatcher: # Create regex matcher. if is_regex: parts[::2] = map(re.escape, parts[::2]) - return re.compile("".join(parts), re.DOTALL).fullmatch + pattern = re.compile("".join(parts), re.DOTALL) + return _MessagePatternMatcher(msg, pattern) # Recreate the pattern with all escape sequences replaced. - pattern = "".join(parts) + msg = "".join(parts) # Create simple matcher. - return pattern.__eq__ + return _MessageMatcher(msg)

Allow matching on `extra` fields passed to `logging` calls This would need to be a recursive partial match. Support ELLIPSIS marker With using doctest you can use `...` to mean regex-like `.*` catchall. When converting to logot I stumbled upon `AssertionError: Not logged:` until realizing I had `...` inside the expected output. Support `loguru`-style message matching Originally intended as be part of #28, this has been moved to a separate issue following discussion with `loguru` devs in https://github.com/Delgan/loguru/issues/1072. 👍 ## Proposed API Support `.format()`-style placeholders like this: ``` py from logot.contrib.loguru import logged logot.wait_for(logged.info("Hello {}")) ``` Support matching structured data like this (requires #25): ``` py logot.wait_for( logged.bind(foo="bar").info("Hello {}") ) ``` ## Implementation Parsing of the format-style placeholders should be nice and easy using [`string.Formatter.parse()`](https://docs.python.org/3/library/string.html#string.Formatter.parse). Probably easier than the normal `%`-style placeholder parsing!

Just found out about https://logot.readthedocs.io/latest/log-message-matching.html which wasn't clear from the Github README alone, that solved my issue. Above idea may still be interesting. I've flirted with the idea of supporting alternate match syntax (see #31). If there was demand for this, I'd consider making a generic mechanism to match using other syntaxes, of which `...` would be a good option. I'm marking this idea as `wontfix` for now, as there are no immediate plans. If other people stumble upon this issue and think it would be worthwhile, however, feel free to comment and we can think about ways it could be implemented! ❤️ Meanwhile, I've updated the `README` with some deep links to the `logot` docs (see #125). Hopefully this will help other people find the [log message matching](https://logot.readthedocs.io/latest/log-message-matching.html) docs in future! 🙇 Matching the style of every 3rd-party log integration is going to get tedious. Let's not.

2024-02-25T13:18:32

0.0

[]

etianen/logot

etianen__logot-94

02aacaafa7657ae596ea6a41a30a57fd5e241fe6

diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 1309bb24..f83073b6 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -19,6 +19,8 @@ jobs: - python-version: "3.10" - python-version: "3.11" - python-version: "3.12" + - lib-versions: "loguru~=0.6.0" + - lib-versions: "loguru~=0.7.0" - lib-versions: "pytest~=7.0" - lib-versions: "pytest~=8.0" - lib-versions: "trio~=0.22.0" diff --git a/README.md b/README.md index ddff7abf..64d0b111 100644 --- a/README.md +++ b/README.md @@ -16,6 +16,8 @@ def test_something(logot: Logot) -> None: logot.assert_logged(logged.info("Something was done")) ``` +`logot` provides first-class integrations for popular testing (e.g. [`pytest`](https://logot.readthedocs.io/latest/using-pytest.html), [`unittest`](https://logot.readthedocs.io/latest/using-unittest.html)), asynchronous (e.g. [`asyncio`](https://logot.readthedocs.io/latest/index.html#index-testing-threaded), [`trio`](https://logot.readthedocs.io/latest/integrations/trio.html)) and logging frameworks (e.g. [`logging`](https://logot.readthedocs.io/latest/log-capturing.html), [`loguru`](https://logot.readthedocs.io/latest/integrations/loguru.html)). It can be extended to support many others. 💪 + ## Documentation 📖 diff --git a/docs/api/index.rst b/docs/api/index.rst index 3f7d8718..2d64d0b1 100644 --- a/docs/api/index.rst +++ b/docs/api/index.rst @@ -13,7 +13,7 @@ Import the :mod:`logot` API in your tests: Modules ------- -Learn about the public :mod:`logot` modules with the following reference guides: +Learn about public :mod:`logot` modules with the following reference guides: .. toctree:: :maxdepth: 1 diff --git a/docs/api/logot.loguru.rst b/docs/api/logot.loguru.rst new file mode 100644 index 00000000..ebf8f998 --- /dev/null +++ b/docs/api/logot.loguru.rst @@ -0,0 +1,10 @@ +:mod:`logot.loguru` +=================== + +.. automodule:: logot.loguru + + +API reference +------------- + +.. autoclass:: LoguruCapturer diff --git a/docs/conf.py b/docs/conf.py index 794643b0..489d5e4d 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -25,9 +25,10 @@ autodoc_member_order = "bysource" intersphinx_mapping = { - "python": ("https://docs.python.org/3", None), + "python": ("https://docs.python.org/3/", None), + "loguru": ("https://loguru.readthedocs.io/en/latest/", None), "pytest": ("https://docs.pytest.org/en/latest/", None), - "trio": ("https://trio.readthedocs.io/en/latest", None), + "trio": ("https://trio.readthedocs.io/en/latest/", None), } nitpicky = True diff --git a/docs/index.rst b/docs/index.rst index 12f2c662..5da9e915 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -15,8 +15,10 @@ Log-based testing 🪵 .. note:: - These examples all show using :mod:`logot` with :mod:`pytest`. See :doc:`/using-unittest` to learn how to use - :mod:`logot` with :mod:`unittest`. + :mod:`logot` provides first-class integrations for popular testing (e.g. :doc:`pytest </using-pytest>`, + :doc:`unittest </using-unittest>`), asynchronous (e.g. :ref:`asyncio <index-testing-threaded>`, + :doc:`trio </integrations/trio>`) and logging frameworks (e.g. :doc:`logging </log-capturing>`, + :doc:`loguru </integrations/loguru>`). It can be extended to support many others. 💪 Why test logging? 🤔 @@ -100,7 +102,16 @@ Use :meth:`Logot.await_for` to pause your test until the expected logs arrive or asyncio.create_task(app.start()) await logot.await_for(logged.info("App started")) -.. include:: /include/testing-async-notes.rst +.. note:: + + Use the ``timeout`` argument to :meth:`Logot.await_for` to configure how long to wait before the test fails. This can + be configured globally with the ``timeout`` argument to :class:`Logot`, defaulting to :attr:`Logot.DEFAULT_TIMEOUT`. + +.. seealso:: + + See :doc:`/log-pattern-matching` for examples of how to wait for logs that may arrive in an unpredictable order. + + See :ref:`integrations-async` for other supported asynchronous frameworks (e.g. :doc:`trio </integrations/trio>`). Testing synchronous code diff --git a/docs/installing.rst b/docs/installing.rst index 98f5d900..677e259d 100644 --- a/docs/installing.rst +++ b/docs/installing.rst @@ -23,10 +23,11 @@ Installing package ``extras`` .. code:: bash - pip install 'logot[pytest,trio]' + pip install 'logot[loguru,pytest,trio]' Use the following package ``extras`` for supported 3rd-party integrations: +- ``loguru`` - :doc:`/integrations/loguru` - ``pytest`` - :doc:`/using-pytest` - ``trio`` - :doc:`/integrations/trio` diff --git a/docs/integrations/index.rst b/docs/integrations/index.rst index 16a731e9..2e27d61b 100644 --- a/docs/integrations/index.rst +++ b/docs/integrations/index.rst @@ -19,7 +19,7 @@ Asynchronous frameworks Integrations with 3rd-party asynchronous frameworks extend :meth:`Logot.await_for`, allowing you to :ref:`test asynchronous code <index-testing-async>` using your framework of choice. 💪 -Learn about supported 3rd-party asynchronous frameworks: +Supported frameworks: .. toctree:: :maxdepth: 1 @@ -41,11 +41,13 @@ Logging frameworks Integrations with 3rd-party logging frameworks extend :meth:`Logot.capturing`, allowing you to :doc:`capture logs </log-capturing>` using your framework of choice. 💪 -Learn about supported 3rd-party logging frameworks: +Supported frameworks: .. toctree:: :maxdepth: 1 + loguru + .. seealso:: See :doc:`/log-capturing` usage guide. diff --git a/docs/integrations/loguru.rst b/docs/integrations/loguru.rst new file mode 100644 index 00000000..09eaa852 --- /dev/null +++ b/docs/integrations/loguru.rst @@ -0,0 +1,90 @@ +Using with :mod:`loguru` +======================== + +.. currentmodule:: logot + +:mod:`logot` makes it easy to capture logs from :mod:`loguru`: + +.. code:: python + + from logot.loguru import LoguruCapturer + + with Logot(capturer=LoguruCapturer).capturing() as logot: + do_something() + logot.assert_logged(logged.info("App started")) + + +Installing +---------- + +Ensure :mod:`logot` is installed alongside a compatible :mod:`loguru` version by adding the ``loguru`` extra: + +.. code:: bash + + pip install 'logot[loguru]' + +.. seealso:: + + See :ref:`installing-extras` usage guide. + + +Enabling for :mod:`pytest` +-------------------------- + +Enable :mod:`loguru` support in your :external+pytest:doc:`pytest configuration <reference/customize>`: + +.. code:: ini + + # pytest.ini or .pytest.ini + [pytest] + logot_capturer = logot.loguru.LoguruCapturer + +.. code:: toml + + # pyproject.toml + [tool.pytest.ini_options] + logot_capturer = "logot.loguru.LoguruCapturer" + +.. seealso:: + + See :doc:`/using-pytest` usage guide. + + +Enabling for :mod:`unittest` +---------------------------- + +Enable :mod:`loguru` support in your :class:`logot.unittest.LogotTestCase`: + +.. code:: python + + from logot.loguru import LoguruCapturer + + class MyAppTest(LogotTestCase): + logot_capturer = LoguruCapturer + +.. seealso:: + + See :doc:`/using-unittest` usage guide. + + +Enabling manually +----------------- + +Enable :mod:`loguru` support for your :class:`Logot` instance: + +.. code:: python + + from logot.loguru import LoguruCapturer + + logot = Logot(capturer=LoguruCapturer) + +Enable :mod:`loguru` support for a single :meth:`Logot.capturing` call: + +.. code:: python + + with Logot().capturing(capturer=LoguruCapturer) as logot: + do_something() + +.. seealso:: + + See :class:`Logot` and :meth:`Logot.capturing` API reference. diff --git a/docs/integrations/trio.rst b/docs/integrations/trio.rst index 601fecdb..cb12bda0 100644 --- a/docs/integrations/trio.rst +++ b/docs/integrations/trio.rst @@ -14,7 +14,14 @@ Use :meth:`Logot.await_for` to pause your test until the expected logs arrive or nursery.start_soon(app.start()) await logot.await_for(logged.info("App started")) -.. include:: /include/testing-async-notes.rst +.. note:: + + Use the ``timeout`` argument to :meth:`Logot.await_for` to configure how long to wait before the test fails. This can + be configured globally with the ``timeout`` argument to :class:`Logot`, defaulting to :attr:`Logot.DEFAULT_TIMEOUT`. + +.. seealso:: + + See :doc:`/log-pattern-matching` for examples of how to wait for logs that may arrive in an unpredictable order. Installing diff --git a/docs/log-capturing.rst b/docs/log-capturing.rst index 73fecf35..300306b0 100644 --- a/docs/log-capturing.rst +++ b/docs/log-capturing.rst @@ -11,6 +11,10 @@ Log capturing do_something() logot.assert_logged(logged.info("App started")) +.. seealso:: + + See :ref:`integrations-logging` for other supported logging frameworks (e.g. :doc:`loguru </integrations/loguru>`). + Test framework integrations --------------------------- diff --git a/logot/_logging.py b/logot/_logging.py index dc30a708..557a7225 100644 --- a/logot/_logging.py +++ b/logot/_logging.py @@ -23,7 +23,7 @@ def start_capturing(self, logot: Logot, /, *, level: Level, logger: Logger) -> N handler = self._handler = _Handler(level, logot) # If the logger is less verbose than the handler, force it to the necessary verboseness. self._prev_levelno = logger.level - if handler.level < logger.level: + if handler.level < logger.getEffectiveLevel(): logger.setLevel(handler.level) # Add the handler. logger.addHandler(handler) diff --git a/logot/_logot.py b/logot/_logot.py index 0243007d..f0e26924 100644 --- a/logot/_logot.py +++ b/logot/_logot.py @@ -1,9 +1,9 @@ from __future__ import annotations +from _thread import allocate_lock from abc import ABC, abstractmethod from collections import deque from contextlib import AbstractContextManager -from threading import Lock from types import TracebackType from typing import Any, Callable, ClassVar, Generic @@ -96,7 +96,7 @@ def __init__( self.capturer = capturer self.timeout = validate_timeout(timeout) self.async_waiter = async_waiter - self._lock = Lock() + self._lock = allocate_lock() self._queue: deque[Captured] = deque() self._wait: _Wait[Any] | None = None diff --git a/logot/_loguru.py b/logot/_loguru.py new file mode 100644 index 00000000..8a1d6651 --- /dev/null +++ b/logot/_loguru.py @@ -0,0 +1,29 @@ +from __future__ import annotations + +from functools import partial + +import loguru + +from logot._capture import Captured +from logot._logot import Capturer, Logot +from logot._typing import Level, Logger + + +class LoguruCapturer(Capturer): + """ + A :class:`logot.Capturer` implementation for :mod:`loguru`. + """ + + __slots__ = ("_handler_id",) + + def start_capturing(self, logot: Logot, /, *, level: Level, logger: Logger) -> None: + self._handler_id = loguru.logger.add(partial(_sink, logot=logot), level=level, filter=logger) + + def stop_capturing(self) -> None: + loguru.logger.remove(self._handler_id) + + +def _sink(msg: loguru.Message, *, logot: Logot) -> None: + record = msg.record + level = record["level"] + logot.capture(Captured(level.name, record["message"], levelno=level.no)) diff --git a/logot/_wait.py b/logot/_wait.py index 3177d8c0..771d2bb7 100644 --- a/logot/_wait.py +++ b/logot/_wait.py @@ -1,7 +1,7 @@ from __future__ import annotations +from _thread import LockType, allocate_lock from abc import ABC, abstractmethod -from threading import Lock from typing import Protocol, TypeVar @@ -14,9 +14,9 @@ def release(self) -> None: W = TypeVar("W", bound=Waiter) -def create_threading_waiter() -> Lock: +def create_threading_waiter() -> LockType: # Create an already-acquired lock. This will be released by `release()`. - lock = Lock() + lock = allocate_lock() lock.acquire() return lock diff --git a/logot/loguru.py b/logot/loguru.py new file mode 100644 index 00000000..2f195e2b --- /dev/null +++ b/logot/loguru.py @@ -0,0 +1,10 @@ +""" +Integration API for :mod:`loguru`. + +.. seealso:: + + See :doc:`/integrations/loguru` usage guide. +""" +from __future__ import annotations + +from logot._loguru import LoguruCapturer as LoguruCapturer diff --git a/poetry.lock b/poetry.lock index 1652b9ab..c037c881 100644 --- a/poetry.lock +++ b/poetry.lock @@ -483,6 +483,24 @@ files = [ six = "*" tornado = {version = "*", markers = "python_version > \"2.7\""} +[[package]] +name = "loguru" +version = "0.7.2" +description = "Python logging made (stupidly) simple" +optional = true +python-versions = ">=3.5" +files = [ + {file = "loguru-0.7.2-py3-none-any.whl", hash = "sha256:003d71e3d3ed35f0f8984898359d65b79e5b21943f78af86aa5491210429b8eb"}, + {file = "loguru-0.7.2.tar.gz", hash = "sha256:e671a53522515f34fd406340ee968cb9ecafbc4b36c679da03c18fd8d0bd51ac"}, +] + +[package.dependencies] +colorama = {version = ">=0.3.4", markers = "sys_platform == \"win32\""} +win32-setctime = {version = ">=1.0.0", markers = "sys_platform == \"win32\""} + +[package.extras] +dev = ["Sphinx (==7.2.5)", "colorama (==0.4.5)", "colorama (==0.4.6)", "exceptiongroup (==1.1.3)", "freezegun (==1.1.0)", "freezegun (==1.2.2)", "mypy (==v0.910)", "mypy (==v0.971)", "mypy (==v1.4.1)", "mypy (==v1.5.1)", "pre-commit (==3.4.0)", "pytest (==6.1.2)", "pytest (==7.4.0)", "pytest-cov (==2.12.1)", "pytest-cov (==4.1.0)", "pytest-mypy-plugins (==1.9.3)", "pytest-mypy-plugins (==3.0.0)", "sphinx-autobuild (==2021.3.14)", "sphinx-rtd-theme (==1.3.0)", "tox (==3.27.1)", "tox (==4.11.0)"] + [[package]] name = "markupsafe" version = "2.1.5" @@ -1065,6 +1083,20 @@ h2 = ["h2 (>=4,<5)"] socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"] zstd = ["zstandard (>=0.18.0)"] +[[package]] +name = "win32-setctime" +version = "1.1.0" +description = "A small Python utility to set file creation time on Windows" +optional = true +python-versions = ">=3.5" +files = [ + {file = "win32_setctime-1.1.0-py3-none-any.whl", hash = "sha256:231db239e959c2fe7eb1d7dc129f11172354f98361c4fa2d6d2d7e278baa8aad"}, + {file = "win32_setctime-1.1.0.tar.gz", hash = "sha256:15cf5750465118d6929ae4de4eb46e8edae9a5634350c01ba582df868e932cb2"}, +] + +[package.extras] +dev = ["black (>=19.3b0)", "pytest (>=4.6.2)"] + [[package]] name = "zipp" version = "3.17.0" @@ -1081,10 +1113,11 @@ docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.link testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"] [extras] +loguru = ["loguru"] pytest = ["pytest"] trio = ["trio"] [metadata] lock-version = "2.0" python-versions = "^3.8" -content-hash = "5ff89ddb47ec98abf8aed490978f89739e5fea0fd852da4f3c4cf53542ff3d26" +content-hash = "0ca1d41b6c4e51dd9144e15b25677c70853c62008b00ba20c4a69d394f19afe7" diff --git a/pyproject.toml b/pyproject.toml index 993c0262..2d9b7feb 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -21,11 +21,13 @@ packages = [{ include = "logot" }] [tool.poetry.dependencies] python = "^3.8" +loguru = { version = ">=0.6,<0.8", optional = true } pytest = { version = ">=7,<9", optional = true } trio = { version = ">=0.22,<0.25", optional = true } -typing-extensions = { version = ">=4.9.0", python = "<3.10" } +typing-extensions = { version = ">=4.9", python = "<3.10" } [tool.poetry.extras] +loguru = ["loguru"] pytest = ["pytest"] trio = ["trio"]

Support `loguru` log capture Supporting a basic [loguru](https://github.com/Delgan/loguru) integration would be very easy by providing a context manager via a helper func using their `LogHandler` integration. Alternatively, a `Logot` subclass called `Logurot` would be cheese and fun. That would allow the `pytest` plugin to provide a `logurot` fixture too (if `loguru` is installed).

Further API discussion and feedback from `loguru` devs in https://github.com/Delgan/loguru/issues/1072 ❤️ ## Proposed API ``` py from logot.contrib.loguru import LoguruCapture with Logot().capturing(LoguruCapture): pass ``` Any arguments given to `capturing` would be propagated to the `LoguruCapture` constructor. This would then unify capture between stdlib logging and `loguru` logging. The default capture backend would be the stdlib capture, and an `@overload` would ensure good typing. 💪

2024-02-09T19:42:22

0.0

[]

etianen/logot

etianen__logot-91

e7d3f7047cbf13be9cb87c15051656062018e3a7

diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 381abb95..26d215ef 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -21,6 +21,9 @@ jobs: - python-version: "3.12" - lib-versions: "pytest~=7.0" - lib-versions: "pytest~=8.0" + - lib-versions: "trio~=0.22.0" + - lib-versions: "trio~=0.23.0" + - lib-versions: "trio~=0.24.0" fail-fast: false env: COVERAGE_FILE: .coverage.${{ matrix.python-version }}${{ matrix.lib-versions }} @@ -39,7 +42,7 @@ jobs: - name: Install dependencies run: poetry install --all-extras - name: Install lib versions - run: poetry run pip install -e .[pytest] ${{ matrix.lib-versions }} + run: poetry run pip install -e .[pytest,trio] ${{ matrix.lib-versions }} if: ${{ matrix.lib-versions }} # Run checks. - name: Check (ruff) diff --git a/docs/api/logot.trio.rst b/docs/api/logot.trio.rst new file mode 100644 index 00000000..3f7f4cd1 --- /dev/null +++ b/docs/api/logot.trio.rst @@ -0,0 +1,10 @@ +:mod:`logot.trio` +================= + +.. automodule:: logot.trio + + +API reference +------------- + +.. autoclass:: TrioWaiter diff --git a/docs/conf.py b/docs/conf.py index b5c1ecc9..794643b0 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -27,6 +27,7 @@ intersphinx_mapping = { "python": ("https://docs.python.org/3", None), "pytest": ("https://docs.pytest.org/en/latest/", None), + "trio": ("https://trio.readthedocs.io/en/latest", None), } nitpicky = True diff --git a/docs/index.rst b/docs/index.rst index 0735d8a3..12f2c662 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -100,14 +100,7 @@ Use :meth:`Logot.await_for` to pause your test until the expected logs arrive or asyncio.create_task(app.start()) await logot.await_for(logged.info("App started")) -.. note:: - - Use the ``timeout`` argument to :meth:`Logot.await_for` to configure how long to wait before the test fails. This can - be configured globally with the ``timeout`` argument to :class:`Logot`, defaulting to :attr:`Logot.DEFAULT_TIMEOUT`. - -.. seealso:: - - See :doc:`/log-pattern-matching` for examples of how to wait for logs that may arrive in an unpredictable order. +.. include:: /include/testing-async-notes.rst Testing synchronous code @@ -153,6 +146,7 @@ Learn more about :mod:`logot` with the following guides: using-pytest using-unittest installing + integrations/index api/index .. toctree:: diff --git a/docs/installing.rst b/docs/installing.rst index 6a4249db..98f5d900 100644 --- a/docs/installing.rst +++ b/docs/installing.rst @@ -18,15 +18,21 @@ Install :mod:`logot` like any other Python library: Installing package ``extras`` ----------------------------- -:mod:`logot` provides package ``extras`` to ensure compatibility with supported 3rd-party integrations. +:mod:`logot` provides package ``extras`` to ensure compatibility with supported +:doc:`3rd-party integrations </integrations/index>`. .. code:: bash - pip install 'logot[pytest]' + pip install 'logot[pytest,trio]' -Package extras for supported 3rd-party integrations: +Use the following package ``extras`` for supported 3rd-party integrations: -- ``pytest`` - :doc:`using-pytest` +- ``pytest`` - :doc:`/using-pytest` +- ``trio`` - :doc:`/integrations/trio` + +.. seealso:: + + See :doc:`/integrations/index` usage guide. Installing with Poetry diff --git a/docs/integrations/index.rst b/docs/integrations/index.rst new file mode 100644 index 00000000..16a731e9 --- /dev/null +++ b/docs/integrations/index.rst @@ -0,0 +1,53 @@ +3rd-party integrations +====================== + +.. currentmodule:: logot + +:mod:`logot` integrates with a number of 3rd-party frameworks to extend its functionality. + +.. note:: + + Adding support for additional 3rd-party frameworks is an excellent way of + `contributing <https://github.com/etianen/logot/blob/main/CONTRIBUTING.md>`_ to :mod:`logot`! 🙇 + + +.. _integrations-async: + +Asynchronous frameworks +----------------------- + +Integrations with 3rd-party asynchronous frameworks extend :meth:`Logot.await_for`, allowing you to +:ref:`test asynchronous code <index-testing-async>` using your framework of choice. 💪 + +Learn about supported 3rd-party asynchronous frameworks: + +.. toctree:: + :maxdepth: 1 + + trio + +.. seealso:: + + See :ref:`index-testing-async` usage guide. + + See :class:`AsyncWaiter` API reference for integrating with 3rd-party asynchronous frameworks. + + +.. _integrations-logging: + +Logging frameworks +------------------ + +Integrations with 3rd-party logging frameworks extend :meth:`Logot.capturing`, allowing you to +:doc:`capture logs </log-capturing>` using your framework of choice. 💪 + +Learn about supported 3rd-party logging frameworks: + +.. toctree:: + :maxdepth: 1 + +.. seealso:: + + See :doc:`/log-capturing` usage guide. + + See :class:`Capturer` API reference for integrating with 3rd-party logging frameworks. diff --git a/docs/integrations/trio.rst b/docs/integrations/trio.rst new file mode 100644 index 00000000..601fecdb --- /dev/null +++ b/docs/integrations/trio.rst @@ -0,0 +1,92 @@ +Using with :mod:`trio` +====================== + +.. currentmodule:: logot + +Use :meth:`Logot.await_for` to pause your test until the expected logs arrive or the ``timeout`` expires: + +.. code:: python + + from logot import Logot, logged + + async def test_app(logot: Logot) -> None: + async with trio.open_nursery() as nursery: + nursery.start_soon(app.start()) + await logot.await_for(logged.info("App started")) + +.. include:: /include/testing-async-notes.rst + + +Installing +---------- + +Ensure :mod:`logot` is installed alongside a compatible :mod:`trio` version by adding the ``trio`` extra: + +.. code:: bash + + pip install 'logot[trio]' + +.. seealso:: + + See :ref:`installing-extras` usage guide. + + +Enabling for :mod:`pytest` +-------------------------- + +Enable :mod:`trio` support in your :external+pytest:doc:`pytest configuration <reference/customize>`: + +.. code:: ini + + # pytest.ini or .pytest.ini + [pytest] + logot_async_waiter = logot.trio.TrioWaiter + +.. code:: toml + + # pyproject.toml + [tool.pytest.ini_options] + logot_async_waiter = "logot.trio.TrioWaiter" + +.. seealso:: + + See :doc:`/using-pytest` usage guide. + + +Enabling for :mod:`unittest` +---------------------------- + +Enable :mod:`trio` support in your :class:`logot.unittest.LogotTestCase`: + +.. code:: python + + from logot.trio import TrioWaiter + + class MyAppTest(LogotTestCase): + logot_async_waiter = TrioWaiter + +.. seealso:: + + See :doc:`/using-unittest` usage guide. + + +Enabling manually +----------------- + +Enable :mod:`trio` support for your :class:`Logot` instance: + +.. code:: python + + from logot.trio import TrioWaiter + + logot = Logot(async_waiter=TrioWaiter) + +Enable :mod:`trio` support for a single :meth:`Logot.await_for` call: + +.. code:: python + + await logot.await_for(logged.info("App started"), async_waiter=TrioWaiter) + +.. seealso:: + + See :class:`Logot` and :meth:`Logot.await_for` API reference. diff --git a/docs/log-capturing.rst b/docs/log-capturing.rst index 574c4a14..73fecf35 100644 --- a/docs/log-capturing.rst +++ b/docs/log-capturing.rst @@ -21,10 +21,8 @@ Use a supported test framework integration for automatic log capturing in tests: - :doc:`/using-unittest` -.. _log-capturing-logging: - -Capturing :mod:`logging` logs ------------------------------ +Configuring +----------- The :meth:`Logot.capturing` method defaults to capturing **all** records from the root logger. Customize this with the ``level`` and ``logger`` arguments to :meth:`Logot.capturing`: @@ -41,27 +39,3 @@ careful to avoid capturing duplicate logs with overlapping calls to :meth:`Logot .. seealso:: See :class:`Logot` and :meth:`Logot.capturing` API reference. - - -.. _log-capturing-3rd-party: - -Capturing 3rd-party logs ------------------------- - -Any 3rd-party logging library can be integrated with :mod:`logot` by sending :class:`Captured` logs to -:meth:`Logot.capture`: - -.. code:: python - - def on_foo_log(logot: Logot, record: FooRecord) -> None: - logot.capture(Captured(record.levelname, record.msg)) - - foo_logger.add_handler(on_foo_log) - -.. note:: - - Using a context manager to set up and tear down log capture for every test run is *highly recommended*! - -.. seealso:: - - See :class:`Captured` and :meth:`Logot.capture` API reference. diff --git a/logot/_capture.py b/logot/_capture.py index 2e7a8045..0e7bf54d 100644 --- a/logot/_capture.py +++ b/logot/_capture.py @@ -8,17 +8,13 @@ class Captured: A captured log record. Send :class:`Captured` logs to :meth:`Logot.capture` to integrate with - :ref:`3rd-party logging frameworks <log-capturing-3rd-party>`. + :ref:`3rd-party logging frameworks <integrations-logging>`. .. note:: - This class is for integration with :ref:`3rd-party logging frameworks <log-capturing-3rd-party>`. It is not + This class is for integration with :ref:`3rd-party logging frameworks <integrations-logging>`. It is not generally used when writing tests. - .. seealso:: - - See :ref:`log-capturing-3rd-party` usage guide. - :param levelname: See :attr:`Captured.levelname`. :param msg: See :attr:`Captured.msg`. :param levelno: See :attr:`Captured.levelno`. diff --git a/logot/_logged.py b/logot/_logged.py index fe6304be..3e10491a 100644 --- a/logot/_logged.py +++ b/logot/_logged.py @@ -16,13 +16,11 @@ class Logged(ABC): .. important:: - :class:`Logged` instances are immutable and can be reused between tests. - - .. note:: - This is an abstract class and cannot be instantiated. Use the helpers in :mod:`logot.logged` to create log patterns. + :class:`Logged` instances are immutable and can be reused between tests. + .. seealso:: See :doc:`/log-pattern-matching` usage guide. diff --git a/logot/_logot.py b/logot/_logot.py index 24a74d12..0243007d 100644 --- a/logot/_logot.py +++ b/logot/_logot.py @@ -63,7 +63,7 @@ class Logot: .. note:: - This is for integration with :ref:`3rd-party logging frameworks <log-capturing-3rd-party>`. + This is for integration with :ref:`3rd-party logging frameworks <integrations-logging>`. Defaults to :attr:`Logot.DEFAULT_CAPTURER`. """ @@ -81,7 +81,7 @@ class Logot: .. note:: - This is for integration with 3rd-party asynchronous frameworks. + This is for integration with :ref:`3rd-party asynchronous frameworks <integrations-async>`. Defaults to :attr:`Logot.DEFAULT_ASYNC_WAITER`. """ @@ -121,7 +121,7 @@ def capturing( :attr:`Logot.DEFAULT_LEVEL`. :param logger: A logger name to capture logs from. Defaults to :attr:`Logot.DEFAULT_LOGGER`. :param capturer: Protocol used to capture logs. This is for integration with - :ref:`3rd-party logging frameworks <log-capturing-3rd-party>`. Defaults to :attr:`Logot.capturer`. + :ref:`3rd-party logging frameworks <integrations-logging>`. Defaults to :attr:`Logot.capturer`. """ if capturer is None: capturer = self.capturer @@ -139,13 +139,9 @@ def capture(self, captured: Captured) -> None: .. note:: - This method is for integration with :ref:`3rd-party logging frameworks <log-capturing-3rd-party>`. It is not + This method is for integration with :ref:`3rd-party logging frameworks <integrations-logging>`. It is not generally used when writing tests. - .. seealso:: - - See :ref:`log-capturing-3rd-party` usage guide. - :param captured: The captured log. """ with self._lock: @@ -215,7 +211,7 @@ async def await_for( :param logged: The expected :doc:`log pattern </log-pattern-matching>`. :param timeout: How long to wait (in seconds) before failing the test. Defaults to :attr:`Logot.timeout`. :param async_waiter: Protocol used to pause tests until expected logs arrive. This is for integration with - 3rd-party asynchronous frameworks. Defaults to :attr:`Logot.async_waiter`. + :ref:`3rd-party asynchronous frameworks <integrations-async>`. Defaults to :attr:`Logot.async_waiter`. :raises AssertionError: If the expected ``log`` pattern does not arrive within ``timeout`` seconds. """ if async_waiter is None: @@ -286,7 +282,7 @@ class Capturer(ABC): .. note:: - This class is for integration with :ref:`3rd-party logging frameworks <log-capturing-3rd-party>`. It is not + This class is for integration with :ref:`3rd-party logging frameworks <integrations-logging>`. It is not generally used when writing tests. """ diff --git a/logot/_trio.py b/logot/_trio.py new file mode 100644 index 00000000..da78d2f5 --- /dev/null +++ b/logot/_trio.py @@ -0,0 +1,25 @@ +from __future__ import annotations + +import trio +from trio.lowlevel import current_trio_token + +from logot._wait import AsyncWaiter + + +class TrioWaiter(AsyncWaiter): + """ + A :class:`logot.AsyncWaiter` implementation for :mod:`trio`. + """ + + __slots__ = ("_token", "_event") + + def __init__(self) -> None: + self._token = current_trio_token() + self._event = trio.Event() + + def release(self) -> None: + self._token.run_sync_soon(self._event.set) + + async def wait(self, *, timeout: float) -> None: + with trio.move_on_after(timeout): + await self._event.wait() diff --git a/logot/_wait.py b/logot/_wait.py index 765c4cf1..3177d8c0 100644 --- a/logot/_wait.py +++ b/logot/_wait.py @@ -27,8 +27,8 @@ class AsyncWaiter(ABC): .. note:: - This class is for integration with 3rd-party asynchronous frameworks. It is not generally used when writing - tests. + This class is for integration with :ref:`3rd-party asynchronous frameworks <integrations-async>`. It is not + generally used when writing tests. """ __slots__ = () diff --git a/logot/logging.py b/logot/logging.py index cdab473e..8064ce00 100644 --- a/logot/logging.py +++ b/logot/logging.py @@ -3,7 +3,7 @@ .. seealso:: - See :ref:`log-capturing-logging` usage guide. + See :doc:`/log-capturing` usage guide. 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+mypy-extensions = ">=0.4.2" +packaging = "*" +trio = ">=0.16.0" +typing-extensions = ">=3.7.4" + +[package.extras] +mypy = ["mypy (>=1.0)"] + [[package]] name = "typing-extensions" version = "4.9.0" @@ -893,10 +1065,26 @@ h2 = ["h2 (>=4,<5)"] socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"] zstd = ["zstandard (>=0.18.0)"] +[[package]] +name = "zipp" +version = "3.17.0" +description = "Backport of pathlib-compatible object wrapper for zip files" +optional = false +python-versions = ">=3.8" +files = [ + {file = "zipp-3.17.0-py3-none-any.whl", hash = "sha256:0e923e726174922dce09c53c59ad483ff7bbb8e572e00c7f7c46b88556409f31"}, + {file = "zipp-3.17.0.tar.gz", hash = "sha256:84e64a1c28cf7e91ed2078bb8cc8c259cb19b76942096c8d7b84947690cabaf0"}, +] + +[package.extras] +docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"] +testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"] + [extras] pytest = ["pytest"] +trio = ["trio"] [metadata] lock-version = "2.0" python-versions = "^3.8" -content-hash = "f21c1997c5ae3160aabaca80fbea4a93281ba8676a414139bf9f8a26ded97b19" +content-hash = "5ff89ddb47ec98abf8aed490978f89739e5fea0fd852da4f3c4cf53542ff3d26" diff --git a/pyproject.toml b/pyproject.toml index 5a4e9c94..993c0262 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -21,11 +21,13 @@ packages = [{ include = "logot" }] [tool.poetry.dependencies] python = "^3.8" -pytest = { version = ">=7.0.0", optional = true } +pytest = { version = ">=7,<9", optional = true } +trio = { version = ">=0.22,<0.25", optional = true } typing-extensions = { version = ">=4.9.0", python = "<3.10" } [tool.poetry.extras] pytest = ["pytest"] +trio = ["trio"] [tool.poetry.group.coverage.dependencies] coverage = "^7.4.1" @@ -34,6 +36,7 @@ coverage = "^7.4.1" hypothesis = "^6.96.1" mypy = "^1.8.0" ruff = "^0.1.11" +trio-typing = "^0.10.0" [tool.poetry.group.docs] optional = true

Support `trio` We can support [trio](https://trio.readthedocs.io/en/stable/) by allowing the async waiter implementation to be customized in the `Logot` constructor.

2024-02-08T22:35:56

0.0

[]

etianen/logot

etianen__logot-85

dc5f98669b888ce0d73c9873016eab3b1e277c6b

diff --git a/docs/api/logot.asyncio.rst b/docs/api/logot.asyncio.rst index ca72a8c0..bbf8282d 100644 --- a/docs/api/logot.asyncio.rst +++ b/docs/api/logot.asyncio.rst @@ -8,5 +8,3 @@ API reference ------------- .. autoclass:: AsyncioWaiter - :members: - :exclude-members: release,wait diff --git a/docs/api/logot.logging.rst b/docs/api/logot.logging.rst new file mode 100644 index 00000000..71a7fef1 --- /dev/null +++ b/docs/api/logot.logging.rst @@ -0,0 +1,10 @@ +:mod:`logot.logging` +==================== + +.. automodule:: logot.logging + + +API reference +------------- + +.. autoclass:: LoggingCapturer diff --git a/docs/api/logot.rst b/docs/api/logot.rst index 3daf2f32..43300559 100644 --- a/docs/api/logot.rst +++ b/docs/api/logot.rst @@ -10,10 +10,13 @@ API reference .. autoclass:: Logot :members: -.. autoclass:: Logged +.. autoclass:: Capturer + :members: .. autoclass:: Captured :members: +.. autoclass:: Logged + .. autoclass:: AsyncWaiter :members: diff --git a/docs/log-capturing.rst b/docs/log-capturing.rst index ae5fea31..574c4a14 100644 --- a/docs/log-capturing.rst +++ b/docs/log-capturing.rst @@ -21,6 +21,8 @@ Use a supported test framework integration for automatic log capturing in tests: - :doc:`/using-unittest` +.. _log-capturing-logging: + Capturing :mod:`logging` logs ----------------------------- diff --git a/logot/__init__.py b/logot/__init__.py index c1d33cfe..f45f38f6 100644 --- a/logot/__init__.py +++ b/logot/__init__.py @@ -9,5 +9,6 @@ from logot._capture import Captured as Captured from logot._logged import Logged as Logged +from logot._logot import Capturer as Capturer from logot._logot import Logot as Logot from logot._wait import AsyncWaiter as AsyncWaiter diff --git a/logot/_logging.py b/logot/_logging.py new file mode 100644 index 00000000..4fe6e285 --- /dev/null +++ b/logot/_logging.py @@ -0,0 +1,45 @@ +from __future__ import annotations + +import logging + +from logot._capture import Captured +from logot._logot import Capturer, Logot + + +class LoggingCapturer(Capturer): + """ + A :class:`logot.Capturer` implementation for :mod:`logging`. + + .. note:: + + This is the default :class:`logot.Capturer` implementation. + """ + + __slots__ = ("_logger", "_handler", "_prev_levelno") + + def start_capturing(self, logot: Logot, /, *, level: str | int, logger: str | None) -> None: + logger = self._logger = logging.getLogger(logger) + handler = self._handler = _Handler(level, logot) + # If the logger is less verbose than the handler, force it to the necessary verboseness. + self._prev_levelno = logger.level + if handler.level < logger.level: + logger.setLevel(handler.level) + # Add the handler. + logger.addHandler(handler) + + def stop_capturing(self) -> None: + # Remove the handler and restore the previous level. + self._logger.removeHandler(self._handler) + self._logger.setLevel(self._prev_levelno) + + +class _Handler(logging.Handler): + __slots__ = ("_logot",) + + def __init__(self, level: str | int, logot: Logot) -> None: + super().__init__(level) + self._logot = logot + + def emit(self, record: logging.LogRecord) -> None: + captured = Captured(record.levelname, record.getMessage(), levelno=record.levelno) + self._logot.capture(captured) diff --git a/logot/_logot.py b/logot/_logot.py index e26f53cf..d9546694 100644 --- a/logot/_logot.py +++ b/logot/_logot.py @@ -1,6 +1,6 @@ from __future__ import annotations -import logging +from abc import ABC, abstractmethod from collections import deque from contextlib import AbstractContextManager from threading import Lock @@ -22,11 +22,12 @@ class Logot: See :doc:`/index` usage guide. + :param capturer: See :attr:`Logot.capturer`. :param timeout: See :attr:`Logot.timeout`. :param async_waiter: See :attr:`Logot.async_waiter`. """ - __slots__ = ("timeout", "async_waiter", "_lock", "_queue", "_wait") + __slots__ = ("capturer", "timeout", "async_waiter", "_lock", "_queue", "_wait") DEFAULT_LEVEL: ClassVar[str | int] = "DEBUG" """ @@ -40,14 +41,30 @@ class Logot: This is the root logger. """ + DEFAULT_CAPTURER: ClassVar[Callable[[], Capturer]] = LazyCallable("logot.logging", "LoggingCapturer") + """ + The default :attr:`capturer` for new :class:`Logot` instances. + """ + DEFAULT_TIMEOUT: ClassVar[float] = 3.0 """ - The default ``timeout`` (in seconds) for new :class:`Logot` instances. + The default :attr:`timeout` (in seconds) for new :class:`Logot` instances. """ DEFAULT_ASYNC_WAITER: ClassVar[Callable[[], AsyncWaiter]] = LazyCallable("logot.asyncio", "AsyncioWaiter") """ - The default ``async_waiter`` for new :class:`Logot` instances. + The default :attr:`async_waiter` for new :class:`Logot` instances. + """ + + capturer: Callable[[], Capturer] + """ + The default ``capturer`` used by :meth:`capturing`. + + .. note:: + + This is for integration with :ref:`3rd-party logging frameworks <log-capturing-3rd-party>`. + + Defaults to :attr:`Logot.DEFAULT_CAPTURER`. """ timeout: float @@ -71,9 +88,11 @@ class Logot: def __init__( self, *, + capturer: Callable[[], Capturer] = DEFAULT_CAPTURER, timeout: float = DEFAULT_TIMEOUT, async_waiter: Callable[[], AsyncWaiter] = DEFAULT_ASYNC_WAITER, ) -> None: + self.capturer = capturer self.timeout = validate_timeout(timeout) self.async_waiter = async_waiter self._lock = Lock() @@ -84,7 +103,8 @@ def capturing( self, *, level: str | int = DEFAULT_LEVEL, - logger: logging.Logger | str | None = DEFAULT_LOGGER, + logger: str | None = DEFAULT_LOGGER, + capturer: Callable[[], Capturer] | None = None, ) -> AbstractContextManager[Logot]: """ Captures logs emitted at the given ``level`` by the given ``logger`` for the duration of the context. @@ -98,11 +118,16 @@ def capturing( :param level: A log level name (e.g. ``"DEBUG"``) or numeric level (e.g. :data:`logging.DEBUG`). Defaults to :attr:`Logot.DEFAULT_LEVEL`. - :param logger: A logger or logger name to capture logs from. Defaults to :attr:`Logot.DEFAULT_LOGGER`. + :param logger: A logger name to capture logs from. Defaults to :attr:`Logot.DEFAULT_LOGGER`. + :param capturer: Protocol used to capture logs. This is for integration with + :ref:`3rd-party logging frameworks <log-capturing-3rd-party>`. Defaults to :attr:`Logot.capturer`. """ + if capturer is None: + capturer = self.capturer + capturer_obj = capturer() level = validate_level(level) logger = validate_logger(logger) - return _Capturing(self, _Handler(level, self), logger=logger) + return _Capturing(self, capturer_obj, level=level, logger=logger) def capture(self, captured: Captured) -> None: """ @@ -128,7 +153,7 @@ def capture(self, captured: Captured) -> None: self._wait.logged = self._wait.logged._reduce(captured) # If the waiter has fully reduced, release the blocked caller. if self._wait.logged is None: - self._wait.waiter.release() + self._wait.waiter_obj.release() return # Otherwise, buffer the captured log. self._queue.append(captured) @@ -168,13 +193,13 @@ def wait_for( :param timeout: How long to wait (in seconds) before failing the test. Defaults to :attr:`Logot.timeout`. :raises AssertionError: If the expected ``log`` pattern does not arrive within ``timeout`` seconds. """ - waiter = self._start_waiting(logged, create_threading_waiter, timeout=timeout) - if waiter is None: + wait = self._start_waiting(logged, create_threading_waiter, timeout=timeout) + if wait is None: return try: - waiter.waiter.acquire(timeout=waiter.timeout) + wait.waiter_obj.acquire(timeout=wait.timeout) finally: - self._stop_waiting(waiter) + self._stop_waiting(wait) async def await_for( self, @@ -194,13 +219,13 @@ async def await_for( """ if async_waiter is None: async_waiter = self.async_waiter - waiter = self._start_waiting(logged, async_waiter, timeout=timeout) - if waiter is None: + wait = self._start_waiting(logged, async_waiter, timeout=timeout) + if wait is None: return try: - await waiter.waiter.wait(timeout=waiter.timeout) + await wait.waiter_obj.wait(timeout=wait.timeout) finally: - self._stop_waiting(waiter) + self._stop_waiting(wait) def clear(self) -> None: """ @@ -226,7 +251,8 @@ def _start_waiting( if logged is None: return None # All done! - wait = self._wait = _Wait(logged=logged, timeout=timeout, waiter=waiter()) + waiter_obj = waiter() + wait = self._wait = _Wait(logged=logged, timeout=timeout, waiter_obj=waiter_obj) return wait def _stop_waiting(self, wait: _Wait[Any]) -> None: @@ -253,21 +279,50 @@ def __repr__(self) -> str: return f"Logot(timeout={self.timeout!r}, async_waiter={self.async_waiter!r})" +class Capturer(ABC): + """ + Protocol used by :meth:`Logot.capturing` to capture logs. + + .. note:: + + This class is for integration with :ref:`3rd-party logging frameworks <log-capturing-3rd-party>`. It is not + generally used when writing tests. + """ + + __slots__ = () + + @abstractmethod + def start_capturing(self, logot: Logot, /, *, level: str | int, logger: str | None) -> None: + """ + Starts capturing logs for the given :class:`Logot`. + + Captured logs should be converted to a :class:`Captured` instance and sent to :meth:`Logot.capture`. + + :param logot: The :class:`Logot` instance. + :param level: A log level name (e.g. ``"DEBUG"``) or numeric level (e.g. :data:`logging.DEBUG`). + :param logger: A logger name to capture logs from. + """ + raise NotImplementedError + + @abstractmethod + def stop_capturing(self) -> None: + """ + Stops capturing logs. + """ + raise NotImplementedError + + class _Capturing: - __slots__ = ("_logot", "_handler", "_logger", "_prev_levelno") + __slots__ = ("_logot", "_capturer_obj", "_level", "_logger") - def __init__(self, logot: Logot, handler: logging.Handler, *, logger: logging.Logger) -> None: + def __init__(self, logot: Logot, capturer: Capturer, *, level: str | int, logger: str | None) -> None: self._logot = logot - self._handler = handler + self._capturer_obj = capturer + self._level = level self._logger = logger def __enter__(self) -> Logot: - # If the logger is less verbose than the handler, force it to the necessary verboseness. - self._prev_levelno = self._logger.level - if self._handler.level < self._logger.level: - self._logger.setLevel(self._handler.level) - # Add the handler. - self._logger.addHandler(self._handler) + self._capturer_obj.start_capturing(self._logot, level=self._level, logger=self._logger) return self._logot def __exit__( @@ -276,28 +331,13 @@ def __exit__( exc_value: BaseException | None, traceback: TracebackType | None, ) -> None: - # Remove the handler. - self._logger.removeHandler(self._handler) - # Restore the previous level. - self._logger.setLevel(self._prev_levelno) - - -class _Handler(logging.Handler): - __slots__ = ("_logot",) - - def __init__(self, level: str | int, logot: Logot) -> None: - super().__init__(level) - self._logot = logot - - def emit(self, record: logging.LogRecord) -> None: - captured = Captured(record.levelname, record.getMessage(), levelno=record.levelno) - self._logot.capture(captured) + self._capturer_obj.stop_capturing() class _Wait(Generic[W]): - __slots__ = ("logged", "timeout", "waiter") + __slots__ = ("logged", "timeout", "waiter_obj") - def __init__(self, *, logged: Logged | None, timeout: float, waiter: W) -> None: + def __init__(self, *, logged: Logged | None, timeout: float, waiter_obj: W) -> None: self.logged = logged self.timeout = timeout - self.waiter = waiter + self.waiter_obj = waiter_obj diff --git a/logot/_validate.py b/logot/_validate.py index 6b47a573..58997704 100644 --- a/logot/_validate.py +++ b/logot/_validate.py @@ -1,7 +1,5 @@ from __future__ import annotations -import logging - def validate_level(level: str | int) -> str | int: # Handle `str` or `int` level. @@ -11,12 +9,9 @@ def validate_level(level: str | int) -> str | int: raise TypeError(f"Invalid level: {level!r}") -def validate_logger(logger: logging.Logger | str | None) -> logging.Logger: +def validate_logger(logger: str | None) -> str | None: # Handle `None` or `str` logger. if logger is None or isinstance(logger, str): - return logging.getLogger(logger) - # Handle `Logger` logger. - if isinstance(logger, logging.Logger): return logger # Handle invalid logger. raise TypeError(f"Invalid logger: {logger!r}") diff --git a/logot/logging.py b/logot/logging.py new file mode 100644 index 00000000..cdab473e --- /dev/null +++ b/logot/logging.py @@ -0,0 +1,10 @@ +""" +Integration API for :mod:`logging`. + +.. seealso:: + + See :ref:`log-capturing-logging` usage guide. +""" +from __future__ import annotations + +from logot._logging import LoggingCapturer as LoggingCapturer

Support `loguru` log capture Supporting a basic [loguru](https://github.com/Delgan/loguru) integration would be very easy by providing a context manager via a helper func using their `LogHandler` integration. Alternatively, a `Logot` subclass called `Logurot` would be cheese and fun. That would allow the `pytest` plugin to provide a `logurot` fixture too (if `loguru` is installed). Only import `logging` if actually used Although less heavyweight than `asyncio` (see #81), `logging` is still quite chunky and part of proving our support for _any_ logging framework (see #28) is not giving special preference to `logging`.

Further API discussion and feedback from `loguru` devs in https://github.com/Delgan/loguru/issues/1072 ❤️ ## Proposed API ``` py from logot.contrib.loguru import LoguruCapture with Logot().capturing(LoguruCapture): pass ``` Any arguments given to `capturing` would be propagated to the `LoguruCapture` constructor. This would then unify capture between stdlib logging and `loguru` logging. The default capture backend would be the stdlib capture, and an `@overload` would ensure good typing. 💪

2024-02-07T20:49:42

0.0

[]

etianen/logot

etianen__logot-84

2e7e25e0c99fc47917f7f14b9084b5d6093fefd9

diff --git a/logot/_logot.py b/logot/_logot.py index 240007fa..e26f53cf 100644 --- a/logot/_logot.py +++ b/logot/_logot.py @@ -250,7 +250,7 @@ def _reduce(self, logged: Logged | None) -> Logged | None: return logged def __repr__(self) -> str: - return f"Logot(timeout={self.timeout!r})" + return f"Logot(timeout={self.timeout!r}, async_waiter={self.async_waiter!r})" class _Capturing:

Only importing `asyncio` if actually used Closes #81

2024-02-06T15:44:25

0.0

[]

etianen/logot

etianen__logot-83

ff2caa3a0935fc955c7415ddc2053b16ec367c38

diff --git a/docs/conf.py b/docs/conf.py index bf52ec02..b5c1ecc9 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -23,7 +23,6 @@ ] autodoc_member_order = "bysource" -autodoc_typehints = "both" intersphinx_mapping = { "python": ("https://docs.python.org/3", None), diff --git a/logot/_import.py b/logot/_import.py index 324ab0a4..abd506f1 100644 --- a/logot/_import.py +++ b/logot/_import.py @@ -1,7 +1,7 @@ from __future__ import annotations from importlib import import_module -from typing import Any +from typing import Any, Callable def import_any(module: str, name: str) -> Any: @@ -12,3 +12,23 @@ def import_any(module: str, name: str) -> Any: def import_any_parsed(name: str) -> Any: module, name = name.rsplit(".", 1) return import_any(module, name) + + +class LazyCallable: + __slots__ = ("_module", "_name", "_resolved") + + _resolved: Callable[..., Any] + + def __init__(self, module: str, name: str) -> None: + self._module = module + self._name = name + + def __call__(self, /, *args: Any, **kwargs: Any) -> Any: + try: + resolved = self._resolved + except AttributeError: + resolved = self._resolved = import_any(self._module, self._name) + return resolved(*args, **kwargs) + + def __repr__(self) -> str: + return f"{self._module}.{self._name}" diff --git a/logot/_logot.py b/logot/_logot.py index 51e16ce4..240007fa 100644 --- a/logot/_logot.py +++ b/logot/_logot.py @@ -7,8 +7,8 @@ from types import TracebackType from typing import Any, Callable, ClassVar, Generic -from logot._asyncio import AsyncioWaiter from logot._capture import Captured +from logot._import import LazyCallable from logot._logged import Logged from logot._validate import validate_level, validate_logger, validate_timeout from logot._wait import AsyncWaiter, W, create_threading_waiter @@ -45,7 +45,7 @@ class Logot: The default ``timeout`` (in seconds) for new :class:`Logot` instances. """ - DEFAULT_ASYNC_WAITER: ClassVar[Callable[[], AsyncWaiter]] = AsyncioWaiter + DEFAULT_ASYNC_WAITER: ClassVar[Callable[[], AsyncWaiter]] = LazyCallable("logot.asyncio", "AsyncioWaiter") """ The default ``async_waiter`` for new :class:`Logot` instances. """

Only import `asyncio` if actually used It's a big heavy framework we don't always need to import. Since we're only importing 3rd-party libs when actually used, it would be ideal to treat heavyweight stdlib modules the same. Since `asyncio` is the default async waiter, probably a lazy import is best to keep the default async waiter zero-config.

2024-02-06T15:39:13

0.0

[]

etianen/logot

etianen__logot-78

0027d37668f5ae1bdc19dd153f1d056910d83f7b

diff --git a/docs/api/logot.asyncio.rst b/docs/api/logot.asyncio.rst new file mode 100644 index 00000000..ca72a8c0 --- /dev/null +++ b/docs/api/logot.asyncio.rst @@ -0,0 +1,12 @@ +:mod:`logot.asyncio` +==================== + +.. automodule:: logot.asyncio + + +API reference +------------- + +.. autoclass:: AsyncioWaiter + :members: + :exclude-members: release,wait diff --git a/docs/api/logot.rst b/docs/api/logot.rst index b6f2f247..3daf2f32 100644 --- a/docs/api/logot.rst +++ b/docs/api/logot.rst @@ -14,3 +14,6 @@ API reference .. autoclass:: Captured :members: + +.. autoclass:: AsyncWaiter + :members: diff --git a/docs/index.rst b/docs/index.rst index c2756476..0735d8a3 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -60,6 +60,8 @@ Testing this code with :mod:`logot` is easy! logot.wait_for(logged.info("Poll finished")) +.. _index-testing-threaded: + Testing threaded code --------------------- @@ -83,6 +85,8 @@ Use :meth:`Logot.wait_for` to pause your test until the expected logs arrive or See :doc:`/log-pattern-matching` for examples of how to wait for logs that may arrive in an unpredictable order. +.. _index-testing-async: + Testing asynchronous code ------------------------- diff --git a/logot/__init__.py b/logot/__init__.py index b64dee6f..c1d33cfe 100644 --- a/logot/__init__.py +++ b/logot/__init__.py @@ -10,3 +10,4 @@ from logot._capture import Captured as Captured from logot._logged import Logged as Logged from logot._logot import Logot as Logot +from logot._wait import AsyncWaiter as AsyncWaiter diff --git a/logot/_asyncio.py b/logot/_asyncio.py index 8f99fb3d..0dcd7e03 100644 --- a/logot/_asyncio.py +++ b/logot/_asyncio.py @@ -6,14 +6,22 @@ class AsyncioWaiter(AsyncWaiter): + """ + A :class:`logot.AsyncWaiter` implementation for :mod:`asyncio`. + + .. note:: + + This is the default :class:`logot.AsyncWaiter` implementation. + """ + __slots__ = ("_loop", "_future") def __init__(self) -> None: - # Create an unresolved `asyncio.Future`. This will be resolved by `notify()`. + # Create an unresolved `asyncio.Future`. This will be resolved by `release()`. self._loop = asyncio.get_running_loop() self._future: asyncio.Future[None] = self._loop.create_future() - def notify(self) -> None: + def release(self) -> None: self._loop.call_soon_threadsafe(self._resolve) async def wait(self, *, timeout: float) -> None: @@ -27,5 +35,5 @@ def _resolve(self) -> None: try: self._future.set_result(None) except asyncio.InvalidStateError: # pragma: no cover - # It's possible that the timeout and the `notify()` will both occur in the same tick of the event loop. + # It's possible that the timeout and the `release()` will both occur in the same tick of the event loop. pass diff --git a/logot/_import.py b/logot/_import.py new file mode 100644 index 00000000..324ab0a4 --- /dev/null +++ b/logot/_import.py @@ -0,0 +1,14 @@ +from __future__ import annotations + +from importlib import import_module +from typing import Any + + +def import_any(module: str, name: str) -> Any: + module_obj = import_module(module) + return getattr(module_obj, name) + + +def import_any_parsed(name: str) -> Any: + module, name = name.rsplit(".", 1) + return import_any(module, name) diff --git a/logot/_logot.py b/logot/_logot.py index d10b2e82..51e16ce4 100644 --- a/logot/_logot.py +++ b/logot/_logot.py @@ -5,13 +5,13 @@ from contextlib import AbstractContextManager from threading import Lock from types import TracebackType -from typing import ClassVar +from typing import Any, Callable, ClassVar, Generic from logot._asyncio import AsyncioWaiter from logot._capture import Captured from logot._logged import Logged from logot._validate import validate_level, validate_logger, validate_timeout -from logot._wait import AbstractWaiter, ThreadedWaiter +from logot._wait import AsyncWaiter, W, create_threading_waiter class Logot: @@ -23,9 +23,10 @@ class Logot: See :doc:`/index` usage guide. :param timeout: See :attr:`Logot.timeout`. + :param async_waiter: See :attr:`Logot.async_waiter`. """ - __slots__ = ("timeout", "_lock", "_queue", "_waiter") + __slots__ = ("timeout", "async_waiter", "_lock", "_queue", "_wait") DEFAULT_LEVEL: ClassVar[str | int] = "DEBUG" """ @@ -41,25 +42,43 @@ class Logot: DEFAULT_TIMEOUT: ClassVar[float] = 3.0 """ - The default timeout (in seconds) for new :class:`Logot` instances. + The default ``timeout`` (in seconds) for new :class:`Logot` instances. + """ + + DEFAULT_ASYNC_WAITER: ClassVar[Callable[[], AsyncWaiter]] = AsyncioWaiter + """ + The default ``async_waiter`` for new :class:`Logot` instances. """ timeout: float """ - The default timeout (in seconds) for calls to :meth:`wait_for` and :meth:`await_for`. + The default ``timeout`` (in seconds) for calls to :meth:`wait_for` and :meth:`await_for`. Defaults to :attr:`Logot.DEFAULT_TIMEOUT`. """ + async_waiter: Callable[[], AsyncWaiter] + """ + The default ``async_waiter`` for calls to :meth:`await_for`. + + .. note:: + + This is for integration with 3rd-party asynchronous frameworks. + + Defaults to :attr:`Logot.DEFAULT_ASYNC_WAITER`. + """ + def __init__( self, *, timeout: float = DEFAULT_TIMEOUT, + async_waiter: Callable[[], AsyncWaiter] = DEFAULT_ASYNC_WAITER, ) -> None: self.timeout = validate_timeout(timeout) + self.async_waiter = async_waiter self._lock = Lock() self._queue: deque[Captured] = deque() - self._waiter: AbstractWaiter | None = None + self._wait: _Wait[Any] | None = None def capturing( self, @@ -105,11 +124,11 @@ def capture(self, captured: Captured) -> None: """ with self._lock: # If there is a waiter that has not been fully reduced, attempt to reduce it. - if self._waiter is not None and self._waiter._logged is not None: - self._waiter._logged = self._waiter._logged._reduce(captured) - # If the waiter has fully reduced, notify the blocked caller. - if self._waiter._logged is None: - self._waiter.notify() + if self._wait is not None and self._wait.logged is not None: + self._wait.logged = self._wait.logged._reduce(captured) + # If the waiter has fully reduced, release the blocked caller. + if self._wait.logged is None: + self._wait.waiter.release() return # Otherwise, buffer the captured log. self._queue.append(captured) @@ -136,7 +155,12 @@ def assert_not_logged(self, logged: Logged) -> None: if reduced is None: raise AssertionError(f"Logged:\n\n{logged}") - def wait_for(self, logged: Logged, *, timeout: float | None = None) -> None: + def wait_for( + self, + logged: Logged, + *, + timeout: float | None = None, + ) -> None: """ Waits for the expected ``log`` pattern to arrive or the ``timeout`` to expire. @@ -144,25 +168,37 @@ def wait_for(self, logged: Logged, *, timeout: float | None = None) -> None: :param timeout: How long to wait (in seconds) before failing the test. Defaults to :attr:`Logot.timeout`. :raises AssertionError: If the expected ``log`` pattern does not arrive within ``timeout`` seconds. """ - waiter = ThreadedWaiter() - timeout = self._start_waiting(logged, waiter, timeout=timeout) + waiter = self._start_waiting(logged, create_threading_waiter, timeout=timeout) + if waiter is None: + return try: - waiter.wait(timeout=timeout) + waiter.waiter.acquire(timeout=waiter.timeout) finally: self._stop_waiting(waiter) - async def await_for(self, logged: Logged, *, timeout: float | None = None) -> None: + async def await_for( + self, + logged: Logged, + *, + timeout: float | None = None, + async_waiter: Callable[[], AsyncWaiter] | None = None, + ) -> None: """ Waits *asynchronously* for the expected ``log`` pattern to arrive or the ``timeout`` to expire. :param logged: The expected :doc:`log pattern </log-pattern-matching>`. :param timeout: How long to wait (in seconds) before failing the test. Defaults to :attr:`Logot.timeout`. + :param async_waiter: Protocol used to pause tests until expected logs arrive. This is for integration with + 3rd-party asynchronous frameworks. Defaults to :attr:`Logot.async_waiter`. :raises AssertionError: If the expected ``log`` pattern does not arrive within ``timeout`` seconds. """ - waiter = AsyncioWaiter() - timeout = self._start_waiting(logged, waiter, timeout=timeout) + if async_waiter is None: + async_waiter = self.async_waiter + waiter = self._start_waiting(logged, async_waiter, timeout=timeout) + if waiter is None: + return try: - await waiter.wait(timeout=timeout) + await waiter.waiter.wait(timeout=waiter.timeout) finally: self._stop_waiting(waiter) @@ -170,10 +206,11 @@ def clear(self) -> None: """ Clears any captured logs. """ - with self._lock: - self._queue.clear() + self._queue.clear() - def _start_waiting(self, logged: Logged | None, waiter: AbstractWaiter, *, timeout: float | None) -> float: + def _start_waiting( + self, logged: Logged | None, waiter: Callable[[], W], *, timeout: float | None + ) -> _Wait[W] | None: with self._lock: # If no timeout is provided, use the default timeout. # Otherwise, validate and use the provided timeout. @@ -182,24 +219,23 @@ def _start_waiting(self, logged: Logged | None, waiter: AbstractWaiter, *, timeo else: timeout = validate_timeout(timeout) # Ensure no other waiters. - if self._waiter is not None: # pragma: no cover + if self._wait is not None: # pragma: no cover raise RuntimeError("Multiple concurrent waiters are not supported") - # Set a waiter. - waiter = self._waiter = waiter # Apply an immediate reduction. - logged = waiter._logged = self._reduce(logged) + logged = self._reduce(logged) if logged is None: - waiter.notify() + return None # All done! - return timeout + wait = self._wait = _Wait(logged=logged, timeout=timeout, waiter=waiter()) + return wait - def _stop_waiting(self, waiter: AbstractWaiter) -> None: + def _stop_waiting(self, wait: _Wait[Any]) -> None: with self._lock: # Clear the waiter. - self._waiter = None + self._wait = None # Error if the waiter logs are not fully reduced. - if waiter._logged is not None: - raise AssertionError(f"Not logged:\n\n{waiter._logged}") + if wait.logged is not None: + raise AssertionError(f"Not logged:\n\n{wait.logged}") def _reduce(self, logged: Logged | None) -> Logged | None: # Drain the queue until the log is fully reduced. @@ -256,3 +292,12 @@ def __init__(self, level: str | int, logot: Logot) -> None: def emit(self, record: logging.LogRecord) -> None: captured = Captured(record.levelname, record.getMessage(), levelno=record.levelno) self._logot.capture(captured) + + +class _Wait(Generic[W]): + __slots__ = ("logged", "timeout", "waiter") + + def __init__(self, *, logged: Logged | None, timeout: float, waiter: W) -> None: + self.logged = logged + self.timeout = timeout + self.waiter = waiter diff --git a/logot/_typing.py b/logot/_typing.py index ac192bd2..85c53071 100644 --- a/logot/_typing.py +++ b/logot/_typing.py @@ -1,7 +1,7 @@ from __future__ import annotations import sys -from typing import TypeVar +from typing import Any, TypeVar if sys.version_info >= (3, 10): from typing import ParamSpec as ParamSpec @@ -12,3 +12,5 @@ P = ParamSpec("P") T = TypeVar("T") + +MISSING: Any = object() diff --git a/logot/_wait.py b/logot/_wait.py index 61b77df7..765c4cf1 100644 --- a/logot/_wait.py +++ b/logot/_wait.py @@ -2,47 +2,49 @@ from abc import ABC, abstractmethod from threading import Lock +from typing import Protocol, TypeVar -from logot._logged import Logged +class Waiter(Protocol): + @abstractmethod + def release(self) -> None: + raise NotImplementedError -class AbstractWaiter(ABC): - __slots__ = ("_logged", "_timeout") - # This protected attr is populated by `Logot._start_waiting`. - _logged: Logged | None +W = TypeVar("W", bound=Waiter) - @abstractmethod - def notify(self) -> None: - raise NotImplementedError +def create_threading_waiter() -> Lock: + # Create an already-acquired lock. This will be released by `release()`. + lock = Lock() + lock.acquire() + return lock -class Waiter(AbstractWaiter): - __slots__ = () - @abstractmethod - def wait(self, *, timeout: float) -> None: - raise NotImplementedError +class AsyncWaiter(ABC): + """ + Protocol used by :meth:`Logot.await_for` to pause tests until expected logs arrive. + + .. note:: + This class is for integration with 3rd-party asynchronous frameworks. It is not generally used when writing + tests. + """ -class AsyncWaiter(AbstractWaiter): __slots__ = () @abstractmethod - async def wait(self, *, timeout: float) -> None: + def release(self) -> None: + """ + Releases the test waiting on :meth:`wait`, allowing it to resume immediately. + """ raise NotImplementedError + @abstractmethod + async def wait(self, *, timeout: float) -> None: + """ + Waits *asynchronously* for :meth:`release` to be called or the ``timeout`` to expire. -class ThreadedWaiter(Waiter): - __slots__ = ("_lock",) - - def __init__(self) -> None: - # Create an already-acquired lock. This will be released by `notify()`. - self._lock = Lock() - self._lock.acquire() - - def notify(self) -> None: - self._lock.release() - - def wait(self, *, timeout: float) -> None: - self._lock.acquire(timeout=timeout) + :param timeout: How long to wait (in seconds) before resuming. + """ + raise NotImplementedError diff --git a/logot/asyncio.py b/logot/asyncio.py new file mode 100644 index 00000000..25a47532 --- /dev/null +++ b/logot/asyncio.py @@ -0,0 +1,10 @@ +""" +Integration API for :mod:`asyncio`. + +.. seealso:: + + See :ref:`index-testing-async` usage guide. +""" +from __future__ import annotations + +from logot._asyncio import AsyncioWaiter as AsyncioWaiter

Support `tornado` Once we support `trio` via #78, `tornado` support should be easy.

2024-02-04T15:37:53

0.0

[]

etianen/logot

etianen__logot-66

63b77e5128cac63d83028e2bd1b09435796ac0aa

diff --git a/logot/_format.py b/logot/_format.py index fd55340a..978fbe0d 100644 --- a/logot/_format.py +++ b/logot/_format.py @@ -1,7 +1,5 @@ from __future__ import annotations -import logging - def format_level(level: str | int) -> str: # Format `str` level. @@ -9,8 +7,7 @@ def format_level(level: str | int) -> str: return level # Format `int` level. if isinstance(level, int): - levelname: str = logging.getLevelName(level) - return levelname + return f"Level {level}" # Handle invalid level. raise TypeError(f"Invalid level: {level!r}") diff --git a/logot/_logot.py b/logot/_logot.py index c9690b0f..df4817a0 100644 --- a/logot/_logot.py +++ b/logot/_logot.py @@ -33,7 +33,7 @@ class Logot: The default ``level`` used by :meth:`capturing`. """ - DEFAULT_LOGGER: ClassVar[logging.Logger | str | None] = None + DEFAULT_LOGGER: ClassVar[str | None] = None """ The default ``logger`` used by :meth:`capturing`. diff --git a/pyproject.toml b/pyproject.toml index 04f406d1..f7ae1881 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "logot" -version = "0.3.2" +version = "0.3.3" description = "Test whether your code is logging correctly 🪵" authors = ["Dave Hall <[email protected]>"] license = "MIT"

Support `loguru` log capture Supporting a basic [loguru](https://github.com/Delgan/loguru) integration would be very easy by providing a context manager via a helper func using their `LogHandler` integration. Alternatively, a `Logot` subclass called `Logurot` would be cheese and fun. That would allow the `pytest` plugin to provide a `logurot` fixture too (if `loguru` is installed).

Further API discussion and feedback from `loguru` devs in https://github.com/Delgan/loguru/issues/1072 ❤️ ## Proposed API ``` py from logot.contrib.loguru import LoguruCapture with Logot().capturing(LoguruCapture): pass ``` Any arguments given to `capturing` would be propagated to the `LoguruCapture` constructor. This would then unify capture between stdlib logging and `loguru` logging. The default capture backend would be the stdlib capture, and an `@overload` would ensure good typing. 💪

2024-02-02T21:28:09

0.0

[]

etianen/logot

etianen__logot-61

0b16d95299331bb29721f628350ef127eae429f0

diff --git a/docs/api/logot.logged.rst b/docs/api/logot.logged.rst index 944b74c0..78c25466 100644 --- a/docs/api/logot.logged.rst +++ b/docs/api/logot.logged.rst @@ -7,14 +7,14 @@ API reference ------------- -.. autofunction:: logot.logged.log +.. autofunction:: log -.. autofunction:: logot.logged.debug +.. autofunction:: debug -.. autofunction:: logot.logged.info +.. autofunction:: info -.. autofunction:: logot.logged.warning +.. autofunction:: warning -.. autofunction:: logot.logged.error +.. autofunction:: error -.. autofunction:: logot.logged.critical +.. autofunction:: critical diff --git a/docs/api/logot.rst b/docs/api/logot.rst index 45b7ab0d..b6f2f247 100644 --- a/docs/api/logot.rst +++ b/docs/api/logot.rst @@ -7,10 +7,10 @@ API reference ------------- -.. autoclass:: logot.Logot +.. autoclass:: Logot :members: -.. autoclass:: logot.Logged +.. autoclass:: Logged -.. autoclass:: logot.Captured +.. autoclass:: Captured :members: diff --git a/docs/conf.py b/docs/conf.py index ce597b03..bf52ec02 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -22,7 +22,6 @@ "sphinx.ext.intersphinx", ] -autoclass_content = "both" autodoc_member_order = "bysource" autodoc_typehints = "both" diff --git a/docs/installing.rst b/docs/installing.rst index 10cff8f5..bdc291d3 100644 --- a/docs/installing.rst +++ b/docs/installing.rst @@ -24,7 +24,7 @@ Installing package ``extras`` pip install 'logot[pytest]' -Supported 3rd-party integrations: +Package extras for supported 3rd-party integrations: - ``pytest`` - :doc:`usage-pytest` diff --git a/docs/log-capturing.rst b/docs/log-capturing.rst index 5e1a4ac9..2ec74c96 100644 --- a/docs/log-capturing.rst +++ b/docs/log-capturing.rst @@ -11,14 +11,18 @@ Log capturing app.start() logot.wait_for(logged.info("App started")) -.. note:: - If using :mod:`pytest`, you can probably just use the pre-configured ``logot`` fixture included in the bundled - :doc:`pytest plugin </usage-pytest>` and skip manually configuring log capture. 💪 +Test framework integrations +--------------------------- + +Use a supported test framework integration for automatic log capturing in tests: + +- :doc:`/usage-pytest` +- :doc:`/usage-unittest` Capturing :mod:`logging` logs -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +----------------------------- The :meth:`Logot.capturing` method defaults to capturing **all** records from the root logger. Customize this with the ``level`` and ``logger`` arguments to :meth:`Logot.capturing`: @@ -40,7 +44,7 @@ careful to avoid capturing duplicate logs with overlapping calls to :meth:`Logot .. _log-capturing-3rd-party: Capturing 3rd-party logs -~~~~~~~~~~~~~~~~~~~~~~~~ +------------------------ Any 3rd-party logging library can be integrated with :mod:`logot` by sending :class:`Captured` logs to :meth:`Logot.capture`: diff --git a/pyproject.toml b/pyproject.toml index b0defef2..43bc0103 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "logot" -version = "0.3.1" +version = "0.3.2" description = "Log-based testing" authors = ["Dave Hall <[email protected]>"] license = "MIT"

Create a `unittest.TestCase` mixin that starts log capture on every test This would make it much easier to get started with `unittest`!

2024-02-01T23:15:03

0.0

[]

etianen/logot

etianen__logot-45

59a14727ace7c64a8478e4cfb6d3c35fd7e3ea72

diff --git a/docs/api.rst b/docs/api.rst index eaa9a3de..d887474a 100644 --- a/docs/api.rst +++ b/docs/api.rst @@ -41,10 +41,12 @@ Import the :mod:`logot` API in your tests: .. autoclass:: logot.Captured - .. autoattribute:: levelno + .. autoattribute:: levelname .. autoattribute:: msg + .. autoattribute:: levelno + :mod:`logot.logged` ------------------- diff --git a/docs/captured.rst b/docs/captured.rst index 7f86568a..187bf232 100644 --- a/docs/captured.rst +++ b/docs/captured.rst @@ -48,7 +48,7 @@ Any 3rd-party logging library can be integrated with :mod:`logot` by sending :cl .. code:: python def on_foo_log(logot: Logot, record: FooRecord) -> None: - logot.capture(Captured(record.levelno, record.msg)) + logot.capture(Captured(record.levelname, record.msg, levelno=record.levelno)) foo_logger.add_handler(on_foo_log) diff --git a/logot/_captured.py b/logot/_captured.py index 21eeecd3..da2aef2c 100644 --- a/logot/_captured.py +++ b/logot/_captured.py @@ -1,10 +1,6 @@ from __future__ import annotations -import logging -from typing import Final - from logot._format import format_log -from logot._validate import validate_levelno class Captured: @@ -23,31 +19,43 @@ class Captured: See :ref:`captured-3rd-party` usage guide. - :param level: The log level (e.g. :data:`logging.DEBUG`) or string name (e.g. ``"DEBUG"``). + :param levelname: The log level name (e.g. ``"DEBUG"``). :param msg: The log message. + :param levelno: The log level number (e.g. :data:`logging.DEBUG`). """ - __slots__ = ("levelno", "msg") + __slots__ = ("levelname", "msg", "levelno") - levelno: Final[int] + levelname: str """ - The integer log level (e.g. :data:`logging.DEBUG`). + The log level name (e.g. ``"DEBUG"``). """ - msg: Final[str] + msg: str """ The log message. """ - def __init__(self, level: int | str, msg: str) -> None: - self.levelno = validate_levelno(level) + levelno: int + """ + The log level number (e.g. :data:`logging.DEBUG`). + """ + + def __init__(self, levelname: str, msg: str, *, levelno: int) -> None: + self.levelname = levelname self.msg = msg + self.levelno = levelno def __eq__(self, other: object) -> bool: - return isinstance(other, Captured) and other.levelno == self.levelno and other.msg == self.msg + return ( + isinstance(other, Captured) + and other.levelname == self.levelname + and other.msg == self.msg + and other.levelno == self.levelno + ) def __repr__(self) -> str: - return f"Captured({logging.getLevelName(self.levelno)!r}, {self.msg!r})" + return f"Captured({self.levelname!r}, {self.msg!r}, levelno={self.levelno!r})" def __str__(self) -> str: - return format_log(self.levelno, self.msg) + return format_log(self.levelname, self.msg) diff --git a/logot/_format.py b/logot/_format.py index d0ec82cb..6564284d 100644 --- a/logot/_format.py +++ b/logot/_format.py @@ -3,5 +3,14 @@ import logging -def format_log(levelno: int, msg: str) -> str: - return f"[{logging.getLevelName(levelno)}] {msg}" +def format_level(level: str | int) -> str: + # Format `str` level. + if isinstance(level, str): + return level + # Format `int` level. + level: str = logging.getLevelName(level) + return level + + +def format_log(levelname: str, msg: str) -> str: + return f"[{levelname}] {msg}" diff --git a/logot/_logged.py b/logot/_logged.py index 6c11678b..6400537f 100644 --- a/logot/_logged.py +++ b/logot/_logged.py @@ -1,12 +1,11 @@ from __future__ import annotations -import logging from abc import ABC, abstractmethod from logot._captured import Captured -from logot._format import format_log +from logot._format import format_level, format_log from logot._match import compile_matcher -from logot._validate import validate_levelno +from logot._validate import validate_level class Logged(ABC): @@ -58,14 +57,14 @@ def _str(self, *, indent: str) -> str: raise NotImplementedError -def log(level: int | str, msg: str) -> Logged: +def log(level: str | int, msg: str) -> Logged: """ Creates a :doc:`log pattern <logged>` representing a log record at the given ``level`` with the given ``msg``. - :param level: A log level (e.g. :data:`logging.DEBUG`) or string name (e.g. ``"DEBUG"``). + :param level: A log level name (e.g. ``"DEBUG"``) or numeric constant (e.g. :data:`logging.DEBUG`). :param msg: A log :doc:`message pattern <match>`. """ - return _RecordLogged(validate_levelno(level), msg) + return _RecordLogged(validate_level(level), msg) def debug(msg: str) -> Logged: @@ -74,7 +73,7 @@ def debug(msg: str) -> Logged: :param msg: A log :doc:`message pattern <match>`. """ - return _RecordLogged(logging.DEBUG, msg) + return _RecordLogged("DEBUG", msg) def info(msg: str) -> Logged: @@ -83,7 +82,7 @@ def info(msg: str) -> Logged: :param msg: A log :doc:`message pattern <match>`. """ - return _RecordLogged(logging.INFO, msg) + return _RecordLogged("INFO", msg) def warning(msg: str) -> Logged: @@ -92,7 +91,7 @@ def warning(msg: str) -> Logged: :param msg: A log :doc:`message pattern <match>`. """ - return _RecordLogged(logging.WARNING, msg) + return _RecordLogged("WARNING", msg) def error(msg: str) -> Logged: @@ -101,7 +100,7 @@ def error(msg: str) -> Logged: :param msg: A log :doc:`message pattern <match>`. """ - return _RecordLogged(logging.ERROR, msg) + return _RecordLogged("ERROR", msg) def critical(msg: str) -> Logged: @@ -110,30 +109,39 @@ def critical(msg: str) -> Logged: :param msg: A log :doc:`message pattern <match>`. """ - return _RecordLogged(logging.CRITICAL, msg) + return _RecordLogged("CRITICAL", msg) class _RecordLogged(Logged): - __slots__ = ("_levelno", "_msg", "_matcher") + __slots__ = ("_level", "_msg", "_matcher") - def __init__(self, levelno: int, msg: str) -> None: - self._levelno = levelno + def __init__(self, level: str | int, msg: str) -> None: + self._level = level self._msg = msg self._matcher = compile_matcher(msg) def __eq__(self, other: object) -> bool: - return isinstance(other, _RecordLogged) and other._levelno == self._levelno and other._msg == self._msg + return isinstance(other, _RecordLogged) and other._level == self._level and other._msg == self._msg def __repr__(self) -> str: - return f"log({logging.getLevelName(self._levelno)!r}, {self._msg!r})" + return f"log({self._level!r}, {self._msg!r})" def _reduce(self, captured: Captured) -> Logged | None: - if self._levelno == captured.levelno and self._matcher(captured.msg): - return None - return self + # Match `str` level. + if isinstance(self._level, str): + if self._level != captured.levelname: + return self + # Match `int` level. + elif self._level != captured.levelno: + return self + # Match message. + if not self._matcher(captured.msg): + return self + # We matched! + return None def _str(self, *, indent: str) -> str: - return format_log(self._levelno, self._msg) + return format_log(format_level(self._level), self._msg) class _ComposedLogged(Logged): diff --git a/logot/_logot.py b/logot/_logot.py index c0441d3d..c1b802be 100644 --- a/logot/_logot.py +++ b/logot/_logot.py @@ -9,7 +9,7 @@ from logot._captured import Captured from logot._logged import Logged -from logot._validate import validate_levelno, validate_logger, validate_timeout +from logot._validate import validate_level, validate_logger, validate_timeout from logot._waiter import AsyncWaiter, SyncWaiter, Waiter W = TypeVar("W", bound=Waiter) @@ -29,11 +29,9 @@ class Logot: __slots__ = ("_timeout", "_lock", "_queue", "_waiter") - DEFAULT_LEVEL: ClassVar[int | str] = logging.NOTSET + DEFAULT_LEVEL: ClassVar[str | int] = "DEBUG" """ The default ``level`` used by :meth:`capturing`. - - This is :data:`logging.NOTSET`, specifying that all logs are captured. """ DEFAULT_LOGGER: ClassVar[logging.Logger | str | None] = None @@ -45,9 +43,7 @@ class Logot: DEFAULT_TIMEOUT: ClassVar[float] = 3.0 """ - The default ``timeout`` used by :meth:`wait_for` and :meth:`await_for`. - - This is 3 seconds. + The default ``timeout`` (in seconds) used by :meth:`wait_for` and :meth:`await_for`. """ def __init__( @@ -63,7 +59,7 @@ def __init__( def capturing( self, *, - level: int | str = DEFAULT_LEVEL, + level: str | int = DEFAULT_LEVEL, logger: logging.Logger | str | None = DEFAULT_LOGGER, ) -> AbstractContextManager[Logot]: """ @@ -76,13 +72,13 @@ def capturing( See :doc:`captured` usage guide. - :param level: A log level (e.g. :data:`logging.DEBUG`) or string name (e.g. ``"DEBUG"``). Defaults to - :data:`logging.NOTSET`, specifying that all logs are captured. - :param logger: A logger or logger name to capture logs from. Defaults to the root logger. + :param level: A log level name (e.g. ``"DEBUG"``) or numeric constant (e.g. :data:`logging.DEBUG`). Defaults to + :attr:`DEFAULT_LEVEL`. + :param logger: A logger or logger name to capture logs from. Defaults to :attr:`DEFAULT_LOGGER`. """ - levelno = validate_levelno(level) + level = validate_level(level) logger = validate_logger(logger) - return _Capturing(self, _Handler(self, levelno=levelno), logger=logger) + return _Capturing(self, _Handler(level, self), logger=logger) def capture(self, captured: Captured) -> None: """ @@ -245,10 +241,10 @@ def __exit__( class _Handler(logging.Handler): __slots__ = ("_logot",) - def __init__(self, logot: Logot, *, levelno: int) -> None: - super().__init__(levelno) + def __init__(self, level: str | int, logot: Logot) -> None: + super().__init__(level) self._logot = logot def emit(self, record: logging.LogRecord) -> None: - captured = Captured(record.levelno, record.getMessage()) + captured = Captured(record.levelname, record.getMessage(), levelno=record.levelno) self._logot.capture(captured) diff --git a/logot/_validate.py b/logot/_validate.py index ecf28c3e..6b47a573 100644 --- a/logot/_validate.py +++ b/logot/_validate.py @@ -3,16 +3,10 @@ import logging -def validate_levelno(level: int | str) -> int: - # Handle `int` level. - if isinstance(level, int): +def validate_level(level: str | int) -> str | int: + # Handle `str` or `int` level. + if isinstance(level, (str, int)): return level - # Handle `str` level. - if isinstance(level, str): - levelno = logging.getLevelName(level) - if not isinstance(levelno, int): - raise ValueError(f"Unknown level: {level!r}") - return levelno # Handle invalid level. raise TypeError(f"Invalid level: {level!r}") diff --git a/pyproject.toml b/pyproject.toml index 78858450..75b875c1 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "logot" -version = "0.2.0" +version = "0.3.0" description = "Log-based testing" authors = ["Dave Hall <[email protected]>"] license = "MIT"

Support `loguru` log capture Supporting a basic [loguru](https://github.com/Delgan/loguru) integration would be very easy by providing a context manager via a helper func using their `LogHandler` integration. Alternatively, a `Logot` subclass called `Logurot` would be cheese and fun. That would allow the `pytest` plugin to provide a `logurot` fixture too (if `loguru` is installed).

Further API discussion and feedback from `loguru` devs in https://github.com/Delgan/loguru/issues/1072 ❤️ ## Proposed API ``` py from logot.contrib.loguru import LoguruCapture with Logot().capturing(LoguruCapture): pass ``` Any arguments given to `capturing` would be propagated to the `LoguruCapture` constructor. This would then unify capture between stdlib logging and `loguru` logging. The default capture backend would be the stdlib capture, and an `@overload` would ensure good typing. 💪

2024-01-29T16:35:15

0.0

[]

etianen/logot

etianen__logot-40

64d0ead2f41d5cfeff1a5d562bd087ed68135ba9

diff --git a/logot/_validate.py b/logot/_validate.py index 1bdc96ca..ecf28c3e 100644 --- a/logot/_validate.py +++ b/logot/_validate.py @@ -6,8 +6,6 @@ def validate_levelno(level: int | str) -> int: # Handle `int` level. if isinstance(level, int): - if logging.getLevelName(level).startswith("Level "): - raise ValueError(f"Unknown level: {level!r}") return level # Handle `str` level. if isinstance(level, str):

Support `loguru` log capture Supporting a basic [loguru](https://github.com/Delgan/loguru) integration would be very easy by providing a context manager via a helper func using their `LogHandler` integration. Alternatively, a `Logot` subclass called `Logurot` would be cheese and fun. That would allow the `pytest` plugin to provide a `logurot` fixture too (if `loguru` is installed).

Further API discussion and feedback from `loguru` devs in https://github.com/Delgan/loguru/issues/1072 ❤️ ## Proposed API ``` py from logot.contrib.loguru import LoguruCapture with Logot().capturing(LoguruCapture): pass ``` Any arguments given to `capturing` would be propagated to the `LoguruCapture` constructor. This would then unify capture between stdlib logging and `loguru` logging. The default capture backend would be the stdlib capture, and an `@overload` would ensure good typing. 💪

2024-01-28T22:48:20

0.0

[]

etianen/logot

etianen__logot-38

b1c26621eeec94b15448bc4c74f61cdf361758c3

diff --git a/docs/api.rst b/docs/api.rst index aa70017f..eaa9a3de 100644 --- a/docs/api.rst +++ b/docs/api.rst @@ -19,6 +19,8 @@ Import the :mod:`logot` API in your tests: .. automethod:: capturing + .. automethod:: capture + .. automethod:: wait_for .. automethod:: await_for @@ -37,6 +39,12 @@ Import the :mod:`logot` API in your tests: .. autoclass:: logot.Logged +.. autoclass:: logot.Captured + + .. autoattribute:: levelno + + .. autoattribute:: msg + :mod:`logot.logged` ------------------- diff --git a/docs/captured.rst b/docs/captured.rst new file mode 100644 index 00000000..7f86568a --- /dev/null +++ b/docs/captured.rst @@ -0,0 +1,61 @@ +Log capturing +============= + +.. currentmodule:: logot + +:mod:`logot` makes it easy to capture logs from the stdlib :mod:`logging` module: + +.. code:: python + + with Logot().capturing() as logot: + app.start() + logot.wait_for(logged.info("App started")) + +.. note:: + + If using :mod:`pytest`, you can probably just use the pre-configured ``logot`` fixture included in the bundled + :doc:`pytest plugin <pytest>` and skip manually configuring log capture. 💪 + + +Capturing :mod:`logging` logs +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The :meth:`Logot.capturing` method defaults to capturing **all** records from the root logger. Customize this with the +``level`` and ``logger`` arguments to :meth:`Logot.capturing`: + +.. code:: python + + with Logot().capturing(level=logging.WARNING, logger="app") as logot: + app.start() + logot.wait_for(logged.info("App started")) + +For advanced use-cases, multiple :meth:`Logot.capturing` calls on the same :class:`Logot` instance are supported. Be +careful to avoid capturing duplicate logs with overlapping calls to :meth:`Logot.capturing`! + +.. seealso:: + + See :class:`Logot` and :meth:`Logot.capturing` API reference. + + +.. _captured-3rd-party: + +Capturing 3rd-party logs +~~~~~~~~~~~~~~~~~~~~~~~~ + +Any 3rd-party logging library can be integrated with :mod:`logot` by sending :class:`Captured` logs to +:meth:`Logot.capture`: + +.. code:: python + + def on_foo_log(logot: Logot, record: FooRecord) -> None: + logot.capture(Captured(record.levelno, record.msg)) + + foo_logger.add_handler(on_foo_log) + +.. note:: + + Using a context manager to set up and tear down log capture for every test run is *highly recommended*! + +.. seealso:: + + See :class:`Captured` and :meth:`Logot.capture` API reference. diff --git a/docs/index.rst b/docs/index.rst index bf1df2f0..2b95d97b 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -150,6 +150,7 @@ Learn more about :mod:`logot` with the following guides: match logged + captured pytest unittest api diff --git a/logot/__init__.py b/logot/__init__.py index 9b3b47a9..b63b7410 100644 --- a/logot/__init__.py +++ b/logot/__init__.py @@ -1,4 +1,5 @@ from __future__ import annotations +from logot._captured import Captured as Captured from logot._logged import Logged as Logged from logot._logot import Logot as Logot diff --git a/logot/_captured.py b/logot/_captured.py new file mode 100644 index 00000000..21eeecd3 --- /dev/null +++ b/logot/_captured.py @@ -0,0 +1,53 @@ +from __future__ import annotations + +import logging +from typing import Final + +from logot._format import format_log +from logot._validate import validate_levelno + + +class Captured: + """ + A captured log record. + + Send :class:`Captured` logs to :meth:`Logot.capture` to integrate with + :ref:`3rd-party logging frameworks <captured-3rd-party>` + + .. note:: + + This class is for integration with :ref:`3rd-party logging frameworks <captured-3rd-party>`. It is not generally + used when writing tests. + + .. seealso:: + + See :ref:`captured-3rd-party` usage guide. + + :param level: The log level (e.g. :data:`logging.DEBUG`) or string name (e.g. ``"DEBUG"``). + :param msg: The log message. + """ + + __slots__ = ("levelno", "msg") + + levelno: Final[int] + """ + The integer log level (e.g. :data:`logging.DEBUG`). + """ + + msg: Final[str] + """ + The log message. + """ + + def __init__(self, level: int | str, msg: str) -> None: + self.levelno = validate_levelno(level) + self.msg = msg + + def __eq__(self, other: object) -> bool: + return isinstance(other, Captured) and other.levelno == self.levelno and other.msg == self.msg + + def __repr__(self) -> str: + return f"Captured({logging.getLevelName(self.levelno)!r}, {self.msg!r})" + + def __str__(self) -> str: + return format_log(self.levelno, self.msg) diff --git a/logot/_format.py b/logot/_format.py new file mode 100644 index 00000000..d0ec82cb --- /dev/null +++ b/logot/_format.py @@ -0,0 +1,7 @@ +from __future__ import annotations + +import logging + + +def format_log(levelno: int, msg: str) -> str: + return f"[{logging.getLevelName(levelno)}] {msg}" diff --git a/logot/_logged.py b/logot/_logged.py index c7fc7df8..6c11678b 100644 --- a/logot/_logged.py +++ b/logot/_logged.py @@ -3,6 +3,8 @@ import logging from abc import ABC, abstractmethod +from logot._captured import Captured +from logot._format import format_log from logot._match import compile_matcher from logot._validate import validate_levelno @@ -27,14 +29,14 @@ class Logged(ABC): __slots__ = () - def __rshift__(self, log: Logged) -> Logged: - return _OrderedAllLogged.from_compose(self, log) + def __rshift__(self, logged: Logged) -> Logged: + return _OrderedAllLogged.from_compose(self, logged) - def __and__(self, log: Logged) -> Logged: - return _UnorderedAllLogged.from_compose(self, log) + def __and__(self, logged: Logged) -> Logged: + return _UnorderedAllLogged.from_compose(self, logged) - def __or__(self, log: Logged) -> Logged: - return _AnyLogged.from_compose(self, log) + def __or__(self, logged: Logged) -> Logged: + return _AnyLogged.from_compose(self, logged) def __str__(self) -> str: return self._str(indent="") @@ -48,7 +50,7 @@ def __repr__(self) -> str: raise NotImplementedError @abstractmethod - def _reduce(self, record: logging.LogRecord) -> Logged | None: + def _reduce(self, captured: Captured) -> Logged | None: raise NotImplementedError @abstractmethod @@ -63,7 +65,7 @@ def log(level: int | str, msg: str) -> Logged: :param level: A log level (e.g. :data:`logging.DEBUG`) or string name (e.g. ``"DEBUG"``). :param msg: A log :doc:`message pattern <match>`. """ - return _LogRecordLogged(validate_levelno(level), msg) + return _RecordLogged(validate_levelno(level), msg) def debug(msg: str) -> Logged: @@ -72,7 +74,7 @@ def debug(msg: str) -> Logged: :param msg: A log :doc:`message pattern <match>`. """ - return _LogRecordLogged(logging.DEBUG, msg) + return _RecordLogged(logging.DEBUG, msg) def info(msg: str) -> Logged: @@ -81,7 +83,7 @@ def info(msg: str) -> Logged: :param msg: A log :doc:`message pattern <match>`. """ - return _LogRecordLogged(logging.INFO, msg) + return _RecordLogged(logging.INFO, msg) def warning(msg: str) -> Logged: @@ -90,7 +92,7 @@ def warning(msg: str) -> Logged: :param msg: A log :doc:`message pattern <match>`. """ - return _LogRecordLogged(logging.WARNING, msg) + return _RecordLogged(logging.WARNING, msg) def error(msg: str) -> Logged: @@ -99,7 +101,7 @@ def error(msg: str) -> Logged: :param msg: A log :doc:`message pattern <match>`. """ - return _LogRecordLogged(logging.ERROR, msg) + return _RecordLogged(logging.ERROR, msg) def critical(msg: str) -> Logged: @@ -108,10 +110,10 @@ def critical(msg: str) -> Logged: :param msg: A log :doc:`message pattern <match>`. """ - return _LogRecordLogged(logging.CRITICAL, msg) + return _RecordLogged(logging.CRITICAL, msg) -class _LogRecordLogged(Logged): +class _RecordLogged(Logged): __slots__ = ("_levelno", "_msg", "_matcher") def __init__(self, levelno: int, msg: str) -> None: @@ -120,117 +122,117 @@ def __init__(self, levelno: int, msg: str) -> None: self._matcher = compile_matcher(msg) def __eq__(self, other: object) -> bool: - return isinstance(other, _LogRecordLogged) and other._levelno == self._levelno and other._msg == self._msg + return isinstance(other, _RecordLogged) and other._levelno == self._levelno and other._msg == self._msg def __repr__(self) -> str: return f"log({logging.getLevelName(self._levelno)!r}, {self._msg!r})" - def _reduce(self, record: logging.LogRecord) -> Logged | None: - if self._levelno == record.levelno and self._matcher(record.getMessage()): + def _reduce(self, captured: Captured) -> Logged | None: + if self._levelno == captured.levelno and self._matcher(captured.msg): return None return self def _str(self, *, indent: str) -> str: - return f"[{logging.getLevelName(self._levelno)}] {self._msg}" + return format_log(self._levelno, self._msg) class _ComposedLogged(Logged): - __slots__ = ("_logs",) + __slots__ = ("_logged_items",) - def __init__(self, logs: tuple[Logged, ...]) -> None: - assert len(logs) > 1 - self._logs = logs + def __init__(self, logged_items: tuple[Logged, ...]) -> None: + assert len(logged_items) > 1 + self._logged_items = logged_items def __eq__(self, other: object) -> bool: - return isinstance(other, self.__class__) and other._logs == self._logs + return isinstance(other, self.__class__) and other._logged_items == self._logged_items @classmethod - def from_compose(cls, log_a: Logged, log_b: Logged) -> Logged: - # If possible, flatten nested logs of the same type. - if isinstance(log_a, cls): - if isinstance(log_b, cls): - return cls((*log_a._logs, *log_b._logs)) - return cls((*log_a._logs, log_b)) - if isinstance(log_b, cls): - return cls((log_a, *log_b._logs)) - # Wrap the logs without flattening. - return cls((log_a, log_b)) + def from_compose(cls, logged_a: Logged, logged_b: Logged) -> Logged: + # If possible, flatten nested logged items of the same type. + if isinstance(logged_a, cls): + if isinstance(logged_b, cls): + return cls((*logged_a._logged_items, *logged_b._logged_items)) + return cls((*logged_a._logged_items, logged_b)) + if isinstance(logged_b, cls): + return cls((logged_a, *logged_b._logged_items)) + # Wrap the logged items without flattening. + return cls((logged_a, logged_b)) @classmethod - def from_reduce(cls, logs: tuple[Logged, ...]) -> Logged | None: - assert logs - # If there is a single log, do not wrap it. - if len(logs) == 1: - return logs[0] - # Wrap the logs. - return cls(logs) + def from_reduce(cls, logged_items: tuple[Logged, ...]) -> Logged | None: + assert logged_items + # If there is a single logged item, do not wrap it. + if len(logged_items) == 1: + return logged_items[0] + # Wrap the logged items. + return cls(logged_items) class _OrderedAllLogged(_ComposedLogged): __slots__ = () def __repr__(self) -> str: - return f"({' >> '.join(map(repr, self._logs))})" + return f"({' >> '.join(map(repr, self._logged_items))})" - def _reduce(self, record: logging.LogRecord) -> Logged | None: - log = self._logs[0] - reduced_log = log._reduce(record) + def _reduce(self, captured: Captured) -> Logged | None: + logged = self._logged_items[0] + reduced = logged._reduce(captured) # Handle full reduction. - if reduced_log is None: - return _OrderedAllLogged.from_reduce(self._logs[1:]) + if reduced is None: + return _OrderedAllLogged.from_reduce(self._logged_items[1:]) # Handle partial reduction. - if reduced_log is not log: - return _OrderedAllLogged((reduced_log, *self._logs[1:])) + if reduced is not logged: + return _OrderedAllLogged((reduced, *self._logged_items[1:])) # Handle no reduction. return self def _str(self, *, indent: str) -> str: - return f"\n{indent}".join(log._str(indent=indent) for log in self._logs) + return f"\n{indent}".join(logged._str(indent=indent) for logged in self._logged_items) class _UnorderedAllLogged(_ComposedLogged): __slots__ = () def __repr__(self) -> str: - return f"({' & '.join(map(repr, self._logs))})" + return f"({' & '.join(map(repr, self._logged_items))})" - def _reduce(self, record: logging.LogRecord) -> Logged | None: - for n, log in enumerate(self._logs): - reduced_log = log._reduce(record) + def _reduce(self, captured: Captured) -> Logged | None: + for n, logged in enumerate(self._logged_items): + reduced = logged._reduce(captured) # Handle full reduction. - if reduced_log is None: - return _UnorderedAllLogged.from_reduce((*self._logs[:n], *self._logs[n + 1 :])) + if reduced is None: + return _UnorderedAllLogged.from_reduce((*self._logged_items[:n], *self._logged_items[n + 1 :])) # Handle partial reduction. - if reduced_log is not log: - return _UnorderedAllLogged((*self._logs[:n], reduced_log, *self._logs[n + 1 :])) + if reduced is not logged: + return _UnorderedAllLogged((*self._logged_items[:n], reduced, *self._logged_items[n + 1 :])) # Handle no reduction. return self def _str(self, *, indent: str) -> str: nested_indent = indent + " " - logs_str = "".join(f"\n{indent}- {log._str(indent=nested_indent)}" for log in self._logs) - return f"Unordered:{logs_str}" + logged_items_str = "".join(f"\n{indent}- {logged._str(indent=nested_indent)}" for logged in self._logged_items) + return f"Unordered:{logged_items_str}" class _AnyLogged(_ComposedLogged): __slots__ = () def __repr__(self) -> str: - return f"({' | '.join(map(repr, self._logs))})" + return f"({' | '.join(map(repr, self._logged_items))})" - def _reduce(self, record: logging.LogRecord) -> Logged | None: - for n, log in enumerate(self._logs): - reduced_log = log._reduce(record) + def _reduce(self, captured: Captured) -> Logged | None: + for n, logged in enumerate(self._logged_items): + reduced = logged._reduce(captured) # Handle full reduction. - if reduced_log is None: + if reduced is None: return None # Handle partial reduction. - if reduced_log is not log: - return _AnyLogged((*self._logs[:n], reduced_log, *self._logs[n + 1 :])) + if reduced is not logged: + return _AnyLogged((*self._logged_items[:n], reduced, *self._logged_items[n + 1 :])) # Handle no reduction. return self def _str(self, *, indent: str) -> str: nested_indent = indent + " " - logs_str = "".join(f"\n{indent}- {log._str(indent=nested_indent)}" for log in self._logs) - return f"Any:{logs_str}" + logged_items_str = "".join(f"\n{indent}- {logged._str(indent=nested_indent)}" for logged in self._logged_items) + return f"Any:{logged_items_str}" diff --git a/logot/_logot.py b/logot/_logot.py index 531ea6ee..c0441d3d 100644 --- a/logot/_logot.py +++ b/logot/_logot.py @@ -6,8 +6,8 @@ from threading import Lock from types import TracebackType from typing import ClassVar, TypeVar -from weakref import WeakValueDictionary +from logot._captured import Captured from logot._logged import Logged from logot._validate import validate_levelno, validate_logger, validate_timeout from logot._waiter import AsyncWaiter, SyncWaiter, Waiter @@ -27,7 +27,7 @@ class Logot: :attr:`DEFAULT_TIMEOUT`. """ - __slots__ = ("_timeout", "_lock", "_seen_records", "_queue", "_waiter") + __slots__ = ("_timeout", "_lock", "_queue", "_waiter") DEFAULT_LEVEL: ClassVar[int | str] = logging.NOTSET """ @@ -57,8 +57,7 @@ def __init__( ) -> None: self._timeout = validate_timeout(timeout) self._lock = Lock() - self._seen_records: WeakValueDictionary[int, logging.LogRecord] = WeakValueDictionary() - self._queue: deque[logging.LogRecord] = deque() + self._queue: deque[Captured] = deque() self._waiter: Waiter | None = None def capturing( @@ -73,6 +72,10 @@ def capturing( If the given ``logger`` level is less verbose than the requested ``level``, it will be temporarily adjusted to the requested ``level`` for the duration of the context. + .. seealso:: + + See :doc:`captured` usage guide. + :param level: A log level (e.g. :data:`logging.DEBUG`) or string name (e.g. ``"DEBUG"``). Defaults to :data:`logging.NOTSET`, specifying that all logs are captured. :param logger: A logger or logger name to capture logs from. Defaults to the root logger. @@ -81,57 +84,86 @@ def capturing( logger = validate_logger(logger) return _Capturing(self, _Handler(self, levelno=levelno), logger=logger) - def wait_for(self, log: Logged, *, timeout: float | None = None) -> None: + def capture(self, captured: Captured) -> None: + """ + Adds the given captured log record to the internal capture queue. + + Any waiters blocked on :meth:`wait_for` to :meth:`await_for` will be notified and wake up if their + :doc:`log pattern <logged>` is satisfied. + + .. note:: + + This method is for integration with :ref:`3rd-party logging frameworks <captured-3rd-party>`. It is not + generally used when writing tests. + + .. seealso:: + + See :ref:`captured-3rd-party` usage guide. + + :param captured: The captured log. + """ + with self._lock: + # If there is a waiter that has not been fully reduced, attempt to reduce it. + if self._waiter is not None and self._waiter.logged is not None: + self._waiter.logged = self._waiter.logged._reduce(captured) + # If the waiter has fully reduced, notify the blocked caller. + if self._waiter.logged is None: + self._waiter.notify() + return + # Otherwise, buffer the captured log. + self._queue.append(captured) + + def wait_for(self, logged: Logged, *, timeout: float | None = None) -> None: """ Waits for the expected ``log`` pattern to arrive or the ``timeout`` to expire. - :param log: The expected :doc:`log pattern <logged>`. + :param logged: The expected :doc:`log pattern <logged>`. :param timeout: How long to wait (in seconds) before failing the test. Defaults to the ``timeout`` passed to :class:`Logot`. :raises AssertionError: If the expected ``log`` pattern does not arrive within ``timeout`` seconds. """ - waiter = self._open_waiter(log, SyncWaiter, timeout=timeout) + waiter = self._open_waiter(logged, SyncWaiter, timeout=timeout) try: waiter.wait() finally: self._close_waiter(waiter) - async def await_for(self, log: Logged, *, timeout: float | None = None) -> None: + async def await_for(self, logged: Logged, *, timeout: float | None = None) -> None: """ Waits *asynchronously* for the expected ``log`` pattern to arrive or the ``timeout`` to expire. - :param log: The expected :doc:`log pattern <logged>`. + :param logged: The expected :doc:`log pattern <logged>`. :param timeout: How long to wait (in seconds) before failing the test. Defaults to the ``timeout`` passed to :class:`Logot`. :raises AssertionError: If the expected ``log`` pattern does not arrive within ``timeout`` seconds. """ - waiter = self._open_waiter(log, AsyncWaiter, timeout=timeout) + waiter = self._open_waiter(logged, AsyncWaiter, timeout=timeout) try: await waiter.wait() finally: self._close_waiter(waiter) - def assert_logged(self, log: Logged) -> None: + def assert_logged(self, logged: Logged) -> None: """ Fails *immediately* if the expected ``log`` pattern has not arrived. - :param log: The expected :doc:`log pattern <logged>`. + :param logged: The expected :doc:`log pattern <logged>`. :raises AssertionError: If the expected ``log`` pattern has not arrived. """ - reduced_log = self._reduce(log) - if reduced_log is not None: - raise AssertionError(f"Not logged:\n\n{reduced_log}") + reduced = self._reduce(logged) + if reduced is not None: + raise AssertionError(f"Not logged:\n\n{reduced}") - def assert_not_logged(self, log: Logged) -> None: + def assert_not_logged(self, logged: Logged) -> None: """ Fails *immediately* if the expected ``log`` pattern **has** arrived. - :param log: The expected :doc:`log pattern <logged>`. + :param logged: The expected :doc:`log pattern <logged>`. :raises AssertionError: If the expected ``log`` pattern **has** arrived. """ - reduced_log = self._reduce(log) - if reduced_log is None: - raise AssertionError(f"Logged:\n\n{log}") + reduced = self._reduce(logged) + if reduced is None: + raise AssertionError(f"Logged:\n\n{logged}") def clear(self) -> None: """ @@ -140,25 +172,7 @@ def clear(self) -> None: with self._lock: self._queue.clear() - def _emit(self, record: logging.LogRecord) -> None: - with self._lock: - # De-duplicate log records. - # Duplicate log records are possible if we have multiple active captures. - record_id = id(record) - if record_id in self._seen_records: # pragma: no cover - return - self._seen_records[record_id] = record - # If there is a waiter that has not been fully reduced, attempt to reduce it. - if self._waiter is not None and self._waiter.log is not None: - self._waiter.log = self._waiter.log._reduce(record) - # If the waiter has fully reduced, notify the blocked caller. - if self._waiter.log is None: - self._waiter.notify() - return - # Otherwise, buffer the log record. - self._queue.append(record) - - def _open_waiter(self, log: Logged, waiter_cls: type[W], *, timeout: float | None) -> W: + def _open_waiter(self, logged: Logged, waiter_cls: type[W], *, timeout: float | None) -> W: with self._lock: # If no timeout is provided, use the default timeout. # Otherwise, validate and use the provided timeout. @@ -170,10 +184,10 @@ def _open_waiter(self, log: Logged, waiter_cls: type[W], *, timeout: float | Non if self._waiter is not None: # pragma: no cover raise RuntimeError("Multiple waiters are not supported") # Set a waiter. - waiter = self._waiter = waiter_cls(log, timeout=timeout) + waiter = self._waiter = waiter_cls(logged, timeout=timeout) # Apply an immediate reduction. - waiter.log = self._reduce(waiter.log) - if waiter.log is None: + waiter.logged = self._reduce(waiter.logged) + if waiter.logged is None: waiter.notify() # All done! return waiter @@ -183,20 +197,20 @@ def _close_waiter(self, waiter: Waiter) -> None: # Clear the waiter. self._waiter = None # Error if the waiter logs are not fully reduced. - if waiter.log is not None: - raise AssertionError(f"Not logged:\n\n{waiter.log}") + if waiter.logged is not None: + raise AssertionError(f"Not logged:\n\n{waiter.logged}") - def _reduce(self, log: Logged | None) -> Logged | None: + def _reduce(self, logged: Logged | None) -> Logged | None: # Drain the queue until the log is fully reduced. # This does not need a lock, since `deque.popleft()` is thread-safe. - while log is not None: + while logged is not None: try: - record = self._queue.popleft() + captured = self._queue.popleft() except IndexError: break - log = log._reduce(record) + logged = logged._reduce(captured) # All done! - return log + return logged class _Capturing: @@ -236,4 +250,5 @@ def __init__(self, logot: Logot, *, levelno: int) -> None: self._logot = logot def emit(self, record: logging.LogRecord) -> None: - self._logot._emit(record) + captured = Captured(record.levelno, record.getMessage()) + self._logot.capture(captured) diff --git a/logot/_waiter.py b/logot/_waiter.py index 5cf950f7..66f5895e 100644 --- a/logot/_waiter.py +++ b/logot/_waiter.py @@ -8,10 +8,10 @@ class Waiter(ABC): - __slots__ = ("log", "_timeout") + __slots__ = ("logged", "_timeout") - def __init__(self, log: Logged, *, timeout: float) -> None: - self.log: Logged | None = log + def __init__(self, logged: Logged, *, timeout: float) -> None: + self.logged: Logged | None = logged self._timeout = timeout @abstractmethod @@ -26,8 +26,8 @@ def wait(self) -> object: class SyncWaiter(Waiter): __slots__ = ("_lock",) - def __init__(self, log: Logged, *, timeout: float) -> None: - super().__init__(log, timeout=timeout) + def __init__(self, logged: Logged, *, timeout: float) -> None: + super().__init__(logged, timeout=timeout) # Create an already-acquired lock. This will be released by `notify()`. self._lock = Lock() self._lock.acquire() @@ -42,8 +42,8 @@ def wait(self) -> None: class AsyncWaiter(Waiter): __slots__ = ("_loop", "_future") - def __init__(self, log: Logged, *, timeout: float) -> None: - super().__init__(log, timeout=timeout) + def __init__(self, logged: Logged, *, timeout: float) -> None: + super().__init__(logged, timeout=timeout) # Create an unresolved `asyncio.Future`. This will be resolved by `notify()`. self._loop = asyncio.get_running_loop() self._future: asyncio.Future[None] = self._loop.create_future() diff --git a/pyproject.toml b/pyproject.toml index 1ce8565b..08300a1a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "logot" -version = "0.1.1" +version = "0.2.0" description = "Log-based testing" authors = ["Dave Hall <[email protected]>"] license = "MIT"

Support `loguru` log capture Supporting a basic [loguru](https://github.com/Delgan/loguru) integration would be very easy by providing a context manager via a helper func using their `LogHandler` integration. Alternatively, a `Logot` subclass called `Logurot` would be cheese and fun. That would allow the `pytest` plugin to provide a `logurot` fixture too (if `loguru` is installed).

Further API discussion and feedback from `loguru` devs in https://github.com/Delgan/loguru/issues/1072 ❤️ ## Proposed API ``` py from logot.contrib.loguru import LoguruCapture with Logot().capturing(LoguruCapture): pass ``` Any arguments given to `capturing` would be propagated to the `LoguruCapture` constructor. This would then unify capture between stdlib logging and `loguru` logging. The default capture backend would be the stdlib capture, and an `@overload` would ensure good typing. 💪

2024-01-28T12:38:59

0.0

[]

etianen/logot

etianen__logot-30

aa171e7a99b0c5958429b0c8ff89c4f701f321e0

diff --git a/pyproject.toml b/pyproject.toml index d30a63a6..417ff007 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -17,7 +17,7 @@ classifiers = [ "Topic :: System :: Logging", "Typing :: Typed", ] -packages = [{ include = "logot" }, { include = "_logot_pytest.py" }] +packages = [{ include = "logot" }] [tool.poetry.dependencies] python = "^3.8" @@ -43,7 +43,7 @@ sphinx = { version = "7.2.6", markers = "python_version >= '3.12'" } sphinx-autobuild = { version = "*", markers = "python_version >= '3.12'" } [tool.poetry.plugins.pytest11] -logot = "_logot_pytest" +logot = "logot._pytest" [tool.coverage.run] source = ["logot", "tests"]

Using `coverage` directly rather than `pytest-cov` This allows us to move the `pytest` plugin into the package namespace. Also switching to `importlib` import mode, following the [pytest recommendations](https://docs.pytest.org/en/7.1.x/explanation/goodpractices.html#choosing-a-test-layout-import-rules).

2024-01-27T14:51:07

0.0

[]

blei-lab/treeffuser

blei-lab__treeffuser-106

d5c72fae3c3d5f939d7c69817ec0f98e2d5f7eef

diff --git a/.gitignore b/.gitignore index 6340d65c..e40f04d2 100644 --- a/.gitignore +++ b/.gitignore @@ -77,6 +77,7 @@ docs/_build # Data files **/raw/** **/data.npy +examples/m5/* # Debug files diff --git a/examples/README_example.ipynb b/examples/README_example.ipynb new file mode 100644 index 00000000..05fcf404 --- /dev/null +++ b/examples/README_example.ipynb @@ -0,0 +1,209 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Quick start: Forecasting with synthetic data\n", + "\n", + "In this notebook, we train Treeffuser on synthethic data and then visualize both the original and model-generated samples to explore how well Treeffuser captures the underlying distribution of the data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Getting started" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We first install `treeffuser` and import the relevant libraries." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install treeffuser\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "from treeffuser import Treeffuser" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We simulate a non-linear, bimodal response of $y$ given $x$, where the two modes follow two different response functions: one is a sine function and the other is a cosine function over $x$." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "seed = 0 # fixing the random seed for reproducibility\n", + "n = 5000 # number of data points\n", + "\n", + "rng = np.random.default_rng(seed=seed)\n", + "x = rng.uniform(0, 2 * np.pi, size=n) # x values in the range [0, 2π)\n", + "z = rng.integers(0, 2, size=n) # response function assignments\n", + "\n", + "y = z * np.sin(x - np.pi / 2) + (1 - z) * np.cos(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We also introduce heteroscedastic, fat-tailed noise from a Laplace distribution, meaning the variability of $y$ increases with $x$ and may result in large outliers." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "y += rng.laplace(scale=x / 30, size=n)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fitting Treffuser and producing samples" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fitting Treeffuser and generating samples is very simple, as Treeffuser adheres to the `sklearn.base.BaseEstimator` class. Fitting amounts to initializing the model and calling the `fit` method, just like any `scikit-learn` estimator. Samples are then generated using the `sample` method." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "model = Treeffuser(sde_initialize_from_data=True, seed=seed)\n", + "model.fit(x, y)\n", + "\n", + "y_samples = model.sample(x, n_samples=1, seed=seed, verbose=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plotting the samples" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We create a scatter plot to visualize both the original data and the samples produced by Treeffuser. The samples closely reflect the underlying response distributions that generated the data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.scatter(x, y, s=1, label=\"observed data\")\n", + "plt.scatter(x, y_samples[0, :], s=1, alpha=0.7, label=\"Treeffuser samples\")\n", + "\n", + "plt.xlabel(\"$x$\")\n", + "plt.ylabel(\"$y$\")\n", + "\n", + "legend = plt.legend(loc=\"upper center\", scatterpoints=1, bbox_to_anchor=(0.5, -0.125), ncol=2)\n", + "for legend_handle in legend.legend_handles:\n", + " legend_handle.set_sizes([32]) # change marker size for legend\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The samples generated by Treeffuser can be used to compute any downstream estimates of interest." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x = np.array(np.pi).reshape((1, 1))\n", + "y_samples = model.sample(x, n_samples=100, verbose=True) # y_samples.shape[0] is 100\n", + "\n", + "# Estimate downstream quantities of interest\n", + "y_mean = y_samples.mean(axis=0) # conditional mean for each x\n", + "y_std = y_samples.std(axis=0) # conditional std for each x\n", + "\n", + "print(f\"Mean of the samples: {y_mean}\")\n", + "print(f\"Standard deviation of the samples: {y_std} \")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For convenience, we also provide a class Samples that can estimate standard quantities." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from treeffuser.samples import Samples\n", + "\n", + "y_samples = Samples(y_samples)\n", + "y_mean = y_samples.sample_mean() # same as before\n", + "y_std = y_samples.sample_std() # same as before\n", + "y_quantiles = y_samples.sample_quantile(q=[0.05, 0.95]) # conditional quantiles for each x\n", + "\n", + "print(f\"Mean of the samples: {y_mean}\")\n", + "print(f\"Standard deviation of the samples: {y_std} \")\n", + "print(f\"5th and 95th quantiles of the samples: {y_quantiles.reshape(-1)}\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/m5-news-vendor-notebook.ipynb b/examples/m5-news-vendor-notebook.ipynb new file mode 100644 index 00000000..e1f8fecc --- /dev/null +++ b/examples/m5-news-vendor-notebook.ipynb @@ -0,0 +1,1754 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Forecasting Walmart sales with Treeffuser\n", + "\n", + "In this tutorial we show how to use Treeffuser to model and forecast Walmart sales using the M5 forecasting dataset from Kaggle." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Getting started\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To get started, we first install `treeffuser` and import the relevant libraries (if needed)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "ExecuteTime": { + "end_time": "2024-10-10T19:30:25.237555Z", + "start_time": "2024-10-10T19:30:23.907554Z" + } + }, + "source": [ + "!pip install treeffuser" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "from pathlib import Path\n", + "\n", + "from tqdm import tqdm\n", + "from treeffuser import Treeffuser\n", + "\n", + "# load autoreload extension\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, create a Kaggle account and download the data from https://www.kaggle.com/competitions/m5-forecasting-accuracy/data.\n", + "\n", + "If you're running this notebook in Colab, manually upload the necessary files (`calendar.csv`, `sales_train_validation.csv`, `sell_prices.csv`) to Colab by clicking the `Files` tab on the left sidebar and selecting `Upload`. Move the files into a new folder named `m5`. Once uploaded, the notebook will be able to read and process the data.\n", + "\n", + "If you're running this on your local machine, you can also use Kaggle's [command-line tool](https://www.kaggle.com/docs/api) and run the following from the command line:\n", + "\n", + "```bash\n", + "cd ./m5 # path to folder where you want to save the data\n", + "kaggle competitions download -c m5-forecasting-accuracy\n", + "```\n", + "\n", + "Use your favorite tool to unzip the archive. In Linux/macOS,\n", + "\n", + "```bash\n", + "unzip m5-forecasting-accuracy.zip\n", + "```\n", + "\n", + "We'll be using the following files: `calendar.csv`, `sales_train_validation.csv`, and `sell_prices.csv`.\n", + "\n", + "\n", + "\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Load the data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "data_path = Path(\"./m5\") # change with path where you extracted the data archive\n", + "\n", + "calendar_df = pd.read_csv(data_path / \"calendar.csv\")\n", + "sales_train_df = pd.read_csv(data_path / \"sales_train_validation.csv\")\n", + "sell_prices_df = pd.read_csv(data_path / \"sell_prices.csv\")\n", + "\n", + "# add explicit columns for the day, month, year for ease of processing\n", + "calendar_df[\"date\"] = pd.to_datetime(calendar_df[\"date\"])\n", + "calendar_df[\"day\"] = calendar_df[\"date\"].dt.day\n", + "calendar_df[\"month\"] = calendar_df[\"date\"].dt.month\n", + "calendar_df[\"year\"] = calendar_df[\"date\"].dt.year" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The data\n", + "\n", + "### Preprocessing\n", + "`sell_prices_df` contains the prices of each item in each store at a given time. The `wm_yr_wk` is a unique identifier for the time." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>store_id</th>\n", + " <th>item_id</th>\n", + " <th>wm_yr_wk</th>\n", + " <th>sell_price</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>CA_1</td>\n", + " <td>HOBBIES_1_001</td>\n", + " <td>11325</td>\n", + " <td>9.58</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>CA_1</td>\n", + " <td>HOBBIES_1_001</td>\n", + " <td>11326</td>\n", 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contains information about the dates on which the products were sold." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>date</th>\n", + " <th>wm_yr_wk</th>\n", + " <th>weekday</th>\n", + " <th>wday</th>\n", + " <th>month</th>\n", + " <th>year</th>\n", + " <th>d</th>\n", + " <th>event_name_1</th>\n", + " <th>event_type_1</th>\n", + " <th>event_name_2</th>\n", + " <th>event_type_2</th>\n", + " <th>snap_CA</th>\n", + " <th>snap_TX</th>\n", + " <th>snap_WI</th>\n", + " <th>day</th>\n", + " 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The sales are grouped by day: for example, the `d_1907` column has the number of units sold on the 1907-th day." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>id</th>\n", + " <th>item_id</th>\n", + " <th>dept_id</th>\n", + " <th>cat_id</th>\n", + " <th>store_id</th>\n", + " <th>state_id</th>\n", + " <th>d_1</th>\n", + " <th>d_2</th>\n", + " <th>d_3</th>\n", + " <th>d_4</th>\n", + " <th>...</th>\n", + " <th>d_1904</th>\n", + " <th>d_1905</th>\n", + " <th>d_1906</th>\n", + " 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<td>...</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>1</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>HOBBIES_1_003_CA_1_validation</td>\n", + " <td>HOBBIES_1_003</td>\n", + " <td>HOBBIES_1</td>\n", + " <td>HOBBIES</td>\n", + " <td>CA_1</td>\n", + " <td>CA</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>...</td>\n", + " <td>2</td>\n", + " <td>1</td>\n", + " <td>2</td>\n", + " <td>1</td>\n", + " <td>1</td>\n", + " <td>1</td>\n", + " <td>0</td>\n", + " <td>1</td>\n", + " <td>1</td>\n", + " <td>1</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>HOBBIES_1_004_CA_1_validation</td>\n", + " <td>HOBBIES_1_004</td>\n", + " <td>HOBBIES_1</td>\n", + " <td>HOBBIES</td>\n", + " <td>CA_1</td>\n", + " <td>CA</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>...</td>\n", + " <td>1</td>\n", + " <td>0</td>\n", + " <td>5</td>\n", + " <td>4</td>\n", + " <td>1</td>\n", + " <td>0</td>\n", + " <td>1</td>\n", + " <td>3</td>\n", + " <td>7</td>\n", + " <td>2</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>HOBBIES_1_005_CA_1_validation</td>\n", + " <td>HOBBIES_1_005</td>\n", + " <td>HOBBIES_1</td>\n", + " <td>HOBBIES</td>\n", + " <td>CA_1</td>\n", + " <td>CA</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>...</td>\n", + " <td>2</td>\n", + " <td>1</td>\n", + " <td>1</td>\n", + " <td>0</td>\n", + " <td>1</td>\n", + " <td>1</td>\n", + " <td>2</td>\n", + " <td>2</td>\n", + " <td>2</td>\n", + " <td>4</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<p>5 rows × 1919 columns</p>\n", + "</div>" + ], + "text/plain": [ + " id item_id dept_id cat_id store_id \\\n", + "0 HOBBIES_1_001_CA_1_validation HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 \n", + "1 HOBBIES_1_002_CA_1_validation HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 \n", + "2 HOBBIES_1_003_CA_1_validation HOBBIES_1_003 HOBBIES_1 HOBBIES CA_1 \n", + "3 HOBBIES_1_004_CA_1_validation HOBBIES_1_004 HOBBIES_1 HOBBIES CA_1 \n", + "4 HOBBIES_1_005_CA_1_validation HOBBIES_1_005 HOBBIES_1 HOBBIES CA_1 \n", + "\n", + " state_id d_1 d_2 d_3 d_4 ... d_1904 d_1905 d_1906 d_1907 d_1908 \\\n", + "0 CA 0 0 0 0 ... 1 3 0 1 1 \n", + "1 CA 0 0 0 0 ... 0 0 0 0 0 \n", + "2 CA 0 0 0 0 ... 2 1 2 1 1 \n", + "3 CA 0 0 0 0 ... 1 0 5 4 1 \n", + "4 CA 0 0 0 0 ... 2 1 1 0 1 \n", + "\n", + " d_1909 d_1910 d_1911 d_1912 d_1913 \n", + "0 1 3 0 1 1 \n", + "1 1 0 0 0 0 \n", + "2 1 0 1 1 1 \n", + "3 0 1 3 7 2 \n", + "4 1 2 2 2 4 \n", + "\n", + "[5 rows x 1919 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sales_train_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To align the sales data with the other DataFrames, we convert `sales_train_df` to a long format. We collapse the daily sales columns `d_{i}` into a single `sales` column, with an additional `day` column indicating the day corresponding to each sales entry." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>item_id</th>\n", + " <th>dept_id</th>\n", + " <th>cat_id</th>\n", + " <th>store_id</th>\n", + " <th>state_id</th>\n", + " <th>d</th>\n", + " <th>sales</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>HOBBIES_1_001</td>\n", + " <td>HOBBIES_1</td>\n", + " <td>HOBBIES</td>\n", + " <td>CA_1</td>\n", + " <td>CA</td>\n", + " <td>d_1</td>\n", + " <td>0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>HOBBIES_1_001</td>\n", + " <td>HOBBIES_1</td>\n", + " <td>HOBBIES</td>\n", + " <td>CA_1</td>\n", + " <td>CA</td>\n", + " <td>d_2</td>\n", + " <td>0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>HOBBIES_1_001</td>\n", + " <td>HOBBIES_1</td>\n", + " <td>HOBBIES</td>\n", + " <td>CA_1</td>\n", + " <td>CA</td>\n", + " <td>d_3</td>\n", + " <td>0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>HOBBIES_1_001</td>\n", + " <td>HOBBIES_1</td>\n", + " <td>HOBBIES</td>\n", + " <td>CA_1</td>\n", + " <td>CA</td>\n", + " <td>d_4</td>\n", + " <td>0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>HOBBIES_1_001</td>\n", + " <td>HOBBIES_1</td>\n", + " <td>HOBBIES</td>\n", + " <td>CA_1</td>\n", + " <td>CA</td>\n", + " <td>d_5</td>\n", + " <td>0</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " item_id dept_id cat_id store_id state_id d sales\n", + "0 HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA d_1 0\n", + "1 HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA d_2 0\n", + "2 HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA d_3 0\n", + "3 HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA d_4 0\n", + "4 HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA d_5 0" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def convert_sales_data_from_wide_to_long(sales_df_wide):\n", + " index_vars = [\"item_id\", \"dept_id\", \"cat_id\", \"store_id\", \"state_id\"]\n", + " sales_df_long = pd.wide_to_long(\n", + " sales_df_wide.iloc[:100, 1:],\n", + " i=index_vars,\n", + " j=\"day\",\n", + " stubnames=[\"d\"],\n", + " sep=\"_\",\n", + " ).reset_index()\n", + "\n", + " sales_df_long = sales_df_long.rename(columns={\"d\": \"sales\", \"day\": \"d\"})\n", + "\n", + " sales_df_long[\"d\"] = \"d_\" + sales_df_long[\"d\"].astype(\n", + " \"str\"\n", + " ) # restore \"d_{i}\" format for day\n", + " return sales_df_long\n", + "\n", + "\n", + "sales_train_df_long = convert_sales_data_from_wide_to_long(sales_train_df)\n", + "sales_train_df_long.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(\n", + " sales_train_df_long[\"sales\"],\n", + " bins=np.arange(0, 10 + 1.5) - 0.5,\n", + " range=[0, 10],\n", + " density=True,\n", + ")\n", + "plt.xticks(range(10))\n", + "plt.ylabel(\"Frequency of number of sales\")\n", + "plt.title(\"Number of sales over the entire timespan\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Train and test sets" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The dataset comprises sales data of 100 items over 1,913 days. For simplicity, we select the data from the first 365 days and discard the rest." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "n_items = 100\n", + "n_days = 1913\n" + ] + } + ], + "source": [ + "print(f\"n_items = {len(sales_train_df_long['item_id'].unique())}\")\n", + "print(f\"n_days = {len(sales_train_df_long['d'].unique())}\")\n", + "\n", + "sales_train_df_long[\"day_number\"] = sales_train_df_long[\"d\"].str.extract(\"(\\d+)\").astype(int)\n", + "data = sales_train_df_long[sales_train_df_long[\"day_number\"] <= 365].copy()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We compute the lags of the previous 30 days and merge the sales, calendar, and price data together." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>item_id</th>\n", + " <th>dept_id</th>\n", + " <th>cat_id</th>\n", + " <th>store_id</th>\n", + " <th>state_id</th>\n", + " <th>d</th>\n", + " <th>sales</th>\n", + " <th>day_number</th>\n", + " <th>sales_lag_1</th>\n", + " <th>sales_lag_2</th>\n", + " <th>...</th>\n", + " <th>year</th>\n", + " <th>event_name_1</th>\n", + " <th>event_type_1</th>\n", + " <th>event_name_2</th>\n", + " <th>event_type_2</th>\n", + " <th>snap_CA</th>\n", + " <th>snap_TX</th>\n", + " <th>snap_WI</th>\n", + " <th>day</th>\n", + " <th>sell_price</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>HOBBIES_1_002</td>\n", + " <td>HOBBIES_1</td>\n", + " <td>HOBBIES</td>\n", + " <td>CA_1</td>\n", + " <td>CA</td>\n", + " <td>d_141</td>\n", + " <td>0</td>\n", + " <td>141</td>\n", + " <td>0.0</td>\n", + " <td>0.0</td>\n", + " <td>...</td>\n", + " <td>2011</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>18</td>\n", + " <td>3.97</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>HOBBIES_1_002</td>\n", + " <td>HOBBIES_1</td>\n", + " <td>HOBBIES</td>\n", + " <td>CA_1</td>\n", + " <td>CA</td>\n", + " <td>d_142</td>\n", + " <td>0</td>\n", + " <td>142</td>\n", + " <td>0.0</td>\n", + " <td>0.0</td>\n", + " <td>...</td>\n", + " <td>2011</td>\n", + " <td>Father's day</td>\n", + " <td>Cultural</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>19</td>\n", + " <td>3.97</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>HOBBIES_1_002</td>\n", + " <td>HOBBIES_1</td>\n", + " <td>HOBBIES</td>\n", + " <td>CA_1</td>\n", + " <td>CA</td>\n", + " <td>d_143</td>\n", + " <td>0</td>\n", + " <td>143</td>\n", + " <td>0.0</td>\n", + " <td>0.0</td>\n", + " <td>...</td>\n", + " <td>2011</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>20</td>\n", + " <td>3.97</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>HOBBIES_1_002</td>\n", + " <td>HOBBIES_1</td>\n", + " <td>HOBBIES</td>\n", + " <td>CA_1</td>\n", + " <td>CA</td>\n", + " <td>d_144</td>\n", + " <td>1</td>\n", + " <td>144</td>\n", + " <td>0.0</td>\n", + " <td>0.0</td>\n", + " <td>...</td>\n", + " <td>2011</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>21</td>\n", + " <td>3.97</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>HOBBIES_1_002</td>\n", + " <td>HOBBIES_1</td>\n", + " <td>HOBBIES</td>\n", + " <td>CA_1</td>\n", + " <td>CA</td>\n", + " <td>d_145</td>\n", + " <td>0</td>\n", + " <td>145</td>\n", + " <td>1.0</td>\n", + " <td>0.0</td>\n", + " <td>...</td>\n", + " <td>2011</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>NaN</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>0</td>\n", + " <td>22</td>\n", + " <td>3.97</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<p>5 rows × 53 columns</p>\n", + "</div>" + ], + "text/plain": [ + " item_id dept_id cat_id store_id state_id d sales \\\n", + "0 HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 CA d_141 0 \n", + "1 HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 CA d_142 0 \n", + "2 HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 CA d_143 0 \n", + "3 HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 CA d_144 1 \n", + "4 HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 CA d_145 0 \n", + "\n", + " day_number sales_lag_1 sales_lag_2 ... year event_name_1 \\\n", + "0 141 0.0 0.0 ... 2011 NaN \n", + "1 142 0.0 0.0 ... 2011 Father's day \n", + "2 143 0.0 0.0 ... 2011 NaN \n", + "3 144 0.0 0.0 ... 2011 NaN \n", + "4 145 1.0 0.0 ... 2011 NaN \n", + "\n", + " event_type_1 event_name_2 event_type_2 snap_CA snap_TX snap_WI day \\\n", + "0 NaN NaN NaN 0 0 0 18 \n", + "1 Cultural NaN NaN 0 0 0 19 \n", + "2 NaN NaN NaN 0 0 0 20 \n", + "3 NaN NaN NaN 0 0 0 21 \n", + "4 NaN NaN NaN 0 0 0 22 \n", + "\n", + " sell_price \n", + "0 3.97 \n", + "1 3.97 \n", + "2 3.97 \n", + "3 3.97 \n", + "4 3.97 \n", + "\n", + "[5 rows x 53 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "n_lags = 30\n", + "\n", + "# sort data before computing lags\n", + "data_index_vars = [\"item_id\", \"dept_id\", \"cat_id\", \"store_id\", \"state_id\"]\n", + "data.sort_values(data_index_vars + [\"day_number\"], inplace=True)\n", + "\n", + "for lag in range(1, n_lags + 1):\n", + " data[f\"sales_lag_{lag}\"] = data.groupby(by=data_index_vars)[\"sales\"].shift(lag)\n", + "\n", + "data = data.merge(calendar_df).merge(sell_prices_df)\n", + "\n", + "data.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Treeffuser can handle **categorical columns**, but the dtype of those columns must be set to `category` in the DataFrame." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "categorical_columns = [\n", + " \"item_id\",\n", + " \"dept_id\",\n", + " \"cat_id\",\n", + " \"store_id\",\n", + " \"state_id\",\n", + " \"d\",\n", + " \"wm_yr_wk\",\n", + " \"weekday\",\n", + " \"event_name_1\",\n", + " \"event_type_1\",\n", + " \"event_name_2\",\n", + " \"event_type_2\",\n", + " \"snap_CA\",\n", + " \"snap_TX\",\n", + " \"snap_WI\",\n", + "]\n", + "data[categorical_columns] = data[categorical_columns].astype(\"category\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, for each item, we take the first 300 days as train data and use the remaining 65 data as test data for evaluation." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(15216, 50)\n", + "(3891, 50)\n" + ] + } + ], + "source": [ + "is_train = data[\"day_number\"] <= 300\n", + "\n", + "y_name = \"sales\"\n", + "x_names = [name for name in data.columns if name != y_name and name not in [\"day_number\", \"date\"]]\n", + "\n", + "X_train, y_train, dates_train = data[is_train][x_names], data[is_train][y_name], data[is_train][\"date\"]\n", + "X_test, y_test, dates_test = data[~is_train][x_names], data[~is_train][y_name], data[~is_train][\"date\"]\n", + "\n", + "print(X_train.shape)\n", + "print(X_test.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Probabilistic predictions with Treeffuser\n", + "\n", + "We regress the sales on the following covariates." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "item_id, dept_id, cat_id, store_id, state_id, d, sales_lag_1, sales_lag_2, sales_lag_3, sales_lag_4, sales_lag_5, sales_lag_6, sales_lag_7, sales_lag_8, sales_lag_9, sales_lag_10, sales_lag_11, sales_lag_12, sales_lag_13, sales_lag_14, sales_lag_15, sales_lag_16, sales_lag_17, sales_lag_18, sales_lag_19, sales_lag_20, sales_lag_21, sales_lag_22, sales_lag_23, sales_lag_24, sales_lag_25, sales_lag_26, sales_lag_27, sales_lag_28, sales_lag_29, sales_lag_30, wm_yr_wk, weekday, wday, month, year, event_name_1, event_type_1, event_name_2, event_type_2, snap_CA, snap_TX, snap_WI, day, sell_price\n" + ] + } + ], + "source": [ + "print(\", \".join(map(str, X_train.columns)))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN\n", + "[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN\n", + "[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN\n", + "[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN\n", + "[LightGBM] [Warning] Categorical features with more bins than the configured maximum bin number found.\n", + "[LightGBM] [Warning] For categorical features, max_bin and max_bin_by_feature may be ignored with a large number of categories.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/achille/Documents/treeffuser-ter/src/treeffuser/_base_tabular_diffusion.py:205: CastFloat32Warning: Input array is not float; it has been recast to float32.\n", + " y = _check_array(y)\n" + ] + }, + { + "data": { + "text/html": [ + 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input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-2);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n", + "#sk-container-id-1 div.sk-label label {\n", + " /* The background is the default theme color */\n", + " color: var(--sklearn-color-text-on-default-background);\n", + "}\n", + "\n", + "/* On hover, darken the color of the background */\n", + "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n", + " color: var(--sklearn-color-text);\n", + " background-color: var(--sklearn-color-unfitted-level-2);\n", + "}\n", + "\n", + "/* Label box, darken color on hover, fitted */\n", + "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n", + " color: var(--sklearn-color-text);\n", + " background-color: var(--sklearn-color-fitted-level-2);\n", + "}\n", + "\n", + "/* Estimator label */\n", + "\n", + "#sk-container-id-1 div.sk-label label {\n", + " font-family: monospace;\n", + " font-weight: bold;\n", + " display: inline-block;\n", + " line-height: 1.2em;\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-label-container {\n", + " text-align: center;\n", + "}\n", + "\n", + "/* Estimator-specific */\n", + "#sk-container-id-1 div.sk-estimator {\n", + " font-family: monospace;\n", + " border: 1px dotted var(--sklearn-color-border-box);\n", + " border-radius: 0.25em;\n", + " box-sizing: border-box;\n", + " margin-bottom: 0.5em;\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-0);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-estimator.fitted {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-0);\n", + "}\n", + "\n", + "/* on hover */\n", + "#sk-container-id-1 div.sk-estimator:hover {\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-2);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-estimator.fitted:hover {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-2);\n", + "}\n", + "\n", + "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n", + "\n", + "/* Common style for \"i\" and \"?\" */\n", + "\n", + ".sk-estimator-doc-link,\n", + "a:link.sk-estimator-doc-link,\n", + "a:visited.sk-estimator-doc-link {\n", + " float: right;\n", + " font-size: smaller;\n", + " line-height: 1em;\n", + " font-family: monospace;\n", + " background-color: var(--sklearn-color-background);\n", + " border-radius: 1em;\n", + " height: 1em;\n", + " width: 1em;\n", + " text-decoration: none !important;\n", + " margin-left: 1ex;\n", + " /* unfitted */\n", + " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n", + " color: var(--sklearn-color-unfitted-level-1);\n", + "}\n", + "\n", + ".sk-estimator-doc-link.fitted,\n", + "a:link.sk-estimator-doc-link.fitted,\n", + "a:visited.sk-estimator-doc-link.fitted {\n", + " /* fitted */\n", + " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n", + " color: var(--sklearn-color-fitted-level-1);\n", + "}\n", + "\n", + "/* On hover */\n", + "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n", + ".sk-estimator-doc-link:hover,\n", + "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n", + ".sk-estimator-doc-link:hover {\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-3);\n", + " color: var(--sklearn-color-background);\n", + " text-decoration: none;\n", + "}\n", + "\n", + "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n", + ".sk-estimator-doc-link.fitted:hover,\n", + "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n", + ".sk-estimator-doc-link.fitted:hover {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-3);\n", + " color: var(--sklearn-color-background);\n", + " text-decoration: none;\n", + "}\n", + "\n", + "/* Span, style for the box shown on hovering the info icon */\n", + ".sk-estimator-doc-link span {\n", + " display: none;\n", + " z-index: 9999;\n", + " position: relative;\n", + " font-weight: normal;\n", + " right: .2ex;\n", + " padding: .5ex;\n", + " margin: .5ex;\n", + " width: min-content;\n", + " min-width: 20ex;\n", + " max-width: 50ex;\n", + " color: var(--sklearn-color-text);\n", + " box-shadow: 2pt 2pt 4pt #999;\n", + " /* unfitted */\n", + " background: var(--sklearn-color-unfitted-level-0);\n", + " border: .5pt solid var(--sklearn-color-unfitted-level-3);\n", + "}\n", + "\n", + ".sk-estimator-doc-link.fitted span {\n", + " /* fitted */\n", + " background: var(--sklearn-color-fitted-level-0);\n", + " border: var(--sklearn-color-fitted-level-3);\n", + "}\n", + "\n", + ".sk-estimator-doc-link:hover span {\n", + " display: block;\n", + "}\n", + "\n", + "/* \"?\"-specific style due to the `<a>` HTML tag */\n", + "\n", + "#sk-container-id-1 a.estimator_doc_link {\n", + " float: right;\n", + " font-size: 1rem;\n", + " line-height: 1em;\n", + " font-family: monospace;\n", + " background-color: var(--sklearn-color-background);\n", + " border-radius: 1rem;\n", + " height: 1rem;\n", + " width: 1rem;\n", + " text-decoration: none;\n", + " /* unfitted */\n", + " color: var(--sklearn-color-unfitted-level-1);\n", + " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n", + "}\n", + "\n", + "#sk-container-id-1 a.estimator_doc_link.fitted {\n", + " /* fitted */\n", + " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n", + " color: var(--sklearn-color-fitted-level-1);\n", + "}\n", + "\n", + "/* On hover */\n", + "#sk-container-id-1 a.estimator_doc_link:hover {\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-3);\n", + " color: var(--sklearn-color-background);\n", + " text-decoration: none;\n", + "}\n", + "\n", + "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-3);\n", + "}\n", + "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Treeffuser(extra_lightgbm_params={}, seed=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow \"> Treeffuser<span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></label><div class=\"sk-toggleable__content \"><pre>Treeffuser(extra_lightgbm_params={}, seed=0)</pre></div> </div></div></div></div>" + ], + "text/plain": [ + "Treeffuser(extra_lightgbm_params={}, seed=0)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = Treeffuser(seed=0)\n", + "model.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [12:39<00:00, 7.59s/it] \n" + ] + } + ], + "source": [ + "y_test_samples = model.sample(X_test, n_samples=100, seed=0, n_steps=50, verbose=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Newsvendor model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We illustrate the practical relevance of accurate probabilistic predictions with an application to inventory management, using the newsvendor model \\citep{arrow1951optimal}. \n", + "\n", + "Assume that every day we decide how many units $q$ of an item to buy. \n", + "We buy at a cost $c$ and sell at a price $p$. \n", + "However, the demand $y$ is random, introducing uncertainty in our decision. \n", + "The goal is to maximize the expected profit:\n", + "$$\\max_{q} p~\\mathbb{E}\\left[\\min(q, y)\\right] - c q.$$\n", + "The optimal solution to the newsvendor problem is to buy $q = F^{-1}\\left( \\frac{p-c}{p} \\right)$ units, where $F^{-1}$ is the quantile function of the distribution of $y$. \n", + "\n", + "Using Treeffuser, we can compute the quantiles from the samples and forecast the optimal quantity of units to buy.\n", + "\n", + "To compute profits, we use the observed prices, assume a margin of $50\\%$ over all products, and assume the actual number of sales of an item correspond to the demand of this item. We let Treeffuser, learn the conditional distribution of the demand of each item, estimate their quantiles, and thus determine the optimal quantity to buy. \n", + "\n", + "We use the held-out data to compute the profit made if Treeffuser was used to forecast the demand of each item and to manage the inventory of each item." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def newsvendor_utility(y_true, quantity_ordered, prices, stocking_cost):\n", + " \"\"\"\n", + " The newsvendor utility function with stock q, demand y, selling price p, stocking cost c is given by\n", + " $$ U(y, q, p, c) = p * min(y, q) - c * q $$\n", + " \"\"\"\n", + " utility = prices * np.minimum(y_true, quantity_ordered) - stocking_cost * quantity_ordered\n", + " return utility\n", + "\n", + "\n", + "def newsvendor_optimal_quantity(y_samples, prices, stocking_cost):\n", + " \"\"\"\n", + " Returns the optimal quantity to order for the newsvendor problem.\n", + " \n", + " It is given theoeretically by:\n", + " $$ q* = argmax_{q} E[U(y, q, p, c)] $$ \n", + " which has a closed form solution,\n", + " $$ q* = F^{-1}( (p - c) / p) $$\n", + " where F is the CDF of the demand distribution\n", + " \"\"\"\n", + " # compute the target quantiles (p - c) / p\n", + " target_quantiles = (prices - stocking_cost) / prices\n", + " target_quantiles = np.maximum(target_quantiles, 0.0)\n", + "\n", + " # compute the empirical quantities corresponding to the target quantiles\n", + " res = []\n", + " for i in range(y_samples.shape[1]):\n", + " optimal_quantities = np.quantile(y_samples[:, i], target_quantiles[i])\n", + " res.append(optimal_quantities)\n", + " optimal_quantities = np.array(res)\n", + " return optimal_quantities\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "185.07666666666668" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# we don't know the profit margin of each item, so we assume it is 50%.\n", + "profit_margin = 0.5\n", + "\n", + "prices = X_test[\"sell_price\"].values\n", + "stocking_cost = prices / (1 + profit_margin)\n", + "\n", + "# Compute optimal quantities. \n", + "optimal_quantities = newsvendor_optimal_quantity(y_test_samples, prices, stocking_cost)\n", + "\n", + "# Treeffuser models continuous responses, hence we cast the predicted quantities into int\n", + "optimal_quantities = optimal_quantities.astype(int)\n", + "\n", + "profit = newsvendor_utility(y_test, optimal_quantities, prices, stocking_cost)\n", + "profit.sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "X_test_results = pd.DataFrame({\n", + " \"date\": dates_test,\n", + " \"profit\": profit\n", + "})\n", + "daily_profit = X_test_results.groupby(\"date\").sum().sort_values(\"date\").reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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v1KDoUpZUQ9A/2noReQVatAyshm6NvCSJQS5Y7BARUaVToNVh2b5rAICXOtYxaNUp1jG4qNiJistAZl5BhcZ3+Pod/HkmGUoFMG9Ak4euCkCPxmKHiIgqnS2nEhGfnocaLvYY1Saw1H0CPJxR17MKtDqBw9fuVFhsBVod5v5+HgAwum0gmvi5V9h7yxWLHSIiktzDpiExtcL7WnUmPl0HTvZlz8vWqX7R5aOKvJS15kgsrtzKhkcVe8zoXr/C3lfOWOwQEZGkfj+dhKZzd2HtkZsV8n5f7r2G2LRceFSxx+i2pbfqFLu/305FFGSpWflYuvsKAGBWzwao6mxv9vesDFjsEBGRZCJj0zHz59PIVhfi87+uIr9Aa9b3++vCLXy5p2gx6ff6NkIVh4fPrdsmyAMOtkokZ+bjamq2WWMDgI+3X0aWuhCh/u4Yft9QeHoyLHaIiEgS8em5mLQmEhqtDgCQnqPBr1GJZnu/m3dy8PrP0QCAF9oFYlBz/0c+x9HOBm3r3BuCbuZV0CNj0/G/qAQAwLwBIRxqbkIsdoiIqMJl5RfgxdUnkJajQRM/N8zsUdQ35ftDN8wyY3GuphAvrY1EVn4hwgOr4d0+jcv93OLZlM3Zb0erE3j/t6JOySNaBiAsoKrZ3qsyYrFDREQVqlCrw6s/ncKVW9nwcnXA92NaYmyHILg62uLG7Rzsu2zaSfyEEHjrf2dx+VYWarg4YPmoFrC3Lf/XX3G/neMx6cjVFJo0tmI/HY/D+SQV3BxtMatXA7O8R2XGYoeIiCrUR9suYv/l23C0U+L7MS3h6+4EFwdbPHdvYr/vDtww6fut/Ocmfj+dBFulAstHtYC3m6NRz69Towr8qzlBo9Xh6I00k8YGFK1/9dmuywCAN3o0QHUXB5O/R2XHYoeIiCrMj0djsfLeEghLhoehmX9V/WNjO9SGrVKBYzHpOJuQaZL3O3YjDR9tuwgAeLt3I7QO8jD6NRQKxb+XsszQb+eTnZeRkVuAhj6uGNWm5EzO9ORY7BARUYU4dPUO5tybLG9mj/ro3dTX4HFfdyf0C/UDAPzfwSdv3bmlysfL609BqxMYEOaHcR1qP/Zrmavfzh+nk/DT8TgAwPwBIbC14deyOfBflYiIzC7mTg6mrouEVicwqHlNvNylXqn7TXg6CACw9WwyEjPyHvv9NIU6TPkxEney1Wjo44qFg5s+0ZIL7evVgK1SgZtpubh5J+exX+d+kbHpeOOX0wCAiU8HPVarE5UPix0iIjIrIQRm/3oGqnsjoR5WeDTxc0f7utWh1Qms+ifmsd/z4x2XEBWXAVdHW3w7OhzO9g+fT+dRXBxs0bJ2NQDAgatP3roTl3Zv2H2hDt0be+OtiEZP/JpUNhY7RERkVlvPJuPojXQ42CqxdERYqYtu3m/i03UAAD8dj4cq3/gFOHeeT8EPh4oKpSXDw1C7RhXjgy5F8dIRO86lQPsEw+Mz8wowbtVxpOVoEFLTDV+MDIMN59QxKxY7RERkNrmaQny0taiD8NTO9RDg4fzI53Sq74l6Xi7IVhdi4/F4o94vPj0X/7nv0lD3xt7GB12GZxp5QaEADl9PwwsrjuGWKt/o1yjQ6jB1XSSu386Br7sjfhjT6olbnejRWOwQEZHZfL3vGpIz8+FfzQkvdapTrucolQpMeKqo787Kf2JQcG+G5UfRFOrwyvooqPIL0bxWVczq1fCx4y5NsLcrPhsaCic7G/xzLQ0RXxzEvkvlnxNICIF3N5/DP9fSUMXeBj+MaWX0MHh6PCx2iIjILG7eycH/HSi6nPR+38aPvHx1v4HNa6KGiz2SMvOx7WxyuZ6zaPslnE7IhLuTHb56tjnszDCyaUi4P/549Sk08nVDeo4G41adwAd/XoC68NFrev33wA1sPBkPpQJY9lwLNPZzM3l8VDoWO0REZBbz/7wAjVaHjvU9jb6c5Ghngxfa1QZQNAz9USuO7zyfghX3OjQvHhYK/2qPvlz2uOp5uWDz1PYY274ovh8OxWDIN4cRU8oorQKtDsmZefj5RDwWbb8EAJjTrwm6NPQyW3xUEi8UEhGRye25eAt7L6XCzkaBOf0aP9aw79FtA7F8/zWcS1Rh76VUdGtUesF0fz+dSR3r4BkT9tMpi6OdDeb2b4Kn6tXAfzadxrlEFfp+eRA9m/ggLUeD1Cw1UlX5SM/V4P46bWz72hhzr0iiisNih4iITCq/QIv5f14AAIx/Kgh1PV0e63U8qthjSAt/rDsWhxdXn0Swlwt6hfigZxMfNPFzg0KhKNFP5z89K3ZdqWcae2P7ax3x2oZTOBaTjl9PlVy13VapgKerA3o09sZ7fcu/ACmZDosdIiIyqR8OxSA2LRderg54tWvwE73WGz0a4HaWGvsup+Jqajau7r2Gr/Zeg381J/Rq4oO7uQX6fjrLnmthln46j+Lj7oj1E9ti86lEpGTmwcvNEV6uDvBydYS3mwOqOdtDyaHlklKIR10IrQRUKhXc3d2RmZkJNzd2GCMielxJGXnotvhv5BVo8cXIMAwIq2mS11XlF2DvxVTsOJeC/VdSkV9gOELr+xdaVsjlK7Is5f3+ZssOERGZzEfbLiKvQIvWtT3Q/946V6bg5miHgc1rYmDzmsjTaPH3ldvYeT4Fh6/fweg2gSx06KFY7BARkUkcuHIbW88kQ6kA5vZv8kRrUT2Mk70NeoX4oFeIj1len+TH6Iuba9asgVqtLrFdo9FgzZo1JgmKiIisy8VkFV5eHwUAeL5tIOeQIYtidLEzbtw4ZGZmltielZWFcePGGfVaBw4cQL9+/eDn5weFQoEtW7boHysoKMCbb76Jpk2bokqVKvDz88MLL7yApKQkg9fo3LkzFAqFwW3kyJHGpkVERI8pPj0XL6w4jqz8QrSqXQ2ze3NRS7IsRhc7QohSmyYTEhLg7u5u1Gvl5OQgNDQUy5YtK/FYbm4uoqKi8N577yEqKgq//vorrly5gv79+5fYd+LEiUhOTtbf/vvf/xoVBxERPZ472Wo8/8Mx3M5So6GPK74f08qomZKJKkK5++w0b95c33LSrVs32Nr++1StVouYmBj06tXLqDePiIhAREREqY+5u7tj9+7dBtu++uortG7dGnFxcahVq5Z+u7OzM3x8eO2WiKgiZeUXYOzK47iZlgv/ak5YPb413J3spA6LqIRyFzsDBw4EAERHR6Nnz55wcfl3kih7e3vUrl0bQ4YMMXmA98vMzIRCoUDVqlUNtq9btw4//vgjvL29ERERgTlz5sDV1bXM11Gr1Qb9jlQqlblCJiKSpfwCLSaticS5RBWqV7HH2hfbcFFLsljlLnbmzJkDAKhduzZGjBgBR8eKPajz8/Px1ltv4bnnnjMYSz9q1CgEBQXBx8cH586dw+zZs3H69OkSrUL3W7hwIebNm1cRYRMRyY5WJ/D6xmgcuVG0eveqca0RVKOK1GERlcliJhVUKBTYvHmzvgXpfgUFBRg2bBji4uKwf//+h04cFBkZiZYtWyIyMhItWrQodZ/SWnYCAgI4qSAR0SMIIfDOlnNYfywO9jZKrBrXCu3r1ZA6LKqkTDqpoIeHB65cuYIaNWqgWrVqD507IT093fhoH6KgoADDhw9HTEwM9u7d+8hipEWLFrCzs8PVq1fLLHYcHBzg4OBg0jiJiOQuK78A8/64gE2RCVAogKUjw1jokFUoV7Hz+eef6/vALF261JzxGCgudK5evYp9+/ahevXqj3zO+fPnUVBQAF9f3wqIkIiocjgek44ZP0cj4W4eFArggwEh6N2Un7NkHcpV7Jw+fRpDhw6Fg4MDgoKC0L59e4PRWI8rOzsb165d09+PiYlBdHQ0PDw84Ofnh6FDhyIqKgp//vkntFotUlJSABS1NNnb2+P69etYt24devfujRo1auDChQt444030Lx5c3To0OGJ4yMiquzUhVos2XUF3x28ASEA/2pOWDwsFG3qPPrHJ5GlKFefHTs7OyQkJMDb2xs2NjZITk6Gl5fXE7/5/v370aVLlxLbx4wZg7lz5yIoKKjU5+3btw+dO3dGfHw8Ro8ejXPnziE7OxsBAQHo06cP5syZAw8Pj3LHwYVAiYhKupCkwoyfo3EpJQsAMKJlAN7t2wiujhxeTpbBpH12ateujS+//BI9evSAEAJHjhxBtWrVSt23Y8eO5Q6yc+fOeFit9ag6LCAgAH///Xe534+IiB5NqxP474Hr+Hz3FRRoBapXsceiIc3QnYttkpUqV8vOli1bMHnyZKSmpkKhUJRZhCgUCmi1WpMHaW5s2SEi+tfCbRfx3wM3AAA9GntjweCmqOHCQR1kecr7/W3U0PPs7Gy4ubnh8uXLZV7GMnbJCEvAYoeIqEh6jgbtF+1BfoEOHwxogtFtA822ejnRkzLpZaxiLi4u2LdvH4KCgkzSQZmIiCzL2iOxyC/QIaSmGwsdkg2jK5ZOnTpBq9Xif//7Hy5evAiFQoFGjRphwIABsLHh4m9ERNYqT6PF6iM3AQCTOtZloUOyYXSxc+3aNfTp0wcJCQlo0KABhBC4cuUKAgICsHXrVtStW9cccRIRkZltikpAeo4G/tWc0DuEiyuTfCiNfcK0adNQp04dxMfHIyoqCqdOnUJcXByCgoIwbdo0c8RIRERmptUJfH+wqFPyhKeCYGtj9NcDkcUyumXn77//xtGjRw3msalevToWLVrEifyIiKzUrvMpiE3LRVVnOwxvFSB1OEQmZXTp7uDggKysrBLbs7OzYW9vb5KgiIio4ggh8O29oebPtw2Esz0HoJC8GF3s9O3bF5MmTcKxY8cghIAQAkePHsXkyZPRv39/c8RIRERmdDwmHafjM2Bvq8SY9rWlDofI5Iwudr788kvUrVsX7dq1g6OjIxwdHdGhQwfUq1cPX3zxhTliJCIiM/ruXqvO0HB/Th5IsmRUW6UQApmZmfjpp5+QlJSEixcvQgiBxo0bo169euaKkYiIzOTqrSzsuZQKhQKY+HQdqcMhMguji53g4GCcP38ewcHBLHCIiKzcd/ctCxFUo4rE0RCZh1GXsZRKJYKDg5GWlmaueIiIqILcUuVjS3QiAOClTpwjjeTL6D47n3zyCf7zn//g3Llz5oiHiIgqyIp/YlCgFWhVuxpa1KomdThEZmP0+MLRo0cjNzcXoaGhsLe3h5OTk8Hj6enpJguOiIjMIyu/AOuPxgEoWhqCSM6MLnaWLl1qhjCIiKgirTsWhyx1Iep6VkG3hl5Sh0NkVkYXO2PGjDFHHEREVEHOJ2Xi891XAAAvdawLpZILfpK8PdY0mVqtFps3by6x6rmtLWfdJCKyZKr8AkxdFwV1oQ5dGnhiaLi/1CERmZ3R1cm5c+cwYMAApKSkoEGDBgCAK1euwNPTE7///juaNm1q8iCJiOjJCSEw65cziE3LRc2qTvh8RBhbdahSMHo01oQJE9CkSRMkJCQgKioKUVFRiI+PR7NmzTBp0iRzxEhERCbww6EY7DifAjsbBZaPaoGqzlzPkCoHo1t2Tp8+jZMnT6JatX+HKVarVg0fffQRWrVqZdLgiIjINCJj07Fo+yUAwHt9GyM0oKq0ARFVIKNbdho0aIBbt26V2J6amsoZlYmILFBathovrzuFQp1Av1A/PN82UOqQiCqU0cXOggULMG3aNGzatAkJCQlISEjApk2bMH36dHz88cdQqVT6GxERSUurE5i+MRopqnzU9ayChYObQqFgPx2qXBRCCGHME5TKf+uj4hOm+CXuv69QKKDVak0Vp1mpVCq4u7sjMzMTbm5uUodDZHI6nWBH1Erq891X8MWeq3Cys8Fvr3RAfW9XqUMiMpnyfn8b3Wdn3759TxQYEVWs7w/ewCc7L2PJ8FD0beYndThUgQ5cuY0v914FAHw0KISFDlVaRhc7nTp1MkccRGQGW04l4sOtFwEA3+y/bvHFTlZ+AarY27IVygRSVfl4fWM0hACea1MLg1twPh2qvIzus0NE1uHwtTv4z6bT+vvnk1S4kGS5fen+PJOE8A//wus/R0sditXT6QRe/zkaaTkaNPRxxft9G0sdEpGkWOwQydDllCy8tDYSBVqBPs180bOJNwDgl8h4iSMr3W/RiXhtQzQ0hTr8deEWtDqjuhLSA775+zr+uZYGJzsbLHuuBRztbKQOiUhSkhY7Bw4cQL9+/eDn5weFQoEtW7YYPC6EwNy5c+Hn5wcnJyd07twZ58+fN9hHrVbj1VdfRY0aNVClShX0798fCQkJFZgFkWVJyczH2JXHkaUuROvaHlg8LBQjW9UCAPwWnQRNoU7iCA1tPpWA1zdG6wucHI0WV1OzJI7KekXGpmPJvXWv5g9ognpeLhJHRCQ9SYudnJwchIaGYtmyZaU+/sknn2DJkiVYtmwZTpw4AR8fH3Tv3h1ZWf9+EE6fPh2bN2/Ghg0bcOjQIWRnZ6Nv375WMxKMyJSy8gswduVxJGcWDTP+7oVwONrZ4OngGvBydUB6jgZ7L5WcJ0sq/4tMwIyfT0MngGdbB6B1kAcA4HR8hrSBWanM3AJM+6mocBwY5sd1r4jukbTYiYiIwIcffojBgweXeEwIgaVLl+Kdd97B4MGDERISgtWrVyM3Nxfr168HAGRmZuKHH37A4sWL8cwzz6B58+b48ccfcfbsWfz1118VnQ6RpDSFOkz5MQqXUrJQw8UBq8a11i8HYGuj1HdQ/fmkZbR8/nwyHjM3nYYQwKg2tfDRwKZoUatoZvZoFjtGE0LgrV/PIDEjD4HVnfHhIM6nQ1SsXKOxmjdvXu6TJioq6okCKhYTE4OUlBT06NFDv83BwQGdOnXC4cOH8dJLLyEyMhIFBQUG+/j5+SEkJASHDx9Gz549S31ttVoNtVqtv88JEKkinU/KxP7Lt/HiU0Em60tR/EV36NodONvbYOXYVgjwcDbYZ1hLf3z793Xsv5yKVFU+vNwcTfLeDyrQ6vDLyQRUcbBBI1831KlRBbY2hr+rNhyPw1u/ngUAPN82EPMHNIFCoUDYvSUMTsVlmCU2OVt3LA7bzxWte/XVs83h4mD0YFsi2SrX2TBw4ED9/+fn52P58uVo3Lgx2rVrBwA4evQozp8/j6lTp5ossJSUFACAt7e3wXZvb2/Exsbq97G3tzdYp6t4n+Lnl2bhwoWYN2+eyWIlKq/0HA3GrDiBO9lq3M3R4F0TjJJJzszDB39ewLazKbBRKvD1cy3Q1N+9xH51PV3QolZVRMVl4NdTiZjcqe4Tv3dpluy+gm/2X9fft7dVor63Cxr5uKGRrxtyNYX4bFdRn5Kx7WtjTr/G+h9TxcXOlVtZyNUUwtmeX9jlcTFZhfl/XgAAvNmrIZr5V5U2ICILU65Pkjlz5uj/f8KECZg2bRo++OCDEvvEx5t+pMeDLUrFszM/zKP2mT17NmbMmKG/r1KpEBAQ8GSBEj2CEALvbjmLO9lFrYorD9/EkHB/NPJ9vFm71YVafH8wBsv2XkNegRZKBbBgUAi6NPQq8znDWwYgKi4Dv5yMx0sd65j8Msf5pEx8d+AGAKBpTXfcuJ2NHI0W5xJVOJdo2II6vkMQ3uvbyCAGH3dH+Lg5IkWVj7MJmWhTp7pJ45OjXE0hXv3pFDSFOnRt6IUXnwqSOiQii2P0z6ZffvkFJ0+eLLF99OjRaNmyJVasWGGSwHx8fAAUtd74+vrqt6empupbe3x8fKDRaHD37l2D1p3U1FS0b9++zNd2cHCAg4ODSeIkKq/fTydh29kU2CoVCA2oisjYu3hn81lsmtze6En09l1Oxbzfz+NmWi4AIDywGub1b4KQmiVbdO7Xp5kv5v5xHtdv5+BUfIa+j4wpFGp1mP3rWWh1AhEhPvhmdDh0OoH4u7m4mKzCheQsXEhS4cbtbPRt5ovXu9cvtdgKC6iKHedTEB2fwWLnEXQ6gbd/PYtrqdnwcnXAp0ObsZ8OUSmM7qDs5OSEQ4cOldh+6NAhODqarg9AUFAQfHx8sHv3bv02jUaDv//+W1/IhIeHw87OzmCf5ORknDt37qHFDlFFS87Mw3tbzgEAXu0ajK+fa4Eq9jZFrSxGzH0Tl5aLCatPYtzKE7iZlgtPVwcsGR6KTZPbPbLQAQBXRzv0Din68fCLiTsqrzp8E2cSMuHqaIt5/ZsAAJRKBQKrV0GvEF/M6F4f349pib0zO2NGjwZlfimH1aoKgJ2UH0UIgfl/XsCW6CTYKBVYOjIM1V34I46oNEa37EyfPh1TpkxBZGQk2rZtC6Coz86KFSvw/vvvG/Va2dnZuHbtmv5+TEwMoqOj4eHhgVq1amH69OlYsGABgoODERwcjAULFsDZ2RnPPfccAMDd3R0vvvgi3njjDVSvXh0eHh6YOXMmmjZtimeeecbY1IjMQgiBWZvOQJVfiFB/d0ztUhd2Nkq83r0+Ptx6EQu3X0L3xj7wqGL/0Nf56Xgc5vx+HppCHWyVCozrUBvTugXD1dHOqHiGtvTHr6cS8cfpJLzftzGc7J+8k3R8ei4W3+uH807vRk/U+Tn0Xn8TDj9/uMW7rmDV4ZsAgM+GNUP7ujWkDYjIghld7Lz11luoU6cOvvjiC/0Q8EaNGmHVqlUYPny4Ua918uRJdOnSRX+/uB/NmDFjsGrVKsyaNQt5eXmYOnUq7t69izZt2mDXrl1wdf13MbvPP/8ctra2GD58OPLy8tCtWzesWrUKNjacMZQsw4/H4nDw6h042CqxeHgY7O6NTBrbvjY2RSbgUkoWFm2/iE+Ghpb5Gt8fvKFf46pDveqY268Jgh9zUce2QdUR4OGE+PQ87DifjEHNn2wuFiEE3t58FnkFWrQJ8sCIVk/W/62ZvzuUCiApM9+so8as2bd/X8eyfUU/FD8YGPLEf0MiuVMIISr9vOzlXSKeyFgxd3LQ+4uDyCvQYk6/xhjXwbDzaGRsOoZ8cwQAsGlyO7Ss7VHiNb7edw2f7rwMAJjSuS5m9Sz7ElB5ffHXVXz+1xW0r1sd6ye2faLX+jWqaGJAe1sldrz2NOp4PvmMvb2WHsCllCz89/lw9Gzi88SvJyc/Ho3Fu/cuib4V0dBso+qIrEF5v78fa1LBjIwMfP/993j77beRnp4OoGh+ncTExMeLlkiGCrU6vPFzNPIKtGhftzrGtKtdYp/wQA+MaFnUEvLulnMo0P67lIMQAkt2XdYXOq8/U98khQ4ADAmvCYUCOHw9DfHpuY/9OmnZanxwb8jza92CTVLoAP8OQWe/HUObTyXgvd+KCp1XutRjoUNUTkYXO2fOnEH9+vXx8ccf49NPP0VGRgYAYPPmzZg9e7ap4yOyWv89cANRcRlwdbDFp8NCyxxx9VZEQ1RztsOllCysvtcHQwiBhdsv4cu91/T7vPZMsMlG2vhXc0b7ukUjnf4X9fgdlT/48wLu5hagoY8rJnWsY5LYACD0XrHDfjv/2nU+BTN/OQMhii6BvtGjvtQhEVkNo4udGTNmYOzYsbh69arB6KuIiAgcOHDApMERWasLSSos/auow+6c/k1Qs6pTmftWq2KP2RGNAACf776CpIw8zP39vH6+mjn9GpvlF/yw8KIWpU2RCdA9xirj+y6nYkt0EpQKYNGQZvq+SKZQ3LJzJiGTK6ADOHj1Nl5ZfwpancDQcH+837cxh5gTGcHoT6cTJ07gpZdeKrG9Zs2aD521mKgyef+3cyjQCvRo7I0hLWo+cv+h4f4ID6yGHI0W/b46hNVHYqFQAAsGNS3Rz8dUeoX4wNXRFgl383D0RppRz81RF+LdzUWXU8Z1CNIXJ6ZS39sVzvY2yFYX4vrtbJO+trXZe+kWJqw+CY1Wh4gQHywa3NToeZmIKjujix1HR8dS15K6fPkyPD09TRIUkTU7HZ+Bk7F3YWejwAcDQ8r1C1ypVODDgSGwUSqQlqOBUgF8NjQUz7WpZbY4He1s0C/UDwCwcPsl5Bdoy/W8opmgzyExIw81qzphRnfTX06xUSrQ9N68QdGVeJ2srWeSMWlNJNSFOnRv7I2lI8NKrDNGRI9m9FkzYMAAzJ8/HwUFBQCKlnOIi4vDW2+9hSFDhpg8QCJrs/KfGABAv2Z+8DZi2HQjXzfM6tkAnq4O+GJkcwwJN/9w4le61EM1ZzucTczE3N/Pl+s5a4/GYvOpRNgoFVg8PBRVzLTgpL6TckKGWV7f0v18Mh6v/hSFQp3AgDA/LB/VAg62nFKD6HEYXex89tlnuH37Nry8vJCXl4dOnTqhXr16cHV1xUcffWSOGImsxi1VPraeTQaAx7r89FKnujj+djd9i4u5+VV1wpfPNodCAWw4EY+NJ+Ieun9kbDrm/1E0+uqtXg3R1ozLOeiLnUrYsrP68E3M2nQGOgE82zoAS+6bn4mIjGf0TzI3NzccOnQIe/fuRVRUFHQ6HVq0aMEZi4lQNAdKgVagZWC1UlceL4+K7nj6dLAnZvZogE93XsZ7v51HY1/3UmNPzcrH1HVFLQ19mvpiwtPmXXCyeNmIy7eykKfRmmSmZ2tw/7xKE54Kwjt9GrEzMtETMvqnwpo1a6BWq9G1a1fMnDkTs2bNwjPPPAONRoM1a9aYI0Yiq5BfoMX6Y0UtI+bqVGwuUzrVxTONvKAp1GHyj5G4m6MxeLxAq8Mr60/hlkqNel4u+LgCFpz0dXeCt5sDtDqBs4mZZn0vSyCEwCc7LukLnde6BbPQITIRo4udcePGITOz5AdPVlYWxo0bZ5KgiKzR76eTkJajgZ+7I3o28ZY6HKMolQosHh6GwOrOSMzIw2sbow2GfH+8/RKOx6TDxcEW344Oh4uZ+uk8yBrXyToVdxcHr9426jmFWh3e++0clu+/DgB4u3fDMleFJyLjGV3sCCFKPQETEhLg7v54zfZE1k4IgZX/3AQAvNC+tlWOmHF3ssO3o8PhaKfEgSu38cWeqwCAP04n4ftDRZ2uPxvWDPW8TDNLcnmUdwV0daH2seYKMqVUVT5e23AKg5YfxvM/HMebm86Ua4RbZm4Bxq48gR+PxkGhAD4cGIJJHTkzMpEplfvnWfPmzaFQKKBQKNCtWzfY2v77VK1Wi5iYGPTq1cssQRJZumMx6biYrIKjnRIjn3AhTCk18nXDwsFN8frG0/hyz1W4Otji83uTI07uVBe9QnwrNJ7yLBtxOSULI787gvrervhpYtsKn4OmUKvD2qOxWLLrCrLUhSj+LbjxZDzOJWXim1HhqFXdudTnXr+djQmrTyLmTg6c7W2wZHgYeoVwLTAiUyt3sTNw4EAAQHR0NHr27AkXl39/3dnb26N27docek6VVvFw88Et/FHV2V7iaJ7MoOb+OBWXgTVHYvHRtqKV1tvXrY6ZEixP0My/KhQKIDEjD6lZ+fByNRzKn5lXgJfWnsTd3AIci0nHH2eSMCDs0ZM4mkpU3F28u/kcLiQXzT0W6u+ODwaGQJVXiGkbTuF8kgp9vzqIz0eEoVsjw0ubB67cxsvro5CVX4iaVZ3wfy+0RGM/LkRMZA7lLnbmzJkDAKhduzZGjBhhsFQEUWUWn56L3RduAQDGta8tbTAm8m6fxjibmIlTcRnwdXfEV882l+TSnIuDLYK9XHDlVjZOx2eie+N/P3d0OoHXN0bjZloubJUKFOoEPt15Gb1CfMw+H83dHA0+2XkJPx2PBwC4OdpiVq+GeLZ1Ldjca1n689Wn8PL6KJyKy8CLq0/i5S51MaN7AygVwKrDN/HBnxegE0DLwGr49vlw1HBxMGvMRJWZ0b0Mx4wZY444iKzWmiM3oRPA08E1EOztKnU4JmFvq8T/vdAS647GoV+oL6pL+EUcFlAVV25lIzr+Lro3/rd15Is9V7H3UiocbJX4cUIbvLwuCgl387D2SCwmPG26RUnvpyksumT15Z6ryMwrmlh1aLg/3opoWKJY8avqhI2T2mHBtotYdfgmvt53HdHxGfCv6oyNJ4uKpGHh/vhwUAgnCyQyM6OLHa1Wi88//xw///wz4uLioNEYDlFNT083WXBEli5HXYgNJ4q+uMZ1qC1tMCZWw8UBrz0TLHUYCAuohp9PJhj02/nrwi19B+qPBjVFq9oemNG9Pt769SyW7buGYS0D4O5kZ7IYhBDYfi4FH++4hNi0XABAQx9XfDAwBK1qe5T5PHtbJeb2b4Lmtarirf+dxT/X0gCkQakA3u7dCC8+FcQRV0QVwOh26Xnz5mHJkiUYPnw4MjMzMWPGDAwePBhKpRJz5841Q4hEluvXqARk5RciqEYVdK7vJXU4sqRfAT0+EzqdQMydHLy+MRoA8EK7QAy9t6zG0HB/1PNyQUZuAb65N4TbFKLi7mLot0cwdV0UYtNy4enqgEWDm2LrtKcfWujcb0BYTfz2SgcEe7nA3ckOP4xphQlP12GhQ1RBjG7ZWbduHf7v//4Pffr0wbx58/Dss8+ibt26aNasGY4ePYpp06aZI04ii6PTCaw8fBMAMKZdIFeiNpP63i5wsrNBlroQZxMzMfOX08hSF6JlYDW826exfj9bGyXe6tUQE9acxIp/YvBCu0D4VXV67PeNT8/Foh2XsPVM0fIfTnY2mNSxDiZ1rPNY64HV93bFzukdUaDT8bIVUQUzumUnJSUFTZs2BQC4uLjoJxjs27cvtm7datroiCzY31dv48btHLg62GJoS+sdbm7pbG2U+hXQJ609iaup2fBydcDyUS1gb2v4EdatkRdaB3lAU6jDkt1XHuv9hBBYdywW3T//G1vPJEOhAIa39Me+mZ3xevf6T7TwqVKpYKFDJAGjix1/f38kJxf90qlXrx527doFADhx4gQcHDiagCoHnU7oL5UMaxlQYTMKV1bFkwveUqlhZ6PAN6NbwKuUFeUVCgXe7t0IAPC/qARcvDckvLzSczSYtDYS72w+h/wCHdrW8cC2aU/jk6Gh8HHnCFQia2V0sTNo0CDs2bMHAPDaa6/hvffeQ3BwMF544QWMHz/e5AESWaIV/8TgeEw6HO2UsuuYbImK++0AwPv9miA8sOy+MmEBVdGnmS+EABZtv1Tu9zh49TZ6Lj2A3Rduwd5GiXf7NML6CW3RyJdz3xBZO6N/ji5atEj//0OHDoW/vz8OHz6MevXqoX///iYNjsgSXUhS4ZMdRYs1vtunMQI8Sp8dl0ynU31PdKhXHc0DqmF0m1qP3P8/PRpg57kU/H3lNv65dgcd6tUoc191oRaf7risXxKjnpcLvhgZhiZ+XP6GSC4UQghpF5SxACqVCu7u7sjMzISbG3/FUdnyC7To99UhXE3NxjONvPF/L4RzRI2Fmvv7eaw6fBMhNd3w+8tPlehArinU4XxSJmb/ehaXUrIAAM+3DcTbvRvByZ79aoisQXm/v8vVsvP777+X+43ZukNytnDbRVxNzYanqwM+HtKUhY4Fe7VrPWyKTMC5RBWW7rkKdyc73LyTg5tpRbfEu3koXju0ehV7fDK0WYklHYhIHspV7BSvi/UoCoUCWu2jV/klskb7LqVi9ZFYAMBnw0IlnVWYHq26iwOmdK6LT3dexpf3JiB8kLO9DTrV98S8AU1KrLtFRPJRrmJHp9OZOw4ii3Y7S43/bDoNABjfIQid6ntKHBGVx/gOQTgek45bqnwE1aiC2jWqIKh60X9rV3eGp6sDW+eIKgGOlyV6BCEEZm06jTvZGjT0ccWsXg2kDonKycneBqvHt5Y6DCKSmNHFzvz58x/6+Pvvv//YwRBZojVHYrHv8m3Y2yrxxcjmcLRj51UiImtidLGzefNmg/sFBQWIiYmBra0t6taty2KHZOXKrSx8tO0iAODtiIZo4COPVc2JiCoToycVPHXqlMHt3LlzSE5ORrdu3fD666+bPMDatWtDoVCUuL388ssAgLFjx5Z4rG3btiaPgyqnN/93BppCHTrV98SY9rWlDoeIiB6DSfrsuLm5Yf78+ejbty+ef/55U7yk3okTJwxGeJ07dw7du3fHsGHD9Nt69eqFlStX6u/b29ubNAaqnM4lZuJUXAbsbBT4dGgzdmQlIrJSJuugnJGRoV8U1JQ8PQ1HvSxatAh169ZFp06d9NscHBzg4+Nj8vemym1TZAIAoEdjn1LXYSIiIutgdLHz5ZdfGtwXQiA5ORlr165Fr169TBZYaTQaDX788UfMmDHD4Ff2/v374eXlhapVq6JTp0746KOP4OXlVebrqNVqqNVq/X2VyrjFAkn+8gu02HwqEQAwvBVXNCcismZGFzuff/65wX2lUglPT0+MGTMGs2fPNllgpdmyZQsyMjIwduxY/baIiAgMGzYMgYGBiImJwXvvvYeuXbsiMjKyzFXYFy5ciHnz5pk1VrJuuy/cQmZeAfzcHfHUQ9ZVIiIiy2dVa2P17NkT9vb2+OOPP8rcJzk5GYGBgdiwYQMGDx5c6j6ltewEBARwbSzSe/6HYzh49Q6mda2HGT04rw4RkSUy6dpYliA2NhZ//fUXfv3114fu5+vri8DAQFy9Wvr08EBRH5+yWn2IEu7m4tC1OwCAoeG8hEVEZO2MLnby8/Px1VdfYd++fUhNTS2xlERUVJTJgrvfypUr4eXlhT59+jx0v7S0NMTHx8PX19cscZD8bYpMgBBA+7rVUau6s9ThEBHREzK62Bk/fjx2796NoUOHonXr1hUyHFen02HlypUYM2YMbG3/DTk7Oxtz587FkCFD4Ovri5s3b+Ltt99GjRo1MGjQILPHRfKj0wn8crJoFNbwlmzVISKSA6OLna1bt2Lbtm3o0KGDOeIp1V9//YW4uDiMHz/eYLuNjQ3Onj2LNWvWICMjA76+vujSpQs2btwIV1fOdEvGO3w9DYkZeXB1tEWvEE5nQEQkB0YXOzVr1qzwQqJHjx4orR+1k5MTdu7cWaGxkLz9fDIeADAgzI9rYBERyYTRy0UsXrwYb775JmJjY80RD5FkMnMLsON8CgBgRMtaEkdDRESmYnTLTsuWLZGfn486derA2dkZdnZ2Bo+np6ebLDiiivTb6URoCnVo6OOKkJqcgoCISC6MLnaeffZZJCYmYsGCBfD29uZ6QSQbG08UXcIa0SqAxzURkYwYXewcPnwYR44cQWhoqDniIZLEucRMnE9Swd5GiYFhNaUOh4iITMjoPjsNGzZEXl6eOWIhkswv9zomd2/ijWpV7CWOhoiITMnoYmfRokV44403sH//fqSlpUGlUhnciKxNfoEWW6KTAHBuHSIiOTL6MlbxyubdunUz2C6EgEKhgFarNU1kRBVkFxf9JCKSNaOLnX379pkjDiLJ/HyvY/LQcH/YKNkxmYhIbowudjp16mSOOIgkcS4xE4eu3YFSAQzjJSwiIlkyutg5cODAQx/v2LHjYwdDVNG+2nsVANA/1A8BHlz0k4hIjowudjp37lxi2/1zkrDPDlmLSykq7Dx/CwoF8ErXelKHQ0REZmL0aKy7d+8a3FJTU7Fjxw60atUKu3btMkeMRGaxbO81AEDvEF/U8+LCsUREcmV0y467u3uJbd27d4eDgwNef/11REZGmiQwInO6lpqFrWeTAbBVh4hI7oxu2SmLp6cnLl++bKqXIzKrr/ddhxBA98beaOTLdbCIiOTM6JadM2fOGNwXQiA5ORmLFi3iEhJkFW7eycFv0YkAgGldgyWOhoiIzM3oYicsLAwKhQJCCIPtbdu2xYoVK0wWGJG5LN9/DToBdG7giab+JS/LEhGRvBhd7MTExBjcVyqV8PT0hKOjo8mCIjKX+PRc/BpV1KrzKlt1iIgqBaOLncDAQHPEQVQhvvn7Ogp1Ak/Vq4HwwGpSh0NERBWg3B2U9+7di8aNG5e62GdmZiaaNGmCgwcPmjQ4IlNKzszDppMJAIBXOQKLiKjSKHexs3TpUkycOBFubiVHrri7u+Oll17CkiVLTBockSn99+8b0Gh1aB3kgTZ1qksdDhERVZByFzunT5/Wr3hemh49enCOHbJYqVn5+Ol4HACOwCIiqmzKXezcunULdnZ2ZT5ua2uL27dvmyQoIlMSQuDrvdegLtShea2q6FCPrTpERJVJuTso16xZE2fPnkW9eqX3dThz5gx8fX1NFhjRk9LpBHZduIVl+67iXGJRX7Np3YIN1nIjIiL5K3ex07t3b7z//vuIiIgoMcw8Ly8Pc+bMQd++fU0eIJGxtDqBrWeT8fXea7h8KwsA4Gxvg8md6qJzfU+JoyMiooqmEA/ODliGW7duoUWLFrCxscErr7yCBg0aQKFQ4OLFi/j666+h1WoRFRUFb29vc8dsciqVCu7u7sjMzCy1AzZZhwKtDr9FJ2H5vmu4cScHAODqYIsx7Wtj/FNB8KhiL3GERERkSuX9/i53y463tzcOHz6MKVOmYPbs2foZlBUKBXr27Inly5dbZaFD8pBfoMXw/x7BmYRMAEBVZzuM7xCEMe1rw92p7L5mREQkf0ZNKhgYGIht27bh7t27uHbtGoQQCA4ORrVqnJyNpLV412WcSciEm6Mtpnaph9FtA+HiYPScmUREJEOP9W1QrVo1tGrVytSxED2WEzfT8f2homVMlo4MQ9eGbGEkIqJ/lXvouRTmzp0LhUJhcPPx8dE/LoTA3Llz4efnBycnJ3Tu3Bnnz5+XMGKqaLmaQvznl9MQAhgW7s9Ch4iISrDoYgcAmjRpguTkZP3t7Nmz+sc++eQTLFmyBMuWLcOJEyfg4+OD7t27IysrS8KIqSJ9suMybqblwtfdEe/1ayx1OEREZIEsvtixtbWFj4+P/ubpWTR0WAiBpUuX4p133sHgwYMREhKC1atXIzc3F+vXr5c4aqoIh6/fwarDNwEAHw9pBjdHdkQmIqKSLL7YuXr1Kvz8/BAUFISRI0fixo0bAICYmBikpKSgR48e+n0dHBzQqVMnHD58WKpwqYJkqwsxa9MZAMCzrWuhI+fPISKiMlj0cJU2bdpgzZo1qF+/Pm7duoUPP/wQ7du3x/nz55GSkgIAJYa7e3t7IzY29qGvq1aroVar9fdLW8mdLNuCbReRcDcPNas64Z0+jaQOh4iILJhFFzsRERH6/2/atCnatWuHunXrYvXq1Wjbti0AlJj6XwjxyOUAFi5ciHnz5pk+YKoQB67cxvpjRYt6fjqsGYeYExHRQ1n8Zaz7ValSBU2bNsXVq1f1o7KKW3iKpaamPnJyw9mzZyMzM1N/i4+PN1vMZFqq/AK8+b+iy1dj2gWifd0aEkdERESWzqqKHbVajYsXL8LX1xdBQUHw8fHB7t279Y9rNBr8/fffaN++/UNfx8HBAW5ubgY3sg4f/nkByZn5CKzujDcjGkodDhERWQGLbv+fOXMm+vXrh1q1aiE1NRUffvghVCoVxowZA4VCgenTp2PBggUIDg5GcHAwFixYAGdnZzz33HNSh05mcClFhZ9PJkChAD4dGgpne4s+fImIyEJY9LdFQkICnn32Wdy5cweenp5o27Ytjh49isDAQADArFmzkJeXh6lTp+Lu3bto06YNdu3aBVdXV4kjJ3P47kDRSLzeIb5oHeQhcTRERGQtyr3quZxx1XPLl5SRh46f7EOhTuC3lzsgNKCq1CEREZHEyvv9bVV9dqjyWvlPDAp1Am3reLDQISIio7DYIYuXmVegH2r+Uqe6EkdDRETWhsUOWbx1x2KRo9GigbcrOnOmZCIiMhKLHbJo6kItVv5zEwAwqWOdR04YSURE9CAWO2TRtpxKxO0sNXzcHNEv1E/qcIiIyAqx2CGLpdMJ/XDzF58Kgr0tD1ciIjIevz3IYu25lIrrt3Pg6mCLka0DpA6HiIisFIsdsljfHbgOABjVNhCujnYSR0NERNaKxQ5ZpMjYuzhx8y7sbZQY16G21OEQEZEVY7FDFqm4VWdgcz94uzlKHA0REVkzFjtkcW7czsauC7cAFA03JyIiehIsdsji/N/BGAgBPNPIC/W8uKgrERE9GRY7ZFGSMvLwv6gEAFwagoiITIPFDlmUz3ZehqZQh9ZBHmgZWE3qcIiISAZY7JDFOJeYiV9PJQIA3u3TiEtDEBGRSbDYIYsghMCHWy8AAAaG+aGZf1VpAyIiItlgsUMWYc/FVBy9kQ57WyVm9mwgdThERCQjLHZIcgVaHRZsvwigaA0s/2rOEkdERERywmKHJLfheBxu3M6BRxV7TOnMEVhERGRaLHZIUqr8Anz+11UAwOvPBMONa2AREZGJsdghSX2z/zrSczSo41kFI1vXkjocIiKSIRY7JJmEu7n44VAMAODtiEaws+HhSEREpmcrdQAkX8dupKFQJxAeWA2OdjYlHi+eQLBtHQ90a+QlQYRERFQZsNghs9h3ORXjVp4AANjbKNEisCra162B9nWrIzSgKi4kqbAlOgkA8G6fxpxAkIiIzIbFDplcjroQ724+BwCoYm+DHI0WR2+k4+iNdCzZDTjb28DZvujQG9y8JkJquksZLhERyRyLHTK5z3ZdRmJGHvyrOWHX6x2RkpmPw9fTcOR6Go7cSEN6jga5Gi0cOIEgERFVABY7ZFKn4u5i1eGbAIAFg5rC2d4WdTxdUMfTBaPbBkKnE7h8Kwsnbqajka8b/Ko6SRswERHJHosdMpkCrQ6zfz0LIYouT3Ws71liH6VSgUa+bmjk6yZBhEREVBlxrC+ZzHcHbuBSShY8qtjj3b6NpQ6HiIgIgIUXOwsXLkSrVq3g6uoKLy8vDBw4EJcvXzbYZ+zYsVAoFAa3tm3bShRx5XXjdja+2FM0E/L7fRvDo4q9xBEREREVsehi5++//8bLL7+Mo0ePYvfu3SgsLESPHj2Qk5NjsF+vXr2QnJysv23btk2iiCsnnU5g9q9noSnUoWN9TwwI85M6JCIiIj2L7rOzY8cOg/srV66El5cXIiMj0bFjR/12BwcH+Pj4VHR4dM/Gk/E4FpMOJzsbfDQwhHPmEBGRRbHolp0HZWZmAgA8PDwMtu/fvx9eXl6oX78+Jk6ciNTU1Ie+jlqthkqlMrjR40lV5WPBtosAgDd61EeAh7PEERERERmymmJHCIEZM2bgqaeeQkhIiH57REQE1q1bh71792Lx4sU4ceIEunbtCrVaXeZrLVy4EO7u7vpbQEBARaQgS3N+P4+s/EKE+rtjXIcgqcMhIiIqQSGEEFIHUR4vv/wytm7dikOHDsHf37/M/ZKTkxEYGIgNGzZg8ODBpe6jVqsNiiGVSoWAgABkZmbCzY1DosvrwJXbeGHFcdgoFfjjlafQ2I//dkREVHFUKhXc3d0f+f1t0X12ir366qv4/fffceDAgYcWOgDg6+uLwMBAXL16tcx9HBwc4ODgYOowKxWdTmDR9ksAgDHtarPQISIii2XRxY4QAq+++io2b96M/fv3Iyjo0ZdJ0tLSEB8fD19f3wqIsPL640wSLiSr4Opgi1e61pM6HCIiojJZdJ+dl19+GT/++CPWr18PV1dXpKSkICUlBXl5eQCA7OxszJw5E0eOHMHNmzexf/9+9OvXDzVq1MCgQYMkjl6+1IVafLqzaL6jyZ3rck4dIiKyaBbdsvPNN98AADp37mywfeXKlRg7dixsbGxw9uxZrFmzBhkZGfD19UWXLl2wceNGuLq6ShBx5bD+WBwS7ubBy9UB4zrUljocIiKih7LoYudRfaednJywc+fOCoqGACArvwBf7b0GAHjtmWA421v0IURERGTZl7HI8vzfwRik52hQp0YVDG/JIftERGT5WOxQuaVm5eP7gzcAAP/p2QB2Njx8iIjI8vHbisrtqz3XkKvRIjSgKnqFcHkOIiKyDix2qFxu3snBT8fjAACzIxpy/SsiIrIaLHaoXD7bdRmFOoHODTzRtk51qcMhIiIqNxY79EhnEjLw55lkKBTArJ4NpQ6HiIjIKCx26KGE+HdZiEFhNbksBBERWR1OkkJlKtTqsHD7JRy+ngZ7GyVe715f6pCIiIiMxmKHSpWWrcYr60/hyI00AEVDzQM8nCWOioiIyHgsdqiEswmZmPxjJBIz8lDF3gaLh4eiVwgXViUiIuvEYocMbIpMwNubz0JTqENQjSr47vlwBHtznTEiIrJeLHYIAFCg1eHDPy9g9ZFYAEC3hl74fGQY3BztJI6MiIjoybDYIdzOUuPldVE4fjMdAPBat2C81i0YSiUnDiQiIuvHYqeSO5OQgZfWRiI5Mx+uDrZYMiIM3Rt7Sx0WERGRybDYqcT+F5mA2ff659TxrIL/e6El6nq6SB0WERGRSbHYqYQKtTos2HYJK/6JAQA808gLS0awfw4REckTi51KJj1Hg1fWR+Hw9aL5c6Z1C8Z09s8hIiIZY7FTiZxPysSkNffPnxOGXiE+UodFRERkVix2KoGLySr8eDQWmyIToC7UoXZ1Z3z3QkvU5/w5RERUCbDYkSl1oRbbz6Zg7dFYRMbe1W/vVN8TX45sDndn9s8hIqLKgcWOzMSn52LdsTj8fDIe6TkaAICtUoGeTXwwum0g2tbxgELB/jlERFR5sNiRgczcAmw7l4wtpxJx/GY6hCja7uvuiGdb18LIVgHwcnOUNkgiIiKJsNixUvkFWuy9lIotpxKx//JtaLQ6/WNPB9fA6LaB6NbQC7Y2SgmjJCIikh6LHSuTnqPBJzsuYeuZZGSpC/XbG/q4YkBYTfQP80PNqk4SRkhERGRZWOxYEa1OYOq6SBy9UbSGlZ+7I/qH1cTA5n5o6OMmcXRERESWicWOFVn61xUcvZEOZ3sb/Pf5cHSoW4OTARIRET0Cix0rsf9yKr7aew0AsHBwUzwd7ClxRERERNaBvVetQHJmHl7fGA0AGNWmFgaE1ZQ2ICIiIivCYsfCFWh1eHX9KdzNLUATPze817ex1CERERFZFdkUO8uXL0dQUBAcHR0RHh6OgwcPSh2SSXy28zJOxt6Fq4Mtlo9qAUc7G6lDIiIisiqyKHY2btyI6dOn45133sGpU6fw9NNPIyIiAnFxcVKH9kR2X7iF/x64AQD4dFgzBFavInFERERE1kcWxc6SJUvw4osvYsKECWjUqBGWLl2KgIAAfPPNN1KH9tji03Pxxs/RAIBxHWqjV4ivtAERERFZKasfjaXRaBAZGYm33nrLYHuPHj1w+PDhUp+jVquhVqv191UqlVljLI1WJ3AtNRv5BVpohYBOJ6DViXv/D3y68xJU+YUIDaiK2RGNKjw+IiIiubD6YufOnTvQarXw9vY22O7t7Y2UlJRSn7Nw4ULMmzevIsIr4UKSCluiE/FbdCJuqdQP3dfdyQ5fP9cc9rayaIAjIiKShNUXO8UeXMlbCFHm6t6zZ8/GjBkz9PdVKhUCAgLMFltyZh5+i07CllOJuJSSpd9exd4GVZ3toVQCNgoFlEoFbBQK2CgVcHW0xcweDeBfzdlscREREVUGVl/s1KhRAzY2NiVacVJTU0u09hRzcHCAg4OD2WP7LToRG47H42hMmn4lcnsbJbo18sLA5jXRuYEnHGw5uoqIiMicrL7Ysbe3R3h4OHbv3o1Bgwbpt+/evRsDBgyQMDLg0NU7OHIjDQDQOsgDg5vXRESIL9yd7SSNi4iIqDKx+mIHAGbMmIHnn38eLVu2RLt27fDdd98hLi4OkydPljSuZ9vUQu0aVTAgzI+Xo4iIiCQii2JnxIgRSEtLw/z585GcnIyQkBBs27YNgYGBksbVolY1tKhVTdIYiIiIKjuFEMW9SSovlUoFd3d3ZGZmws3NTepwiIiIqBzK+/3NMc1EREQkayx2iIiISNZY7BAREZGssdghIiIiWWOxQ0RERLLGYoeIiIhkjcUOERERyRqLHSIiIpI1FjtEREQkayx2iIiISNZksTbWkypeMUOlUkkcCREREZVX8ff2o1a+YrEDICsrCwAQEBAgcSRERERkrKysLLi7u5f5OBcCBaDT6ZCUlARXV1coFAqpw3kklUqFgIAAxMfHW+3CpczBcsghD+ZgGeSQAyCPPCpLDkIIZGVlwc/PD0pl2T1z2LIDQKlUwt/fX+owjObm5ma1B3Ex5mA55JAHc7AMcsgBkEcelSGHh7XoFGMHZSIiIpI1FjtEREQkayx2rJCDgwPmzJkDBwcHqUN5bMzBcsghD+ZgGeSQAyCPPJiDIXZQJiIiIlljyw4RERHJGosdIiIikjUWO0RERCRrLHaIiIhI1ljsEBERkayx2LEw2dnZUofwxKKiovTrjRE9qdOnT+P69etSh0EyweOpcmKxYyGSk5MxatQoPPvss3jxxRcRFRUldUhGS0pKQo8ePdClSxdER0dLHc5jS0lJwfz587F8+XJs27ZN6nAeS3JyMl555RUsWLAAa9askTqcx3Lr1i30798fzzzzDHbu3Im8vDypQ3osPJ4sg1yOp+TkZEybNg1vvvkmvvzyS6nDeSzJycmYOnUq3nvvPaxatapC3pPFjgX48ccfERISAo1Gg4iICOzduxcff/wxUlJSpA6t3GbNmoXAwEA4Ozvj4sWLePrpp6UO6bF88MEHqFevHo4fP45Vq1Zh0KBBWL9+PYCiBeeswYoVK9CkSRPExsbixo0bmDx5Ml5++WVcu3ZN6tDKLT4+Hn379oVCocDhw4fx/PPPw8nJSeqwjMbjyTLI5XiaO3cugoODERsbi9TUVEyfPh0ffPABAOs5njZv3oyGDRvi5s2buHTpEsaPH4/Jkyfj8uXL5n1jQZIqLCwU3bt3Fx9++KF+28aNG4W3t7fIyMiQMLLy0Wg04pVXXhEKhUJs2LBBv/3WrVsSRmW8wsJCsWjRItG2bVuxdetWIYQQKpVKvPXWW6JWrVoSR1d+2dnZolOnTmLZsmX6bdu3bxeurq5iypQpQqfTSRhd+X377beiZ8+e+vtnz54VsbGxoqCgQMKoyo/Hk2Wx9uOpoKBALFq0SHTq1Els375dv/29994TderUkTAy4w0YMEC89tpr+vtbt24VLVu2FBMmTDDr8cSWHYmdPXsWN27cgJ+fn35bbm4uhgwZgszMTAkjKx87Ozs8/fTT6NixI+7cuYNLly5h0KBBGDJkCDp16oTvvvsOGo1G6jAfycbGBhqNBl27dkWvXr0AAK6urujUqRNsbW2t5hr/gQMHcP78eXTp0gU6nQ46nQ49e/ZE1apV8euvv1r8JQhx79fpmTNn4Ofnh/T0dHTp0gXDhw9Hu3btMGrUKBw4cEDiKB+Nx5NlsebjSQgBW1tbtGvXDnPmzEGPHj30jxUUFGDy5MlWcUlOCIHk5GRcv34dAQEB+u29e/fGyJEjERUVZdZLWix2KlBOTg6uXr0KlUql39a4cWNUr14dP/zwA7777jsMHz4c48ePx6lTp9CsWTNMnz4dqampEkZtSKVS4ejRo0hMTNRvGzx4MEJCQvDhhx/iqaeeQmBgIIYNG4aGDRti2rRpWLZsmcWdjBqNRh+TTqcDAPznP//BRx99BKVSqf/STU9Ph6OjI+rWrStZrGUpLYcmTZrg7t27uHXrFpRKJZRKJU6ePImgoCC0aNECW7dutajO46WdEzqdDnfv3oWLiwveeustBAcH43//+x+WLl0KlUqFd955x+Iu8ZZ2XsycOdOqjqfScrC24+n+c6KYEMLqjqf7zwuFQgEA6NixI7p06QKlUgmVSoWBAwfi448/xoYNGxAaGopNmzYhNzdX4sj/lZWVhQMHDugvdyoUCnh5eSEzM1P/eVVYWAgAGDlyJOrVq4fff/8dGRkZ5gnIbG1GZGD+/PkiKChIhIWFiaCgILFt2zZ9E+rhw4fFF198IXr37i1atGghTpw4ITIzM8XKlStFu3btxJw5c6QN/p4FCxYINzc3ERISItzc3MTSpUtFbGysEEKIAwcOiDFjxojff//d4DnTpk0ToaGh4uzZs1KEXKpFixaJ+vXrix07dpT6uFar1f//K6+8IkaNGiWEKLpkZykelsOkSZOEu7u7mDVrlpg+fbpQKpVi8eLFYt68eaJx48YiMTFRgohLKu2cKCwsFEII8eWXXwqFQiFq1aolTp06pX/O1q1bRbt27cTSpUslirqk0s6LhIQEIUTRsWQNx9ODOXz++ef6HMaPH28Vx1Np50Tx8fTVV19ZzfFU2nlxP41GI3744QfRu3dvcejQIXHmzBkxdepU0bhxY/0lU6nNnz9feHp6ivDwcOHk5CQWL14s7ty5I4QQ4vXXXxd169bV/22KL10tW7ZMhIaGiuPHj5slJhY7Znbz5k3Rv39/0aRJE7F161axZ88eMWbMGOHr6yuSk5MN9u3WrZvBtXEhhOjRo4eYPHmy5B+M27ZtE40aNRKbN28WN27cEB999JFo0qSJGDdunH6f6OhokZ+fL4T4t2BISUkRCoVCHDt2TJK475eWliYmT54smjVrJtzc3MTgwYPF7du3S923+N+7VatWYsmSJQaPSdlPobw5zJo1S/Tp00d06tRJ/+EfGxsrnJycRFxcXEWHbaA850ReXp5o0qSJcHV1FadPn9Y/V6PRiGbNmokvvvhCqvANlHVejB8/3mA/Sz2ehCg9h8aNG4sXX3xRv8/MmTMt9nh62DlR/DmUn59v8cfTw86LlJQUg31zcnJKPL9atWpi/fr1FRVuqa5fvy66d+8umjZtKnbs2CESExPFvHnzhKenp754vnjxonBzcxOLFy8WQvz7N1KpVEKpVIqjR4+aJTYWO2a2YcMG0bFjR3Hx4kWD7W5ubgatIDdv3hSBgYHixIkT+m05OTmibdu2YsGCBRUWb1mmTZsmmjdvbrDtq6++Eg0aNBDffvutEMKwRaT4A/ynn34SXl5eBh8wUrlx44aYNWuW2Lp1qzh48KBQKBTip59+Moj7fnFxccLT01PcvHlTCFHUMfPZZ58VMTExFRi1oUflcH+Hywc/ED/88EMREhIi0tPTy8y5IpT3nNiwYYNQKBRi0aJF+uMpKytLhIaGirVr11ZozGV52Hnx3XffCSH+/ZtY4vEkxMNzWL58uRCiqIXEUo+nR50Txf+19OOpPOdFcdwPFsgnTpwQtWrVMui8LIUDBw6IOXPmiGvXrum3JSYmirp164r4+HghRNH5MH/+fOHi4iJOnjyp3+/EiRMiMDDQbD+MWeyYSfHBmJ6eLn755ReDx1JSUkSDBg3Erl27DPZt3ry56NSpk1i7dq2IiooSffv2FU2aNJG8UNBqtWLKlCli5MiR+pYbIYRISkoSL730kggNDRXZ2dn67cX5XLx4UfTo0UNMnDixwmMuTWFhof6ymxBCDB8+XDRr1qzML5sff/xRdOvWTcTHx4uIiAhha2sr3njjjQqKtnTG5qDT6URBQYE4f/686Nixo/jggw8qKNLSYxGifOdEseeff140atRIDBs2TPzxxx+iT58+Ijw8XP8rUUrlOS+ysrL02y3xeDI2B0s6nooZc05Y4vH0OOfF/c+7fPmy6Nu3rxgyZIjB30oKGo1GX9QU3x80aJB46qmnxPz588WVK1f0xWenTp1EWFiYmDVrlvjnn39E165dRbdu3UpttTIFFjtm9GD1XfxHvnDhgqhevbq4cuWKwfbLly+L8PBw0aBBA1GnTh0xfPhwkZ6eXrFBP6A4h4ULF4qAgIASHyC///67aNmypfi///s/IUTRUNV58+aJsWPHCmdnZzFq1CihUqkqOuyHKs4pLS1N2NnZiYULFxp80Bc//sILLwiFQiHs7OxEnz599NecLcGjchCi6Evgjz/+EJMmTRIODg7iueeeMyhKpVDec6L4en5mZqZYs2aN6Nixo2jdurUYMWKE5OeEEMafFzqdzuKOJ2NzKCgosLjj6X4POyeKW9cs/XgqVtZ5USw3N1csXLhQTJgwQbi4uIhnn31WZGZmVli85XHx4kXh7OwsWrVqJd5//30RFhYm2rZtqz+eUlNTxdtvvy3CwsJE/fr1xeDBg8Xdu3fNFg+LHTMq6wD+9ttvRcuWLQ32KX4sIyNDXLt2TfKm7WLFXzp5eXnCzc3NYD4gIYqus7Zu3VosXLhQv+3rr78WQ4cONbgkJ7UH/xbFH35z584Vnp6eBp0Wi3MeN26caNq0qcFjUjImh+Lj6fjx42LevHkW87cw5py4n1qttogvpWKPc15MmDDBoo6nx8nh5MmTFn08lee8FsLyjqfHOS++++47MXr0aINLQVJ6ML7s7Gxx8OBBfS5qtVpERESIl156SeTm5ur3y8zMFElJSWaPj8XOE0hLSyvREa7Y/X0nHux1PmrUKDFjxgz942fOnBFnzpwxd7ilSkpKEocPHy61uHpwwq3PPvtMuLq6lvigCwsLE1OmTNHfl+L6fXnzKP5b3P/BV7NmTTFp0iSRnp4udu3aJVavXi2EEBXeJGyOHCqasTk87JyQcgRfec9tIR5+XkydOlV/v6KPJ3PkUNGM/Yx92DmxZs2aCoi4dKb8riju1lDRHdvLm0Px/98fX/H+Tz/9tBgwYICZIy0di53H9PbbbwtPT0/x0UcflbmPTqcTs2bNEj/++KPQarVCp9OJjIwM0bBhQ7Fz506RlJQkhg0bJhQKhfjzzz8rMPoir732mqhevbpo1aqVcHZ2Fl9//XWJWZuLcyjuwBceHi66deumH+IYGRkpQkNDS72mXFGMyaP4byHEvx8sv/76q7CxsRFNmzYVCoWixIg4a8zh66+/toocLO2cEMK4c9tSz4vKloOlnhNCyOO74nH/Fvc7duyY6Nixo2TnNYsdI929e1eMHz9etG7dWoSHh4t+/frpfw3dX8muWrVKeHh4iLCwMINWmxMnTggfHx8xefJk4eTkJHr06GHQua4ixMbGin79+ol27dqJf/75R8TExIiZM2eKkJAQgw+2+3OIiooSQhRdQx40aJCwt7cXPXr0EM7OzmLkyJFm61Rm6jwebEFLSEgQs2bNEgqFQowcOdKgcx1zqJgcLOGcEOLxzm1LOy8qaw6Wdk4IIY/viifN4cKFC+LMmTPivffeE9WrVxeTJ0+W5LtCCBY7RsvPzxfz5s0TmzdvFvv37xctWrQQs2fPNpgHJycnR3zwwQfim2++MWhWFeLfya1at24t2S+mrVu3ihEjRpS41uvj4yM2btwohChqcn8wh+KDOzMzU+zatUssW7ZMHDp0qGKDv8/j5lFMrVaL6dOnCw8PD7Fv376KCtsAc5D2nLj/A/txz22pzwvmYDnnhCnzkOq8MGUO33//vWjevLlo1aqV2LNnT4XlUBoWO49Q/Ie//494f9P8G2+8ITp06FBi5soHm/GKX0elUomVK1eaKdrSFb938bXUxMRE8c8//+gf12q1QqPRiPDwcLFu3TqD7ZbEHHk8OFmXuTGHkq8jxTkhRNGIlgdH4T3OuS0l5lC6ij4nhDBdHlKeF6bOQa1WW8SEskKw2HmoxYsXlxihUKz4jxsbGys6dOggJk6cqO+8VVbHMSlmSn0wh7J6/cfGxgoXFxfJ5/QpixzyYA4lSTV78FtvvSVatGghnnnmGfHFF1/oh+3qdLrHOrelwBwsh6nzkCI/ufwtysJipxTHjx8XnTt3FgqFQrRo0UIcPnxYCFF29bp06VIRHh5uUIU/OKS8opU3h2I//fSTaNq0aamPS3kwyyEP5mBIyuNJrVaLoUOHisaNG4sNGzaIF154QTRu3Fj06dPHYL/iuC3x3GYORaTOQQh55CGHHMqDq56XYufOnahRowZWrFih/y8Ag9WL7zd58mR4e3tj+/btOHv2LNatW4cFCxbonyMFY3OIjIxEu3bt9PHu27cPf/zxBwDoV92VghzyYA6WkQMAXL9+HadPn8bSpUsxYsQIrF69Gt999x327t2LTz/9tEQulnhuMwfLyAGQRx5yyKFcpKqyLFFxdRobG6v/5bpw4ULRpk0b8fPPPwshSlauxfe3bNki6tSpI6pXry7s7e3FZ599VoGR/+txcigsLBTNmzcXGzduFDdu3BBdu3YV9vb2+s6lUpBDHszBMnK4X2RkpFAoFCItLU0IYTiLcLVq1QxmqrW0c7sYc7CMHISQRx5yyKE8WOw8wvXr18XAgQPFwIED9TNuPvjhfu3aNf1U8FOmTLGoKdSFeHQOp0+fFq6urvr1ekaMGGFxSzwIIY88mIO0Tp06JZo0aSK++uorIcS/H+wajUYEBQXp16oqHpBgiec2c7CMHISQRx5yyKE8WOw8RPEf/YcffhBt2rQRS5YsKXW///znP8Lf31+yWZAfpjw5bNq0SSgUCtG1a1eLmcr+QXLIgzlUXHxlSU9PFwMHDhQjRozQT1FfPKps8eLFws/Pz6Bwk+LcZg6WkYMQ8shDDjmYQqUrdh6cZfN+D06hfv/icePGjRNdu3bVN+lFRkaWeM2KYqociieHio+PF3v37jVnyKWSQx7MwTJyEKJoSoj7473/vLx/+w8//CBCQ0PF0qVLDZ7//fffiyZNmoibN2/qn1vR5zZzsIwchJBHHnLIwVQqTbGj0WjElClTxIQJE4QQpa/bUbzfqlWrSjy2bds20blzZzFq1CjRtWtXoVAoKnwhOXPkIMXKy3LIgzlYRg7F8U2dOlW0b99edO/eXcyfP18f4/0f6Hl5eeKnn34SQggxZswY0a5dO4OibO7cuaJz584VG/w9zMEychBCHnnIIQdTqxTFztGjR0XHjh2Fp6ensLOz088M+uAv2S+++EJ4eHiIIUOGlChkYmNjRd26dfXTj1f0pFVyyEEIeeTBHIpInYMQQuzatUvUq1dPdOrUSWzevFmMHz9eNGjQQLzzzjsG+xXnUbwI4enTp8WoUaOEvb29mDJlipg0aZJwdXUV33zzjRCiYofHMwfLyEEuecghB3OoFMXO0qVLxYsvvii2bdsmBg8eLNq0aVNin+XLl4ugoCCxbt26En/UPXv2CBcXFxEWFlZiSvyKIocchJBHHszBMnLIzMwUEyZMEC+//LJ++nq1Wi3mzJkjevbsqV+D5/487m+x0ul0YsGCBWLixImid+/eBrNAM4fKlYNc8pBDDuYi62Kn+I8YHx8vzp8/L4QQYseOHcLT01N8//33QoiiA0GIoqa9snqV37lzR6xfv74CIi5JDjkIIY88mMO/pD6ehCjqj7Bq1Sp9J+jiguzNN98UHTt21O9XWh6W8iuVOVhGDkLIIw855GAusit2itfsKOsPd+fOHfHqq6+KgIAAfZP9wzpcSXEAyCEHIeSRB3MoSerjqazYimOfMmWKGDt2rBDC8j7AmYPlkEMecsiholjwdIfG2bp1K/z9/dG3b18cPnwYCoWi1Jldq1evjtGjR8PR0RFvvfXWI1+3Imd7lUMOgDzyYA5lk/p4UiqV0Ol0JfYrnr01KioKTz31VIXG+CjMwXLIIQ855FDhJC21TOTgwYOiV69e4pVXXhERERGiZcuWD90/JydHfPrpp8Ld3V3ExsYKIYTYt2+ffuEzKcghByHkkQdzsIwchDA+jxs3bghPT09x6dIl/bbr168LIUofWl8RmEMRqXMQQh55yCEHKVh1sVPcHHflyhWxZMkScePGDXHy5Enh7Oys739QVvPelStXROfOnUXr1q1FeHi48PDwEDExMRUVup4cchBCHnkwB8vIQYjHz+Obb74RLVq0EEIIERUVJVq3bi08PT1LzBdUEZiDZeQghDzykEMOUrLKYicyMlJkZGQYbCuuUAsKCsQbb7whPD09RX5+fpmvcfbsWdGsWTOhUCjE1KlT9Z0yK4occhBCHnkwhyJS5yDE4+dR/EXw6quviqFDh4rXX39dKJVK8eKLLz40Z3NgDpaRgxDyyEMOOVgCqyp2Nm3aJPz9/UXdunVFrVq1xPvvvy+Sk5OFEEV/2OI/7o0bN0RAQIB+TY8HO2QdPHhQBAYGirZt24pr164xh8cghzyYg2XkIIRp8tBqtSIwMFAoFArRuXNn/Wgz5lC5cpBLHnLIwZJYTbFz4sQJ0bBhQ7F06VJx+vRpsXz5cuHp6SmmTJmiX621uNrV6XRi+fLlwtbWVty4cUMIUTScNisrSwghRGJiojhy5AhzqMR5MAfLyMFUeeTk5Ii8vDyxYMECsXPnTuZQSXOQSx5yyMHSWHyxU1ylfvPNN8Lf39+gw+SyZctE27ZtxQcffFDieWlpaaJ9+/ZiwIABIjIyUvTo0UOsXbtWknU95JCDEPLIgzlYRg5CmC6P7t27i7Vr11ZY3PdjDpaRgxDyyEMOOVgqiy92is2aNUt07dpVPwOkEEJkZ2eLl19+WbRv316cO3dOCGHYu3zlypVCoVAIpVIp+vbtK3Jzcys87vvJIQch5JEHc7CMHIQwTR73P1cKzMEychBCHnnIIQdLY3HFzq5du8Srr74qli5dKo4dO6bf/ttvvwlHR8cSQ+Z27dolOnToIJYsWaLfV61Wi6+//loolUrRqVMn/YHBHIwjhzyYg2XkUByXtefBHCwjh+K4rD0POeRgLSym2ElKShJ9+/YVXl5eYtSoUaJp06bC3d1dfwDk5eWJhg0bikmTJgkhDIfYPf3002Lq1Kn6+ykpKeK1114Tq1evZg6PQQ55MAfLyEEIeeTBHCwjByHkkYcccrA2FlHs5OTkiDFjxogRI0boO1gJIUSrVq30U1wXFhaKNWvWCKVSWWJxslGjRokuXbpUaMwPkkMOQsgjD+ZgGTkIIY88mINl5CCEPPKQQw7WyCKWi3B2doaDgwPGjh2LoKAgFBYWAgD69u2LixcvAgBsbGwwfPhwDBgwABMmTMDff/8NIQRSUlJw9epVjBo1SsoUZJEDII88mINl5ADIIw/mYBk5APLIQw45WCWpqqwHFS9HL8S/PdJHjx4tJk6caLAtLy9PdO7cWXh5eYkePXoIPz8/0bZtWxEXF1fxQT9ADjkIIY88mINl5CCEPPJgDpaRgxDyyEMOOVgbhRClrAxoITp27Ijx48dj7NixEEJAp9PBxsYGt27dwpkzZ3DixAnUrl0bzz33nNShlkkOOQDyyIM5WA455MEcLIcc8pBDDhZNmhrr0a5fvy68vb3FyZMn9dukmL7+ScghByHkkQdzsBxyyIM5WA455CGHHCydRfTZuZ+419B06NAhuLi4IDw8HAAwb948vPbaa0hNTZUyvHKRQw6APPJgDpZDDnkwB8shhzzkkIO1sJU6gAcpFAoAwPHjxzFkyBDs3r0bkyZNQm5uLtauXQsvLy+JI3w0OeQAyCMP5mA55JAHc7AccshDDjlYDamalB4mLy9P1KtXTygUCuHg4CAWLVokdUhGk0MOQsgjD+ZgOeSQB3OwHHLIQw45WAOL7aDcvXt3BAcHY8mSJXB0dJQ6nMcihxwAeeTBHCyHHPJgDpZDDnnIIQdLZ7HFjlarhY2NjdRhPBE55ADIIw/mYDnkkAdzsBxyyEMOOVg6iy12iIiIiEzB4kZjEREREZkSix0iIiKSNRY7REREJGssdoiIiEjWWOwQERGRrLHYISIiIlljsUNERESyxmKHiKzC2LFjoVAooFAoYGdnB29vb3Tv3h0rVqyATqcr9+usWrUKVatWNV+gRGRxWOwQkdXo1asXkpOTcfPmTWzfvh1dunTBa6+9hr59+6KwsFDq8IjIQrHYISKr4eDgAB8fH9SsWRMtWrTA22+/jd9++w3bt2/HqlWrAABLlixB06ZNUaVKFQQEBGDq1KnIzs4GAOzfvx/jxo1DZmamvpVo7ty5AACNRoNZs2ahZs2aqFKlCtq0aYP9+/dLkygRmRSLHSKyal27dkVoaCh+/fVXAIBSqcSXX36Jc+fOYfXq1di7dy9mzZoFAGjfvj2WLl0KNzc3JCcnIzk5GTNnzgQAjBs3Dv/88w82bNiAM2fOYNiwYejVqxeuXr0qWW5EZBpcG4uIrMLYsWORkZGBLVu2lHhs5MiROHPmDC5cuFDisV9++QVTpkzBnTt3ABT12Zk+fToyMjL0+1y/fh3BwcFISEiAn5+ffvszzzyD1q1bY8GCBSbPh4gqjq3UARARPSkhBBQKBQBg3759WLBgAS5cuACVSoXCwkLk5+cjJycHVapUKfX5UVFREEKgfv36BtvVajWqV69u9viJyLxY7BCR1bt48SKCgoIQGxuL3r17Y/Lkyfjggw/g4eGBQ4cO4cUXX0RBQUGZz9fpdLCxsUFkZCRsbGwMHnNxcTF3+ERkZix2iMiq7d27F2fPnsXrr7+OkydPorCwEIsXL4ZSWdQl8eeffzbY397eHlqt1mBb8+bNodVqkZqaiqeffrrCYieiisFih4ishlqtRkpKCrRaLW7duoUdO3Zg4cKF6Nu3L1544QWcPXsWhYWF+Oqrr9CvXz/8888/+Pbbbw1eo3bt2sjOzsaePXsQGhoKZ2dn1K9fH6NGjcILL7yAxYsXo3nz5rhz5w727t2Lpk2bonfv3hJlTESmwNFYRGQ1duzYAV9fX9SuXRu9evXCvn378OWXX+K3336DjY0NwsLCsGTJEnz88ccICQnBunXrsHDhQoPXaN++PSZPnowRI0bA09MTn3zyCQBg5cqVeOGFF/DGG2+gQYMG6N+/P44dO4aAgAApUiUiE+JoLCIiIpI1tuwQERGRrLHYISIiIlljsUNERESyxmKHiIiIZI3FDhEREckaix0iIiKSNRY7REREJGssdoiIiEjWWOwQERGRrLHYISIiIlljsUNERESyxmKHiIiIZO3/AXm76IXofSVPAAAAAElFTkSuQmCC", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import matplotlib.dates as mdates\n", + "\n", + "\n", + "plt.plot(pd.to_datetime(daily_profit[\"date\"]), daily_profit[\"profit\"].cumsum())\n", + "plt.ylabel(\"Cumulated profit\")\n", + "plt.xlabel(\"Date\")\n", + "plt.gca().xaxis.set_major_locator(mdates.WeekdayLocator(byweekday=mdates.MONDAY))\n", + "plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))\n", + "plt.gcf().autofmt_xdate()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/pyproject.toml b/pyproject.toml index c55dda0f..1557a56d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -28,6 +28,7 @@ tqdm = "^4.66.4" lightgbm = "^4.3.0" ml-collections = "^0.1.1" scikit-learn = "^1.5.0" +pandas = "^2.0.0" [tool.poetry.dev-dependencies] pytest = "^8.2.2" diff --git a/src/treeffuser/_base_tabular_diffusion.py b/src/treeffuser/_base_tabular_diffusion.py index bca3327c..cd5fa9c2 100644 --- a/src/treeffuser/_base_tabular_diffusion.py +++ b/src/treeffuser/_base_tabular_diffusion.py @@ -6,6 +6,7 @@ import einops import numpy as np +import pandas as pd from jaxtyping import Float from numpy import ndarray from sklearn.base import BaseEstimator @@ -35,19 +36,20 @@ def _check_array(array: ndarray[float]): array = array.reshape(-1, 1) # Cast floats - try: - if not np.issubdtype(array.dtype, np.floating): + if not np.issubdtype(array.dtype, np.floating): + try: array = np.asarray(array, dtype=np.float32) warnings.warn( "Input array is not float; it has been recast to float32.", CastFloat32Warning, stacklevel=2, ) - except ValueError as e: - # Raise the ValueError preserving the original exception context, see B904 from flake8-bugbear - raise ValueError( - "Input array is not float and cannot be converted to float32." - ) from e # + except ValueError as e: + # raise the ValueError preserving the original exception context, see B904 from flake8-bugbear + raise ValueError( + "Input array is not float and cannot be converted to float32." + "Please check if you have encoded the categorical variables as numerical values." + ) from e return array @@ -57,6 +59,32 @@ def _check_array(array: ndarray[float]): ################################################### +def _ensure_numerical_columns(X: pd.DataFrame): + """ + Convert the categorical columns of a DataFrame to numerical columns. + Raise an error if the columns are not numerical or categorical. + Copy the DataFrame to avoid modifying the original, but only if necessary. + """ + X_has_been_copied = False + for c in X.columns: + if not isinstance(X[c].dtype, pd.CategoricalDtype) and not np.issubdtype( + X[c].dtype, np.number + ): + raise ValueError( + f"Dtypes of columns in `X` must be int, float, bool or category. " + f"Column {c} has dtype {X[c].dtype}." + f"Convert the column to a numerical dtype or a category, e.g.," + f"X['{c}'] = X['{c}'].astype('category')." + ) + if isinstance(X[c].dtype, pd.CategoricalDtype): + if X_has_been_copied is False: + # copy the DataFrame to avoid modifying the original + X = X.copy() + X_has_been_copied = True + X[c] = X[c].cat.codes + return X + + class BaseTabularDiffusion(BaseEstimator, abc.ABC): """ Abstract class for the tabular diffusions. Every particular @@ -72,6 +100,7 @@ def __init__( self.sde_initialize_from_data = sde_initialize_from_data self.score_model = None self._is_fitted = False + self._x_dataframe_columns = None self._x_scaler = None self._x_dim = None self._x_cat_idx = None @@ -90,26 +119,126 @@ def get_new_score_model(self) -> ScoreModel: Return the score model. """ - def _validate_data( + def _preprocess_and_validate_data( self, - X: ndarray | None = None, - y: ndarray | None = None, + X: ndarray | pd.DataFrame | None = None, + y: ndarray | pd.Series | pd.DataFrame | None = None, + cat_idx: list[int] | None = None, validate_X: bool = True, validate_y: bool = True, - ) -> tuple[Float[ndarray, "batch x_dim"] | None, Float[ndarray, "batch y_dim"] | None]: - """Reshape X and/or y to adhere to the (batch, n_dim) convention, and cast them as flows.""" - if validate_X and X is not None: + reset_data_schema: bool = False, + ) -> tuple[ + Float[ndarray, "batch x_dim"] | None, Float[ndarray, "batch y_dim"] | None, list[int] + ]: + """ + Preprocess and validate the input data by methods like `fit`, `predict`, `sample`, ... + (1) It transforms the different possible inputs (pandas, numpy) into numpy arrays. + (2) It checks and convert the shapes to be 2d arrays. (3) It detects categorical columns + in DataFrames. (4) It ensure that the input in `predict` has the same shape/columns as the + input in `fit` (see parameter `reset` below). + + Parameters + ---------- + X : np.ndarray or pd.DataFrame, optional + Input data with shape (batch, x_dim). + y : np.ndarray or pd.Series or pd.DataFrame, optional + Target data with shape (batch, y_dim) or (batch). + cat_idx : List[int], optional + If X is a np.ndarray, list of indices of categorical features in X. + If X is a DataFrame, setting `cat_idx` will raise an error. Instead, ensure that the + categorical columns have dtype `category`, and they will be automatically detected as + categorical features. E.g., X['column_name'] = X['column_name'].astype('category'). + validate_X : bool, optional + If True, validate the input data `X` and raise an error if no `X` is provided. + Default is True. + validate_y : bool, optional + If True, validate the target data `y` and raise an error if no `y` is provided. + Default is True. + reset_data_schema : bool, optional + If True, reset the cached schema of X and y (their shape, the name of the columns, + the categorical indices). + If False, check that X and y have the same shape and columns as the cached properties. + It should be set to True when `_preprocess_and_validate_data` is called from the `fit` + method and False when called from `predict` or `sample`, to ensure that the prediction + data is consistent with the training data. Default is False. + """ + if validate_X: + if X is None: + raise ValueError("Input data `X` cannot be None.") + if isinstance(X, pd.DataFrame): + if cat_idx is not None and cat_idx != []: + raise ValueError( + "Categorical indices `cat_idx` should not be provided when `X` is a DataFrame." + "Instead, ensure that the categorical columns have dtype `category`." + "E.g., X['column_name'] = X['column_name'].astype('category')." + ) + cat_idx = [ + X.columns.get_loc(col) for col in X.select_dtypes("category").columns + ] + # check dtype of columns and convert categorical columns to numerical + X = _ensure_numerical_columns(X) + # at that point, X contains only numerical columns + if reset_data_schema: + # store the columns of the DataFrame + self._x_dataframe_columns = X.columns + self._x_cat_idx = cat_idx + self._x_dim = X.shape[1] + else: + # check coherence of the input data with the training data + # ensure the columns of the DataFrame are the same as the ones used during training + if not all(col in X.columns for col in self._x_dataframe_columns): + raise ValueError( + "Column names in `X` are not present in the DataFrame but were present during training." + f"{set(self._x_dataframe_columns) - set(X.columns)}" + ) + # reorder the columns of the DataFrame to match the order of the training data + X = X[self._x_dataframe_columns] + X = X.values + + elif isinstance(X, np.ndarray): + if cat_idx is not None: + for idx in cat_idx: + if idx < 0 or idx >= X.shape[1]: + raise ValueError( + f"Invalid indices in `cat_idx`: {idx} is not in " + f"[0, {X.shape[1]}-1] (the shape of X)." + ) + else: + cat_idx = [] + + if reset_data_schema: + self._x_cat_idx = cat_idx + else: + raise TypeError( + "Input data `X` must be a numpy array or a pandas DataFrame." + f"Reveived {type(X).__name}." + ) X = _check_array(X) - if validate_y and y is not None: + if validate_y: + if y is None: + raise ValueError("Target data `y` cannot be None.") + if isinstance(y, pd.Series) or isinstance(y, pd.DataFrame): + y = y.values + elif not isinstance(y, np.ndarray): + raise TypeError( + "Target data `y` must be a numpy array, a pandas Series or a pandas DataFrame." + f"Received {type(y).__name}." + ) + + if reset_data_schema: + # store the original number of dimensions of the response + self._y_original_ndim = y.ndim + self._y_dim = y.shape[1] if y.ndim > 1 else 1 + y = _check_array(y) - return X, y + return X, y, self._x_cat_idx def fit( self, - X: Float[ndarray, "batch x_dim"], - y: Float[ndarray, "batch y_dim"], + X: Float[ndarray, "batch x_dim"] | pd.DataFrame, + y: Float[ndarray, "batch y_dim"] | pd.Series | pd.DataFrame, cat_idx: list[int] | None = None, ): """ @@ -117,12 +246,16 @@ def fit( Parameters ---------- - X : np.ndarray + X : np.ndarray or pd.DataFrame Input data with shape (batch, x_dim). - y : np.ndarray + y : np.ndarray or pd.Series or pd.DataFrame Target data with shape (batch, y_dim). cat_idx : List[int], optional - List of indices of categorical features in X. Default is None. + If X is a np.ndarray, list of indices of categorical features in X. + If X is a DataFrame, setting `cat_idx` will raise an error. Instead, ensure that the + categorical columns have dtype `category`, and they will be automatically detected as + categorical features. E.g., X['column_name'] = X['column_name'].astype('category'). + Default is None. Returns ------- @@ -134,13 +267,6 @@ def fit( The method handles 2D inputs (["batch x_dim"], ["batch y_dim"]) by convention, but also works with 1D inputs (["batch"]) for single-dimensional data. """ - if cat_idx is not None: - for idx in cat_idx: - if idx < 0 or idx >= X.shape[1]: - raise ValueError( - f"Invalid indices in `cat_idx`: {idx} is not in " - f"[0, {X.shape[1]}-1] (the shape of X)." - ) self.sde = self.get_new_sde() self.score_model = self.get_new_score_model() self._x_scaler = ScalerMixedTypes() @@ -149,14 +275,11 @@ def fit( # store the original number of dimensions of the response # before reshaping the data so as to ensure that predictions # have the same shape as the user-inputted response - self._y_original_ndim = y.ndim - X, y = self._validate_data(X=X, y=y) - - self._y_dim = y.shape[1] - x_transformed = self._x_scaler.fit_transform( - X, - cat_idx=cat_idx, + X, y, cat_idx = self._preprocess_and_validate_data( + X=X, y=y, cat_idx=cat_idx, reset_data_schema=True ) + + x_transformed = self._x_scaler.fit_transform(X, cat_idx=cat_idx) y_transformed = self._y_scaler.fit_transform(y) if self.sde_initialize_from_data: @@ -168,10 +291,10 @@ def fit( def sample( self, - X: Float[ndarray, "batch x_dim"], + X: Float[ndarray, "batch x_dim"] | pd.DataFrame, n_samples: int, n_parallel: int = 10, - n_steps: int = 100, + n_steps: int = 50, seed=None, verbose: bool = False, ) -> Float[ndarray, "n_samples batch y_dim"]: @@ -180,7 +303,7 @@ def sample( Parameters ---------- - X : np.ndarray + X : np.ndarray or pd.DataFrame Input data with shape (batch, x_dim). n_samples : int Number of samples to draw for each input. @@ -211,10 +334,10 @@ def sample( if not self._is_fitted: raise ValueError("The model has not been fitted yet.") - X, _ = self._validate_data(X=X, validate_y=False) + X, _, _ = self._preprocess_and_validate_data(X=X, validate_y=False) y_samples = self._sample_without_validation( - X, n_samples, n_parallel, n_steps, seed, verbose + X, n_samples, n_parallel=n_parallel, n_steps=n_steps, seed=seed, verbose=verbose ) # Ensure output aligns with original shape provided by user @@ -243,7 +366,7 @@ def _sample_without_validation( y_samples = [] x_batched = None - pbar = tqdm(total=n_samples, disable=~verbose) + pbar = tqdm(total=n_samples, disable=not verbose) while n_samples_sampled < n_samples: batch_size_samples = min(n_parallel, n_samples - n_samples_sampled) y_batch = self.sde.sample_from_theoretical_prior( @@ -286,7 +409,7 @@ def _score_fn(y, t): def predict( self, - X: Float[ndarray, "batch x_dim"], + X: Float[ndarray, "batch x_dim"] | pd.DataFrame, tol: float = 1e-3, max_samples: int = 100, verbose: bool = False, @@ -299,7 +422,7 @@ def predict( Parameters ---------- - X : Float[ndarray, "batch x_dim"] + X : Float[ndarray, "batch x_dim"] or pd.DataFrame Input data with shape (batch, x_dim). tol : float, optional Tolerance for the stopping criterion based on the relative change in the mean estimate. Default is 1e-3. @@ -324,11 +447,10 @@ def predict( The method handles 2D inputs (["batch x_dim"], ["batch y_dim"]) by convention, but also works with 1D inputs (["batch"]) for single-dimensional data. """ - X, _ = self._validate_data(X=X, validate_y=False) - if not self._is_fitted: raise ValueError("The model has not been fitted yet.") + X, _, _ = self._preprocess_and_validate_data(X=X, validate_y=False) y_preds = self._predict_from_sample(X, tol, max_samples, verbose) if self._y_original_ndim == 1: @@ -420,7 +542,7 @@ def predict_distribution( if not self._is_fitted: raise ValueError("The model has not been fitted yet.") - X, _ = self._validate_data(X=X, validate_y=False) + X, _, _ = self._preprocess_and_validate_data(X=X, validate_y=False) y_samples = self._sample_without_validation(X=X, n_samples=n_samples, verbose=verbose) @@ -473,7 +595,7 @@ def compute_nll( if not self._is_fitted: raise ValueError("The model has not been fitted yet.") - X, y = self._validate_data(X=X, y=y) + X, y, _ = self._preprocess_and_validate_data(X=X, y=y) return self._compute_nll_from_sample(X, y, n_samples, bandwidth, verbose) diff --git a/src/treeffuser/samples.py b/src/treeffuser/samples.py index 39d6dbed..216f0c48 100644 --- a/src/treeffuser/samples.py +++ b/src/treeffuser/samples.py @@ -320,6 +320,9 @@ def to_numpy(self) -> Float[np.ndarray, "n_samples batch y_dim"]: """ return self._samples + def __str__(self) -> str: + return str(self._samples) + def __getitem__(self, key): """ Prevent the user from removing the first or second dimension of the samples.

Support for pandas dataframe Support pandas Dataframe as input to Treeffuser. In particular, users won't have to manually specific the categorical columns (with `cat_idx`). Proposed in: #86 Support for pandas dataframe Support pandas Dataframe as input to Treeffuser. In particular, users won't have to manually specific the categorical columns (with `cat_idx`). Proposed in: #86

2024-10-14T19:31:35

0.0

[]

blei-lab/treeffuser

blei-lab__treeffuser-50

44c0bebdf90239a7fc39183053761baa8bce2ca7

diff --git a/src/treeffuser/treeffuser.py b/src/treeffuser/treeffuser.py index dafb751a..4ce04dec 100644 --- a/src/treeffuser/treeffuser.py +++ b/src/treeffuser/treeffuser.py @@ -84,6 +84,8 @@ def fit(self, X: Float[ndarray, "batch x_dim"], y: Float[ndarray, "batch y_dim"] Returns an instance of the model for chaining. """ _check_arguments(X, y) + X = np.asarray(X, dtype=np.float32) + y = np.asarray(y, dtype=np.float32) x_transformed = self._x_preprocessor.fit_transform(X) y_transformed = self._y_preprocessor.fit_transform(y) @@ -135,7 +137,8 @@ def sample( while n_samples_sampled < n_samples: batch_size_samples = min(n_parallel, n_samples - n_samples_sampled) y_batch = self._sde.sample_from_theoretical_prior( - (batch_size_samples * batch_size_x, y_dim) + (batch_size_samples * batch_size_x, y_dim), + seed=seed, ) if x_batched is None or x_batched.shape[0] != batch_size_samples: # Reuse the same batch of x as much as possible

Achille/fix int float bug If the dtype is int, the normalized data would stay as int and be mostly -1, 0 and 1. Cast to float to avoid that!

2024-04-24T02:36:25

0.0

[]

blei-lab/treeffuser

blei-lab__treeffuser-33

6d0dd64a5a1d55b85685db45784b3ead0c781d8f

diff --git a/src/treeffuser/_score_models.py b/src/treeffuser/_score_models.py index f7517e84..ff9ec0a5 100644 --- a/src/treeffuser/_score_models.py +++ b/src/treeffuser/_score_models.py @@ -79,8 +79,8 @@ def _make_training_data( Creates the training data for the score model. This functions assumes that 1. Score is parametrized as score(y, x, t) = GBT(y, x, t) / std(t) 2. The loss that we want to use is - || sigma(t) * score(y_perturbed, x, t) - (mean(y, t) - y_perturbed)/sigma ||^2 - Which corresponds to the standard denoising objective with weights sigma(t)**2 + || std(t) * score(y_perturbed, x, t) - (mean(y, t) - y_perturbed)/std(t) ||^2 + Which corresponds to the standard denoising objective with weights std(t)**2 This ends up meaning that we optimize || GBT(y_perturbed, x, t) - (-z)||^2 where z is the noise added to y_perturbed. diff --git a/src/treeffuser/sde/base_sde.py b/src/treeffuser/sde/base_sde.py index 1d5816c7..e42d7dc3 100644 --- a/src/treeffuser/sde/base_sde.py +++ b/src/treeffuser/sde/base_sde.py @@ -26,7 +26,7 @@ class BaseSDE(abc.ABC): @abc.abstractmethod def drift_and_diffusion( self, y: Float[ndarray, "batch y_dim"], t: Float[ndarray, "batch 1"] - ) -> (Float[ndarray, "batch y_dim"], Float[ndarray, "batch y_dim"]): + ) -> tuple[Float[ndarray, "batch y_dim"], Float[ndarray, "batch y_dim"]]: """ Computes the drift and diffusion at a given time `t` for a given state `Y=y`. @@ -76,7 +76,7 @@ def __init__( def drift_and_diffusion( self, y: Float[ndarray, "batch y_dim"], t: Float[ndarray, "batch 1"] - ) -> (Float[ndarray, "batch y_dim"], Float[ndarray, "batch y_dim"]): + ) -> tuple[Float[ndarray, "batch y_dim"], Float[ndarray, "batch y_dim"]]: drift, diffusion = self.sde.drift_and_diffusion(y, self.t_reverse_origin - t) drift = -drift + diffusion**2 * self.score_fn(y, self.t_reverse_origin - t) return drift, diffusion @@ -114,7 +114,7 @@ def __init__( def drift_and_diffusion( self, y: Float[ndarray, "batch y_dim"], t: Float[ndarray, "batch 1"] - ) -> (Float[ndarray, "batch y_dim"], Float[ndarray, "batch y_dim"]): + ) -> tuple[Float[ndarray, "batch y_dim"], Float[ndarray, "batch y_dim"]]: return self.drift_fn(y, t), self.diffusion_fn(y, t) def __repr__(self): @@ -150,7 +150,7 @@ def _register(cls): return _register -def get_sde(name): +def get_sde(name: str): """ Function to retrieve a registered SDE by its name. diff --git a/src/treeffuser/sde/initialize.py b/src/treeffuser/sde/initialize.py new file mode 100644 index 00000000..1fa7775b --- /dev/null +++ b/src/treeffuser/sde/initialize.py @@ -0,0 +1,177 @@ +import warnings +from typing import Callable +from typing import Optional +from typing import Tuple +from typing import Union + +import numpy as np +from jaxtyping import Float + +from .base_sde import get_sde +from .parameter_schedule import LinearSchedule +from .sdes import VESDE +from .sdes import VPSDE +from .sdes import SubVPSDE + + +class ConvergenceWarning(Warning): + # Indicates hyperparameter initialization didn't converge. + pass + + +def initialize_sde( + name: str, + y0: Float[np.ndarray, "batch y_dim"], + T: float = 1, + KL_tol: Optional[float] = 10 ** (-5), + verbose: bool = False, +) -> Union[VESDE, VPSDE, SubVPSDE]: + """ + Initializes an SDE model based on the given name and initial data. + + For all SDEs, it sets `hyperparam_min` to 0.01. For VESDE, it sets `hyperparam_max` + to the maxiumum pairwise distance in y0, following [1]. For VPSDE and Sub-VPSDE, it + sets `hyperparam_max` to the smallest value that controls the KL divergence between + the perturbation kernel at T and the theorethical prior. + + Parameters + ---------- + name : str + The SDE model to initialize ('vesde', 'vpsde', 'sub-vpsde'). + y0 : np.ndarray + The data array with the training outcome. + T : float, optional + End time of the SDE, default is 1. + KL_tol : float, optional + KL divergence tolerance for initialization, default is 1e-5. This is only used + for VPSDE and Sub-VPSDE. + + Returns + ------- + An instance of the specified SDE model. + + References + ------- + [1] Song, Y. and Ermon, S., 2020. Improved techniques for training score-based + generative models. NeurIPS (2020). + """ + if name.lower() == "vesde": + hyperparam_min, hyperparam_max = _initialize_vesde(y0) + elif name.lower() == "vpsde": + hyperparam_min, hyperparam_max = _initialize_vpsde(y0, T, KL_tol) + elif name.lower() == "sub-vpsde": + hyperparam_min, hyperparam_max = _initialize_subvpsde(y0, T, KL_tol) + else: + raise NotImplementedError + + sde = get_sde(name)(hyperparam_min, hyperparam_max) + if verbose: + print(sde) + return sde + + +def _initialize_vesde(y0: Float[np.ndarray, "batch y_dim"]) -> Tuple[float, float]: + hyperparam_min = 0.01 + if y0.shape[1] == 1: + max_pairwise_difference = y0.max() - y0.min() + else: + y0_aug = y0[:, np.newaxis, :] + max_pairwise_difference = np.max(np.sqrt(np.sum((y0_aug - y0) ** 2, axis=-1))) + return hyperparam_min, max_pairwise_difference + + +def _initialize_vpsde( + y0: Float[np.ndarray, "batch y_dim"], T: float = 1, KL_tol: float = 10 ** (-5) +) -> Tuple[float, float]: + hyperparam_min = 0.01 + y0_max = y0.max() + + def KL_helper(hyperparam_max): + schedule = LinearSchedule(hyperparam_min, hyperparam_max) + hyperparam_integral = schedule.get_integral(T) + KL = _KL_univariate_gaussians( + y0_max * np.exp(-0.5 * hyperparam_integral), + (1 - np.exp(-hyperparam_integral)) ** 0.5, + 0, + 1, + ) + return KL + + hyperparam_max_ini = 100 + hyperparam_max = _bisect(KL_helper, hyperparam_min, hyperparam_max_ini, KL_tol) + + if hyperparam_max == hyperparam_max_ini: + warnings.warn( + f"Hyperparameter initialization did not converge: setting `hyperparam_max` to {hyperparam_max_ini}.", + ConvergenceWarning, + stacklevel=1, + ) + + return hyperparam_min, hyperparam_max + + +def _initialize_subvpsde( + y0: Float[np.ndarray, "batch y_dim"], T: float = 1, KL_tol: float = 10 ** (-5) +) -> Tuple[float, float]: + hyperparam_min = 0.01 + y0_max = y0.max() + + def KL_helper(hyperparam_max): + schedule = LinearSchedule(hyperparam_min, hyperparam_max) + hyperparam_integral = schedule.get_integral(T) + KL = _KL_univariate_gaussians( + y0_max * np.exp(-0.5 * hyperparam_integral), 1 - np.exp(-hyperparam_integral), 0, 1 + ) + return KL + + hyperparam_max_ini = 100 + hyperparam_max = _bisect(KL_helper, hyperparam_min, hyperparam_max_ini, KL_tol) + + if hyperparam_max == hyperparam_max_ini: + warnings.warn( + f"Hyperparameter initialization did not converge: setting `hyperparam_max` to {hyperparam_max_ini}.", + ConvergenceWarning, + stacklevel=1, + ) + + return hyperparam_min, hyperparam_max + + +def _KL_univariate_gaussians( + loc_1: float, scale_1: float, loc_2: float = 0, scale_2: float = 1 +) -> float: + """ + Computes the KL divergence between two univariate Gaussians. + + The KL divergence between two Gaussians `N(loc_1, scale_1)` and `N(loc_2, scale_2)` is: + `log (scale_2 / scale_1) + (scale_1^2 + (loc_1 - loc_2) ^ 2) / (2 * scale_2^2) - .5`. + """ + return ( + np.log(scale_2 / scale_1) + + (scale_1**2 + (loc_1 - loc_2) ** 2) / (2 * scale_2**2) + - 1 / 2 + ) + + +def _bisect( + f: Callable[[float], float], + a: float, + b: float, + tol: float = 1e-5, + x_tol: float = 1e-5, + max_iter: int = 1000, +) -> float: + """ + Return an x such that |f(x)| <= tol and |f(x - x_tol)| > tol. + + It assumes that f is continuous and decreasing in [a, b], and that |f(a)| > tol and |f(b)| <= tol. + """ + for _ in range(max_iter): + x = (a + b) / 2 + if np.abs(f(x)) > tol: + a = x + elif np.abs(f(x - x_tol)) > tol: + return x + else: + b = x + return x diff --git a/src/treeffuser/sde/sdes.py b/src/treeffuser/sde/sdes.py index c4f99bc8..43466460 100644 --- a/src/treeffuser/sde/sdes.py +++ b/src/treeffuser/sde/sdes.py @@ -1,4 +1,5 @@ import abc +from typing import Dict from typing import Optional from typing import Union @@ -15,7 +16,7 @@ class DiffusionSDE(BaseSDE): """ Abstract class representing a diffusion SDE: - `dY = (A(t) + B(t) Y) dt + \\sigma(t) dW` where `\\sigma(t)` is a time-varying + `dY = (A(t) + B(t) Y) dt + C(t) dW` where `C(t)` is a time-varying diffusion coefficient independent of `Y`, and the drift is an affine function of Y. As a result, the conditional distribution p_t(y | y0) is Gaussian. """ @@ -25,6 +26,16 @@ def T(self) -> float: """End time of the SDE.""" return 1.0 + @abc.abstractmethod + def get_hyperparams(self) -> Dict: + """ + Return a dictionary with the hyperparameters of the SDE. + + The hyperparameters parametrize the drift and diffusion coefficients of the + SDE. + """ + raise NotImplementedError + @abc.abstractmethod def sample_from_theoretical_prior( self, shape: tuple[int, ...], seed: Optional[int] = None @@ -46,7 +57,7 @@ def get_mean_std_pt_given_y0( self, y0: Float[ndarray, "batch y_dim"], t: Float[ndarray, "batch 1"], - ) -> (Float[ndarray, "batch y_dim"], Float[ndarray, "batch y_dim"]): + ) -> tuple[Float[ndarray, "batch y_dim"], Float[ndarray, "batch y_dim"]]: """ Our diffusion SDEs have conditional distributions p_t(y | y0) that are Gaussian. This method returns their mean and standard deviation. @@ -107,57 +118,64 @@ class VESDE(DiffusionSDE): """ Variance-exploding SDE (VESDE): `dY = 0 dt + \\sqrt{2 \\sigma(t) \\sigma'(t)} dW` - where `sigma(t)` is a time-varying diffusion coefficient. + where `sigma(t)` is a time-varying diffusion coefficient with exponential schedule: - The SDE converges to a normal distribution with variance `sigma_max**2`. + `\\sigma(t) = hyperparam_min * (hyperparam_max / hyperparam_min) ^ t` + + The SDE converges to a normal distribution with variance `hyperparam_max ^ 2`. Parameters ---------- - sigma_min : float + hyperparam_min : float Minimum value of the diffusion coefficient. - sigma_max : float + hyperparam_max : float Maximum value of the diffusion coefficient. """ - def __init__(self, sigma_min=0.01, sigma_max=20): - self.sigma_min = sigma_min - self.sigma_max = sigma_max - self.sigma_schedule = ExponentialSchedule(sigma_min, sigma_max) + def __init__(self, hyperparam_min=0.01, hyperparam_max=20): + self.hyperparam_min = hyperparam_min + self.hyperparam_max = hyperparam_max + self.hyperparam_schedule = ExponentialSchedule(hyperparam_min, hyperparam_max) + + def get_hyperparams(self): + return {"hyperparam_min": self.hyperparam_min, "hyperparam_max": self.hyperparam_max} def drift_and_diffusion( self, y: Float[ndarray, "batch y_dim"], t: Float[ndarray, "batch 1"] - ) -> (Float[ndarray, "batch y_dim"], Float[ndarray, "batch y_dim"]): - sigma = self.sigma_schedule(t) - sigma_prime = self.sigma_schedule.get_derivative(t) + ) -> tuple[Float[ndarray, "batch y_dim"], Float[ndarray, "batch y_dim"]]: + hyperparam = self.hyperparam_schedule(t) + hyperparam_prime = self.hyperparam_schedule.get_derivative(t) drift = 0 - diffusion = np.sqrt(2 * sigma * sigma_prime) + diffusion = np.sqrt(2 * hyperparam * hyperparam_prime) return drift, diffusion def sample_from_theoretical_prior( self, shape: tuple[int, ...], seed: Optional[int] = None ) -> Float[ndarray, "batch x_dim y_dim"]: - # This assumes that sigma_max is large enough + # This assumes that hyperparam_max is large enough rng = np.random.default_rng(seed) - return rng.normal(0, self.sigma_max, shape) + return rng.normal(0, self.hyperparam_max, shape) def get_mean_std_pt_given_y0( self, y0: Float[ndarray, "batch y_dim"], t: Float[ndarray, "batch 1"], - ) -> (Float[ndarray, "batch y_dim"], Float[ndarray, "batch y_dim"]): + ) -> tuple[Float[ndarray, "batch y_dim"], Float[ndarray, "batch y_dim"]]: """ The conditional distribution is Gaussian with: mean: `y0` - variance: `sigma(t)**2 - sigma(0)**2` + variance: `hyperparam(t)**2 - hyperparam(0)**2` """ mean = y0 - std = (self.sigma_schedule(t) ** 2 - self.sigma_schedule(np.zeros_like(t)) ** 2) ** 0.5 + std = ( + self.hyperparam_schedule(t) ** 2 - self.hyperparam_schedule(np.zeros_like(t)) ** 2 + ) ** 0.5 std = np.broadcast_to(std, y0.shape) return mean, std def __repr__(self): - return f"VESDE(sigma_min={self.sigma_min}, sigma_max={self.sigma_max})" + return f"VESDE(hyperparam_min={self.hyperparam_min}, hyperparam_max={self.hyperparam_max})" @_register_sde(name="vpsde") @@ -165,35 +183,39 @@ class VPSDE(DiffusionSDE): """ Variance-preserving SDE (VPSDE): `dY = -0.5 \\beta(t) Y dt + \\sqrt{\\beta(t)} dW` - where `beta(t)` is a time-varying diffusion coefficient. + where `beta(t)` is a time-varying coefficient with linear schedule: + `\\beta(t) = hyperparam_min + (hyperparam_max - hyperparam_min) * t.` - The SDE converges to a standard normal distribution for large `beta(t)`. + The SDE converges to a standard normal distribution for large `hyperparam_max`. Parameters ---------- - beta_min : float - Minimum value of the diffusion coefficient. - beta_max : float - Maximum value of the diffusion coefficient. + hyperparam_min : float + Minimum value of the time-varying coefficient `\\beta(t)`. + hyperparam_max : float + Maximum value of the time-varying coefficient `\\beta(t)`. """ - def __init__(self, beta_min=0.01, beta_max=20): - self.beta_min = beta_min - self.beta_max = beta_max - self.beta_schedule = LinearSchedule(beta_min, beta_max) + def __init__(self, hyperparam_min=0.01, hyperparam_max=20): + self.hyperparam_min = hyperparam_min + self.hyperparam_max = hyperparam_max + self.hyperparam_schedule = LinearSchedule(hyperparam_min, hyperparam_max) + + def get_hyperparams(self): + return {"hyperparam_min": self.hyperparam_min, "hyperparam_max": self.hyperparam_max} def drift_and_diffusion( self, y: Float[ndarray, "batch y_dim"], t: Float[ndarray, "batch 1"] - ) -> (Float[ndarray, "batch y_dim"], Float[ndarray, "batch y_dim"]): - beta_t = self.beta_schedule(t) - drift = -0.5 * beta_t * y - diffusion = np.sqrt(beta_t) + ) -> tuple[Float[ndarray, "batch y_dim"], Float[ndarray, "batch y_dim"]]: + hyperparam_t = self.hyperparam_schedule(t) + drift = -0.5 * hyperparam_t * y + diffusion = np.sqrt(hyperparam_t) return drift, diffusion def sample_from_theoretical_prior( self, shape: tuple[int, ...], seed: Optional[int] = None ) -> Float[ndarray, "batch x_dim y_dim"]: - # Assume that beta_max is large enough so that the SDE has converged to N(0,1). + # Assume that hyperparam_max is large enough so that the SDE has converged to N(0,1). rng = np.random.default_rng(seed) return rng.normal(0, 1, shape) @@ -201,22 +223,22 @@ def get_mean_std_pt_given_y0( self, y0: Float[ndarray, "batch y_dim"], t: Float[ndarray, "batch 1"], - ) -> (Float[ndarray, "batch y_dim"], Float[ndarray, "batch y_dim"]): + ) -> tuple[Float[ndarray, "batch y_dim"], Float[ndarray, "batch y_dim"]]: """ The conditional distribution is Gaussian with: - mean: `y0 * exp(-0.5 * \\int_0^t1 beta(s) ds)` - variance: `1 - exp(-\\int_0^t1 beta(s) ds)` + mean: `y0 * exp(-0.5 * \\int_0^t1 \\beta(s) ds)` + variance: `1 - exp(-\\int_0^t1 \\beta(s) ds)` """ - beta_integral = self.beta_schedule.get_integral(t) - mean = y0 * np.exp(-0.5 * beta_integral) - std = (1 - np.exp(-beta_integral)) ** 0.5 + hyperparam_integral = self.hyperparam_schedule.get_integral(t) + mean = y0 * np.exp(-0.5 * hyperparam_integral) + std = (1 - np.exp(-hyperparam_integral)) ** 0.5 mean = np.broadcast_to(mean, y0.shape) std = np.broadcast_to(std, y0.shape) return mean, std def __repr__(self): - return f"VPSDE(beta_min={self.beta_min}, beta_max={self.beta_max})" + return f"VPSDE(hyperparam_min={self.hyperparam_min}, hyperparam_max={self.hyperparam_max})" @_register_sde(name="sub-vpsde") @@ -224,37 +246,41 @@ class SubVPSDE(DiffusionSDE): """ Sub-Variance-preserving SDE (SubVPSDE): `dY = -0.5 \\beta(t) Y dt + \\sqrt{\\beta(t) (1 - e^{-2 \\int_0^t \\beta(s) ds})} dW` - where `beta(t)` is a time-varying diffusion coefficient. + where `beta(t)` is a time-varying coefficient with linear schedule: + `\\beta(t) = hyperparam_min + (hyperparam_max - hyperparam_min) * t.` - The SDE converges to a standard normal distribution for large `beta(t)`. + The SDE converges to a standard normal distribution for large `hyperparam_max`. Parameters ---------- - beta_min : float - Minimum value of the diffusion coefficient. - beta_max : float - Maximum value of the diffusion coefficient. + hyperparam_min : float + Minimum value of the time-varying coefficient `\\beta(t)`. + hyperparam_max : float + Maximum value of the time-varying coefficient `\\beta(t)`. """ - def __init__(self, beta_min=0.01, beta_max=20): - self.beta_min = beta_min - self.beta_max = beta_max - self.beta_schedule = LinearSchedule(beta_min, beta_max) + def __init__(self, hyperparam_min=0.01, hyperparam_max=20): + self.hyperparam_min = hyperparam_min + self.hyperparam_max = hyperparam_max + self.hyperparam_schedule = LinearSchedule(hyperparam_min, hyperparam_max) + + def get_hyperparams(self): + return {"hyperparam_min": self.hyperparam_min, "hyperparam_max": self.hyperparam_max} def drift_and_diffusion( self, y: Float[ndarray, "batch y_dim"], t: Float[ndarray, "batch 1"] - ) -> (Float[ndarray, "batch y_dim"], Float[ndarray, "batch y_dim"]): - beta_t = self.beta_schedule(t) - drift = -0.5 * beta_t * y - beta_integral = self.beta_schedule.get_integral(t) - discount = 1.0 - np.exp(-2 * beta_integral) - diffusion = np.sqrt(beta_t * discount) + ) -> tuple[Float[ndarray, "batch y_dim"], Float[ndarray, "batch y_dim"]]: + hyperparam_t = self.hyperparam_schedule(t) + drift = -0.5 * hyperparam_t * y + hyperparam_integral = self.hyperparam_schedule.get_integral(t) + discount = 1.0 - np.exp(-2 * hyperparam_integral) + diffusion = np.sqrt(hyperparam_t * discount) return drift, diffusion def sample_from_theoretical_prior( self, shape: tuple[int, ...], seed: Optional[int] = None ) -> Float[ndarray, "batch x_dim y_dim"]: - # Assume that beta_max is large enough so that the SDE has converged to N(0,1). + # Assume that hyperparam_max is large enough so that the SDE has converged to N(0,1). rng = np.random.default_rng(seed) return rng.normal(0, 1, shape) @@ -262,16 +288,19 @@ def get_mean_std_pt_given_y0( self, y0: Float[ndarray, "batch y_dim"], t: Float[ndarray, "batch 1"], - ) -> (Float[ndarray, "batch y_dim"], Float[ndarray, "batch y_dim"]): + ) -> tuple[Float[ndarray, "batch y_dim"], Float[ndarray, "batch y_dim"]]: """ The conditional distribution is Gaussian with: - mean: `y0 * exp(-0.5 * \\int_0^t1 beta(s) ds)` - variance: `[1 - exp(-\\int_0^t1 beta(s) ds)]^2` + mean: `y0 * exp(-0.5 * \\int_0^t1 \\beta(s) ds)` + variance: `[1 - exp(-\\int_0^t1 \\beta(s) ds)]^2` """ - beta_integral = self.beta_schedule.get_integral(t) - mean = y0 * np.exp(-0.5 * beta_integral) - std = 1 - np.exp(-beta_integral) + hyperparam_integral = self.hyperparam_schedule.get_integral(t) + mean = y0 * np.exp(-0.5 * hyperparam_integral) + std = 1 - np.exp(-hyperparam_integral) mean = np.broadcast_to(mean, y0.shape) std = np.broadcast_to(std, y0.shape) return mean, std + + def __repr__(self): + return f"VPSDE(hyperparam_min={self.hyperparam_min}, hyperparam_max={self.hyperparam_max})" diff --git a/src/treeffuser/treeffuser.py b/src/treeffuser/treeffuser.py index 076e8a1f..2270e396 100644 --- a/src/treeffuser/treeffuser.py +++ b/src/treeffuser/treeffuser.py @@ -8,6 +8,7 @@ import numpy as np from einops import rearrange from jaxtyping import Float +from ml_collections import ConfigDict from ml_collections import FrozenConfigDict from numpy import ndarray from sklearn.base import BaseEstimator @@ -18,6 +19,7 @@ from treeffuser._score_models import Score from treeffuser.sde import get_sde from treeffuser.sde import sdeint +from treeffuser.sde.initialize import initialize_sde def _check_arguments( @@ -39,17 +41,21 @@ class Treeffuser(BaseEstimator, abc.ABC): different parameters and methods. """ - def __init__(self, sde_name: str = "vesde"): + def __init__( + self, + sde_name: str = "vesde", + sde_initialize_with_data: Optional[bool] = False, + sde_manual_hyperparams: Optional[dict] = None, + ): + self._sde_name = sde_name + self._sde_initialize_with_data = sde_initialize_with_data + self._sde_manual_hyperparams = sde_manual_hyperparams self._score_model = None self._is_fitted = False self._x_preprocessor = Preprocessor() self._y_preprocessor = Preprocessor() self._y_dim = None - # TODO: We are using the defaults but we should change this - self._sde = get_sde(sde_name)() - self._sde_name = sde_name - @property @abc.abstractmethod def score_config(self) -> FrozenConfigDict: @@ -74,6 +80,18 @@ def fit(self, X: Float[ndarray, "batch x_dim"], y: Float[ndarray, "batch y_dim"] """ _check_arguments(X, y) + if self._sde_initialize_with_data: + self._sde = initialize_sde(self._sde_name, y) + else: + sde_cls = get_sde(self._sde_name) + if self._sde_manual_hyperparams: + self._sde = sde_cls(**self._sde_manual_hyperparams) + else: + self._sde = sde_cls() + + self._score_config.update({"sde": self._sde}) + self._score_config = FrozenConfigDict(self._score_config) + x_transformed = self._x_preprocessor.fit_transform(X) y_transformed = self._y_preprocessor.fit_transform(y) @@ -152,8 +170,10 @@ def __init__( self, # Diffusion model args sde_name: Optional[str] = "vesde", - # Score estimator args + sde_initialize_with_data: Optional[bool] = False, + sde_manual_hyperparams: Optional[dict] = None, n_repeats: Optional[int] = 10, + # Score estimator args n_estimators: Optional[int] = 100, eval_percent: Optional[float] = None, early_stopping_rounds: Optional[int] = None, @@ -172,8 +192,13 @@ def __init__( """ Diffusion model args ------------------------------- + sde_name (str): The SDE name. + sde_initialize_with_data (bool): Whether to initialize the SDE hyperparameters + with data. + sde_manual_hyperparams: (dict): A dictionary for explicitly setting the SDE + hyperparameters, overriding default or data-based initializations. n_repeats (int): How many times to repeat the training dataset. i.e how - many noisy versions of a point to generate for training. + many noisy versions of a point to generate for training. LightGBM args ------------------------------- eval_percent (float): Percentage of the training data to use for validation. @@ -199,10 +224,18 @@ def __init__( n_jobs (int): Number of parallel threads. If set to -1, the number is set to the number of available cores. """ - super().__init__(sde_name=sde_name) - self._score_config = FrozenConfigDict( + if sde_initialize_with_data and sde_manual_hyperparams is not None: + raise Warning( + "Manual hypeparameter setting will override data-based initialization." + ) + + super().__init__( + sde_name=sde_name, + sde_initialize_with_data=sde_initialize_with_data, + sde_manual_hyperparams=sde_manual_hyperparams, + ) + self._score_config = ConfigDict( { - "sde": self._sde, "n_repeats": n_repeats, "n_estimators": n_estimators, "eval_percent": eval_percent,

Come up with good initialization of SDE hyper-params There are good guidelines for VESDE. There must be for the other ones and we should probably implement them.

2024-03-16T06:09:04

0.0

[]

kinde-oss/kinde-python-sdk

kinde-oss__kinde-python-sdk-17

ff1907c27c565c05304237aef2576f10ab9dbd77

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 4ad5204..a8c3ad5 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -62,4 +62,4 @@ jobs: pip install -r requirements.txt - name: Test with unittest - run: python -m unittest kinde_sdk.test + run: python -m unittest diff --git a/kinde_sdk/kinde_api_client.py b/kinde_sdk/kinde_api_client.py index 5110ef8..079571b 100644 --- a/kinde_sdk/kinde_api_client.py +++ b/kinde_sdk/kinde_api_client.py @@ -120,7 +120,8 @@ def fetch_token(self, authorization_response: Optional[str] = None) -> None: self._clear_decoded_tokens() def refresh_token(self) -> None: - if refresh_token := self.__access_token_obj.get("refresh_token"): + refresh_token = self.__access_token_obj.get("refresh_token") + if refresh_token: self.__access_token_obj = self.client.refresh_token( self.token_endpoint, refresh_token=refresh_token, @@ -165,7 +166,8 @@ def _decode_token_if_needed(self, token_name: str) -> None: "When grant_type is CLIENT_CREDENTIALS use fetch_token().\n" 'For other grant_type use "get_login_url()" or "get_register_url()".' ) - if token := self.__access_token_obj.get(token_name): + token = self.__access_token_obj.get(token_name) + if token: self.__decoded_tokens[token_name] = jwt.decode( token, options={"verify_signature": False} ) diff --git a/requirements.txt b/requirements.txt index d623a84..0412070 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,3 +2,4 @@ urllib3==1.26.14 # https://github.com/urllib3/urllib3 python-dateutil==2.8.2 # https://github.com/dateutil/dateutil Authlib==1.2.0 # https://github.com/lepture/authlib pyjwt==2.6.0 # https://github.com/jpadilla/pyjwt +requests==2.28.2 # https://github.com/psf/requests

[#184459799] fix - scope and audience

2023-02-20T14:20:09

0.0

[]

ArgonneCPAC/diffstar

ArgonneCPAC__diffstar-63

099ae3d313439e803bd5b76225234439b62ff631

diff --git a/diffstar/data_loaders/load_smah_data.py b/diffstar/data_loaders/load_smah_data.py index 554b179..de3b5fa 100644 --- a/diffstar/data_loaders/load_smah_data.py +++ b/diffstar/data_loaders/load_smah_data.py @@ -1,10 +1,12 @@ """ """ + import os import warnings import h5py import numpy as np +from umachine_pyio.load_mock import load_mock_from_binaries from ..defaults import SFR_MIN from ..utils import _get_dt_array @@ -12,7 +14,10 @@ TASSO = "/Users/aphearin/work/DATA/diffmah_data" BEBOP = "/lcrc/project/halotools/diffmah_data" LAPTOP = "/Users/alarcon/Documents/diffmah_data" - +BEBOP_SMDPL = os.path.join( + "/lcrc/project/galsampler/SMDPL/", + "dr1_no_merging_upidh/sfh_binary_catalogs/a_1.000000/", +) H_BPL = 0.678 H_TNG = 0.6774 H_MDPL = H_BPL @@ -38,30 +43,107 @@ def load_fit_mah(filename, data_drn=BEBOP): logmp: ndarray of shape (n_gal, ) Base-10 logarithm of the present day peak halo mass + logmp: ndarray of shape (n_gal, ) + Base-10 logarithm of the present day peak halo mass """ - fitting_data = dict() fn = os.path.join(data_drn, filename) with h5py.File(fn, "r") as hdf: - for key in hdf.keys(): - if key == "halo_id": - fitting_data[key] = hdf[key][...] - else: - fitting_data["fit_" + key] = hdf[key][...] - - mah_fit_params = np.array( - [ - fitting_data["fit_mah_logtc"], - fitting_data["fit_mah_k"], - fitting_data["fit_early_index"], - fitting_data["fit_late_index"], - ] - ).T - logmp = fitting_data["fit_logmp_fit"] + mah_fit_params = np.array( + [ + hdf["logm0"][:], + hdf["logtc"][:], + hdf["early_index"][:], + hdf["late_index"][:], + ] + ).T + logmp = hdf["logm0"][:] return mah_fit_params, logmp +def load_fit_mah_tpeak(filename, data_drn=BEBOP): + """Load the best fit diffmah parameter data + + Parameters + ---------- + filename : string + Name of the h5 file where the diffmah best fit parameters are stored + + data_drn : string + Filepath where the Diffstar best-fit parameters are stored + + Returns + ------- + mah_fit_params: ndarray of shape (n_gal, 4) + Best fit parameters for each halo: + (logtc, k, early_index, late_index) + + logmp: ndarray of shape (n_gal, ) + Base-10 logarithm of the present day peak halo mass + + logmp: ndarray of shape (n_gal, ) + Base-10 logarithm of the present day peak halo mass + """ + + fn = os.path.join(data_drn, filename) + with h5py.File(fn, "r") as hdf: + mah_fit_params = np.array( + [ + hdf["logm0"][:], + hdf["logtc"][:], + hdf["early_index"][:], + hdf["late_index"][:], + ] + ).T + t_peak = hdf["t_peak"][:] + logmp = hdf["logm0"][:] + + return mah_fit_params, logmp, t_peak + + +def load_fit_sfh(filename, data_drn=BEBOP): + """Load the best fit diffmah parameter data + + Parameters + ---------- + filename : string + Name of the h5 file where the diffmah best fit parameters are stored + + data_drn : string + Filepath where the Diffstar best-fit parameters are stored + + Returns + ------- + sfh_fit_params: ndarray of shape (n_gal, 4) + Best fit parameters for each halo: + (logtc, k, early_index, late_index) + + """ + + fn = os.path.join(data_drn, filename) + with h5py.File(fn, "r") as hdf: + ms_fit_params = np.array( + [ + hdf["lgmcrit"][:], + hdf["lgy_at_mcrit"][:], + hdf["indx_lo"][:], + hdf["indx_hi"][:], + hdf["tau_dep"][:], + ] + ).T + q_fit_params = np.array( + [ + hdf["lg_qt"][:], + hdf["qlglgdt"][:], + hdf["lg_drop"][:], + hdf["lg_rejuv"][:], + ] + ).T + + return ms_fit_params, q_fit_params + + def load_bolshoi_data(gal_type, data_drn=BEBOP): """Load the stellar mass histories from UniverseMachine simulation applied to the Bolshoi-Planck (BPL) simulation. @@ -397,3 +479,65 @@ def load_mdpl_small_data(gal_type, data_drn=BEBOP): log_smahs = np.where(sm_cumsum == 0, 0, np.log10(sm_cumsum)) return halo_ids, log_smahs, sfrh, mdpl_t, dt + + +def load_SMDPL_data(subvols, data_drn=BEBOP_SMDPL): + """Load the stellar mass histories from UniverseMachine simulation + applied to the Bolshoi-Planck (BPL) simulation. + + The loaded stellar mass data has units of Msun assuming the h = H_BPL + from the cosmology of the underlying simulation. + + The output stellar mass data has units of Msun/h, or units of + Mstar[h=H_BPL] using the h value of the simulation. + + H_BPL is defined at the top of the module. + + Parameters + ---------- + gal_type : string + Name of the galaxy type of the file being loaded. Options are + 'cens': central galaxies + 'sats': satellite galaxies + 'orphans': orphan galaxies + data_drn : string + Filepath where the Diffstar best-fit parameters are stored. + + Returns + ------- + halo_ids: ndarray of shape (n_gal, ) + IDs of the halos in the file. + log_smahs: ndarray of shape (n_gal, n_times) + Cumulative stellar mass history in units of Msun assuming h=1. + sfrh: ndarray of shape (n_gal, n_times) + Star formation rate history in units of Msun/yr assuming h=1. + bpl_t : ndarray of shape (n_times, ) + Cosmic time of each simulated snapshot in Gyr + dt : ndarray of shape (n_times, ) + Cosmic time steps between each simulated snapshot in Gyr + """ + + galprops = ["halo_id", "sfr_history_main_prog", "mpeak_history_main_prog"] + mock = load_mock_from_binaries(subvols, root_dirname=data_drn, galprops=galprops) + + SMDPL_t = np.loadtxt(os.path.join(data_drn, "smdpl_cosmic_time.txt")) + + halo_ids = mock["halo_id"] + dt = _get_dt_array(SMDPL_t) + sfrh = mock["sfr_history_main_prog"] + sm_cumsum = np.cumsum(sfrh * dt, axis=1) * 1e9 + + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + log_smahs = np.where(sm_cumsum == 0, 0, np.log10(sm_cumsum)) + + lgmh_min = 7.0 + mh_min = 10**lgmh_min + msk = mock["mpeak_history_main_prog"] < mh_min + clipped_mahs = np.where(msk, 1.0, mock["mpeak_history_main_prog"]) + log_mahs = np.log10(clipped_mahs) + log_mahs = np.maximum.accumulate(log_mahs, axis=1) + + logmp = log_mahs[:, -1] + + return halo_ids, log_smahs, sfrh, SMDPL_t, dt, log_mahs, logmp diff --git a/diffstar/fitting_helpers/fit_smah_helpers.py b/diffstar/fitting_helpers/fit_smah_helpers.py index 6a4a888..cd4f369 100644 --- a/diffstar/fitting_helpers/fit_smah_helpers.py +++ b/diffstar/fitting_helpers/fit_smah_helpers.py @@ -1,5 +1,6 @@ """ """ + import os import warnings @@ -180,7 +181,7 @@ def get_weights( t_sim, log_smah_sim, log_fstar_sim, - fstar_indx_high, + fstar_tdelay, dlogm_cut, t_fit_min, mass_fit_min, @@ -195,8 +196,9 @@ def get_weights( Base-10 log of cumulative stellar mass in Msun units. log_fstar_sim : ndarray of shape (nt, ) Base-10 log of SFH averaged over a time period in Msun/yr units. - fstar_indx_high: ndarray of shape (n_times_fstar, ) - Indices from np.searchsorted(t, t - fstar_tdelay)[index_select] + fstar_tdelay: float + Time interval in Gyr for fstar definition. + fstar = (mstar(t) - mstar(t-fstar_tdelay)) / fstar_tdelay[Gyr] dlogm_cut : float, optional Additional quantity used to place a cut on which simulated snapshots are used to define the target halo SFH. @@ -236,9 +238,9 @@ def get_weights( weight_fstar = np.ones_like(t_sim) weight_fstar[~mask] = 1e10 - weight_fstar = weight_fstar[fstar_indx_high] weight_fstar[log_fstar_sim.max() - log_fstar_sim < 0.1] = 0.5 weight_fstar[weight_fstar == -10.0] = 1e10 + weight_fstar[t_sim < fstar_tdelay + 0.01] = 1e10 return weight, weight_fstar @@ -264,8 +266,6 @@ def loss_free(params, loss_data): log_sm_target, sfr_target, fstar_target, - index_select, - fstar_indx_high, fstar_tdelay, ssfrh_floor, weight, @@ -277,15 +277,7 @@ def loss_free(params, loss_data): q_params = params[5:9] _res = calculate_sm_sfr_fstar_history_from_mah( - lgt, - dt, - dmhdt, - log_mah, - sfr_params, - q_params, - index_select, - fstar_indx_high, - fstar_tdelay, + lgt, dt, dmhdt, log_mah, sfr_params, q_params, fstar_tdelay ) mstar, sfr, fstar = _res @@ -390,10 +382,6 @@ def get_loss_data_free( Star formation history in Msun/yr. log_fstar_sim : ndarray of shape (nt, ) Base-10 log of cumulative SFH averaged over a timescale in Msun/yr. - index_select: ndarray of shape (n_times_fstar, ) - Snapshot indices used in fstar computation. - fstar_indx_high: ndarray of shape (n_times_fstar, ) - Indices of np.searchsorted(t, t - fstar_tdelay)[index_select] fstar_tdelay: float Time interval in Gyr for fstar definition. fstar = (mstar(t) - mstar(t-fstar_tdelay)) / fstar_tdelay[Gyr] @@ -409,20 +397,16 @@ def get_loss_data_free( Base-10 log of the cosmic time where SFH target history peaks. """ - fstar_indx_high = np.searchsorted(t_sim, t_sim - fstar_tdelay) - _mask = t_sim > fstar_tdelay + fstar_tdelay / 2.0 - index_select = np.arange(len(t_sim))[_mask] - fstar_indx_high = fstar_indx_high[_mask] smh = 10**log_smah_sim - fstar_sim = compute_fstar(t_sim, smh, index_select, fstar_indx_high, fstar_tdelay) + fstar_sim = compute_fstar(t_sim, smh, fstar_tdelay) with warnings.catch_warnings(): warnings.simplefilter("ignore") - ssfrh = fstar_sim / smh[index_select] + ssfrh = fstar_sim / smh ssfrh = np.clip(ssfrh, ssfrh_floor, np.inf) - fstar_sim = ssfrh * smh[index_select] + fstar_sim = ssfrh * smh log_fstar_sim = np.where( fstar_sim == 0.0, np.log10(fstar_sim.max()) - 3.0, np.log10(fstar_sim) ) @@ -435,13 +419,13 @@ def get_loss_data_free( t_sim, log_smah_sim, log_fstar_sim, - fstar_indx_high, + fstar_tdelay, dlogm_cut, t_fit_min, mass_fit_min, ) - t_fstar_max = logt[index_select][np.argmax(log_fstar_sim)] + t_fstar_max = logt[np.argmax(log_fstar_sim)] default_sfr_params = np.array(DEFAULT_U_MS_PARAMS) default_sfr_params[0] = np.clip(0.3 * (logmp - 11.0) + 11.4, 11.0, 13.0) @@ -469,8 +453,6 @@ def get_loss_data_free( log_smah_sim, sfrh, log_fstar_sim, - index_select, - fstar_indx_high, fstar_tdelay, ssfrh_floor, weight, @@ -516,8 +498,6 @@ def loss_fixed_noquench(params, loss_data): log_sm_target, sfr_target, fstar_target, - index_select, - fstar_indx_high, fstar_tdelay, ssfrh_floor, weight, @@ -529,15 +509,7 @@ def loss_fixed_noquench(params, loss_data): sfr_params = params _res = calculate_sm_sfr_fstar_history_from_mah( - lgt, - dt, - dmhdt, - log_mah, - sfr_params, - q_params, - index_select, - fstar_indx_high, - fstar_tdelay, + lgt, dt, dmhdt, log_mah, sfr_params, q_params, fstar_tdelay ) mstar, sfr, fstar = _res @@ -642,10 +614,6 @@ def get_loss_data_fixed_noquench( Star formation history in Msun/yr. log_fstar_sim : ndarray of shape (nt, ) Base-10 log of cumulative SFH averaged over a timescale in Msun/yr. - index_select: ndarray of shape (n_times_fstar, ) - Snapshot indices used in fstar computation. - fstar_indx_high: ndarray of shape (n_times_fstar, ) - Indices of np.searchsorted(t, t - fstar_tdelay)[index_select] fstar_tdelay: float Time interval in Gyr for fstar definition. fstar = (mstar(t) - mstar(t-fstar_tdelay)) / fstar_tdelay[Gyr] @@ -663,20 +631,16 @@ def get_loss_data_fixed_noquench( Fixed values of the unbounded quenching parameters """ - fstar_indx_high = np.searchsorted(t_sim, t_sim - fstar_tdelay) - _mask = t_sim > fstar_tdelay + fstar_tdelay / 2.0 - index_select = np.arange(len(t_sim))[_mask] - fstar_indx_high = fstar_indx_high[_mask] smh = 10**log_smah_sim - fstar_sim = compute_fstar(t_sim, smh, index_select, fstar_indx_high, fstar_tdelay) + fstar_sim = compute_fstar(t_sim, smh, fstar_tdelay) with warnings.catch_warnings(): warnings.simplefilter("ignore") - ssfrh = fstar_sim / smh[index_select] + ssfrh = fstar_sim / smh ssfrh = np.clip(ssfrh, ssfrh_floor, np.inf) - fstar_sim = ssfrh * smh[index_select] + fstar_sim = ssfrh * smh log_fstar_sim = np.where( fstar_sim == 0.0, np.log10(fstar_sim.max()) - 3.0, np.log10(fstar_sim) ) @@ -689,13 +653,13 @@ def get_loss_data_fixed_noquench( t_sim, log_smah_sim, log_fstar_sim, - fstar_indx_high, + fstar_tdelay, dlogm_cut, t_fit_min, mass_fit_min, ) - t_fstar_max = logt[index_select][np.argmax(log_fstar_sim)] + t_fstar_max = logt[np.argmax(log_fstar_sim)] default_sfr_params = np.array(DEFAULT_U_MS_PARAMS) default_sfr_params[0] = np.clip(0.3 * (logmp - 11.0) + 11.4, 11.0, 13.0) @@ -722,8 +686,6 @@ def get_loss_data_fixed_noquench( log_smah_sim, sfrh, log_fstar_sim, - index_select, - fstar_indx_high, fstar_tdelay, ssfrh_floor, weight, @@ -770,8 +732,6 @@ def loss_fixed_hi(params, loss_data): log_sm_target, sfr_target, fstar_target, - index_select, - fstar_indx_high, fstar_tdelay, ssfrh_floor, weight, @@ -784,15 +744,7 @@ def loss_fixed_hi(params, loss_data): q_params = params[4:8] _res = calculate_sm_sfr_fstar_history_from_mah( - lgt, - dt, - dmhdt, - log_mah, - sfr_params, - q_params, - index_select, - fstar_indx_high, - fstar_tdelay, + lgt, dt, dmhdt, log_mah, sfr_params, q_params, fstar_tdelay ) mstar, sfr, fstar = _res @@ -897,10 +849,6 @@ def get_loss_data_fixed_hi( Star formation history in Msun/yr. log_fstar_sim : ndarray of shape (nt, ) Base-10 log of cumulative SFH averaged over a timescale in Msun/yr. - index_select: ndarray of shape (n_times_fstar, ) - Snapshot indices used in fstar computation. - fstar_indx_high: ndarray of shape (n_times_fstar, ) - Indices of np.searchsorted(t, t - fstar_tdelay)[index_select] fstar_tdelay: float Time interval in Gyr for fstar definition. fstar = (mstar(t) - mstar(t-fstar_tdelay)) / fstar_tdelay[Gyr] @@ -918,20 +866,16 @@ def get_loss_data_fixed_hi( Fixed value of the unbounded diffstar parameter indx_hi """ - fstar_indx_high = np.searchsorted(t_sim, t_sim - fstar_tdelay) - _mask = t_sim > fstar_tdelay + fstar_tdelay / 2.0 - index_select = np.arange(len(t_sim))[_mask] - fstar_indx_high = fstar_indx_high[_mask] smh = 10**log_smah_sim - fstar_sim = compute_fstar(t_sim, smh, index_select, fstar_indx_high, fstar_tdelay) + fstar_sim = compute_fstar(t_sim, smh, fstar_tdelay) with warnings.catch_warnings(): warnings.simplefilter("ignore") - ssfrh = fstar_sim / smh[index_select] + ssfrh = fstar_sim / smh ssfrh = np.clip(ssfrh, ssfrh_floor, np.inf) - fstar_sim = ssfrh * smh[index_select] + fstar_sim = ssfrh * smh log_fstar_sim = np.where( fstar_sim == 0.0, np.log10(fstar_sim.max()) - 3.0, np.log10(fstar_sim) ) @@ -944,13 +888,13 @@ def get_loss_data_fixed_hi( t_sim, log_smah_sim, log_fstar_sim, - fstar_indx_high, + fstar_tdelay, dlogm_cut, t_fit_min, mass_fit_min, ) - t_fstar_max = logt[index_select][np.argmax(log_fstar_sim)] + t_fstar_max = logt[np.argmax(log_fstar_sim)] default_sfr_params = np.array(DEFAULT_U_MS_PARAMS) default_sfr_params[0] = np.clip(0.3 * (logmp - 11.0) + 11.4, 11.0, 13.0) @@ -981,8 +925,6 @@ def get_loss_data_fixed_hi( log_smah_sim, sfrh, log_fstar_sim, - index_select, - fstar_indx_high, fstar_tdelay, ssfrh_floor, weight, @@ -1039,8 +981,6 @@ def loss_fixed_hi_rej(params, loss_data): log_sm_target, sfr_target, fstar_target, - index_select, - fstar_indx_high, fstar_tdelay, ssfrh_floor, weight, @@ -1058,15 +998,7 @@ def loss_fixed_hi_rej(params, loss_data): q_params = [*params[4:7], u_lg_rejuv] _res = calculate_sm_sfr_fstar_history_from_mah( - lgt, - dt, - dmhdt, - log_mah, - sfr_params, - q_params, - index_select, - fstar_indx_high, - fstar_tdelay, + lgt, dt, dmhdt, log_mah, sfr_params, q_params, fstar_tdelay ) mstar, sfr, fstar = _res @@ -1172,10 +1104,6 @@ def get_loss_data_fixed_hi_rej( Star formation history in Msun/yr. log_fstar_sim : ndarray of shape (nt, ) Base-10 log of cumulative SFH averaged over a timescale in Msun/yr. - index_select: ndarray of shape (n_times_fstar, ) - Snapshot indices used in fstar computation. - fstar_indx_high: ndarray of shape (n_times_fstar, ) - Indices of np.searchsorted(t, t - fstar_tdelay)[index_select] fstar_tdelay: float Time interval in Gyr for fstar definition. fstar = (mstar(t) - mstar(t-fstar_tdelay)) / fstar_tdelay[Gyr] @@ -1193,20 +1121,16 @@ def get_loss_data_fixed_hi_rej( Fixed value of the unbounded diffstar parameter indx_hi """ - fstar_indx_high = np.searchsorted(t_sim, t_sim - fstar_tdelay) - _mask = t_sim > fstar_tdelay + fstar_tdelay / 2.0 - index_select = np.arange(len(t_sim))[_mask] - fstar_indx_high = fstar_indx_high[_mask] smh = 10**log_smah_sim - fstar_sim = compute_fstar(t_sim, smh, index_select, fstar_indx_high, fstar_tdelay) + fstar_sim = compute_fstar(t_sim, smh, fstar_tdelay) with warnings.catch_warnings(): warnings.simplefilter("ignore") - ssfrh = fstar_sim / smh[index_select] + ssfrh = fstar_sim / smh ssfrh = np.clip(ssfrh, ssfrh_floor, np.inf) - fstar_sim = ssfrh * smh[index_select] + fstar_sim = ssfrh * smh log_fstar_sim = np.where( fstar_sim == 0.0, np.log10(fstar_sim.max()) - 3.0, np.log10(fstar_sim) ) @@ -1219,13 +1143,13 @@ def get_loss_data_fixed_hi_rej( t_sim, log_smah_sim, log_fstar_sim, - fstar_indx_high, + fstar_tdelay, dlogm_cut, t_fit_min, mass_fit_min, ) - t_fstar_max = logt[index_select][np.argmax(log_fstar_sim)] + t_fstar_max = logt[np.argmax(log_fstar_sim)] default_sfr_params = np.array(DEFAULT_U_MS_PARAMS) default_sfr_params[0] = np.clip(0.3 * (logmp - 11.0) + 11.4, 11.0, 13.0) @@ -1259,8 +1183,6 @@ def get_loss_data_fixed_hi_rej( log_smah_sim, sfrh, log_fstar_sim, - index_select, - fstar_indx_high, fstar_tdelay, ssfrh_floor, weight, @@ -1321,8 +1243,6 @@ def loss_fixed_hi_depl(params, loss_data): log_sm_target, sfr_target, fstar_target, - index_select, - fstar_indx_high, fstar_tdelay, ssfrh_floor, weight, @@ -1336,15 +1256,7 @@ def loss_fixed_hi_depl(params, loss_data): q_params = params[3:7] _res = calculate_sm_sfr_fstar_history_from_mah( - lgt, - dt, - dmhdt, - log_mah, - sfr_params, - q_params, - index_select, - fstar_indx_high, - fstar_tdelay, + lgt, dt, dmhdt, log_mah, sfr_params, q_params, fstar_tdelay ) mstar, sfr, fstar = _res @@ -1450,10 +1362,6 @@ def get_loss_data_fixed_hi_depl( Star formation history in Msun/yr. log_fstar_sim : ndarray of shape (nt, ) Base-10 log of cumulative SFH averaged over a timescale in Msun/yr. - index_select: ndarray of shape (n_times_fstar, ) - Snapshot indices used in fstar computation. - fstar_indx_high: ndarray of shape (n_times_fstar, ) - Indices of np.searchsorted(t, t - fstar_tdelay)[index_select] fstar_tdelay: float Time interval in Gyr for fstar definition. fstar = (mstar(t) - mstar(t-fstar_tdelay)) / fstar_tdelay[Gyr] @@ -1473,20 +1381,16 @@ def get_loss_data_fixed_hi_depl( Fixed value of the unbounded diffstar parameter tau_dep """ - fstar_indx_high = np.searchsorted(t_sim, t_sim - fstar_tdelay) - _mask = t_sim > fstar_tdelay + fstar_tdelay / 2.0 - index_select = np.arange(len(t_sim))[_mask] - fstar_indx_high = fstar_indx_high[_mask] smh = 10**log_smah_sim - fstar_sim = compute_fstar(t_sim, smh, index_select, fstar_indx_high, fstar_tdelay) + fstar_sim = compute_fstar(t_sim, smh, fstar_tdelay) with warnings.catch_warnings(): warnings.simplefilter("ignore") - ssfrh = fstar_sim / smh[index_select] + ssfrh = fstar_sim / smh ssfrh = np.clip(ssfrh, ssfrh_floor, np.inf) - fstar_sim = ssfrh * smh[index_select] + fstar_sim = ssfrh * smh log_fstar_sim = np.where( fstar_sim == 0.0, np.log10(fstar_sim.max()) - 3.0, np.log10(fstar_sim) ) @@ -1499,13 +1403,13 @@ def get_loss_data_fixed_hi_depl( t_sim, log_smah_sim, log_fstar_sim, - fstar_indx_high, + fstar_tdelay, dlogm_cut, t_fit_min, mass_fit_min, ) - t_fstar_max = logt[index_select][np.argmax(log_fstar_sim)] + t_fstar_max = logt[np.argmax(log_fstar_sim)] default_sfr_params = np.array(DEFAULT_U_MS_PARAMS) default_sfr_params[0] = np.clip(0.3 * (logmp - 11.0) + 11.4, 11.0, 13.0) @@ -1535,8 +1439,6 @@ def get_loss_data_fixed_hi_depl( log_smah_sim, sfrh, log_fstar_sim, - index_select, - fstar_indx_high, fstar_tdelay, ssfrh_floor, weight, @@ -1590,8 +1492,6 @@ def loss_fixed_depl_noquench(params, loss_data): log_sm_target, sfr_target, fstar_target, - index_select, - fstar_indx_high, fstar_tdelay, ssfrh_floor, weight, @@ -1604,15 +1504,7 @@ def loss_fixed_depl_noquench(params, loss_data): sfr_params = [*params[0:4], fixed_tau] _res = calculate_sm_sfr_fstar_history_from_mah( - lgt, - dt, - dmhdt, - log_mah, - sfr_params, - q_params, - index_select, - fstar_indx_high, - fstar_tdelay, + lgt, dt, dmhdt, log_mah, sfr_params, q_params, fstar_tdelay ) mstar, sfr, fstar = _res @@ -1719,10 +1611,6 @@ def get_loss_data_fixed_depl_noquench( Star formation history in Msun/yr. log_fstar_sim : ndarray of shape (nt, ) Base-10 log of cumulative SFH averaged over a timescale in Msun/yr. - index_select: ndarray of shape (n_times_fstar, ) - Snapshot indices used in fstar computation. - fstar_indx_high: ndarray of shape (n_times_fstar, ) - Indices of np.searchsorted(t, t - fstar_tdelay)[index_select] fstar_tdelay: float Time interval in Gyr for fstar definition. fstar = (mstar(t) - mstar(t-fstar_tdelay)) / fstar_tdelay[Gyr] @@ -1742,20 +1630,16 @@ def get_loss_data_fixed_depl_noquench( Fixed value of the unbounded quenching parameters """ - fstar_indx_high = np.searchsorted(t_sim, t_sim - fstar_tdelay) - _mask = t_sim > fstar_tdelay + fstar_tdelay / 2.0 - index_select = np.arange(len(t_sim))[_mask] - fstar_indx_high = fstar_indx_high[_mask] smh = 10**log_smah_sim - fstar_sim = compute_fstar(t_sim, smh, index_select, fstar_indx_high, fstar_tdelay) + fstar_sim = compute_fstar(t_sim, smh, fstar_tdelay) with warnings.catch_warnings(): warnings.simplefilter("ignore") - ssfrh = fstar_sim / smh[index_select] + ssfrh = fstar_sim / smh ssfrh = np.clip(ssfrh, ssfrh_floor, np.inf) - fstar_sim = ssfrh * smh[index_select] + fstar_sim = ssfrh * smh log_fstar_sim = np.where( fstar_sim == 0.0, np.log10(fstar_sim.max()) - 3.0, np.log10(fstar_sim) ) @@ -1768,13 +1652,13 @@ def get_loss_data_fixed_depl_noquench( t_sim, log_smah_sim, log_fstar_sim, - fstar_indx_high, + fstar_tdelay, dlogm_cut, t_fit_min, mass_fit_min, ) - t_fstar_max = logt[index_select][np.argmax(log_fstar_sim)] + t_fstar_max = logt[np.argmax(log_fstar_sim)] default_sfr_params = np.array(DEFAULT_U_MS_PARAMS) default_sfr_params[0] = np.clip(0.3 * (logmp - 11.0) + 11.4, 11.0, 13.0) @@ -1802,8 +1686,6 @@ def get_loss_data_fixed_depl_noquench( log_smah_sim, sfrh, log_fstar_sim, - index_select, - fstar_indx_high, fstar_tdelay, ssfrh_floor, weight, diff --git a/diffstar/fitting_helpers/fit_smah_helpers_tpeak.py b/diffstar/fitting_helpers/fit_smah_helpers_tpeak.py new file mode 100644 index 0000000..69a0127 --- /dev/null +++ b/diffstar/fitting_helpers/fit_smah_helpers_tpeak.py @@ -0,0 +1,467 @@ +""" +""" + +import warnings + +import h5py +import numpy as np +from diffmah.defaults import LGT0 +from diffmah.diffmah_kernels import _diffmah_kern +from jax import grad +from jax import jit as jjit +from jax import numpy as jnp + +from ..defaults import ( + DEFAULT_MS_PARAMS, + DEFAULT_MS_PDICT, + DEFAULT_Q_PARAMS, + DEFAULT_Q_PDICT, +) +from ..kernels.main_sequence_kernels_tpeak import ( + _get_bounded_sfr_params, + _get_unbounded_sfr_params, +) +from ..kernels.quenching_kernels import ( + _get_bounded_q_params, + _get_bounded_qt, + _get_unbounded_q_params, +) +from ..utils import _sigmoid +from .fitting_kernels import calculate_sm_sfr_fstar_history_from_mah, compute_fstar + +T_FIT_MIN = 1.0 # Only fit snapshots above this threshold. Gyr units. +DLOGM_CUT = 3.5 # Only fit SMH within this dex of the present day stellar mass. +MIN_MASS_CUT = 7.0 # Only fit SMH above this threshold. Log10(Msun) units. +FSTAR_TIME_DELAY = 1.0 # Time period of averaged SFH (aka fstar). Gyr units. +SSFRH_FLOOR = 1e-12 # Clip SFH to this minimum sSFR value. 1/yr units. + + +def get_header(): + """ """ + colnames = ["halo_id"] + colnames.extend(list(DEFAULT_MS_PDICT.keys())) + colnames.extend(list(DEFAULT_Q_PDICT.keys())) + colnames.extend(["loss", "success"]) + header_str = "# " + " ".join(colnames) + "\n" + return header_str, colnames + + +def get_header_unbound(): + """ """ + colnames = ["halo_id"] + colnames.extend(["u_" + s for s in list(DEFAULT_MS_PDICT.keys())]) + colnames.extend(["u_" + s for s in list(DEFAULT_Q_PDICT.keys())]) + colnames.extend(["loss", "success"]) + out = "# " + " ".join(colnames) + "\n" + return out + + +def write_collated_data(outname, data, colnames): + + nrows, ncols = np.shape(data) + + assert len(colnames) == ncols, "data mismatched with header" + with h5py.File(outname, "w") as hdf: + for i, name in enumerate(colnames): + if (name == "halo_id") | (name == "success"): + hdf[name] = data[:, i].astype(int) + else: + hdf[name] = data[:, i].astype(float) + + +@jjit +def loss_default(params, loss_data): + """ + MSE loss function for fitting individual stellar mass histories. + The parameters k, indx_hi are fixed. + + """ + ( + lgt, + dt, + dmhdt, + log_mah, + sm_target, + log_sm_target, + sfr_target, + fstar_target, + fstar_tdelay, + ssfrh_floor, + weight, + weight_fstar, + t_fstar_max, + fixed_hi, + ) = loss_data + + u_sfr_params = [*params[0:3], fixed_hi, params[3]] + u_q_params = params[4:8] + + _res = calculate_sm_sfr_fstar_history_from_mah( + lgt, + dt, + dmhdt, + log_mah, + u_sfr_params, + u_q_params, + fstar_tdelay, + ) + + mstar, sfr, fstar = _res + fstar = jnp.where(fstar > 0.0, jnp.log10(fstar), mstar * ssfrh_floor) + mstar = jnp.log10(mstar) + + sfr_res = 1e8 * (sfr - sfr_target) / sm_target + sfr_res = jnp.clip(sfr_res, -1.0, 1.0) + + loss = jnp.mean(((mstar - log_sm_target) / weight) ** 2) + loss += jnp.mean(((fstar - fstar_target) / weight_fstar) ** 2) + loss += jnp.mean((sfr_res / weight) ** 2) + + qt = _get_bounded_qt(u_q_params[0]) + loss += _sigmoid(qt - t_fstar_max, 0.0, 50.0, 100.0, 0.0) + sfr_params = _get_bounded_sfr_params(*u_sfr_params) + ( + lgmcrit, + lgy_at_mcrit, + indx_lo, + indx_hi, + tau_dep, + ) = sfr_params + loss += _sigmoid(indx_lo, 0.0, 10.0, 1.0, 0.0) + + return loss + + +loss_grad_default = jjit(grad(loss_default, argnums=(0))) + + +def loss_grad_default_np(params, data): + return np.array(loss_grad_default(params, data)).astype(float) + + +def get_loss_data_default( + t_sim, + dt, + sfrh, + log_smah_sim, + logmp, + mah_params, + t_peak, + dlogm_cut=DLOGM_CUT, + t_fit_min=T_FIT_MIN, + mass_fit_min=MIN_MASS_CUT, + fstar_tdelay=FSTAR_TIME_DELAY, + ssfrh_floor=SSFRH_FLOOR, + lgt0=LGT0, +): + """Retrieve the target data passed to the optimizer when fitting the halo + SFH model for the case in which the parameters k, indx_hi are fixed. + + Parameters + ---------- + t_sim : ndarray of shape (nt, ) + Cosmic time of each simulated snapshot in Gyr units. + dt : ndarray of shape (nt, ) + Cosmic time steps between each simulated snapshot in Gyr units. + sfrh : ndarray of shape (nt, ) + Star formation history of simulated snapshots in Msun/yr units. + log_smah_sim : ndarray of shape (nt, ) + Base-10 log of cumulative stellar mass in Msun units. + logmp : float + Base-10 log present day halo mass in Msun units. + mah_params : ndarray of shape (4, ) + Best fit diffmah halo parameters. Includes (logtc, k, early, late). + dlogm_cut : float, optional + Additional quantity used to place a cut on which simulated snapshots + are used to define the target halo SFH. + Snapshots will not be used when log_smah_sim falls below + log_smah_sim[-1] - dlogm_cut. + Default is set as global at top of module. + t_fit_min : float, optional + Additional quantity used to place a cut on which simulated snapshots are used to + define the target halo SFH. The value of t_fit_min defines the minimum cosmic + time in Gyr used to define the target SFH. + Default is set as global at top of module. + mass_fit_min : float + Quantity used to place a cut on which simulated snapshots are used to + define the target halo SFH. + The value mass_fit_min is the base-10 log of the minimum stellar mass in the SFH + used as target data. The final mass_fit_min cut is equal to + min(log_smah_sim[-1] - 0.5, mass_fit_min). + Default is set as global at top of module. + fstar_tdelay : float + Time interval in Gyr for fstar definition. + fstar = mstar(t) - mstar(t-fstar_tdelay) + Default is set as global at top of module. + ssfrh_floor : float + Lower bound value of star formation history used in the fits. + SFH(t) = max(SFH(t), SMH(t) * ssfrh_floor) + Default is set as global at top of module. + + Returns + ------- + p_init : ndarray of shape (5, ) + Initial guess at the unbounded value of the best-fit parameter. + Here we have p_init = (u_lgm, u_lgy, u_l, u_h, u_dt) + loss_data : sequence consisting of the following data + logt: ndarray of shape (nt, ) + Base-10 log of cosmic time of each simulated snapshot in Gyr. + dt : ndarray of shape (nt, ) + Cosmic time steps between each simulated snapshot in Gyr + dmhdt : ndarray of shape (nt, ) + Diffmah halo mass accretion rate in units of Msun/yr. + log_mah : ndarray of shape (nt, ) + Diffmah halo mass accretion history in units of Msun. + smh : ndarray of shape (nt, ) + Cumulative stellar mass history in Msun. + log_smah_sim : ndarray of shape (nt, ) + Base-10 log of cumulative stellar mass in Msun. + sfrh : ndarray of shape (nt, ) + Star formation history in Msun/yr. + log_fstar_sim : ndarray of shape (nt, ) + Base-10 log of cumulative SFH averaged over a timescale in Msun/yr. + fstar_tdelay: float + Time interval in Gyr for fstar definition. + fstar = (mstar(t) - mstar(t-fstar_tdelay)) / fstar_tdelay[Gyr] + ssfrh_floor : float + Lower bound value of star formation history used in the fits. + weight : ndarray of shape (nt, ) + Weight for each snapshot, to effectively remove from the fit + the SMH snapshots that fall below the threshold mass. + weight_fstar : ndarray of shape (n_times_fstar, ) + Weight for each snapshot, to effectively remove from the fit + the SFH snapshots that fall below the threshold mass. + t_fstar_max : float + Base-10 log of the cosmic time where SFH target history peaks. + fixed_hi : float + Fixed value of the unbounded diffstar parameter indx_hi + + """ + smh = 10**log_smah_sim + + fstar_sim = compute_fstar(t_sim, smh, fstar_tdelay) + + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + ssfrh = fstar_sim / smh + ssfrh = np.clip(ssfrh, ssfrh_floor, np.inf) + fstar_sim = ssfrh * smh + log_fstar_sim = np.where( + fstar_sim == 0.0, np.log10(fstar_sim.max()) - 3.0, np.log10(fstar_sim) + ) + + logt = jnp.log10(t_sim) + dmhdt, log_mah = _diffmah_kern(mah_params, t_sim, t_peak, lgt0) + + weight, weight_fstar = get_weights( + t_sim, + log_smah_sim, + log_fstar_sim, + fstar_tdelay, + dlogm_cut, + t_fit_min, + mass_fit_min, + ) + + t_fstar_max = logt[np.argmax(log_fstar_sim)] + + default_sfr_params = np.array(DEFAULT_MS_PARAMS) + default_sfr_params[0] = np.clip(0.3 * (logmp - 11.0) + 11.4, 11.0, 13.0) + default_sfr_params[1] = np.clip(0.2 * (logmp - 11.0) - 0.7, -1.5, -0.2) + default_sfr_params[2] = np.clip(0.7 * (logmp - 11.0) - 0.3, 0.2, 3.0) + default_sfr_params[4] = np.clip(-8.0 * (logmp - 11.0) + 15, 2.0, 15.0) + u_default_sfr_params = np.array(_get_unbounded_sfr_params(*default_sfr_params)) + + sfr_ms_params = np.zeros(4) + sfr_ms_params[0:3] = u_default_sfr_params[0:3] + sfr_ms_params[3] = u_default_sfr_params[4] + fixed_hi = u_default_sfr_params[3] + + sfr_ms_params_err = np.array([0.5, 0.5, 1.0, 3.0]) + + default_q_params = np.array(DEFAULT_Q_PARAMS) + default_q_params[0] = np.clip(-0.5 * (logmp - 11.0) + 1.5, 0.7, 1.5) + default_q_params[2] = -2.0 + q_params = np.array(_get_unbounded_q_params(*default_q_params)) + q_params_err = np.array([0.3, 0.5, 0.3, 0.3]) + + loss_data = ( + logt, + dt, + dmhdt, + log_mah, + smh, + log_smah_sim, + sfrh, + log_fstar_sim, + fstar_tdelay, + ssfrh_floor, + weight, + weight_fstar, + t_fstar_max, + fixed_hi, + ) + p_init = ( + np.concatenate((sfr_ms_params, q_params)), + np.concatenate((sfr_ms_params_err, q_params_err)), + ) + return p_init, loss_data + + +def get_outline_default(halo_id, loss_data, p_best, loss_best, success): + """Return the string storing fitting results that will be written to disk""" + fixed_hi = loss_data[-1] + u_sfr_params = np.zeros(5) + u_sfr_params[0:3] = p_best[0:3] + u_sfr_params[3] = fixed_hi + u_sfr_params[4] = p_best[3] + u_q_params = p_best[4:8] + + sfr_params = _get_bounded_sfr_params(*u_sfr_params) + q_params = _get_bounded_q_params(*u_q_params) + _d = np.concatenate((sfr_params, q_params)).astype("f4") + data_out = (*_d, float(loss_best)) + out = str(halo_id) + " " + " ".join(["{:.5e}".format(x) for x in data_out]) + out = out + " " + str(success) + return out + "\n" + + +def get_weights( + t_sim, + log_smah_sim, + log_fstar_sim, + fstar_tdelay, + dlogm_cut, + t_fit_min, + mass_fit_min, +): + """Calculate weights to mask target SMH and fstar target data. + + Parameters + ---------- + t_sim : ndarray of shape (nt, ) + Cosmic time of each simulated snapshot in Gyr units. + log_smah_sim : ndarray of shape (nt, ) + Base-10 log of cumulative stellar mass in Msun units. + log_fstar_sim : ndarray of shape (nt, ) + Base-10 log of SFH averaged over a time period in Msun/yr units. + fstar_tdelay: float + Time interval in Gyr for fstar definition. + fstar = (mstar(t) - mstar(t-fstar_tdelay)) / fstar_tdelay[Gyr] + dlogm_cut : float, optional + Additional quantity used to place a cut on which simulated snapshots + are used to define the target halo SFH. + Snapshots will not be used when log_smah_sim falls below + log_smah_sim[-1] - dlogm_cut. + t_fit_min : float, optional + Additional quantity used to place a cut on which simulated snapshots are used to + define the target halo SFH. The value of t_fit_min defines the minimum cosmic + time in Gyr used to define the target SFH. + mass_fit_min : float + Quantity used to place a cut on which simulated snapshots are used to + define the target halo SFH. + The value mass_fit_min is the base-10 log of the minimum stellar mass in the SFH + used as target data. The final mass_fit_min cut is equal to + min(log_smah_sim[-1] - 0.5, mass_fit_min). + + Returns + ------- + weight : ndarray of shape (nt, ) + Weight for each snapshot, to effectively remove from the fit + the SMH snapshots that fall below the threshold mass. + weight_fstar : ndarray of shape (nt, ) + Weight for each snapshot, to effectively remove from the fit + the SFH snapshots that fall below the threshold mass. + + """ + mass_fit_min = min(log_smah_sim[-1] - 0.5, mass_fit_min) + + mask = log_smah_sim > (log_smah_sim[-1] - dlogm_cut) + mask &= log_smah_sim > mass_fit_min + mask &= t_sim >= t_fit_min + + weight = np.ones_like(t_sim) + weight[~mask] = 1e10 + weight[log_smah_sim[-1] - log_smah_sim < 0.1] = 0.5 + weight = jnp.array(weight) + + weight_fstar = np.ones_like(t_sim) + weight_fstar[~mask] = 1e10 + weight_fstar[log_fstar_sim.max() - log_fstar_sim < 0.1] = 0.5 + weight_fstar[weight_fstar == -10.0] = 1e10 + weight_fstar[t_sim < fstar_tdelay + 0.01] = 1e10 + + return weight, weight_fstar + + +@jjit +def loss_default_clipssfrh(params, loss_data): + """ + MSE loss function for fitting individual stellar mass histories. + The parameters k, indx_hi are fixed. + + """ + ( + lgt, + dt, + dmhdt, + log_mah, + sm_target, + log_sm_target, + sfr_target, + fstar_target, + fstar_tdelay, + ssfrh_floor, + weight, + weight_fstar, + t_fstar_max, + fixed_hi, + ) = loss_data + + u_sfr_params = [*params[0:3], fixed_hi, params[3]] + u_q_params = params[4:8] + + _res = calculate_sm_sfr_fstar_history_from_mah( + lgt, + dt, + dmhdt, + log_mah, + u_sfr_params, + u_q_params, + fstar_tdelay, + ) + + mstar, sfr, fstar = _res + fstar = jnp.clip(fstar, mstar * ssfrh_floor, jnp.inf) + fstar = jnp.where(fstar > 0.0, jnp.log10(fstar), mstar * ssfrh_floor) + + mstar = jnp.log10(mstar) + + sfr_res = 1e8 * (sfr - sfr_target) / sm_target + sfr_res = jnp.clip(sfr_res, -1.0, 1.0) + + loss = jnp.mean(((mstar - log_sm_target) / weight) ** 2) + loss += jnp.mean(((fstar - fstar_target) / weight_fstar) ** 2) + loss += jnp.mean((sfr_res / weight) ** 2) + + qt = _get_bounded_qt(u_q_params[0]) + loss += _sigmoid(qt - t_fstar_max, 0.0, 50.0, 100.0, 0.0) + + sfr_params = _get_bounded_sfr_params(*u_sfr_params) + ( + lgmcrit, + lgy_at_mcrit, + indx_lo, + indx_hi, + tau_dep, + ) = sfr_params + loss += _sigmoid(indx_lo, 0.0, 10.0, 1.0, 0.0) + loss += _sigmoid(lgy_at_mcrit, 0.0, 20.0, 0.0, 1.0) + return loss + + +loss_grad_default_clipssfrh = jjit(grad(loss_default_clipssfrh, argnums=(0))) + + +def loss_grad_default_clipssfrh_np(params, data): + return np.array(loss_grad_default_clipssfrh(params, data)).astype(float) diff --git a/diffstar/fitting_helpers/fitting_kernels.py b/diffstar/fitting_helpers/fitting_kernels.py index 19d2bc3..806604e 100644 --- a/diffstar/fitting_helpers/fitting_kernels.py +++ b/diffstar/fitting_helpers/fitting_kernels.py @@ -1,5 +1,6 @@ """ """ + from diffmah.individual_halo_assembly import _calc_halo_history from jax import jit as jjit from jax import numpy as jnp @@ -13,7 +14,6 @@ _sfr_eff_plaw, ) from ..kernels.quenching_kernels import _quenching_kern_u_params -from ..utils import jax_np_interp @jjit @@ -24,8 +24,6 @@ def calculate_sm_sfr_fstar_history_from_mah( log_mah, u_ms_params, u_q_params, - index_select, - index_high, fstar_tdelay, fb=FB, ): @@ -58,12 +56,6 @@ def calculate_sm_sfr_fstar_history_from_mah( u_q_params : ndarray of shape (4, ) Quenching model unbounded parameters. Includes (u_qt, u_qs, u_drop, u_rejuv) - index_select: ndarray of shape (n_times_fstar, ) - Snapshot indices used in fstar computation - - index_high: ndarray of shape (n_times_fstar, ) - Indices of np.searchsorted(t, t - fstar_tdelay)[index_select] - fstar_tdelay: float Time interval in Gyr for fstar definition. fstar = (mstar(t) - mstar(t-fstar_tdelay)) / fstar_tdelay[Gyr] @@ -82,7 +74,7 @@ def calculate_sm_sfr_fstar_history_from_mah( """ sfr = _sfr_history_from_mah(lgt, dt, dmhdt, log_mah, u_ms_params, u_q_params, fb=fb) mstar = _integrate_sfr(sfr, dt) - fstar = compute_fstar(10**lgt, mstar, index_select, index_high, fstar_tdelay) + fstar = compute_fstar(10**lgt, mstar, fstar_tdelay) return mstar, sfr, fstar @@ -140,8 +132,6 @@ def calculate_histories( mah_params, u_ms_params, u_q_params, - index_select, - index_high, fstar_tdelay, fb=FB, ): @@ -173,12 +163,6 @@ def calculate_histories( Quenching model unbounded parameters. Includes (u_qt, u_qs, u_drop, u_rejuv) - index_select: ndarray of shape (n_times_fstar, ) - Snapshot indices used in fstar computation. - - index_high: ndarray of shape (n_times_fstar, ) - Indices of np.searchsorted(t, t - fstar_tdelay)[index_select] - fstar_tdelay: float Time interval in Gyr for fstar definition. fstar = (mstar(t) - mstar(t-fstar_tdelay)) / fstar_tdelay[Gyr] @@ -209,8 +193,6 @@ def calculate_histories( log_mah, u_ms_params, u_q_params, - index_select, - index_high, fstar_tdelay, fb=fb, ) @@ -218,7 +200,7 @@ def calculate_histories( calculate_histories_vmap = jjit( - vmap(calculate_histories, in_axes=(None, None, 0, 0, 0, None, None, None, None)) + vmap(calculate_histories, in_axes=(None, None, 0, 0, 0, None, None)) ) @@ -229,7 +211,7 @@ def _integrate_sfr(sfr, dt): @jjit -def compute_fstar(tarr, mstar, index_select, index_high, fstar_tdelay): +def compute_fstar(tarr, mstar, fstar_tdelay): """Time averaged SFH that has ocurred over some previous time period fstar = (mstar(t) - mstar(t-fstar_tdelay)) / fstar_tdelay @@ -242,12 +224,6 @@ def compute_fstar(tarr, mstar, index_select, index_high, fstar_tdelay): mstar : ndarray of shape (n_times, ) Stellar mass history in Msun units - index_select: ndarray of shape (n_times_fstar, ) - Snapshot indices used in fstar computation - - index_high: ndarray of shape (n_times_fstar, ) - Indices of np.searchsorted(t, t - fstar_tdelay)[index_select] - fstar_tdelay: float Time interval in Gyr units for fstar definition. fstar = (mstar(t) - mstar(t-fstar_tdelay)) / fstar_tdelay @@ -257,18 +233,17 @@ def compute_fstar(tarr, mstar, index_select, index_high, fstar_tdelay): fstar : ndarray of shape (n_times) SFH averaged over timescale fstar_tdelay in units of Msun/yr assuming h=1 + Notes + ------- + for t-fstar_tdelay < 0 t<tarr, and jnp.interp returns by default mstar[0], + so fstar will always be positive. """ - mstar_high = mstar[index_select] - mstar_low = jax_np_interp( - tarr[index_select] - fstar_tdelay, tarr, mstar, index_high - ) - fstar = (mstar_high - mstar_low) / fstar_tdelay / 1e9 + mstar_low = jnp.interp(tarr - fstar_tdelay, tarr, mstar) + fstar = (mstar - mstar_low) / fstar_tdelay / 1e9 + fstar = jnp.where(fstar > 0.0, fstar, 0.0) return fstar -compute_fstar_vmap = jjit(vmap(compute_fstar, in_axes=(None, 0, *[None] * 3))) - - @jjit def _sfr_history_from_mah(lgt, dtarr, dmhdt, log_mah, sfr_params, q_params, fb=FB): """Star formation history of an individual galaxy. diff --git a/docs/source/demo_diffstar_fitter.ipynb b/docs/source/demo_diffstar_fitter.ipynb index 87a6e86..5b2e6af 100644 --- a/docs/source/demo_diffstar_fitter.ipynb +++ b/docs/source/demo_diffstar_fitter.ipynb @@ -207,7 +207,7 @@ "\n", "dmhdt_fit, log_mah_fit = loss_data[2:4]\n", "lgt = np.log10(tarr)\n", - "index_select, index_high, fstar_tdelay = loss_data[8:11]\n", + "fstar_tdelay = loss_data[8]\n", " \n", "_histories = calculate_sm_sfr_fstar_history_from_mah(\n", " lgt,\n", @@ -216,8 +216,6 @@ " log_mah_fit,\n", " u_sfr_fit_params,\n", " u_q_fit_params,\n", - " index_select,\n", - " index_high,\n", " fstar_tdelay,\n", ")\n", "smh_fit, sfh_fit, fstar_fit = _histories" @@ -331,7 +329,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.12.5" } }, "nbformat": 4, diff --git a/scripts/history_fitting_script_SMDPL_tpeak.py b/scripts/history_fitting_script_SMDPL_tpeak.py new file mode 100644 index 0000000..11f447f --- /dev/null +++ b/scripts/history_fitting_script_SMDPL_tpeak.py @@ -0,0 +1,189 @@ +import argparse +import os +import subprocess +from glob import glob +from time import time + +import numpy as np +from mpi4py import MPI + +import diffstar.fitting_helpers.fit_smah_helpers_tpeak as fitsmah +from diffstar.data_loaders.load_smah_data import load_fit_mah_tpeak, load_SMDPL_data +from diffstar.fitting_helpers.utils import minimizer_wrapper + +BEBOP_SMDPL = "/lcrc/project/galsampler/SMDPL/dr1_no_merging_upidh/sfh_binary_catalogs/a_1.000000/" +BEBOP_SMDPL_MAH = ( + "/lcrc/project/halotools/SMDPL/dr1_no_merging_upidh/diffmah_tpeak_fits/" +) + +TMP_OUTPAT = "tmp_sfh_fits_rank_{0}.dat" +NUM_SUBVOLS_SMDPL = 576 + + +if __name__ == "__main__": + comm = MPI.COMM_WORLD + rank, nranks = comm.Get_rank(), comm.Get_size() + + parser = argparse.ArgumentParser() + + parser.add_argument("outdir", help="Output directory") + parser.add_argument("-outbase", help="Basename of the output hdf5 file") + parser.add_argument( + "-indir_diffmah", help="Directory of mah parameters", default=BEBOP_SMDPL_MAH + ) + parser.add_argument("-indir", help="Input directory", default=BEBOP_SMDPL) + parser.add_argument( + "-fstar_tdelay", + help="Time interval in Gyr for fstar definition.", + type=float, + default=1.0, + ) + parser.add_argument( + "-mass_fit_min", + help="Minimum mass included in stellar mass histories.", + type=float, + default=fitsmah.MIN_MASS_CUT, + ) + parser.add_argument( + "-ssfrh_floor", + help="Clipping floor for sSFH", + type=float, + default=fitsmah.SSFRH_FLOOR, + ) + parser.add_argument("-test", help="Short test run?", type=bool, default=False) + parser.add_argument("-istart", help="First subvolume in loop", type=int, default=0) + parser.add_argument("-iend", help="Last subvolume in loop", type=int, default=-1) + parser.add_argument( + "-num_subvols_tot", help="Total # subvols", type=int, default=NUM_SUBVOLS_SMDPL + ) + + args = parser.parse_args() + + indir = args.indir + indir_diffmah = args.indir_diffmah + outbase = args.outbase + istart, iend = args.istart, args.iend + num_subvols_tot = args.num_subvols_tot # needed for string formatting + HEADER, colnames_out = fitsmah.get_header() + + kwargs = { + "fstar_tdelay": args.fstar_tdelay, + "mass_fit_min": args.mass_fit_min, + "ssfrh_floor": args.ssfrh_floor, + } + + start = time() + + all_avail_subvol_names = [ + os.path.basename(drn) for drn in glob(os.path.join(indir, "subvol_*")) + ] + all_avail_subvolumes = [int(s.split("_")[1]) for s in all_avail_subvol_names] + all_avail_subvolumes = sorted(all_avail_subvolumes) + + if args.test: + subvolumes = [ + all_avail_subvolumes[0], + ] + else: + subvolumes = np.arange(istart, iend) + + for isubvol in subvolumes: + isubvol_start = time() + + nchar_subvol = len(str(num_subvols_tot)) + subvol_str = f"subvol_{isubvol:0{nchar_subvol}d}" + + subvolumes_i = [isubvol] + + subvol_data_str = indir + _data = load_SMDPL_data(subvolumes_i, subvol_data_str) + halo_ids, log_smahs, sfrhs, tarr, dt, log_mahs, logmp = _data + + subvol_diffmah_str = f"{subvol_str}_diffmah_fits.h5" + mah_fit_params, logmp_fit, t_peak_arr = load_fit_mah_tpeak( + subvol_diffmah_str, data_drn=indir_diffmah + ) + + if rank == 0: + print("Number of galaxies in mock = {}".format(len(halo_ids))) + + # Get data for rank + if args.test: + nhalos_tot = nranks * 5 + else: + nhalos_tot = len(halo_ids) + + indx_all = np.arange(0, nhalos_tot).astype("i8") + indx = np.array_split(indx_all, nranks)[rank] + + halo_ids_for_rank = halo_ids[indx] + log_smahs_for_rank = log_smahs[indx] + sfrhs_for_rank = sfrhs[indx] + mah_params_for_rank = mah_fit_params[indx] + logmp_for_rank = logmp[indx] + t_peak_for_rank = t_peak_arr[indx] + + nhalos_for_rank = len(halo_ids_for_rank) + + rank_basepat = "_".join((subvol_str, outbase, TMP_OUTPAT)) + rank_outname = os.path.join(args.outdir, rank_basepat).format(rank) + + # breakpoint() + with open(rank_outname, "w") as fout: + fout.write(HEADER) + + for i in range(nhalos_for_rank): + halo_id = halo_ids_for_rank[i] + lgsmah = log_smahs_for_rank[i, :] + sfrh = sfrhs_for_rank[i, :] + mah_params = mah_params_for_rank[i] + logmp_halo = logmp_for_rank[i] + t_peak = t_peak_for_rank[i] + + p_init, loss_data = fitsmah.get_loss_data_default( + tarr, dt, sfrh, lgsmah, logmp_halo, mah_params, t_peak, **kwargs + ) + _res = minimizer_wrapper( + fitsmah.loss_default_clipssfrh, + fitsmah.loss_grad_default_clipssfrh_np, + p_init, + loss_data, + ) + p_best, loss_best, success = _res + outline = fitsmah.get_outline_default( + halo_id, loss_data, p_best, loss_best, success + ) + + fout.write(outline) + + comm.Barrier() + isubvol_end = time() + + msg = "\n\nWallclock runtime to fit {0} galaxies with {1} ranks = {2:.1f} seconds\n\n" + if rank == 0: + print("\nFinished with subvolume {}".format(isubvol)) + runtime = isubvol_end - isubvol_start + print(msg.format(nhalos_tot, nranks, runtime)) + + # collate data from ranks and rewrite to disk + pat = os.path.join(args.outdir, rank_basepat) + fit_data_fnames = [pat.format(i) for i in range(nranks)] + collector = [] + for fit_fn in fit_data_fnames: + assert os.path.isfile(fit_fn) + fit_data = np.genfromtxt(fit_fn, dtype="str") + collector.append(fit_data) + subvol_i_fit_results = np.concatenate(collector) + + subvol_str = f"subvol_{isubvol:0{nchar_subvol}d}" + outbn = "_".join((subvol_str, outbase)) + ".h5" + outfn = os.path.join(args.outdir, outbn) + + # fitsmah.write_collated_data(outfn, subvol_i_fit_results, chunk_arr=None) + fitsmah.write_collated_data(outfn, subvol_i_fit_results, colnames_out) + + # clean up ASCII data for subvol_i + bnpat = subvol_str + "*.dat" + fnpat = os.path.join(args.outdir, bnpat) + command = "rm " + fnpat + subprocess.os.system(command)

Resolve some failing tests due to change of arguments to compute_fstar Still WIP: 2 test failures remain

2024-08-27T16:01:19

0.0

[]

ArgonneCPAC/diffstar

ArgonneCPAC__diffstar-60

82d0a77048b0f882a553df4ec3a433f1780e3611

diff --git a/diffstar/data_loaders/load_smah_data.py b/diffstar/data_loaders/load_smah_data.py index a46ca94..de3b5fa 100644 --- a/diffstar/data_loaders/load_smah_data.py +++ b/diffstar/data_loaders/load_smah_data.py @@ -1,17 +1,16 @@ """ """ + import os import warnings import h5py import numpy as np +from umachine_pyio.load_mock import load_mock_from_binaries from ..defaults import SFR_MIN from ..utils import _get_dt_array -from umachine_pyio.load_mock import load_mock_from_binaries - - TASSO = "/Users/aphearin/work/DATA/diffmah_data" BEBOP = "/lcrc/project/halotools/diffmah_data" LAPTOP = "/Users/alarcon/Documents/diffmah_data" @@ -541,4 +540,4 @@ def load_SMDPL_data(subvols, data_drn=BEBOP_SMDPL): logmp = log_mahs[:, -1] - return halo_ids, log_smahs, sfrh, SMDPL_t, dt, log_mahs, logmp \ No newline at end of file + return halo_ids, log_smahs, sfrh, SMDPL_t, dt, log_mahs, logmp diff --git a/diffstar/fitting_helpers/fit_smah_helpers.py b/diffstar/fitting_helpers/fit_smah_helpers.py index 6a4a888..0012eba 100644 --- a/diffstar/fitting_helpers/fit_smah_helpers.py +++ b/diffstar/fitting_helpers/fit_smah_helpers.py @@ -1,5 +1,6 @@ """ """ + import os import warnings diff --git a/diffstar/fitting_helpers/fit_smah_helpers_tpeak.py b/diffstar/fitting_helpers/fit_smah_helpers_tpeak.py index 2df66d7..c7e97f2 100644 --- a/diffstar/fitting_helpers/fit_smah_helpers_tpeak.py +++ b/diffstar/fitting_helpers/fit_smah_helpers_tpeak.py @@ -1,42 +1,33 @@ """ """ -import os + import warnings import h5py import numpy as np - +from diffmah.defaults import LGT0 +from diffmah.diffmah_kernels import _diffmah_kern from jax import grad from jax import jit as jjit from jax import numpy as jnp -from diffmah.diffmah_kernels import _diffmah_kern -from diffmah.defaults import LGT0 - from ..defaults import ( - DEFAULT_MS_PDICT, - DEFAULT_Q_PDICT, - DEFAULT_U_MS_PARAMS, - DEFAULT_U_Q_PARAMS, DEFAULT_MS_PARAMS, + DEFAULT_MS_PDICT, DEFAULT_Q_PARAMS, + DEFAULT_Q_PDICT, ) from ..kernels.main_sequence_kernels_tpeak import ( - _get_bounded_sfr_params_vmap, _get_bounded_sfr_params, _get_unbounded_sfr_params, ) from ..kernels.quenching_kernels import ( - _get_bounded_lg_drop, - _get_bounded_q_params_vmap, _get_bounded_q_params, _get_bounded_qt, _get_unbounded_q_params, - _get_unbounded_qrejuv, ) -from .fitting_kernels import calculate_sm_sfr_fstar_history_from_mah, compute_fstar - from ..utils import _sigmoid +from .fitting_kernels import calculate_sm_sfr_fstar_history_from_mah, compute_fstar T_FIT_MIN = 1.0 # Only fit snapshots above this threshold. Gyr units. DLOGM_CUT = 3.5 # Only fit SMH within this dex of the present day stellar mass. @@ -52,7 +43,6 @@ def get_header(): colnames.extend(list(DEFAULT_Q_PDICT.keys())) colnames.extend(["loss", "success"]) header_str = "# " + " ".join(colnames) + "\n" - dtypes = [] return header_str, colnames @@ -130,7 +120,7 @@ def loss_default(params, loss_data): qt = _get_bounded_qt(u_q_params[0]) loss += _sigmoid(qt - t_fstar_max, 0.0, 50.0, 100.0, 0.0) sfr_params = _get_bounded_sfr_params(*u_sfr_params) - ( + ( lgmcrit, lgy_at_mcrit, indx_lo, @@ -148,6 +138,7 @@ def loss_default(params, loss_data): def loss_grad_default_np(params, data): return np.array(loss_grad_default(params, data)).astype(float) + def get_loss_data_default( t_sim, dt, @@ -260,7 +251,6 @@ def get_loss_data_default( ) logt = jnp.log10(t_sim) - logtmp = np.log10(t_sim[-1]) dmhdt, log_mah = _diffmah_kern(mah_params, t_sim, t_peak, lgt0) weight, weight_fstar = get_weights( @@ -458,7 +448,7 @@ def loss_default_clipssfrh(params, loss_data): loss += _sigmoid(qt - t_fstar_max, 0.0, 50.0, 100.0, 0.0) sfr_params = _get_bounded_sfr_params(*u_sfr_params) - ( + ( lgmcrit, lgy_at_mcrit, indx_lo, @@ -473,4 +463,4 @@ def loss_default_clipssfrh(params, loss_data): def loss_grad_default_clipssfrh_np(params, data): - return np.array(loss_grad_default_clipssfrh(params, data)).astype(float) \ No newline at end of file + return np.array(loss_grad_default_clipssfrh(params, data)).astype(float) diff --git a/diffstar/fitting_helpers/fitting_kernels.py b/diffstar/fitting_helpers/fitting_kernels.py index 7c216e4..806604e 100644 --- a/diffstar/fitting_helpers/fitting_kernels.py +++ b/diffstar/fitting_helpers/fitting_kernels.py @@ -1,5 +1,6 @@ """ """ + from diffmah.individual_halo_assembly import _calc_halo_history from jax import jit as jjit from jax import numpy as jnp @@ -13,7 +14,6 @@ _sfr_eff_plaw, ) from ..kernels.quenching_kernels import _quenching_kern_u_params -from ..utils import jax_np_interp @jjit @@ -235,10 +235,10 @@ def compute_fstar(tarr, mstar, fstar_tdelay): Notes ------- - for t-fstar_tdelay < 0 t<tarr, and jnp.interp returns by default mstar[0], + for t-fstar_tdelay < 0 t<tarr, and jnp.interp returns by default mstar[0], so fstar will always be positive. """ - mstar_low = jnp.interp(tarr - fstar_tdelay, tarr, mstar) + mstar_low = jnp.interp(tarr - fstar_tdelay, tarr, mstar) fstar = (mstar - mstar_low) / fstar_tdelay / 1e9 fstar = jnp.where(fstar > 0.0, fstar, 0.0) return fstar diff --git a/scripts/history_fitting_script_SMDPL_tpeak.py b/scripts/history_fitting_script_SMDPL_tpeak.py index c3fde86..11f447f 100644 --- a/scripts/history_fitting_script_SMDPL_tpeak.py +++ b/scripts/history_fitting_script_SMDPL_tpeak.py @@ -1,27 +1,20 @@ - -import os -import sys -import h5py import argparse -import warnings -import numpy as np - -from time import time +import os +import subprocess from glob import glob +from time import time -from umachine_pyio.load_mock import load_mock_from_binaries -from astropy.cosmology import Planck15 -from diffstar.utils import _get_dt_array -import diffstar.fitting_helpers.fit_smah_helpers_tpeak as fitsmah -from diffstar.fitting_helpers.utils import minimizer_wrapper -from diffstar.data_loaders.load_smah_data import load_SMDPL_data, load_fit_mah_tpeak - +import numpy as np from mpi4py import MPI -import subprocess +import diffstar.fitting_helpers.fit_smah_helpers_tpeak as fitsmah +from diffstar.data_loaders.load_smah_data import load_fit_mah_tpeak, load_SMDPL_data +from diffstar.fitting_helpers.utils import minimizer_wrapper BEBOP_SMDPL = "/lcrc/project/galsampler/SMDPL/dr1_no_merging_upidh/sfh_binary_catalogs/a_1.000000/" -BEBOP_SMDPL_MAH = "/lcrc/project/halotools/SMDPL/dr1_no_merging_upidh/diffmah_tpeak_fits/" +BEBOP_SMDPL_MAH = ( + "/lcrc/project/halotools/SMDPL/dr1_no_merging_upidh/diffmah_tpeak_fits/" +) TMP_OUTPAT = "tmp_sfh_fits_rank_{0}.dat" NUM_SUBVOLS_SMDPL = 576 @@ -30,12 +23,14 @@ if __name__ == "__main__": comm = MPI.COMM_WORLD rank, nranks = comm.Get_rank(), comm.Get_size() - + parser = argparse.ArgumentParser() parser.add_argument("outdir", help="Output directory") parser.add_argument("-outbase", help="Basename of the output hdf5 file") - parser.add_argument("-indir_diffmah", help="Directory of mah parameters", default=BEBOP_SMDPL_MAH) + parser.add_argument( + "-indir_diffmah", help="Directory of mah parameters", default=BEBOP_SMDPL_MAH + ) parser.add_argument("-indir", help="Input directory", default=BEBOP_SMDPL) parser.add_argument( "-fstar_tdelay", @@ -50,7 +45,10 @@ default=fitsmah.MIN_MASS_CUT, ) parser.add_argument( - "-ssfrh_floor", help="Clipping floor for sSFH", type=float, default=fitsmah.SSFRH_FLOOR, + "-ssfrh_floor", + help="Clipping floor for sSFH", + type=float, + default=fitsmah.SSFRH_FLOOR, ) parser.add_argument("-test", help="Short test run?", type=bool, default=False) parser.add_argument("-istart", help="First subvolume in loop", type=int, default=0) @@ -81,7 +79,7 @@ ] all_avail_subvolumes = [int(s.split("_")[1]) for s in all_avail_subvol_names] all_avail_subvolumes = sorted(all_avail_subvolumes) - + if args.test: subvolumes = [ all_avail_subvolumes[0], @@ -100,9 +98,11 @@ subvol_data_str = indir _data = load_SMDPL_data(subvolumes_i, subvol_data_str) halo_ids, log_smahs, sfrhs, tarr, dt, log_mahs, logmp = _data - + subvol_diffmah_str = f"{subvol_str}_diffmah_fits.h5" - mah_fit_params, logmp_fit, t_peak_arr = load_fit_mah_tpeak(subvol_diffmah_str, data_drn=indir_diffmah) + mah_fit_params, logmp_fit, t_peak_arr = load_fit_mah_tpeak( + subvol_diffmah_str, data_drn=indir_diffmah + ) if rank == 0: print("Number of galaxies in mock = {}".format(len(halo_ids))) @@ -112,7 +112,7 @@ nhalos_tot = nranks * 5 else: nhalos_tot = len(halo_ids) - + indx_all = np.arange(0, nhalos_tot).astype("i8") indx = np.array_split(indx_all, nranks)[rank] @@ -139,16 +139,20 @@ mah_params = mah_params_for_rank[i] logmp_halo = logmp_for_rank[i] t_peak = t_peak_for_rank[i] - p_init, loss_data = fitsmah.get_loss_data_default( tarr, dt, sfrh, lgsmah, logmp_halo, mah_params, t_peak, **kwargs ) _res = minimizer_wrapper( - fitsmah.loss_default_clipssfrh, fitsmah.loss_grad_default_clipssfrh_np, p_init, loss_data + fitsmah.loss_default_clipssfrh, + fitsmah.loss_grad_default_clipssfrh_np, + p_init, + loss_data, ) p_best, loss_best, success = _res - outline = fitsmah.get_outline_default(halo_id, loss_data, p_best, loss_best, success) + outline = fitsmah.get_outline_default( + halo_id, loss_data, p_best, loss_best, success + ) fout.write(outline)

Fix bugs in unit tests of new tpeak-based SFH model This PR resolves a failing unit test. The failure was due to a bug in the test, not the source code: within the unit test, the new SFH kernel was just being called with the arguments in the wrong order. @alexalar remember to use relative imports when importing diffstar from within diffstar (which is the only other change brought in with this PR).

2024-08-26T19:22:13

0.0

[]

scholarly-python-package/scholarly

scholarly-python-package__scholarly-483

00cf1d88f06de4f753c16cdaae3a2c9414accae5

diff --git a/scholarly/_navigator.py b/scholarly/_navigator.py index 0fbda9d..8bea767 100644 --- a/scholarly/_navigator.py +++ b/scholarly/_navigator.py @@ -119,6 +119,14 @@ def _get_page(self, pagerequest: str, premium: bool = False) -> str: if resp.status_code == 200 and not has_captcha: return resp.text + elif resp.status_code == 404: + # If the scholar_id was approximate, it first appears as + # 404 (or 302), and then gets redirected to the correct profile. + # In such cases, we need to try again with the same session. + # See https://github.com/scholarly-python-package/scholarly/issues/469. + self.logger.debug("Got a 404 error. Attempting with same proxy") + tries += 1 + continue elif has_captcha: self.logger.info("Got a captcha request.") session = pm._handle_captcha2(pagerequest) diff --git a/scholarly/_proxy_generator.py b/scholarly/_proxy_generator.py index f451ec5..49d5bd5 100644 --- a/scholarly/_proxy_generator.py +++ b/scholarly/_proxy_generator.py @@ -451,7 +451,7 @@ def _handle_captcha2(self, url): return self._session def _new_session(self, **kwargs): - init_kwargs = {} + init_kwargs = {"follow_redirects": True} init_kwargs.update(kwargs) proxies = {} if self._session: @@ -610,7 +610,7 @@ def ScraperAPI(self, API_KEY, country_code=None, premium=False, render=False): # https://www.scraperapi.com/documentation/ self._TIMEOUT = 60 - prefix = "http://scraperapi" + prefix = "http://scraperapi.retry_404=true" if country_code is not None: prefix += ".country_code=" + country_code if premium: @@ -624,7 +624,7 @@ def ScraperAPI(self, API_KEY, country_code=None, premium=False, render=False): for _ in range(3): proxy_works = self._use_proxy(http=f'{prefix}:{API_KEY}@proxy-server.scraperapi.com:8001') if proxy_works: - proxies = {'http://': f"http://scraperapi:{API_KEY}@proxy-server.scraperapi.com:8001",} + proxies = {'http://': f"{prefix}:{API_KEY}@proxy-server.scraperapi.com:8001",} self.logger.info("ScraperAPI proxy setup successfully") self._new_session(verify=False, proxies=proxies) return proxy_works diff --git a/scholarly/author_parser.py b/scholarly/author_parser.py index 53dad8e..d46038e 100644 --- a/scholarly/author_parser.py +++ b/scholarly/author_parser.py @@ -440,6 +440,14 @@ def fill(self, author, sections: list = [], sortby="citedby", publication_limit: url = '{0}&pagesize={1}'.format(url_citations, _PAGESIZE) soup = self.nav._get_soup(url) + # Update scholar_id + scholar_id = re.findall(_CITATIONAUTHRE, soup.find("link", rel="canonical").get('href', ""))[0] + if scholar_id != author['scholar_id']: + self.nav.logger.warning("Changing the scholar_id following redirect from %s to %s. " + "To avoid this warning, use %s to look up this scholar.", + author['scholar_id'], scholar_id, scholar_id) + author["scholar_id"] = scholar_id + if sections == []: for i in self._sections: if i not in author['filled']: diff --git a/setup.py b/setup.py index cadec5a..b4ea378 100644 --- a/setup.py +++ b/setup.py @@ -5,7 +5,7 @@ setuptools.setup( name='scholarly', - version='1.7.10', + version='1.7.11', author='Steven A. Cholewiak, Panos Ipeirotis, Victor Silva, Arun Kannawadi', author_email='[email protected], [email protected], [email protected], [email protected]', description='Simple access to Google Scholar authors and citations',

Fill author cannot handle 302 redirects **Describe the bug** When calling `fill()` on an author record whose scholar_id has a 302 redirect, scholarly gets stuck in a loop on the original URL. **To Reproduce** ``` from scholarly import scholarly import logging logging.basicConfig(level=logging.INFO) scholar_id = 'oMaIg8sAAAAJ' author = scholarly.search_author_id(scholar_id, filled = True) ``` Results in logging such as: ``` INFO:scholarly:Getting https://scholar.google.com/citations?hl=en&user=oMaIg8sAAAAJ&pagesize=100 INFO:scholarly:Getting https://scholar.google.com/citations?hl=en&user=oMaIg8sAAAAJ&cstart=100&pagesize=100 INFO:scholarly:Getting https://scholar.google.com/citations?hl=en&user=oMaIg8sAAAAJ&cstart=200&pagesize=100 INFO:scholarly:Getting https://scholar.google.com/citations?hl=en&user=oMaIg8sAAAAJ&cstart=300&pagesize=100 INFO:scholarly:Getting https://scholar.google.com/citations?hl=en&user=oMaIg8sAAAAJ&cstart=400&pagesize=100 ... ``` **Expected behavior** The 302 redirect will be observed and results will be the same as for searching for `scholar_id = 'PEJ42J0AAAAJ'` **Desktop (please complete the following information):** - Proxy service: None - python version: 3.9.14 - OS: Linux - Version: Centos Stream 9

With no proxy and `FreeProxies`, I could not fetch this (see #465 ), but then when I tried with `ScraperAPI`, I got 404 (not found) response for `scholar_id = 'oMaIg8sAAAAJ'` and success with `scholar_id = 'PEJ42J0AAAAJ'`. It is true that the code does not handle 302 redirects, but I did not encounter this. Is this a consistent issue? Yes, I found it was a recurring issue. I had a script iterating through a list of scholar_ids and if it encountered one which effectively had a 302 redirect (when viewed in a browser), it would get stuck in the kind of loop identified in the logging referenced in my original post.

2023-01-16T21:48:55

0.0

[]

scholarly-python-package/scholarly

scholarly-python-package__scholarly-478

599639ac54085c92088fec8e1a92d6b0d03f0cd1

diff --git a/scholarly/_navigator.py b/scholarly/_navigator.py index 3d85642..fcb454d 100644 --- a/scholarly/_navigator.py +++ b/scholarly/_navigator.py @@ -74,12 +74,12 @@ def use_proxy(self, pg1: ProxyGenerator, pg2: ProxyGenerator = None): self._session1 = self.pm1.get_session() self._session2 = self.pm2.get_session() - def _new_session(self, premium=True): + def _new_session(self, premium=True, **kwargs): self.got_403 = False if premium: - self._session1 = self.pm1._new_session() + self._session1 = self.pm1._new_session(**kwargs) else: - self._session2 = self.pm2._new_session() + self._session2 = self.pm2._new_session(**kwargs) def _get_page(self, pagerequest: str, premium: bool = False) -> str: @@ -112,7 +112,8 @@ def _get_page(self, pagerequest: str, premium: bool = False) -> str: w = random.uniform(1,2) time.sleep(w) resp = session.get(pagerequest, timeout=timeout) - self.logger.debug("Session proxy config is {}".format(pm._proxies)) + if premium is False: # premium methods may contain sensitive information + self.logger.debug("Session proxy config is {}".format(pm._proxies)) has_captcha = self._requests_has_captcha(resp.text) diff --git a/scholarly/_proxy_generator.py b/scholarly/_proxy_generator.py index 01f7d77..676b28a 100644 --- a/scholarly/_proxy_generator.py +++ b/scholarly/_proxy_generator.py @@ -183,11 +183,11 @@ def _use_proxy(self, http: str, https: str = None) -> bool: :returns: whether or not the proxy was set up successfully :rtype: {bool} """ - if https is None: - https = http if http[:4] != "http": http = "http://" + http - if https[:5] != "https": + if https is None: + https = http + elif https[:5] != "https": https = "https://" + https proxies = {'http://': http, 'https://': https} @@ -205,7 +205,7 @@ def _use_proxy(self, http: str, https: str = None) -> bool: if self._proxy_works: self._proxies = proxies - self._new_session() + self._new_session(proxies=proxies) return self._proxy_works @@ -444,8 +444,9 @@ def _handle_captcha2(self, url): return self._session - def _new_session(self): + def _new_session(self, **kwargs): init_kwargs = {} + init_kwargs.update(kwargs) proxies = {} if self._session: proxies = self._proxies @@ -612,8 +613,9 @@ def ScraperAPI(self, API_KEY, country_code=None, premium=False, render=False): for _ in range(3): proxy_works = self._use_proxy(http=f'{prefix}:{API_KEY}@proxy-server.scraperapi.com:8001') if proxy_works: + proxies = {'http://': f"http://scraperapi:{API_KEY}@proxy-server.scraperapi.com:8001",} self.logger.info("ScraperAPI proxy setup successfully") - self._session.verify = False + self._new_session(verify=False, proxies=proxies) return proxy_works if (r["requestCount"] >= r["requestLimit"]): diff --git a/setup.py b/setup.py index b1a3164..0a78ae0 100644 --- a/setup.py +++ b/setup.py @@ -5,7 +5,7 @@ setuptools.setup( name='scholarly', - version='1.7.7', + version='1.7.8', author='Steven A. Cholewiak, Panos Ipeirotis, Victor Silva, Arun Kannawadi', author_email='[email protected], [email protected], [email protected], [email protected]', description='Simple access to Google Scholar authors and citations',

v1.7.7 is incompatible with ScraperAPI

2022-12-28T02:23:50

0.0

[]

scholarly-python-package/scholarly

scholarly-python-package__scholarly-451

ca4623a20749d826d2db4e0bc101191f1db4ff1b

diff --git a/.github/workflows/lint.yaml b/.github/workflows/lint.yaml index cca9756..4c62384 100644 --- a/.github/workflows/lint.yaml +++ b/.github/workflows/lint.yaml @@ -2,10 +2,6 @@ name: lint on: workflow_call: - push: - branches: [main, develop] - pull_request: - branches: [main, develop] jobs: lint: diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml index 1fd1152..3894ee1 100644 --- a/.github/workflows/pythonpackage.yml +++ b/.github/workflows/pythonpackage.yml @@ -10,11 +10,6 @@ on: branches: [main, develop] pull_request: branches: [main, develop] - workflow_run: - workflows: ["lint"] -# branches: [main] - types: - - completed jobs: lint: diff --git a/scholarly/_scholarly.py b/scholarly/_scholarly.py index faf6ab0..dd27c04 100644 --- a/scholarly/_scholarly.py +++ b/scholarly/_scholarly.py @@ -1,20 +1,24 @@ """scholarly.py""" import requests +import re import os import copy import csv import pprint -from typing import Dict, List +import datetime +import re +from typing import Dict, List, Union from ._navigator import Navigator from ._proxy_generator import ProxyGenerator from dotenv import find_dotenv, load_dotenv from .author_parser import AuthorParser from .publication_parser import PublicationParser, _SearchScholarIterator -from .data_types import Author, AuthorSource, Journal, Publication, PublicationSource +from .data_types import Author, AuthorSource, CitesPerYear, Journal, Publication, PublicationSource _AUTHSEARCH = '/citations?hl=en&view_op=search_authors&mauthors={0}' _KEYWORDSEARCH = '/citations?hl=en&view_op=search_authors&mauthors=label:{0}' _KEYWORDSEARCHBASE = '/citations?hl=en&view_op=search_authors&mauthors={}' +_KEYWORDSEARCH_PATTERN = "[-: #(),;]+" # Unallowed characters in the keywords. _PUBSEARCH = '/scholar?hl=en&q={0}' _CITEDBYSEARCH = '/scholar?hl=en&cites={0}' _ORGSEARCH = "/citations?view_op=view_org&hl=en&org={0}" @@ -155,11 +159,11 @@ def search_pubs(self, sort_by=sort_by, include_last_year=include_last_year, start_index=start_index) return self.__nav.search_publications(url) - def search_citedby(self, publication_id: int, **kwargs): + def search_citedby(self, publication_id: Union[int, str], **kwargs): """Searches by Google Scholar publication id and returns a generator of Publication objects. :param publication_id: Google Scholar publication id - :type publication_id: int + :type publication_id: int or str For the remaining parameters, see documentation of `search_pubs`. """ @@ -248,6 +252,25 @@ def bibtex(self, object: Publication)->str: self.logger.warning("Object not supported for bibtex exportation") return + @staticmethod + def _bin_citations_by_year(cites_per_year: CitesPerYear, year_end): + years = [] + y_hi, y_lo = year_end, year_end + running_count = 0 + for y in sorted(cites_per_year, reverse=True): + if running_count + cites_per_year[y] <= 1000: + running_count += cites_per_year[y] + y_lo = y + else: + running_count = cites_per_year[y] + years.append((y_hi, y_lo)) + y_hi = y + + if running_count > 0: + years.append((y_hi, y_lo)) + + return years + def citedby(self, object: Publication)->_SearchScholarIterator: """Searches Google Scholar for other articles that cite this Publication and returns a Publication generator. @@ -255,13 +278,44 @@ def citedby(self, object: Publication)->_SearchScholarIterator: :param object: The Publication object for the bibtex exportation :type object: Publication """ - if object['container_type'] == "Publication": - publication_parser = PublicationParser(self.__nav) - return publication_parser.citedby(object) - else: + + if object['container_type'] != "Publication": self.logger.warning("Object not supported for bibtex exportation") return + if object["num_citations"] <= 1000: + return PublicationParser(self.__nav).citedby(object) + + self.logger.debug("Since the paper titled %s has %d citations (>1000), " + "fetching it on an annual basis.", object["bib"]["title"], object["num_citations"]) + + year_end = int(datetime.date.today().year) + + if object["source"] == PublicationSource.AUTHOR_PUBLICATION_ENTRY: + self.fill(object) + years = self._bin_citations_by_year(object.get("cites_per_year", {}), year_end) + else: + try: + year_low = int(object["bib"]["pub_year"]) + except KeyError: + self.logger.warning("Unknown publication year for paper %s, may result in incorrect number " + "of citedby papers.", object["bib"]["title"]) + return PublicationParser(self.__nav).citedby(object) + + # Go one year at a time in decreasing order + years = zip(range(year_end, year_low-1, -1), range(year_end, year_low-1, -1)) + + # Extract cites_id. Note: There could be multiple ones, separated by commas. + m = re.search("cites=[\d+,]*", object["citedby_url"]) + pub_id = m.group()[6:] + for y_hi, y_lo in years: + sub_citations = self.search_citedby(publication_id=pub_id, year_low=y_lo, year_high=y_hi) + if sub_citations.total_results and (sub_citations.total_results > 1000): + self.logger.warn("The paper titled %s has %d citations in the year %d. " + "Due to the limitation in Google Scholar, fetching only 1000 results " + "from that year.", object["bib"]["title"], sub_citations.total_results, y_lo) + yield from sub_citations + def search_author_id(self, id: str, filled: bool = False, sortby: str = "citedby", publication_limit: int = 0)->Author: """Search by author id and return a single Author object :param sortby: select the order of the citations in the author page. Either by 'citedby' or 'year'. Defaults to 'citedby'. @@ -321,7 +375,9 @@ def search_keyword(self, keyword: str): 'source': 'SEARCH_AUTHOR_SNIPPETS', 'url_picture': 'https://scholar.google.com/citations?view_op=medium_photo&user=lHrs3Y4AAAAJ'} """ - url = _KEYWORDSEARCH.format(requests.utils.quote(keyword)) + + reg_keyword = re.sub(_KEYWORDSEARCH_PATTERN, "_", keyword) + url = _KEYWORDSEARCH.format(requests.utils.quote(reg_keyword)) return self.__nav.search_authors(url) def search_keywords(self, keywords: List[str]): @@ -355,8 +411,8 @@ def search_keywords(self, keywords: List[str]): 'url_picture': 'https://scholar.google.com/citations?view_op=medium_photo&user=_cMw1IUAAAAJ'} """ - - formated_keywords = ['label:'+requests.utils.quote(keyword) for keyword in keywords] + reg_keywords = (re.sub(_KEYWORDSEARCH_PATTERN, "_", keyword) for keyword in keywords) + formated_keywords = ['label:'+requests.utils.quote(keyword) for keyword in reg_keywords] formated_keywords = '+'.join(formated_keywords) url = _KEYWORDSEARCHBASE.format(formated_keywords) return self.__nav.search_authors(url) diff --git a/setup.py b/setup.py index de9012c..ff1ef97 100644 --- a/setup.py +++ b/setup.py @@ -5,7 +5,7 @@ setuptools.setup( name='scholarly', - version='1.7.2', + version='1.7.3', author='Steven A. Cholewiak, Panos Ipeirotis, Victor Silva, Arun Kannawadi', author_email='[email protected], [email protected], [email protected], [email protected]', description='Simple access to Google Scholar authors and citations',

Query fails to collect data for some keywords, which results in StopIteration in the next() **Describe the bug** Query works with some keywords but triggers StopIteration in the next(search_query) with others even if that keyword works manually on google scholar. **To Reproduce** ``` from scholarly import scholarly, ProxyGenerator # pg = ProxyGenerator() # success = pg.FreeProxies() # scholarly.use_proxy(pg) test = ["reinforcement", "reinforcement learning"] #it words with "reinforcement" but not with "reinforcement learning" search_query = scholarly.search_keyword(test[1]) # Retrieve the first result from the iterator first_author_result = next(search_query) #this line results in the StopIteration error scholarly.pprint(first_author_result) # Retrieve all the details for the author author = scholarly.fill(first_author_result ) scholarly.pprint(author) # Take a closer look at the first publication first_publication = author['publications'][0] first_publication_filled = scholarly.fill(first_publication) scholarly.pprint(first_publication_filled) # Print the titles of the author's publications publication_titles = [pub['bib']['title'] for pub in author['publications']] print(publication_titles) # Which papers cited that publication? citations = [citation['bib']['title'] for citation in scholarly.citedby(first_publication_filled)] print(citations) ``` **Expected behavior** I expected the code to work with any keyword and even with combinations of precise keywords with quotation marks as in the manual search on google scholar. **Desktop (please complete the following information):** - Proxy service: I am new to data crawlers, so I am not sure if I need a service or not - python version: 3.9 - OS: Windows - Version 10 **Do you plan on contributing?** - Not now, but maybe in the future when I learn more about crawlers

Thanks for the bug report. This is indeed a bug caused by the space between the keywords not replaced by a + internally when the URL is generated. This will be fixed in v1.7.3.

2022-10-20T18:00:50

0.0

[]

scholarly-python-package/scholarly

scholarly-python-package__scholarly-409

868ab58f15e841b0a57213f992f572edd075518b

diff --git a/.github/workflows/publish-to-pypi.yml b/.github/workflows/publish-to-pypi.yml index f02572de..b593dd6c 100644 --- a/.github/workflows/publish-to-pypi.yml +++ b/.github/workflows/publish-to-pypi.yml @@ -1,5 +1,7 @@ name: Publish package to PyPI -on: push +on: + push: + branches: [main] jobs: build-n-publish: diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml index fa7d9582..4325ce80 100644 --- a/.github/workflows/pythonpackage.yml +++ b/.github/workflows/pythonpackage.yml @@ -43,7 +43,12 @@ jobs: # uses: typilus/[email protected] - name: Install Chrome uses: browser-actions/setup-chrome@latest - - name: Test with unittest + continue-on-error: true + with: + fail_ci_if_error: false + - name: Run unittests + id: unittests + continue-on-error: true env: CONNECTION_METHOD: ${{ secrets.CONNECTION_METHOD }} PASSWORD: ${{ secrets.PASSWORD }} @@ -52,20 +57,23 @@ jobs: SCRAPER_API_KEY: ${{ secrets.SCRAPER_API_KEY }} run: | coverage run -m unittest -v test_module.TestScholarly - coverage xml - - name: Validate the repository yaml for codecov + - name: Generate coverage report (macOS only) if: - "matrix.os == 'ubuntu-latest'" - run: + "matrix.os == 'macos-latest'" + run: | curl --data-binary @.github/.codecov.yml https://codecov.io/validate | head -n 1 - - name: Upload code coverage + coverage xml + - name: Upload code coverage (macOS only) if: - "matrix.os != 'windows-latest'" + "matrix.os == 'macos-latest'" uses: codecov/codecov-action@v2 with: directory: ./ - fail_ci_if_error: true + fail_ci_if_error: false files: ./coverage.xml flags: unittests name: codecov-umbrella verbose: true + - name: Check if unittests failed + if: "steps.unittests.outcome == 'failure'" + run: exit 1 diff --git a/scholarly/_proxy_generator.py b/scholarly/_proxy_generator.py index 96a96d00..39059dcd 100644 --- a/scholarly/_proxy_generator.py +++ b/scholarly/_proxy_generator.py @@ -49,6 +49,7 @@ def __init__(self): self._tor_control_port = None self._tor_password = None self._session = None + self._webdriver = None self._TIMEOUT = 5 self._new_session() @@ -458,7 +459,10 @@ def _close_session(self): if self._session: self._session.close() if self._webdriver: - self._webdriver.quit() + try: + self._webdriver.quit() + except Exception as e: + self.logger.warning("Could not close webdriver cleanly: %s", e) def _fp_coroutine(self, timeout=1, wait_time=120): """A coroutine to continuosly yield free proxies

Fix test_download_mandates_csv in GHA **Describe the bug** The `test_download_mandates_csv` test case is throwing errors in Github Actions, despite the logic being correct. The test passes locally on my computer. **To Reproduce** ```python python -m unittest -v test_module.TestScholarly.test_download_mandates_csv ``` **Expected behavior** The test should pass without any FAIL or ERROR messages in Github Actions **Screenshots** <img width="937" alt="image" src="https://user-images.githubusercontent.com/5103390/156935746-7ff753a1-15ff-49b2-b7ed-26ce45f2ec76.png"> <img width="781" alt="image" src="https://user-images.githubusercontent.com/5103390/156935762-56fe1b48-cf76-406a-8fc2-71b2c816a7ab.png"> **Desktop (please complete the following information):** - Proxy service: [e.g. ScraperAPI] - python version: [e.g. 3.8] - OS: [e.g. macOS] - Version [e.g. 1.6]

2022-03-26T05:59:49

0.0

[]

scholarly-python-package/scholarly

scholarly-python-package__scholarly-401

24e86d607e391e690b1ac6f2204275e96ac09486

diff --git a/README.md b/README.md index befc5abf..c6404cb3 100644 --- a/README.md +++ b/README.md @@ -57,9 +57,12 @@ If neither installed, `scholarly` will fetch only up to 20 co-authors. ```bash pip3 install scholarly[tor] ``` - + If you use `zsh` (which is now the default in latest macOS), you should type this as + ```zsh + pip3 install scholarly'[tor]' + ``` **Note:** Tor option is unavailable with conda installation. - + ## Tests To check if your installation is succesful, run the tests by executing the `test_module.py` file as: diff --git a/scholarly/_navigator.py b/scholarly/_navigator.py index 19b5c7f4..ef7c4d21 100644 --- a/scholarly/_navigator.py +++ b/scholarly/_navigator.py @@ -163,7 +163,12 @@ def _get_page(self, pagerequest: str, premium: bool = False) -> str: self.logger.info("Retrying with a new session.") tries += 1 - session, timeout = pm.get_next_proxy(num_tries = tries, old_timeout = timeout, old_proxy=session.proxies.get('http', None)) + try: + session, timeout = pm.get_next_proxy(num_tries = tries, old_timeout = timeout, old_proxy=session.proxies.get('http', None)) + except Exception: + self.logger.info("No other secondary connections possible. " + "Using the primary proxy for all requests.") + break # If secondary proxy does not work, try again primary proxy. if not premium: diff --git a/scholarly/_scholarly.py b/scholarly/_scholarly.py index bbd68ba2..a9692756 100644 --- a/scholarly/_scholarly.py +++ b/scholarly/_scholarly.py @@ -2,14 +2,15 @@ import requests import os import copy +import csv import pprint -from typing import List +from typing import Dict, List from ._navigator import Navigator from ._proxy_generator import ProxyGenerator from dotenv import find_dotenv, load_dotenv from .author_parser import AuthorParser from .publication_parser import PublicationParser, _SearchScholarIterator -from .data_types import Author, AuthorSource, Publication, PublicationSource +from .data_types import Author, AuthorSource, Journal, Publication, PublicationSource _AUTHSEARCH = '/citations?hl=en&view_op=search_authors&mauthors={0}' _KEYWORDSEARCH = '/citations?hl=en&view_op=search_authors&mauthors=label:{0}' @@ -17,6 +18,7 @@ _PUBSEARCH = '/scholar?hl=en&q={0}' _CITEDBYSEARCH = '/scholar?hl=en&cites={0}' _ORGSEARCH = "/citations?view_op=view_org&hl=en&org={0}" +_MANDATES_URL = "https://scholar.google.com/citations?view_op=mandates_leaderboard_csv" class _Scholarly: @@ -27,6 +29,13 @@ def __init__(self): self.env = os.environ.copy() self.__nav = Navigator() self.logger = self.__nav.logger + self._journal_categories = None + + @property + def journal_categories(self): + if self._journal_categories is None: + self._journal_categories = self.get_journal_categories() + return self._journal_categories def set_retries(self, num_retries: int)->None: """Sets the number of retries in case of errors @@ -465,6 +474,40 @@ def search_author_by_organization(self, organization_id: int): url = _ORGSEARCH.format(organization_id) return self.__nav.search_authors(url) + def download_mandates_csv(self, filename: str, overwrite: bool = False, + include_links: bool =True): + """ + Download the CSV file of the current mandates. + """ + if (not overwrite) and os.path.exists(filename): + raise ValueError(f"{filename} already exists. Either provide a " + "different filename or allow overwriting by " + "setting overwrite=True") + text = self.__nav._get_page(_MANDATES_URL, premium=False) + if include_links: + soup = self.__nav._get_soup("/citations?view_op=mandates_leaderboard") + text = text.replace("Funder,", "Funder,Policy,Cached,", 1) + for agency in soup.find_all("td", class_="gsc_mlt_t"): + cached = agency.find("span", class_="gs_a").a["href"] + name = agency.a.text + if name != "cached": + policy = agency.a['href'] + else: + name = agency.text[:-10] + policy = "" + + if "," in name: + text = text.replace(f'"{name}",', f'"{name}",{policy},{cached},') + else: + text = text.replace(f"{name},", f"{name},{policy},{cached},") + try: + with open(filename, 'w') as f: + f.write(text) + except IOError: + self.logger.error("Error writing mandates as %s", filename) + finally: + return text + # TODO: Make it a public method in v1.6 def _construct_url(self, baseurl: str, patents: bool = True, citations: bool = True, year_low: int = None, @@ -495,3 +538,86 @@ def _construct_url(self, baseurl: str, patents: bool = True, # improve str below return url + yr_lo + yr_hi + citations + patents + sortby + start + + def get_journal_categories(self): + """ + Get a dict of journal categories and subcategories. + """ + soup = self.__nav._get_soup("/citations?view_op=top_venues&hl=en&vq=en") + categories = {} + for category in soup.find_all("a", class_="gs_md_li"): + if not "vq=" in category['href']: + continue + vq = category['href'].split("&vq=")[1] + categories[category.text] = {} + categories[category.text][None] = vq + + for category in categories: + vq = categories[category][None] + if vq=="en": + continue + soup = self.__nav._get_soup(f"/citations?view_op=top_venues&hl=en&vq={vq}") + for subcategory in soup.find_all("a", class_="gs_md_li"): + if not f"&vq={vq}_" in subcategory['href']: + continue + categories[category][subcategory.text] = subcategory['href'].split("&vq=")[1] + + #print(categories) + return categories + + def get_journals(self, category='English', subcategory=None, include_comments: bool = False) -> Dict[int, Journal]: + try: + cat = self.journal_categories[category] + try: + subcat = cat[subcategory] + url = f"/citations?view_op=top_venues&hl=en&vq={subcat}" + soup = self.__nav._get_soup(url) + + ranks = soup.find_all("td", class_="gsc_mvt_p") + names = soup.find_all("td", class_="gsc_mvt_t") + h5indices = soup.find_all("a", class_="gs_ibl gsc_mp_anchor") + h5medians = soup.find_all("span", class_="gs_ibl") + + + #import pdb; pdb.set_trace() + result = {} + for rank, name, h5index, h5median in zip(ranks, names, h5indices, h5medians): + url_citations = h5index['href'] + comment = "" + if include_comments: + soup = self.__nav._get_soup(url_citations) + try: + for cmt in soup.find_all('ul', class_='gsc_mlhd_list')[1].find_all('li'): + comment += cmt.text+"; " + except IndexError: + pass + result[int(rank.text[:-1])] = Journal(name=name.text, + h5_index=int(h5index.text), + h5_median=int(h5median.text), + url_citations=url_citations, + comment=comment + ) + #print(result) + return result + except KeyError: + raise ValueError("Invalid subcategory: %s for %s. Choose one from %s" % (subcategory, category, cat.keys())) + except KeyError: + raise ValueError("Invalid category: %s. Choose one from %s", category, self.journal_categories.keys()) + + def save_journals_csv(self, filename, category="English", subcategory=None, include_comments=False): + """ + Save a list of journals to a file in CSV format. + """ + journals = self.get_journals(category, subcategory, include_comments) + try: + with open(filename, 'w') as f: + csv_writer = csv.writer(f) + header = ['Publication', 'h5-index', 'h5-median'] + ['Comment']*include_comments + csv_writer.writerow(header) + for rank, journal in journals.items(): + row = [journal['name'], journal['h5_index'], journal['h5_median']] + [journal.get('comment', '')]*include_comments + csv_writer.writerow(row) + except IOError: + self.logger.error("Error writing journals as %s", filename) + finally: + return journals \ No newline at end of file diff --git a/scholarly/author_parser.py b/scholarly/author_parser.py index 740d3e1c..f1f9bd0b 100644 --- a/scholarly/author_parser.py +++ b/scholarly/author_parser.py @@ -56,8 +56,11 @@ def get_author(self, __data)->Author: author['email_domain'] = re.sub(_EMAILAUTHORRE, r'@', email.text) int_class = self._find_tag_class_name(__data, 'a', 'one_int') - interests = __data.find_all('a', class_=int_class) - author['interests'] = [i.text.strip() for i in interests] + if int_class: + interests = __data.find_all('a', class_=int_class) + author['interests'] = [i.text.strip() for i in interests] + else: + author['interests'] = [] citedby_class = self._find_tag_class_name(__data, 'div', 'cby') citedby = __data.find('div', class_=citedby_class) diff --git a/scholarly/data_types.py b/scholarly/data_types.py index c54b20ef..85f9897a 100644 --- a/scholarly/data_types.py +++ b/scholarly/data_types.py @@ -61,6 +61,7 @@ class PublicationSource(str, Enum): ''' PUBLICATION_SEARCH_SNIPPET = "PUBLICATION_SEARCH_SNIPPET" AUTHOR_PUBLICATION_ENTRY = "AUTHOR_PUBLICATION_ENTRY" + JOURNAL_CITATION_LIST = "JOURNAL_CITATION_LIST" class AuthorSource(str, Enum): @@ -134,6 +135,27 @@ class BibEntry(TypedDict, total=False): publisher: str +class Mandate(TypedDict, total=False): + """ + :class:`Mandate <Mandate>` A funding mandate for a given year + + :param agency: name of the funding agency + :param url_policy: url of the policy for this mandate + :param url_policy_cached: url of the policy cached by Google Scholar + :param effective_date: date from which the policy is effective + :param embargo: period within which the article must be publicly available + :param acknowledgement: text in the paper acknowledging the funding + :param grant: grant ID that supported this work + """ + agency: str + url_policy: str + url_policy_cached: str + effective_date: str + embargo: str + acknowledgement: str + grant: str + + class Publication(TypedDict, total=False): """ :class:`Publication <Publication>` object used to represent a publication entry on Google Scholar. @@ -170,6 +192,7 @@ class Publication(TypedDict, total=False): values. (source: AUTHOR_PUBLICATION_ENTRY) :param public_access: Boolean corresponding to whether the article is available or not in accordance with public access mandates. + :param mandates: List of mandates with funding information and public access requirements. :param url_related_articles: the url containing link for related articles of a publication (needs fill() for AUTHOR_PUBLICATION_ENTRIES) :param url_add_sclib: (source: PUBLICATION_SEARCH_SNIPPET) :param url_scholarbib: the url containing links for @@ -189,6 +212,7 @@ class Publication(TypedDict, total=False): cites_per_year: CitesPerYear author_pub_id: str public_access: bool + mandates: List[Mandate] eprint_url: str pub_url: str url_add_sclib: str @@ -248,3 +272,23 @@ class Author(TypedDict, total=False): coauthors: List # List of authors. No self dict functionality available container_type: str source: AuthorSource + +class Journal(TypedDict, total=False): + """ + :class:`Journal <Journal>` object used to represent a journal entry on Google Scholar. + (When source is not specified, the field is present in all sources) + + + :param name: The name of the journal + :param h5-index: h5-index is the h-index for articles published in the journal during the last 5 complete years. + :param h5-median: h5-median for a publication is the median number of citations for the articles that make up its h5-index. + :param url_citations: The URL for the cached citations page of the journal + :param comment: String representing the ranking for the journal in various categories + + """ + + name: str + h5_index: int + h5_median: int + url_citations: str + comment: str diff --git a/scholarly/publication_parser.py b/scholarly/publication_parser.py index 173e06cd..04f40bb5 100644 --- a/scholarly/publication_parser.py +++ b/scholarly/publication_parser.py @@ -2,10 +2,9 @@ import bibtexparser import arrow from bibtexparser.bibdatabase import BibDatabase -from .data_types import BibEntry, Publication, PublicationSource +from .data_types import BibEntry, Mandate, Publication, PublicationSource -_HOST = 'https://scholar.google.com{0}' _SCHOLARPUBRE = r'cites=([\d,]*)' _CITATIONPUB = '/citations?hl=en&view_op=view_citation&citation_for_view={0}' _SCHOLARPUB = '/scholar?hl=en&oi=bibs&cites={0}' @@ -13,6 +12,7 @@ _BIBCITE = '/scholar?q=info:{0}:scholar.google.com/\ &output=cite&scirp={1}&hl=en' _CITEDBYLINK = '/scholar?cites={0}' +_MANDATES_URL = '/citations?view_op=view_mandate&hl=en&citation_for_view={0}' _BIB_MAPPING = { 'ENTRYTYPE': 'pub_type', @@ -48,6 +48,7 @@ class _SearchScholarIterator(object): def __init__(self, nav, url: str): self._url = url + self._pubtype = PublicationSource.PUBLICATION_SEARCH_SNIPPET if "/scholar?" in url else PublicationSource.JOURNAL_CITATION_LIST self._nav = nav self._load_url(url) self.total_results = self._get_total_results() @@ -57,7 +58,7 @@ def _load_url(self, url: str): # this is temporary until setup json file self._soup = self._nav._get_soup(url) self._pos = 0 - self._rows = self._soup.find_all('div', class_='gs_r gs_or gs_scl') + self._rows = self._soup.find_all('div', class_='gs_r gs_or gs_scl') + self._soup.find_all('div', class_='gsc_mpat_ttl') def _get_total_results(self): if self._soup.find("div", class_="gs_pda"): @@ -80,7 +81,7 @@ def __next__(self): if self._pos < len(self._rows): row = self._rows[self._pos] self._pos += 1 - res = self.pub_parser.get_publication(row, PublicationSource.PUBLICATION_SEARCH_SNIPPET) + res = self.pub_parser.get_publication(row, self._pubtype) return res elif self._soup.find(class_='gs_ico gs_ico_nav_next'): url = self._soup.find( @@ -141,6 +142,9 @@ def get_publication(self, __data, pubtype: PublicationSource)->Publication: return self._citation_pub(__data, publication) elif publication['source'] == PublicationSource.PUBLICATION_SEARCH_SNIPPET: return self._scholar_pub(__data, publication) + elif publication['source'] == PublicationSource.JOURNAL_CITATION_LIST: + return publication + # TODO: self._journal_pub(__data, publication) else: return publication @@ -346,6 +350,11 @@ def fill(self, publication: Publication)->Publication: if soup.find('div', class_='gsc_vcd_title_ggi'): publication['eprint_url'] = soup.find( 'div', class_='gsc_vcd_title_ggi').a['href'] + + if publication.get('public_access', None): + publication['mandates'] = [] + self._fill_public_access_mandates(publication) + publication['filled'] = True elif publication['source'] == PublicationSource.PUBLICATION_SEARCH_SNIPPET: bibtex_url = self._get_bibtex(publication['url_scholarbib']) @@ -400,3 +409,31 @@ def _get_bibtex(self, bib_url) -> str: if link.string.lower() == "bibtex": return link.get('href') return '' + + def _fill_public_access_mandates(self, publication: Publication) -> None: + """Fills the public access mandates""" + if publication.get('public_access', None): + soup = self.nav._get_soup(_MANDATES_URL.format(publication['author_pub_id'])) + mandates = soup.find_all('li') + for mandate in mandates: + m = Mandate() + m['agency'] = mandate.find('span', class_='gsc_md_mndt_name').text + m['url_policy'] = mandate.find('div', class_='gsc_md_mndt_title').a['href'] + m['url_policy_cached'] = mandate.find('span', class_='gs_a').a['href'] + for desc in mandate.find_all('div', class_='gsc_md_mndt_desc'): + match = re.search("Effective date: [0-9]{4}/[0-9]{1,2}", desc.text) + if match: + m['effective_date'] = re.sub(pattern="Effective date: ", repl="", + string=desc.text[match.start() : match.end()]) + + match = re.search("Embargo: ", desc.text) + if match: + m['embargo'] = re.sub(pattern="Embargo: ", repl="", string=desc.text[match.end():]) + + if "Grant: " in desc.text: + m['grant'] = desc.text.split("Grant: ")[1] + + if "Funding acknowledgment" in desc.text: + m['acknowledgement'] = desc.find('span', class_='gs_gray').text + + publication['mandates'].append(m) diff --git a/setup.py b/setup.py index 9784f44b..eeb28150 100644 --- a/setup.py +++ b/setup.py @@ -5,7 +5,7 @@ setuptools.setup( name='scholarly', - version='1.5.1', + version='1.6.0', author='Steven A. Cholewiak, Panos Ipeirotis, Victor Silva, Arun Kannawadi', author_email='[email protected], [email protected], [email protected], [email protected]', description='Simple access to Google Scholar authors and citations',

StopIteration reached when fetching free proxies **Describe the bug** When FreeProxies are used, StopIteration may be reached if all of the available proxies have stopped working. This may happen before `scholarly` switches to using premium proxies. **To Reproduce** See https://github.com/arunkannawadi/scholarly/runs/4613129195?check_suite_focus=true#step:7:85 **Expected behavior** When StopIteration is raised, `scholarly` needs to switch to premium proxy. If premium proxy is also FreeProxy, it should exit more graciously with a helpful message. **Screenshots** ![image](https://user-images.githubusercontent.com/5103390/147187722-51a691cd-77b5-495d-a1aa-aeeacfd2d53b.png) **Desktop (please complete the following information):** - Proxy service: FreeProxy - python version: [e.g. 3.8] - OS: ubuntu-latest in CI - Version 1.5.1 **Do you plan on contributing?** Your response below will clarify whether the maintainers can expect you to fix the bug you reported. - [x] Yes, I will create a Pull Request with the bugfix.

2022-03-03T19:41:52

0.0

[]

scholarly-python-package/scholarly

scholarly-python-package__scholarly-400

1b223441a8bc8eb8796d2022e516e600c7a9f17e

diff --git a/README.md b/README.md index befc5abf..c6404cb3 100644 --- a/README.md +++ b/README.md @@ -57,9 +57,12 @@ If neither installed, `scholarly` will fetch only up to 20 co-authors. ```bash pip3 install scholarly[tor] ``` - + If you use `zsh` (which is now the default in latest macOS), you should type this as + ```zsh + pip3 install scholarly'[tor]' + ``` **Note:** Tor option is unavailable with conda installation. - + ## Tests To check if your installation is succesful, run the tests by executing the `test_module.py` file as: diff --git a/scholarly/_navigator.py b/scholarly/_navigator.py index 19b5c7f4..ef7c4d21 100644 --- a/scholarly/_navigator.py +++ b/scholarly/_navigator.py @@ -163,7 +163,12 @@ def _get_page(self, pagerequest: str, premium: bool = False) -> str: self.logger.info("Retrying with a new session.") tries += 1 - session, timeout = pm.get_next_proxy(num_tries = tries, old_timeout = timeout, old_proxy=session.proxies.get('http', None)) + try: + session, timeout = pm.get_next_proxy(num_tries = tries, old_timeout = timeout, old_proxy=session.proxies.get('http', None)) + except Exception: + self.logger.info("No other secondary connections possible. " + "Using the primary proxy for all requests.") + break # If secondary proxy does not work, try again primary proxy. if not premium: diff --git a/scholarly/_scholarly.py b/scholarly/_scholarly.py index bbd68ba2..a9692756 100644 --- a/scholarly/_scholarly.py +++ b/scholarly/_scholarly.py @@ -2,14 +2,15 @@ import requests import os import copy +import csv import pprint -from typing import List +from typing import Dict, List from ._navigator import Navigator from ._proxy_generator import ProxyGenerator from dotenv import find_dotenv, load_dotenv from .author_parser import AuthorParser from .publication_parser import PublicationParser, _SearchScholarIterator -from .data_types import Author, AuthorSource, Publication, PublicationSource +from .data_types import Author, AuthorSource, Journal, Publication, PublicationSource _AUTHSEARCH = '/citations?hl=en&view_op=search_authors&mauthors={0}' _KEYWORDSEARCH = '/citations?hl=en&view_op=search_authors&mauthors=label:{0}' @@ -17,6 +18,7 @@ _PUBSEARCH = '/scholar?hl=en&q={0}' _CITEDBYSEARCH = '/scholar?hl=en&cites={0}' _ORGSEARCH = "/citations?view_op=view_org&hl=en&org={0}" +_MANDATES_URL = "https://scholar.google.com/citations?view_op=mandates_leaderboard_csv" class _Scholarly: @@ -27,6 +29,13 @@ def __init__(self): self.env = os.environ.copy() self.__nav = Navigator() self.logger = self.__nav.logger + self._journal_categories = None + + @property + def journal_categories(self): + if self._journal_categories is None: + self._journal_categories = self.get_journal_categories() + return self._journal_categories def set_retries(self, num_retries: int)->None: """Sets the number of retries in case of errors @@ -465,6 +474,40 @@ def search_author_by_organization(self, organization_id: int): url = _ORGSEARCH.format(organization_id) return self.__nav.search_authors(url) + def download_mandates_csv(self, filename: str, overwrite: bool = False, + include_links: bool =True): + """ + Download the CSV file of the current mandates. + """ + if (not overwrite) and os.path.exists(filename): + raise ValueError(f"{filename} already exists. Either provide a " + "different filename or allow overwriting by " + "setting overwrite=True") + text = self.__nav._get_page(_MANDATES_URL, premium=False) + if include_links: + soup = self.__nav._get_soup("/citations?view_op=mandates_leaderboard") + text = text.replace("Funder,", "Funder,Policy,Cached,", 1) + for agency in soup.find_all("td", class_="gsc_mlt_t"): + cached = agency.find("span", class_="gs_a").a["href"] + name = agency.a.text + if name != "cached": + policy = agency.a['href'] + else: + name = agency.text[:-10] + policy = "" + + if "," in name: + text = text.replace(f'"{name}",', f'"{name}",{policy},{cached},') + else: + text = text.replace(f"{name},", f"{name},{policy},{cached},") + try: + with open(filename, 'w') as f: + f.write(text) + except IOError: + self.logger.error("Error writing mandates as %s", filename) + finally: + return text + # TODO: Make it a public method in v1.6 def _construct_url(self, baseurl: str, patents: bool = True, citations: bool = True, year_low: int = None, @@ -495,3 +538,86 @@ def _construct_url(self, baseurl: str, patents: bool = True, # improve str below return url + yr_lo + yr_hi + citations + patents + sortby + start + + def get_journal_categories(self): + """ + Get a dict of journal categories and subcategories. + """ + soup = self.__nav._get_soup("/citations?view_op=top_venues&hl=en&vq=en") + categories = {} + for category in soup.find_all("a", class_="gs_md_li"): + if not "vq=" in category['href']: + continue + vq = category['href'].split("&vq=")[1] + categories[category.text] = {} + categories[category.text][None] = vq + + for category in categories: + vq = categories[category][None] + if vq=="en": + continue + soup = self.__nav._get_soup(f"/citations?view_op=top_venues&hl=en&vq={vq}") + for subcategory in soup.find_all("a", class_="gs_md_li"): + if not f"&vq={vq}_" in subcategory['href']: + continue + categories[category][subcategory.text] = subcategory['href'].split("&vq=")[1] + + #print(categories) + return categories + + def get_journals(self, category='English', subcategory=None, include_comments: bool = False) -> Dict[int, Journal]: + try: + cat = self.journal_categories[category] + try: + subcat = cat[subcategory] + url = f"/citations?view_op=top_venues&hl=en&vq={subcat}" + soup = self.__nav._get_soup(url) + + ranks = soup.find_all("td", class_="gsc_mvt_p") + names = soup.find_all("td", class_="gsc_mvt_t") + h5indices = soup.find_all("a", class_="gs_ibl gsc_mp_anchor") + h5medians = soup.find_all("span", class_="gs_ibl") + + + #import pdb; pdb.set_trace() + result = {} + for rank, name, h5index, h5median in zip(ranks, names, h5indices, h5medians): + url_citations = h5index['href'] + comment = "" + if include_comments: + soup = self.__nav._get_soup(url_citations) + try: + for cmt in soup.find_all('ul', class_='gsc_mlhd_list')[1].find_all('li'): + comment += cmt.text+"; " + except IndexError: + pass + result[int(rank.text[:-1])] = Journal(name=name.text, + h5_index=int(h5index.text), + h5_median=int(h5median.text), + url_citations=url_citations, + comment=comment + ) + #print(result) + return result + except KeyError: + raise ValueError("Invalid subcategory: %s for %s. Choose one from %s" % (subcategory, category, cat.keys())) + except KeyError: + raise ValueError("Invalid category: %s. Choose one from %s", category, self.journal_categories.keys()) + + def save_journals_csv(self, filename, category="English", subcategory=None, include_comments=False): + """ + Save a list of journals to a file in CSV format. + """ + journals = self.get_journals(category, subcategory, include_comments) + try: + with open(filename, 'w') as f: + csv_writer = csv.writer(f) + header = ['Publication', 'h5-index', 'h5-median'] + ['Comment']*include_comments + csv_writer.writerow(header) + for rank, journal in journals.items(): + row = [journal['name'], journal['h5_index'], journal['h5_median']] + [journal.get('comment', '')]*include_comments + csv_writer.writerow(row) + except IOError: + self.logger.error("Error writing journals as %s", filename) + finally: + return journals \ No newline at end of file diff --git a/scholarly/author_parser.py b/scholarly/author_parser.py index 740d3e1c..f1f9bd0b 100644 --- a/scholarly/author_parser.py +++ b/scholarly/author_parser.py @@ -56,8 +56,11 @@ def get_author(self, __data)->Author: author['email_domain'] = re.sub(_EMAILAUTHORRE, r'@', email.text) int_class = self._find_tag_class_name(__data, 'a', 'one_int') - interests = __data.find_all('a', class_=int_class) - author['interests'] = [i.text.strip() for i in interests] + if int_class: + interests = __data.find_all('a', class_=int_class) + author['interests'] = [i.text.strip() for i in interests] + else: + author['interests'] = [] citedby_class = self._find_tag_class_name(__data, 'div', 'cby') citedby = __data.find('div', class_=citedby_class) diff --git a/scholarly/data_types.py b/scholarly/data_types.py index c54b20ef..85f9897a 100644 --- a/scholarly/data_types.py +++ b/scholarly/data_types.py @@ -61,6 +61,7 @@ class PublicationSource(str, Enum): ''' PUBLICATION_SEARCH_SNIPPET = "PUBLICATION_SEARCH_SNIPPET" AUTHOR_PUBLICATION_ENTRY = "AUTHOR_PUBLICATION_ENTRY" + JOURNAL_CITATION_LIST = "JOURNAL_CITATION_LIST" class AuthorSource(str, Enum): @@ -134,6 +135,27 @@ class BibEntry(TypedDict, total=False): publisher: str +class Mandate(TypedDict, total=False): + """ + :class:`Mandate <Mandate>` A funding mandate for a given year + + :param agency: name of the funding agency + :param url_policy: url of the policy for this mandate + :param url_policy_cached: url of the policy cached by Google Scholar + :param effective_date: date from which the policy is effective + :param embargo: period within which the article must be publicly available + :param acknowledgement: text in the paper acknowledging the funding + :param grant: grant ID that supported this work + """ + agency: str + url_policy: str + url_policy_cached: str + effective_date: str + embargo: str + acknowledgement: str + grant: str + + class Publication(TypedDict, total=False): """ :class:`Publication <Publication>` object used to represent a publication entry on Google Scholar. @@ -170,6 +192,7 @@ class Publication(TypedDict, total=False): values. (source: AUTHOR_PUBLICATION_ENTRY) :param public_access: Boolean corresponding to whether the article is available or not in accordance with public access mandates. + :param mandates: List of mandates with funding information and public access requirements. :param url_related_articles: the url containing link for related articles of a publication (needs fill() for AUTHOR_PUBLICATION_ENTRIES) :param url_add_sclib: (source: PUBLICATION_SEARCH_SNIPPET) :param url_scholarbib: the url containing links for @@ -189,6 +212,7 @@ class Publication(TypedDict, total=False): cites_per_year: CitesPerYear author_pub_id: str public_access: bool + mandates: List[Mandate] eprint_url: str pub_url: str url_add_sclib: str @@ -248,3 +272,23 @@ class Author(TypedDict, total=False): coauthors: List # List of authors. No self dict functionality available container_type: str source: AuthorSource + +class Journal(TypedDict, total=False): + """ + :class:`Journal <Journal>` object used to represent a journal entry on Google Scholar. + (When source is not specified, the field is present in all sources) + + + :param name: The name of the journal + :param h5-index: h5-index is the h-index for articles published in the journal during the last 5 complete years. + :param h5-median: h5-median for a publication is the median number of citations for the articles that make up its h5-index. + :param url_citations: The URL for the cached citations page of the journal + :param comment: String representing the ranking for the journal in various categories + + """ + + name: str + h5_index: int + h5_median: int + url_citations: str + comment: str diff --git a/scholarly/publication_parser.py b/scholarly/publication_parser.py index 173e06cd..04f40bb5 100644 --- a/scholarly/publication_parser.py +++ b/scholarly/publication_parser.py @@ -2,10 +2,9 @@ import bibtexparser import arrow from bibtexparser.bibdatabase import BibDatabase -from .data_types import BibEntry, Publication, PublicationSource +from .data_types import BibEntry, Mandate, Publication, PublicationSource -_HOST = 'https://scholar.google.com{0}' _SCHOLARPUBRE = r'cites=([\d,]*)' _CITATIONPUB = '/citations?hl=en&view_op=view_citation&citation_for_view={0}' _SCHOLARPUB = '/scholar?hl=en&oi=bibs&cites={0}' @@ -13,6 +12,7 @@ _BIBCITE = '/scholar?q=info:{0}:scholar.google.com/\ &output=cite&scirp={1}&hl=en' _CITEDBYLINK = '/scholar?cites={0}' +_MANDATES_URL = '/citations?view_op=view_mandate&hl=en&citation_for_view={0}' _BIB_MAPPING = { 'ENTRYTYPE': 'pub_type', @@ -48,6 +48,7 @@ class _SearchScholarIterator(object): def __init__(self, nav, url: str): self._url = url + self._pubtype = PublicationSource.PUBLICATION_SEARCH_SNIPPET if "/scholar?" in url else PublicationSource.JOURNAL_CITATION_LIST self._nav = nav self._load_url(url) self.total_results = self._get_total_results() @@ -57,7 +58,7 @@ def _load_url(self, url: str): # this is temporary until setup json file self._soup = self._nav._get_soup(url) self._pos = 0 - self._rows = self._soup.find_all('div', class_='gs_r gs_or gs_scl') + self._rows = self._soup.find_all('div', class_='gs_r gs_or gs_scl') + self._soup.find_all('div', class_='gsc_mpat_ttl') def _get_total_results(self): if self._soup.find("div", class_="gs_pda"): @@ -80,7 +81,7 @@ def __next__(self): if self._pos < len(self._rows): row = self._rows[self._pos] self._pos += 1 - res = self.pub_parser.get_publication(row, PublicationSource.PUBLICATION_SEARCH_SNIPPET) + res = self.pub_parser.get_publication(row, self._pubtype) return res elif self._soup.find(class_='gs_ico gs_ico_nav_next'): url = self._soup.find( @@ -141,6 +142,9 @@ def get_publication(self, __data, pubtype: PublicationSource)->Publication: return self._citation_pub(__data, publication) elif publication['source'] == PublicationSource.PUBLICATION_SEARCH_SNIPPET: return self._scholar_pub(__data, publication) + elif publication['source'] == PublicationSource.JOURNAL_CITATION_LIST: + return publication + # TODO: self._journal_pub(__data, publication) else: return publication @@ -346,6 +350,11 @@ def fill(self, publication: Publication)->Publication: if soup.find('div', class_='gsc_vcd_title_ggi'): publication['eprint_url'] = soup.find( 'div', class_='gsc_vcd_title_ggi').a['href'] + + if publication.get('public_access', None): + publication['mandates'] = [] + self._fill_public_access_mandates(publication) + publication['filled'] = True elif publication['source'] == PublicationSource.PUBLICATION_SEARCH_SNIPPET: bibtex_url = self._get_bibtex(publication['url_scholarbib']) @@ -400,3 +409,31 @@ def _get_bibtex(self, bib_url) -> str: if link.string.lower() == "bibtex": return link.get('href') return '' + + def _fill_public_access_mandates(self, publication: Publication) -> None: + """Fills the public access mandates""" + if publication.get('public_access', None): + soup = self.nav._get_soup(_MANDATES_URL.format(publication['author_pub_id'])) + mandates = soup.find_all('li') + for mandate in mandates: + m = Mandate() + m['agency'] = mandate.find('span', class_='gsc_md_mndt_name').text + m['url_policy'] = mandate.find('div', class_='gsc_md_mndt_title').a['href'] + m['url_policy_cached'] = mandate.find('span', class_='gs_a').a['href'] + for desc in mandate.find_all('div', class_='gsc_md_mndt_desc'): + match = re.search("Effective date: [0-9]{4}/[0-9]{1,2}", desc.text) + if match: + m['effective_date'] = re.sub(pattern="Effective date: ", repl="", + string=desc.text[match.start() : match.end()]) + + match = re.search("Embargo: ", desc.text) + if match: + m['embargo'] = re.sub(pattern="Embargo: ", repl="", string=desc.text[match.end():]) + + if "Grant: " in desc.text: + m['grant'] = desc.text.split("Grant: ")[1] + + if "Funding acknowledgment" in desc.text: + m['acknowledgement'] = desc.find('span', class_='gs_gray').text + + publication['mandates'].append(m)

StopIteration reached when fetching free proxies **Describe the bug** When FreeProxies are used, StopIteration may be reached if all of the available proxies have stopped working. This may happen before `scholarly` switches to using premium proxies. **To Reproduce** See https://github.com/arunkannawadi/scholarly/runs/4613129195?check_suite_focus=true#step:7:85 **Expected behavior** When StopIteration is raised, `scholarly` needs to switch to premium proxy. If premium proxy is also FreeProxy, it should exit more graciously with a helpful message. **Screenshots** ![image](https://user-images.githubusercontent.com/5103390/147187722-51a691cd-77b5-495d-a1aa-aeeacfd2d53b.png) **Desktop (please complete the following information):** - Proxy service: FreeProxy - python version: [e.g. 3.8] - OS: ubuntu-latest in CI - Version 1.5.1 **Do you plan on contributing?** Your response below will clarify whether the maintainers can expect you to fix the bug you reported. - [x] Yes, I will create a Pull Request with the bugfix.

2022-03-03T02:58:29

0.0

[]

vippsas/zeroeventhub

vippsas__zeroeventhub-11

655014ea9336d37655981ca037dadd0ae4f041a8

diff --git a/README.md b/README.md index f32e736..7c5de4a 100644 --- a/README.md +++ b/README.md @@ -47,6 +47,7 @@ but the intended uses is closer to AMQP and Kafka. The third component is tooling around this protocol: * [Client/server implementations for Go](go/README.md) +* [Client implementation for Python](python/zeroeventhub/README.md) * [mssql-changefeed](https://github.com/vippsas/mssql-changefeed) offers the primitives needed to use ZeroEventHub together with an Azure SQL database @@ -77,4 +78,4 @@ the aggregate is computed on demand from the event tables. ## Further reading Head to [the specification](SPEC.md) for technical details -and discussions. \ No newline at end of file +and discussions. diff --git a/python/zeroeventhub/.flake8 b/python/zeroeventhub/.flake8 new file mode 100644 index 0000000..129982e --- /dev/null +++ b/python/zeroeventhub/.flake8 @@ -0,0 +1,6 @@ +[flake8] +ignore = F401,W503 +max-line-length = 100 +exclude = + .venv + __pycache__ diff --git a/python/zeroeventhub/.gitignore b/python/zeroeventhub/.gitignore new file mode 100644 index 0000000..617462a --- /dev/null +++ b/python/zeroeventhub/.gitignore @@ -0,0 +1,7 @@ +# vim +**/*.swp + +# python +**/__pycache__/**/* +.coverage +dist diff --git a/python/zeroeventhub/.pylintrc b/python/zeroeventhub/.pylintrc new file mode 100644 index 0000000..667e087 --- /dev/null +++ b/python/zeroeventhub/.pylintrc @@ -0,0 +1,5 @@ +[MAIN] +load-plugins=pylint.extensions.docparams + +[BASIC] +no-docstring-rgx=^$ diff --git a/python/zeroeventhub/README.md b/python/zeroeventhub/README.md new file mode 100644 index 0000000..1415542 --- /dev/null +++ b/python/zeroeventhub/README.md @@ -0,0 +1,74 @@ +# ZeroEventHub + +This README file contains information specific to the Python port of the ZeroEventHub. +Please see the [main readme file](../../README.md) for an overview of what this project is about. + +## Client + +We recommend that you store the latest checkpoint/cursor for each partition in the client's +database. Example of simple single-partition consumption. *Note about the example*: + +* Things starting with "my" is supplied by you +* Things starting with "their" is supplied by the service you connect to + +```python +# Step 1: Setup +their_partition_count = 1 # documented contract with server +zeh_session = requests.Session() # you can setup the authentication on the session +client = zeroeventhub.Client(their_service_url, their_partition_count, zeh_session) + +# Step 2: Load the cursors from last time we ran +cursors = my_get_cursors_from_db() +if not cursors: + # we have never run before, so we can get all events with FIRST_CURSOR + # (if we just want to receive new events from now, we would use LAST_CURSOR) + cursors = [ + zeroeventhub.Cursor(partition_id, zeroeventhub.FIRST_CURSOR) + for partition_id in range(their_partition_count) + ] + +# Step 3: Enter listening loop... +page_of_events = PageEventReceiver() +while myStillWantToReadEvents: + # Step 4: Use ZeroEventHub client to fetch the next page of events. + client.fetch_events( + cursors, + my_page_size_hint, + page_of_events + ) + + # Step 5: Write the effect of changes to our own database and the updated + # cursor value in the same transaction. + with db.begin_transaction() as tx: + my_write_effect_of_events_to_db(tx, page_of_events.events) + + my_write_cursors_to_db(tx, page_of_events.latest_checkpoints) + + tx.commit() + + cursors = page_of_events.latest_checkpoints + + page_of_events.clear() +``` + +## Development + +To run the test suite, assuming you already have Python 3.10 or later installed and on your `PATH`: +```sh +pip install poetry==1.3.1 +poetry config virtualenvs.in-project true +poetry install --sync +poetry run coverage run --branch -m pytest +poetry run coverage html +``` + +Then, you can open the `htmlcov/index.html` file in your browser to look at the code coverage report. + +Also, to pass the CI checks, you may want to run the following before pushing your changes: + +```sh +poetry run pylint ./zeroeventhub/ +poetry run flake8 +poetry run mypy --check-untyped-defs ./tests/ +poetry run mypy --disallow-untyped-defs ./zeroeventhub/ +``` diff --git a/python/zeroeventhub/poetry.lock b/python/zeroeventhub/poetry.lock new file mode 100644 index 0000000..682547a --- /dev/null +++ b/python/zeroeventhub/poetry.lock @@ -0,0 +1,1868 @@ +# This file is automatically @generated by Poetry and should not be changed by hand. + +[[package]] +name = "astroid" +version = "2.13.3" +description = "An abstract syntax tree for Python with inference support." +category = "dev" +optional = false +python-versions = ">=3.7.2" +files = [ + {file = "astroid-2.13.3-py3-none-any.whl", hash = "sha256:14c1603c41cc61aae731cad1884a073c4645e26f126d13ac8346113c95577f3b"}, + {file = "astroid-2.13.3.tar.gz", hash = "sha256:6afc22718a48a689ca24a97981ad377ba7fb78c133f40335dfd16772f29bcfb1"}, +] + +[package.dependencies] +lazy-object-proxy = ">=1.4.0" +typing-extensions = {version = ">=4.0.0", markers = 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b/python/zeroeventhub/zeroeventhub/__init__.py @@ -0,0 +1,8 @@ +"""ZeroEventHub module""" + +from .client import Client +from .cursor import Cursor, FIRST_CURSOR, LAST_CURSOR +from .event_receiver import EventReceiver +from .errors import APIError, ErrCursorsMissing +from .constants import ALL_HEADERS +from .page_event_receiver import Event, PageEventReceiver diff --git a/python/zeroeventhub/zeroeventhub/client.py b/python/zeroeventhub/zeroeventhub/client.py new file mode 100644 index 0000000..3c6a26b --- /dev/null +++ b/python/zeroeventhub/zeroeventhub/client.py @@ -0,0 +1,161 @@ +"""Module containing client-side related code for ZeroEventHub""" + +import json +from typing import Dict, Optional, Any, Sequence +import requests + +from .cursor import Cursor +from .event_receiver import EventReceiver +from .errors import ErrCursorsMissing + + +class Client: + """ + Client-side code to query a ZeroEventHub server to fetch events and pass them to the supplied + EventReceiver instance. + """ + + def __init__( + self, + url: str, + partition_count: int, + session: Optional[requests.Session] = None, + ) -> None: + + """ + Initializes a new instance of the Client class. + + :param url: The base URL for the service. + :param partition_count: The number of partitions the ZeroEventHub server has. + :param session: An optional session under which to make the HTTP requests. + This allows one time setup of authentication etc. on the session, + and increases performance if fetching events frequently due to + connection pooling. + """ + self.url = url + self.partition_count = partition_count + self._client_owns_session = not bool(session) + self._session = session or requests.Session() + + def __del__(self) -> None: + """ + Close the session when this Client instance is destroyed if we created it + during initialization + """ + if self._client_owns_session: + self._session.close() + + @property + def session(self) -> requests.Session: + """Return the session being used by this client.""" + return self._session + + def fetch_events( + self, + cursors: Sequence[Cursor], + page_size_hint: Optional[int], + event_receiver: EventReceiver, + headers: Optional[Sequence[str]] = None, + ) -> None: + """ + Fetch events from the server using the provided context, cursors, page size hint, + event receiver, and headers. + + :param cursors: A sequence of cursors to be used in the request. + :param page_size_hint: An optional hint for the page size of the response. + :param event_receiver: An event receiver to handle the received events. + :param headers: An optional sequence containing event headers desired in the response. + :raises APIError: if cursors are missing. + :raises ValueError: if an exception occurs while the event receiver handles the response. + :raises requests.exceptions.RequestException: if unable to call the endpoint successfully. + :raises requests.HTTPError: if response status code does not indicate success. + :raises json.JSONDecodeError: if a line from the response cannot be decoded into JSON. + """ + self._validate_inputs(cursors) + req = self._build_request(cursors, page_size_hint, headers) + + with self.session.send(req, stream=True) as res: + self._process_response(res, event_receiver) + + def _validate_inputs(self, cursors: Sequence[Cursor]) -> None: + """ + Validate that the input cursors are not empty. + + :param cursors: A sequence of cursors to be used in the request. + :raises APIError: if cursors are missing. + """ + if not cursors: + raise ErrCursorsMissing + + def _build_request( + self, + cursors: Sequence[Cursor], + page_size_hint: Optional[int], + headers: Optional[Sequence[str]], + ) -> requests.PreparedRequest: + """ + Build the http request using the provided inputs. + + :param cursors: A sequence of cursors to be used in the request. + :param page_size_hint: An optional hint for the page size of the response. + :param headers: An optional sequence containing event headers desired in the response. + :return: the http request + """ + params: Dict[str, str | int] = { + "n": self.partition_count, + } + if page_size_hint: + params["pagesizehint"] = page_size_hint + + for cursor in cursors: + params[f"cursor{cursor.partition_id}"] = cursor.cursor + + if headers: + params["headers"] = ",".join(headers) + + return requests.Request("GET", self.url, params=params).prepare() + + def _process_response(self, res: requests.Response, event_receiver: EventReceiver) -> None: + """ + Process the response from the server. + + :param res: the server response + :param event_receiver: An event receiver to handle the received events. + :raises requests.HTTPError: if response status code does not indicate success. + :raises json.JSONDecodeError: if a line from the response cannot be decoded into JSON. + :raises ValueError: error while EventReceiver handles checkpoint or event. + """ + res.raise_for_status() + + for line in res.iter_lines(): + checkpoint_or_event = json.loads(line) + self._process_checkpoint_or_event(checkpoint_or_event, event_receiver) + + def _process_checkpoint_or_event( + self, checkpoint_or_event: Dict[str, Any], event_receiver: EventReceiver + ) -> None: + """ + Process a line of response from the server. + + :param checkpoint_or_event: A dictionary containing a checkpoint or event. + :param event_receiver: An event receiver to handle the received events. + :raises ValueError: if an error occurred in the event receiver while + the event or checkpoint was being processed. + """ + + if checkpoint_or_event.get("cursor", None) is not None: + try: + event_receiver.checkpoint( + checkpoint_or_event["partition"], checkpoint_or_event["cursor"] + ) + except Exception as error: + raise ValueError("error while receiving checkpoint") from error + else: + try: + event_receiver.event( + checkpoint_or_event["partition"], + checkpoint_or_event.get("headers", None), + checkpoint_or_event["data"], + ) + except Exception as error: + raise ValueError("error while receiving event") from error diff --git a/python/zeroeventhub/zeroeventhub/constants.py b/python/zeroeventhub/zeroeventhub/constants.py new file mode 100644 index 0000000..7062506 --- /dev/null +++ b/python/zeroeventhub/zeroeventhub/constants.py @@ -0,0 +1,7 @@ +"""Module containing constants which are relevant for both Client and Server""" + +ALL_HEADERS = ("_all",) +""" +ALL_HEADERS is a special value for `headers` argument representing a request for returning +all headers available. +""" diff --git a/python/zeroeventhub/zeroeventhub/cursor.py b/python/zeroeventhub/zeroeventhub/cursor.py new file mode 100644 index 0000000..3633273 --- /dev/null +++ b/python/zeroeventhub/zeroeventhub/cursor.py @@ -0,0 +1,23 @@ +"""This module defines a Cursor dataclass for use by the client and server""" + +from dataclasses import dataclass + + +@dataclass +class Cursor: + """ + A dataclass encapsulating both the partition ID and the actual cursor within this partition. + + :param partition_id: The partition ID + :param cursor: The cursor within the partition + """ + + partition_id: int + cursor: str + + +FIRST_CURSOR = "_first" +"""FIRST_CURSOR is a special cursor: starts at the first event.""" + +LAST_CURSOR = "_last" +"""LAST_CURSOR is a special cursor: starts at the last available event.""" diff --git a/python/zeroeventhub/zeroeventhub/errors.py b/python/zeroeventhub/zeroeventhub/errors.py new file mode 100644 index 0000000..1688696 --- /dev/null +++ b/python/zeroeventhub/zeroeventhub/errors.py @@ -0,0 +1,30 @@ +""" +This module defines a custom error class, APIError, and several specific error instances that can +be used to indicate specific error conditions in an API. + +The specific error instances (e.g. `ErrCursorsMissing`) are pre-defined instances +of the APIError class, with specific error messages and codes. They can be used to indicate +specific error conditions in your API. +""" + + +class APIError(ValueError): + """ + APIError has two attributes, `message` and `code`, which are set during initialization. The + `message` attribute represents a human-readable error message, and the `code` attribute is an + HTTP status code that can be used to indicate the type of error that occurred. + """ + + def __init__(self, message: str, code: int): + self.message = message + self.code = code + + def __str__(self) -> str: + return self.message + + def status(self) -> int: + """Return the HTTP status code associated with this APIError""" + return self.code + + +ErrCursorsMissing = APIError("cursors are missing", 400) diff --git a/python/zeroeventhub/zeroeventhub/event_receiver.py b/python/zeroeventhub/zeroeventhub/event_receiver.py new file mode 100644 index 0000000..f7a0dcd --- /dev/null +++ b/python/zeroeventhub/zeroeventhub/event_receiver.py @@ -0,0 +1,28 @@ +"""Module to define the EventReceiver interface""" + +from typing import Protocol, Dict, Any, Optional + + +class EventReceiver(Protocol): + """ + EventReceiver is an interface describing an abstraction for handling either + events or checkpoints. + Checkpoint in this context is basically a cursor. + """ + + def event(self, partition_id: int, headers: Optional[Dict[str, str]], data: Any) -> None: + """ + Event method processes actual events. + + :param partition_id: the partition id + :param headers: the headers + :param data: the data + """ + + def checkpoint(self, partition_id: int, cursor: str) -> None: + """ + Checkpoint method processes cursors. + + :param partition_id: the partition id + :param cursor: the cursor + """ diff --git a/python/zeroeventhub/zeroeventhub/page_event_receiver.py b/python/zeroeventhub/zeroeventhub/page_event_receiver.py new file mode 100644 index 0000000..f0d92db --- /dev/null +++ b/python/zeroeventhub/zeroeventhub/page_event_receiver.py @@ -0,0 +1,70 @@ +"""Module to make it easy to receive a page of events""" + +from typing import Dict, Any, Sequence, Optional, List +from dataclasses import dataclass +from .event_receiver import EventReceiver +from .cursor import Cursor + + +@dataclass +class Event: + """All properties received relating to a certain event.""" + partition_id: int + headers: Optional[Dict[str, str]] + data: Any + + +class PageEventReceiver(EventReceiver): + """ + Receive a page of events + """ + + def __init__(self) -> None: + """Initialize the PageEventReceiver with empty state.""" + self._events: List[Event] = [] + self._checkpoints: List[Cursor] = [] + self._latest_checkpoints: Dict[int, str] = {} + + def clear(self) -> None: + """Clear the received events and checkpoints, ready to handle a new page.""" + self._events.clear() + self._checkpoints.clear() + self._latest_checkpoints.clear() + + @property + def events(self) -> Sequence[Event]: + """Return the page of events received.""" + return self._events + + @property + def checkpoints(self) -> Sequence[Cursor]: + """Return the page of checkpoints received.""" + return self._checkpoints + + @property + def latest_checkpoints(self) -> Sequence[Cursor]: + """Only return the latest checkpoint for each partition.""" + return [ + Cursor(partition_id, cursor) + for partition_id, cursor in self._latest_checkpoints.items() + ] + + def event(self, partition_id: int, headers: Optional[Dict[str, str]], data: Any) -> None: + """ + Add the given event to the list. + + :param partition_id: the partition id + :param headers: the headers + :param data: the data + """ + self._events.append(Event(partition_id, headers, data)) + + def checkpoint(self, partition_id: int, cursor: str) -> None: + """ + Add the given cursor to the list. + + :param partition_id: the partition id + :param cursor: the cursor + """ + self._checkpoints.append(Cursor(partition_id, cursor)) + self._latest_checkpoints[partition_id] = cursor

Create Python client library for zero event hub

2023-02-03T13:45:34

0.0

[]

entropicalabs/openqaoa

entropicalabs__openqaoa-291

9053e7883b5f35a9b01fe5795bd35065d47b202c

diff --git a/CHANGELOG.md b/CHANGELOG.md index 1c41097fd..a680ea372 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,3 +1,10 @@ +## Version v0.2.3 (October 25th, 2023) + +This version brings the following changes: +* Fix QPU tests due to provider changes +* Fix `backend_qpu`/`device.backend_device` duplicate +* Compatibility of QAOA and RQAOA with Azure Quantum sessions + ## Version v0.2.2 (September 20th, 2023) This version fixes the version of `pyquil` to prevent breaking changes occuring with v4.0.0. @@ -11,7 +18,7 @@ This version fixes an issue detecting the plugins when installing `openqaoa-core This new version brings an import change to OpenQAOA, separately installable plugins to access different providers. -## What's changed +### What's changed This version brings the following new features: * Independently installable plugins @@ -27,7 +34,7 @@ This version brings the following new features: ## Version v0.1.3 (April 21st, 2023) This version brings a set of new features in OpenQAOA -## What's Changed +### What's Changed * QAOA workflows now support `evaluate_circuit` which lets compute the energy and measurement outcomes for a QAOA circuit for a specified set of parameter values. * The library design-wise is moving closer to modularization goal where each hardware provider backend can be installed independently (this feature is expected to fully functional in the next release) * OpenQAOA v0.1.3 brings in a list of new optimization problems contributed by @alemonbar, including: @@ -48,7 +55,7 @@ This release brings the following new features: * The ability to plug in custom qubit routing solutions for QAOA circuits. * AWS managed jobs are now supported through OpenQAOA -## What's Changed +### What's Changed * Refactor * The new `GateMapLabel` introduces an updated and a more consistent way to label QAOA gates in the circuit. @@ -63,7 +70,7 @@ This is OpenQAOA's first major release! V0.1.0 contains many new features and qu **notice**: the license has been changed from `apache 2.0` to `MIT` -## What's Changed +### What's Changed * Refactor * The code underwent a considerable refactoring effort. The most noticeable change is in the new `openqaoa-core` and other library plugins in the form `openqaoa-xyz`. @@ -85,7 +92,7 @@ This is OpenQAOA's first major release! V0.1.0 contains many new features and qu This release brings improvements to RQAOA workflow and AWS authentication, and a bugfix to TSP problem class. -## What's Changed +### What's Changed * Refactor * Authentication Refactor by @shahidee44 in https://github.com/entropicalabs/openqaoa/pull/126 * RQAOA workflow by @raulconchello in https://github.com/entropicalabs/openqaoa/pull/109 @@ -105,7 +112,7 @@ This release brings improvements to RQAOA workflow and AWS authentication, and a A release to fix two QPU-related bugs -## What's Changed +### What's Changed * Fixes * Now save_intermediate works for backends with no jobid #110 * Fix a bug that prevented the correct usage of Rigetti's QPUs when on QCS #116 diff --git a/src/openqaoa-braket/openqaoa_braket/backends/qaoa_braket_qpu.py b/src/openqaoa-braket/openqaoa_braket/backends/qaoa_braket_qpu.py index 829d76bd8..d996490c5 100644 --- a/src/openqaoa-braket/openqaoa_braket/backends/qaoa_braket_qpu.py +++ b/src/openqaoa-braket/openqaoa_braket/backends/qaoa_braket_qpu.py @@ -99,9 +99,9 @@ def __init__( "Cannot attach a bigger circuit" " to the QAOA routine" ) - if self.device.provider_connected and self.device.qpu_connected: - self.backend_qpu = self.device.backend_device - elif ( + # if self.device.provider_connected and self.device.qpu_connected: + # self.backend_qpu = self.device.backend_device + if ( self.device.provider_connected is True and self.device.qpu_connected is False ): @@ -114,7 +114,7 @@ def __init__( raise Exception( "Connection to AWS was made. A device name was not specified." ) - else: + elif not (self.device.provider_connected and self.device.qpu_connected): raise Exception("Error connecting to AWS.") if self.device.n_qubits < self.n_qubits: @@ -217,7 +217,7 @@ def get_counts(self, params: QAOAVariationalBaseParams, n_shots=None) -> dict: max_job_retries = 5 while job_state == False: - job = self.backend_qpu.run( + job = self.device.backend_device.run( circuit, (self.device.s3_bucket_name, self.device.folder_name), shots=n_shots, diff --git a/src/openqaoa-core/openqaoa/algorithms/rqaoa/rqaoa_workflow.py b/src/openqaoa-core/openqaoa/algorithms/rqaoa/rqaoa_workflow.py index 507e9ca7f..4adee4ba4 100644 --- a/src/openqaoa-core/openqaoa/algorithms/rqaoa/rqaoa_workflow.py +++ b/src/openqaoa-core/openqaoa/algorithms/rqaoa/rqaoa_workflow.py @@ -1,5 +1,6 @@ import time import numpy as np +from typing import Optional, Callable from .rqaoa_workflow_properties import RqaoaParameters from ..baseworkflow import Workflow, check_compiled @@ -11,10 +12,19 @@ ground_state_hamiltonian, exp_val_hamiltonian_termwise, generate_timestamp, + get_mixer_hamiltonian, +) +from ...qaoa_components import ( + Hamiltonian, + QAOADescriptor, + create_qaoa_variational_params, ) from ...backends.qaoa_analytical_sim import QAOABackendAnalyticalSimulator from . import rqaoa_utils from .rqaoa_result import RQAOAResult +from ...backends.wrapper import SPAMTwirlingWrapper +from ...optimizers.qaoa_optimizer import get_optimizer +from ...backends.qaoa_backend import get_qaoa_backend class RQAOA(Workflow): @@ -295,7 +305,7 @@ def compile(self, problem: QUBO = None, verbose: bool = False): ), f"Schedule is incomplete, add {np.abs(n_qubits - n_cutoff - counter) - sum(self.rqaoa_parameters.steps)} more eliminations" # Create the qaoa object with the properties - self.__q = QAOA(self.device) + self.__q = WrappedQAOA(self.device) self.__q.circuit_properties = self.circuit_properties self.__q.backend_properties = self.backend_properties self.__q.classical_optimizer = self.classical_optimizer @@ -307,6 +317,8 @@ def compile(self, problem: QUBO = None, verbose: bool = False): "algorithm" ] = "qaoa" # change the algorithm name to qaoa, since this is a qaoa object + # connect to the QPU specified + self.device.check_connection() # compile qaoa object self.__q.compile(problem, verbose=verbose) @@ -610,3 +622,150 @@ def _serializable_dict( ]["input_parameters"]["rqaoa_parameters"]["n_cutoff"] return serializable_dict + + +class WrappedQAOA(QAOA): + """ + A class implementing the QAOA object to use in RQAOA, here check_connection() is used outside of the QAOA + compilation to make sure it's only used once. + + Args: + QAOA (_type_): _description_ + """ + + def __init__(self, device=DeviceLocal("vectorized")): + super().__init__(device) + + def compile( + self, + problem: QUBO = None, + verbose: bool = False, + routing_function: Optional[Callable] = None, + ): + """ + Override of QAOA.compile(). Initialise the trainable parameters for QAOA according to the specified + strategies and by passing the problem statement. The device.compile() is called in RQAOA.compile() only. + + .. note:: + Compilation is necessary because it is the moment where the problem statement and + the QAOA instructions are used to build the actual QAOA circuit. + + .. tip:: + Set Verbose to false if you are running batch computations! + + Parameters + ---------- + problem: `Problem` + QUBO problem to be solved by QAOA + verbose: bool + Set True to have a summary of QAOA to displayed after compilation + """ + # if isinstance(routing_function,Callable): + # #assert that routing_function is supported only for Standard QAOA. + # if ( + # self.backend_properties.append_state is not None or\ + # self.backend_properties.prepend_state is not None or\ + # self.circuit_properties.mixer_hamiltonian is not 'x' or\ + + # ) + + # we compile the method of the parent class to genereate the id and + # check the problem is a QUBO object and save it + Workflow.compile(self, problem=problem) + + self.cost_hamil = Hamiltonian.classical_hamiltonian( + terms=problem.terms, coeffs=problem.weights, constant=problem.constant + ) + + self.mixer_hamil = get_mixer_hamiltonian( + n_qubits=self.cost_hamil.n_qubits, + mixer_type=self.circuit_properties.mixer_hamiltonian, + qubit_connectivity=self.circuit_properties.mixer_qubit_connectivity, + coeffs=self.circuit_properties.mixer_coeffs, + ) + + self.qaoa_descriptor = QAOADescriptor( + self.cost_hamil, + self.mixer_hamil, + p=self.circuit_properties.p, + routing_function=routing_function, + device=self.device, + ) + self.variate_params = create_qaoa_variational_params( + qaoa_descriptor=self.qaoa_descriptor, + params_type=self.circuit_properties.param_type, + init_type=self.circuit_properties.init_type, + variational_params_dict=self.circuit_properties.variational_params_dict, + linear_ramp_time=self.circuit_properties.linear_ramp_time, + q=self.circuit_properties.q, + seed=self.circuit_properties.seed, + total_annealing_time=self.circuit_properties.annealing_time, + ) + + backend_dict = self.backend_properties.__dict__.copy() + + self.backend = get_qaoa_backend( + qaoa_descriptor=self.qaoa_descriptor, + device=self.device, + **backend_dict, + ) + + # Implementing SPAM Twirling error mitigation requires wrapping the backend. + # However, the BaseWrapper can have many more use cases. + if ( + self.error_mitigation_properties.error_mitigation_technique + == "spam_twirling" + ): + self.backend = SPAMTwirlingWrapper( + backend=self.backend, + n_batches=self.error_mitigation_properties.n_batches, + calibration_data_location=self.error_mitigation_properties.calibration_data_location, + ) + + self.optimizer = get_optimizer( + vqa_object=self.backend, + variational_params=self.variate_params, + optimizer_dict=self.classical_optimizer.asdict(), + ) + + # Set the header properties + self.header["target"] = self.device.device_name + self.header["cloud"] = self.device.device_location + + metadata = { + "p": self.circuit_properties.p, + "param_type": self.circuit_properties.param_type, + "init_type": self.circuit_properties.init_type, + "optimizer_method": self.classical_optimizer.method, + } + + self.set_exp_tags(tags=metadata) + + self.compiled = True + + if verbose: + print("\t \033[1m ### Summary ###\033[0m") + print("OpenQAOA has been compiled with the following properties") + print( + f"Solving QAOA with \033[1m {self.device.device_name} \033[0m on" + f"\033[1m{self.device.device_location}\033[0m" + ) + print( + f"Using p={self.circuit_properties.p} with {self.circuit_properties.param_type}" + f"parameters initialized as {self.circuit_properties.init_type}" + ) + + if hasattr(self.backend, "n_shots"): + print( + f"OpenQAOA will optimize using \033[1m{self.classical_optimizer.method}" + f"\033[0m, with up to \033[1m{self.classical_optimizer.maxiter}" + f"\033[0m maximum iterations. Each iteration will contain" + f"\033[1m{self.backend_properties.n_shots} shots\033[0m" + ) + else: + print( + f"OpenQAOA will optimize using \033[1m{self.classical_optimizer.method}\033[0m," + "with up to \033[1m{self.classical_optimizer.maxiter}\033[0m maximum iterations" + ) + + return None diff --git a/src/openqaoa-core/openqaoa/qaoa_components/variational_parameters/variational_baseparams.py b/src/openqaoa-core/openqaoa/qaoa_components/variational_parameters/variational_baseparams.py index eb628010e..6f057a462 100644 --- a/src/openqaoa-core/openqaoa/qaoa_components/variational_parameters/variational_baseparams.py +++ b/src/openqaoa-core/openqaoa/qaoa_components/variational_parameters/variational_baseparams.py @@ -62,36 +62,35 @@ def __str__(self): def mixer_1q_angles(self) -> np.ndarray: """2D array with the X-rotation angles. - 1st index goes over p and the 2nd index over the qubits to - apply X-rotations on. + 1st index goes over p and the 2nd index over the qubits, + to apply X-rotations on. """ raise NotImplementedError() @property def mixer_2q_angles(self) -> np.ndarray: - """2D array with the X-rotation angles. + """2D array with the XX and YY-rotation angles. - 1st index goes over p and the 2nd index over the qubits to - apply X-rotations on. + 1st index goes over p and the 2nd index over the qubit pairs, + to apply XX and YY-rotations on. """ raise NotImplementedError() @property def cost_1q_angles(self) -> np.ndarray: - """2D array with the ZZ-rotation angles. + """2D array with the Z-rotation angles. - 1st index goes over the p and the 2nd index over the qubit - pairs, to apply ZZ-rotations on. + 1st index goes over the p and the 2nd index over the qubits, + to apply Z-rotations on. """ raise NotImplementedError() @property def cost_2q_angles(self) -> np.ndarray: - """2D array with Z-rotation angles. + """2D array with ZZ-rotation angles. 1st index goes over the p and the 2nd index over the qubit - pairs, to apply Z-rotations on. These are needed by - ``qaoa.cost_function.make_qaoa_memory_map`` + pairs, to apply ZZ-rotations on. """ raise NotImplementedError() diff --git a/src/openqaoa-qiskit/openqaoa_qiskit/backends/qaoa_qiskit_qpu.py b/src/openqaoa-qiskit/openqaoa_qiskit/backends/qaoa_qiskit_qpu.py index 0a2e27cdb..874e1096b 100644 --- a/src/openqaoa-qiskit/openqaoa_qiskit/backends/qaoa_qiskit_qpu.py +++ b/src/openqaoa-qiskit/openqaoa_qiskit/backends/qaoa_qiskit_qpu.py @@ -105,9 +105,9 @@ def __init__( "Cannot attach a bigger circuit" "to the QAOA routine" ) - if self.device.provider_connected and self.device.qpu_connected: - self.backend_qpu = self.device.backend_device - elif self.device.provider_connected and self.device.qpu_connected in [ + # if self.device.provider_connected and self.device.qpu_connected: + # self.backend_qpu = self.device.backend_device + if self.device.provider_connected and self.device.qpu_connected in [ False, None, ]: @@ -117,7 +117,7 @@ def __init__( ) ) - else: + elif not (self.device.provider_connected and self.device.qpu_connected): raise Exception( "Error connecting to {}.".format(self.device.device_location.upper()) ) @@ -160,7 +160,7 @@ def qaoa_circuit(self, params: QAOAVariationalBaseParams) -> QuantumCircuit: if self.qaoa_descriptor.routed is True: transpiled_circuit = transpile( circuit_with_angles, - self.backend_qpu, + self.device.backend_device, initial_layout=self.initial_qubit_mapping, optimization_level=self.qiskit_optimziation_level, routing_method="none", @@ -168,7 +168,7 @@ def qaoa_circuit(self, params: QAOAVariationalBaseParams) -> QuantumCircuit: else: transpiled_circuit = transpile( circuit_with_angles, - self.backend_qpu, + self.device.backend_device, initial_layout=self.initial_qubit_mapping, optimization_level=self.qiskit_optimziation_level, ) @@ -241,7 +241,7 @@ def get_counts(self, params: QAOAVariationalBaseParams, n_shots=None) -> dict: # initial_layout only passed if not azure device # if type(self.device).__name__ == "DeviceAzure": # job = self.backend_qpu.run(circuit, **input_items) - job = self.backend_qpu.run(circuit, shots=n_shots) + job = self.device.backend_device.run(circuit, shots=n_shots) api_contact = False no_of_api_retries = 0

Fix: Typo in docstring ## Description Fix docstring typo for the following in `variational_baseparams.py`: - cost_1q_angles - cost_2q_angles - mixer_2q_angles ## Checklist [//]: <> (- [ ] My code follows the style guidelines of this project) - [X] I have performed a self-review of my code. - [X] My changes generate no new warnings - [X] New and existing unit tests pass locally with my changes. [//]: <> (- [ ] Any dependent changes have been merged and published in downstream modules)

2023-10-25T03:21:19

0.0

[]

entropicalabs/openqaoa

entropicalabs__openqaoa-284

750dce1cbc2614629db7a76d87b34d068357d77c

diff --git a/CHANGELOG.md b/CHANGELOG.md index 9eb1373d..1c41097f 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,3 +1,11 @@ +## Version v0.2.2 (September 20th, 2023) + +This version fixes the version of `pyquil` to prevent breaking changes occuring with v4.0.0. + +## Version v0.2.1 (September 11th, 2023) + +This version fixes an issue detecting the plugins when installing `openqaoa-core` from PyPI. + ## Version v0.2.0 (September 8th, 2023) This new version brings an import change to OpenQAOA, separately installable plugins to access diff --git a/_version.py b/_version.py index edbf19f9..b5fdc753 100644 --- a/_version.py +++ b/_version.py @@ -1,1 +1,1 @@ -__version__ = "0.2.0-rc1" +__version__ = "0.2.2" diff --git a/src/openqaoa-azure/openqaoa_azure/_version.py b/src/openqaoa-azure/openqaoa_azure/_version.py index edbf19f9..b5fdc753 100644 --- a/src/openqaoa-azure/openqaoa_azure/_version.py +++ b/src/openqaoa-azure/openqaoa_azure/_version.py @@ -1,1 +1,1 @@ -__version__ = "0.2.0-rc1" +__version__ = "0.2.2" diff --git a/src/openqaoa-braket/openqaoa_braket/_version.py b/src/openqaoa-braket/openqaoa_braket/_version.py index edbf19f9..b5fdc753 100644 --- a/src/openqaoa-braket/openqaoa_braket/_version.py +++ b/src/openqaoa-braket/openqaoa_braket/_version.py @@ -1,1 +1,1 @@ -__version__ = "0.2.0-rc1" +__version__ = "0.2.2" diff --git a/src/openqaoa-core/openqaoa/_version.py b/src/openqaoa-core/openqaoa/_version.py index edbf19f9..b5fdc753 100644 --- a/src/openqaoa-core/openqaoa/_version.py +++ b/src/openqaoa-core/openqaoa/_version.py @@ -1,1 +1,1 @@ -__version__ = "0.2.0-rc1" +__version__ = "0.2.2" diff --git a/src/openqaoa-core/openqaoa/backends/plugin_finder.py b/src/openqaoa-core/openqaoa/backends/plugin_finder.py index d0d9b7d5..fd5d1122 100644 --- a/src/openqaoa-core/openqaoa/backends/plugin_finder.py +++ b/src/openqaoa-core/openqaoa/backends/plugin_finder.py @@ -13,7 +13,7 @@ def plugin_finder_dict() -> list: if sys.version_info >= (3, 10): available_plugins = entry_points().select(group="openqaoa.plugins") else: - available_plugins = entry_points()["openqaoa.plugins"] + available_plugins = entry_points().get("openqaoa.plugins", []) output_dict = dict() for each_plugin_entry_point in available_plugins: diff --git a/src/openqaoa-core/openqaoa/utilities.py b/src/openqaoa-core/openqaoa/utilities.py index 262bee49..c42391a3 100644 --- a/src/openqaoa-core/openqaoa/utilities.py +++ b/src/openqaoa-core/openqaoa/utilities.py @@ -8,6 +8,7 @@ import numpy as np import uuid import matplotlib.pyplot as plt +from matplotlib import colormaps import networkx as nx import datetime @@ -424,7 +425,7 @@ def plot_graph(G: nx.Graph, ax=None, colormap="seismic") -> None: """ # Create plot figure - fig = plt.figure(figsize=(10, 6)) + fig, ax = plt.subplots(figsize=(10, 6), ncols=1) # Extract all graph attributes biases_and_nodes = nx.get_node_attributes(G, "weight") @@ -446,7 +447,7 @@ def plot_graph(G: nx.Graph, ax=None, colormap="seismic") -> None: cmap=cmap, norm=plt.Normalize(vmin=edge_vmin, vmax=edge_vmax) ) # Add colormap to plot - cbar = plt.colorbar(sm, pad=0.08) + cbar = plt.colorbar(sm, ax=ax, pad=0.08) cbar.ax.set_ylabel("Edge Weights", rotation=270, labelpad=15) else: weights = [1] * len(G.edges()) @@ -460,7 +461,7 @@ def plot_graph(G: nx.Graph, ax=None, colormap="seismic") -> None: vmin = min(biases) vmax = max(biases) sm2 = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=vmin, vmax=vmax)) - cbar2 = plt.colorbar(sm2, location="left") + cbar2 = plt.colorbar(sm2, ax=ax, location="left") cbar2.ax.set_ylabel("Single Qubit Biases", rotation=90) # Draw graph diff --git a/src/openqaoa-core/requirements.txt b/src/openqaoa-core/requirements.txt index 876a2b7d..2f114503 100644 --- a/src/openqaoa-core/requirements.txt +++ b/src/openqaoa-core/requirements.txt @@ -9,3 +9,4 @@ autograd>=1.4 semantic_version>=2.10 autoray>=0.3.1 requests +ipython>=8.2.0 diff --git a/src/openqaoa-pyquil/openqaoa_pyquil/_version.py b/src/openqaoa-pyquil/openqaoa_pyquil/_version.py index edbf19f9..b5fdc753 100644 --- a/src/openqaoa-pyquil/openqaoa_pyquil/_version.py +++ b/src/openqaoa-pyquil/openqaoa_pyquil/_version.py @@ -1,1 +1,1 @@ -__version__ = "0.2.0-rc1" +__version__ = "0.2.2" diff --git a/src/openqaoa-pyquil/requirements.txt b/src/openqaoa-pyquil/requirements.txt index cde21f45..19adc00d 100644 --- a/src/openqaoa-pyquil/requirements.txt +++ b/src/openqaoa-pyquil/requirements.txt @@ -1,1 +1,1 @@ -pyquil>=3.1.0 \ No newline at end of file +pyquil>=3.1.0,<4.0.0 \ No newline at end of file diff --git a/src/openqaoa-qiskit/openqaoa_qiskit/_version.py b/src/openqaoa-qiskit/openqaoa_qiskit/_version.py index edbf19f9..b5fdc753 100644 --- a/src/openqaoa-qiskit/openqaoa_qiskit/_version.py +++ b/src/openqaoa-qiskit/openqaoa_qiskit/_version.py @@ -1,1 +1,1 @@ -__version__ = "0.2.0-rc1" +__version__ = "0.2.2"

Temporary fix for pyquil breaking changes ## Description - **Temporary ignore breaking changes of pyquil 4.0.0**

2023-09-19T07:46:08

0.0

[]

entropicalabs/openqaoa

entropicalabs__openqaoa-241

d82f2a700f00d92f384a6e1af9db7781061f37b1

diff --git a/src/openqaoa-core/qaoa_components/ansatz_constructor/operators.py b/src/openqaoa-core/qaoa_components/ansatz_constructor/operators.py index 113efb0a1..08871517b 100644 --- a/src/openqaoa-core/qaoa_components/ansatz_constructor/operators.py +++ b/src/openqaoa-core/qaoa_components/ansatz_constructor/operators.py @@ -5,6 +5,9 @@ from typing import List, Union, Tuple from sympy import Symbol import numpy as np +from scipy.sparse import kron as sparse_kron +from functools import reduce +from scipy.sparse import csr_matrix Identity = np.array(([1, 0], [0, 1]), dtype=complex) PauliX = np.array(([0, 1], [1, 0]), dtype=complex) @@ -686,3 +689,45 @@ def classical_hamiltonian( raise ValueError("Hamiltonian only supports Linear and Quadratic terms") return cls(pauli_ops, pauli_coeffs, constant) + + @property + def as_matrix(self): + """ + Build sparse matrix of Hamiltonian. Note this includes self.constant value. + + Returns + ------- + H_mat: + sparse matrix of Hamiltonian. + """ + + if self.n_qubits>20: + raise ValueError('number of qubits exceeds maximum of 20') + + p_mat_dict = {'X': csr_matrix([[0, 1], + [1, 0]]), + 'Y': csr_matrix([[0., -1j], + [1j, 0.]]), + 'Z': csr_matrix([[1, 0], + [0, -1]]), + 'I': csr_matrix([[1, 0], [0, 1]])} + + converter = lambda s: p_mat_dict[s] + + # vectorized function to map single qubit pauli strings to sparse matrix + vfunc = np.vectorize(converter) + + base = np.array(['I' for _ in range(self.n_qubits)]) + + H_mat = reduce(sparse_kron, vfunc(base)) * self.constant + for P, coeff in zip(self.terms, self.coeffs): + temp = base.copy() + + # replace single qubit pauli I in base term + # with single qubit pauli operators according to qubit_indices and pauli_strs in P + temp[np.array(P.qubit_indices)] = np.array(list(P.pauli_str)) + + # take kronecker product of sparse list of matrices + H_mat += reduce(sparse_kron, vfunc(temp)) * coeff + + return H_mat \ No newline at end of file

Implement `as_matrix` method for the `Hamiltonian` class ### Prerequisites Before raising this feature request, I made sure: - [x] It does not lead to duplication. - [x] It has not already been requested. ### Is your feature request related to a problem? Please describe. The class `Hamiltonian` in OpenQAOA defined in `src/openqaoa-src/openqaoa/qaoa_components/ansatz_constructor/operators.py` is responsible for constructing the `Cost` and `Mixer` Hamiltonians in QAOA. It fundamentally describes the Hamiltonian object via list of `PauliOp`s defined on a set of qubits and a corresponding list of their coefficients. Implicitly, the definition of the Hamiltonian is then assumed as follows: $$H = \sum_i \alpha_{i} P_i + \sum_{ij} J_{ij} P_{ij},$$ where $\ P_i, P_{ij}$ are one-qubit and two-qubit `PauliOp`s respectively. Implement a method in this class that returns the matrix representation of the `Hamiltonian` object. This will be a $2^n x 2^n$ dimensional sum over all Pauli entries as shown above. ### Describe the solution you'd like - Implement the method - Use numpy sparse matrix where relevant - Implement checks preventing users from creating matrices beyond 20 qubits to prevent memory overflow - Bonus task: If the Hamiltonian can be expressed as a sparse matrix, shift the qubit restriction to a higher number depending on the sparsity of the matrix ```{python} class Hamiltonian: def as_matrix(self): "Implements a numpy array (sparse where relevant) representation of the Hamiltonian object." #TODO return mat ```

Hello @vishal-ph! I am new to OpenQAOA and woud like to implement this functionality. Could you please assign it to me and if possible show any relavant literature to show matrix rep? @manulpatel, thanks for taking the initiative! In OpenQAOA, the Hamiltonian object is a sum over the terms of the Pauli and Identity operators set tensored over n-qubits. The goal is to represent the sum of these terms in a matrix form. To get started, you may want to start looking at the matrix representation of the Pauli operators here https://en.wikipedia.org/wiki/Pauli_matrices and think about their tensor products.

2023-05-28T15:21:55

0.0

[]

entropicalabs/openqaoa

entropicalabs__openqaoa-192

ad5e943499211b61e5a03aa53685d849aee4c6f2

diff --git a/docs/source/index.rst b/docs/source/index.rst index 1eb621726..e020290b5 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -247,6 +247,7 @@ Contents notebooks/11_Mixer_example.ipynb notebooks/12_testing_azure.ipynb notebooks/13_optimizers.ipynb + notebooks/14_qaoa_benchmark.ipynb notebooks/X_dumping_data.ipynb diff --git a/examples/13_optimizers.ipynb b/examples/13_optimizers.ipynb index 3c9c5cd04..3b806e532 100644 --- a/examples/13_optimizers.ipynb +++ b/examples/13_optimizers.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# X - QAOA parameters optimization with different optimization algorithms\n", + "# 13 - QAOA parameters optimization with different optimization algorithms\n", "\n", "This notebook will provide a brief overview of all the optimizers available in OpenQAOA for optimizing the QAOA parameters. If you would like to learn more about what an optimizer is and obtain a more comprehensive explanation of all the methods implemented in the library, please refer to our documentation on this topic at https://openqaoa.entropicalabs.com/optimizers/shot-adaptive-optimizers/." ] diff --git a/examples/14_qaoa_benchmark.ipynb b/examples/14_qaoa_benchmark.ipynb new file mode 100644 index 000000000..a9a5a3330 --- /dev/null +++ b/examples/14_qaoa_benchmark.ipynb @@ -0,0 +1,744 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 14 - QAOA Benchmark\n", + "\n", + "This notebook will provide an overview of QAOA Benchmarks." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create a problem instance\n", + "We begin by creating a problem instance for a simple `MaximumCut` problem for a random graph created using the `random_instance` method of the class." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from openqaoa.problems import MaximumCut\n", + "\n", + "# Use the MinimumVertexCover class to instantiate the problem, and get the QUBO representation of the problem\n", + "prob = MaximumCut.random_instance(n_nodes=7, edge_probability=0.9, seed=10)\n", + "qubo = prob.qubo" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create a QAOA object with the device to benchmark" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then, we create the `QAOA` object with the device that we want to benchmark" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "qpu_credentials ={\n", + " \"hub\": \"ibm-q\",\n", + " \"group\": \"open\", \n", + " \"project\": \"main\"\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from openqaoa import QAOA, create_device\n", + "\n", + "# create the QAOA object\n", + "q = QAOA()\n", + "\n", + "# set device and backend properties\n", + "qiskit_cloud = create_device(location='ibmq', name='ibm_oslo', **qpu_credentials, as_emulator=True)\n", + "q.set_device(qiskit_cloud)\n", + "q.set_backend_properties(n_shots=1000)\n", + "\n", + "# set properties\n", + "q.set_circuit_properties(p=1, param_type='standard')\n", + "\n", + "# compile with the problem\n", + "q.compile(qubo)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the code above, one can see that the `.set_classical_optimizer` method has not been used. This is because the benchmark process does not use the optimization loop." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Benchmark" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we create the benchmark object. This object has different methods that will allow us to benchmark the corresponding device and to plot the values taken." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from openqaoa import QAOABenchmark\n", + "\n", + "# create the QAOABenchmark object, passing the QAOA object to benchmark as an argument\n", + "benchmark = QAOABenchmark(q)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When initializing the object two attributes are set: `.qaoa` and `.reference`.\n", + "- `.qaoa` will be the QAOA object passed, the object to be benchmarked.\n", + "- `.reference` will be a QAOA object with the same circuit properties as `.qaoa` but with an `analytical_simulator` backend. It will also be compiled with the same problem. This object will be the reference for the `.qaoa` object to be compared with." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".qaoa: <class 'openqaoa.algorithms.qaoa.qaoa_workflow.QAOA'> ,\t\t .qaoa.backend: <class 'openqaoa_qiskit.backends.qaoa_qiskit_qpu.QAOAQiskitQPUBackend'>\n", + ".reference: <class 'openqaoa.algorithms.qaoa.qaoa_workflow.QAOA'> ,\t .reference.backend: <class 'openqaoa.backends.qaoa_analytical_sim.QAOABackendAnalyticalSimulator'>\n" + ] + } + ], + "source": [ + "print('.qaoa:', type(benchmark.qaoa), ',\\t\\t .qaoa.backend:', type(benchmark.qaoa.backend))\n", + "print('.reference:', type(benchmark.reference), ',\\t .reference.backend:', type(benchmark.reference.backend))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run Benchmark" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `.run` method of the `QAOABenchmark` object will run the benchmark. \n", + "\n", + "It evaluates the QAOA circuit of the benchmarked object for a given number of points and ranges per parameter. <br>\n", + "It also evaluates the QAOA circuit of the reference object with the same given points.\n", + "\n", + "It has two required positional arguments: `n_points_axis` and `ranges`. \n", + "- `n_points_axis`: The number of points per axis\n", + "- `ranges`: The sweep ranges of the parameters. The expected format is a list of tuples: (min, max) or (value,). One tuple per parameter." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting the main plot with the following parameters:\n", + "\tParameter 0: 0 to 1.57, with 33 values\n", + "\tParameter 1: 0 to 1.57, with 33 values\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 650x500 with 2 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "Running reference.\n", + "Point 1089 out of 1089. Expected remaining time to complete: 00:00:00, it will be finished at 03:03:28. \n", + "Plotting the reference plot with the following parameters:\n", + "\tParameter 0: 0 to 1.57, with 33 values\n", + "\tParameter 1: 0 to 1.57, with 33 values\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 650x500 with 2 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "benchmark.run(n_points_axis=2**5+1, ranges=[(0, 3.14/2), (0, 3.14/2)], plot=True, plot_every=100) \n", + "# with plot=True, the plot is shown every `plot_every` evaluated points and at the end of the execution" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Showing results" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After running we can plot the results and get the mean difference and the standard deviation of the difference between the benchmarked and the reference:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-0.5407383175835657" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "benchmark.difference_mean" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.3463625010931868" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "benchmark.difference_std" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting the main plot with the following parameters:\n", + "\tParameter 0: 0 to 1.57, with 33 values\n", + "\tParameter 1: 0 to 1.57, with 33 values\n", + "Plotting the reference plot with the following parameters:\n", + "\tParameter 0: 0 to 1.57, with 33 values\n", + "\tParameter 1: 0 to 1.57, with 33 values\n", + "Plotting the difference plot with the following parameters:\n", + "\tParameter 0: 0 to 1.57, with 33 values\n", + "\tParameter 1: 0 to 1.57, with 33 values\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 650x1500 with 6 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "benchmark.plot(main=True, reference=True, difference=True, labels=['beta', 'gamma'])\n", + "# with main=True, the plot of the benchmarked points is shown\n", + "# with reference=True, the plot of the reference points is shown\n", + "# with difference=True, the plot of the difference between the benchmarked and the reference points is shown\n", + "# with labels=['beta', 'gamma'], the labels of the axes are set to 'beta' and 'gamma', if not specified, default labels are used" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To access the raw data of the benchmark, use the following attributes:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.274, -0.074, 0.052, ..., -0.336, 0.196, -0.042],\n", + " [-0.026, -0.012, -0.48 , ..., 0.276, 0.148, -0.016],\n", + " [-0.19 , -0.192, -0.344, ..., 0.492, 0.13 , -0.096],\n", + " ...,\n", + " [-0.04 , -0.122, 0.15 , ..., -0.012, -0.044, 0.19 ],\n", + " [ 0.034, 0.05 , -0.056, ..., -0.054, -0.01 , -0.004],\n", + " [-0.202, 0.168, 0.074, ..., -0.09 , -0.214, 0.018]])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "benchmark.values # the values of the benchmarked points" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n", + " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n", + " [ 0.00000000e+00, -3.30596324e-01, -6.36201769e-01, ...,\n", + " 6.90397010e-01, 3.48489595e-01, 5.50253502e-03],\n", + " [ 0.00000000e+00, -6.07268558e-01, -1.14705888e+00, ...,\n", + " 1.33292239e+00, 6.62233861e-01, 1.02865946e-02],\n", + " ...,\n", + " [ 0.00000000e+00, -1.32919377e-02, -3.59491490e-02, ...,\n", + " -3.36256986e-03, 3.38913220e-03, 1.35164298e-04],\n", + " [ 0.00000000e+00, -2.52121083e-03, -7.71505865e-03, ...,\n", + " -3.32741998e-03, -2.75178585e-04, 1.81992750e-05],\n", + " [ 0.00000000e+00, -3.69897000e-07, -1.45593300e-06, ...,\n", + " -1.46058600e-06, -3.72279000e-07, -1.90000000e-11]])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "benchmark.values_reference # the values of the reference points" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.274 , -0.074 , 0.052 , ..., -0.336 ,\n", + " 0.196 , -0.042 ],\n", + " [-0.026 , 0.31859632, 0.15620177, ..., -0.41439701,\n", + " -0.20048959, -0.02150254],\n", + " [-0.19 , 0.41526856, 0.80305888, ..., -0.84092239,\n", + " -0.53223386, -0.10628659],\n", + " ...,\n", + " [-0.04 , -0.10870806, 0.18594915, ..., -0.00863743,\n", + " -0.04738913, 0.18986484],\n", + " [ 0.034 , 0.05252121, -0.04828494, ..., -0.05067258,\n", + " -0.00972482, -0.0040182 ],\n", + " [-0.202 , 0.16800037, 0.07400146, ..., -0.08999854,\n", + " -0.21399963, 0.018 ]])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "benchmark.difference # the difference between the benchmarked and the reference points" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run 1D Benchmark" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One can also run the benchmark fixing the value of any parameter:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running benchmark.\n", + "Point 100 out of 100. Expected remaining time to complete: 00:00:00, it will be finished at 03:03:42. \n", + "Running reference.\n", + "Point 100 out of 100. Expected remaining time to complete: 00:00:00, it will be finished at 03:03:42. \n" + ] + } + ], + "source": [ + "benchmark.run(n_points_axis=100, ranges=[(0, 3.14/2), (1,)])\n", + "# in this case the first parameter is swept from 0 to 3.14/2, while the second is fixed to 1" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting the main plot with the following parameters:\n", + "\tParameter 0: 0 to 1.57, with 100 values\n", + "\tParameter 1: 1\n", + "Plotting the reference plot with the following parameters:\n", + "\tParameter 0: 0 to 1.57, with 100 values\n", + "\tParameter 1: 1\n", + "Plotting the difference plot with the following parameters:\n", + "\tParameter 0: 0 to 1.57, with 100 values\n", + "\tParameter 1: 1\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 650x500 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "benchmark.plot(main=True, reference=True, difference=True, labels=['beta', 'cost'], title=\"comparison\", one_plot=True)\n", + "# with one_plot=True, the three plots are shown in the same figure" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Benchmark with different circuit properties" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The same procedure can be applied to an object with circuit properties that are not the standard ones, for example: p=2 and param_type='extended'.\n", + "\n", + "However, for this properties the `analytical_simulator` backend is not available, so the `vectorized` backend is used instead for the reference." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# create the QAOA object\n", + "q2 = QAOA()\n", + "\n", + "# set device and backend properties\n", + "q2.set_device(qiskit_cloud)\n", + "q2.set_backend_properties(n_shots=1000)\n", + "\n", + "# set properties\n", + "q2.set_circuit_properties(p=2, param_type='extended')\n", + "\n", + "# compile with the problem\n", + "q2.compile(MaximumCut.random_instance(n_nodes=3, edge_probability=0.9, seed=10).qubo) #use a smaller problem to speed up the example" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: vectorized simulator will be used for the reference, since the analytical simulator is not available for the circuit properties of the benchmarked QAOA object\n" + ] + } + ], + "source": [ + "# create the new QAOABenchmark object, in this case the reference will use the vectorized simulator not the analytical one\n", + "benchmark2 = QAOABenchmark(q2)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running benchmark.\n", + "Point 1089 out of 1089. Expected remaining time to complete: 00:00:00, it will be finished at 03:05:02. \n", + "Running reference.\n", + "Point 1089 out of 1089. Expected remaining time to complete: 00:00:00, it will be finished at 03:05:03. \n" + ] + } + ], + "source": [ + "benchmark2.run(n_points_axis=2**5+1, ranges=[(1,), (1,), (1,), (0, 3.14), (1,), (1,), (-3.14, 3.14), (1,), (1,), (1,), (1,), (1,)]) \n", + "# see that the ranges are now 12-dimensional, one tuple is needed for each parameter. \n", + "# in this case, we are fixing 10 parameters, and sweeping the other two\n", + "# one can sweep more than " + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 650x1500 with 6 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "benchmark2.plot(main=True, reference=True, difference=True, verbose=False, title=['benchmarked', 'analytical', 'difference'],) " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compare different QPUs or simulators" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this section we will show how one can compare the performance of two different QPUs or simulators.\n", + "\n", + "For that we will follow the same steps as before. Now, we will define two different QAOA objects (one for each QPU to compare) and the respective QAOABenchmark objects, and then ploting the values together.\n", + "\n", + "Here we will compare the IBM QPU with a shot-based simulator:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# we start defining the QAOA object for the ibm qpu:\n", + "\n", + "# create the QAOA object\n", + "q_ibm = QAOA()\n", + "\n", + "# set device and backend properties\n", + "qiskit_cloud = create_device(location='ibmq', name='ibm_oslo', **qpu_credentials, as_emulator=True)\n", + "q_ibm.set_device(qiskit_cloud)\n", + "q_ibm.set_backend_properties(n_shots=1000)\n", + "\n", + "# set properties\n", + "q_ibm.set_circuit_properties(p=1, param_type='standard')\n", + "\n", + "# compile with the problem\n", + "q_ibm.compile(qubo)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# now, we define the QAOA object for the shot-based simulator:\n", + "\n", + "# create the QAOA object\n", + "q_simulator = QAOA()\n", + "\n", + "# set device and backend properties\n", + "qiskit_simulator = create_device(location='local', name='qiskit.shot_simulator')\n", + "q_simulator.set_device(qiskit_simulator)\n", + "q_simulator.set_backend_properties(n_shots=1000)\n", + "\n", + "# set properties\n", + "q_simulator.set_circuit_properties(p=1, param_type='standard')\n", + "\n", + "# compile with the problem\n", + "q_simulator.compile(qubo)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running benchmark.\n", + "Point 100 out of 100. Expected remaining time to complete: 00:00:00, it will be finished at 03:05:21. \n", + "Running reference.\n", + "Point 100 out of 100. Expected remaining time to complete: 00:00:00, it will be finished at 03:05:21. \n", + "Running benchmark.\n", + "Point 100 out of 100. Expected remaining time to complete: 00:00:00, it will be finished at 03:05:23. \n" + ] + } + ], + "source": [ + "# next step is to create the QAOABenchmark objects\n", + "\n", + "benchmark_ibm = QAOABenchmark(q_ibm)\n", + "benchmark_simulator = QAOABenchmark(q_simulator)\n", + "\n", + "# now we can run the benchmarks\n", + "benchmark_ibm.run(n_points_axis=100, ranges=[(3.14/4,), (0, 3.14/2)], plot=False)\n", + "benchmark_simulator.run(n_points_axis=100, ranges=[(3.14/4,), (0, 3.14/2)], plot=False, run_reference=False) #here we set reference=False because the reference is the same as in the ibm case" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# last step is to plot the results\n", + "\n", + "import matplotlib.pyplot as plt\n", + "fig, ax = plt.subplots()\n", + "benchmark_ibm.plot(main=True, reference=True, difference=False, ax=ax, labels_legend=['ibm', 'reference'], verbose=False)\n", + "benchmark_simulator.plot(main=True, reference=False, difference=False, ax=ax, labels_legend='simulator', verbose=False)\n", + "ax.legend()\n", + "ax.set_title('comparison QPU vs. simulator')\n", + "ax.set_ylabel('cost')\n", + "ax.set_xlabel('gamma')\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "OQ", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + }, + "vscode": { + "interpreter": { + "hash": "2374e833df7d90fe0779e245413a800a00448ba720136fdc7edf28b169de91eb" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/src/openqaoa-core/__init__.py b/src/openqaoa-core/__init__.py index 6eee62d87..4db56ed3a 100644 --- a/src/openqaoa-core/__init__.py +++ b/src/openqaoa-core/__init__.py @@ -1,3 +1,3 @@ -from .algorithms import QAOA, RQAOA +from .algorithms import QAOA, RQAOA, QAOABenchmark from .problems import QUBO from .backends import create_device diff --git a/src/openqaoa-core/algorithms/__init__.py b/src/openqaoa-core/algorithms/__init__.py index 3b4557928..069c4fd75 100644 --- a/src/openqaoa-core/algorithms/__init__.py +++ b/src/openqaoa-core/algorithms/__init__.py @@ -1,3 +1,3 @@ -from .qaoa import QAOA, QAOAResult +from .qaoa import QAOA, QAOAResult, QAOABenchmark from .rqaoa import RQAOA, RQAOAResult from .jobs.managed_job import AWSJobs diff --git a/src/openqaoa-core/algorithms/baseworkflow.py b/src/openqaoa-core/algorithms/baseworkflow.py index 702e0477e..15eb2dd16 100644 --- a/src/openqaoa-core/algorithms/baseworkflow.py +++ b/src/openqaoa-core/algorithms/baseworkflow.py @@ -27,12 +27,16 @@ DEVICE_ACCESS_OBJECT_MAPPER, ) + def check_compiled(func): def wrapper(self, *args, **kwargs): result = func(self, *args, **kwargs) if self.compiled: - raise ValueError("Cannot change properties of the object after compilation.") + raise ValueError( + "Cannot change properties of the object after compilation." + ) return result + return wrapper @@ -533,11 +537,17 @@ def dump( options = {**options, **{"complex_to_string": True}} - project_id = self.header["project_id"] if not self.header["project_id"] is None else "None" + project_id = ( + self.header["project_id"] + if not self.header["project_id"] is None + else "None" + ) # get the full name if prepend_id == False and file_name == "": - raise ValueError("dump method missing argument: 'file_name'. Otherwise 'prepend_id' must be specified as True.") + raise ValueError( + "dump method missing argument: 'file_name'. Otherwise 'prepend_id' must be specified as True." + ) elif prepend_id == False: file = file_path + file_name elif file_name == "": diff --git a/src/openqaoa-core/algorithms/qaoa/__init__.py b/src/openqaoa-core/algorithms/qaoa/__init__.py index 435b000b4..7b5ba4677 100644 --- a/src/openqaoa-core/algorithms/qaoa/__init__.py +++ b/src/openqaoa-core/algorithms/qaoa/__init__.py @@ -1,2 +1,3 @@ from .qaoa_workflow import QAOA from .qaoa_result import QAOAResult +from .qaoa_benchmark import QAOABenchmark diff --git a/src/openqaoa-core/algorithms/qaoa/qaoa_benchmark.py b/src/openqaoa-core/algorithms/qaoa/qaoa_benchmark.py new file mode 100644 index 000000000..f99d12a42 --- /dev/null +++ b/src/openqaoa-core/algorithms/qaoa/qaoa_benchmark.py @@ -0,0 +1,656 @@ +import numpy as np +from math import log2 +from random import shuffle +from typing import List, Union +from matplotlib import pyplot as plt +from copy import deepcopy +from IPython.display import clear_output +import time + +from . import QAOA +from ...backends import create_device + + +class QAOABenchmark: + """ + Benchmark is a class that implements benchmarking for QAOA. + + Attributes + ---------- + qaoa : QAOA + The QAOA object to be benchmarked. + reference : QAOA + The reference QAOA object, which will be used to compare the results of the benchmarked QAOA object. + ranges : List[tuple] + The ranges of the variate parameters of the benchmarked QAOA object. + ranges_reference : List[tuple] + The ranges of the variate parameters of the reference QAOA object. + values : np.ndarray + The values of the benchmarked QAOA object. + values_reference : np.ndarray + The values of the reference QAOA object. + difference : np.ndarray + The difference between the values of the benchmarked QAOA object and the values of the reference QAOA object. + difference_mean : float + The mean of the difference between the values of the benchmarked QAOA object and the values of the reference QAOA object. + """ + + def __init__(self, qaoa: QAOA): + """ + Constructor for the Benchmark class. + + Parameters + ---------- + qaoa : QAOA + The QAOA object to be benchmarked. + """ + # check if the QAOA object inputted is valid and save it + assert isinstance(qaoa, QAOA), "`qaoa` must be an instance of QAOA" + assert qaoa.compiled, "`qaoa` must be compiled before benchmarking" + self.qaoa = qaoa + + # create a reference QAOA object, which will be used to compare the results of the benchmarked QAOA object + self.reference = QAOA() + self.reference.circuit_properties = ( + self.qaoa.circuit_properties + ) # copy the circuit properties of the benchmarked QAOA object + try: + self.reference.set_device( + create_device(location="local", name="analytical_simulator") + ) + self.reference.compile(self.qaoa.problem) + except: + self.reference.set_device( + create_device(location="local", name="vectorized") + ) + self.reference.compile(self.qaoa.problem) + print( + "Warning: vectorized simulator will be used for the reference, since the analytical simulator is not available for the circuit properties of the benchmarked QAOA object" + ) + + # initialize values and ranges + self.values = None + self.values_reference = None + self.ranges = None + self.ranges_reference = None + + @property + def difference(self): + "The difference between the values of the benchmarked QAOA object and the values of the reference QAOA object." + if self.values is None: + raise Exception( + "You must run the benchmark before calculating the difference" + ) + if self.values_reference is None: + raise Exception( + "You must run the reference before calculating the difference" + ) + if self.values.shape != self.values_reference.shape: + raise Exception( + "The ranges and number of points of the values and reference values must be the same" + ) + return self.values - self.values_reference + + @property + def difference_mean(self): + "The mean of the difference between the values of the benchmarked QAOA object and the values of the reference QAOA object." + return np.mean(self.difference) + + @property + def difference_std(self): + "The standard deviation of the difference between the values of the benchmarked QAOA object and the values of the reference QAOA object." + return np.std(self.difference) + + def __assert_run_inputs( + self, + n_points_axis: int, + ranges: List[tuple], + run_main: bool, + run_reference: bool, + plot: bool, + plot_difference: bool, + plot_options: dict, + verbose: bool, + ): + "Private method that checks the inputs of the run (and run_reference) method." + assert isinstance( + n_points_axis, int + ), "The number of points per axis must be an integer" + assert ( + isinstance(ranges, list) or ranges is None + ), "The ranges argument must be a list of tuples: (min, max) or (value,)" + assert len(ranges) == len( + self.qaoa.variate_params + ), "The number of ranges must be equal to the number of variate parameters, which is {}".format( + len(self.qaoa.variate_params) + ) + assert all( + [isinstance(r, tuple) or isinstance(r, list) for r in ranges] + ), "Each range must be a tuple: (min, max) or (value,)" + assert all( + [len(r) == 1 or len(r) == 2 for r in ranges] + ), "Each range must be a tuple of length 1 or 2: (min, max) or (value,)" + assert ( + len([r for r in ranges if len(r) == 2]) > 0 + ), "At least one range must be a tuple of length 2: (min, max)" + for bools in [ + "run_main", + "run_reference", + "plot", + "plot_difference", + "verbose", + ]: + assert isinstance( + eval(bools), bool + ), "The {} argument must be a boolean".format(bools) + assert ( + run_main or run_reference + ), "You must run the main or the reference or both" + assert isinstance( + plot_options, dict + ), "The plot_options argument must be a dictionary" + + def __print_expected_time(self, k, n_points): + "Private method that prints the expected remaining time to complete the benchmark." + print("\rPoint {} out of {}.".format(k + 1, n_points), end="") + if k == 0: + self.__start_time = time.time() + elif k > 0: + expected_time = (time.time() - self.__start_time) * (n_points - k) / k + print( + " Expected remaining time to complete: {}, it will be finished at {}.".format( + time.strftime("%H:%M:%S", time.localtime(expected_time)), + time.strftime( + "%H:%M:%S", time.localtime(time.time() + expected_time) + ), + ), + end="", + ) + + def run( + self, + n_points_axis, + ranges: List[tuple], + run_main: bool = True, + run_reference: bool = True, + plot: bool = False, + plot_every: int = 1000, + plot_difference: bool = False, + plot_options: dict = {}, + verbose: bool = True, + ): + """ + Evaluates the QAOA circuit of the benchmarked (and the reference) QAOA object for a given number of points per axis and ranges. + + Parameters + ---------- + n_points_axis : int + The number of points per axis. It is recommended to use a number that is a power of 2 + 1: 2**k+1 (where, k is an integer). + Using this number will ensure that the points evaluated are ordered such that there are k rounds, each round covers the whole range, + and each round has the double number of points (per axis) of the previous round. + ranges : List[tuple] + The sweep ranges of the parameters. The expected format is a list of tuples: (min, max) or (value,). One tuple per parameter. + If the length of the tuple is 1, the parameter will be fixed to the value of the tuple. + If the length of the tuple is 2, the parameter will be swept from the first value of the tuple to the second value of the tuple. + run_main : bool, optional + If True, the benchmarked QAOA object will be evaluated for the requested points. + The default is True. + run_reference : bool, optional + If True, the reference QAOA object will be evaluated for the same points. + The default is True. + plot : bool, optional + If True, the values will be plotted. + The default is False. + plot_every : int, optional + The number of evaluations after which the plot will be updated. + The default is 1000. + plot_difference : bool, optional + If True, the difference between the values of the benchmarked QAOA object and the values of the reference QAOA object will be plotted. + The default is False. + plot_options : dict, optional + The options for the plot. The expected format is a dictionary with the keys of the plot method of this class. + The default is {}. + verbose : bool, optional + If True, the expected remaining time to complete the benchmark will be printed. + The default is True. + """ + + # check the inputs + self.__assert_run_inputs( + n_points_axis, + ranges, + run_main, + run_reference, + plot, + plot_difference, + plot_options, + verbose, + ) + + # plot options + plot_options = {**{"verbose": verbose}, **plot_options} + + # save the ranges + if run_main: + self.ranges = ranges + if run_reference: + self.ranges_reference = ranges + + # get only the ranges that will be swept, and the number of parameters swept + ranges_to_use = [r for r in ranges if len(r) == 2] + n_params = len(ranges_to_use) + + # prepare the axes of the grid + axes = [ + np.linspace(r[0], r[1], n_points_axis) for r in ranges_to_use + ] # the starting and ending points are given by the ranges and the number of points is given by n_points_axis + + # function that given the values used for the parameters swept, returns the values of all the parameters in order + params = lambda new_params: [ + r[0] if len(r) == 1 else new_params.pop(0) for r in ranges + ] + + # copy the parameters object, which will be updated before each evaluation + params_obj = deepcopy(self.qaoa.variate_params) + + # initialize the array that will contain the values of the benchmarked QAOA object + if run_main: + self.values = np.zeros([n_points_axis for _ in range(n_params)]) + if run_reference: + self.values_reference = np.zeros([n_points_axis for _ in range(n_params)]) + + # initialize the arrays that will contain the difference between the values of the benchmarked QAOA object and the values of the reference QAOA object + both_qaoa = [ + qaoa + for qaoa, boolean in zip( + [self.qaoa, self.reference], [run_main, run_reference] + ) + if boolean + ] + both_values = [ + values + for values, boolean in zip( + [self.values, self.values_reference], [run_main, run_reference] + ) + if boolean + ] + both_strings = [ + string + for string, boolean in zip( + ["benchmark", "reference"], [run_main, run_reference] + ) + if boolean + ] + + # loop over the benchmarked QAOA object and the reference QAOA object (if requested) + for qaoa, values, string in zip(both_qaoa, both_values, both_strings): + + if verbose: + print(f"Running {string}.") + + # evaluate all the points in the grid, in the order provided by the function __ordered_points. We loop over the indices of the grid. + for k, i_point in enumerate(self.__ordered_points(n_params, n_points_axis)): + + if verbose: + self.__print_expected_time( + k, n_points_axis**n_params + ) # print the expected remaining time, info for the user + + new_params = [ + axis[i] for axis, i in zip(axes, i_point) + ] # from the indices of the grid, get the values of the parameters that will be evaluated + params_obj.update_from_raw(params(new_params)) + values[tuple(i_point[::-1])] = qaoa.backend.expectation(params_obj) + + # plot every 'plot_every' points and at the end + if ( + ( + (k % plot_every == 0 and k > 0) + or k + 1 == n_points_axis**n_params + ) + and plot + and string == "benchmark" + ): + clear_output(wait=True) + + if (not k + 1 == n_points_axis**n_params) and n_params == 1: + plot_opt = {"marker": "o", "linestyle": "", "markersize": 3} + else: + plot_opt = {} + self.plot(plot_options=plot_opt, **plot_options) + + if verbose: + print( + " " + ) # print a blank line, necessary because previous print: end="" + + # plot the reference if requested + if plot and run_reference: + self.plot(main=False, reference=True, difference=False, **plot_options) + + # plot the difference if requested and the reference has been run + if plot_difference: + try: + self.plot(main=False, reference=False, difference=True, **plot_options) + except: + print( + "The difference cannot be plotted because the reference has not been run." + ) + + def plot( + self, + ax: plt.Axes = None, + title: Union[str, List[str]] = None, + labels: List[str] = None, + labels_legend: Union[str, List[str]] = None, + main: bool = True, + reference: bool = False, + difference: bool = False, + one_plot: bool = False, + # params_to_plot:List[int] = None, TODO: difficult because you need to specify the values of the other parameters + plot_options: dict = {}, + verbose: bool = True, + ): + """ + Plots the values of the benchmarked QAOA object and/or the values of the reference QAOA + object and/or the difference between the values of the benchmarked QAOA object and the values of the reference QAOA object. + + Parameters + ---------- + ax : plt.Axes, optional + The matplotlib Axes object to plot on. If None, a new figure will be created. + The default is None. + title : Union[str, List[str]], optional + The title of the plot. The expected format is a string or a list of strings: one for each plot. + If None, the title will be the default: `main plot`, `reference plot` or `difference plot`. + The default is None. + labels : List[str], optional + The labels of the axes. The expected format is a list of two strings: one for each axis. + If None, the labels will be the number of the parameters. + The default is None. + labels_legend : Union[str, List[str]], optional + The labels of the legend. The expected format is a string or a list of strings: one for each line. + It is only available if the sweep is over just one parameter, and if `one_plot` is True or `ax` is not None. + If None, the labels will be the default: `main`, `reference` or `difference`. + The default is None. + main : bool, optional + If True, the values of the benchmarked QAOA object will be plotted. + The default is True. + reference : bool, optional + If True, the values of the reference QAOA object will be plotted. + The default is False. + difference : bool, optional + If True, the difference between the values of the benchmarked QAOA object and the values of the reference QAOA object will be plotted. + The default is False. + one_plot : bool, optional + If True, the values of the benchmarked QAOA object, + the values of the reference QAOA object and the difference between the values of the benchmarked QAOA object + and the values of the reference QAOA object will be plotted in the same plot. + It is not available if the sweep is over more than one parameter. + The default is False. + plot_options : dict, optional + The options for the plot (plt.plot for one parameter, plt.pcolorfast for two parameters). + """ + + # check the inputs + assert ( + isinstance(ax, plt.Axes) or ax is None + ), "ax must be a matplotlib Axes or None" + assert ( + isinstance(labels, list) or labels is None + ), "labels must be a list of two strings or None" + assert ( + all([isinstance(label, str) for label in labels]) + if labels is not None + else True + ), "labels must be a list of two strings or None" + assert ( + len(labels) == 2 if labels is not None else True + ), "labels must be a list of two strings, one for each axis" + for plot in ["main", "reference", "difference"]: + assert isinstance(eval(plot), bool), plot + " must be a boolean" + assert ( + main or reference or difference + ), "You must specify at least one of the main, reference or difference plots" + assert isinstance(plot_options, dict), "plot_options must be a dictionary" + + # if labels_legend is not a list or tuple, make it a list + if not isinstance(labels_legend, (list, tuple)): + labels_legend = [labels_legend for _ in range(3)] + + # create a dictionary where the keys are the three possible plots and the values are a tuple + # with the boolean (which says if the plot is requested) and the values to plot + values = { + "main": (main, self.values if main else None), + "reference": (reference, self.values_reference if reference else None), + "difference": (difference, self.difference if difference else None), + } + + # create the figure and axes if the axis are not provided + ax_input = ax # save the input axis + nrows = 1 + if ax_input is None: + nrows = ( + 1 if one_plot else sum([1 if values[key][0] else 0 for key in values]) + ) + fig, ax = plt.subplots(nrows=nrows, ncols=1, figsize=(6.5, 5 * nrows)) + + # plot the requested plots + count_sp = 0 # counter of subplots + for key in values: + if values[key][0]: # skip the plot if it is not requested + + # raise an exception if the values for a plo requested are not available + if values[key][1] is None: + raise Exception( + "You must run the benchmark before plotting the results, there are no values for the " + + key + + " plot" + ) + + # get the ranges to use, use the reference ranges if the key is "reference", otherwise use the main ranges + ranges = self.ranges if key != "reference" else self.ranges_reference + ranges_to_use = [r for r in ranges if len(r) == 2] + n_params = len(ranges_to_use) + + # create the axes, the range is provided by the ranges_to_use and the number of values is provided by the values[key][1].shape + axes = [ + np.linspace(r[0], r[1], values[key][1].shape[i]) + for i, r in enumerate(ranges_to_use) + ] + + # skip the plot if there are more than two parameters to plot, and print a warning + if n_params > 2: + print( + "Only 1 or 2 parameters can be plotted using this method. You are trying to plot " + + str(n_params) + + " parameters. You can use the argument params_to_plot to specify which parameters to plot." + ) + break + # if verbose, print the parameters used for the plot + elif verbose: + print( + "Plotting the " + key + " plot with the following parameters:" + ) + for i, r in enumerate(ranges): + if len(r) == 2: + print( + "\tParameter " + + str(i) + + ": " + + str(r[0]) + + " to " + + str(r[1]) + + ", with " + + str(values[key][1].shape[i]) + + " values" + ) + else: + print("\tParameter " + str(i) + ": " + str(r[0])) + + ## plot the values + + # if we are plotting only one plot, use the same axes, otherwise different axes for each subplot + axis = ax if nrows == 1 else ax[count_sp] + + # set the labels and plot the values + if n_params == 1: + axis.set_xlabel( + "Parameter {}".format( + [i for i, r in enumerate(ranges) if len(r) == 2][0] + ) + ) + axis.set_ylabel("Expectation value") + + label_legend = ( + labels_legend[count_sp] + if labels_legend[count_sp] is not None + else key + ) + axis.plot(*axes, values[key][1], label=label_legend, **plot_options) + else: + if one_plot: + raise Exception( + "For two parameters, you must specify one_plot=False" + ) + + axis.set_xlabel( + "Parameter {}".format( + [i for i, r in enumerate(ranges) if len(r) == 2][0] + ) + ) + axis.set_ylabel( + "Parameter {}".format( + [i for i, r in enumerate(ranges) if len(r) == 2][1] + ) + ) + plot = axis.pcolorfast(*axes, values[key][1], **plot_options) + _ = plt.colorbar(plot) + + # set the title + if not one_plot and title is None: + axis.set_title(key + " plot") + elif not title is None: + if not isinstance(title, list) and not isinstance(title, tuple): + axis.set_title(title) + else: + if len(title) < nrows: + raise Exception( + "If title is a list or tuple, it must have the same length as the number of plots" + ) + axis.set_title(title[count_sp]) + + # replace the labels if specified + if labels is not None: + axis.set_xlabel(labels[0]) + axis.set_ylabel(labels[1]) + + # increase the counter for the axes + count_sp += 1 + + # add legend if one_plot + if one_plot: + ax.legend() + + # show the figure if the axis were not provided + if ax_input is None: + if nrows > 1: + fig.subplots_adjust(hspace=0.3) # add some space between the subplots + plt.show() + plt.close(fig) + + @staticmethod + def __ordered_points(n_params, n_points_axis): + """ + This method creates a grid of points for each parameter, and then creates all the combinations of these points. + The points are ordered such that we evaluate the grid by rounds, the first round the resolution is very low, + next rounds the resolution is higher, + and also, every n_points = 2**n_params one point is used at each round, and the points are shuffled. + + It works very well for n_points_axis = 2**k + 1 (where k is any integer), but it also works for other values. + + Parameters + ---------- + n_params : int + The number of parameters to sweep. + n_points_axis : int + The number of points per axis. + + Returns + ------- + points : list + The list of points to evaluate in order. + """ + + assert ( + isinstance(n_params, int) and n_params > 0 + ), "The number of parameters must be an integer, and greater than 0" + assert ( + isinstance(n_points_axis, int) and n_points_axis > 3 + ), "The number of points per axis must be an integer, and greater than 3" + + ## we create a grid of points for each axis + axis_points = list(range(n_points_axis)) + + ## first we create all the points to evaluate and we order them such that every n_points = 2**n_params one point is used at each round, and the points are shuffled + + # we separate the points in two lists, [0, 2, 4, ...] and [1, 3, 5, ...] + axis_points_separated = ( + [[i] for i in axis_points[::2]], + [[i] for i in axis_points[1::2]], + ) + + # we create a list of lists, where each list will tell how to combine the two lists of points to create the points for each round + zero_one = [[0], [1]] + order_list = zero_one + for _ in range(n_params - 1): + order_list = [order + y for order in order_list for y in zero_one] + + # the variable points will be a list of lists, where each list is a round of points to evaluate + points = [ + axis_points_separated[order[0]] for order in order_list + ] # we start with the first axis + + # we create the points for each round by combining the points of the previous round with the points of the next axis + for k in range(1, n_params): + for x in range(len(points)): + points[x] = [ + point_i + point_j + for point_i in points[x] + for point_j in axis_points_separated[order_list[x][k]] + ] + + # we shuffle the points at each round + for i in range(len(points)): + shuffle(points[i]) + + # the final list of points to evaluate is a concatenation of all the rounds + ordered_points = [] + for round in range(len(points)): + ordered_points += points[round] + + ## then we reorder the points such that the first round we have a grid of points with very low resolution, and then at each round we increase the resolution + values = np.zeros([n_points_axis for _ in range(n_params)]) + + used_axis_points = [] + reordered_points = [] + for round in range(1, int(log2(len(axis_points) - 1)) + 1): + new = [ + k + for k in axis_points[:: (len(axis_points) - 1) // 2**round] + if k not in used_axis_points + ] + used_axis_points += new + + points_ = ordered_points.copy() + for point in ordered_points: + if ( + all([i in used_axis_points for i in point]) + and values[tuple(point)] == 0 + ): + values[tuple(point)] = round + reordered_points.append(points_.pop(points_.index(point))) + + ordered_points = points_ # there are less points now + + return reordered_points diff --git a/src/openqaoa-core/algorithms/qaoa/qaoa_workflow.py b/src/openqaoa-core/algorithms/qaoa/qaoa_workflow.py index 4d86be5d7..73a0173d4 100644 --- a/src/openqaoa-core/algorithms/qaoa/qaoa_workflow.py +++ b/src/openqaoa-core/algorithms/qaoa/qaoa_workflow.py @@ -1,8 +1,13 @@ -from typing import List, Callable, Optional -import requests +from typing import List, Callable, Optional, Union, Dict +from copy import deepcopy +import numpy as np + from .qaoa_result import QAOAResult from ..workflow_properties import CircuitProperties from ..baseworkflow import Workflow, check_compiled +from ...backends.basebackend import QAOABaseBackendStatevector +from ...backends import QAOABackendAnalyticalSimulator +from ...backends.cost_function import cost_function from ...backends.devices_core import DeviceLocal, DeviceBase from ...backends.qaoa_backend import get_qaoa_backend from ...problems import QUBO @@ -11,6 +16,9 @@ QAOADescriptor, create_qaoa_variational_params, ) +from ...qaoa_components.variational_parameters.variational_baseparams import ( + QAOAVariationalBaseParams, +) from ...utilities import get_mixer_hamiltonian, generate_timestamp from ...optimizers.qaoa_optimizer import get_optimizer @@ -305,6 +313,101 @@ def optimize(self, verbose=False): print(f"optimization completed.") return + def evaluate_circuit( + self, + params: Union[List[float], Dict[str, List[float]], QAOAVariationalBaseParams], + ): + """ + A method to evaluate the QAOA circuit at a given set of parameters + + Parameters + ---------- + params: list or dict or QAOAVariationalBaseParams or None + List of parameters or dictionary of parameters. Which will be used to evaluate the QAOA circuit. + If None, the variational parameters of the QAOA object will be used. + + Returns + ------- + result: dict + A dictionary containing the results of the evaluation: + - "expectation": the expectation value of the cost Hamiltonian + - "uncertainty": the uncertainty of the expectation value of the cost Hamiltonian + - "measurement_results": either the state of the QAOA circuit output (if the QAOA circuit is + evaluated on a state simulator) or the counts of the QAOA circuit output + (if the QAOA circuit is evaluated on a QPU or shot-based simulator) + """ + + # before evaluating the circuit we check that the QAOA object has been compiled + if self.compiled == False: + raise ValueError("Please compile the QAOA before optimizing it!") + + ## Check the type of the input parameters and save them as a QAOAVariationalBaseParams object at the variable `params_obj` + + # if the parameters are passed as a dictionary we copy and update the variational parameters of the QAOA object + if isinstance(params, dict): + params_obj = deepcopy(self.variate_params) + # we check that the dictionary contains all the parameters of the QAOA object that are not empty + for key, value in params_obj.asdict().items(): + if value.size > 0: + assert ( + key in params.keys() + ), f"The parameter `{key}` is missing from the input dictionary" + params_obj.update_from_dict(params) + + # if the parameters are passed as a list we copy and update the variational parameters of the QAOA object + elif isinstance(params, list) or isinstance(params, np.ndarray): + assert len(params) == len( + self.variate_params + ), "The number of parameters does not match the number of parameters in the QAOA circuit" + params_obj = deepcopy(self.variate_params) + params_obj.update_from_raw(params) + + # if the parameters are passed as a QAOAVariationalBaseParams object we just take it as it is + elif isinstance(params, QAOAVariationalBaseParams): + # check whether the input params object is supported for circuit evaluation + assert ( + len(self.variate_params.mixer_1q_angles) == len(params.mixer_1q_angles) + and len(self.variate_params.mixer_2q_angles) + == len(self.variate_params.mixer_2q_angles) + and len(self.variate_params.cost_1q_angles) + == len(self.variate_params.cost_1q_angles) + and len(self.variate_params.cost_2q_angles) + == len(self.variate_params.cost_2q_angles) + ), "Specify a supported params object" + params_obj = params + + # if the parameters are passed in a different format, we raise an error + else: + raise TypeError( + f"The input params must be a list or a dictionary. Instead, received {type(params)}" + ) + + ## Evaluate the QAOA circuit and return the results + output_dict = { + "cost": None, + "uncertainty": None, + "measurement_results": None, + } + # if the backend is the analytical simulator, we just return the expectation value of the cost Hamiltonian + if isinstance(self.backend, QAOABackendAnalyticalSimulator): + output_dict.update({"cost": self.backend.expectation(params_obj)[0]}) + + else: + cost, uncertainty = self.backend.expectation_w_uncertainty(params_obj) + measurement_results = ( + self.backend.measurement_outcomes + if isinstance(self.backend.measurement_outcomes, dict) + else self.backend.measurement_outcomes.tolist() + ) + output_dict.update( + { + "cost": cost, + "uncertainty": uncertainty, + "measurement_results": measurement_results, + } + ) + return output_dict + def _serializable_dict( self, complex_to_string: bool = False, intermediate_mesurements: bool = True ): diff --git a/src/openqaoa-core/backends/qaoa_device.py b/src/openqaoa-core/backends/qaoa_device.py index eb49640bb..2b27aa453 100644 --- a/src/openqaoa-core/backends/qaoa_device.py +++ b/src/openqaoa-core/backends/qaoa_device.py @@ -6,6 +6,7 @@ from openqaoa_pyquil.backends import DevicePyquil from openqaoa_azure.backends import DeviceAzure + def device_class_arg_mapper( device_class: DeviceBase, hub: str = None, @@ -56,6 +57,7 @@ def device_class_arg_mapper( } return final_device_kwargs + LOCATION_MAPPER = { "ibmq": DeviceQiskit, "qcs": DevicePyquil, @@ -64,6 +66,7 @@ def device_class_arg_mapper( "azure": DeviceAzure, } + def create_device(location: str, name: str, **kwargs): """ This function returns an instance of the appropriate device class. @@ -85,8 +88,10 @@ def create_device(location: str, name: str, **kwargs): """ location = location.lower() if location not in LOCATION_MAPPER: - raise ValueError(f"Invalid device location, Choose from: {list(LOCATION_MAPPER.keys())}") - + raise ValueError( + f"Invalid device location, Choose from: {list(LOCATION_MAPPER.keys())}" + ) + device_class = LOCATION_MAPPER[location] return device_class(device_name=name, **kwargs) diff --git a/src/openqaoa-core/backends/qaoa_vectorized.py b/src/openqaoa-core/backends/qaoa_vectorized.py index 7ee3207bf..7cf6683a6 100644 --- a/src/openqaoa-core/backends/qaoa_vectorized.py +++ b/src/openqaoa-core/backends/qaoa_vectorized.py @@ -890,7 +890,7 @@ def expectation(self, params: Type[QAOAVariationalBaseParams]) -> float: # Reshape wavefunction wavefn_ = self.wavefn - self.measurement_outcomes = self.wavefn.flatten() + self.measurement_outcomes = wavefn_.flatten() # Compute the expectation value and its standard deviation ham_wf = self.ham_op * wavefn_ @@ -922,7 +922,7 @@ def expectation_w_uncertainty( # Reshape wavefunction wavefn_ = self.wavefn - self.measurement_outcomes = self.wavefn.flatten() + self.measurement_outcomes = wavefn_.flatten() # Compute the expectation value and its standard deviation ham_wf = self.ham_op * wavefn_ diff --git a/src/openqaoa-core/qaoa_components/variational_parameters/fourierparams.py b/src/openqaoa-core/qaoa_components/variational_parameters/fourierparams.py index 70586bfb8..69614dc94 100644 --- a/src/openqaoa-core/qaoa_components/variational_parameters/fourierparams.py +++ b/src/openqaoa-core/qaoa_components/variational_parameters/fourierparams.py @@ -57,8 +57,8 @@ def __init__( super().__init__(qaoa_descriptor) assert q is not None, f"Depth q for {type(self).__name__} must be specified" self.q = q - self.u = u self.v = v + self.u = u self.betas = dct(self.v, n=self.p) self.gammas = dst(self.u, n=self.p) diff --git a/src/openqaoa-core/qaoa_components/variational_parameters/variational_baseparams.py b/src/openqaoa-core/qaoa_components/variational_parameters/variational_baseparams.py index d1d0f925d..86278a19e 100644 --- a/src/openqaoa-core/qaoa_components/variational_parameters/variational_baseparams.py +++ b/src/openqaoa-core/qaoa_components/variational_parameters/variational_baseparams.py @@ -132,6 +132,55 @@ def raw(self) -> np.ndarray: """ raise NotImplementedError() + def update_from_dict(self, new_values: dict): + """ + Update all the parameters from a dictionary. + + The input has the same format as the output of ``self.asdict()``. + + Parameters + ---------- + new_values: `dict` + A dictionary with the new parameters. Must have the same keys as + the output of ``self.asdict()``. + + """ + + assert isinstance(new_values, dict), f"Expected dict, got {type(new_values)}" + + for key, value in new_values.items(): + if key not in self.asdict().keys(): + raise KeyError( + f"'{key}' not in {self.__class__.__name__}, expected keys: {list(self.asdict().keys())}" + ) + else: + if getattr(self, key).shape != np.array(value).shape: + raise ValueError( + f"Shape of '{key}' does not match. Expected shape {getattr(self, key).shape}, got {np.array(value).shape}." + ) + + raw_params = [] + for key, value in self.asdict().items(): + if key in new_values.keys(): + raw_params += list(np.array(new_values[key]).flatten()) + else: + raw_params += list(np.array(value).flatten()) + + self.update_from_raw(raw_params) + + def asdict(self) -> dict: + """ + Return the parameters as a dictionary. + + Returns + ------- + dict: + The parameters as a dictionary. Has the same output format as the + expected input of ``self.update_from_dict``. + + """ + return {k[2:]: v for k, v in self.__dict__.items() if k[0:2] == "__"} + @classmethod def linear_ramp_from_hamiltonian( cls, qaoa_descriptor: QAOADescriptor, time: float = None

QAOA benchmark Benchmark object for qaoa - [x] Qaoa Benchmark object - [x] decide the import - [x] Create reference object - [x] Run method for benchmarked object and reference - [x] When running the benchmark it evaluates the points such that at each round there is more resolution - [x] Plot method for results - [x] Difference property between benchmarked values and reference - [x] Mean difference property - [x] Notebook - [x] Tests For examples read notebook 14 (new), here is a simple example: ``` from openqaoa import QAOA, create_device, QAOABenchmark from openqaoa.problems import QUBO qaoa = QAOA() qaoa.set_device(create_device(name='qiskit.shot_simulator', location='local')) qaoa.compile(QUBO.random_instance(5)) benchmark = QAOABenchmark(qaoa) benchmark.run(n_points_axis=2**4, ranges=[(0,1),(0,1)], plot=True) ```

2023-02-27T12:12:58

0.0

[]

trustpilot/python-trustpilot

trustpilot__python-trustpilot-17

31df744e23ae64cc76736f4abf71d7ba5950862e

diff --git a/HISTORY.md b/HISTORY.md index 8a646c9..805aed4 100644 --- a/HISTORY.md +++ b/HISTORY.md @@ -156,3 +156,10 @@ DELETED DO NOT USE\!\! ### 8.1.0 (2020-04-15) - reintroduce cli script + +### 9.0.0 (2020-04-29) + +- username/passwrd are now optional +- dot-env support in cli +- `patch` command exposed in cli +- output format can now be `raw` or `json`, (default=json) diff --git a/README.md b/README.md index 2faf6c0..b922b79 100644 --- a/README.md +++ b/README.md @@ -10,7 +10,8 @@ Python HTTP client for [Trustpilot](https://developers.trustpilot.com/). - GET, POST, PUT, DELETE, HEAD, OPTIONS and PATCH methods are exposed on module level - Implements session factory and default singleton session - Provides a simple hook system -- CLI tool with basic HTTP commands +- [CLI](#CLI) tool with basic HTTP commands + ## Installation @@ -20,158 +21,49 @@ Install the package from [PyPI](http://pypi.python.org/pypi/) using [pip](https: pip install trustpilot ``` -## Getting Started - -This client is using the [Requests](http://docs.python-requests.org/en/master/) library. Responses are standard [`requests.Response`](http://docs.python-requests.org/en/master/api/#requests.Response) objects. You can use it as a factory or as a singleton. +## Usage -### Use the singleton session - -Use the built-in `default session` to instantiate a globally accessible session. +_(for **full usage documentation** checkout [docs](https://github.com/trustpilot/python-trustpilot/blob/master/docs/README.md))_ ```python from trustpilot import client client.default_session.setup( - api_host="https://api.trustpilot.com", - api_version="v1", - api_key="YOUR_API_KEY", - api_secret="YOUR_API_SECRET", - username="YOUR_TRUSTPILOT_BUSINESS_USERNAME", - password="YOUR_TRUSTPILOT_BUSINESS_PASSWORD" + api_host="https://api.trustpilot.com" + api_key="YOUR_API_KEY" ) response = client.get("/foo/bar") +status_code = response.status_code ``` You can rely on environment variables for the setup of sessions so ```bash $ env -TRUSTPILOT_API_HOST=foobar.com -TRUSTPILOT_API_VERSION=v1 +TRUSTPILOT_API_HOST=https://api.trustpilot.com TRUSTPILOT_API_KEY=foo TRUSTPILOT_API_SECRET=bar -TRUSTPILOT_USERNAME=username -TRUSTPILOT_PASSWORD=password ``` -Will work with the implicit `default_session` and the `TrustpilotSession.setup` method. - -```python -from trustpilot import client -client.get("/foo/bar") -``` - -### Instantiate your own session - -You can create as many sessions as you like, as long as you pass them around yourself. - -```python -from trustpilot import client -session = client.TrustpilotSession( - api_host="https://api.trustpilot.com", - api_version="v1", - api_key="YOUR_API_KEY", - api_secret="YOUR_API_SECRET", - username="YOUR_TRUSTPILOT_BUSINESS_USERNAME", - password="YOUR_TRUSTPILOT_BUSINESS_PASSWORD" -) -response = session.get("/foo/bar") -``` - -## Async client - -Since version `3.0.0` you are able to use the `async_client` for `asyncio` usecases. - -To use the default `async_client` session, using `env-vars` for settings, import is as following: - -```python -import asyncio -from trustpilot import async_client -loop = asyncio.get_event_loop() - -async def get_response(): - response = await async_client.get('/foo/bar') - response_json = await response.json() - -loop.run_until_complete(get_response()) -``` - -Or instantiate the session yourself with: - -```python -import asyncio -from trustpilot import async_client -loop = asyncio.get_event_loop() - -session = async_client.TrustpilotAsyncSession( - api_host="https://api.trustpilot.com", - api_version="v1", - api_key="YOUR_API_KEY", - api_secret="YOUR_API_SECRET", - username="YOUR_TRUSTPILOT_BUSINESS_USERNAME", - password="YOUR_TRUSTPILOT_BUSINESS_PASSWORD" -) +### CLI -async def get_response(): - response = await session.get('/foo/bar') - response_json = await response.json() - -loop.run_until_complete(get_response()) -``` - -### Advanced async usage - -The async client uses an _asynccontextmanager_ under the hood to perform the supported request methods. -A side effect of the implementation is that it buffers up all the content before returning it to the calling scope. - -You can get around this limitation by using the _asynccontextmanager_ directly like in the next example. - -**Example with stream reading the raw aiohttp response object:** - -``` -import asyncio -from trustpilot import async_client -loop = asyncio.get_event_loop() - -async def get_response(): - async with session.request_context_manager('get', "/v1/foo/bar") as resp: - result = True - while True: - chunk = resp.content.read(8) - if not chunk: - break - result += chunk - return result - -loop.run_until_complete(get_response()) -``` - -## Setup User Agent - -A UserAgent header can be specified in two ways: - -1. By populating the `TRUSTPILOT_USER_AGENT` environment var -2. By creating your own (async/sync)-client instance, or calling `setup` on the `default_session`, and supplying the kwargs `user_agent=foobar` - -If no user-agent is given it will autopopulate using the function in `get_user_agent` function in [auth.py](./trustpilot/auth.py) - -## CLI - -A command line tool `trustpilot_api_client` is bundled with the module. To invoke it, use: +The `trustpilot_api_client` command is bundled with the install ```bash Usage: trustpilot_api_client [OPTIONS] COMMAND [ARGS]... Options: - --host TEXT host name - --version TEST api version - --key TEXT api key - --secret TEXT api secret - --token_issuer_host TEXT token issuer host name - --username TEXT Trustpilot username - --password TEXT Trustpilot password - -c TEXT json config file name - -v, --verbose verbosity level - --help Show this message and exit. + --host TEXT Host name + --version TEXT Api version + --key TEXT Api key + --secret TEXT Api secret + --token_issuer_host TEXT Token issuer host name + --username TEXT Trustpilot username + --password TEXT Trustpilot password + -c, --config FILENAME Json config file name + -e, --env FILENAME Dot env file + -of, --outputformat [json|raw] Output format, default=json + -v, --verbose Verbosity level + --help Show this message and exit. Commands: create-access-token Get an access token @@ -181,15 +73,36 @@ Commands: put Send a PUT request with specified data ``` -In order to use the **-c** option please supply the filename of a JSON in the following format: +You can also supply the variables with: + +**--config/-c** : As JSON config file in the following format: ```json { "TRUSTPILOT_API_HOST": "foo", - "TRUSTPILOT_API_VERSION": "v1", "TRUSTPILOT_API_KEY": "bar", "TRUSTPILOT_API_SECRET": "baz", + "TRUSTPILOT_API_VERSION": "v1", "TRUSTPILOT_USERNAME": "username", "TRUSTPILOT_PASSWORD": "password" } ``` + +or **--env/-e** : As DotEnv config file in the following format: + +```ini +TRUSTPILOT_API_HOST=foo +TRUSTPILOT_API_KEY=bar +TRUSTPILOT_API_SECRET=baz +TRUSTPILOT_API_VERSION=v1 +TRUSTPILOT_USERNAME=username +TRUSTPILOT_PASSWORD=password +``` + +## Changelog + +see [HISTORY.md](https://github.com/trustpilot/python-trustpilot/blob/master/HISTORY.md) + +## Issues / DEV + +Report issues [here](https://github.com/trustpilot/python-trustpilot/issues) and we welcome collaboration through PullRequests :-) diff --git a/docs/README.md b/docs/README.md new file mode 100644 index 0000000..19c769c --- /dev/null +++ b/docs/README.md @@ -0,0 +1,208 @@ +# Usage + +Complete instructions for how to use the **Trustpilot** module and `trustpilot_api_client` commandline tool. + +## Installation + +Install the package from [PyPI](http://pypi.python.org/pypi/) using [pip](https://pip.pypa.io/): + +``` +pip install trustpilot +``` + +## Getting Started + +This client is using the [Requests](http://docs.python-requests.org/en/master/) library. Responses are standard [`requests.Response`](http://docs.python-requests.org/en/master/api/#requests.Response) objects. You can use it as a factory or as a singleton. + +### Use the singleton session + +Use the built-in `default session` to instantiate a globally accessible session. + +```python +from trustpilot import client +client.default_session.setup( + api_host="https://api.trustpilot.com", # optional, default: https://api.tp-staging.com + api_key="YOUR_API_KEY", # required + api_secret="YOUR_API_SECRET", # optional + api_version="v1", # optional, default: v1 + username="YOUR_TRUSTPILOT_BUSINESS_USERNAME", # optional + password="YOUR_TRUSTPILOT_BUSINESS_PASSWORD" # optional +) +response = client.get("/foo/bar") +status_code = response.status_code + +``` + +You can rely on environment variables for the setup of sessions so + +```bash +$ env +TRUSTPILOT_API_HOST=https://api.trustpilot.com +TRUSTPILOT_API_KEY=foo +TRUSTPILOT_API_SECRET=bar +``` + +> optionally supply: +> ```ini +> TRUSTPILOT_API_VERSION=v1 +> TRUSTPILOT_USERNAME=username +> TRUSTPILOT_PASSWORD=password +> ``` + +Will work with the implicit `default_session` and the `TrustpilotSession.setup` method. + +```python +from trustpilot import client +client.get("/foo/bar") +``` + +### Instantiate your own session + +You can create as many sessions as you like, as long as you pass them around yourself. + +```python +from trustpilot import client +session = client.TrustpilotSession( + api_host="https://api.trustpilot.com", + api_key="YOUR_API_KEY", + api_secret="YOUR_API_SECRET", + api_version="v1", + username="YOUR_TRUSTPILOT_BUSINESS_USERNAME", + password="YOUR_TRUSTPILOT_BUSINESS_PASSWORD" +) +response = session.get("/foo/bar") +``` + +## Async client + +Since version `3.0.0` you are able to use the `async_client` for `asyncio` usecases. +The `async_client` uses [aiohttp](https://docs.aiohttp.org/en/stable/) read here for the signarue of its [request](https://docs.aiohttp.org/en/stable/client_reference.html#aiohttp.ClientSession.request) and [response](https://docs.aiohttp.org/en/stable/client_reference.html#response-object) + +To use the default `async_client` session, using `env-vars` for settings, import is as following: + +```python +import asyncio +from trustpilot import async_client +loop = asyncio.get_event_loop() + +async def get_response(): + response = await async_client.get('/foo/bar') + response_json = await response.json() + status_code = response.status + +loop.run_until_complete(get_response()) +``` + +Or instantiate the session yourself with: + +```python +import asyncio +from trustpilot import async_client +loop = asyncio.get_event_loop() + +session = async_client.TrustpilotAsyncSession( + api_host="https://api.trustpilot.com", # optional, default: https://api.tp-staging.com + api_key="YOUR_API_KEY", # required + api_secret="YOUR_API_SECRET", # optional + api_version="v1", # optional + username="YOUR_TRUSTPILOT_BUSINESS_USERNAME", # optional + password="YOUR_TRUSTPILOT_BUSINESS_PASSWORD" # optional +) + +async def get_response(): + response = await session.get('/foo/bar') + response_json = await response.json() + +loop.run_until_complete(get_response()) +``` + +### Advanced async usage + +The async client uses an _asynccontextmanager_ under the hood to perform the supported request methods. +A side effect of the implementation is that it buffers up all the content before returning it to the calling scope. + +You can get around this limitation by using the _asynccontextmanager_ directly like in the next example. + +**Example with stream reading the raw aiohttp response object:** + +``` +import asyncio +from trustpilot import async_client +loop = asyncio.get_event_loop() + +async def get_response(): + async with session.request_context_manager('get', "/v1/foo/bar") as resp: + result = True + while True: + chunk = resp.content.read(8) + if not chunk: + break + result += chunk + return result + +loop.run_until_complete(get_response()) +``` + +## Setup User Agent + +A UserAgent header can be specified in two ways: + +1. By populating the `TRUSTPILOT_USER_AGENT` environment var +2. By creating your own (async/sync)-client instance, or calling `setup` on the `default_session`, and supplying the kwargs `user_agent=foobar` + +If no user-agent is given it will autopopulate using the function in `get_user_agent` function in [auth.py](./trustpilot/auth.py) + +## CLI + +The `trustpilot_api_client` command is bundled with the install + +```bash +Usage: trustpilot_api_client [OPTIONS] COMMAND [ARGS]... + +Options: + --host TEXT Host name + --version TEXT Api version + --key TEXT Api key + --secret TEXT Api secret + --token_issuer_host TEXT Token issuer host name + --username TEXT Trustpilot username + --password TEXT Trustpilot password + -c, --config FILENAME Json config file name + -e, --env FILENAME Dot env file + -of, --outputformat [json|raw] Output format, default=json + -v, --verbose Verbosity level + --help Show this message and exit. + +Commands: + create-access-token Get an access token + delete Send a DELETE request + get Send a GET request + post Send a POST request with specified data + put Send a PUT request with specified data +``` + +You can also supply the variables through with: + +**--config/-c** : As JSON config file in the following format: + +```json +{ + "TRUSTPILOT_API_HOST": "foo", + "TRUSTPILOT_API_VERSION": "v1", + "TRUSTPILOT_API_KEY": "bar", + "TRUSTPILOT_API_SECRET": "baz", + "TRUSTPILOT_USERNAME": "username", + "TRUSTPILOT_PASSWORD": "password" +} +``` + +**--env/-e** : As DotEnv config file in the following format: + +```ini +TRUSTPILOT_API_HOST=foo +TRUSTPILOT_API_VERSION=v1 +TRUSTPILOT_API_KEY=bar +TRUSTPILOT_API_SECRET=baz +TRUSTPILOT_USERNAME=username +TRUSTPILOT_PASSWORD=password +``` \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 0b71920..72d7541 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "trustpilot" -version = "8.1.0" +version = "9.0.0" description = "trustpilot api client including cli tool" authors = [ "Johannes Valbjørn <[email protected]>", diff --git a/trustpilot/async_client.py b/trustpilot/async_client.py index 89e5046..6b4927a 100644 --- a/trustpilot/async_client.py +++ b/trustpilot/async_client.py @@ -19,8 +19,8 @@ def __init__(self, *args, **kwargs): def setup( self, api_host=None, - api_version=None, api_key=None, + api_version=None, api_secret=None, username=None, password=None, @@ -31,10 +31,20 @@ def setup( **kwargs ): - self.api_version = api_version or environ.get("TRUSTPILOT_API_VERSION", "v1") self.api_host = api_host or environ.get( "TRUSTPILOT_API_HOST", "https://api.trustpilot.com" ) + try: + self.api_key = api_key or environ["TRUSTPILOT_API_KEY"] + self.api_secret = api_secret or environ.get("TRUSTPILOT_API_SECRET", "") + self.username = username or environ.get("TRUSTPILOT_USERNAME") + self.password = password or environ.get("TRUSTPILOT_PASSWORD") + self.access_token = access_token + except KeyError as e: + logger.debug("Not auth setup, missing env-var or setup for {}".format(e)) + + self.api_version = api_version or environ.get("TRUSTPILOT_API_VERSION", "v1") + self.token_issuer_host = token_issuer_host or self.api_host self.access_token = access_token self.token_issuer_path = token_issuer_path or environ.get( @@ -51,15 +61,6 @@ def setup( "'{}' is not a valid api_host url".format(api_host) ) - try: - self.api_key = api_key or environ["TRUSTPILOT_API_KEY"] - self.api_secret = api_secret or environ.get("TRUSTPILOT_API_SECRET", "") - self.username = username or environ.get("TRUSTPILOT_USERNAME") - self.password = password or environ.get("TRUSTPILOT_PASSWORD") - self.access_token = access_token - except KeyError as e: - logger.debug("Not auth setup, missing env-var or setup for {}".format(e)) - return self async def get_request_auth_headers(self): diff --git a/trustpilot/auth.py b/trustpilot/auth.py index 0048f5f..a2d1d4e 100644 --- a/trustpilot/auth.py +++ b/trustpilot/auth.py @@ -22,11 +22,15 @@ def create_access_token_request_params(session): api_version=session.api_version.rstrip("/"), token_issuer_path=session.token_issuer_path, ) - data = { - "grant_type": "password", - "username": session.username, - "password": session.password, - } + + data = {} + + if session.username and session.password: + data = { + "grant_type": "password", + "username": session.username, + "password": session.password, + } headers = { "Authorization": "Basic {}".format( diff --git a/trustpilot/cli.py b/trustpilot/cli.py index 5c8efda..093faa2 100644 --- a/trustpilot/cli.py +++ b/trustpilot/cli.py @@ -19,36 +19,62 @@ def get_verbosity(ctx): return ctx.meta.get("trustpilot.verbosity", 0) [email protected]_context +def get_output_format(ctx): + output_format = ctx.meta.get("trustpilot.outputformat", "json") or "json" + return output_format + + def format_response(response): content = response.text - try: - content = response.json() - except ValueError: - pass - output = OrderedDict() - output["url"] = response.url - output["status"] = response.status_code - if get_verbosity(): - headers = response.headers - output["headers"] = OrderedDict((k, headers[k]) for k in headers) - output["content"] = content - return json.dumps(output, indent=2) + output_format = get_output_format() + + if output_format == "raw": + lines = [] + lines.extend(["url", response.url, "\n"]) + lines.extend(["status", str(response.status_code), "\n"]) + if get_verbosity(): + lines.extend( + [ + e + for t in [ + (f"headers.{key}", str(value), "\n") + for key, value in response.headers.items() + ] + for e in t + ] + ) + lines.extend(["content", content]) + return "\n".join(lines) + elif output_format == "json": + try: + content = response.json() + except ValueError: + pass + output = OrderedDict() + output["url"] = response.url + output["status"] = response.status_code + if get_verbosity(): + headers = response.headers + output["headers"] = OrderedDict((k, headers[k]) for k in headers) + output["content"] = content + return json.dumps(output, indent=2) @click.group(invoke_without_command=True) @click.pass_context [email protected]("--host", type=str, help="host name", envvar="TRUSTPILOT_API_HOST") [email protected]("--host", type=str, help="Host name", envvar="TRUSTPILOT_API_HOST") @click.option( - "--version", type=str, help="api version (e.g. v1)", envvar="TRUSTPILOT_API_VERSION" + "--version", type=str, help="Api version", envvar="TRUSTPILOT_API_VERSION" ) [email protected]("--key", type=str, help="api key", envvar="TRUSTPILOT_API_KEY") [email protected]("--secret", type=str, help="api secret", envvar="TRUSTPILOT_API_SECRET") [email protected]("--key", type=str, help="Api key", envvar="TRUSTPILOT_API_KEY") [email protected]("--secret", type=str, help="Api secret", envvar="TRUSTPILOT_API_SECRET") @click.option( "--token_issuer_host", type=str, default="", - help="token issuer host name", + help="Token issuer host name", envvar="TRUSTPILOT_API_TOKEN_ISSUER_HOST", ) @click.option( @@ -65,8 +91,16 @@ def format_response(response): help="Trustpilot password", envvar="TRUSTPILOT_PASSWORD", ) [email protected]("-c", type=str, help="json config file name") [email protected]("-v", "--verbose", count=True, help="verbosity level") [email protected]("--config", "-c", type=click.File("r"), help="Json config file name") [email protected]("--env", "-e", type=click.File("r"), help="Dot env file") [email protected]( + "--outputformat", + "-of", + type=click.Choice(["json", "raw"], case_sensitive=False), + default="json", + help="Output format, default=json", +) [email protected]("-v", "--verbose", count=True, help="Verbosity level") def cli(ctx, **kwargs): splash = r""" _____ _ _ _ _ @@ -88,16 +122,30 @@ def cli(ctx, **kwargs): splash = click.style(splash, fg="green") + "\n" values_dict = {} - config_filename = kwargs.pop("c") + config_file = kwargs.pop("config") - if config_filename: - with open(config_filename, "r") as f: - values_dict = json.load(f) + if config_file: + values_dict.update(json.load(config_file)) + + env_file = kwargs.pop("env") + if env_file: + env_file_lines = env_file.read().split("\n") + values_dict.update( + dict( + (key, value[0]) + for key, *value in (line.split("=", 1) for line in env_file_lines) + if key + ) + ) # setup verbosity level for global access verbosity = kwargs.get("verbose") ctx.meta["trustpilot.verbosity"] = verbosity + # setup output format + output_format = kwargs.get("outputformat") + ctx.meta["trustpilot.outputformat"] = output_format + # setup logging (increasing information levels) # _ : content, url, status_code # v : headers @@ -132,8 +180,10 @@ def cli(ctx, **kwargs): kwargs.pop("token_issuer_host") or values_dict.get("TRUSTPILOT_API_TOKEN_ISSUER_HOST", None) ), - username=kwargs.pop("username") or values_dict["TRUSTPILOT_USERNAME"], - password=kwargs.pop("password") or values_dict["TRUSTPILOT_PASSWORD"], + username=kwargs.pop("username") + or values_dict.get("TRUSTPILOT_USERNAME", None), + password=kwargs.pop("password") + or values_dict.get("TRUSTPILOT_PASSWORD", None), ) except KeyError as key: raise SystemExit("Missing argument: {}".format(key)) @@ -209,5 +259,23 @@ def put(path, data, content_type): click.echo(format_response(response)) +@cli_command [email protected]("path") [email protected]("--data", type=str, help="json_data to post") [email protected]( + "--content-type", + type=str, + default="application/json", + help="content-type, default=application/json", +) +def patch(path, data, content_type): + """ + Send a PATCH request with specified data + """ + headers = {"content-type": content_type} + response = client.patch(url=path, data=data, headers=headers) + click.echo(format_response(response)) + + if __name__ == "__main__": cli() diff --git a/trustpilot/client.py b/trustpilot/client.py index d015083..89120e1 100644 --- a/trustpilot/client.py +++ b/trustpilot/client.py @@ -39,8 +39,8 @@ def __init__(self, **kwargs): def setup( self, api_host=None, - api_version=None, api_key=None, + api_version=None, api_secret=None, username=None, password=None, @@ -51,10 +51,20 @@ def setup( **kwargs ): - self.api_version = api_version or environ.get("TRUSTPILOT_API_VERSION", "v1") self.api_host = api_host or environ.get( "TRUSTPILOT_API_HOST", "https://api.trustpilot.com" ) + + try: + self.api_key = api_key or environ["TRUSTPILOT_API_KEY"] + self.api_secret = api_secret or environ.get("TRUSTPILOT_API_SECRET", "") + self.username = username or environ.get("TRUSTPILOT_USERNAME") + self.password = password or environ.get("TRUSTPILOT_PASSWORD") + self.access_token = access_token + except KeyError as e: + logger.debug("Not auth setup, missing env-var or setup for {}".format(e)) + + self.api_version = api_version or environ.get("TRUSTPILOT_API_VERSION", "v1") self.token_issuer_host = token_issuer_host or self.api_host self.access_token = access_token self.token_issuer_path = token_issuer_path or environ.get( @@ -71,15 +81,7 @@ def setup( "'{}' is not a valid api_host url".format(api_host) ) - try: - self.api_key = api_key or environ["TRUSTPILOT_API_KEY"] - self.api_secret = api_secret or environ.get("TRUSTPILOT_API_SECRET", "") - self.username = username or environ.get("TRUSTPILOT_USERNAME") - self.password = password or environ.get("TRUSTPILOT_PASSWORD") - self.access_token = access_token - self.hooks["response"] = self._post_request_callback - except KeyError as e: - logger.debug("Not auth setup, missing env-var or setup for {}".format(e)) + self.hooks["response"] = self._post_request_callback return self

Backend application authentication We're interested in making some API calls on a schedule from our server, but it seems (also from the [docs](https://developers.trustpilot.com/authentication)) that all authentication options require a personal account password. Is this true, or is there an API-key based auth option that I'm not seeing?

we have identified the issue and we are working on it. sorry for taking so long to reply. Will keep you updated on the progress made and mention you when the issue is fixed

2020-04-29T08:52:25

0.0

[]

pyapp-kit/psygnal

pyapp-kit__psygnal-293

c643ad05f78d597bc1633be3e22a52837bf9162d

diff --git a/src/psygnal/_evented_model.py b/src/psygnal/_evented_model.py index d139bb9c..60bc4222 100644 --- a/src/psygnal/_evented_model.py +++ b/src/psygnal/_evented_model.py @@ -8,6 +8,7 @@ ClassVar, Dict, Iterator, + Mapping, NamedTuple, Set, Type, @@ -23,13 +24,8 @@ from ._group_descriptor import _check_field_equality, _pick_equality_operator from ._signal import Signal -NULL = object() -ALLOW_PROPERTY_SETTERS = "allow_property_setters" -FIELD_DEPENDENCIES = "field_dependencies" -GUESS_PROPERTY_DEPENDENCIES = "guess_property_dependencies" PYDANTIC_V1 = pydantic.version.VERSION.startswith("1") - if TYPE_CHECKING: from inspect import Signature @@ -38,7 +34,7 @@ from pydantic._internal import _utils as utils from typing_extensions import dataclass_transform as dataclass_transform # py311 - from ._signal import SignalInstance + from ._signal import RecursionMode, SignalInstance EqOperator = Callable[[Any, Any], bool] else: @@ -58,6 +54,14 @@ def dataclass_transform(*args, **kwargs): return lambda a: a +NULL = object() +ALLOW_PROPERTY_SETTERS = "allow_property_setters" +FIELD_DEPENDENCIES = "field_dependencies" +GUESS_PROPERTY_DEPENDENCIES = "guess_property_dependencies" +RECURSION_MODE = "recursion_mode" +DEFAULT_RECURSION_MODE: "RecursionMode" = "deferred" + + @contextmanager def no_class_attributes() -> Iterator[None]: # pragma: no cover """Context in which pydantic_main.ClassAttribute just passes value 2. @@ -213,10 +217,31 @@ def __new__( model_fields = _get_fields(cls) model_config = _get_config(cls) + recursion_cfg = model_config.get(RECURSION_MODE, {}) + default_recursion: RecursionMode = DEFAULT_RECURSION_MODE + recursion_map: Mapping[str, RecursionMode] = {} + if isinstance(recursion_cfg, str): + if recursion_cfg not in {"immediate", "deferred"}: + raise ValueError( + f"Invalid recursion mode {default_recursion!r}. Must be " + "either 'immediate' or 'deferred'" + ) + default_recursion = recursion_cfg + else: + if not isinstance(recursion_cfg, Mapping) or not all( + x in {"immediate", "deferred"} for x in recursion_cfg.values() + ): + raise ValueError( + f"Invalid recursion mode {recursion_cfg!r}. Must be a mapping " + "of field names to 'immediate' or 'deferred'." + ) + recursion_map = recursion_cfg + for n, f in model_fields.items(): cls.__eq_operators__[n] = _pick_equality_operator(f.annotation) if not f.frozen: - signals[n] = Signal(f.annotation) + recursion = recursion_map.get(n, default_recursion) + signals[n] = Signal(f.annotation, recursion_mode=recursion) # If a field type has a _json_encode method, add it to the json # encoders for this model. @@ -244,7 +269,8 @@ def __new__( for key, attr in namespace.items(): if isinstance(attr, property) and attr.fset is not None: cls.__property_setters__[key] = attr - signals[key] = Signal(object) + recursion = recursion_map.get(key, default_recursion) + signals[key] = Signal(object, recursion_mode=recursion) else: for b in cls.__bases__: with suppress(AttributeError): diff --git a/src/psygnal/_signal.py b/src/psygnal/_signal.py index e53d6a19..0a063384 100644 --- a/src/psygnal/_signal.py +++ b/src/psygnal/_signal.py @@ -5,6 +5,7 @@ import threading import warnings import weakref +from collections import deque from contextlib import contextmanager, suppress from functools import lru_cache, partial, reduce from inspect import Parameter, Signature, isclass @@ -49,11 +50,13 @@ # function that takes two args tuples. it will be passed to itertools.reduce ReducerTwoArgs = Callable[[tuple, tuple], tuple] ReducerFunc = Union[ReducerOneArg, ReducerTwoArgs] + RecursionMode = Literal["immediate", "deferred"] __all__ = ["Signal", "SignalInstance", "_compiled"] _NULL = object() F = TypeVar("F", bound=Callable) RECURSION_LIMIT = sys.getrecursionlimit() +DEFAULT_RECURSION_MODE: RecursionMode = "immediate" class Signal: @@ -107,6 +110,16 @@ class attribute that is bound to the signal will be used. default None Whether to check the callback parameter types against `signature` when connecting a new callback. This can also be provided at connection time using `.connect(..., check_types=True)`. By default, False. + recursion_mode : Literal["immediate", "deferred"] + How to handle recursive emissions, when this signal is emitted by a callback + while this signal is being emitted. + + - "immediate": Nested emission events immediately begin a deeper emission loop: + calling all callbacks with the arguments of the nested emitter before + returning to continue the original emission loop. This is the default + behavior. + - "deferred": Nested emission events are deferred until the current emission + loop is complete. """ # _signature: Signature # callback signature for this signal @@ -120,11 +133,13 @@ def __init__( name: str | None = None, check_nargs_on_connect: bool = True, check_types_on_connect: bool = False, + recursion_mode: RecursionMode = DEFAULT_RECURSION_MODE, ) -> None: self._name = name self.description = description self._check_nargs_on_connect = check_nargs_on_connect self._check_types_on_connect = check_types_on_connect + self._recursion_mode = recursion_mode self._signal_instance_class: type[SignalInstance] = SignalInstance self._signal_instance_cache: dict[int, SignalInstance] = {} @@ -220,6 +235,7 @@ def _create_signal_instance( name=name or self._name, check_nargs_on_connect=self._check_nargs_on_connect, check_types_on_connect=self._check_types_on_connect, + recursion_mode=self._recursion_mode, ) @classmethod @@ -310,6 +326,16 @@ class Emitter: Whether to check the callback parameter types against `signature` when connecting a new callback. This can also be provided at connection time using `.connect(..., check_types=True)`. By default, False. + recursion_mode : Literal["immediate", "deferred"] + How to handle recursive emissions, when this signal is emitted by a callback + while this signal is being emitted. + + - "immediate": Nested emission events immediately begin a deeper emission loop: + calling all callbacks with the arguments of the nested emitter before + returning to continue the original emission loop. This is the default + behavior. + - "deferred": Nested emission events are deferred until the current emission + loop is complete. Raises ------ @@ -330,11 +356,8 @@ def __init__( name: str | None = None, check_nargs_on_connect: bool = True, check_types_on_connect: bool = False, + recursion_mode: RecursionMode = "immediate", ) -> None: - self._name = name - self._instance: Callable = self._instance_ref(instance) - self._args_queue: list[tuple] = [] # filled when paused - if isinstance(signature, (list, tuple)): signature = _build_signature(*signature) elif not isinstance(signature, Signature): # pragma: no cover @@ -343,6 +366,15 @@ def __init__( "instance of `inspect.Signature`" ) + if recursion_mode not in ("immediate", "deferred"): # pragma: no cover + raise ValueError( + "recursion_mode must be one of 'immediate' or 'deferred', not " + f"{recursion_mode!r}" + ) + + self._name = name + self._instance: Callable = self._instance_ref(instance) + self._args_queue: list[tuple] = [] # filled when paused self._signature = signature self._check_nargs_on_connect = check_nargs_on_connect self._check_types_on_connect = check_types_on_connect @@ -350,7 +382,14 @@ def __init__( self._is_blocked: bool = False self._is_paused: bool = False self._lock = threading.RLock() - self._emit_queue: list[tuple] = [] + self._emit_queue: deque[tuple] = deque() + self._recursion_mode = recursion_mode + self._run_emit_loop_inner: Callable[[], None] + if self._recursion_mode == "immediate": + self._run_emit_loop_inner = self._run_emit_loop_immediate + else: + self._run_emit_loop_inner = self._run_emit_loop_deferred + # whether any slots in self._slots have a priority other than 0 self._priority_in_use = False @@ -954,7 +993,7 @@ def emit( checks fail. """ if self._is_blocked: - return None + return if check_nargs: try: @@ -976,7 +1015,7 @@ def emit( if self._is_paused: self._args_queue.append(args) - return None + return if SignalInstance._debug_hook is not None: from ._group import EmissionInfo @@ -984,12 +1023,11 @@ def emit( SignalInstance._debug_hook(EmissionInfo(self, args)) self._run_emit_loop(args) - return None def __call__( self, *args: Any, check_nargs: bool = False, check_types: bool = False ) -> None: - """Alias for `emit()`.""" + """Alias for `emit()`. But prefer using `emit()` for clarity.""" return self.emit( *args, check_nargs=check_nargs, @@ -997,33 +1035,44 @@ def __call__( ) def _run_emit_loop(self, args: tuple[Any, ...]) -> None: - # allow receiver to query sender with Signal.current_emitter() with self._lock: self._emit_queue.append(args) - if len(self._emit_queue) > 1: - return None + return try: with Signal._emitting(self): - i = 0 - while i < len(self._emit_queue): - args = self._emit_queue[i] - for caller in self._slots: - caller.cb(args) - if len(self._emit_queue) > RECURSION_LIMIT: - raise RecursionError - i += 1 + # allow receiver to query sender with Signal.current_emitter() + self._run_emit_loop_inner() except RecursionError as e: raise RecursionError( - f"RecursionError in {caller.slot_repr()} when " + f"RecursionError when " f"emitting signal {self.name!r} with args {args}" ) from e except Exception as e: - raise EmitLoopError(cb=caller, args=args, exc=e, signal=self) from e + raise EmitLoopError( + cb=self._caller, args=self._args, exc=e, signal=self + ) from e finally: self._emit_queue.clear() - return None + def _run_emit_loop_immediate(self) -> None: + self._args = args = self._emit_queue.popleft() + caller = None + for caller in self._slots: + self._caller = caller + caller.cb(args) + + def _run_emit_loop_deferred(self) -> None: + i = 0 + caller = None + while i < len(self._emit_queue): + self._args = args = self._emit_queue[i] + for caller in self._slots: + self._caller = caller + caller.cb(args) + if len(self._emit_queue) > RECURSION_LIMIT: + raise RecursionError + i += 1 def block(self, exclude: Iterable[str | SignalInstance] = ()) -> None: """Block this signal from emitting. @@ -1172,6 +1221,7 @@ def __getstate__(self) -> dict: "_check_types_on_connect", "_emit_queue", "_priority_in_use", + "_recursion_mode", ) dd = {slot: getattr(self, slot) for slot in attrs} dd["_instance"] = self._instance() @@ -1194,6 +1244,10 @@ def __setstate__(self, state: dict) -> None: else: setattr(self, k, v) self._lock = threading.RLock() + if self._recursion_mode == "immediate": + self._run_emit_loop_inner = self._run_emit_loop_immediate + else: # pragma: no cover + self._run_emit_loop_inner = self._run_emit_loop_deferred class _SignalBlocker: diff --git a/src/psygnal/containers/_evented_set.py b/src/psygnal/containers/_evented_set.py index 4a312d35..9f68a8a2 100644 --- a/src/psygnal/containers/_evented_set.py +++ b/src/psygnal/containers/_evented_set.py @@ -218,7 +218,7 @@ class SetEvents(SignalGroup): added or removed from the set. """ - items_changed = Signal(tuple, tuple) + items_changed = Signal(tuple, tuple, recursion_mode="deferred") class EventedSet(_BaseMutableSet[_T]):

RecursionError with v0.10.0 * psygnal version: 0.10.0 * Python version: 3.11.4 * Operating System: Windows 11 ### Description I found in a specific case, psygnal<0.10 works fine but recursion error occurs in the emission loop with 0.10.0. ### What I Did This is a simple example that class `A` has an evented property `value` and class `Linker` can link the values of different `A` instances. ``` python from psygnal import SignalGroup, Signal class Events(SignalGroup): value = Signal(int) class A: def __init__(self): self.events = Events() self._value = 0 @property def value(self): return self._value @value.setter def value(self, val: int): self._value = val self.events.value.emit(val) class Linker: def __init__(self, objects: list[A]): self._objs = objects for obj in objects: obj.events.value.connect(self.set_values) self._updating = False def set_values(self, val: int): if self._updating: return # avoid recursion error self._updating = True try: for obj in self._objs: obj.value = val finally: self._updating = False a0 = A() a1 = A() linker = Linker([a0, a1]) a0.value = -1 assert a1.value == -1 ``` ``` RecursionError Traceback (most recent call last) File src\psygnal\_signal.py:1014, in _run_emit_loop() RecursionError: During handling of the above exception, another exception occurred: RecursionError Traceback (most recent call last) Cell In[1], line 42 38 a1 = A() 40 linker = Linker([a0, a1]) ---> 42 a0.value = -1 43 assert a1.value == -1 Cell In[1], line 18, in A.value(self, val) 15 @value.setter 16 def value(self, val: int): 17 self._value = val ---> 18 self.events.value.emit(val) File src\psygnal\_signal.py:986, in emit() File src\psygnal\_signal.py:1017, in _run_emit_loop() RecursionError: RecursionError in __main__.Linker.set_values when emitting signal 'value' with args (-1,) ```

Hi @hanjinliu, sorry you've hit this error. The main change that occurred in #281 was to ensure that, when a signal is emitted, *all* registered callbacks are called before the next emission event. This means that if one of the callbacks also triggers an emit event of the same signal (as your `value.setter` does here) that emission will wait until all callbacks have been called with the first emission. In other words, it's a bread-first callback pattern instead of a depth first callback pattern. (more on that in a moment) One solution that should work for you in both cases is to not emit unnecessary events in your value.setter if the value hasn't actually changed: ```python @value.setter def value(self, val: int) -> None: before, self._value = self._value, val if val != before: self.value_changed.emit(val) ``` ---- However, your comment prompted me to go back and check what Qt's default behavior is for calling callbacks. And I realized it does *not* do a breadth-first callback pattern. Emission of a signal immediately invokes all callbacks, even if it happens in the middle of another emit event. For example: ```python from qtpy import QtCore class Emitter(QtCore.QObject): sig = QtCore.Signal(int) def cb1(value: int) -> None: print("calling cb1 with", value) if value != 2: emitter.sig.emit(2) def cb2(value: int) -> None: print("calling cb2 with", value) app = QtCore.QCoreApplication([]) emitter = Emitter() emitter.sig.connect(cb1) emitter.sig.connect(cb2) emitter.sig.emit(1) ``` prints ```python calling cb1 with 1 calling cb1 with 2 calling cb2 with 2 calling cb2 with 1 ``` So, @Czaki, with #281, we actually changed the behavior of psygnal to be unlike Qt, and possibly be more surprising than the previous behavior. Can you weigh in on what prompted your PR in #281, and why you think we should be calling all callbacks first before entering the second emission round? i think (but am not sure) that the motivation came from some behavior you were observing with EventedSet in napari, is that correct? Is it possible that that specific issue could have been solved without changing the callback order of nested emission events? as a slightly deeper example: ```python from qtpy import QtCore class Emitter(QtCore.QObject): sig = QtCore.Signal(int) def cb1(value: int) -> None: print("calling cb1 with", value) if value == 1: emitter.sig.emit(2) def cb2(value: int) -> None: print("calling cb2 with", value) if value == 2: emitter.sig.emit(3) def cb3(value: int) -> None: print("calling cb3 with", value) app = QtCore.QCoreApplication([]) emitter = Emitter() emitter.sig.connect(cb1) emitter.sig.connect(cb2) emitter.sig.connect(cb3) emitter.sig.emit(1) ``` ``` calling cb1 with 1 calling cb1 with 2 calling cb2 with 2 calling cb1 with 3 calling cb2 with 3 calling cb3 with 3 calling cb3 with 2 calling cb2 with 1 calling cb3 with 1 ``` I could confirm that also for native objects the order is same as you spotted: ```python from qtpy.QtWidgets import QApplication, QListWidget app = QApplication([]) w = QListWidget() w.addItems(["a", "b", "c", "d", "e"]) w.currentRowChanged.connect(lambda x: w.setCurrentRow(2)) w.currentRowChanged.connect(lambda x: print("currentRowChanged", x)) w.show() w.setCurrentRow(1) ``` gives: ``` currentRowChanged 2 currentRowChanged 1 ``` The problem is that in such a situation you cannot depend on values emitted by signal, as the last signal contains different value than the current state in the object, that introduces inconsistency in the system. If you decide to revert this change, then there will be a need to make sender dispatch thread safe. Thanks @Czaki, your example indeed makes it very clear what the problem is with the default pattern. So, while I do think the default behavior should return to how it was, I think we need an option flag. There are three places where I can imagine having this (not mutually exclusive). ```python # defined on the signal level class Thing: sig = Signal(int, depth_first=False) t = Thing() # declared at the connection level t.sig.connect(print, depth_first=False) # declared at emission time t.sig.emit(1, depth_first=False) ``` ... option 2 (a connection parameter) would perhaps be the weirdest one, with possibly confusing results and a more difficult implementation, but it would be the only one where the receiver gets to specify how they want to be called. There's many ways we could go with this, and I think it's a worthwhile feature/switch. Thoughts? There is also a need to allow passing such selection to evented model. I prefer declaration in constructor call `Signal(int, depth_first=False)` Also qt is not consistent in order of event emmision ```python from qtpy.QtWidgets import QApplication, QListWidget from qtpy.QtCore import Qt app = QApplication([]) w = QListWidget() w.addItems(["a", "b", "c", "d", "e"]) w.currentRowChanged.connect(lambda x: w.setCurrentRow(2), Qt.QueuedConnection) w.currentRowChanged.connect(lambda x: print("currentRowChanged", x), Qt.QueuedConnection) w.show() w.setCurrentRow(1) app.exec_() ``` provides ``` currentRowChanged 1 currentRowChanged 2 ``` right, `Qt::ConnectionType` does give the connector (not the emitter) some degree of control over the order in which they will be called. so it seems that there is probably at least a user case for both declaration in the signal with `Signal(int, depth_first=False)`, as well as at connection time. I'll put together a proposal

2024-03-08T21:54:44

0.0

[]

pyapp-kit/psygnal

pyapp-kit__psygnal-277

e9ee49698b53aa441e069e0c14b56bc42fbad08d

diff --git a/src/psygnal/_evented_model_v1.py b/src/psygnal/_evented_model_v1.py index f9bd6d3c..a481b8bc 100644 --- a/src/psygnal/_evented_model_v1.py +++ b/src/psygnal/_evented_model_v1.py @@ -362,10 +362,10 @@ def __setattr__(self, name: str, value: Any) -> None: deps_with_callbacks = { dep_name for dep_name in self.__field_dependents__.get(name, ()) - if len(group[dep_name]) > 1 + if len(group[dep_name]) } if ( - len(signal_instance) < 2 # the signal itself has no listeners + len(signal_instance) < 1 # the signal itself has no listeners and not deps_with_callbacks # no dependent properties with listeners and not len(group._psygnal_relay) # no listeners on the SignalGroup ): diff --git a/src/psygnal/_evented_model_v2.py b/src/psygnal/_evented_model_v2.py index 75b82375..c4413854 100644 --- a/src/psygnal/_evented_model_v2.py +++ b/src/psygnal/_evented_model_v2.py @@ -348,10 +348,10 @@ def __setattr__(self, name: str, value: Any) -> None: deps_with_callbacks = { dep_name for dep_name in self.__field_dependents__.get(name, ()) - if len(group[dep_name]) > 1 + if len(group[dep_name]) } if ( - len(signal_instance) < 2 # the signal itself has no listeners + len(signal_instance) < 1 # the signal itself has no listeners and not deps_with_callbacks # no dependent properties with listeners and not len(group._psygnal_relay) # no listeners on the SignalGroup ): diff --git a/src/psygnal/_group.py b/src/psygnal/_group.py index c79071cf..98e19c6d 100644 --- a/src/psygnal/_group.py +++ b/src/psygnal/_group.py @@ -12,12 +12,14 @@ import warnings from typing import ( + TYPE_CHECKING, Any, Callable, ClassVar, ContextManager, Iterable, Iterator, + Literal, Mapping, NamedTuple, ) @@ -26,6 +28,9 @@ from ._mypyc import mypyc_attr +if TYPE_CHECKING: + from psygnal._weak_callback import WeakCallback + __all__ = ["EmissionInfo", "SignalGroup"] @@ -63,14 +68,29 @@ def __init__( self._signals = signals self._sig_was_blocked: dict[str, bool] = {} + def _append_slot(self, slot: WeakCallback) -> None: + super()._append_slot(slot) + if len(self._slots) == 1: + self._connect_relay() + + def _connect_relay(self) -> None: # silence any warnings about failed weakrefs (will occur in compiled version) with warnings.catch_warnings(): warnings.simplefilter("ignore") - for sig in signals.values(): + for sig in self._signals.values(): sig.connect( self._slot_relay, check_nargs=False, check_types=False, unique=True ) + def _remove_slot(self, slot: int | WeakCallback | Literal["all"]) -> None: + super()._remove_slot(slot) + if not self._slots: + self._disconnect_relay() + + def _disconnect_relay(self) -> None: + for sig in self._signals.values(): + sig.disconnect(self._slot_relay) + def _slot_relay(self, *args: Any) -> None: if emitter := Signal.current_emitter(): info = EmissionInfo(emitter, args) diff --git a/src/psygnal/_group_descriptor.py b/src/psygnal/_group_descriptor.py index 8776e437..d751f479 100644 --- a/src/psygnal/_group_descriptor.py +++ b/src/psygnal/_group_descriptor.py @@ -268,7 +268,7 @@ def _setattr_and_emit_(self: object, name: str, value: Any) -> None: # don't emit if the signal doesn't exist or has no listeners signal: SignalInstance = group[name] - if len(signal) < 2 and not len(group._psygnal_relay): + if len(signal) < 1: return super_setattr(self, name, value) with _changes_emitted(self, name, signal): diff --git a/src/psygnal/_signal.py b/src/psygnal/_signal.py index ea3f5d9e..ee516cdc 100644 --- a/src/psygnal/_signal.py +++ b/src/psygnal/_signal.py @@ -521,14 +521,28 @@ def _wrapper( finalize=self._try_discard, on_ref_error=_on_ref_err, ) - if thread is None: - self._slots.append(cb) - else: - self._slots.append(QueuedCallback(cb, thread=thread)) + if thread is not None: + cb = QueuedCallback(cb, thread=thread) + self._append_slot(cb) return slot return _wrapper if slot is None else _wrapper(slot) + def _append_slot(self, slot: WeakCallback) -> None: + """Append a slot to the list of slots.""" + # implementing this as a method allows us to override/extend it in subclasses + self._slots.append(slot) + + def _remove_slot(self, slot: Literal["all"] | int | WeakCallback) -> None: + """Remove a slot from the list of slots.""" + # implementing this as a method allows us to override/extend it in subclasses + if slot == "all": + self._slots.clear() + elif isinstance(slot, int): + self._slots.pop(slot) + else: + self._slots.remove(cast("WeakCallback", slot)) + def _try_discard(self, callback: WeakCallback, missing_ok: bool = True) -> None: """Try to discard a callback from the list of slots. @@ -540,7 +554,7 @@ def _try_discard(self, callback: WeakCallback, missing_ok: bool = True) -> None: If `True`, do not raise an error if the callback is not found in the list. """ try: - self._slots.remove(callback) + self._remove_slot(callback) except ValueError: if not missing_ok: raise @@ -633,7 +647,7 @@ def connect_setattr( finalize=self._try_discard, on_ref_error=on_ref_error, ) - self._slots.append(caller) + self._append_slot(caller) return caller def disconnect_setattr( @@ -747,7 +761,7 @@ def connect_setitem( finalize=self._try_discard, on_ref_error=on_ref_error, ) - self._slots.append(caller) + self._append_slot(caller) return caller @@ -859,12 +873,12 @@ def disconnect(self, slot: Callable | None = None, missing_ok: bool = True) -> N with self._lock: if slot is None: # NOTE: clearing an empty list is actually a RuntimeError in Qt - self._slots.clear() + self._remove_slot("all") return idx = self._slot_index(slot) if idx != -1: - self._slots.pop(idx) + self._remove_slot(idx) elif not missing_ok: raise ValueError(f"slot is not connected: {slot}")

[idea] Connect SignalRelay to signals only when have callback ### Description Currently, SignalRelay is always connected to all signals in SignalGroup. It means that emitting an event always triggers some callback. Maybe SignalRelay should connect itself to all signals in a group only if something is connected to it itself?

2024-02-21T13:07:23

0.0

[]

pyapp-kit/psygnal

pyapp-kit__psygnal-255

b0d2057025e6b8b9facc0210d01222bfdc356a8a

diff --git a/src/psygnal/containers/_evented_dict.py b/src/psygnal/containers/_evented_dict.py index 2eb24773..ad504766 100644 --- a/src/psygnal/containers/_evented_dict.py +++ b/src/psygnal/containers/_evented_dict.py @@ -1,12 +1,12 @@ """Dict that emits events when altered.""" +from __future__ import annotations from typing import ( - Dict, + TYPE_CHECKING, Iterable, Iterator, Mapping, MutableMapping, - Optional, Sequence, Tuple, Type, @@ -14,6 +14,9 @@ Union, ) +if TYPE_CHECKING: + from typing import Self + from psygnal._group import SignalGroup from psygnal._signal import Signal @@ -38,13 +41,13 @@ class TypedMutableMapping(MutableMapping[_K, _V]): def __init__( self, - data: Optional[DictArg] = None, + data: DictArg | None = None, *, basetype: TypeOrSequenceOfTypes = (), **kwargs: _V, ): - self._dict: Dict[_K, _V] = {} - self._basetypes: Tuple[Type[_V], ...] = ( + self._dict: dict[_K, _V] = {} + self._basetypes: tuple[type[_V], ...] = ( tuple(basetype) if isinstance(basetype, Sequence) else (basetype,) ) self.update({} if data is None else data, **kwargs) @@ -76,19 +79,20 @@ def _type_check(self, value: _V) -> _V: ) return value - def __newlike__( - self, mapping: MutableMapping[_K, _V] - ) -> "TypedMutableMapping[_K, _V]": + def __newlike__(self, mapping: MutableMapping[_K, _V]) -> Self: new = self.__class__() # separating this allows subclasses to omit these from their `__init__` new._basetypes = self._basetypes new.update(mapping) return new - def copy(self) -> "TypedMutableMapping[_K, _V]": + def copy(self) -> Self: """Return a shallow copy of the dictionary.""" return self.__newlike__(self) + def __copy__(self) -> Self: + return self.copy() + class DictEvents(SignalGroup): """Events available on [EventedDict][psygnal.containers.EventedDict]. @@ -145,7 +149,7 @@ class EventedDict(TypedMutableMapping[_K, _V]): def __init__( self, - data: Optional[DictArg] = None, + data: DictArg | None = None, *, basetype: TypeOrSequenceOfTypes = (), **kwargs: _V, @@ -172,4 +176,4 @@ def __delitem__(self, key: _K) -> None: self.events.removed.emit(key, item) def __repr__(self) -> str: - return f"EventedDict({super().__repr__()})" + return f"{self.__class__.__name__}({super().__repr__()})" diff --git a/src/psygnal/containers/_evented_list.py b/src/psygnal/containers/_evented_list.py index 4cd15986..a1a7c722 100644 --- a/src/psygnal/containers/_evented_list.py +++ b/src/psygnal/containers/_evented_list.py @@ -23,7 +23,16 @@ """ from __future__ import annotations # pragma: no cover -from typing import Any, Iterable, MutableSequence, TypeVar, Union, cast, overload +from typing import ( + TYPE_CHECKING, + Any, + Iterable, + MutableSequence, + TypeVar, + Union, + cast, + overload, +) from psygnal._group import EmissionInfo, SignalGroup from psygnal._signal import Signal, SignalInstance @@ -32,6 +41,9 @@ _T = TypeVar("_T") Index = Union[int, slice] +if TYPE_CHECKING: + from typing import Self + class ListEvents(SignalGroup): """Events available on [EventedList][psygnal.containers.EventedList]. @@ -133,10 +145,10 @@ def __getitem__(self, key: int) -> _T: ... @overload - def __getitem__(self, key: slice) -> EventedList[_T]: + def __getitem__(self, key: slice) -> Self: ... - def __getitem__(self, key: Index) -> _T | EventedList[_T]: + def __getitem__(self, key: Index) -> _T | Self: """Return self[key].""" result = self._data[key] return self.__newlike__(result) if isinstance(result, list) else result @@ -200,21 +212,24 @@ def _pre_remove(self, index: int) -> None: if self._child_events: self._disconnect_child_emitters(self[index]) - def __newlike__(self, iterable: Iterable[_T]) -> EventedList[_T]: + def __newlike__(self, iterable: Iterable[_T]) -> Self: """Return new instance of same class.""" return self.__class__(iterable) - def copy(self) -> EventedList[_T]: + def copy(self) -> Self: """Return a shallow copy of the list.""" return self.__newlike__(self) - def __add__(self, other: Iterable[_T]) -> EventedList[_T]: + def __copy__(self) -> Self: + return self.copy() + + def __add__(self, other: Iterable[_T]) -> Self: """Add other to self, return new object.""" copy = self.copy() copy.extend(other) return copy - def __iadd__(self, other: Iterable[_T]) -> EventedList[_T]: + def __iadd__(self, other: Iterable[_T]) -> Self: """Add other to self in place (self += other).""" self.extend(other) return self diff --git a/src/psygnal/containers/_evented_set.py b/src/psygnal/containers/_evented_set.py index f03c53e0..10f53f1b 100644 --- a/src/psygnal/containers/_evented_set.py +++ b/src/psygnal/containers/_evented_set.py @@ -6,10 +6,11 @@ from psygnal import Signal, SignalGroup if TYPE_CHECKING: + from typing import Self + from typing_extensions import Final _T = TypeVar("_T") -_Cls = TypeVar("_Cls", bound="_BaseMutableSet") class BailType: @@ -89,15 +90,13 @@ def _do_discard(self, item: _T) -> None: # -------- To match set API - def __copy__(self: _Cls) -> _Cls: - inst = self.__class__.__new__(self.__class__) - inst.__dict__.update(self.__dict__) - return inst + def __copy__(self) -> Self: + return self.copy() - def copy(self: _Cls) -> _Cls: + def copy(self) -> Self: return self.__class__(self) - def difference(self: _Cls, *s: Iterable[_T]) -> _Cls: + def difference(self, *s: Iterable[_T]) -> Self: """Return the difference of two or more sets as a new set. (i.e. all elements that are in this set but not the others.) @@ -110,7 +109,7 @@ def difference_update(self, *s: Iterable[_T]) -> None: for i in chain(*s): self.discard(i) - def intersection(self: _Cls, *s: Iterable[_T]) -> _Cls: + def intersection(self, *s: Iterable[_T]) -> Self: """Return the intersection of two sets as a new set. (i.e. all elements that are in both sets.) @@ -133,7 +132,7 @@ def issuperset(self, __s: Iterable[Any]) -> bool: """Report whether this set contains another set.""" return set(self).issuperset(__s) - def symmetric_difference(self: _Cls, __s: Iterable[_T]) -> _Cls: + def symmetric_difference(self, __s: Iterable[_T]) -> Self: """Return the symmetric difference of two sets as a new set. (i.e. all elements that are in exactly one of the sets.) @@ -150,7 +149,7 @@ def symmetric_difference_update(self, __s: Iterable[_T]) -> None: for i in __s: self.discard(i) if i in self else self.add(i) - def union(self: _Cls, *s: Iterable[_T]) -> _Cls: + def union(self, *s: Iterable[_T]) -> Self: """Return the union of sets as a new set. (i.e. all elements that are in either set.)

Copy operator of evented sets lead to sharing inner structures between copies ### Original issue https://github.com/napari/napari/issues/6620 ### Description When debugging linked issue, I noticed that `copu` of `pysygnal.containers.Selection` shares internal structures with the original one. As I understand the code of the copy operator, it does only shallow copy of internal structures. https://github.com/pyapp-kit/psygnal/blob/b0d2057025e6b8b9facc0210d01222bfdc356a8a/src/psygnal/containers/_evented_set.py#L92-L95 There is any reason to not implement copy as: ``` def __copy__(self: _Cls) -> _Cls: return self.copy() ``` Where `copy()` is implemented as: https://github.com/pyapp-kit/psygnal/blob/b0d2057025e6b8b9facc0210d01222bfdc356a8a/src/psygnal/containers/_evented_set.py#L97-L98 Current copy may also copy callbacks connected to signals.

yeah, I agree that's a tricky result, and I agree it's more surprising to retain any of the mutable characteristics of one set (including callbacks) in the other set. However, in order to mimic native set behavior as much as possible, I don't think it can simply be `self.copy()`, because the members of the set should be copied over (as they would in `set.copy()`). But, perhaps it could be ```python def __copy__(self: _Cls) -> _Cls: new = self.copy() new._data.update(self._data) return new ``` thoughts? but ```python def copy(self: _Cls) -> _Cls: return self.__class__(self) ``` Also copy elements, as it takes iterable as constructor arguments. oh right! Sorry, completely missed that. So, yep, I agree then with your original proposal

2024-01-25T20:37:38

0.0

[]

pyapp-kit/psygnal

pyapp-kit__psygnal-234

12552913920ce0bfea4288a5bc40f605a4f11e6c

diff --git a/src/psygnal/_evented_model_v1.py b/src/psygnal/_evented_model_v1.py index 280600f8..ed680734 100644 --- a/src/psygnal/_evented_model_v1.py +++ b/src/psygnal/_evented_model_v1.py @@ -40,7 +40,7 @@ def dataclass_transform(*args, **kwargs): _NULL = object() ALLOW_PROPERTY_SETTERS = "allow_property_setters" -PROPERTY_DEPENDENCIES = "property_dependencies" +FIELD_DEPENDENCIES = "field_dependencies" GUESS_PROPERTY_DEPENDENCIES = "guess_property_dependencies" @@ -185,16 +185,26 @@ class Config: """ deps: Dict[str, Set[str]] = {} - cfg_deps = getattr(cls.__config__, PROPERTY_DEPENDENCIES, {}) # sourcery skip + cfg_deps = getattr(cls.__config__, FIELD_DEPENDENCIES, {}) # sourcery skip + if not cfg_deps: + cfg_deps = getattr(cls.__config__, "property_dependencies", {}) + if cfg_deps: + warnings.warn( + "The 'property_dependencies' configuration key is deprecated. " + "Use 'field_dependencies' instead", + DeprecationWarning, + stacklevel=2, + ) + if cfg_deps: if not isinstance(cfg_deps, dict): # pragma: no cover raise TypeError( f"Config property_dependencies must be a dict, not {cfg_deps!r}" ) for prop, fields in cfg_deps.items(): - if prop not in cls.__property_setters__: + if prop not in {*cls.__fields__, *cls.__property_setters__}: raise ValueError( - "Fields with dependencies must be property.setters." + "Fields with dependencies must be fields or property.setters." f"{prop!r} is not." ) for field in fields: @@ -344,21 +354,28 @@ def __setattr__(self, name: str, value: Any) -> None: # grab current value before = getattr(self, name, object()) + # if we have any dependent attributes, we need to grab their values + # before we set the new value, so that we can emit events for them + # after the new value is set (only if they have changed). + deps_before: Dict[str, Any] = { + dep: getattr(self, dep) for dep in self.__field_dependents__.get(name, ()) + } # set value using original setter signal_instance: SignalInstance = getattr(self._events, name) with signal_instance.blocked(): self._super_setattr_(name, value) - # if different we emit the event with new value + # if the value has changed we emit the event with new value after = getattr(self, name) - if not _check_field_equality(type(self), name, after, before): signal_instance.emit(after) # emit event - # emit events for any dependent computed property setters as well - for dep in self.__field_dependents__.get(name, ()): - getattr(self.events, dep).emit(getattr(self, dep)) + # also emit events for any dependent computed property setters as well + for dep, before_val in deps_before.items(): + after_val = getattr(self, dep) + if not _check_field_equality(type(self), dep, after_val, before_val): + getattr(self._events, dep).emit(after_val) # expose the private SignalGroup publically @property diff --git a/src/psygnal/_evented_model_v2.py b/src/psygnal/_evented_model_v2.py index e40bdd98..89cd9ff9 100644 --- a/src/psygnal/_evented_model_v2.py +++ b/src/psygnal/_evented_model_v2.py @@ -41,7 +41,7 @@ def dataclass_transform(*args, **kwargs): _NULL = object() ALLOW_PROPERTY_SETTERS = "allow_property_setters" -PROPERTY_DEPENDENCIES = "property_dependencies" +FIELD_DEPENDENCIES = "field_dependencies" GUESS_PROPERTY_DEPENDENCIES = "guess_property_dependencies" @@ -182,16 +182,26 @@ class Config: """ deps: Dict[str, Set[str]] = {} - cfg_deps = cls.model_config.get(PROPERTY_DEPENDENCIES, {}) # sourcery skip + cfg_deps = cls.model_config.get(FIELD_DEPENDENCIES, {}) # sourcery skip + if not cfg_deps: + cfg_deps = cls.model_config.get("property_dependencies", {}) + if cfg_deps: + warnings.warn( + "The 'property_dependencies' configuration key is deprecated. " + "Use 'field_dependencies' instead", + DeprecationWarning, + stacklevel=2, + ) + if cfg_deps: if not isinstance(cfg_deps, dict): # pragma: no cover raise TypeError( f"Config property_dependencies must be a dict, not {cfg_deps!r}" ) for prop, fields in cfg_deps.items(): - if prop not in cls.__property_setters__: + if prop not in {*cls.model_fields, *cls.__property_setters__}: raise ValueError( - "Fields with dependencies must be property.setters." + "Fields with dependencies must be fields or property.setters." f"{prop!r} is not." ) for field in fields: @@ -330,21 +340,28 @@ def __setattr__(self, name: str, value: Any) -> None: # grab current value before = getattr(self, name, object()) + # if we have any dependent attributes, we need to grab their values + # before we set the new value, so that we can emit events for them + # after the new value is set (only if they have changed). + deps_before: Dict[str, Any] = { + dep: getattr(self, dep) for dep in self.__field_dependents__.get(name, ()) + } # set value using original setter signal_instance: SignalInstance = getattr(self._events, name) with signal_instance.blocked(): self._super_setattr_(name, value) - # if different we emit the event with new value + # if the value has changed we emit the event with new value after = getattr(self, name) - if not _check_field_equality(type(self), name, after, before): signal_instance.emit(after) # emit event - # emit events for any dependent computed property setters as well - for dep in self.__field_dependents__.get(name, ()): - getattr(self.events, dep).emit(getattr(self, dep)) + # also emit events for any dependent attributes as well + for dep, before_val in deps_before.items(): + after_val = getattr(self, dep) + if not _check_field_equality(type(self), dep, after_val, before_val): + getattr(self._events, dep).emit(after_val) # expose the private SignalGroup publically @property

EventedModel does not emit events for all value changes in a root validator * psygnal version: `main` at the time of writing (a4a3348fb6f81c3aeefc8a1c723360c9a26db23e) * Python version: 3.7.16 * Operating System: macOS (Intel x86-64) ### Description When I define an `EventedModel` with a root validator that coerces some field values based on others, changes in derived/constrained values do not trigger events to be emitted. Coming here from [an issue on napari](https://github.com/napari/napari/pull/4819#issuecomment-1703093535) where similar problems with its similar `EventedModel` cause problems for `Dims`. No fix for that yet. ### What I Did Here's the simplest and also somewhat artificial reproducer I could write, where a model has two ints (`x` and `y`) and the root validator ensures `x >= y`. ```python from psygnal import EventedModel from pydantic import root_validator class Model(EventedModel): x: int y: int class Config: validate_assignment = True @root_validator def check(cls, values): x = values['x'] if values['y'] > x: values['y'] = x return values m = Model(x=2, y=1) m.events.x.connect(lambda x: print(f'x is now {x}')) m.events.y.connect(lambda y: print(f'y is now {y}')) print(m) # => Model(x=2, y=1) m.x = 0 # x change is printed, but not y print(m) # => Model(x=0, y=0) ``` I think this is a bug because I'd expect the change in `y` to emit an event. ### Thoughts I'm not sure this is a critical bug because I've at least had half-baked thoughts about alternatives/workarounds (e.g. define `x` and `y` as properties and then use `property_dependencies`). Taking that half-baked idea a little further, does it make any sense to allow `property_dependencies` to include `fields` as well as properties? Other ideas/workarounds are welcome!

thanks as always for the super clear issue! :) I think we should fix this (without forcing the workaround of making them properties), but it could be done in a bunch of ways, let me know which of the following sounds best to you: 1. Your idea of allowing `property_dependencies` to include fields would be the simplest and the most explicit. we would just remove [this exception](https://github.com/pyapp-kit/psygnal/blob/main/src/psygnal/_evented_model_v1.py#L195-L199), and allow: ```python class Config: validate_assignment = True property_dependencies = {"y": ["x"]} ``` It would be the most performant, and the least magical, but it would mean `events.y` will *always* be emitted whenever `x` is changed, regardless of whether the change in `x` actually changed the value of `y`. (for what it's worth, that's already the case with property dependencies anyway) 2. We could extend that idea to actually check whether the value of y changed before emitting. That would result in the most "expected" behavior, but it would mean that anytime `__setattr__` is called on a field, we'd need to fetch the values of all dependent fields before and after actually setting the field, then compare each of them to determine whether an emission is required. So, could be costly in terms of performance. 3. An alternative to the explicit/mandatory `property_dependencies` configuration would be to detect whether the class had a root validator, then simply check *all* fields in the model before and after setting any field. That's of course the worst in terms of performance, but "just does the right thing" without requiring the user to notice this special case. 4. We could do number 3, but require opt-in with a config setting thoughts? 5. as a variant/extension of number 2, we could add an additional config setting that gates the checking. allowing the user to decide whether they'd prefer more accurate event emission, or better peformance

2023-09-16T20:30:13

0.0

[]

pyapp-kit/psygnal

pyapp-kit__psygnal-216

8ec0671696ff76505c2a76263f8e94a78d349f28

diff --git a/src/psygnal/_evented_model.py b/src/psygnal/_evented_model.py index c739fe62..551574db 100644 --- a/src/psygnal/_evented_model.py +++ b/src/psygnal/_evented_model.py @@ -348,13 +348,14 @@ def __setattr__(self, name: str, value: Any) -> None: before = getattr(self, name, object()) # set value using original setter - self._super_setattr_(name, value) + signal_instance: SignalInstance = getattr(self._events, name) + with signal_instance.blocked(): + self._super_setattr_(name, value) # if different we emit the event with new value after = getattr(self, name) if not _check_field_equality(type(self), name, after, before): - signal_instance: SignalInstance = getattr(self.events, name) signal_instance.emit(after) # emit event # emit events for any dependent computed property setters as well

Setting derived properties in evented models causes duplicate events * psygnal version: 0.9.1 * Python version: 3.9.6 * Operating System: macOS 13.4 (Intel) ### Description Setting a derived property when using an evented model causes the associated event to be fired twice. Coming from the same titled issue over on napari: https://github.com/napari/napari/issues/6032 Our solution (https://github.com/napari/napari/pull/6035) was to block the event in `__setattr__`, but would be curious if you had better ideas. ### What I Did ``` from psygnal import EventedModel class Model(EventedModel): a: int @property def b(self) -> int: return self.a + 1 @b.setter def b(self, b) -> None: self.a = b - 1 class Config: allow_property_setters = True property_dependencies = {"b": ["a"]} m = Model(a=0) m.events.a.connect(lambda _: print('a')) m.events.b.connect(lambda _: print('b')) m.a = 1 # prints "a" and "b" once each, as expected m.b = 3 # prints "a" once, but "b" twice ```

Thanks! I appreciate the ping. Will have a look

2023-07-06T10:52:28

0.0

[]

rnag/dataclass-wizard

rnag__dataclass-wizard-111

e7a56014cd5e4d514dfc015729ec47046a90296e

diff --git a/dataclass_wizard/parsers.py b/dataclass_wizard/parsers.py index f7c338c..23cbb59 100644 --- a/dataclass_wizard/parsers.py +++ b/dataclass_wizard/parsers.py @@ -84,6 +84,14 @@ def __post_init__(self, *_): val: type(val) for val in get_args(self.base_type) } + def __contains__(self, item) -> bool: + """ + Return true if the LiteralParser is expected to handle the specified item + type. Checks that the item is incorporated in the given expected values of + the Literal. + """ + return item in self.value_to_type.keys() + def __call__(self, o: Any) -> M: """ Checks for Literal equivalence, as mentioned here:

[BUG] Incomplete Support for Union or | Type Deserialization in Common Python Types (e.g., `int`, `Enum`, `Literal`) * Dataclass Wizard version: 0.22.1 * Python version: 3.10.4 * Operating System: Ubuntu 22.04 ### Description Using the dataclass-wizard to deserialize a YAML file fails when trying to use string literals as field options. In my particular case, a value in the config can be set manually or derived automatically depending on user preference. In this instance, the only valid string value for `x` should be `Auto`. Any other value should fail to deserialize. However, since it appears that the parser is unaware of the possible equality of str/num/bytes to a Literal this fails. ### What I Did ```python >>> from dataclass_wizard import YAMLWizard >>> from typing import Literal >>> from dataclasses import dataclass >>> @dataclass ... class Example(YAMLWizard): ... x: Literal["Auto"] | float ... >>> config_file = """ ... x: Auto ... """ >>> Example.from_yaml(config_file) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "~/.venv/lib/python3.10/site-packages/dataclass_wizard/wizard_mixins.py", line 136, in from_yaml return fromdict(cls, o) if isinstance(o, dict) else fromlist(cls, o) File "~/.venv/lib/python3.10/site-packages/dataclass_wizard/loaders.py", line 536, in fromdict return load(d) File "~/.venv/lib/python3.10/site-packages/dataclass_wizard/loaders.py", line 619, in cls_fromdict cls_kwargs[field_name] = field_to_parser[field_name]( File "~/.venv/lib/python3.10/site-packages/dataclass_wizard/parsers.py", line 250, in __call__ raise ParseError( dataclass_wizard.errors.ParseError: Failure parsing field `x` in class `Example`. Expected a type [typing.Literal['Auto'], <class 'float'>], got str. value: 'Auto' error: Object was not in any of Union types tag_key: '__tag__' json_object: '{"x": "Auto"}' ```

I don't think this has anything to do with Literals - anything that needs to be deserialized in a union is not being deserialized. ```python >>> from dataclasses import dataclass >>> from dataclass_wizard import JSONWizard >>> @dataclass ... class B: ... thing: str ... >>> >>> @dataclass ... class A(JSONWizard): ... one_or_more: B | list[B] ... >>> >>> result = A.from_dict({ ... 'one_or_more': { ... 'thing': 'value' ... } ... }) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/usr/local/lib/python3.10/site-packages/dataclass_wizard/loaders.py", line 536, in fromdict return load(d) File "/usr/local/lib/python3.10/site-packages/dataclass_wizard/loaders.py", line 619, in cls_fromdict cls_kwargs[field_name] = field_to_parser[field_name]( File "/usr/local/lib/python3.10/site-packages/dataclass_wizard/parsers.py", line 250, in __call__ raise ParseError( dataclass_wizard.errors.ParseError: Failure parsing field `one_or_more` in class `A`. Expected a type [<class '__main__.B'>, <class 'list'>], got dict. value: {'thing': 'value'} error: Object was not in any of Union types tag_key: '__tag__' json_object: '{"one_or_more": {"thing": "value"}}' >>> ``` Got the same issue here. Mixing primitives with dataclasses in unions seems to be the problem: ```python from dataclass_wizard import fromdict from dataclasses import dataclass @dataclass class MoreDetails: arg: str @dataclass class Config: # Unions with primitives and dataclasses entry: str | MoreDetails | int # Works fromdict(Config, { "entry": "a" }) # Works too fromdict(Config, { "entry": 1 }) # Fails, fails to interpret the dict as a dataclass fromdict(Config, { "entry": {"arg": "str"} }) ``` ```pytb Traceback (most recent call last): File "/mnt/c/Users/fakui/Seafile/Programming/projects/cc_crapton/./ha_modbus_mqtt/dataclasswz_bug.py", line 29, in <module> fromdict(Config2, { File "/home/fakui/.local/lib/python3.10/site-packages/dataclass_wizard/loaders.py", line 536, in fromdict return load(d) File "/home/fakui/.local/lib/python3.10/site-packages/dataclass_wizard/loaders.py", line 619, in cls_fromdict cls_kwargs[field_name] = field_to_parser[field_name]( File "/home/fakui/.local/lib/python3.10/site-packages/dataclass_wizard/parsers.py", line 250, in __call__ raise ParseError( dataclass_wizard.errors.ParseError: Failure parsing field `entry` in class `Config2`. Expected a type [<class '__main__.MoreDetails'>, <class '__main__.Config'>], got dict. value: {'arg': '1'} error: Object was not in any of Union types tag_key: '__tag__' json_object: '{"entry": {"arg": "1"}}' ``` dataclass-wizard version: 0.22.2

2024-02-21T14:20:35

0.0

[]

rnag/dataclass-wizard

rnag__dataclass-wizard-105

e3fe0760422613eaf79e0236840a50f27caec064

diff --git a/.gitignore b/.gitignore index d510d86..c783fc2 100644 --- a/.gitignore +++ b/.gitignore @@ -95,3 +95,6 @@ venv/ ENV/ env.bak/ venv.bak/ + +# File created by pytest +testing.json diff --git a/dataclass_wizard/parsers.py b/dataclass_wizard/parsers.py index 2e87db5..e3c810e 100644 --- a/dataclass_wizard/parsers.py +++ b/dataclass_wizard/parsers.py @@ -401,10 +401,7 @@ def __post_init__(self, cls: Type, # Total count should be `Infinity` here, since the variadic form # accepts any number of possible arguments. self.total_count: N = float('inf') - # Check for the count of parsers which don't handle `NoneType` - this - # should exclude the parsers for `Union` types that have `None` in the - # list of args. - self.required_count = 0 if None in self.first_elem_parser[0] else 1 + self.required_count = 0 def __call__(self, o: M) -> M: """

Loading a Variadic Tuple fails for length 0 * Dataclass Wizard version: 0.22.2 * Python version: both python 3.10 and python 3.12 * Operating System: mac ### Description If you have a dataclass with: `my_tuple: tuple[int, ...]` then, trying to parse the following json fails `{my_tuple: []}` because the tuple is empty. However, if there is at least one int, then this succeeds. ### What I Did ``` from dataclasses import dataclass from dataclass_wizard import fromdict @dataclass class Test: my_ints: tuple[int, ...] fromdict(Test, {"my_ints": []}) ``` fails with ``` dataclass_wizard.errors.ParseError: Failure parsing field `my_ints` in class `Test`. Expected a type [], got list. value: [] error: Wrong number of elements. desired_count: '1 - inf' actual_count: 0 json_object: '{"my_ints": []}' ```

2024-01-14T20:41:30

0.0

[]

mehta-lab/recOrder

mehta-lab__recOrder-399

858f3d41ba129544ad6df8c53d9437e02b53f7ed

diff --git a/recOrder/cli/apply_inverse_transfer_function.py b/recOrder/cli/apply_inverse_transfer_function.py index 17080e95..d28b7de6 100644 --- a/recOrder/cli/apply_inverse_transfer_function.py +++ b/recOrder/cli/apply_inverse_transfer_function.py @@ -2,6 +2,7 @@ import numpy as np import torch from iohub import open_ome_zarr +from iohub.ngff_meta import TransformationMeta from waveorder.models import ( inplane_oriented_thick_pol3d, isotropic_fluorescent_thick_3d, @@ -122,6 +123,9 @@ def apply_inverse_transfer_function_cli( 1, ) + input_dataset.data.shape[3:], # chunk by YX + transform=[ + TransformationMeta(type="scale", scale=input_dataset.scale) + ], ) else: output_array = output_dataset[0]

Passing the voxel (z,y,x) in micrometers in zattrs per position As I was playing with some datasets for mantis reconstructions using #392, I noticed we don't pass any coordinateTransforms or metadata that can be useful for the rest of the analysis pipelines. It would be useful to pass from the raw zarr the voxel size (z,y,x) dimensions in microns. In fact, these can also be applied upon creation of the zarr stores to load in napari.

2023-08-09T00:08:36

0.0

[]

mehta-lab/recOrder

mehta-lab__recOrder-391

96dca0163777d7f066ce880e3f6c1bbaa687a536

diff --git a/setup.cfg b/setup.cfg index b2191912..57daeb28 100644 --- a/setup.cfg +++ b/setup.cfg @@ -34,7 +34,6 @@ packages = find: include_package_data = True python_requires = >=3.9 setup_requires = setuptools_scm -# add your package requirements here install_requires = waveorder==2.0.0rc1 pycromanager==0.27.2 @@ -43,11 +42,9 @@ install_requires = colorspacious>=1.1.2 pyqtgraph>=0.12.3 napari-ome-zarr>=0.3.2 # drag and drop convenience - napari[all];'arm64' not in platform_machine # with Qt5 and skimage - napari;'arm64' in platform_machine # without Qt and skimage - PyQt6;'arm64' in platform_machine + napari[pyqt6_experimental] importlib-metadata - iohub==0.1.0.dev3 + iohub==0.1.0.dev4 [options.extras_require] dev =

Native support for Apple Silicon **Problem** `recOrder` does not currently support Apple Silicon (M1, M2, etc.) platforms natively due to the incompatibility between `pip` and PyQt5 (PyQt5 does not have a wheel for `arm_64` on PyPI). Native installation would be beneficial for both performance and potential GPU-accelerated processing/ML with a Metal backend. **Proposed solution** Closely track [napari's progress on supporting Qt6](https://github.com/napari/napari/issues/3880), since PyQt6/PySide6 have wheels available for the Apple Silicon platform on PyPI. And we can upgrade `recOrder` accordingly when napari makes the move. (They already have initial support merged into the main branch). This may evolve into a broader set of [1.0.0](https://github.com/mehta-lab/recOrder/milestone/3) issues if napari starts supporting Qt 6 in a stable release soon enough. **Alternatives** - Offer a conda environment file that installs PyQt5 from `conda-forge`. This would require the removal of explicit dependency on `napari` in `setup.cfg`. #187

napari 0.4.17 has added experimental support for Qt6. `pip install napari pyqt6` now works on macOS_arm64. The only blocking factor is now the `napari[all]` line in `recOrder`'s `setup.cfg`, which really is an alias for `napari pyqt5 scikit-image`. Changing to `napari` allows for editable install of `recOrder` on Apple Silicon with `pip install -e <dir-to-recOrder>`: <img width="1260" alt="recOrder-napari_arm64" src="https://user-images.githubusercontent.com/67518483/202587161-b13242fd-745d-4f67-822e-dea330f3f35f.png"> The glitching in style sheet rendering is [a known issue](https://github.com/napari/napari/pull/3707#issuecomment-1152997251) for napari with Qt6. @ziw-liu I am trying to setup an environment for mantis project on my osx_arm64 laptop. I have been able to install all dependencies, including `nidaqmax`, `dexp`, `pyqt6`, `napari`, `pycromanager`. But, when I try `pip install -e <dir-to-recOrder>` in the main branch, pip still tries to build a pyqt5 wheel and fails. How do I bypass pyqt5 dependency when setting up recOrder? Here is the pip output. ``` Collecting PyQt5!=5.15.0,>=5.12.3 Using cached PyQt5-5.15.9.tar.gz (3.2 MB) Installing build dependencies ... done Getting requirements to build wheel ... done Preparing metadata (pyproject.toml) ... - ``` As suggested in https://github.com/mehta-lab/recOrder/issues/193#issuecomment-1319382740, for the current `recOrder` you would need to manually delete this line: https://github.com/mehta-lab/recOrder/blob/7e10ae59a1ed28cf28052b97404a2c0eba4369ff/setup.cfg#L47 before installing. And then install napari with Qt6: ```sh pip install napari pyqt6 ```

2023-08-02T18:50:05

0.0

[]

mehta-lab/recOrder

mehta-lab__recOrder-384

4582bf5940ff2ee2a2d6c1563056fe5873a96559

diff --git a/recOrder/cli/apply_inverse_transfer_function.py b/recOrder/cli/apply_inverse_transfer_function.py index e9bbda37..46cac81a 100644 --- a/recOrder/cli/apply_inverse_transfer_function.py +++ b/recOrder/cli/apply_inverse_transfer_function.py @@ -109,9 +109,10 @@ def apply_inverse_transfer_function_cli( ) # Load data + tczyx_uint16_numpy = input_dataset.data.oindex[:, channel_indices] tczyx_data = torch.tensor( - input_dataset.data.oindex[:, channel_indices], dtype=torch.float32 - ) + np.int32(tczyx_uint16_numpy), dtype=torch.float32 + ) # convert to np.int32 (torch doesn't accept np.uint16), then convert to tensor float32 # Prepare background dataset if settings.birefringence is not None: @@ -164,7 +165,7 @@ def apply_inverse_transfer_function_cli( echo_headline("Reconstructing phase...") # check data shapes - if input_dataset.data.shape[1] != 1: + if tczyx_data.shape[1] != 1: raise ValueError( "You have requested a phase-only reconstruction, but the input dataset has more than one channel." ) @@ -385,10 +386,10 @@ def apply_inv_tf( """ Apply an inverse transfer function to a dataset using a configuration file. - See /examples/settings/ for example configuration files. + See /examples for example configuration files. Example usage:\n - $ recorder apply-inv-tf input.zarr/0/0/0 transfer-function.zarr -c /examples/settings/birefringence.yml -o output.zarr + $ recorder apply-inv-tf input.zarr/0/0/0 transfer-function.zarr -c /examples/birefringence.yml -o output.zarr """ apply_inverse_transfer_function_cli( input_data_path, transfer_function_path, config_path, output_path diff --git a/recOrder/cli/compute_transfer_function.py b/recOrder/cli/compute_transfer_function.py index 729531ff..80b740aa 100644 --- a/recOrder/cli/compute_transfer_function.py +++ b/recOrder/cli/compute_transfer_function.py @@ -211,9 +211,9 @@ def compute_tf(input_data_path, config_path, output_path): """ Compute a transfer function using a dataset and configuration file. - See /examples/settings/ for example configuration files. + See /examples/ for example configuration files. Example usage:\n - $ recorder compute-tf input.zarr/0/0/0 -c /examples/settings/birefringence.yml -o transfer_function.zarr + $ recorder compute-tf input.zarr/0/0/0 -c /examples/birefringence.yml -o transfer_function.zarr """ compute_transfer_function_cli(input_data_path, config_path, output_path) diff --git a/recOrder/cli/reconstruct.py b/recOrder/cli/reconstruct.py index 9844929d..8599a675 100644 --- a/recOrder/cli/reconstruct.py +++ b/recOrder/cli/reconstruct.py @@ -23,10 +23,10 @@ def reconstruct(input_data_path, config_path, output_path): convenience function for a `compute-tf` call followed by a `apply-inv-tf` call. - See /examples/settings/ for example configuration files. + See /examples for example configuration files. Example usage:\n - $ recorder reconstruct input.zarr/0/0/0 -c /examples/settings/birefringence.yml -o output.zarr + $ recorder reconstruct input.zarr/0/0/0 -c /examples/birefringence.yml -o output.zarr """ # Handle transfer function path diff --git a/setup.cfg b/setup.cfg index c7548797..d79769ee 100644 --- a/setup.cfg +++ b/setup.cfg @@ -37,7 +37,7 @@ python_requires = >=3.9 setup_requires = setuptools_scm # add your package requirements here install_requires = - waveorder==2.0.0rc0 + waveorder==2.0.0rc1 pycromanager==0.27.2 click>=8.0.1 natsort>=7.1.1

Fluorescence CLI @ziw-liu I'm requesting your review of this fairly large PR. I have tested this PR on hummingbird, and I'd like to prioritize getting this merged into `0.4.0dev` then `main` so that we can start integration testing for the `0.4.0` release. I'm particularly interested in your suggestions related to (1) the new `/examples/` folder for the new config file formats, (2) the new CLI names and simplified `recOrder reconstruct` CLI, and (3) the new `cli.settings.py` file. **Summary of major changes**: - new fluorescence CLI functionality - a single configuration file design instead of two configuration files (one for `compute-transfer-function` and one for `apply-inverse-transfer-function` - reworked pydantic models for configuration files (now supported birefringence, phase, birefringence + phase, and fluorescence) - reworked `examples/` folder demonstrating new CLI Minor changes: - high-level tests of CLI - new CLI names: `compute-tf` and `apply-inv-tf`. These shortened names are for the CLI only. - basic validation of CLI inputs - automatically generated example configuration files Earlier list of TODOs: - [x] finish high-level `recorder reconstruct` CLI and test - [x] integration testing at the microscope - [x] removal of example scripts with documentation (now replaced by `waveorder` scripts and CLI + example configurations) - [x] documentation of configuration file generation @edyoshikun I'm tagging you as a reviewer, but we'll likely merge before you're back. We'll make sure this is tested against your upcoming use cases before the 0.4.0 release. Run CI/CD tests on all PRs **Before:** Tests run only on PRs to `main` **After:** Tests run on all PRs **Symptom:** In #381 I used some local spot tests, then saw the checkmarks on GH actions and thought everything was working well. On merging #381 into `0.4.0dev`, I found that the tests weren't actually running...the checkmark was only for napari hub checks. Not a big deal; I will fix the tests in `0.4.0dev`.

Preview page for your plugin is ready here: https://preview.napari-hub.org/mehta-lab/recOrder/381 _Updated: 2023-07-08T00:00:23.839831_ I know this has been a lot of work but it's great to see it come together! I am wondering if it makes sense to have the `apply_inverse_transfer_functions` for all recons to be separate functions. We can do it for another PR post merging `0.4.0dev`. I attempted something in #380 at least for the birefringence reconstruction, which I pushed for you to have the code just in case while I was gone. Thanks @edyoshikun. Merging into `0.4.0dev` ## [Codecov](https://app.codecov.io/gh/mehta-lab/recOrder/pull/383?src=pr&el=h1&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=mehta-lab) Report > Merging [#383](https://app.codecov.io/gh/mehta-lab/recOrder/pull/383?src=pr&el=desc&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=mehta-lab) (2836e3a) into [main](https://app.codecov.io/gh/mehta-lab/recOrder/commit/6b2b3cc3a04308d22a11dbfd48ed0ea2c06c822f?el=desc&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=mehta-lab) (6b2b3cc) will **not change** coverage. > The diff coverage is `n/a`. ```diff @@ Coverage Diff @@ ## main #383 +/- ## ===================================== Coverage 1.95% 1.95% ===================================== Files 21 21 Lines 4851 4851 ===================================== Hits 95 95 Misses 4756 4756 ``` The napari hub preview failed in some TypeScript process...

2023-07-25T17:30:00

0.0

[]

mehta-lab/recOrder

mehta-lab__recOrder-375

802cc62cac537749ab5dce5535d7c0a18137f819

diff --git a/docs/images/cli_structure.png b/docs/images/cli_structure.png new file mode 100644 index 00000000..0ddb1dd1 Binary files /dev/null and b/docs/images/cli_structure.png differ diff --git a/docs/microscope-installation-guide.md b/docs/microscope-installation-guide.md index d54a00de..b701bd1f 100644 --- a/docs/microscope-installation-guide.md +++ b/docs/microscope-installation-guide.md @@ -7,6 +7,22 @@ This guide will walk through a complete recOrder installation consisting of: Before you start you will need a machine with Windows 10, a Meadowlark DS5020 connected to a liquid crystal device, and a microscope system compatible with `Micromanager`. +## Install Meadowlark DS5020 and liquid crystals + +Start by installing the Meadowlark DS5020 and liquid crystals using the software on the USB stick provided by Meadowlark. You will need to install the USB drivers and CellDrive5000. + +**Check your installation versions** by opening CellDrive5000 and double clicking the Meadowlark Optics logo. **We have tested `recOrder == 0.4.0` with "PC software version 1.08" and "Controller firmware version 1.04",** and you will need to upgrade if your software and firmware versions are older. + +To upgrade your "PC software version" use these steps: + +- From "Add and remove programs", remove CellDrive5000 and "National Instruments Software". +- From "Device manager", open the "Meadowlark Optics" group, right click `mlousb`, click "Uninstall device", check "Delete the driver software for this device", and click "Uninstall". Uninstall `Meadowlark Optics D5020 LC Driver` following the same steps. +- Using the USB stick provided by Meadowlark, reinstall the USB drivers and CellDrive5000. +- Confirm that "PC software version" == 1.08 +- **Upgrading users:** you will need to reinstall the Meadowlark device to your micromanager configuration file, because the device driver's name has changed to `MeadowlarkLC`. + +To upgrade your DS5020's firmware, use Meadowlark's "Firmware Updater". + ## Install recOrder software (Optional but recommended) install [anaconda](https://www.anaconda.com/products/distribution) and create a virtual environment @@ -27,11 +43,11 @@ should launch napari (may take 15 seconds on a fresh installation) with the recO ## Install and configure `Micromanager` -Install `Micromanager 2.0` nightly build `20220920` (https://micro-manager.org/Micro-Manager_Nightly_Builds). +Download and install [`Micromanager 2.0` nightly build `20230426` (~150 MB link).](https://download.micro-manager.org/nightly/2.0/Windows/MMSetup_64bit_2.0.1_20230426.exe) -**Note:** We have tested recOrder with `20220920`, but most features will work with newer builds. We recommend testing a minimal installation with `20220920` before testing with a different nightly build or additional device drivers. +**Note:** We have tested recOrder with `20230426`, but most features will work with newer builds. We recommend testing a minimal installation with `20230426` before testing with a different nightly build or additional device drivers. -Before launching `Micromanager`, download the Meadowlark device adapters and calibration files from the [release page](https://github.com/mehta-lab/recOrder/releases/) and place these three unzipped files into your `Micromanager` folder (likely `C:\Program Files\Micro-Manager` or similar). +Before launching `Micromanager`, download the USB driver dll from the [release page](https://github.com/mehta-lab/recOrder/releases/) and place this pair of unzipped files into your `Micromanager` folder (likely `C:\Program Files\Micro-Manager` or similar). Launch `Micromanager`, open `Devices > Hardware Configuration Wizard...`, and add the `MeadowlarkLcOpenSource` device to your configuration. Confirm your installation by opening `Devices > Device Property Browser...` and confirming that `MeadowlarkLCOpenSource` properties appear. diff --git a/docs/reconstruction-guide.md b/docs/reconstruction-guide.md index bbf7990e..cbfd5924 100644 --- a/docs/reconstruction-guide.md +++ b/docs/reconstruction-guide.md @@ -1,13 +1,25 @@ # Automating reconstructions -`recOrder` is undergoing changes to the way that it handles automated reconstructions. +`recOrder` uses a configuration-file-based command-line interface (CLI) to perform all reconstructions. -`recOrder==0.2.0` had an offline mode with `config.yml` files that were used to configure reconstructions. These config files could be generated and saved via the GUI, and the reconstructions could be run via GUI or CLI. +## How can I use `recOrder`'s CLI to perform reconstructions? +`recOrder`'s CLI is summarized in the following figure: +<img src="./images/cli_structure.png" align="center"> -Although the offline mode had many valuable and convenient features, we found that it had diverged from the online mode and it was difficult to recreate results between online and offline modes. These design limitations led us to the following plan for our upcoming releases: +The main command `reconstruct` command is composed of two subcommands: `compute-tf` and `apply-inv-tf`. -`recOrder==0.3.0` (release candidate in the coming weeks) will remove the offline mode and use a set of scripts instead. We recommend modifying the reconstruction scripts in `recOrder/examples/` to recreate and automate reconstructions. +A reconstruction can be performed with a single `reconstruct` call. For example: +``` +recorder reconstruct data.zarr/0/0/0 -c config.yml -o reconstruction.zarr +``` +Equivalently, a reconstruction can be performed with a `compute-tf` call followed by an `apply-inv-tf` call. For example: +``` +recorder compute-tf data.zarr/0/0/0 -c config.yml -o tf.zarr +recorder apply-inv-tf data.zarr/0/0/0 tf.zarr -c config.yml -o reconstruction.zarr +``` +Computing the transfer function is typically the most expensive part of the reconstruction, so saving a transfer function then applying it to many datasets can save time. -Although these scripts are not as user friendly as a GUI+CLI solution, we are preparing for a much cleaner solution in `1.0.0`, and we appreciate your patience as we go through this change. We ask users who are having any difficulty with the scripts to [open an issue](https://github.com/mehta-lab/recOrder/issues/new/choose) or [send us an email](mailto:[email protected],[email protected]). +## What types of reconstructions are supported? +See `/recOrder/examples/` for a list of example configuration files. -`recOrder==1.0.0` will use a single mode to acquire and reconstruct the data. We are currently planning a refactor that will enable an "acquire once, quickly iterate your reconstruction" workflow, an "acquire now, reconstruct later" workflow, and a "live acquisition, live reconstruction" workflow, among others. +TODO: Expand this documentation...need docs for each reconstruction type and parameter. diff --git a/examples/3D-bf-to-2D-phase.py b/examples/3D-bf-to-2D-phase.py deleted file mode 100644 index c46b6552..00000000 --- a/examples/3D-bf-to-2D-phase.py +++ /dev/null @@ -1,64 +0,0 @@ -from iohub import read_micromanager, open_ome_zarr -from recOrder.compute.reconstructions import ( - initialize_reconstructor, - reconstruct_phase2D, -) -from recOrder.compute.phantoms import bf_3D_from_phantom -from datetime import datetime -import numpy as np -import napari - -timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - -## Load a dataset - -# Option 1: use random data and run this script as is. -data = bf_3D_from_phantom() # (Z, Y, X) - -# Option 2: load from file -# reader = read_micromanager("/path/to/ome-tiffs/or/zarr/store/") -# position, time, channel = 0, 0, 0 -# data = reader.get_array(position)[time, channel, ...] # read 3D volume - -## Set up a reconstructor. -Z, Y, X = data.shape -reconstructor_args = { - "image_dim": (Y, X), - "mag": 20, # magnification - "pixel_size_um": 6.5, # pixel size in um - "n_slices": Z, # number of slices in z-stack - "z_step_um": 2, # z-step size in um - "in_focus_slice": None, # integer, set to None to use the central slice - "wavelength_nm": 532, - "NA_obj": 0.4, # numerical aperture of objective - "NA_illu": 0.2, # numerical aperture of condenser - "n_obj_media": 1.0, # refractive index of objective immersion media - "mode": "2D", # phase reconstruction mode, "2D" or "3D" - "use_gpu": False, - "gpu_id": 0, -} -reconstructor = initialize_reconstructor( - pipeline="PhaseFromBF", **reconstructor_args -) - -phase2D = reconstruct_phase2D( - data, reconstructor, method="Tikhonov", reg_p=1e0 -) -print(f"Shape of 2D phase data: {np.shape(phase2D)}") - -## Save to zarr -with open_ome_zarr( - "./output/reconstructions_" + timestamp + ".zarr", - layout="fov", - mode="w-", - channel_names=["Phase"], -) as dataset: - # Write to position "0", with length-one time, channel, and z dimensions - dataset["0"] = phase2D[np.newaxis, np.newaxis, np.newaxis, ...] - -# These lines open the reconstructed images -# Alternatively, drag and drop the zarr store into napari and use the recOrder-napari reader. -v = napari.Viewer() -v.add_image(data) -v.add_image(phase2D) -napari.run() diff --git a/examples/3D-bf-to-3D-phase.py b/examples/3D-bf-to-3D-phase.py deleted file mode 100644 index 8ab8b167..00000000 --- a/examples/3D-bf-to-3D-phase.py +++ /dev/null @@ -1,64 +0,0 @@ -from iohub import read_micromanager, open_ome_zarr -from recOrder.compute.reconstructions import ( - initialize_reconstructor, - reconstruct_phase3D, -) -from recOrder.compute.phantoms import bf_3D_from_phantom -from datetime import datetime -import numpy as np -import napari - -timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - -## Load a dataset - -# Option 1: use data simulated from a phantom and run this script as is. -data = bf_3D_from_phantom() # (Z, Y, X) - -# Option 2: load from file -# reader = read_micromanager('/path/to/ome-tiffs/or/zarr/store/') -# position, time, channel = 0, 0, 0 -# data = reader.get_array(position)[time, channel, ...] # read 3D volume - -## Set up a reconstructor. -Z, Y, X = data.shape -reconstructor_args = { - "image_dim": (Y, X), - "mag": 20, # magnification - "pixel_size_um": 6.5, # pixel size in um - "n_slices": Z, # number of slices in z-stack - "z_step_um": 2, # z-step size in um - "wavelength_nm": 532, - "NA_obj": 0.4, # numerical aperture of objective - "NA_illu": 0.2, # numerical aperture of condenser - "n_obj_media": 1.0, # refractive index of objective immersion media - "pad_z": 5, # slices to pad for phase reconstruction boundary artifacts - "mode": "3D", # phase reconstruction mode, "2D" or "3D" - "use_gpu": False, - "gpu_id": 0, -} -reconstructor = initialize_reconstructor( - pipeline="PhaseFromBF", **reconstructor_args -) - -phase3D = reconstruct_phase3D( - data, reconstructor, method="Tikhonov", reg_re=1e-2 -) -print(f"Shape of 3D phase data: {np.shape(phase3D)}") - -## Save to zarr -with open_ome_zarr( - "./output/reconstructions_" + timestamp + ".zarr", - layout="fov", - mode="w-", - channel_names=["Phase"], -) as dataset: - # Write to position "0", with length-one time and channel dimensions - dataset["0"] = phase3D[np.newaxis, np.newaxis, ...] - -# These lines open the reconstructed images -# Alternatively, drag and drop the zarr store into napari and use the recOrder-napari reader. -v = napari.Viewer() -v.add_image(data) -v.add_image(phase3D) -napari.run() diff --git a/examples/3D-fluor-to-3D-density.py b/examples/3D-fluor-to-3D-density.py deleted file mode 100644 index b4a6665b..00000000 --- a/examples/3D-fluor-to-3D-density.py +++ /dev/null @@ -1,61 +0,0 @@ -from iohub import read_micromanager, open_ome_zarr -from recOrder.compute.reconstructions import ( - initialize_reconstructor, - reconstruct_density_from_fluorescence, -) -from recOrder.compute.phantoms import fluorescence_from_phantom -from datetime import datetime -import numpy as np -import napari - -timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - -## Load a dataset - -# Option 1: use data simulated from a phantom and run this script as is. -data = fluorescence_from_phantom() # (Z, Y, X) - -# Option 2: load from file -# reader = read_micromanager('/path/to/ome-tiffs/or/zarr/store/') -# position, time, channel = 0, 0, 0 -# data = reader.get_array(position)[time, channel, ...] # read 3D volume - -## Set up a reconstructor. -Z, Y, X = data.shape -reconstructor_args = { - "image_dim": (Y, X), - "mag": 20, # magnification - "pixel_size_um": 6.5, # pixel size in um - "n_slices": Z, # number of slices in z-stack - "z_step_um": 2, # z-step size in um - "wavelength_nm": 532, - "NA_obj": 0.4, # numerical aperture of objective - "n_obj_media": 1.0, # refractive index of objective immersion media - "pad_z": 5, # slices to pad for phase reconstruction boundary artifacts - "mode": "3D", # phase reconstruction mode, "2D" or "3D" - "use_gpu": False, - "gpu_id": 0, -} -reconstructor = initialize_reconstructor( - pipeline="fluorescence", **reconstructor_args -) - -density = reconstruct_density_from_fluorescence(data, reconstructor, reg=1e-2) -print(f"Shape of 3D density data: {np.shape(density)}") - -## Save to zarr -with open_ome_zarr( - "./output/reconstructions_" + timestamp + ".zarr", - layout="fov", - mode="w-", - channel_names=["Density"], -) as dataset: - # Write to position "0", with length-one time and channel dimensions - dataset["0"] = density[np.newaxis, np.newaxis, ...] - -# These lines open the reconstructed images -# Alternatively, drag and drop the zarr store into napari and use the recOrder-napari reader. -v = napari.Viewer() -v.add_image(data) -v.add_image(density) -napari.run() diff --git a/examples/3D-pol-to-birefringence.py b/examples/3D-pol-to-birefringence.py deleted file mode 100644 index dfff646d..00000000 --- a/examples/3D-pol-to-birefringence.py +++ /dev/null @@ -1,101 +0,0 @@ -from iohub import read_micromanager, open_ome_zarr -from recOrder.io.utils import load_bg -from recOrder.compute.reconstructions import ( - initialize_reconstructor, - reconstruct_qlipp_stokes, - reconstruct_qlipp_birefringence, - reconstruct_phase3D, -) -from recOrder.compute.phantoms import pol_3D_from_phantom -from datetime import datetime -import numpy as np -import napari - -timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - -## Load a dataset - -# Option 1: use random data and run this script as is. -data, bg_data = pol_3D_from_phantom() # (C, Z, Y, X) and (C, Y, X) -C, Z, Y, X = data.shape - -# Option 2: load from file -# reader = read_micromanager('/path/to/ome-tiffs/or/zarr/store/') -# position, time = 0, 0 -# data = reader.get_array(position)[time, ...] -# C, Z, Y, X = data.shape -# bg_data = load_bg("/path/to/recorder/BG", height=Y, width=X) - -## Set up a reconstructor. -reconstructor_args = { - "image_dim": (Y, X), - "mag": 20, # magnification - "pixel_size_um": 6.5, # pixel size in um - "n_slices": Z, # number of slices in z-stack - "z_step_um": 2, # z-step size in um - "wavelength_nm": 532, - "swing": 0.1, - "calibration_scheme": "5-State", # "4-State" or "5-State" - "NA_obj": 0.4, # numerical aperture of objective - "NA_illu": 0.2, # numerical aperture of condenser - "n_obj_media": 1.0, # refractive index of objective immersion media - "pad_z": 5, # slices to pad for phase reconstruction boundary artifacts - "bg_correction": "local_fit", # BG correction method: "None", "local_fit", "global" - "mode": "3D", # phase reconstruction mode, "2D" or "3D" - "use_gpu": False, - "gpu_id": 0, -} -reconstructor = initialize_reconstructor( - pipeline="QLIPP", **reconstructor_args -) - -# Reconstruct background Stokes -bg_stokes = reconstruct_qlipp_stokes(bg_data, reconstructor) -print(f"Shape of BG Stokes: {np.shape(bg_stokes)}") - -# Reconstruct data Stokes w/ background correction -stokes = reconstruct_qlipp_stokes(data, reconstructor, bg_stokes) -print(f"Shape of background corrected data Stokes: {np.shape(stokes)}") - -# Reconstruct Birefringence from Stokes -# Shape of the output birefringence will be (C, Z, Y, X) where -# Channel order = Retardance [nm], Orientation [rad], Brightfield (S0), Degree of Polarization -birefringence = reconstruct_qlipp_birefringence(stokes, reconstructor) -birefringence[0] = ( - birefringence[0] / (2 * np.pi) * reconstructor_args["wavelength_nm"] -) -print(f"Shape of birefringence data: {np.shape(birefringence)}") - -# Reconstruct Phase3D from S0 -S0 = birefringence[2] - -phase3D = reconstruct_phase3D( - S0, reconstructor, method="Tikhonov", reg_re=1e-2 -) -print(f"Shape of 3D phase data: {np.shape(phase3D)}") - -## Save to zarr -with open_ome_zarr( - "./output/reconstructions_" + timestamp + ".zarr", - layout="fov", - mode="w-", - channel_names=[ - "Retardance", - "Orientation", - "BF - computed", - "DoP", - "Phase", - ], -) as dataset: - img = dataset.create_zeros("0", (1, 5, Z, Y, X), dtype=birefringence.dtype) - img[0, 0:4] = birefringence - img[0, 4] = phase3D - -# These lines open the reconstructed images -# Alternatively, drag and drop the zarr store into napari and use the recOrder-napari reader. -v = napari.Viewer() -v.add_image(data) -v.add_image(phase3D) -v.add_image(birefringence, contrast_limits=(0, 25)) -v.dims.current_step = (0, 5, 256, 256) -napari.run() diff --git a/examples/README.md b/examples/README.md index 732ef878..adde2a8e 100644 --- a/examples/README.md +++ b/examples/README.md @@ -1,61 +1,74 @@ -# `recOrder` example scripts +# `recOrder` CLI examples -These scripts demonstrate four types of reconstruction: +`recOrder` uses a configuration-file-based command-line inferface (CLI) to +calculate transfer functions and apply these transfer functions to datasets. -1. 3D brightfield data to 3D phase in `3D-bf-to-3D-phase.py`. -2. 3D brightfield data to 2D phase in `3D-bf-to-2D-phase.py`. -3. 3D + polarization data to retardance, orientation, and 3D phase in `3D-pol-to-birefringence.py`. -4. 3D fluorescence data to 3D fluorophore density (fluorescence deconvolution) in `3D-fluor-to-3D-density.py`. +This page demonstrates `recOrder`'s CLI. ## Getting started ### 1. Check your installation -First, [install `recOrder`](../docs/software-installation-guide.md) and run any of these scripts with +First, [install `recOrder`](../docs/software-installation-guide.md) and run ```bash -python <script-name.py> +recOrder +``` +in a shell. If `recOrder` is installed correctly, you will see a usage string and +``` +recOrder: Computational Toolkit for Label-Free Imaging ``` -Running these scripts without modification will test your installation by running a reconstruction on a simulated phantom. A successful run will open a `napari` window with simulated data and a reconstruction. - -### 2. Reconstruct test data -Next, [download the test data from zenodo (47 MB)](https://zenodo.org/record/6983916/files/recOrder_test_data.zip?download=1), and modify the script to load the test data. For example, in `3D-bf-to-2D-phase.py`, replace `/path/to/ome-tiffs/or/zarr/store/` with `/path/to/recOrder_test_data/2022_08_04_recOrder_pytest_20x_04NA_BF/2T_3P_16Z_128Y_256X_Kazansky_BF_1`. - -### 3. Load and reconstruct your data -Start by loading your data (our readers currently support `.tiff`, `ome.tiff`, `NDTiff`, and `.zarr`, but any `numpy` array will work). Fill in your imaging parameters (or connect your metadata to these parameters), and prototype with this modified script. +### 2. Download and convert a test dataset +Next, [download the test data from zenodo (47 MB)](https://zenodo.org/record/6983916/files/recOrder_test_data.zip?download=1), and convert a dataset to the latest version of `.zarr` with +``` +cd /path/to/ +iohub convert -i /path/to/recOrder_test_data/2022_08_04_recOrder_pytest_20x_04NA/2T_3P_16Z_128Y_256X_Kazansky_1/ +-o ./dataset.zarr +``` -We recommend prototyping a reconstruction with a single position and time point so that you can perform initial iterations quickly. +You can view the test dataset with +``` +napari ./dataset.zarr --plugin recOrder-napari +``` -### 4. Sweep your reconstruction parameters +### 3. Run a reconstruction +Run an example reconstruction with +``` +recOrder reconstruct ./dataset.zarr/0/0/0 -c /path/to/recOrder/examples/settings/birefringence-and-phase.yml -o ./reconstruction.zarr +``` +then view the reconstruction with +``` +napari ./reconstruction.zarr --plugin recOrder-napari +``` -You may need to test several parameters to find a value that yields the best results for your application. For example, choosing a regularization parameter is commonly semi-empirical: we recommend choosing a regularization parameter that gives results that aren't too noisy or too smooth. +Try modifying the configuration file to see how the regularization parameter changes the results. -The `sweep-regularization.py` script demonstrates a `3D-bf-to-2D-phase` reconstruction with multiple regularization parameters. We recommend running and understanding this script before modifying your reconstruction script to sweep over regularization or other uncertain parameters to help you settle on a set of reconstruction parameters. +### 4. Parallelize over positions or time points -### 5. Reconstruct a multi-modal dataset in a single script +TODO: @Ed I think we'll use the same strategy as ```mantis deskew``` -You may need to perform several reconstructions on a multi-modal dataset. For example, you would like to perform a `3D-fluor-to-3D-density` reconstruction on the fluorescence channels and a `3D-bf-2D-phase` reconstruction on the brightfield channel. +## FAQ +1. **Q: Which configuration file should I use?** -The `multi-modal-recon.py` script demonstrates this type of reconstruction. We recommend running and understanding this script before modifying single-modality reconstructions to run a multi-modal reconstruction. + If you are acquiring: -### 6. Parallelize over positions or time points + **3D data with calibrated liquid-crystal polarizers via `recOrder`** use `birefringence.yml`. -Once you've settled on a script that performs a reconstruction, the script can be applied to multiple datasets with a python `for` loop (slowest), `multiprocessing` (faster, see `parallel-reconstruct.py` for an example), or batch processing with an HPC scheduler e.g. `slurm` (fastest). + **3D fluorescence data** use `fluorescence.yml`. -## FAQ -1. **Q: Which script should I use?** + **3D brightfield data** use `phase.yml`. - If you are acquiring: + **Multi-modal data**, start by reconstructing the individual modaliities, each with a single config file and CLI call. Then combine the reconstructions by ***TODO: @Ziwen do can you help me append to the zarrs to help me fix this? *** - **3D data with calibrated liquid-crystal polarizers via `recOrder`** use `3D-pol-to-birefringence.py`. +2. **Q: Should I use `reconstruction_dimension` = 2 or 3? - **3D fluorescence data** use `3D-fluor-to-3D-density.py`. + If your downstream processing requires 3D information or if you're unsure, then you should use `reconstruction_dimension = 3`. If your sample is very thin compared to the depth of field of the microscope, if you're in a noise-limited regime, or if your downstream processing requires 2D information, then you should use `reconstruction_dimension = 2`. Empirically, we have found that 2D reconstructions reduce the noise in our reconstructions because it uses 3D information to make a single estimate for each pixel. - **3D brightfield data** use `3D-bf-to-3D-phase.py` or `3D-bf-to-2D-phase.py`, and decide if you need a 3D or 2D phase reconstruction. +3. **Q: What regularization parameter should I use?** - If your downstream processing requires 3D information or if you're unsure, then you should use `3D-bf-to-3D-phase.py`. If your sample is very thin compared to the depth of field of the microscope, if you're in a noise-limited regime, or if your downstream processing requires 2D phase information, then you should use `3D-bf-to-2D-phase.py`. Empirically, we have found that `3D-bf-to-2D-phase.py` reduces the noise in our reconstructions because it uses 3D information to make a single phase estimate for each pixel. + We recommend starting with the defaults then testing over a few orders of magnitude and choosing a result that isn't too noisy or too smooth. - **Multi-modal data**, start by reconstructing the individual modaliities, then combine the reconstructions using `multi-modal-recon.py` as a guide. +### Developers note -2. **Q: What regularization parameter should I use?** +These configuration files are automatically generated when the tests run. See `/tests/cli_tests/test_settings.py` - `test_generate_example_settings`. - We recommend starting with the defaults then testing over a few orders of magnitude and choosing a result that isn't too noisy or too smooth. +To keep these settings up to date, run `pytest` locally when `cli/settings.py` changes. diff --git a/examples/birefringence-and-phase.yml b/examples/birefringence-and-phase.yml new file mode 100644 index 00000000..fcf30b0c --- /dev/null +++ b/examples/birefringence-and-phase.yml @@ -0,0 +1,30 @@ +input_channel_names: +- State0 +- State1 +- State2 +- State3 +reconstruction_dimension: 3 +birefringence: + transfer_function: + swing: 0.1 + apply_inverse: + wavelength_illumination: 0.532 + background_path: '' + remove_estimated_background: false + orientation_flip: false + orientation_rotate: false +phase: + transfer_function: + wavelength_illumination: 0.532 + yx_pixel_size: 0.325 + z_pixel_size: 2.0 + z_padding: 0 + index_of_refraction_media: 1.3 + numerical_aperture_detection: 1.2 + numerical_aperture_illumination: 0.5 + axial_flip: false + apply_inverse: + reconstruction_algorithm: Tikhonov + regularization_strength: 0.001 + TV_rho_strength: 0.001 + TV_iterations: 1 diff --git a/examples/birefringence.yml b/examples/birefringence.yml new file mode 100644 index 00000000..45ffd268 --- /dev/null +++ b/examples/birefringence.yml @@ -0,0 +1,15 @@ +input_channel_names: +- State0 +- State1 +- State2 +- State3 +reconstruction_dimension: 3 +birefringence: + transfer_function: + swing: 0.1 + apply_inverse: + wavelength_illumination: 0.532 + background_path: '' + remove_estimated_background: false + orientation_flip: false + orientation_rotate: false diff --git a/examples/fluorescence.yml b/examples/fluorescence.yml new file mode 100644 index 00000000..02d47666 --- /dev/null +++ b/examples/fluorescence.yml @@ -0,0 +1,16 @@ +input_channel_names: +- GFP +reconstruction_dimension: 3 +fluorescence: + transfer_function: + yx_pixel_size: 0.325 + z_pixel_size: 2.0 + z_padding: 0 + index_of_refraction_media: 1.3 + numerical_aperture_detection: 1.2 + wavelength_emission: 0.507 + apply_inverse: + reconstruction_algorithm: Tikhonov + regularization_strength: 0.001 + TV_rho_strength: 0.001 + TV_iterations: 1 diff --git a/examples/multi-modal-recon.py b/examples/multi-modal-recon.py deleted file mode 100644 index c9aadff6..00000000 --- a/examples/multi-modal-recon.py +++ /dev/null @@ -1,97 +0,0 @@ -from iohub import read_micromanager, open_ome_zarr -from recOrder.compute.reconstructions import ( - initialize_reconstructor, - reconstruct_phase3D, - reconstruct_density_from_fluorescence, -) -from recOrder.compute.phantoms import ( - bf_3D_from_phantom, - fluorescence_from_phantom, -) -from datetime import datetime -import numpy as np -import napari - -timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - -## Load a dataset - -## Option 1: use data simulated from a phantom and run this script as is. -bf_data = bf_3D_from_phantom() # (Z, Y, X) -fluor_data = fluorescence_from_phantom() # (Z, Y, X) - -## Option 2: load from file. Uncomment all lines with a single #. -# reader = read_micromanager('/path/to/ome-tiffs/or/zarr/store/') -# position, time = 0, 0 - -## Set these by hand or use `reader.channel_names.index('BF')` to read from metadata -# bf_index = 0 -# fluor_index = 1 - -## Read -# bf_data = reader.get_array(position)[time, bf_index, ...] -# fluor_data = reader.get_array(position)[time, fluor_index, ...] - -## Set up the reconstructors. - -# Set arguments that are shared by both modalities -reconstructor_args = { - "image_dim": bf_data.shape[1:], # (Y, X) - "mag": 20, # magnification - "pixel_size_um": 6.5, # pixel size in um - "z_step_um": 2, # z-step size in um - "NA_obj": 0.4, # numerical aperture of objective - "NA_illu": 0.2, # numerical aperture of condenser - "n_obj_media": 1.0, # refractive index of objective immersion media - "pad_z": 5, # slices to pad for phase reconstruction boundary artifacts - "mode": "3D", # phase reconstruction mode, "2D" or "3D" - "use_gpu": False, - "gpu_id": 0, -} - -# Initialize reconstructors with parameters that are not shared -bf_reconstructor = initialize_reconstructor( - pipeline="PhaseFromBF", - wavelength_nm=532, - n_slices=bf_data.shape[0], - **reconstructor_args -) - -fluor_reconstructor = initialize_reconstructor( - pipeline="fluorescence", - wavelength_nm=450, - n_slices=fluor_data.shape[0], - **reconstructor_args -) - -# Reconstruct -phase = reconstruct_phase3D( - bf_data, bf_reconstructor, method="Tikhonov", reg_re=1e-2 -) - -density = reconstruct_density_from_fluorescence( - fluor_data, fluor_reconstructor, reg=1e-2 -) - -## Save to zarr -with open_ome_zarr( - "./output/reconstructions_" + timestamp + ".zarr", - layout="fov", - mode="w-", - channel_names=["Phase"], -) as dataset: - # Write to position "0", with length-one time dimension - dataset["0"] = phase[np.newaxis, np.newaxis, ...] - dataset.append_channel("Density") - dataset["0"][0, -1] = density - -# These lines open the reconstructed images -# Alternatively, drag and drop the zarr store into napari and use the recOrder-napari reader. -v = napari.Viewer() -v.add_image(bf_data) -v.add_image(fluor_data) -v.add_image(phase) -v.add_image(density) -v.dims.current_step = (15, 256, 256) - -napari.run() diff --git a/examples/parallel-reconstruct.py b/examples/parallel-reconstruct.py deleted file mode 100644 index 8cfc4ec3..00000000 --- a/examples/parallel-reconstruct.py +++ /dev/null @@ -1,184 +0,0 @@ -import shutil -import os -import numpy as np -import multiprocessing as mp -from wget import download -from datetime import datetime -from iohub import open_ome_zarr -from iohub.convert import TIFFConverter -from recOrder.io.utils import load_bg -from recOrder.compute.reconstructions import ( - initialize_reconstructor, - reconstruct_qlipp_stokes, - reconstruct_qlipp_birefringence, - reconstruct_phase3D, -) - -timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - -# This example demonstrates a 3D-pol-to-birfringence reconstruction applied -# to each position and time point of a micromanager dataset. The example -# uses multiprocessing to apply the reconstructions in parallel. - -# This example will download a ~50 MB micro-manager test dataset to your -# working directory, convert it to a zarr store, then reconstruct physical -# paramaters from each position and time point. - -# Choose the number of processes to parallel over. -# You should have at least this many cores available with enough RAM -# available to each process to complete the reconstruction. -N_processes = 6 - - -def precomputation(): - - # download test data and convert to zarr - # use this section for a test run, then delete it and load directly from - # a zarr store for a real reconstruction run - data_folder = os.path.join(os.getcwd(), "data_temp") - os.makedirs(data_folder, exist_ok=True) - url = "https://zenodo.org/record/6983916/files/recOrder_test_data.zip?download=1" - zip_file = "recOrder_test_Data.zip" - output = os.path.join(data_folder, zip_file) - if not os.path.exists(output): - print("Downloading test files...") - download(url, out=output) - shutil.unpack_archive(output, extract_dir=data_folder) - - temp_path = os.path.join(data_folder, timestamp + "_temp.zarr") - converter = TIFFConverter( - os.path.join( - data_folder, - "2022_08_04_recOrder_pytest_20x_04NA/2T_3P_16Z_128Y_256X_Kazansky_1", - ), - temp_path, - ) - converter.run() - - # setup input reader - reader = open_ome_zarr(temp_path, mode="r+") - T, C, Z, Y, X = reader["0/0/0"].data.shape - dtype = reader["0/0/0"].data.dtype - P = len(list(reader.positions())) - bg_data = load_bg( - os.path.join(data_folder, "2022_08_04_recOrder_pytest_20x_04NA/BG/"), - height=Y, - width=X, - ) - - # setup reconstructor. - reconstructor_args = { - "image_dim": (Y, X), - "mag": 20, # magnification - "pixel_size_um": 6.5, # pixel size in um - "n_slices": Z, # number of slices in z-stack - "z_step_um": 2, # z-step size in um - "wavelength_nm": 532, - "swing": 0.1, - "calibration_scheme": "4-State", # "4-State" or "5-State" - "NA_obj": 0.4, # numerical aperture of objective - "NA_illu": 0.2, # numerical aperture of condenser - "n_obj_media": 1.0, # refractive index of objective immersion media - "pad_z": 5, # slices to pad for phase reconstruction boundary artifacts - "bg_correction": "global", # BG correction method: "None", "local_fit", "global" - "mode": "3D", # phase reconstruction mode, "2D" or "3D" - "use_gpu": False, - "gpu_id": 0, - } - reconstructor = initialize_reconstructor( - pipeline="QLIPP", **reconstructor_args - ) - - # reconstruct background Stokes - bg_stokes = reconstruct_qlipp_stokes(bg_data, reconstructor) - - # setup output zarr - writer = open_ome_zarr( - "./output/reconstructions_" + timestamp + ".zarr", - layout="hcs", - mode="w-", - channel_names=[ - "Retardance", - "Orientation", - "BF - computed", - "DoP", - "Phase", - ], - ) - for p in range(P): - position = writer.create_position("0", str(p), "0") - position.create_zeros(name="0", shape=(T, 5, Z, Y, X), dtype=dtype) - - return reader, writer, reconstructor, bg_stokes - - -# define the work to be done on a single process -# in this example a single process will loop through a set of CZYX volumes, and -# for each volume it will read the data, apply a reconstruction, then save the -# result to a hcs-format zarr -def single_process( - reader, writer, reconstructor, bg_stokes, vol_start, vol_end -): - T, C, Z, Y, X = reader["0/0/0"].data.shape - for vol in range(vol_start, vol_end + 1): - p = int(np.floor(vol / T)) - t = int(vol % T) - print(f"Reconstructing vol={vol}, pos={p}, time={t}") - - # read from input zarr - data = reader["0/" + str(p) + "/0"]["0"][t] - - # reconstruct data Stokes w/ background correction - stokes = reconstruct_qlipp_stokes(data, reconstructor, bg_stokes) - - # reconstruct birefringence from Stokes - birefringence = reconstruct_qlipp_birefringence(stokes, reconstructor) - birefringence[0] = ( - birefringence[0] / (2 * np.pi) * (reconstructor.lambda_illu / 1000) - ) - - # reconstruct phase3D from S0 - S0 = birefringence[2] - phase3D = reconstruct_phase3D( - S0, reconstructor, method="Tikhonov", reg_re=1e-2 - ) - - # write output - writer["0/" + str(p) + "/0"]["0"][t, 0:4] = birefringence - writer["0/" + str(p) + "/0"]["0"][t, 4] = phase3D - - -# prepare the processes -# we will apply the same reconstruction to each position and time point, so we -# need to split the CZYX volumes among the processes -if __name__ == "__main__": - reader, writer, reconstructor, bg_stokes = precomputation() - processes = [] - V = len(list(reader.positions())) * reader["0/0/0"].data.shape[0] - vol_per_process = int(np.ceil(V / N_processes)) - print("vol_per_process", vol_per_process) - for i in range(N_processes): - vol_start = i * vol_per_process - vol_end = np.minimum(vol_start + vol_per_process - 1, V - 1) - print(f"Preparing volumes {vol_start}-{vol_end} for process {i}") - processes.append( - mp.Process( - target=single_process, - args=( - reader, - writer, - reconstructor, - bg_stokes, - vol_start, - vol_end, - ), - ) - ) - print("Starting processes...") - # run the processes - for process in processes: - process.start() - for process in processes: - process.join() - - writer.close() diff --git a/examples/phase.yml b/examples/phase.yml new file mode 100644 index 00000000..d48c6aa6 --- /dev/null +++ b/examples/phase.yml @@ -0,0 +1,18 @@ +input_channel_names: +- BF +reconstruction_dimension: 3 +phase: + transfer_function: + wavelength_illumination: 0.532 + yx_pixel_size: 0.325 + z_pixel_size: 2.0 + z_padding: 0 + index_of_refraction_media: 1.3 + numerical_aperture_detection: 1.2 + numerical_aperture_illumination: 0.5 + axial_flip: false + apply_inverse: + reconstruction_algorithm: Tikhonov + regularization_strength: 0.001 + TV_rho_strength: 0.001 + TV_iterations: 1 diff --git a/examples/sweep-regularization.py b/examples/sweep-regularization.py deleted file mode 100644 index fca6570c..00000000 --- a/examples/sweep-regularization.py +++ /dev/null @@ -1,75 +0,0 @@ -from iohub import read_micromanager, open_ome_zarr -from recOrder.compute.reconstructions import ( - initialize_reconstructor, - reconstruct_phase3D, -) -from recOrder.compute.phantoms import bf_3D_from_phantom -from datetime import datetime -import numpy as np -import napari - -timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - -## Load a dataset - -# Option 1: use data simulated from a phantom and run this script as is. -data = bf_3D_from_phantom() # (Z, Y, X) - -# Option 2: load from file -# reader = read_micromanager('/path/to/ome-tiffs/or/zarr/store/') -# position, time, channel = 0, 0, 0 -# data = reader.get_array(position)[time, channel, ...] # read 3D volume - -## Set up a reconstructor. -Z, Y, X = data.shape -reconstructor_args = { - "image_dim": (Y, X), - "mag": 20, # magnification - "pixel_size_um": 6.5, # pixel size in um - "n_slices": Z, # number of slices in z-stack - "z_step_um": 2, # z-step size in um - "wavelength_nm": 532, - "NA_obj": 0.4, # numerical aperture of objective - "NA_illu": 0.2, # numerical aperture of condenser - "n_obj_media": 1.0, # refractive index of objective immersion media - "pad_z": 5, # slices to pad for phase reconstruction boundary artifacts - "mode": "3D", # phase reconstruction mode, "2D" or "3D" - "use_gpu": False, - "gpu_id": 0, -} -reconstructor = initialize_reconstructor( - pipeline="PhaseFromBF", **reconstructor_args -) - -# Setup a single writer and viewer -reg_powers = 10.0 ** np.arange(-3, 3) - -dataset = open_ome_zarr( - "./output/reconstructions_" + timestamp + ".zarr", - layout="fov", - mode="w-", - channel_names=[f"{reg:.1e}" for reg in reg_powers], -) - -dataset.create_zeros( - "0", shape=(1, len(reg_powers), Z, Y, X), dtype=np.float32 -) - -v = napari.Viewer() -v.add_image(data) - -# Loop through regularizations -for i, reg in enumerate(reg_powers): - print(f"Reconstructing with 3D phase with reg = {reg}") - - phase3D = reconstruct_phase3D( - data, reconstructor, method="Tikhonov", reg_re=reg - ) - - # Save each regularization into a "position" of the output zarr - dataset["0"][0, i, ...] = phase3D - - # Add the reconstructions to the viewer - v.add_image(phase3D, name=f"reg = {reg}") - -napari.run() diff --git a/recOrder/acq/acquisition_workers.py b/recOrder/acq/acquisition_workers.py index 25dd073f..91bbe938 100644 --- a/recOrder/acq/acquisition_workers.py +++ b/recOrder/acq/acquisition_workers.py @@ -4,31 +4,25 @@ from qtpy.QtCore import Signal from iohub import open_ome_zarr from iohub.convert import TIFFConverter -from recOrder.compute.reconstructions import ( - initialize_reconstructor, - reconstruct_qlipp_birefringence, - reconstruct_qlipp_stokes, - reconstruct_phase2D, - reconstruct_phase3D, +from recOrder.cli import settings +from recOrder.cli.compute_transfer_function import ( + compute_transfer_function_cli, +) +from recOrder.cli.apply_inverse_transfer_function import ( + apply_inverse_transfer_function_cli, ) from recOrder.acq.acq_functions import ( generate_acq_settings, acquire_from_settings, ) -from recOrder.io.utils import load_bg, extract_reconstruction_parameters -from recOrder.compute.reconstructions import QLIPPBirefringenceCompute -from recOrder.io.metadata_reader import MetadataReader, get_last_metadata_file -from recOrder.io.utils import ram_message, rec_bkg_to_wo_bkg +from recOrder.io.utils import model_to_yaml +from recOrder.io.utils import ram_message from napari.qt.threading import WorkerBaseSignals, WorkerBase from napari.utils.notifications import show_warning import logging -import tifffile as tiff import numpy as np import os -import zarr import shutil -import time -import glob # type hint/check from typing import TYPE_CHECKING @@ -39,6 +33,81 @@ from recOrder.calib.Calibration import QLIPP_Calibration +def _generate_reconstruction_config_from_gui( + reconstruction_config_path, + mode, + calib_window, + input_channel_names, +): + if mode == "birefringence" or mode == "all": + if calib_window.bg_option == "None": + background_path = "" + remove_estimated_background = False + elif calib_window.bg_option == "Measured": + background_path = calib_window.acq_bg_directory + remove_estimated_background = False + elif calib_window.bg_option == "Estimated": + background_path = "" + remove_estimated_background = True + elif calib_window.bg_option == "Measured + Estimated": + background_path = calib_window.acq_bg_directory + remove_estimated_background = True + + birefringence_transfer_function_settings = ( + settings.BirefringenceTransferFunctionSettings( + swing=calib_window.swing, + ) + ) + birefringence_apply_inverse_settings = settings.BirefringenceApplyInverseSettings( + wavelength_illumination=calib_window.recon_wavelength / 1000, + background_path=background_path, + remove_estimated_background=remove_estimated_background, + orientation_flip=False, # TODO connect this to a recOrder button + orientation_rotate=False, # TODO connect this to a recOrder button + ) + birefringence_settings = settings.BirefringenceSettings( + transfer_function=birefringence_transfer_function_settings, + apply_inverse=birefringence_apply_inverse_settings, + ) + else: + birefringence_settings = None + + if mode == "phase" or mode == "all": + phase_transfer_function_settings = ( + settings.PhaseTransferFunctionSettings( + wavelength_illumination=calib_window.recon_wavelength / 1000, + yx_pixel_size=calib_window.ps / calib_window.mag, + z_pixel_size=calib_window.z_step, + z_padding=calib_window.pad_z, + index_of_refraction_media=calib_window.n_media, + numerical_aperture_detection=calib_window.obj_na, + numerical_aperture_illumination=calib_window.cond_na, + axial_flip=False, # TODO: connect this to a recOrder button + ) + ) + phase_apply_inverse_settings = settings.FourierApplyInverseSettings( + reconstruction_algorithm=calib_window.phase_regularizer, + regularization_strength=calib_window.ui.le_phase_strength.text(), + TV_rho_strength=calib_window.ui.le_rho.text(), + TV_iterations=calib_window.ui.le_itr.text(), + ) + phase_settings = settings.PhaseSettings( + transfer_function=phase_transfer_function_settings, + apply_inverse=phase_apply_inverse_settings, + ) + else: + phase_settings = None + + reconstruction_settings = settings.ReconstructionSettings( + input_channel_names=input_channel_names, + reconstruction_dimension=int(calib_window.acq_mode[0]), + birefringence=birefringence_settings, + phase=phase_settings, + ) + + model_to_yaml(reconstruction_settings, reconstruction_config_path) + + class PolarizationAcquisitionSignals(WorkerBaseSignals): """ Custom Signals class that includes napari native signals @@ -60,16 +129,6 @@ class BFAcquisitionSignals(WorkerBaseSignals): aborted = Signal() -class ListeningSignals(WorkerBaseSignals): - """ - Custom Signals class that includes napari native signals - """ - - store_emitter = Signal(object) - dim_emitter = Signal(tuple) - aborted = Signal() - - class BFAcquisitionWorker(WorkerBase): """ Class to execute a brightfield acquisition. First step is to snap the images follow by a second @@ -162,6 +221,17 @@ def work(self): channel_group = group break + # Create and validate reconstruction settings + self.config_path = os.path.join( + self.snap_dir, "reconstruction_settings.yml" + ) + _generate_reconstruction_config_from_gui( + self.config_path, + "phase", + self.calib_window, + input_channel_names=["BF"], + ) + # Acquire 3D stack logging.debug("Acquiring 3D stack") @@ -196,14 +266,14 @@ def work(self): # Reconstruct snapped images self.n_slices = stack.shape[2] - phase, meta = self._reconstruct(stack[0]) + phase = self._reconstruct() self._check_abort() - # Save images - logging.debug("Saving Images") - self._save_imgs(phase, meta) + # # Save images + # logging.debug("Saving Images") + # self._save_imgs(phase, meta) - self._check_abort() + # self._check_abort() logging.info("Finished Acquisition") logging.debug("Finished Acquisition") @@ -211,221 +281,41 @@ def work(self): # Emit the images and let thread know function is finished self.phase_image_emitter.emit(phase) - def _reconstruct(self, stack): + def _reconstruct(self): """ - Method to reconstruct, given a 2D or 3D stack. - This function also checks to see if the reconstructor needs to be updated from previous acquisitions - - Parameters - ---------- - stack: (nd-array) Dimensions are (C, Z, Y, X) - - Returns - ------- - + Method to reconstruct """ - - self.img_dim = (stack.shape[-2], stack.shape[-1], stack.shape[-3]) - self._check_abort() - - # Initialize the reconstuctor - - # if no reconstructor has been initialized before, create new reconstructor - if not self.calib_window.phase_reconstructor: - logging.debug("Computing new reconstructor") - - recon = initialize_reconstructor( - "PhaseFromBF", - image_dim=(stack.shape[-2], stack.shape[-1]), - wavelength_nm=self.calib_window.recon_wavelength, - NA_obj=self.calib_window.obj_na, - NA_illu=self.calib_window.cond_na, - mag=self.calib_window.mag, - n_slices=self.img_dim[-1], - z_step_um=self.calib_window.z_step, - pad_z=self.calib_window.pad_z, - pixel_size_um=self.calib_window.ps, - n_obj_media=self.calib_window.n_media, - mode=self.calib_window.acq_mode, - use_gpu=self.calib_window.use_gpu, - gpu_id=self.calib_window.gpu_id, - ) - - # Emit reconstructor to be saved for later reconstructions - self.phase_reconstructor_emitter.emit(recon) - - # if previous reconstructor exists - else: - self._check_abort() - - # compute new reconstructor if the old reconstructor properties have been modified - if self._reconstructor_changed(stack.shape): - logging.debug( - "Reconstruction settings changed, updating reconstructor" - ) - - recon = initialize_reconstructor( - "PhaseFromBF", - image_dim=(stack.shape[-2], stack.shape[-1]), - wavelength_nm=self.calib_window.recon_wavelength, - NA_obj=self.calib_window.obj_na, - NA_illu=self.calib_window.cond_na, - mag=self.calib_window.mag, - n_slices=self.n_slices, - z_step_um=self.calib_window.z_step, - pad_z=self.calib_window.pad_z, - pixel_size_um=self.calib_window.ps, - n_obj_media=self.calib_window.n_media, - mode=self.calib_window.acq_mode, - use_gpu=self.calib_window.use_gpu, - gpu_id=self.calib_window.gpu_id, - ) - - # Emit reconstructor to be saved for later reconstructions - self.phase_reconstructor_emitter.emit(recon) - - # use previous reconstructor - else: - logging.debug("Using previous reconstruction settings") - recon = self.calib_window.phase_reconstructor - - # Begin reconstruction with stokes (needed for birefringence or phase) - logging.debug("Reconstructing...") self._check_abort() - regularizer = self.calib_window.phase_regularizer - reg = float(self.calib_window.ui.le_phase_strength.text()) - - # Perform deconvolution - if self.dim == "2D": - phase = reconstruct_phase2D( - stack[0], - recon, - method=regularizer, - reg_p=reg, - itr=int(self.calib_window.ui.le_itr.text()), - rho=float(self.calib_window.ui.le_rho.text()), - ) - else: - phase = reconstruct_phase3D( - stack[0], - recon, - method=regularizer, - reg_re=reg, - itr=int(self.calib_window.ui.le_itr.text()), - rho=float(self.calib_window.ui.le_rho.text()), - ) - - self._check_abort() - - # Update metadata in zarr attributes with reconstruction parameters - meta = extract_reconstruction_parameters( - recon, magnification=self.calib_window.mag + # Create i/o paths + transfer_function_path = os.path.join( + self.snap_dir, "transfer_function.zarr" + ) + reconstruction_path = os.path.join( + self.snap_dir, "reconstruction.zarr" ) - meta["regularization_method"] = regularizer - meta["regularization_strength"] = reg - if regularizer == "TV": - meta["rho"] = float(self.calib_window.ui.le_rho.text()) - meta["itr"] = int(self.calib_window.ui.le_itr.text()) - - # return both variables, could contain images or could be null - return phase, meta - - def _save_imgs(self, phase, meta=None): - """ - function to save images. - Parameters - ---------- - phase: (nd-array or None) phase image or stack + input_data_path = os.path.join(self.latest_out_path, "0", "0", "0") - Returns - ------- - - """ - prefix = self.calib_window.save_name - name = ( - f"ReconstructionSnap.zarr" - if not prefix - else f"{prefix}_ReconstructionSnap.zarr" + # TODO: skip if config files match + compute_transfer_function_cli( + input_data_path, + self.config_path, + transfer_function_path, ) - with open_ome_zarr( - os.path.join(self.snap_dir, name), - layout="fov", - mode="w-", - channel_names=["Phase" + str(phase.ndim) + "D"], - ) as dataset: - dataset["0"] = phase[ - (5 - phase.ndim) * (np.newaxis,) + (Ellipsis,) - ] - dataset.zattrs["recOrder"] = meta - - def _reconstructor_changed(self, stack_shape: tuple): - """Function to check if the reconstructor has changed from the previous one in memory. - Serves to check if the worker attributes and reconstructor attributes have diverged. + apply_inverse_transfer_function_cli( + input_data_path, + transfer_function_path, + self.config_path, + reconstruction_path, + ) - Parameters - ---------- - stack_shape : tuple - shape of the stack array to reconstruct + # Read reconstruction to pass to emitters + with open_ome_zarr(reconstruction_path, mode="r") as dataset: + phase = dataset["0"][0] - Returns - ------- - bool - whether a new reconstructor should be initialized - """ - # Attributes that are directly equivalent to worker attributes - attr_list = { - "acq_mode": "phase_deconv", - "pad_z": "pad_z", - "n_media": "n_media", - "use_gpu": "use_gpu", - "gpu_id": "gpu_id", - } - # attributes that are modified upon passing them to reconstructor - attr_modified_list = { - "obj_na": "NA_obj", - "cond_na": "NA_illu", - "wavelength": "lambda_illu", - "n_slices": "N_defocus", - "swing": "chi", - "ps": "ps", - } - self._check_abort() - old_recon = self.calib_window.phase_reconstructor - # check if X/Y shape has changed - if (old_recon.N, old_recon.M) != stack_shape[-2:]: - return True - # check if equivalent attributes have diverged - for key, value in attr_list.items(): - if getattr(self.calib_window, key) != getattr(old_recon, value): - return True - # modify attributes to be equivalent and check for divergence - for key, value in attr_modified_list.items(): - if key == "wavelength": - if ( - self.calib_window.wavelength - * 1e-3 - / self.calib_window.n_media - != old_recon.lambda_illu - ): - return True - elif key == "n_slices": - if getattr(self, key) != getattr(old_recon, value): - return True - elif key == "ps": - if getattr(self.calib_window, key) / float( - self.calib_window.mag - ) != getattr(old_recon, value): - return True - else: - if getattr( - self.calib_window, key - ) / self.calib_window.n_media != getattr(old_recon, value): - return True - # not changed - return False + return phase def _cleanup_acq(self): # Get display windows @@ -459,10 +349,10 @@ def _cleanup_acq(self): if not save_prefix else f"{save_prefix}_RawBFDataSnap.zarr" ) - out_path = os.path.join(self.snap_dir, name) + self.latest_out_path = os.path.join(self.snap_dir, name) converter = TIFFConverter( os.path.join(dir_, prefix), - out_path, + self.latest_out_path, data_type="ometiff", grid_layout=False, label_positions=False, @@ -565,6 +455,17 @@ def work(self): self._check_abort() + # Create and validate reconstruction settings + self.config_path = os.path.join( + self.snap_dir, "reconstruction_settings.yml" + ) + _generate_reconstruction_config_from_gui( + self.config_path, + self.mode, + self.calib_window, + input_channel_names=channels, + ) + # Acquire 2D stack if self.dim == "2D": logging.debug("Acquiring 2D stack") @@ -615,17 +516,17 @@ def work(self): # Reconstruct snapped images self._check_abort() self.n_slices = stack.shape[2] - birefringence, phase, meta = self._reconstruct(stack[0]) + birefringence, phase = self._reconstruct() self._check_abort() if self.calib_window.orientation_offset: birefringence = self._orientation_offset(birefringence) # Save images - logging.debug("Saving Images") - self._save_imgs(birefringence, phase, meta) + # logging.debug("Saving Images") + # self._save_imgs(birefringence, phase) - self._check_abort() + # self._check_abort() logging.info("Finished Acquisition") logging.debug("Finished Acquisition") @@ -653,7 +554,16 @@ def _orientation_offset(self, birefringence: np.ndarray): "This will affect both visualization and the saved reconstruction." ) ) + + # TODO: Move these to waveorder, connect to the `orientation_flip` + # and `orientation_rotate` parameters, and connect to recOrder buttons. + + # 90 degree flip birefringence[1] = np.fmod(birefringence[1] + np.pi / 2, np.pi) + + # Reflection + birefringence[1] = np.fmod(-birefringence[1], np.pi) + np.pi + return birefringence def _check_exposure(self) -> None: @@ -700,407 +610,49 @@ def _acquire(self) -> np.ndarray: return stack - def _reconstruct(self, stack): + def _reconstruct(self): """ - Method to reconstruct, given a 2D or 3D stack. First need to initialize the reconstructor given + Method to reconstruct. First need to initialize the reconstructor given what type of acquisition it is (birefringence only skips a lot of heavy compute needed for phase). This function also checks to see if the reconstructor needs to be updated from previous acquisitions - Parameters - ---------- - stack: (nd-array) Dimensions are (C, Z, Y, X) - - Returns - ------- - """ - - # get rid of z-dimension if 2D acquisition - stack = stack[:, 0] if self.n_slices == 1 else stack - self._check_abort() - wo_background_correction = rec_bkg_to_wo_bkg( - self.calib_window.bg_option + # Create config and i/o paths + transfer_function_path = os.path.join( + self.snap_dir, "transfer_function.zarr" ) - - # Initialize the heavy reconstuctor - if self.mode == "phase" or self.mode == "all": - self._check_abort() - - # if no reconstructor has been initialized before, create new reconstructor - if not self.calib_window.phase_reconstructor: - logging.debug("Computing new reconstructor") - - recon = initialize_reconstructor( - "QLIPP", - image_dim=(stack.shape[-2], stack.shape[-1]), - wavelength_nm=self.calib_window.recon_wavelength, - swing=self.calib.swing, - calibration_scheme=self.calib.calib_scheme, - NA_obj=self.calib_window.obj_na, - NA_illu=self.calib_window.cond_na, - mag=self.calib_window.mag, - n_slices=self.n_slices, - z_step_um=self.calib_window.z_step, - pad_z=self.calib_window.pad_z, - pixel_size_um=self.calib_window.ps, - bg_correction=wo_background_correction, - n_obj_media=self.calib_window.n_media, - mode=self.calib_window.acq_mode, - use_gpu=self.calib_window.use_gpu, - gpu_id=self.calib_window.gpu_id, - ) - - # Emit reconstructor to be saved for later reconstructions - self.phase_reconstructor_emitter.emit(recon) - - # if previous reconstructor exists - else: - self._check_abort() - - # compute new reconstructor if the old reconstructor properties have been modified - if self._reconstructor_changed(): - logging.debug( - "Reconstruction settings changed, updating reconstructor" - ) - - recon = initialize_reconstructor( - "QLIPP", - image_dim=(stack.shape[-2], stack.shape[-1]), - wavelength_nm=self.calib_window.recon_wavelength, - swing=self.calib.swing, - calibration_scheme=self.calib.calib_scheme, - NA_obj=self.calib_window.obj_na, - NA_illu=self.calib_window.cond_na, - mag=self.calib_window.mag, - n_slices=self.n_slices, - z_step_um=self.calib_window.z_step, - pad_z=self.calib_window.pad_z, - pixel_size_um=self.calib_window.ps, - bg_correction=wo_background_correction, - n_obj_media=self.calib_window.n_media, - mode=self.calib_window.acq_mode, - use_gpu=self.calib_window.use_gpu, - gpu_id=self.calib_window.gpu_id, - ) - - self.phase_reconstructor_emitter.emit(recon) - - # use previous reconstructor - else: - logging.debug("Using previous reconstruction settings") - recon = self.calib_window.phase_reconstructor - - # if phase isn't desired, initialize the lighter birefringence only reconstructor - # no need to save this reconstructor for later as it is pretty quick to compute - else: - self._check_abort() - logging.debug("Creating birefringence only reconstructor") - recon = initialize_reconstructor( - "birefringence", - image_dim=(stack.shape[-2], stack.shape[-1]), - calibration_scheme=self.calib.calib_scheme, - wavelength_nm=self.calib_window.recon_wavelength, - swing=self.calib.swing, - bg_correction=wo_background_correction, - n_slices=self.n_slices, - ) - - # Prepare background corrections for waveorder - # This block mimics qlipp_pipeline.py L110-119. - if self.calib_window.bg_option in ["global", "local_fit+"]: - logging.debug("Loading BG Data") - self._check_abort() - bg_data = self._load_bg( - self.calib_window.acq_bg_directory, - stack.shape[-2], - stack.shape[-1], - ) - self._check_abort() - bg_stokes = recon.Stokes_recon(bg_data) - self._check_abort() - bg_stokes = recon.Stokes_transform(bg_stokes) - self._check_abort() - elif self.calib_window.bg_option == "local_fit": - bg_stokes = np.zeros((5, stack.shape[-2], stack.shape[-1])) - bg_stokes[ - 0, ... - ] = 1 # Set background to "identity" Stokes parameters. - else: - logging.debug("No Background Correction method chosen") - bg_stokes = None - - # Begin reconstruction with stokes (needed for birefringence or phase) - logging.debug("Reconstructing...") - self._check_abort() - stokes = reconstruct_qlipp_stokes(stack, recon, bg_stokes) - self._check_abort() - - # initialize empty variables to pass along - birefringence = None - phase = None - - regularizer = self.calib_window.phase_regularizer - - reg = float(self.calib_window.ui.le_phase_strength.text()) - - # reconstruct both phase and birefringence - if self.mode == "all": - if self.calib_window.acq_mode == "2D": - birefringence = reconstruct_qlipp_birefringence( - np.mean(stokes, axis=1), recon - ) - else: - birefringence = reconstruct_qlipp_birefringence(stokes, recon) - birefringence[0] = ( - birefringence[0] - / (2 * np.pi) - * self.calib_window.recon_wavelength - ) - self._check_abort() - - if self.calib_window.acq_mode == "2D": - phase = reconstruct_phase2D( - stokes[0], - recon, - method=regularizer, - reg_p=reg, - itr=int(self.calib_window.ui.le_itr.text()), - rho=float(self.calib_window.ui.le_rho.text()), - ) - else: - phase = reconstruct_phase3D( - stokes[0], - recon, - method=regularizer, - reg_re=reg, - itr=int(self.calib_window.ui.le_itr.text()), - rho=float(self.calib_window.ui.le_rho.text()), - ) - - self._check_abort() - - # reconstruct phase only - elif self.mode == "phase": - if self.calib_window.acq_mode == "2D": - phase = reconstruct_phase2D( - stokes[0], - recon, - method=regularizer, - reg_p=reg, - itr=int(self.calib_window.ui.le_itr.text()), - rho=float(self.calib_window.ui.le_rho.text()), - ) - else: - phase = reconstruct_phase3D( - stokes[0], - recon, - method=regularizer, - reg_re=reg, - itr=int(self.calib_window.ui.le_itr.text()), - rho=float(self.calib_window.ui.le_rho.text()), - ) - self._check_abort() - - # reconstruct birefringence only - elif self.mode == "birefringence": - birefringence = reconstruct_qlipp_birefringence(stokes, recon) - birefringence[0] = ( - birefringence[0] - / (2 * np.pi) - * self.calib_window.recon_wavelength - ) - self._check_abort() - - else: - raise ValueError("Reconstruction Mode Not Understood") - - meta = extract_reconstruction_parameters(recon, self.calib_window.mag) - meta["regularization_method"] = regularizer - meta["regularization_strength"] = reg - if regularizer == "TV": - meta["rho"] = float(self.calib_window.ui.le_rho.text()) - meta["itr"] = int(self.calib_window.ui.le_itr.text()) - - # return both variables, could contain images or could be null - return birefringence, phase, meta - - def _save_imgs(self, birefringence, phase, meta=None): - """ - function to save images. - Makes sure file names do not overlap, i.e. nothing is overwritten. - - Parameters - ---------- - birefringence: (nd-array) birefringence image(s) - phase: (nd-array or None) phase image(s) - - Returns - ------- - - """ - prefix = self.calib_window.save_name - - name = ( - f"ReconstructionSnap.zarr" - if not prefix - else f"{prefix}_ReconstructionSnap.zarr" + reconstruction_path = os.path.join( + self.snap_dir, "reconstruction.zarr" ) - with open_ome_zarr( - os.path.join(self.snap_dir, name), - layout="fov", - mode="w-", - channel_names=["Retardance", "Orientation", "BF", "Pol"], - ) as dataset: - if birefringence.ndim == 3: - birefringence = birefringence[:, np.newaxis] # CYX -> CZYX - birefringence = birefringence[np.newaxis] # CZYX -> TCZYX - dataset["0"] = birefringence - - if phase is not None: - dataset.append_channel( - "Phase" + str(phase.ndim) + "D", resize_arrays=True - ) - if phase.ndim == 2: - phase = phase[np.newaxis] # YX -> ZYX - dataset["0"][0, 4] = phase - - dataset.zattrs["recOrder"] = meta - - def _load_bg(self, path, height, width): - """ - # TODO: remove ROI for 1.0.0 - - Load background and calibration metadata. - - Parameters - ---------- - path: (str) path to the BG folder - height: (int) height of BG image - width: (int) widht of BG image - - Returns - ------- - - """ - - # TODO: Change to just accept ROI - try: - metadata_path = get_last_metadata_file(path) - metadata = MetadataReader(metadata_path) - roi = metadata.ROI - except: - roi = None - - bg_data = load_bg(path, height, width, roi) - - return bg_data + input_data_path = os.path.join(self.latest_out_path, "0", "0", "0") - def _reconstructor_changed(self): - """ - Function to check if the reconstructor has changed from the previous one in memory. - Serves to check if the worker attributes and reconstructor attributes have diverged. - - Returns - ------- + # TODO: skip if config files match + compute_transfer_function_cli( + input_data_path, + self.config_path, + transfer_function_path, + ) - """ + apply_inverse_transfer_function_cli( + input_data_path, + transfer_function_path, + self.config_path, + reconstruction_path, + ) - changed = None - - # Attributes that are directly equivalent to worker attributes - attr_list = { - "acq_mode": "phase_deconv", - "pad_z": "pad_z", - "n_media": "n_media", - "bg_option": "bg_option", - "use_gpu": "use_gpu", - "gpu_id": "gpu_id", - } - - # attributes that are modified upon passing them to reconstructor - attr_modified_list = { - "obj_na": "NA_obj", - "cond_na": "NA_illu", - "wavelength": "lambda_illu", - "n_slices": "N_defocus", - "swing": "chi", - "ps": "ps", - } + # Read reconstruction to pass to emitters + with open_ome_zarr(reconstruction_path, mode="r") as dataset: + czyx_data = dataset["0"][0] + birefringence = czyx_data[0:4] + try: + phase = czyx_data[4] + except: + phase = None - self._check_abort() - # check if equivalent attributes have diverged - for key, value in attr_list.items(): - if getattr(self.calib_window, key) != getattr( - self.calib_window.phase_reconstructor, value - ): - changed = True - break - else: - changed = False - - if not changed: - # modify attributes to be equivalent and check for divergence - for key, value in attr_modified_list.items(): - if key == "swing": - if self.calib.calib_scheme == "5-State": - if ( - self.calib.swing * 2 * np.pi - != self.calib_window.phase_reconstructor.chi - ): - changed = True - else: - changed = False - else: - if ( - self.calib.swing - != self.calib_window.phase_reconstructor.chi - ): - changed = True - else: - changed = False - - elif key == "wavelength": - if ( - self.calib_window.recon_wavelength - * 1e-3 - / self.calib_window.n_media - != self.calib_window.phase_reconstructor.lambda_illu - ): - changed = True - break - else: - changed = False - elif key == "n_slices": - if getattr(self, key) != getattr( - self.calib_window.phase_reconstructor, value - ): - changed = True - break - else: - changed = False - - elif key == "ps": - if getattr(self.calib_window, key) / float( - self.calib_window.mag - ) != getattr(self.calib_window.phase_reconstructor, value): - changed = True - break - else: - changed = False - else: - if getattr( - self.calib_window, key - ) / self.calib_window.n_media != getattr( - self.calib_window.phase_reconstructor, value - ): - changed = True - else: - changed = False - - return changed + return birefringence, phase def _cleanup_acq(self): # Get display windows @@ -1134,10 +686,10 @@ def _cleanup_acq(self): if not save_prefix else f"{save_prefix}_RawPolDataSnap.zarr" ) - out_path = os.path.join(self.snap_dir, name) + self.latest_out_path = os.path.join(self.snap_dir, name) converter = TIFFConverter( os.path.join(dir_, prefix), - out_path, + self.latest_out_path, data_type="ometiff", grid_layout=False, label_positions=False, @@ -1149,378 +701,3 @@ def _cleanup_acq(self): break else: continue - - -class ListeningWorker(WorkerBase): - """ - Class to execute a birefringence/phase acquisition. First step is to snap the images follow by a second - step of reconstructing those images. - """ - - def __init__(self, calib_window, bg_data): - super().__init__(SignalsClass=ListeningSignals) - - # Save current state of GUI window - self.calib_window = calib_window - - # Init properties - self.n_slices = None - self.n_channels = None - self.n_frames = None - self.n_pos = None - self.shape = None - self.dtype = None - self.root = None - self.prefix = None - self.store = None - self.save_directory = None - self.bg_data = bg_data - self.reconstructor = None - - def _check_abort(self): - if self.abort_requested: - self.aborted.emit() - raise TimeoutError("Stop Requested") - - def get_byte_offset(self, offsets, page): - """ - Gets the byte offset from the tiff tag metadata. - - 210 accounts for header data + page header data. - 162 accounts page header data - - Parameters - ---------- - offsets: (dict) Offset dictionary list - page: (int) Page to look at for the offset - - Returns - ------- - byte offset: (int) byte offset for the image array - - """ - - if page == 0: - array_offset = offsets[page] + 210 - else: - array_offset = offsets[page] + 162 - - return array_offset - - def listen_for_images( - self, array, file, offsets, interval, dim3, dim2, dim1, dim0, dim_order - ): - """ - - Parameters - ---------- - array: (nd-array) numpy array of size (C, Z) - file: (string) filepath corresponding to the desired tiff image - offsets: (dict) dictionary of offsets corresponding to byte offsets of pixel data in tiff image - interval: (int) time interval between timepoints in seconds - dim3: (int) outermost dimension to value begin at - dim2: (int) first-inner dimension value to begin at - dim1: (int) second-inner dimension value to begin at - dim0: (int) innermost dimension value to begin at - dim_order: (int) 1, 2, 3, or 4 corresponding to the dimensionality ordering of the acquisition (MM-provided) - - Returns - ------- - array: (nd-array) partially filled array of size (C, Z) to continue filling in next iteration - index: (int) current page number at end of function - dim3: (int) dimension values corresponding to where the next iteration should begin - dim2: (int) dimension values corresponding to where the next iteration should begin - dim1: (int) dimension values corresponding to where the next iteration should begin - dim0: (int) dimension values corresponding to where the next iteration should begin - - """ - - # Order dimensions that we will loop through in order to match the acquisition - if dim_order == 0: - dims = [ - [dim3, self.n_frames], - [dim2, self.n_pos], - [dim1, self.n_slices], - [dim0, self.n_channels], - ] - channel_dim = 0 - elif dim_order == 1: - dims = [ - [dim3, self.n_frames], - [dim2, self.n_pos], - [dim1, self.n_channels], - [dim0, self.n_slices], - ] - channel_dim = 1 - elif dim_order == 2: - dims = [ - [dim3, self.n_pos], - [dim2, self.n_frames], - [dim1, self.n_slices], - [dim0, self.n_channels], - ] - channel_dim = 0 - else: - dims = [ - [dim3, self.n_pos], - [dim2, self.n_frames], - [dim1, self.n_channels], - [dim0, self.n_slices], - ] - channel_dim = 1 - - # Loop through dimensions in the order they are acquired - idx = 0 - for dim3 in range(dims[0][0], dims[0][1]): - for dim2 in range(dims[1][0], dims[1][1]): - for dim1 in range(dims[2][0], dims[2][1]): - for dim0 in range(dims[3][0], dims[3][1]): - # GET OFFSET AND WAIT - if idx > 0: - try: - offset = self.get_byte_offset(offsets, idx) - except IndexError: - # NEED TO STOP BECAUSE RAN OUT OF PAGES OR REACHED END OF ACQ - return array, idx, dim3, dim2, dim1, dim0 - - # Checks if the offset in metadata is 0, but technically self.get_byte_offset() adds - # 162 to every offset to account for tiff file header bytes - while offset == 162: - self._check_abort() - tf = tiff.TiffFile(file) - time.sleep(interval) - offsets = tf.micromanager_metadata["IndexMap"][ - "Offset" - ] - tf.close() - offset = self.get_byte_offset(offsets, idx) - else: - offset = self.get_byte_offset(offsets, idx) - - if idx < ( - self.n_slices - * self.n_channels - * self.n_frames - * self.n_pos - ): - # Assign dimensions based off acquisition order to correctly add image to array - if dim_order == 0: - t, p, c, z = dim3, dim2, dim0, dim1 - elif dim_order == 1: - t, p, c, z = dim3, dim2, dim1, dim0 - elif dim_order == 2: - t, p, c, z = dim2, dim3, dim0, dim1 - else: - t, p, c, z = dim2, dim3, dim1, dim0 - - self._check_abort() - - # Add Image to array - img = np.memmap( - file, - dtype=self.dtype, - mode="r", - offset=offset, - shape=self.shape, - ) - array[c, z] = img - - # If Channel first, compute birefringence here - if ( - channel_dim == 0 - and dim0 == self.n_channels - 1 - ): - self._check_abort() - - # Compute birefringence - self.compute_and_save(array[:, z], p, t, z) - - idx += 1 - - # Reset Range to 0 to account for starting this function in middle of a dimension - if idx < ( - self.n_slices - * self.n_channels - * self.n_frames - * self.n_pos - ): - dims[2][0] = 0 - dims[3][0] = 0 - - # If z-first, compute the birefringence here - if channel_dim == 1 and dim1 == self.n_channels - 1: - # Assign dimensions based off acquisition order to correctly add image to array - if dim_order == 0: - t, p, c, z = dim3, dim2, dim0, dim1 - elif dim_order == 1: - t, p, c, z = dim3, dim2, dim1, dim0 - elif dim_order == 2: - t, p, c, z = dim2, dim3, dim0, dim1 - else: - t, p, c, z = dim2, dim3, dim1, dim0 - - self._check_abort() - self.compute_and_save(array, p, t, dim0) - else: - continue - - # Reset range to 0 to account for starting this function in the middle of a dimension - if idx < ( - self.n_slices - * self.n_channels - * self.n_frames - * self.n_pos - ): - dims[1][0] = 0 - - # Return at the end of the acquisition - return array, idx, dim3, dim2, dim1, dim0 - - def compute_and_save(self, array, p, t, z): - if self.n_slices == 1: - array = array[:, 0] - - birefringence = self.reconstructor.reconstruct(array) - - # If the napari layer doesn't exist yet, create the zarr store to emit to napari - if self.prefix not in self.calib_window.viewer.layers: - self.store = zarr.open( - os.path.join(self.root, self.prefix + ".zarr") - ) - self.store.zeros( - name="Birefringence", - shape=( - self.n_pos, - self.n_frames, - 2, - self.n_slices, - self.shape[0], - self.shape[1], - ), - chunks=(1, 1, 1, 1, self.shape[0], self.shape[1]), - overwrite=True, - ) - - # Add data and send out the store. Once the store has been emitted, we can add data to the store - # without needing to emit the store again (thanks to napari's handling of zarr datasets) - if len(birefringence.shape) == 4: - self.store["Birefringence"][p, t] = birefringence - else: - self.store["Birefringence"][p, t, :, z] = birefringence - - self.store_emitter.emit(self.store) - - else: - if len(birefringence.shape) == 4: - self.store["Birefringence"][p, t] = birefringence - else: - self.store["Birefringence"][p, t, :, z] = birefringence - - # Emit the current dimensions so that we can update the napari dimension slider - self.dim_emitter.emit((p, t, z)) - - def work(self): - acq_man = self.calib_window.mm.acquisitions() - - while not acq_man.isAcquisitionRunning(): - pass - - time.sleep(1) - - # Get all of the dataset dimensions, settings - s = acq_man.getAcquisitionSettings() - self.root = s.root() - self.prefix = s.prefix() - self.n_frames, self.interval = ( - (s.numFrames(), s.intervalMs() / 1000) if s.useFrames() else (1, 1) - ) - self.n_channels = s.channels().size() if s.useChannels() else 1 - self.n_slices = s.slices().size() if s.useChannels() else 1 - self.n_pos = ( - 1 - if not s.usePositionList() - else self.calib_window.mm.getPositionListManager() - .getPositionList() - .getNumberOfPositions() - ) - dim_order = s.acqOrderMode() - - # Get File Path corresponding to current dataset - path = os.path.join(self.root, self.prefix) - files = [fn for fn in glob.glob(path + "*") if ".zarr" not in fn] - index = max([int(x.strip(path + "_")) for x in files]) - - self.prefix = self.prefix + f"_{index}" - full_path = os.path.join(self.root, self.prefix) - first_file_path = os.path.join( - full_path, self.prefix + "_MMStack.ome.tif" - ) - - file = tiff.TiffFile(first_file_path) - file_path = first_file_path - self.shape = ( - file.micromanager_metadata["Summary"]["Height"], - file.micromanager_metadata["Summary"]["Width"], - ) - self.dtype = file.pages[0].dtype - offsets = file.micromanager_metadata["IndexMap"]["Offset"] - file.close() - - self._check_abort() - - # Init Reconstruction Class - self.reconstructor = QLIPPBirefringenceCompute( - self.shape, - self.calib_window.calib_scheme, - self.calib_window.wavelength, - self.calib_window.swing, - self.n_slices, - self.calib_window.bg_option, - self.bg_data, - ) - - self._check_abort() - - # initialize dimensions / array for the loop - idx, dim3, dim2, dim1, dim0 = 0, 0, 0, 0, 0 - array = np.zeros( - (self.n_channels, self.n_slices, self.shape[0], self.shape[1]) - ) - file_count = 0 - total_idx = 0 - - # Run until the function has collected the totality of the data - while total_idx < ( - self.n_slices * self.n_channels * self.n_frames * self.n_pos - ): - self._check_abort() - - # this will loop through reading images in a single file as it's being written - # when it has successfully loaded all of the images from the file, it'll move on to the next - array, idx, dim3, dim2, dim1, dim0 = self.listen_for_images( - array, - file_path, - offsets, - self.interval, - dim3, - dim2, - dim1, - dim0, - dim_order, - ) - - total_idx += idx - - # If acquisition is not finished, grab the next file and listen for images - if ( - total_idx - != self.n_slices * self.n_channels * self.n_frames * self.n_pos - ): - time.sleep(1) - file_count += 1 - file_path = os.path.join( - full_path, self.prefix + f"_MMStack_{file_count}.ome.tif" - ) - file = tiff.TiffFile(file_path) - offsets = file.micromanager_metadata["IndexMap"]["Offset"] - file.close() diff --git a/recOrder/calib/Calibration.py b/recOrder/calib/Calibration.py index d445d9d7..d8f50fbb 100644 --- a/recOrder/calib/Calibration.py +++ b/recOrder/calib/Calibration.py @@ -1,8 +1,7 @@ import numpy as np import matplotlib.pyplot as plt -import matplotlib.patches as patches -import tifffile as tiff import time +from iohub import open_ome_zarr from recOrder.io.core_functions import * from recOrder.calib.Optimization import BrentOptimizer, MinScalarOptimizer from mpl_toolkits.axes_grid1.axes_divider import make_axes_locatable @@ -18,7 +17,7 @@ from datetime import datetime from importlib_metadata import version -LC_DEVICE_NAME = "MeadowlarkLcOpenSource" +LC_DEVICE_NAME = "MeadowlarkLC" class QLIPP_Calibration: @@ -107,17 +106,18 @@ def __init__( # Set Mode # TODO: make sure LC or TriggerScope are loaded in the respective modes allowed_modes = ["MM-Retardance", "MM-Voltage", "DAC"] - assert ( - lc_control_mode in allowed_modes - ), f"LC control mode must be one of {allowed_modes}" + if lc_control_mode not in allowed_modes: + raise ValueError(f"LC control mode must be one of {allowed_modes}") self.mode = lc_control_mode self.LC_DAC_conversion = 4 # convert between the input range of LCs (0-20V) and the output range of the DAC (0-5V) # Initialize calibration class allowed_interp_methods = ["schnoor_fit", "linear"] - assert ( - interp_method in allowed_interp_methods - ), f"LC calibration data interpolation method must be one of {allowed_interp_methods}" + if interp_method not in allowed_interp_methods: + raise ValueError( + "LC calibration data interpolation method must be one of " + f"{allowed_interp_methods}" + ) dir_path = mmc.getDeviceAdapterSearchPaths().get( 0 ) # MM device adapter directory @@ -294,7 +294,6 @@ def define_lc_state(self, state, lca_retardance, lcb_retardance): ) def opt_lc(self, x, device_property, reference, normalize=False): - if isinstance(x, list) or isinstance(x, tuple): x = x[0] @@ -321,7 +320,6 @@ def opt_lc(self, x, device_property, reference, normalize=False): return np.abs(mean - reference) def opt_lc_cons(self, x, device_property, reference, mode): - self.set_lc(x, device_property) swing = (self.lca_ext - x) * self.ratio @@ -374,7 +372,6 @@ def opt_lc_grid(self, a_min, a_max, b_min, b_max, step): # coarse search for lca in np.arange(a_min, a_max, step): for lcb in np.arange(b_min, b_max, step): - self.set_lc(lca, "LCA") self.set_lc(lcb, "LCB") @@ -832,7 +829,6 @@ def calculate_extinction( ) def calc_inst_matrix(self): - if self.calib_scheme == "4-State": chi = self.swing inst_mat = np.array( @@ -872,7 +868,6 @@ def calc_inst_matrix(self): return inst_mat def write_metadata(self, notes=None): - inst_mat = self.calc_inst_matrix() inst_mat = np.around(inst_mat, decimals=5).tolist() @@ -990,7 +985,6 @@ def _capture_state(self, state: str, n_avg: int): return np.mean(imgs, axis=0) def _plot_bg_images(self, imgs): - img_names = ( ["Extinction", "0", "60", "120"] if len(imgs) == 4 @@ -1079,39 +1073,36 @@ def capture_bg(self, n_avg, directory): self.mmc.setAutoShutter(False) self.open_shutter() - logging.debug("Capturing Background State0") - state0 = self._capture_state("State0", n_avg) - logging.debug("Saving Background State0") - tiff.imsave(os.path.join(directory, "State0.tif"), state0) - - logging.debug("Capturing Background State1") - state1 = self._capture_state("State1", n_avg) - logging.debug("Saving Background State1") - tiff.imsave(os.path.join(directory, "State1.tif"), state1) - - logging.debug("Capturing Background State2") - state2 = self._capture_state("State2", n_avg) - logging.debug("Saving Background State2") - tiff.imsave(os.path.join(directory, "State2.tif"), state2) - - logging.debug("Capturing Background State3") - state3 = self._capture_state("State3", n_avg) - logging.debug("Saving Background State3") - tiff.imsave(os.path.join(directory, "State3.tif"), state3) - - imgs = [state0, state1, state2, state3] - - if self.calib_scheme == "5-State": - logging.debug("Capturing Background State4") - state4 = self._capture_state("State4", n_avg) - logging.debug("Saving Background State4") - tiff.imsave(os.path.join(directory, "State4.tif"), state4) - imgs.append(state4) + num_states = int(self.calib_scheme[0]) + + # Acquire background data + yx_list = [] + for channel in range(num_states): + logging.debug(f"Capturing Background State{channel}") + yx_list.append(self._capture_state(f"State{channel}", n_avg)) + logging.debug(f"Saving Background State{channel}") + cyx_data = np.array(yx_list) + + # Save to zarr + with open_ome_zarr( + os.path.join(directory, "background.zarr"), + layout="hcs", + mode="w", + channel_names=[f"State{i}" for i in range(num_states)], + ) as dataset: + position = dataset.create_position("0", "0", "0") + position.create_zeros( + name="0", + shape=(1, num_states, 1, cyx_data.shape[1], cyx_data.shape[2]), + dtype=np.float32, + chunks=(1, 1, 1, cyx_data.shape[1], cyx_data.shape[2]), + ) + position["0"][0, :, 0] = cyx_data # save to 1C1YX array # self._plot_bg_images(np.asarray(imgs)) self.reset_shutter() - return np.asarray(imgs) + return cyx_data class CalibrationData: @@ -1377,7 +1368,6 @@ def interpolate_data(self, raw_data, calib_wavelengths): self.curve = self.spline(x_range) elif self.wavelength > 630: - new_a1_y = np.interp(x_range, x_range, spline546(x_range)) new_a2_y = np.interp(x_range, x_range, spline630(x_range)) @@ -1399,7 +1389,6 @@ def interpolate_data(self, raw_data, calib_wavelengths): self.curve = self.spline(x_range) elif 490 < self.wavelength < 546: - new_a1_y = np.interp(x_range, x_range, spline490(x_range)) new_a2_y = np.interp(x_range, x_range, spline546(x_range)) @@ -1416,7 +1405,6 @@ def interpolate_data(self, raw_data, calib_wavelengths): self.curve = self.spline(x_range) elif 546 < self.wavelength < 630: - new_a1_y = np.interp(x_range, x_range, spline546(x_range)) new_a2_y = np.interp(x_range, x_range, spline630(x_range)) diff --git a/recOrder/calib/calibration_workers.py b/recOrder/calib/calibration_workers.py index 64d704ea..5ad56cca 100644 --- a/recOrder/calib/calibration_workers.py +++ b/recOrder/calib/calibration_workers.py @@ -4,15 +4,17 @@ from qtpy.QtCore import Signal from iohub import open_ome_zarr from napari.qt.threading import WorkerBaseSignals, WorkerBase, thread_worker -from recOrder.compute.reconstructions import ( - initialize_reconstructor, - reconstruct_qlipp_birefringence, - reconstruct_qlipp_stokes, -) +from recOrder.cli import settings from recOrder.io.core_functions import set_lc_state, snap_and_average -from recOrder.io.utils import MockEmitter +from recOrder.io.utils import MockEmitter, model_to_yaml from recOrder.calib.Calibration import LC_DEVICE_NAME from recOrder.io.metadata_reader import MetadataReader, get_last_metadata_file +from recOrder.cli.compute_transfer_function import ( + compute_transfer_function_cli, +) +from recOrder.cli.apply_inverse_transfer_function import ( + apply_inverse_transfer_function_cli, +) import os import shutil import numpy as np @@ -234,7 +236,6 @@ def _calibrate_4state(self): self._check_abort() def _calibrate_5state(self): - search_radius = np.min((self.calib.swing, 0.05)) self.calib.calib_scheme = "5-State" @@ -306,7 +307,6 @@ def __init__(self, calib_window, calib): super().__init__(calib_window, calib) def work(self): - # Make the background folder bg_path = os.path.join( self.calib_window.directory, @@ -315,7 +315,6 @@ def work(self): if not os.path.exists(bg_path): os.mkdir(bg_path) else: - # increment background paths idx = 1 while os.path.exists(bg_path + f"_{idx}"): @@ -334,36 +333,60 @@ def work(self): # capture and return background images imgs = self.calib.capture_bg(self.calib_window.n_avg, bg_path) - img_dim = (imgs.shape[-2], imgs.shape[-1]) - self.calib_window._dump_gui_state(bg_path) - # initialize reconstructor - recon = initialize_reconstructor( - "birefringence", - image_dim=img_dim, - calibration_scheme=self.calib.calib_scheme, - wavelength_nm=self.calib.wavelength, - swing=self.calib.swing, - bg_correction="None", + # build background-specific reconstruction settings + reconstruction_settings = settings.ReconstructionSettings( + input_channel_names=[ + f"State{i}" + for i in range(int(self.calib_window.calib_scheme[0])) + ], + reconstruction_dimension=2, + birefringence=settings.BirefringenceSettings( + transfer_function=settings.BirefringenceTransferFunctionSettings( + swing=self.calib_window.swing + ), + apply_inverse=settings.BirefringenceApplyInverseSettings( + wavelength_illumination=self.calib_window.recon_wavelength + / 1000, + background_path="", + remove_estimated_background=False, + orientation_flip=False, + ), + ), ) - self._check_abort() - - # Reconstruct birefringence from BG images - stokes = reconstruct_qlipp_stokes(imgs, recon, None) - - self._check_abort() + reconstruction_config_path = os.path.join( + bg_path, "reconstruction_settings.yml" + ) + model_to_yaml(reconstruction_settings, reconstruction_config_path) - self.birefringence = reconstruct_qlipp_birefringence(stokes, recon) + input_data_path = os.path.join( + bg_path, "background.zarr", "0", "0", "0" + ) + transfer_function_path = os.path.join( + bg_path, "transfer_function.zarr" + ) + reconstruction_path = os.path.join(bg_path, "reconstruction.zarr") - self._check_abort() + compute_transfer_function_cli( + input_data_path, + reconstruction_config_path, + transfer_function_path, + ) - # Convert retardance to nm - self.retardance = ( - self.birefringence[0] / (2 * np.pi) * self.calib.wavelength + apply_inverse_transfer_function_cli( + input_data_path, + transfer_function_path, + reconstruction_config_path, + reconstruction_path, ) + # Load reconstructions from file for layers + with open_ome_zarr(reconstruction_path, mode="r") as dataset: + self.retardance = dataset["0"][0, 0, 0] + self.birefringence = dataset["0"][0, :, 0] + # Save metadata file and emit imgs meta_file = os.path.join(bg_path, "calibration_metadata.txt") self._write_meta_file(meta_file) @@ -385,9 +408,6 @@ def work(self): self._check_abort() - self._save_bg_recon(bg_path) - self._check_abort() - # Emit background images + background birefringence self.bg_image_emitter.emit(imgs) self.bire_image_emitter.emit((self.retardance, self.birefringence[1])) @@ -395,45 +415,6 @@ def work(self): # Emit bg path self.bg_path_update_emitter.emit(bg_path) - def _save_bg_recon(self, bg_path: StrOrBytesPath): - bg_recon_path = os.path.join(bg_path, "reconstruction") - # create the reconstruction directory - if os.path.isdir(bg_recon_path): - shutil.rmtree(bg_recon_path) - elif os.path.isfile(bg_recon_path): - os.remove(bg_recon_path) - else: - os.mkdir(bg_recon_path) - # save raw reconstruction to zarr store - with open_ome_zarr( - os.path.join(bg_recon_path, "reconstruction"), - layout="fov", - mode="w-", - channel_names=[ - "Retardance", - "Orientation", - "BF - computed", - "DoP", - ], - ) as dataset: - dataset["0"] = self.birefringence[np.newaxis, :, np.newaxis, ...] - - # save intensity trace visualization - import matplotlib.pyplot as plt - - plt.imsave( - os.path.join(bg_recon_path, "retardance.png"), - self.retardance, - cmap="gray", - ) - plt.imsave( - os.path.join(bg_recon_path, "orientation.png"), - self.birefringence[1], - cmap="hsv", - vmin=0, - vmax=np.pi, - ) - @thread_worker def load_calibration(calib, metadata: MetadataReader): diff --git a/recOrder/cli/apply_inverse_transfer_function.py b/recOrder/cli/apply_inverse_transfer_function.py new file mode 100644 index 00000000..46cac81a --- /dev/null +++ b/recOrder/cli/apply_inverse_transfer_function.py @@ -0,0 +1,396 @@ +import click +import numpy as np +import torch +from iohub import open_ome_zarr +from recOrder.cli.printing import echo_headline, echo_settings +from recOrder.cli.settings import ReconstructionSettings +from recOrder.cli.parsing import ( + input_data_path_argument, + config_path_option, + output_dataset_option, +) +from recOrder.io import utils +from waveorder.models import ( + inplane_oriented_thick_pol3d, + isotropic_thin_3d, + phase_thick_3d, + isotropic_fluorescent_thick_3d, +) + + +def _check_background_consistency(background_shape, data_shape): + data_cyx_shape = (data_shape[1],) + data_shape[3:] + if background_shape != data_cyx_shape: + raise ValueError( + f"Background shape {background_shape} does not match data shape {data_cyx_shape}" + ) + + +def apply_inverse_transfer_function_cli( + input_data_path, transfer_function_path, config_path, output_path +): + echo_headline("Starting reconstruction...") + + # Load datasets + transfer_function_dataset = open_ome_zarr(transfer_function_path) + input_dataset = open_ome_zarr(input_data_path) + + # Load config file + settings = utils.yaml_to_model(config_path, ReconstructionSettings) + + # Check input channel names + if not set(settings.input_channel_names).issubset( + input_dataset.channel_names + ): + raise ValueError( + f"Each of the input_channel_names = {settings.input_channel_names} in {config_path} must appear in the dataset {input_data_path} which currently contains channel_names = {input_dataset.channel_names}." + ) + + # Find channel indices + channel_indices = [] + for input_channel_name in settings.input_channel_names: + channel_indices.append( + input_dataset.channel_names.index(input_channel_name) + ) + + # Load dataset shape + t_shape = input_dataset.data.shape[0] + + # Simplify important settings names + recon_biref = settings.birefringence is not None + recon_phase = settings.phase is not None + recon_fluo = settings.fluorescence is not None + recon_dim = settings.reconstruction_dimension + + # Prepare output dataset + channel_names = [] + if recon_biref: + channel_names.append("Retardance") + channel_names.append("Orientation") + channel_names.append("BF") + channel_names.append("Pol") + if recon_phase: + if recon_dim == 2: + channel_names.append("Phase2D") + elif recon_dim == 3: + channel_names.append("Phase3D") + if recon_fluo: + fluor_name = settings.input_channel_names[0] + if recon_dim == 2: + channel_names.append(fluor_name + "2D") + elif recon_dim == 3: + channel_names.append(fluor_name + "3D") + + if recon_dim == 2: + output_z_shape = 1 + elif recon_dim == 3: + output_z_shape = input_dataset.data.shape[2] + + output_shape = ( + t_shape, + len(channel_names), + output_z_shape, + ) + input_dataset.data.shape[3:] + + # Create output dataset + output_dataset = open_ome_zarr( + output_path, layout="fov", mode="w", channel_names=channel_names + ) + output_array = output_dataset.create_zeros( + name="0", + shape=output_shape, + dtype=np.float32, + chunks=( + 1, + 1, + 1, + ) + + input_dataset.data.shape[3:], # chunk by YX + ) + + # Load data + tczyx_uint16_numpy = input_dataset.data.oindex[:, channel_indices] + tczyx_data = torch.tensor( + np.int32(tczyx_uint16_numpy), dtype=torch.float32 + ) # convert to np.int32 (torch doesn't accept np.uint16), then convert to tensor float32 + + # Prepare background dataset + if settings.birefringence is not None: + biref_inverse_dict = settings.birefringence.apply_inverse.dict() + + # Resolve background path into array + background_path = biref_inverse_dict["background_path"] + biref_inverse_dict.pop("background_path") + if background_path != "": + cyx_no_sample_data = utils.load_background(background_path) + _check_background_consistency( + cyx_no_sample_data.shape, input_dataset.data.shape + ) + else: + cyx_no_sample_data = None + + # Main reconstruction logic + # Eight different cases [2, 3] x [biref only, phase only, biref and phase, fluorescence] + + # [biref only] [2 or 3] + if recon_biref and (not recon_phase): + echo_headline("Reconstructing birefringence with settings:") + echo_settings(settings.birefringence.apply_inverse) + echo_headline("Reconstructing birefringence...") + + # Load transfer function + intensity_to_stokes_matrix = torch.tensor( + transfer_function_dataset["intensity_to_stokes_matrix"][0, 0, 0] + ) + + for time_index in range(t_shape): + # Apply + reconstructed_parameters = ( + inplane_oriented_thick_pol3d.apply_inverse_transfer_function( + tczyx_data[time_index], + intensity_to_stokes_matrix, + cyx_no_sample_data=cyx_no_sample_data, + project_stokes_to_2d=(recon_dim == 2), + **biref_inverse_dict, + ) + ) + # Save + for param_index, parameter in enumerate(reconstructed_parameters): + output_array[time_index, param_index] = parameter + + # [phase only] + if recon_phase and (not recon_biref): + echo_headline("Reconstructing phase with settings:") + echo_settings(settings.phase.apply_inverse) + echo_headline("Reconstructing phase...") + + # check data shapes + if tczyx_data.shape[1] != 1: + raise ValueError( + "You have requested a phase-only reconstruction, but the input dataset has more than one channel." + ) + + # [phase only, 2] + if recon_dim == 2: + # Load transfer functions + absorption_transfer_function = torch.tensor( + transfer_function_dataset["absorption_transfer_function"][0, 0] + ) + phase_transfer_function = torch.tensor( + transfer_function_dataset["phase_transfer_function"][0, 0] + ) + + for time_index in range(t_shape): + # Apply + ( + _, + yx_phase, + ) = isotropic_thin_3d.apply_inverse_transfer_function( + tczyx_data[time_index, 0], + absorption_transfer_function, + phase_transfer_function, + **settings.phase.apply_inverse.dict(), + ) + + # Save + output_array[time_index, -1, 0] = yx_phase + + # [phase only, 3] + elif recon_dim == 3: + # Load transfer functions + real_potential_transfer_function = torch.tensor( + transfer_function_dataset["real_potential_transfer_function"][ + 0, 0 + ] + ) + imaginary_potential_transfer_function = torch.tensor( + transfer_function_dataset[ + "imaginary_potential_transfer_function" + ][0, 0] + ) + + # Apply + for time_index in range(t_shape): + zyx_phase = phase_thick_3d.apply_inverse_transfer_function( + tczyx_data[time_index, 0], + real_potential_transfer_function, + imaginary_potential_transfer_function, + z_padding=settings.phase.transfer_function.z_padding, + z_pixel_size=settings.phase.transfer_function.z_pixel_size, + wavelength_illumination=settings.phase.transfer_function.wavelength_illumination, + **settings.phase.apply_inverse.dict(), + ) + # Save + output_array[time_index, -1] = zyx_phase + + # [biref and phase] + if recon_biref and recon_phase: + echo_headline("Reconstructing phase with settings:") + echo_settings(settings.phase.apply_inverse) + echo_headline("Reconstructing birefringence with settings:") + echo_settings(settings.birefringence.apply_inverse) + echo_headline("Reconstructing...") + + # Load birefringence transfer function + intensity_to_stokes_matrix = torch.tensor( + transfer_function_dataset["intensity_to_stokes_matrix"][0, 0, 0] + ) + + # [biref and phase, 2] + if recon_dim == 2: + # Load phase transfer functions + absorption_transfer_function = torch.tensor( + transfer_function_dataset["absorption_transfer_function"][0, 0] + ) + phase_transfer_function = torch.tensor( + transfer_function_dataset["phase_transfer_function"][0, 0] + ) + + for time_index in range(t_shape): + # Apply + reconstructed_parameters_2d = inplane_oriented_thick_pol3d.apply_inverse_transfer_function( + tczyx_data[time_index], + intensity_to_stokes_matrix, + cyx_no_sample_data=cyx_no_sample_data, + project_stokes_to_2d=True, + **biref_inverse_dict, + ) + + reconstructed_parameters_3d = inplane_oriented_thick_pol3d.apply_inverse_transfer_function( + tczyx_data[time_index], + intensity_to_stokes_matrix, + cyx_no_sample_data=cyx_no_sample_data, + project_stokes_to_2d=False, + **biref_inverse_dict, + ) + + brightfield_3d = reconstructed_parameters_3d[2] + + ( + _, + yx_phase, + ) = isotropic_thin_3d.apply_inverse_transfer_function( + brightfield_3d, + absorption_transfer_function, + phase_transfer_function, + **settings.phase.apply_inverse.dict(), + ) + + # Save + for param_index, parameter in enumerate( + reconstructed_parameters_2d + ): + output_array[time_index, param_index] = parameter + output_array[time_index, -1, 0] = yx_phase + + # [biref and phase, 3] + elif recon_dim == 3: + # Load phase transfer functions + intensity_to_stokes_matrix = torch.tensor( + transfer_function_dataset["intensity_to_stokes_matrix"][ + 0, 0, 0 + ] + ) + # Load transfer functions + real_potential_transfer_function = torch.tensor( + transfer_function_dataset["real_potential_transfer_function"][ + 0, 0 + ] + ) + imaginary_potential_transfer_function = torch.tensor( + transfer_function_dataset[ + "imaginary_potential_transfer_function" + ][0, 0] + ) + + # Apply + for time_index in range(t_shape): + reconstructed_parameters_3d = inplane_oriented_thick_pol3d.apply_inverse_transfer_function( + tczyx_data[time_index], + intensity_to_stokes_matrix, + cyx_no_sample_data=cyx_no_sample_data, + project_stokes_to_2d=False, + **biref_inverse_dict, + ) + + brightfield_3d = reconstructed_parameters_3d[2] + + zyx_phase = phase_thick_3d.apply_inverse_transfer_function( + brightfield_3d, + real_potential_transfer_function, + imaginary_potential_transfer_function, + z_padding=settings.phase.transfer_function.z_padding, + z_pixel_size=settings.phase.transfer_function.z_pixel_size, + wavelength_illumination=settings.phase.transfer_function.wavelength_illumination, + **settings.phase.apply_inverse.dict(), + ) + # Save + for param_index, parameter in enumerate( + reconstructed_parameters_3d + ): + output_array[time_index, param_index] = parameter + output_array[time_index, -1] = zyx_phase + + # [fluo] + if recon_fluo: + echo_headline("Reconstructing fluorescence with settings:") + echo_settings(settings.fluorescence.apply_inverse) + echo_headline("Reconstructing...") + + # [fluo, 2] + if recon_dim == 2: + raise NotImplementedError + # [fluo, 3] + elif recon_dim == 3: + # Load transfer functions + optical_transfer_function = torch.tensor( + transfer_function_dataset["optical_transfer_function"][0, 0] + ) + + # Apply + for time_index in range(t_shape): + zyx_recon = isotropic_fluorescent_thick_3d.apply_inverse_transfer_function( + tczyx_data[time_index, 0], + optical_transfer_function, + settings.fluorescence.transfer_function.z_padding, + **settings.fluorescence.apply_inverse.dict(), + ) + + # Save + output_array[time_index, 0] = zyx_recon + + output_dataset.zattrs["settings"] = settings.dict() + + echo_headline(f"Closing {output_path}\n") + output_dataset.close() + transfer_function_dataset.close() + input_dataset.close() + + echo_headline( + f"Recreate this reconstruction with:\n$ recorder apply-inv-tf {input_data_path} {transfer_function_path} -c {config_path} -o {output_path}" + ) + + [email protected]() [email protected]_option("-h", "--help") +@input_data_path_argument() [email protected]( + "transfer_function_path", + type=click.Path(exists=True), +) +@config_path_option() +@output_dataset_option(default="./reconstruction.zarr") +def apply_inv_tf( + input_data_path, transfer_function_path, config_path, output_path +): + """ + Apply an inverse transfer function to a dataset using a configuration file. + + See /examples for example configuration files. + + Example usage:\n + $ recorder apply-inv-tf input.zarr/0/0/0 transfer-function.zarr -c /examples/birefringence.yml -o output.zarr + """ + apply_inverse_transfer_function_cli( + input_data_path, transfer_function_path, config_path, output_path + ) diff --git a/recOrder/cli/compute_transfer_function.py b/recOrder/cli/compute_transfer_function.py new file mode 100644 index 00000000..80b740aa --- /dev/null +++ b/recOrder/cli/compute_transfer_function.py @@ -0,0 +1,219 @@ +import click +import numpy as np +from iohub import open_ome_zarr +from recOrder.cli.printing import echo_settings, echo_headline +from recOrder.cli.settings import ReconstructionSettings +from recOrder.cli.parsing import ( + input_data_path_argument, + config_path_option, + output_dataset_option, +) +from recOrder.io import utils +from waveorder.models import ( + inplane_oriented_thick_pol3d, + isotropic_thin_3d, + phase_thick_3d, + isotropic_fluorescent_thick_3d, +) + + +def generate_and_save_birefringence_transfer_function(settings, dataset): + """Generates and saves the birefringence transfer function to the dataset, based on the settings. + + Parameters + ---------- + settings: ReconstructionSettings + dataset: NGFF Node + The dataset that will be updated. + """ + echo_headline("Generating birefringence transfer function with settings:") + echo_settings(settings.birefringence.transfer_function) + + # Calculate transfer functions + intensity_to_stokes_matrix = ( + inplane_oriented_thick_pol3d.calculate_transfer_function( + scheme=str(len(settings.input_channel_names)) + "-State", + **settings.birefringence.transfer_function.dict(), + ) + ) + # Save + dataset[ + "intensity_to_stokes_matrix" + ] = intensity_to_stokes_matrix.cpu().numpy()[None, None, None, ...] + + +def generate_and_save_phase_transfer_function(settings, dataset, zyx_shape): + """Generates and saves the phase transfer function to the dataset, based on the settings. + + Parameters + ---------- + settings: ReconstructionSettings + dataset: NGFF Node + The dataset that will be updated. + zyx_shape : tuple + A tuple of integers specifying the input data's shape in (Z, Y, X) order + """ + echo_headline("Generating phase transfer function with settings:") + echo_settings(settings.phase.transfer_function) + + if settings.reconstruction_dimension == 2: + # Convert zyx_shape and z_pixel_size into yx_shape and z_position_list + settings_dict = settings.phase.transfer_function.dict() + settings_dict["yx_shape"] = [zyx_shape[1], zyx_shape[2]] + settings_dict["z_position_list"] = list( + -(np.arange(zyx_shape[0]) - zyx_shape[0] // 2) + * settings_dict["z_pixel_size"] + ) + + # Remove unused parameters + settings_dict.pop("z_pixel_size") + settings_dict.pop("z_padding") + + # Calculate transfer functions + ( + absorption_transfer_function, + phase_transfer_function, + ) = isotropic_thin_3d.calculate_transfer_function( + **settings_dict, + ) + + # Save + dataset[ + "absorption_transfer_function" + ] = absorption_transfer_function.cpu().numpy()[None, None, ...] + dataset[ + "phase_transfer_function" + ] = phase_transfer_function.cpu().numpy()[None, None, ...] + + elif settings.reconstruction_dimension == 3: + # Calculate transfer functions + ( + real_potential_transfer_function, + imaginary_potential_transfer_function, + ) = phase_thick_3d.calculate_transfer_function( + zyx_shape=zyx_shape, + **settings.phase.transfer_function.dict(), + ) + # Save + dataset[ + "real_potential_transfer_function" + ] = real_potential_transfer_function.cpu().numpy()[None, None, ...] + dataset[ + "imaginary_potential_transfer_function" + ] = imaginary_potential_transfer_function.cpu().numpy()[ + None, None, ... + ] + + +def generate_and_save_fluorescence_transfer_function( + settings, dataset, zyx_shape +): + """Generates and saves the fluorescence transfer function to the dataset, based on the settings. + + Parameters + ---------- + settings: ReconstructionSettings + dataset: NGFF Node + The dataset that will be updated. + zyx_shape : tuple + A tuple of integers specifying the input data's shape in (Z, Y, X) order + """ + echo_headline("Generating fluorescence transfer function with settings:") + echo_settings(settings.fluorescence.transfer_function) + + if settings.reconstruction_dimension == 2: + raise NotImplementedError + elif settings.reconstruction_dimension == 3: + # Calculate transfer functions + optical_transfer_function = ( + isotropic_fluorescent_thick_3d.calculate_transfer_function( + zyx_shape=zyx_shape, + **settings.fluorescence.transfer_function.dict(), + ) + ) + # Save + dataset[ + "optical_transfer_function" + ] = optical_transfer_function.cpu().numpy()[None, None, ...] + + +def compute_transfer_function_cli(input_data_path, config_path, output_path): + """CLI command to compute the transfer function given a configuration file path + and a desired output path. + + Parameters + ---------- + input_data_path : string + Path to the input data file. + config_path : string + Path of the configuration file. + output_path : string + Path of the output file. + """ + + # Load config file + settings = utils.yaml_to_model(config_path, ReconstructionSettings) + + echo_headline( + f"Generating transfer functions and storing in {output_path}\n" + ) + + # Read shape from input dataset + input_dataset = open_ome_zarr(input_data_path, layout="fov", mode="r") + zyx_shape = input_dataset.data.shape[ + 2: + ] # only loads a single position "0" + + # Check input channel names + if not set(settings.input_channel_names).issubset( + input_dataset.channel_names + ): + raise ValueError( + f"Each of the input_channel_names = {settings.input_channel_names} in {config_path} must appear in the dataset {input_data_path} which currently contains channel_names = {input_dataset.channel_names}." + ) + + # Prepare output dataset + output_dataset = open_ome_zarr( + output_path, layout="fov", mode="w", channel_names=["None"] + ) + + # Pass settings to appropriate calculate_transfer_function and save + if settings.birefringence is not None: + generate_and_save_birefringence_transfer_function( + settings, output_dataset + ) + if settings.phase is not None: + generate_and_save_phase_transfer_function( + settings, output_dataset, zyx_shape + ) + if settings.fluorescence is not None: + generate_and_save_fluorescence_transfer_function( + settings, output_dataset, zyx_shape + ) + + # Write settings to metadata + output_dataset.zattrs["settings"] = settings.dict() + + echo_headline(f"Closing {output_path}\n") + output_dataset.close() + + echo_headline( + f"Recreate this transfer function with:\n$ recorder compute-tf {input_data_path} -c {config_path} -o {output_path}" + ) + + [email protected]() [email protected]_option("-h", "--help") +@input_data_path_argument() +@config_path_option() +@output_dataset_option(default="./transfer-function.zarr") +def compute_tf(input_data_path, config_path, output_path): + """ + Compute a transfer function using a dataset and configuration file. + + See /examples/ for example configuration files. + + Example usage:\n + $ recorder compute-tf input.zarr/0/0/0 -c /examples/birefringence.yml -o transfer_function.zarr + """ + compute_transfer_function_cli(input_data_path, config_path, output_path) diff --git a/recOrder/cli/main.py b/recOrder/cli/main.py new file mode 100644 index 00000000..ad39a932 --- /dev/null +++ b/recOrder/cli/main.py @@ -0,0 +1,16 @@ +import click +from recOrder.cli.view import view +from recOrder.cli.reconstruct import reconstruct +from recOrder.cli.compute_transfer_function import compute_tf +from recOrder.cli.apply_inverse_transfer_function import apply_inv_tf + + [email protected]() +def cli(): + """\033[92mrecOrder: Computational Toolkit for Label-Free Imaging\033[0m\n""" + + +cli.add_command(view) +cli.add_command(reconstruct) +cli.add_command(compute_tf) +cli.add_command(apply_inv_tf) diff --git a/recOrder/cli/parsing.py b/recOrder/cli/parsing.py new file mode 100644 index 00000000..298536c0 --- /dev/null +++ b/recOrder/cli/parsing.py @@ -0,0 +1,54 @@ +import click +from typing import Callable +from iohub.ngff import open_ome_zarr, Plate + + +def _validate_fov_path( + ctx: click.Context, opt: click.Option, value: str +) -> None: + dataset = open_ome_zarr(value) + if isinstance(dataset, Plate): + raise ValueError( + "Please supply a single position instead of an HCS plate. Likely fix: replace 'input.zarr' with 'input.zarr/0/0/0'" + ) + return value + + +def input_data_path_argument() -> Callable: + def decorator(f: Callable) -> Callable: + return click.argument( + "input-data-path", + type=click.Path(exists=True), + callback=_validate_fov_path, + nargs=1, + )(f) + + return decorator + + +def config_path_option() -> Callable: + def decorator(f: Callable) -> Callable: + return click.option( + "--config-path", "-c", required=True, help="Path to config.yml" + )(f) + + return decorator + + +def output_dataset_option(default) -> Callable: + click_options = [ + click.option( + "--output-path", + "-o", + default=default, + help="Path to output.zarr", + ) + ] + # good place to add chunking, overwrite flag, etc + + def decorator(f: Callable) -> Callable: + for opt in click_options: + f = opt(f) + return f + + return decorator diff --git a/recOrder/cli/printing.py b/recOrder/cli/printing.py new file mode 100644 index 00000000..8d0f6e7a --- /dev/null +++ b/recOrder/cli/printing.py @@ -0,0 +1,10 @@ +import click +import yaml + + +def echo_settings(settings): + click.echo(yaml.dump(settings.dict(), default_flow_style=False, sort_keys=False)) + + +def echo_headline(headline): + click.echo(click.style(headline, fg="green")) diff --git a/recOrder/cli/reconstruct.py b/recOrder/cli/reconstruct.py new file mode 100644 index 00000000..8599a675 --- /dev/null +++ b/recOrder/cli/reconstruct.py @@ -0,0 +1,44 @@ +import click +import os +from recOrder.cli.compute_transfer_function import ( + compute_transfer_function_cli, +) +from recOrder.cli.apply_inverse_transfer_function import ( + apply_inverse_transfer_function_cli, +) +from recOrder.cli.parsing import ( + input_data_path_argument, + config_path_option, + output_dataset_option, +) + + [email protected]() [email protected]_option("-h", "--help") +@input_data_path_argument() +@config_path_option() +@output_dataset_option(default="./reconstruction.zarr") +def reconstruct(input_data_path, config_path, output_path): + """Reconstruct a dataset using a configuration file. This is a + convenience function for a `compute-tf` call followed by a `apply-inv-tf` + call. + + See /examples for example configuration files. + + Example usage:\n + $ recorder reconstruct input.zarr/0/0/0 -c /examples/birefringence.yml -o output.zarr + """ + + # Handle transfer function path + output_directory = os.path.dirname(output_path) + transfer_function_path = os.path.join( + output_directory, "transfer_function.zarr" + ) + + # Compute transfer function and apply inverse + compute_transfer_function_cli( + input_data_path, config_path, transfer_function_path + ) + apply_inverse_transfer_function_cli( + input_data_path, transfer_function_path, config_path, output_path + ) diff --git a/recOrder/cli/settings.py b/recOrder/cli/settings.py new file mode 100644 index 00000000..051dae7a --- /dev/null +++ b/recOrder/cli/settings.py @@ -0,0 +1,178 @@ +import os +from pydantic import ( + BaseModel, + Extra, + NonNegativeInt, + NonNegativeFloat, + PositiveInt, + PositiveFloat, + root_validator, + validator, +) +from typing import Literal, List, Optional + +# This file defines the configuration settings for the CLI. + +# Example settings files in `/examples/settings/` are autmatically generated +# by the tests in `/tests/cli_tests/test_settings.py` - `test_generate_example_settings`. + +# To keep the example settings up to date, run `pytest` locally when this file changes. + + +# All settings classes inherit from MyBaseModel, which forbids extra parameters to guard against typos +class MyBaseModel(BaseModel, extra=Extra.forbid): + pass + + +# Bottom level settings +class WavelengthIllumination(MyBaseModel): + wavelength_illumination: PositiveFloat = 0.532 + + +class BirefringenceTransferFunctionSettings(MyBaseModel): + swing: float = 0.1 + + @validator("swing") + def swing_range(cls, v): + if v <= 0 or v >= 1.0: + raise ValueError(f"swing = {v} should be between 0 and 1.") + return v + + +class BirefringenceApplyInverseSettings(WavelengthIllumination): + background_path: str = "" + remove_estimated_background: bool = False + orientation_flip: bool = False + orientation_rotate: bool = False + + @validator("background_path") + def check_background_path(cls, v): + if v == "": + return v + + raw_dir = r"{}".format(v) + if not os.path.isdir(raw_dir): + raise ValueError(f"{v} is not a existing directory") + return raw_dir + + +class FourierTransferFunctionSettings(MyBaseModel): + yx_pixel_size: PositiveFloat = 6.5 / 20 + z_pixel_size: PositiveFloat = 2.0 + z_padding: NonNegativeInt = 0 + index_of_refraction_media: PositiveFloat = 1.3 + numerical_aperture_detection: PositiveFloat = 1.2 + + @validator("numerical_aperture_detection") + def na_det(cls, v, values): + n = values["index_of_refraction_media"] + if v > n: + raise ValueError( + f"numerical_aperture_detection = {v} must be less than or equal to index_of_refraction_media = {n}" + ) + return v + + @validator("z_pixel_size") + def warn_unit_consistency(cls, v, values): + yx_pixel_size = values["yx_pixel_size"] + ratio = yx_pixel_size / v + if ratio < 1.0 / 20 or ratio > 20: + raise Warning( + f"yx_pixel_size ({yx_pixel_size}) / z_pixel_size ({v}) = {ratio}. Did you use consistent units?" + ) + return v + + +class FourierApplyInverseSettings(MyBaseModel): + reconstruction_algorithm: Literal["Tikhonov", "TV"] = "Tikhonov" + regularization_strength: NonNegativeFloat = 1e-3 + TV_rho_strength: PositiveFloat = 1e-3 + TV_iterations: NonNegativeInt = 1 + + +class PhaseTransferFunctionSettings( + FourierTransferFunctionSettings, + WavelengthIllumination, +): + numerical_aperture_illumination: NonNegativeFloat = 0.5 + axial_flip: bool = False + + @validator("numerical_aperture_illumination") + def na_ill(cls, v, values): + n = values.get("index_of_refraction_media") + if v > n: + raise ValueError( + f"numerical_aperture_illumination = {v} must be less than or equal to index_of_refraction_media = {n}" + ) + return v + + +class FluorescenceTransferFunctionSettings(FourierTransferFunctionSettings): + wavelength_emission: PositiveFloat = 0.507 + + @validator("wavelength_emission") + def warn_unit_consistency(cls, v, values): + yx_pixel_size = values.get("yx_pixel_size") + ratio = yx_pixel_size / v + if ratio < 1.0 / 20 or ratio > 20: + raise Warning( + f"yx_pixel_size ({yx_pixel_size}) / wavelength_illumination ({v}) = {ratio}. Did you use consistent units?" + ) + return v + + +# Second level settings +class BirefringenceSettings(MyBaseModel): + transfer_function: BirefringenceTransferFunctionSettings = ( + BirefringenceTransferFunctionSettings() + ) + apply_inverse: BirefringenceApplyInverseSettings = ( + BirefringenceApplyInverseSettings() + ) + + +class PhaseSettings(MyBaseModel): + transfer_function: PhaseTransferFunctionSettings = ( + PhaseTransferFunctionSettings() + ) + apply_inverse: FourierApplyInverseSettings = FourierApplyInverseSettings() + + +class FluorescenceSettings(MyBaseModel): + transfer_function: FluorescenceTransferFunctionSettings = ( + FluorescenceTransferFunctionSettings() + ) + apply_inverse: FourierApplyInverseSettings = FourierApplyInverseSettings() + + +# Top level settings +class ReconstructionSettings(MyBaseModel): + input_channel_names: List[str] = [f"State{i}" for i in range(4)] + reconstruction_dimension: Literal[2, 3] = 3 + birefringence: Optional[BirefringenceSettings] + phase: Optional[PhaseSettings] + fluorescence: Optional[FluorescenceSettings] + + @root_validator(pre=False) + def validate_reconstruction_types(cls, values): + if (values.get("birefringence") or values.get("phase")) and values.get( + "fluorescence" + ) is not None: + raise ValueError( + '"fluorescence" cannot be present alongside "birefringence" or "phase". Please use one configuration file for a "fluorescence" reconstruction and another configuration file for a "birefringence" and/or "phase" reconstructions.' + ) + num_channel_names = len(values.get("input_channel_names")) + if values.get("birefringence") is None: + if ( + values.get("phase") is None + and values.get("fluorescence") is None + ): + raise ValueError( + "Provide settings for either birefringence, phase, birefringence + phase, or fluorescence." + ) + if num_channel_names != 1: + raise ValueError( + f"{num_channel_names} channels names provided. Please provide a single channel for fluorescence/phase reconstructions." + ) + + return values diff --git a/recOrder/scripts/cli.py b/recOrder/cli/view.py similarity index 97% rename from recOrder/scripts/cli.py rename to recOrder/cli/view.py index 5e54fe53..0461aa9a 100644 --- a/recOrder/scripts/cli.py +++ b/recOrder/cli/view.py @@ -1,7 +1,7 @@ import click import napari -v = napari.Viewer() # open viewer right away to use on hpc +#v = napari.Viewer() # open viewer right away to use on hpc import numpy as np from recOrder.io.utils import ret_ori_overlay from iohub.reader import print_info, _infer_format @@ -170,14 +170,7 @@ def text_overlay(): napari.run() [email protected]() -def cli(): - print( - "\033[92mrecOrder: Computational Toolkit for Label-Free Imaging\033[0m\n" - ) - - [email protected]() [email protected]() @click.help_option("-h", "--help") @click.argument("filename") @click.option( diff --git a/recOrder/compute/__init__.py b/recOrder/compute/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/recOrder/compute/phantoms.py b/recOrder/compute/phantoms.py deleted file mode 100644 index e31dcccb..00000000 --- a/recOrder/compute/phantoms.py +++ /dev/null @@ -1,157 +0,0 @@ -import numpy as np -import waveorder as wo - - -def pol_3D_from_phantom(): - # Adapted from waveorder/examples/2D_QLIPP_simulation/2D_QLIPP_forward.py - - N = 512 # number of pixel in y dimension - M = 512 # number of pixel in x dimension - mag = 20 # magnification - ps = 6.5 / mag # effective pixel size - lambda_illu = 0.532 # wavelength - n_media = 1 # refractive index in the media - NA_obj = 0.4 # objective NA - NA_illu = 0.2 # illumination NA (condenser) - N_defocus = 11 - z_defocus = ( - np.r_[:N_defocus] - N_defocus // 2 - ) * 2 # a set of defocus planes - chi = 0.1 * 2 * np.pi # swing of Polscope analyzer - - # Generate phantom - star, theta, _ = wo.genStarTarget(N, M) - phase_value = 1 # average phase in radians (optical path length) - phi_s = star * (phase_value + 0.15) # slower OPL across target - phi_f = star * (phase_value - 0.15) # faster OPL across target - mu_s = np.zeros((N, M)) # absorption - mu_f = mu_s.copy() - t_eigen = np.zeros((2, N, M), complex) # complex specimen transmission - t_eigen[0] = np.exp(-mu_s + 1j * phi_s) - t_eigen[1] = np.exp(-mu_f + 1j * phi_f) - _, _, fxx, fyy = wo.gen_coordinate((N, M), ps) - sa = theta % np.pi # slow axes. - - # Generate source - Source_cont = wo.gen_Pupil(fxx, fyy, NA_illu, lambda_illu) - Source_discrete = wo.Source_subsample( - Source_cont, lambda_illu * fxx, lambda_illu * fyy, subsampled_NA=0.1 - ) - - # Generate simulator - simulator = wo.waveorder_microscopy_simulator( - (N, M), - lambda_illu, - ps, - NA_obj, - NA_illu, - z_defocus, - chi, - n_media=n_media, - illu_mode="Arbitrary", - Source=Source_discrete, - ) - - # Simulate measurements - I_meas, _ = simulator.simulate_waveorder_measurements( - t_eigen, sa, multiprocess=False - ) - - # Add background - photon_count = 300000 - ext_ratio = 10000 - const_bg = photon_count / (0.5 * (1 - np.cos(chi))) / ext_ratio - I_meas_noise = ( - np.random.poisson(I_meas / np.max(I_meas) * photon_count + const_bg) - ).astype("float64") - - # Return data and background - data = I_meas_noise.transpose(0, 3, 1, 2) - bkg = np.ones((5, N, M)) * const_bg - return data, bkg - - -def bf_3D_from_phantom(): - # Adapted from waveorder/3D_PODT_phase_simulation/3D_PODT_Phase_forward.py - - N = 512 # number of pixel in y dimension - M = 512 # number of pixel in x dimension - mag = 20 # magnification - ps = 6.5 / mag # effective pixel size - lambda_illu = 0.532 # wavelength - n_media = 1 # refractive index in the media - NA_obj = 0.4 # objective NA - NA_illu = 0.2 # illumination NA (condenser) - N_defocus = 31 - psz = 2 - z_defocus = ( - np.r_[:N_defocus] - N_defocus // 2 - ) * psz # a set of defocus planes - chi = 0.1 # not used - - # Generate sample - phantom = np.zeros((N, M, N_defocus), dtype=np.complex64) - star, _, _ = wo.genStarTarget(N, M) - n_sample = 1.50 - t_obj = np.exp(1j * 2 * np.pi * psz * (star * n_sample - n_media)) - phantom[:, :, N_defocus // 2] = t_obj - phantom += n_media - - # Generate source - _, _, fxx, fyy = wo.gen_coordinate((N, M), ps) - Source_cont = wo.gen_Pupil(fxx, fyy, NA_illu, lambda_illu) - Source_discrete = wo.Source_subsample( - Source_cont, lambda_illu * fxx, lambda_illu * fyy, subsampled_NA=0.1 - ) - - # Simulate - simulator = wo.waveorder_microscopy_simulator( - (N, M), - lambda_illu, - ps, - NA_obj, - NA_illu, - z_defocus, - chi, - n_media=n_media, - illu_mode="Arbitrary", - Source=Source_discrete, - ) - data = simulator.simulate_3D_scalar_measurements(phantom) - return data.transpose((2, 0, 1)) - - -def fluorescence_from_phantom(): - # Adapted from waveorder/examples/fluorescence_deconvolution/fluorescence_deconv.ipynb - - N = 512 # number of pixel in y dimension - M = 512 # number of pixel in x dimension - mag = 20 # magnification - ps = 6.5 / mag # effective pixel size - psz = 2 - lambda_emiss = [0.532] # wavelength - n_media = 1 # refractive index in the media - NA_obj = 0.4 # objective NA - N_defocus = 11 - - fluor_setup = wo.fluorescence_microscopy( - (N, M, N_defocus), - lambda_emiss, - ps, - psz, - NA_obj, - n_media=n_media, - deconv_mode="3D-WF", - pad_z=10, - use_gpu=False, - gpu_id=0, - ) - - star, _, _ = wo.genStarTarget(N, M) - - f = np.zeros((N, M, fluor_setup.N_defocus_3D)) - f[:, :, fluor_setup.N_defocus_3D // 2] = star - F = np.fft.fftn(f) - H = fluor_setup.OTF_WF_3D[0, ...] - g = np.abs(np.fft.ifftn(F * H)) - return g.transpose((2, 0, 1)) diff --git a/recOrder/compute/reconstructions.py b/recOrder/compute/reconstructions.py deleted file mode 100644 index 06167ddf..00000000 --- a/recOrder/compute/reconstructions.py +++ /dev/null @@ -1,492 +0,0 @@ -from waveorder.waveorder_reconstructor import ( - waveorder_microscopy, - fluorescence_microscopy, -) -import numpy as np -import time - - -def initialize_reconstructor( - pipeline, - image_dim=None, - wavelength_nm=None, - swing=None, - calibration_scheme=None, - NA_obj=1.3, - NA_illu=0.5, - mag=None, - n_slices=None, - z_step_um=None, - in_focus_slice=None, - pad_z=0, - pixel_size_um=None, - bg_correction="None", - n_obj_media=1.3, - mode="3D", - use_gpu=False, - gpu_id=0, -): - """ - Initialize the QLIPP reconstructor for downstream tasks. See tags next to parameters - for which parameters are needed for each pipeline - - Parameters - ---------- - - pipeline : string - 'birefringence', 'QLIPP', 'PhaseFromBF', 'fluorescence' - - image_dim : tuple - (height, width) of images in pixels - - wavelength_nm : int - wavelength of illumination in nm - - swing : float - swing used for calibration in waves - - calibration_scheme: str - '4-State' or '5-State' - - NA_obj : float - numerical aperture of the detection objective - - NA_illu : float - numerical aperture of the illumination condenser - - mag : float - magnification used for imaging (e.g. 20 for 20x) - - n_slices : int - number of slices in the z-stack - - z_step_um : float - z step size of the image space - - in_focus_slice : int or None - index of the in-focus z slice used for 2D-from-3D reconstructions - if None, the middle z slice is used as the in focus slice - - pad_z : float - how many padding slices to add for phase computation - - pixel_size_um : float - pixel size of the camera in um - - bg_correction : str - 'local' for estimating background with scipy uniform filter - 'local_fit' for estimating background with polynomial fit - 'None' for no BG correction - 'Global' for normal background subtraction with the provided background - - n_obj_media : float - refractive index of the objective immersion media - - mode : str - '2D' or '3D' (phase reconstruction only) - - use_gpu : bool - option to use gpu or not - - gpu_id : int - number refering to which gpu will be used - - - Returns - ------- - reconstructor : object - reconstruction object initialized with reconstruction parameters - - """ - - anisotropy_only = False - - if NA_obj > n_obj_media: - raise ValueError("Parameters are non-physical: NA_obj > n_obj_media") - if NA_illu > n_obj_media: - raise ValueError("Parameters are non-physical: NA_illu > n_obj_media") - - if pipeline == "QLIPP" or pipeline == "PhaseFromBF": - - if not NA_obj: - raise ValueError("Please specify NA_obj in function parameters") - if not NA_illu: - raise ValueError("Please specify NA_illu in function parameters") - if not mag: - raise ValueError( - "Please specify mag (magnification) in function parameters" - ) - if not wavelength_nm: - raise ValueError( - "Please specify the wavelength for reconstruction" - ) - if not n_slices: - raise ValueError("Please specify n_slices in function parameters") - if not z_step_um: - raise ValueError("Please specify z_step_um in function parameters") - if not pixel_size_um: - raise ValueError("Please specify NA_obj in function parameters") - if not n_obj_media: - raise ValueError("Please specify NA_obj in function parameters") - if not image_dim: - raise ValueError("Please specify image_dim in function parameters") - - if pipeline == "QLIPP": - if not calibration_scheme: - raise ValueError( - "Please specify qlipp_scheme (calibration scheme) for QLIPP reconstruction" - ) - if not swing: - raise ValueError("Please specify swing in function parameters") - - elif pipeline == "birefringence": - - anisotropy_only = True - - if not calibration_scheme: - raise ValueError( - "Please specify qlipp_scheme (calibration scheme) for QLIPP reconstruction" - ) - if not wavelength_nm: - raise ValueError( - "Please specify the wavelength for QLIPP reconstruction" - ) - if not swing: - raise ValueError("Please specify swing in function parameters") - elif pipeline == "fluorescence": - if not NA_obj: - raise ValueError( - "Please specify NA_obj for fluorescence reconstruction." - ) - else: - raise ValueError(f"Pipeline {pipeline} not understood") - - # Modify user inputs to fit waveorder input requirements - lambda_illu = wavelength_nm / 1000 if wavelength_nm else None - n_defocus = n_slices if n_slices else 0 - z_step_um = 0 if not z_step_um else z_step_um - - if in_focus_slice is None: - in_focus_slice = n_defocus // 2 - - z_defocus = ( - -(np.r_[:n_defocus] - in_focus_slice) * z_step_um - ) # assumes stack starts from the bottom - ps = pixel_size_um / mag if pixel_size_um else None - cali = True - NA_obj = 0 if not NA_obj else NA_obj - NA_illu = 0 if not NA_illu else NA_illu - - if calibration_scheme == "4-State": - inst_mat = np.array( - [ - [1, 0, 0, -1], - [1, np.sin(2 * np.pi * swing), 0, -np.cos(2 * np.pi * swing)], - [ - 1, - -0.5 * np.sin(2 * np.pi * swing), - np.sqrt(3) * np.cos(np.pi * swing) * np.sin(np.pi * swing), - -np.cos(2 * np.pi * swing), - ], - [ - 1, - -0.5 * np.sin(2 * np.pi * swing), - -np.sqrt(3) / 2 * np.sin(2 * np.pi * swing), - -np.cos(2 * np.pi * swing), - ], - ] - ) - n_channel = 4 - - elif calibration_scheme == "5-State": - swing = swing * 2 * np.pi - inst_mat = None - n_channel = 5 - - elif calibration_scheme == "PhaseFromBF": - inst_mat = None - n_channel = 1 - swing = 0 - else: - inst_mat = None - n_channel = 1 - swing = 0 - - print("Initializing Reconstructor...") - start_time = time.time() - if pipeline != "fluorescence": - recon = waveorder_microscopy( - img_dim=image_dim, - lambda_illu=lambda_illu, - ps=ps, - NA_obj=NA_obj, - NA_illu=NA_illu, - z_defocus=z_defocus, - chi=swing, - n_media=n_obj_media, - cali=cali, - bg_option=bg_correction, - A_matrix=inst_mat, - QLIPP_birefringence_only=anisotropy_only, - pad_z=pad_z, - phase_deconv=mode, - illu_mode="BF", - use_gpu=use_gpu, - gpu_id=gpu_id, - ) - recon.N_channel = n_channel - else: - - recon = fluorescence_microscopy( - img_dim=image_dim + (n_slices,), - lambda_emiss=[lambda_illu], - ps=ps, - psz=z_step_um, - NA_obj=NA_obj, - n_media=n_obj_media, - deconv_mode="3D-WF", - pad_z=pad_z, - use_gpu=use_gpu, - gpu_id=gpu_id, - ) - - elapsed_time = (time.time() - start_time) / 60 - print(f"Finished Initializing Reconstructor ({elapsed_time:0.2f} min)") - - return recon - - -def reconstruct_qlipp_stokes(data, recon, bg_stokes=None): - """ - From intensity data, use the waveorder.waveorder_microscopy (recon) to build a stokes array - if recon background correction flag is selected, will also perform backgroudn correction - - Parameters - ---------- - data : np.ndarray or zarr array - intensity data of shape: (C, Z, Y, X) - - recon : waveorder.waveorder_microscopy object - initialized by initialize_reconstructor - - bg_stokes : np.ndarray (5, Y, X) or (4, Y, X) - stokes array representing background data - - Returns - ------- - stokes_stack : np.ndarray - array representing stokes array - has shape: (C, Z, Y, X), where C = 5 - or (C, Y, X) if not reconstructing a z-stack - """ - - stokes_data = recon.Stokes_recon(np.copy(data)) - stokes_data = recon.Stokes_transform(stokes_data) - - # Don't do background correction if BG data isn't provided - if recon.bg_option == "None" or bg_stokes is None: - return stokes_data # C(Z)YX - - # Compute Stokes with background correction - else: - if len(np.shape(stokes_data)) == 4: - s_image = recon.Polscope_bg_correction( - np.transpose(stokes_data, (-4, -2, -1, -3)), bg_stokes - ) - s_image = np.transpose(s_image, (0, 3, 1, 2)) # Tranpose to CZYX - else: - s_image = recon.Polscope_bg_correction( - stokes_data, bg_stokes - ) # CYX - - return s_image - - -def reconstruct_qlipp_birefringence(stokes, recon): - """ - From stokes data, use waveorder.waveorder_microscopy (recon) to build a birefringence array - - Parameters - ---------- - stokes : np.ndarray or zarr array - stokes array generated by reconstruct_qlipp_stokes - dimensions: (C, Z, Y, X) or (C, Y, X) - - recon : waveorder.waveorder_microscopy object - initialized by initialize_reconstructor - - Returns - ------- - recon_data : np.ndarray - volume of shape (C, Z, Y, X) or (C, Y, X) containing reconstructed birefringence data. - """ - - if stokes.ndim != 4 and stokes.ndim != 3: - raise ValueError(f"Incompatible stokes dimension: {stokes.shape}") - - return recon.Polarization_recon(stokes) - - -def reconstruct_phase2D( - S0, recon, method="Tikhonov", reg_p=1e-4, rho=1, lambda_p=1e-4, itr=50 -): - """ - Reconstruct 2D phase from a given S0 or BF stack. - - Parameters - ---------- - S0: (nd-array) BF/S0 stack of dimensions (Z, Y, X) - recon: (waveorder_microscopy Object): initialized reconstructor object - method: (str) Regularization method 'Tikhonov' or 'TV' - reg_p: (float) Tikhonov regularization parameters - rho: (float) TV regularization parameter - lambda_p: (float) TV regularization parameter - itr: (int) TV Regularization number of iterations - - Returns - ------- - phase2D: (nd-array) Phase2D image of size (Y, X) - - """ - - S0 = np.transpose(S0, (1, 2, 0)) - _, phase2D = recon.Phase_recon( - np.copy(S0).astype("float"), - method=method, - reg_p=reg_p, - rho=rho, - lambda_p=lambda_p, - itr=itr, - verbose=False, - ) - - return phase2D - - -def reconstruct_phase3D( - S0, recon, method="Tikhonov", reg_re=1e-4, rho=1e-3, lambda_re=1e-4, itr=50 -): - """ - Reconstruct 2D phase from a given S0 or BF stack. - - Parameters - ---------- - S0: (nd-array) BF/S0 stack of dimensions (Z, Y, X) - recon: (waveorder_microscopy Object): initialized reconstructor object - method: (str) Regularization method 'Tikhonov' or 'TV' - reg_p: (float) Tikhonov regularization parameters - rho: (float) TV regularization parameter - lambda_p: (float) TV regularization parameter - itr: (int) TV Regularization number of iterations - - Returns - ------- - phase3D: (nd-array) Phase2D image of size (Z, Y, X) - - """ - - S0 = np.transpose(S0, (1, 2, 0)) - phase3D = recon.Phase_recon_3D( - np.copy(S0).astype("float"), - method=method, - reg_re=reg_re, - rho=rho, - lambda_re=lambda_re, - itr=itr, - verbose=False, - ) - - phase3D = np.transpose(phase3D, (-1, -3, -2)) - - return phase3D - - -def reconstruct_density_from_fluorescence(data3D, recon, bg_level=0, reg=1e-2): - """ - Reconstruct 3D density from fluorescence intensity - - Parameters - ---------- - data3D: (nd-array) Stack of dimensions (Z, Y, X) - recon: (fluorescence_microscopy Object): initialized reconstructor object - reg: (float) Tikhonov regularization parameters - - Returns - ------- - density: (nd-array) density image of size (Z, Y, X) - - """ - - data3D = np.transpose(data3D, (1, 2, 0)) - density = recon.deconvolve_fluor_3D( - np.copy(data3D).astype("float"), - bg_level=[bg_level], - reg=[reg], - ) - - return np.transpose(density, (-1, -3, -2)) - - -class QLIPPBirefringenceCompute: - """ - Convenience Class for computing QLIPP birefringence only. - """ - - def __init__( - self, - shape, - scheme, - wavelength, - swing, - n_slices, - bg_option, - bg_data=None, - ): - - self.shape = shape - self.scheme = scheme - self.wavelength = wavelength - self.swing = swing - self.n_slices = n_slices - self.bg_option = bg_option - - self.reconstructor = initialize_reconstructor( - pipeline="birefringence", - image_dim=self.shape, - calibration_scheme=self.scheme, - wavelength_nm=self.wavelength, - swing=self.swing, - bg_correction=self.bg_option, - n_slices=self.n_slices, - ) - - if bg_option != "None": - self.bg_stokes = reconstruct_qlipp_stokes( - bg_data, self.reconstructor - ) - else: - self.bg_stokes = None - - def reconstruct(self, array): - """ - reconstructs raw data into birefringence data - - Parameters - ---------- - array: (nd-array) of image shape (C, Z, Y, X) or (C, Y, X) with minimum C=4 - - Returns - ------- - birefringence: (nd-array) birefringence array of size (2, Z, Y, X) or (2, Y, X) - first channel is retardance [nm] second channel is orientation [0, pi] - - """ - stokes = reconstruct_qlipp_stokes( - array, self.reconstructor, self.bg_stokes - ) - birefringence = reconstruct_qlipp_birefringence( - stokes, self.reconstructor - ) - birefringence[0] = birefringence[0] / (2 * np.pi) * self.wavelength - - return birefringence[0:2] diff --git a/recOrder/io/utils.py b/recOrder/io/utils.py index 3c6731ab..e1b418b3 100644 --- a/recOrder/io/utils.py +++ b/recOrder/io/utils.py @@ -1,15 +1,21 @@ +from typing import Literal import glob import logging import os import psutil +import torch import textwrap import tifffile as tiff import numpy as np +import yaml from colorspacious import cspace_convert +from iohub import open_ome_zarr from matplotlib.colors import hsv_to_rgb from waveorder.waveorder_reconstructor import waveorder_microscopy +from recOrder.cli import settings +# TO BE DEPRECATED def extract_reconstruction_parameters(reconstructor, magnification=None): """ Function that extracts the reconstruction parameters from a waveorder reconstructor. Works for waveorder_microscopy class. @@ -53,91 +59,12 @@ def extract_reconstruction_parameters(reconstructor, magnification=None): return attr_dict -def load_bg(bg_path, height, width, ROI=None): - """ - Parameters - ---------- - bg_path : (str) path to the folder containing background images - height : (int) height of image in pixels # Remove for 1.0.0 - width : (int) width of image in pixels # Remove for 1.0.0 - ROI : (tuple) ROI of the background images to use, if None, full FOV will be used # Remove for 1.0.0 - - Returns - ------- - bg_data : (ndarray) Array of background data w/ dimensions (N_channel, Y, X) - """ - - bg_paths = glob.glob(os.path.join(bg_path, "*.tif")) - bg_paths.sort() - - # Backwards compatibility warning - if ROI is not None and ROI != ( - 0, - 0, - width, - height, - ): # TODO: Remove for 1.0.0 - warning_msg = """ - Earlier versions of recOrder (0.1.2 and earlier) would have averaged over the background ROI. - This behavior is now considered a bug, and future versions of recOrder (0.2.0 and later) - will not average over the background. - """ - logging.warning(warning_msg) - - # Load background images - bg_img_list = [] - for bg_path in bg_paths: - bg_img_list.append(tiff.imread(bg_path)) - bg_img_arr = np.array(bg_img_list) # CYX - - # Error if shapes do not match - # TODO: 1.0.0 move these validation check to waveorder's Polscope_bg_correction - if bg_img_arr.shape[1:] != (height, width): - error_msg = "The background image has a different X/Y size than the acquired image." - raise ValueError(error_msg) - - return bg_img_arr # CYX - - -def create_grid_from_coordinates(xy_coords, rows, columns): - """ - Function to create a grid from XY-position coordinates. Useful for generating HCS Zarr metadata. - - Parameters - ---------- - xy_coords: (list) XY Stage position list in the order in which it was acquired: (X, Y) tuple. - rows: (int) number of rows in the grid-like acquisition - columns: (int) number of columns in the grid-like acquisition - - Returns - ------- - pos_index_grid (array) A grid-like array mimicking the shape of the acquisition where the value in the array - corresponds to the position index at that location. - """ - - coords = dict() - coords_list = [] - for idx, pos in enumerate(xy_coords): - coords[idx] = pos - coords_list.append(pos) - - # sort by X and then by Y - coords_list.sort(key=lambda x: x[0]) - coords_list.sort(key=lambda x: x[1]) - - # reshape XY coordinates into their proper 2D shape - grid = np.reshape(coords_list, (rows, columns, 2)) - pos_index_grid = np.zeros((rows, columns), "uint16") - keys = list(coords.keys()) - vals = list(coords.values()) - - for row in range(rows): - for col in range(columns): - - # append position index (key) into a final grid by indexed into the coordinate map (values) - pos_index_grid[row, col] = keys[vals.index(list(grid[row, col]))] - - return pos_index_grid +def load_background(background_path): + with open_ome_zarr( + os.path.join(background_path, "background.zarr", "0", "0", "0") + ) as dataset: + cyx_data = dataset["0"][0, :, 0] + return torch.tensor(cyx_data, dtype=torch.float32) class MockEmitter: @@ -145,48 +72,6 @@ def emit(self, value): pass -def get_unimodal_threshold(input_image): - """Determines optimal unimodal threshold - https://users.cs.cf.ac.uk/Paul.Rosin/resources/papers/unimodal2.pdf - https://www.mathworks.com/matlabcentral/fileexchange/45443-rosin-thresholding - :param np.array input_image: generate mask for this image - :return float best_threshold: optimal lower threshold for the foreground - hist - """ - - hist_counts, bin_edges = np.histogram( - input_image, - bins=256, - range=(input_image.min(), np.percentile(input_image, 99.5)), - ) - bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2 - - # assuming that background has the max count - max_idx = np.argmax(hist_counts) - int_with_max_count = bin_centers[max_idx] - p1 = [int_with_max_count, hist_counts[max_idx]] - - # find last non-empty bin - pos_counts_idx = np.where(hist_counts > 0)[0] - last_binedge = pos_counts_idx[-1] - p2 = [bin_centers[last_binedge], hist_counts[last_binedge]] - - best_threshold = -np.inf - max_dist = -np.inf - for idx in range(max_idx, last_binedge, 1): - x0 = bin_centers[idx] - y0 = hist_counts[idx] - a = [p1[0] - p2[0], p1[1] - p2[1]] - b = [x0 - p2[0], y0 - p2[1]] - cross_ab = a[0] * b[1] - b[0] * a[1] - per_dist = np.linalg.norm(cross_ab) / np.linalg.norm(a) - if per_dist > max_dist: - best_threshold = x0 - max_dist = per_dist - assert best_threshold > -np.inf, "Error in unimodal thresholding" - return best_threshold - - def ram_message(): """ Determine if the system's RAM capacity is sufficient for running reconstruction. @@ -282,7 +167,9 @@ def generic_hsv_overlay( return overlay_final[0] if mode == "2D" else overlay_final -def ret_ori_overlay(retardance, orientation, ret_max=10, cmap="JCh"): +def ret_ori_overlay( + retardance, orientation, ret_max=10, cmap: Literal["JCh", "HSV"] = "JCh" +): """ This function will create an overlay of retardance and orientation with two different colormap options. HSV is the standard Hue, Saturation, Value colormap while JCh is a similar colormap but is perceptually uniform. @@ -301,14 +188,14 @@ def ret_ori_overlay(retardance, orientation, ret_max=10, cmap="JCh"): """ if retardance.shape != orientation.shape: raise ValueError( - f"Retardance and Orientation shapes do not match: {retardance.shape} vs. {orientation.shape}" + "Retardance and Orientation shapes do not match: " + f"{retardance.shape} vs. {orientation.shape}" ) # Prepare input and output arrays ret_ = np.clip(retardance, 0, ret_max) # clip and copy - ori_ = ( - np.copy(orientation) * 360 / np.pi - ) # convert 180 degree range into 360 to match periodicity of hue. + # Convert 180 degree range into 360 to match periodicity of hue. + ori_ = orientation * 360 / np.pi overlay_final = np.zeros_like(retardance) # FIX ME: this binning code leads to artifacts. @@ -350,3 +237,96 @@ def ret_ori_overlay(retardance, orientation, ret_max=10, cmap="JCh"): raise ValueError(f"Colormap {cmap} not understood") return overlay_final + + +def model_to_yaml(model, yaml_path): + """ + Save a model's dictionary representation to a YAML file. + + Parameters + ---------- + model : object + The model object to convert to YAML. + yaml_path : str + The path to the output YAML file. + + Raises + ------ + TypeError + If the `model` object does not have a `dict()` method. + + Notes + ----- + This function converts a model object into a dictionary representation + using the `dict()` method. It removes any fields with None values before + writing the dictionary to a YAML file. + + Examples + -------- + >>> from my_model import MyModel + >>> model = MyModel() + >>> model_to_yaml(model, 'model.yaml') + + """ + if not hasattr(model, "dict"): + raise TypeError("The 'model' object does not have a 'dict()' method.") + + model_dict = model.dict() + + # Remove None-valued fields + clean_model_dict = { + key: value for key, value in model_dict.items() if value is not None + } + + with open(yaml_path, "w+") as f: + yaml.dump( + clean_model_dict, f, default_flow_style=False, sort_keys=False + ) + + +def yaml_to_model(yaml_path, model): + """ + Load model settings from a YAML file and create a model instance. + + Parameters + ---------- + yaml_path : str + The path to the YAML file containing the model settings. + model : class + The model class used to create an instance with the loaded settings. + + Returns + ------- + object + An instance of the model class with the loaded settings. + + Raises + ------ + TypeError + If the provided model is not a class or does not have a callable constructor. + FileNotFoundError + If the YAML file specified by `yaml_path` does not exist. + + Notes + ----- + This function loads model settings from a YAML file using `yaml.safe_load()`. + It then creates an instance of the provided `model` class using the loaded settings. + + Examples + -------- + >>> from my_model import MyModel + >>> model = yaml_to_model('model.yaml', MyModel) + + """ + if not callable(getattr(model, "__init__", None)): + raise TypeError( + "The provided model must be a class with a callable constructor." + ) + + try: + with open(yaml_path, "r") as file: + raw_settings = yaml.safe_load(file) + except FileNotFoundError: + raise FileNotFoundError(f"The YAML file '{yaml_path}' does not exist.") + + return model(**raw_settings) diff --git a/recOrder/plugin/main_widget.py b/recOrder/plugin/main_widget.py index 985d3b2e..599e44c5 100644 --- a/recOrder/plugin/main_widget.py +++ b/recOrder/plugin/main_widget.py @@ -1,44 +1,51 @@ # TODO: remove in Python 3.11 from __future__ import annotations -from recOrder.calib.Calibration import QLIPP_Calibration, LC_DEVICE_NAME +import json +import logging +import os +import textwrap +import time +from os.path import dirname +from pathlib import Path, PurePath + +# type hint/check +from typing import TYPE_CHECKING + +import dask.array as da +import numpy as np +import yaml +from dask import delayed +from napari import Viewer +from napari.components import LayerList +from napari.qt.threading import create_worker +from napari.utils.events import Event +from napari.utils.notifications import show_info, show_warning +from numpy.typing import NDArray +from numpydoc.docscrape import NumpyDocString +from packaging import version from pycromanager import Core, Studio, zmq_bridge -from qtpy.QtCore import Slot, Signal, Qt -from qtpy.QtWidgets import QWidget, QFileDialog, QSizePolicy, QSlider -from qtpy.QtGui import QPixmap, QColor +from qtpy.QtCore import Qt, Signal, Slot +from qtpy.QtGui import QColor, QPixmap +from qtpy.QtWidgets import QFileDialog, QSizePolicy, QSlider, QWidget from superqt import QDoubleRangeSlider, QRangeSlider +from waveorder.waveorder_reconstructor import waveorder_microscopy + +from recOrder.acq.acquisition_workers import ( + BFAcquisitionWorker, + PolarizationAcquisitionWorker, +) from recOrder.calib import Calibration +from recOrder.calib.Calibration import LC_DEVICE_NAME, QLIPP_Calibration from recOrder.calib.calibration_workers import ( - CalibrationWorker, BackgroundCaptureWorker, + CalibrationWorker, load_calibration, ) -from recOrder.acq.acquisition_workers import ( - PolarizationAcquisitionWorker, - BFAcquisitionWorker, -) -from recOrder.plugin import gui from recOrder.io.core_functions import set_lc_state, snap_and_average from recOrder.io.metadata_reader import MetadataReader from recOrder.io.utils import ret_ori_overlay -from waveorder.waveorder_reconstructor import waveorder_microscopy -from pathlib import Path, PurePath -from napari import Viewer -from napari.utils.notifications import show_warning, show_info -from napari.qt.threading import create_worker -from numpydoc.docscrape import NumpyDocString -from packaging import version -import numpy as np -from numpy.typing import NDArray -import os -from os.path import dirname -import json -import logging -import textwrap -import yaml - -# type hint/check -from typing import TYPE_CHECKING +from recOrder.plugin import gui # avoid runtime import error if TYPE_CHECKING: @@ -218,6 +225,12 @@ def __init__(self, napari_viewer: Viewer): self.ui.qbutton_acq_ret_ori_phase.clicked[bool].connect( self.acq_ret_ori_phase ) + + # hook to render overlay + # acquistion updates existing layers and moves them to the top which triggers this event + self.viewer.layers.events.moved.connect(self.handle_layers_updated) + self.viewer.layers.events.inserted.connect(self.handle_layers_updated) + # Commenting for 0.3.0. Consider debugging or deleting for 1.0.0. # self.ui.cb_colormap.currentIndexChanged[int].connect( # self.enter_colormap @@ -303,7 +316,7 @@ def __init__(self, napari_viewer: Viewer): self.orientation_offset = False self.pad_z = 0 self.phase_reconstructor = None - self.acq_bg_directory = None + self.acq_bg_directory = "" self.auto_shutter = True self.lca_dac = None self.lcb_dac = None @@ -786,7 +799,7 @@ def _check_requirements_for_acq(self, mode): """ # check if a QLIPP_Calibration object has been initialized - if not self.calib: + if mode != "phase" and not self.calib: raise RuntimeError("Please run or load calibration first.") # initialize the variable to keep track of the success of the requirement check @@ -817,7 +830,10 @@ def _check_requirements_for_acq(self, mode): raise_error = True # check background path if 'Measured' or 'Measured + Estimated' is selected - if self.bg_option == "local_fit+" or self.bg_option == "global": + if ( + self.bg_option == "Measured" + or self.bg_option == "Measured + Estimated" + ): success = self._check_line_edit("bg_path") if not success: raise_error = True @@ -879,7 +895,7 @@ def connect_to_mm(self): ------- """ - RECOMMENDED_MM = "20220920" + RECOMMENDED_MM = "20230426" ZMQ_TARGET_VERSION = "4.2.0" try: self.mmc = Core(convert_camel_case=False) @@ -1127,6 +1143,7 @@ def _add_or_update_image_layer( if name in self.viewer.layers: self.viewer.layers[name].data = image if move_to_top: + logging.debug(f"Moving layer {name} to the top.") src_index = self.viewer.layers.index(name) self.viewer.layers.move(src_index, dest_index=-1) else: @@ -1146,29 +1163,62 @@ def handle_bg_bire_image_update(self, value): value[1], "Background Orientation", cmap="hsv" ) - def _draw_bire_overlay(self, overlay): - self._add_or_update_image_layer( - overlay, "BirefringenceOverlay" + self.acq_mode, cmap="rgb" + def handle_layers_updated(self, event: Event): + layers: LayerList = event.source + latest_layer_name = layers[-1].name + channels = ["Retardance", "Orientation"] + for ch in channels: + if latest_layer_name.startswith(ch): + suffix = latest_layer_name.replace(ch, "") + other_name = channels[1 - channels.index(ch)] + suffix + overlay_name = "BirefringenceOverlay" + suffix + if other_name in layers and overlay_name not in layers: + logging.info( + "Detected updated birefringence layers: " + f"'{latest_layer_name}', '{other_name}'" + ) + self._draw_bire_overlay( + [ch + suffix, other_name], overlay_name + ) + if latest_layer_name.startswith(channels[1]): + logging.info( + "Detected orientation layer in updated layer list." + "Setting its colormap to HSV." + ) + self.viewer.layers[latest_layer_name].colormap = "hsv" + + def _draw_bire_overlay( + self, source_names: list[str, str], overlay_name: str + ): + def _layer_data(name: str): + return self.viewer.layers[name].data + + def _draw(overlay): + self._add_or_update_image_layer(overlay, overlay_name, cmap="rgb") + + orientation_name, retardance_name = sorted(source_names) + retardance = _layer_data(retardance_name) + orientation = _layer_data(orientation_name) + worker = create_worker( + ret_ori_overlay, + retardance=retardance, + orientation=orientation, + ret_max=np.percentile(np.ravel(retardance), 99.99), + cmap=self.colormap, ) + worker.start() + logging.info(f"Updating the birefringence overlay layer.") + if retardance.size >= 2 * 2048 * 2048: + show_info("Generating large overlay. This might take a moment...") + worker.returned.connect(_draw) @Slot(object) def handle_bire_image_update(self, value: NDArray): # generate overlay in a separate thread - overlay_worker = create_worker( - ret_ori_overlay, - retardance=value[0], - orientation=value[1], - ret_max=np.percentile(value[0], 99.99), - cmap=self.colormap, - ) - overlay_worker.returned.connect(self._draw_bire_overlay) - overlay_worker.start() for i, channel in enumerate(("Retardance", "Orientation")): - name = channel + self.acq_mode + name = channel cmap = "gray" if channel != "Orientation" else "hsv" self._add_or_update_image_layer(value[i], name, cmap=cmap) - if self.acq_mode == "3D": - show_info("Generating 3D overlay. This might take a moment...") @Slot(object) def handle_phase_image_update(self, value): @@ -1521,17 +1571,17 @@ def enter_bg_correction(self): self.ui.label_bg_path.setHidden(False) self.ui.le_bg_path.setHidden(False) self.ui.qbutton_browse_bg_path.setHidden(False) - self.bg_option = "global" + self.bg_option = "Measured" elif state == 2: self.ui.label_bg_path.setHidden(True) self.ui.le_bg_path.setHidden(True) self.ui.qbutton_browse_bg_path.setHidden(True) - self.bg_option = "local_fit" + self.bg_option = "Estimated" elif state == 3: self.ui.label_bg_path.setHidden(False) self.ui.le_bg_path.setHidden(False) self.ui.qbutton_browse_bg_path.setHidden(False) - self.bg_option = "local_fit+" + self.bg_option = "Measured + Estimated" @Slot() def enter_gpu_id(self): diff --git a/setup.cfg b/setup.cfg index 8bf83809..d79769ee 100644 --- a/setup.cfg +++ b/setup.cfg @@ -37,8 +37,8 @@ python_requires = >=3.9 setup_requires = setuptools_scm # add your package requirements here install_requires = - waveorder==1.0.0rc0 - pycromanager==0.24.1 + waveorder==2.0.0rc1 + pycromanager==0.27.2 click>=8.0.1 natsort>=7.1.1 colorspacious>=1.1.2 @@ -59,14 +59,15 @@ dev = tox pre-commit black + hypothesis [options.package_data] * = *.yaml [options.entry_points] console_scripts = - recorder = recOrder.scripts.cli:cli - recOrder = recOrder.scripts.cli:cli + recorder = recOrder.cli.main:cli + recOrder = recOrder.cli.main:cli napari.manifest = recOrder = recOrder:napari.yaml

[BUG] Rare background correction failure In using `recOrder` over the last week I had >95% success rate with background corrections. Once the scope was running background corrections correctly, it would continue to do so indefinitely. But ~1/10 times I would use an incorrect image as the background correction and give an incorrect reconstruction. I was always able to "fix" this by acquiring and reconstructing again. One time I had a dramatic failure: - I took a background image beside the Kazansky target - I took a background-corrected acquisition of the Kazansky target (looked as expected) - I changed samples and took a background image (looked as expected) - **I moved onto the real sample, took an acquisition, and the result was the Kazansky target**. A repeat acquisition was as expected. I haven't had time to trace this further, but this is high priority bug. I suspect the recOrder side because the new waveorder doesn't carry any state. Fix 0.4.0dev tests This PR against `0.4.0dev` includes the test fixes that should have appeared in #381 (see #383 for CI/CD fix).

2023-06-07T20:45:05

0.0

[]

mehta-lab/recOrder

mehta-lab__recOrder-229

6b4adbc798cef9486d46df6ab106cfed4abaa9db

diff --git a/.git-blame-ignore-revs b/.git-blame-ignore-revs new file mode 100644 index 00000000..31e202a2 --- /dev/null +++ b/.git-blame-ignore-revs @@ -0,0 +1,4 @@ +# .git-blame-ignore-revs +# created as described in: https://docs.github.com/en/repositories/working-with-files/using-files/viewing-a-file#ignore-commits-in-the-blame-view +# black-format all `.py` files except recorder_ui.py +958af2070cfb32397e7b7e8bfa7d022ec10b67af \ No newline at end of file diff --git a/docs/development-guide.md b/docs/development-guide.md index 0f1460c8..c45d9bda 100644 --- a/docs/development-guide.md +++ b/docs/development-guide.md @@ -84,4 +84,14 @@ pyuic5 -x recOrder_ui.ui -o recOrder_ui.py ``` Note: `pyuic5` is installed alongside `PyQt5`, so you can expect to find it installed in your `recOrder` conda environement. -Note: although much of the GUI is specified in the generated `recOrder_ui.py` file, the `main_widget.py` file makes extensive modifications to the GUI. \ No newline at end of file +Note: although much of the GUI is specified in the generated `recOrder_ui.py` file, the `main_widget.py` file makes extensive modifications to the GUI. + +## Make `git blame` ignore formatting commits +**Note:** `git --version` must be `>=2.23` to use this feature. + +If you would like `git blame` to ignore formatting commits, run this line: +```sh + git config --global blame.ignoreRevsFile .git-blame-ignore-revs +``` + +The `\.git-blame-ignore-revs` file contains a list of commit hashes corresponding to formatting commits. If you make a formatting commit, please add the commit's hash to this file. \ No newline at end of file diff --git a/recOrder/__init__.py b/recOrder/__init__.py index 6d76432a..f9ca19c7 100644 --- a/recOrder/__init__.py +++ b/recOrder/__init__.py @@ -1,4 +1,4 @@ -#todo: format overall init +# todo: format overall init # from .plugin.widget.napari_plugin_entry_point import napari_experimental_provide_dock_widget -# name = "recOrder" \ No newline at end of file +# name = "recOrder" diff --git a/recOrder/acq/acq_functions.py b/recOrder/acq/acq_functions.py index cc60e730..80219ca3 100644 --- a/recOrder/acq/acq_functions.py +++ b/recOrder/acq/acq_functions.py @@ -6,8 +6,19 @@ import time import glob -def generate_acq_settings(mm, channel_group, channels=None, zstart=None, zend=None, zstep=None, - save_dir=None, prefix=None, keep_shutter_open_channels=False, keep_shutter_open_slices=False): + +def generate_acq_settings( + mm, + channel_group, + channels=None, + zstart=None, + zend=None, + zstep=None, + save_dir=None, + prefix=None, + keep_shutter_open_channels=False, + keep_shutter_open_slices=False, +): """ This function generates a json file specific to the micromanager SequenceSettings. It has default parameters for a multi-channels z-stack acquisition but does not yet @@ -46,58 +57,67 @@ def generate_acq_settings(mm, channel_group, channels=None, zstart=None, zend=No do_z = False # Structure of the channel properties - channel_dict = {'channelGroup': channel_group, - 'config': None, - 'exposure': None, - 'zOffset': 0, - 'doZStack': do_z, - 'color': {'value': -16747854, 'falpha': 0.0}, - 'skipFactorFrame': 0, - 'useChannel': True if channels else False} + channel_dict = { + "channelGroup": channel_group, + "config": None, + "exposure": None, + "zOffset": 0, + "doZStack": do_z, + "color": {"value": -16747854, "falpha": 0.0}, + "skipFactorFrame": 0, + "useChannel": True if channels else False, + } channel_list = None if channels: # Append all the channels with their current exposure settings channel_list = [] for chan in channels: - #todo: think about how to deal with missing exposure - exposure = app.getChannelExposureTime(channel_group, chan, 10) # sets exposure to 10 if not found + # todo: think about how to deal with missing exposure + exposure = app.getChannelExposureTime( + channel_group, chan, 10 + ) # sets exposure to 10 if not found channel = channel_dict.copy() - channel['config'] = chan - channel['exposure'] = exposure + channel["config"] = chan + channel["exposure"] = exposure channel_list.append(channel) # set other parameters - original_json['numFrames'] = 1 - original_json['intervalMs'] = 0 - original_json['relativeZSlice'] = True - original_json['slicesFirst'] = True - original_json['timeFirst'] = False - original_json['keepShutterOpenSlices'] = keep_shutter_open_slices - original_json['keepShutterOpenChannels'] = keep_shutter_open_channels - original_json['useAutofocus'] = False - original_json['saveMode'] = 'MULTIPAGE_TIFF' - original_json['save'] = True if save_dir else False - original_json['root'] = save_dir if save_dir else '' - original_json['prefix'] = prefix if prefix else 'Untitled' - original_json['channels'] = channel_list - original_json['zReference'] = 0.0 - original_json['channelGroup'] = channel_group - original_json['usePositionList'] = False - original_json['shouldDisplayImages'] = True - original_json['useSlices'] = do_z - original_json['useFrames'] = False - original_json['useChannels'] = True if channels else False - original_json['slices'] = list(np.arange(float(zstart), float(zend+zstep), float(zstep))) if zstart else [] - original_json['sliceZStepUm'] = zstep - original_json['sliceZBottomUm'] = zstart - original_json['sliceZTopUm'] = zend - original_json['acqOrderMode'] = 1 + original_json["numFrames"] = 1 + original_json["intervalMs"] = 0 + original_json["relativeZSlice"] = True + original_json["slicesFirst"] = True + original_json["timeFirst"] = False + original_json["keepShutterOpenSlices"] = keep_shutter_open_slices + original_json["keepShutterOpenChannels"] = keep_shutter_open_channels + original_json["useAutofocus"] = False + original_json["saveMode"] = "MULTIPAGE_TIFF" + original_json["save"] = True if save_dir else False + original_json["root"] = save_dir if save_dir else "" + original_json["prefix"] = prefix if prefix else "Untitled" + original_json["channels"] = channel_list + original_json["zReference"] = 0.0 + original_json["channelGroup"] = channel_group + original_json["usePositionList"] = False + original_json["shouldDisplayImages"] = True + original_json["useSlices"] = do_z + original_json["useFrames"] = False + original_json["useChannels"] = True if channels else False + original_json["slices"] = ( + list(np.arange(float(zstart), float(zend + zstep), float(zstep))) + if zstart + else [] + ) + original_json["sliceZStepUm"] = zstep + original_json["sliceZBottomUm"] = zstart + original_json["sliceZTopUm"] = zend + original_json["acqOrderMode"] = 1 return original_json -def acquire_from_settings(mm, settings, grab_images = True): + +def acquire_from_settings(mm, settings, grab_images=True): """ Function to acquire an MDA acquisition with the native MM MDA Engine. Assumes single position acquisition. @@ -121,17 +141,20 @@ def acquire_from_settings(mm, settings, grab_images = True): time.sleep(3) - #TODO: speed improvements in reading the data with pycromanager acquisition? + # TODO: speed improvements in reading the data with pycromanager acquisition? if grab_images: # get the most recent acquisition if multiple - path = os.path.join(settings['root'], settings['prefix']) - files = glob.glob(path+'*') - index = max([int(x.split(path + '_')[1]) for x in files]) + path = os.path.join(settings["root"], settings["prefix"]) + files = glob.glob(path + "*") + index = max([int(x.split(path + "_")[1]) for x in files]) - reader = WaveorderReader(path+f'_{index}', 'ometiff', extract_data=True) + reader = WaveorderReader( + path + f"_{index}", "ometiff", extract_data=True + ) return reader.get_array(0) + def acquire_2D(mm, mmc, scheme, snap_manager=None): """ Acquire a 2D stack with pycromanager (data transfer over ZMQ) given the acquisition scheme. @@ -152,20 +175,20 @@ def acquire_2D(mm, mmc, scheme, snap_manager=None): if not snap_manager: snap_manager = mm.getSnapLiveManager() - set_lc_state(mmc, 'State0') + set_lc_state(mmc, "State0") state0 = snap_and_get_image(snap_manager) - set_lc_state(mmc, 'State1') + set_lc_state(mmc, "State1") state1 = snap_and_get_image(snap_manager) - set_lc_state(mmc, 'State2') + set_lc_state(mmc, "State2") state2 = snap_and_get_image(snap_manager) - set_lc_state(mmc, 'State3') + set_lc_state(mmc, "State3") state3 = snap_and_get_image(snap_manager) - if scheme == '5-State': - set_lc_state(mmc, 'State4') + if scheme == "5-State": + set_lc_state(mmc, "State4") state4 = snap_and_get_image(snap_manager) return np.asarray([state0, state1, state2, state3, state4]) @@ -198,12 +221,14 @@ def acquire_3D(mm, mmc, scheme, z_start, z_end, z_step, snap_manager=None): stage = mmc.getFocusDevice() current_z = mmc.getPosition(stage) - n_channels = 4 if scheme == '4-State' else 5 + n_channels = 4 if scheme == "4-State" else 5 stack = [] for c in range(n_channels): - set_lc_state(mmc, f'State{c}') + set_lc_state(mmc, f"State{c}") z_stack = [] - for z in np.arange(current_z+z_start, current_z+z_end+z_step, z_step): + for z in np.arange( + current_z + z_start, current_z + z_end + z_step, z_step + ): mmc.setPosition(stage, z) z_stack.append(snap_and_get_image(snap_manager)) diff --git a/recOrder/calib/Calibration.py b/recOrder/calib/Calibration.py index 658ea7a2..7490e831 100644 --- a/recOrder/calib/Calibration.py +++ b/recOrder/calib/Calibration.py @@ -18,14 +18,22 @@ from datetime import datetime from importlib_metadata import version -LC_DEVICE_NAME = 'MeadowlarkLcOpenSource' - - -class QLIPP_Calibration(): - - def __init__(self, mmc, mm, group='Channel', lc_control_mode='MM-Retardance', interp_method='schnoor_fit', - wavelength=532, optimization='min_scalar', print_details=True): - ''' +LC_DEVICE_NAME = "MeadowlarkLcOpenSource" + + +class QLIPP_Calibration: + def __init__( + self, + mmc, + mm, + group="Channel", + lc_control_mode="MM-Retardance", + interp_method="schnoor_fit", + wavelength=532, + optimization="min_scalar", + print_details=True, + ): + """ Parameters ---------- @@ -54,7 +62,7 @@ def __init__(self, mmc, mm, group='Channel', lc_control_mode='MM-Retardance', in LC retardance optimization method, 'min_scalar' (default) or 'brent' print_details : bool Set verbose option - ''' + """ # Micromanager API self.mm = mm @@ -62,46 +70,70 @@ def __init__(self, mmc, mm, group='Channel', lc_control_mode='MM-Retardance', in self.snap_manager = mm.getSnapLiveManager() # Meadowlark LC Device Adapter Property Names - self.PROPERTIES = {'LCA': (LC_DEVICE_NAME, 'Retardance LC-A [in waves]'), - 'LCB': (LC_DEVICE_NAME, 'Retardance LC-B [in waves]'), - 'LCA-Voltage': (LC_DEVICE_NAME, 'Voltage (V) LC-A'), - 'LCB-Voltage': (LC_DEVICE_NAME, 'Voltage (V) LC-B'), - 'LCA-DAC': ('TS_DAC01', 'Volts'), - 'LCB-DAC': ('TS_DAC02', 'Volts'), - 'State0': (LC_DEVICE_NAME, 'Pal. elem. 00; enter 0 to define; 1 to activate'), - 'State1': (LC_DEVICE_NAME, 'Pal. elem. 01; enter 0 to define; 1 to activate'), - 'State2': (LC_DEVICE_NAME, 'Pal. elem. 02; enter 0 to define; 1 to activate'), - 'State3': (LC_DEVICE_NAME, 'Pal. elem. 03; enter 0 to define; 1 to activate'), - 'State4': (LC_DEVICE_NAME, 'Pal. elem. 04; enter 0 to define; 1 to activate') - } + self.PROPERTIES = { + "LCA": (LC_DEVICE_NAME, "Retardance LC-A [in waves]"), + "LCB": (LC_DEVICE_NAME, "Retardance LC-B [in waves]"), + "LCA-Voltage": (LC_DEVICE_NAME, "Voltage (V) LC-A"), + "LCB-Voltage": (LC_DEVICE_NAME, "Voltage (V) LC-B"), + "LCA-DAC": ("TS_DAC01", "Volts"), + "LCB-DAC": ("TS_DAC02", "Volts"), + "State0": ( + LC_DEVICE_NAME, + "Pal. elem. 00; enter 0 to define; 1 to activate", + ), + "State1": ( + LC_DEVICE_NAME, + "Pal. elem. 01; enter 0 to define; 1 to activate", + ), + "State2": ( + LC_DEVICE_NAME, + "Pal. elem. 02; enter 0 to define; 1 to activate", + ), + "State3": ( + LC_DEVICE_NAME, + "Pal. elem. 03; enter 0 to define; 1 to activate", + ), + "State4": ( + LC_DEVICE_NAME, + "Pal. elem. 04; enter 0 to define; 1 to activate", + ), + } self.group = group # GUI Emitter self.intensity_emitter = MockEmitter() self.plot_sequence_emitter = MockEmitter() - #Set Mode + # Set Mode # TODO: make sure LC or TriggerScope are loaded in the respective modes - allowed_modes = ['MM-Retardance', 'MM-Voltage', 'DAC'] - assert lc_control_mode in allowed_modes, f'LC control mode must be one of {allowed_modes}' + allowed_modes = ["MM-Retardance", "MM-Voltage", "DAC"] + assert ( + lc_control_mode in allowed_modes + ), f"LC control mode must be one of {allowed_modes}" self.mode = lc_control_mode self.LC_DAC_conversion = 4 # convert between the input range of LCs (0-20V) and the output range of the DAC (0-5V) # Initialize calibration class - allowed_interp_methods = ['schnoor_fit', 'linear'] - assert interp_method in allowed_interp_methods,\ - f'LC calibration data interpolation method must be one of {allowed_interp_methods}' - dir_path = mmc.getDeviceAdapterSearchPaths().get(0) # MM device adapter directory - self.calib = CalibrationData(os.path.join(dir_path, 'mmgr_dal_MeadowlarkLC.csv'), interp_method=interp_method, - wavelength=wavelength) + allowed_interp_methods = ["schnoor_fit", "linear"] + assert ( + interp_method in allowed_interp_methods + ), f"LC calibration data interpolation method must be one of {allowed_interp_methods}" + dir_path = mmc.getDeviceAdapterSearchPaths().get( + 0 + ) # MM device adapter directory + self.calib = CalibrationData( + os.path.join(dir_path, "mmgr_dal_MeadowlarkLC.csv"), + interp_method=interp_method, + wavelength=wavelength, + ) # Optimizer - if optimization == 'min_scalar': + if optimization == "min_scalar": self.optimizer = MinScalarOptimizer(self) - elif optimization == 'brent': + elif optimization == "brent": self.optimizer = BrentOptimizer(self) else: - raise ModuleNotFoundError(f'No optimizer named {optimization}') + raise ModuleNotFoundError(f"No optimizer named {optimization}") # User / Calculated Parameters self.swing = None @@ -111,7 +143,7 @@ def __init__(self, mmc, mm, group='Channel', lc_control_mode='MM-Retardance', in self.ROI = None self.ratio = 1.793 self.print_details = print_details - self.calib_scheme = '4-State' + self.calib_scheme = "4-State" # LC States self.lca_ext = None @@ -151,8 +183,8 @@ def __init__(self, mmc, mm, group='Channel', lc_control_mode='MM-Retardance', in self._shutter_state = None def set_dacs(self, lca_dac, lcb_dac): - self.PROPERTIES['LCA-DAC'] = (f'TS_{lca_dac}', 'Volts') - self.PROPERTIES['LCB-DAC'] = (f'TS_{lcb_dac}', 'Volts') + self.PROPERTIES["LCA-DAC"] = (f"TS_{lca_dac}", "Volts") + self.PROPERTIES["LCB-DAC"] = (f"TS_{lcb_dac}", "Volts") def set_wavelength(self, wavelength): self.calib.set_wavelength(wavelength) @@ -174,15 +206,15 @@ def set_lc(self, retardance, LC: str): """ - if self.mode == 'MM-Retardance': - set_lc_waves(self.mmc, self.PROPERTIES[f'{LC}'], retardance) - elif self.mode == 'MM-Voltage': + if self.mode == "MM-Retardance": + set_lc_waves(self.mmc, self.PROPERTIES[f"{LC}"], retardance) + elif self.mode == "MM-Voltage": volts = self.calib.get_voltage(retardance) - set_lc_voltage(self.mmc, self.PROPERTIES[f'{LC}-Voltage'], volts) - elif self.mode == 'DAC': + set_lc_voltage(self.mmc, self.PROPERTIES[f"{LC}-Voltage"], volts) + elif self.mode == "DAC": volts = self.calib.get_voltage(retardance) dac_volts = volts / self.LC_DAC_conversion - set_lc_daq(self.mmc, self.PROPERTIES[f'{LC}-DAC'], dac_volts) + set_lc_daq(self.mmc, self.PROPERTIES[f"{LC}-DAC"], dac_volts) def get_lc(self, LC: str): """ @@ -198,13 +230,15 @@ def get_lc(self, LC: str): LC retardance in waves """ - if self.mode == 'MM-Retardance': - retardance = get_lc(self.mmc, self.PROPERTIES[f'{LC}']) - elif self.mode == 'MM-Voltage': - volts = get_lc(self.mmc, self.PROPERTIES[f'{LC}-Voltage']) # returned value is in volts + if self.mode == "MM-Retardance": + retardance = get_lc(self.mmc, self.PROPERTIES[f"{LC}"]) + elif self.mode == "MM-Voltage": + volts = get_lc( + self.mmc, self.PROPERTIES[f"{LC}-Voltage"] + ) # returned value is in volts retardance = self.calib.get_retardance(volts) - elif self.mode == 'DAC': - dac_volts = get_lc(self.mmc, self.PROPERTIES[f'{LC}-DAC']) + elif self.mode == "DAC": + dac_volts = get_lc(self.mmc, self.PROPERTIES[f"{LC}-DAC"]) volts = dac_volts * self.LC_DAC_conversion retardance = self.calib.get_retardance(volts) @@ -228,20 +262,37 @@ def define_lc_state(self, state, lca_retardance, lcb_retardance): """ - if self.mode == 'MM-Retardance': - self.set_lc(lca_retardance, 'LCA') - self.set_lc(lcb_retardance, 'LCB') + if self.mode == "MM-Retardance": + self.set_lc(lca_retardance, "LCA") + self.set_lc(lcb_retardance, "LCB") define_meadowlark_state(self.mmc, self.PROPERTIES[state]) - elif self.mode == 'DAC': - lca_volts = self.calib.get_voltage(lca_retardance) / self.LC_DAC_conversion - lcb_volts = self.calib.get_voltage(lcb_retardance) / self.LC_DAC_conversion - define_config_state(self.mmc, self.group, state, - [self.PROPERTIES['LCA-DAC'], self.PROPERTIES['LCB-DAC']], [lca_volts, lcb_volts]) - elif self.mode == 'MM-Voltage': + elif self.mode == "DAC": + lca_volts = ( + self.calib.get_voltage(lca_retardance) / self.LC_DAC_conversion + ) + lcb_volts = ( + self.calib.get_voltage(lcb_retardance) / self.LC_DAC_conversion + ) + define_config_state( + self.mmc, + self.group, + state, + [self.PROPERTIES["LCA-DAC"], self.PROPERTIES["LCB-DAC"]], + [lca_volts, lcb_volts], + ) + elif self.mode == "MM-Voltage": lca_volts = self.calib.get_voltage(lca_retardance) lcb_volts = self.calib.get_voltage(lcb_retardance) - define_config_state(self.mmc, self.group, state, - [self.PROPERTIES['LCA-Voltage'], self.PROPERTIES['LCB-Voltage']], [lca_volts, lcb_volts]) + define_config_state( + self.mmc, + self.group, + state, + [ + self.PROPERTIES["LCA-Voltage"], + self.PROPERTIES["LCB-Voltage"], + ], + [lca_volts, lcb_volts], + ) def opt_lc(self, x, device_property, reference, normalize=False): @@ -259,8 +310,8 @@ def opt_lc(self, x, device_property, reference, normalize=False): val = (mean - min_) / (max_ - min_) ref = (reference - min_) / (max_ - min_) - logging.debug(f'LC-Value: {x}') - logging.debug(f'F-Value:{val - ref}\n') + logging.debug(f"LC-Value: {x}") + logging.debug(f"F-Value:{val - ref}\n") return val - ref else: @@ -275,11 +326,11 @@ def opt_lc_cons(self, x, device_property, reference, mode): self.set_lc(x, device_property) swing = (self.lca_ext - x) * self.ratio - if mode == '60': - self.set_lc(self.lcb_ext + swing, 'LCB') + if mode == "60": + self.set_lc(self.lcb_ext + swing, "LCB") - if mode == '120': - self.set_lc(self.lcb_ext - swing, 'LCB') + if mode == "120": + self.set_lc(self.lcb_ext - swing, "LCB") mean = snap_and_average(self.snap_manager) logging.debug(str(mean)) @@ -325,8 +376,8 @@ def opt_lc_grid(self, a_min, a_max, b_min, b_max, step): for lca in np.arange(a_min, a_max, step): for lcb in np.arange(b_min, b_max, step): - self.set_lc(lca, 'LCA') - self.set_lc(lcb, 'LCB') + self.set_lc(lca, "LCA") + self.set_lc(lcb, "LCB") # current_int = np.mean(snap_image(calib.mmc)) current_int = snap_and_average(self.snap_manager) @@ -336,7 +387,10 @@ def opt_lc_grid(self, a_min, a_max, b_min, b_max, step): better_lca = lca better_lcb = lcb min_int = current_int - logging.debug("update (%f, %f, %f)" % (min_int, better_lca, better_lcb)) + logging.debug( + "update (%f, %f, %f)" + % (min_int, better_lca, better_lcb) + ) logging.debug("coarse search done") logging.debug("better lca = " + str(better_lca)) @@ -351,11 +405,11 @@ def opt_lc_grid(self, a_min, a_max, b_min, b_max, step): # ========== Optimization wrappers ============= # ============================================== def opt_Iext(self): - self.plot_sequence_emitter.emit('Coarse') - logging.info('Calibrating State0 (Extinction)...') - logging.debug('Calibrating State0 (Extinction)...') + self.plot_sequence_emitter.emit("Coarse") + logging.info("Calibrating State0 (Extinction)...") + logging.debug("Calibrating State0 (Extinction)...") - set_lc_state(self.mmc, self.group, 'State0') + set_lc_state(self.mmc, self.group, "State0") time.sleep(2) # Perform exhaustive search with step 0.1 over range: @@ -366,15 +420,17 @@ def opt_Iext(self): logging.debug(f"Starting first grid search, step = {step}") logging.debug(f"================================") - best_lca, best_lcb, i_ext_ = self.opt_lc_grid(0.01, 0.5, 0.25, 0.75, step) + best_lca, best_lcb, i_ext_ = self.opt_lc_grid( + 0.01, 0.5, 0.25, 0.75, step + ) logging.debug("grid search done") logging.debug("lca = " + str(best_lca)) logging.debug("lcb = " + str(best_lcb)) logging.debug("intensity = " + str(i_ext_)) - self.set_lc(best_lca, 'LCA') - self.set_lc(best_lcb, 'LCB') + self.set_lc(best_lca, "LCA") + self.set_lc(best_lcb, "LCB") logging.debug(f"================================") logging.debug(f"Starting fine search") @@ -383,29 +439,34 @@ def opt_Iext(self): # Perform brent optimization around results of 2nd grid search # threshold not very necessary here as intensity value will # vary between exposure/lamp intensities - self.plot_sequence_emitter.emit('Fine') - lca, lcb, I_ext = self.optimizer.optimize(state='ext', lca_bound=0.1, lcb_bound=0.1, - reference=self.I_Black, thresh=1, n_iter=5) + self.plot_sequence_emitter.emit("Fine") + lca, lcb, I_ext = self.optimizer.optimize( + state="ext", + lca_bound=0.1, + lcb_bound=0.1, + reference=self.I_Black, + thresh=1, + n_iter=5, + ) # Set the Extinction state to values output from optimization - self.define_lc_state('State0', lca, lcb) + self.define_lc_state("State0", lca, lcb) self.lca_ext = lca self.lcb_ext = lcb self.I_Ext = I_ext logging.debug("fine search done") - logging.info(f'LCA State0 (Extinction) = {lca:.3f}') - logging.debug(f'LCA State0 (Extinction) = {lca:.5f}') - logging.info(f'LCB State0 (Extinction) = {lcb:.3f}') - logging.debug(f'LCB State0 (Extinction) = {lcb:.5f}') - logging.info(f'Intensity (Extinction) = {I_ext:.0f}') - logging.debug(f'Intensity (Extinction) = {I_ext:.3f}') + logging.info(f"LCA State0 (Extinction) = {lca:.3f}") + logging.debug(f"LCA State0 (Extinction) = {lca:.5f}") + logging.info(f"LCB State0 (Extinction) = {lcb:.3f}") + logging.debug(f"LCB State0 (Extinction) = {lcb:.5f}") + logging.info(f"Intensity (Extinction) = {I_ext:.0f}") + logging.debug(f"Intensity (Extinction) = {I_ext:.3f}") logging.debug("--------done--------") logging.info("--------done--------") - def opt_I0(self): """ no optimization performed for this. Simply apply swing and read intensity @@ -414,25 +475,27 @@ def opt_I0(self): mean of image """ - logging.info('Calibrating State1 (I0)...') - logging.debug('Calibrating State1 (I0)...') + logging.info("Calibrating State1 (I0)...") + logging.debug("Calibrating State1 (I0)...") self.lca_0 = self.lca_ext - self.swing self.lcb_0 = self.lcb_ext - self.set_lc(self.lca_0, 'LCA') - self.set_lc(self.lcb_0, 'LCB') + self.set_lc(self.lca_0, "LCA") + self.set_lc(self.lcb_0, "LCB") - self.define_lc_state('State1', self.lca_0, self.lcb_0) + self.define_lc_state("State1", self.lca_0, self.lcb_0) intensity = snap_and_average(self.snap_manager) self.I_Elliptical = intensity - self.swing0 = np.sqrt((self.lcb_0 - self.lcb_ext) ** 2 + (self.lca_0 - self.lca_ext) ** 2) - - logging.info(f'LCA State1 (I0) = {self.lca_0:.3f}') - logging.debug(f'LCA State1 (I0) = {self.lca_0:.5f}') - logging.info(f'LCB State1 (I0) = {self.lcb_0:.3f}') - logging.debug(f'LCB State1 (I0) = {self.lcb_0:.5f}') - logging.info(f'Intensity (I0) = {intensity:.0f}') - logging.debug(f'Intensity (I0) = {intensity:.3f}') + self.swing0 = np.sqrt( + (self.lcb_0 - self.lcb_ext) ** 2 + (self.lca_0 - self.lca_ext) ** 2 + ) + + logging.info(f"LCA State1 (I0) = {self.lca_0:.3f}") + logging.debug(f"LCA State1 (I0) = {self.lca_0:.5f}") + logging.info(f"LCB State1 (I0) = {self.lcb_0:.3f}") + logging.debug(f"LCB State1 (I0) = {self.lcb_0:.5f}") + logging.info(f"Intensity (I0) = {intensity:.0f}") + logging.debug(f"Intensity (I0) = {intensity:.3f}") logging.info("--------done--------") logging.debug("--------done--------") @@ -449,25 +512,34 @@ def opt_I45(self, lca_bound, lcb_bound): intensity value at optimized state """ self.inten = [] - logging.info('Calibrating State2 (I45)...') - logging.debug('Calibrating State2 (I45)...') - - self.set_lc(self.lca_ext, 'LCA') - self.set_lc(self.lcb_ext - self.swing, 'LCB') - - self.lca_45, self.lcb_45, intensity = self.optimizer.optimize('45', lca_bound, lcb_bound, - reference=self.I_Elliptical, n_iter=5, thresh=.01) - - self.define_lc_state('State2', self.lca_45, self.lcb_45) - - self.swing45 = np.sqrt((self.lcb_45 - self.lcb_ext) ** 2 + (self.lca_45 - self.lca_ext) ** 2) - - logging.info(f'LCA State2 (I45) = {self.lca_45:.3f}') - logging.debug(f'LCA State2 (I45) = {self.lca_45:.5f}') - logging.info(f'LCB State2 (I45) = {self.lcb_45:.3f}') - logging.debug(f'LCB State2 (I45) = {self.lcb_45:.5f}') - logging.info(f'Intensity (I45) = {intensity:.0f}') - logging.debug(f'Intensity (I45) = {intensity:.3f}') + logging.info("Calibrating State2 (I45)...") + logging.debug("Calibrating State2 (I45)...") + + self.set_lc(self.lca_ext, "LCA") + self.set_lc(self.lcb_ext - self.swing, "LCB") + + self.lca_45, self.lcb_45, intensity = self.optimizer.optimize( + "45", + lca_bound, + lcb_bound, + reference=self.I_Elliptical, + n_iter=5, + thresh=0.01, + ) + + self.define_lc_state("State2", self.lca_45, self.lcb_45) + + self.swing45 = np.sqrt( + (self.lcb_45 - self.lcb_ext) ** 2 + + (self.lca_45 - self.lca_ext) ** 2 + ) + + logging.info(f"LCA State2 (I45) = {self.lca_45:.3f}") + logging.debug(f"LCA State2 (I45) = {self.lca_45:.5f}") + logging.info(f"LCB State2 (I45) = {self.lcb_45:.3f}") + logging.debug(f"LCB State2 (I45) = {self.lcb_45:.5f}") + logging.info(f"Intensity (I45) = {intensity:.0f}") + logging.debug(f"Intensity (I45) = {intensity:.3f}") logging.info("--------done--------") logging.debug("--------done--------") @@ -485,40 +557,54 @@ def opt_I60(self, lca_bound, lcb_bound): """ self.inten = [] - logging.info('Calibrating State2 (I60)...') - logging.debug('Calibrating State2 (I60)...') + logging.info("Calibrating State2 (I60)...") + logging.debug("Calibrating State2 (I60)...") # Calculate Initial Swing for initial guess to optimize around # Based on ratio calculated from ellpiticity/orientation of LC simulation - swing_ell = np.sqrt((self.lca_ext - self.lca_0) ** 2 + (self.lcb_ext - self.lcb_0) ** 2) - lca_swing = np.sqrt(swing_ell ** 2 / (1 + self.ratio ** 2)) + swing_ell = np.sqrt( + (self.lca_ext - self.lca_0) ** 2 + (self.lcb_ext - self.lcb_0) ** 2 + ) + lca_swing = np.sqrt(swing_ell**2 / (1 + self.ratio**2)) lcb_swing = self.ratio * lca_swing # Optimization - self.set_lc(self.lca_ext + lca_swing, 'LCA') - self.set_lc(self.lcb_ext + lcb_swing, 'LCB') + self.set_lc(self.lca_ext + lca_swing, "LCA") + self.set_lc(self.lcb_ext + lcb_swing, "LCB") - self.lca_60, self.lcb_60, intensity = self.optimizer.optimize('60', lca_bound, lcb_bound, - reference=self.I_Elliptical, - n_iter=5, thresh=.01) + self.lca_60, self.lcb_60, intensity = self.optimizer.optimize( + "60", + lca_bound, + lcb_bound, + reference=self.I_Elliptical, + n_iter=5, + thresh=0.01, + ) - self.define_lc_state('State2', self.lca_60, self.lcb_60) + self.define_lc_state("State2", self.lca_60, self.lcb_60) - self.swing60 = np.sqrt((self.lcb_60 - self.lcb_ext) ** 2 + (self.lca_60 - self.lca_ext) ** 2) + self.swing60 = np.sqrt( + (self.lcb_60 - self.lcb_ext) ** 2 + + (self.lca_60 - self.lca_ext) ** 2 + ) # Print comparison of target swing, target ratio # Ratio determines the orientation of the elliptical state # should be close to target. Swing will vary to optimize ellipticity - logging.debug(f'ratio: swing_LCB / swing_LCA = {(self.lcb_ext - self.lcb_60) / (self.lca_ext - self.lca_60):.4f} \ - | target ratio: {-self.ratio}') - logging.debug(f'total swing = {self.swing60:.4f} | target = {swing_ell}') + logging.debug( + f"ratio: swing_LCB / swing_LCA = {(self.lcb_ext - self.lcb_60) / (self.lca_ext - self.lca_60):.4f} \ + | target ratio: {-self.ratio}" + ) + logging.debug( + f"total swing = {self.swing60:.4f} | target = {swing_ell}" + ) logging.info(f"LCA State2 (I60) = {self.lca_60:.3f}") logging.debug(f"LCA State2 (I60) = {self.lca_60:.5f}") logging.info(f"LCB State2 (I60) = {self.lcb_60:.3f}") logging.debug(f"LCB State2 (I60) = {self.lcb_60:.5f}") - logging.info(f'Intensity (I60) = {intensity:.0f}') - logging.debug(f'Intensity (I60) = {intensity:.3f}') + logging.info(f"Intensity (I60) = {intensity:.0f}") + logging.debug(f"Intensity (I60) = {intensity:.3f}") logging.info("--------done--------") logging.debug("--------done--------") @@ -534,28 +620,36 @@ def opt_I90(self, lca_bound, lcb_bound): lca, lcb value at optimized state intensity value at optimized state """ - logging.info('Calibrating State3 (I90)...') - logging.debug('Calibrating State3 (I90)...') + logging.info("Calibrating State3 (I90)...") + logging.debug("Calibrating State3 (I90)...") self.inten = [] - self.set_lc(self.lca_ext + self.swing, 'LCA') - self.set_lc(self.lcb_ext, 'LCB') - - self.lca_90, self.lcb_90, intensity = self.optimizer.optimize('90', lca_bound, lcb_bound, - reference=self.I_Elliptical, - n_iter=5, thresh=.01) - - self.define_lc_state('State3', self.lca_90, self.lcb_90) - - self.swing90 = np.sqrt((self.lcb_90 - self.lcb_ext) ** 2 + (self.lca_90 - self.lca_ext) ** 2) - - logging.info(f'LCA State3 (I90) = {self.lca_90:.3f}') - logging.debug(f'LCA State3 (I90) = {self.lca_90:.5f}') - logging.info(f'LCB State3 (I90) = {self.lcb_90:.3f}') - logging.debug(f'LCB State3 (I90) = {self.lcb_90:.5f}') - logging.info(f'Intensity (I90) = {intensity:.0f}') - logging.debug(f'Intensity (I90) = {intensity:.3f}') + self.set_lc(self.lca_ext + self.swing, "LCA") + self.set_lc(self.lcb_ext, "LCB") + + self.lca_90, self.lcb_90, intensity = self.optimizer.optimize( + "90", + lca_bound, + lcb_bound, + reference=self.I_Elliptical, + n_iter=5, + thresh=0.01, + ) + + self.define_lc_state("State3", self.lca_90, self.lcb_90) + + self.swing90 = np.sqrt( + (self.lcb_90 - self.lcb_ext) ** 2 + + (self.lca_90 - self.lca_ext) ** 2 + ) + + logging.info(f"LCA State3 (I90) = {self.lca_90:.3f}") + logging.debug(f"LCA State3 (I90) = {self.lca_90:.5f}") + logging.info(f"LCB State3 (I90) = {self.lcb_90:.3f}") + logging.debug(f"LCB State3 (I90) = {self.lcb_90:.5f}") + logging.info(f"Intensity (I90) = {intensity:.0f}") + logging.debug(f"Intensity (I90) = {intensity:.3f}") logging.info("--------done--------") logging.debug("--------done--------") @@ -571,39 +665,53 @@ def opt_I120(self, lca_bound, lcb_bound): lca, lcb value at optimized state intensity value at optimized state """ - logging.info('Calibrating State3 (I120)...') - logging.debug('Calibrating State3 (I120)...') + logging.info("Calibrating State3 (I120)...") + logging.debug("Calibrating State3 (I120)...") # Calculate Initial Swing for initial guess to optimize around # Based on ratio calculated from ellpiticity/orientation of LC simulation - swing_ell = np.sqrt((self.lca_ext - self.lca_0) ** 2 + (self.lcb_ext - self.lcb_0) ** 2) - lca_swing = np.sqrt(swing_ell ** 2 / (1 + self.ratio ** 2)) + swing_ell = np.sqrt( + (self.lca_ext - self.lca_0) ** 2 + (self.lcb_ext - self.lcb_0) ** 2 + ) + lca_swing = np.sqrt(swing_ell**2 / (1 + self.ratio**2)) lcb_swing = self.ratio * lca_swing # Brent Optimization - self.set_lc(self.lca_ext + lca_swing, 'LCA') - self.set_lc(self.lcb_ext - lcb_swing, 'LCB') + self.set_lc(self.lca_ext + lca_swing, "LCA") + self.set_lc(self.lcb_ext - lcb_swing, "LCB") - self.lca_120, self.lcb_120, intensity = self.optimizer.optimize('120', lca_bound, lcb_bound, - reference=self.I_Elliptical, - n_iter=5, thresh=.01) + self.lca_120, self.lcb_120, intensity = self.optimizer.optimize( + "120", + lca_bound, + lcb_bound, + reference=self.I_Elliptical, + n_iter=5, + thresh=0.01, + ) - self.define_lc_state('State3', self.lca_120, self.lcb_120) + self.define_lc_state("State3", self.lca_120, self.lcb_120) - self.swing120 = np.sqrt((self.lcb_120 - self.lcb_ext) ** 2 + (self.lca_120 - self.lca_ext) ** 2) + self.swing120 = np.sqrt( + (self.lcb_120 - self.lcb_ext) ** 2 + + (self.lca_120 - self.lca_ext) ** 2 + ) # Print comparison of target swing, target ratio # Ratio determines the orientation of the elliptical state # should be close to target. Swing will vary to optimize ellipticity - logging.debug(f'ratio: swing_LCB / swing_LCA = {(self.lcb_ext - self.lcb_120) / (self.lca_ext - self.lca_120):.4f}\ - | target ratio: {self.ratio}') - logging.debug(f'total swing = {self.swing120:.4f} | target = {swing_ell}') + logging.debug( + f"ratio: swing_LCB / swing_LCA = {(self.lcb_ext - self.lcb_120) / (self.lca_ext - self.lca_120):.4f}\ + | target ratio: {self.ratio}" + ) + logging.debug( + f"total swing = {self.swing120:.4f} | target = {swing_ell}" + ) logging.info(f"LCA State3 (I120) = {self.lca_120:.3f}") logging.debug(f"LCA State3 (I120) = {self.lca_120:.5f}") logging.info(f"LCB State3 (I120) = {self.lcb_120:.3f}") logging.debug(f"LCB State3 (I120) = {self.lcb_120:.5f}") - logging.info(f'Intensity (I120) = {intensity:.0f}') - logging.debug(f'Intensity (I120) = {intensity:.3f}') + logging.info(f"Intensity (I120) = {intensity:.0f}") + logging.debug(f"Intensity (I120) = {intensity:.3f}") logging.info("--------done--------") logging.debug("--------done--------") @@ -620,33 +728,41 @@ def opt_I135(self, lca_bound, lcb_bound): intensity value at optimized state """ - logging.info('Calibrating State4 (I135)...') - logging.debug('Calibrating State4 (I135)...') + logging.info("Calibrating State4 (I135)...") + logging.debug("Calibrating State4 (I135)...") self.inten = [] - self.set_lc(self.lca_ext, 'LCA') - self.set_lc(self.lcb_ext + self.swing, 'LCB') - - self.lca_135, self.lcb_135, intensity = self.optimizer.optimize('135', lca_bound, lcb_bound, - reference=self.I_Elliptical, - n_iter=5, thresh=.01) - - self.define_lc_state('State4', self.lca_135, self.lcb_135) - - self.swing135 = np.sqrt((self.lcb_135 - self.lcb_ext) ** 2 + (self.lca_135 - self.lca_ext) ** 2) - - logging.info(f'LCA State4 (I135) = {self.lca_135:.3f}') - logging.debug(f'LCA State4 (I135) = {self.lca_135:.5f}') - logging.info(f'LCB State4 (I135) = {self.lcb_135:.3f}') - logging.debug(f'LCB State4 (I135) = {self.lcb_135:.5f}') - logging.info(f'Intensity (I135) = {intensity:.0f}') - logging.debug(f'Intensity (I135) = {intensity:.3f}') + self.set_lc(self.lca_ext, "LCA") + self.set_lc(self.lcb_ext + self.swing, "LCB") + + self.lca_135, self.lcb_135, intensity = self.optimizer.optimize( + "135", + lca_bound, + lcb_bound, + reference=self.I_Elliptical, + n_iter=5, + thresh=0.01, + ) + + self.define_lc_state("State4", self.lca_135, self.lcb_135) + + self.swing135 = np.sqrt( + (self.lcb_135 - self.lcb_ext) ** 2 + + (self.lca_135 - self.lca_ext) ** 2 + ) + + logging.info(f"LCA State4 (I135) = {self.lca_135:.3f}") + logging.debug(f"LCA State4 (I135) = {self.lca_135:.5f}") + logging.info(f"LCB State4 (I135) = {self.lcb_135:.3f}") + logging.debug(f"LCB State4 (I135) = {self.lcb_135:.5f}") + logging.info(f"Intensity (I135) = {intensity:.0f}") + logging.debug(f"Intensity (I135) = {intensity:.3f}") logging.info("--------done--------") logging.debug("--------done--------") def open_shutter(self): - if self.shutter_device == '': # no shutter - input('Please manually open the shutter and press <Enter>') + if self.shutter_device == "": # no shutter + input("Please manually open the shutter and press <Enter>") else: self.mmc.setShutterOpen(True) @@ -658,9 +774,13 @@ def reset_shutter(self): ------- """ - if self.shutter_device == '': # no shutter - input('Please reset the shutter to its original state and press <Enter>') - logging.info("This is the end of the command-line instructions. You can return to the napari window.") + if self.shutter_device == "": # no shutter + input( + "Please reset the shutter to its original state and press <Enter>" + ) + logging.info( + "This is the end of the command-line instructions. You can return to the napari window." + ) else: self.mmc.setAutoShutter(self._auto_shutter_state) self.mmc.setShutterOpen(self._shutter_state) @@ -669,8 +789,10 @@ def close_shutter_and_calc_blacklevel(self): self._auto_shutter_state = self.mmc.getAutoShutter() self._shutter_state = self.mmc.getShutterOpen() - if self.shutter_device == '': # no shutter - show_warning('No shutter found. Please follow the command-line instructions...') + if self.shutter_device == "": # no shutter + show_warning( + "No shutter found. Please follow the command-line instructions..." + ) shutter_warning_msg = """ recOrder could not find an automatic shutter configured through Micro-Manager. >>> If you would like manually enter the black level, enter an integer or float and press <Enter> @@ -678,7 +800,7 @@ def close_shutter_and_calc_blacklevel(self): """ in_string = input(shutter_warning_msg) - if in_string.isdigit(): # True if positive integer + if in_string.isdigit(): # True if positive integer self.I_Black = float(in_string) return else: @@ -699,7 +821,10 @@ def get_full_roi(self): # Get Image Parameters self.mmc.snapImage() self.mmc.getImage() - self.height, self.width = self.mmc.getImageHeight(), self.mmc.getImageWidth() + self.height, self.width = ( + self.mmc.getImageHeight(), + self.mmc.getImageWidth(), + ) self.ROI = (0, 0, self.width, self.height) def check_and_get_roi(self): @@ -711,15 +836,24 @@ def check_and_get_roi(self): for i in range(size): win = windows.get(i).toFront() time.sleep(0.05) - roi = self.mm.displays().getActiveDataViewer().getImagePlus().getRoi() + roi = ( + self.mm.displays() + .getActiveDataViewer() + .getImagePlus() + .getRoi() + ) if roi != None: boxes.append(roi) if len(boxes) == 0: - raise ValueError('No ROI Bounding Box Found, Please Draw Bounding Box on the Preview (live) Window') + raise ValueError( + "No ROI Bounding Box Found, Please Draw Bounding Box on the Preview (live) Window" + ) if len(boxes) > 1: - raise ValueError('More than one Bounding Box Found, Please Remove any box not on the preview (live) window') + raise ValueError( + "More than one Bounding Box Found, Please Remove any box not on the preview (live) window" + ) if len(boxes) == 1: rect = boxes[0].getBounds() @@ -729,50 +863,83 @@ def display_and_check_ROI(self, rect): img = snap_image(self.mmc) - print('Will Calibrate Using this ROI:') + print("Will Calibrate Using this ROI:") fig, ax = plt.subplots() - ax.imshow(np.reshape(img, (self.height, self.width)), 'gray') - box = patches.Rectangle((rect.x, rect.y), rect.width, rect.height, linewidth=2, edgecolor='r', facecolor='none') + ax.imshow(np.reshape(img, (self.height, self.width)), "gray") + box = patches.Rectangle( + (rect.x, rect.y), + rect.width, + rect.height, + linewidth=2, + edgecolor="r", + facecolor="none", + ) ax.add_patch(box) plt.show() - cont = input('Would You Like to Calibrate Using this ROI? (Yes/No): \t') + cont = input( + "Would You Like to Calibrate Using this ROI? (Yes/No): \t" + ) - if cont in ['Yes', 'Y', 'yes', 'ye', 'y', '']: + if cont in ["Yes", "Y", "yes", "ye", "y", ""]: return True - if cont in ['No', 'N', 'no', 'n']: + if cont in ["No", "N", "no", "n"]: return False else: - raise ValueError('Did not understand your answer, please check spelling') - - def calculate_extinction(self, swing, black_level, intensity_extinction, intensity_elliptical): - return np.round((1 / np.sin(np.pi * swing) ** 2) * \ - (intensity_elliptical - black_level) / (intensity_extinction - black_level), 2) + raise ValueError( + "Did not understand your answer, please check spelling" + ) + + def calculate_extinction( + self, swing, black_level, intensity_extinction, intensity_elliptical + ): + return np.round( + (1 / np.sin(np.pi * swing) ** 2) + * (intensity_elliptical - black_level) + / (intensity_extinction - black_level), + 2, + ) def calc_inst_matrix(self): - if self.calib_scheme == '4-State': + if self.calib_scheme == "4-State": chi = self.swing - inst_mat = np.array([[1, 0, 0, -1], - [1, np.sin(2 * np.pi * chi), 0, -np.cos(2 * np.pi * chi)], - [1, -0.5 * np.sin(2 * np.pi * chi), - np.sqrt(3) * np.cos(np.pi * chi) * np.sin(np.pi * chi), -np.cos(2 * np.pi * chi)], - [1, -0.5 * np.sin(2 * np.pi * chi), -np.sqrt(3) / 2 * np.sin(2 * np.pi * chi), - -np.cos(2 * np.pi * chi)]]) + inst_mat = np.array( + [ + [1, 0, 0, -1], + [1, np.sin(2 * np.pi * chi), 0, -np.cos(2 * np.pi * chi)], + [ + 1, + -0.5 * np.sin(2 * np.pi * chi), + np.sqrt(3) * np.cos(np.pi * chi) * np.sin(np.pi * chi), + -np.cos(2 * np.pi * chi), + ], + [ + 1, + -0.5 * np.sin(2 * np.pi * chi), + -np.sqrt(3) / 2 * np.sin(2 * np.pi * chi), + -np.cos(2 * np.pi * chi), + ], + ] + ) return inst_mat - if self.calib_scheme == '5-State': + if self.calib_scheme == "5-State": chi = self.swing * 2 * np.pi - inst_mat = np.array([[1, 0, 0, -1], - [1, np.sin(chi), 0, -np.cos(chi)], - [1, 0, np.sin(chi), -np.cos(chi)], - [1, -np.sin(chi), 0, -np.cos(chi)], - [1, 0, -np.sin(chi), -np.cos(chi)]]) + inst_mat = np.array( + [ + [1, 0, 0, -1], + [1, np.sin(chi), 0, -np.cos(chi)], + [1, 0, np.sin(chi), -np.cos(chi)], + [1, -np.sin(chi), 0, -np.cos(chi)], + [1, 0, -np.sin(chi), -np.cos(chi)], + ] + ) return inst_mat @@ -781,57 +948,86 @@ def write_metadata(self, notes=None): inst_mat = self.calc_inst_matrix() inst_mat = np.around(inst_mat, decimals=5).tolist() - metadata = {'Summary': { - 'Timestamp': str(datetime.now()), - 'recOrder-napari version': version('recOrder-napari'), - 'waveorder version': version('waveorder')}, - 'Calibration': { - 'Calibration scheme': self.calib_scheme, - 'Swing (waves)': self.swing, - 'Wavelength (nm)': self.wavelength, - 'Retardance to voltage interpolation method': self.calib.interp_method, - 'LC control mode': self.mode, - 'Black level': np.round(self.I_Black, 2), - 'Extinction ratio': self.extinction_ratio, - 'ROI (x, y, width, height)': self.ROI}, - 'Notes': notes - } - - if self.calib_scheme == '4-State': - metadata['Calibration'].update({ - 'Channel names': [f"State{i}" for i in range(4)], - 'LC retardance': {f'LC{i}_{j}': np.around(getattr(self, f'lc{i.lower()}_{j}'), decimals=6) - for j in ['ext', '0', '60', '120'] - for i in ['A', 'B']}, - 'LC voltage': {f'LC{i}_{j}': np.around(self.calib.get_voltage(getattr(self, f'lc{i.lower()}_{j}')), decimals=4) - for j in ['ext', '0', '60', '120'] - for i in ['A', 'B']}, - 'Swing_0': np.around(self.swing0, decimals=3), - 'Swing_60': np.around(self.swing60, decimals=3), - 'Swing_120': np.around(self.swing120, decimals=3), - 'Instrument matrix': inst_mat - }) - - elif self.calib_scheme == '5-State': - metadata['Calibration'].update({ - 'Channel names': [f"State{i}" for i in range(5)], - 'LC retardance': {f'LC{i}_{j}': np.around(getattr(self, f'lc{i.lower()}_{j}'), decimals=6) - for j in ['ext', '0', '45', '90', '135'] - for i in ['A', 'B']}, - 'LC voltage': {f'LC{i}_{j}': np.around(self.calib.get_voltage(getattr(self, f'lc{i.lower()}_{j}')), decimals=4) - for j in ['ext', '0', '45', '90', '135'] - for i in ['A', 'B']}, - 'Swing_0': np.around(self.swing0, decimals=3), - 'Swing_45': np.around(self.swing45, decimals=3), - 'Swing_90': np.around(self.swing90, decimals=3), - 'Swing_135': np.around(self.swing135, decimals=3), - 'Instrument matrix': inst_mat - }) - - if not self.meta_file.endswith('.txt'): - self.meta_file += '.txt' - - with open(self.meta_file, 'w') as metafile: + metadata = { + "Summary": { + "Timestamp": str(datetime.now()), + "recOrder-napari version": version("recOrder-napari"), + "waveorder version": version("waveorder"), + }, + "Calibration": { + "Calibration scheme": self.calib_scheme, + "Swing (waves)": self.swing, + "Wavelength (nm)": self.wavelength, + "Retardance to voltage interpolation method": self.calib.interp_method, + "LC control mode": self.mode, + "Black level": np.round(self.I_Black, 2), + "Extinction ratio": self.extinction_ratio, + "ROI (x, y, width, height)": self.ROI, + }, + "Notes": notes, + } + + if self.calib_scheme == "4-State": + metadata["Calibration"].update( + { + "Channel names": [f"State{i}" for i in range(4)], + "LC retardance": { + f"LC{i}_{j}": np.around( + getattr(self, f"lc{i.lower()}_{j}"), decimals=6 + ) + for j in ["ext", "0", "60", "120"] + for i in ["A", "B"] + }, + "LC voltage": { + f"LC{i}_{j}": np.around( + self.calib.get_voltage( + getattr(self, f"lc{i.lower()}_{j}") + ), + decimals=4, + ) + for j in ["ext", "0", "60", "120"] + for i in ["A", "B"] + }, + "Swing_0": np.around(self.swing0, decimals=3), + "Swing_60": np.around(self.swing60, decimals=3), + "Swing_120": np.around(self.swing120, decimals=3), + "Instrument matrix": inst_mat, + } + ) + + elif self.calib_scheme == "5-State": + metadata["Calibration"].update( + { + "Channel names": [f"State{i}" for i in range(5)], + "LC retardance": { + f"LC{i}_{j}": np.around( + getattr(self, f"lc{i.lower()}_{j}"), decimals=6 + ) + for j in ["ext", "0", "45", "90", "135"] + for i in ["A", "B"] + }, + "LC voltage": { + f"LC{i}_{j}": np.around( + self.calib.get_voltage( + getattr(self, f"lc{i.lower()}_{j}") + ), + decimals=4, + ) + for j in ["ext", "0", "45", "90", "135"] + for i in ["A", "B"] + }, + "Swing_0": np.around(self.swing0, decimals=3), + "Swing_45": np.around(self.swing45, decimals=3), + "Swing_90": np.around(self.swing90, decimals=3), + "Swing_135": np.around(self.swing135, decimals=3), + "Instrument matrix": inst_mat, + } + ) + + if not self.meta_file.endswith(".txt"): + self.meta_file += ".txt" + + with open(self.meta_file, "w") as metafile: json.dump(metadata, metafile, indent=1) def _add_colorbar(self, mappable): @@ -868,14 +1064,22 @@ def _capture_state(self, state: str, n_avg: int): def _plot_bg_images(self, imgs): - img_names = ['Extinction', '0', '60', '120'] if len(imgs) == 4 else ['Extinction', '0', '45', '90', 135] - fig, ax = plt.subplots(2, 2, figsize=(20, 20)) if len(imgs) == 4 else plt.subplots(3, 2, figsize=(20, 20)) + img_names = ( + ["Extinction", "0", "60", "120"] + if len(imgs) == 4 + else ["Extinction", "0", "45", "90", 135] + ) + fig, ax = ( + plt.subplots(2, 2, figsize=(20, 20)) + if len(imgs) == 4 + else plt.subplots(3, 2, figsize=(20, 20)) + ) img_idx = 0 for ax1 in range(len(ax[:, 0])): for ax2 in range(len(ax[0, :])): if img_idx < len(imgs): - im = ax[ax1, ax2].imshow(imgs[img_idx], 'gray') + im = ax[ax1, ax2].imshow(imgs[img_idx], "gray") ax[ax1, ax2].set_title(img_names[img_idx]) self._add_colorbar(im) else: @@ -904,7 +1108,9 @@ def pol_states(self): elif self.calib_scheme == "5-State": pols = ("ext", "0", "45", "90", "135") else: - raise ValueError(f"Invalid calibration state: {self.calib_scheme}.") + raise ValueError( + f"Invalid calibration state: {self.calib_scheme}." + ) return pols @property @@ -919,14 +1125,14 @@ def lc_states(self): lc_sides = ["A", "B"] return { f"LC{lc_side}": [ - self.__getattribute__("lc" + lc_side.lower() + "_" + pol) + self.__getattribute__("lc" + lc_side.lower() + "_" + pol) for pol in self.pol_states ] for lc_side in lc_sides } def capture_bg(self, n_avg, directory): - """" + """ " This function will capture an image at every state and save to specified directory This may throw errors depending on the micromanager config file-- @@ -940,38 +1146,38 @@ def capture_bg(self, n_avg, directory): if not os.path.exists(directory): os.makedirs(directory) - logging.info('Capturing Background') + logging.info("Capturing Background") self._auto_shutter_state = self.mmc.getAutoShutter() self._shutter_state = self.mmc.getShutterOpen() self.open_shutter() - logging.debug('Capturing Background State0') + logging.debug("Capturing Background State0") - state0 = self._capture_state('State0', n_avg) - logging.debug('Saving Background State0') - tiff.imsave(os.path.join(directory, 'State0.tif'), state0) + state0 = self._capture_state("State0", n_avg) + logging.debug("Saving Background State0") + tiff.imsave(os.path.join(directory, "State0.tif"), state0) - logging.debug('Capturing Background State1') - state1 = self._capture_state('State1', n_avg) - logging.debug('Saving Background State1') - tiff.imsave(os.path.join(directory, 'State1.tif'), state1) + logging.debug("Capturing Background State1") + state1 = self._capture_state("State1", n_avg) + logging.debug("Saving Background State1") + tiff.imsave(os.path.join(directory, "State1.tif"), state1) - logging.debug('Capturing Background State2') - state2 = self._capture_state('State2', n_avg) - logging.debug('Saving Background State2') - tiff.imsave(os.path.join(directory, 'State2.tif'), state2) + logging.debug("Capturing Background State2") + state2 = self._capture_state("State2", n_avg) + logging.debug("Saving Background State2") + tiff.imsave(os.path.join(directory, "State2.tif"), state2) - logging.debug('Capturing Background State3') - state3 = self._capture_state('State3', n_avg) - logging.debug('Saving Background State3') - tiff.imsave(os.path.join(directory, 'State3.tif'), state3) + logging.debug("Capturing Background State3") + state3 = self._capture_state("State3", n_avg) + logging.debug("Saving Background State3") + tiff.imsave(os.path.join(directory, "State3.tif"), state3) imgs = [state0, state1, state2, state3] - if self.calib_scheme == '5-State': - logging.debug('Capturing Background State4') - state4 = self._capture_state('State4', n_avg) - logging.debug('Saving Background State4') - tiff.imsave(os.path.join(directory, 'State4.tif'), state4) + if self.calib_scheme == "5-State": + logging.debug("Capturing Background State4") + state4 = self._capture_state("State4", n_avg) + logging.debug("Saving Background State4") + tiff.imsave(os.path.join(directory, "State4.tif"), state4) imgs.append(state4) # self._plot_bg_images(np.asarray(imgs)) @@ -985,7 +1191,7 @@ class CalibrationData: Interpolates LC calibration data between retardance (in waves), voltage (in mV), and wavelength (in nm) """ - def __init__(self, path, wavelength=532, interp_method='linear'): + def __init__(self, path, wavelength=532, interp_method="linear"): """ Parameters @@ -999,21 +1205,25 @@ def __init__(self, path, wavelength=532, interp_method='linear'): """ header, raw_data = self.read_data(path) - self.calib_wavelengths = np.array([i[:3] for i in header[1::3]]).astype('double') + self.calib_wavelengths = np.array( + [i[:3] for i in header[1::3]] + ).astype("double") self.wavelength = None self.V_min = 0.0 self.V_max = 20.0 - if interp_method in ['linear', 'schnoor_fit']: + if interp_method in ["linear", "schnoor_fit"]: self.interp_method = interp_method else: - raise ValueError('Unknown interpolation method.') + raise ValueError("Unknown interpolation method.") self.set_wavelength(wavelength) - if interp_method == 'linear': - self.interpolate_data(raw_data, self.calib_wavelengths) # calib_wavelengths is not used, values hardcoded - elif interp_method == 'schnoor_fit': + if interp_method == "linear": + self.interpolate_data( + raw_data, self.calib_wavelengths + ) # calib_wavelengths is not used, values hardcoded + elif interp_method == "schnoor_fit": self.fit_params = self.fit_data(raw_data, self.calib_wavelengths) self.ret_min = self.get_retardance(self.V_max) @@ -1055,10 +1265,10 @@ def read_data(path): Calibration data. Voltage is in millivolts and retardance is in nanometers """ - with open(path, 'r') as f: - header = f.readline().strip().split(',') + with open(path, "r") as f: + header = f.readline().strip().split(",") - raw_data = np.loadtxt(path, delimiter=',', comments='-', skiprows=3) + raw_data = np.loadtxt(path, delimiter=",", comments="-", skiprows=3) return header, raw_data @staticmethod @@ -1080,7 +1290,7 @@ def schnoor_fit(V, a, b1, b2, c, d, e, wavelength): Retardance in nanometers """ - retardance = a + (b1 + b2 / wavelength ** 2) / (1 + (V / c) ** d) ** e + retardance = a + (b1 + b2 / wavelength**2) / (1 + (V / c) ** d) ** e return retardance @@ -1104,7 +1314,9 @@ def schnoor_fit_inv(retardance, a, b1, b2, c, d, e, wavelength): """ - voltage = c * (((b1 + b2 / wavelength ** 2) / (retardance - a)) ** (1 / e) - 1) ** (1 / d) + voltage = c * ( + ((b1 + b2 / wavelength**2) / (retardance - a)) ** (1 / e) - 1 + ) ** (1 / d) return voltage @@ -1115,24 +1327,37 @@ def _fun(x, wavelengths, xdata, ydata): return res.flatten() def set_wavelength(self, wavelength): - if len(self.calib_wavelengths) == 1 and wavelength != self.calib_wavelengths: - raise ValueError("Calibration is not provided at this wavelength. " - "Wavelength dependence of LC retardance vs voltage cannot be extrapolated.") - - if wavelength < self.calib_wavelengths.min() or \ - wavelength > self.calib_wavelengths.max(): - warnings.warn("Specified wavelength is outside of the calibration range. " - "LC retardance vs voltage data will be extrapolated at this wavelength.") + if ( + len(self.calib_wavelengths) == 1 + and wavelength != self.calib_wavelengths + ): + raise ValueError( + "Calibration is not provided at this wavelength. " + "Wavelength dependence of LC retardance vs voltage cannot be extrapolated." + ) + + if ( + wavelength < self.calib_wavelengths.min() + or wavelength > self.calib_wavelengths.max() + ): + warnings.warn( + "Specified wavelength is outside of the calibration range. " + "LC retardance vs voltage data will be extrapolated at this wavelength." + ) self.wavelength = wavelength - if self.interp_method == 'linear': + if self.interp_method == "linear": # Interpolation of calib beyond this range produce strange results. if self.wavelength < 450: self.wavelength = 450 - warnings.warn("Wavelength is limited to 450-720 nm for this interpolation method.") + warnings.warn( + "Wavelength is limited to 450-720 nm for this interpolation method." + ) if self.wavelength > 720: self.wavelength = 720 - warnings.warn("Wavelength is limited to 450-720 nm for this interpolation method.") + warnings.warn( + "Wavelength is limited to 450-720 nm for this interpolation method." + ) def fit_data(self, raw_data, calib_wavelengths): """ @@ -1150,13 +1375,18 @@ def fit_data(self, raw_data, calib_wavelengths): ------- """ - xdata = raw_data[:, 0::3] / 1000 # convert to volts - ydata = raw_data[:, 1::3] # in nanometers + xdata = raw_data[:, 0::3] / 1000 # convert to volts + ydata = raw_data[:, 1::3] # in nanometers x0 = [10, 1000, 1e7, 1, 10, 0.1] - p = least_squares(self._fun, x0, method='trf', args=(calib_wavelengths, xdata, ydata), - bounds=((-np.inf, 0, 0, 0, 0, 0), (np.inf,)*6), - x_scale=[10, 1000, 1e7, 1, 10, 0.1]) + p = least_squares( + self._fun, + x0, + method="trf", + args=(calib_wavelengths, xdata, ydata), + bounds=((-np.inf, 0, 0, 0, 0, 0), (np.inf,) * 6), + x_scale=[10, 1000, 1e7, 1, 10, 0.1], + ) if not p.success: raise RuntimeError("Schnoor fit to calibration data did not work.") @@ -1166,7 +1396,9 @@ def fit_data(self, raw_data, calib_wavelengths): slope, intercept, r_value, *_ = linregress(y, y_hat) r_squared = r_value**2 if r_squared < 0.999: - warnings.warn(f'Schnoor fit has R2 value of {r_squared:.5f}, fit may not have worked well.') + warnings.warn( + f"Schnoor fit has R2 value of {r_squared:.5f}, fit may not have worked well." + ) return p.x @@ -1203,8 +1435,16 @@ def interpolate_data(self, raw_data, calib_wavelengths): fact1 = np.abs(490 - wavelength_new) / (546 - 490) fact2 = np.abs(546 - wavelength_new) / (546 - 490) - temp_curve = np.asarray([[i, 2 * new_a1_y[i] - (fact1 * new_a1_y[i] + fact2 * new_a2_y[i])] - for i in range(len(new_a1_y))]) + temp_curve = np.asarray( + [ + [ + i, + 2 * new_a1_y[i] + - (fact1 * new_a1_y[i] + fact2 * new_a2_y[i]), + ] + for i in range(len(new_a1_y)) + ] + ) self.spline = interp1d(temp_curve[:, 0], temp_curve[:, 1]) self.curve = self.spline(x_range) @@ -1217,12 +1457,19 @@ def interpolate_data(self, raw_data, calib_wavelengths): fact1 = np.abs(630 - wavelength_new) / (630 - 546) fact2 = np.abs(546 - wavelength_new) / (630 - 546) - temp_curve = np.asarray([[i, 2 * new_a1_y[i] - (fact1 * new_a1_y[i] + fact2 * new_a2_y[i])] - for i in range(len(new_a1_y))]) + temp_curve = np.asarray( + [ + [ + i, + 2 * new_a1_y[i] + - (fact1 * new_a1_y[i] + fact2 * new_a2_y[i]), + ] + for i in range(len(new_a1_y)) + ] + ) self.spline = interp1d(temp_curve[:, 0], temp_curve[:, 1]) self.curve = self.spline(x_range) - elif 490 < self.wavelength < 546: new_a1_y = np.interp(x_range, x_range, spline490(x_range)) @@ -1231,7 +1478,12 @@ def interpolate_data(self, raw_data, calib_wavelengths): fact1 = np.abs(490 - self.wavelength) / (546 - 490) fact2 = np.abs(546 - self.wavelength) / (546 - 490) - temp_curve = np.asarray([[i, fact1 * new_a1_y[i] + fact2 * new_a2_y[i]] for i in range(len(new_a1_y))]) + temp_curve = np.asarray( + [ + [i, fact1 * new_a1_y[i] + fact2 * new_a2_y[i]] + for i in range(len(new_a1_y)) + ] + ) self.spline = interp1d(temp_curve[:, 0], temp_curve[:, 1]) self.curve = self.spline(x_range) @@ -1243,7 +1495,12 @@ def interpolate_data(self, raw_data, calib_wavelengths): fact1 = np.abs(546 - self.wavelength) / (630 - 546) fact2 = np.abs(630 - self.wavelength) / (630 - 546) - temp_curve = np.asarray([[i, fact1 * new_a1_y[i] + fact2 * new_a2_y[i]] for i in range(len(new_a1_y))]) + temp_curve = np.asarray( + [ + [i, fact1 * new_a1_y[i] + fact2 * new_a2_y[i]] + for i in range(len(new_a1_y)) + ] + ) self.spline = interp1d(temp_curve[:, 0], temp_curve[:, 1]) self.curve = self.spline(x_range) @@ -1260,7 +1517,7 @@ def interpolate_data(self, raw_data, calib_wavelengths): self.spline = spline630 else: - raise ValueError(f'Wavelength {self.wavelength} not understood') + raise ValueError(f"Wavelength {self.wavelength} not understood") def get_voltage(self, retardance): """ @@ -1277,19 +1534,21 @@ def get_voltage(self, retardance): """ - retardance = np.asarray(retardance, dtype='double') + retardance = np.asarray(retardance, dtype="double") voltage = None - ret_nanometers = retardance*self.wavelength + ret_nanometers = retardance * self.wavelength if retardance < self.ret_min: voltage = self.V_max elif retardance > self.ret_max: voltage = self.V_min else: - if self.interp_method == 'linear': + if self.interp_method == "linear": voltage = np.abs(self.curve - ret_nanometers).argmin() / 1000 - elif self.interp_method == 'schnoor_fit': - voltage = self.schnoor_fit_inv(ret_nanometers, *self.fit_params, self.wavelength) + elif self.interp_method == "schnoor_fit": + voltage = self.schnoor_fit_inv( + ret_nanometers, *self.fit_params, self.wavelength + ) return voltage @@ -1308,21 +1567,25 @@ def get_retardance(self, volts): """ - volts = np.asarray(volts, dtype='double') + volts = np.asarray(volts, dtype="double") ret_nanometers = None if volts < self.V_min: volts = self.V_min elif volts >= self.V_max: - if self.interp_method == 'linear': - volts = self.V_max - 1e-3 # interpolation breaks down at upper boundary + if self.interp_method == "linear": + volts = ( + self.V_max - 1e-3 + ) # interpolation breaks down at upper boundary else: volts = self.V_max - if self.interp_method == 'linear': + if self.interp_method == "linear": ret_nanometers = self.spline(volts * 1000) - elif self.interp_method == 'schnoor_fit': - ret_nanometers = self.schnoor_fit(volts, *self.fit_params, self.wavelength) + elif self.interp_method == "schnoor_fit": + ret_nanometers = self.schnoor_fit( + volts, *self.fit_params, self.wavelength + ) retardance = ret_nanometers / self.wavelength return retardance diff --git a/recOrder/calib/Optimization.py b/recOrder/calib/Optimization.py index 88ea8933..6e6893b0 100644 --- a/recOrder/calib/Optimization.py +++ b/recOrder/calib/Optimization.py @@ -3,68 +3,104 @@ from recOrder.io.core_functions import snap_and_average import logging -class BrentOptimizer: +class BrentOptimizer: def __init__(self, calib): self.calib = calib - def _check_bounds(self, lca_bound, lcb_bound): - current_lca = self.calib.get_lc('LCA') - current_lcb = self.calib.get_lc('LCB') + current_lca = self.calib.get_lc("LCA") + current_lcb = self.calib.get_lc("LCB") # check that bounds don't exceed range of LC - lca_lower_bound = 0.01 if (current_lca - lca_bound) <= 0.01 else current_lca - lca_bound - lca_upper_bound = 1.6 if (current_lca + lca_bound) >= 1.6 else current_lca + lca_bound - - lcb_lower_bound = 0.01 if current_lcb - lcb_bound <= 0.01 else current_lcb - lcb_bound - lcb_upper_bound = 1.6 if current_lcb + lcb_bound >= 1.6 else current_lcb + lcb_bound - - return lca_lower_bound, lca_upper_bound, lcb_lower_bound, lcb_upper_bound - - def opt_lca(self, cost_function, lower_bound, upper_bound, reference, cost_function_args): - - xopt, fval, ierr, numfunc = optimize.fminbound(cost_function, - x1=lower_bound, - x2=upper_bound, - disp=0, - args=cost_function_args, - full_output=True) + lca_lower_bound = ( + 0.01 + if (current_lca - lca_bound) <= 0.01 + else current_lca - lca_bound + ) + lca_upper_bound = ( + 1.6 + if (current_lca + lca_bound) >= 1.6 + else current_lca + lca_bound + ) + + lcb_lower_bound = ( + 0.01 + if current_lcb - lcb_bound <= 0.01 + else current_lcb - lcb_bound + ) + lcb_upper_bound = ( + 1.6 if current_lcb + lcb_bound >= 1.6 else current_lcb + lcb_bound + ) + + return ( + lca_lower_bound, + lca_upper_bound, + lcb_lower_bound, + lcb_upper_bound, + ) + + def opt_lca( + self, + cost_function, + lower_bound, + upper_bound, + reference, + cost_function_args, + ): + + xopt, fval, ierr, numfunc = optimize.fminbound( + cost_function, + x1=lower_bound, + x2=upper_bound, + disp=0, + args=cost_function_args, + full_output=True, + ) lca = xopt - lcb = self.calib.get_lc(self.calib.mmc, self.calib.PROPERTIES['LCA']) + lcb = self.calib.get_lc(self.calib.mmc, self.calib.PROPERTIES["LCA"]) abs_intensity = fval + reference difference = fval / reference * 100 - logging.debug('\tOptimize lca ...') + logging.debug("\tOptimize lca ...") logging.debug(f"\tlca = {lca:.5f}") logging.debug(f"\tlcb = {lcb:.5f}") - logging.debug(f'\tIntensity = {abs_intensity}') - logging.debug(f'\tIntensity Difference = {difference:.4f}%') + logging.debug(f"\tIntensity = {abs_intensity}") + logging.debug(f"\tIntensity Difference = {difference:.4f}%") return [lca, lcb, abs_intensity, difference] - def opt_lcb(self, cost_function, lower_bound, upper_bound, reference, cost_function_args): - - xopt, fval, ierr, numfunc = optimize.fminbound(cost_function, - x1=lower_bound, - x2=upper_bound, - disp=0, - args=cost_function_args, - full_output=True) - - lca = self.calib.get_lc(self.calib.mmc, self.calib.PROPERTIES['LCA']) + def opt_lcb( + self, + cost_function, + lower_bound, + upper_bound, + reference, + cost_function_args, + ): + + xopt, fval, ierr, numfunc = optimize.fminbound( + cost_function, + x1=lower_bound, + x2=upper_bound, + disp=0, + args=cost_function_args, + full_output=True, + ) + + lca = self.calib.get_lc(self.calib.mmc, self.calib.PROPERTIES["LCA"]) lcb = xopt abs_intensity = fval + reference difference = fval / reference * 100 - logging.debug('\tOptimize lcb ...') + logging.debug("\tOptimize lcb ...") logging.debug(f"\tlca = {lca:.5f}") logging.debug(f"\tlcb = {lcb:.5f}") - logging.debug(f'\tIntensity = {abs_intensity}') - logging.debug(f'\tIntensity Difference = {difference:.4f}%') + logging.debug(f"\tIntensity = {abs_intensity}") + logging.debug(f"\tIntensity Difference = {difference:.4f}%") return [lca, lcb, abs_intensity, difference] @@ -76,40 +112,64 @@ def optimize(self, state, lca_bound, lcb_bound, reference, thresh, n_iter): optimal = [] while not converged: - logging.debug(f'iteration: {iteration}') + logging.debug(f"iteration: {iteration}") - lca_lower_bound, lca_upper_bound,\ - lcb_lower_bound, lcb_upper_bound = self._check_bounds(lca_bound, lcb_bound) + ( + lca_lower_bound, + lca_upper_bound, + lcb_lower_bound, + lcb_upper_bound, + ) = self._check_bounds(lca_bound, lcb_bound) - if state == 'ext': + if state == "ext": - results_lca = self.opt_lca(self.calib.opt_lc, lca_lower_bound, lca_upper_bound, - reference, (self.calib.PROPERTIES['LCA'], reference)) + results_lca = self.opt_lca( + self.calib.opt_lc, + lca_lower_bound, + lca_upper_bound, + reference, + (self.calib.PROPERTIES["LCA"], reference), + ) - self.calib.set_lc(self.calib.mmc, results_lca[0], 'LCA') + self.calib.set_lc(self.calib.mmc, results_lca[0], "LCA") optimal.append(results_lca) - results_lcb = self.opt_lcb(self.calib.opt_lc, lcb_lower_bound, lcb_upper_bound, - reference, (self.calib.PROPERTIES['LCB'], reference)) + results_lcb = self.opt_lcb( + self.calib.opt_lc, + lcb_lower_bound, + lcb_upper_bound, + reference, + (self.calib.PROPERTIES["LCB"], reference), + ) - self.calib.set_lc(self.calib.mmc, results_lca[1], 'LCB') + self.calib.set_lc(self.calib.mmc, results_lca[1], "LCB") optimal.append(results_lcb) results = results_lcb - if state == '45' or state == '135': + if state == "45" or state == "135": - results = self.opt_lcb(self.calib.opt_lc, lca_lower_bound, lca_upper_bound, - reference, (self.calib.PROPERTIES['LCB'], reference)) + results = self.opt_lcb( + self.calib.opt_lc, + lca_lower_bound, + lca_upper_bound, + reference, + (self.calib.PROPERTIES["LCB"], reference), + ) optimal.append(results) - if state == '60': + if state == "60": - results = self.opt_lca(self.calib.opt_lc_cons, lca_lower_bound, lca_upper_bound, - reference, (reference, '60')) + results = self.opt_lca( + self.calib.opt_lc_cons, + lca_lower_bound, + lca_upper_bound, + reference, + (reference, "60"), + ) swing = (self.calib.lca_ext - results[0]) * self.calib.ratio lca = results[0] @@ -117,16 +177,26 @@ def optimize(self, state, lca_bound, lcb_bound, reference, thresh, n_iter): optimal.append([lca, lcb, results[2], results[3]]) - if state == '90': + if state == "90": - results = self.opt_lca(self.calib.opt_lc, lca_lower_bound, lca_upper_bound, - reference, (self.calib.PROPERTIES['LCA'], reference)) + results = self.opt_lca( + self.calib.opt_lc, + lca_lower_bound, + lca_upper_bound, + reference, + (self.calib.PROPERTIES["LCA"], reference), + ) optimal.append(results) - if state == '120': - results = self.opt_lca(self.calib.opt_lc_cons, lca_lower_bound, lca_upper_bound, - reference, (reference, '120')) + if state == "120": + results = self.opt_lca( + self.calib.opt_lc_cons, + lca_lower_bound, + lca_upper_bound, + reference, + (reference, "120"), + ) swing = (self.calib.lca_ext - results[0]) * self.calib.ratio lca = results[0] @@ -143,13 +213,17 @@ def optimize(self, state, lca_bound, lcb_bound, reference, thresh, n_iter): # if loop preforms more than n_iter iterations, stop elif iteration >= n_iter: - logging.debug(f'Exceeded {n_iter} Iterations: Search discontinuing') + logging.debug( + f"Exceeded {n_iter} Iterations: Search discontinuing" + ) converged = True optimal = np.asarray(optimal) opt = np.where(optimal == np.min(np.abs(optimal[:, 0])))[0] - logging.debug(f'Lowest Inten: {optimal[opt, 0]}, lca = {optimal[opt, 1]}, lcb = {optimal[opt, 2]}') + logging.debug( + f"Lowest Inten: {optimal[opt, 0]}, lca = {optimal[opt, 1]}, lcb = {optimal[opt, 2]}" + ) return optimal[-1, 0], optimal[-1, 1], optimal[-1, 2] @@ -157,110 +231,195 @@ def optimize(self, state, lca_bound, lcb_bound, reference, thresh, n_iter): class MinScalarOptimizer: - def __init__(self, calib): self.calib = calib def _check_bounds(self, lca_bound, lcb_bound): - current_lca = self.calib.get_lc('LCA') - current_lcb = self.calib.get_lc('LCB') + current_lca = self.calib.get_lc("LCA") + current_lcb = self.calib.get_lc("LCB") - if self.calib.mode == 'voltage': + if self.calib.mode == "voltage": # check that bounds don't exceed range of LC - lca_lower_bound = 0.01 if (current_lca - lca_bound) <= 0.01 else current_lca - lca_bound - lca_upper_bound = 2.2 if (current_lca + lca_bound) >= 2.2 else current_lca + lca_bound - - lcb_lower_bound = 0.01 if current_lcb - lcb_bound <= 0.01 else current_lcb - lcb_bound - lcb_upper_bound = 2.2 if current_lcb + lcb_bound >= 2.2 else current_lcb + lcb_bound + lca_lower_bound = ( + 0.01 + if (current_lca - lca_bound) <= 0.01 + else current_lca - lca_bound + ) + lca_upper_bound = ( + 2.2 + if (current_lca + lca_bound) >= 2.2 + else current_lca + lca_bound + ) + + lcb_lower_bound = ( + 0.01 + if current_lcb - lcb_bound <= 0.01 + else current_lcb - lcb_bound + ) + lcb_upper_bound = ( + 2.2 + if current_lcb + lcb_bound >= 2.2 + else current_lcb + lcb_bound + ) else: # check that bounds don't exceed range of LC - lca_lower_bound = 0.01 if (current_lca - lca_bound) <= 0.01 else current_lca - lca_bound - lca_upper_bound = 1.6 if (current_lca + lca_bound) >= 1.6 else current_lca + lca_bound - - lcb_lower_bound = 0.01 if current_lcb - lcb_bound <= 0.01 else current_lcb - lcb_bound - lcb_upper_bound = 1.6 if current_lcb + lcb_bound >= 1.6 else current_lcb + lcb_bound - - return lca_lower_bound, lca_upper_bound, lcb_lower_bound, lcb_upper_bound - - def opt_lca(self, cost_function, lower_bound, upper_bound, reference, cost_function_args): - - res = optimize.minimize_scalar(cost_function, bounds=(lower_bound, upper_bound), - method='bounded', args=cost_function_args) + lca_lower_bound = ( + 0.01 + if (current_lca - lca_bound) <= 0.01 + else current_lca - lca_bound + ) + lca_upper_bound = ( + 1.6 + if (current_lca + lca_bound) >= 1.6 + else current_lca + lca_bound + ) + + lcb_lower_bound = ( + 0.01 + if current_lcb - lcb_bound <= 0.01 + else current_lcb - lcb_bound + ) + lcb_upper_bound = ( + 1.6 + if current_lcb + lcb_bound >= 1.6 + else current_lcb + lcb_bound + ) + + return ( + lca_lower_bound, + lca_upper_bound, + lcb_lower_bound, + lcb_upper_bound, + ) + + def opt_lca( + self, + cost_function, + lower_bound, + upper_bound, + reference, + cost_function_args, + ): + + res = optimize.minimize_scalar( + cost_function, + bounds=(lower_bound, upper_bound), + method="bounded", + args=cost_function_args, + ) lca = res.x - lcb = self.calib.get_lc('LCB') + lcb = self.calib.get_lc("LCB") abs_intensity = res.fun + reference difference = res.fun / reference * 100 - logging.debug('\tOptimize lca ...') + logging.debug("\tOptimize lca ...") logging.debug(f"\tlca = {lca:.5f}") logging.debug(f"\tlcb = {lcb:.5f}") - logging.debug(f'\tIntensity = {abs_intensity}') - logging.debug(f'\tIntensity Difference = {difference:.4f}%') + logging.debug(f"\tIntensity = {abs_intensity}") + logging.debug(f"\tIntensity Difference = {difference:.4f}%") return [lca, lcb, abs_intensity, difference] - def opt_lcb(self, cost_function, lower_bound, upper_bound, reference, cost_function_args): - - res = optimize.minimize_scalar(cost_function, bounds=(lower_bound, upper_bound), - method='bounded', args=cost_function_args) - - lca = self.calib.get_lc('LCA') + def opt_lcb( + self, + cost_function, + lower_bound, + upper_bound, + reference, + cost_function_args, + ): + + res = optimize.minimize_scalar( + cost_function, + bounds=(lower_bound, upper_bound), + method="bounded", + args=cost_function_args, + ) + + lca = self.calib.get_lc("LCA") lcb = res.x abs_intensity = res.fun + reference difference = res.fun / reference * 100 - logging.debug('\tOptimize lcb ...') + logging.debug("\tOptimize lcb ...") logging.debug(f"\tlca = {lca:.5f}") logging.debug(f"\tlcb = {lcb:.5f}") - logging.debug(f'\tIntensity = {abs_intensity}') - logging.debug(f'\tIntensity Difference = {difference:.4f}%') + logging.debug(f"\tIntensity = {abs_intensity}") + logging.debug(f"\tIntensity Difference = {difference:.4f}%") return [lca, lcb, abs_intensity, difference] - def optimize(self, state, lca_bound, lcb_bound, reference, thresh=None, n_iter=None): + def optimize( + self, state, lca_bound, lcb_bound, reference, thresh=None, n_iter=None + ): - lca_lower_bound, lca_upper_bound, lcb_lower_bound, lcb_upper_bound = self._check_bounds(lca_bound, lcb_bound) + ( + lca_lower_bound, + lca_upper_bound, + lcb_lower_bound, + lcb_upper_bound, + ) = self._check_bounds(lca_bound, lcb_bound) - if state == 'ext': + if state == "ext": optimal = [] - results_lca = self.opt_lca(self.calib.opt_lc, lca_lower_bound, lca_upper_bound, - reference, ('LCA', reference)) + results_lca = self.opt_lca( + self.calib.opt_lc, + lca_lower_bound, + lca_upper_bound, + reference, + ("LCA", reference), + ) - self.calib.set_lc(results_lca[0], 'LCA') + self.calib.set_lc(results_lca[0], "LCA") optimal.append(results_lca) - results_lcb = self.opt_lcb(self.calib.opt_lc, lcb_lower_bound, lcb_upper_bound, - reference, ('LCB', reference)) + results_lcb = self.opt_lcb( + self.calib.opt_lc, + lcb_lower_bound, + lcb_upper_bound, + reference, + ("LCB", reference), + ) - self.calib.set_lc(results_lcb[1], 'LCB') + self.calib.set_lc(results_lcb[1], "LCB") optimal.append(results_lcb) # ============BEGIN FINE SEARCH================= - logging.debug(f'\n\tBeginning Finer Search\n') - lca_lower_bound = results_lcb[0] - .01 - lca_upper_bound = results_lcb[0] + .01 - lcb_lower_bound = results_lcb[1] - .01 - lcb_upper_bound = results_lcb[1] + .01 + logging.debug(f"\n\tBeginning Finer Search\n") + lca_lower_bound = results_lcb[0] - 0.01 + lca_upper_bound = results_lcb[0] + 0.01 + lcb_lower_bound = results_lcb[1] - 0.01 + lcb_upper_bound = results_lcb[1] + 0.01 - results_lca = self.opt_lca(self.calib.opt_lc, lca_lower_bound, lca_upper_bound, - reference, ('LCA', reference)) + results_lca = self.opt_lca( + self.calib.opt_lc, + lca_lower_bound, + lca_upper_bound, + reference, + ("LCA", reference), + ) - self.calib.set_lc(results_lca[0], 'LCA') + self.calib.set_lc(results_lca[0], "LCA") optimal.append(results_lca) - results_lcb = self.opt_lcb(self.calib.opt_lc, lcb_lower_bound, lcb_upper_bound, - reference, ('LCB', reference)) + results_lcb = self.opt_lcb( + self.calib.opt_lc, + lcb_lower_bound, + lcb_upper_bound, + reference, + ("LCB", reference), + ) - self.calib.set_lc(results_lcb[1], 'LCB') + self.calib.set_lc(results_lcb[1], "LCB") optimal.append(results_lcb) @@ -273,30 +432,50 @@ def optimize(self, state, lca_bound, lcb_bound, reference, thresh=None, n_iter=N lcb = float(optimal[opt][0][1]) results = optimal[opt][0] - if state == '45' or state == '135': - results = self.opt_lcb(self.calib.opt_lc, lcb_lower_bound, lcb_upper_bound, - reference, ('LCB', reference)) + if state == "45" or state == "135": + results = self.opt_lcb( + self.calib.opt_lc, + lcb_lower_bound, + lcb_upper_bound, + reference, + ("LCB", reference), + ) lca = results[0] lcb = results[1] - if state == '60': - results = self.opt_lca(self.calib.opt_lc_cons, lca_lower_bound, lca_upper_bound, - reference, ('LCA', reference, '60')) + if state == "60": + results = self.opt_lca( + self.calib.opt_lc_cons, + lca_lower_bound, + lca_upper_bound, + reference, + ("LCA", reference, "60"), + ) swing = (self.calib.lca_ext - results[0]) * self.calib.ratio lca = results[0] lcb = self.calib.lcb_ext + swing - if state == '90': - results = self.opt_lca(self.calib.opt_lc, lca_lower_bound, lca_upper_bound, - reference, ('LCA', reference)) + if state == "90": + results = self.opt_lca( + self.calib.opt_lc, + lca_lower_bound, + lca_upper_bound, + reference, + ("LCA", reference), + ) lca = results[0] lcb = results[1] - if state == '120': - results = self.opt_lca(self.calib.opt_lc_cons, lca_lower_bound, lca_upper_bound, - reference, ('LCB', reference, '120')) + if state == "120": + results = self.opt_lca( + self.calib.opt_lc_cons, + lca_lower_bound, + lca_upper_bound, + reference, + ("LCB", reference, "120"), + ) swing = (self.calib.lca_ext - results[0]) * self.calib.ratio lca = results[0] diff --git a/recOrder/compute/__init__.py b/recOrder/compute/__init__.py index 62937c77..d4cc5512 100644 --- a/recOrder/compute/__init__.py +++ b/recOrder/compute/__init__.py @@ -1,1 +1,1 @@ -from .qlipp_compute import * \ No newline at end of file +from .qlipp_compute import * diff --git a/recOrder/compute/qlipp_compute.py b/recOrder/compute/qlipp_compute.py index ad009307..e99b0c2a 100644 --- a/recOrder/compute/qlipp_compute.py +++ b/recOrder/compute/qlipp_compute.py @@ -3,10 +3,25 @@ import time -def initialize_reconstructor(pipeline, image_dim=None, wavelength_nm=None, swing=None, calibration_scheme=None, - NA_obj=None, NA_illu=None, mag=None, n_slices=None, z_step_um=None, - pad_z=0, pixel_size_um=None, bg_correction='local_fit', n_obj_media=1.0, mode='3D', - use_gpu=False, gpu_id=0): +def initialize_reconstructor( + pipeline, + image_dim=None, + wavelength_nm=None, + swing=None, + calibration_scheme=None, + NA_obj=None, + NA_illu=None, + mag=None, + n_slices=None, + z_step_um=None, + pad_z=0, + pixel_size_um=None, + bg_correction="local_fit", + n_obj_media=1.0, + mode="3D", + use_gpu=False, + gpu_id=0, +): """ Initialize the QLIPP reconstructor for downstream tasks. See tags next to parameters for which parameters are needed for each pipeline @@ -74,76 +89,100 @@ def initialize_reconstructor(pipeline, image_dim=None, wavelength_nm=None, swing reconstructor : object reconstruction object initialized with reconstruction parameters - """ + """ anisotropy_only = False - if pipeline == 'QLIPP' or pipeline == 'PhaseFromBF': + if pipeline == "QLIPP" or pipeline == "PhaseFromBF": if not NA_obj: - raise ValueError('Please specify NA_obj in function parameters') + raise ValueError("Please specify NA_obj in function parameters") if not NA_illu: - raise ValueError('Please specify NA_illu in function parameters') + raise ValueError("Please specify NA_illu in function parameters") if not mag: - raise ValueError('Please specify mag (magnification) in function parameters') + raise ValueError( + "Please specify mag (magnification) in function parameters" + ) if not wavelength_nm: - raise ValueError('Please specify the wavelength for reconstruction') + raise ValueError( + "Please specify the wavelength for reconstruction" + ) if not n_slices: - raise ValueError('Please specify n_slices in function parameters') + raise ValueError("Please specify n_slices in function parameters") if not z_step_um: - raise ValueError('Please specify z_step_um in function parameters') + raise ValueError("Please specify z_step_um in function parameters") if not pixel_size_um: - raise ValueError('Please specify NA_obj in function parameters') + raise ValueError("Please specify NA_obj in function parameters") if not n_obj_media: - raise ValueError('Please specify NA_obj in function parameters') + raise ValueError("Please specify NA_obj in function parameters") if not image_dim: - raise ValueError('Please specify image_dim in function parameters') + raise ValueError("Please specify image_dim in function parameters") - if pipeline == 'QLIPP': + if pipeline == "QLIPP": if not calibration_scheme: - raise ValueError('Please specify qlipp_scheme (calibration scheme) for QLIPP reconstruction') + raise ValueError( + "Please specify qlipp_scheme (calibration scheme) for QLIPP reconstruction" + ) if not swing: - raise ValueError('Please specify swing in function parameters') + raise ValueError("Please specify swing in function parameters") - elif pipeline == 'birefringence': + elif pipeline == "birefringence": anisotropy_only = True if not calibration_scheme: - raise ValueError('Please specify qlipp_scheme (calibration scheme) for QLIPP reconstruction') + raise ValueError( + "Please specify qlipp_scheme (calibration scheme) for QLIPP reconstruction" + ) if not wavelength_nm: - raise ValueError('Please specify the wavelength for QLIPP reconstruction') + raise ValueError( + "Please specify the wavelength for QLIPP reconstruction" + ) if not swing: - raise ValueError('Please specify swing in function parameters') + raise ValueError("Please specify swing in function parameters") else: - raise ValueError(f'Pipeline {pipeline} not understood') + raise ValueError(f"Pipeline {pipeline} not understood") # Modify user inputs to fit waveorder input requirements lambda_illu = wavelength_nm / 1000 if wavelength_nm else None n_defocus = n_slices if n_slices else 0 z_step_um = 0 if not z_step_um else z_step_um - z_defocus = -(np.r_[:n_defocus] - n_defocus // 2) * z_step_um # assumes stack starts from the bottom + z_defocus = ( + -(np.r_[:n_defocus] - n_defocus // 2) * z_step_um + ) # assumes stack starts from the bottom ps = pixel_size_um / mag if pixel_size_um else None cali = True NA_obj = 0 if not NA_obj else NA_obj NA_illu = 0 if not NA_illu else NA_illu - if calibration_scheme == '4-State': - inst_mat = np.array([[1, 0, 0, -1], - [1, np.sin(2 * np.pi * swing), 0, -np.cos(2 * np.pi * swing)], - [1, -0.5 * np.sin(2 * np.pi * swing), np.sqrt(3) * np.cos(np.pi * swing) * np.sin(np.pi * swing), - -np.cos(2 * np.pi * swing)], - [1, -0.5 * np.sin(2 * np.pi * swing), -np.sqrt(3) / 2 * np.sin(2 * np.pi * swing), - -np.cos(2 * np.pi * swing)]]) + if calibration_scheme == "4-State": + inst_mat = np.array( + [ + [1, 0, 0, -1], + [1, np.sin(2 * np.pi * swing), 0, -np.cos(2 * np.pi * swing)], + [ + 1, + -0.5 * np.sin(2 * np.pi * swing), + np.sqrt(3) * np.cos(np.pi * swing) * np.sin(np.pi * swing), + -np.cos(2 * np.pi * swing), + ], + [ + 1, + -0.5 * np.sin(2 * np.pi * swing), + -np.sqrt(3) / 2 * np.sin(2 * np.pi * swing), + -np.cos(2 * np.pi * swing), + ], + ] + ) n_channel = 4 - elif calibration_scheme == '5-State': + elif calibration_scheme == "5-State": swing = swing * 2 * np.pi inst_mat = None n_channel = 5 - elif calibration_scheme == 'PhaseFromBF': + elif calibration_scheme == "PhaseFromBF": inst_mat = None n_channel = 1 swing = 0 @@ -152,34 +191,37 @@ def initialize_reconstructor(pipeline, image_dim=None, wavelength_nm=None, swing n_channel = 1 swing = 0 - print('Initializing Reconstructor...') + print("Initializing Reconstructor...") start_time = time.time() print(bg_correction) - recon = waveorder_microscopy(img_dim=image_dim, - lambda_illu=lambda_illu, - ps=ps, - NA_obj=NA_obj, - NA_illu=NA_illu, - z_defocus=z_defocus, - chi=swing, - n_media=n_obj_media, - cali=cali, - bg_option=bg_correction, - A_matrix=inst_mat, - QLIPP_birefringence_only=anisotropy_only, - pad_z=pad_z, - phase_deconv=mode, - illu_mode='BF', - use_gpu=use_gpu, - gpu_id=gpu_id) + recon = waveorder_microscopy( + img_dim=image_dim, + lambda_illu=lambda_illu, + ps=ps, + NA_obj=NA_obj, + NA_illu=NA_illu, + z_defocus=z_defocus, + chi=swing, + n_media=n_obj_media, + cali=cali, + bg_option=bg_correction, + A_matrix=inst_mat, + QLIPP_birefringence_only=anisotropy_only, + pad_z=pad_z, + phase_deconv=mode, + illu_mode="BF", + use_gpu=use_gpu, + gpu_id=gpu_id, + ) recon.N_channel = n_channel elapsed_time = (time.time() - start_time) / 60 - print(f'Finished Initializing Reconstructor ({elapsed_time:0.2f} min)') + print(f"Finished Initializing Reconstructor ({elapsed_time:0.2f} min)") return recon + def reconstruct_qlipp_stokes(data, recon, bg_stokes=None): """ From intensity data, use the waveorder.waveorder_microscopy (recon) to build a stokes array @@ -208,19 +250,24 @@ def reconstruct_qlipp_stokes(data, recon, bg_stokes=None): stokes_data = recon.Stokes_transform(stokes_data) # Don't do background correction if BG data isn't provided - if recon.bg_option == 'None': - return stokes_data # C(Z)YX + if recon.bg_option == "None": + return stokes_data # C(Z)YX # Compute Stokes with background correction else: if len(np.shape(stokes_data)) == 4: - s_image = recon.Polscope_bg_correction(np.transpose(stokes_data, (-4, -2, -1, -3)), bg_stokes) - s_image = np.transpose(s_image, (0, 3, 1, 2)) # Tranpose to CZYX + s_image = recon.Polscope_bg_correction( + np.transpose(stokes_data, (-4, -2, -1, -3)), bg_stokes + ) + s_image = np.transpose(s_image, (0, 3, 1, 2)) # Tranpose to CZYX else: - s_image = recon.Polscope_bg_correction(stokes_data, bg_stokes) # CYX + s_image = recon.Polscope_bg_correction( + stokes_data, bg_stokes + ) # CYX return s_image + def reconstruct_qlipp_birefringence(stokes, recon): """ From stokes data, use waveorder.waveorder_microscopy (recon) to build a birefringence array @@ -245,16 +292,21 @@ def reconstruct_qlipp_birefringence(stokes, recon): elif stokes.ndim == 3: pass else: - raise ValueError(f'Incompatible stokes dimension: {stokes.shape}') + raise ValueError(f"Incompatible stokes dimension: {stokes.shape}") birefringence = recon.Polarization_recon(np.copy(stokes)) # Return the transposed birefringence array with channel first - return np.transpose(birefringence, (-4, -1, -3, -2)) if len(birefringence.shape) == 4 else birefringence + return ( + np.transpose(birefringence, (-4, -1, -3, -2)) + if len(birefringence.shape) == 4 + else birefringence + ) -def reconstruct_phase2D(S0, recon, method='Tikhonov', reg_p=1e-4, rho=1, - lambda_p=1e-4, itr=50): +def reconstruct_phase2D( + S0, recon, method="Tikhonov", reg_p=1e-4, rho=1, lambda_p=1e-4, itr=50 +): """ Reconstruct 2D phase from a given S0 or BF stack. @@ -275,13 +327,22 @@ def reconstruct_phase2D(S0, recon, method='Tikhonov', reg_p=1e-4, rho=1, """ S0 = np.transpose(S0, (1, 2, 0)) - _, phase2D = recon.Phase_recon(np.copy(S0).astype('float'), method=method, reg_p=reg_p, rho=rho, lambda_p=lambda_p, itr=itr, verbose=False) + _, phase2D = recon.Phase_recon( + np.copy(S0).astype("float"), + method=method, + reg_p=reg_p, + rho=rho, + lambda_p=lambda_p, + itr=itr, + verbose=False, + ) return phase2D -def reconstruct_phase3D(S0, recon, method='Tikhonov', reg_re=1e-4, - rho=1e-3, lambda_re=1e-4, itr=50): +def reconstruct_phase3D( + S0, recon, method="Tikhonov", reg_re=1e-4, rho=1e-3, lambda_re=1e-4, itr=50 +): """ Reconstruct 2D phase from a given S0 or BF stack. @@ -302,19 +363,36 @@ def reconstruct_phase3D(S0, recon, method='Tikhonov', reg_re=1e-4, """ S0 = np.transpose(S0, (1, 2, 0)) - phase3D = recon.Phase_recon_3D(np.copy(S0).astype('float'), method=method, reg_re=reg_re, rho=rho, lambda_re=lambda_re, - itr=itr, verbose=False) + phase3D = recon.Phase_recon_3D( + np.copy(S0).astype("float"), + method=method, + reg_re=reg_re, + rho=rho, + lambda_re=lambda_re, + itr=itr, + verbose=False, + ) phase3D = np.transpose(phase3D, (-1, -3, -2)) return phase3D + class QLIPPBirefringenceCompute: """ Convenience Class for computing QLIPP birefringence only. """ - def __init__(self, shape, scheme, wavelength, swing, n_slices, bg_option, bg_data=None): + def __init__( + self, + shape, + scheme, + wavelength, + swing, + n_slices, + bg_option, + bg_data=None, + ): self.shape = shape self.scheme = scheme @@ -323,16 +401,20 @@ def __init__(self, shape, scheme, wavelength, swing, n_slices, bg_option, bg_dat self.n_slices = n_slices self.bg_option = bg_option - self.reconstructor = initialize_reconstructor(pipeline='birefringence', - image_dim=self.shape, - calibration_scheme=self.scheme, - wavelength_nm=self.wavelength, - swing=self.swing, - bg_correction=self.bg_option, - n_slices=self.n_slices) - - if bg_option != 'None': - self.bg_stokes = reconstruct_qlipp_stokes(bg_data, self.reconstructor) + self.reconstructor = initialize_reconstructor( + pipeline="birefringence", + image_dim=self.shape, + calibration_scheme=self.scheme, + wavelength_nm=self.wavelength, + swing=self.swing, + bg_correction=self.bg_option, + n_slices=self.n_slices, + ) + + if bg_option != "None": + self.bg_stokes = reconstruct_qlipp_stokes( + bg_data, self.reconstructor + ) else: self.bg_stokes = None @@ -350,8 +432,12 @@ def reconstruct(self, array): first channel is retardance [nm] second channel is orientation [0, pi] """ - stokes = reconstruct_qlipp_stokes(array, self.reconstructor, self.bg_stokes) - birefringence = reconstruct_qlipp_birefringence(stokes, self.reconstructor) + stokes = reconstruct_qlipp_stokes( + array, self.reconstructor, self.bg_stokes + ) + birefringence = reconstruct_qlipp_birefringence( + stokes, self.reconstructor + ) birefringence[0] = birefringence[0] / (2 * np.pi) * self.wavelength return birefringence[0:2] diff --git a/recOrder/io/__init__.py b/recOrder/io/__init__.py index 2730cc46..58c8e239 100644 --- a/recOrder/io/__init__.py +++ b/recOrder/io/__init__.py @@ -1,4 +1,4 @@ from .utils import * from .config_reader import ConfigReader from ._reader import napari_get_reader -from .metadata_reader import MetadataReader \ No newline at end of file +from .metadata_reader import MetadataReader diff --git a/recOrder/io/_reader.py b/recOrder/io/_reader.py index 27466956..6df5c4bd 100644 --- a/recOrder/io/_reader.py +++ b/recOrder/io/_reader.py @@ -2,9 +2,10 @@ import zarr from typing import Tuple, List, Dict, Union + def napari_get_reader(path): if isinstance(path, str): - if '.zarr' in path: + if ".zarr" in path: return ome_zarr_reader else: return ome_tif_reader @@ -12,29 +13,34 @@ def napari_get_reader(path): return None -def ome_zarr_reader(path: Union[str, List[str]]) -> List[Tuple[zarr.Array, Dict]]: +def ome_zarr_reader( + path: Union[str, List[str]] +) -> List[Tuple[zarr.Array, Dict]]: reader = WaveorderReader(path) results = list() - zs = zarr.open(path, 'r') + zs = zarr.open(path, "r") names = [] dict_ = zs.attrs.asdict() - wells = dict_['plate']['wells'] + wells = dict_["plate"]["wells"] for well in wells: - path = well['path'] + path = well["path"] well_dict = zs[path].attrs.asdict() - for name in well_dict['well']['images']: - names.append(name['path']) + for name in well_dict["well"]["images"]: + names.append(name["path"]) for pos in range(reader.get_num_positions()): meta = dict() name = names[pos] - meta['name'] = name + meta["name"] = name results.append((reader.get_zarr(pos), meta)) return results -def ome_tif_reader(path: Union[str, List[str]]) -> List[Tuple[zarr.Array, Dict]]: + +def ome_tif_reader( + path: Union[str, List[str]] +) -> List[Tuple[zarr.Array, Dict]]: reader = WaveorderReader(path) results = list() @@ -42,9 +48,9 @@ def ome_tif_reader(path: Union[str, List[str]]) -> List[Tuple[zarr.Array, Dict]] for pos in range(npos): meta = dict() if npos == 1: - meta['name'] = 'Pos000_000' + meta["name"] = "Pos000_000" else: - meta['name'] = reader.stage_positions[pos]['Label'][2:] + meta["name"] = reader.stage_positions[pos]["Label"][2:] results.append((reader.get_zarr(pos), meta)) - return results \ No newline at end of file + return results diff --git a/recOrder/io/config_reader.py b/recOrder/io/config_reader.py index 5dd107cd..0744f94c 100644 --- a/recOrder/io/config_reader.py +++ b/recOrder/io/config_reader.py @@ -4,72 +4,81 @@ import warnings DATASET = { - 'method': None, - 'mode': None, - 'data_dir': None, - 'save_dir': None, - 'data_type': 'zarr', - 'data_save_name': None, - 'positions': 'all', - 'timepoints': 'all', - 'background': None, - 'background_ROI': None, - 'calibration_metadata': None + "method": None, + "mode": None, + "data_dir": None, + "save_dir": None, + "data_type": "zarr", + "data_save_name": None, + "positions": "all", + "timepoints": "all", + "background": None, + "background_ROI": None, + "calibration_metadata": None, } PROCESSING = { - 'output_channels': None, - 'qlipp_birefringence_only': False, - 'background_correction': 'None', - 'use_gpu': False, - 'gpu_id': 0, - 'wavelength': None, - 'pixel_size': None, - 'magnification': None, - 'NA_objective': None, - 'NA_condenser': None, - 'n_objective_media': 1.003, - 'brightfield_channel_index': 0, - 'z_step': None, - 'focus_zidx': None, - 'reg': 1e-4, - 'phase_denoiser_2D': 'Tikhonov', - 'Tik_reg_abs_2D': 1e-4, - 'Tik_reg_ph_2D': 1e-4, - 'rho_2D': 1, - 'itr_2D': 50, - 'TV_reg_abs_2D': 1e-4, - 'TV_reg_ph_2D': 1e-4, - 'phase_denoiser_3D': 'Tikhonov', - 'rho_3D': 1e-3, - 'itr_3D': 50, - 'Tik_reg_ph_3D': 1e-4, - 'TV_reg_ph_3D': 5e-5, - 'pad_z': 0 + "output_channels": None, + "qlipp_birefringence_only": False, + "background_correction": "None", + "use_gpu": False, + "gpu_id": 0, + "wavelength": None, + "pixel_size": None, + "magnification": None, + "NA_objective": None, + "NA_condenser": None, + "n_objective_media": 1.003, + "brightfield_channel_index": 0, + "z_step": None, + "focus_zidx": None, + "reg": 1e-4, + "phase_denoiser_2D": "Tikhonov", + "Tik_reg_abs_2D": 1e-4, + "Tik_reg_ph_2D": 1e-4, + "rho_2D": 1, + "itr_2D": 50, + "TV_reg_abs_2D": 1e-4, + "TV_reg_ph_2D": 1e-4, + "phase_denoiser_3D": "Tikhonov", + "rho_3D": 1e-3, + "itr_3D": 50, + "Tik_reg_ph_3D": 1e-4, + "TV_reg_ph_3D": 5e-5, + "pad_z": 0, } -class ConfigReader(): + +class ConfigReader: """ Config Reader handles all of the requirements necessary for running the pipeline. Default values are used for those that do not need to be specified. CLI will always overried config """ - def __init__(self, cfg_path=None, data_dir=None, save_dir=None, method=None, mode=None, name=None, immutable=True): + def __init__( + self, + cfg_path=None, + data_dir=None, + save_dir=None, + method=None, + mode=None, + name=None, + immutable=True, + ): self.immutable = immutable - if data_dir is not None: - setattr(self, 'data_dir', data_dir) + setattr(self, "data_dir", data_dir) if save_dir is not None: - setattr(self, 'save_dir', save_dir) + setattr(self, "save_dir", save_dir) if method is not None: - setattr(self, 'method', method) + setattr(self, "method", method) if mode is not None: - setattr(self, 'mode', mode) + setattr(self, "mode", mode) if name is not None: - setattr(self, 'data_save_name', name) + setattr(self, "data_save_name", name) # initialize defaults for key, value in DATASET.items(): @@ -87,7 +96,7 @@ def __init__(self, cfg_path=None, data_dir=None, save_dir=None, method=None, mod self.read_config(cfg_path, data_dir, save_dir, method, mode, name) # Create yaml dict to save in save_dir - setattr(self, 'yaml_dict', self._create_yaml_dict()) + setattr(self, "yaml_dict", self._create_yaml_dict()) # self._save_yaml() # Override set attribute function to disallow changes after init @@ -100,54 +109,106 @@ def __setattr__(self, name, value): def _check_assertions(self, data_dir, save_dir, method, mode, name): # assert main fields of config - assert 'dataset' in self.config, \ - 'dataset is a required field in the config yaml file' - assert 'processing' in self.config, \ - 'processing is a required field in the config yaml file' - - if not method: assert 'method' in self.config['dataset'], \ - 'Please provide method in config file or CLI argument' - if not mode: assert 'mode' in self.config['dataset'], \ - 'Please provide mode in config file or CLI argument' - if not data_dir: assert 'data_dir' in self.config['dataset'], \ - 'Please provide data_dir in config file or CLI argument' - if not save_dir: assert 'save_dir' in self.config['dataset'], \ - 'Please provide save_dir in config file or CLI argument' - - if self.config['dataset']['positions'] != 'all' and not isinstance(self.config['dataset']['positions'], int): - assert isinstance(self.config['dataset']['positions'], list), \ - 'if not single integer value or "all", positions must be list (nested lists/tuples allowed)' - if self.config['dataset']['timepoints'] != 'all' and not isinstance(self.config['dataset']['timepoints'], int): - assert isinstance(self.config['dataset']['timepoints'], list), \ - 'if not single integer value or "all", timepoints must be list (nested lists/tuples allowed)' - - for key,value in PROCESSING.items(): - if key == 'output_channels': - if 'Phase3D' in self.config['processing'][key] or 'Phase2D' in self.config['processing'][key]: - assert 'wavelength' in self.config['processing'], 'wavelength required for phase reconstruction' - assert 'pixel_size' in self.config['processing'], 'pixel_size required for phase reconstruction' - assert 'magnification' in self.config['processing'], 'magnification required for phase reconstruction' - assert 'NA_objective' in self.config['processing'], 'NA_objective required for phase reconstruction' - assert 'NA_condenser' in self.config['processing'], 'NA_condenser required for phase reconstruction' - - if 'Phase3D' in self.config['processing'][key] and 'Phase2D' in self.config['processing'][key]: - raise KeyError(f'Both Phase3D and Phase2D cannot be specified in {key}. Please compute separately') - - if 'Phase3D' in self.config['processing'][key] and self.config['dataset']['mode'] == '2D': - raise KeyError(f'Specified mode is 2D and Phase3D was specified for reconstruction. ' - 'Only 2D reconstructions can be performed in 2D mode') - - if 'Phase2D' in self.config['processing'][key] and self.config['dataset']['mode'] == '3D': - raise KeyError(f'Specified mode is 3D and Phase2D was specified for reconstruction. ' - 'Only 3D reconstructions can be performed in 3D mode') - - elif key == 'background_correction' and self.config['dataset']['method'] == 'QLIPP': - mode = self.config['processing'][key] - if mode == 'None' or mode == 'local_fit' or mode == 'local_fit+': + assert ( + "dataset" in self.config + ), "dataset is a required field in the config yaml file" + assert ( + "processing" in self.config + ), "processing is a required field in the config yaml file" + + if not method: + assert ( + "method" in self.config["dataset"] + ), "Please provide method in config file or CLI argument" + if not mode: + assert ( + "mode" in self.config["dataset"] + ), "Please provide mode in config file or CLI argument" + if not data_dir: + assert ( + "data_dir" in self.config["dataset"] + ), "Please provide data_dir in config file or CLI argument" + if not save_dir: + assert ( + "save_dir" in self.config["dataset"] + ), "Please provide save_dir in config file or CLI argument" + + if self.config["dataset"]["positions"] != "all" and not isinstance( + self.config["dataset"]["positions"], int + ): + assert isinstance( + self.config["dataset"]["positions"], list + ), 'if not single integer value or "all", positions must be list (nested lists/tuples allowed)' + if self.config["dataset"]["timepoints"] != "all" and not isinstance( + self.config["dataset"]["timepoints"], int + ): + assert isinstance( + self.config["dataset"]["timepoints"], list + ), 'if not single integer value or "all", timepoints must be list (nested lists/tuples allowed)' + + for key, value in PROCESSING.items(): + if key == "output_channels": + if ( + "Phase3D" in self.config["processing"][key] + or "Phase2D" in self.config["processing"][key] + ): + assert ( + "wavelength" in self.config["processing"] + ), "wavelength required for phase reconstruction" + assert ( + "pixel_size" in self.config["processing"] + ), "pixel_size required for phase reconstruction" + assert ( + "magnification" in self.config["processing"] + ), "magnification required for phase reconstruction" + assert ( + "NA_objective" in self.config["processing"] + ), "NA_objective required for phase reconstruction" + assert ( + "NA_condenser" in self.config["processing"] + ), "NA_condenser required for phase reconstruction" + + if ( + "Phase3D" in self.config["processing"][key] + and "Phase2D" in self.config["processing"][key] + ): + raise KeyError( + f"Both Phase3D and Phase2D cannot be specified in {key}. Please compute separately" + ) + + if ( + "Phase3D" in self.config["processing"][key] + and self.config["dataset"]["mode"] == "2D" + ): + raise KeyError( + f"Specified mode is 2D and Phase3D was specified for reconstruction. " + "Only 2D reconstructions can be performed in 2D mode" + ) + + if ( + "Phase2D" in self.config["processing"][key] + and self.config["dataset"]["mode"] == "3D" + ): + raise KeyError( + f"Specified mode is 3D and Phase2D was specified for reconstruction. " + "Only 3D reconstructions can be performed in 3D mode" + ) + + elif ( + key == "background_correction" + and self.config["dataset"]["method"] == "QLIPP" + ): + mode = self.config["processing"][key] + if ( + mode == "None" + or mode == "local_fit" + or mode == "local_fit+" + ): pass else: - assert self.config['dataset']['background'] is not None, \ - 'path to background data must be specified for this background correction method' + assert ( + self.config["dataset"]["background"] is not None + ), "path to background data must be specified for this background correction method" def save_yaml(self, dir_=None, name=None): self.immutable = False @@ -155,84 +216,96 @@ def save_yaml(self, dir_=None, name=None): self.immutable = True dir_ = self.save_dir if dir_ is None else dir_ - name = f'config_{self.data_save_name}.yml' if name is None else name - if not name.endswith('.yml'): - name += '.yml' + name = f"config_{self.data_save_name}.yml" if name is None else name + if not name.endswith(".yml"): + name += ".yml" if not os.path.exists(dir_): os.mkdir(dir_) - with open(os.path.join(dir_, name), 'w') as file: + with open(os.path.join(dir_, name), "w") as file: yaml.dump(self.yaml_dict, file) file.close() def _create_yaml_dict(self): - yaml_dict = {'dataset': {}, - 'processing': {}} + yaml_dict = {"dataset": {}, "processing": {}} for key, value in DATASET.items(): - yaml_dict['dataset'][key] = getattr(self, key) + yaml_dict["dataset"][key] = getattr(self, key) for key, value in PROCESSING.items(): - yaml_dict['processing'][key] = getattr(self, key) + yaml_dict["processing"][key] = getattr(self, key) return yaml_dict def _use_default_name(self): path = pathlib.PurePath(self.data_dir) - super().__setattr__('data_save_name', path.name) + super().__setattr__("data_save_name", path.name) def _parse_dataset(self): - for key, value in self.config['dataset'].items(): + for key, value in self.config["dataset"].items(): if key in DATASET.keys(): # This section will prioritize the command line arguments over the # config arguments (data_dir, save_dir, data_save_name) - if key == 'name' and self.data_save_name: + if key == "name" and self.data_save_name: continue - elif key == 'data_dir' and self.data_dir: + elif key == "data_dir" and self.data_dir: continue - elif key == 'save_dir' and self.save_dir: + elif key == "save_dir" and self.save_dir: continue - elif key == 'mode' and self.mode: + elif key == "mode" and self.mode: continue - elif key == 'method' and self.method: + elif key == "method" and self.method: continue # this section appends the rest of the dataset parameters specified - elif key == 'positions' or key == 'timepoints': + elif key == "positions" or key == "timepoints": if isinstance(value, str): - if value == 'all': + if value == "all": super().__setattr__(key, [value]) else: - raise KeyError(f'{key} value {value} not understood,\ - please specify a list or "all"') + raise KeyError( + f'{key} value {value} not understood,\ + please specify a list or "all"' + ) elif isinstance(value, int): super().__setattr__(key, [value]) elif isinstance(value, tuple): if len(value) == 2: super().__setattr__(key, value) else: - raise KeyError(f'{key} value {value} is not a tuple with length of 2') + raise KeyError( + f"{key} value {value} is not a tuple with length of 2" + ) elif isinstance(value, list): super().__setattr__(key, value) else: - raise KeyError(f'{key} value {value} format not understood. \ - Must be list with nested tuple or list or "all"') + raise KeyError( + f'{key} value {value} format not understood. \ + Must be list with nested tuple or list or "all"' + ) else: super().__setattr__(key, value) else: - warnings.warn(f'yaml DATASET config field {key} is not recognized') + warnings.warn( + f"yaml DATASET config field {key} is not recognized" + ) def _parse_processing(self): - for key, value in self.config['processing'].items(): + for key, value in self.config["processing"].items(): if key in PROCESSING.keys(): super().__setattr__(key, value) else: - warnings.warn(f'yaml PROCESSING config field {key} is not recognized') + warnings.warn( + f"yaml PROCESSING config field {key} is not recognized" + ) - if 'Phase3D' not in self.output_channels and 'Phase2D' not in self.output_channels: - super().__setattr__('qlipp_birefringence_only', True) + if ( + "Phase3D" not in self.output_channels + and "Phase2D" not in self.output_channels + ): + super().__setattr__("qlipp_birefringence_only", True) def read_config(self, cfg_path, data_dir, save_dir, method, mode, name): @@ -244,6 +317,6 @@ def read_config(self, cfg_path, data_dir, save_dir, method, mode, name): self._check_assertions(data_dir, save_dir, method, mode, name) self._parse_dataset() self._parse_processing() - + if not self.data_save_name: self._use_default_name() diff --git a/recOrder/io/core_functions.py b/recOrder/io/core_functions.py index 8c432512..2c12529b 100644 --- a/recOrder/io/core_functions.py +++ b/recOrder/io/core_functions.py @@ -18,7 +18,9 @@ def snap_image(mmc): """ mmc.snapImage() - time.sleep(0.3) # sleep after snap to make sure the image we grab is the correct one + time.sleep( + 0.3 + ) # sleep after snap to make sure the image we grab is the correct one return mmc.getImage() @@ -65,12 +67,16 @@ def snap_and_get_image(snap_manager): """ snap_manager.snap(True) - time.sleep(0.3) # sleep after snap to make sure the image we grab is the correct one + time.sleep( + 0.3 + ) # sleep after snap to make sure the image we grab is the correct one # get pixels + dimensions height = snap_manager.getDisplay().getDisplayedImages().get(0).getHeight() width = snap_manager.getDisplay().getDisplayedImages().get(0).getWidth() - array = snap_manager.getDisplay().getDisplayedImages().get(0).getRawPixels() + array = ( + snap_manager.getDisplay().getDisplayedImages().get(0).getRawPixels() + ) return np.reshape(array, (height, width)) @@ -91,7 +97,9 @@ def snap_and_average(snap_manager, display=True): """ snap_manager.snap(display) - time.sleep(0.3) # sleep after snap to make sure the image we grab is the correct one + time.sleep( + 0.3 + ) # sleep after snap to make sure the image we grab is the correct one return snap_manager.getDisplay().getImagePlus().getStatistics().umean @@ -117,11 +125,13 @@ def set_lc_waves(mmc, device_property: tuple, value: float): prop_name = device_property[1] if value > 1.6 or value < 0.001: - raise ValueError(f"Requested retardance value is {value} waves. " - f"Retardance must be greater than 0.001 and less than 1.6 waves.") + raise ValueError( + f"Requested retardance value is {value} waves. " + f"Retardance must be greater than 0.001 and less than 1.6 waves." + ) mmc.setProperty(device_name, prop_name, str(value)) - time.sleep(20/1000) + time.sleep(20 / 1000) def set_lc_voltage(mmc, device_property: tuple, value: float): @@ -145,8 +155,10 @@ def set_lc_voltage(mmc, device_property: tuple, value: float): prop_name = device_property[1] if value > 20.0 or value < 0.0: - raise ValueError(f"Requested LC voltage is {value} V. " - f"LC voltage must be greater than 0.0 and less than 20.0 V.") + raise ValueError( + f"Requested LC voltage is {value} V. " + f"LC voltage must be greater than 0.0 and less than 20.0 V." + ) mmc.setProperty(device_name, prop_name, str(value)) time.sleep(20 / 1000) @@ -173,7 +185,9 @@ def set_lc_daq(mmc, device_property: tuple, value: float): prop_name = device_property[1] if value > 5.0 or value < 0.0: - raise ValueError("DAC voltage must be greater than 0.0 and less than 5.0") + raise ValueError( + "DAC voltage must be greater than 0.0 and less than 5.0" + ) mmc.setProperty(device_name, prop_name, str(value)) time.sleep(20 / 1000) @@ -229,7 +243,9 @@ def define_meadowlark_state(mmc, device_property: tuple): mmc.waitForDevice(device_name) -def define_config_state(mmc, group: str, config: str, device_properties: list, values: list): +def define_config_state( + mmc, group: str, config: str, device_properties: list, values: list +): """ Define config state by specifying the values for all device properties in this config @@ -274,4 +290,4 @@ def set_lc_state(mmc, group: str, config: str): """ mmc.setConfig(group, config) - time.sleep(20/1000) # delay for LC settle time \ No newline at end of file + time.sleep(20 / 1000) # delay for LC settle time diff --git a/recOrder/io/metadata_reader.py b/recOrder/io/metadata_reader.py index a77230d7..65fce0b0 100644 --- a/recOrder/io/metadata_reader.py +++ b/recOrder/io/metadata_reader.py @@ -4,14 +4,20 @@ def load_json(path): - with open(path, 'r') as f: + with open(path, "r") as f: data = json.load(f) return data def get_last_metadata_file(path): - last_metadata_file = natsorted([file for file in os.listdir(path) if file.startswith('calibration_metadata')])[-1] + last_metadata_file = natsorted( + [ + file + for file in os.listdir(path) + if file.startswith("calibration_metadata") + ] + )[-1] return os.path.join(path, last_metadata_file) @@ -30,38 +36,42 @@ def __init__(self, path: str): self.metadata_path = path self.json_metadata = load_json(self.metadata_path) - self.Timestamp = self.get_summary_calibration_attr('Timestamp') - self.recOrder_napari_verion = self.get_summary_calibration_attr('recOrder-napari version') - self.waveorder_version = self.get_summary_calibration_attr('waveorder version') + self.Timestamp = self.get_summary_calibration_attr("Timestamp") + self.recOrder_napari_verion = self.get_summary_calibration_attr( + "recOrder-napari version" + ) + self.waveorder_version = self.get_summary_calibration_attr( + "waveorder version" + ) self.Calibration_scheme = self.get_calibration_scheme() self.Swing = self.get_swing() - self.Wavelength = self.get_summary_calibration_attr('Wavelength (nm)') + self.Wavelength = self.get_summary_calibration_attr("Wavelength (nm)") self.Black_level = self.get_black_level() self.Extinction_ratio = self.get_extinction_ratio() - self.ROI = tuple(self.get_roi()) # JSON does not preserve tuples + self.ROI = tuple(self.get_roi()) # JSON does not preserve tuples self.Channel_names = self.get_channel_names() - self.LCA_retardance = self.get_lc_retardance('LCA') - self.LCB_retardance = self.get_lc_retardance('LCB') - self.LCA_voltage = self.get_lc_voltage('LCA') - self.LCB_voltage = self.get_lc_voltage('LCB') + self.LCA_retardance = self.get_lc_retardance("LCA") + self.LCB_retardance = self.get_lc_retardance("LCB") + self.LCA_voltage = self.get_lc_voltage("LCA") + self.LCB_voltage = self.get_lc_voltage("LCB") self.Swing_measured = self.get_swing_measured() self.Notes = self.get_notes() def get_summary_calibration_attr(self, attr): try: - val = self.json_metadata['Summary'][attr] + val = self.json_metadata["Summary"][attr] except KeyError: try: - val = self.json_metadata['Calibration'][attr] + val = self.json_metadata["Calibration"][attr] except KeyError: val = None return val def get_cal_states(self): - if self.Calibration_scheme == '4-State': - states = ['ext', '0', '60', '120'] - elif self.Calibration_scheme == '5-State': - states = ['ext', '0', '45', '90', '135'] + if self.Calibration_scheme == "4-State": + states = ["ext", "0", "60", "120"] + elif self.Calibration_scheme == "5-State": + states = ["ext", "0", "45", "90", "135"] return states def get_lc_retardance(self, lc): @@ -79,13 +89,28 @@ def get_lc_retardance(self, lc): val = None try: - val = [self.json_metadata['Calibration']['LC retardance'][f'{lc}_{state}'] for state in states] + val = [ + self.json_metadata["Calibration"]["LC retardance"][ + f"{lc}_{state}" + ] + for state in states + ] except KeyError: - states[0] = 'Ext' - if lc == 'LCA': - val = [self.json_metadata['Summary'][f'[LCA_{state}, LCB_{state}]'][0] for state in states] - elif lc == 'LCB': - val = [self.json_metadata['Summary'][f'[LCA_{state}, LCB_{state}]'][1] for state in states] + states[0] = "Ext" + if lc == "LCA": + val = [ + self.json_metadata["Summary"][ + f"[LCA_{state}, LCB_{state}]" + ][0] + for state in states + ] + elif lc == "LCB": + val = [ + self.json_metadata["Summary"][ + f"[LCA_{state}, LCB_{state}]" + ][1] + for state in states + ] return val @@ -103,68 +128,82 @@ def get_lc_voltage(self, lc): states = self.get_cal_states() val = None - if 'Calibration' in self.json_metadata: - lc_voltage = self.json_metadata['Calibration']['LC voltage'] + if "Calibration" in self.json_metadata: + lc_voltage = self.json_metadata["Calibration"]["LC voltage"] if lc_voltage: - val = [self.json_metadata['Calibration']['LC voltage'][f'{lc}_{state}'] for state in states] + val = [ + self.json_metadata["Calibration"]["LC voltage"][ + f"{lc}_{state}" + ] + for state in states + ] return val def get_swing(self): try: - val = self.json_metadata['Calibration']['Swing (waves)'] + val = self.json_metadata["Calibration"]["Swing (waves)"] except KeyError: - val = self.json_metadata['Summary']['Swing (fraction)'] + val = self.json_metadata["Summary"]["Swing (fraction)"] return val def get_swing_measured(self): states = self.get_cal_states() try: - val = [self.json_metadata['Calibration'][f'Swing_{state}'] for state in states[1:]] + val = [ + self.json_metadata["Calibration"][f"Swing_{state}"] + for state in states[1:] + ] except KeyError: - val = [self.json_metadata['Summary'][f'Swing{state}'] for state in states[1:]] + val = [ + self.json_metadata["Summary"][f"Swing{state}"] + for state in states[1:] + ] return val def get_calibration_scheme(self): try: - val = self.json_metadata['Calibration']['Calibration scheme'] + val = self.json_metadata["Calibration"]["Calibration scheme"] except KeyError: - val = self.json_metadata['Summary']['Acquired Using'] + val = self.json_metadata["Summary"]["Acquired Using"] return val def get_black_level(self): try: - val = self.json_metadata['Calibration']['Black level'] + val = self.json_metadata["Calibration"]["Black level"] except KeyError: - val = self.json_metadata['Summary']['BlackLevel'] + val = self.json_metadata["Summary"]["BlackLevel"] return val def get_extinction_ratio(self): try: - val = self.json_metadata['Calibration']['Extinction ratio'] + val = self.json_metadata["Calibration"]["Extinction ratio"] except KeyError: - val = self.json_metadata['Summary']['Extinction Ratio'] + val = self.json_metadata["Summary"]["Extinction Ratio"] return val def get_roi(self): try: - val = self.json_metadata['Calibration']['ROI (x, y, width, height)'] + val = self.json_metadata["Calibration"][ + "ROI (x, y, width, height)" + ] except KeyError: - val = self.json_metadata['Summary']['ROI Used (x, y, width, height)'] + val = self.json_metadata["Summary"][ + "ROI Used (x, y, width, height)" + ] return val def get_channel_names(self): try: - val = self.json_metadata['Calibration']['Channel names'] + val = self.json_metadata["Calibration"]["Channel names"] except KeyError: - val = self.json_metadata['Summary']['ChNames'] + val = self.json_metadata["Summary"]["ChNames"] return val def get_notes(self): try: - val = self.json_metadata['Notes'] + val = self.json_metadata["Notes"] except KeyError: val = None return val - diff --git a/recOrder/io/utils.py b/recOrder/io/utils.py index 7ab08c87..00eb91ca 100644 --- a/recOrder/io/utils.py +++ b/recOrder/io/utils.py @@ -282,7 +282,9 @@ def generic_hsv_overlay( return overlay_final[0] if mode == "2D" else overlay_final -def ret_ori_overlay(retardance, orientation, ret_max = 10, mode="2D", cmap="JCh"): +def ret_ori_overlay( + retardance, orientation, ret_max=10, mode="2D", cmap="JCh" +): """ This function will create an overlay of retardance and orientation with two different colormap options. HSV is the standard Hue, Saturation, Value colormap while JCh is a similar colormap but is perceptually uniform. diff --git a/recOrder/io/zarr_converter.py b/recOrder/io/zarr_converter.py index b030f42e..cae4c775 100644 --- a/recOrder/io/zarr_converter.py +++ b/recOrder/io/zarr_converter.py @@ -17,7 +17,14 @@ class ZarrConverter: for tiled acquisitions) """ - def __init__(self, input_dir, output_dir, data_type=None, replace_position_names=False, format_hcs=False): + def __init__( + self, + input_dir, + output_dir, + data_type=None, + replace_position_names=False, + format_hcs=False, + ): """ Parameters @@ -32,26 +39,33 @@ def __init__(self, input_dir, output_dir, data_type=None, replace_position_names format_hcs: bool """ - if not output_dir.endswith('.zarr'): - raise ValueError('Please specify .zarr at the end of your output') + if not output_dir.endswith(".zarr"): + raise ValueError("Please specify .zarr at the end of your output") # Init File IO Properties - self.version = 'recOrder converter version=0.5' + self.version = "recOrder converter version=0.5" self.data_directory = input_dir self.save_directory = os.path.dirname(output_dir) # self.files = glob.glob(os.path.join(self.data_directory, '*.tif')) self.meta_file = None - print('Initializing Data...') - self.reader = WaveorderReader(self.data_directory, data_type, extract_data=False) + print("Initializing Data...") + self.reader = WaveorderReader( + self.data_directory, data_type, extract_data=False + ) self.data_type = self.reader.data_type - print('Finished initializing data') + print("Finished initializing data") - self.summary_metadata = self.reader.mm_meta['Summary'] if self.reader.mm_meta else None + self.summary_metadata = ( + self.reader.mm_meta["Summary"] if self.reader.mm_meta else None + ) self.save_name = os.path.basename(output_dir) - if self.data_type != 'upti': - self.mfile_name = os.path.join(self.save_directory, f'{self.save_name.strip(".zarr")}_ImagePlaneMetadata.txt') - self.meta_file = open(self.mfile_name, 'a') + if self.data_type != "upti": + self.mfile_name = os.path.join( + self.save_directory, + f'{self.save_name.strip(".zarr")}_ImagePlaneMetadata.txt', + ) + self.meta_file = open(self.mfile_name, "a") self.replace_position_names = replace_position_names self.format_hcs = format_hcs @@ -78,19 +92,28 @@ def __init__(self, input_dir, output_dir, data_type=None, replace_position_names self.dim = (self.p, self.t, self.c, self.z, self.y, self.x) self.focus_z = self.z // 2 self.prefix_list = [] - print(f'Found Dataset {self.save_name} w/ dimensions (P, T, C, Z, Y, X): {self.dim}') + print( + f"Found Dataset {self.save_name} w/ dimensions (P, T, C, Z, Y, X): {self.dim}" + ) # Generate coordinate set self._gen_coordset() # Initialize Metadata Dictionary self.metadata = dict() - self.metadata['recOrder_Converter_Version'] = self.version - self.metadata['Summary'] = self.summary_metadata + self.metadata["recOrder_Converter_Version"] = self.version + self.metadata["Summary"] = self.summary_metadata # initialize metadata if HCS desired, init writer - self.hcs_meta = self._generate_hcs_metadata() if self.format_hcs else None - self.writer = WaveorderWriter(self.save_directory, hcs=self.format_hcs, hcs_meta=self.hcs_meta, verbose=False) + self.hcs_meta = ( + self._generate_hcs_metadata() if self.format_hcs else None + ) + self.writer = WaveorderWriter( + self.save_directory, + hcs=self.format_hcs, + hcs_meta=self.hcs_meta, + verbose=False, + ) self.writer.create_zarr_root(self.save_name) def _gen_coordset(self): @@ -105,28 +128,38 @@ def _gen_coordset(self): """ # if acquisition information is not present, make an arbitrary dimension order - if not self.summary_metadata or 'AxisOrder' not in self.summary_metadata.keys(): + if ( + not self.summary_metadata + or "AxisOrder" not in self.summary_metadata.keys() + ): self.p_dim = 0 self.t_dim = 1 self.c_dim = 2 self.z_dim = 3 - self.dim_order = ['position', 'time', 'channel', 'z'] + self.dim_order = ["position", "time", "channel", "z"] # Assume data was collected slice first - dims = [self.reader.slices, self.reader.channels, self.reader.frames, self.reader.get_num_positions()] + dims = [ + self.reader.slices, + self.reader.channels, + self.reader.frames, + self.reader.get_num_positions(), + ] # get the order in which the data was collected to minimize i/o calls else: # 4 possible dimensions: p, c, t, z n_dim = 4 - hashmap = {'position': self.p, - 'time': self.t, - 'channel': self.c, - 'z': self.z} + hashmap = { + "position": self.p, + "time": self.t, + "channel": self.c, + "z": self.z, + } - self.dim_order = copy.copy(self.summary_metadata['AxisOrder']) + self.dim_order = copy.copy(self.summary_metadata["AxisOrder"]) dims = [] for i in range(n_dim): @@ -137,14 +170,19 @@ def _gen_coordset(self): # Reverse the dimension order and gather dimension indices self.dim_order.reverse() - self.p_dim = self.dim_order.index('position') - self.t_dim = self.dim_order.index('time') - self.c_dim = self.dim_order.index('channel') - self.z_dim = self.dim_order.index('z') + self.p_dim = self.dim_order.index("position") + self.t_dim = self.dim_order.index("time") + self.c_dim = self.dim_order.index("channel") + self.z_dim = self.dim_order.index("z") # create array of coordinate tuples with innermost dimension being the first dim acquired - self.coords = [(dim3, dim2, dim1, dim0) for dim3 in range(dims[3]) for dim2 in range(dims[2]) - for dim1 in range(dims[1]) for dim0 in range(dims[0])] + self.coords = [ + (dim3, dim2, dim1, dim0) + for dim3 in range(dims[3]) + for dim2 in range(dims[2]) + for dim1 in range(dims[1]) + for dim0 in range(dims[0]) + ] def _get_position_coords(self): @@ -152,16 +190,16 @@ def _get_position_coords(self): col_max = 0 coords_list = [] - #TODO: read rows, cols directly from XY corods - #TODO: account for non MM2gamma meta? + # TODO: read rows, cols directly from XY corods + # TODO: account for non MM2gamma meta? for idx, pos in enumerate(self.reader.stage_positions): - coords_list.append(pos['XYStage']) - row = pos['GridRow'] - col = pos['GridCol'] + coords_list.append(pos["XYStage"]) + row = pos["GridRow"] + col = pos["GridCol"] row_max = row if row > row_max else row_max col_max = col if col > col_max else col_max - return coords_list, row_max+1, col_max+1 + return coords_list, row_max + 1, col_max + 1 def _generate_hcs_metadata(self): @@ -170,20 +208,31 @@ def _generate_hcs_metadata(self): position_grid = create_grid_from_coordinates(position_list, rows, cols) # Build metadata based off of position grid - hcs_meta = {'plate': { - 'acquisitions': [{'id': 1, - 'maximumfieldcount': 1, - 'name': 'Dataset', - 'starttime': 0}], - 'columns': [{'name': f'Col_{i}'} for i in range(cols)], - - 'field_count': 1, - 'name': 'name', - 'rows': [{'name': f'Row_{i}'} for i in range(rows)], - 'version': '0.1', - 'wells': [{'path': f'Row_{i}/Col_{j}'} for i in range(rows) for j in range(cols)]}, - - 'well': [{'images': [{'path': f'Pos_{pos:03d}'}]} for pos in position_grid.flatten()] + hcs_meta = { + "plate": { + "acquisitions": [ + { + "id": 1, + "maximumfieldcount": 1, + "name": "Dataset", + "starttime": 0, + } + ], + "columns": [{"name": f"Col_{i}"} for i in range(cols)], + "field_count": 1, + "name": "name", + "rows": [{"name": f"Row_{i}"} for i in range(rows)], + "version": "0.1", + "wells": [ + {"path": f"Row_{i}/Col_{j}"} + for i in range(rows) + for j in range(cols) + ], + }, + "well": [ + {"images": [{"path": f"Pos_{pos:03d}"}]} + for pos in position_grid.flatten() + ], } return hcs_meta @@ -206,7 +255,7 @@ def _generate_plane_metadata(self, tiff_file, page): """ for tag in tiff_file.pages[page].tags.values(): - if tag.name == 'MicroManagerMetadata': + if tag.name == "MicroManagerMetadata": return tag.value else: continue @@ -227,10 +276,12 @@ def _perform_image_check(self, tiff_image, coord): """ - zarr_array = self.writer.sub_writer.current_pos_group['arr_0'] - zarr_img = zarr_array[coord[self.dim_order.index('time')], - coord[self.dim_order.index('channel')], - coord[self.dim_order.index('z')]] + zarr_array = self.writer.sub_writer.current_pos_group["arr_0"] + zarr_img = zarr_array[ + coord[self.dim_order.index("time")], + coord[self.dim_order.index("channel")], + coord[self.dim_order.index("z")], + ] return np.array_equal(zarr_img, tiff_image) @@ -259,11 +310,11 @@ def _get_position_names(self): for p in range(self.p): if self.p > 1: try: - name = self.summary_metadata['StagePositions'][p]['Label'] + name = self.summary_metadata["StagePositions"][p]["Label"] except KeyError: - name = '' + name = "" else: - name = '' + name = "" self.pos_names.append(name) def check_file_changed(self, last_file, current_file): @@ -323,8 +374,8 @@ def get_channel_clims(self, pos): for chan in range(self.c): img = self.get_image_array(pos, t=0, c=chan, z=self.focus_z) clip = 0.01 - low = np.percentile(img, clip*100) - high = np.percentile(img, (1-clip)*100) + low = np.percentile(img, clip * 100) + high = np.percentile(img, (1 - clip) * 100) clims.append((low, high)) return clims @@ -342,24 +393,27 @@ def init_zarr_structure(self): """ - chan_names = self._get_channel_names() self._get_position_names() for pos in range(self.p): clims = self.get_channel_clims(pos) name = self.pos_names[pos] if self.replace_position_names else None - self.writer.init_array(pos, - data_shape=(self.t if self.t != 0 else 1, - self.c if self.c != 0 else 1, - self.z if self.z != 0 else 1, - self.y, - self.x), - chunk_size=(1, 1, 1, self.y, self.x), - chan_names=chan_names, - clims=clims, - dtype=self.dtype, - position_name=name) + self.writer.init_array( + pos, + data_shape=( + self.t if self.t != 0 else 1, + self.c if self.c != 0 else 1, + self.z if self.z != 0 else 1, + self.y, + self.x, + ), + chunk_size=(1, 1, 1, self.y, self.x), + chan_names=chan_names, + clims=clims, + dtype=self.dtype, + position_name=name, + ) def run_conversion(self): """ @@ -372,25 +426,27 @@ def run_conversion(self): """ # Run setup - print('Running Conversion...') - print('Setting up zarr') + print("Running Conversion...") + print("Setting up zarr") # self._gather_index_maps() self.init_zarr_structure() last_file = None - #Format bar for CLI display - bar_format = 'Status: |{bar}|{n_fmt}/{total_fmt} (Time Remaining: {remaining}), {rate_fmt}{postfix}]' + # Format bar for CLI display + bar_format = "Status: |{bar}|{n_fmt}/{total_fmt} (Time Remaining: {remaining}), {rate_fmt}{postfix}]" # Run through every coordinate and convert image + grab image metadata, statistics # loop is done in order in which the images were acquired - print('Converting Images...') + print("Converting Images...") for coord in tqdm(self.coords, bar_format=bar_format): - if self.data_type == 'ometiff': + if self.data_type == "ometiff": # re-order coordinates into zarr format - coord_reorder = (coord[self.p_dim], - coord[self.t_dim], - coord[self.c_dim], - coord[self.z_dim]) + coord_reorder = ( + coord[self.p_dim], + coord[self.t_dim], + coord[self.c_dim], + coord[self.z_dim], + ) # Only load tiff file if it has changed from previous run current_file = self.reader.reader.coord_map[coord_reorder][0] @@ -403,29 +459,49 @@ def run_conversion(self): meta = dict() plane_meta = self._generate_plane_metadata(tf, page) - meta[f'{coord_reorder}'] = plane_meta + meta[f"{coord_reorder}"] = plane_meta json.dump(meta, self.meta_file, indent=1) - elif self.data_type == 'pycromanager': + elif self.data_type == "pycromanager": # write page metadata - plane_metadata = self.reader.reader.get_image_metadata(coord[self.p_dim], - coord[self.t_dim], - coord[self.c_dim], - coord[self.z_dim]) - - json.dump({f'FrameKey-{coord[self.p_dim]}-{coord[self.t_dim]}-' - f'{coord[self.c_dim]}-{coord[self.z_dim]}': plane_metadata}, - self.meta_file, indent=1) + plane_metadata = self.reader.reader.get_image_metadata( + coord[self.p_dim], + coord[self.t_dim], + coord[self.c_dim], + coord[self.z_dim], + ) + + json.dump( + { + f"FrameKey-{coord[self.p_dim]}-{coord[self.t_dim]}-" + f"{coord[self.c_dim]}-{coord[self.z_dim]}": plane_metadata + }, + self.meta_file, + indent=1, + ) # get the memory mapped image - img_raw = self.get_image_array(coord[self.p_dim], coord[self.t_dim], coord[self.c_dim], coord[self.z_dim]) + img_raw = self.get_image_array( + coord[self.p_dim], + coord[self.t_dim], + coord[self.c_dim], + coord[self.z_dim], + ) # Write the data - self.writer.write(img_raw, coord[self.p_dim], coord[self.t_dim], coord[self.c_dim], coord[self.z_dim]) + self.writer.write( + img_raw, + coord[self.p_dim], + coord[self.t_dim], + coord[self.c_dim], + coord[self.z_dim], + ) # Perform image check if not self._perform_image_check(img_raw, coord): - raise ValueError('Converted zarr image does not match the raw data. Conversion Failed') + raise ValueError( + "Converted zarr image does not match the raw data. Conversion Failed" + ) # Put summary metadata into zarr store and cleanup self.writer.store.attrs.update(self.metadata) diff --git a/recOrder/pipelines/base.py b/recOrder/pipelines/base.py index 2c4b6264..38d89d99 100644 --- a/recOrder/pipelines/base.py +++ b/recOrder/pipelines/base.py @@ -20,5 +20,7 @@ def reconstruct_birefringence_volume(self, stokes): pass @abstractmethod - def write_data(self, p, t, pt_data, stokes, birefringence, deconvolve2D, deconvolve3D): - pass \ No newline at end of file + def write_data( + self, p, t, pt_data, stokes, birefringence, deconvolve2D, deconvolve3D + ): + pass diff --git a/recOrder/pipelines/phase_from_bf_pipeline.py b/recOrder/pipelines/phase_from_bf_pipeline.py index 19aa1b46..9124bb37 100644 --- a/recOrder/pipelines/phase_from_bf_pipeline.py +++ b/recOrder/pipelines/phase_from_bf_pipeline.py @@ -2,13 +2,24 @@ from waveorder.io.reader import WaveorderReader from waveorder.io.writer import WaveorderWriter from recOrder.io.utils import MockEmitter -from recOrder.compute.qlipp_compute import reconstruct_phase2D, reconstruct_phase3D, initialize_reconstructor +from recOrder.compute.qlipp_compute import ( + reconstruct_phase2D, + reconstruct_phase3D, + initialize_reconstructor, +) import numpy as np from recOrder.pipelines.base import PipelineInterface -class PhaseFromBF(PipelineInterface): - def __init__(self, config: ConfigReader, data: WaveorderReader, writer: WaveorderWriter, num_t: int, emitter=MockEmitter()): +class PhaseFromBF(PipelineInterface): + def __init__( + self, + config: ConfigReader, + data: WaveorderReader, + writer: WaveorderWriter, + num_t: int, + emitter=MockEmitter(), + ): # Dataset Parameters self.config = config @@ -22,37 +33,44 @@ def __init__(self, config: ConfigReader, data: WaveorderReader, writer: Waveorde self.t = num_t self.output_channels = self.config.output_channels self._check_output_channels(self.output_channels) - self.mode = '2D' if 'Phase2D' in self.output_channels else '3D' + self.mode = "2D" if "Phase2D" in self.output_channels else "3D" self.bf_chan_idx = self.config.brightfield_channel_index self.slices = self.data.slices self.focus_slice = None - if self.mode == '2D': + if self.mode == "2D": self.slices = 1 self.focus_slice = self.config.focus_zidx # Set image dimensions / writer parameters self.img_dim = (self.data.height, self.data.width, self.data.slices) - self.data_shape = (self.t, len(self.output_channels), self.slices, self.img_dim[0], self.img_dim[1]) + self.data_shape = ( + self.t, + len(self.output_channels), + self.slices, + self.img_dim[0], + self.img_dim[1], + ) self.chunk_size = (1, 1, 1, self.data_shape[-2], self.data_shape[-1]) # Initialize Reconstructor - self.reconstructor = initialize_reconstructor(pipeline='PhaseFromBF', - image_dim=(self.img_dim[0], self.img_dim[1]), - wavelength_nm=self.config.wavelength, - NA_obj=self.config.NA_objective, - NA_illu=self.config.NA_condenser, - n_obj_media=self.config.n_objective_media, - mag=self.config.magnification, - n_slices=self.data.slices, - z_step_um=self.data.z_step_size, - pad_z=self.config.pad_z, - pixel_size_um=self.config.pixel_size, - mode=self.mode, - use_gpu=self.config.use_gpu, - gpu_id=self.config.gpu_id) - + self.reconstructor = initialize_reconstructor( + pipeline="PhaseFromBF", + image_dim=(self.img_dim[0], self.img_dim[1]), + wavelength_nm=self.config.wavelength, + NA_obj=self.config.NA_objective, + NA_illu=self.config.NA_condenser, + n_obj_media=self.config.n_objective_media, + mag=self.config.magnification, + n_slices=self.data.slices, + z_step_um=self.data.z_step_size, + pad_z=self.config.pad_z, + pixel_size_um=self.config.pixel_size, + mode=self.mode, + use_gpu=self.config.use_gpu, + gpu_id=self.config.gpu_id, + ) def _check_output_channels(self, output_channels): """ @@ -69,12 +87,14 @@ def _check_output_channels(self, output_channels): """ for channel in output_channels: - if 'Phase3D' in channel: + if "Phase3D" in channel: continue - elif 'Phase2D' in channel: + elif "Phase2D" in channel: continue - elif 'Phase3D' in channel and 'Phase2D' in channel: - raise KeyError('Simultaneous 2D and 3D phase reconstruction not supported') + elif "Phase3D" in channel and "Phase2D" in channel: + raise KeyError( + "Simultaneous 2D and 3D phase reconstruction not supported" + ) else: continue @@ -124,19 +144,33 @@ def deconvolve_volume(self, bf_data): phase2D = None phase3D = None - if 'Phase3D' in self.output_channels: - phase3D = reconstruct_phase3D(bf_data, self.reconstructor, method=self.config.phase_denoiser_3D, - reg_re=self.config.Tik_reg_ph_3D, rho=self.config.rho_3D, - lambda_re=self.config.TV_reg_ph_3D, itr=self.config.itr_3D) - - if 'Phase2D' in self.output_channels: - phase2D = reconstruct_phase2D(bf_data, self.reconstructor, method=self.config.phase_denoiser_2D, - reg_p=self.config.Tik_reg_ph_2D, rho=self.config.rho_2D, - lambda_p=self.config.TV_reg_ph_2D, itr=self.config.itr_2D) + if "Phase3D" in self.output_channels: + phase3D = reconstruct_phase3D( + bf_data, + self.reconstructor, + method=self.config.phase_denoiser_3D, + reg_re=self.config.Tik_reg_ph_3D, + rho=self.config.rho_3D, + lambda_re=self.config.TV_reg_ph_3D, + itr=self.config.itr_3D, + ) + + if "Phase2D" in self.output_channels: + phase2D = reconstruct_phase2D( + bf_data, + self.reconstructor, + method=self.config.phase_denoiser_2D, + reg_p=self.config.Tik_reg_ph_2D, + rho=self.config.rho_2D, + lambda_p=self.config.TV_reg_ph_2D, + itr=self.config.itr_2D, + ) return phase2D, phase3D - def write_data(self, p, t, pt_data, stokes, birefringence, phase2D, phase3D): + def write_data( + self, p, t, pt_data, stokes, birefringence, phase2D, phase3D + ): """ This function will iteratively write the data into its proper position, time, channel, z index. Dimensions differ between data type to make compute easier with waveOrder backend. @@ -150,19 +184,19 @@ def write_data(self, p, t, pt_data, stokes, birefringence, phase2D, phase3D): birefringence: (nd-array) None or nd-array w/ dimensions (C, Z, Y, X) phase2D: (nd-array) None or nd-array w/ dimensions (Y, X) phase3D: (nd-array) None or nd-array w/ dimensions (Z, Y, X) - + Returns ------- Writes a zarr array to to given save directory. """ - z = 0 if self.mode == '2D' else None - slice_ = self.focus_slice if self.mode == '2D' else slice(None) + z = 0 if self.mode == "2D" else None + slice_ = self.focus_slice if self.mode == "2D" else slice(None) for chan in range(len(self.output_channels)): - if 'Phase3D' in self.output_channels[chan]: + if "Phase3D" in self.output_channels[chan]: self.writer.write(phase3D, p=p, t=t, c=chan, z=z) - elif 'Phase2D' in self.output_channels[chan]: + elif "Phase2D" in self.output_channels[chan]: self.writer.write(phase2D, p=p, t=t, c=chan, z=z) - self.dimension_emitter.emit((p, t, chan)) \ No newline at end of file + self.dimension_emitter.emit((p, t, chan)) diff --git a/recOrder/pipelines/pipeline_manager.py b/recOrder/pipelines/pipeline_manager.py index 4289f6e1..bd9ff77e 100644 --- a/recOrder/pipelines/pipeline_manager.py +++ b/recOrder/pipelines/pipeline_manager.py @@ -18,18 +18,25 @@ class PipelineManager: """ - def __init__(self, config: ConfigReader, overwrite: bool = False, emitter=MockEmitter()): + def __init__( + self, + config: ConfigReader, + overwrite: bool = False, + emitter=MockEmitter(), + ): self._check_ram() - + start = time.time() - print('Reading Data...') + print("Reading Data...") data = WaveorderReader(config.data_dir, extract_data=True) end = time.time() - print(f'Finished Reading Data ({(end - start) / 60 :0.1f} min)') + print(f"Finished Reading Data ({(end - start) / 60 :0.1f} min)") self.config = config self.data = data - self.use_hcs = True if isinstance(self.data.reader, ZarrReader) else False + self.use_hcs = ( + True if isinstance(self.data.reader, ZarrReader) else False + ) self._gen_coord_set() @@ -45,25 +52,47 @@ def __init__(self, config: ConfigReader, overwrite: bool = False, emitter=MockEm # Delete previous data if overwrite is true, helpful for making quick config changes since the writer # doesn't by default allow you to overwrite data if overwrite: - path = os.path.join(self.config.save_dir, self.config.data_save_name) - path = path+'.zarr' if not path.endswith('.zarr') else path + path = os.path.join( + self.config.save_dir, self.config.data_save_name + ) + path = path + ".zarr" if not path.endswith(".zarr") else path if os.path.exists(path): shutil.rmtree(path) # Writer Parameters - self.writer = WaveorderWriter(self.config.save_dir, hcs=self.use_hcs, hcs_meta=self.hcs_meta, verbose=False) + self.writer = WaveorderWriter( + self.config.save_dir, + hcs=self.use_hcs, + hcs_meta=self.hcs_meta, + verbose=False, + ) self.writer.create_zarr_root(self.config.data_save_name) # Pipeline Initiation - if self.config.method == 'QLIPP': - self.pipeline = QLIPP(self.config, self.data, self.writer, self.config.mode, self.num_t, emitter=emitter) - - elif self.config.method == 'PhaseFromBF': - self.pipeline = PhaseFromBF(self.config, self.data, self.writer, self.num_t, emitter=emitter) + if self.config.method == "QLIPP": + self.pipeline = QLIPP( + self.config, + self.data, + self.writer, + self.config.mode, + self.num_t, + emitter=emitter, + ) + + elif self.config.method == "PhaseFromBF": + self.pipeline = PhaseFromBF( + self.config, + self.data, + self.writer, + self.num_t, + emitter=emitter, + ) else: - raise NotImplementedError(f'Method {self.config.method} is not currently implemented ' - 'please specify one of the following: QLIPP or PhaseFromBF') + raise NotImplementedError( + f"Method {self.config.method} is not currently implemented " + "please specify one of the following: QLIPP or PhaseFromBF" + ) def _check_ram(self): """ @@ -99,10 +128,10 @@ def _update_hcs_meta_from_config(self, hcs_meta): for idx, p in enumerate(self.p_indices): # for every position, grab it from the original HCS metadata and place it into new list - well_meta = meta_new['well'] + well_meta = meta_new["well"] well_meta_new[idx] = well_meta[p] - wells = meta_new['plate']['wells'] + wells = meta_new["plate"]["wells"] wells_new[idx] = wells[p] # Filter out any blank entries @@ -113,18 +142,18 @@ def _update_hcs_meta_from_config(self, hcs_meta): rows_new = [] cols_new = [] for well in wells_new: - split = well['path'].split('/') - if not {'name': split[0]} in rows_new: - rows_new.append({'name': split[0]}) + split = well["path"].split("/") + if not {"name": split[0]} in rows_new: + rows_new.append({"name": split[0]}) - if not {'name': split[1]} in cols_new: - cols_new.append({'name': split[1]}) + if not {"name": split[1]} in cols_new: + cols_new.append({"name": split[1]}) # Group all of the metadata together in one dictionary - meta_new['plate']['rows'] = rows_new - meta_new['plate']['columns'] = cols_new - meta_new['plate']['wells'] = wells_new - meta_new['well'] = well_meta_new + meta_new["plate"]["rows"] = rows_new + meta_new["plate"]["columns"] = cols_new + meta_new["plate"]["wells"] = wells_new + meta_new["well"] = well_meta_new return meta_new @@ -146,7 +175,7 @@ def _gen_coord_set(self): # run through the different possible config specifications (ranges, single entries, 'all', etc.) cnt = 0 for p_entry in self.config.positions: - if p_entry == 'all': + if p_entry == "all": for p in range(self.data.get_num_positions()): p_indices.add(p) self.indices_map[p] = cnt @@ -167,11 +196,13 @@ def _gen_coord_set(self): self.indices_map[p] = cnt cnt += 1 else: - raise ValueError(f'Did not understand entry {p_entry} in config specified positions') + raise ValueError( + f"Did not understand entry {p_entry} in config specified positions" + ) # run through the different possible config specifications (ranges, single entries, 'all', etc.) for t_entry in self.config.timepoints: - if t_entry == 'all': + if t_entry == "all": for t in range(self.data.frames): t_indices.add(t) break @@ -181,10 +212,12 @@ def _gen_coord_set(self): for t in t_entry: t_indices.add(t) elif isinstance(t_entry, tuple): - for t in range(t_entry[0],t_entry[1]): + for t in range(t_entry[0], t_entry[1]): t_indices.add(t) else: - raise ValueError(f'Did not understand entry {t_entry} in config specified positions') + raise ValueError( + f"Did not understand entry {t_entry} in config specified positions" + ) self.num_t = len(t_indices) self.num_p = len(p_indices) @@ -201,48 +234,63 @@ def try_init_array(self, pt): # If not doing the full position, we still want semantic information on which positions # were reconstructed, so append the filename to the position (which would have otherwise been arbitrary) if not self.hcs_meta: - name = f'Pos_{pt[0]:03d}' + name = f"Pos_{pt[0]:03d}" else: name = None - self.pipeline.writer.init_array(self.indices_map[pt[0]], - self.pipeline.data_shape, - self.pipeline.chunk_size, - self.pipeline.output_channels, - position_name=name) + self.pipeline.writer.init_array( + self.indices_map[pt[0]], + self.pipeline.data_shape, + self.pipeline.chunk_size, + self.pipeline.output_channels, + position_name=name, + ) # assumes array exists already if there is an error thrown except: pass - #TODO: use arbol print statements - #TODO: Refactor Birefringence to Anisotropy + # TODO: use arbol print statements + # TODO: Refactor Birefringence to Anisotropy def run(self): - print(f'Beginning Reconstruction...') + print(f"Beginning Reconstruction...") for pt in sorted(self.pt_set): start_time = time.time() self.try_init_array(pt) - pt_data = self.data.get_zarr(pt[0])[pt[1]] # (C, Z, Y, X) virtual + pt_data = self.data.get_zarr(pt[0])[pt[1]] # (C, Z, Y, X) virtual # will return pt_data if the pipeline does not compute stokes stokes = self.pipeline.reconstruct_stokes_volume(pt_data) # will return None if the pipeline doesn't support birefringence reconstruction - birefringence = self.pipeline.reconstruct_birefringence_volume(stokes) + birefringence = self.pipeline.reconstruct_birefringence_volume( + stokes + ) # will return phase volumes - deconvolve2D, deconvolve3D = self.pipeline.deconvolve_volume(stokes) - - self.pipeline.write_data(self.indices_map[pt[0]], pt[1], pt_data, stokes, - birefringence, deconvolve2D, deconvolve3D) + deconvolve2D, deconvolve3D = self.pipeline.deconvolve_volume( + stokes + ) + + self.pipeline.write_data( + self.indices_map[pt[0]], + pt[1], + pt_data, + stokes, + birefringence, + deconvolve2D, + deconvolve3D, + ) end_time = time.time() - print(f'Finishing Reconstructing P = {pt[0]}, T = {pt[1]} ({(end_time-start_time)/60:0.2f}) min') + print( + f"Finishing Reconstructing P = {pt[0]}, T = {pt[1]} ({(end_time-start_time)/60:0.2f}) min" + ) existing_meta = self.writer.store.attrs.asdict().copy() - existing_meta['Config'] = self.config.yaml_dict - self.writer.store.attrs.put(existing_meta) \ No newline at end of file + existing_meta["Config"] = self.config.yaml_dict + self.writer.store.attrs.put(existing_meta) diff --git a/recOrder/pipelines/qlipp_pipeline.py b/recOrder/pipelines/qlipp_pipeline.py index 83f66eed..b3f5dd97 100644 --- a/recOrder/pipelines/qlipp_pipeline.py +++ b/recOrder/pipelines/qlipp_pipeline.py @@ -3,8 +3,13 @@ from waveorder.io.writer import WaveorderWriter from recOrder.io import MetadataReader from recOrder.io.utils import load_bg, MockEmitter, rec_bkg_to_wo_bkg -from recOrder.compute.qlipp_compute import reconstruct_qlipp_birefringence, reconstruct_qlipp_stokes, \ - reconstruct_phase2D, reconstruct_phase3D, initialize_reconstructor +from recOrder.compute.qlipp_compute import ( + reconstruct_qlipp_birefringence, + reconstruct_qlipp_stokes, + reconstruct_phase2D, + reconstruct_phase3D, + initialize_reconstructor, +) import numpy as np from recOrder.pipelines.base import PipelineInterface @@ -15,7 +20,15 @@ class QLIPP(PipelineInterface): This class contains methods to reconstruct an entire QLIPP dataset """ - def __init__(self, config: ConfigReader, data: WaveorderReader, writer: WaveorderWriter, mode: str, num_t: int, emitter=MockEmitter()): + def __init__( + self, + config: ConfigReader, + data: WaveorderReader, + writer: WaveorderWriter, + mode: str, + num_t: int, + emitter=MockEmitter(), + ): """ Parameters ---------- @@ -41,11 +54,13 @@ def __init__(self, config: ConfigReader, data: WaveorderReader, writer: Waveorde self._check_output_channels(self.output_channels) if self.data.channels < 4: - raise ValueError(f'Number of Channels is {data.channels}, cannot be less than 4') + raise ValueError( + f"Number of Channels is {data.channels}, cannot be less than 4" + ) self.slices = self.data.slices self.focus_slice = None - if self.mode == '2D': + if self.mode == "2D": self.slices = 1 self.focus_slice = self.config.focus_zidx @@ -53,68 +68,92 @@ def __init__(self, config: ConfigReader, data: WaveorderReader, writer: Waveorde # Metadata self.chan_names = self.data.channel_names - self.bg_path = self.config.background if self.config.background else None + self.bg_path = ( + self.config.background if self.config.background else None + ) if self.config.calibration_metadata: self.calib_meta = MetadataReader(self.config.calibration_metadata) self.calib_scheme = self.calib_meta.Calibration_scheme - self.bg_roi = self.calib_meta.ROI # TODO: remove for 1.0.0 + self.bg_roi = self.calib_meta.ROI # TODO: remove for 1.0.0 else: self.calib_meta = None - self.calib_scheme = '4-State' - self.bg_roi = None # TODO: remove for 1.0.0 + self.calib_scheme = "4-State" + self.bg_roi = None # TODO: remove for 1.0.0 # identify the image indicies corresponding to each polarization orientation - self.s0_idx, self.s1_idx, \ - self.s2_idx, self.s3_idx, \ - self.s4_idx, = self.parse_channel_idx(self.data.channel_names) + ( + self.s0_idx, + self.s1_idx, + self.s2_idx, + self.s3_idx, + self.s4_idx, + ) = self.parse_channel_idx(self.data.channel_names) # Writer Parameters - self.data_shape = (self.t, len(self.output_channels), self.slices, self.img_dim[0], self.img_dim[1]) + self.data_shape = ( + self.t, + len(self.output_channels), + self.slices, + self.img_dim[0], + self.img_dim[1], + ) self.chunk_size = (1, 1, 1, self.data_shape[-2], self.data_shape[-1]) - wo_background_correction = rec_bkg_to_wo_bkg(self.config.background_correction) + wo_background_correction = rec_bkg_to_wo_bkg( + self.config.background_correction + ) # Initialize Reconstructor if self.no_phase: - self.reconstructor = initialize_reconstructor(pipeline='birefringence', - image_dim=(self.img_dim[0], self.img_dim[1]), - wavelength_nm=self.config.wavelength, - swing=self.calib_meta.Swing, - calibration_scheme=self.calib_scheme, - n_slices=self.data.slices, - pad_z=self.config.pad_z, - bg_correction=wo_background_correction, - mode=self.mode, - use_gpu=self.config.use_gpu, - gpu_id=self.config.gpu_id) + self.reconstructor = initialize_reconstructor( + pipeline="birefringence", + image_dim=(self.img_dim[0], self.img_dim[1]), + wavelength_nm=self.config.wavelength, + swing=self.calib_meta.Swing, + calibration_scheme=self.calib_scheme, + n_slices=self.data.slices, + pad_z=self.config.pad_z, + bg_correction=wo_background_correction, + mode=self.mode, + use_gpu=self.config.use_gpu, + gpu_id=self.config.gpu_id, + ) else: - self.reconstructor = initialize_reconstructor(pipeline='QLIPP', - image_dim=(self.img_dim[0], self.img_dim[1]), - wavelength_nm=self.config.wavelength, - swing=self.calib_meta.Swing, - calibration_scheme=self.calib_scheme, - NA_obj=self.config.NA_objective, - NA_illu=self.config.NA_condenser, - n_obj_media=self.config.n_objective_media, - mag=self.config.magnification, - n_slices=self.data.slices, - z_step_um=self.data.z_step_size, - pad_z=self.config.pad_z, - pixel_size_um=self.config.pixel_size, - bg_correction=wo_background_correction, - mode=self.mode, - use_gpu=self.config.use_gpu, - gpu_id=self.config.gpu_id) + self.reconstructor = initialize_reconstructor( + pipeline="QLIPP", + image_dim=(self.img_dim[0], self.img_dim[1]), + wavelength_nm=self.config.wavelength, + swing=self.calib_meta.Swing, + calibration_scheme=self.calib_scheme, + NA_obj=self.config.NA_objective, + NA_illu=self.config.NA_condenser, + n_obj_media=self.config.n_objective_media, + mag=self.config.magnification, + n_slices=self.data.slices, + z_step_um=self.data.z_step_size, + pad_z=self.config.pad_z, + pixel_size_um=self.config.pixel_size, + bg_correction=wo_background_correction, + mode=self.mode, + use_gpu=self.config.use_gpu, + gpu_id=self.config.gpu_id, + ) # Prepare background corrections for waveorder - if self.config.background_correction in ['global', 'local_fit+']: - bg_data = load_bg(self.bg_path, self.img_dim[0], self.img_dim[1], self.bg_roi) # TODO: remove ROI for 1.0.0 + if self.config.background_correction in ["global", "local_fit+"]: + bg_data = load_bg( + self.bg_path, self.img_dim[0], self.img_dim[1], self.bg_roi + ) # TODO: remove ROI for 1.0.0 self.bg_stokes = self.reconstructor.Stokes_recon(bg_data) - self.bg_stokes = self.reconstructor.Stokes_transform(self.bg_stokes) - elif self.config.background_correction == 'local_fit': + self.bg_stokes = self.reconstructor.Stokes_transform( + self.bg_stokes + ) + elif self.config.background_correction == "local_fit": self.bg_stokes = np.zeros((5, self.img_dim[0], self.img_dim[1])) - self.bg_stokes[0, ...] = 1 # Set background to "identity" Stokes parameters. + self.bg_stokes[ + 0, ... + ] = 1 # Set background to "identity" Stokes parameters. else: self.bg_stokes = None @@ -122,14 +161,17 @@ def _check_output_channels(self, output_channels): self.no_birefringence = True self.no_phase = True for channel in output_channels: - if 'Retardance' in channel or 'Orientation' in channel or 'Brightfield' in channel: + if ( + "Retardance" in channel + or "Orientation" in channel + or "Brightfield" in channel + ): self.no_birefringence = False - if 'Phase3D' in channel or 'Phase2D' in channel: + if "Phase3D" in channel or "Phase2D" in channel: self.no_phase = False else: continue - def reconstruct_stokes_volume(self, data): """ This method reconstructs a stokes volume from raw data @@ -146,16 +188,20 @@ def reconstruct_stokes_volume(self, data): """ - if self.calib_scheme == '4-State': - LF_array = np.zeros([4, self.data.slices, self.data.height, self.data.width]) + if self.calib_scheme == "4-State": + LF_array = np.zeros( + [4, self.data.slices, self.data.height, self.data.width] + ) LF_array[0] = data[self.s0_idx] LF_array[1] = data[self.s1_idx] LF_array[2] = data[self.s2_idx] LF_array[3] = data[self.s3_idx] - elif self.calib_scheme == '5-State': - LF_array = np.zeros([5, self.data.slices, self.data.height, self.data.width]) + elif self.calib_scheme == "5-State": + LF_array = np.zeros( + [5, self.data.slices, self.data.height, self.data.width] + ) LF_array[0] = data[self.s0_idx] LF_array[1] = data[self.s1_idx] LF_array[2] = data[self.s2_idx] @@ -163,9 +209,13 @@ def reconstruct_stokes_volume(self, data): LF_array[4] = data[self.s4_idx] else: - raise NotImplementedError(f"calibration scheme {self.calib_scheme} not implemented") + raise NotImplementedError( + f"calibration scheme {self.calib_scheme} not implemented" + ) - stokes = reconstruct_qlipp_stokes(LF_array, self.reconstructor, self.bg_stokes) + stokes = reconstruct_qlipp_stokes( + LF_array, self.reconstructor, self.bg_stokes + ) return stokes @@ -189,15 +239,27 @@ def deconvolve_volume(self, stokes): phase2D = None phase3D = None - if 'Phase3D' in self.output_channels: - phase3D = reconstruct_phase3D(stokes[0], self.reconstructor, method=self.config.phase_denoiser_3D, - reg_re=self.config.Tik_reg_ph_3D, rho=self.config.rho_3D, - lambda_re=self.config.TV_reg_ph_3D, itr=self.config.itr_3D) - - if 'Phase2D' in self.output_channels: - phase2D = reconstruct_phase2D(stokes[0], self.reconstructor, method=self.config.phase_denoiser_2D, - reg_p=self.config.Tik_reg_ph_2D, rho=self.config.rho_2D, - lambda_p=self.config.TV_reg_ph_2D, itr=self.config.itr_2D) + if "Phase3D" in self.output_channels: + phase3D = reconstruct_phase3D( + stokes[0], + self.reconstructor, + method=self.config.phase_denoiser_3D, + reg_re=self.config.Tik_reg_ph_3D, + rho=self.config.rho_3D, + lambda_re=self.config.TV_reg_ph_3D, + itr=self.config.itr_3D, + ) + + if "Phase2D" in self.output_channels: + phase2D = reconstruct_phase2D( + stokes[0], + self.reconstructor, + method=self.config.phase_denoiser_2D, + reg_p=self.config.Tik_reg_ph_2D, + rho=self.config.rho_2D, + lambda_p=self.config.TV_reg_ph_2D, + itr=self.config.itr_2D, + ) return phase2D, phase3D @@ -221,10 +283,18 @@ def reconstruct_birefringence_volume(self, stokes): return None else: return reconstruct_qlipp_birefringence( - stokes[:, slice(None) if self.slices != 1 else self.focus_slice, :, :], - self.reconstructor) - - def write_data(self, p, t, pt_data, stokes, birefringence, phase2D, phase3D): + stokes[ + :, + slice(None) if self.slices != 1 else self.focus_slice, + :, + :, + ], + self.reconstructor, + ) + + def write_data( + self, p, t, pt_data, stokes, birefringence, phase2D, phase3D + ): """ This function will iteratively write the data into its proper position, time, channel, z index. Dimensions differ between data type to make compute easier with waveOrder backend. @@ -245,30 +315,38 @@ def write_data(self, p, t, pt_data, stokes, birefringence, phase2D, phase3D): """ - z = 0 if self.mode == '2D' else None - slice_ = self.focus_slice if self.mode == '2D' else slice(None) + z = 0 if self.mode == "2D" else None + slice_ = self.focus_slice if self.mode == "2D" else slice(None) # stokes = np.transpose(stokes, (-1, -4, -3, -2)) if len(stokes.shape) == 4 else stokes - + for chan in range(len(self.output_channels)): - if 'Retardance' in self.output_channels[chan]: + if "Retardance" in self.output_channels[chan]: ret = birefringence[0] / (2 * np.pi) * self.config.wavelength self.writer.write(ret, p=p, t=t, c=chan, z=z) - elif 'Orientation' in self.output_channels[chan]: + elif "Orientation" in self.output_channels[chan]: self.writer.write(birefringence[1], p=p, t=t, c=chan, z=z) - elif 'Brightfield' in self.output_channels[chan]: + elif "Brightfield" in self.output_channels[chan]: self.writer.write(birefringence[2], p=p, t=t, c=chan, z=z) - elif 'Phase3D' in self.output_channels[chan]: + elif "Phase3D" in self.output_channels[chan]: self.writer.write(phase3D, p=p, t=t, c=chan, z=z) - elif 'Phase2D' in self.output_channels[chan]: + elif "Phase2D" in self.output_channels[chan]: self.writer.write(phase2D, p=p, t=t, c=chan, z=z) - elif 'S0' in self.output_channels[chan]: - self.writer.write(stokes[0, slice_, :, :], p=p, t=t, c=chan, z=z) - elif 'S1' in self.output_channels[chan]: - self.writer.write(stokes[1, slice_, :, :], p=p, t=t, c=chan, z=z) - elif 'S2' in self.output_channels[chan]: - self.writer.write(stokes[2, slice_, :, :], p=p, t=t, c=chan, z=z) - elif 'S3' in self.output_channels[chan]: - self.writer.write(stokes[3, slice_, :, :], p=p, t=t, c=chan, z=z) + elif "S0" in self.output_channels[chan]: + self.writer.write( + stokes[0, slice_, :, :], p=p, t=t, c=chan, z=z + ) + elif "S1" in self.output_channels[chan]: + self.writer.write( + stokes[1, slice_, :, :], p=p, t=t, c=chan, z=z + ) + elif "S2" in self.output_channels[chan]: + self.writer.write( + stokes[2, slice_, :, :], p=p, t=t, c=chan, z=z + ) + elif "S3" in self.output_channels[chan]: + self.writer.write( + stokes[3, slice_, :, :], p=p, t=t, c=chan, z=z + ) self.dimension_emitter.emit((p, t, chan)) @@ -292,26 +370,27 @@ def parse_channel_idx(self, channel_list): s2_idx = None s3_idx = None s4_idx = None - if 'PolScope_Plugin_Version' in self.calib_meta.json_metadata['Summary']: + if ( + "PolScope_Plugin_Version" + in self.calib_meta.json_metadata["Summary"] + ): open_pol = True else: open_pol = False for channel in range(len(channel_list)): - if 'State0' in channel_list[channel]: + if "State0" in channel_list[channel]: s0_idx = channel - elif 'State1' in channel_list[channel]: + elif "State1" in channel_list[channel]: s1_idx = channel - elif 'State2' in channel_list[channel]: + elif "State2" in channel_list[channel]: s2_idx = channel - elif 'State3' in channel_list[channel]: + elif "State3" in channel_list[channel]: s3_idx = channel - elif 'State4' in channel_list[channel]: + elif "State4" in channel_list[channel]: s4_idx = channel if open_pol: s1_idx, s2_idx, s3_idx, s4_idx = s4_idx, s3_idx, s1_idx, s2_idx return s0_idx, s1_idx, s2_idx, s3_idx, s4_idx - - diff --git a/recOrder/plugin/__init__.py b/recOrder/plugin/__init__.py index ce49ae06..bd7e37d3 100644 --- a/recOrder/plugin/__init__.py +++ b/recOrder/plugin/__init__.py @@ -1,2 +1,2 @@ # data viewers that depend on napari -name = "plugin" \ No newline at end of file +name = "plugin" diff --git a/recOrder/plugin/qtdesigner/mplwidget.py b/recOrder/plugin/qtdesigner/mplwidget.py index fbfd01b5..dbe70f6f 100644 --- a/recOrder/plugin/qtdesigner/mplwidget.py +++ b/recOrder/plugin/qtdesigner/mplwidget.py @@ -1,11 +1,12 @@ # Imports from qtpy import QtWidgets import matplotlib -matplotlib.use('QT5Agg') + +matplotlib.use("QT5Agg") from matplotlib.figure import Figure from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as Canvas -matplotlib.use('QT5Agg') +matplotlib.use("QT5Agg") # Matplotlib canvas class to create figure class MplCanvas(Canvas): @@ -13,14 +14,19 @@ def __init__(self): self.fig = Figure() self.ax = self.fig.add_subplot(111) Canvas.__init__(self, self.fig) - Canvas.setSizePolicy(self, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Expanding) + Canvas.setSizePolicy( + self, + QtWidgets.QSizePolicy.Expanding, + QtWidgets.QSizePolicy.Expanding, + ) Canvas.updateGeometry(self) + # Matplotlib widget class MplWidget(QtWidgets.QWidget): def __init__(self, parent=None): - QtWidgets.QWidget.__init__(self, parent) # Inherit from QWidget - self.canvas = MplCanvas() # Create canvas object - self.vbl = QtWidgets.QVBoxLayout() # Set box for plotting + QtWidgets.QWidget.__init__(self, parent) # Inherit from QWidget + self.canvas = MplCanvas() # Create canvas object + self.vbl = QtWidgets.QVBoxLayout() # Set box for plotting self.vbl.addWidget(self.canvas) self.setLayout(self.vbl) diff --git a/recOrder/plugin/widget/loading_widget.py b/recOrder/plugin/widget/loading_widget.py index 2256b2fc..0ca69ac7 100644 --- a/recOrder/plugin/widget/loading_widget.py +++ b/recOrder/plugin/widget/loading_widget.py @@ -2,10 +2,12 @@ from qtpy.QtGui import QPalette, QPainter, QBrush, QColor, QPen from qtpy.QtWidgets import QWidget, QPushButton + class Overlay(QWidget): """ ADAPTED FROM AYDIN https://github.com/royerlab/aydin/blob/master/aydin/gui/_qt/custom_widgets/overlay.py """ + def __init__(self, parent=None): # super().__init__(self, parent) @@ -14,7 +16,6 @@ def __init__(self, parent=None): palette.setColor(palette.Background, Qt.transparent) self.setPalette(palette) - def paintEvent(self, event): painter = QPainter() painter.begin(self) @@ -58,4 +59,4 @@ def timerEvent(self, event): def hideEvent(self, event): self.killTimer(self.timer) - self.hide() \ No newline at end of file + self.hide() diff --git a/recOrder/plugin/widget/main_widget.py b/recOrder/plugin/widget/main_widget.py index 627ff15b..9f681307 100644 --- a/recOrder/plugin/widget/main_widget.py +++ b/recOrder/plugin/widget/main_widget.py @@ -5,8 +5,16 @@ from qtpy.QtGui import QPixmap, QColor from superqt import QDoubleRangeSlider, QRangeSlider from recOrder.calib import Calibration -from recOrder.plugin.workers.calibration_workers import CalibrationWorker, BackgroundCaptureWorker, load_calibration -from recOrder.plugin.workers.acquisition_workers import PolarizationAcquisitionWorker, ListeningWorker, BFAcquisitionWorker +from recOrder.plugin.workers.calibration_workers import ( + CalibrationWorker, + BackgroundCaptureWorker, + load_calibration, +) +from recOrder.plugin.workers.acquisition_workers import ( + PolarizationAcquisitionWorker, + ListeningWorker, + BFAcquisitionWorker, +) from recOrder.plugin.workers.reconstruction_workers import ReconstructionWorker from recOrder.plugin.qtdesigner import recOrder_ui from recOrder.io.core_functions import set_lc_state, snap_and_average @@ -27,6 +35,7 @@ import pathlib import textwrap + class MainWidget(QWidget): """ This is the main recOrder widget that houses all of the GUI components of recOrder. @@ -52,10 +61,10 @@ def __init__(self, napari_viewer: Viewer): # Connect to MicroManager # I'm disconnecting and hiding this button for 0.2.0, TODO: reinstate for 1.0.0 # In 1.0.0 we'll only have a single "Switch to Online/Offline" button in the "Calibration tab" - + # self.ui.qbutton_mm_connect.clicked[bool].connect(self.connect_to_mm) self.ui.qbutton_mm_connect.hide() - + # Calibration Tab # Remove QT creator calibration mode items @@ -63,35 +72,49 @@ def __init__(self, napari_viewer: Viewer): self.ui.cb_calib_mode.removeItem(0) # Populate calibration modes from docstring - cal_docs = NumpyDocString(Calibration.QLIPP_Calibration.__init__.__doc__) - mode_docs = ' '.join(cal_docs['Parameters'][3].desc).split('* ')[1:] + cal_docs = NumpyDocString( + Calibration.QLIPP_Calibration.__init__.__doc__ + ) + mode_docs = " ".join(cal_docs["Parameters"][3].desc).split("* ")[1:] for i, mode_doc in enumerate(mode_docs): - mode_name, mode_tooltip = mode_doc.split(': ') - wrapped_tooltip = '\n'.join(textwrap.wrap(mode_tooltip, width=70)) + mode_name, mode_tooltip = mode_doc.split(": ") + wrapped_tooltip = "\n".join(textwrap.wrap(mode_tooltip, width=70)) self.ui.cb_calib_mode.addItem(mode_name) - self.ui.cb_calib_mode.setItemData(i, wrapped_tooltip, Qt.ToolTipRole) + self.ui.cb_calib_mode.setItemData( + i, wrapped_tooltip, Qt.ToolTipRole + ) self.ui.qbutton_browse.clicked[bool].connect(self.browse_dir_path) self.ui.le_directory.editingFinished.connect(self.enter_dir_path) self.ui.le_directory.setText(str(Path.cwd())) self.ui.le_swing.editingFinished.connect(self.enter_swing) - self.ui.le_swing.setText('0.1') + self.ui.le_swing.setText("0.1") self.enter_swing() self.ui.le_wavelength.editingFinished.connect(self.enter_wavelength) - self.ui.le_wavelength.setText('532') + self.ui.le_wavelength.setText("532") self.enter_wavelength() - self.ui.cb_calib_scheme.currentIndexChanged[int].connect(self.enter_calib_scheme) - self.ui.cb_calib_mode.currentIndexChanged[int].connect(self.enter_calib_mode) + self.ui.cb_calib_scheme.currentIndexChanged[int].connect( + self.enter_calib_scheme + ) + self.ui.cb_calib_mode.currentIndexChanged[int].connect( + self.enter_calib_mode + ) self.ui.cb_lca.currentIndexChanged[int].connect(self.enter_dac_lca) self.ui.cb_lcb.currentIndexChanged[int].connect(self.enter_dac_lcb) - self.ui.chb_use_roi.stateChanged[int].connect(self.enter_use_cropped_roi) + self.ui.chb_use_roi.stateChanged[int].connect( + self.enter_use_cropped_roi + ) self.ui.qbutton_calibrate.clicked[bool].connect(self.run_calibration) self.ui.qbutton_load_calib.clicked[bool].connect(self.load_calibration) - self.ui.qbutton_calc_extinction.clicked[bool].connect(self.calc_extinction) - self.ui.cb_config_group.currentIndexChanged[int].connect(self.enter_config_group) + self.ui.qbutton_calc_extinction.clicked[bool].connect( + self.calc_extinction + ) + self.ui.cb_config_group.currentIndexChanged[int].connect( + self.enter_config_group + ) # Capture Background self.ui.le_bg_folder.editingFinished.connect(self.enter_bg_folder_name) @@ -99,110 +122,164 @@ def __init__(self, napari_viewer: Viewer): self.ui.qbutton_capture_bg.clicked[bool].connect(self.capture_bg) # Advanced - self.ui.cb_loglevel.currentIndexChanged[int].connect(self.enter_log_level) + self.ui.cb_loglevel.currentIndexChanged[int].connect( + self.enter_log_level + ) self.ui.qbutton_push_note.clicked[bool].connect(self.push_note) # Acquisition Tab self.ui.qbutton_gui_mode.clicked[bool].connect(self.change_gui_mode) - self.ui.qbutton_browse_save_dir.clicked[bool].connect(self.browse_save_path) + self.ui.qbutton_browse_save_dir.clicked[bool].connect( + self.browse_save_path + ) self.ui.le_save_dir.editingFinished.connect(self.enter_save_path) self.ui.le_save_dir.setText(str(Path.cwd())) self.ui.le_data_save_name.editingFinished.connect(self.enter_save_name) - self.ui.qbutton_listen.clicked[bool].connect(self.listen_and_reconstruct) + self.ui.qbutton_listen.clicked[bool].connect( + self.listen_and_reconstruct + ) self.ui.le_zstart.editingFinished.connect(self.enter_zstart) - self.ui.le_zstart.setText('-1') + self.ui.le_zstart.setText("-1") self.enter_zstart() self.ui.le_zend.editingFinished.connect(self.enter_zend) - self.ui.le_zend.setText('1') + self.ui.le_zend.setText("1") self.enter_zend() self.ui.le_zstep.editingFinished.connect(self.enter_zstep) - self.ui.le_zstep.setText('0.25') + self.ui.le_zstep.setText("0.25") self.enter_zstep() self.ui.chb_use_gpu.stateChanged[int].connect(self.enter_use_gpu) self.ui.le_gpu_id.editingFinished.connect(self.enter_gpu_id) - self.ui.le_recon_wavelength.setText('532') # This parameter seems to be wired differently than others...investigate later + self.ui.le_recon_wavelength.setText( + "532" + ) # This parameter seems to be wired differently than others...investigate later self.ui.le_obj_na.editingFinished.connect(self.enter_obj_na) - self.ui.le_obj_na.setText('1.3') + self.ui.le_obj_na.setText("1.3") self.enter_obj_na() self.ui.le_cond_na.editingFinished.connect(self.enter_cond_na) - self.ui.le_cond_na.setText('0.5') + self.ui.le_cond_na.setText("0.5") self.enter_cond_na() self.ui.le_mag.editingFinished.connect(self.enter_mag) - self.ui.le_mag.setText('60') + self.ui.le_mag.setText("60") self.enter_mag() self.ui.le_ps.editingFinished.connect(self.enter_ps) - self.ui.le_ps.setText('6.9') + self.ui.le_ps.setText("6.9") self.enter_ps() self.ui.le_n_media.editingFinished.connect(self.enter_n_media) - self.ui.le_n_media.setText('1.3') + self.ui.le_n_media.setText("1.3") self.enter_n_media() self.ui.le_pad_z.editingFinished.connect(self.enter_pad_z) - self.ui.chb_pause_updates.stateChanged[int].connect(self.enter_pause_updates) - self.ui.cb_birefringence.currentIndexChanged[int].connect(self.enter_birefringence_dim) + self.ui.chb_pause_updates.stateChanged[int].connect( + self.enter_pause_updates + ) + self.ui.cb_birefringence.currentIndexChanged[int].connect( + self.enter_birefringence_dim + ) self.ui.cb_phase.currentIndexChanged[int].connect(self.enter_phase_dim) # Populate background correction GUI element for i in range(3): self.ui.cb_bg_method.removeItem(0) - bg_options = ['None','Measured','Estimated','Measured + Estimated'] - tooltips = ['No background correction.', - 'Correct sample images with a background image acquired at an empty field of view, loaded from "Background Path".', - 'Estimate sample background by fitting a 2D surface to the sample images. Works well when structures are spatially distributed across the field of view and a clear background is unavailable.', - 'Apply "Measured" background correction and then "Estimated" background correction. Use to remove residual background after the sample retardance is corrected with measured background.'] + bg_options = ["None", "Measured", "Estimated", "Measured + Estimated"] + tooltips = [ + "No background correction.", + 'Correct sample images with a background image acquired at an empty field of view, loaded from "Background Path".', + "Estimate sample background by fitting a 2D surface to the sample images. Works well when structures are spatially distributed across the field of view and a clear background is unavailable.", + 'Apply "Measured" background correction and then "Estimated" background correction. Use to remove residual background after the sample retardance is corrected with measured background.', + ] for i, bg_option in enumerate(bg_options): - wrapped_tooltip = '\n'.join(textwrap.wrap(tooltips[i], width=70)) + wrapped_tooltip = "\n".join(textwrap.wrap(tooltips[i], width=70)) self.ui.cb_bg_method.addItem(bg_option) - self.ui.cb_bg_method.setItemData(i, wrapped_tooltip, Qt.ToolTipRole) - self.ui.cb_bg_method.currentIndexChanged[int].connect(self.enter_bg_correction) + self.ui.cb_bg_method.setItemData( + i, wrapped_tooltip, Qt.ToolTipRole + ) + self.ui.cb_bg_method.currentIndexChanged[int].connect( + self.enter_bg_correction + ) self.ui.le_bg_path.editingFinished.connect(self.enter_acq_bg_path) - self.ui.qbutton_browse_bg_path.clicked[bool].connect(self.browse_acq_bg_path) - self.ui.qbutton_acq_birefringence.clicked[bool].connect(self.acq_birefringence) + self.ui.qbutton_browse_bg_path.clicked[bool].connect( + self.browse_acq_bg_path + ) + self.ui.qbutton_acq_birefringence.clicked[bool].connect( + self.acq_birefringence + ) self.ui.qbutton_acq_phase.clicked[bool].connect(self.acq_phase) - self.ui.qbutton_acq_birefringence_phase.clicked[bool].connect(self.acq_birefringence_phase) - self.ui.cb_colormap.currentIndexChanged[int].connect(self.enter_colormap) - self.ui.chb_display_volume.stateChanged[int].connect(self.enter_use_full_volume) - self.ui.le_overlay_slice.editingFinished.connect(self.enter_display_slice) - self.ui.slider_value.sliderMoved[tuple].connect(self.handle_val_slider_move) - self.ui.slider_saturation.sliderMoved[tuple].connect(self.handle_sat_slider_move) + self.ui.qbutton_acq_birefringence_phase.clicked[bool].connect( + self.acq_birefringence_phase + ) + self.ui.cb_colormap.currentIndexChanged[int].connect( + self.enter_colormap + ) + self.ui.chb_display_volume.stateChanged[int].connect( + self.enter_use_full_volume + ) + self.ui.le_overlay_slice.editingFinished.connect( + self.enter_display_slice + ) + self.ui.slider_value.sliderMoved[tuple].connect( + self.handle_val_slider_move + ) + self.ui.slider_saturation.sliderMoved[tuple].connect( + self.handle_sat_slider_move + ) # Display Tab - self.viewer.layers.events.inserted.connect(self._add_layer_to_display_boxes) - self.viewer.layers.events.removed.connect(self._remove_layer_from_display_boxes) - self.ui.qbutton_create_overlay.clicked[bool].connect(self.create_overlay) - self.ui.cb_saturation.currentIndexChanged[int].connect(self.update_sat_scale) - self.ui.cb_value.currentIndexChanged[int].connect(self.update_value_scale) + self.viewer.layers.events.inserted.connect( + self._add_layer_to_display_boxes + ) + self.viewer.layers.events.removed.connect( + self._remove_layer_from_display_boxes + ) + self.ui.qbutton_create_overlay.clicked[bool].connect( + self.create_overlay + ) + self.ui.cb_saturation.currentIndexChanged[int].connect( + self.update_sat_scale + ) + self.ui.cb_value.currentIndexChanged[int].connect( + self.update_value_scale + ) self.ui.le_sat_max.editingFinished.connect(self.enter_sat_max) self.ui.le_sat_min.editingFinished.connect(self.enter_sat_min) self.ui.le_val_max.editingFinished.connect(self.enter_val_max) self.ui.le_val_min.editingFinished.connect(self.enter_val_min) # Reconstruction - self.ui.qbutton_browse_data_dir.clicked[bool].connect(self.browse_data_dir) - self.ui.qbutton_browse_calib_meta.clicked[bool].connect(self.browse_calib_meta) + self.ui.qbutton_browse_data_dir.clicked[bool].connect( + self.browse_data_dir + ) + self.ui.qbutton_browse_calib_meta.clicked[bool].connect( + self.browse_calib_meta + ) self.ui.qbutton_load_config.clicked[bool].connect(self.load_config) self.ui.qbutton_save_config.clicked[bool].connect(self.save_config) - self.ui.qbutton_load_default_config.clicked[bool].connect(self.load_default_config) + self.ui.qbutton_load_default_config.clicked[bool].connect( + self.load_default_config + ) self.ui.cb_method.currentIndexChanged[int].connect(self.enter_method) self.ui.cb_mode.currentIndexChanged[int].connect(self.enter_mode) - self.ui.le_calibration_metadata.editingFinished.connect(self.enter_calib_meta) + self.ui.le_calibration_metadata.editingFinished.connect( + self.enter_calib_meta + ) self.ui.qbutton_reconstruct.clicked[bool].connect(self.reconstruct) - self.ui.cb_phase_denoiser.currentIndexChanged[int].connect(self.enter_phase_denoiser) + self.ui.cb_phase_denoiser.currentIndexChanged[int].connect( + self.enter_phase_denoiser + ) # Logging log_box = QtLogger(self.ui.te_log) - log_box.setFormatter(logging.Formatter('%(levelname)s - %(message)s')) + log_box.setFormatter(logging.Formatter("%(levelname)s - %(message)s")) logging.getLogger().addHandler(log_box) logging.getLogger().setLevel(logging.INFO) @@ -210,7 +287,7 @@ def __init__(self, napari_viewer: Viewer): self.mm_status_changed.connect(self.handle_mm_status_update) # Instantiate Attributes: - self.gui_mode = 'offline' + self.gui_mode = "offline" self.bridge = None self.mm = None self.mmc = None @@ -221,21 +298,27 @@ def __init__(self, napari_viewer: Viewer): self.directory = str(Path.cwd()) # Reconstruction / Calibration Parameter Defaults - self.calib_scheme = '4-State' - self.calib_mode = 'MM-Retardance' - self.interp_method = 'schnoor_fit' - self.config_group = 'Channel' - self.calib_channels = ['State0', 'State1', 'State2', 'State3', 'State4'] + self.calib_scheme = "4-State" + self.calib_mode = "MM-Retardance" + self.interp_method = "schnoor_fit" + self.config_group = "Channel" + self.calib_channels = [ + "State0", + "State1", + "State2", + "State3", + "State4", + ] self.last_calib_meta_file = None self.use_cropped_roi = False - self.bg_folder_name = 'BG' + self.bg_folder_name = "BG" self.n_avg = 5 self.intensity_monitor = [] self.save_directory = str(Path.cwd()) self.save_name = None - self.bg_option = 'None' - self.birefringence_dim = '2D' - self.phase_dim = '2D' + self.bg_option = "None" + self.birefringence_dim = "2D" + self.phase_dim = "2D" self.gpu_id = 0 self.use_gpu = False self.pad_z = 0 @@ -245,13 +328,13 @@ def __init__(self, napari_viewer: Viewer): self.lca_dac = None self.lcb_dac = None self.pause_updates = False - self.method = 'QLIPP' - self.mode = '3D' + self.method = "QLIPP" + self.mode = "3D" self.calib_path = str(Path.cwd()) self.data_dir = str(Path.cwd()) self.config_path = str(Path.cwd()) self.save_config_path = str(Path.cwd()) - self.colormap = 'HSV' + self.colormap = "HSV" self.use_full_volume = False self.display_slice = 0 self.last_p = 0 @@ -264,26 +347,36 @@ def __init__(self, napari_viewer: Viewer): # Init Plot self.plot_item = self.ui.plot_widget.getPlotItem() self.plot_item.enableAutoRange() - self.plot_item.setLabel('left', 'Intensity') + self.plot_item.setLabel("left", "Intensity") self.ui.plot_widget.setBackground((32, 34, 40)) - self.plot_sequence = 'Coarse' + self.plot_sequence = "Coarse" # Init thread worker self.worker = None # Display/Initialiaze GUI Images (plotting legends, recOrder logo) - recorder_dir = dirname(dirname(dirname(dirname(os.path.abspath(__file__))))) - jch_legend_path = os.path.join(recorder_dir, 'docs/images/JCh_legend.png') - hsv_legend_path = os.path.join(recorder_dir, 'docs/images/HSV_legend.png') + recorder_dir = dirname( + dirname(dirname(dirname(os.path.abspath(__file__)))) + ) + jch_legend_path = os.path.join( + recorder_dir, "docs/images/JCh_legend.png" + ) + hsv_legend_path = os.path.join( + recorder_dir, "docs/images/HSV_legend.png" + ) self.jch_pixmap = QPixmap(jch_legend_path) self.hsv_pixmap = QPixmap(hsv_legend_path) self.ui.label_orientation_image.setPixmap(self.hsv_pixmap) - logo_path = os.path.join(recorder_dir, 'docs/images/recOrder_plugin_logo.png') + logo_path = os.path.join( + recorder_dir, "docs/images/recOrder_plugin_logo.png" + ) logo_pixmap = QPixmap(logo_path) self.ui.label_logo.setPixmap(logo_pixmap) # Get default config file - self.default_offline_config = os.path.join(recorder_dir, "recOrder/plugin/config_offline_default.yml") + self.default_offline_config = os.path.join( + recorder_dir, "recOrder/plugin/config_offline_default.yml" + ) # Hide initial UI elements for later implementation or for later pop-up purposes self.ui.label_lca.hide() @@ -308,7 +401,9 @@ def __init__(self, napari_viewer: Viewer): self.ui.chb_pause_updates.setHidden(True) # Hide temporarily unsupported "Overlay" functions - self.ui.tabWidget.setTabText(self.ui.tabWidget.indexOf(self.ui.Display), "Orientation Legend") + self.ui.tabWidget.setTabText( + self.ui.tabWidget.indexOf(self.ui.Display), "Orientation Legend" + ) self.ui.label_orientation_legend.setHidden(True) self.ui.DisplayOptions.setHidden(True) @@ -316,9 +411,11 @@ def __init__(self, napari_viewer: Viewer): self.ui.chb_use_roi.setHidden(True) # Set initial UI Properties - self.ui.le_gui_mode.setStyleSheet("border: 1px solid rgb(200,0,0); color: rgb(200,0,0);") - self.ui.te_log.setStyleSheet('background-color: rgb(32,34,40);') - self.ui.le_mm_status.setText('Not Connected') + self.ui.le_gui_mode.setStyleSheet( + "border: 1px solid rgb(200,0,0); color: rgb(200,0,0);" + ) + self.ui.te_log.setStyleSheet("background-color: rgb(32,34,40);") + self.ui.le_mm_status.setText("Not Connected") self.ui.le_mm_status.setStyleSheet("border: 1px solid yellow;") self.ui.le_sat_min.setStyleSheet("background-color: rgba(0, 0, 0, 0);") self.ui.le_sat_max.setStyleSheet("background-color: rgba(0, 0, 0, 0);") @@ -327,16 +424,22 @@ def __init__(self, napari_viewer: Viewer): self.setStyleSheet("QTabWidget::tab-bar {alignment: center;}") self.red_text = QColor(200, 0, 0, 255) self.original_tab_text = self.ui.tabWidget_3.tabBar().tabTextColor(0) - self.ui.tabWidget.parent().setObjectName('recOrder') # make sure the top says recOrder and not 'Form' - self.ui.tabWidget_2.setCurrentIndex(0) # set focus to "Plot" tab by default - self.ui.tabWidget_3.setCurrentIndex(0) # set focus to "General" tab by default + self.ui.tabWidget.parent().setObjectName( + "recOrder" + ) # make sure the top says recOrder and not 'Form' + self.ui.tabWidget_2.setCurrentIndex( + 0 + ) # set focus to "Plot" tab by default + self.ui.tabWidget_3.setCurrentIndex( + 0 + ) # set focus to "General" tab by default # No "Optional" text on offline mode's calibration metadata box self.ui.le_calibration_metadata.setPlaceholderText("") # disable wheel events for combo boxes for attr_name in dir(self.ui): - if 'cb_' in attr_name: + if "cb_" in attr_name: attr = getattr(self.ui, attr_name) attr.wheelEvent = lambda event: None @@ -371,15 +474,19 @@ def _demote_slider_offline(self, ui_slider, range_): # Add back the sliders as range sliders with the same properties ui_slider = QSlider(getattr(self.ui, slider_parent)) - sizePolicy.setHeightForWidth(ui_slider.sizePolicy().hasHeightForWidth()) + sizePolicy.setHeightForWidth( + ui_slider.sizePolicy().hasHeightForWidth() + ) ui_slider.setSizePolicy(sizePolicy) ui_slider.setOrientation(Qt.Horizontal) ui_slider.setObjectName(slider_name) - self.ui.gridLayout_26.addWidget(ui_slider, - slider_position[0], - slider_position[1], - slider_position[2], - slider_position[3]) + self.ui.gridLayout_26.addWidget( + ui_slider, + slider_position[0], + slider_position[1], + slider_position[2], + slider_position[3], + ) ui_slider.setRange(range_[0], range_[1]) def _promote_slider_offline(self, ui_slider, range_): @@ -411,15 +518,19 @@ def _promote_slider_offline(self, ui_slider, range_): # Add back the sliders as range sliders with the same properties ui_slider = QRangeSlider(getattr(self.ui, slider_parent)) - sizePolicy.setHeightForWidth(ui_slider.sizePolicy().hasHeightForWidth()) + sizePolicy.setHeightForWidth( + ui_slider.sizePolicy().hasHeightForWidth() + ) ui_slider.setSizePolicy(sizePolicy) ui_slider.setOrientation(Qt.Horizontal) ui_slider.setObjectName(slider_name) - self.ui.gridLayout_26.addWidget(ui_slider, - slider_position[0], - slider_position[1], - slider_position[2], - slider_position[3]) + self.ui.gridLayout_26.addWidget( + ui_slider, + slider_position[0], + slider_position[1], + slider_position[2], + slider_position[3], + ) ui_slider.setRange(range_[0], range_[1]) def _promote_slider_init(self): @@ -437,39 +548,59 @@ def _promote_slider_init(self): # Get Information from regular sliders value_slider_idx = self.ui.gridLayout_17.indexOf(self.ui.slider_value) - value_slider_position = self.ui.gridLayout_17.getItemPosition(value_slider_idx) + value_slider_position = self.ui.gridLayout_17.getItemPosition( + value_slider_idx + ) value_slider_parent = self.ui.slider_value.parent().objectName() - saturation_slider_idx = self.ui.gridLayout_17.indexOf(self.ui.slider_saturation) - saturation_slider_position = self.ui.gridLayout_17.getItemPosition(saturation_slider_idx) - saturation_slider_parent = self.ui.slider_saturation.parent().objectName() + saturation_slider_idx = self.ui.gridLayout_17.indexOf( + self.ui.slider_saturation + ) + saturation_slider_position = self.ui.gridLayout_17.getItemPosition( + saturation_slider_idx + ) + saturation_slider_parent = ( + self.ui.slider_saturation.parent().objectName() + ) # Remove regular sliders from the UI self.ui.gridLayout_17.removeWidget(self.ui.slider_value) self.ui.gridLayout_17.removeWidget(self.ui.slider_saturation) # Add back the sliders as range sliders with the same properties - self.ui.slider_saturation = QDoubleRangeSlider(getattr(self.ui, saturation_slider_parent)) - sizePolicy.setHeightForWidth(self.ui.slider_saturation.sizePolicy().hasHeightForWidth()) + self.ui.slider_saturation = QDoubleRangeSlider( + getattr(self.ui, saturation_slider_parent) + ) + sizePolicy.setHeightForWidth( + self.ui.slider_saturation.sizePolicy().hasHeightForWidth() + ) self.ui.slider_saturation.setSizePolicy(sizePolicy) self.ui.slider_saturation.setOrientation(Qt.Horizontal) self.ui.slider_saturation.setObjectName("slider_saturation") - self.ui.gridLayout_17.addWidget(self.ui.slider_saturation, - saturation_slider_position[0], - saturation_slider_position[1], - saturation_slider_position[2], - saturation_slider_position[3]) + self.ui.gridLayout_17.addWidget( + self.ui.slider_saturation, + saturation_slider_position[0], + saturation_slider_position[1], + saturation_slider_position[2], + saturation_slider_position[3], + ) self.ui.slider_saturation.setRange(0, 100) - self.ui.slider_value = QDoubleRangeSlider(getattr(self.ui, value_slider_parent)) - sizePolicy.setHeightForWidth(self.ui.slider_value.sizePolicy().hasHeightForWidth()) + self.ui.slider_value = QDoubleRangeSlider( + getattr(self.ui, value_slider_parent) + ) + sizePolicy.setHeightForWidth( + self.ui.slider_value.sizePolicy().hasHeightForWidth() + ) self.ui.slider_value.setSizePolicy(sizePolicy) self.ui.slider_value.setOrientation(Qt.Horizontal) self.ui.slider_value.setObjectName("slider_value") - self.ui.gridLayout_17.addWidget(self.ui.slider_value, - value_slider_position[0], - value_slider_position[1], - value_slider_position[2], - value_slider_position[3]) + self.ui.gridLayout_17.addWidget( + self.ui.slider_value, + value_slider_position[0], + value_slider_position[1], + value_slider_position[2], + value_slider_position[3], + ) self.ui.slider_value.setRange(0, 100) def _hide_acquisition_ui(self, val: bool): @@ -490,10 +621,12 @@ def _hide_acquisition_ui(self, val: bool): # Calibration Tab self.ui.tabWidget.setTabEnabled(0, not val) if val: - self.ui.tabWidget.setStyleSheet("QTabBar::tab::disabled {width: 0; height: 0; margin: 0; padding: 0; border: none;} ") + self.ui.tabWidget.setStyleSheet( + "QTabBar::tab::disabled {width: 0; height: 0; margin: 0; padding: 0; border: none;} " + ) else: self.ui.tabWidget.setStyleSheet("") - self.ui.le_mm_status.setText('Not Connected') + self.ui.le_mm_status.setText("Not Connected") self.ui.le_mm_status.setStyleSheet("border: 1px solid yellow;") self.mmc = None self.mm = None @@ -528,7 +661,7 @@ def _hide_offline_ui(self, val: bool): # Processing Settings self.ui.groupBox_2.setHidden(val) - + def _enable_buttons(self): """ enables the buttons that were disabled during acquisition, calibration, or reconstruction @@ -586,8 +719,10 @@ def _handle_error(self, exc): """ - self.ui.tb_calib_assessment.setText(f'Error: {str(exc)}') - self.ui.tb_calib_assessment.setStyleSheet("border: 1px solid rgb(200,0,0);") + self.ui.tb_calib_assessment.setText(f"Error: {str(exc)}") + self.ui.tb_calib_assessment.setStyleSheet( + "border: 1px solid rgb(200,0,0);" + ) # Reset ROI if it was cropped down during reconstruction if self.use_cropped_roi: @@ -619,7 +754,9 @@ def _handle_load_finished(self): ------- """ - self.ui.tb_calib_assessment.setText('Previous calibration successfully loaded') + self.ui.tb_calib_assessment.setText( + "Previous calibration successfully loaded" + ) self.ui.tb_calib_assessment.setStyleSheet("border: 1px solid green;") self.ui.progress_bar.setValue(100) @@ -642,13 +779,22 @@ def _add_layer_to_display_boxes(self, val): """ for layer in self.viewer.layers: - if 'Overlay' in layer.name: + if "Overlay" in layer.name: continue - if layer.name not in [self.ui.cb_hue.itemText(i) for i in range(self.ui.cb_hue.count())]: + if layer.name not in [ + self.ui.cb_hue.itemText(i) + for i in range(self.ui.cb_hue.count()) + ]: self.ui.cb_hue.addItem(layer.name) - if layer.name not in [self.ui.cb_saturation.itemText(i) for i in range(self.ui.cb_saturation.count())]: + if layer.name not in [ + self.ui.cb_saturation.itemText(i) + for i in range(self.ui.cb_saturation.count()) + ]: self.ui.cb_saturation.addItem(layer.name) - if layer.name not in [self.ui.cb_value.itemText(i) for i in range(self.ui.cb_value.count())]: + if layer.name not in [ + self.ui.cb_value.itemText(i) + for i in range(self.ui.cb_value.count()) + ]: self.ui.cb_value.addItem(layer.name) def _remove_layer_from_display_boxes(self, val): @@ -687,15 +833,16 @@ def _set_tab_red(self, name, state): """ # this map corresponds to the tab index in the TabWidget GUI element - name_map = {'General': 0, - 'Processing': 1} + name_map = {"General": 0, "Processing": 1} index = name_map[name] if state: self.ui.tabWidget_3.tabBar().setTabTextColor(index, self.red_text) else: - self.ui.tabWidget_3.tabBar().setTabTextColor(index, self.original_tab_text) + self.ui.tabWidget_3.tabBar().setTabTextColor( + index, self.original_tab_text + ) def _check_line_edit(self, name): """ @@ -710,10 +857,10 @@ def _check_line_edit(self, name): ------- """ - le = getattr(self.ui, f'le_{name}') + le = getattr(self.ui, f"le_{name}") text = le.text() - if text == '': + if text == "": le.setStyleSheet("border: 1px solid rgb(200,0,0);") return False else: @@ -736,59 +883,79 @@ def _check_requirements_for_acq(self, mode): """ # Initialize all tabs in their default style (not red) - self._set_tab_red('General', False) - self._set_tab_red('Processing', False) - + self._set_tab_red("General", False) + self._set_tab_red("Processing", False) + # initialize the variable to keep track of the success of the requirement check raise_error = False # define the fields required for the specific acquisition modes. Matches LineEdit object names - phase_required = {'wavelength', 'mag', 'cond_na', 'obj_na', 'n_media', - 'phase_strength', 'ps', 'zstep'} + phase_required = { + "wavelength", + "mag", + "cond_na", + "obj_na", + "n_media", + "phase_strength", + "ps", + "zstep", + } # Initalize all fields in their default style (not red). for field in phase_required: - le = getattr(self.ui, f'le_{field}') + le = getattr(self.ui, f"le_{field}") le.setStyleSheet("") - + # Check generally required fields - if mode == 'birefringence' or mode == 'phase': - success = self._check_line_edit('save_dir') + if mode == "birefringence" or mode == "phase": + success = self._check_line_edit("save_dir") if not success: raise_error = True - self._set_tab_red('General', True) + self._set_tab_red("General", True) # check background path if 'Measured' or 'Measured + Estimated' is selected - if self.bg_option == 'local_fit+' or self.bg_option == 'global': - success = self._check_line_edit('bg_path') + if self.bg_option == "local_fit+" or self.bg_option == "global": + success = self._check_line_edit("bg_path") if not success: raise_error = True - self._set_tab_red('General', True) + self._set_tab_red("General", True) # Check phase specific fields - if mode == 'phase': + if mode == "phase": # add in extra requirement is user is acquiring PhaseFromBF if self.ui.chb_phase_from_bf.isChecked(): - cont = self._check_line_edit('recon_wavelength') - tab = getattr(self.ui, f'le_recon_wavelength').parent().parent().objectName() + cont = self._check_line_edit("recon_wavelength") + tab = ( + getattr(self.ui, f"le_recon_wavelength") + .parent() + .parent() + .objectName() + ) if not cont: raise_error = True self._set_tab_red(tab, True) for field in phase_required: cont = self._check_line_edit(field) - tab = getattr(self.ui, f'le_{field}').parent().parent().objectName() + tab = ( + getattr(self.ui, f"le_{field}") + .parent() + .parent() + .objectName() + ) if not cont: raise_error = True - if field != 'zstep': + if field != "zstep": self._set_tab_red(tab, True) else: continue # Alert the user to check and enter in the missing parameters if raise_error: - raise ValueError('Please enter in all of the parameters necessary for the acquisition') + raise ValueError( + "Please enter in all of the parameters necessary for the acquisition" + ) def _check_requirements_for_reconstruction(self): """ @@ -802,8 +969,8 @@ def _check_requirements_for_reconstruction(self): """ # Initalize all tab elements and reconstruct button to default state (not red) - self._set_tab_red('General', False) - self._set_tab_red('Processing', False) + self._set_tab_red("General", False) + self._set_tab_red("Processing", False) self.ui.qbutton_reconstruct.setStyleSheet("") # initalize the success variable of the requirement check @@ -811,38 +978,73 @@ def _check_requirements_for_reconstruction(self): # gather the specified output channels (will determine which requirements are necessary to look at) output_channels = self.ui.le_output_channels.text() - output_channels = output_channels.split(',') - output_channels = [chan.replace(' ', '') for chan in output_channels] + output_channels = output_channels.split(",") + output_channels = [chan.replace(" ", "") for chan in output_channels] # intialize the reconstruction specific required fields - always_required = {'data_dir', 'save_dir', 'positions', 'timepoints', 'output_channels'} - birefringence_required = {'calibration_metadata', 'recon_wavelength'} - phase_required = {'recon_wavelength', 'mag', 'obj_na', 'cond_na', 'n_media', - 'phase_strength', 'ps'} - + always_required = { + "data_dir", + "save_dir", + "positions", + "timepoints", + "output_channels", + } + birefringence_required = {"calibration_metadata", "recon_wavelength"} + phase_required = { + "recon_wavelength", + "mag", + "obj_na", + "cond_na", + "n_media", + "phase_strength", + "ps", + } + # intialize all UI elements in the default state for field in always_required: - le = getattr(self.ui, f'le_{field}') + le = getattr(self.ui, f"le_{field}") le.setStyleSheet("") for field in phase_required: - le = getattr(self.ui, f'le_{field}') + le = getattr(self.ui, f"le_{field}") le.setStyleSheet("") for field in always_required: cont = self._check_line_edit(field) if not cont: success = False - if field == 'data_dir' or field == 'save_dir': - self._set_tab_red('General', True) - if field == 'positions' or field == 'timepoints' or field == 'output_channels': - self._set_tab_red('Processing', True) + if field == "data_dir" or field == "save_dir": + self._set_tab_red("General", True) + if ( + field == "positions" + or field == "timepoints" + or field == "output_channels" + ): + self._set_tab_red("Processing", True) else: continue - possible_channels = ['Retardance', 'Orientation', 'BF', 'S0', 'S1', 'S2', 'S3', 'Phase2D', 'Phase3D'] - bire_channels = ['Retardance', 'Orientation', 'BF', 'S0', 'S1', 'S2', 'S3'] + possible_channels = [ + "Retardance", + "Orientation", + "BF", + "S0", + "S1", + "S2", + "S3", + "Phase2D", + "Phase3D", + ] + bire_channels = [ + "Retardance", + "Orientation", + "BF", + "S0", + "S1", + "S2", + "S3", + ] qlipp_channel_present = False bire_channel_present = False - if self.method == 'QLIPP': + if self.method == "QLIPP": for channel in output_channels: if channel in possible_channels: qlipp_channel_present = True @@ -853,66 +1055,94 @@ def _check_requirements_for_reconstruction(self): if bire_channel_present: for field in birefringence_required: cont = self._check_line_edit(field) - tab = getattr(self.ui, f'le_{field}').parent().parent().objectName() + tab = ( + getattr(self.ui, f"le_{field}") + .parent() + .parent() + .objectName() + ) if not cont: - if field != 'calibration_metadata': + if field != "calibration_metadata": self._set_tab_red(tab, True) success = False else: # self._set_tab_red(tab, False) continue - if 'Phase2D' in output_channels or 'Phase3D' in output_channels: + if ( + "Phase2D" in output_channels + or "Phase3D" in output_channels + ): for field in phase_required: cont = self._check_line_edit(field) - tab = getattr(self.ui, f'le_{field}').parent().parent().objectName() + tab = ( + getattr(self.ui, f"le_{field}") + .parent() + .parent() + .objectName() + ) if not cont: self._set_tab_red(tab, True) success = False else: self._set_tab_red(tab, False) continue - if 'Phase2D' in output_channels and self.mode == '2D': - cont = self._check_line_edit('focus_zidx') + if "Phase2D" in output_channels and self.mode == "2D": + cont = self._check_line_edit("focus_zidx") if not cont: - self._set_tab_red('Processing', True) + self._set_tab_red("Processing", True) success = False else: - self._set_tab_red('Processing', True) - self.ui.le_output_channels.setStyleSheet("border: 1px solid rgb(200,0,0);") - print('User did not specify any QLIPP Specific Channels') + self._set_tab_red("Processing", True) + self.ui.le_output_channels.setStyleSheet( + "border: 1px solid rgb(200,0,0);" + ) + print("User did not specify any QLIPP Specific Channels") success = False - elif self.method == 'PhaseFromBF': - cont = self._check_line_edit('bf_chan') - tab = getattr(self.ui, f'le_bf_chan').parent().parent().objectName() + elif self.method == "PhaseFromBF": + cont = self._check_line_edit("bf_chan") + tab = ( + getattr(self.ui, f"le_bf_chan").parent().parent().objectName() + ) if not cont: self._set_tab_red(tab, True) success = False - if 'Phase2D' in output_channels or 'Phase3D' in output_channels: + if "Phase2D" in output_channels or "Phase3D" in output_channels: for field in phase_required: cont = self._check_line_edit(field) - tab = getattr(self.ui, f'le_{field}').parent().parent().objectName() + tab = ( + getattr(self.ui, f"le_{field}") + .parent() + .parent() + .objectName() + ) if not cont: self._set_tab_red(tab, True) success = False else: continue - if 'Phase2D' in output_channels and self.mode == '2D': - cont = self._check_line_edit('focus_zidx') + if "Phase2D" in output_channels and self.mode == "2D": + cont = self._check_line_edit("focus_zidx") if not cont: - self._set_tab_red('Processing', True) + self._set_tab_red("Processing", True) success = False else: - self._set_tab_red('Processing', True) - self.ui.le_output_channels.setStyleSheet("border: 1px solid rgb(200,0,0);") - print('User did not specify any PhaseFromBF Specific Channels (Phase2D, Phase3D)') + self._set_tab_red("Processing", True) + self.ui.le_output_channels.setStyleSheet( + "border: 1px solid rgb(200,0,0);" + ) + print( + "User did not specify any PhaseFromBF Specific Channels (Phase2D, Phase3D)" + ) success = False else: - print('Error in parameter checks') - self.ui.qbutton_reconstruct.setStyleSheet("border: 1px solid rgb(200,0,0);") + print("Error in parameter checks") + self.ui.qbutton_reconstruct.setStyleSheet( + "border: 1px solid rgb(200,0,0);" + ) success = False return success @@ -937,119 +1167,167 @@ def _populate_config_from_app(self): self.save_directory = self.ui.le_save_dir.text() self.config_reader.method = self.method self.config_reader.mode = self.mode - self.config_reader.data_save_name = self.ui.le_data_save_name.text() if self.ui.le_data_save_name.text() != '' \ - else pathlib.PurePath(self.data_dir).name - self.config_reader.calibration_metadata = self.ui.le_calibration_metadata.text() + self.config_reader.data_save_name = ( + self.ui.le_data_save_name.text() + if self.ui.le_data_save_name.text() != "" + else pathlib.PurePath(self.data_dir).name + ) + self.config_reader.calibration_metadata = ( + self.ui.le_calibration_metadata.text() + ) self.config_reader.background = self.ui.le_bg_path.text() self.config_reader.background_correction = self.bg_option # Assumes that positions/timepoints can either be 'all'; '[all]'; 1, 2, 3, N; (start, end) positions = self.ui.le_positions.text() - positions = positions.replace(' ', '') - if positions == 'all' or positions == "['all']" or positions == '[all]': - self.config_reader.positions = ['all'] - elif positions.startswith('[') and positions.endswith(']'): - vals = positions[1:-1].split(',') + positions = positions.replace(" ", "") + if ( + positions == "all" + or positions == "['all']" + or positions == "[all]" + ): + self.config_reader.positions = ["all"] + elif positions.startswith("[") and positions.endswith("]"): + vals = positions[1:-1].split(",") if len(vals) != 2: - self._set_tab_red('Processing', True) - self.ui.le_positions.setStyleSheet("border: 1px solid rgb(200,0,0);") + self._set_tab_red("Processing", True) + self.ui.le_positions.setStyleSheet( + "border: 1px solid rgb(200,0,0);" + ) else: - self._set_tab_red('Processing', False) + self._set_tab_red("Processing", False) self.ui.le_positions.setStyleSheet("") self.config_reader.positions = [(int(vals[0]), int(vals[1]))] - elif positions.startswith('(') and positions.endswith(')'): + elif positions.startswith("(") and positions.endswith(")"): self.config_reader.positions = [eval(positions)] else: - vals = positions.split(',') + vals = positions.split(",") vals = map(lambda x: int(x), vals) self.config_reader.positions = list(vals) timepoints = self.ui.le_timepoints.text() - timepoints = timepoints.replace(' ', '') - if timepoints == 'all' or timepoints == "['all']" or timepoints == '[all]': - self.config_reader.timepoints = ['all'] - elif timepoints.startswith('[') and timepoints.endswith(']'): - vals = timepoints[1:-1].split(',') + timepoints = timepoints.replace(" ", "") + if ( + timepoints == "all" + or timepoints == "['all']" + or timepoints == "[all]" + ): + self.config_reader.timepoints = ["all"] + elif timepoints.startswith("[") and timepoints.endswith("]"): + vals = timepoints[1:-1].split(",") if len(vals) != 2: - self._set_tab_red('Processing', True) - self.ui.le_timepoints.setStyleSheet("border: 1px solid rgb(200,0,0);") + self._set_tab_red("Processing", True) + self.ui.le_timepoints.setStyleSheet( + "border: 1px solid rgb(200,0,0);" + ) else: - self._set_tab_red('Processing', False) + self._set_tab_red("Processing", False) self.ui.le_timepoints.setStyleSheet("") self.config_reader.timepoints = [(int(vals[0]), int(vals[1]))] - elif timepoints.startswith('(') and timepoints.endswith(')'): + elif timepoints.startswith("(") and timepoints.endswith(")"): self.config_reader.timepoints = [eval(timepoints)] else: - vals = timepoints.split(',') + vals = timepoints.split(",") vals = map(lambda x: int(x), vals) self.config_reader.timepoints = list(vals) attrs = dir(self.ui) - skip = ['wavelength', 'pixel_size', 'magnification', 'NA_objective', 'NA_condenser', 'n_objective_media'] + skip = [ + "wavelength", + "pixel_size", + "magnification", + "NA_objective", + "NA_condenser", + "n_objective_media", + ] # TODO: Figure out how to catch errors in regularizer strength field for key, value in PROCESSING.items(): if key not in skip: - if key == 'background_correction': - bg_map = {0: 'None', 1: 'global', 2: 'local_fit', 3: 'local_fit+'} - setattr(self.config_reader, key, bg_map[self.ui.cb_bg_method.currentIndex()]) - - elif key == 'output_channels': + if key == "background_correction": + bg_map = { + 0: "None", + 1: "global", + 2: "local_fit", + 3: "local_fit+", + } + setattr( + self.config_reader, + key, + bg_map[self.ui.cb_bg_method.currentIndex()], + ) + + elif key == "output_channels": # Reset style sheets self.ui.le_output_channels.setStyleSheet("") self.ui.cb_mode.setStyleSheet("") - self._set_tab_red('Processing', False) + self._set_tab_red("Processing", False) # Make a list of the channels from the line edit string field_text = self.ui.le_output_channels.text() - channels = field_text.split(',') - channels = [i.replace(' ', '') for i in channels] + channels = field_text.split(",") + channels = [i.replace(" ", "") for i in channels] setattr(self.config_reader, key, channels) - if 'Phase3D' in channels and 'Phase2D' in channels: - self.ui.le_output_channels.setStyleSheet("border: 1px solid rgb(200,0,0);") - self._set_tab_red('Processing', True) + if "Phase3D" in channels and "Phase2D" in channels: + self.ui.le_output_channels.setStyleSheet( + "border: 1px solid rgb(200,0,0);" + ) + self._set_tab_red("Processing", True) + raise KeyError( + f"Both Phase3D and Phase2D cannot be specified in output_channels. Please compute " + f"separately" + ) + + if "Phase3D" in channels and self.mode == "2D": + self._set_tab_red("Processing", True) + self.ui.le_output_channels.setStyleSheet( + "border: 1px solid rgb(200,0,0);" + ) + self.ui.cb_mode.setStyleSheet( + "border: 1px solid rgb(200,0,0);" + ) raise KeyError( - f'Both Phase3D and Phase2D cannot be specified in output_channels. Please compute ' - f'separately') - - if 'Phase3D' in channels and self.mode == '2D': - self._set_tab_red('Processing', True) - self.ui.le_output_channels.setStyleSheet("border: 1px solid rgb(200,0,0);") - self.ui.cb_mode.setStyleSheet("border: 1px solid rgb(200,0,0);") - raise KeyError(f'Specified mode is 2D and Phase3D was specified for reconstruction. ' - 'Only 2D reconstructions can be performed in 2D mode') - - if 'Phase2D' in channels and self.mode == '3D': - self._set_tab_red('Processing', True) - self.ui.le_output_channels.setStyleSheet("border: 1px solid rgb(200,0,0);") - self.ui.cb_mode.setStyleSheet("border: 1px solid rgb(200,0,0);") - raise KeyError(f'Specified mode is 3D and Phase2D was specified for reconstruction. ' - 'Only 3D reconstructions can be performed in 3D mode') - - elif key == 'pad_z': + f"Specified mode is 2D and Phase3D was specified for reconstruction. " + "Only 2D reconstructions can be performed in 2D mode" + ) + + if "Phase2D" in channels and self.mode == "3D": + self._set_tab_red("Processing", True) + self.ui.le_output_channels.setStyleSheet( + "border: 1px solid rgb(200,0,0);" + ) + self.ui.cb_mode.setStyleSheet( + "border: 1px solid rgb(200,0,0);" + ) + raise KeyError( + f"Specified mode is 3D and Phase2D was specified for reconstruction. " + "Only 3D reconstructions can be performed in 3D mode" + ) + + elif key == "pad_z": val = self.ui.le_pad_z.text() setattr(self.config_reader, key, int(val)) - elif key == 'gpu_id': + elif key == "gpu_id": val = self.ui.le_gpu_id.text() setattr(self.config_reader, key, int(val)) - elif key == 'use_gpu': + elif key == "use_gpu": if self.ui.chb_use_gpu.isChecked(): setattr(self.config_reader, key, True) else: setattr(self.config_reader, key, False) - elif key == 'focus_zidx': + elif key == "focus_zidx": val = self.ui.le_focus_zidx.text() - if val == '': + if val == "": setattr(self.config_reader, key, None) else: setattr(self.config_reader, key, int(val)) else: - attr_name = f'le_{key}' + attr_name = f"le_{key}" if attr_name in attrs: le = getattr(self.ui, attr_name) try: @@ -1063,44 +1341,108 @@ def _populate_config_from_app(self): continue # Manually enter in phase regularization - if self.mode == '3D': + if self.mode == "3D": if self.ui.cb_phase_denoiser.currentIndex() == 0: - setattr(self.config_reader, 'Tik_reg_ph_3D', float(self.ui.le_phase_strength.text())) + setattr( + self.config_reader, + "Tik_reg_ph_3D", + float(self.ui.le_phase_strength.text()), + ) else: - setattr(self.config_reader, 'TV_reg_ph_3D', float(self.ui.le_phase_strength.text())) - setattr(self.config_reader, 'rho_3D', float(self.ui.le_rho.text())) - setattr(self.config_reader, 'itr_3D', int(self.ui.le_itr.text())) + setattr( + self.config_reader, + "TV_reg_ph_3D", + float(self.ui.le_phase_strength.text()), + ) + setattr( + self.config_reader, "rho_3D", float(self.ui.le_rho.text()) + ) + setattr( + self.config_reader, "itr_3D", int(self.ui.le_itr.text()) + ) else: if self.ui.cb_phase_denoiser.currentIndex() == 0: - setattr(self.config_reader, 'Tik_reg_ph_2D', float(self.ui.le_phase_strength.text())) + setattr( + self.config_reader, + "Tik_reg_ph_2D", + float(self.ui.le_phase_strength.text()), + ) else: - setattr(self.config_reader, 'TV_reg_ph_2D', float(self.ui.le_phase_strength.text())) - setattr(self.config_reader, 'rho_2D', float(self.ui.le_rho.text())) - setattr(self.config_reader, 'itr_2D', int(self.ui.le_itr.text())) - - if self.method == 'PhaseFromBF': - setattr(self.config_reader, 'wavelength', int(self.ui.le_recon_wavelength.text())) - setattr(self.config_reader, 'brightfield_channel_index', - int(self.ui.le_bf_chan.text())) + setattr( + self.config_reader, + "TV_reg_ph_2D", + float(self.ui.le_phase_strength.text()), + ) + setattr( + self.config_reader, "rho_2D", float(self.ui.le_rho.text()) + ) + setattr( + self.config_reader, "itr_2D", int(self.ui.le_itr.text()) + ) + + if self.method == "PhaseFromBF": + setattr( + self.config_reader, + "wavelength", + int(self.ui.le_recon_wavelength.text()), + ) + setattr( + self.config_reader, + "brightfield_channel_index", + int(self.ui.le_bf_chan.text()), + ) focus_zidx = self.ui.le_focus_zidx.text() - setattr(self.config_reader, 'focus_zidx', int(focus_zidx) if focus_zidx != '' else None) + setattr( + self.config_reader, + "focus_zidx", + int(focus_zidx) if focus_zidx != "" else None, + ) else: - setattr(self.config_reader, 'wavelength', int(self.ui.le_recon_wavelength.text())) + setattr( + self.config_reader, + "wavelength", + int(self.ui.le_recon_wavelength.text()), + ) # Parse name mismatch fields - setattr(self.config_reader, 'NA_objective', float(self.ui.le_obj_na.text()) if self.ui.le_obj_na.text() != '' - else None) - setattr(self.config_reader, 'NA_condenser', float(self.ui.le_cond_na.text()) if self.ui.le_cond_na.text() != '' - else None) - setattr(self.config_reader, 'pixel_size', float(self.ui.le_ps.text()) if self.ui.le_ps.text() != '' - else None) - setattr(self.config_reader, 'n_objective_media', float(self.ui.le_n_media.text()) - if self.ui.le_n_media.text() != '' else None) - setattr(self.config_reader, 'magnification', float(self.ui.le_mag.text()) if self.ui.le_mag.text() != '' - else None) + setattr( + self.config_reader, + "NA_objective", + float(self.ui.le_obj_na.text()) + if self.ui.le_obj_na.text() != "" + else None, + ) + setattr( + self.config_reader, + "NA_condenser", + float(self.ui.le_cond_na.text()) + if self.ui.le_cond_na.text() != "" + else None, + ) + setattr( + self.config_reader, + "pixel_size", + float(self.ui.le_ps.text()) + if self.ui.le_ps.text() != "" + else None, + ) + setattr( + self.config_reader, + "n_objective_media", + float(self.ui.le_n_media.text()) + if self.ui.le_n_media.text() != "" + else None, + ) + setattr( + self.config_reader, + "magnification", + float(self.ui.le_mag.text()) + if self.ui.le_mag.text() != "" + else None, + ) def _populate_from_config(self): """ @@ -1118,47 +1460,53 @@ def _populate_from_config(self): self.save_directory = self.config_reader.save_dir self.ui.le_save_dir.setText(self.config_reader.save_dir) self.ui.le_data_save_name.setText(self.config_reader.data_save_name) - self.ui.le_calibration_metadata.setText(self.config_reader.calibration_metadata) + self.ui.le_calibration_metadata.setText( + self.config_reader.calibration_metadata + ) self.ui.le_bg_path.setText(self.config_reader.background) self.mode = self.config_reader.mode - self.ui.cb_mode.setCurrentIndex(0) if self.mode == '3D' else self.ui.cb_mode.setCurrentIndex(1) + self.ui.cb_mode.setCurrentIndex( + 0 + ) if self.mode == "3D" else self.ui.cb_mode.setCurrentIndex(1) self.method = self.config_reader.method - if self.method == 'QLIPP': + if self.method == "QLIPP": self.ui.cb_method.setCurrentIndex(0) - elif self.method == 'PhaseFromBF': + elif self.method == "PhaseFromBF": self.ui.cb_method.setCurrentIndex(1) else: - print(f'Did not understand method from config: {self.method}') + print(f"Did not understand method from config: {self.method}") self.ui.cb_method.setStyleSheet("border: 1px solid rgb(200,0,0);") self.bg_option = self.config_reader.background_correction - if self.bg_option == 'None': + if self.bg_option == "None": self.ui.cb_bg_method.setCurrentIndex(0) - elif self.bg_option == 'global': + elif self.bg_option == "global": self.ui.cb_bg_method.setCurrentIndex(1) - elif self.bg_option == 'local_fit': + elif self.bg_option == "local_fit": self.ui.cb_bg_method.setCurrentIndex(2) - elif self.bg_option == 'local_fit+': + elif self.bg_option == "local_fit+": self.ui.cb_bg_method.setCurrentIndex(3) else: - print(f'Did not understand method from config: {self.method}') + print(f"Did not understand method from config: {self.method}") self.ui.cb_method.setStyleSheet("border: 1px solid rgb(200,0,0);") if isinstance(self.config_reader.positions, list): positions = self.config_reader.positions - text = '' + text = "" for idx, pos in enumerate(positions): - text += f'{pos}, ' if idx != len(positions) - 1 else f'{pos}' + text += f"{pos}, " if idx != len(positions) - 1 else f"{pos}" self.ui.le_positions.setText(text) else: self.ui.le_positions.setText(str(self.config_reader.positions)) if isinstance(self.config_reader.timepoints, list): timepoints = self.config_reader.timepoints - text = '' + text = "" for idx, time in enumerate(timepoints): - text += f'{time}, ' if idx != len(timepoints) - 1 else f'{time}' + text += ( + f"{time}, " if idx != len(timepoints) - 1 else f"{time}" + ) self.ui.le_timepoints.setText(text) else: self.ui.le_timepoints.setText(str(self.config_reader.timepoints)) @@ -1166,11 +1514,15 @@ def _populate_from_config(self): # Parse Processing name mismatch fields wavelengths = self.config_reader.wavelength if not isinstance(wavelengths, list): - self.ui.le_recon_wavelength.setText(str(int(self.config_reader.wavelength))) + self.ui.le_recon_wavelength.setText( + str(int(self.config_reader.wavelength)) + ) else: - text = '' + text = "" for idx, chan in enumerate(wavelengths): - text += f'{chan}, ' if idx != len(wavelengths) - 1 else f'{chan}' + text += ( + f"{chan}, " if idx != len(wavelengths) - 1 else f"{chan}" + ) self.ui.le_recon_wavelength.setText(text) self.ui.le_obj_na.setText(str(self.config_reader.NA_objective)) @@ -1180,72 +1532,92 @@ def _populate_from_config(self): self.ui.le_mag.setText(str(self.config_reader.magnification)) # Parse PhaseFromBF - if self.method == 'PhaseFromBF': - self.ui.le_bf_chan.setText(str(self.config_reader.brightfield_channel_index)) - + if self.method == "PhaseFromBF": + self.ui.le_bf_chan.setText( + str(self.config_reader.brightfield_channel_index) + ) + # Parse processing automatically denoiser = None for key, val in PROCESSING.items(): - if key == 'output_channels': + if key == "output_channels": channels = self.config_reader.output_channels - text = '' + text = "" for idx, chan in enumerate(channels): - text += f'{chan}, ' if idx != len(channels)-1 else f'{chan}' + text += ( + f"{chan}, " if idx != len(channels) - 1 else f"{chan}" + ) self.ui.le_output_channels.setText(text) - elif key == 'focus_zidx': + elif key == "focus_zidx": indices = self.config_reader.focus_zidx if isinstance(indices, int): self.ui.le_focus_zidx.setText(str(indices)) elif isinstance(indices, list): - text = '' + text = "" for idx, val in enumerate(indices): - text += f'{val}, ' if idx != len(indices) - 1 else f'{val}' + text += ( + f"{val}, " if idx != len(indices) - 1 else f"{val}" + ) self.ui.le_focus_zidx.setText(text) - elif key == 'use_gpu': + elif key == "use_gpu": state = getattr(self.config_reader, key) self.ui.chb_use_gpu.setChecked(state) - elif key == 'gpu_id': + elif key == "gpu_id": val = str(int(getattr(self.config_reader, key))) self.ui.le_gpu_id.setText(val) - elif key == 'pad_z': + elif key == "pad_z": val = str(int(getattr(self.config_reader, key))) self.ui.le_pad_z.setText(val) - elif key == 'focus_zidx': + elif key == "focus_zidx": val = str(int(getattr(self.config_reader, key))) self.ui.le_focus_zidx.setText(val) - elif hasattr(self.ui, f'le_{key}'): - le = getattr(self.ui, f'le_{key}') - le.setText(str(getattr(self.config_reader, key)) if not isinstance(getattr(self.config_reader, key), - str) else getattr(self.config_reader, - key)) - elif hasattr(self.ui, f'cb_{key}'): - cb = getattr(self.ui, f'cb_{key}') + elif hasattr(self.ui, f"le_{key}"): + le = getattr(self.ui, f"le_{key}") + le.setText( + str(getattr(self.config_reader, key)) + if not isinstance(getattr(self.config_reader, key), str) + else getattr(self.config_reader, key) + ) + elif hasattr(self.ui, f"cb_{key}"): + cb = getattr(self.ui, f"cb_{key}") items = [cb.itemText(i) for i in range(cb.count())] cfg_attr = getattr(self.config_reader, key) self.ui.cb_mode.setCurrentIndex(items.index(cfg_attr)) - elif key == 'phase_denoiser_2D' or key == 'phase_denoiser_3D': + elif key == "phase_denoiser_2D" or key == "phase_denoiser_3D": cb = self.ui.cb_phase_denoiser - cfg_attr = getattr(self.config_reader, f'phase_denoiser_{self.mode}') + cfg_attr = getattr( + self.config_reader, f"phase_denoiser_{self.mode}" + ) denoiser = cfg_attr - cb.setCurrentIndex(0) if cfg_attr == 'Tikhonov' else cb.setCurrentIndex(1) + cb.setCurrentIndex( + 0 + ) if cfg_attr == "Tikhonov" else cb.setCurrentIndex(1) else: - if denoiser == 'Tikhonov': - strength = getattr(self.config_reader, f'Tik_reg_ph_{self.mode}') + if denoiser == "Tikhonov": + strength = getattr( + self.config_reader, f"Tik_reg_ph_{self.mode}" + ) self.ui.le_phase_strength.setText(str(strength)) else: - strength = getattr(self.config_reader, f'TV_reg_ph_{self.mode}') + strength = getattr( + self.config_reader, f"TV_reg_ph_{self.mode}" + ) self.ui.le_phase_strength.setText(str(strength)) - self.ui.le_rho.setText(str(getattr(self.config_reader, f'rho_{self.mode}'))) - self.ui.le_itr.setText(str(getattr(self.config_reader, f'itr_{self.mode}'))) + self.ui.le_rho.setText( + str(getattr(self.config_reader, f"rho_{self.mode}")) + ) + self.ui.le_itr.setText( + str(getattr(self.config_reader, f"itr_{self.mode}")) + ) @Slot(bool) def change_gui_mode(self): @@ -1256,28 +1628,31 @@ def change_gui_mode(self): ------- """ - if self.gui_mode == 'offline': - self.ui.qbutton_gui_mode.setText('Switch to Offline') - self.ui.le_gui_mode.setText('Online') - self.ui.le_gui_mode.setStyleSheet("border: 1px solid green; color: green;") + if self.gui_mode == "offline": + self.ui.qbutton_gui_mode.setText("Switch to Offline") + self.ui.le_gui_mode.setText("Online") + self.ui.le_gui_mode.setStyleSheet( + "border: 1px solid green; color: green;" + ) self._hide_offline_ui(True) self._hide_acquisition_ui(False) - self.gui_mode = 'online' + self.gui_mode = "online" self.connect_to_mm() else: - self.ui.qbutton_gui_mode.setText('Switch to Online') - self.ui.le_gui_mode.setText('Offline') - self.ui.le_gui_mode.setStyleSheet("border: 1px solid rgb(200,0,0); color: rgb(200,0,0);") + self.ui.qbutton_gui_mode.setText("Switch to Online") + self.ui.le_gui_mode.setText("Offline") + self.ui.le_gui_mode.setStyleSheet( + "border: 1px solid rgb(200,0,0); color: rgb(200,0,0);" + ) self._hide_offline_ui(False) self._hide_acquisition_ui(True) - self.gui_mode = 'offline' + self.gui_mode = "offline" self.ui.cb_config_group.clear() - #Make sure button is still visible + # Make sure button is still visible self.ui.qbutton_mm_connect.setEnabled(True) - @Slot(bool) def connect_to_mm(self): """ @@ -1289,8 +1664,8 @@ def connect_to_mm(self): ------- """ - RECOMMENDED_MM = '20220920' - ZMQ_TARGET_VERSION = '4.2.0' + RECOMMENDED_MM = "20220920" + ZMQ_TARGET_VERSION = "4.2.0" try: # Try to open Bridge. Requires micromanager to be open with server running. # This does not fail gracefully, so I'm wrapping it in its own try-except block. @@ -1299,20 +1674,35 @@ def connect_to_mm(self): self.mmc = self.bridge.get_core() self.mm = self.bridge.get_studio() except: - print(("Could not establish pycromanager bridge.\n" - "Is micromanager open?\n" - "Is Tools > Options > Run server on port 4827 checked?\n" - f"Are you using nightly build {RECOMMENDED_MM}?")) + print( + ( + "Could not establish pycromanager bridge.\n" + "Is micromanager open?\n" + "Is Tools > Options > Run server on port 4827 checked?\n" + f"Are you using nightly build {RECOMMENDED_MM}?" + ) + ) raise EnvironmentError # Warn the user if there is a MicroManager/ZMQ version mismatch - self.bridge._main_socket.send({"command": "connect", "debug": False}) + self.bridge._main_socket.send( + {"command": "connect", "debug": False} + ) reply_json = self.bridge._main_socket.receive(timeout=500) - zmq_mm_version = reply_json['version'] + zmq_mm_version = reply_json["version"] if zmq_mm_version != ZMQ_TARGET_VERSION: - upgrade_str = 'upgrade' if version.parse(zmq_mm_version) < version.parse(ZMQ_TARGET_VERSION) else 'downgrade' - print(("WARNING: This version of Micromanager has not been tested with recOrder.\n" - f"Please {upgrade_str} to MicroManager nightly build {RECOMMENDED_MM}.")) + upgrade_str = ( + "upgrade" + if version.parse(zmq_mm_version) + < version.parse(ZMQ_TARGET_VERSION) + else "downgrade" + ) + print( + ( + "WARNING: This version of Micromanager has not been tested with recOrder.\n" + f"Please {upgrade_str} to MicroManager nightly build {RECOMMENDED_MM}." + ) + ) # Find config group containing calibration channels # calib_channels is typically ['State0', 'State1', 'State2', ...] @@ -1330,9 +1720,16 @@ def connect_to_mm(self): config_list = [] for j in range(configs.size()): config_list.append(configs.get(j)) - if np.all([np.any([ch in config for config in config_list]) for ch in self.calib_channels]): + if np.all( + [ + np.any([ch in config for config in config_list]) + for ch in self.calib_channels + ] + ): if not config_group_found: - self.config_group = group # set to first config group found + self.config_group = ( + group # set to first config group found + ) config_group_found = True self.ui.cb_config_group.addItem(group) # not entirely sure what this part does, but I left it in @@ -1341,28 +1738,32 @@ def connect_to_mm(self): if ch not in self.calib_channels: self.ui.cb_acq_channel.addItem(ch) if not config_group_found: - msg = f'No config group contains channels {self.calib_channels}. ' \ - 'Please refer to the recOrder wiki on how to set up the config properly.' - self.ui.cb_config_group.setStyleSheet("border: 1px solid rgb(200,0,0);") + msg = ( + f"No config group contains channels {self.calib_channels}. " + "Please refer to the recOrder wiki on how to set up the config properly." + ) + self.ui.cb_config_group.setStyleSheet( + "border: 1px solid rgb(200,0,0);" + ) raise KeyError(msg) - # set startup LC control mode _devices = self.mmc.getLoadedDevices() loaded_devices = [_devices.get(i) for i in range(_devices.size())] if LC_DEVICE_NAME in loaded_devices: - config_desc = self.mmc.getConfigData('Channel','State0').getVerbose() - if 'String send to' in config_desc: - self.calib_mode = 'MM-Retardance' + config_desc = self.mmc.getConfigData( + "Channel", "State0" + ).getVerbose() + if "String send to" in config_desc: + self.calib_mode = "MM-Retardance" self.ui.cb_calib_mode.setCurrentIndex(0) - if 'Voltage (V)' in config_desc: - self.calib_mode = 'MM-Voltage' + if "Voltage (V)" in config_desc: + self.calib_mode = "MM-Voltage" self.ui.cb_calib_mode.setCurrentIndex(1) else: - self.calib_mode = 'DAC' + self.calib_mode = "DAC" self.ui.cb_calib_mode.setCurrentIndex(2) - self.mm_status_changed.emit(True) except: @@ -1371,20 +1772,22 @@ def connect_to_mm(self): @Slot(bool) def handle_mm_status_update(self, value): if value: - self.ui.le_mm_status.setText('Success!') + self.ui.le_mm_status.setText("Success!") self.ui.le_mm_status.setStyleSheet("background-color: green;") - #Disabling the button + # Disabling the button self.ui.qbutton_mm_connect.setEnabled(False) else: - #Make sure button is still visible if it fails + # Make sure button is still visible if it fails self.ui.qbutton_mm_connect.setEnabled(True) - self.ui.le_mm_status.setText('Failed.') - self.ui.le_mm_status.setStyleSheet("background-color: rgb(200,0,0);") + self.ui.le_mm_status.setText("Failed.") + self.ui.le_mm_status.setStyleSheet( + "background-color: rgb(200,0,0);" + ) @Slot(tuple) def handle_progress_update(self, value): self.ui.progress_bar.setValue(value[0]) - self.ui.label_progress.setText('Progress: ' + value[1]) + self.ui.label_progress.setText("Progress: " + value[1]) @Slot(str) def handle_extinction_update(self, value): @@ -1409,12 +1812,17 @@ def handle_plot_update(self, value): self.intensity_monitor.append(value) self.ui.plot_widget.plot(self.intensity_monitor) - if self.plot_sequence[0] == 'Coarse': + if self.plot_sequence[0] == "Coarse": self.plot_item.autoRange() else: - self.plot_item.setRange(xRange=(self.plot_sequence[1], len(self.intensity_monitor)), - yRange=(0, np.max(self.intensity_monitor[self.plot_sequence[1]:])), - padding=0.1) + self.plot_item.setRange( + xRange=(self.plot_sequence[1], len(self.intensity_monitor)), + yRange=( + 0, + np.max(self.intensity_monitor[self.plot_sequence[1] :]), + ), + padding=0.1, + ) @Slot(str) def handle_calibration_assessment_update(self, value): @@ -1424,12 +1832,18 @@ def handle_calibration_assessment_update(self, value): def handle_calibration_assessment_msg_update(self, value): self.ui.tb_calib_assessment.setText(value) - if self.calib_assessment_level == 'good': - self.ui.tb_calib_assessment.setStyleSheet("border: 1px solid green;") - elif self.calib_assessment_level == 'okay': - self.ui.tb_calib_assessment.setStyleSheet("border: 1px solid rgb(252,190,3);") - elif self.calib_assessment_level == 'bad': - self.ui.tb_calib_assessment.setStyleSheet("border: 1px solid rgb(200,0,0);") + if self.calib_assessment_level == "good": + self.ui.tb_calib_assessment.setStyleSheet( + "border: 1px solid green;" + ) + elif self.calib_assessment_level == "okay": + self.ui.tb_calib_assessment.setStyleSheet( + "border: 1px solid rgb(252,190,3);" + ) + elif self.calib_assessment_level == "bad": + self.ui.tb_calib_assessment.setStyleSheet( + "border: 1px solid rgb(200,0,0);" + ) else: pass @@ -1443,99 +1857,127 @@ def handle_lc_states_emit(self, value: tuple[tuple, dict[str, list]]): 2-tuple consisting of a tuple of polarization state names and a dictionary of LC retardance values. """ pol_states, lc_values = value - - # Calculate circle - theta = np.linspace(0, 2*np.pi, 100) - x_circ = self.swing*np.cos(theta) + lc_values["LCA"][0] - y_circ = self.swing*np.sin(theta) + lc_values["LCB"][0] + + # Calculate circle + theta = np.linspace(0, 2 * np.pi, 100) + x_circ = self.swing * np.cos(theta) + lc_values["LCA"][0] + y_circ = self.swing * np.sin(theta) + lc_values["LCB"][0] import matplotlib.pyplot as plt - plt.close('all') - with plt.rc_context({ - "axes.spines.right": False, - "axes.spines.top": False, - }) and plt.ion(): + + plt.close("all") + with plt.rc_context( + { + "axes.spines.right": False, + "axes.spines.top": False, + } + ) and plt.ion(): plt.figure("Calibrated LC States") - plt.scatter(lc_values["LCA"], lc_values["LCB"], c='r') - plt.plot(x_circ, y_circ, 'k--', alpha=0.25) + plt.scatter(lc_values["LCA"], lc_values["LCB"], c="r") + plt.plot(x_circ, y_circ, "k--", alpha=0.25) plt.axis("equal") plt.xlabel("LCA retardance") plt.ylabel("LCB retardance") for i, pol in enumerate(pol_states): plt.annotate( - pol, + pol, xy=(lc_values["LCA"][i], lc_values["LCB"][i]), - xycoords='data', - xytext=(10,10), # annotation offset - textcoords='offset points' - ) + xycoords="data", + xytext=(10, 10), # annotation offset + textcoords="offset points", + ) @Slot(object) def handle_bg_image_update(self, value): - if 'Background Images' in self.viewer.layers: - self.viewer.layers['Background Images'].data = value + if "Background Images" in self.viewer.layers: + self.viewer.layers["Background Images"].data = value else: - self.viewer.add_image(value, name='Background Images', colormap='gray') + self.viewer.add_image( + value, name="Background Images", colormap="gray" + ) @Slot(object) def handle_bg_bire_image_update(self, value): # Separate Background Retardance and Background Orientation # Add new layer if none exists, otherwise update layer data - if 'Background Retardance' in self.viewer.layers: - self.viewer.layers['Background Retardance'].data = value[0] + if "Background Retardance" in self.viewer.layers: + self.viewer.layers["Background Retardance"].data = value[0] else: - self.viewer.add_image(value[0], name='Background Retardance', colormap='gray') + self.viewer.add_image( + value[0], name="Background Retardance", colormap="gray" + ) - if 'Background Orientation' in self.viewer.layers: - self.viewer.layers['Background Orientation'].data = value[1] + if "Background Orientation" in self.viewer.layers: + self.viewer.layers["Background Orientation"].data = value[1] else: - self.viewer.add_image(value[1], name='Background Orientation', colormap='gray') + self.viewer.add_image( + value[1], name="Background Orientation", colormap="gray" + ) @Slot(object) def handle_bire_image_update(self, value): - channel_names = {'Orientation': 1, - 'Retardance': 0, - } + channel_names = { + "Orientation": 1, + "Retardance": 0, + } # Compute Overlay if birefringence acquisition is 2D - if self.birefringence_dim == '2D': - channel_names['BirefringenceOverlay'] = None - overlay = ret_ori_overlay(retardance=value[0], - orientation=value[1], - ret_max= np.percentile(value[0], 99.99), - cmap=self.colormap) + if self.birefringence_dim == "2D": + channel_names["BirefringenceOverlay"] = None + overlay = ret_ori_overlay( + retardance=value[0], + orientation=value[1], + ret_max=np.percentile(value[0], 99.99), + cmap=self.colormap, + ) for key, chan in channel_names.items(): - if key == 'BirefringenceOverlay': - if key+self.birefringence_dim in self.viewer.layers: - self.viewer.layers[key+self.birefringence_dim].data = overlay + if key == "BirefringenceOverlay": + if key + self.birefringence_dim in self.viewer.layers: + self.viewer.layers[ + key + self.birefringence_dim + ].data = overlay else: - self.viewer.add_image(overlay, name=key+self.birefringence_dim, rgb=True) + self.viewer.add_image( + overlay, name=key + self.birefringence_dim, rgb=True + ) else: - if key+self.birefringence_dim in self.viewer.layers: - self.viewer.layers[key+self.birefringence_dim].data = value[chan] + if key + self.birefringence_dim in self.viewer.layers: + self.viewer.layers[ + key + self.birefringence_dim + ].data = value[chan] else: - cmap = 'gray' if key != 'Orientation' else 'hsv' - self.viewer.add_image(value[chan], name=key+self.birefringence_dim, colormap=cmap) + cmap = "gray" if key != "Orientation" else "hsv" + self.viewer.add_image( + value[chan], + name=key + self.birefringence_dim, + colormap=cmap, + ) @Slot(object) def handle_phase_image_update(self, value): - name = 'Phase2D' if self.phase_dim == '2D' else 'Phase3D' + name = "Phase2D" if self.phase_dim == "2D" else "Phase3D" # Add new layer if none exists, otherwise update layer data if name in self.viewer.layers: self.viewer.layers[name].data = value else: - self.viewer.add_image(value, name=name, colormap='gray') - - if 'Phase' not in [self.ui.cb_saturation.itemText(i) for i in range(self.ui.cb_saturation.count())]: - self.ui.cb_saturation.addItem('Retardance') - if 'Phase' not in [self.ui.cb_value.itemText(i) for i in range(self.ui.cb_value.count())]: - self.ui.cb_value.addItem('Retardance') + self.viewer.add_image(value, name=name, colormap="gray") + + if "Phase" not in [ + self.ui.cb_saturation.itemText(i) + for i in range(self.ui.cb_saturation.count()) + ]: + self.ui.cb_saturation.addItem("Retardance") + if "Phase" not in [ + self.ui.cb_value.itemText(i) + for i in range(self.ui.cb_value.count()) + ]: + self.ui.cb_value.addItem("Retardance") @Slot(object) def handle_qlipp_reconstructor_update(self, value): @@ -1593,19 +2035,27 @@ def handle_reconstruction_dim_update(self, value): layer_name = self.worker.manager.config.data_save_name if p == 0 and t == 0 and c == 0: - self.reconstruction_data = WaveorderReader(self.reconstruction_data_path, 'zarr') - self.viewer.add_image(self.reconstruction_data.get_zarr(p), name=layer_name + f'_Pos_{p:03d}') - - self.viewer.dims.set_axis_label(0, 'T') - self.viewer.dims.set_axis_label(1, 'C') - self.viewer.dims.set_axis_label(2, 'Z') + self.reconstruction_data = WaveorderReader( + self.reconstruction_data_path, "zarr" + ) + self.viewer.add_image( + self.reconstruction_data.get_zarr(p), + name=layer_name + f"_Pos_{p:03d}", + ) + self.viewer.dims.set_axis_label(0, "T") + self.viewer.dims.set_axis_label(1, "C") + self.viewer.dims.set_axis_label(2, "Z") # Add each new position as a new layer in napari - name = layer_name + f'_Pos_{p:03d}' + name = layer_name + f"_Pos_{p:03d}" if name not in self.viewer.layers: - self.reconstruction_data = WaveorderReader(self.reconstruction_data_path, 'zarr') - self.viewer.add_image(self.reconstruction_data.get_zarr(p), name=name) + self.reconstruction_data = WaveorderReader( + self.reconstruction_data_path, "zarr" + ) + self.viewer.add_image( + self.reconstruction_data.get_zarr(p), name=name + ) # update the napari dimension slider position if the user hasn't specified to pause updates if not self.pause_updates: @@ -1616,7 +2066,7 @@ def handle_reconstruction_dim_update(self, value): @Slot(bool) def browse_dir_path(self): - result = self._open_file_dialog(self.current_dir_path, 'dir') + result = self._open_file_dialog(self.current_dir_path, "dir") self.directory = result self.current_dir_path = result self.ui.le_directory.setText(result) @@ -1625,20 +2075,20 @@ def browse_dir_path(self): @Slot(bool) def browse_save_path(self): - result = self._open_file_dialog(self.current_save_path, 'dir') + result = self._open_file_dialog(self.current_save_path, "dir") self.save_directory = result self.current_save_path = result self.ui.le_save_dir.setText(result) @Slot(bool) def browse_data_dir(self): - path = self._open_file_dialog(self.data_dir, 'dir') + path = self._open_file_dialog(self.data_dir, "dir") self.data_dir = path self.ui.le_data_dir.setText(self.data_dir) @Slot(bool) def browse_calib_meta(self): - path = self._open_file_dialog(self.calib_path, 'file') + path = self._open_file_dialog(self.calib_path, "file") self.calib_path = path self.ui.le_calibration_metadata.setText(self.calib_path) @@ -1650,7 +2100,7 @@ def enter_dir_path(self): self.save_directory = path self.ui.le_save_dir.setText(path) else: - self.ui.le_directory.setText('Path Does Not Exist') + self.ui.le_directory.setText("Path Does Not Exist") @Slot() def enter_swing(self): @@ -1664,27 +2114,27 @@ def enter_wavelength(self): def enter_calib_scheme(self): index = self.ui.cb_calib_scheme.currentIndex() if index == 0: - self.calib_scheme = '4-State' + self.calib_scheme = "4-State" else: - self.calib_scheme = '5-State' + self.calib_scheme = "5-State" @Slot() def enter_calib_mode(self): index = self.ui.cb_calib_mode.currentIndex() if index == 0: - self.calib_mode = 'MM-Retardance' + self.calib_mode = "MM-Retardance" self.ui.label_lca.hide() self.ui.label_lcb.hide() self.ui.cb_lca.hide() self.ui.cb_lcb.hide() elif index == 1: - self.calib_mode = 'MM-Voltage' + self.calib_mode = "MM-Voltage" self.ui.label_lca.hide() self.ui.label_lcb.hide() self.ui.cb_lca.hide() self.ui.cb_lcb.hide() elif index == 2: - self.calib_mode = 'DAC' + self.calib_mode = "DAC" self.ui.cb_lca.clear() self.ui.cb_lcb.clear() self.ui.cb_lca.show() @@ -1692,18 +2142,18 @@ def enter_calib_mode(self): self.ui.label_lca.show() self.ui.label_lcb.show() - cfg = self.mmc.getConfigData(self.config_group, 'State0') + cfg = self.mmc.getConfigData(self.config_group, "State0") # Update the DAC combo boxes with available DAC's from the config. Necessary for the user # to specify which DAC output corresponds to which LC for voltage-space calibration memory = set() for i in range(cfg.size()): prop = cfg.getSetting(i) - if 'TS_DAC' in prop.getDeviceLabel(): + if "TS_DAC" in prop.getDeviceLabel(): dac = prop.getDeviceLabel()[-2:] if dac not in memory: - self.ui.cb_lca.addItem('DAC'+dac) - self.ui.cb_lcb.addItem('DAC'+dac) + self.ui.cb_lca.addItem("DAC" + dac) + self.ui.cb_lcb.addItem("DAC" + dac) memory.add(dac) else: continue @@ -1731,7 +2181,7 @@ def enter_config_group(self): ------- """ - #if/else takes care of the clearing of config + # if/else takes care of the clearing of config if self.ui.cb_config_group.count() != 0: self.mmc = self.bridge.get_core() self.mm = self.bridge.get_studio() @@ -1740,12 +2190,12 @@ def enter_config_group(self): self.config_group = self.ui.cb_config_group.currentText() config = self.mmc.getAvailableConfigs(self.config_group) - channels = [] + channels = [] for i in range(config.size()): channels.append(config.get(i)) # Check to see if any states are missing - states = ['State0', 'State1', 'State2', 'State3', 'State4'] + states = ["State0", "State1", "State2", "State3", "State4"] missing = [] for state in states: if state not in channels: @@ -1753,14 +2203,18 @@ def enter_config_group(self): # if states are missing, set the combo box red and alert the user if len(missing) != 0: - msg = f'The chosen config group ({self.config_group}) is missing states: {missing}. '\ - 'Please refer to the recOrder wiki on how to set up the config properly.' - - self.ui.cb_config_group.setStyleSheet("border: 1px solid rgb(200,0,0);") + msg = ( + f"The chosen config group ({self.config_group}) is missing states: {missing}. " + "Please refer to the recOrder wiki on how to set up the config properly." + ) + + self.ui.cb_config_group.setStyleSheet( + "border: 1px solid rgb(200,0,0);" + ) raise KeyError(msg) else: self.ui.cb_config_group.setStyleSheet("") - + @Slot() def enter_use_cropped_roi(self): state = self.ui.chb_use_roi.checkState() @@ -1792,7 +2246,7 @@ def enter_save_path(self): self.save_directory = path self.current_save_path = path else: - self.ui.le_save_dir.setText('Path Does Not Exist') + self.ui.le_save_dir.setText("Path Does Not Exist") @Slot() def enter_save_name(self): @@ -1815,17 +2269,17 @@ def enter_zstep(self): def enter_birefringence_dim(self): state = self.ui.cb_birefringence.currentIndex() if state == 0: - self.birefringence_dim = '2D' + self.birefringence_dim = "2D" elif state == 1: - self.birefringence_dim = '3D' + self.birefringence_dim = "3D" @Slot() def enter_phase_dim(self): state = self.ui.cb_phase.currentIndex() if state == 0: - self.phase_dim = '2D' + self.phase_dim = "2D" elif state == 1: - self.phase_dim = '3D' + self.phase_dim = "3D" @Slot() def enter_phase_denoiser(self): @@ -1849,7 +2303,7 @@ def enter_acq_bg_path(self): self.acq_bg_directory = path self.current_bg_path = path else: - self.ui.le_bg_path.setText('Path Does Not Exist') + self.ui.le_bg_path.setText("Path Does Not Exist") @Slot(str) def handle_bg_path_update(self, value: str): @@ -1876,7 +2330,7 @@ def handle_bg_path_update(self, value: str): @Slot(bool) def browse_acq_bg_path(self): - result = self._open_file_dialog(self.current_bg_path, 'dir') + result = self._open_file_dialog(self.current_bg_path, "dir") self.acq_bg_directory = result self.current_bg_path = result self.ui.le_bg_path.setText(result) @@ -1888,22 +2342,22 @@ def enter_bg_correction(self): self.ui.label_bg_path.setHidden(True) self.ui.le_bg_path.setHidden(True) self.ui.qbutton_browse_bg_path.setHidden(True) - self.bg_option = 'None' + self.bg_option = "None" elif state == 1: self.ui.label_bg_path.setHidden(False) self.ui.le_bg_path.setHidden(False) self.ui.qbutton_browse_bg_path.setHidden(False) - self.bg_option = 'global' + self.bg_option = "global" elif state == 2: self.ui.label_bg_path.setHidden(True) self.ui.le_bg_path.setHidden(True) self.ui.qbutton_browse_bg_path.setHidden(True) - self.bg_option = 'local_fit' + self.bg_option = "local_fit" elif state == 3: self.ui.label_bg_path.setHidden(False) self.ui.le_bg_path.setHidden(False) self.ui.qbutton_browse_bg_path.setHidden(False) - self.bg_option = 'local_fit+' + self.bg_option = "local_fit+" @Slot() def enter_gpu_id(self): @@ -1969,29 +2423,31 @@ def enter_method(self): idx = self.ui.cb_method.currentIndex() if idx == 0: - self.method = 'QLIPP' + self.method = "QLIPP" self.ui.label_bf_chan.hide() self.ui.le_bf_chan.hide() - self.ui.label_chan_desc.setText('Retardance, Orientation, BF, Phase3D, Phase2D, S0, S1, S2, S3') + self.ui.label_chan_desc.setText( + "Retardance, Orientation, BF, Phase3D, Phase2D, S0, S1, S2, S3" + ) elif idx == 1: - self.method = 'PhaseFromBF' + self.method = "PhaseFromBF" self.ui.label_bf_chan.show() self.ui.le_bf_chan.show() - self.ui.label_bf_chan.setText('Brightfield Channel Index') - self.ui.le_bf_chan.setPlaceholderText('int') - self.ui.label_chan_desc.setText('Phase3D, Phase2D') + self.ui.label_bf_chan.setText("Brightfield Channel Index") + self.ui.le_bf_chan.setPlaceholderText("int") + self.ui.label_chan_desc.setText("Phase3D, Phase2D") @Slot(int) def enter_mode(self): idx = self.ui.cb_mode.currentIndex() if idx == 0: - self.mode = '3D' + self.mode = "3D" self.ui.label_focus_zidx.hide() self.ui.le_focus_zidx.hide() else: - self.mode = '2D' + self.mode = "2D" self.ui.label_focus_zidx.show() self.ui.le_focus_zidx.show() @@ -1999,8 +2455,10 @@ def enter_mode(self): def enter_data_dir(self): entry = self.ui.le_data_dir.text() if not os.path.exists(entry): - self.ui.le_data_dir.setStyleSheet("border: 1px solid rgb(200,0,0);") - self.ui.le_data_dir.setText('Path Does Not Exist') + self.ui.le_data_dir.setStyleSheet( + "border: 1px solid rgb(200,0,0);" + ) + self.ui.le_data_dir.setText("Path Does Not Exist") else: self.ui.le_data_dir.setStyleSheet("") self.data_dir = entry @@ -2009,8 +2467,10 @@ def enter_data_dir(self): def enter_calib_meta(self): entry = self.ui.le_calibration_metadata.text() if not os.path.exists(entry): - self.ui.le_calibration_metadata.setStyleSheet("border: 1px solid rgb(200,0,0);") - self.ui.le_calibration_metadata.setText('Path Does Not Exist') + self.ui.le_calibration_metadata.setStyleSheet( + "border: 1px solid rgb(200,0,0);" + ) + self.ui.le_calibration_metadata.setText("Path Does Not Exist") else: self.ui.le_calibration_metadata.setStyleSheet("") self.calib_path = entry @@ -2030,23 +2490,30 @@ def enter_colormap(self): state = self.ui.cb_colormap.currentIndex() if state == 0: self.ui.label_orientation_image.setPixmap(self.jch_pixmap) - self.colormap = 'JCh' + self.colormap = "JCh" else: self.ui.label_orientation_image.setPixmap(self.hsv_pixmap) - self.colormap = 'HSV' + self.colormap = "HSV" # Update the birefringence overlay to new colormap if the colormap has changed if prev_cmap != self.colormap: - #TODO: Handle case where there are multiple snaps - if 'BirefringenceOverlay2D' in self.viewer.layers: - if 'Retardance2D' in self.viewer.layers and 'Orientation2D' in self.viewer.layers: - - overlay = ret_ori_overlay(retardance=self.viewer.layers['Retardance2D'].data, - orientation=self.viewer.layers['Orientation2D'].data, - ret_max= np.percentile(self.viewer.layers['Retardance2D'].data, 99.99), - cmap=self.colormap) - - self.viewer.layers['BirefringenceOverlay2D'].data = overlay + # TODO: Handle case where there are multiple snaps + if "BirefringenceOverlay2D" in self.viewer.layers: + if ( + "Retardance2D" in self.viewer.layers + and "Orientation2D" in self.viewer.layers + ): + + overlay = ret_ori_overlay( + retardance=self.viewer.layers["Retardance2D"].data, + orientation=self.viewer.layers["Orientation2D"].data, + ret_max=np.percentile( + self.viewer.layers["Retardance2D"].data, 99.99 + ), + cmap=self.colormap, + ) + + self.viewer.layers["BirefringenceOverlay2D"].data = overlay @Slot(int) def enter_use_full_volume(self): @@ -2101,24 +2568,26 @@ def push_note(self): # make sure the user has performed a calibration in this session (or loaded a previous one) if not self.last_calib_meta_file: - raise ValueError('No calibration has been performed yet so there is no previous metadata file') + raise ValueError( + "No calibration has been performed yet so there is no previous metadata file" + ) else: note = self.ui.le_notes_field.text() # Open the existing calibration metadata file and append the notes - with open(self.last_calib_meta_file, 'r') as file: + with open(self.last_calib_meta_file, "r") as file: current_json = json.load(file) # Append note to the end of the old note (so we don't overwrite previous notes) or write a new # note in the blank notes field - old_note = current_json['Notes'] - if old_note is None or old_note == '' or old_note == note: - current_json['Notes'] = note + old_note = current_json["Notes"] + if old_note is None or old_note == "" or old_note == note: + current_json["Notes"] = note else: - current_json['Notes'] = old_note + ', ' + note + current_json["Notes"] = old_note + ", " + note # dump the contents into the metadata file - with open(self.last_calib_meta_file, 'w') as file: + with open(self.last_calib_meta_file, "w") as file: json.dump(current_json, file, indent=1) @Slot(bool) @@ -2134,13 +2603,15 @@ def calc_extinction(self): """ # Snap images from the extinction state and first elliptical state - set_lc_state(self.mmc, self.config_group, 'State0') + set_lc_state(self.mmc, self.config_group, "State0") extinction = snap_and_average(self.calib.snap_manager) - set_lc_state(self.mmc, self.config_group, 'State1') + set_lc_state(self.mmc, self.config_group, "State1") state1 = snap_and_average(self.calib.snap_manager) # Calculate extinction based off captured intensities - extinction = self.calib.calculate_extinction(self.swing, self.calib.I_Black, extinction, state1) + extinction = self.calib.calculate_extinction( + self.swing, self.calib.I_Black, extinction, state1 + ) self.ui.le_extinction.setText(str(extinction)) @property @@ -2149,19 +2620,20 @@ def _microscope_params(self): A dictionary containing microscope parameters from the current GUI Unused in 0.2.0 --- candidate for deletion """ + def _param_value(param_name: str, ui=self.ui): # refer to main widget class ui attribute names ui_attr_name = "le_" + param_name param_text = ui.__getattribute__(ui_attr_name).text() # handle blank string - return float(param_text) if param_text != '' else None + return float(param_text) if param_text != "" else None return { - 'n_objective_media': _param_value("n_media"), - 'objective_NA': _param_value("obj_na"), - 'condenser_NA': _param_value("cond_na"), - 'magnification': _param_value("mag"), - 'pixel_size': _param_value("ps") + "n_objective_media": _param_value("n_media"), + "objective_NA": _param_value("obj_na"), + "condenser_NA": _param_value("cond_na"), + "magnification": _param_value("mag"), + "pixel_size": _param_value("ps"), } @Slot(bool) @@ -2170,7 +2642,7 @@ def load_calibration(self): Uses previous JSON calibration metadata to load previous calibration """ - metadata_path = self._open_file_dialog(self.current_dir_path, 'file') + metadata_path = self._open_file_dialog(self.current_dir_path, "file") metadata = MetadataReader(metadata_path) # Update Properties @@ -2178,15 +2650,21 @@ def load_calibration(self): self.swing = metadata.Swing # Initialize calibration class - self.calib = QLIPP_Calibration(self.mmc, self.mm, group=self.config_group, lc_control_mode=self.calib_mode, - interp_method=self.interp_method, wavelength=self.wavelength) + self.calib = QLIPP_Calibration( + self.mmc, + self.mm, + group=self.config_group, + lc_control_mode=self.calib_mode, + interp_method=self.interp_method, + wavelength=self.wavelength, + ) self.calib.swing = self.swing self.ui.le_swing.setText(str(self.swing)) self.calib.wavelength = self.wavelength self.ui.le_wavelength.setText(str(self.wavelength)) # Update Calibration Scheme Combo Box - if metadata.Calibration_scheme == '4-State': + if metadata.Calibration_scheme == "4-State": self.ui.cb_calib_scheme.setCurrentIndex(0) else: self.ui.cb_calib_scheme.setCurrentIndex(1) @@ -2218,21 +2696,27 @@ def run_calibration(self): """ self._check_MM_config_setup() - - self.calib = QLIPP_Calibration(self.mmc, self.mm, group=self.config_group, lc_control_mode=self.calib_mode, - interp_method=self.interp_method, wavelength=self.wavelength) - if self.calib_mode == 'DAC': + self.calib = QLIPP_Calibration( + self.mmc, + self.mm, + group=self.config_group, + lc_control_mode=self.calib_mode, + interp_method=self.interp_method, + wavelength=self.wavelength, + ) + + if self.calib_mode == "DAC": self.calib.set_dacs(self.lca_dac, self.lcb_dac) # Reset Styling - self.ui.tb_calib_assessment.setText('') + self.ui.tb_calib_assessment.setText("") self.ui.tb_calib_assessment.setStyleSheet("") # Save initial autoshutter state for when we set it back later self.auto_shutter = self.mmc.getAutoShutter() - logging.info('Starting Calibration') + logging.info("Starting Calibration") # Initialize displays + parameters for calibration self.ui.progress_bar.setValue(0) @@ -2240,7 +2724,9 @@ def run_calibration(self): self.intensity_monitor = [] self.calib.swing = self.swing self.calib.wavelength = self.wavelength - self.calib.meta_file = os.path.join(self.directory, 'calibration_metadata.txt') + self.calib.meta_file = os.path.join( + self.directory, "calibration_metadata.txt" + ) # Make sure Live Mode is off if self.calib.snap_manager.getIsLiveModeOn(): @@ -2253,10 +2739,16 @@ def run_calibration(self): self.worker.progress_update.connect(self.handle_progress_update) self.worker.extinction_update.connect(self.handle_extinction_update) self.worker.intensity_update.connect(self.handle_plot_update) - self.worker.calib_assessment.connect(self.handle_calibration_assessment_update) - self.worker.calib_assessment_msg.connect(self.handle_calibration_assessment_msg_update) + self.worker.calib_assessment.connect( + self.handle_calibration_assessment_update + ) + self.worker.calib_assessment_msg.connect( + self.handle_calibration_assessment_msg_update + ) self.worker.calib_file_emit.connect(self.handle_calib_file_update) - self.worker.plot_sequence_emit.connect(self.handle_plot_sequence_update) + self.worker.plot_sequence_emit.connect( + self.handle_plot_sequence_update + ) self.worker.lc_states.connect(self.handle_lc_states_emit) self.worker.started.connect(self._disable_buttons) self.worker.finished.connect(self._enable_buttons) @@ -2268,38 +2760,59 @@ def run_calibration(self): @property def _channel_descriptions(self): return [ - self.mmc.getConfigData(self.config_group, calib_channel).getVerbose() + self.mmc.getConfigData( + self.config_group, calib_channel + ).getVerbose() for calib_channel in self.calib_channels ] def _check_MM_config_setup(self): # Warns the user if the MM configuration is not correctly set up. desc = self._channel_descriptions - if self.calib_mode == 'MM-Retardance': - if all('String send to' in s for s in desc) and not any('Voltage (V)' in s for s in desc): + if self.calib_mode == "MM-Retardance": + if all("String send to" in s for s in desc) and not any( + "Voltage (V)" in s for s in desc + ): return else: - msg = ' \n'.join(textwrap.wrap("In \'MM-Retardance\' mode each preset must include the " \ - "\'String send to\' property, and no \'Voltage\' properties.", width=40)) + msg = " \n".join( + textwrap.wrap( + "In 'MM-Retardance' mode each preset must include the " + "'String send to' property, and no 'Voltage' properties.", + width=40, + ) + ) show_warning(msg) - - elif self.calib_mode == 'MM-Voltage': - if all('Voltage (V) LC-A' in s for s in desc) and all('Voltage (V) LC-B' in s for s in desc) and not any('String send to' in s for s in desc): + + elif self.calib_mode == "MM-Voltage": + if ( + all("Voltage (V) LC-A" in s for s in desc) + and all("Voltage (V) LC-B" in s for s in desc) + and not any("String send to" in s for s in desc) + ): return else: - msg = ' \n'.join(textwrap.wrap("In \'MM-Voltage\' mode each preset must include the \'Voltage (V) LC-A\' " \ - "property, the \'Voltage (V) LC-B\' property, and no \'String send to\' properties.", width=40)) + msg = " \n".join( + textwrap.wrap( + "In 'MM-Voltage' mode each preset must include the 'Voltage (V) LC-A' " + "property, the 'Voltage (V) LC-B' property, and no 'String send to' properties.", + width=40, + ) + ) show_warning(msg) - elif self.calib_mode == 'DAC': + elif self.calib_mode == "DAC": _devices = self.mmc.getLoadedDevices() loaded_devices = [_devices.get(i) for i in range(_devices.size())] if LC_DEVICE_NAME in loaded_devices: - show_warning("In \'DAC\' mode the MeadowLarkLC device adapter must not be loaded in MM.") - + show_warning( + "In 'DAC' mode the MeadowLarkLC device adapter must not be loaded in MM." + ) + else: - raise ValueError(f'self.calib_mode = {self.calib_mode} is an unrecognized state.') - + raise ValueError( + f"self.calib_mode = {self.calib_mode} is an unrecognized state." + ) @Slot(bool) def capture_bg(self): @@ -2316,20 +2829,22 @@ def capture_bg(self): no_calibration_message = """Capturing a background requires calibrated liquid crystals. \ Please either run a calibration or load a calibration from file.""" raise RuntimeError(no_calibration_message) - + # Init worker and thread self.worker = BackgroundCaptureWorker(self, self.calib) # Connect Handlers self.worker.bg_image_emitter.connect(self.handle_bg_image_update) - self.worker.bire_image_emitter.connect(self.handle_bg_bire_image_update) + self.worker.bire_image_emitter.connect( + self.handle_bg_bire_image_update + ) self.worker.started.connect(self._disable_buttons) self.worker.finished.connect(self._enable_buttons) self.worker.errored.connect(self._handle_error) self.ui.qbutton_stop_calib.clicked.connect(self.worker.quit) self.worker.aborted.connect(self._handle_calib_abort) - + # Connect to BG Correction Path self.worker.bg_path_update_emitter.connect(self.handle_bg_path_update) @@ -2345,10 +2860,12 @@ def acq_birefringence(self): """ - self._check_requirements_for_acq('birefringence') + self._check_requirements_for_acq("birefringence") # Init Worker and thread - self.worker = PolarizationAcquisitionWorker(self, self.calib, 'birefringence') + self.worker = PolarizationAcquisitionWorker( + self, self.calib, "birefringence" + ) # Connect Handlers self.worker.bire_image_emitter.connect(self.handle_bire_image_update) @@ -2366,17 +2883,21 @@ def acq_phase(self): Wrapper function to acquire phase stack and plot in napari """ - self._check_requirements_for_acq('phase') + self._check_requirements_for_acq("phase") # Init worker and thread if self.ui.chb_phase_from_bf.isChecked(): self.worker = BFAcquisitionWorker(self) else: - self.worker = PolarizationAcquisitionWorker(self, self.calib, 'phase') + self.worker = PolarizationAcquisitionWorker( + self, self.calib, "phase" + ) # Connect Handlers self.worker.phase_image_emitter.connect(self.handle_phase_image_update) - self.worker.phase_reconstructor_emitter.connect(self.handle_qlipp_reconstructor_update) + self.worker.phase_reconstructor_emitter.connect( + self.handle_qlipp_reconstructor_update + ) self.worker.meta_emitter.connect(self.handle_meta_update) self.worker.started.connect(self._disable_buttons) self.worker.finished.connect(self._enable_buttons) @@ -2391,15 +2912,17 @@ def acq_birefringence_phase(self): Wrapper function to acquire both birefringence and phase stack and plot in napari """ - self._check_requirements_for_acq('phase') + self._check_requirements_for_acq("phase") # Init worker # Init worker and thread - self.worker = PolarizationAcquisitionWorker(self, self.calib, 'all') + self.worker = PolarizationAcquisitionWorker(self, self.calib, "all") # connect handlers self.worker.phase_image_emitter.connect(self.handle_phase_image_update) - self.worker.phase_reconstructor_emitter.connect(self.handle_qlipp_reconstructor_update) + self.worker.phase_reconstructor_emitter.connect( + self.handle_qlipp_reconstructor_update + ) self.worker.bire_image_emitter.connect(self.handle_bire_image_update) self.worker.meta_emitter.connect(self.handle_meta_update) self.worker.started.connect(self._disable_buttons) @@ -2422,12 +2945,14 @@ def listen_and_reconstruct(self): """ # Init reconstructor - if self.bg_option != 'None': + if self.bg_option != "None": metadata_file = get_last_metadata_file(self.current_bg_path) metadata = MetadataReader(metadata_file) roi = metadata.ROI height, width = roi[2], roi[3] - bg_data = load_bg(self.current_bg_path, height, width, roi) # TODO: remove ROI for 1.0.0 + bg_data = load_bg( + self.current_bg_path, height, width, roi + ) # TODO: remove ROI for 1.0.0 else: bg_data = None @@ -2454,14 +2979,20 @@ def reconstruct(self): success = self._check_requirements_for_reconstruction() if not success: - raise ValueError('Please make sure all necessary parameters are set before reconstruction') + raise ValueError( + "Please make sure all necessary parameters are set before reconstruction" + ) self._populate_config_from_app() self.worker = ReconstructionWorker(self, self.config_reader) # connect handlers - self.worker.dimension_emitter.connect(self.handle_reconstruction_dim_update) - self.worker.store_emitter.connect(self.handle_reconstruction_store_update) + self.worker.dimension_emitter.connect( + self.handle_reconstruction_dim_update + ) + self.worker.store_emitter.connect( + self.handle_reconstruction_store_update + ) self.worker.started.connect(self._disable_buttons) self.worker.finished.connect(self._enable_buttons) self.worker.errored.connect(self._handle_acq_error) @@ -2471,7 +3002,7 @@ def reconstruct(self): @Slot(bool) def save_config(self): - path = self._open_file_dialog(self.save_config_path, 'save') + path = self._open_file_dialog(self.save_config_path, "save") self.save_config_path = path name = PurePath(self.save_config_path).name dir_ = self.save_config_path.strip(name) @@ -2479,10 +3010,12 @@ def save_config(self): if isinstance(self.config_reader.positions, tuple): pos = self.config_reader.positions - self.config_reader.positions = f'[!!python/tuple [{pos[0]},{pos[1]}]]' + self.config_reader.positions = ( + f"[!!python/tuple [{pos[0]},{pos[1]}]]" + ) if isinstance(self.config_reader.timepoints, tuple): t = self.config_reader.timepoints - self.config_reader.timepoints = f'[!!python/tuple [{t[0]},{t[1]}]]' + self.config_reader.timepoints = f"[!!python/tuple [{t[0]},{t[1]}]]" self.config_reader.save_yaml(dir_=dir_, name=name) @@ -2495,8 +3028,8 @@ def load_config(self): ------- """ - path = self._open_file_dialog(self.save_config_path, 'file') - if path == '': + path = self._open_file_dialog(self.save_config_path, "file") + if path == "": pass else: self.config_path = path @@ -2517,7 +3050,7 @@ def update_sat_scale(self): min_, max_ = np.min(data), np.max(data) self.ui.slider_saturation.setMinimum(min_) self.ui.slider_saturation.setMaximum(max_) - self.ui.slider_saturation.setSingleStep((max_ - min_)/250) + self.ui.slider_saturation.setSingleStep((max_ - min_) / 250) self.ui.slider_saturation.setValue((min_, max_)) self.ui.le_sat_max.setText(str(np.round(max_, 3))) self.ui.le_sat_min.setText(str(np.round(min_, 3))) @@ -2531,7 +3064,7 @@ def update_value_scale(self): min_, max_ = np.min(data), np.max(data) self.ui.slider_value.setMinimum(min_) self.ui.slider_value.setMaximum(max_) - self.ui.slider_value.setSingleStep((max_ - min_)/250) + self.ui.slider_value.setSingleStep((max_ - min_) / 250) self.ui.slider_value.setValue((min_, max_)) self.ui.le_val_max.setText(str(np.round(max_, 3))) self.ui.le_val_min.setText(str(np.round(min_, 3))) @@ -2547,25 +3080,50 @@ def create_overlay(self): """ - if self.ui.cb_hue.count() == 0 or self.ui.cb_saturation.count() == 0 or self.ui.cb_value == 0: - raise ValueError('Cannot create overlay until all 3 combo boxes are populated') + if ( + self.ui.cb_hue.count() == 0 + or self.ui.cb_saturation.count() == 0 + or self.ui.cb_value == 0 + ): + raise ValueError( + "Cannot create overlay until all 3 combo boxes are populated" + ) # Gather channel data - H = self.viewer.layers[self.ui.cb_hue.itemText(self.ui.cb_hue.currentIndex())].data - S = self.viewer.layers[self.ui.cb_saturation.itemText(self.ui.cb_saturation.currentIndex())].data - V = self.viewer.layers[self.ui.cb_value.itemText(self.ui.cb_value.currentIndex())].data - + H = self.viewer.layers[ + self.ui.cb_hue.itemText(self.ui.cb_hue.currentIndex()) + ].data + S = self.viewer.layers[ + self.ui.cb_saturation.itemText( + self.ui.cb_saturation.currentIndex() + ) + ].data + V = self.viewer.layers[ + self.ui.cb_value.itemText(self.ui.cb_value.currentIndex()) + ].data - #TODO: this is a temp fix which handles on data with n-dimensions of 4, 3, or 2 which automatically + # TODO: this is a temp fix which handles on data with n-dimensions of 4, 3, or 2 which automatically # chooses the first timepoint if H.ndim > 2 or S.ndim > 2 or V.ndim > 2: if H.ndim == 4: # assumes this is a (T, Z, Y, X) array read from napari-ome-zarr - H = H[0, self.display_slice] if not self.use_full_volume else H[0] + H = ( + H[0, self.display_slice] + if not self.use_full_volume + else H[0] + ) if S.ndim == 4: - S = S[0, self.display_slice] if not self.use_full_volume else S[0] + S = ( + S[0, self.display_slice] + if not self.use_full_volume + else S[0] + ) if V.ndim == 4: - V = V[0, self.display_slice] if not self.use_full_volume else V[0] + V = ( + V[0, self.display_slice] + if not self.use_full_volume + else V[0] + ) if H.ndim == 3: # assumes this is a (Z, Y, X) array collected from acquisition module @@ -2577,31 +3135,37 @@ def create_overlay(self): if S.ndim == 3: S = S[self.display_slice] if not self.use_full_volume else S - mode = '2D' if not self.use_full_volume else '3D' + mode = "2D" if not self.use_full_volume else "3D" H_name = self.ui.cb_hue.itemText(self.ui.cb_hue.currentIndex()) - H_scale = (np.min(H), np.max(H)) if 'Orientation' not in H_name else (0, np.pi) + H_scale = ( + (np.min(H), np.max(H)) + if "Orientation" not in H_name + else (0, np.pi) + ) S_scale = self.ui.slider_saturation.value() V_scale = self.ui.slider_value.value() - hsv_image = generic_hsv_overlay(H, S, V, H_scale, S_scale, V_scale, mode=mode) + hsv_image = generic_hsv_overlay( + H, S, V, H_scale, S_scale, V_scale, mode=mode + ) # Create overlay layer name idx = 0 - while f'HSV_Overlay_{idx}' in self.viewer.layers: + while f"HSV_Overlay_{idx}" in self.viewer.layers: idx += 1 # add overlay image to napari - self.viewer.add_image(hsv_image, name=f'HSV_Overlay_{idx}', rgb=True) + self.viewer.add_image(hsv_image, name=f"HSV_Overlay_{idx}", rgb=True) @Slot(object) def add_listener_data(self, store): - self.viewer.add_image(store['Birefringence'], name=self.worker.prefix) - self.viewer.dims.set_axis_label(0, 'P') - self.viewer.dims.set_axis_label(1, 'T') - self.viewer.dims.set_axis_label(2, 'C') - self.viewer.dims.set_axis_label(3, 'Z') + self.viewer.add_image(store["Birefringence"], name=self.worker.prefix) + self.viewer.dims.set_axis_label(0, "P") + self.viewer.dims.set_axis_label(1, "T") + self.viewer.dims.set_axis_label(2, "C") + self.viewer.dims.set_axis_label(3, "Z") @Slot(tuple) def update_dims(self, dims): @@ -2619,9 +3183,7 @@ def _reset_listening(self): def _open_file_dialog(self, default_path, type): - return self._open_dialog("select a directory", - str(default_path), - type) + return self._open_dialog("select a directory", str(default_path), type) def _open_dialog(self, title, ref, type): """ @@ -2641,23 +3203,20 @@ def _open_dialog(self, title, ref, type): options = QFileDialog.Options() options |= QFileDialog.DontUseNativeDialog - if type == 'dir': - path = QFileDialog.getExistingDirectory(None, - title, - ref, - options=options) - elif type == 'file': - path = QFileDialog.getOpenFileName(None, - title, - ref, - options=options)[0] - elif type == 'save': - path = QFileDialog.getSaveFileName(None, - 'Choose a save name', - ref, - options=options)[0] + if type == "dir": + path = QFileDialog.getExistingDirectory( + None, title, ref, options=options + ) + elif type == "file": + path = QFileDialog.getOpenFileName( + None, title, ref, options=options + )[0] + elif type == "save": + path = QFileDialog.getSaveFileName( + None, "Choose a save name", ref, options=options + )[0] else: - raise ValueError('Did not understand file dialogue type') + raise ValueError("Did not understand file dialogue type") return path diff --git a/recOrder/plugin/widget/napari_plugin_entry_point.py b/recOrder/plugin/widget/napari_plugin_entry_point.py index 23ee4576..f7fdcbf7 100644 --- a/recOrder/plugin/widget/napari_plugin_entry_point.py +++ b/recOrder/plugin/widget/napari_plugin_entry_point.py @@ -1,1 +1,1 @@ -from recOrder.plugin.widget.main_widget import MainWidget \ No newline at end of file +from recOrder.plugin.widget.main_widget import MainWidget diff --git a/recOrder/plugin/workers/acquisition_workers.py b/recOrder/plugin/workers/acquisition_workers.py index 4887d214..0dcb585f 100644 --- a/recOrder/plugin/workers/acquisition_workers.py +++ b/recOrder/plugin/workers/acquisition_workers.py @@ -1,7 +1,15 @@ from qtpy.QtCore import Signal -from recOrder.compute.qlipp_compute import initialize_reconstructor, \ - reconstruct_qlipp_birefringence, reconstruct_qlipp_stokes, reconstruct_phase2D, reconstruct_phase3D -from recOrder.acq.acq_functions import generate_acq_settings, acquire_from_settings +from recOrder.compute.qlipp_compute import ( + initialize_reconstructor, + reconstruct_qlipp_birefringence, + reconstruct_qlipp_stokes, + reconstruct_phase2D, + reconstruct_phase3D, +) +from recOrder.acq.acq_functions import ( + generate_acq_settings, + acquire_from_settings, +) from recOrder.io.utils import load_bg, extract_reconstruction_parameters from recOrder.compute import QLIPPBirefringenceCompute from recOrder.io.zarr_converter import ZarrConverter @@ -42,6 +50,7 @@ class BFAcquisitionSignals(WorkerBaseSignals): meta_emitter = Signal(dict) aborted = Signal() + class ListeningSignals(WorkerBaseSignals): """ Custom Signals class that includes napari native signals @@ -65,23 +74,39 @@ def __init__(self, calib_window): self.calib_window = calib_window # Init Properties - self.prefix = 'recOrderPluginSnap' + self.prefix = "recOrderPluginSnap" self.dm = self.calib_window.mm.displays() - self.dim = '2D' if self.calib_window.ui.cb_phase.currentIndex() == 0 else '3D' + self.dim = ( + "2D" if self.calib_window.ui.cb_phase.currentIndex() == 0 else "3D" + ) self.img_dim = None - save_dir = self.calib_window.save_directory if self.calib_window.save_directory else self.calib_window.directory + save_dir = ( + self.calib_window.save_directory + if self.calib_window.save_directory + else self.calib_window.directory + ) if save_dir is None: - raise ValueError('save directory is empty, please specify a directory in the plugin') + raise ValueError( + "save directory is empty, please specify a directory in the plugin" + ) # increment filename one more than last found saved snap i = 0 prefix = self.calib_window.save_name - snap_dir = f'recOrderPluginSnap_{i}' if not prefix else f'{prefix}_recOrderPluginSnap_{i}' + snap_dir = ( + f"recOrderPluginSnap_{i}" + if not prefix + else f"{prefix}_recOrderPluginSnap_{i}" + ) while os.path.exists(os.path.join(save_dir, snap_dir)): i += 1 - snap_dir = f'recOrderPluginSnap_{i}' if not prefix else f'{prefix}_recOrderPluginSnap_{i}' + snap_dir = ( + f"recOrderPluginSnap_{i}" + if not prefix + else f"{prefix}_recOrderPluginSnap_{i}" + ) self.snap_dir = os.path.join(save_dir, snap_dir) os.mkdir(self.snap_dir) @@ -89,7 +114,7 @@ def __init__(self, calib_window): def _check_abort(self): if self.abort_requested: self.aborted.emit() - raise TimeoutError('Stop Requested') + raise TimeoutError("Stop Requested") def _check_ram(self): """ @@ -106,7 +131,7 @@ def work(self): Function that runs the 2D or 3D acquisition and reconstructs the data """ self._check_ram() - logging.info('Running Acquisition...') + logging.info("Running Acquisition...") self._check_abort() channel_idx = self.calib_window.ui.cb_acq_channel.currentIndex() @@ -126,23 +151,27 @@ def work(self): break # Acquire 3D stack - logging.debug('Acquiring 3D stack') + logging.debug("Acquiring 3D stack") # Generate MDA Settings - settings = generate_acq_settings(self.calib_window.mm, - channel_group=channel_group, - channels=[channel], - zstart=self.calib_window.z_start, - zend=self.calib_window.z_end, - zstep=self.calib_window.z_step, - save_dir=self.snap_dir, - prefix=self.prefix) + settings = generate_acq_settings( + self.calib_window.mm, + channel_group=channel_group, + channels=[channel], + zstart=self.calib_window.z_start, + zend=self.calib_window.z_end, + zstep=self.calib_window.z_step, + save_dir=self.snap_dir, + prefix=self.prefix, + ) self._check_abort() # Acquire from MDA settings uses MM MDA GUI # Returns (1, 4/5, Z, Y, X) array - stack = acquire_from_settings(self.calib_window.mm, settings, grab_images=True) + stack = acquire_from_settings( + self.calib_window.mm, settings, grab_images=True + ) self._check_abort() # Cleanup acquisition by closing window, converting to zarr, and deleting temp directory @@ -155,13 +184,13 @@ def work(self): self._check_abort() # Save images - logging.debug('Saving Images') + logging.debug("Saving Images") self._save_imgs(phase, meta) self._check_abort() - logging.info('Finished Acquisition') - logging.debug('Finished Acquisition') + logging.info("Finished Acquisition") + logging.debug("Finished Acquisition") # Emit the images and let thread know function is finished self.phase_image_emitter.emit(phase) @@ -188,22 +217,26 @@ def _reconstruct(self, stack): # if no reconstructor has been initialized before, create new reconstructor if not self.calib_window.phase_reconstructor: - logging.debug('Computing new reconstructor') - - recon = initialize_reconstructor('PhaseFromBF', - image_dim=(stack.shape[-2], stack.shape[-1]), - wavelength_nm=int(self.calib_window.ui.le_recon_wavelength.text()), - NA_obj=self.calib_window.obj_na, - NA_illu=self.calib_window.cond_na, - mag=self.calib_window.mag, - n_slices=self.img_dim[-1], - z_step_um=self.calib_window.z_step, - pad_z=self.calib_window.pad_z, - pixel_size_um=self.calib_window.ps, - n_obj_media=self.calib_window.n_media, - mode=self.calib_window.phase_dim, - use_gpu=self.calib_window.use_gpu, - gpu_id=self.calib_window.gpu_id) + logging.debug("Computing new reconstructor") + + recon = initialize_reconstructor( + "PhaseFromBF", + image_dim=(stack.shape[-2], stack.shape[-1]), + wavelength_nm=int( + self.calib_window.ui.le_recon_wavelength.text() + ), + NA_obj=self.calib_window.obj_na, + NA_illu=self.calib_window.cond_na, + mag=self.calib_window.mag, + n_slices=self.img_dim[-1], + z_step_um=self.calib_window.z_step, + pad_z=self.calib_window.pad_z, + pixel_size_um=self.calib_window.ps, + n_obj_media=self.calib_window.n_media, + mode=self.calib_window.phase_dim, + use_gpu=self.calib_window.use_gpu, + gpu_id=self.calib_window.gpu_id, + ) # Emit reconstructor to be saved for later reconstructions self.phase_reconstructor_emitter.emit(recon) @@ -214,65 +247,81 @@ def _reconstruct(self, stack): # compute new reconstructor if the old reconstructor properties have been modified if self._reconstructor_changed(): - logging.debug('Reconstruction settings changed, updating reconstructor') - - recon = initialize_reconstructor('PhaseFromBF', - image_dim=(stack.shape[-2], stack.shape[-1]), - wavelength_nm=int(self.calib_window.ui.le_recon_wavelength.text()), - NA_obj=self.calib_window.obj_na, - NA_illu=self.calib_window.cond_na, - mag=self.calib_window.mag, - n_slices=self.n_slices, - z_step_um=self.calib_window.z_step, - pad_z=self.calib_window.pad_z, - pixel_size_um=self.calib_window.ps, - n_obj_media=self.calib_window.n_media, - mode=self.calib_window.phase_dim, - use_gpu=self.calib_window.use_gpu, - gpu_id=self.calib_window.gpu_id) + logging.debug( + "Reconstruction settings changed, updating reconstructor" + ) + + recon = initialize_reconstructor( + "PhaseFromBF", + image_dim=(stack.shape[-2], stack.shape[-1]), + wavelength_nm=int( + self.calib_window.ui.le_recon_wavelength.text() + ), + NA_obj=self.calib_window.obj_na, + NA_illu=self.calib_window.cond_na, + mag=self.calib_window.mag, + n_slices=self.n_slices, + z_step_um=self.calib_window.z_step, + pad_z=self.calib_window.pad_z, + pixel_size_um=self.calib_window.ps, + n_obj_media=self.calib_window.n_media, + mode=self.calib_window.phase_dim, + use_gpu=self.calib_window.use_gpu, + gpu_id=self.calib_window.gpu_id, + ) # Emit reconstructor to be saved for later reconstructions self.phase_reconstructor_emitter.emit(recon) # use previous reconstructor else: - logging.debug('Using previous reconstruction settings') + logging.debug("Using previous reconstruction settings") recon = self.calib_window.phase_reconstructor # Begin reconstruction with stokes (needed for birefringence or phase) - logging.debug('Reconstructing...') + logging.debug("Reconstructing...") self._check_abort() - regularizer = 'Tikhonov' if self.calib_window.ui.cb_phase_denoiser.currentIndex() == 0 else 'TV' + regularizer = ( + "Tikhonov" + if self.calib_window.ui.cb_phase_denoiser.currentIndex() == 0 + else "TV" + ) reg = float(self.calib_window.ui.le_phase_strength.text()) # Perform deconvolution - if self.dim == '2D': - - phase = reconstruct_phase2D(stack[0], - recon, - method=regularizer, - reg_p=reg, - itr=int(self.calib_window.ui.le_itr.text()), - rho=float(self.calib_window.ui.le_rho.text())) + if self.dim == "2D": + + phase = reconstruct_phase2D( + stack[0], + recon, + method=regularizer, + reg_p=reg, + itr=int(self.calib_window.ui.le_itr.text()), + rho=float(self.calib_window.ui.le_rho.text()), + ) else: - phase = reconstruct_phase3D(stack[0], - recon, - method=regularizer, - reg_re=reg, - itr=int(self.calib_window.ui.le_itr.text()), - rho=float(self.calib_window.ui.le_rho.text())) + phase = reconstruct_phase3D( + stack[0], + recon, + method=regularizer, + reg_re=reg, + itr=int(self.calib_window.ui.le_itr.text()), + rho=float(self.calib_window.ui.le_rho.text()), + ) self._check_abort() # Update metadata in zarr attributes with reconstruction parameters - meta = extract_reconstruction_parameters(recon, magnification=self.calib_window.mag) - meta['regularization_method'] = regularizer - meta['regularization_strength'] = reg - if regularizer == 'TV': - meta['rho'] = float(self.calib_window.ui.le_rho.text()) - meta['itr'] = int(self.calib_window.ui.le_itr.text()) + meta = extract_reconstruction_parameters( + recon, magnification=self.calib_window.mag + ) + meta["regularization_method"] = regularizer + meta["regularization_strength"] = reg + if regularizer == "TV": + meta["rho"] = float(self.calib_window.ui.le_rho.text()) + meta["itr"] = int(self.calib_window.ui.le_itr.text()) # return both variables, could contain images or could be null return phase, meta @@ -295,29 +344,37 @@ def _save_imgs(self, phase, meta=None): # initialize chunk_size = (1, 1, 1, phase.shape[-2], phase.shape[-1]) prefix = self.calib_window.save_name - name = f'PhaseSnap.zarr' if not prefix else f'{prefix}_PhaseSnap.zarr' + name = f"PhaseSnap.zarr" if not prefix else f"{prefix}_PhaseSnap.zarr" # create zarr root and position group writer.create_zarr_root(name) # Check if 2D if len(phase.shape) == 2: - writer.init_array(0, (1, 1, 1, phase.shape[-2], phase.shape[-1]), chunk_size, ['Phase2D']) + writer.init_array( + 0, + (1, 1, 1, phase.shape[-2], phase.shape[-1]), + chunk_size, + ["Phase2D"], + ) z = 0 # Check if 3D else: - writer.init_array(0, (1, 1, phase.shape[-3], phase.shape[-2], phase.shape[-1]), chunk_size, - ['Phase3D']) + writer.init_array( + 0, + (1, 1, phase.shape[-3], phase.shape[-2], phase.shape[-1]), + chunk_size, + ["Phase3D"], + ) z = slice(0, phase.shape[-3]) - # Write data to disk writer.write(phase, p=0, t=0, c=0, z=z) current_meta = writer.store.attrs.asdict() - current_meta['recOrder'] = meta + current_meta["recOrder"] = meta writer.store.attrs.put(current_meta) def _reconstructor_changed(self): @@ -333,26 +390,30 @@ def _reconstructor_changed(self): changed = None # Attributes that are directly equivalent to worker attributes - attr_list = {'phase_dim': 'phase_deconv', - 'pad_z': 'pad_z', - 'n_media': 'n_media', - 'use_gpu': 'use_gpu', - 'gpu_id': 'gpu_id' - } + attr_list = { + "phase_dim": "phase_deconv", + "pad_z": "pad_z", + "n_media": "n_media", + "use_gpu": "use_gpu", + "gpu_id": "gpu_id", + } # attributes that are modified upon passing them to reconstructor - attr_modified_list = {'obj_na': 'NA_obj', - 'cond_na': 'NA_illu', - 'wavelength': 'lambda_illu', - 'n_slices': 'N_defocus', - 'swing': 'chi', - 'ps': 'ps' - } + attr_modified_list = { + "obj_na": "NA_obj", + "cond_na": "NA_illu", + "wavelength": "lambda_illu", + "n_slices": "N_defocus", + "swing": "chi", + "ps": "ps", + } self._check_abort() # check if equivalent attributes have diverged for key, value in attr_list.items(): - if getattr(self.calib_window, key) != getattr(self.calib_window.phase_reconstructor, value): + if getattr(self.calib_window, key) != getattr( + self.calib_window.phase_reconstructor, value + ): changed = True break else: @@ -361,29 +422,40 @@ def _reconstructor_changed(self): if not changed: # modify attributes to be equivalent and check for divergence for key, value in attr_modified_list.items(): - if key == 'wavelength': - if self.calib_window.wavelength * 1e-3 / self.calib_window.n_media != \ - self.calib_window.phase_reconstructor.lambda_illu: + if key == "wavelength": + if ( + self.calib_window.wavelength + * 1e-3 + / self.calib_window.n_media + != self.calib_window.phase_reconstructor.lambda_illu + ): changed = True break else: changed = False - elif key == 'n_slices': - if getattr(self, key) != getattr(self.calib_window.phase_reconstructor, value): + elif key == "n_slices": + if getattr(self, key) != getattr( + self.calib_window.phase_reconstructor, value + ): changed = True break else: changed = False - elif key == 'ps': - if getattr(self.calib_window, key) / float(self.calib_window.mag) != getattr(self.calib_window.phase_reconstructor, value): + elif key == "ps": + if getattr(self.calib_window, key) / float( + self.calib_window.mag + ) != getattr(self.calib_window.phase_reconstructor, value): changed = True break else: changed = False else: - if getattr(self.calib_window, key)/self.calib_window.n_media != \ - getattr(self.calib_window.phase_reconstructor, value): + if getattr( + self.calib_window, key + ) / self.calib_window.n_media != getattr( + self.calib_window.phase_reconstructor, value + ): changed = True else: changed = False @@ -410,14 +482,27 @@ def _cleanup_acq(self): closed = disp.isClosed() dp.close() - # Try to delete the data, sometime it isn't cleaned up quickly enough and will # return an error. In this case, catch the error and then try to close again (seems to work). try: - save_prefix = self.calib_window.save_name if self.calib_window.save_name else None - name = f'RawBFDataSnap.zarr' if not save_prefix else f'{save_prefix}_RawBFDataSnap.zarr' + save_prefix = ( + self.calib_window.save_name + if self.calib_window.save_name + else None + ) + name = ( + f"RawBFDataSnap.zarr" + if not save_prefix + else f"{save_prefix}_RawBFDataSnap.zarr" + ) out_path = os.path.join(self.snap_dir, name) - converter = ZarrConverter(os.path.join(dir_, prefix), out_path, 'ometiff', False, False) + converter = ZarrConverter( + os.path.join(dir_, prefix), + out_path, + "ometiff", + False, + False, + ) converter.run_conversion() shutil.rmtree(os.path.join(dir_, prefix)) except PermissionError as ex: @@ -426,6 +511,7 @@ def _cleanup_acq(self): else: continue + # TODO: Cache common OTF's on local computers and use those for reconstruction class PolarizationAcquisitionWorker(WorkerBase): """ @@ -443,28 +529,45 @@ def __init__(self, calib_window, calib, mode): self.calib = calib self.mode = mode self.n_slices = None - self.prefix = 'recOrderPluginSnap' + self.prefix = "recOrderPluginSnap" self.dm = self.calib_window.mm.displays() self.channel_group = self.calib_window.config_group # Determine whether 2D or 3D acquisition is needed - if self.mode == 'birefringence' and self.calib_window.birefringence_dim == '2D': - self.dim = '2D' + if ( + self.mode == "birefringence" + and self.calib_window.birefringence_dim == "2D" + ): + self.dim = "2D" else: - self.dim = '3D' + self.dim = "3D" - save_dir = self.calib_window.save_directory if self.calib_window.save_directory else self.calib_window.directory + save_dir = ( + self.calib_window.save_directory + if self.calib_window.save_directory + else self.calib_window.directory + ) if save_dir is None: - raise ValueError('save directory is empty, please specify a directory in the plugin') + raise ValueError( + "save directory is empty, please specify a directory in the plugin" + ) # increment filename one more than last found saved snap i = 0 prefix = self.calib_window.save_name - snap_dir = f'recOrderPluginSnap_{i}' if not prefix else f'{prefix}_recOrderPluginSnap_{i}' + snap_dir = ( + f"recOrderPluginSnap_{i}" + if not prefix + else f"{prefix}_recOrderPluginSnap_{i}" + ) while os.path.exists(os.path.join(save_dir, snap_dir)): i += 1 - snap_dir = f'recOrderPluginSnap_{i}' if not prefix else f'{prefix}_recOrderPluginSnap_{i}' + snap_dir = ( + f"recOrderPluginSnap_{i}" + if not prefix + else f"{prefix}_recOrderPluginSnap_{i}" + ) self.snap_dir = os.path.join(save_dir, snap_dir) os.mkdir(self.snap_dir) @@ -472,7 +575,7 @@ def __init__(self, calib_window, calib, mode): def _check_abort(self): if self.abort_requested: self.aborted.emit() - raise TimeoutError('Stop Requested') + raise TimeoutError("Stop Requested") def _check_ram(self): """ @@ -489,51 +592,55 @@ def work(self): Function that runs the 2D or 3D acquisition and reconstructs the data """ self._check_ram() - logging.info('Running Acquisition...') + logging.info("Running Acquisition...") # List the Channels to acquire, if 5-state then append 5th channel - channels = ['State0', 'State1', 'State2', 'State3'] - if self.calib_window.calib_scheme == '5-State': - channels.append('State4') + channels = ["State0", "State1", "State2", "State3"] + if self.calib_window.calib_scheme == "5-State": + channels.append("State4") self._check_abort() # Acquire 2D stack - if self.dim == '2D': - logging.debug('Acquiring 2D stack') + if self.dim == "2D": + logging.debug("Acquiring 2D stack") # Generate MDA Settings - self.settings = generate_acq_settings(self.calib_window.mm, - channel_group=self.channel_group, - channels=channels, - save_dir=self.snap_dir, - prefix=self.prefix, - keep_shutter_open_channels=True) + self.settings = generate_acq_settings( + self.calib_window.mm, + channel_group=self.channel_group, + channels=channels, + save_dir=self.snap_dir, + prefix=self.prefix, + keep_shutter_open_channels=True, + ) self._check_abort() # acquire images stack = self._acquire() # Acquire 3D stack else: - logging.debug('Acquiring 3D stack') + logging.debug("Acquiring 3D stack") # Generate MDA Settings - self.settings = generate_acq_settings(self.calib_window.mm, - channel_group=self.channel_group, - channels=channels, - zstart=self.calib_window.z_start, - zend=self.calib_window.z_end, - zstep=self.calib_window.z_step, - save_dir=self.snap_dir, - prefix=self.prefix, - keep_shutter_open_channels=True, - keep_shutter_open_slices=True) + self.settings = generate_acq_settings( + self.calib_window.mm, + channel_group=self.channel_group, + channels=channels, + zstart=self.calib_window.z_start, + zend=self.calib_window.z_end, + zstep=self.calib_window.z_step, + save_dir=self.snap_dir, + prefix=self.prefix, + keep_shutter_open_channels=True, + keep_shutter_open_slices=True, + ) self._check_abort() # set acquisition order to channel-first - self.settings['slicesFirst'] = False - self.settings['acqOrderMode'] = 0 # TIME_POS_SLICE_CHANNEL + self.settings["slicesFirst"] = False + self.settings["acqOrderMode"] = 0 # TIME_POS_SLICE_CHANNEL # acquire images stack = self._acquire() @@ -548,13 +655,13 @@ def work(self): self._check_abort() # Save images - logging.debug('Saving Images') + logging.debug("Saving Images") self._save_imgs(birefringence, phase, meta) self._check_abort() - logging.info('Finished Acquisition') - logging.debug('Finished Acquisition') + logging.info("Finished Acquisition") + logging.debug("Finished Acquisition") # Emit the images and let thread know function is finished self.bire_image_emitter.emit(birefringence) @@ -565,19 +672,21 @@ def _check_exposure(self) -> None: """ Check that all LF channels have the same exposure settings. If not, abort Acquisition. """ - logging.debug('Verifying exposure times...') + logging.debug("Verifying exposure times...") # parse exposure times channel_exposures = [] - for channel in self.settings['channels']: - channel_exposures.append(channel['exposure']) + for channel in self.settings["channels"]: + channel_exposures.append(channel["exposure"]) channel_exposures = np.array(channel_exposures) # check if exposure times are equal if not np.all(channel_exposures == channel_exposures[0]): - error_exposure_msg = f'The MDA exposure times are not equal! Aborting Acquisition.\n' \ - f'Please manually set the exposure times to the same value from the MDA menu.' + error_exposure_msg = ( + f"The MDA exposure times are not equal! Aborting Acquisition.\n" + f"Please manually set the exposure times to the same value from the MDA menu." + ) - raise ValueError(error_exposure_msg) + raise ValueError(error_exposure_msg) self._check_abort() @@ -594,7 +703,9 @@ def _acquire(self) -> np.ndarray: # Acquire from MDA settings uses MM MDA GUI # Returns (1, 4/5, Z, Y, X) array - stack = acquire_from_settings(self.calib_window.mm, self.settings, grab_images=True) + stack = acquire_from_settings( + self.calib_window.mm, self.settings, grab_images=True + ) self._check_abort() return stack @@ -619,35 +730,38 @@ def _reconstruct(self, stack): self._check_abort() - wo_background_correction = rec_bkg_to_wo_bkg(self.calib_window.bg_option) + wo_background_correction = rec_bkg_to_wo_bkg( + self.calib_window.bg_option + ) # Initialize the heavy reconstuctor - if self.mode == 'phase' or self.mode == 'all': + if self.mode == "phase" or self.mode == "all": self._check_abort() # if no reconstructor has been initialized before, create new reconstructor if not self.calib_window.phase_reconstructor: - logging.debug('Computing new reconstructor') - - - recon = initialize_reconstructor('QLIPP', - image_dim=(stack.shape[-2], stack.shape[-1]), - wavelength_nm=self.calib_window.wavelength, - swing=self.calib_window.swing, - calibration_scheme=self.calib_window.calib_scheme, - NA_obj=self.calib_window.obj_na, - NA_illu=self.calib_window.cond_na, - mag=self.calib_window.mag, - n_slices=self.n_slices, - z_step_um=self.calib_window.z_step, - pad_z=self.calib_window.pad_z, - pixel_size_um=self.calib_window.ps, - bg_correction=wo_background_correction, - n_obj_media=self.calib_window.n_media, - mode=self.calib_window.phase_dim, - use_gpu=self.calib_window.use_gpu, - gpu_id=self.calib_window.gpu_id) + logging.debug("Computing new reconstructor") + + recon = initialize_reconstructor( + "QLIPP", + image_dim=(stack.shape[-2], stack.shape[-1]), + wavelength_nm=self.calib_window.wavelength, + swing=self.calib_window.swing, + calibration_scheme=self.calib_window.calib_scheme, + NA_obj=self.calib_window.obj_na, + NA_illu=self.calib_window.cond_na, + mag=self.calib_window.mag, + n_slices=self.n_slices, + z_step_um=self.calib_window.z_step, + pad_z=self.calib_window.pad_z, + pixel_size_um=self.calib_window.ps, + bg_correction=wo_background_correction, + n_obj_media=self.calib_window.n_media, + mode=self.calib_window.phase_dim, + use_gpu=self.calib_window.use_gpu, + gpu_id=self.calib_window.gpu_id, + ) # Emit reconstructor to be saved for later reconstructions self.phase_reconstructor_emitter.emit(recon) @@ -658,66 +772,78 @@ def _reconstruct(self, stack): # compute new reconstructor if the old reconstructor properties have been modified if self._reconstructor_changed(): - logging.debug('Reconstruction settings changed, updating reconstructor') - - recon = initialize_reconstructor('QLIPP', - image_dim=(stack.shape[-2], stack.shape[-1]), - wavelength_nm=self.calib_window.wavelength, - swing=self.calib_window.swing, - calibration_scheme=self.calib_window.calib_scheme, - NA_obj=self.calib_window.obj_na, - NA_illu=self.calib_window.cond_na, - mag=self.calib_window.mag, - n_slices=self.n_slices, - z_step_um=self.calib_window.z_step, - pad_z=self.calib_window.pad_z, - pixel_size_um=self.calib_window.ps, - bg_correction=wo_background_correction, - n_obj_media=self.calib_window.n_media, - mode=self.calib_window.phase_dim, - use_gpu=self.calib_window.use_gpu, - gpu_id=self.calib_window.gpu_id) + logging.debug( + "Reconstruction settings changed, updating reconstructor" + ) + + recon = initialize_reconstructor( + "QLIPP", + image_dim=(stack.shape[-2], stack.shape[-1]), + wavelength_nm=self.calib_window.wavelength, + swing=self.calib_window.swing, + calibration_scheme=self.calib_window.calib_scheme, + NA_obj=self.calib_window.obj_na, + NA_illu=self.calib_window.cond_na, + mag=self.calib_window.mag, + n_slices=self.n_slices, + z_step_um=self.calib_window.z_step, + pad_z=self.calib_window.pad_z, + pixel_size_um=self.calib_window.ps, + bg_correction=wo_background_correction, + n_obj_media=self.calib_window.n_media, + mode=self.calib_window.phase_dim, + use_gpu=self.calib_window.use_gpu, + gpu_id=self.calib_window.gpu_id, + ) self.phase_reconstructor_emitter.emit(recon) # use previous reconstructor else: - logging.debug('Using previous reconstruction settings') + logging.debug("Using previous reconstruction settings") recon = self.calib_window.phase_reconstructor # if phase isn't desired, initialize the lighter birefringence only reconstructor # no need to save this reconstructor for later as it is pretty quick to compute else: self._check_abort() - logging.debug('Creating birefringence only reconstructor') - recon = initialize_reconstructor('birefringence', - image_dim=(stack.shape[-2], stack.shape[-1]), - calibration_scheme=self.calib_window.calib_scheme, - wavelength_nm=self.calib_window.wavelength, - swing=self.calib_window.swing, - bg_correction=wo_background_correction, - n_slices=self.n_slices) + logging.debug("Creating birefringence only reconstructor") + recon = initialize_reconstructor( + "birefringence", + image_dim=(stack.shape[-2], stack.shape[-1]), + calibration_scheme=self.calib_window.calib_scheme, + wavelength_nm=self.calib_window.wavelength, + swing=self.calib_window.swing, + bg_correction=wo_background_correction, + n_slices=self.n_slices, + ) # Prepare background corrections for waveorder # This block mimics qlipp_pipeline.py L110-119. - if self.calib_window.bg_option in ['global', 'local_fit+']: - logging.debug('Loading BG Data') + if self.calib_window.bg_option in ["global", "local_fit+"]: + logging.debug("Loading BG Data") self._check_abort() - bg_data = self._load_bg(self.calib_window.acq_bg_directory, stack.shape[-2], stack.shape[-1]) + bg_data = self._load_bg( + self.calib_window.acq_bg_directory, + stack.shape[-2], + stack.shape[-1], + ) self._check_abort() bg_stokes = recon.Stokes_recon(bg_data) self._check_abort() bg_stokes = recon.Stokes_transform(bg_stokes) self._check_abort() - elif self.calib_window.bg_option == 'local_fit': + elif self.calib_window.bg_option == "local_fit": bg_stokes = np.zeros((5, stack.shape[-2], stack.shape[-1])) - bg_stokes[0, ...] = 1 # Set background to "identity" Stokes parameters. + bg_stokes[ + 0, ... + ] = 1 # Set background to "identity" Stokes parameters. else: - logging.debug('No Background Correction method chosen') + logging.debug("No Background Correction method chosen") bg_stokes = None # Begin reconstruction with stokes (needed for birefringence or phase) - logging.debug('Reconstructing...') + logging.debug("Reconstructing...") self._check_abort() stokes = reconstruct_qlipp_stokes(stack, recon, bg_stokes) self._check_abort() @@ -726,68 +852,86 @@ def _reconstruct(self, stack): birefringence = None phase = None - regularizer = 'Tikhonov' if self.calib_window.ui.cb_phase_denoiser.currentIndex() == 0 else 'TV' + regularizer = ( + "Tikhonov" + if self.calib_window.ui.cb_phase_denoiser.currentIndex() == 0 + else "TV" + ) reg = float(self.calib_window.ui.le_phase_strength.text()) # reconstruct both phase and birefringence - if self.mode == 'all': - if self.calib_window.birefringence_dim == '2D': - birefringence = reconstruct_qlipp_birefringence(stokes[:, stokes.shape[1]//2, :, :], recon) + if self.mode == "all": + if self.calib_window.birefringence_dim == "2D": + birefringence = reconstruct_qlipp_birefringence( + stokes[:, stokes.shape[1] // 2, :, :], recon + ) else: birefringence = reconstruct_qlipp_birefringence(stokes, recon) - birefringence[0] = birefringence[0] / (2 * np.pi) * self.calib_window.wavelength + birefringence[0] = ( + birefringence[0] / (2 * np.pi) * self.calib_window.wavelength + ) self._check_abort() - if self.calib_window.phase_dim == '2D': - phase = reconstruct_phase2D(stokes[0], - recon, - method=regularizer, - reg_p=reg, - itr=int(self.calib_window.ui.le_itr.text()), - rho=float(self.calib_window.ui.le_rho.text())) + if self.calib_window.phase_dim == "2D": + phase = reconstruct_phase2D( + stokes[0], + recon, + method=regularizer, + reg_p=reg, + itr=int(self.calib_window.ui.le_itr.text()), + rho=float(self.calib_window.ui.le_rho.text()), + ) else: - phase = reconstruct_phase3D(stokes[0], - recon, - method=regularizer, - reg_re=reg, - itr=int(self.calib_window.ui.le_itr.text()), - rho=float(self.calib_window.ui.le_rho.text())) + phase = reconstruct_phase3D( + stokes[0], + recon, + method=regularizer, + reg_re=reg, + itr=int(self.calib_window.ui.le_itr.text()), + rho=float(self.calib_window.ui.le_rho.text()), + ) self._check_abort() # reconstruct phase only - elif self.mode == 'phase': - if self.calib_window.phase_dim == '2D': - phase = reconstruct_phase2D(stokes[0], - recon, - method=regularizer, - reg_p=reg, - itr=int(self.calib_window.ui.le_itr.text()), - rho=float(self.calib_window.ui.le_rho.text())) + elif self.mode == "phase": + if self.calib_window.phase_dim == "2D": + phase = reconstruct_phase2D( + stokes[0], + recon, + method=regularizer, + reg_p=reg, + itr=int(self.calib_window.ui.le_itr.text()), + rho=float(self.calib_window.ui.le_rho.text()), + ) else: - phase = reconstruct_phase3D(stokes[0], - recon, - method=regularizer, - reg_re=reg, - itr=int(self.calib_window.ui.le_itr.text()), - rho=float(self.calib_window.ui.le_rho.text())) + phase = reconstruct_phase3D( + stokes[0], + recon, + method=regularizer, + reg_re=reg, + itr=int(self.calib_window.ui.le_itr.text()), + rho=float(self.calib_window.ui.le_rho.text()), + ) self._check_abort() # reconstruct birefringence only - elif self.mode == 'birefringence': + elif self.mode == "birefringence": birefringence = reconstruct_qlipp_birefringence(stokes, recon) - birefringence[0] = birefringence[0] / (2 * np.pi) * self.calib_window.wavelength + birefringence[0] = ( + birefringence[0] / (2 * np.pi) * self.calib_window.wavelength + ) self._check_abort() else: - raise ValueError('Reconstruction Mode Not Understood') + raise ValueError("Reconstruction Mode Not Understood") meta = extract_reconstruction_parameters(recon, self.calib_window.mag) - meta['regularization_method'] = regularizer - meta['regularization_strength'] = reg - if regularizer == 'TV': - meta['rho'] = float(self.calib_window.ui.le_rho.text()) - meta['itr'] = int(self.calib_window.ui.le_itr.text()) + meta["regularization_method"] = regularizer + meta["regularization_strength"] = reg + if regularizer == "TV": + meta["rho"] = float(self.calib_window.ui.le_rho.text()) + meta["itr"] = int(self.calib_window.ui.le_itr.text()) # return both variables, could contain images or could be null return birefringence, phase, meta @@ -811,32 +955,62 @@ def _save_imgs(self, birefringence, phase, meta=None): if birefringence is not None: # initialize - chunk_size = (1, 1, 1, birefringence.shape[-2],birefringence.shape[-1]) + chunk_size = ( + 1, + 1, + 1, + birefringence.shape[-2], + birefringence.shape[-1], + ) # increment filename one more than last found saved snap prefix = self.calib_window.save_name - name = f'BirefringenceSnap.zarr' if not prefix else f'{prefix}_BirefringenceSnap.zarr' + name = ( + f"BirefringenceSnap.zarr" + if not prefix + else f"{prefix}_BirefringenceSnap.zarr" + ) # create zarr root and position group writer.create_zarr_root(name) # Check if 2D if len(birefringence.shape) == 3: - writer.init_array(0, (1, 4, 1, birefringence.shape[-2], birefringence.shape[-1]), - chunk_size, ['Retardance', 'Orientation', 'BF', 'Pol']) + writer.init_array( + 0, + ( + 1, + 4, + 1, + birefringence.shape[-2], + birefringence.shape[-1], + ), + chunk_size, + ["Retardance", "Orientation", "BF", "Pol"], + ) z = 0 # Check if 3D else: - writer.init_array(0, (1, 4, birefringence.shape[-3], birefringence.shape[-2], birefringence.shape[-1]), - chunk_size, ['Retardance', 'Orientation', 'BF', 'Pol']) + writer.init_array( + 0, + ( + 1, + 4, + birefringence.shape[-3], + birefringence.shape[-2], + birefringence.shape[-1], + ), + chunk_size, + ["Retardance", "Orientation", "BF", "Pol"], + ) z = slice(0, birefringence.shape[-3]) # Write the data to disk writer.write(birefringence, p=0, t=0, c=slice(0, 4), z=z) current_meta = writer.store.attrs.asdict() - current_meta['recOrder'] = meta + current_meta["recOrder"] = meta writer.store.attrs.put(current_meta) if phase is not None: @@ -846,26 +1020,38 @@ def _save_imgs(self, birefringence, phase, meta=None): # increment filename one more than last found saved snap prefix = self.calib_window.save_name - name = f'PhaseSnap.zarr' if not prefix else f'{prefix}_PhaseSnap.zarr' + name = ( + f"PhaseSnap.zarr" if not prefix else f"{prefix}_PhaseSnap.zarr" + ) # create zarr root and position group writer.create_zarr_root(name) # Check if 2D if len(phase.shape) == 2: - writer.init_array(0, (1, 1, 1, phase.shape[-2], phase.shape[-1]), chunk_size, ['Phase2D']) + writer.init_array( + 0, + (1, 1, 1, phase.shape[-2], phase.shape[-1]), + chunk_size, + ["Phase2D"], + ) z = 0 # Check if 3D else: - writer.init_array(0, (1, 1, phase.shape[-3], phase.shape[-2], phase.shape[-1]), chunk_size, ['Phase3D']) + writer.init_array( + 0, + (1, 1, phase.shape[-3], phase.shape[-2], phase.shape[-1]), + chunk_size, + ["Phase3D"], + ) z = slice(0, phase.shape[-3]) # Write data to disk writer.write(phase, p=0, t=0, c=0, z=z) current_meta = writer.store.attrs.asdict() - current_meta['recOrder'] = meta + current_meta["recOrder"] = meta writer.store.attrs.put(current_meta) def _load_bg(self, path, height, width): @@ -885,7 +1071,7 @@ def _load_bg(self, path, height, width): """ - #TODO: Change to just accept ROI + # TODO: Change to just accept ROI try: metadata_path = get_last_metadata_file(path) metadata = MetadataReader(metadata_path) @@ -910,27 +1096,31 @@ def _reconstructor_changed(self): changed = None # Attributes that are directly equivalent to worker attributes - attr_list = {'phase_dim': 'phase_deconv', - 'pad_z': 'pad_z', - 'n_media': 'n_media', - 'bg_option': 'bg_option', - 'use_gpu': 'use_gpu', - 'gpu_id': 'gpu_id' - } + attr_list = { + "phase_dim": "phase_deconv", + "pad_z": "pad_z", + "n_media": "n_media", + "bg_option": "bg_option", + "use_gpu": "use_gpu", + "gpu_id": "gpu_id", + } # attributes that are modified upon passing them to reconstructor - attr_modified_list = {'obj_na': 'NA_obj', - 'cond_na': 'NA_illu', - 'wavelength': 'lambda_illu', - 'n_slices': 'N_defocus', - 'swing': 'chi', - 'ps': 'ps' - } + attr_modified_list = { + "obj_na": "NA_obj", + "cond_na": "NA_illu", + "wavelength": "lambda_illu", + "n_slices": "N_defocus", + "swing": "chi", + "ps": "ps", + } self._check_abort() # check if equivalent attributes have diverged for key, value in attr_list.items(): - if getattr(self.calib_window, key) != getattr(self.calib_window.phase_reconstructor, value): + if getattr(self.calib_window, key) != getattr( + self.calib_window.phase_reconstructor, value + ): changed = True break else: @@ -939,41 +1129,58 @@ def _reconstructor_changed(self): if not changed: # modify attributes to be equivalent and check for divergence for key, value in attr_modified_list.items(): - if key == 'swing': - if self.calib_window.calib_scheme == '5-State': - if self.calib_window.swing * 2 * np.pi != self.calib_window.phase_reconstructor.chi: + if key == "swing": + if self.calib_window.calib_scheme == "5-State": + if ( + self.calib_window.swing * 2 * np.pi + != self.calib_window.phase_reconstructor.chi + ): changed = True else: changed = False else: - if self.calib_window.swing != self.calib_window.phase_reconstructor.chi: + if ( + self.calib_window.swing + != self.calib_window.phase_reconstructor.chi + ): changed = True else: changed = False - elif key == 'wavelength': - if self.calib_window.wavelength * 1e-3 / self.calib_window.n_media != \ - self.calib_window.phase_reconstructor.lambda_illu: + elif key == "wavelength": + if ( + self.calib_window.wavelength + * 1e-3 + / self.calib_window.n_media + != self.calib_window.phase_reconstructor.lambda_illu + ): changed = True break else: changed = False - elif key == 'n_slices': - if getattr(self, key) != getattr(self.calib_window.phase_reconstructor, value): + elif key == "n_slices": + if getattr(self, key) != getattr( + self.calib_window.phase_reconstructor, value + ): changed = True break else: changed = False - elif key == 'ps': - if getattr(self.calib_window, key) / float(self.calib_window.mag) != getattr(self.calib_window.phase_reconstructor, value): + elif key == "ps": + if getattr(self.calib_window, key) / float( + self.calib_window.mag + ) != getattr(self.calib_window.phase_reconstructor, value): changed = True break else: changed = False else: - if getattr(self.calib_window, key)/self.calib_window.n_media != \ - getattr(self.calib_window.phase_reconstructor, value): + if getattr( + self.calib_window, key + ) / self.calib_window.n_media != getattr( + self.calib_window.phase_reconstructor, value + ): changed = True else: changed = False @@ -1000,14 +1207,27 @@ def _cleanup_acq(self): closed = disp.isClosed() dp.close() - # Try to delete the data, sometime it isn't cleaned up quickly enough and will # return an error. In this case, catch the error and then try to close again (seems to work). try: - save_prefix = self.calib_window.save_name if self.calib_window.save_name else None - name = f'RawPolDataSnap.zarr' if not save_prefix else f'{save_prefix}_RawPolDataSnap.zarr' + save_prefix = ( + self.calib_window.save_name + if self.calib_window.save_name + else None + ) + name = ( + f"RawPolDataSnap.zarr" + if not save_prefix + else f"{save_prefix}_RawPolDataSnap.zarr" + ) out_path = os.path.join(self.snap_dir, name) - converter = ZarrConverter(os.path.join(dir_, prefix), out_path, 'ometiff', False, False) + converter = ZarrConverter( + os.path.join(dir_, prefix), + out_path, + "ometiff", + False, + False, + ) converter.run_conversion() shutil.rmtree(os.path.join(dir_, prefix)) except PermissionError as ex: @@ -1046,7 +1266,7 @@ def __init__(self, calib_window, bg_data): def _check_abort(self): if self.abort_requested: self.aborted.emit() - raise TimeoutError('Stop Requested') + raise TimeoutError("Stop Requested") def get_byte_offset(self, offsets, page): """ @@ -1073,7 +1293,9 @@ def get_byte_offset(self, offsets, page): return array_offset - def listen_for_images(self, array, file, offsets, interval, dim3, dim2, dim1, dim0, dim_order): + def listen_for_images( + self, array, file, offsets, interval, dim3, dim2, dim1, dim0, dim_order + ): """ Parameters @@ -1101,16 +1323,36 @@ def listen_for_images(self, array, file, offsets, interval, dim3, dim2, dim1, di # Order dimensions that we will loop through in order to match the acquisition if dim_order == 0: - dims = [[dim3, self.n_frames], [dim2, self.n_pos], [dim1, self.n_slices], [dim0, self.n_channels]] + dims = [ + [dim3, self.n_frames], + [dim2, self.n_pos], + [dim1, self.n_slices], + [dim0, self.n_channels], + ] channel_dim = 0 elif dim_order == 1: - dims = [[dim3, self.n_frames], [dim2, self.n_pos], [dim1, self.n_channels], [dim0, self.n_slices]] + dims = [ + [dim3, self.n_frames], + [dim2, self.n_pos], + [dim1, self.n_channels], + [dim0, self.n_slices], + ] channel_dim = 1 elif dim_order == 2: - dims = [[dim3, self.n_pos], [dim2, self.n_frames], [dim1, self.n_slices], [dim0, self.n_channels]] + dims = [ + [dim3, self.n_pos], + [dim2, self.n_frames], + [dim1, self.n_slices], + [dim0, self.n_channels], + ] channel_dim = 0 else: - dims = [[dim3, self.n_pos], [dim2, self.n_frames], [dim1, self.n_channels], [dim0, self.n_slices]] + dims = [ + [dim3, self.n_pos], + [dim2, self.n_frames], + [dim1, self.n_channels], + [dim0, self.n_slices], + ] channel_dim = 1 # Loop through dimensions in the order they are acquired @@ -1134,13 +1376,20 @@ def listen_for_images(self, array, file, offsets, interval, dim3, dim2, dim1, di self._check_abort() tf = tiff.TiffFile(file) time.sleep(interval) - offsets = tf.micromanager_metadata['IndexMap']['Offset'] + offsets = tf.micromanager_metadata["IndexMap"][ + "Offset" + ] tf.close() offset = self.get_byte_offset(offsets, idx) else: offset = self.get_byte_offset(offsets, idx) - if idx < (self.n_slices * self.n_channels * self.n_frames * self.n_pos): + if idx < ( + self.n_slices + * self.n_channels + * self.n_frames + * self.n_pos + ): # Assign dimensions based off acquisition order to correctly add image to array if dim_order == 0: @@ -1155,11 +1404,20 @@ def listen_for_images(self, array, file, offsets, interval, dim3, dim2, dim1, di self._check_abort() # Add Image to array - img = np.memmap(file, dtype=self.dtype, mode='r', offset=offset, shape=self.shape) + img = np.memmap( + file, + dtype=self.dtype, + mode="r", + offset=offset, + shape=self.shape, + ) array[c, z] = img # If Channel first, compute birefringence here - if channel_dim == 0 and dim0 == self.n_channels - 1: + if ( + channel_dim == 0 + and dim0 == self.n_channels - 1 + ): self._check_abort() # Compute birefringence @@ -1168,7 +1426,12 @@ def listen_for_images(self, array, file, offsets, interval, dim3, dim2, dim1, di idx += 1 # Reset Range to 0 to account for starting this function in middle of a dimension - if idx < (self.n_slices * self.n_channels * self.n_frames * self.n_pos): + if idx < ( + self.n_slices + * self.n_channels + * self.n_frames + * self.n_pos + ): dims[2][0] = 0 dims[3][0] = 0 @@ -1191,7 +1454,12 @@ def listen_for_images(self, array, file, offsets, interval, dim3, dim2, dim1, di continue # Reset range to 0 to account for starting this function in the middle of a dimension - if idx < (self.n_slices * self.n_channels * self.n_frames * self.n_pos): + if idx < ( + self.n_slices + * self.n_channels + * self.n_frames + * self.n_pos + ): dims[1][0] = 0 # Return at the end of the acquisition @@ -1206,31 +1474,37 @@ def compute_and_save(self, array, p, t, z): # If the napari layer doesn't exist yet, create the zarr store to emit to napari if self.prefix not in self.calib_window.viewer.layers: - self.store = zarr.open(os.path.join(self.root, self.prefix+'.zarr')) - self.store.zeros(name='Birefringence', - shape=(self.n_pos, - self.n_frames, - 2, - self.n_slices, - self.shape[0], - self.shape[1]), - chunks=(1, 1, 1, 1, self.shape[0], self.shape[1]), - overwrite=True) + self.store = zarr.open( + os.path.join(self.root, self.prefix + ".zarr") + ) + self.store.zeros( + name="Birefringence", + shape=( + self.n_pos, + self.n_frames, + 2, + self.n_slices, + self.shape[0], + self.shape[1], + ), + chunks=(1, 1, 1, 1, self.shape[0], self.shape[1]), + overwrite=True, + ) # Add data and send out the store. Once the store has been emitted, we can add data to the store # without needing to emit the store again (thanks to napari's handling of zarr datasets) if len(birefringence.shape) == 4: - self.store['Birefringence'][p, t] = birefringence + self.store["Birefringence"][p, t] = birefringence else: - self.store['Birefringence'][p, t, :, z] = birefringence + self.store["Birefringence"][p, t, :, z] = birefringence self.store_emitter.emit(self.store) else: if len(birefringence.shape) == 4: - self.store['Birefringence'][p, t] = birefringence + self.store["Birefringence"][p, t] = birefringence else: - self.store['Birefringence'][p, t, :, z] = birefringence + self.store["Birefringence"][p, t, :, z] = birefringence # Emit the current dimensions so that we can update the napari dimension slider self.dim_emitter.emit((p, t, z)) @@ -1247,68 +1521,97 @@ def work(self): s = acq_man.getAcquisitionSettings() self.root = s.root() self.prefix = s.prefix() - self.n_frames, self.interval = (s.numFrames(), s.intervalMs() / 1000) if s.useFrames() else (1, 1) + self.n_frames, self.interval = ( + (s.numFrames(), s.intervalMs() / 1000) if s.useFrames() else (1, 1) + ) self.n_channels = s.channels().size() if s.useChannels() else 1 self.n_slices = s.slices().size() if s.useChannels() else 1 - self.n_pos = 1 if not s.usePositionList() else \ - self.calib_window.mm.getPositionListManager().getPositionList().getNumberOfPositions() + self.n_pos = ( + 1 + if not s.usePositionList() + else self.calib_window.mm.getPositionListManager() + .getPositionList() + .getNumberOfPositions() + ) dim_order = s.acqOrderMode() # Get File Path corresponding to current dataset path = os.path.join(self.root, self.prefix) - files = [fn for fn in glob.glob(path + '*') if '.zarr' not in fn] - index = max([int(x.strip(path + '_')) for x in files]) + files = [fn for fn in glob.glob(path + "*") if ".zarr" not in fn] + index = max([int(x.strip(path + "_")) for x in files]) - self.prefix = self.prefix + f'_{index}' + self.prefix = self.prefix + f"_{index}" full_path = os.path.join(self.root, self.prefix) - first_file_path = os.path.join(full_path, self.prefix + '_MMStack.ome.tif') + first_file_path = os.path.join( + full_path, self.prefix + "_MMStack.ome.tif" + ) file = tiff.TiffFile(first_file_path) file_path = first_file_path - self.shape = (file.micromanager_metadata['Summary']['Height'], file.micromanager_metadata['Summary']['Width']) + self.shape = ( + file.micromanager_metadata["Summary"]["Height"], + file.micromanager_metadata["Summary"]["Width"], + ) self.dtype = file.pages[0].dtype - offsets = file.micromanager_metadata['IndexMap']['Offset'] + offsets = file.micromanager_metadata["IndexMap"]["Offset"] file.close() self._check_abort() # Init Reconstruction Class - self.reconstructor = QLIPPBirefringenceCompute(self.shape, - self.calib_window.calib_scheme, - self.calib_window.wavelength, - self.calib_window.swing, - self.n_slices, - self.calib_window.bg_option, - self.bg_data) + self.reconstructor = QLIPPBirefringenceCompute( + self.shape, + self.calib_window.calib_scheme, + self.calib_window.wavelength, + self.calib_window.swing, + self.n_slices, + self.calib_window.bg_option, + self.bg_data, + ) self._check_abort() # initialize dimensions / array for the loop idx, dim3, dim2, dim1, dim0 = 0, 0, 0, 0, 0 - array = np.zeros((self.n_channels, self.n_slices, self.shape[0], self.shape[1])) + array = np.zeros( + (self.n_channels, self.n_slices, self.shape[0], self.shape[1]) + ) file_count = 0 total_idx = 0 # Run until the function has collected the totality of the data - while total_idx < (self.n_slices * self.n_channels * self.n_frames * self.n_pos): + while total_idx < ( + self.n_slices * self.n_channels * self.n_frames * self.n_pos + ): self._check_abort() # this will loop through reading images in a single file as it's being written # when it has successfully loaded all of the images from the file, it'll move on to the next - array, idx, dim3, dim2, dim1, dim0 = self.listen_for_images(array, - file_path, - offsets, - self.interval, - dim3, dim2, dim1, dim0, dim_order) + array, idx, dim3, dim2, dim1, dim0 = self.listen_for_images( + array, + file_path, + offsets, + self.interval, + dim3, + dim2, + dim1, + dim0, + dim_order, + ) total_idx += idx # If acquisition is not finished, grab the next file and listen for images - if total_idx != self.n_slices * self.n_channels * self.n_frames * self.n_pos: + if ( + total_idx + != self.n_slices * self.n_channels * self.n_frames * self.n_pos + ): time.sleep(1) file_count += 1 - file_path = os.path.join(full_path, self.prefix + f'_MMStack_{file_count}.ome.tif') + file_path = os.path.join( + full_path, self.prefix + f"_MMStack_{file_count}.ome.tif" + ) file = tiff.TiffFile(file_path) - offsets = file.micromanager_metadata['IndexMap']['Offset'] + offsets = file.micromanager_metadata["IndexMap"]["Offset"] file.close() diff --git a/recOrder/plugin/workers/calibration_workers.py b/recOrder/plugin/workers/calibration_workers.py index 24911a47..6183729a 100644 --- a/recOrder/plugin/workers/calibration_workers.py +++ b/recOrder/plugin/workers/calibration_workers.py @@ -3,8 +3,11 @@ from qtpy.QtCore import Signal from napari.qt.threading import WorkerBaseSignals, WorkerBase, thread_worker -from recOrder.compute.qlipp_compute import initialize_reconstructor, \ - reconstruct_qlipp_birefringence, reconstruct_qlipp_stokes +from recOrder.compute.qlipp_compute import ( + initialize_reconstructor, + reconstruct_qlipp_birefringence, + reconstruct_qlipp_stokes, +) from recOrder.io.core_functions import set_lc_state, snap_and_average from recOrder.io.utils import MockEmitter from recOrder.calib.Calibration import LC_DEVICE_NAME @@ -19,6 +22,7 @@ # type hint/check from typing import TYPE_CHECKING + # avoid runtime import error if TYPE_CHECKING: from _typeshed import StrOrBytesPath @@ -68,7 +72,7 @@ def __init_subclass__(cls, signals: WorkerBaseSignals): """ super().__init_subclass__() cls.signals = signals - + def __init__(self, calib_window: MainWidget, calib: QLIPP_Calibration): """Initialize the worker object. @@ -90,17 +94,20 @@ def _check_abort(self): """ if self.abort_requested: self.aborted.emit() - raise TimeoutError('Stop Requested.') - + raise TimeoutError("Stop Requested.") + def _write_meta_file(self, meta_file: str): self.calib.meta_file = meta_file - self.calib.write_metadata(notes=self.calib_window.ui.le_notes_field.text()) + self.calib.write_metadata( + notes=self.calib_window.ui.le_notes_field.text() + ) class CalibrationWorker(CalibrationWorkerBase, signals=CalibrationSignals): """ Class to execute calibration """ + def __init__(self, calib_window, calib): super().__init__(calib_window, calib) @@ -109,44 +116,48 @@ def work(self): Runs the full calibration algorithm and emits necessary signals. """ - self.plot_sequence_emit.emit('Coarse') + self.plot_sequence_emit.emit("Coarse") self.calib.intensity_emitter = self.intensity_update self.calib.plot_sequence_emitter = self.plot_sequence_emit self.calib.get_full_roi() - self.progress_update.emit((1, 'Calculating Blacklevel...')) + self.progress_update.emit((1, "Calculating Blacklevel...")) self._check_abort() # Check if change of ROI is needed if self.calib_window.use_cropped_roi: rect = self.calib.check_and_get_roi() - self.calib_window.mmc.setROI(rect.x, rect.y, rect.width, rect.height) + self.calib_window.mmc.setROI( + rect.x, rect.y, rect.width, rect.height + ) self.calib.ROI = (rect.x, rect.y, rect.width, rect.height) self._check_abort() - logging.info('Calculating Black Level ...') - logging.debug('Calculating Black Level ...') + logging.info("Calculating Black Level ...") + logging.debug("Calculating Black Level ...") self.calib.close_shutter_and_calc_blacklevel() # Calculate Blacklevel - logging.info(f'Black Level: {self.calib.I_Black:.0f}\n') - logging.debug(f'Black Level: {self.calib.I_Black:.0f}\n') + logging.info(f"Black Level: {self.calib.I_Black:.0f}\n") + logging.debug(f"Black Level: {self.calib.I_Black:.0f}\n") self._check_abort() - self.progress_update.emit((10, 'Calibrating Extinction State...')) + self.progress_update.emit((10, "Calibrating Extinction State...")) # Open shutter self.calib.open_shutter() # Set LC Wavelength: self.calib.set_wavelength(int(self.calib_window.wavelength)) - if self.calib_window.calib_mode == 'MM-Retardance': - self.calib_window.mmc.setProperty(LC_DEVICE_NAME, 'Wavelength', self.calib_window.wavelength) + if self.calib_window.calib_mode == "MM-Retardance": + self.calib_window.mmc.setProperty( + LC_DEVICE_NAME, "Wavelength", self.calib_window.wavelength + ) self._check_abort() # Optimize States - self._calibrate_4state() if self.calib_window.calib_scheme == '4-State' else self._calibrate_5state() + self._calibrate_4state() if self.calib_window.calib_scheme == "4-State" else self._calibrate_5state() # Reset shutter autoshutter self.calib.reset_shutter() @@ -158,8 +169,12 @@ def work(self): self._check_abort() # Calculate Extinction - extinction_ratio = self.calib.calculate_extinction(self.calib.swing, self.calib.I_Black, self.calib.I_Ext, - self.calib.I_Elliptical) + extinction_ratio = self.calib.calculate_extinction( + self.calib.swing, + self.calib.I_Black, + self.calib.I_Ext, + self.calib.I_Elliptical, + ) self._check_abort() # Update main GUI with extinction ratio @@ -183,7 +198,7 @@ def _meta_path(metadata_idx: str): # Write Metadata self._write_meta_file(meta_file) self.calib_file_emit.emit(self.calib.meta_file) - self.progress_update.emit((100, 'Finished')) + self.progress_update.emit((100, "Finished")) self._check_abort() @@ -205,9 +220,9 @@ def _calibrate_4state(self): Run through the 4-state calibration algorithm """ - search_radius = np.min((self.calib.swing/self.calib.ratio, 0.05)) + search_radius = np.min((self.calib.swing / self.calib.ratio, 0.05)) - self.calib.calib_scheme = '4-State' + self.calib.calib_scheme = "4-State" self._check_abort() @@ -215,23 +230,23 @@ def _calibrate_4state(self): self.calib.opt_Iext() self._check_abort() - self.progress_update.emit((60, 'Calibrating State 1...')) + self.progress_update.emit((60, "Calibrating State 1...")) # Optimize first elliptical (reference) state self.calib.opt_I0() - self.progress_update.emit((65, 'Calibrating State 2...')) + self.progress_update.emit((65, "Calibrating State 2...")) self._check_abort() # Optimize 60 deg state self.calib.opt_I60(search_radius, search_radius) - self.progress_update.emit((75, 'Calibrating State 3...')) + self.progress_update.emit((75, "Calibrating State 3...")) self._check_abort() # Optimize 120 deg state self.calib.opt_I120(search_radius, search_radius) - self.progress_update.emit((85, 'Writing Metadata...')) + self.progress_update.emit((85, "Writing Metadata...")) self._check_abort() @@ -239,35 +254,35 @@ def _calibrate_5state(self): search_radius = np.min((self.calib.swing, 0.05)) - self.calib.calib_scheme = '5-State' + self.calib.calib_scheme = "5-State" # Optimize Extinction State self.calib.opt_Iext() - self.progress_update.emit((50, 'Calibrating State 1...')) + self.progress_update.emit((50, "Calibrating State 1...")) self._check_abort() # Optimize First elliptical state self.calib.opt_I0() - self.progress_update.emit((55, 'Calibrating State 2...')) + self.progress_update.emit((55, "Calibrating State 2...")) self._check_abort() # Optimize 45 deg state self.calib.opt_I45(search_radius, search_radius) - self.progress_update.emit((65, 'Calibrating State 3...')) + self.progress_update.emit((65, "Calibrating State 3...")) self._check_abort() # Optimize 90 deg state self.calib.opt_I90(search_radius, search_radius) - self.progress_update.emit((75, 'Calibrating State 4...')) + self.progress_update.emit((75, "Calibrating State 4...")) self._check_abort() # Optimize 135 deg state self.calib.opt_I135(search_radius, search_radius) - self.progress_update.emit((85, 'Writing Metadata...')) + self.progress_update.emit((85, "Writing Metadata...")) self._check_abort() @@ -278,22 +293,28 @@ def _assess_calibration(self): """ if self.calib.extinction_ratio >= 100: - self.calib_assessment.emit('good') - self.calib_assessment_msg.emit('Successful Calibration') + self.calib_assessment.emit("good") + self.calib_assessment_msg.emit("Successful Calibration") elif 80 <= self.calib.extinction_ratio < 100: - self.calib_assessment.emit('okay') - self.calib_assessment_msg.emit('Successful Calibration, Okay Extinction Ratio') + self.calib_assessment.emit("okay") + self.calib_assessment_msg.emit( + "Successful Calibration, Okay Extinction Ratio" + ) else: - self.calib_assessment.emit('bad') - message = ("Possibilities are: a) linear polarizer and LC are not oriented properly, " - "b) circular analyzer has wrong handedness, " - "c) the condenser is not setup for Kohler illumination, " - "d) a component, such as autofocus dichroic or sample chamber, distorts the polarization state") + self.calib_assessment.emit("bad") + message = ( + "Possibilities are: a) linear polarizer and LC are not oriented properly, " + "b) circular analyzer has wrong handedness, " + "c) the condenser is not setup for Kohler illumination, " + "d) a component, such as autofocus dichroic or sample chamber, distorts the polarization state" + ) - self.calib_assessment_msg.emit('Poor Extinction. '+message) + self.calib_assessment_msg.emit("Poor Extinction. " + message) -class BackgroundCaptureWorker(CalibrationWorkerBase, signals=BackgroundSignals): +class BackgroundCaptureWorker( + CalibrationWorkerBase, signals=BackgroundSignals +): """ Class to execute background capture. """ @@ -304,22 +325,27 @@ def __init__(self, calib_window, calib): def work(self): # Make the background folder - bg_path = os.path.join(self.calib_window.directory, self.calib_window.ui.le_bg_folder.text()) + bg_path = os.path.join( + self.calib_window.directory, + self.calib_window.ui.le_bg_folder.text(), + ) if not os.path.exists(bg_path): os.mkdir(bg_path) else: # increment background paths idx = 1 - while os.path.exists(bg_path+f'_{idx}'): + while os.path.exists(bg_path + f"_{idx}"): idx += 1 - bg_path = bg_path+f'_{idx}' - for state_file in glob.glob(os.path.join(bg_path, 'State*')): + bg_path = bg_path + f"_{idx}" + for state_file in glob.glob(os.path.join(bg_path, "State*")): os.remove(state_file) - if os.path.exists(os.path.join(bg_path, 'calibration_metadata.txt')): - os.remove(os.path.join(bg_path, 'calibration_metadata.txt')) + if os.path.exists( + os.path.join(bg_path, "calibration_metadata.txt") + ): + os.remove(os.path.join(bg_path, "calibration_metadata.txt")) self._check_abort() @@ -328,12 +354,14 @@ def work(self): img_dim = (imgs.shape[-2], imgs.shape[-1]) # initialize reconstructor - recon = initialize_reconstructor('birefringence', - image_dim=img_dim, - calibration_scheme=self.calib_window.calib_scheme, - wavelength_nm=self.calib_window.wavelength, - swing=self.calib_window.swing, - bg_correction='None') + recon = initialize_reconstructor( + "birefringence", + image_dim=img_dim, + calibration_scheme=self.calib_window.calib_scheme, + wavelength_nm=self.calib_window.wavelength, + swing=self.calib_window.swing, + bg_correction="None", + ) self._check_abort() @@ -347,25 +375,27 @@ def work(self): self._check_abort() # Convert retardance to nm - self.retardance = self.birefringence[0] / (2 * np.pi) * self.calib_window.wavelength + self.retardance = ( + self.birefringence[0] / (2 * np.pi) * self.calib_window.wavelength + ) # Save metadata file and emit imgs - meta_file = os.path.join(bg_path, 'calibration_metadata.txt') + meta_file = os.path.join(bg_path, "calibration_metadata.txt") self._write_meta_file(meta_file) # Update last calibration file note = self.calib_window.ui.le_notes_field.text() - with open(self.calib_window.last_calib_meta_file, 'r') as file: + with open(self.calib_window.last_calib_meta_file, "r") as file: current_json = json.load(file) - old_note = current_json['Notes'] - if old_note is None or old_note == '' or old_note == note: - current_json['Notes'] = note + old_note = current_json["Notes"] + if old_note is None or old_note == "" or old_note == note: + current_json["Notes"] = note else: - current_json['Notes'] = old_note + ', ' + note + current_json["Notes"] = old_note + ", " + note - with open(self.calib_window.last_calib_meta_file, 'w') as file: + with open(self.calib_window.last_calib_meta_file, "w") as file: json.dump(current_json, file, indent=1) self._check_abort() @@ -397,26 +427,25 @@ def _save_bg_recon(self, bg_path: StrOrBytesPath): position=0, data_shape=(1, 2, 1, rows, columns), chunk_size=(1, 1, 1, rows, columns), - chan_names=['Retardance', 'Orientation'] + chan_names=["Retardance", "Orientation"], ) writer.write(self.birefringence[:2], p=0, t=0, z=0) # save intensity trace visualization import matplotlib.pyplot as plt + plt.imsave( - os.path.join(bg_recon_path, - "retardance.png"), + os.path.join(bg_recon_path, "retardance.png"), self.retardance, - cmap="gray" + cmap="gray", ) plt.imsave( - os.path.join(bg_recon_path, - "orientation.png"), - self.birefringence[1], + os.path.join(bg_recon_path, "orientation.png"), + self.birefringence[1], cmap="hsv", vmin=0, - vmax=np.pi - ) - + vmax=np.pi, + ) + @thread_worker def load_calibration(calib, metadata: MetadataReader): @@ -438,13 +467,17 @@ def load_calibration(calib, metadata: MetadataReader): def _set_calib_attrs(calib, metadata): """Set the retardance attributes in the recOrder Calibration object""" if calib.calib_scheme == "4-State": - lc_states = ['ext', '0', '60', '120'] + lc_states = ["ext", "0", "60", "120"] elif calib.calib_scheme == "5-State": - lc_states = ['ext', '0', '45', '90', '135'] + lc_states = ["ext", "0", "45", "90", "135"] else: - raise ValueError("Invalid calibration scheme in metadata: {calib.calib_scheme}") + raise ValueError( + "Invalid calibration scheme in metadata: {calib.calib_scheme}" + ) for side in ("A", "B"): - retardance_values = metadata.__getattribute__("LC" + side + "_retardance") + retardance_values = metadata.__getattribute__( + "LC" + side + "_retardance" + ) for i, state in enumerate(lc_states): # set the retardance value attribute (e.g. 'lca_0') retardance_name = "lc" + side.lower() + "_" + state @@ -456,19 +489,27 @@ def _set_calib_attrs(calib, metadata): _set_calib_attrs(calib, metadata) - for state, lca, lcb in zip([f'State{i}' for i in range(5)], metadata.LCA_retardance, metadata.LCB_retardance): + for state, lca, lcb in zip( + [f"State{i}" for i in range(5)], + metadata.LCA_retardance, + metadata.LCB_retardance, + ): calib.define_lc_state(state, lca, lcb) # Calculate black level after loading these properties calib.intensity_emitter = MockEmitter() calib.close_shutter_and_calc_blacklevel() calib.open_shutter() - set_lc_state(calib.mmc, calib.group, 'State0') + set_lc_state(calib.mmc, calib.group, "State0") calib.I_Ext = snap_and_average(calib.snap_manager) - set_lc_state(calib.mmc, calib.group, 'State1') + set_lc_state(calib.mmc, calib.group, "State1") calib.I_Elliptical = snap_and_average(calib.snap_manager) calib.reset_shutter() - yield str(calib.calculate_extinction(calib.swing, calib.I_Black, calib.I_Ext, calib.I_Elliptical)) + yield str( + calib.calculate_extinction( + calib.swing, calib.I_Black, calib.I_Ext, calib.I_Elliptical + ) + ) return calib diff --git a/recOrder/plugin/workers/reconstruction_workers.py b/recOrder/plugin/workers/reconstruction_workers.py index 3832b7cf..e6976bf6 100644 --- a/recOrder/plugin/workers/reconstruction_workers.py +++ b/recOrder/plugin/workers/reconstruction_workers.py @@ -4,6 +4,7 @@ import time import os + class ReconstructionSignals(WorkerBaseSignals): """ Custom Signals class that includes napari native signals @@ -15,7 +16,6 @@ class ReconstructionSignals(WorkerBaseSignals): class ReconstructionWorker(WorkerBase): - def __init__(self, calib_window, config): super().__init__(SignalsClass=ReconstructionSignals) @@ -26,17 +26,25 @@ def __init__(self, calib_window, config): def _check_abort(self): if self.abort_requested: self.aborted.emit() - raise TimeoutError('Stop Requested') + raise TimeoutError("Stop Requested") def work(self): - self.manager = PipelineManager(self.config, emitter=self.dimension_emitter) - - store_path = os.path.join(self.manager.config.save_dir, self.manager.config.data_save_name) - store_path = store_path + '.zarr' if not store_path.endswith('.zarr') else store_path + self.manager = PipelineManager( + self.config, emitter=self.dimension_emitter + ) + + store_path = os.path.join( + self.manager.config.save_dir, self.manager.config.data_save_name + ) + store_path = ( + store_path + ".zarr" + if not store_path.endswith(".zarr") + else store_path + ) self.store_emitter.emit(store_path) - print(f'Beginning Reconstruction...') + print(f"Beginning Reconstruction...") for pt in sorted(self.manager.pt_set): start_time = time.time() @@ -45,7 +53,9 @@ def work(self): self._check_abort() - pt_data = self.manager.data.get_zarr(pt[0])[pt[1]] # (C, Z, Y, X) virtual + pt_data = self.manager.data.get_zarr(pt[0])[ + pt[1] + ] # (C, Z, Y, X) virtual self._check_abort() @@ -53,25 +63,39 @@ def work(self): self._check_abort() - birefringence = self.manager.pipeline.reconstruct_birefringence_volume(stokes) + birefringence = ( + self.manager.pipeline.reconstruct_birefringence_volume(stokes) + ) self._check_abort() # will return phase volumes - deconvolve2D, deconvolve3D = self.manager.pipeline.deconvolve_volume(stokes) + ( + deconvolve2D, + deconvolve3D, + ) = self.manager.pipeline.deconvolve_volume(stokes) self._check_abort() - self.manager.pipeline.write_data(self.manager.indices_map[pt[0]], pt[1], pt_data, stokes, - birefringence, deconvolve2D, deconvolve3D) + self.manager.pipeline.write_data( + self.manager.indices_map[pt[0]], + pt[1], + pt_data, + stokes, + birefringence, + deconvolve2D, + deconvolve3D, + ) self._check_abort() end_time = time.time() - print(f'Finishing Reconstructing P = {pt[0]}, T = {pt[1]} ({(end_time - start_time) / 60:0.2f}) min') + print( + f"Finishing Reconstructing P = {pt[0]}, T = {pt[1]} ({(end_time - start_time) / 60:0.2f}) min" + ) existing_meta = self.manager.writer.store.attrs.asdict().copy() - existing_meta['Config'] = self.manager.config.yaml_dict + existing_meta["Config"] = self.manager.config.yaml_dict self.manager.writer.store.attrs.put(existing_meta) self._check_abort() diff --git a/recOrder/scripts/cli.py b/recOrder/scripts/cli.py index 0bddc2ee..506dc5c2 100644 --- a/recOrder/scripts/cli.py +++ b/recOrder/scripts/cli.py @@ -10,18 +10,20 @@ class ShowUsageOnMissingError(click.Command): def __call__(self, *args, **kwargs): try: return super(ShowUsageOnMissingError, self).__call__( - *args, standalone_mode=False, **kwargs) + *args, standalone_mode=False, **kwargs + ) except click.MissingParameter as exc: exc.ctx = None exc.show(file=sys.stdout) click.echo() try: - super(ShowUsageOnMissingError, self).__call__(['--help']) + super(ShowUsageOnMissingError, self).__call__(["--help"]) except SystemExit: sys.exit(exc.exit_code) + @click.command(cls=ShowUsageOnMissingError) [email protected]_option('-h', '--help') [email protected]_option("-h", "--help") def help(): """\033[92mrecOrder: Computational Toolkit for Label-Free Imaging\033[0m @@ -40,41 +42,106 @@ def help(): """ print(help.__doc__) + @click.command(cls=ShowUsageOnMissingError) [email protected]_option('-h', '--help') [email protected]('--method', required=False, type=str, help='mode of reconstruction: \ - QLIPP, IPS, UPTI') [email protected]('--mode', required=False, type=str, help='mode of reconstruction: \ - 2D, 3D, Stokes') [email protected]('--data_dir', required=False, type=click.Path(exists=True), help='path to the data') [email protected]('--save_dir', required=False, type=click.Path(), help='path to the save directory') [email protected]('--name', required=False, type=str, help='name to use for saving the data') [email protected]('--config', required=True, type=click.Path(exists=True), help='path to config yml file') [email protected]('--overwrite', required=False, type=bool, - help='whether or not to overwrite any previous data under this name') -def reconstruct(method, mode, data_dir, save_dir, name, config, overwrite=False): [email protected]_option("-h", "--help") [email protected]( + "--method", + required=False, + type=str, + help="mode of reconstruction: \ + QLIPP, IPS, UPTI", +) [email protected]( + "--mode", + required=False, + type=str, + help="mode of reconstruction: \ + 2D, 3D, Stokes", +) [email protected]( + "--data_dir", + required=False, + type=click.Path(exists=True), + help="path to the data", +) [email protected]( + "--save_dir", + required=False, + type=click.Path(), + help="path to the save directory", +) [email protected]( + "--name", required=False, type=str, help="name to use for saving the data" +) [email protected]( + "--config", + required=True, + type=click.Path(exists=True), + help="path to config yml file", +) [email protected]( + "--overwrite", + required=False, + type=bool, + help="whether or not to overwrite any previous data under this name", +) +def reconstruct( + method, mode, data_dir, save_dir, name, config, overwrite=False +): if config: if not os.path.exists(config): - raise ValueError('Specified config path does not exist') + raise ValueError("Specified config path does not exist") else: - config = ConfigReader(config, data_dir, save_dir, method, mode, name, - immutable=False) + config = ConfigReader( + config, data_dir, save_dir, method, mode, name, immutable=False + ) config.save_yaml() else: - config = ConfigReader(None, data_dir, save_dir, method, mode, name, immutable=False) + config = ConfigReader( + None, data_dir, save_dir, method, mode, name, immutable=False + ) config.save_yaml() manager = PipelineManager(config, overwrite) manager.run() + @click.command(cls=ShowUsageOnMissingError) [email protected]_option('-h', '--help') [email protected]('--input', required=True, type=click.Path(exists=True), help='path to the raw data folder containing ome.tifs') [email protected]('--output', required=True, type=str, help='full path to save the zarr store (../../Experiment.zarr') [email protected]('--data_type', required=False, type=str, help='Data type, "ometiff", "upti", "zarr"') [email protected]('--replace_pos_name', required=False, type=bool, help='whether or not to append position name to data') [email protected]('--format_hcs', required=False, type=bool, help='whether or not to format the data as an HCS "well-plate"') [email protected]_option("-h", "--help") [email protected]( + "--input", + required=True, + type=click.Path(exists=True), + help="path to the raw data folder containing ome.tifs", +) [email protected]( + "--output", + required=True, + type=str, + help="full path to save the zarr store (../../Experiment.zarr", +) [email protected]( + "--data_type", + required=False, + type=str, + help='Data type, "ometiff", "upti", "zarr"', +) [email protected]( + "--replace_pos_name", + required=False, + type=bool, + help="whether or not to append position name to data", +) [email protected]( + "--format_hcs", + required=False, + type=bool, + help='whether or not to format the data as an HCS "well-plate"', +) def convert(input, output, data_type, replace_pos_name, format_hcs): - converter = ZarrConverter(input, output, data_type, replace_pos_name, format_hcs) - converter.run_conversion() \ No newline at end of file + converter = ZarrConverter( + input, output, data_type, replace_pos_name, format_hcs + ) + converter.run_conversion() diff --git a/recOrder/scripts/launch_napari.py b/recOrder/scripts/launch_napari.py index c9c6cd61..40043651 100644 --- a/recOrder/scripts/launch_napari.py +++ b/recOrder/scripts/launch_napari.py @@ -9,4 +9,4 @@ def main(): if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/recOrder/scripts/md5_check_sum.py b/recOrder/scripts/md5_check_sum.py index a3c39d0f..14ba4648 100644 --- a/recOrder/scripts/md5_check_sum.py +++ b/recOrder/scripts/md5_check_sum.py @@ -5,19 +5,28 @@ import pathlib as path import click + @click.command() [email protected]('--zarr_path', required=True, type=str, help='path to the save directory') [email protected]('--raw_stats_path', required=True, type=str, help='path to the statistics file of the raw data') [email protected]( + "--zarr_path", required=True, type=str, help="path to the save directory" +) [email protected]( + "--raw_stats_path", + required=True, + type=str, + help="path to the statistics file of the raw data", +) def parse_args(zarr_path, raw_stats_path): """parse command line arguments and return class with the arguments""" - class Args(): + class Args: def __init__(self, zarr_path, raw_stats_path): self.zarr_path = zarr_path self.raw_stats_path = raw_stats_path return Args(zarr_path, raw_stats_path) + def md5(fname): hash_md5 = hashlib.md5() with open(fname, "rb") as f: @@ -25,15 +34,16 @@ def md5(fname): hash_md5.update(chunk) return hash_md5.hexdigest() + def gen_stats_file(zarr_path, save_path): name = path.PurePath(zarr_path).name - if name.endswith('.zarr'): + if name.endswith(".zarr"): name = name[:-5] - file_name = os.path.join(save_path, name+'_ZarrStatistics.txt') - file = open(file_name, 'w') + file_name = os.path.join(save_path, name + "_ZarrStatistics.txt") + file = open(file_name, "w") - zstore = zarr.open(zarr_path, 'r')['array'] + zstore = zarr.open(zarr_path, "r")["array"] shape = zstore.shape for p in range(shape[0]): @@ -45,7 +55,9 @@ def gen_stats_file(zarr_path, save_path): mean = np.mean(image) median = np.median(image) std = np.std(image) - file.write(f'Coord: {(p, t, c, z)}, Mean: {mean}, Median: {median}, Std: {std}\n') + file.write( + f"Coord: {(p, t, c, z)}, Mean: {mean}, Median: {median}, Std: {std}\n" + ) file.close() return file_name @@ -62,11 +74,6 @@ def gen_stats_file(zarr_path, save_path): converted_md5 = md5(conv_stats_path) if raw_md5 != converted_md5: - print('MD5 check sum failed. Potential Error in Conversion') + print("MD5 check sum failed. Potential Error in Conversion") else: - print('MD5 check sum passed. Conversion successful') - - - - - + print("MD5 check sum passed. Conversion successful") diff --git a/recOrder/scripts/repeat-cal-acq-rec.py b/recOrder/scripts/repeat-cal-acq-rec.py index 9e63beeb..912f5679 100644 --- a/recOrder/scripts/repeat-cal-acq-rec.py +++ b/recOrder/scripts/repeat-cal-acq-rec.py @@ -71,11 +71,16 @@ def measure_fov(mmc: Core): """ pixel_size = float(mmc.getPixelSizeUm()) if pixel_size == 0: - float(input("Pixel size is not calibrated. Please provide an estimate (in microns):")) + float( + input( + "Pixel size is not calibrated. Please provide an estimate (in microns):" + ) + ) fov_x = pixel_size * float(mmc.getImageWidth()) fov_y = pixel_size * float(mmc.getImageHeight()) return fov_x, fov_y + def rand_shift(length: float): """Randomly signed shift of a certain length. @@ -92,12 +97,13 @@ def rand_shift(length: float): sign = random.randint(0, 1) * 2 - 1 return sign * length + def main(): viewer = napari.Viewer() app = MainWidget(viewer) viewer.window.add_dock_widget(app) app.ui.qbutton_gui_mode.click() - app.calib_scheme = '5-State' + app.calib_scheme = "5-State" app.directory = SAVE_DIR app.save_directory = SAVE_DIR @@ -112,7 +118,7 @@ def main(): with stage_detour(app, dx, dy) as app: print(f"Calibration repeat # {cal_repeat}") app.swing = SWING - + print(f"Calibrating with swing = {SWING}") app.run_calibration() time.sleep(90) @@ -124,12 +130,15 @@ def main(): app.last_calib_meta_file = app.calib.meta_file app.capture_bg() time.sleep(20) - app.ui.cb_bg_method.setCurrentIndex(1) # Set to "Measured" bg correction + app.ui.cb_bg_method.setCurrentIndex( + 1 + ) # Set to "Measured" bg correction app.enter_bg_correction() app.save_name = f"cal-{cal_repeat}-bkg-{bkg_repeat}" app.enter_acq_bg_path() app.acq_birefringence() time.sleep(15) + if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/recOrder/scripts/repeat-calibration.py b/recOrder/scripts/repeat-calibration.py index 56d04df7..a2a61283 100644 --- a/recOrder/scripts/repeat-calibration.py +++ b/recOrder/scripts/repeat-calibration.py @@ -8,12 +8,13 @@ SWINGS = [0.1, 0.03, 0.01, 0.005] REPEATS = 5 + def main(): viewer = napari.Viewer() recorder = MainWidget(viewer) viewer.window.add_dock_widget(recorder) recorder.ui.qbutton_gui_mode.click() - recorder.calib_scheme = '5-State' + recorder.calib_scheme = "5-State" for repeat in range(REPEATS): for swing in SWINGS: @@ -22,6 +23,7 @@ def main(): recorder.directory = SAVE_DIR recorder.run_calibration() time.sleep(100) - + + if __name__ == "__main__": - main() \ No newline at end of file + main()

Format with black Automatic formatting of `.py` documents with `black`. Did not modify the auto-generated `./recOrder/plugin/qtdesigner/recOrder_ui.py`. Enforce consistent code formatting in development **Problem** Although we depend on the `black` formatter in the `[dev]` requirements, it is not currently consistently used, and there are unconventional formatting (wrt PEP 8) widespread in the code base. **Proposed solution** - [x] We should arrange a PR that formats the existing code. (Done: #229) - [x] The contributors should be required to format their code with the `black` formatter we specify in the `[dev]` requirement before pushing to the repository. (#185) - [ ] (Optionally) we can also [incorporate formatting formatting in our GItHub Actions workflow](https://black.readthedocs.io/en/stable/integrations/github_actions.html).. **Example** `black` can be executed from the command line if the user has the `[dev]` environment activated. If you are using a modern IDE, formatting can also be done by selecting the `black` installed in their virtual environment as the formatter backend (VS Code: `^/⌘ ⇧ P` and type 'format document with...').

# [Codecov](https://codecov.io/gh/mehta-lab/recOrder/pull/179?src=pr&el=h1&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=mehta-lab) Report > Merging [#179](https://codecov.io/gh/mehta-lab/recOrder/pull/179?src=pr&el=desc&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=mehta-lab) (543f06a) into [main](https://codecov.io/gh/mehta-lab/recOrder/commit/2c6eb2de379ab123ac6eeb8409a9c3d4c3bb3eb1?el=desc&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=mehta-lab) (2c6eb2d) will **decrease** coverage by `0.04%`. > The diff coverage is `21.49%`. ```diff @@ Coverage Diff @@ ## main #179 +/- ## ========================================== - Coverage 11.21% 11.16% -0.05% ========================================== Files 45 45 Lines 5984 6010 +26 ========================================== Hits 671 671 - Misses 5313 5339 +26 ``` | [Impacted Files](https://codecov.io/gh/mehta-lab/recOrder/pull/179?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=mehta-lab) | Coverage Δ | | |---|---|---| | [recOrder/acq/acq\_functions.py](https://codecov.io/gh/mehta-lab/recOrder/pull/179/diff?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=mehta-lab#diff-cmVjT3JkZXIvYWNxL2FjcV9mdW5jdGlvbnMucHk=) | `0.00% <0.00%> (ø)` | | | [recOrder/calib/Calibration.py](https://codecov.io/gh/mehta-lab/recOrder/pull/179/diff?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=mehta-lab#diff-cmVjT3JkZXIvY2FsaWIvQ2FsaWJyYXRpb24ucHk=) | `0.00% <0.00%> (ø)` | | | [recOrder/calib/Optimization.py](https://codecov.io/gh/mehta-lab/recOrder/pull/179/diff?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=mehta-lab#diff-cmVjT3JkZXIvY2FsaWIvT3B0aW1pemF0aW9uLnB5) | `0.00% <0.00%> (ø)` | | | [recOrder/compute/fluorescence\_compute.py](https://codecov.io/gh/mehta-lab/recOrder/pull/179/diff?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=mehta-lab#diff-cmVjT3JkZXIvY29tcHV0ZS9mbHVvcmVzY2VuY2VfY29tcHV0ZS5weQ==) | `0.00% <0.00%> (ø)` | | | [recOrder/compute/qlipp\_compute.py](https://codecov.io/gh/mehta-lab/recOrder/pull/179/diff?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=mehta-lab#diff-cmVjT3JkZXIvY29tcHV0ZS9xbGlwcF9jb21wdXRlLnB5) | `0.00% <0.00%> (ø)` | | | [recOrder/io/\_\_init\_\_.py](https://codecov.io/gh/mehta-lab/recOrder/pull/179/diff?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=mehta-lab#diff-cmVjT3JkZXIvaW8vX19pbml0X18ucHk=) | `0.00% <0.00%> (ø)` | | | [recOrder/io/\_reader.py](https://codecov.io/gh/mehta-lab/recOrder/pull/179/diff?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=mehta-lab#diff-cmVjT3JkZXIvaW8vX3JlYWRlci5weQ==) | `0.00% <0.00%> (ø)` | | | [recOrder/io/config\_reader.py](https://codecov.io/gh/mehta-lab/recOrder/pull/179/diff?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=mehta-lab#diff-cmVjT3JkZXIvaW8vY29uZmlnX3JlYWRlci5weQ==) | `0.00% <0.00%> (ø)` | | | [recOrder/io/core\_functions.py](https://codecov.io/gh/mehta-lab/recOrder/pull/179/diff?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=mehta-lab#diff-cmVjT3JkZXIvaW8vY29yZV9mdW5jdGlvbnMucHk=) | `0.00% <0.00%> (ø)` | | | [recOrder/io/metadata\_reader.py](https://codecov.io/gh/mehta-lab/recOrder/pull/179/diff?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=mehta-lab#diff-cmVjT3JkZXIvaW8vbWV0YWRhdGFfcmVhZGVyLnB5) | `0.00% <0.00%> (ø)` | | | ... and [31 more](https://codecov.io/gh/mehta-lab/recOrder/pull/179/diff?src=pr&el=tree-more&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=mehta-lab) | | Help us with your feedback. Take ten seconds to tell us [how you rate us](https://about.codecov.io/nps?utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=mehta-lab). Have a feature suggestion? [Share it here.](https://app.codecov.io/gh/feedback/?utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=mehta-lab) Preview page for your plugin is ready here: https://preview.napari-hub.org/mehta-lab/recOrder/179 _Updated: 2022-09-24T00:17:29.595624_ 'Initial formatting' is done manually here to break up the process into commits each modifying a single submodule for easier bug-fix traceback. This should not be necessary given the reliability of `black`, and is only performed out of an abundance of precaution. We should consider [enforcing formatting in our GItHub Actions workflow](https://black.readthedocs.io/en/stable/integrations/github_actions.html). This PR will start to address #176. @talonchandler I think we should try to merge this relatively soon to avoid more conflicts. Roger...I just took a quick flip through and highlighted some of the points that are really bizarre. Sorry I let this slip. Can you merge main into this branch, reformat, then I'll test Monday morning? Fixed two typos I spotted in my sift through the changes. This PR is getting increasingly cluttered with conflicts now. I'm tempted to close it and open a new one following [instructions here](https://black.readthedocs.io/en/stable/guides/introducing_black_to_your_project.html#avoiding-ruining-git-blame). Any suggestions @talonchandler? I'm very in favor of preserving git-blame info if possible (I use this info for context quite often). How about we do this on Wednesday (I just sent an invite for hummingbird)? [GitHub's doc on formatter & `git blame`](https://docs.github.com/en/repositories/working-with-files/using-files/viewing-a-file#ignore-commits-in-the-blame-view) @ziw-liu and I are closing together. We will revisit black formatting after release. Another issue to also think about is enforcing consistent Markdown documents with a linter, e.g. this one that is available as both a [GitHub Action](https://github.com/marketplace/actions/markdownlint-cli2-action) and an [editor plugin](https://marketplace.visualstudio.com/items?itemName=DavidAnson.vscode-markdownlint).

2022-10-08T00:49:40

0.0

[]

GiadaLalli/ISN-tractor

GiadaLalli__ISN-tractor-49

7f6558cbc84828e2ca178366d413be9c18fc7d91

diff --git a/benchmark/dense_isn_offline.py b/benchmark/dense_isn_offline.py index 36de2ea..ba4f8b2 100644 --- a/benchmark/dense_isn_offline.py +++ b/benchmark/dense_isn_offline.py @@ -1,22 +1,17 @@ """Implementation of the offline LionessR algorithm using an offline Pearson metric.""" import torch as t -from numpy import float32 +from numpy import float64 def dense_isn_offline(data, device=None): """Lionessr using offline pearson metric.""" - xt = t.tensor(data.T.to_numpy(dtype=float32, copy=False), device=device) + xt = t.tensor(data.T.to_numpy(dtype=float64, copy=False), device=device) nrsamples = xt.size(1) net = t.corrcoef(xt) agg = net.flatten() - # output = t.empty((net.size(0) * net.size(1), nrsamples)) - for i in range(nrsamples): ss = t.corrcoef(t.cat((xt[:, :i], xt[:, i + 1 :]), dim=1)).flatten() yield nrsamples * (agg - ss) + ss - # output[:, i] = nrsamples * (agg - ss) + ss - - # return output.T diff --git a/isn_tractor/ibisn.py b/isn_tractor/ibisn.py index 11aec67..c3d1beb 100644 --- a/isn_tractor/ibisn.py +++ b/isn_tractor/ibisn.py @@ -510,8 +510,8 @@ def dense_isn( """ Network computation based on the Lioness algorithm """ - num_samples = t.tensor(data.shape[0], dtype=t.float32) - orig = t.from_numpy(data.to_numpy(dtype=np.float32, copy=False)).to(device) + num_samples = t.tensor(data.shape[0], dtype=t.float64) + orig = t.from_numpy(data.to_numpy(dtype=np.float64, copy=False)).to(device) orig_transpose = t.transpose(orig, 0, 1) dot_prod = t.matmul(orig_transpose, orig) mean_vect = t.sum(orig, dim=0)

Discrepency between online/offline `dense_isn` computation As reported by @marouenbg: This code, ```python import pandas as pd import numpy as np import isn_tractor.ibisn as it import time def dataframe(size, values): n_rows, n_cols = size mapped = "mapped" if "mapped" in values else "unmapped" if "discrete" in values: data = np.random.randint(0, 3, size=(n_rows, n_cols)) elif "continuous" in values: data = np.random.uniform(0, 100, size=(n_rows, n_cols)) else: raise ValueError("Values must contain either 'discrete' or 'continuous'") col_names = [mapped + "_" + "feature" + "_" + str(i) for i in range(n_cols)] index_names = ["sample_" + str(i) for i in range(n_rows)] df = pd.DataFrame(data, index=index_names, columns=col_names) return df dfs = [] sizes = [ (200, 1000), (200, 2000), (200, 3000), (500, 1000), (500, 2000), (500, 3000), (1000, 1000), (1000, 2000), (2000, 1000), ] for i, size in enumerate(sizes): df_name = f"m_df{i+1}" df = dataframe(size, ["mapped", "continuous"]) locals()[df_name] = df dfs.append(df) diffs=[] for df in dfs: st = it.dense_isn(df) a = list(st) se = it.dense_isn_offline(df) b = list(se) diffs.append(np.max(list(np.abs(a[0] - b[0])))) ``` Produces: `diffs == [0.00072455406, 0.00076186657, 0.0007597208, 0.0010197759, 0.0010986328, 0.001281023, 0.0018423535, 0.0021594092,0.0035106242]`

Thanks James, the fix could be as simple as setting the inputs to double maybe.

2024-10-11T10:21:15

0.0

[]

resgroup/wind-up

resgroup__wind-up-39

980f6db15b2f4f33642c807ffef19780c46fc03a

diff --git a/examples/smarteole_example.ipynb b/examples/smarteole_example.ipynb index 16d7d11..e294b1b 100644 --- a/examples/smarteole_example.ipynb +++ b/examples/smarteole_example.ipynb @@ -1275,7 +1275,7 @@ { "data": { "text/markdown": [ - "filter_stuck_data set 0 rows [0.0%] to NA" + "filter_stuck_data set 7443 rows [7.6%] to NA" ], "text/plain": [ "<IPython.core.display.Markdown object>" @@ -2561,7 +2561,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -2581,7 +2581,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -2786,7 +2786,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8a3fe823eaff4180889d356dd1d4cab4", + "model_id": "a2457ea9318744fd80c413a42676fcd0", "version_major": 2, "version_minor": 0 }, @@ -2908,7 +2908,7 @@ { "data": { "text/markdown": [ - "{'ref': 'SMV7', 'ref_ws_col': 'ref_ws_est_blend', 'distance_m': 314.4465998943834, 'bearing_deg': 173.69483366777283, 'ref_max_northing_error_v_reanalysis': np.float64(2.6590754551807976), 'ref_max_northing_error_v_wf': np.float64(2.842170943040401e-14), 'ref_max_ws_drift': np.float64(0.08697706942338845), 'ref_max_ws_drift_pp_period': np.float64(0.08697706942338845), 'ref_powercurve_shift': np.float64(0.003117456887993697), 'ref_rpm_shift': np.float64(0.0015313319638985412), 'ref_pitch_shift': np.float64(-0.05548555519736481), 'detrend_pre_r2_improvement': np.float64(0.004942384513915488), 'detrend_post_r2_improvement': np.float64(0.0011487228730558963), 'mean_power_pre': np.float64(1149.2323289820358), 'mean_power_post': np.float64(1148.4157066585956), 'mean_test_yaw_offset_pre': np.float64(-0.022511156888877885), 'mean_test_yaw_offset_post': np.float64(3.8213264238880478), 'mean_test_yaw_offset_command_pre': np.float64(0.0002638323353293413), 'mean_test_yaw_offset_command_post': np.float64(6.636967675544794), 'mean_ref_yaw_offset_command_pre': np.float64(0.0), 'test_ref_warning_counts': 0, 'time_calculated': Timestamp('2024-09-26 13:14:03.984554+0000', tz='UTC'), 'uplift_frc': np.float64(-0.010361770845398625), 'unc_one_sigma_frc': np.float64(0.0057851151530948705), 't_value_one_sigma': np.float64(1.000630119597717), 'missing_bins_unc_scale_factor': 1, 'pp_valid_hours_pre': np.float64(132.5), 'pp_valid_hours_post': np.float64(136.0), 'pp_valid_hours': np.float64(268.5), 'pp_data_coverage': np.float64(0.11496467565831728), 'pp_invalid_bin_count': np.int64(16), 'uplift_noadj_frc': np.float64(-0.011505660672016103), 'unc_one_sigma_noadj_frc': np.float64(0.0057851151530948705), 'poweronly_uplift_frc': np.float64(-0.012003308408347353), 'reversed_uplift_frc': np.float64(-0.009715528755112396), 'reversal_error': np.float64(0.0022877796532349576), 'unc_one_sigma_lowerbound_frc': np.float64(0.0011438898266174788), 'unc_one_sigma_bootstrap_frc': np.float64(0.0049406532714673), 'uplift_p5_frc': np.float64(-0.000846103203498606), 'uplift_p95_frc': np.float64(-0.019877438487298643), 'wind_up_version': '0.1.9', 'test_wtg': 'SMV6', 'test_pw_col': 'test_pw_clipped', 'lt_wtg_hours_raw': 0, 'lt_wtg_hours_filt': 0, 'test_max_ws_drift': np.float64(0.03653354205095605), 'test_max_ws_drift_pp_period': np.float64(0.03653354205095605), 'test_powercurve_shift': np.float64(0.0010615707256107498), 'test_rpm_shift': np.float64(0.0011316163321652972), 'test_pitch_shift': np.float64(-0.037158903030505286), 'preprocess_warning_counts': 0, 'test_warning_counts': 0}" + "{'ref': 'SMV7', 'ref_ws_col': 'ref_ws_est_blend', 'distance_m': 314.4465998943834, 'bearing_deg': 173.69483366777283, 'ref_max_northing_error_v_reanalysis': np.float64(2.6590754551807976), 'ref_max_northing_error_v_wf': np.float64(2.842170943040401e-14), 'ref_max_ws_drift': np.float64(0.08697706942338845), 'ref_max_ws_drift_pp_period': np.float64(0.08697706942338845), 'ref_powercurve_shift': np.float64(0.003117456887993697), 'ref_rpm_shift': np.float64(0.0015313319638985412), 'ref_pitch_shift': np.float64(-0.05548555519736481), 'detrend_pre_r2_improvement': np.float64(0.004942384513915488), 'detrend_post_r2_improvement': np.float64(0.0011487228730558963), 'mean_power_pre': np.float64(1149.2323289820358), 'mean_power_post': np.float64(1148.4157066585956), 'mean_test_yaw_offset_pre': np.float64(-0.022511156888877885), 'mean_test_yaw_offset_post': np.float64(3.8213264238880478), 'mean_test_yaw_offset_command_pre': np.float64(0.0002638323353293413), 'mean_test_yaw_offset_command_post': np.float64(6.636967675544794), 'mean_ref_yaw_offset_command_pre': np.float64(0.0), 'test_ref_warning_counts': 0, 'time_calculated': Timestamp('2024-11-04 14:53:53.646317+0000', tz='UTC'), 'uplift_frc': np.float64(-0.010361770845398625), 'unc_one_sigma_frc': np.float64(0.0057851151530948705), 't_value_one_sigma': np.float64(1.000630119597717), 'missing_bins_unc_scale_factor': 1, 'pp_valid_hours_pre': np.float64(132.5), 'pp_valid_hours_post': np.float64(136.0), 'pp_valid_hours': np.float64(268.5), 'pp_data_coverage': np.float64(0.11496467565831728), 'pp_invalid_bin_count': np.int64(16), 'uplift_noadj_frc': np.float64(-0.011505660672016103), 'unc_one_sigma_noadj_frc': np.float64(0.0057851151530948705), 'poweronly_uplift_frc': np.float64(-0.012003308408347353), 'reversed_uplift_frc': np.float64(-0.009715528755112396), 'reversal_error': np.float64(0.0022877796532349576), 'unc_one_sigma_lowerbound_frc': np.float64(0.0011438898266174788), 'unc_one_sigma_bootstrap_frc': np.float64(0.0049406532714673), 'uplift_p5_frc': np.float64(-0.000846103203498606), 'uplift_p95_frc': np.float64(-0.019877438487298643), 'wind_up_version': '0.1.9', 'test_wtg': 'SMV6', 'test_pw_col': 'test_pw_clipped', 'lt_wtg_hours_raw': 0, 'lt_wtg_hours_filt': 0, 'test_max_ws_drift': np.float64(0.03653354205095605), 'test_max_ws_drift_pp_period': np.float64(0.03653354205095605), 'test_powercurve_shift': np.float64(0.0010615707256107498), 'test_rpm_shift': np.float64(0.0011316163321652972), 'test_pitch_shift': np.float64(-0.037158903030505286), 'preprocess_warning_counts': 0, 'test_warning_counts': 0}" ], "text/plain": [ "<IPython.core.display.Markdown object>" @@ -3523,7 +3523,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -3543,7 +3543,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -3748,7 +3748,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2168cd5405004d16b8116014d42d5fe5", + "model_id": "df804baf4f2041b18c2dc67189a83f67", "version_major": 2, "version_minor": 0 }, @@ -3870,7 +3870,7 @@ { "data": { "text/markdown": [ - "{'ref': 'SMV7', 'ref_ws_col': 'ref_ws_est_blend', 'distance_m': 591.1178519927024, 'bearing_deg': 190.23567745705736, 'ref_max_northing_error_v_reanalysis': np.float64(2.6590754551807976), 'ref_max_northing_error_v_wf': np.float64(2.842170943040401e-14), 'ref_max_ws_drift': np.float64(0.08697706942338845), 'ref_max_ws_drift_pp_period': np.float64(0.08697706942338845), 'ref_powercurve_shift': np.float64(0.003117456887993697), 'ref_rpm_shift': np.float64(0.0015313319638985412), 'ref_pitch_shift': np.float64(-0.05548555519736481), 'detrend_pre_r2_improvement': np.float64(0.09621188863947527), 'detrend_post_r2_improvement': np.float64(0.11890364717818414), 'mean_power_pre': np.float64(955.493497245509), 'mean_power_post': np.float64(993.6911992736077), 'mean_test_yaw_offset_pre': np.float64(-2.2725466102034675), 'mean_test_yaw_offset_post': np.float64(-2.876981850327039), 'mean_test_yaw_offset_command_pre': np.float64(0.0), 'mean_test_yaw_offset_command_post': np.float64(0.0), 'mean_ref_yaw_offset_command_pre': np.float64(0.0), 'test_ref_warning_counts': 0, 'time_calculated': Timestamp('2024-09-26 13:15:57.997400+0000', tz='UTC'), 'uplift_frc': np.float64(0.030879346731271313), 'unc_one_sigma_frc': np.float64(0.01167847006525424), 't_value_one_sigma': np.float64(1.0006277462668354), 'missing_bins_unc_scale_factor': 1, 'pp_valid_hours_pre': np.float64(133.0), 'pp_valid_hours_post': np.float64(137.16666666666669), 'pp_valid_hours': np.float64(270.1666666666667), 'pp_data_coverage': np.float64(0.11567829872261472), 'pp_invalid_bin_count': np.int64(16), 'uplift_noadj_frc': np.float64(0.030509447623790466), 'unc_one_sigma_noadj_frc': np.float64(0.01004760180633488), 'poweronly_uplift_frc': np.float64(0.029990866525649328), 'reversed_uplift_frc': np.float64(0.03073066474061102), 'reversal_error': np.float64(0.0007397982149616941), 'unc_one_sigma_lowerbound_frc': np.float64(0.00036989910748084706), 'unc_one_sigma_bootstrap_frc': np.float64(0.01167847006525424), 'uplift_p5_frc': np.float64(0.050088720575348945), 'uplift_p95_frc': np.float64(0.01166997288719368), 'wind_up_version': '0.1.9', 'test_wtg': 'SMV5', 'test_pw_col': 'test_pw_clipped', 'lt_wtg_hours_raw': 0, 'lt_wtg_hours_filt': 0, 'test_max_ws_drift': np.float64(0.10726609831004863), 'test_max_ws_drift_pp_period': np.float64(0.10726609831004863), 'test_powercurve_shift': np.float64(-0.005678000921447213), 'test_rpm_shift': np.float64(0.0013951853610039144), 'test_pitch_shift': np.float64(-0.02783487184623068), 'preprocess_warning_counts': 0, 'test_warning_counts': 0}" + "{'ref': 'SMV7', 'ref_ws_col': 'ref_ws_est_blend', 'distance_m': 591.1178519927024, 'bearing_deg': 190.23567745705736, 'ref_max_northing_error_v_reanalysis': np.float64(2.6590754551807976), 'ref_max_northing_error_v_wf': np.float64(2.842170943040401e-14), 'ref_max_ws_drift': np.float64(0.08697706942338845), 'ref_max_ws_drift_pp_period': np.float64(0.08697706942338845), 'ref_powercurve_shift': np.float64(0.003117456887993697), 'ref_rpm_shift': np.float64(0.0015313319638985412), 'ref_pitch_shift': np.float64(-0.05548555519736481), 'detrend_pre_r2_improvement': np.float64(0.09621188863947527), 'detrend_post_r2_improvement': np.float64(0.11890364717818414), 'mean_power_pre': np.float64(955.493497245509), 'mean_power_post': np.float64(993.6911992736077), 'mean_test_yaw_offset_pre': np.float64(-2.2725466102034675), 'mean_test_yaw_offset_post': np.float64(-2.876981850327039), 'mean_test_yaw_offset_command_pre': np.float64(0.0), 'mean_test_yaw_offset_command_post': np.float64(0.0), 'mean_ref_yaw_offset_command_pre': np.float64(0.0), 'test_ref_warning_counts': 0, 'time_calculated': Timestamp('2024-11-04 14:55:21.795722+0000', tz='UTC'), 'uplift_frc': np.float64(0.030879346731271313), 'unc_one_sigma_frc': np.float64(0.01167847006525424), 't_value_one_sigma': np.float64(1.0006277462668354), 'missing_bins_unc_scale_factor': 1, 'pp_valid_hours_pre': np.float64(133.0), 'pp_valid_hours_post': np.float64(137.16666666666669), 'pp_valid_hours': np.float64(270.1666666666667), 'pp_data_coverage': np.float64(0.11567829872261472), 'pp_invalid_bin_count': np.int64(16), 'uplift_noadj_frc': np.float64(0.030509447623790466), 'unc_one_sigma_noadj_frc': np.float64(0.01004760180633488), 'poweronly_uplift_frc': np.float64(0.029990866525649328), 'reversed_uplift_frc': np.float64(0.03073066474061102), 'reversal_error': np.float64(0.0007397982149616941), 'unc_one_sigma_lowerbound_frc': np.float64(0.00036989910748084706), 'unc_one_sigma_bootstrap_frc': np.float64(0.01167847006525424), 'uplift_p5_frc': np.float64(0.050088720575348945), 'uplift_p95_frc': np.float64(0.01166997288719368), 'wind_up_version': '0.1.9', 'test_wtg': 'SMV5', 'test_pw_col': 'test_pw_clipped', 'lt_wtg_hours_raw': 0, 'lt_wtg_hours_filt': 0, 'test_max_ws_drift': np.float64(0.10726609831004863), 'test_max_ws_drift_pp_period': np.float64(0.10726609831004863), 'test_powercurve_shift': np.float64(-0.005678000921447213), 'test_rpm_shift': np.float64(0.0013951853610039144), 'test_pitch_shift': np.float64(-0.02783487184623068), 'preprocess_warning_counts': 0, 'test_warning_counts': 0}" ], "text/plain": [ "<IPython.core.display.Markdown object>" @@ -3999,7 +3999,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.4" + "version": "3.12.5" } }, "nbformat": 4, diff --git a/pyproject.toml b/pyproject.toml index 2e83e4a..14076a0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "res-wind-up" -version = "0.1.9" +version = "0.1.10" authors = [ { name = "Alex Clerc", email = "[email protected]" } ] diff --git a/wind_up/scada_funcs.py b/wind_up/scada_funcs.py index 187afa4..48e4802 100644 --- a/wind_up/scada_funcs.py +++ b/wind_up/scada_funcs.py @@ -28,7 +28,15 @@ def filter_stuck_data(df: pd.DataFrame) -> pd.DataFrame: - diffdf = df.groupby("TurbineName", observed=False).ffill().fillna(0).diff() + """ + Filter out rows where all columns (except timestamp columns) are the same as the previous row + (only where the wind is greater than a low wind threshold). + + :param df: time series where the index is a MultiIndex with levels "TurbineName" and "<your timestamp index name>" + :return: dataframe with stuck data rows set to NA + """ + timestamp_columns = [col for col, data_type in df.dtypes.to_dict().items() if "date" in data_type.name] + diffdf = df.drop(columns=timestamp_columns).groupby("TurbineName", observed=False).ffill().fillna(0).diff() stuck_data = (diffdf == 0).all(axis=1) very_low_wind_threshold = 1.5 very_low_wind = df["WindSpeedMean"] < very_low_wind_threshold

Timestamp column can be converted to type "object" which potentially causes the `.diff()` to fail https://github.com/resgroup/wind-up/blob/980f6db15b2f4f33642c807ffef19780c46fc03a/wind_up/scada_funcs.py#L31 Example error when the `.diff()` is run: `TypeError: unsupported operand type(s) for -: 'int' and 'Timestamp'`

2024-11-04T13:39:55

0.0

[]

AMYPAD/AmyPET

AMYPAD__AmyPET-16

1af0de595ba4e46bcc4669402ab7562a441620c7

diff --git a/.gitignore b/.gitignore index 3eacabe1..19d59b27 100644 --- a/.gitignore +++ b/.gitignore @@ -1,11 +1,12 @@ -*.py[co] +*.py[cod] __pycache__/ # build -/amypad/_dist_ver.py +/amypet/_dist_ver.py /build/ /dist/ /*.egg*/ /.coverage* /coverage.xml +/.pytest_cache/ diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 62ffebe4..b5f649e4 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -31,9 +31,11 @@ repos: - id: flake8 args: [-j8] additional_dependencies: + - flake8-broken-line - flake8-bugbear - flake8-comprehensions - flake8-debugger + - flake8-isort - flake8-string-format - repo: https://github.com/google/yapf rev: v0.31.0 @@ -41,10 +43,10 @@ repos: - id: yapf args: [-i] - repo: https://github.com/PyCQA/isort - rev: 5.9.3 + rev: 5.10.1 hooks: - id: isort - hooks: - id: mbeautify repo: https://github.com/AMYPAD/miutil - rev: v0.9.0 + rev: v0.9.1 diff --git a/README.rst b/README.rst index 29fdb60a..5cbfab0e 100644 --- a/README.rst +++ b/README.rst @@ -1,6 +1,6 @@ |Logo| -AMYPAD +AMYPET ====== Amyloid imaging to prevent Alzheimer's Disease. @@ -13,8 +13,8 @@ Choose one of the following: .. code:: sh - pip install amypad # command line interface (CLI) version - pip install amypad[gui] # CLI and graphical user interface (GUI) version + pip install amypet # command line interface (CLI) version + pip install amypet[gui] # CLI and graphical user interface (GUI) version Usage @@ -22,8 +22,8 @@ Usage .. code:: sh - amypad --help # CLI version - amypad.gui # GUI version + amypet --help # CLI version + amypet.gui # GUI version .. |Logo| image:: https://amypad.eu/wp-content/themes/AMYPAD/images/AMYPAD_Logo.jpg diff --git a/amypad/__init__.py b/amypet/__init__.py similarity index 90% rename from amypad/__init__.py rename to amypet/__init__.py index 7b1b7acb..f7b99085 100644 --- a/amypad/__init__.py +++ b/amypet/__init__.py @@ -14,7 +14,7 @@ __version__ = "UNKNOWN" try: - __licence__ = get_distribution("amypad").get_metadata("LICENCE.md") + __licence__ = get_distribution("amypet").get_metadata("LICENCE.md") except (DistributionNotFound, FileNotFoundError): try: __licence__ = (Path(__file__).parent.parent / "LICENCE.md").read_text() diff --git a/amypad/__main__.py b/amypet/__main__.py similarity index 100% rename from amypad/__main__.py rename to amypet/__main__.py diff --git a/amypad/centiloid/__init__.py b/amypet/centiloid/__init__.py similarity index 100% rename from amypad/centiloid/__init__.py rename to amypet/centiloid/__init__.py diff --git a/amypad/centiloid/f_1CorregisterEstimate.m b/amypet/centiloid/f_1CorregisterEstimate.m similarity index 100% rename from amypad/centiloid/f_1CorregisterEstimate.m rename to amypet/centiloid/f_1CorregisterEstimate.m diff --git a/amypad/centiloid/f_2CorregisterEstimate.m b/amypet/centiloid/f_2CorregisterEstimate.m similarity index 100% rename from amypad/centiloid/f_2CorregisterEstimate.m rename to amypet/centiloid/f_2CorregisterEstimate.m diff --git a/amypad/centiloid/f_3Segment.m b/amypet/centiloid/f_3Segment.m similarity index 100% rename from amypad/centiloid/f_3Segment.m rename to amypet/centiloid/f_3Segment.m diff --git a/amypad/centiloid/f_4Normalise.m b/amypet/centiloid/f_4Normalise.m similarity index 100% rename from amypad/centiloid/f_4Normalise.m rename to amypet/centiloid/f_4Normalise.m diff --git a/amypad/centiloid/f_Quant_centiloid.m b/amypet/centiloid/f_Quant_centiloid.m similarity index 100% rename from amypad/centiloid/f_Quant_centiloid.m rename to amypet/centiloid/f_Quant_centiloid.m diff --git a/amypad/centiloid/f_acpcReorientation.m b/amypet/centiloid/f_acpcReorientation.m similarity index 100% rename from amypad/centiloid/f_acpcReorientation.m rename to amypet/centiloid/f_acpcReorientation.m diff --git a/amypad/centiloid/f_noNaN.m b/amypet/centiloid/f_noNaN.m similarity index 100% rename from amypad/centiloid/f_noNaN.m rename to amypet/centiloid/f_noNaN.m diff --git a/amypad/cli.py b/amypet/cli.py similarity index 100% rename from amypad/cli.py rename to amypet/cli.py diff --git a/amypad/config_icon.png b/amypet/config_icon.png similarity index 100% rename from amypad/config_icon.png rename to amypet/config_icon.png diff --git a/amypad/gui.py b/amypet/gui.py similarity index 94% rename from amypad/gui.py rename to amypet/gui.py index d2c89c64..bec07df3 100755 --- a/amypad/gui.py +++ b/amypet/gui.py @@ -232,15 +232,15 @@ def print_not_none(value, **kwargs): # progress_expr="float(percent or 0)", # hide_progress_msg=True, # richtext_controls=True, -@Gooey(default_size=(768, 768), program_name="amypad", sidebar_title="pipeline", +@Gooey(default_size=(768, 768), program_name="amypet", sidebar_title="pipeline", image_dir=resource_filename(__name__, ""), show_restart_button=False, header_bg_color="#ffffff", sidebar_bg_color="#a3b5cd", body_bg_color="#a3b5cd", footer_bg_color="#2a569f", terminal_font_family="monospace", menu=[{ "name": "Help", "items": [{ "type": "Link", "menuTitle": "🌐 View source (online)", - "url": "https://github.com/AMYPAD/amypad"}, { - "type": "AboutDialog", "menuTitle": "🔍 About", "name": "AMYPAD Pipeline", - "description": "GUI to run AMYPAD tools", "version": __version__, + "url": "https://github.com/AMYPAD/amypet"}, { + "type": "AboutDialog", "menuTitle": "🔍 About", "name": "AMYPET Pipeline", + "description": "GUI to run AMYPET tools", "version": __version__, "copyright": "2021", "website": "https://amypad.eu", "developer": "https://github.com/AMYPAD", "license": __licence__}]}]) def main(args=None, gui_mode=True): @@ -249,9 +249,9 @@ def main(args=None, gui_mode=True): import niftypad.api import niftypad.models - from amypad import centiloid, imscroll, imtrimup + from amypet import centiloid, imscroll, imtrimup - parser = fix_subparser(MyParser(prog=None if gui_mode else "amypad"), gui_mode=gui_mode) + parser = fix_subparser(MyParser(prog=None if gui_mode else "amypet"), gui_mode=gui_mode) sub_kwargs = {} if sys.version_info[:2] >= (3, 7): sub_kwargs["required"] = True @@ -290,11 +290,11 @@ def argparser(prog, description=None, epilog=None, formatter_class=None): <src> : Input file/folder [default: FileChooser] Options: - -d PATH, --dst PATH : Output file/folder (default: input folder) - [default: FileChooser] - -m MODEL, --model MODEL : model [default: srtmb_basis] - -p FILE, --params FILE : config file hint (relative to `--input`) - [default: FileChooser] + --dst PATH : Output file/folder (default: input folder) + [default: FileChooser] + --model MODEL : model [default: srtmb_basis] + --params FILE : config file hint (relative to `src` input). + Default: search for {config,params}.{yaml,json}. --w W : (default: None) --r1 R1 : [default: 0.905:float] --k2p K2P : [default: 0.000250:float] @@ -333,7 +333,7 @@ def argparser(prog, description=None, epilog=None, formatter_class=None): args = [i for i in args if i not in ("--dry-run",)] # strip args if gui_mode: - print(" ".join([Path(sys.executable).name, "-m amypad"] + args)) + print(" ".join([Path(sys.executable).name, "-m amypet"] + args)) if opts.dry_run: pass elif hasattr(opts, "main__"): # Cmd diff --git a/amypad/imscroll.py b/amypet/imscroll.py similarity index 100% rename from amypad/imscroll.py rename to amypet/imscroll.py diff --git a/amypad/imtrimup.py b/amypet/imtrimup.py similarity index 100% rename from amypad/imtrimup.py rename to amypet/imtrimup.py diff --git a/amypad/program_icon.png b/amypet/program_icon.png similarity index 100% rename from amypad/program_icon.png rename to amypet/program_icon.png diff --git a/amypad/utils.py b/amypet/utils.py similarity index 100% rename from amypad/utils.py rename to amypet/utils.py diff --git a/pyproject.toml b/pyproject.toml index a77e1bab..f49fc768 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -2,5 +2,5 @@ requires = ["setuptools>=42", "wheel", "setuptools_scm[toml]>=3.4"] [tool.setuptools_scm] -write_to = "amypad/_dist_ver.py" +write_to = "amypet/_dist_ver.py" write_to_template = "__version__ = '{version}'\n" diff --git a/setup.cfg b/setup.cfg index 27218c72..2a100733 100644 --- a/setup.cfg +++ b/setup.cfg @@ -1,5 +1,5 @@ [metadata] -name=amypad +name=amypet description=Amyloid imaging to prevent Alzheimer's Disease long_description=file: README.rst long_description_content_type=text/x-rst @@ -7,14 +7,14 @@ license=MPL 2.0 license_file=LICENCE.md url=https://amypad.eu project_urls= - Repository=https://github.com/AMYPAD/amypad - Changelog=https://github.com/AMYPAD/amypad/releases - Documentation=https://github.com/AMYPAD/amypad/#amypad + Repository=https://github.com/AMYPAD/amypet + Changelog=https://github.com/AMYPAD/amypet/releases + Documentation=https://github.com/AMYPAD/amypet/#amypet maintainer=Casper da Costa-Luis [email protected] keywords=pet, alzheimers platforms=any -provides=amypad +provides=amypet classifiers= Development Status :: 3 - Alpha Intended Audience :: Developers @@ -61,8 +61,8 @@ gui=Gooey>=1.0.8 niftypet=niftypet>=0.0.1 [options.entry_points] console_scripts= - amypad=amypad.cli:main - amypad.gui=amypad.gui:main + amypet=amypet.cli:main + amypet.gui=amypet.gui:main [options.packages.find] exclude=tests [options.package_data] @@ -87,12 +87,12 @@ split_before_closing_bracket=False [isort] profile=black line_length=99 -known_first_party=amypad,tests +known_first_party=amypet,tests [tool:pytest] timeout=10 log_level=INFO python_files=tests/test_*.py -addopts=-v --tb=short -rxs -W=error -n=auto --durations=0 --durations-min=1 --cov=amypad --cov-report=term-missing --cov-report=xml +addopts=-v --tb=short -rxs -W=error -n=auto --durations=0 --durations-min=1 --cov=amypet --cov-report=term-missing --cov-report=xml filterwarnings= ignore:numpy.ufunc size changed.*:RuntimeWarning

rename to amypet - AMYPAD = org - amypet = package

2021-12-01T08:33:28

0.0

[]

AMYPAD/AmyPET

AMYPAD__AmyPET-3

5314ca01168ec3eeaa3c953e45addabba7edeceb

diff --git a/.github/workflows/comment-bot.yml b/.github/workflows/comment-bot.yml new file mode 100644 index 00000000..b44ee7ba --- /dev/null +++ b/.github/workflows/comment-bot.yml @@ -0,0 +1,52 @@ +name: Comment Bot +on: + issue_comment: + types: [created] + pull_request_review_comment: + types: [created] +jobs: + tag: # /tag <tagname> <commit> + if: startsWith(github.event.comment.body, '/tag ') + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v2 + - name: React Seen + uses: actions/github-script@v2 + with: + script: | + const perm = await github.repos.getCollaboratorPermissionLevel({ + owner: context.repo.owner, repo: context.repo.repo, + username: context.payload.comment.user.login}) + post = (context.eventName == "issue_comment" + ? github.reactions.createForIssueComment + : github.reactions.createForPullRequestReviewComment) + if (!["admin", "write"].includes(perm.data.permission)){ + post({ + owner: context.repo.owner, repo: context.repo.repo, + comment_id: context.payload.comment.id, content: "laugh"}) + throw "Permission denied for user " + context.payload.comment.user.login + } + post({ + owner: context.repo.owner, repo: context.repo.repo, + comment_id: context.payload.comment.id, content: "eyes"}) + github-token: ${{ secrets.GH_TOKEN }} + - name: Tag Commit + run: | + git clone https://${GITHUB_TOKEN}@github.com/${GITHUB_REPOSITORY} repo + git -C repo tag $(echo "$BODY" | awk '{print $2" "$3}') + git -C repo push --tags + rm -rf repo + env: + BODY: ${{ github.event.comment.body }} + GITHUB_TOKEN: ${{ secrets.GH_TOKEN }} + - name: React Success + uses: actions/github-script@v2 + with: + script: | + post = (context.eventName == "issue_comment" + ? github.reactions.createForIssueComment + : github.reactions.createForPullRequestReviewComment) + post({ + owner: context.repo.owner, repo: context.repo.repo, + comment_id: context.payload.comment.id, content: "rocket"}) + github-token: ${{ secrets.GH_TOKEN }} diff --git a/.gitignore b/.gitignore index 753afde1..3eacabe1 100644 --- a/.gitignore +++ b/.gitignore @@ -1,9 +1,11 @@ -*.py[cod] +*.py[co] __pycache__/ + +# build /amypad/_dist_ver.py -/.eggs/ -/*.egg-info/ /build/ /dist/ +/*.egg*/ + /.coverage* /coverage.xml diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 00000000..afba6eee --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,46 @@ +default_language_version: + python: python3 +repos: +- repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.0.1 + hooks: + - id: check-added-large-files + - id: check-case-conflict + - id: check-docstring-first + - id: check-executables-have-shebangs + - id: check-toml + - id: check-merge-conflict + - id: check-yaml + - id: debug-statements + - id: end-of-file-fixer + - id: mixed-line-ending + - id: sort-simple-yaml + - id: trailing-whitespace +- repo: local + hooks: + - id: todo + name: Check TODO + language: pygrep + args: [-i] + entry: TODO + types: [text] + exclude: ^(.pre-commit-config.yaml|.github/workflows/test.yml)$ +- repo: https://gitlab.com/pycqa/flake8 + rev: 3.9.2 + hooks: + - id: flake8 + args: [-j8] + additional_dependencies: + - flake8-bugbear + - flake8-comprehensions + - flake8-debugger + - flake8-string-format +- repo: https://github.com/google/yapf + rev: v0.31.0 + hooks: + - id: yapf + args: [-i] +- repo: https://github.com/PyCQA/isort + rev: 5.9.3 + hooks: + - id: isort diff --git a/LICENCE.md b/LICENCE.md index 957e9cbb..53871088 100644 --- a/LICENCE.md +++ b/LICENCE.md @@ -1,13 +1,7 @@ -Copyright 2020 AMYPAD +Copyright (c) 2021 AMYPAD -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at +Mozilla Public License Version 2.0 (MPL-2.0) - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. +This Source Code Form is subject to the terms of the Mozilla Public +License, v. 2.0. If a copy of the MPL was not distributed with this +file, You can obtain one at http://mozilla.org/MPL/2.0/. diff --git a/README.rst b/README.rst index b6b87b51..29fdb60a 100644 --- a/README.rst +++ b/README.rst @@ -3,7 +3,28 @@ AMYPAD ====== -Amyloid imaging to prevent Alzheimer's Disease +Amyloid imaging to prevent Alzheimer's Disease. + + +Install +------- + +Choose one of the following: + +.. code:: sh + + pip install amypad # command line interface (CLI) version + pip install amypad[gui] # CLI and graphical user interface (GUI) version + + +Usage +----- + +.. code:: sh + + amypad --help # CLI version + amypad.gui # GUI version + .. |Logo| image:: https://amypad.eu/wp-content/themes/AMYPAD/images/AMYPAD_Logo.jpg :target: https://amypad.eu diff --git a/amypad/__init__.py b/amypad/__init__.py index 812265af..7b1b7acb 100644 --- a/amypad/__init__.py +++ b/amypad/__init__.py @@ -1,3 +1,7 @@ +from pathlib import Path + +from pkg_resources import DistributionNotFound, get_distribution + # version detector. Precedence: installed dist, git, 'UNKNOWN' try: from ._dist_ver import __version__ @@ -8,3 +12,11 @@ __version__ = get_version(root="..", relative_to=__file__) except (ImportError, LookupError): __version__ = "UNKNOWN" + +try: + __licence__ = get_distribution("amypad").get_metadata("LICENCE.md") +except (DistributionNotFound, FileNotFoundError): + try: + __licence__ = (Path(__file__).parent.parent / "LICENCE.md").read_text() + except FileNotFoundError: + __licence__ = "MPL-2.0" diff --git a/amypad/__main__.py b/amypad/__main__.py new file mode 100755 index 00000000..3bb6d4b2 --- /dev/null +++ b/amypad/__main__.py @@ -0,0 +1,4 @@ +#!/usr/bin/env python3 +from .cli import main + +main() diff --git a/amypad/cli.py b/amypad/cli.py new file mode 100755 index 00000000..7a3c47b1 --- /dev/null +++ b/amypad/cli.py @@ -0,0 +1,12 @@ +#!/usr/bin/env python3 +import sys +from functools import partial + +if "--ignore-gooey" not in sys.argv: + sys.argv.insert(1, "--ignore-gooey") # must be before gooey import +from .gui import main as gui_main + +main = partial(gui_main, gui_mode=False) + +if __name__ == "__main__": # pragma: no cover + main() diff --git a/amypad/config_icon.png b/amypad/config_icon.png new file mode 100644 index 00000000..5e8a23a5 Binary files /dev/null and b/amypad/config_icon.png differ diff --git a/amypad/gui.py b/amypad/gui.py new file mode 100755 index 00000000..d819d8c1 --- /dev/null +++ b/amypad/gui.py @@ -0,0 +1,352 @@ +#!/usr/bin/env python3 +import logging +import re +import sys +from argparse import SUPPRESS, ArgumentParser, RawDescriptionHelpFormatter +from functools import partial +from pathlib import Path +from subprocess import PIPE, Popen +from textwrap import dedent +from weakref import WeakSet + +import shtab +from argopt import argopt +from pkg_resources import resource_filename + +try: + from . import __licence__, __version__ +except ImportError: + __version__, __licence__ = "", "MPL-2.0" + +log = logging.getLogger(__name__) +WIDGETS = ( + "FileChooser", + "MultiFileChooser", + "DirChooser", + "FileSaver", + "MultiFileSaver", + "Slider", +) +RE_DEFAULT = re.compile(f"\\[default: (None:.*?|{'|'.join(WIDGETS)})\\]", flags=re.M) +RE_PRECOLON = re.compile(r"^\s*:\s*", flags=re.M) +ENCODING = sys.getfilesystemencoding() + + +def patch_argument_kwargs(kwargs, gooey=True): + kwargs = kwargs.copy() + if "help" in kwargs: + kwargs["help"] = RE_PRECOLON.sub("", RE_DEFAULT.sub("", kwargs["help"])) + + default = kwargs.get("default", None) + if default in WIDGETS: + if gooey: + kwargs["widget"] = default + kwargs["default"] = None + elif gooey: + typ = kwargs.get("type", None) + if typ == open: + nargs = kwargs.get("nargs", 1) + if nargs and (nargs > 1 if isinstance(nargs, int) else nargs in "+*"): + kwargs['widget'] = "MultiFileChooser" + else: + kwargs['widget'] = "FileChooser" + elif typ == int: + kwargs['widget'] = "IntegerField" + kwargs['gooey_options'] = {'min': 0, 'max': 1_000} + elif typ == float: + kwargs['widget'] = "DecimalField" + kwargs['gooey_options'] = {'min': -1, 'max': 1e3, 'increment': 1e-6} + + return kwargs + + +try: + from gooey import Gooey + from gooey import GooeyParser as BaseParser + + patch_argument_kwargs = partial(patch_argument_kwargs, gooey=True) +except ImportError: + BaseParser = ArgumentParser + patch_argument_kwargs = partial(patch_argument_kwargs, gooey=False) + + def Gooey(**_): + def wrapper(func): + return func + + return wrapper + + +class MyParser(BaseParser): + def add_argument(self, *args, **kwargs): + kwargs = patch_argument_kwargs(kwargs) + log.debug("%r, %r", args, kwargs) + return super(MyParser, self).add_argument(*args, **kwargs) + + +class CmdException(Exception): + def __init__(self, code, cmd, stdout, stderr): + super(CmdException, self).__init__( + dedent("""\ + Code {:d}: + === command === + {} + === stderr === + {}=== stdout === + {}===""").format(code, cmd, stderr, stdout)) + + +class Base(object): + _instances = WeakSet() + + def __init__(self, python_deps=None, matlab_deps=None, version=__version__): + self.python_deps = python_deps or [] + self.matlab_deps = matlab_deps or [] + self.version = version + + def __new__(cls, *_, **__): + self = object.__new__(cls) + cls._instances.add(self) + return self + + def __str__(self): + pydeps = "" + if self.python_deps: + pydeps = "\n - " + "\n - ".join(self.python_deps) + mdeps = "" + if self.matlab_deps: + mdeps = "\n - " + "\n - ".join(self.matlab_deps) + + return dedent("""\ + . + version: {} + python_deps:{} + matlab_deps:{}""")[2:].format(self.version, pydeps, mdeps) + + +class Cmd(Base): + def __init__( + self, + cmd, + doc, + version=None, + argparser=MyParser, + formatter_class=RawDescriptionHelpFormatter, + **kwargs, + ): + """ + Args: + cmd (list): e.g. `[sys.executable, "-m", "miutil.cuinfo"]` + doc (str): an `argopt`-compatible docstring for `cmd` + version (str): optional + """ + super(Cmd, self).__init__(**kwargs) + self.parser = argopt( + dedent(doc), + argparser=argparser, + formatter_class=formatter_class, + version=version, + ) + self.parser.set_defaults(main__=self.main) + self.cmd = cmd + + def __str__(self): + return dedent("""\ + {} + {}""").format(self.parser.prog, + super(Cmd, self).__str__()) + + def main(self, args, verify_args=True): + """ + Args: + args (list): list of arguments (e.g. `sys.argv[1:]`) + verify_args (bool): whether to parse args to ensure no input errors + """ + try: + if verify_args: + self.parser.parse_args(args=args) + except SystemExit as exc: + if exc.code: + raise + else: + # return check_output(self.cmd + args, stderr=STDOUT).decode("U8") + out = Popen(self.cmd + args, stdout=PIPE, stderr=PIPE) + stdout, stderr = out.communicate() + if out.returncode != 0: + raise CmdException( + out.returncode, + str(self), + stdout.decode(ENCODING), + stderr.decode(ENCODING), + ) + return stdout.decode(ENCODING) + + +class Func(Base): + def __init__( + self, + func, + doc, + version=None, + argparser=MyParser, + formatter_class=RawDescriptionHelpFormatter, + **kwargs, + ): + """ + Args: + func (callable): e.g. `miutil.hasext` + doc (str): an `argopt`-compatible docstring for `func` + version (str): optional + """ + super(Func, self).__init__(**kwargs) + self.parser = argopt( + dedent(doc), + argparser=argparser, + formatter_class=formatter_class, + version=version, + ) + self.parser.set_defaults(run__=func) + # self.func = func + + def __str__(self): + return dedent("""\ + {} + {}""").format(self.parser.prog, + super(Func, self).__str__()) + + +def fix_subparser(subparser, gui_mode=True): + subparser.add_argument( + "--dry-run", + action="store_true", + help="don't run command (implies print_command)" if gui_mode else SUPPRESS, + ) + return subparser + + +def print_not_none(value, **kwargs): + if value is not None: + print(value, **kwargs) + + +# progress_regex="^\s*(?P<percent>\d[.\d]*)%|", +# progress_expr="float(percent or 0)", +# hide_progress_msg=True, +# richtext_controls=True, +@Gooey(default_size=(768, 768), program_name="amypad", sidebar_title="pipeline", + image_dir=resource_filename(__name__, ""), show_restart_button=False, + header_bg_color="#ffffff", sidebar_bg_color="#a3b5cd", body_bg_color="#a3b5cd", + footer_bg_color="#2a569f", terminal_font_family="monospace", menu=[{ + "name": "Help", "items": [{ + "type": "Link", "menuTitle": "🌐 View source (online)", + "url": "https://github.com/AMYPAD/amypad"}, { + "type": "AboutDialog", "menuTitle": "🔍 About", "name": "AMYPAD Pipeline", + "description": "GUI to run AMYPAD tools", "version": __version__, + "copyright": "2021", "website": "https://amypad.eu", + "developer": "https://github.com/AMYPAD", "license": __licence__}]}]) +def main(args=None, gui_mode=True): + logging.basicConfig(level=logging.INFO) + import miutil.cuinfo + import niftypad.api + import niftypad.models + + parser = fix_subparser(MyParser(prog=None if gui_mode else "amypad"), gui_mode=gui_mode) + sub_kwargs = {} + if sys.version_info[:2] >= (3, 7): + sub_kwargs["required"] = True + subparsers = parser.add_subparsers(help="pipeline to run", **sub_kwargs) + if not gui_mode: + subparser = subparsers.add_parser("completion", help="Print tab completion scripts") + shtab.add_argument_to(subparser, "shell", parent=parser) + + def argparser(prog, description=None, epilog=None, formatter_class=None): + """handle (prog, description, epilog) => (title, help)""" + return fix_subparser( + subparsers.add_parser( + {"miutil.cuinfo": "cuinfo"}.get(prog, prog), # override + help="\n".join([description or "", epilog or ""]).strip(), + ), + gui_mode=gui_mode, + ) + + kinetic_model = Func( + niftypad.api.kinetic_model, + """\ + Kinetic modelling + + Usage: + kinetic_model [options] <src> + + Arguments: + <src> : Input file/folder [default: FileChooser] + + Options: + -d PATH, --dst PATH : Output file/folder (default: input folder) + [default: FileChooser] + -m MODEL, --model MODEL : model [default: srtmb_basis] + -p FILE, --params FILE : config file hint (relative to `--input`) + [default: FileChooser] + --w W : (default: None) + --r1 R1 : [default: 0.905:float] + --k2p K2P : [default: 0.000250:float] + --beta_lim BETA_LIM : (default: None) + --n_beta N_BETA : [default: 40:int] + --linear_phase_start LINEAR_PHASE_START : [default: 500:int] + --linear_phase_end LINEAR_PHASE_END : (default: None) + --km_outputs KM_OUTPUTS : (default: None) + --thr THR : [default: 0.1:float] + """, + version=niftypad.__version__, + python_deps=["niftypad>=1.1.0"], + argparser=argparser, + ) + opts = kinetic_model.parser._get_optional_actions() + model = next(i for i in opts if i.dest == "model") + model.choices = niftypad.models.NAMES + + # example of how to wrap any CLI command using an `argopt`-style docstring + Cmd( + [sys.executable, "-m", "miutil.cuinfo"], + miutil.cuinfo.__doc__, + version=miutil.__version__, + python_deps=["miutil[cuda]>=0.8.0"], + argparser=argparser, + ) + + # example of how to wrap any callable using an `argopt`-style docstring + Func( + miutil.hasext, + """\ + Check if a given filename has a given extension + + Usage: + hasext <fname> <ext> + + Arguments: + <fname> : path to file [default: FileChooser] + <ext> : extension (with or without `.` prefix) + """, + version=miutil.__version__, + python_deps=["miutil>=0.8.0"], + argparser=argparser, + ) + + if args is None: + args = sys.argv[1:] + args = [i for i in args if i not in ("--ignore-gooey",)] + opts = parser.parse_args(args=args) + args = [i for i in args if i not in ("--dry-run",)] # strip args + + if gui_mode: + print(" ".join([Path(sys.executable).name, "-m amypad"] + args)) + if opts.dry_run: + pass + elif hasattr(opts, "main__"): # Cmd + print_not_none(opts.main__(args[1:], verify_args=False), end="") + elif hasattr(opts, "run__"): # Func + kwargs = {k: v + for (k, v) in opts._get_kwargs() if k not in ("dry_run", "run__")} # strip opts + print_not_none(opts.run__(*opts._get_args(), **kwargs)) + + +if __name__ == "__main__": # pragma: no cover + main() diff --git a/amypad/program_icon.png b/amypad/program_icon.png new file mode 100644 index 00000000..43d28359 Binary files /dev/null and b/amypad/program_icon.png differ diff --git a/setup.cfg b/setup.cfg index 770da816..e7035614 100644 --- a/setup.cfg +++ b/setup.cfg @@ -1,61 +1,93 @@ [metadata] -name = amypad -description = Amyloid imaging to prevent Alzheimer's Disease -long_description = file: README.rst -long_description_content_type = text/x-rst -license = Apache 2.0 -license_file = LICENCE.md -url = https://amypad.eu -project_urls = - Repository = https://github.com/AMYPAD/amypad - Changelog = https://github.com/AMYPAD/amypad/releases - Documentation = https://github.com/AMYPAD/amypad/#amypad -maintainer = Casper da Costa-Luis -maintainer_email = [email protected] -keywords = pet, alzheimers -platforms = any -provides = amypad -classifiers = +name=amypad +description=Amyloid imaging to prevent Alzheimer's Disease +long_description=file: README.rst +long_description_content_type=text/x-rst +license=MPL 2.0 +license_file=LICENCE.md +url=https://amypad.eu +project_urls= + Repository=https://github.com/AMYPAD/amypad + Changelog=https://github.com/AMYPAD/amypad/releases + Documentation=https://github.com/AMYPAD/amypad/#amypad +maintainer=Casper da Costa-Luis [email protected] +keywords=pet, alzheimers +platforms=any +provides=amypad +classifiers= Development Status :: 3 - Alpha + Intended Audience :: Developers Intended Audience :: Education Intended Audience :: Healthcare Industry Intended Audience :: Science/Research - License :: OSI Approved :: Apache Software License + License :: OSI Approved :: Mozilla Public License 2.0 (MPL 2.0) Operating System :: Microsoft :: Windows Operating System :: POSIX :: Linux Programming Language :: Python :: 3 - Programming Language :: Python :: 3.5 Programming Language :: Python :: 3.6 Programming Language :: Python :: 3.7 Programming Language :: Python :: 3.8 Programming Language :: Python :: 3.9 + Programming Language :: Python :: 3 :: Only Topic :: Scientific/Engineering :: Medical Science Apps. + Topic :: Software Development :: Libraries + Topic :: Software Development :: Libraries :: Python Modules + Topic :: Software Development :: User Interfaces + Topic :: System :: Installation/Setup + Topic :: Utilities [options] -zip_safe = False -setup_requires = setuptools>=42; setuptools_scm[toml]>=3.4 -install_requires = - argparse; python_version == "2.6" -include_package_data = True -packages = find: +setup_requires=setuptools>=42; setuptools_scm[toml]>=3.4 +install_requires= + argopt + miutil[cuda]>=0.8.0 + niftypad>=1.1.0 + setuptools + shtab>1.3.2 +packages=find: +python_requires=>=3.6 [options.extras_require] -dev = - black; python_version >= "3.0" - flake8 - twine - wheel +dev= + pre-commit pytest pytest-cov pytest-timeout pytest-xdist - codecov -niftypet = niftypet>=0.0.1 +gui=Gooey>=1.0.8 +niftypet=niftypet>=0.0.1 +niftypad= +[options.entry_points] +console_scripts= + amypad=amypad.cli:main + amypad.gui=amypad.gui:main +[options.packages.find] +exclude=tests [options.package_data] -* = *.md, *.rst, *.m +*=*.md, *.rst, *.m, *.png [flake8] -max_line_length = 88 -extend-ignore = E203 -exclude = .git,__pycache__,build,dist,.eggs +max_line_length=99 +extend-ignore=E261,P1 +exclude=.git,__pycache__,build,dist,.eggs -[bdist_wheel] -universal = 1 +[yapf] +spaces_before_comment=15, 20 +arithmetic_precedence_indication=true +allow_split_before_dict_value=false +coalesce_brackets=True +column_limit=99 +each_dict_entry_on_separate_line=False +space_between_ending_comma_and_closing_bracket=False +split_before_named_assigns=False +split_before_closing_bracket=False + +[isort] +profile=black +line_length=99 +known_first_party=amypad,tests + +[tool:pytest] +timeout=10 +log_level=INFO +python_files=tests/test_*.py +addopts=-v --tb=short -rxs -W=error -n=auto --durations=0 --durations-min=1 --cov=amypad --cov-report=term-missing --cov-report=xml diff --git a/setup.py b/setup.py index 60684932..d5d43d7c 100644 --- a/setup.py +++ b/setup.py @@ -1,3 +1,3 @@ from setuptools import setup -setup() +setup(use_scm_version=True)

GUI launch error ```python Traceback (most recent call last): ... File "\AppData\Local\Continuum\anaconda3\lib\site-packages\amypad\gui.py", line 278, in main import miutil.cuinfo File "\AppData\Local\Continuum\anaconda3\lib\site-packages\miutil\__init__.py", line 1, in <module> from .fdio import * # NOQA File "\AppData\Local\Continuum\anaconda3\lib\site-packages\miutil\fdio.py", line 22, in <module> from tqdm.utils import CallbackIOWrapper ImportError: cannot import name 'CallbackIOWrapper' from 'tqdm.utils' (\AppData\Local\Continuum\anaconda3\lib\site-packages\tqdm\utils.py) ``` - [x] https://github.com/AMYPAD/miutil needs to specify [tqdm>=4.40.0](https://tqdm.github.io/releases/#v4400-2019-12-01) - [ ] bump `miutil>=0.8.0`

2020-11-28T05:27:35

0.0

[]

jessepollak/mixpanel-python-async

jessepollak__mixpanel-python-async-20

c7b7e52b34d6b871c0c5bd1d5aa8cd48e787fd0b

diff --git a/CHANGELOG.md b/CHANGELOG.md index 319a580..12f3ae9 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,17 @@ project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). ## [Unreleased] +### Added + +* New parameters for the `AsyncBufferedConsumer` constructor: `import_url`, + `request_timeout`, `groups_url`, `api_host`, `retry_limit`, `retry_backoff_factor`, + `verify_cert`. + +### Changed + +* Extra arguments (`*args`, `**kwargs`) are no longer accepted in the + `AsyncBufferedConsumer` constructor. + ## [0.2.0] - 2019-07-11 ### Added diff --git a/README.md b/README.md index 103fe53..0b901d2 100644 --- a/README.md +++ b/README.md @@ -34,14 +34,17 @@ For most users, the default configuration should work perfectly. For more specif * `flush_after (datetime.timedelta)` - *defaults to 10 seconds* - the time period after which the AsyncBufferedConsumer will flush the events upon receiving a new event (no matter what the event queue size is) - * `flush_first (bool)` - *defaults to True* - whether the consumer should always flush the first event. - * `max_size (int)` - *defaults to 20* - how big a given event queue can get before it is flushed by the consumer - * `events_url (str)` - *defaults to standard Mixpanel API URL* - the Mixpanel API URL that track events will be sent to - * `people_url (str)` - *defaults to standard Mixpanel API URL* - the Mixpanel API URL that people events will be sent to +* `import_url (str)` - *defaults to standard Mixpanel API URL* - the Mixpanel API URL that import events will be sent to +* `request_timeout (int)` - *defaults to `None` (no timeout)* - Connection timeout in seconds. +* `groups_url (str)` - *defaults to standard Mixpanel API URL* - the Mixpanel API URL that groups events will be sent to +* `api_host (str)` - *defaults to api.mixpanel.com* - Mixpanel API domain for all requests unless overridden by above URLs +* `retry_limit (int)` - *defaults to 4* - Number of times to retry each request in case of an error, 0 to fail after first attempt. +* `retry_backoff_factor` - *defaults to 0.25* - Factor which controls sleep duration between retries: `sleep_seconds = backoff_factor * (2 ^ (retry_count - 1))` +* `verify_cert` - *defaults to `True`*- Whether to verify the server certificate. `True` is recommended. ### Usage diff --git a/mixpanel_async/async_buffered_consumer.py b/mixpanel_async/async_buffered_consumer.py index 2b5d1d3..c03fc81 100644 --- a/mixpanel_async/async_buffered_consumer.py +++ b/mixpanel_async/async_buffered_consumer.py @@ -53,8 +53,21 @@ class AsyncBufferedConsumer(SynchronousBufferedConsumer): ALL = "ALL" ENDPOINT = "ENDPOINT" - def __init__(self, flush_after=timedelta(0, 10), flush_first=True, max_size=20, - events_url=None, people_url=None, *args, **kwargs): + def __init__( + self, + flush_after=timedelta(0, 10), + flush_first=True, + max_size=20, + events_url=None, + people_url=None, + import_url=None, + request_timeout=None, + groups_url=None, + api_host="api.mixpanel.com", + retry_limit=4, + retry_backoff_factor=0.25, + verify_cert=True, + ): ''' Create a new instance of a AsyncBufferedConsumer class. @@ -65,13 +78,30 @@ def __init__(self, flush_after=timedelta(0, 10), flush_first=True, max_size=20, the consumer receives :param max_size (int): the number of events in queue that will trigger the queue to be flushed asynchronously - :param events_url: the Mixpanel API URL that track events will be sent to - :param people_url: the Mixpanel API URL that people events will be sent to + :param events_url: Mixpanel API URL that track events will be sent to + :param people_url: Mixpanel API URL that people events will be sent to + :param import_url: Mixpanel API URL that import events will be sent to + :param request_timeout: Connection timeout in seconds + :param groups_url: Mixpanel API URL that groups events will be sent to + :param api_host: Mixpanel API domain for all requests unless overriden by above URLs + :param retry_limit: Number of times to retry each request in case of an error, 0 + to fail after first attempt. + :param retry_backoff_factor: Factor which controls sleep duration between retries: + sleep_seconds = backoff_factor * (2 ^ (retry_count - 1)) + :param verify_cert: Whether to verify the server certificate. "True" is + recommended. ''' super(AsyncBufferedConsumer, self).__init__( max_size=max_size, events_url=events_url, - people_url=people_url + people_url=people_url, + import_url=import_url, + request_timeout=request_timeout, + groups_url=groups_url, + api_host=api_host, + retry_limit=retry_limit, + retry_backoff_factor=retry_backoff_factor, + verify_cert=verify_cert, ) # remove the minimum max size that the SynchronousBufferedConsumer

Parameter api_host is not passed to the BufferedConsumer Hello! The parameter `api_host` is used when we want to use mixpanel's EU servers: `consumer = AsyncBufferedConsumer(api_host="api-eu.mixpanel.com")` That parameter is not passed to the mixpanel consumer when using AsyncBufferedConsumer. There is also some other parameters not passed, like `request_timeout`, `verify_cert`, etc.

@bbc2 Any chance you have time to look into this?

2021-02-17T18:02:56

0.0

[]

h4l/sortedcontainers-stubs

h4l__sortedcontainers-stubs-4

edae30a8abdce8f0997dd1d0f5b65217879559df

diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 70c4769..c39982d 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -17,7 +17,6 @@ jobs: outputs: test_targets: ${{ steps.expand_bake_matrix.outputs.test_targets }} lint_targets: ${{ steps.expand_bake_matrix.outputs.lint_targets }} - mypy_targets: ${{ steps.expand_bake_matrix.outputs.mypy_targets }} steps: - name: Checkout @@ -30,7 +29,7 @@ jobs: id: expand_bake_matrix run: | docker buildx bake --print \ - | jq -cer '{test: .group.test.targets, lint: .group.lint.targets, mypy: .group.mypy.targets} + | jq -cer '{test: .group.test.targets, lint: .group.lint.targets} | to_entries[] | {name: .key, targets: .value} | "\(.name)_targets=\(.targets | tojson)"' \ @@ -85,28 +84,3 @@ jobs: set: | *.cache-from=type=gha *.cache-to=type=gha,mode=max - - mypy: - name: Run mypy under several Python versions - runs-on: ubuntu-latest - needs: - - expand_bake_matrix - strategy: - matrix: - target: ${{ fromJSON(needs.expand_bake_matrix.outputs.mypy_targets) }} - - steps: - - name: Checkout - uses: actions/checkout@c85c95e3d7251135ab7dc9ce3241c5835cc595a9 - - - name: Set up Docker Buildx - uses: docker/setup-buildx-action@4c0219f9ac95b02789c1075625400b2acbff50b1 - - - name: Run Lint Matrix - uses: docker/bake-action@f32f8b8d70bc284af19f8148dd14ad1d2fbc6c28 - with: - targets: ${{ matrix.target }} - files: docker-bake.hcl - set: | - *.cache-from=type=gha - *.cache-to=type=gha,mode=max diff --git a/CHANGELOG.md b/CHANGELOG.md index 4c26d78..1729640 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -11,7 +11,28 @@ minor version; the patch version increments independently to release fixes. ## [Unreleased] -- Nothing yet +### Fixed + +- Using the SortedList (and the other) types as a function that creates an + instance of the type works in pyright now. + ([#3](https://github.com/h4l/sortedcontainers-stubs/issues/3)) + +### Changed + +- Because of the fix to allow pyright to allow using the types as functions, the + constructors with no arguments had to be widened to permit assignment to + non-comparable/hashable types. The result is that it's unfortunately not a + type error to do something like `sl: SortedList[type] = SortedList()` even + though subsequently adding multiple values to this list will fail at runtime. + + It doesn't seem to be possible to type the signature of `__new__` to constrain + `SortedList()` to only allow comparable element types in a standard way (mypy + supported the previous method which worked, but other checkers don't). + +- The signature definition of SortedDict.setdefault was restructured to satisfy + pyright, but the effect of the signature itself remains the same. + +- pyright runs in CI now ## [2.4.1] — 2023-10-10 diff --git a/Dockerfile b/Dockerfile index 5b92546..9ba48cd 100644 --- a/Dockerfile +++ b/Dockerfile @@ -4,24 +4,28 @@ FROM python:${PYTHON_VER:?} AS python-base FROM python-base AS poetry -RUN pip install poetry +RUN --mount=type=cache,target=/root/.cache pip install poetry +RUN python -m venv /venv +ENV VIRTUAL_ENV=/venv \ + PATH="/venv/bin:$PATH" +RUN poetry config virtualenvs.create false WORKDIR /workspace COPY pyproject.toml poetry.lock /workspace/ # Poetry needs these to exist to setup the editable install RUN mkdir sortedcontainers-stubs && touch README.md -RUN poetry install +RUN --mount=type=cache,target=/root/.cache poetry install FROM poetry AS test RUN --mount=source=.,target=/workspace,rw \ --mount=type=cache,uid=1000,target=.pytest_cache \ - poetry run pytest + pytest FROM poetry AS lint-flake8 RUN --mount=source=.,target=/workspace,rw \ - poetry run flake8 + flake8 FROM poetry AS lint-black @@ -38,3 +42,15 @@ FROM poetry AS lint-mypy RUN --mount=source=.,target=/workspace,rw \ --mount=type=cache,target=.mypy_cache \ poetry run mypy . + + +FROM python-base AS pyright +RUN apt-get update && apt-get install -y --no-install-recommends nodejs npm +RUN npm install -g pyright +WORKDIR /workspace +COPY --link --from=poetry /venv /venv + + +FROM pyright AS lint-pyright +RUN --mount=source=.,target=/workspace,rw \ + pyright --pythonpath /venv/bin/python . diff --git a/docker-bake.hcl b/docker-bake.hcl index 73ccb4b..11a5ccd 100644 --- a/docker-bake.hcl +++ b/docker-bake.hcl @@ -1,5 +1,5 @@ group "default" { - targets = ["test", "lint", "mypy"] + targets = ["test", "lint"] } target "test" { @@ -15,23 +15,10 @@ target "test" { output = ["type=cacheonly"] } -target "mypy" { - name = "mypy_python-${replace(py, ".", "-")}" - matrix = { - py = ["3.8", "3.9", "latest"], - } - args = { - PYTHON_VER = py == "latest" ? "slim" : "${py}-slim" - } - target = "lint-mypy" - no-cache-filter = ["lint-mypy"] - output = ["type=cacheonly"] -} - target "lint" { name = "lint-${lint_type}" matrix = { - lint_type = ["flake8", "black", "isort"], + lint_type = ["flake8", "black", "isort", "mypy", "pyright"], } args = { PYTHON_VER = "slim" diff --git a/pyproject.toml b/pyproject.toml index e3b05de..54ed13e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -56,3 +56,7 @@ extra_standard_library = ["typing_extensions"] [tool.mypy] strict = true + +[tool.pyright] +include = ["sortedcontainers-stubs", "test"] +strict = ["sortedcontainers-stubs", "test"] diff --git a/sortedcontainers-stubs/_typing.pyi b/sortedcontainers-stubs/_typing.pyi index 74a5d1f..f7be9d4 100644 --- a/sortedcontainers-stubs/_typing.pyi +++ b/sortedcontainers-stubs/_typing.pyi @@ -1,8 +1,10 @@ from collections.abc import Hashable -from typing import Any, Protocol +from typing import Any, Protocol, TypeVar from typing_extensions import TypeAlias -from _typeshed import SupportsDunderGT, SupportsDunderLT, _T_contra # noqa: F401 +from _typeshed import SupportsDunderGT, SupportsDunderLT + +_T_contra = TypeVar("_T_contra", contravariant=True) class SupportsHashableAndDunderGT( Hashable, SupportsDunderGT[_T_contra], Protocol[_T_contra] diff --git a/sortedcontainers-stubs/sorteddict.pyi b/sortedcontainers-stubs/sorteddict.pyi index a08587a..09f6390 100644 --- a/sortedcontainers-stubs/sorteddict.pyi +++ b/sortedcontainers-stubs/sorteddict.pyi @@ -39,10 +39,15 @@ _OrderT = TypeVar( KeyFunc: TypeAlias = Callable[[_KT], _OrderT] class SortedDict(MutableMapping[_KT, _VT]): + # mypy 1.5.1 complains about overlapping overloaded function signatures with + # incompatible return types. This seems to be because the signatures using + # kwargs can would overlap when no kwargs are present, but there's no way to + # type a non-empty kwargs param. + + # Should return SortedDict[_OrderT, _VT] but we can't reliably express that. + # See comment on SortedList.__new__. @overload - def __new__( - cls, - ) -> SortedDict[_OrderT, _VT]: ... + def __new__(cls) -> Self: ... # type: ignore[misc] @overload def __new__( cls, @@ -69,17 +74,6 @@ class SortedDict(MutableMapping[_KT, _VT]): /, **kwargs: _VT, ) -> SortedKeyDict[str, _VT, _OrderT]: ... - # mypy 1.5.1 complains: "Overloaded function signatures 5 and 6 overlap with - # incompatible return types [misc]". If no kwargs are passed, they would - # indeed overlap, but there's no way to specify non-empty kwargs. We need - # this overload to prevent kwargs adding str keys to non-str __entries. - # - # Type-enforcement seems to work correct despite the error we ignore here... - # Alternatively, we would need to not type calls with both entries and - # kwargs. - # - # Ideally str would be "str or a superclass of str", but I can't find a way - # to type that. @overload def __new__( # type: ignore[misc] cls, @@ -125,7 +119,10 @@ class SortedDict(MutableMapping[_KT, _VT]): def pop(self, key: _KT, /, default: _T) -> _VT | _T: ... def popitem(self, index: int = ...) -> tuple[_KT, _VT]: ... def peekitem(self, index: int = ...) -> tuple[_KT, _VT]: ... - def setdefault(self, key: _KT, default: _VT = ...) -> _VT: ... + @overload + def setdefault(self: MutableMapping[_KT, _T | None], key: _KT) -> _T | None: ... + @overload + def setdefault(self, key: _KT, default: _VT) -> _VT: ... # Our override of update is strictly incompatible in that we only allow # kwargs when _KT is str. This is consistent with dict()'s constructor # typing, and allowing kwargs with non-str _KT causes TypeErrors at runtime. @@ -146,7 +143,7 @@ class SortedDict(MutableMapping[_KT, _VT]): @overload def update(self, __iterable: Iterable[tuple[_KT, _VT]]) -> None: ... @overload - def update(self: SortedDict[str, _VT], **kwargs: _VT) -> None: ... + def update(self: SortedDict[str, _VT], **kwargs: _VT) -> None: ... # type: ignore[override,unused-ignore] def __reduce__( self, ) -> tuple[ diff --git a/sortedcontainers-stubs/sortedlist.pyi b/sortedcontainers-stubs/sortedlist.pyi index f3fe0be..1cf8883 100644 --- a/sortedcontainers-stubs/sortedlist.pyi +++ b/sortedcontainers-stubs/sortedlist.pyi @@ -19,8 +19,15 @@ class SortedList(MutableSequence[_T]): DEFAULT_LOAD_FACTOR: Final[int] = ... @overload def __new__(cls, key: KeyFunc[_T, _OrderT]) -> SortedKeyList[_T, _OrderT]: ... + # Returning SortedList[Any] is a compromise. + # The signature should be something like: + # (cls: type[SortedList[_OrderT]], ...) -> SortedList[_OrderT] + # but neither mypy nor pyright support typing cls like this. If we leave cls + # bare then mypy correctly constrains the return type to only allow _OrderT + # elements, but pylance doesn't like the solo type argument, and does not + # allow the type to be used as () -> SortedList[...]. @overload - def __new__(cls, iterable: None = ..., key: None = ...) -> SortedList[_OrderT]: ... + def __new__(cls, iterable: None = ..., key: None = ...) -> Self: ... @overload def __new__( cls, iterable: Iterable[_OrderT], key: None = ... diff --git a/sortedcontainers-stubs/sortedset.pyi b/sortedcontainers-stubs/sortedset.pyi index 0bf4085..27c9538 100644 --- a/sortedcontainers-stubs/sortedset.pyi +++ b/sortedcontainers-stubs/sortedset.pyi @@ -21,8 +21,10 @@ class SortedSet(MutableSet[_T], Sequence[_T]): def __new__(cls, iterable: Iterable[_OrderT]) -> SortedSet[_OrderT]: ... @overload def __new__(cls, key: KeyFunc[_T, _OrderT]) -> SortedKeySet[_T, _OrderT]: ... + # Should return SortedSet[_OrderT] but we can't reliably express that. + # See comment on SortedList.__new__. @overload - def __new__(cls, iterable: None = ..., key: None = ...) -> SortedSet[_OrderT]: ... + def __new__(cls, iterable: None = ..., key: None = ...) -> Self: ... @overload def __new__(cls, iterable: Iterable[_OrderT], key: None) -> SortedSet[_OrderT]: ... @overload

Problem using typed sortedcontainer with defaultfactory so with dataclass, it is supposed to use default_factory to create stuff, I can't really find a way to make annotation without problems sample code: ``` from dataclasses import dataclass, field from sortedcontainers import SortedList @dataclass class Example(): samples: SortedList[str] = field(default_factory=SortedList) ``` error: ``` Argument of type "type[SortedList[_T@SortedList]]" cannot be assigned to parameter "default_factory" of type "() -> _T@field" in function "field" No overloaded function matches type "() -> SortedList[str]" ```

Thanks for the report. It's probably because of the way `SortedList.__new__` has several overloads but no explicit empty argument overload. I'll see if I can tweak the overloads to make this work.

2023-10-28T07:16:38

0.0

[]

Mityuha/fresh-bakery

Mityuha__fresh-bakery-44

c58d3fea7d953c7d9a4324be5536f82096f78999

diff --git a/pyproject.toml b/pyproject.toml index 18cec60..4727209 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -33,7 +33,7 @@ profile = "black" [tool.poetry] name = "fresh-bakery" -version = "0.3.5" +version = "0.3.6" description = "Bake your dependencies stupidly simple!" readme = "README.md" license = "MIT" @@ -166,7 +166,8 @@ deps = py{36,37}: pylint==2.13.9 py{38,39,310,311,312}: pylint==3.1.0 - pytest==7.0.1 + py{36,37}: pytest==7.0.1 + py{38,39,310,311,312}: pytest>=8.3.2 pytest-mock==3.6.1 typing_extensions==4.1.1 pytest-trio==0.7.0 @@ -175,6 +176,7 @@ deps = py36: mypy==0.971 py37: mypy==1.4.1 py{38,39,310,311,312}: mypy==1.10.0 + commands = black --check bakery/ tests/ isort --check bakery tests/

Patch cakes with the Cake object only Now it's possible to patch attributes this way (from the docs): ```python async def test_example_3(bakery_mock: BakeryMock) -> None: bakery_mock.settings = lambda dsn: "any value" async with bakery_mock(MyBakery): assert MyBakery().settings == "any value" ``` As the practice show such a behaviour is an error prone and leads to confusion. There is a suggestion to get rid of any implicit behaviour in favour of explicit Cake objects' behaviour, like this ```python async def test_example_3(bakery_mock: BakeryMock) -> None: bakery_mock.settings = Cake(lambda dsn: "any value") async with bakery_mock(MyBakery): assert MyBakery().settings == "any value" ```

2024-08-24T10:45:40

0.0

[]

Mityuha/fresh-bakery

Mityuha__fresh-bakery-33

9df814817391ad3044edb0539a81e2c1396fbc08

diff --git a/README.md b/README.md index 4af1d5f..e8ededb 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ <p align="center"> - <a href="https://fresh-bakery.readthedocs.io/en/latest/"><img width="300px" src="https://user-images.githubusercontent.com/17745407/187294435-a3bc6b26-b7df-43e5-abd3-0d7a7f92b71e.png" alt='fresh-bakery'></a> + <a href="https://fresh-bakery.readthedocs.io/en/latest/"><img width="300px" src="https://github.com/Mityuha/fresh-bakery/assets/17745407/9ad83683-03dc-43af-b66f-f8a010bde264" alt='fresh-bakery'></a> </p> <p align="center"> <em>🍰 The little DI framework that tastes like a cake. 🍰</em> @@ -25,6 +25,7 @@ It is [nearly] production-ready, and gives you the following: * Zero dependencies. * `Mypy` compatible (no probably need for `# type: ignore`). * `FastAPI` fully compatible. +* `Litestar` compatible. * `Pytest` fully compatible (Fresh Bakery encourages the use of `pytest`). * Ease of testing. * Easily extended (contribution is welcome). diff --git a/bakery/bakery.py b/bakery/bakery.py index 91f9051..acb4209 100644 --- a/bakery/bakery.py +++ b/bakery/bakery.py @@ -71,15 +71,13 @@ async def aopen(cls: Type[T]) -> T: cls.__bakery_visitors__ += 1 - cakes: Dict[str, Any] = {} exception: Optional[Union[Exception, BaseException]] = None # let's bake all your cakes - name: str cake: Cakeable[Any] - for name, cake in cls.__bakery_items__.items(): + for cake in cls.__bakery_items__.values(): try: - cakes[name] = await bake(cake) + await bake(cake) except (Exception, BaseException) as exc: # pylint: disable=broad-except exception = exc logger.error(f'{cake} cannot be baked: {exc}') diff --git a/bakery/baking.py b/bakery/baking.py index f5a69fc..bd5bbd3 100644 --- a/bakery/baking.py +++ b/bakery/baking.py @@ -8,7 +8,7 @@ "determine_baking_method", "unbake", ] - +from copy import deepcopy from inspect import isawaitable, iscoroutinefunction from typing import Any, AsyncContextManager, ContextManager, Optional, TypeVar @@ -104,6 +104,8 @@ class Ingredients: Cake internal state. """ + # pylint: disable=too-many-instance-attributes + def __init__( self, recipe: Any, @@ -133,6 +135,19 @@ def __init__( self.result = self.recipe self.is_baked = True + self.__name__: Final = getattr(self.recipe, "__name__", None) or self.__call__.__name__ + self.__code__: Final = getattr(self.recipe, "__code__", None) or self.__call__.__code__ + self.__defaults__: Final = ( + getattr(self.recipe, "__defaults__", None) or self.__call__.__defaults__ + ) + self.__kwdefaults__: Final = ( + getattr(self.recipe, "__kwdefaults__", None) or self.__call__.__kwdefaults__ + ) + self.__annotations__: Final = ( + getattr(self.recipe, "__annotations__", None) or self.__call__.__annotations__ + ) + self._is_coroutine: Final = getattr(self.recipe, "_is_coroutine", False) + def __repr__(self) -> str: """Repr.""" name: str = self.name or "<anon>" @@ -149,6 +164,17 @@ def __copy__(self) -> "Ingredients": ingr_copy.name = self.name return ingr_copy + def __deepcopy__(self, memo: Any) -> "Ingredients": + """Deep copy all ingredients and technologies.""" + ingr_copy: Ingredients = Ingredients( + self.recipe, + *deepcopy(self.recipe_args), + cake_baking_method=self.cake_baking_method, + **deepcopy(self.recipe_kwargs), + ) + ingr_copy.name = self.name + return ingr_copy + def __call__(self) -> Any: """Just return result.""" return self.result diff --git a/bakery/cake.py b/bakery/cake.py index 57ece83..ece4a81 100644 --- a/bakery/cake.py +++ b/bakery/cake.py @@ -12,6 +12,7 @@ Callable, ContextManager, Optional, + Set, TypeVar, cast, overload, @@ -28,6 +29,7 @@ IngredientsProto, assert_baked, cake_ingredients, + is_baked, is_cake, ) @@ -38,11 +40,22 @@ class Pastry(CakeRecipe): Your item ingredients and cooking method stored here. """ + __CAKE_ATTRIBUTE_ERROR_NAMES__: Final[Set[str]] = { + "func", # partial + "__wrapped__", # functools special attribute + } + def __init__( self, ingredients: Ingredients, ): self.__ingredients: Final[Ingredients] = ingredients + self.__name__: Final = ingredients.__name__ + self.__code__: Final = ingredients.__code__ + self.__defaults__: Final = ingredients.__defaults__ + self.__kwdefaults__: Final = ingredients.__kwdefaults__ + self.__annotations__: Final = ingredients.__annotations__ + self._is_coroutine: Final = ingredients._is_coroutine def __set_name__(self, _: Any, name: str) -> None: self.__ingredients.name = name @@ -54,19 +67,49 @@ def __repr__(self) -> str: def __copy__(self) -> "Cakeable[Any]": """Copy itself with all ingredients and technologies. + If cake is not baked then such a cake's copy is cake itself. + 'Copy cake' just sounds like 'cupcake'. And I like cupcakes ;) """ - assert_baked(cast(Cakeable[Any], self)) # we only copy proven recipes! + if not is_baked(self): + return self + ingr_copy: Ingredients = self.__ingredients.__copy__() return cast(Cakeable[Any], Pastry(ingr_copy)) + def __deepcopy__(self, memo: Any) -> "Cakeable[Any]": + """Deep copy with all ingredients and technologies. + + If cake is not baked then such a cake's deep copy is cake itself. + """ + if not is_baked(self): + return self + + ingr_copy: Ingredients = self.__ingredients.__deepcopy__(memo) + return cast(Cakeable[Any], Pastry(ingr_copy)) + def __call__(self) -> Any: """Just return whole cake.""" assert_baked(cast(Cakeable[Any], self)) return self.__ingredients() def __getattr__(self, piece_name: str) -> PieceOfCake: - """Cut a piece of cake.""" + """Cut a piece of cake. + + With __CAKE_ATTRIBUTE_ERROR_NAMES__ you can no longer write + the things like: + class MyBakery(Bakery): + ctrl: Any = Cake(Controller) + controller_func: Any = Cake(some_func, ctrl.func) # or __wrapped__ + + There is the place where AttribureError exception will occured. + You can make a simple refactoring: + class MyBakery(Bakery): + ctrl: Any = Cake(Controller) + controller_func: Any = Cake(some_func, Cake(getattr, ctrl, "func")) + """ + if piece_name in self.__CAKE_ATTRIBUTE_ERROR_NAMES__: + raise AttributeError(piece_name) # explicit __getattr__ call to avoid collisions return PieceOfCake(self).__getattr__(piece_name) diff --git a/docs/index.md b/docs/index.md index 8fec128..1e33319 100644 --- a/docs/index.md +++ b/docs/index.md @@ -1,9 +1,10 @@ # Fresh Bakery Fresh Bakery is a lightweight [Dependency Injection](dependency_injection.md) framework. -Fresh Bakery can only be used within async applications, so if you have a sync one you probably should look for some alternatives. +Actually, the only two asyncronious things about Fresh Bakery are its opening and its closing. +It doesn't add up how you open and close your bakery: just make it asynchronously. That's it. -Fresh Bakery is suitable for integrating against any async Web framework, such as [Starlette][starlette], [aiohttp][aiohttp], [FastAPI][fastapi] and so forth. +Fresh Bakery is suitable for integrating against any async Web framework, such as [Starlette][starlette], [aiohttp][aiohttp], [FastAPI][fastapi], [Litestar][litestar] and so forth. **Requirements**: Python 3.6+ @@ -115,3 +116,4 @@ async def main() -> None: [starlette]: https://github.com/encode/starlette [aiohttp]: https://github.com/aio-libs/aiohttp [fastapi]: https://github.com/tiangolo/fastapi +[litestar]: https://github.com/litestar-org/litestar diff --git a/pyproject.toml b/pyproject.toml index 042717c..f5bd85d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -32,7 +32,7 @@ use_parentheses= true [tool.poetry] name = "fresh-bakery" -version = "0.2.1" +version = "0.3.1" description = "Bake your dependencies stupidly simple!" readme = "README.md" license = "MIT"

Delete unused dict on bakery open Delete [unused dict](https://github.com/Mityuha/fresh-bakery/blob/main/bakery/bakery.py#L74) in Bakery.aopen method

2023-07-09T16:45:16

0.0

[]

ucla-mobility/OpenCDA

ucla-mobility__OpenCDA-200

8e0b167c26c3b631aa17ec4d0c9ac98514f26240

diff --git a/README.md b/README.md index e8072e21..8fdb2bd4 100644 --- a/README.md +++ b/README.md @@ -21,12 +21,12 @@ The key features of OpenCDA are: Users could refer to [OpenCDA documentation](https://opencda-documentation.readthedocs.io/en/latest/) for more details. ## What's New -### March 2022 +### March 2023 * OpenCDA now supports Docker Installation! Many thanks to @GoodarzMehr! * OpenCDA has make the configuration system better! We provide a `default.yaml` as a template for all scenarios. Users now can only change the parts that are different from the default parameters, which makes the configuration file much cleaner. -### Jan 2022 +### Jan 2023 * Our paper [The OpenCDA Open-source Ecosystem for Cooperative Driving Automation Research](https://ieeexplore.ieee.org/document/10045043) has been accepted by **IEEE Transactions on Intelligent Vehicles**. We extend the scope of the original OpenCDA simulation framework to a ecosystem, which contains a model zoo, a suite of driving simulators at various resolutions, large-scale real-world and simulated datasets, complete devel-opment toolkits for benchmark training/testing, and a scenario database/generator. @@ -102,7 +102,7 @@ The arxiv link to the paper: https://arxiv.org/abs/2107.06260 Also, under this LICENSE, OpenCDA is for non-commercial research only. Researchers can modify the source code for their own research only. Contracted work that generates corporate revenues and other general commercial use are prohibited under this LICENSE. See the LICENSE file for details and possible opportunities for commercial use. ## Contributors -OpenCDA is supported by the [UCLA Mobility Lab](https://mobility-lab.seas.ucla.edu/). <br> +OpenCDA is mainly supported by the [UCLA Mobility Lab](https://mobility-lab.seas.ucla.edu/). <br> ### Lab Principal Investigator: - Dr. Jiaqi Ma ([linkedin](https://www.linkedin.com/in/jiaqi-ma-17037838/), @@ -118,3 +118,6 @@ OpenCDA is supported by the [UCLA Mobility Lab](https://mobility-lab.seas.ucla.e - Zonglin Meng([linkedin](https://www.linkedin.com/in/zonglin-meng-a393b31ab/) - Dr. Xin Xia ([linkedin](https://www.linkedin.com/in/yi-guo-4008baaa/)) +### External Contributor Acknowledgements +- We would like to acknowledge the great contributions from UC Davis Professor [Junshan Zhang's](https://faculty.engineering.ucdavis.edu/jzhang/) group, particularly to Dr. [Wei Shao](https://scholar.google.com.au/citations?user=zbqNhWwAAAAJ&hl=en), who played a crucial role in this collaboration. Their expertise enabled the seamless integration of openScenario with OpenCDA. +- We would like to thank @GoodarzMehr for his outstanding contribution in creating the OpenCDA Dockerfile. diff --git a/docs/md_files/coperception/setup_scenario_runner.md b/docs/md_files/coperception/setup_scenario_runner.md new file mode 100644 index 00000000..70564372 --- /dev/null +++ b/docs/md_files/coperception/setup_scenario_runner.md @@ -0,0 +1,30 @@ +## Scenario Runner + +To enable the scenario runner, follow the official docs: +https://carla-scenariorunner.readthedocs.io/en/latest/ to set up the environments. + +### Details for each step: +1. Clone the Scenario Runner repo. Find the match version with your installed Carla. We'll use `0.9.12` as the following example +2. The `SCENARIO_RUNNER_ROOT="/to/scenario_runner/installation/path"`. In our example, we use `${HOME}/scenario_runner-0.9.12` +3. Set up the environment in `.bashrc` like +``` +export SCENARIO_RUNNER_ROOT=${HOME}/scenario_runner-0.9.12 +export PYTHONPATH=$SCENARIO_RUNNER_ROOT:$PYTHONPATH +export PYTHONPATH=$PYTHONPATH:${CARLA_ROOT}/PythonAPI/carla/dist/carla-0.9.12-py3.7-linux-x86_64.egg +export PYTHONPATH=$PYTHONPATH:${CARLA_ROOT}/PythonAPI/carla +``` +4. source your environment file as `source .bashrc` +5. create an empty `__init__.py` under the scenario runner repo so that the folder is treated as a package + +### Verify the installation +1. verify with the import +``` +python -c 'import scenario_runner' +``` +2. Verify with python prompt +``` +>> import scenario_runner as sr +>> dir(sr) +['CarlaDataProvider', 'LooseVersion', 'OpenScenario', 'OpenScenarioConfiguration', 'RawTextHelpFormatter', 'RouteParser', 'RouteScenario', 'ScenarioConfigurationParser', 'ScenarioManager', 'ScenarioRunner', 'VERSION', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__spec__', 'argparse', 'carla', 'datetime', 'glob', 'importlib', 'inspect', 'json', 'main', 'os', 'pkg_resources', 'print_function', 'signal', 'sys', 'time', 'traceback'] +``` + diff --git a/docs/md_files/installation.md b/docs/md_files/installation.md index 0004168d..eb025c0e 100644 --- a/docs/md_files/installation.md +++ b/docs/md_files/installation.md @@ -144,9 +144,16 @@ export SUMO_HOME=/usr/share/sumo ``` --- +### 5. Install OpenScenario (Optional) +If you want to use OpenScenario to conduct scenario testing, e.g. `python opencda.py -t openscenario_carla -v 0.9.12`, you need to install OpenScenario first. <br> + +Please follow the [openscenario installation](coperception/setup_scenario_runner.md) to install OpenScenario. + ## Docker Installation OpenCDA provides docker image for users to run directly. +Note: Openscenario is not supported in docker image yet. + ### 1. Prerequisite First, make sure that you have installed `docker` in your ubuntu system. If you have nvidia gpu, it is recommended to install `nvidia-docker` as well. diff --git a/opencda/core/common/misc.py b/opencda/core/common/misc.py index 6fb0bd15..7beab1bc 100644 --- a/opencda/core/common/misc.py +++ b/opencda/core/common/misc.py @@ -57,6 +57,30 @@ def draw_trajetory_points(world, waypoints, z=0.25, life_time=lt) +def draw_prediction_points(world, points, z=0.5, lt=0.06, size=0.05): + """ + This function draws prediction points on the carla server. + + Parameters: + world (carla.World): The world object where points will be drawn. + points (list): A list of points to be drawn. Each point is a tuple (x, y, yaw). + z (float, optional): The z-coordinate for the points. Default is 0.5. + lt (float, optional): The lifetime of the points in seconds. Default is 0.06. + size (float, optional): The size of the points. Default is 0.05. + + Returns: + None + """ + for point in points: + color = carla.Color(255, 103, 0) + x, y, yaw = point + world.debug.draw_point(carla.Location(x, y, z), size=size, color=color, life_time=lt) + + +def draw_prediction_bbx(world, x, y, color=carla.Color(255, 0, 0), z=2.0, lt=0.06, size=0.05): + """ Draw the bounding box of the predicted vehicle.""" + world.debug.draw_point(carla.Location(x, y, z), size=size, color=color, life_time=lt) + def draw_waypoints(world, waypoints, z=0.5): """ Draw a list of waypoints at a certain height given in z. diff --git a/opencda/core/plan/behavior_agent.py b/opencda/core/plan/behavior_agent.py index 847d9b5c..1e9a1644 100644 --- a/opencda/core/plan/behavior_agent.py +++ b/opencda/core/plan/behavior_agent.py @@ -14,12 +14,14 @@ import numpy as np import carla +from opencda.core.common.misc import draw_prediction_points from opencda.core.common.misc import get_speed, positive, cal_distance_angle from opencda.core.plan.collision_check import CollisionChecker from opencda.core.plan.local_planner_behavior import LocalPlanner from opencda.core.plan.global_route_planner import GlobalRoutePlanner from opencda.core.plan.global_route_planner_dao import GlobalRoutePlannerDAO from opencda.core.plan.planer_debug_helper import PlanDebugHelper +from opencda.core.sensing.prediction.physics_model import PredictionManager class BehaviorAgent(object): @@ -90,6 +92,7 @@ def __init__(self, vehicle, carla_map, config_yaml): self._ego_pos = None self._ego_speed = 0.0 self._map = carla_map + self._cav_world = self.vehicle.get_world() # speed related, check yaml file to see the meaning self.max_speed = config_yaml['max_speed'] @@ -104,7 +107,8 @@ def __init__(self, vehicle, carla_map, config_yaml): self.ttc = 1000 # collision checker self._collision_check = CollisionChecker( - time_ahead=config_yaml['collision_time_ahead']) + time_ahead=config_yaml['collision_time_ahead'], + cav_world=self._cav_world) self.ignore_traffic_light = config_yaml['ignore_traffic_light'] self.overtake_allowed = config_yaml['overtake_allowed'] self.overtake_allowed_origin = config_yaml['overtake_allowed'] @@ -145,6 +149,23 @@ def __init__(self, vehicle, carla_map, config_yaml): # print message in debug mode self.debug = False if 'debug' not in \ config_yaml else config_yaml['debug'] + # prediction + self.enable_prediction = False + self.prediction_scan_window = 0 + if 'local_planner' in config_yaml and 'enable_prediction' in config_yaml['local_planner'] and \ + config_yaml['local_planner']['enable_prediction']: + print("Prediction is enabled") + print(f"Prediction model used: {config_yaml['local_planner']['prediction_model']}") + local_planner_config = config_yaml['local_planner'] + dt = local_planner_config['dt'] + self.enable_prediction = local_planner_config['enable_prediction'] + self.prediction_scan_window = config_yaml['local_planner']['prediction_scan_window'] # override zero + self.prediction_manager = PredictionManager( + observed_length=int(local_planner_config['observation_seconds'] // dt), + predict_length=int(local_planner_config['observation_seconds'] // dt), + dt=dt, + model=config_yaml['local_planner']['prediction_model'] + ) def update_information(self, ego_pos, ego_speed, objects): """ @@ -168,6 +189,9 @@ def update_information(self, ego_pos, ego_speed, objects): self.break_distance = self._ego_speed / 3.6 * self.emergency_param # update the localization info to trajectory planner self.get_local_planner().update_information(ego_pos, ego_speed) + # prediction + if self.enable_prediction: + self.prediction_manager.update_information(objects) self.objects = objects # current version only consider about vehicles @@ -403,7 +427,10 @@ def traffic_light_manager(self, waypoint): self.light_id_to_ignore = -1 return 0 - def collision_manager(self, rx, ry, ryaw, waypoint, adjacent_check=False): + def collision_manager(self, rx, ry, ryaw, + waypoint, + adjacent_check=False, + obstacle_vehicle_predictions={}): """ This module is in charge of warning in case of a collision. @@ -423,6 +450,9 @@ def collision_manager(self, rx, ry, ryaw, waypoint, adjacent_check=False): adjacent_check : boolean Whether it is a check for adjacent lane. + + obstacle_vehicle_predictions: dict + dict of vehicle and its predicted future paths """ def dist(v): @@ -432,24 +462,57 @@ def dist(v): min_distance = 100000 target_vehicle = None - for vehicle in self.obstacle_vehicles: - collision_free = self._collision_check.collision_circle_check( - rx, ry, ryaw, vehicle, self._ego_speed / 3.6, self._map, - adjacent_check=adjacent_check) - if not collision_free: - vehicle_state = True + if self.enable_prediction: + for v_id, predictions in obstacle_vehicle_predictions.items(): + vehicle = predictions['vehicle'] + # not collide with prediction points + collision_free_prediction = self._collision_check.collision_circle_check_enable_prediction( + rx, ry, ryaw, + predictions['vehicle'], + predictions['points'], + self._ego_speed / 3.6, + self.prediction_scan_window, + adjacent_check=adjacent_check + ) + + # not collide with original check + collision_free = self._collision_check.collision_circle_check( + rx, ry, ryaw, vehicle, self._ego_speed / 3.6, self._map, + adjacent_check=adjacent_check) + + # either collide with original check or the prediction + if not collision_free or not collision_free_prediction: + vehicle_state = True + + # the vehicle length is typical 3 meters, + # so we need to consider that when calculating the distance + distance = positive(dist(vehicle) - 3) + + # updating the minimal distance and the closet vehicle + if distance < min_distance: + min_distance = distance + target_vehicle = vehicle + return vehicle_state, target_vehicle, min_distance + + else: + for vehicle in self.obstacle_vehicles: + collision_free = self._collision_check.collision_circle_check( + rx, ry, ryaw, vehicle, self._ego_speed / 3.6, self._map, + adjacent_check=adjacent_check) + if not collision_free: + vehicle_state = True - # the vehicle length is typical 3 meters, - # so we need to consider that when calculating the distance - distance = positive(dist(vehicle) - 3) + # the vehicle length is typical 3 meters, + # so we need to consider that when calculating the distance + distance = positive(dist(vehicle) - 3) - if distance < min_distance: - min_distance = distance - target_vehicle = vehicle + if distance < min_distance: + min_distance = distance + target_vehicle = vehicle - return vehicle_state, target_vehicle, min_distance + return vehicle_state, target_vehicle, min_distance - def overtake_management(self, obstacle_vehicle): + def overtake_management(self, obstacle_vehicle, obstacle_vehicle_predictions): """ Overtake behavior. @@ -489,7 +552,9 @@ def overtake_management(self, obstacle_vehicle): overtake=True, world=self.vehicle.get_world()) vehicle_state, _, _ = self.collision_manager( rx, ry, ryaw, self._map.get_waypoint( - self._ego_pos.location), True) + self._ego_pos.location), + True, + obstacle_vehicle_predictions) if not vehicle_state: print("left overtake is operated") self.overtake_counter = 100 @@ -690,10 +755,10 @@ def check_lane_change_permission(self, lane_change_allowed, collision_detector_e # * overtake hasn't happened : if previously we have been doing an overtake, then lane change should not be allowed. # * destination is not pushed : if we have been doing destination pushed, then lane change should not be allowed. lane_change_enabled_flag = collision_detector_enabled and \ - self.get_local_planner().lane_id_change and \ - self.get_local_planner().lane_lateral_change and \ - self.overtake_counter <= 0 and \ - not self.destination_push_flag + self.get_local_planner().lane_id_change and \ + self.get_local_planner().lane_lateral_change and \ + self.overtake_counter <= 0 and \ + not self.destination_push_flag if lane_change_enabled_flag: lane_change_allowed = lane_change_allowed and self.lane_change_management() if not lane_change_allowed: @@ -769,6 +834,13 @@ def run_step( ego_vehicle_loc = self._ego_pos.location ego_vehicle_wp = self._map.get_waypoint(ego_vehicle_loc) waipoint_buffer = self.get_local_planner().get_waypoint_buffer() + # prediction + obstacle_vehicle_predictions = {} + if self.enable_prediction: + obstacle_vehicle_predictions = self.prediction_manager.predict() + for v_id, predictions in obstacle_vehicle_predictions.items(): + draw_prediction_points(self._cav_world, predictions['points']) + # ttc reset to 1000 at the beginning self.ttc = 1000 # when overtake_counter > 0, another overtake/lane change is forbidden @@ -817,13 +889,16 @@ def run_step( rx, ry, rk, ryaw = self._local_planner.generate_path() # check whether lane change is allowed - self.lane_change_allowed = self.check_lane_change_permission(lane_change_allowed, collision_detector_enabled, rk) + self.lane_change_allowed = self.check_lane_change_permission(lane_change_allowed, collision_detector_enabled, + rk) # 3. Collision check is_hazard = False if collision_detector_enabled: is_hazard, obstacle_vehicle, distance = self.collision_manager( - rx, ry, ryaw, ego_vehicle_wp) + rx, ry, ryaw, ego_vehicle_wp, + False, + obstacle_vehicle_predictions) car_following_flag = False if not is_hazard: @@ -872,7 +947,7 @@ def run_step( # front obstacle if self._ego_speed >= obstacle_speed - 5: - car_following_flag = self.overtake_management(obstacle_vehicle) + car_following_flag = self.overtake_management(obstacle_vehicle, obstacle_vehicle_predictions) else: car_following_flag = True @@ -890,6 +965,4 @@ def run_step( target_speed, target_loc = self._local_planner.run_step( rx, ry, rk, target_speed=self.max_speed - self.speed_lim_dist if not target_speed else target_speed) - return target_speed, target_loc - - + return target_speed, target_loc \ No newline at end of file diff --git a/opencda/core/plan/collision_check.py b/opencda/core/plan/collision_check.py index 22adf811..37f4cbe2 100644 --- a/opencda/core/plan/collision_check.py +++ b/opencda/core/plan/collision_check.py @@ -10,7 +10,7 @@ import carla import numpy as np -from opencda.core.common.misc import cal_distance_angle, draw_trajetory_points +from opencda.core.common.misc import cal_distance_angle, draw_prediction_bbx from opencda.core.plan.spline import Spline2D @@ -21,15 +21,20 @@ class CollisionChecker: Parameters ---------- time_ahead : float - how many seconds we look ahead in advance for collision check. + How many seconds we look ahead in advance for collision check. + cav_world : carla.world + The simulation world. circle_radius : float The radius of the collision checking circle. circle_offsets : float The offset between collision checking circle and the trajectory point. """ - def __init__(self, time_ahead=1.2, circle_radius=1.0, circle_offsets=None): - + def __init__(self, time_ahead=3, + cav_world=None, + circle_radius=1.0, + circle_offsets=None): + self._cav_world = cav_world self.time_ahead = time_ahead self._circle_offsets = [-1.0, 0, @@ -144,14 +149,14 @@ def adjacent_lane_collision_check( target_wpt_previous = target_wpt.previous(diff_s) target_wpt_previous = target_wpt_previous[0] - target_wpt_middle = target_wpt_previous.next(diff_s/2)[0] + target_wpt_middle = target_wpt_previous.next(diff_s / 2)[0] x, y = [target_wpt_next.transform.location.x, target_wpt_middle.transform.location.x, target_wpt_previous.transform.location.x], \ - [target_wpt_next.transform.location.y, - target_wpt_middle.transform.location.y, - target_wpt_previous.transform.location.y] + [target_wpt_next.transform.location.y, + target_wpt_middle.transform.location.y, + target_wpt_previous.transform.location.y] ds = 0.1 sp = Spline2D(x, y) @@ -188,7 +193,6 @@ def collision_circle_check( """ Use circled collision check to see whether potential hazard on the forwarding path. - Args: -adjacent_check (boolean): Indicator of whether do adjacent check. Note: always give full path for adjacent lane check. @@ -205,10 +209,12 @@ def collision_circle_check( collision_free = True # detect x second ahead. in case the speed is very slow, # there is some minimum threshold for the check distance + ### Look ahead how many points in the path list ### distance_check = min(max(int(self.time_ahead * speed / 0.1), 90), len(path_x)) \ if not adjacent_check else len(path_x) + ### Get the 3D location of the vehicle being checked ### obstacle_vehicle_loc = obstacle_vehicle.get_location() obstacle_vehicle_yaw = \ carla_map.get_waypoint(obstacle_vehicle_loc).transform.rotation.yaw @@ -248,7 +254,6 @@ def collision_circle_check( corrected_extent_x, obstacle_vehicle_loc.y + corrected_extent_y]]) - # compute whether the distance between the four corners of the # vehicle to the trajectory point collision_dists = spatial.distance.cdist( @@ -261,3 +266,91 @@ def collision_circle_check( break return collision_free + + def collision_circle_check_enable_prediction( + self, + path_x, + path_y, + path_yaw, + obstacle_vehicle, + vehicle_predictions, + speed, + prediction_scan_window, + adjacent_check=False): + """ + This function checks for potential collisions between a vehicle and obstacles along a given path. + + Parameters: + path_x (list): The x-coordinates of the path. + path_y (list): The y-coordinates of the path. + path_yaw (list): The yaw angles of the path. + obstacle_vehicle (Vehicle): The obstacle vehicle to check for collisions. + vehicle_predictions (list): The predicted positions of the vehicle. + speed (float): The speed of the vehicle. + prediction_scan_window (int): The time window for prediction scan. + adjacent_check (bool, optional): Whether to check for adjacent collisions. Default is False. + + Returns: + collision_free (bool): True if the path is collision-free, False otherwise. + """ + collision_free = True + dt = 0.05 + max_time_on_path = min(int((len(path_x) * 0.1 / max(speed, 2.0) / dt)), + len(vehicle_predictions)) + time_check = min(max(int(self.time_ahead / dt), int(2 / dt)), + max_time_on_path) \ + if not adjacent_check else max_time_on_path + # decide to align on the time axis, need to convert the points to distances + # 1. the distance between every two point is 0.1m + # 2. the distance traveled is speed * time + # 3. find the corresponding index in the original array + # check every 1 sec -> 1 / 0.05 = 20. every 20 points for the prediction points + interval = int(0.5 / dt) + + # align on the time axis + for time in range(0, time_check, interval): + # calculating for corresponding ego index + distance_traveled = time * dt * speed + ego_idx = int(distance_traveled // 0.1) + # ego circle (3 points) + ptx, pty, yaw = path_x[ego_idx], path_y[ego_idx], path_yaw[ego_idx] + circle_locations = np.zeros((len(self._circle_offsets), 2)) + circle_offsets = np.array(self._circle_offsets) + circle_locations[:, 0] = ptx + circle_offsets * cos(yaw) + circle_locations[:, 1] = pty + circle_offsets * sin(yaw) + draw_prediction_bbx(self._cav_world, ptx, pty, carla.Color(0, 255, 0)) + + # if enable the scan window, examine through + # [vehicle_predictions[i - window], vehicle_predictions[i + window]] + # calculating surrounding vehicle + scan_range = [time - prediction_scan_window, time + prediction_scan_window] + for idx in scan_range: + x, y, obstacle_vehicle_yaw = vehicle_predictions[idx] + # obstacle_vehicle_yaw = math.radians(obstacle_vehicle_yaw) + draw_prediction_bbx(self._cav_world, x, y) + dx, dy = obstacle_vehicle.bounding_box.extent.x, obstacle_vehicle.bounding_box.extent.y + vehicle_extent = np.array([dx, dy]) + corner_points = np.array([ + [-dx, -dy], + [dx, -dy], + [-dx, dy], + [dx, dy] + ]) + rotation_matrix = np.array([ + [np.cos(obstacle_vehicle_yaw), -np.sin(obstacle_vehicle_yaw)], + [np.sin(obstacle_vehicle_yaw), np.cos(obstacle_vehicle_yaw)] + ]) + rotated_points = corner_points @ rotation_matrix.T + obstacle_vehicle_bbx_array = rotated_points + np.array([x, y]) + for pt in obstacle_vehicle_bbx_array.tolist(): + draw_prediction_bbx(self._cav_world, pt[0], pt[1], carla.Color(0, 0, 255), z=3.0) + + collision_dists = spatial.distance.cdist( + obstacle_vehicle_bbx_array, circle_locations) + + collision_dists = np.subtract(collision_dists, self._circle_radius) + collision_free = collision_free and not np.any(collision_dists < 0) + + if not collision_free: + break + return collision_free diff --git a/opencda/core/sensing/perception/obstacle_vehicle.py b/opencda/core/sensing/perception/obstacle_vehicle.py index 83ca5996..c016fe09 100644 --- a/opencda/core/sensing/perception/obstacle_vehicle.py +++ b/opencda/core/sensing/perception/obstacle_vehicle.py @@ -133,6 +133,12 @@ def get_velocity(self): """ return self.velocity + def get_carla_id(self): + """ + Return the carla id + """ + return self.carla_id + def set_carla_id(self, id): """ Set carla id according to the carla.vehicle. diff --git a/opencda/core/sensing/prediction/physics_model.py b/opencda/core/sensing/prediction/physics_model.py new file mode 100644 index 00000000..f933b7e2 --- /dev/null +++ b/opencda/core/sensing/prediction/physics_model.py @@ -0,0 +1,282 @@ +# -*- coding: utf-8 -*- +""" +Physics-based trajectory prediction model +""" +import math +import numpy as np +from collections import deque + +def check_positive_integer(value, name): + assert isinstance(value, int) and value > 0, f"{name} must be int and greater than 0" + +def check_positive_real_number(value, name): + assert (isinstance(value, int) or isinstance(value, float)) and value > 0, f"{name} must be real number and greater than 0" + +def angle_diff(x, y): + """ + Get the smallest angle difference between 2 angles: the angle from y to x. + Parameters + ---------- + x : float + To angle. + + y : float + From angle. + + Returns + ------- + diff : float + Angle difference from y to x. + + """ + # calculate angle difference, modulo to [0, 2*pi] + period = 2 * np.pi + diff = (x - y + period / 2) % period - period / 2 + if diff > np.pi: + diff = diff - (2 * np.pi) # shift (pi, 2*pi] to (-pi, 0] + return diff + + +class TrajectoryData: + def __init__(self, l): + self.observed_traj = deque(maxlen=l) + self.observed_velocity = deque(maxlen=l) + self.observed_yaw = deque(maxlen=l) + self.yaw_rate = 0 + self.acc = [0, 0] + + def add(self, pose, v, time_diff, current_yaw): + # use current yaw from system to avoid getting 0.0 + # yaw when velocity is very slow. + # current_yaw = np.arctan2(v[1], v[0]) + if len(self.observed_velocity): + # update acceleration + past_v = self.observed_velocity[-1] + self.acc = [(v[i] - past_v[i]) / time_diff for i in range(2)] + # update yaw rate + past_yaw = self.observed_yaw[-1] + self.yaw_rate = angle_diff(current_yaw, past_yaw) / time_diff + + self.observed_traj.append(pose) + self.observed_velocity.append(v) + self.observed_yaw.append(current_yaw) + + +def get_kinematics(trajectory_data, observed_length): + observed_traj = list(trajectory_data.observed_traj)[-observed_length:] + velocity = list(trajectory_data.observed_velocity)[-1] + yaw = trajectory_data.observed_yaw[-1] + yaw_rate = trajectory_data.yaw_rate + acc = trajectory_data.acc + return observed_traj, velocity, acc, yaw, yaw_rate + + + +class PredictionManager: + def __init__(self, observed_length, predict_length, dt, model="ConstantVelocityHeading"): + check_positive_integer(observed_length, "observed_length") + check_positive_integer(predict_length, "predict_length") + check_positive_real_number(dt, "dt") + + model_classes = { + "ConstantVelocityHeading": ConstantVelocityHeading, + "ConstantAccelerationHeading": ConstantAccelerationHeading, + "ConstantSpeedYawRate": ConstantSpeedYawRate, + "ConstantMagnitudeAccelAndYawRate": ConstantMagnitudeAccelAndYawRate, + "PhysicsOracle": PhysicsOracle + } + + self.observed_length = observed_length + self.predict_length = predict_length + self.dt = dt + self.model = model_classes[model](self.observed_length, self.predict_length, self.dt) + self.vehicle_trajectory_data = {} + self.objects = None + self.vehicles = {} + + def update_information(self, objects): + # only keep track of the vehicles that is visible in current frame + self.vehicle_trajectory_data = {v.get_carla_id(): self.vehicle_trajectory_data.get(v.get_carla_id(), TrajectoryData(self.observed_length)) \ + for v in objects['vehicles']} + self.objects = objects + self.vehicles = {v.get_carla_id(): v for v in objects['vehicles']} + for vehicle in objects['vehicles']: + location = vehicle.get_location() + x, y = location.x, location.y + v = vehicle.get_velocity() + yaw = math.radians(vehicle.get_transform().rotation.yaw) + self.vehicle_trajectory_data[vehicle.get_carla_id()].add([x, y], [v.x, v.y], self.dt, yaw) + + def predict(self): + predictions = {} + for vehicle_id, vehicle in self.vehicles.items(): + kinematics_data = get_kinematics(self.vehicle_trajectory_data[vehicle_id], self.observed_length) + predictions.update({ + vehicle_id: { + 'vehicle': vehicle, + 'points': self.model(kinematics_data) + } + }) + return predictions + + +class Baseline: + """ + Baseline class for physics-based trajectory prediction model. + Parameters + ---------- + observed_length : int + Observed trajectory length. + + predict_length : int + Predicted trajectory length. + + dt : float + Time discretization interval. + + """ + + def __init__(self, observed_length, predict_length, dt): + check_positive_integer(observed_length, "observed_length") + check_positive_integer(predict_length, "predict_length") + check_positive_real_number(dt, "dt") + self.observed_length = observed_length + self.predict_length = predict_length + self.dt = dt + + +class ConstantVelocityHeading(Baseline): + def __call__(self, kinematics_data): + observed_traj, velocity, acc, yaw, yaw_rate = kinematics_data + x = observed_traj[-1][0] + y = observed_traj[-1][1] + vx, vy = velocity + pred_traj = constant_velocity_heading(x, y, vx, vy, yaw, self.predict_length, self.dt) + return pred_traj + + +class ConstantAccelerationHeading(Baseline): + def __call__(self, kinematics_data): + observed_traj, velocity, acc, yaw, yaw_rate = kinematics_data + x = observed_traj[-1][0] + y = observed_traj[-1][1] + vx, vy = velocity + ax, ay = acc + pred_traj = constant_acceleration_and_heading(x, y, vx, vy, yaw, ax, ay, self.predict_length, self.dt) + return pred_traj + + +class ConstantSpeedYawRate(Baseline): + def __call__(self, kinematics_data): + observed_traj, velocity, acc, yaw, yaw_rate = kinematics_data + x = observed_traj[-1][0] + y = observed_traj[-1][1] + vx, vy = velocity + pred_traj = constant_speed_and_yaw_rate(x, y, vx, vy, yaw, yaw_rate, self.predict_length, self.dt) + return pred_traj + + +class ConstantMagnitudeAccelAndYawRate(Baseline): + def __call__(self, kinematics_data): + observed_traj, velocity, acc, yaw, yaw_rate = kinematics_data + x = observed_traj[-1][0] + y = observed_traj[-1][1] + vx, vy = velocity + ax, ay = acc + pred_traj = constant_magnitude_accel_and_yaw_rate(x, y, vx, vy, ax, ay, + yaw, yaw_rate, self.predict_length, self.dt) + return pred_traj + + +class PhysicsOracle(Baseline): + def __call__(self, kinematics_data, ground_truth): + assert len(ground_truth.shape) == 2 and ground_truth.shape[0] == self.predict_length \ + and ground_truth.shape[1] == 2 + models = [ + ConstantVelocityHeading, + ConstantAccelerationHeading, + ConstantSpeedYawRate, + ConstantMagnitudeAccelAndYawRate + ] + models = [model(self.observed_length, self.predict_length, self.dt) for model in models] + all_preds = [model(kinematics_data) for model in models] + oracles = sorted(all_preds, + key=lambda x: np.linalg.norm(np.array(x) - ground_truth, ord="fro")) + oracle = oracles[0] + return oracle + + +def constant_velocity_heading(x, y, vx, vy, yaw, predict_length, dt): + """ + Predict trajectories based on constant velocity. + Parameters + ---------- + x : float + Vehicle's current x-axis position. + + y : float + Vehicle's current x-axis position. + + vx : float + Vehicle's current x-axis velocity. + + vy : float + Vehicle's current y-axis velocity. + + yaw : float + Vehicle's current yaw angle + + predict_length : int + Predicted trajectory length. + + dt : float + Time discretization interval. + + Returns + ------- + preds : np.array + Predicted trajectory with shape (predict_length, 2) + + """ + preds = [] + for i in range(1, predict_length + 1): + t = i * dt + preds.append((x + vx * t, y + vy * t, yaw)) + return np.array(preds) + + +def constant_acceleration_and_heading(x, y, vx, vy, yaw, ax, ay, predict_length, dt): + preds = [] + for i in range(1, predict_length + 1): + t = i * dt + half_time_squared = 1 / 2 * t * t + preds.append((x + vx * t + half_time_squared * ax, y + vy * t + half_time_squared * ay, yaw)) + return np.array(preds) + + +def constant_speed_and_yaw_rate(x, y, vx, vy, yaw, yaw_rate, predict_length, dt): + preds = [] + distance_step = np.sqrt(vx ** 2 + vy ** 2) * dt + yaw_step = yaw_rate * dt + for i in range(1, predict_length + 1): + x += distance_step * np.cos(yaw) + y += distance_step * np.sin(yaw) + yaw += yaw_step + preds.append((x, y, yaw)) + return np.array(preds) + + +def constant_magnitude_accel_and_yaw_rate(x, y, vx, vy, ax, ay, yaw, yaw_rate, predict_length, dt): + preds = [] + a_value = np.sqrt(ax ** 2 + ay ** 2) + v_value = np.sqrt(vx ** 2 + vy ** 2) + speed_step = a_value * dt + yaw_step = yaw_rate * dt + for i in range(1, predict_length + 1): + distance_step = dt * v_value + x += distance_step * np.cos(yaw) + y += distance_step * np.sin(yaw) + v_value += speed_step + yaw += yaw_step + preds.append((x, y, yaw)) + return np.array(preds) diff --git a/requirements.txt b/requirements.txt index c91e774e..91e2354d 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,6 +1,6 @@ matplotlib==3.4.2 networkx==2.5.1 -numpy==1.2.0 +numpy==1.20.0 pillow pyparsing==2.4.7 six==1.16.0 @@ -13,3 +13,4 @@ scipy==1.6.3 seaborn lxml shapely==1.8.4 +omegaconf

Coperception - basic docs for setting up the scenario runner ![image](https://user-images.githubusercontent.com/8717276/232376028-2e881157-6fc4-4837-99be-6426a1d3757b.png)

2023-04-09T04:49:13

0.0

[]

ucla-mobility/OpenCDA

ucla-mobility__OpenCDA-196

a29544a93564c88e1f1dcda5b26731cf361fefdc

diff --git a/opencda/core/common/cav_world.py b/opencda/core/common/cav_world.py index 794bab8f..db492a85 100644 --- a/opencda/core/common/cav_world.py +++ b/opencda/core/common/cav_world.py @@ -55,6 +55,12 @@ def __init__(self, apply_ml=False): # this is used only when co-simulation activated. self.sumo2carla_ids = {} + def destroy(self): + for vehicle_manager in self._vehicle_manager_dict.values(): + vehicle_manager.destroy() + for rsu_manager in self._rsu_manager_dict.values(): + rsu_manager.destory() + def update_vehicle_manager(self, vehicle_manager): """ Update created CAV manager to the world. diff --git a/opencda/core/map/map_manager.py b/opencda/core/map/map_manager.py index 92e7f645..70f70aed 100644 --- a/opencda/core/map/map_manager.py +++ b/opencda/core/map/map_manager.py @@ -289,7 +289,10 @@ def generate_lane_cross_info(self): while nxt.road_id == waypoint.road_id \ and nxt.lane_id == waypoint.lane_id: waypoints.append(nxt) - nxt = nxt.next(self.lane_sample_resolution)[0] + nxt_list = nxt.next(self.lane_sample_resolution) + if len(nxt_list) == 0: + break + nxt = nxt_list[0] # waypoint is the centerline, we need to calculate left lane mark left_marking = [lateral_shift(w.transform, -w.lane_width * 0.5) for

Develop This PR mainly include the docker build for OpenCDA.

2023-03-23T01:32:21

0.0

[]

ucla-mobility/OpenCDA

ucla-mobility__OpenCDA-191

e95071dbd640c2c732e98d053da40d5ef785a185

diff --git a/.gitignore b/.gitignore index 65583e2c..67e1370c 100644 --- a/.gitignore +++ b/.gitignore @@ -148,4 +148,4 @@ evaluation_outputs/ docs/tmp.py video_dumping *data_dumping* - +ready* diff --git a/docs/md_files/yaml_define.md b/docs/md_files/yaml_define.md index 6cc442d6..77267000 100644 --- a/docs/md_files/yaml_define.md +++ b/docs/md_files/yaml_define.md @@ -1,16 +1,26 @@ ## Yaml Rule -To construct a scenario testing in OpenCDA, users have to first write a yml file to define the simulation parameters. To help reuse the parameters across different modules within the system, we adopt the `name anchor`. To know more details about the `named anchor` feature in yaml file, [read this blog](https://anil.io/blog/symfony/yaml/using-variables-in-yaml-files/). +To create a scenario test in OpenCDA, you need to start by writing a yml file to define the simulation parameters. This can be a bit tricky, so we've provided a helpful starting point in the form of a yaml file called default.yaml. -Below we demonstrate an example in `platoon_2lanefree_carla` scenario. Our example yaml files for various scenes are stored in the path`opencda/scenario_testing/config_yaml`. +The default.yaml file provides default parameters for a scenario, which you can modify as needed to create your own scenario. Instead of starting from scratch, you can use this file as a template and only change the parts that are different from the default parameters. +If you're not sure where to start, we've included example yaml files for various scenarios in the opencda/scenario_testing/config_yaml directory. You can use these as a guide or a starting point for your own scenario. + +Below show an concrete example: ```yaml -# platoon_2lanefree_carla.yaml -world: # define the CARLA server setting - sync_mode: true # whether to use sync mode - client_port: 2000 # client port to connect to the server - fixed_delta_seconds: &delta 0.05 # fixed time step - weather: # set weather parameters +# default.yaml +description: |- + Copyright 2021 <UCLA Mobility Lab> + Author: Runsheng Xu <[email protected]> + Content: This is the template scenario testing configuration file that other scenarios could directly refer + +# define carla simulation setting +world: + sync_mode: true + client_port: 2000 + fixed_delta_seconds: 0.05 + seed: 11 # seed for numpy and random + weather: sun_altitude_angle: 15 # 90 is the midday and -90 is the midnight cloudiness: 0 # 0 is the clean sky and 100 is the thickest cloud precipitation: 0 # rain, 100 is the heaviest rain @@ -22,58 +32,277 @@ world: # define the CARLA server setting wetness: 0 -vehicle_base: &vehicle_base # define cav default parameters - sensing: &base_sensing # define sensing parameters - perception: &base_perception # define perception related settings - localization: &base_localize # define localization related settings - map_manager: &base_map_manager # define HDMap manager - behavior: &base_behavior # define planning related parameters - controller: &base_controller # define controller - type: pid_controller # define the type of controller - args: ... # define the detailed parmaters of the controller - v2x: &base_v2x # define v2x configuration - -carla_traffic_manager: # define the background traffic controled by carla.TrafficManager - global_speed_perc: -100 # define the default speed of traffic flow - auto_lane_change: false # whether lane change is allowed in the traffic flow - ...: # some other parameters - random: true # whether the car models of traffic flow are random selected - vehicle_list: # define all human drive vehicles' position and individual speed - - spawn_position: [x,y,z,pitch,yaw,roll] - vehicle_speed_perc: -200 # this speed will overwrite the default traffic flow speed - # this is another we to define carla traffic flow by giving the number of - # spawn vehicles and the spawn range. - # vehicle_list : 10 - # range: [x1, y1, x2, y2] - -platoon_base: &platoon_base # define the platoon default characteristics +# Define the basic parameters of the rsu +rsu_base: + sensing: + perception: + activate: false # when not activated, objects positions will be retrieved from server directly + camera: + visualize: 4 # how many camera images need to be visualized. 0 means no visualization for camera + num: 4 # how many cameras are mounted on the vehicle. Maximum 3(frontal, left and right cameras) + # relative positions (x,y,z,yaw) of the camera. len(positions) should be equal to camera num + positions: + - [2.5, 0, 1.0, 0] + - [0.0, 0.3, 1.8, 100] + - [0.0, -0.3, 1.8, -100] + - [-2.0, 0.0, 1.5, 180] + lidar: # lidar sensor configuration, check CARLA sensor reference for more details + visualize: true + channels: 32 + range: 120 + points_per_second: 1000000 + rotation_frequency: 20 # the simulation is 20 fps + upper_fov: 2 + lower_fov: -25 + dropoff_general_rate: 0.3 + dropoff_intensity_limit: 0.7 + dropoff_zero_intensity: 0.4 + noise_stddev: 0.02 + localization: + activate: true # when not activated, ego position will be retrieved from server directly + dt: ${world.fixed_delta_seconds} # used for kalman filter + gnss: # gnss sensor configuration + noise_alt_stddev: 0.05 + noise_lat_stddev: 3e-6 + noise_lon_stddev: 3e-6 + +# Basic parameters of the vehicles +vehicle_base: + sensing: # include perception and localization + perception: + activate: false # when not activated, objects positions will be retrieved from server directly + camera: + visualize: 1 # how many camera images need to be visualized. 0 means no visualization for camera + num: 1 # how many cameras are mounted on the vehicle. + positions: # relative positions (x,y,z,yaw) of the camera. len(positions) should be equal to camera num + - [2.5, 0, 1.0, 0] + lidar: # lidar sensor configuration, check CARLA sensor reference for more details + visualize: true + channels: 32 + range: 50 + points_per_second: 100000 + rotation_frequency: 20 # the simulation is 20 fps + upper_fov: 10.0 + lower_fov: -30.0 + dropoff_general_rate: 0.0 + dropoff_intensity_limit: 1.0 + dropoff_zero_intensity: 0.0 + noise_stddev: 0.0 + + localization: + activate: false # when not activated, ego position will be retrieved from server directly + dt: ${world.fixed_delta_seconds} # used for kalman filter + gnss: # gnss sensor configuration + noise_alt_stddev: 0.001 + noise_lat_stddev: 1.0e-6 + noise_lon_stddev: 1.0e-6 + heading_direction_stddev: 0.1 # degree + speed_stddev: 0.2 + debug_helper: + show_animation: false # whether to show real-time trajectory plotting + x_scale: 1.0 # used to multiply with the x coordinate to make the error on x axis clearer + y_scale: 100.0 # used to multiply with the y coordinate to make the error on y axis clearer + + map_manager: + pixels_per_meter: 2 # rasterization map resolution + raster_size: [224, 224] # the rasterize map size (pixel) + lane_sample_resolution: 0.1 # for every 0.1m, we draw a point of lane + visualize: true # whether to visualize the rasteraization map + activate: true # whether activate the map manager + + safety_manager: # used to watch the safety status of the cav + print_message: true # whether to print the message if hazard happens + collision_sensor: + history_size: 30 + col_thresh: 1 + stuck_dector: + len_thresh: 500 + speed_thresh: 0.5 + offroad_dector: [ ] + traffic_light_detector: # whether the vehicle violate the traffic light + light_dist_thresh: 20 + + behavior: + max_speed: 111 # maximum speed, km/h + tailgate_speed: 121 # when a vehicles needs to be close to another vehicle asap + speed_lim_dist: 3 # max_speed - speed_lim_dist = target speed + speed_decrease: 15 # used in car following mode to decrease speed for distance keeping + safety_time: 4 # ttc safety thresholding for decreasing speed + emergency_param: 0.4 # used to identify whether a emergency stop needed + ignore_traffic_light: true # whether to ignore traffic light + overtake_allowed: true # whether overtake allowed, typically false for platoon leader + collision_time_ahead: 1.5 # used for collision checking + overtake_counter_recover: 35 # the vehicle can not do another overtake during next certain steps + sample_resolution: 4.5 # the unit distance between two adjacent waypoints in meter + local_planner: # trajectory planning related + buffer_size: 12 # waypoint buffer size + trajectory_update_freq: 15 # used to control trajectory points updating frequency + waypoint_update_freq: 9 # used to control waypoint updating frequency + min_dist: 3 # used to pop out the waypoints too close to current location + trajectory_dt: 0.20 # for every dt seconds, we sample a trajectory point from the trajectory path as next goal state + debug: false # whether to draw future/history waypoints + debug_trajectory: false # whether to draw the trajectory points and path + + controller: + type: pid_controller # this has to be exactly the same name as the controller py file + args: + lat: + k_p: 0.75 + k_d: 0.02 + k_i: 0.4 + lon: + k_p: 0.37 + k_d: 0.024 + k_i: 0.032 + dynamic: false # whether use dynamic pid setting + dt: ${world.fixed_delta_seconds} # this should be equal to your simulation time-step + max_brake: 1.0 + max_throttle: 1.0 + max_steering: 0.3 + v2x: # communication related + enabled: true + communication_range: 35 + + +# define the background traffic control by carla +carla_traffic_manager: + sync_mode: true # has to be same as the world setting + global_distance: 5 # the minimum distance in meters that vehicles have to keep with the rest + # Sets the difference the vehicle's intended speed and its current speed limit. + # Carla default speed is 30 km/h, so -100 represents 60 km/h, + # and 20 represents 24 km/h + global_speed_perc: -100 + set_osm_mode: true # Enables or disables the OSM mode. + auto_lane_change: false + ignore_lights_percentage: 0 # whether set the traffic ignore traffic lights + random: false # whether to random select vehicles' color and model + vehicle_list: [] # define in each scenario. If set to ~, then the vehicles be spawned in a certain range + # Used only when vehicle_list is ~ + # x_min, x_max, y_min, y_max, x_step, y_step, vehicle_num + range: [] + +# define the platoon basic characteristics +platoon_base: + max_capacity: 10 + inter_gap: 0.6 # desired time gap + open_gap: 1.2 # open gap + warm_up_speed: 55 # required speed before cooperative merging + change_leader_speed: true # whether to assign leader multiple speed to follow + leader_speeds_profile: [ 85, 95 ] # different speed for leader to follow + stage_duration: 10 # how long should the leader keeps in the current velocity stage + +# define tne scenario in each specific scenario +scenario: + single_cav_list: [] + platoon_list: [] +``` +The above yaml file is the `default.yaml`. If the users wants to create a platoon joining scenario in highway, +here is how we create `platoon_joining_2lanefree_carla.yaml`: + +```yaml +# platoon_joining_2lanefree_carla.yaml +vehicle_base: + sensing: + perception: + camera: + visualize: 0 # how many camera images need to be visualized. 0 means no visualization for camera + num: 0 # how many cameras are mounted on the vehicle. Maximum 3(frontal, left and right cameras) + # relative positions (x,y,z,yaw) of the camera. len(positions) should be equal to camera num + positions: [] + lidar: + visualize: false + map_manager: + visualize: false + activate: false + behavior: + max_speed: 95 # maximum speed, km/h + tailgate_speed: 105 # when a vehicles needs to be close to another vehicle asap + overtake_allowed: false # whether overtake allowed, typically false for platoon leader + collision_time_ahead: 1.3 # used for collision checking + overtake_counter_recover: 35 # the vehicle can not do another overtake during next certain steps + local_planner: + trajectory_dt: 0.25 # for every dt seconds, we sample a trajectory point from the trajectory path as next goal state + +# define the platoon basic characteristics +platoon_base: max_capacity: 10 inter_gap: 0.6 # desired time gap - open_gap: 1.5 # desired open gap during cut-in join - warm_up_speed: 55 # required minimum speed before cooperative merging - - -scenario: # define each cav's spawn position, driving task, and parameters - platoon_list: # define the platoons - - <<: *platoon_base # the first platoon will take the default platoon parameters - destination: [x, y, z] # platoon destination - members: # define the first platoon's members - - <<: *vehicle_base # the platoon leader(a cav) will take the default cav parameters - spawn_position: ... - behavior: - <<: *base_behavior - max_speed: 100 # overwrite the default target speed defined in &vehicle_base - platoon: *platoon_base # add a new category 'platoon' to the origin vehicle parameters - - <<: *vehicle_base # the second platoon mameber(a cav) - ... - single_cav_list: # define the cavs that are not in any platoon and aim to search and join one. - - <<: *vehicle_base - spawn_position: [x,y,z,pitch,yaw,roll] - destination: [x, y, z] - sensing: &base_sensing # point to default sensing setting - ...: ... # overwrite the default sensing parameters for this cav - + open_gap: 1.5 # open gap + warm_up_speed: 55 # required speed before cooperative merging + +# define the background traffic control by carla +carla_traffic_manager: + global_distance: 4.0 # the minimum distance in meters that vehicles have to keep with the rest + # Sets the difference the vehicle's intended speed and its current speed limit. + # Carla default speed is 30 km/h, so -100 represents 60 km/h, + # and 20 represents 24 km/h + global_speed_perc: -300 + vehicle_list: + - spawn_position: [-285, 8.3, 0.3, 0, 0, 0] + - spawn_position: [-310, 8.3, 0.3, 0, 0, 0] + - spawn_position: [-390, 8.3, 0.3, 0, 0, 0] + - spawn_position: [-320, 4.8, 0.3, 0, 0, 0] + vehicle_speed_perc: -200 + - spawn_position: [-335, 4.8, 0.3, 0, 0, 0] + - spawn_position: [-360, 4.8, 0.3, 0, 0, 0] + - spawn_position: [-400, 4.8, 0.3, 0, 0, 0] + - spawn_position: [-410, 4.8, 0.3, 0, 0, 0] + +# define scenario. In this scenario, a 4-vehicle platoon already exists. +scenario: + platoon_list: + - name: platoon1 + destination: [1000.372955, 8.3, 0.3] + members: # the first one is regarded as leader by default + - name: cav1 + spawn_position: [-350, 8.3, 0.3, 0, 0, 0] # x, y, z, roll, yaw, pitch + perception: + camera: + visualize: 1 # how many camera images need to be visualized. 0 means no visualization for camera + num: 1 # how many cameras are mounted on the vehicle. Maximum 3(frontal, left and right cameras) + # relative positions (x,y,z,yaw) of the camera. len(positions) should be equal to camera num + positions: + - [2.5, 0, 1.0, 0] + lidar: + visualize: true + behavior: + local_planner: + debug_trajectory: true + debug: false + - name: cav2 + spawn_position: [-360, 8.3, 0.3, 0, 0, 0] + - name: cav3 + spawn_position: [-370, 8.3, 0.3, 0, 0, 0] + - name: cav4 + spawn_position: [-380, 8.3, 0.3, 0, 0, 0] + single_cav_list: # this is for merging vehicle or single cav without v2x + - name: single_cav + spawn_position: [-380, 4.8, 0.3, 0, 0, 0] + # when this is defined, the above parameter will be ignored, and a special map function will + # be used to define the spawn position based on the argument + spawn_special: [0.625] + destination: [300, 12.0, 0] + sensing: + perception: + camera: + visualize: 1 # how many camera images need to be visualized. 0 means no visualization for camera + num: 1 # how many cameras are mounted on the vehicle. Maximum 3(frontal, left and right cameras) + # relative positions (x,y,z,yaw) of the camera. len(positions) should be equal to camera num + positions: + - [2.5, 0, 1.0, 0] + lidar: + visualize: true + v2x: + communication_range: 35 + behavior: + overtake_allowed: true + local_planner: + debug_trajectory: true + debug: false ``` +As you can see, the `platoon_joining_2lanefree_carla.yaml` only contains the part that `default.yaml` does not have or has different +parameters. + ### Detailed Explanation --- @@ -174,14 +403,16 @@ carla_traffic_manager: - spawn_position: [100, 100, 0.3, 0 , 20, 0] - spawn_position: [122, 666, 0.3, 0 , 0, 0] ``` -* Set the parameter `vehicle_list` under `carla_traffic_manager` as an integer. The CARLA server will then spawn -the same number of vehicles. If `vehicle_list` is an integer, an additional parameter `range` needs to be set to -give the server the spawn area. In the example shown below, 5 vehicles will be randomly spawn in the restricted -rectangle area `0<x<100, 22<y<334`. +* Set the parameter `vehicle_list` under `carla_traffic_manager` as `~`. The CARLA server will then spawn +the vehicles randomly in a certain rectangle range given by the additional parameter `range`. ```yaml carla_traffic_manager: - vehicle_list : 5 - range: [0, 100, 22, 334] + vehicle_list: ~ # a number or a list + # Used only when vehicle_list is a number. + # x_min, x_max, y_min, y_max, x_step, y_step, veh_num + range: + - [ 2, 10, 0, 200, 3.5, 25, 30] + ``` Other important parameters: * `sync_mode` : bool type, it should be consistent with server's sync setting. diff --git a/opencda.py b/opencda.py index 8eca93c1..20ef5199 100644 --- a/opencda.py +++ b/opencda.py @@ -10,19 +10,22 @@ import importlib import os import sys +from omegaconf import OmegaConf from opencda.version import __version__ def arg_parse(): + # create an argument parser parser = argparse.ArgumentParser(description="OpenCDA scenario runner.") + # add arguments to the parser parser.add_argument('-t', "--test_scenario", required=True, type=str, help='Define the name of the scenario you want to test. The given name must' 'match one of the testing scripts(e.g. single_2lanefree_carla) in ' 'opencda/scenario_testing/ folder' ' as well as the corresponding yaml file in opencda/scenario_testing/config_yaml.') - parser.add_argument("--record", action='store_true', help='whether to record and save the simulation process to' - '.log file') + parser.add_argument("--record", action='store_true', + help='whether to record and save the simulation process to .log file') parser.add_argument("--apply_ml", action='store_true', help='whether ml/dl framework such as sklearn/pytorch is needed in the testing. ' @@ -30,25 +33,41 @@ def arg_parse(): parser.add_argument('-v', "--version", type=str, default='0.9.11', help='Specify the CARLA simulator version, default' 'is 0.9.11, 0.9.12 is also supported.') - + # parse the arguments and return the result opt = parser.parse_args() return opt def main(): + # parse the arguments opt = arg_parse() + # print the version of OpenCDA print("OpenCDA Version: %s" % __version__) - - testing_scenario = importlib.import_module("opencda.scenario_testing.%s" % opt.test_scenario) - + # set the default yaml file + default_yaml = config_yaml = os.path.join( + os.path.dirname(os.path.realpath(__file__)), + 'opencda/scenario_testing/config_yaml/default.yaml') + # set the yaml file for the specific testing scenario config_yaml = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'opencda/scenario_testing/config_yaml/%s.yaml' % opt.test_scenario) + # load the default yaml file and the scenario yaml file as dictionaries + default_dict = OmegaConf.load(default_yaml) + scene_dict = OmegaConf.load(config_yaml) + # merge the dictionaries + scene_dict = OmegaConf.merge(default_dict, scene_dict) + + # import the testing script + testing_scenario = importlib.import_module( + "opencda.scenario_testing.%s" % opt.test_scenario) + # check if the yaml file for the specific testing scenario exists if not os.path.isfile(config_yaml): - sys.exit("opencda/scenario_testing/config_yaml/%s.yaml not found!" % opt.test_cenario) + sys.exit( + "opencda/scenario_testing/config_yaml/%s.yaml not found!" % opt.test_cenario) + # get the function for running the scenario from the testing script scenario_runner = getattr(testing_scenario, 'run_scenario') - # run scenario testing - scenario_runner(opt, config_yaml) + # run the scenario testing + scenario_runner(opt, scene_dict) if __name__ == '__main__': diff --git a/opencda/core/common/data_dumper.py b/opencda/core/common/data_dumper.py index b347f9f0..78b1b3b9 100644 --- a/opencda/core/common/data_dumper.py +++ b/opencda/core/common/data_dumper.py @@ -100,13 +100,14 @@ def run_step(self, if self.count % 2 != 0: return - self.save_rgb_image() - self.save_lidar_points() + self.save_rgb_image(self.count) + # self.save_lidar_points() self.save_yaml_file(perception_manager, localization_manager, - behavior_agent) + behavior_agent, + self.count) - def save_rgb_image(self): + def save_rgb_image(self, count): """ Save camera rgb images to disk. """ @@ -115,7 +116,7 @@ def save_rgb_image(self): frame = camera.frame image = camera.image - image_name = '%06d' % frame + '_' + 'camera%d' % i + '.png' + image_name = '%06d' % count + '_' + 'camera%d' % i + '.png' cv2.imwrite(os.path.join(self.save_parent_folder, image_name), image) @@ -149,7 +150,8 @@ def save_lidar_points(self): def save_yaml_file(self, perception_manager, localization_manager, - behavior_agent): + behavior_agent, + count): """ Save objects positions/spped, true ego position, predicted ego position, sensor transformations. @@ -165,7 +167,7 @@ def save_yaml_file(self, behavior_agent : opencda object OpenCDA behavior agent. """ - frame = self.lidar.frame + frame = count dump_yml = {} vehicle_dict = {} diff --git a/opencda/core/common/vehicle_manager.py b/opencda/core/common/vehicle_manager.py index 99a15afa..5135f082 100644 --- a/opencda/core/common/vehicle_manager.py +++ b/opencda/core/common/vehicle_manager.py @@ -17,6 +17,7 @@ import LocalizationManager from opencda.core.sensing.perception.perception_manager \ import PerceptionManager +from opencda.core.safety.safety_manager import SafetyManager from opencda.core.plan.behavior_agent \ import BehaviorAgent from opencda.core.map.map_manager import MapManager @@ -107,7 +108,9 @@ def __init__( self.map_manager = MapManager(vehicle, carla_map, map_config) - + # safety manager + self.safety_manager = SafetyManager(vehicle=vehicle, + params=config_yaml['safety_manager']) # behavior agent self.agent = None if 'platooning' in application: @@ -181,6 +184,15 @@ def update_info(self): # update the ego pose for map manager self.map_manager.update_information(ego_pos) + # this is required by safety manager + safety_input = {'ego_pos': ego_pos, + 'ego_speed': ego_spd, + 'objects': objects, + 'carla_map': self.carla_map, + 'world': self.vehicle.get_world(), + 'static_bev': self.map_manager.static_bev} + self.safety_manager.update_info(safety_input) + # update ego position and speed to v2x manager, # and then v2x manager will search the nearby cavs self.v2x_manager.update_info(ego_pos, ego_spd) diff --git a/opencda/core/map/map_manager.py b/opencda/core/map/map_manager.py index 4e0a2cdc..92e7f645 100644 --- a/opencda/core/map/map_manager.py +++ b/opencda/core/map/map_manager.py @@ -26,8 +26,6 @@ class MapManager(object): """ This class is used to manage HD Map. We emulate the style of Lyft dataset. - todo: Currently mainly used for map rasterization. - todo: everything is groundtruth loaded from server directly. Parameters ---------- @@ -65,7 +63,6 @@ class MapManager(object): crosswalk_info : dict A dictionary that contains all crosswalk information. - todo: will be implemented in the next version. traffic_light_info : dict A dictionary that contains all traffic light information. @@ -127,15 +124,9 @@ def __init__(self, vehicle, carla_map, config): self.generate_lane_cross_info() # bev maps - self.dynamic_bev = 255 * np.zeros( - shape=(self.raster_size[1], self.raster_size[0], 3), - dtype=np.uint8) - self.static_bev = 255 * np.ones( - shape=(self.raster_size[1], self.raster_size[0], 3), - dtype=np.uint8) - self.vis_bev = 255 * np.ones( - shape=(self.raster_size[1], self.raster_size[0], 3), - dtype=np.uint8) + self.dynamic_bev = None + self.static_bev = None + self.vis_bev = None def update_information(self, ego_pose): """ diff --git a/opencda/core/plan/behavior_agent.py b/opencda/core/plan/behavior_agent.py index d45bb38a..847d9b5c 100644 --- a/opencda/core/plan/behavior_agent.py +++ b/opencda/core/plan/behavior_agent.py @@ -142,6 +142,9 @@ def __init__(self, vehicle, carla_map, config_yaml): # debug helper self.debug_helper = PlanDebugHelper(self.vehicle.id) + # print message in debug mode + self.debug = False if 'debug' not in \ + config_yaml else config_yaml['debug'] def update_information(self, ego_pos, ego_speed, objects): """ @@ -308,14 +311,15 @@ def reroute(self, spawn_points): spawn_points : list List of possible destinations for the agent. """ - - print("Target almost reached, setting new destination...") + if self.debug: + print("Target almost reached, setting new destination...") random.shuffle(spawn_points) new_start = \ self._local_planner.waypoints_queue[-1][0].transform.location destination = spawn_points[0].location if \ spawn_points[0].location != new_start else spawn_points[1].location - print("New destination: " + str(destination)) + if self.debug: + print("New destination: " + str(destination)) self.set_destination(new_start, destination) @@ -390,7 +394,6 @@ def traffic_light_manager(self, waypoint): # indicate no need to stop return 0 - if not waypoint.is_junction and ( self.light_id_to_ignore != light_id or light_id == -1): return 1 @@ -728,13 +731,13 @@ def get_push_destination(self, ego_vehicle_wp, is_intersection): reset_target = \ ego_vehicle_wp.next(max(self._ego_speed / 3.6 * 3, 10.0))[0] - - print( - 'Vehicle id: %d :destination pushed forward because of ' - 'potential collision, reset destination :%f. %f, %f' % - (self.vehicle.id, reset_target.transform.location.x, - reset_target.transform.location.y, - reset_target.transform.location.z)) + if self.debug: + print( + 'Vehicle id: %d :destination pushed forward because of ' + 'potential collision, reset destination :%f. %f, %f' % + (self.vehicle.id, reset_target.transform.location.x, + reset_target.transform.location.y, + reset_target.transform.location.z)) return reset_target def run_step( @@ -791,7 +794,8 @@ def run_step( # 2. when the temporary route is finished, we return to the global route if len(self.get_local_planner().get_waypoints_queue()) == 0 \ and len(self.get_local_planner().get_waypoint_buffer()) <= 2: - print('Destination Reset!') + if self.debug: + print('Destination Reset!') # in case the vehicle is disabled overtaking function # at the beginning self.overtake_allowed = True and self.overtake_allowed_origin @@ -812,7 +816,6 @@ def run_step( # Path generation based on the global route rx, ry, rk, ryaw = self._local_planner.generate_path() - # check whether lane change is allowed self.lane_change_allowed = self.check_lane_change_permission(lane_change_allowed, collision_detector_enabled, rk) diff --git a/opencda/core/plan/local_planner_behavior.py b/opencda/core/plan/local_planner_behavior.py index ddbb0a5e..04d80392 100644 --- a/opencda/core/plan/local_planner_behavior.py +++ b/opencda/core/plan/local_planner_behavior.py @@ -469,7 +469,7 @@ def buffer_filter(self): self._ego_pos.location, self._ego_pos.rotation.yaw) if angle > 90: - print('delete waypoint!') + # print('delete waypoint!') del self._waypoint_buffer[j] continue diff --git a/opencda/core/safety/__init__.py b/opencda/core/safety/__init__.py new file mode 100644 index 00000000..38d0f116 --- /dev/null +++ b/opencda/core/safety/__init__.py @@ -0,0 +1,1 @@ +"""Safety/Human in the loop system related""" \ No newline at end of file diff --git a/opencda/core/safety/safety_manager.py b/opencda/core/safety/safety_manager.py new file mode 100644 index 00000000..ac3069d4 --- /dev/null +++ b/opencda/core/safety/safety_manager.py @@ -0,0 +1,51 @@ +""" +The safety manager is used to collect the AV's hazard status and give the +control back to human if necessary +""" +import numpy as np +import carla + +from opencda.core.safety.sensors import CollisionSensor, \ + TrafficLightDector, StuckDetector, OffRoadDetector + + +class SafetyManager: + """ + A class that manages the safety of a given vehicle in a simulation environment. + + Parameters + ---------- + vehicle: carla.Actor + The vehicle that the SafetyManager is responsible for. + params: dict + A dictionary of parameters that are used to configure the SafetyManager. + """ + def __init__(self, vehicle, params): + self.vehicle = vehicle + self.print_message = params['print_message'] + self.sensors = [CollisionSensor(vehicle, params['collision_sensor']), + StuckDetector(params['stuck_dector']), + OffRoadDetector(params['offroad_dector']), + TrafficLightDector(params['traffic_light_detector'], + vehicle)] + + def update_info(self, data_dict) -> dict: + status_dict = {} + for sensor in self.sensors: + sensor.tick(data_dict) + status_dict.update(sensor.return_status()) + if self.print_message: + print_flag = False + # only print message when it has hazard + for key, val in status_dict.items(): + if val == True: + print_flag = True + break + if print_flag: + print("Safety Warning from the safety manager:") + print(status_dict) + + + def destroy(self): + for sensor in self.sensors: + sensor.destroy() diff --git a/opencda/core/safety/sensors.py b/opencda/core/safety/sensors.py new file mode 100644 index 00000000..fa77fe3f --- /dev/null +++ b/opencda/core/safety/sensors.py @@ -0,0 +1,355 @@ +""" +Sensors related to safety status check +""" +import math +import numpy as np +import carla +import weakref +import shapely +from collections import deque +from typing import List + + +class CollisionSensor(object): + """ + Collision detection sensor. + + Parameters + ---------- + vehicle : carla.Vehicle + The carla.Vehicle, this is for cav. + params : dict + The dictionary containing sensor configurations. + + Attributes + ---------- + image : np.ndarray + Current received rgb image. + sensor : carla.sensor + The carla sensor that mounts at the vehicle. + """ + + def __init__(self, vehicle, params): + world = vehicle.get_world() + + blueprint = world.get_blueprint_library().find('sensor.other.collision') + self.sensor = world.spawn_actor(blueprint, carla.Transform(), + attach_to=vehicle) + + # We need to pass the lambda a weak reference to self to avoid circular + # reference. + weak_self = weakref.ref(self) + self.sensor.listen( + lambda event: CollisionSensor._on_collision(weak_self, event)) + + self.collided = False + self.collided_frame = -1 + self._history = deque(maxlen=params['history_size']) + self._threshold = params['col_thresh'] + + @staticmethod + def _on_collision(weak_self, event) -> None: + self = weak_self() + if not self: + return + impulse = event.normal_impulse + intensity = math.sqrt(impulse.x ** 2 + impulse.y ** 2 + impulse.z ** 2) + self._history.append((event.frame, intensity)) + if intensity > self._threshold: + self.collided = True + self.collided_frame = event.frame + + def return_status(self): + return {'collision': self.collided} + + def tick(self, data_dict): + pass + + def destroy(self) -> None: + """ + Clear collision sensor in Carla world. + """ + self._history.clear() + if self.sensor.is_alive: + self.sensor.stop() + self.sensor.destroy() + + +class StuckDetector(object): + """ + Stuck detector used to detect vehicle stuck in simulator. + It takes speed as input in each tick. + + Parameters + ---------- + params : dict + The dictionary containing sensor configurations. + """ + + def __init__(self, params): + self._speed_queue = deque(maxlen=params['len_thresh']) + self._len_thresh = params['len_thresh'] + self._speed_thresh = params['speed_thresh'] + + self.stuck = False + + def tick(self, data_dict) -> None: + """ + Update one tick + + Parameters + ---------- + data_dict : dict + The data dictionary provided by the upsteam modules. + """ + speed = data_dict['ego_speed'] + self._speed_queue.append(speed) + if len(self._speed_queue) >= self._len_thresh: + if np.average(self._speed_queue) < self._speed_thresh: + self.stuck = True + return + self.stuck = False + + def return_status(self): + return {'stuck': self.stuck} + + def destroy(self): + """ + Clear speed history + """ + self._speed_queue.clear() + + +class OffRoadDetector(object): + """ + A detector to monitor whether + + Parameters + ---------- + params : dict + The dictionary containing sensor configurations. + """ + + def __init__(self, params): + self.off_road = False + + def tick(self, data_dict) -> None: + """ + Update one tick + + Parameters + ---------- + data_dict : dict + The data dictionary provided by the upsteam modules. + """ + # static bev map that indicate where is the road + static_map = data_dict['static_bev'] + if static_map is None: + return + h, w = static_map.shape[0], static_map.shape[1] + # the ego is always at the center of the bev map. If the pixel is + # black, that means the vehicle is off road. + if np.mean(static_map[h // 2, w // 2]) == 255: + self.off_road = True + else: + self.off_road = False + + def return_status(self): + return {'offroad': self.off_road} + + def destroy(self): + pass + + +class TrafficLightDector(object): + """ + Interface of traffic light detector and recorder. It detects next traffic light state, + calculates distance from hero vehicle to the end of this road, and if hero vehicle crosses + this line when correlated light is red, it will record running a red light + """ + + def __init__(self, params, vehicle): + self.ran_light = False + self._map = None + self.veh_extent = vehicle.bounding_box.extent.x + + self._light_dis_thresh = params['light_dist_thresh'] + self._active_light = None + self._last_light = None + + self.total_lights_ran = 0 + self.total_lights = 0 + self.ran_light = False + self.active_light_state = carla.TrafficLightState.Off + self.active_light_dis = 200 + + def tick(self, data_dict): + # Reset the "ran light" flag + self.ran_light = False + + # Extract the active traffic lights, vehicle transform, world, and map from data_dict + active_lights = data_dict['objects']['traffic_lights'] + vehicle_transform = data_dict['ego_pos'] + world = data_dict['world'] + self._map = data_dict['carla_map'] + + # Get the location of the first active traffic light + self._active_light = active_lights[0] if len(active_lights) > 0 \ + else None + vehicle_location = vehicle_transform.location + + # If there is an active traffic light, + # compute the distance between the vehicle and the traffic light + if self._active_light is not None: + light_trigger_location = self._active_light.get_location() + self.active_light_state = self._active_light.get_state() + delta = vehicle_location - light_trigger_location + distance = np.sqrt(sum([delta.x ** 2, delta.y ** 2, delta.z ** 2])) + + # Set the active light distance to the minimum of the + # computed distance and a maximum threshold + self.active_light_dis = min(200, distance) + + # If the vehicle is close enough to the traffic light, + # and the traffic light has changed since the last tick, + # increment the total number of traffic lights seen and set the + # last light to the current light + if self.active_light_dis < self._light_dis_thresh: + if self._last_light is None or self._active_light.actor.id != self._last_light.id: + self.total_lights += 1 + self._last_light = self._active_light.actor + else: + # If there is no active traffic light, set the active light state + # to "Off" and set the active light distance to a default value + self.active_light_state = carla.TrafficLightState.Off + self.active_light_dis = 200 + + # If there is a last light (i.e., a traffic light that was active + # in the previous tick), check if it is currently red + if self._last_light is not None: + if self._last_light.state != carla.TrafficLightState.Red: + return + + # Compute the endpoints of a line segment representing the + # vehicle's position and direction + veh_extent = self.veh_extent + tail_close_pt = self._rotate_point( + carla.Vector3D(-0.8 * veh_extent, 0.0, vehicle_location.z), + vehicle_transform.rotation.yaw + ) + tail_close_pt = vehicle_location + carla.Location(tail_close_pt) + tail_far_pt = self._rotate_point( + carla.Vector3D(-veh_extent - 1, 0.0, vehicle_location.z), + vehicle_transform.rotation.yaw + ) + tail_far_pt = vehicle_location + carla.Location(tail_far_pt) + + # Get the trigger waypoints for the last traffic light + trigger_waypoints = self._get_traffic_light_trigger_waypoints( + self._last_light) + + # For each trigger waypoint, + # check if the vehicle has crossed the stop line + for wp in trigger_waypoints: + tail_wp = self._map.get_waypoint(tail_far_pt) + + # Calculate the dot product (Might be unscaled, + # as only its sign is important) + ve_dir = vehicle_transform.get_forward_vector() + wp_dir = wp.transform.get_forward_vector() + dot_ve_wp = ve_dir.x * wp_dir.x + ve_dir.y * wp_dir.y + ve_dir.z * wp_dir.z + + # Check the lane until all the "tail" has passed + if tail_wp.road_id == wp.road_id and tail_wp.lane_id == wp.lane_id and dot_ve_wp > 0: + # This light is red and is affecting our lane + yaw_wp = wp.transform.rotation.yaw + lane_width = wp.lane_width + location_wp = wp.transform.location + + lft_lane_wp = self._rotate_point( + carla.Vector3D(0.4 * lane_width, 0.0, location_wp.z), + yaw_wp + 90) + lft_lane_wp = location_wp + carla.Location(lft_lane_wp) + rgt_lane_wp = self._rotate_point( + carla.Vector3D(0.4 * lane_width, 0.0, location_wp.z), + yaw_wp - 90) + rgt_lane_wp = location_wp + carla.Location(rgt_lane_wp) + + # Is the vehicle traversing the stop line? + if self._is_vehicle_crossing_line( + (tail_close_pt, tail_far_pt), + (lft_lane_wp, rgt_lane_wp)): + self.ran_light = True + self.total_lights_ran += 1 + self._last_light = None + + def _is_vehicle_crossing_line(self, seg1: List, seg2: List) -> bool: + """ + check if vehicle crosses a line segment + """ + line1 = shapely.geometry.LineString( + [(seg1[0].x, seg1[0].y), (seg1[1].x, seg1[1].y)]) + line2 = shapely.geometry.LineString( + [(seg2[0].x, seg2[0].y), (seg2[1].x, seg2[1].y)]) + inter = line1.intersection(line2) + + return not inter.is_empty + + def _rotate_point(self, point: carla.Vector3D, + angle: float) -> carla.Vector3D: + """ + rotate a given point by a given angle + """ + x_ = math.cos(math.radians(angle)) * point.x - math.sin( + math.radians(angle)) * point.y + y_ = math.sin(math.radians(angle)) * point.x + math.cos( + math.radians(angle)) * point.y + return carla.Vector3D(x_, y_, point.z) + + def _get_traffic_light_trigger_waypoints(self, + traffic_light: carla.Actor) -> \ + List[carla.Waypoint]: + # Get the transform information for the traffic light + base_transform = traffic_light.get_transform() + base_rot = base_transform.rotation.yaw + area_loc = base_transform.transform( + traffic_light.trigger_volume.location) + + # Get the extent of the trigger volume + area_ext = traffic_light.trigger_volume.extent + # Discretize the trigger box into points along the x-axis + x_values = np.arange(-0.9 * area_ext.x, 0.9 * area_ext.x, + 1.0) # 0.9 to avoid crossing to adjacent lanes + + # Create a list of discretized points + area = [] + for x in x_values: + point = self._rotate_point(carla.Vector3D(x, 0, area_ext.z), + base_rot) + point_location = area_loc + carla.Location(x=point.x, y=point.y) + area.append(point_location) + + # Get the waypoints of these points, removing duplicates + ini_wps = [] + for pt in area: + wpx = self._map.get_waypoint(pt) + # As x_values are arranged in order, only the last one has to be checked + if not ini_wps or ini_wps[-1].road_id != wpx.road_id or ini_wps[ + -1].lane_id != wpx.lane_id: + ini_wps.append(wpx) + + # Advance the waypoints until the intersection + wps = [] + for wpx in ini_wps: + while not wpx.is_intersection: + next_wp = wpx.next(0.5)[0] + if next_wp and not next_wp.is_intersection: + wpx = next_wp + else: + break + wps.append(wpx) + + return wps + + def return_status(self): + return {'ran_light': self.ran_light} diff --git a/opencda/core/sensing/perception/perception_manager.py b/opencda/core/sensing/perception/perception_manager.py index ecc777a9..49a7cca8 100644 --- a/opencda/core/sensing/perception/perception_manager.py +++ b/opencda/core/sensing/perception/perception_manager.py @@ -87,7 +87,10 @@ def spawn_point_estimation(relative_position, global_position): pitch = 0 carla_location = carla.Location(x=0, y=0, z=0) + x, y, z, yaw = relative_position + # this is for rsu. It utilizes global position instead of relative + # position to the vehicle if global_position is not None: carla_location = carla.Location( x=global_position[0], @@ -95,28 +98,9 @@ def spawn_point_estimation(relative_position, global_position): z=global_position[2]) pitch = -35 - if relative_position == 'front': - carla_location = carla.Location(x=carla_location.x + 2.5, - y=carla_location.y, - z=carla_location.z + 1.0) - yaw = 0 - - elif relative_position == 'right': - carla_location = carla.Location(x=carla_location.x + 0.0, - y=carla_location.y + 0.3, - z=carla_location.z + 1.8) - yaw = 100 - - elif relative_position == 'left': - carla_location = carla.Location(x=carla_location.x + 0.0, - y=carla_location.y - 0.3, - z=carla_location.z + 1.8) - yaw = -100 - else: - carla_location = carla.Location(x=carla_location.x - 2.0, - y=carla_location.y, - z=carla_location.z + 1.5) - yaw = 180 + carla_location = carla.Location(x=carla_location.x + x, + y=carla_location.y + y, + z=carla_location.z + z) carla_rotation = carla.Rotation(roll=0, yaw=yaw, pitch=pitch) spawn_point = carla.Transform(carla_location, carla_rotation) @@ -379,12 +363,13 @@ def __init__(self, vehicle, config_yaml, cav_world, self.vehicle = vehicle self.carla_world = carla_world if carla_world is not None \ else self.vehicle.get_world() + self._map = self.carla_world.get_map() self.id = infra_id if infra_id is not None else vehicle.id self.activate = config_yaml['activate'] - self.camera_visualize = config_yaml['camera_visualize'] - self.camera_num = min(config_yaml['camera_num'], 4) - self.lidar_visualize = config_yaml['lidar_visualize'] + self.camera_visualize = config_yaml['camera']['visualize'] + self.camera_num = config_yaml['camera']['num'] + self.lidar_visualize = config_yaml['lidar']['visualize'] self.global_position = config_yaml['global_position'] \ if 'global_position' in config_yaml else None @@ -406,7 +391,11 @@ def __init__(self, vehicle, config_yaml, cav_world, # camera visualization is needed if self.activate or self.camera_visualize: self.rgb_camera = [] - mount_position = ['front', 'right', 'left', 'back'] + mount_position = config_yaml['camera']['positions'] + assert len(mount_position) == self.camera_num, \ + "The camera number has to be the same as the length of the" \ + "relative positions list" + for i in range(self.camera_num): self.rgb_camera.append( CameraSensor( @@ -443,6 +432,9 @@ def __init__(self, vehicle, config_yaml, cav_world, # the dictionary contains all objects self.objects = {} + # traffic light detection related + self.traffic_thresh = config_yaml['traffic_light_thresh'] \ + if 'traffic_light_thresh' in config_yaml else 50 def dist(self, a): """ @@ -544,7 +536,6 @@ def activate_mode(self, objects): self.speed_retrieve(objects) if self.camera_visualize: - names = ['front', 'right', 'left', 'back'] for (i, rgb_image) in enumerate(rgb_draw_images): if i > self.camera_num - 1 or i > self.camera_visualize - 1: break @@ -552,8 +543,8 @@ def activate_mode(self, objects): yolo_detection, rgb_image, i) rgb_image = cv2.resize(rgb_image, (0, 0), fx=0.4, fy=0.4) cv2.imshow( - '%s camera of actor %d, perception activated' % - (names[i], self.id), rgb_image) + '%s-th camera of actor %d, perception activated' % + (str(i), self.id), rgb_image) cv2.waitKey(1) if self.lidar_visualize: @@ -590,6 +581,7 @@ def deactivate_mode(self, objects): world = self.carla_world vehicle_list = world.get_actors().filter("*vehicle*") + # todo: hard coded thresh = 50 if not self.data_dump else 120 vehicle_list = [v for v in vehicle_list if self.dist(v) < thresh and @@ -798,16 +790,49 @@ def retrieve_traffic_lights(self, objects): world = self.carla_world tl_list = world.get_actors().filter('traffic.traffic_light*') + vehicle_location = self.ego_pos.location + vehicle_waypoint = self._map.get_waypoint(vehicle_location) + + activate_tl, light_trigger_location = \ + self._get_active_light(tl_list, vehicle_location, vehicle_waypoint) + objects.update({'traffic_lights': []}) - for tl in tl_list: - distance = self.dist(tl) - if distance < 50: - traffic_light = TrafficLight(tl.get_location(), - tl.get_state()) - objects['traffic_lights'].append(traffic_light) + if activate_tl is not None: + traffic_light = TrafficLight(activate_tl, + light_trigger_location, + activate_tl.get_state()) + objects['traffic_lights'].append(traffic_light) return objects + def _get_active_light(self, tl_list, vehicle_location, vehicle_waypoint): + for tl in tl_list: + object_location = \ + TrafficLight.get_trafficlight_trigger_location(tl) + object_waypoint = self._map.get_waypoint(object_location) + + if object_waypoint.road_id != vehicle_waypoint.road_id: + continue + + ve_dir = vehicle_waypoint.transform.get_forward_vector() + wp_dir = object_waypoint.transform.get_forward_vector() + dot_ve_wp = ve_dir.x * wp_dir.x +\ + ve_dir.y * wp_dir.y + \ + ve_dir.z * wp_dir.z + + if dot_ve_wp < 0: + continue + while not object_waypoint.is_intersection: + next_waypoint = object_waypoint.next(0.5)[0] + if next_waypoint and not next_waypoint.is_intersection: + object_waypoint = next_waypoint + else: + break + + return tl, object_waypoint.transform.location + + return None, None + def destroy(self): """ Destroy sensors. diff --git a/opencda/core/sensing/perception/static_obstacle.py b/opencda/core/sensing/perception/static_obstacle.py index 01e64c93..d3f105cb 100644 --- a/opencda/core/sensing/perception/static_obstacle.py +++ b/opencda/core/sensing/perception/static_obstacle.py @@ -6,6 +6,7 @@ # Author: Runsheng Xu <[email protected]> # License: TDG-Attribution-NonCommercial-NoDistrib import sys +import math import numpy as np import carla @@ -71,6 +72,12 @@ class TrafficLight(object): Parameters --------- + tl : carla.Actor + The CARLA traffic actor + + trigger_location : carla.Vector3D + The trigger location of te traffic light. + pos : carla.Location The location of this traffic light. @@ -79,12 +86,42 @@ class TrafficLight(object): """ - def __init__(self, pos, light_state): - self._location = pos + def __init__(self, tl, trigger_location, light_state): + self._location = trigger_location self.state = light_state + self.actor = tl def get_location(self): return self._location def get_state(self): - return self.state \ No newline at end of file + return self.state + + @staticmethod + def get_trafficlight_trigger_location(traffic_light: carla.Actor) \ + -> carla.Vector3D: # pylint: disable=invalid-name + """ + Calculates the yaw of the waypoint that represents the trigger + volume of the traffic light + """ + + def rotate_point(point, angle): + """ + rotate a given point by a given angle + """ + x_ = math.cos(math.radians(angle)) * point.x - math.sin(math.radians(angle)) * point.y + y_ = math.sin(math.radians(angle)) * point.x - math.cos(math.radians(angle)) * point.y + + return carla.Vector3D(x_, y_, point.z) + + base_transform = traffic_light.get_transform() + base_rot = base_transform.rotation.yaw + area_loc = base_transform.transform(traffic_light.trigger_volume.location) + area_ext = traffic_light.trigger_volume.extent + + point = rotate_point(carla.Vector3D(0, 0, area_ext.z), base_rot) + point_location = area_loc + carla.Location(x=point.x, y=point.y) + + return carla.Location(point_location.x, + point_location.y, + point_location.z) diff --git a/opencda/version.py b/opencda/version.py index 51720796..823854a8 100644 --- a/opencda/version.py +++ b/opencda/version.py @@ -1,3 +1,3 @@ """Specifies the current version number of OpenCDA.""" -__version__ = "0.1.2" +__version__ = "0.1.3"

Feature/new config system OpenCDA now has a better configuration system! We have a default.yaml for all scenarios with default parameters, and users just need to change the part that is specific in each scenario yaml.

2023-03-03T17:20:54

0.0

[]

ucla-mobility/OpenCDA

ucla-mobility__OpenCDA-90

391f82a5520c75a0456db1936cc42e6fc0ea606a

diff --git a/opencda/assets/2lane_freeway_simplified/2lane_freeway_simplified.net.xml b/opencda/assets/2lane_freeway_simplified/2lane_freeway_simplified.net.xml index 5cbecdfa..b41857e5 100644 --- a/opencda/assets/2lane_freeway_simplified/2lane_freeway_simplified.net.xml +++ b/opencda/assets/2lane_freeway_simplified/2lane_freeway_simplified.net.xml @@ -1,14 +1,14 @@ <?xml version='1.0' encoding='UTF-8'?> - <net xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" version="1.9" junctionCornerDetail="5" rectangularLaneCut="true" limitTurnSpeed="5.50" xsi:noNamespaceSchemaLocation="http://sumo.dlr.de/xsd/net_file.xsd"> - <location netOffset="1204.72,456.54" convBoundary="0.00,0.00,2831.71,453.54" origBoundary="-1204.72,-456.54,1626.99,-3.00" projParameter="!"/> - <type id="bidirectional" priority="1" speed="1.39" disallow="pedestrian tram rail_urban rail rail_electric rail_fast ship" width="3.65"/> - <type id="biking" priority="-1" speed="5.56" allow="bicycle" width="1.50"/> - <type id="border" priority="0" speed="1.39" disallow="all" discard="1" width="0.10"/> - <type id="driving" priority="1" speed="13.89" disallow="pedestrian tram rail_urban rail rail_electric rail_fast ship" width="3.65"/> - <type id="entry" priority="1" speed="22.22" disallow="pedestrian tram rail_urban rail rail_electric rail_fast ship" width="3.65"/> - <type id="exit" priority="1" speed="22.22" disallow="pedestrian tram rail_urban rail rail_electric rail_fast ship" width="3.65"/> - <type id="median" priority="0" speed="1.39" disallow="all" discard="1" width="0.10"/> - <type id="mwyEntry" priority="1" speed="22.22" allow="private emergency authority army vip passenger hov taxi bus coach delivery truck trailer motorcycle evehicle custom1 custom2" width="3.65"/> - <type id="mwyExit" priority="1" speed="22.22" allow="private emergency authority army vip passenger hov taxi bus coach delivery truck trailer motorcycle evehicle custom1 custom2" width="3.65"/> - <type id="none" priority="0" speed="1.39" disallow="all" discard="1" width="1.00"/> - <type id="offRamp" priority="1" speed="22.22" allow="private emergency authority army vip passenger hov taxi bus coach delivery truck trailer motorcycle evehicle custom1 custom2" width="3.65"/> - <type id="onRamp" priority="1" speed="22.22" allow="private emergency authority army vip passenger hov taxi bus coach delivery truck trailer motorcycle evehicle custom1 custom2" width="3.65"/> - <type id="parking" priority="1" speed="1.39" disallow="pedestrian tram rail_urban rail rail_electric rail_fast ship" width="2.50"/> - <type id="rail" priority="3" speed="33.33" allow="rail_urban rail rail_electric rail_fast" width="3.65"/> - <type id="restricted" priority="0" speed="13.89" disallow="all" width="3.65"/> - <type id="roadWorks" priority="0" speed="1.39" allow="authority" width="3.65"/> - <type id="shoulder" priority="0" speed="1.39" disallow="all" discard="1" width="1.00"/> - <type id="sidewalk" priority="-2" speed="2.78" allow="pedestrian" width="2.50"/> - <type id="special1" priority="1" speed="22.22" allow="custom1" discard="1" width="3.65"/> - <type id="special2" priority="1" speed="22.22" allow="custom2" discard="1" width="3.65"/> - <type id="special3" priority="1" speed="22.22" allow="custom1 custom2" discard="1" width="3.65"/> - <type id="stop" priority="1" speed="13.89" disallow="pedestrian tram rail_urban rail rail_electric rail_fast ship" width="3.65"/> - <type id="tram" priority="2" speed="13.89" allow="tram" width="3.65"/> + <location netOffset="1204.72,456.54" convBoundary="0.00,0.00,2836.67,453.54" origBoundary="-1204.72,-456.54,1631.95,-3.00" projParameter="!"/> <edge id=":1_0" function="internal"> - <lane id=":1_0_0" index="0" disallow="pedestrian tram rail_urban rail rail_electric rail_fast ship" speed="31.31" length="16.98" width="3.66" shape="1188.16,441.22 1191.54,442.45 1195.04,443.46 1199.24,444.14 1204.72,444.39"> + <lane id=":1_0_0" index="0" speed="31.31" length="16.98" width="3.66" shape="1188.16,441.22 1191.54,442.45 1195.04,443.46 1199.24,444.14 1204.72,444.39"> <param key="origId" value="67_-1"/> </lane> </edge> <edge id=":1_1" function="internal"> - <lane id=":1_1_0" index="0" disallow="pedestrian tram rail_urban rail rail_electric rail_fast ship" speed="33.53" length="9.90" width="3.66" shape="1194.82,448.05 1204.72,448.05"> + <lane id=":1_1_0" index="0" speed="33.53" length="9.90" width="3.66" shape="1194.82,448.05 1204.72,448.05"> <param key="origId" value="68_-2"/> </lane> - <lane id=":1_1_1" index="1" disallow="pedestrian tram rail_urban rail rail_electric rail_fast ship" speed="33.53" length="9.90" width="3.66" shape="1194.82,451.71 1204.72,451.71"> + <lane id=":1_1_1" index="1" speed="33.53" length="9.90" width="3.66" shape="1194.82,451.71 1204.72,451.71"> <param key="origId" value="68_-1"/> </lane> </edge> <edge id=":2_0" function="internal"> - <lane id=":2_0_0" index="0" disallow="pedestrian tram rail_urban rail rail_electric rail_fast ship" speed="6.02" length="5.58" width="3.66" shape="1623.10,444.39 1625.25,444.32 1626.85,444.10 1627.89,443.73 1628.37,443.21"> + <lane id=":2_0_0" index="0" speed="6.02" length="5.58" width="3.66" shape="1623.10,444.39 1625.25,444.32 1626.85,444.10 1627.89,443.73 1628.37,443.21"> <param key="origId" value="65_-1"/> </lane> </edge> - <edge id=":2_1" function="internal"> - <lane id=":2_1_0" index="0" disallow="pedestrian tram rail_urban rail rail_electric rail_fast ship" speed="33.53" length="9.06" width="3.66" shape="1623.10,448.05 1632.16,448.05"> - <param key="origId" value="66_-2"/> - </lane> - <lane id=":2_1_1" index="1" disallow="pedestrian tram rail_urban rail rail_electric rail_fast ship" speed="33.53" length="9.06" width="3.66" shape="1623.10,451.71 1632.16,451.71"> - <param key="origId" value="66_-1"/> - </lane> - </edge> <edge id="-60.0.00" from="1" to="2" priority="1" type="driving" shape="1204.72,453.54 1623.10,453.54"> - <lane id="-60.0.00_0" index="0" disallow="pedestrian tram rail_urban rail rail_electric rail_fast ship" speed="33.53" length="418.37" width="3.66" shape="1204.72,444.39 1623.10,444.39" type="driving"> + <lane id="-60.0.00_0" index="0" speed="33.53" length="418.37" width="3.66" shape="1204.72,444.39 1623.10,444.39" type="driving"> <param key="origId" value="60_-3"/> </lane> - <lane id="-60.0.00_1" index="1" disallow="pedestrian tram rail_urban rail rail_electric rail_fast ship" speed="33.53" length="418.37" width="3.66" shape="1204.72,448.05 1623.10,448.05" type="driving"> + <lane id="-60.0.00_1" index="1" speed="33.53" length="418.37" width="3.66" shape="1204.72,448.05 1623.10,448.05" type="driving"> <param key="origId" value="60_-2"/> </lane> - <lane id="-60.0.00_2" index="2" disallow="pedestrian tram rail_urban rail rail_electric rail_fast ship" speed="33.53" length="418.37" width="3.66" shape="1204.72,451.71 1623.10,451.71" type="driving"> + <lane id="-60.0.00_2" index="2" speed="33.53" length="418.37" width="3.66" shape="1204.72,451.71 1623.10,451.71" type="driving"> <param key="origId" value="60_-1"/> </lane> </edge> <edge id="-61.0.00" from="61.begin" to="1" priority="1" type="driving" shape="2.00,0.00 1187.52,442.93"> - <lane id="-61.0.00_0" index="0" disallow="pedestrian tram rail_urban rail rail_electric rail_fast ship" speed="31.29" length="1265.57" width="3.66" shape="2.64,-1.71 1188.16,441.22" type="driving"> + <lane id="-61.0.00_0" index="0" speed="31.29" length="1265.57" width="3.66" shape="2.64,-1.71 1188.16,441.22" type="driving"> <param key="origId" value="61_-1"/> </lane> </edge> - <edge id="-62.0.00" from="2" to="62.end" priority="1" type="driving" shape="1631.71,453.54 2831.71,453.54"> - <lane id="-62.0.00_0" index="0" disallow="pedestrian tram rail_urban rail rail_electric rail_fast ship" speed="33.53" length="1199.56" width="3.66" shape="1632.16,448.05 2831.71,448.05" type="driving"> + <edge id="-62.0.00" from="4" to="3" priority="1" type="driving"> + <lane id="-62.0.00_0" index="0" speed="33.53" length="1200.00" width="3.66" shape="1631.69,448.05 2831.69,448.05" type="driving"> <param key="origId" value="62_-2"/> </lane> - <lane id="-62.0.00_1" index="1" disallow="pedestrian tram rail_urban rail rail_electric rail_fast ship" speed="33.53" length="1199.56" width="3.66" shape="1632.16,451.71 2831.71,451.71" type="driving"> + <lane id="-62.0.00_1" index="1" speed="33.53" length="1200.00" width="3.66" shape="1631.69,451.71 2831.69,451.71" type="driving"> <param key="origId" value="62_-1"/> </lane> </edge> <edge id="-63.0.00" from="63.begin" to="1" priority="1" type="driving" shape="0.00,453.54 1200.00,453.54"> - <lane id="-63.0.00_0" index="0" disallow="pedestrian tram rail_urban rail rail_electric rail_fast ship" speed="33.53" length="1194.82" width="3.66" shape="0.00,448.05 1194.82,448.05" type="driving"> + <lane id="-63.0.00_0" index="0" speed="33.53" length="1194.82" width="3.66" shape="0.00,448.05 1194.82,448.05" type="driving"> <param key="origId" value="63_-2"/> </lane> - <lane id="-63.0.00_1" index="1" disallow="pedestrian tram rail_urban rail rail_electric rail_fast ship" speed="33.53" length="1194.82" width="3.66" shape="0.00,451.71 1194.82,451.71" type="driving"> + <lane id="-63.0.00_1" index="1" speed="33.53" length="1194.82" width="3.66" shape="0.00,451.71 1194.82,451.71" type="driving"> <param key="origId" value="63_-1"/> </lane> </edge> <edge id="-64.0.00" from="2" to="64.end" priority="1" type="driving" shape="1629.16,446.58 1629.23,446.39"> - <lane id="-64.0.00_0" index="0" disallow="pedestrian tram rail_urban rail rail_electric rail_fast ship" speed="39.44" length="0.20" width="3.66" shape="1628.37,443.21 1628.43,443.02" type="driving"> + <lane id="-64.0.00_0" index="0" speed="39.44" length="0.20" width="3.66" shape="1628.37,443.21 1628.43,443.02" type="driving"> <param key="origId" value="64_-1"/> </lane> </edge> + <edge id="-69.0.00" from="2" to="69.end" priority="1" type="driving" shape="2831.67,453.54 2836.67,453.54"> + <lane id="-69.0.00_0" index="0" speed="33.53" length="2.13" width="3.66" shape="2834.55,448.05 2836.67,448.05" type="driving"> + <param key="origId" value="69_-2"/> + </lane> + <lane id="-69.0.00_1" index="1" speed="33.53" length="2.13" width="3.66" shape="2834.55,451.71 2836.67,451.71" type="driving"> + <param key="origId" value="69_-1"/> + </lane> + </edge> <junction id="1" type="priority" x="1196.05" y="448.21" incLanes="-61.0.00_0 -63.0.00_0 -63.0.00_1" intLanes=":1_0_0 :1_1_0 :1_1_1" shape="1204.72,453.54 1204.72,442.56 1201.26,442.53 1198.68,442.40 1196.57,442.10 1194.50,441.56 1192.06,440.72 1188.80,439.51 1187.52,442.93 1190.17,443.94 1192.25,444.76 1193.75,445.40 1194.68,445.86 1195.04,446.13 1194.82,446.22 1194.82,453.54"> <request index="0" response="000" foes="000" cont="0"/> <request index="1" response="000" foes="000" cont="0"/> <request index="2" response="000" foes="000" cont="0"/> </junction> - <junction id="2" type="priority" x="1627.41" y="450.06" incLanes="-60.0.00_0 -60.0.00_1 -60.0.00_2" intLanes=":2_0_0 :2_1_0 :2_1_1" shape="1632.16,453.54 1632.16,446.22 1631.04,441.01 1627.58,439.84 1623.10,442.56 1623.10,453.54"> - <request index="0" response="000" foes="000" cont="0"/> - <request index="1" response="000" foes="000" cont="0"/> - <request index="2" response="000" foes="000" cont="0"/> + <junction id="2" type="priority" x="1627.40" y="450.06" incLanes="-60.0.00_0 -60.0.00_1 -60.0.00_2" intLanes=":2_0_0" shape="2834.55,453.54 2834.55,446.22 1631.04,441.01 1627.58,439.84 1623.10,442.56 1623.10,453.54"> + <request index="0" response="0" foes="0" cont="0"/> </junction> + <junction id="3" type="dead_end" x="2831.69" y="453.54" incLanes="-62.0.00_0 -62.0.00_1" intLanes="" shape="2831.69,446.22 2831.69,453.54"/> + <junction id="4" type="dead_end" x="1631.69" y="453.54" incLanes="" intLanes="" shape="1631.69,453.54 1631.69,446.22"/> <junction id="61.begin" type="dead_end" x="2.00" y="0.00" incLanes="" intLanes="" shape="2.00,-0.00 3.28,-3.43"/> - <junction id="62.end" type="dead_end" x="2831.71" y="453.54" incLanes="-62.0.00_0 -62.0.00_1" intLanes="" shape="2831.71,446.22 2831.71,453.54"/> <junction id="63.begin" type="dead_end" x="0.00" y="453.54" incLanes="" intLanes="" shape="0.00,453.54 0.00,446.22"/> <junction id="64.end" type="dead_end" x="1629.23" y="446.39" incLanes="-64.0.00_0" intLanes="" shape="1625.76,445.22 1629.23,446.39"/> + <junction id="69.end" type="dead_end" x="2836.67" y="453.54" incLanes="-69.0.00_0 -69.0.00_1" intLanes="" shape="2836.67,446.22 2836.67,453.54"/> <connection from="-60.0.00" to="-64.0.00" fromLane="0" toLane="0" via=":2_0_0" dir="r" state="M"> <param key="origId" value="65_-1"/> </connection> - <connection from="-60.0.00" to="-62.0.00" fromLane="1" toLane="0" via=":2_1_0" dir="s" state="M"> - <param key="origId" value="66_-2"/> - </connection> - <connection from="-60.0.00" to="-62.0.00" fromLane="2" toLane="1" via=":2_1_1" dir="s" state="M"> - <param key="origId" value="66_-1"/> - </connection> <connection from="-61.0.00" to="-60.0.00" fromLane="0" toLane="0" via=":1_0_0" dir="s" state="M"> <param key="origId" value="67_-1"/> </connection> @@ -162,6 +133,4 @@ <connection from=":1_1" to="-60.0.00" fromLane="0" toLane="1" dir="s" state="M"/> <connection from=":1_1" to="-60.0.00" fromLane="1" toLane="2" dir="s" state="M"/> <connection from=":2_0" to="-64.0.00" fromLane="0" toLane="0" dir="r" state="M"/> - <connection from=":2_1" to="-62.0.00" fromLane="0" toLane="0" dir="s" state="M"/> - <connection from=":2_1" to="-62.0.00" fromLane="1" toLane="1" dir="s" state="M"/> </net> diff --git a/opencda/assets/2lane_freeway_simplified/2lane_freeway_simplified.rou.xml b/opencda/assets/2lane_freeway_simplified/2lane_freeway_simplified.rou.xml index 6e0ef779..b66f4f9b 100644 --- a/opencda/assets/2lane_freeway_simplified/2lane_freeway_simplified.rou.xml +++ b/opencda/assets/2lane_freeway_simplified/2lane_freeway_simplified.rou.xml @@ -4,7 +4,7 @@ <routes> <vType id="vType_0" minGap="2.00" speedFactor="normc(1.00,0.00)" vClass="passenger" carFollowModel="IDMM" tau="0.6"/> <vType id="DEFAULT_VEHTYPE" minGap="2.50" tau="1.0" color="255,255,255" Class="passenger" accel="0.5"/> - <flow id="flow_0" begin="0.00" departLane="1" departSpeed="15" departPos="-320" from="-63.0.00" to="-62.0.00" via="-60.0.00" end="4800.00" vehsPerHour="1000.00" type="DEFAULT_VEHTYPE"/> - <flow id="flow_1" begin="0.2" departLane="0" departSpeed="15" departPos="-320" from="-63.0.00" to="-62.0.00" via="-60.0.00" end="4800.00" vehsPerHour="1000.00" type="DEFAULT_VEHTYPE"/> + <flow id="flow_0" begin="0.00" departLane="1" departSpeed="15" departPos="-320" from="-63.0.00" to="-60.0.00" via="-60.0.00" end="4800.00" vehsPerHour="1000.00" type="DEFAULT_VEHTYPE"/> + <flow id="flow_1" begin="0.2" departLane="0" departSpeed="15" departPos="-320" from="-63.0.00" to="-60.0.00" via="-60.0.00" end="4800.00" vehsPerHour="1000.00" type="DEFAULT_VEHTYPE"/> </routes> \ No newline at end of file diff --git a/opencda/assets/2lane_freeway_simplified/2lane_freeway_simplified.xodr b/opencda/assets/2lane_freeway_simplified/2lane_freeway_simplified.xodr index 3cc9f6f8..041a27c8 100644 --- a/opencda/assets/2lane_freeway_simplified/2lane_freeway_simplified.xodr +++ b/opencda/assets/2lane_freeway_simplified/2lane_freeway_simplified.xodr @@ -88,11 +88,12 @@ </road> <road name="gneE5" length="1200" id="62" junction="-1"> <link> - <predecessor elementType="junction" elementId="2"/> + <predecessor elementType="junction" elementId="4"/> + <successor elementType="junction" elementId="3"/> </link> <type s="0" type="town"/> <planView> - <geometry s="0.00000000" x="426.99172801" y="-3.0" hdg="0.00000000" length="1200"> + <geometry s="0.00000000" x="426.97" y="-3.0" hdg="0.00000000" length="1200"> <line/> </geometry> </planView> @@ -105,7 +106,7 @@ <center> <lane id="0" type="none" level="true"> <link/> - <roadMark sOffset="0" type="solid" weight="standard" color="standard" width="0.13" laneChange="none"/> + <roadMark sOffset="0" type="solid" weight="standard" color="standard" width="0.13" laneChange="both"/> </lane> </center> <right> @@ -118,7 +119,7 @@ <lane id="-2" type="driving" level="true"> <link/> <width sOffset="0" a="3.6576" b="0" c="0" d="0"/> - <roadMark sOffset="0" type="solid" weight="standard" color="standard" width="0.13" laneChange="none"/> + <roadMark sOffset="0" type="solid" weight="standard" color="standard" width="0.13" laneChange="both"/> <speed sOffset="0" max="33.53"/> </lane> </right> @@ -203,6 +204,47 @@ <objects/> <signals/> </road> + <road name="gneE6" length="5.0" id="69" junction="-1"> + <link> + <predecessor elementType="junction" elementId="2"/> + </link> + <type s="0" type="town"/> + <planView> + <geometry s="0.00000000" x="1626.95" y="-3.0" hdg="0.00000000" length="5.0"> + <line/> + </geometry> + </planView> + <elevationProfile> + <elevation s="0" a="0.00" b="0" c="0" d="0"/> + </elevationProfile> + <lateralProfile/> + <lanes> + <laneSection s="0"> + <center> + <lane id="0" type="none" level="true"> + <link/> + <roadMark sOffset="0" type="solid" weight="standard" color="standard" width="0.13" laneChange="both"/> + </lane> + </center> + <right> + <lane id="-1" type="driving" level="true"> + <link/> + <width sOffset="0" a="3.6576" b="0" c="0" d="0"/> + <roadMark sOffset="0" type="broken" weight="standard" color="standard" width="0.13" laneChange="both"/> + <speed sOffset="0" max="33.53"/> + </lane> + <lane id="-2" type="driving" level="true"> + <link/> + <width sOffset="0" a="3.6576" b="0" c="0" d="0"/> + <roadMark sOffset="0" type="solid" weight="standard" color="standard" width="0.13" laneChange="both"/> + <speed sOffset="0" max="33.53"/> + </lane> + </right> + </laneSection> + </lanes> + <objects/> + <signals/> + </road> <road name="gneJ6_0" length="6.28738509" id="65" junction="2"> <link> @@ -250,7 +292,7 @@ </link> <type s="0" type="town"/> <planView> - <geometry s="0.00000000" x="418.3729844599999" y="-3.0" hdg="0.00000000" length="8.61874355"> + <geometry s="0.00000000" x="418.372" y="-3.0" hdg="0.00000000" length="8.61874355"> <line/> </geometry> </planView> @@ -263,7 +305,7 @@ <center> <lane id="0" type="none" level="true"> <link/> - <roadMark sOffset="0" type="none" weight="standard" color="standard" width="0.00" laneChange="none"/> + <roadMark sOffset="0" type="none" weight="standard" color="standard" width="0.00" laneChange="both"/> </lane> </center> <right> @@ -388,6 +430,14 @@ <laneLink from="-2" to="-2"/> </connection> </junction> + + <junction name="8433034436" id="4"> + <connection id="2" incomingRoad="66" connectingRoad="62" contactPoint="start"> + <laneLink from="-1" to="-1"/> + <laneLink from="-2" to="-2"/> + </connection> + </junction> + <junction name="8433034435" id="1"> <connection id="0" incomingRoad="61" connectingRoad="67" contactPoint="start"> <laneLink from="-1" to="-1"/> @@ -397,4 +447,12 @@ <laneLink from="-2" to="-2"/> </connection> </junction> + + <junction name="8433034434" id="3"> + <connection id="0" incomingRoad="62" connectingRoad="69" contactPoint="start"> + <laneLink from="-1" to="-1"/> + <laneLink from="-2" to="-2"/> + </connection> + </junction> + </OpenDRIVE>

Develop The development branch is nearly ready for the formal version v0.1 except for some part of the documentation still needs to be proofread.

2021-07-25T04:23:22

0.0

[]

flekschas/jupyter-scatter

flekschas__jupyter-scatter-168

939b9cb58acace667a9463b344d3e4cadea871e8

diff --git a/CHANGELOG.md b/CHANGELOG.md index 30719af..af02e45 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,3 +1,7 @@ +## v0.19.1 + +- Fix: revert back to `pd.api.types.is_string_dtype` from `pd.StringDType` because the two function, albeit looking semantically identical, do not do the same thing. The former detects object and string types while the latter only strictly detects string types. This is confusing as `pd.Series(['a', 'b'])` is of type `object`. So `pd.StringDtype.is_dtype(pd.Series(['a', 'b'])) == False` but `pd.api.types.is_string_dtype(pd.Series(['a', 'b'])) == True`. [Wat](https://www.destroyallsoftware.com/talks/wat)?! + ## v0.19.0 - Feat: add contour line annotations [#163](https://github.com/flekschas/jupyter-scatter/issues/163) diff --git a/jscatter/utils.py b/jscatter/utils.py index ddc173f..4e1020f 100644 --- a/jscatter/utils.py +++ b/jscatter/utils.py @@ -1,9 +1,9 @@ import ipywidgets as widgets +import pandas as pd import warnings from matplotlib.colors import LogNorm, PowerNorm, Normalize from numpy import histogram, isnan, sum -from pandas import CategoricalDtype, StringDtype from urllib.parse import urlparse from typing import List, Union @@ -78,13 +78,13 @@ def to_scale_type(norm = None): return 'categorical' def get_scale_type_from_df(data): - if isinstance(data.dtype, CategoricalDtype) or isinstance(data.dtype, StringDtype): + if pd.CategoricalDtype.is_dtype(data) or pd.api.types.is_string_dtype(data): return 'categorical' return 'linear' def get_domain_from_df(data): - if isinstance(data.dtype, CategoricalDtype) or isinstance(data.dtype, StringDtype): + if pd.CategoricalDtype.is_dtype(data) or pd.api.types.is_string_dtype(data): # We need to recreate the categorization in case the data is just a # filtered view, in which case it might contain "missing" indices _data = data.copy().astype(str).astype('category') @@ -120,7 +120,7 @@ def create_labeling(partial_labeling, column: Union[str, None] = None) -> Labeli return labeling def get_histogram_from_df(data, bins=20, range=None): - if isinstance(data.dtype, CategoricalDtype) or isinstance(data.dtype, StringDtype): + if pd.CategoricalDtype.is_dtype(data) or pd.api.types.is_string_dtype(data): # We need to recreate the categorization in case the data is just a # filtered view, in which case it might contain "missing" indices value_counts = data.copy().astype(str).astype('category').cat.codes.value_counts() diff --git a/notebooks/Untitled4.ipynb b/notebooks/Untitled4.ipynb new file mode 100644 index 0000000..920b5ec --- /dev/null +++ b/notebooks/Untitled4.ipynb @@ -0,0 +1,152 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "c8d67f28-de9c-4cd4-b60b-c5bf92db023b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "env: ANYWIDGET_HMR=1\n" + ] + } + ], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "%env ANYWIDGET_HMR=1" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "63452dc6-5726-4242-81a0-fc79b68e64a7", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "473f9edbd47840f5ab4720fc1abf3479", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(VBox(children=(Button(button_style='primary', icon='arrows', layout=Layout(width='36px'), style…" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import jscatter\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "data1 = pd.DataFrame({\n", + " \"x\": np.random.rand(1000),\n", + " \"y\": np.random.rand(1000),\n", + " \"z\": ['a','b']*500,\n", + "})\n", + "scatter = jscatter.Scatter(data=data1, x=\"x\", y=\"y\", width=500, height=500)\n", + "scatter.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "3d052d61-79e7-4f7a-9839-92f8e34a5bbf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<jscatter.jscatter.Scatter at 0x12cc5d640>" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scatter.tooltip(True, properties = ['x','y', 'z']) " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "3b01ac2a-c9bf-48cc-bb8e-34a646528db3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.StringDtype.is_dtype(pd.Series(['a', 'b']))" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "c8d03133-e4e2-43ad-9292-6ea39dcb3bfb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.api.types.is_string_dtype(pd.Series(['a', 'b']))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "069b99ef-74f9-401e-b26e-851589f0328a", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}

tooltip stopped supporting categorical columns set as properties in 0.18.1 ``` import jscatter import numpy as np import pandas as pd data1 = pd.DataFrame({ "x": np.random.rand(1000), "y": np.random.rand(1000), "z": ['a','b']*500, }) scatter = jscatter.Scatter(data=data1, x="x", y="y", width=500, height=500) scatter.show() ``` The following works in 0.16.1: `scatter.tooltip(True, properties = ['x','y', 'z']) ` But in 0.18.1, I get a `TypeError: ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''`

2024-09-26T01:25:49

0.0

[]

flekschas/jupyter-scatter

flekschas__jupyter-scatter-151

750d1511d8397b3a21de758b7097d510502ddb1a

diff --git a/CHANGELOG.md b/CHANGELOG.md index 0e55d5d..cc1acb2 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,6 +1,7 @@ ## v0.18.0 -- Feat: The exported and saved images now include a background color instead of a transparent background. You can still still export or save images with a transparent background by holding down the ALT key while clicking on the download or camera button. +- Feat: add support for line-based annotations via `scatter.annotations()` +- Feat: the exported and saved images now include a background color instead of a transparent background. You can still still export or save images with a transparent background by holding down the ALT key while clicking on the download or camera button. - Refactor: When saving the current view as an image via the camera button on the left side bar, the image gets saved in `scatter.widget.view_data` as a 3D Numpy array (shape: `[height, width, 4]`) instead of a 1D Numpy array. Since the shape is now encoded by the 3D numpy array, `scatter.widget.view_shape` is no longer needed and is removed. - Fix: hide button for activating rotate mouse mode as the rotation did not work (which is easily fixable) and should not be available when axes are shown as the axes are not rotateable. However rotating the plot without rotating the axis leads to incorrect tick marks. - Fix: VSCode integration by updating regl-scatterplot to v1.10.4 ([#37](https://github.com/flekschas/jupyter-scatter/issues/37)) @@ -15,7 +16,7 @@ ## v0.17.0 -- Feat: Add `scatter.show_tooltip(point_idx)` +- Feat: add `scatter.show_tooltip(point_idx)` - Fix: reset scale & norm ranges upon updating the data via `scatter.data()` - Fix: ensure `scatter.axes(labels=['x_label', 'y_label'])` works properly ([#137](https://github.com/flekschas/jupyter-scatter/issues/137])) diff --git a/docs/.vitepress/config.ts b/docs/.vitepress/config.ts index da1870a..a50cb30 100644 --- a/docs/.vitepress/config.ts +++ b/docs/.vitepress/config.ts @@ -71,6 +71,7 @@ export default defineConfig({ { text: 'Selections', link: '/selections' }, { text: 'Link Multiple Scatter Plots', link: '/link-multiple-plots' }, { text: 'Axes & Legends', link: '/axes-legends' }, + { text: 'Annotations', link: '/annotations' }, { text: 'Tooltip', link: '/tooltip' }, { text: 'Scales', link: '/scales' }, { text: 'Connected Scatterplots', link: '/connected-scatterplots' }, diff --git a/docs/annotations.md b/docs/annotations.md new file mode 100644 index 0000000..b841bff --- /dev/null +++ b/docs/annotations.md @@ -0,0 +1,96 @@ +# Annotations + +To help navigating and relating points and clusters, Jupyter Scatter offers +annotations such a lines and rectangles. + +::: info +Currently, Jupyter Scatter supports line-based annotations only. In the future, +we plan to add support for text annotations as well. +::: + +## Line, HLine, VLine, and Rect + +To draw annotations, create instances of `Line`, `HLine`, `VLine`, or `Rect`. +You can then either pass the annotations into the constructor, as shown below, +or call `scatter.annotations()`. + +```py{9-17,22} +import jscatter +import numpy as np + +x1, y1 = np.random.normal(-1, 0.2, 1000), np.random.normal(+1, 0.05, 1000) +x2, y2 = np.random.normal(+1, 0.2, 1000), np.random.normal(+1, 0.05, 1000) +x3, y3 = np.random.normal(+1, 0.2, 1000), np.random.normal(-1, 0.05, 1000) +x4, y4 = np.random.normal(-1, 0.2, 1000), np.random.normal(-1, 0.05, 1000) + +y0 = jscatter.HLine(0) +x0 = jscatter.VLine(0) +c1 = jscatter.Rect(x_start=-1.5, x_end=-0.5, y_start=+0.75, y_end=+1.25) +c2 = jscatter.Rect(x_start=+0.5, x_end=+1.5, y_start=+0.75, y_end=+1.25) +c3 = jscatter.Rect(x_start=+0.5, x_end=+1.5, y_start=-1.25, y_end=-0.75) +c4 = jscatter.Rect(x_start=-1.5, x_end=-0.5, y_start=-1.25, y_end=-0.75) +l = jscatter.Line([ + (-2, -2), (-1.75, -1), (-1.25, -0.5), (1.25, 0.5), (1.75, 1), (2, 2) +]) + +scatter = jscatter.Scatter( + x=np.concatenate((x1, x2, x3, x4)), x_scale=(-2, 2), + y=np.concatenate((y1, y2, y3, y4)), y_scale=(-2, 2), + annotations=[x0, y0, c1, c2, c3, c4, l], + width=400, + height=400, +) +scatter.show() +``` + +<div class="img simple"><div /></div> + +## Line Color & Width + +You can customize the line color and width of `Line`, `HLine`, `VLine`, or +`Rect` via the `line_color` and `line_width` attributes. + +```py +y0 = jscatter.HLine(0, line_color=(0, 0, 0, 0.1)) +x0 = jscatter.VLine(0, line_color=(0, 0, 0, 0.1)) +c1 = jscatter.Rect(x_start=-1.5, x_end=-0.5, y_start=+0.75, y_end=+1.25, line_color="#56B4E9", line_width=2) +c2 = jscatter.Rect(x_start=+0.5, x_end=+1.5, y_start=+0.75, y_end=+1.25, line_color="#56B4E9", line_width=2) +c3 = jscatter.Rect(x_start=+0.5, x_end=+1.5, y_start=-1.25, y_end=-0.75, line_color="#56B4E9", line_width=2) +c4 = jscatter.Rect(x_start=-1.5, x_end=-0.5, y_start=-1.25, y_end=-0.75, line_color="#56B4E9", line_width=2) +l = jscatter.Line( + [(-2, -2), (-1.75, -1), (-1.25, -0.5), (1.25, 0.5), (1.75, 1), (2, 2)], + line_color="red", + line_width=3 +) +``` + +<div class="img styles"><div /></div> + +<style scoped> + .img { + max-width: 100%; + background-position: center; + background-repeat: no-repeat; + background-size: cover; + } + + .img.simple { + width: 316px; + background-image: url(/images/annotations-simple-light.png) + } + .img.simple div { padding-top: 83.2278481% } + + :root.dark .img.simple { + background-image: url(/images/annotations-simple-dark.png) + } + + .img.styles { + width: 314px; + background-image: url(/images/annotations-styles-light.png) + } + .img.styles div { padding-top: 83.75796178% } + + :root.dark .img.styles { + background-image: url(/images/annotations-styles-dark.png) + } +</style> diff --git a/docs/api.md b/docs/api.md index fb446e3..9c28a53 100644 --- a/docs/api.md +++ b/docs/api.md @@ -6,7 +6,7 @@ - [selection()](#scatter.selection) and [filter()](#scatter.filter) - [color()](#scatter.color), [opacity()](#scatter.opacity), and [size()](#scatter.size) - [connect()](#scatter.connect), [connection_color()](#scatter.connection_color), [connection_opacity()](#scatter.connection_opacity), and [connection_size()](#scatter.connection_size) - - [axes()](#scatter.axes) and [legend()](#scatter.legend) + - [axes()](#scatter.axes), [legend()](#scatter.legend), and [annotations()](#scatter.annotations) - [tooltip()](#scatter.tooltip) and [show_tooltip()](#scatter.show_tooltip) - [zoom()](#scatter.zoom) and [camera()](#scatter.camera) - [lasso()](#scatter.lasso), [reticle()](#scatter.reticle), and [mouse()](#scatter.mouse), @@ -357,6 +357,21 @@ Set or get the legend settings. scatter.legend(True, 'top-right', 'small') ``` +### scatter.annotations(_annotations=Undefined_) {#scatter.annotations} + +Set or get annotations. + +**Arguments:** +- `annotations` is a list of annotations (`Line`, `HLine`, `VLine`, or `Rect`) + +**Returns:** either the annotation properties when all arguments are `Undefined` or `self`. + +**Example:** + +```python +from jscatter import HLine, VLine +scatter.annotations([HLine(42), VLine(42)]) +``` ### scatter.tooltip(_enable=Undefined_, _properties=Undefined_, _histograms=Undefined_, _histograms_bins=Undefined_, _histograms_ranges=Undefined_, _histograms_size=Undefined_, _preview=Undefined_, _preview_type=Undefined_, _preview_text_lines=Undefined_, _preview_image_background_color=Undefined_, _preview_image_position=Undefined_, _preview_image_size=Undefined_, _preview_audio_length=Undefined_, _preview_audio_loop=Undefined_, _preview_audio_controls=Undefined_, _size=Undefined_) {#scatter.tooltip} diff --git a/docs/public/images/annotations-simple-dark.png b/docs/public/images/annotations-simple-dark.png new file mode 100644 index 0000000..0c9e065 --- /dev/null +++ b/docs/public/images/annotations-simple-dark.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d01dc4bade1496dff2c9643a870b5805da6c53b0093c916669e0a7103ed0d029 +size 18822 diff --git a/docs/public/images/annotations-simple-light.png b/docs/public/images/annotations-simple-light.png new file mode 100644 index 0000000..61fe341 --- /dev/null +++ b/docs/public/images/annotations-simple-light.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dcdf68d391800cab5365eb7083f9d100f3e9256a5f8082dc2e11f40bf1760736 +size 18348 diff --git a/docs/public/images/annotations-styles-dark.png b/docs/public/images/annotations-styles-dark.png new file mode 100644 index 0000000..6c540de --- /dev/null +++ b/docs/public/images/annotations-styles-dark.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bcbaaf43b01f4270579257aa152f8d8973ddde125891d58514e100e5a28f807d +size 18657 diff --git a/docs/public/images/annotations-styles-light.png b/docs/public/images/annotations-styles-light.png new file mode 100644 index 0000000..9cc06dd --- /dev/null +++ b/docs/public/images/annotations-styles-light.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8b293f4c7d75f60f335b6f6941ffb7488d3093dbb26dacd04076574b5c72dbf8 +size 18173 diff --git a/js/src/codecs.js b/js/src/codecs.js index 1cdd253..0c9affd 100644 --- a/js/src/codecs.js +++ b/js/src/codecs.js @@ -1,3 +1,5 @@ +import { camelToSnake, snakeToCamel } from './utils'; + const DTYPES = { uint8: Uint8Array, int8: Int8Array, @@ -120,3 +122,43 @@ export function Numpy1D(dtype) { } } } + +export function Annotations() { + const pyToJsKey = { x_start: 'x1', x_end: 'x2', y_start: 'y1', y_end: 'y2' }; + const jsToPyKey = { x1: 'x_start', x2: 'x_end', y1: 'y_start', y2: 'y_end' }; + return { + /** + * @param {string[] | null} annotationStrs + * @returns {object[]} + */ + deserialize: (annotationStrs) => { + if (annotationStrs === null) { + return null; + } + return annotationStrs.map((annotationStr) => { + return Object.entries(JSON.parse(annotationStr)).reduce((acc, [key, value]) => { + const jsKey = key in pyToJsKey ? pyToJsKey[key] : snakeToCamel(key); + acc[jsKey] = value; + return acc; + }, {}) + }); + }, + /** + * @param {object[] | null} annotations + * @returns {string[]} + */ + serialize: (annotations) => { + if (annotations === null) { + return null; + } + return annotations.map((annotation) => { + const pyAnnotation = Object.entries(annotation).reduce((acc, [key, value]) => { + const pyKey = key in jsToPyKey ? jsToPyKey[key] : camelToSnake(key); + acc[pyKey] = value; + return acc; + }, {}); + return JSON.stringify(pyAnnotation); + }) + }, + } +} diff --git a/js/src/index.js b/js/src/index.js index c840e45..fe6000f 100644 --- a/js/src/index.js +++ b/js/src/index.js @@ -7,7 +7,7 @@ import { format } from 'd3-format'; import { scaleLinear } from 'd3-scale'; import { select } from 'd3-selection'; -import { Numpy1D, Numpy2D, NumpyImage } from "./codecs"; +import { Annotations, Numpy1D, Numpy2D, NumpyImage } from "./codecs"; import { createHistogram } from "./histogram"; import { createLegend } from "./legend"; import { @@ -59,6 +59,7 @@ const TOOLTIP_OFFSET_REM = 0.5; * different. E.g., size (Python) vs pointSize (JavaScript) */ const properties = { + annotations: 'annotations', backgroundColor: 'backgroundColor', backgroundImage: 'backgroundImage', cameraDistance: 'cameraDistance', @@ -371,6 +372,9 @@ class JupyterScatterView { this.scatterplot .draw(this.points, options) .then(() => { + if (this.annotations) { + this.scatterplot.drawAnnotations(this.annotations); + } if (this.filter?.length && this.model.get('zoom_on_filter')) { this.zoomToHandler(this.filter); } @@ -1766,6 +1770,10 @@ class JupyterScatterView { } } + annotationsHandler(annotations) { + this.scatterplot.drawAnnotations(annotations || []); + } + // Event handlers for JS-triggered events pointoverHandler(pointIndex) { this.hoveringChangedByJs = true; @@ -2426,6 +2434,7 @@ async function render({ model, el }) { filter: Numpy1D('uint32'), view_data: NumpyImage(), zoom_to: Numpy1D('uint32'), + annotations: Annotations(), }), }); view.render(); diff --git a/js/src/utils.js b/js/src/utils.js index f56c727..94ea0fe 100644 --- a/js/src/utils.js +++ b/js/src/utils.js @@ -5,6 +5,10 @@ export function camelToSnake(string) { return string.replace(/[\w]([A-Z])/g, (m) => m[0] + "_" + m[1]).toLowerCase(); } +export function snakeToCamel(string) { + return string.toLowerCase().replace(/[-_][a-z]/g, (group) => group.slice(-1).toUpperCase()); +} + export function toCapitalCase(string) { if (string.length === 0) return string; return string.at(0).toUpperCase() + string.slice(1); diff --git a/jscatter/__init__.py b/jscatter/__init__.py index 8035c25..768f23e 100644 --- a/jscatter/__init__.py +++ b/jscatter/__init__.py @@ -9,5 +9,6 @@ __version__ = "uninstalled" from .jscatter import Scatter, plot +from .annotations import Line, HLine, VLine, Rect from .compose import compose, link from .color_maps import okabe_ito, glasbey_light, glasbey_dark diff --git a/jscatter/annotations.py b/jscatter/annotations.py new file mode 100644 index 0000000..62f597d --- /dev/null +++ b/jscatter/annotations.py @@ -0,0 +1,55 @@ +from __future__ import annotations + +from dataclasses import dataclass +from matplotlib.colors import to_rgba +from typing import List, Tuple, Optional + +from .types import Color + +DEFAULT_1D_LINE_START = None +DEFAULT_1D_LINE_END = None +DEFAULT_LINE_COLOR = '#000000' +DEFAULT_LINE_WIDTH = 1 + +@dataclass +class HLine(): + y: float + x_start: Optional[float] = DEFAULT_1D_LINE_START + x_end: Optional[float] = DEFAULT_1D_LINE_END + line_color: Color = DEFAULT_LINE_COLOR + line_width: int = DEFAULT_LINE_WIDTH + + def __post_init__(self): + self.line_color = to_rgba(self.line_color) + +@dataclass +class VLine(): + x: float + y_start: Optional[float] = DEFAULT_1D_LINE_START + y_end: Optional[float] = DEFAULT_1D_LINE_END + line_color: Color = DEFAULT_LINE_COLOR + line_width: int = DEFAULT_LINE_WIDTH + + def __post_init__(self): + self.line_color = to_rgba(self.line_color) + +@dataclass +class Rect(): + x_start: float + x_end: float + y_start: float + y_end: float + line_color: Color = DEFAULT_LINE_COLOR + line_width: int = DEFAULT_LINE_WIDTH + + def __post_init__(self): + self.line_color = to_rgba(self.line_color) + +@dataclass +class Line(): + vertices: List[Tuple[float]] + line_color: Color = DEFAULT_LINE_COLOR + line_width: int = DEFAULT_LINE_WIDTH + + def __post_init__(self): + self.line_color = to_rgba(self.line_color) diff --git a/jscatter/annotations_traits.py b/jscatter/annotations_traits.py new file mode 100644 index 0000000..777d849 --- /dev/null +++ b/jscatter/annotations_traits.py @@ -0,0 +1,85 @@ +from __future__ import annotations + +import json + +from dataclasses import asdict +from traitlets import TraitType +from typing import Any + +from .annotations import ( + HLine as AHLine, + VLine as AVLine, + Line as ALine, + Rect as ARect, +) + +class Line(TraitType): + info_text = 'line annotation' + + def validate(self, obj: Any, value: Any): + if isinstance(value, ALine): + return value + self.error(obj, value) + +class HLine(TraitType): + info_text = 'horizontal line annotation' + + def validate(self, obj: Any, value: Any): + if isinstance(value, AHLine): + return value + self.error(obj, value) + +class VLine(TraitType): + info_text = 'vertical line annotation' + + def validate(self, obj: Any, value: Any): + if isinstance(value, AVLine): + return value + self.error(obj, value) + +class Rect(TraitType): + info_text = 'rectangle annotation' + + def validate(self, obj: Any, value: Any): + if isinstance(value, ARect): + return value + self.error(obj, value) + +def to_json(value, *args, **kwargs): + d = None if value is None else asdict(value) + return json.dumps(d, allow_nan=False) + +def annotations_to_json(value, *args, **kwargs): + if value is None: + return None + return [to_json(v) for v in value] + +def from_json(value, *args, **kwargs): + d = json.loads(value) + + if 'y' in d: + return AHLine(**d) + + if 'x' in d: + return AVLine(**d) + + if 'x_start' in d and 'x_end' in d and 'y_start' in d and 'y_end' in d: + return ALine(**d) + + if 'vertices' in d: + return ARect(**d) + + return None + +def annotations_from_json(value): + value = json.loads(value) + + if value is None: + return None + + return [from_json(v) for v in value] + +serialization = dict( + to_json=annotations_to_json, + from_json=annotations_from_json, +) diff --git a/jscatter/jscatter.py b/jscatter/jscatter.py index 780daf7..3c2f7d9 100644 --- a/jscatter/jscatter.py +++ b/jscatter/jscatter.py @@ -5,8 +5,9 @@ import pandas as pd from matplotlib.colors import to_rgba, Normalize, LogNorm, PowerNorm, LinearSegmentedColormap, ListedColormap -from typing import Dict, Optional, Union, List, Tuple, Literal +from typing import Dict, Optional, Union, List, Tuple +from .annotations import Line, HLine, VLine, Rect from .encodings import Encodings from .widget import JupyterScatter, SELECTION_DTYPE from .color_maps import okabe_ito, glasbey_light, glasbey_dark @@ -119,6 +120,54 @@ def get_histogram_range(ranges, property): return None +def normalize_annotation(annotation, x_scale, y_scale): + def to_ndc(value, norm): + return (norm(value) * 2) - 1 + + if isinstance(annotation, HLine): + return HLine( + y=to_ndc(annotation.y, y_scale), + x_start=None if annotation.x_start is None else to_ndc(annotation.x_start, x_scale), + x_end=None if annotation.x_end is None else to_ndc(annotation.x_end, x_scale), + line_color=annotation.line_color, + line_width=annotation.line_width, + ) + + if isinstance(annotation, VLine): + return VLine( + x=to_ndc(annotation.x, x_scale), + y_start=None if annotation.y_start is None else to_ndc(annotation.y_start, y_scale), + y_end=None if annotation.y_end is None else to_ndc(annotation.y_end, y_scale), + line_color=annotation.line_color, + line_width=annotation.line_width, + ) + + + if isinstance(annotation, Line): + return Line( + vertices=[ + (to_ndc(v[0], x_scale), to_ndc(v[1], y_scale)) for v in annotation.vertices + ], + line_color=annotation.line_color, + line_width=annotation.line_width, + ) + + if isinstance(annotation, Rect): + return Rect( + x_start=to_ndc(annotation.x_start, x_scale), + x_end=to_ndc(annotation.x_end, x_scale), + y_start=to_ndc(annotation.y_start, y_scale), + y_end=to_ndc(annotation.y_end, y_scale), + line_color=annotation.line_color, + line_width=annotation.line_width, + ) + +def normalize_annotations(annotations, x_scale, y_scale): + if annotations is None: + return None + return [normalize_annotation(a, x_scale, y_scale) for a in annotations] + + class Scatter(): def __init__( @@ -198,6 +247,7 @@ def __init__( kwargs.get('background_image', UNDEF), ) + self._annotations = None self._lasso_color = (0, 0.666666667, 1, 1) self._lasso_on_long_press = True self._lasso_initiator = False @@ -430,6 +480,9 @@ def __init__( kwargs.get('zoom_on_selection', UNDEF), kwargs.get('zoom_on_filter', UNDEF), ) + self.annotations( + kwargs.get('annotations', UNDEF) + ) self.options(kwargs.get('options', UNDEF)) def get_point_list(self): @@ -649,6 +702,16 @@ def x( if 'skip_widget_update' not in kwargs: self.update_widget('x_scale', to_scale_type(self._x_scale)) + if self._annotations is not None: + self.update_widget( + 'annotations', + normalize_annotations( + self._annotations, + self._x_scale, + self._y_scale + ) + ) + if x is not UNDEF: self._x = x if isinstance(x, str): @@ -765,6 +828,16 @@ def y( if 'skip_widget_update' not in kwargs: self.update_widget('y_scale', to_scale_type(self._y_scale)) + if self._annotations is not None: + self.update_widget( + 'annotations', + normalize_annotations( + self._annotations, + self._x_scale, + self._y_scale + ) + ) + if y is not UNDEF: self._y = y if isinstance(y, str): @@ -884,6 +957,17 @@ def xy( self.update_widget('y_title', self._y_by) self.update_widget('axes_labels', self.get_axes_labels()) self.update_widget('points', self.get_point_list()) + + if self._annotations is not None: + self.update_widget( + 'annotations', + normalize_annotations( + self._annotations, + self._x_scale, + self._y_scale + ) + ) + return self return dict( @@ -3658,6 +3742,51 @@ def tooltip( size = self._tooltip_size, ) + def annotations( + self, + annotations: Optional[Union[List[Union[Line, HLine, VLine, Rect]], Undefined]] = UNDEF, + ): + """ + Draw line-based annotatons + + Parameters + ---------- + annotations : list of `Line`, `HLine`, `VLine`, `Rect`, optional + A list of annotations (`Line`, `HLine`, `VLine`, `Rect`) or `None`. + When set to `None` or `[]` all annotations are removed. + + Returns + ------- + self or dict + If no parameters are provided the current annotation settings are + returned as a dictionary. Otherwise, `self` is returned. + + Examples + -------- + >>> scatter.annotations([HLine(0), VLine(0)]) + <jscatter.jscatter.Scatter> + + >>> scatter.annotations() + {'annotations': [HLine(0), VLine(0)]} + """ + + if annotations is not UNDEF: + self._annotations = annotations + self.update_widget( + 'annotations', + normalize_annotations( + self._annotations, + self._x_scale, + self._y_scale + ) + ) + + if any_not([annotations], UNDEF): + return self + + return dict( + annotations = self._annotations + ) def zoom( self, @@ -3937,6 +4066,11 @@ def widget(self): self._widget = JupyterScatter( data=self._data, + annotations=normalize_annotations( + self._annotations, + self._x_scale, + self._y_scale + ), axes=self._axes, axes_color=self.get_axes_color(), axes_grid=self._axes_grid, diff --git a/jscatter/widget.py b/jscatter/widget.py index 8fd5a23..fd4f8fa 100644 --- a/jscatter/widget.py +++ b/jscatter/widget.py @@ -10,7 +10,13 @@ from traitlets import Bool, Dict, Enum, Float, Int, List, Unicode, Union from traittypes import Array -from .utils import to_hex, with_left_label +from .annotations_traits import ( + Line, + HLine, + VLine, + Rect, + serialization as annotation_serialization, +) SELECTION_DTYPE = 'uint32' EVENT_TYPES = { @@ -107,6 +113,13 @@ class JupyterScatter(anywidget.AnyWidget): opacity_histogram_range = List(None, allow_none=True, minlen=2, maxlen=2).tag(sync=True) size_histogram_range = List(None, allow_none=True, minlen=2, maxlen=2).tag(sync=True) + # Annotations + annotations = List( + trait=Union([Line(), HLine(), VLine(), Rect()]), + default_value=None, + allow_none=True, + ).tag(sync=True, **annotation_serialization) + # View properties camera_target = List([0, 0]).tag(sync=True) camera_distance = Float(1).tag(sync=True) diff --git a/notebooks/annotations.ipynb b/notebooks/annotations.ipynb new file mode 100644 index 0000000..3fd5380 --- /dev/null +++ b/notebooks/annotations.ipynb @@ -0,0 +1,85 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "60d8190f-2372-42d9-8816-ba34bedb544c", + "metadata": {}, + "source": [ + "# Annotations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d5d03018-14f2-4a79-a296-340fbd8e296a", + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "%env ANYWIDGET_HMR=1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3785ca6e-c2f8-45a9-afcf-d7284852ad9e", + "metadata": {}, + "outputs": [], + "source": [ + "import jscatter\n", + "import numpy as np\n", + "\n", + "x1, y1 = np.random.normal(-1, 0.2, 1000), np.random.normal(+1, 0.05, 1000)\n", + "x2, y2 = np.random.normal(+1, 0.2, 1000), np.random.normal(+1, 0.05, 1000)\n", + "x3, y3 = np.random.normal(+1, 0.2, 1000), np.random.normal(-1, 0.05, 1000)\n", + "x4, y4 = np.random.normal(-1, 0.2, 1000), np.random.normal(-1, 0.05, 1000)\n", + "\n", + "y0 = jscatter.HLine(0)\n", + "x0 = jscatter.VLine(0)\n", + "c1 = jscatter.Rect(x_start=-1.5, x_end=-0.5, y_start=+0.75, y_end=+1.25)\n", + "c2 = jscatter.Rect(x_start=+0.5, x_end=+1.5, y_start=+0.75, y_end=+1.25)\n", + "c3 = jscatter.Rect(x_start=+0.5, x_end=+1.5, y_start=-1.25, y_end=-0.75)\n", + "c4 = jscatter.Rect(x_start=-1.5, x_end=-0.5, y_start=-1.25, y_end=-0.75)\n", + "\n", + "scatter = jscatter.Scatter(\n", + " x=np.concatenate((x1, x2, x3, x4)), x_scale=(-2, 2),\n", + " y=np.concatenate((y1, y2, y3, y4)), y_scale=(-2, 2),\n", + " annotations=[x0, y0, c1, c2, c3, c4],\n", + " width=400,\n", + " height=400,\n", + ")\n", + "scatter.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7de74320-6941-4a26-9728-1ec870b6f477", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}

axvline and axhline Is there a possibility to show horizontal or vertical (non-selectable) lines in the plot, similar to matplotlibs axvline and axhline?

Unfortunately such a feature is not available yet. The closest thing you can get is drawing grid lines where the axis ticks are. While not the same as a dedicated x/y line the grid lines redraw as you zoom in and should help locating points. You can activate them via `scatter.axes(grid=True)`

2024-08-28T09:07:15

0.0

[]

flekschas/jupyter-scatter

flekschas__jupyter-scatter-132

fd6fad3c749801f73bc2f5ffdbac3e8e7b9470e4

diff --git a/CHANGELOG.md b/CHANGELOG.md index 5555240..39adac1 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,3 +1,7 @@ +## v0.16.2 + +- Fix: reset scale & norm ranges upon updating the data via `scatter.data()` + ## v0.16.1 - Fix: preserve filter state upon changing visual encoding ([#134](https://github.com/flekschas/jupyter-scatter/issues/134)) diff --git a/js/package-lock.json b/js/package-lock.json index fae5aad..f1e1052 100644 --- a/js/package-lock.json +++ b/js/package-lock.json @@ -22,7 +22,7 @@ "gl-matrix": "~3.3.0", "pub-sub-es": "~3.0.0", "regl": "~2.1.0", - "regl-scatterplot": "~1.9.4" + "regl-scatterplot": "~1.9.6" }, "devDependencies": { "esbuild": "^0.19.5", @@ -2348,9 +2348,9 @@ } }, "node_modules/regl-scatterplot": { - "version": "1.9.4", - "resolved": "https://registry.npmjs.org/regl-scatterplot/-/regl-scatterplot-1.9.4.tgz", - "integrity": "sha512-hS/NtmYcJfCJxYBr9w9tHM0j4OTVKRZMwSxajz0T/kTWusOqC6VN5Uo64t6ZvwnymXJbBQTwhDC+9gTVwww2KA==", + "version": "1.9.6", + "resolved": "https://registry.npmjs.org/regl-scatterplot/-/regl-scatterplot-1.9.6.tgz", + "integrity": "sha512-hvroIbBmvLkPoTRgX99DKc3eg2Q6Ex7BwMb6Ih16wfBdJ651NQKu4n6TC/USUc+m89MjBZuA2csySi0Q8z/T3w==", "dependencies": { "@flekschas/utils": "^0.31.0", "dom-2d-camera": "~2.2.5", diff --git a/js/package.json b/js/package.json index cf9304b..7eaee70 100644 --- a/js/package.json +++ b/js/package.json @@ -30,7 +30,7 @@ "gl-matrix": "~3.3.0", "pub-sub-es": "~3.0.0", "regl": "~2.1.0", - "regl-scatterplot": "~1.9.4" + "regl-scatterplot": "~1.9.6" }, "devDependencies": { "esbuild": "^0.19.5", diff --git a/js/src/index.js b/js/src/index.js index 9284b64..98a2568 100644 --- a/js/src/index.js +++ b/js/src/index.js @@ -32,7 +32,6 @@ const AXES_PADDING_X = 60; const AXES_PADDING_X_WITH_LABEL = AXES_PADDING_X + AXES_LABEL_SIZE; const AXES_PADDING_Y = 20; const AXES_PADDING_Y_WITH_LABEL = AXES_PADDING_Y + AXES_LABEL_SIZE; -const TOOLTIP_EVENT_TYPE = 'tooltip'; const TOOLTIP_DEBOUNCE_TIME = 250; const TOOLTIP_MANDATORY_VISUAL_PROPERTIES = (/** @type {const} */ ({ x: 'X', y: 'Y' })); const TOOLTIP_OPTIONAL_VISUAL_PROPERTIES = (/** @type {const} */ ({ color: 'Col', opacity: 'Opa', size: 'Size' })); @@ -97,7 +96,6 @@ const properties = { connectionSize: 'pointConnectionSize', connectionSizeBy: 'pointConnectionSizeBy', viewDownload: 'viewDownload', - viewReset: 'viewReset', viewSync: 'viewSync', hovering: 'hovering', axes: 'axes', @@ -207,6 +205,7 @@ class JupyterScatterView { constructor({ el, model }) { this.el = el; this.model = model; + this.eventTypes = model.get('event_types'); } render() { @@ -385,10 +384,31 @@ class JupyterScatterView { } customEventHandler(event) { - if (event.type === TOOLTIP_EVENT_TYPE) { + if (event.type === this.eventTypes.TOOLTIP) { if (event.index !== this.tooltipPointIdx) return; this.tooltipDataHandlers(event) } + if (event.type === this.eventTypes.VIEW_RESET) { + if (!this.scatterplot) return; + if (event.area) { + this.scatterplot.zoomToArea( + event.area, + { + transition: event.animation > 0, + transitionDuration: event.animation, + transitionEasing: 'quadInOut', + } + ); + } else { + this.scatterplot.zoomToOrigin( + { + transition: event.animation > 0, + transitionDuration: event.animation, + transitionEasing: 'quadInOut', + } + ); + } + } } getOuterDimensions() { @@ -1501,7 +1521,7 @@ class JupyterScatterView { this.tooltipContentsUpdater = (pointIdx) => { this.model.send({ - type: TOOLTIP_EVENT_TYPE, + type: this.events.TOOLTIP, index: pointIdx, properties: this.tooltipPropertiesNonVisual, preview: this.tooltipPreview, @@ -2009,7 +2029,7 @@ class JupyterScatterView { if (newPoints.length === this.scatterplot.get('points').length) { // We assume point correspondence this.scatterplot.draw(newPoints, { - transition: true, + transition: this.model.get('transition_points'), transitionDuration: 3000, transitionEasing: 'quadInOut', preventFilterReset: this.model.get('prevent_filter_reset'), @@ -2337,14 +2357,6 @@ class JupyterScatterView { }); } - viewResetHandler() { - this.scatterplot.reset(); - setTimeout(() => { - this.model.set('view_reset', false); - this.model.save_changes(); - }, 0); - } - optionsHandler(newOptions) { this.scatterplot.set(newOptions); } @@ -2385,7 +2397,7 @@ function modelWithSerializers(model, serializers) { } } -export async function render({ model, el }) { +async function render({ model, el }) { const view = new JupyterScatterView({ el: el, model: modelWithSerializers(model, { @@ -2399,3 +2411,5 @@ export async function render({ model, el }) { view.render(); return () => view.destroy(); } + +export default { render }; diff --git a/jscatter/jscatter.py b/jscatter/jscatter.py index e3bb9f8..295c7b2 100644 --- a/jscatter/jscatter.py +++ b/jscatter/jscatter.py @@ -5,7 +5,7 @@ import pandas as pd from matplotlib.colors import to_rgba, Normalize, LogNorm, PowerNorm, LinearSegmentedColormap, ListedColormap -from typing import Dict, Optional, Union, List, Tuple +from typing import Dict, Optional, Union, List, Tuple, Literal from .encodings import Encodings from .widget import JupyterScatter, SELECTION_DTYPE @@ -452,6 +452,9 @@ def data( self, data: Optional[pd.DataFrame] = None, use_index: Optional[Union[bool, Undefined]] = UNDEF, + reset_scales: Optional[bool] = False, + reset_view: Optional[bool] = False, + animate: Optional[bool] = False, **kwargs ) -> Union[Scatter, dict]: """ @@ -460,13 +463,22 @@ def data( Parameters ---------- data : pd.DataFrame, optional - The data frame that holds the x and y coordinates as well as other - possible dimensions that can be used for color, size, or opacity - encoding. + The new data frame that holds the x and y coordinates as well as + other possible dimensions that can be used for color, size, or + opacity encoding. label_index : bool, optional If `True` and if the data frame's index is not an instance of `RangeIndex` then the selection and filter methods will reference points by the data frame's label index instead of the row index. + reset_scales : bool, optional + If `True`, all scales (and norms) will be reset to the extend of the + the new data. + reset_view : bool, optional + If `True`, the view will be reset to the origin. + animate : bool, optional + If `True`, if the number of points remain the same, and if + `reset_scales` is `False`, the points will transition smoothly. If + `reset_view` is `True`, the view will also transition smoothly. Returns ------- @@ -493,6 +505,8 @@ def data( {'data': <pandas.DataFrame>, 'use_label_index': False} """ if data is not None: + old_n = self._n + try: self._n = len(data) self._data = data @@ -504,6 +518,35 @@ def data( self._points = np.zeros((self._n, 6)) + same_n = old_n == self._n + + if reset_scales == True: + self._x_scale.vmin = None + self._x_scale.vmax = None + self._y_scale.vmin = None + self._y_scale.vmax = None + self._color_norm.vmin = None + self._color_norm.vmax = None + self._opacity_norm.vmin = None + self._opacity_norm.vmax = None + self._size_norm.vmin = None + self._size_norm.vmax = None + self._connection_color_norm.vmin = None + self._connection_color_norm.vmax = None + self._connection_opacity_norm.vmin = None + self._connection_opacity_norm.vmax = None + self._connection_size_norm.vmin = None + self._connection_size_norm.vmax = None + + if 'skip_widget_update' not in kwargs: + # We have to update the following widget props now to ensure + # that the update propagated to the widget prior to updating the + # points below. This seems to be a bug in Jupyter Lab but I + # noticed that the order in which updates are registered on the + # front-end does *not* correspond to the call order in Python! + self.update_widget('transition_points', animate and same_n) + self.update_widget('prevent_filter_reset', False) + self.x(skip_widget_update=True, **self.x()) self.y(skip_widget_update=True, **self.y()) self.color(skip_widget_update=True, **self.color()) @@ -515,13 +558,26 @@ def data( self.connection_size(skip_widget_update=True, **self.connection_size()) if 'skip_widget_update' not in kwargs: - self.update_widget('prevent_filter_reset', False) self.update_widget('points', self.get_point_list()) + self.update_widget('transition_points', animate == True) + self.update_widget('x_domain', self._x_domain) + self.update_widget('x_scale_domain', self._x_scale_domain) + self.update_widget('y_domain', self._y_domain) + self.update_widget('y_scale_domain', self._y_scale_domain) + + if reset_view: + if reset_scales: + self.widget.reset_view() + else: + animation = 3000 if animate and same_n else 0 + self.widget.reset_view( + animation=animation, + data_extent=True, + ) if use_index is not UNDEF: self._data_use_index = use_index - if any_not([data, use_index], UNDEF): return self @@ -576,18 +632,17 @@ def x( if scale is None or scale == 'linear': self._x_scale = create_default_norm() elif scale == 'time': - self._x_scale = create_default_norm(True) + self._x_scale = create_default_norm(is_time=True) elif scale == 'log': - self._x_scale = LogNorm(clip=True) + self._x_scale = LogNorm() elif scale == 'pow': - self._x_scale = PowerNorm(2, clip=True) - elif isinstance(scale, LogNorm) or isinstance(scale, PowerNorm): + self._x_scale = PowerNorm(2) + elif isinstance(scale, LogNorm) or isinstance(scale, PowerNorm) or isinstance(scale, Normalize): self._x_scale = scale - self._x_scale.clip = True else: try: vmin, vmax = scale - self._x_scale = Normalize(vmin, vmax, clip=True) + self._x_scale = Normalize(vmin, vmax) except: pass @@ -604,7 +659,8 @@ def x( self._x_by = 'Custom X Data' if x is not UNDEF or scale is not UNDEF: - self.update_widget('prevent_filter_reset', True) + if 'skip_widget_update' not in kwargs: + self.update_widget('prevent_filter_reset', True) self._points[:, 0] = zerofy_missing_values(self.x_data.values, 'X') self._x_min = np.min(self._points[:, 0]) @@ -692,18 +748,17 @@ def y( if scale is None or scale == 'linear': self._y_scale = create_default_norm() elif scale == 'time': - self._y_scale = create_default_norm(True) + self._y_scale = create_default_norm(is_time=True) elif scale == 'log': - self._y_scale = LogNorm(clip=True) + self._y_scale = LogNorm() elif scale == 'pow': - self._y_scale = PowerNorm(2, clip=True) - elif isinstance(scale, LogNorm) or isinstance(scale, PowerNorm): + self._y_scale = PowerNorm(2) + elif isinstance(scale, LogNorm) or isinstance(scale, PowerNorm) or isinstance(scale, Normalize): self._y_scale = scale - self._y_scale.clip = True else: try: vmin, vmax = scale - self._y_scale = Normalize(vmin, vmax, clip=True) + self._y_scale = Normalize(vmin, vmax) except: pass @@ -1077,13 +1132,12 @@ def color( if callable(norm): try: self._color_norm = norm - self._color_norm.clip = True except: pass else: try: vmin, vmax = norm - self._color_norm = Normalize(vmin, vmax, clip=True) + self._color_norm = Normalize(vmin, vmax) except: if norm is None: self._color_norm = create_default_norm() @@ -1340,13 +1394,12 @@ def opacity( if callable(norm): try: self._opacity_norm = norm - self._opacity_norm.clip = True except: pass else: try: vmin, vmax = norm - self._opacity_norm = Normalize(vmin, vmax, clip=True) + self._opacity_norm = Normalize(vmin, vmax) except: if norm is None: # Reset to default value @@ -1574,13 +1627,12 @@ def size( if callable(norm): try: self._size_norm = norm - self._size_norm.clip = True except: pass else: try: vmin, vmax = norm - self._size_norm = Normalize(vmin, vmax, clip=True) + self._size_norm = Normalize(vmin, vmax) except: if norm is not None: self._size_norm = create_default_norm() @@ -1954,13 +2006,12 @@ def connection_color( if callable(norm): try: self._connection_color_norm = norm - self._connection_color_norm.clip = True except: pass else: try: vmin, vmax = norm - self._connection_color_norm = Normalize(vmin, vmax, clip=True) + self._connection_color_norm = Normalize(vmin, vmax) except: if norm is None: self._connection_color_norm = create_default_norm() @@ -2206,13 +2257,12 @@ def connection_opacity( if callable(norm): try: self._connection_opacity_norm.clip = norm - self._connection_opacity_norm.clip = True except: pass else: try: vmin, vmax = norm - self._connection_opacity_norm = Normalize(vmin, vmax, clip=True) + self._connection_opacity_norm = Normalize(vmin, vmax) except: if norm is None: self._connection_opacity_norm = create_default_norm() @@ -2437,13 +2487,12 @@ def connection_size( if callable(norm): try: self._connection_size_norm = norm - self._connection_size_norm.clip = True except: pass else: try: vmin, vmax = norm - self._connection_size_norm = Normalize(vmin, vmax, clip=True) + self._connection_size_norm = Normalize(vmin, vmax) except: if norm is None: self._connection_size_norm = create_default_norm() diff --git a/jscatter/utils.py b/jscatter/utils.py index 72009b3..913584f 100644 --- a/jscatter/utils.py +++ b/jscatter/utils.py @@ -56,8 +56,8 @@ class TimeNormalize(Normalize): def create_default_norm(is_time=False): if is_time: - return TimeNormalize(clip=True) - return Normalize(clip=True) + return TimeNormalize() + return Normalize() def to_ndc(X, norm): return (norm(X).data * 2) - 1 diff --git a/jscatter/widget.py b/jscatter/widget.py index 922c90a..c79623c 100644 --- a/jscatter/widget.py +++ b/jscatter/widget.py @@ -13,7 +13,10 @@ from .utils import to_hex, with_left_label SELECTION_DTYPE = 'uint32' -TOOLTIP_EVENT_TYPE = 'tooltip'; +EVENT_TYPES = { + "TOOLTIP": "tooltip", + "VIEW_RESET": "view_reset", +} def component_idx_to_name(idx): if idx == 2: @@ -57,6 +60,7 @@ class JupyterScatter(anywidget.AnyWidget): # Data points = Array(default_value=None).tag(sync=True, **ndarray_serialization) + transition_points = Bool(False).tag(sync=True) prevent_filter_reset = Bool(False).tag(sync=True) selection = Array(default_value=None, allow_none=True).tag(sync=True, **ndarray_serialization) filter = Array(default_value=None, allow_none=True).tag(sync=True, **ndarray_serialization) @@ -213,7 +217,6 @@ class JupyterScatter(anywidget.AnyWidget): # be overwritten by the short-hand options other_options = Dict(dict()).tag(sync=True) - view_reset = Bool(False).tag(sync=True) # Used for triggering a view reset view_download = Unicode(None, allow_none=True).tag(sync=True) # Used for triggering a download view_data = Array(default_value=None, allow_none=True, read_only=True).tag(sync=True, **ndarray_serialization) view_shape = List(None, allow_none=True, read_only=True).tag(sync=True) @@ -221,6 +224,8 @@ class JupyterScatter(anywidget.AnyWidget): # For synchronyzing view changes across scatter plot instances view_sync = Unicode(None, allow_none=True).tag(sync=True) + event_types = Dict(EVENT_TYPES, read_only=True).tag(sync=True) + def __init__(self, data, *args, **kwargs): super().__init__(*args, **kwargs) @@ -228,10 +233,10 @@ def __init__(self, data, *args, **kwargs): self.on_msg(self._handle_custom_msg) def _handle_custom_msg(self, event: dict, buffers): - if event["type"] == TOOLTIP_EVENT_TYPE and isinstance(self.data, pd.DataFrame): + if event["type"] == EVENT_TYPES["TOOLTIP"] and isinstance(self.data, pd.DataFrame): data = self.data.iloc[event["index"]] self.send({ - "type": TOOLTIP_EVENT_TYPE, + "type": EVENT_TYPES["TOOLTIP"], "index": event["index"], "preview": data[event["preview"]] if event["preview"] is not None else None, "properties": data[event["properties"]].to_dict() @@ -267,20 +272,33 @@ def click_handler(b): button.on_click(click_handler) return button - def reset_view(self): - self.view_reset = True + def reset_view(self, animation: int = 0, data_extent: bool = False): + if data_extent: + self.send({ + "type": EVENT_TYPES["VIEW_RESET"], + "area": { + "x": self.points[:, 0].min(), + "width": self.points[:, 0].max() - self.points[:, 0].min(), + "y": self.points[:, 1].min(), + "height": self.points[:, 1].max() - self.points[:, 1].min(), + }, + "animation": animation + }) + else: + self.send({ + "type": EVENT_TYPES["VIEW_RESET"], + "animation": animation + }) def create_reset_view_button(self, icon_only=False, width=None): - button = widgets.Button( + button = Button( description='' if icon_only else 'Reset View', - icon='refresh' + icon='refresh', + width=width, ) - if width is not None: - button.layout.width = f'{width}px' - - def click_handler(b): - self.reset_view() + def click_handler(event): + self.reset_view(500, event["alt_key"]) button.on_click(click_handler) return button @@ -363,3 +381,91 @@ def show(self): ) return widgets.HBox([buttons, plots]) + +class Button(anywidget.AnyWidget): + _esm = """ + function render({ model, el }) { + const button = document.createElement('button'); + + button.classList.add('jupyter-widgets'); + button.classList.add('jupyter-button'); + button.classList.add('widget-button'); + + const update = () => { + const description = model.get('description'); + const icon = model.get('icon'); + const width = model.get('width'); + + button.textContent = ''; + + if (icon) { + const i = document.createElement('i'); + i.classList.add('fa', `fa-${icon}`); + + if (!description) { + i.classList.add('center'); + } + + button.appendChild(i); + } + + if (description) { + button.appendChild(document.createTextNode(description)); + } + + if (width) { + button.style.width = `${width}px`; + } + } + + const createEventHandler = (eventType) => (event) => { + model.send({ + type: eventType, + alt_key: event.altKey, + shift_key: event.shiftKey, + meta_key: event.metaKey, + }); + } + + const clickHandler = createEventHandler('click'); + const dblclickHandler = createEventHandler('dblclick'); + + button.addEventListener('click', clickHandler); + button.addEventListener('dblclick', dblclickHandler); + + model.on('change:description', update); + model.on('change:icon', update); + model.on('change:width', update); + + update(); + + el.appendChild(button); + + return () => { + button.removeEventListener('click', clickHandler); + button.removeEventListener('dblclick', dblclickHandler); + }; + } + export default { render } + """ + + description = Unicode().tag(sync=True) + icon = Unicode().tag(sync=True) + width = Int(allow_none=True).tag(sync=True) + + def __init__(self, **kwargs): + super().__init__(**kwargs) + self._click_handler = None + self.on_msg(self._handle_custom_msg) + + def _handle_custom_msg(self, event: dict, buffers): + if event["type"] == "click" and self._click_handler is not None: + self._click_handler(event) + if event["type"] == "dblclick" and self._dblclick_handler is not None: + self._dblclick_handler(event) + + def on_click(self, callback): + self._click_handler = callback + + def on_dblclick(self, callback): + self._dblclick_handler = callback diff --git a/pyproject.toml b/pyproject.toml index 170ba2b..6d126bf 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -15,11 +15,11 @@ classifiers = [ "Intended Audience :: Developers", "Intended Audience :: Science/Research", "Topic :: Multimedia :: Graphics", - "Programming Language :: Python :: 3.7", "Programming Language :: Python :: 3.8", "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", ] keywords = [ "scatter", @@ -32,7 +32,7 @@ keywords = [ "widgets", ] dependencies = [ - "anywidget>=0.2.0", + "anywidget>=0.9.0", "ipywidgets>=7.6,<9", "ipython", "matplotlib", @@ -81,7 +81,7 @@ path = "js" [tool.hatch.envs.default] features = ["dev"] -python = "3.11" +python = "3.12" [tool.hatch.envs.default.scripts] test = "pytest ."

When updating the source data, xy point coordinates are clipped to original data range - [x] Check for duplicate issues. - [x] Use the latest versions of Jupyter Scatter. - [x] Describe the bug: What did you do to trigger it? What was your expected behavior? And what did you experience instead? * Create a scatter with an initial dataframe. * Replace the dataframe with `scatter.data(df_new)` * Points try to migrate to their new locations but are "blocked" by the bounding box of the first dataframe * Doesn't depend on current camera location <img width="620" alt="image" src="https://github.com/flekschas/jupyter-scatter/assets/1270651/4d62afd0-0245-4057-8b02-4d1cd0f969dc"> - [x] Provide an [minimal, reproducible example](https://stackoverflow.com/help/minimal-reproducible-example) of the bug. ```python data1 = pd.DataFrame({ "x": np.random.rand(1000), "y": np.random.rand(1000), }) data2 = pd.DataFrame({ "x": 2*np.random.rand(1000), "y": 2*np.random.rand(1000), }) scatter = Scatter(data=data1, x="x", y="y", width=500, height=500) scatter.show() ``` ```python scatter.data(data=data2) ```

2024-06-10T01:29:15

0.0

[]

prophyle/prophyle

prophyle__prophyle-352

5abb7d78ddb77b062c6b451f0859bd3f5c789ee8

diff --git a/prophyle/__commit.py b/prophyle/__commit.py index bcb05a8f..8849b1dc 100644 --- a/prophyle/__commit.py +++ b/prophyle/__commit.py @@ -1,4 +1,4 @@ -BRANCH = "mof" -SHORTHASH = "01de02c" -REVCOUNT = 1734 +BRANCH = "fix-mimic-kraken" +SHORTHASH = "fe13d9d" +REVCOUNT = 1737 LATESTTAG = "0.3.1.0" diff --git a/prophyle/prophyle_assignment.py b/prophyle/prophyle_assignment.py index 6acd6dbf..73546730 100755 --- a/prophyle/prophyle_assignment.py +++ b/prophyle/prophyle_assignment.py @@ -108,7 +108,7 @@ def process_read(self, krakline, form, measure): if CONFIG['DIAGNOSTICS']: self.diagnostics() - self.select_best_assignments(measure) + self.select_best_assignments(measure, self.tie_lca) if CONFIG['DIAGNOSTICS']: self.diagnostics() @@ -146,7 +146,7 @@ def blocks_to_masks(self, kmer_blocks, kmer_lca): covmask_1block = self.bitarray_block(covmask_len, count + self.k - 1, pos) if kmer_lca: - node_names = [self.tree_index.lca(*node_names)] + node_names = [self.tree_index.lca(node_names)] for node_name in node_names: hitmasks_dict[node_name] |= hitmask_1block @@ -183,8 +183,6 @@ def evaluate_single_assignment(self, nodename): hitmask = bitarray(self.hitmasks_dict[nodename]) covmask = bitarray(self.covmasks_dict[nodename]) - node = self.tree_index.nodename_to_node[nodename] - ########################## # B) Update from ancestors ########################## @@ -199,6 +197,7 @@ def evaluate_single_assignment(self, nodename): hit = hitmask.count() cov = covmask.count() readlen = self.krakline_parser.readlen + kcount = self.tree_index.nodename_to_kmercount[nodename] assignment = { # 1. hit count @@ -206,14 +205,14 @@ def evaluate_single_assignment(self, nodename): #'hitcigar': self.cigar_from_bitmask(hitmask), 'h1': [hit], 'hf': [hit / (readlen - self.k + 1)], - 'h2': [hit / self.tree_index.nodename_to_kmercount[nodename]], + 'h2': [hit / kcount if kcount > 0 else 0], # 2. coverage 'covmask': covmask, #'covcigar': self.cigar_from_bitmask(covmask), 'c1': [cov], 'cf': [cov / readlen], - 'c2': [cov / self.tree_index.nodename_to_kmercount[nodename]], + 'c2': [cov / kcount if kcount > 0 else 0], # 3. other values 'ln': readlen, @@ -221,18 +220,18 @@ def evaluate_single_assignment(self, nodename): return assignment - def select_best_assignments(self, measure): + def select_best_assignments(self, measure, tie_lca=False): """Find the best assignments and save it to self.max_nodenames (max value: self.max_val). Args: measure (str): Measure (h1/c1/h2/c2). + tie_lca (bool): Compute LCA in case of tie. """ self.max_val = 0 self.max_nodenames = [] - for nodename in self.ass_dict: - ass = self.ass_dict[nodename] + for nodename, ass in self.ass_dict.items(): if ass[measure][0] > self.max_val: self.max_val = ass[measure][0] @@ -241,6 +240,9 @@ def select_best_assignments(self, measure): elif ass[measure][0] == self.max_val: self.max_nodenames.append(nodename) + if tie_lca: + self.make_lca_from_winners() + if CONFIG['SORT_NODES']: self.max_nodenames.sort() @@ -252,11 +254,10 @@ def make_lca_from_winners(self): """ # all characteristics are already computed - if len(self.max_nodenames) == 1: + if len(self.max_nodenames) <= 1: return - first_winner = self.ass_dict[max_nodenames[0]] - lca = self.tree_index.lca(winners) + lca = self.tree_index.lca(self.max_nodenames) ass = { 'covmask': None, @@ -267,14 +268,12 @@ def make_lca_from_winners(self): } for tag in ['h1', 'hf', 'h2', 'c1', 'cf', 'c2']: - ass[tag] = [self.ass_dict[nodename][tag] for nodename in self.max_nodenames], + # ass[tag] = [self.ass_dict[nodename][tag] for nodename in self.max_nodenames] + ass[tag] = [0] self.max_nodenames = [lca] self.ass_dict[lca] = ass - asg['covmask'] = None - asg['hitmask'] = None - def print_selected_assignments(self, form): """Print the best assignments. @@ -332,7 +331,6 @@ def print_sam_line(self, node_name, suffix): suffix (str): Suffix with additional tags. """ - tags = [] qname = self.krakline_parser.readname if node_name is not None: @@ -438,7 +436,7 @@ def print_kraken_line(self, *nodenames): #else: # pass else: - nodename = self.tree_index.lca(*nodenames) + nodename = self.tree_index.lca(nodenames) nodenames_lca_seq.extend(count * [nodename]) c = [] runs = itertools.groupby(nodenames_lca_seq) @@ -539,10 +537,10 @@ def __init__(self, tree_newick_fn, k): node = node.up self.nodename_to_upnodenames[nodename].add(node.name) - def lca(self, *node_names): + def lca(self, node_names): """Return LCA for a given list of nodes. - *node_names (list of str): List of node names. + node_names (list of str): List of node names. Returns: str: Name of the LCA. @@ -550,12 +548,12 @@ def lca(self, *node_names): assert len(node_names) > 0 if len(node_names) == 1: return node_names[0] - nodes = list(map(lambda x: self.nodename_to_node[x], node_names)) + nodes = [self.nodename_to_node[n] for n in node_names] lca = nodes[0].get_common_ancestor(nodes) if lca.is_root() and len(lca.children) == 1: lca = lca.children[0] - assert lca.name != "" #, [x.name for x in lca.children] + assert lca.name != "" # , [x.name for x in lca.children] return lca.name

No LCA computation in case of tie In the current version of `prophyle_assignment.py`, the function `make_lca_from_winners` is never called, even though the option `-L` (tie lca) is active. The list of nodes with the highest score is output instead.

2021-08-07T03:44:05

0.0

[]

adamcharnock/django-hordak

adamcharnock__django-hordak-131

41eea6ba7a5a33b0527f5e73657084c5501c4df0

diff --git a/hordak/forms/transactions.py b/hordak/forms/transactions.py index 1454984..f811b18 100644 --- a/hordak/forms/transactions.py +++ b/hordak/forms/transactions.py @@ -21,17 +21,17 @@ class SimpleTransactionForm(forms.ModelForm): * :meth:`hordak.models.Account.transfer_to()`. """ - from_account = TreeNodeChoiceField( + debit_account = TreeNodeChoiceField( queryset=Account.objects.all(), to_field_name="uuid" ) - to_account = TreeNodeChoiceField( + credit_account = TreeNodeChoiceField( queryset=Account.objects.all(), to_field_name="uuid" ) amount = MoneyField(max_digits=MAX_DIGITS, decimal_places=DECIMAL_PLACES) class Meta: model = Transaction - fields = ["amount", "from_account", "to_account", "date", "description"] + fields = ["amount", "debit_account", "credit_account", "date", "description"] def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) @@ -49,12 +49,12 @@ def __init__(self, *args, **kwargs): self.fields["amount"].initial[1] = default_currency def save(self, commit=True): - from_account = self.cleaned_data.get("from_account") - to_account = self.cleaned_data.get("to_account") + debit_account = self.cleaned_data.get("debit_account") + credit_account = self.cleaned_data.get("credit_account") amount = self.cleaned_data.get("amount") - return from_account.transfer_to( - to_account=to_account, + return debit_account.transfer_to( + to_account=credit_account, amount=amount, description=self.cleaned_data.get("description"), date=self.cleaned_data.get("date"), diff --git a/hordak/models/core.py b/hordak/models/core.py index cd07868..f24cf67 100644 --- a/hordak/models/core.py +++ b/hordak/models/core.py @@ -44,7 +44,6 @@ ) from hordak.utilities.currency import Balance from hordak.utilities.db_functions import GetBalance -from hordak.utilities.dreprecation import deprecated #: Debit @@ -328,73 +327,9 @@ def _zero_balance(self): """Get a balance for this account with all currencies set to zero""" return Balance([Money("0", currency) for currency in self.currencies]) - @deprecated( - "transfer_to() has been deprecated. This method does not adhere to expected transfers based on the " - "accounting equation, see notes. Use .accounting_transfer_to() instead. " - "This method will raise an error in v2.0.0." - ) @db_transaction.atomic() def transfer_to(self, to_account, amount, **transaction_kwargs): - """**Deprecated** Please use `.accounting_transfer_to()` instead. Will raise an error in Hordak 2.x. - - Create a transaction which transfers amount to to_account - - This is a shortcut utility method which simplifies the process of - transferring between accounts. - - This method attempts to perform the transaction in an intuitive manner. - For example: - - * Transferring income -> income will result in the former decreasing and the latter increasing - * Transferring asset (i.e. bank) -> income will result in the balance of both increasing - * Transferring asset -> asset will result in the former decreasing and the latter increasing - - .. note:: - - Transfers in any direction between ``{asset | expense} <-> {income | liability | equity}`` - will always result in both balances increasing. This may change in future if it is - found to be unhelpful. - - Transfers to trading accounts will always behave as normal. - - Args: - - to_account (Account): The destination account. - amount (Money): The amount to be transferred. - transaction_kwargs: Passed through to transaction creation. Useful for setting the - transaction `description` field. - """ - if not isinstance(amount, Money): - raise TypeError("amount must be of type Money") - - if to_account.sign == 1 and to_account.type != AccountType.trading: - # Transferring from two positive-signed accounts implies that - # the caller wants to reduce the first account and increase the second - # (which is opposite to the implicit behaviour) - direction = -1 - elif ( - self.type == AccountType.liability - and to_account.type == AccountType.expense - ): - # Transfers from liability -> asset accounts should reduce both. - # For example, moving money from Rent Payable (liability) to your Rent (expense) account - # should use the funds you've built up in the liability account to pay off the expense account. - direction = -1 - else: - direction = 1 - - transaction = Transaction.objects.create(**transaction_kwargs) - Leg.objects.create( - transaction=transaction, account=self, amount=+amount * direction - ) - Leg.objects.create( - transaction=transaction, account=to_account, amount=-amount * direction - ) - return transaction - - @db_transaction.atomic() - def accounting_transfer_to(self, to_account, amount, **transaction_kwargs): - """Create a transaction which transfers amount to to_account using double entry accounting rules + """Create a transaction which credits self and debits `to_account`. See https://en.wikipedia.org/wiki/Double-entry_bookkeeping. @@ -610,7 +545,8 @@ def is_credit(self): def account_balance_after(self): """Get the balance of the account associated with this leg following the transaction""" # TODO: Consider moving to annotation, - # particularly once we can count on Django 1.11's subquery support + # particularly once we can count on Django 1.11's subquery support + # Or use the new LegView. transaction_date = self.transaction.date return self.account.balance( leg_query=( @@ -625,7 +561,8 @@ def account_balance_after(self): def account_balance_before(self): """Get the balance of the account associated with this leg before the transaction""" # TODO: Consider moving to annotation, - # particularly once we can count on Django 1.11's subquery support + # particularly once we can count on Django 1.11's subquery support. + # Or use the new LegView. transaction_date = self.transaction.date return self.account.balance( leg_query=(

Replace `transfer_to()` with `accounting_transfer_to()` We've been warning this would happen in Hordak 2.0.0 for a while.

2024-06-30T20:42:46

0.0

[]

adamcharnock/django-hordak

adamcharnock__django-hordak-90

8c5453ad74f9f9a1c09ae7c23d2cfe8995be7658

diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index a5962088..878445ef 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -70,13 +70,15 @@ jobs: pip install Django==${{ matrix.DJANGO_VERSION }} pip install codecov - - name: Testing + - name: Check Migrations run: | pip install "py-moneyed<3" --upgrade PYTHONPATH=`pwd` ./manage.py makemigrations --check hordak pip install "py-moneyed>=3" --upgrade # Different version of py-moneyed should not create different migrations PYTHONPATH=`pwd` ./manage.py makemigrations --check hordak + - name: Testing + run: | PYTHONPATH=`pwd` python -Wall -W error::DeprecationWarning -m coverage run ./manage.py test hordak pip install -e .[subqueries] PYTHONPATH=`pwd` python -Wall -W error::DeprecationWarning -m coverage run --append ./manage.py test hordak # Test with subquery diff --git a/CHANGES.txt b/CHANGES.txt index bc8e6b4c..8cce58da 100644 --- a/CHANGES.txt +++ b/CHANGES.txt @@ -6,6 +6,14 @@ Version 1.14.0, This function will raise an error in Hordak 2.0. * Now using Django's locale to `Balance.__str__`'s `format_money` allowing better formatting and specs. +* Changed `Account.currencies` from `ArrayField => JSONField` to allow database agnostic work. + * **Breaking Change** + * `Account.currencies` input must be valid JSON now. + * This is mostly important in 2 areas + * `Forms`, but also if you performed a `", ".join(currencies_str_arr)`. + * `Fixtures` + * Previous: `EUR, USD` => `["EUR", "USD"]` + * Form complains if it is not valid JSON and returns an explanation. Version 1.13.0, Fri 17 Feb 2023 =============================== diff --git a/hordak/defaults.py b/hordak/defaults.py index dccd0e8e..8adbc391 100644 --- a/hordak/defaults.py +++ b/hordak/defaults.py @@ -8,18 +8,22 @@ DEFAULT_CURRENCY = getattr(settings, "DEFAULT_CURRENCY", "EUR") + +def default_currency(): + return DEFAULT_CURRENCY + + CURRENCIES = getattr(settings, "HORDAK_CURRENCIES", getattr(settings, "CURRENCIES", [])) -def default_currencies(): - default_currs = getattr( - settings, "HORDAK_CURRENCIES", getattr(settings, "CURRENCIES", []) - ) +# Expected to be an array of currencies ["EUR", "USD", "GBP"] +def project_currencies() -> list: + project_currs = CURRENCIES - if callable(default_currs): - return default_currs() + if callable(project_currs): + return project_currs() - return default_currs + return project_currs DECIMAL_PLACES = getattr(settings, "HORDAK_DECIMAL_PLACES", 2) diff --git a/hordak/forms/accounts.py b/hordak/forms/accounts.py index c5dd522c..b0fd7529 100644 --- a/hordak/forms/accounts.py +++ b/hordak/forms/accounts.py @@ -1,4 +1,7 @@ +import json + from django import forms +from djmoney.settings import CURRENCY_CHOICES from hordak.models import Account @@ -16,6 +19,8 @@ class Meta: model = Account exclude = ["full_code"] + currencies = forms.JSONField() + def __init__(self, *args, **kwargs): self.is_updating = bool(kwargs.get("instance")) and kwargs["instance"].pk @@ -33,11 +38,40 @@ def __init__(self, *args, **kwargs): super(AccountForm, self).__init__(*args, **kwargs) + try: + self.tmp_currencies = args[0].get("currencies") + except IndexError: + self.tmp_currencies = kwargs.get("data", {}).get("currencies") + if self.is_updating: del self.fields["type"] del self.fields["currencies"] del self.fields["is_bank_account"] + def _check_currencies_json(self): + currencies = self.tmp_currencies + + if isinstance(self.tmp_currencies, str): + try: + currencies = json.loads(self.tmp_currencies) + except json.JSONDecodeError: + if "currencies" in self.fields: + self.add_error( + "currencies", + 'Currencies needs to be valid JSON (i.e. ["USD", "EUR"] or ["USD"])' + + f" - {self.tmp_currencies} is not valid JSON.", + ) + + return + + for currency in currencies: + if currency not in [choice[0] for choice in CURRENCY_CHOICES]: + if "currencies" in self.fields: + self.add_error( + "currencies", + f"Select a valid choice. {currency} is not one of the available choices.", + ) + def clean(self): cleaned_data = super(AccountForm, self).clean() is_bank_account = ( @@ -60,4 +94,6 @@ def clean(self): ): raise forms.ValidationError("Bank accounts may only have one currency.") + self._check_currencies_json() + return cleaned_data diff --git a/hordak/migrations/0030_alter_leg_amount_currency.py b/hordak/migrations/0030_alter_leg_amount_currency.py index b830ed9b..bd1793b3 100644 --- a/hordak/migrations/0030_alter_leg_amount_currency.py +++ b/hordak/migrations/0030_alter_leg_amount_currency.py @@ -2,6 +2,8 @@ import djmoney.models.fields from django.db import migrations +import hordak.models.core +import hordak.defaults class Migration(migrations.Migration): @@ -14,318 +16,8 @@ class Migration(migrations.Migration): model_name="leg", name="amount_currency", field=djmoney.models.fields.CurrencyField( - choices=[ - ("XUA", "ADB Unit of Account"), - ("AFN", "Afghan Afghani"), - ("AFA", "Afghan Afghani (1927–2002)"), - ("ALL", "Albanian Lek"), - ("ALK", "Albanian Lek (1946–1965)"), - ("DZD", "Algerian Dinar"), - ("ADP", "Andorran Peseta"), - ("AOA", "Angolan Kwanza"), - ("AOK", "Angolan Kwanza (1977–1991)"), - ("AON", "Angolan New Kwanza (1990–2000)"), - ("AOR", "Angolan Readjusted Kwanza (1995–1999)"), - ("ARA", "Argentine Austral"), - ("ARS", "Argentine Peso"), - ("ARM", "Argentine Peso (1881–1970)"), - ("ARP", "Argentine Peso (1983–1985)"), - ("ARL", "Argentine Peso Ley (1970–1983)"), - ("AMD", "Armenian Dram"), - ("AWG", "Aruban Florin"), - ("AUD", "Australian Dollar"), - ("ATS", "Austrian Schilling"), - ("AZN", "Azerbaijani Manat"), - ("AZM", "Azerbaijani Manat (1993–2006)"), - ("BSD", "Bahamian Dollar"), - ("BHD", "Bahraini Dinar"), - ("BDT", "Bangladeshi Taka"), - ("BBD", "Barbadian Dollar"), - ("BYN", "Belarusian Ruble"), - ("BYB", "Belarusian Ruble (1994–1999)"), - ("BYR", "Belarusian Ruble (2000–2016)"), - ("BEF", "Belgian Franc"), - ("BEC", "Belgian Franc (convertible)"), - ("BEL", "Belgian Franc (financial)"), - ("BZD", "Belize Dollar"), - ("BMD", "Bermudan Dollar"), - ("BTN", "Bhutanese Ngultrum"), - ("BOB", "Bolivian Boliviano"), - ("BOL", "Bolivian Boliviano (1863–1963)"), - ("BOV", "Bolivian Mvdol"), - ("BOP", "Bolivian Peso"), - ("BAM", "Bosnia-Herzegovina Convertible Mark"), - ("BAD", "Bosnia-Herzegovina Dinar (1992–1994)"), - ("BAN", "Bosnia-Herzegovina New Dinar (1994–1997)"), - ("BWP", "Botswanan Pula"), - ("BRC", "Brazilian Cruzado (1986–1989)"), - ("BRZ", "Brazilian Cruzeiro (1942–1967)"), - ("BRE", "Brazilian Cruzeiro (1990–1993)"), - ("BRR", "Brazilian Cruzeiro (1993–1994)"), - ("BRN", "Brazilian New Cruzado (1989–1990)"), - ("BRB", "Brazilian New Cruzeiro (1967–1986)"), - ("BRL", "Brazilian Real"), - ("GBP", "British Pound"), - ("BND", "Brunei Dollar"), - ("BGL", "Bulgarian Hard Lev"), - ("BGN", "Bulgarian Lev"), - ("BGO", "Bulgarian Lev (1879–1952)"), - ("BGM", "Bulgarian Socialist Lev"), - ("BUK", "Burmese Kyat"), - ("BIF", "Burundian Franc"), - ("XPF", "CFP Franc"), - ("KHR", "Cambodian Riel"), - ("CAD", "Canadian Dollar"), - ("CVE", "Cape Verdean Escudo"), - ("KYD", "Cayman Islands Dollar"), - ("XAF", "Central African CFA Franc"), - ("CLE", "Chilean Escudo"), - ("CLP", "Chilean Peso"), - ("CLF", "Chilean Unit of Account (UF)"), - ("CNX", "Chinese People’s Bank Dollar"), - ("CNY", "Chinese Yuan"), - ("CNH", "Chinese Yuan (offshore)"), - ("COP", "Colombian Peso"), - ("COU", "Colombian Real Value Unit"), - ("KMF", "Comorian Franc"), - ("CDF", "Congolese Franc"), - ("CRC", "Costa Rican Colón"), - ("HRD", "Croatian Dinar"), - ("HRK", "Croatian Kuna"), - ("CUC", "Cuban Convertible Peso"), - ("CUP", "Cuban Peso"), - ("CYP", "Cypriot Pound"), - ("CZK", "Czech Koruna"), - ("CSK", "Czechoslovak Hard Koruna"), - ("DKK", "Danish Krone"), - ("DJF", "Djiboutian Franc"), - ("DOP", "Dominican Peso"), - ("NLG", "Dutch Guilder"), - ("XCD", "East Caribbean Dollar"), - ("DDM", "East German Mark"), - ("ECS", "Ecuadorian Sucre"), - ("ECV", "Ecuadorian Unit of Constant Value"), - ("EGP", "Egyptian Pound"), - ("GQE", "Equatorial Guinean Ekwele"), - ("ERN", "Eritrean Nakfa"), - ("EEK", "Estonian Kroon"), - ("ETB", "Ethiopian Birr"), - ("EUR", "Euro"), - ("XBA", "European Composite Unit"), - ("XEU", "European Currency Unit"), - ("XBB", "European Monetary Unit"), - ("XBC", "European Unit of Account (XBC)"), - ("XBD", "European Unit of Account (XBD)"), - ("FKP", "Falkland Islands Pound"), - ("FJD", "Fijian Dollar"), - ("FIM", "Finnish Markka"), - ("FRF", "French Franc"), - ("XFO", "French Gold Franc"), - ("XFU", "French UIC-Franc"), - ("GMD", "Gambian Dalasi"), - ("GEK", "Georgian Kupon Larit"), - ("GEL", "Georgian Lari"), - ("DEM", "German Mark"), - ("GHS", "Ghanaian Cedi"), - ("GHC", "Ghanaian Cedi (1979–2007)"), - ("GIP", "Gibraltar Pound"), - ("XAU", "Gold"), - ("GRD", "Greek Drachma"), - ("GTQ", "Guatemalan Quetzal"), - ("GWP", "Guinea-Bissau Peso"), - ("GNF", "Guinean Franc"), - ("GNS", "Guinean Syli"), - ("GYD", "Guyanaese Dollar"), - ("HTG", "Haitian Gourde"), - ("HNL", "Honduran Lempira"), - ("HKD", "Hong Kong Dollar"), - ("HUF", "Hungarian Forint"), - ("IMP", "IMP"), - ("ISK", "Icelandic Króna"), - ("ISJ", "Icelandic Króna (1918–1981)"), - ("INR", "Indian Rupee"), - ("IDR", "Indonesian Rupiah"), - ("IRR", "Iranian Rial"), - ("IQD", "Iraqi Dinar"), - ("IEP", "Irish Pound"), - ("ILS", "Israeli New Shekel"), - ("ILP", "Israeli Pound"), - ("ILR", "Israeli Shekel (1980–1985)"), - ("ITL", "Italian Lira"), - ("JMD", "Jamaican Dollar"), - ("JPY", "Japanese Yen"), - ("JOD", "Jordanian Dinar"), - ("KZT", "Kazakhstani Tenge"), - ("KES", "Kenyan Shilling"), - ("KWD", "Kuwaiti Dinar"), - ("KGS", "Kyrgystani Som"), - ("LAK", "Laotian Kip"), - ("LVL", "Latvian Lats"), - ("LVR", "Latvian Ruble"), - ("LBP", "Lebanese Pound"), - ("LSL", "Lesotho Loti"), - ("LRD", "Liberian Dollar"), - ("LYD", "Libyan Dinar"), - ("LTL", "Lithuanian Litas"), - ("LTT", "Lithuanian Talonas"), - ("LUL", "Luxembourg Financial Franc"), - ("LUC", "Luxembourgian Convertible Franc"), - ("LUF", "Luxembourgian Franc"), - ("MOP", "Macanese Pataca"), - ("MKD", "Macedonian Denar"), - ("MKN", "Macedonian Denar (1992–1993)"), - ("MGA", "Malagasy Ariary"), - ("MGF", "Malagasy Franc"), - ("MWK", "Malawian Kwacha"), - ("MYR", "Malaysian Ringgit"), - ("MVR", "Maldivian Rufiyaa"), - ("MVP", "Maldivian Rupee (1947–1981)"), - ("MLF", "Malian Franc"), - ("MTL", "Maltese Lira"), - ("MTP", "Maltese Pound"), - ("MRU", "Mauritanian Ouguiya"), - ("MRO", "Mauritanian Ouguiya (1973–2017)"), - ("MUR", "Mauritian Rupee"), - ("MXV", "Mexican Investment Unit"), - ("MXN", "Mexican Peso"), - ("MXP", "Mexican Silver Peso (1861–1992)"), - ("MDC", "Moldovan Cupon"), - ("MDL", "Moldovan Leu"), - ("MCF", "Monegasque Franc"), - ("MNT", "Mongolian Tugrik"), - ("MAD", "Moroccan Dirham"), - ("MAF", "Moroccan Franc"), - ("MZE", "Mozambican Escudo"), - ("MZN", "Mozambican Metical"), - ("MZM", "Mozambican Metical (1980–2006)"), - ("MMK", "Myanmar Kyat"), - ("NAD", "Namibian Dollar"), - ("NPR", "Nepalese Rupee"), - ("ANG", "Netherlands Antillean Guilder"), - ("TWD", "New Taiwan Dollar"), - ("NZD", "New Zealand Dollar"), - ("NIO", "Nicaraguan Córdoba"), - ("NIC", "Nicaraguan Córdoba (1988–1991)"), - ("NGN", "Nigerian Naira"), - ("KPW", "North Korean Won"), - ("NOK", "Norwegian Krone"), - ("OMR", "Omani Rial"), - ("PKR", "Pakistani Rupee"), - ("XPD", "Palladium"), - ("PAB", "Panamanian Balboa"), - ("PGK", "Papua New Guinean Kina"), - ("PYG", "Paraguayan Guarani"), - ("PEI", "Peruvian Inti"), - ("PEN", "Peruvian Sol"), - ("PES", "Peruvian Sol (1863–1965)"), - ("PHP", "Philippine Peso"), - ("XPT", "Platinum"), - ("PLN", "Polish Zloty"), - ("PLZ", "Polish Zloty (1950–1995)"), - ("PTE", "Portuguese Escudo"), - ("GWE", "Portuguese Guinea Escudo"), - ("QAR", "Qatari Rial"), - ("XRE", "RINET Funds"), - ("RHD", "Rhodesian Dollar"), - ("RON", "Romanian Leu"), - ("ROL", "Romanian Leu (1952–2006)"), - ("RUB", "Russian Ruble"), - ("RUR", "Russian Ruble (1991–1998)"), - ("RWF", "Rwandan Franc"), - ("SVC", "Salvadoran Colón"), - ("WST", "Samoan Tala"), - ("SAR", "Saudi Riyal"), - ("RSD", "Serbian Dinar"), - ("CSD", "Serbian Dinar (2002–2006)"), - ("SCR", "Seychellois Rupee"), - ("SLL", "Sierra Leonean Leone"), - ("XAG", "Silver"), - ("SGD", "Singapore Dollar"), - ("SKK", "Slovak Koruna"), - ("SIT", "Slovenian Tolar"), - ("SBD", "Solomon Islands Dollar"), - ("SOS", "Somali Shilling"), - ("ZAR", "South African Rand"), - ("ZAL", "South African Rand (financial)"), - ("KRH", "South Korean Hwan (1953–1962)"), - ("KRW", "South Korean Won"), - ("KRO", "South Korean Won (1945–1953)"), - ("SSP", "South Sudanese Pound"), - ("SUR", "Soviet Rouble"), - ("ESP", "Spanish Peseta"), - ("ESA", "Spanish Peseta (A account)"), - ("ESB", "Spanish Peseta (convertible account)"), - ("XDR", "Special Drawing Rights"), - ("LKR", "Sri Lankan Rupee"), - ("SHP", "St. Helena Pound"), - ("XSU", "Sucre"), - ("SDD", "Sudanese Dinar (1992–2007)"), - ("SDG", "Sudanese Pound"), - ("SDP", "Sudanese Pound (1957–1998)"), - ("SRD", "Surinamese Dollar"), - ("SRG", "Surinamese Guilder"), - ("SZL", "Swazi Lilangeni"), - ("SEK", "Swedish Krona"), - ("CHF", "Swiss Franc"), - ("SYP", "Syrian Pound"), - ("STN", "São Tomé & Príncipe Dobra"), - ("STD", "São Tomé & Príncipe Dobra (1977–2017)"), - ("TVD", "TVD"), - ("TJR", "Tajikistani Ruble"), - ("TJS", "Tajikistani Somoni"), - ("TZS", "Tanzanian Shilling"), - ("XTS", "Testing Currency Code"), - ("THB", "Thai Baht"), - ( - "XXX", - "The codes assigned for transactions where no currency is involved", - ), - ("TPE", "Timorese Escudo"), - ("TOP", "Tongan Paʻanga"), - ("TTD", "Trinidad & Tobago Dollar"), - ("TND", "Tunisian Dinar"), - ("TRY", "Turkish Lira"), - ("TRL", "Turkish Lira (1922–2005)"), - ("TMT", "Turkmenistani Manat"), - ("TMM", "Turkmenistani Manat (1993–2009)"), - ("USD", "US Dollar"), - ("USN", "US Dollar (Next day)"), - ("USS", "US Dollar (Same day)"), - ("UGX", "Ugandan Shilling"), - ("UGS", "Ugandan Shilling (1966–1987)"), - ("UAH", "Ukrainian Hryvnia"), - ("UAK", "Ukrainian Karbovanets"), - ("AED", "United Arab Emirates Dirham"), - ("UYW", "Uruguayan Nominal Wage Index Unit"), - ("UYU", "Uruguayan Peso"), - ("UYP", "Uruguayan Peso (1975–1993)"), - ("UYI", "Uruguayan Peso (Indexed Units)"), - ("UZS", "Uzbekistani Som"), - ("VUV", "Vanuatu Vatu"), - ("VES", "Venezuelan Bolívar"), - ("VEB", "Venezuelan Bolívar (1871–2008)"), - ("VEF", "Venezuelan Bolívar (2008–2018)"), - ("VND", "Vietnamese Dong"), - ("VNN", "Vietnamese Dong (1978–1985)"), - ("CHE", "WIR Euro"), - ("CHW", "WIR Franc"), - ("XOF", "West African CFA Franc"), - ("YDD", "Yemeni Dinar"), - ("YER", "Yemeni Rial"), - ("YUN", "Yugoslavian Convertible Dinar (1990–1992)"), - ("YUD", "Yugoslavian Hard Dinar (1966–1990)"), - ("YUM", "Yugoslavian New Dinar (1994–2002)"), - ("YUR", "Yugoslavian Reformed Dinar (1992–1993)"), - ("ZWN", "ZWN"), - ("ZRN", "Zairean New Zaire (1993–1998)"), - ("ZRZ", "Zairean Zaire (1971–1993)"), - ("ZMW", "Zambian Kwacha"), - ("ZMK", "Zambian Kwacha (1968–2012)"), - ("ZWD", "Zimbabwean Dollar (1980–2008)"), - ("ZWR", "Zimbabwean Dollar (2008)"), - ("ZWL", "Zimbabwean Dollar (2009)"), - ], - default="EUR", + choices=hordak.models.core.get_currency_choices(), + default=hordak.defaults.get_internal_currency, editable=False, max_length=3, ), diff --git a/hordak/migrations/0034_alter_account_currencies_alter_leg_amount_currency.py b/hordak/migrations/0034_alter_account_currencies_alter_leg_amount_currency.py index 90d343c2..f5a15453 100644 --- a/hordak/migrations/0034_alter_account_currencies_alter_leg_amount_currency.py +++ b/hordak/migrations/0034_alter_account_currencies_alter_leg_amount_currency.py @@ -4,6 +4,7 @@ import djmoney.models.fields from django.db import migrations, models import hordak.models.core +import hordak.defaults class Migration(migrations.Migration): @@ -21,7 +22,7 @@ class Migration(migrations.Migration): max_length=3, ), db_index=True, - default=hordak.models.core.default_currencies, + default=hordak.defaults.DEFAULT_CURRENCY, size=None, verbose_name="currencies", ), @@ -31,7 +32,7 @@ class Migration(migrations.Migration): name="amount_currency", field=djmoney.models.fields.CurrencyField( choices=hordak.models.core.get_currency_choices(), - default=hordak.models.core.get_internal_currency, + default=hordak.defaults.get_internal_currency, editable=False, max_length=3, ), diff --git a/hordak/migrations/0035_account_currencies_json.py b/hordak/migrations/0035_account_currencies_json.py new file mode 100644 index 00000000..2fc51859 --- /dev/null +++ b/hordak/migrations/0035_account_currencies_json.py @@ -0,0 +1,36 @@ +# Generated by Django 4.2 on 2023-05-16 01:31 + +from django.db import migrations, models + +import hordak + + +def copy_currencies_data(apps, schema_editor): + MyModel = apps.get_model( + "hordak", "Account" + ) # replace 'myapp' and 'MyModel' with your actual app and model names + table_name = MyModel._meta.db_table + with schema_editor.connection.cursor() as cursor: + cursor.execute( + f""" + UPDATE {table_name} SET currencies_json = array_to_json(currencies)::jsonb; + """ + ) + + +class Migration(migrations.Migration): + dependencies = [ + ("hordak", "0034_alter_account_currencies_alter_leg_amount_currency"), + ] + + operations = [ + migrations.AddField( + model_name="account", + name="currencies_json", + field=models.JSONField( + db_index=True, + default=hordak.defaults.project_currencies, + ), + ), + migrations.RunPython(copy_currencies_data), + ] diff --git a/hordak/migrations/0036_remove_currencies_and_rename_account_currencies_json_to_currencies.py b/hordak/migrations/0036_remove_currencies_and_rename_account_currencies_json_to_currencies.py new file mode 100644 index 00000000..d9b5558c --- /dev/null +++ b/hordak/migrations/0036_remove_currencies_and_rename_account_currencies_json_to_currencies.py @@ -0,0 +1,32 @@ +# Generated by Django 4.2 on 2023-05-16 01:36 + +from django.db import migrations, models + +import hordak + + +class Migration(migrations.Migration): + dependencies = [ + ("hordak", "0035_account_currencies_json"), + ] + + operations = [ + migrations.RemoveField( + model_name="account", + name="currencies", + ), + migrations.RenameField( + model_name="account", + old_name="currencies_json", + new_name="currencies", + ), + migrations.AlterField( + model_name="account", + name="currencies", + field=models.JSONField( + db_index=True, + default=hordak.models.core.project_currencies, + verbose_name="currencies", + ), + ), + ] diff --git a/hordak/migrations/0037_auto_20230516_0142.py b/hordak/migrations/0037_auto_20230516_0142.py new file mode 100644 index 00000000..dcf46d3f --- /dev/null +++ b/hordak/migrations/0037_auto_20230516_0142.py @@ -0,0 +1,43 @@ +# Generated by Django 4.2 on 2023-05-16 01:42 + +from django.db import migrations + + +class Migration(migrations.Migration): + dependencies = [ + ( + "hordak", + "0036_remove_currencies_and_rename_account_currencies_json_to_currencies", + ), + ] + + operations = [ + migrations.RunSQL( + """ + CREATE OR REPLACE FUNCTION check_leg_and_account_currency_match() + RETURNS trigger AS + $$ + DECLARE + account RECORD; + BEGIN + + IF (TG_OP = 'DELETE') THEN + RETURN OLD; + END IF; + + PERFORM * FROM hordak_account WHERE id = NEW.account_id AND currencies::jsonb @> to_jsonb(ARRAY[NEW.amount_currency]::text[]); + + IF NOT FOUND THEN + SELECT * INTO account FROM hordak_account WHERE id = NEW.account_id; + + RAISE EXCEPTION 'Destination Account#% does not support currency %. Account currencies: %', account.id, NEW.amount_currency, account.currencies; + END IF; + + RETURN NEW; + END; + $$ + LANGUAGE plpgsql + """, + "DROP FUNCTION check_leg_and_account_currency_match()", + ), + ] diff --git a/hordak/models/core.py b/hordak/models/core.py index 03b5e22a..f8a75fd5 100644 --- a/hordak/models/core.py +++ b/hordak/models/core.py @@ -21,7 +21,6 @@ create a transaction for the statement line. """ -from django.contrib.postgres.fields.array import ArrayField from django.db import models from django.db import transaction as db_transaction from django.db.models import JSONField @@ -38,8 +37,8 @@ from hordak.defaults import ( DECIMAL_PLACES, MAX_DIGITS, - default_currencies, get_internal_currency, + project_currencies, ) from hordak.utilities.currency import Balance from hordak.utilities.dreprecation import deprecated @@ -142,10 +141,9 @@ class Account(MPTTModel): "statements into it and that it only supports a single currency", verbose_name=_("is bank account"), ) - currencies = ArrayField( - models.CharField(max_length=3, choices=get_currency_choices()), + currencies = JSONField( db_index=True, - default=default_currencies, + default=project_currencies, verbose_name=_("currencies"), )

Mark accounts as 'placeholder' This idea is from GnuCash. Accounts marked as placeholders cannot have transactions associated with them. Typically this is useful for parent accounts, as we normally only wish transactions to be associated with leaf accounts.

2023-05-16T02:55:26

0.0

[]

adamcharnock/django-hordak

adamcharnock__django-hordak-65

ab9249c46c83e61bf7e70b1a69fffc08f92a01a0

diff --git a/hordak/views/accounts.py b/hordak/views/accounts.py index 64413c7b..6d752d86 100644 --- a/hordak/views/accounts.py +++ b/hordak/views/accounts.py @@ -1,6 +1,5 @@ from django.contrib.auth.mixins import LoginRequiredMixin from django.urls.base import reverse_lazy -from django.views import View from django.views.generic.detail import SingleObjectMixin from django.views.generic.edit import CreateView, UpdateView from django.views.generic.list import ListView @@ -78,7 +77,8 @@ class AccountUpdateView(LoginRequiredMixin, UpdateView): success_url = reverse_lazy("hordak:accounts_list") -class AccountTransactionsView(LoginRequiredMixin, SingleObjectMixin, View): +# TODO: remove ignore comment once https://github.com/typeddjango/django-stubs/issues/873 is fixed +class AccountTransactionsView(LoginRequiredMixin, SingleObjectMixin, ListView): # type: ignore template_name = "hordak/accounts/account_transactions.html" model = Leg slug_field = "uuid"

class AccountTransactionsView(LoginRequiredMixin, SingleObjectMixin, View): ->class AccountTransactionsView(LoginRequiredMixin, SingleObjectMixin, ListView): Hi! after migrating from django-hordak==1.10.0 to django-hordak==1.10.1 there is error when trying to view account transactions: **'super' object has no attribute 'get'** /usr/local/lib/python3.9/site-packages/hordak/views/accounts.py, line 89, in get return super(AccountTransactionsView, self).get(request, *args, **kwargs) Please verify if instead of View in class AccountTransactionsView(LoginRequiredMixin, SingleObjectMixin, **View**): should be ListView, i.e.: class AccountTransactionsView(LoginRequiredMixin, SingleObjectMixin, **ListView**): like it was in previous version. Best regards, Pawel

2022-03-15T10:30:35

0.0

[]

tmux-python/tmuxp

tmux-python__tmuxp-897

5daa1ae89b3a5295bea716be7d2860fb12880eda

diff --git a/CHANGES b/CHANGES index 74d7187c6f..df1dbc7baf 100644 --- a/CHANGES +++ b/CHANGES @@ -24,6 +24,7 @@ $ pipx install --suffix=@next 'tmuxp' --pip-args '\--pre' --force - libtmux: 0.24.1 -> 0.25.0, maintenance release (#896) Improve styling via pydocstyle. +- `config_reader`: Move to `tmuxp._internal` (#897) ## tmuxp 1.33.0 (2023-12-21) diff --git a/docs/api.md b/docs/api.md index 7e1ac090a0..eb8c67a595 100644 --- a/docs/api.md +++ b/docs/api.md @@ -110,7 +110,7 @@ If you need an internal API stabilized please [file an issue](https://github.com ## Configuration reader ```{eval-rst} -.. automodule:: tmuxp.config_reader +.. automodule:: tmuxp._internal.config_reader ``` ## Workspace Builder diff --git a/src/tmuxp/_internal/__init__.py b/src/tmuxp/_internal/__init__.py new file mode 100644 index 0000000000..01dccbcfcb --- /dev/null +++ b/src/tmuxp/_internal/__init__.py @@ -0,0 +1,1 @@ +"""Internal APIs for tmuxp.""" diff --git a/src/tmuxp/config_reader.py b/src/tmuxp/_internal/config_reader.py similarity index 95% rename from src/tmuxp/config_reader.py rename to src/tmuxp/_internal/config_reader.py index 6943aff30d..74f1989acd 100644 --- a/src/tmuxp/config_reader.py +++ b/src/tmuxp/_internal/config_reader.py @@ -55,13 +55,13 @@ def load(cls, format: "FormatLiteral", content: str) -> "ConfigReader": >>> cfg = ConfigReader.load("json", '{ "session_name": "my session" }') >>> cfg - <tmuxp.config_reader.ConfigReader object at ...> + <tmuxp._internal.config_reader.ConfigReader object at ...> >>> cfg.content {'session_name': 'my session'} >>> cfg = ConfigReader.load("yaml", 'session_name: my session') >>> cfg - <tmuxp.config_reader.ConfigReader object at ...> + <tmuxp._internal.config_reader.ConfigReader object at ...> >>> cfg.content {'session_name': 'my session'} """ @@ -133,7 +133,7 @@ def from_file(cls, path: pathlib.Path) -> "ConfigReader": >>> cfg = ConfigReader.from_file(yaml_file) >>> cfg - <tmuxp.config_reader.ConfigReader object at ...> + <tmuxp._internal.config_reader.ConfigReader object at ...> >>> cfg.content {'session_name': 'my session'} @@ -150,7 +150,7 @@ def from_file(cls, path: pathlib.Path) -> "ConfigReader": >>> cfg = ConfigReader.from_file(json_file) >>> cfg - <tmuxp.config_reader.ConfigReader object at ...> + <tmuxp._internal.config_reader.ConfigReader object at ...> >>> cfg.content {'session_name': 'my session'} diff --git a/src/tmuxp/cli/convert.py b/src/tmuxp/cli/convert.py index 88d995d67d..75a09ab98d 100644 --- a/src/tmuxp/cli/convert.py +++ b/src/tmuxp/cli/convert.py @@ -4,7 +4,7 @@ import pathlib import typing as t -from tmuxp.config_reader import ConfigReader +from tmuxp._internal.config_reader import ConfigReader from tmuxp.workspace.finders import find_workspace_file, get_workspace_dir from .. import exc diff --git a/src/tmuxp/cli/freeze.py b/src/tmuxp/cli/freeze.py index f82dbb1032..e36795fa18 100644 --- a/src/tmuxp/cli/freeze.py +++ b/src/tmuxp/cli/freeze.py @@ -7,7 +7,7 @@ from libtmux.server import Server -from tmuxp.config_reader import ConfigReader +from tmuxp._internal.config_reader import ConfigReader from tmuxp.exc import TmuxpException from tmuxp.workspace.finders import get_workspace_dir diff --git a/src/tmuxp/cli/import_config.py b/src/tmuxp/cli/import_config.py index 70f4bf71f5..8bab8d918a 100644 --- a/src/tmuxp/cli/import_config.py +++ b/src/tmuxp/cli/import_config.py @@ -5,7 +5,7 @@ import sys import typing as t -from tmuxp.config_reader import ConfigReader +from tmuxp._internal.config_reader import ConfigReader from tmuxp.workspace.finders import find_workspace_file from ..workspace import importers diff --git a/src/tmuxp/cli/load.py b/src/tmuxp/cli/load.py index 4311e87efc..7f426938e4 100644 --- a/src/tmuxp/cli/load.py +++ b/src/tmuxp/cli/load.py @@ -14,7 +14,8 @@ from tmuxp.types import StrPath -from .. import config_reader, exc, log, util +from .. import exc, log, util +from .._internal import config_reader from ..workspace import loader from ..workspace.builder import WorkspaceBuilder from ..workspace.finders import find_workspace_file, get_workspace_dir diff --git a/src/tmuxp/workspace/builder.py b/src/tmuxp/workspace/builder.py index 4fb6f7c60d..0efa22f150 100644 --- a/src/tmuxp/workspace/builder.py +++ b/src/tmuxp/workspace/builder.py @@ -97,7 +97,7 @@ class WorkspaceBuilder: 1. Load JSON / YAML file via via :class:`pathlib.Path`:: - from tmuxp import config_reader + from tmuxp._internal import config_reader session_config = config_reader.ConfigReader._load(raw_yaml) The reader automatically detects the file type from :attr:`pathlib.suffix`. @@ -105,7 +105,7 @@ class WorkspaceBuilder: We can also parse raw file:: import pathlib - from tmuxp import config_reader + from tmuxp._internal import config_reader session_config = config_reader.ConfigReader._from_file( pathlib.Path('path/to/config.yaml')

Move `config_reader` to `_internal` `config_reader` is a generic utility / helper.

2023-12-21T18:59:15

0.0

[]

tmux-python/tmuxp

tmux-python__tmuxp-859

41d027ada3836bd0f3a572652d1900f942f58907

diff --git a/CHANGES b/CHANGES index bc92b1c36f..da2ec72306 100644 --- a/CHANGES +++ b/CHANGES @@ -25,6 +25,12 @@ $ pipx install --suffix=@next 'tmuxp' --pip-args '\--pre' --force ### Development +- **Improved typings** + + Now [`mypy --strict`] compliant (#859) + + [`mypy --strict`]: https://mypy.readthedocs.io/en/stable/command_line.html#cmdoption-mypy-strict + - Poetry 1.5.1 -> 1.6.1 (#885) ## tmuxp 1.30.1 (2023-09-09) @@ -35,7 +41,7 @@ _Maintenance only, no bug fixes or new features_ - Cut last python 3.7 release (EOL was June 27th, 2023) - For security updates, a 1.30.x branch can be maintained for a limited time, + For security updates, a 1.30.x branch can be maintained for a limited time, if necessary. ## tmuxp 1.30.0 (2023-09-04) @@ -49,6 +55,7 @@ _Maintenance only, no bug fixes or new features_ This includes fixes made by hand alongside ruff's automated fixes. The more stringent rules include import sorting, and still runs almost instantly against the whole codebase. + - CI: `black . --check` now runs on pushes and pull requests ### Packaging diff --git a/docs/_ext/aafig.py b/docs/_ext/aafig.py index 3770613d68..d3885405e2 100644 --- a/docs/_ext/aafig.py +++ b/docs/_ext/aafig.py @@ -21,6 +21,10 @@ from sphinx.errors import SphinxError from sphinx.util.osutil import ensuredir, relative_uri +if t.TYPE_CHECKING: + from sphinx.application import Sphinx + + try: import aafigure except ImportError: @@ -32,14 +36,18 @@ DEFAULT_FORMATS = {"html": "svg", "latex": "pdf", "text": None} -def merge_dict(dst, src): +def merge_dict( + dst: t.Dict[str, t.Optional[str]], src: t.Dict[str, t.Optional[str]] +) -> t.Dict[str, t.Optional[str]]: for k, v in src.items(): if k not in dst: dst[k] = v return dst -def get_basename(text, options, prefix="aafig"): +def get_basename( + text: str, options: t.Dict[str, str], prefix: t.Optional[str] = "aafig" +) -> str: options = options.copy() if "format" in options: del options["format"] @@ -52,7 +60,7 @@ class AafigError(SphinxError): category = "aafig error" -class AafigDirective(images.Image): +class AafigDirective(images.Image): # type:ignore """ Directive to insert an ASCII art figure to be rendered by aafigure. """ @@ -71,7 +79,7 @@ class AafigDirective(images.Image): option_spec = images.Image.option_spec.copy() option_spec.update(own_option_spec) - def run(self): + def run(self) -> t.List[nodes.Node]: aafig_options = {} own_options_keys = [self.own_option_spec.keys(), "scale"] for k, v in self.options.items(): @@ -93,7 +101,7 @@ def run(self): return [image_node] -def render_aafig_images(app, doctree): +def render_aafig_images(app: "Sphinx", doctree: nodes.Node) -> None: format_map = app.builder.config.aafig_format merge_dict(format_map, DEFAULT_FORMATS) if aafigure is None: @@ -144,7 +152,9 @@ def __init__(self, *args: object, **kwargs: object) -> None: return super().__init__("aafigure module not installed", *args, **kwargs) -def render_aafigure(app, text, options): +def render_aafigure( + app: "Sphinx", text: str, options: t.Dict[str, str] +) -> t.Tuple[str, str, t.Optional[str], t.Optional[str]]: """ Render an ASCII art figure into the requested format output file. """ @@ -186,7 +196,7 @@ def render_aafigure(app, text, options): finally: if f is not None: f.close() - return relfn, outfn, id, extra + return relfn, outfn, None, extra except AafigError: pass @@ -204,10 +214,10 @@ def render_aafigure(app, text, options): with open(metadata_fname, "w") as f: f.write(extra) - return relfn, outfn, id, extra + return relfn, outfn, None, extra -def setup(app): +def setup(app: "Sphinx") -> None: app.add_directive("aafig", AafigDirective) app.connect("doctree-read", render_aafig_images) app.add_config_value("aafig_format", DEFAULT_FORMATS, "html") diff --git a/docs/conf.py b/docs/conf.py index fea1c2e785..f7c5dd4b7a 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -11,7 +11,6 @@ if t.TYPE_CHECKING: from sphinx.application import Sphinx - # Get the project root dir, which is the parent dir of this cwd = pathlib.Path(__file__).parent project_root = cwd.parent @@ -177,7 +176,7 @@ aafig_default_options = {"scale": 0.75, "aspect": 0.5, "proportional": True} -def linkcode_resolve(domain, info): +def linkcode_resolve(domain: str, info: t.Dict[str, str]) -> t.Union[None, str]: """ Determine the URL corresponding to Python object @@ -210,7 +209,8 @@ def linkcode_resolve(domain, info): except AttributeError: pass else: - obj = unwrap(obj) + if callable(obj): + obj = unwrap(obj) try: fn = inspect.getsourcefile(obj) diff --git a/pyproject.toml b/pyproject.toml index 72ea545623..d3ece281a0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -138,10 +138,12 @@ exclude_lines = [ ] [tool.mypy] +strict = true files = [ "src/", "tests/", ] +enable_incomplete_feature = ["Unpack"] [[tool.mypy.overrides]] module = [ diff --git a/src/tmuxp/_compat.py b/src/tmuxp/_compat.py index 805d6c1cc5..1b0009e3a1 100644 --- a/src/tmuxp/_compat.py +++ b/src/tmuxp/_compat.py @@ -12,7 +12,7 @@ else: import pdb - breakpoint = pdb.set_trace # type: ignore + breakpoint = pdb.set_trace console_encoding = sys.__stdout__.encoding diff --git a/src/tmuxp/_types.py b/src/tmuxp/_types.py new file mode 100644 index 0000000000..79eac5c9c2 --- /dev/null +++ b/src/tmuxp/_types.py @@ -0,0 +1,27 @@ +"""Internal, :const:`typing.TYPE_CHECKING` guarded :term:`type annotations <annotation>` + +These are _not_ to be imported at runtime as `typing_extensions` is not +bundled with tmuxp. Usage example: + +>>> import typing as t + +>>> if t.TYPE_CHECKING: +... from tmuxp._types import PluginConfigSchema +... +""" +import typing as t + +from typing_extensions import NotRequired, TypedDict + + +class PluginConfigSchema(TypedDict): + plugin_name: NotRequired[str] + tmux_min_version: NotRequired[str] + tmux_max_version: NotRequired[str] + tmux_version_incompatible: NotRequired[t.List[str]] + libtmux_min_version: NotRequired[str] + libtmux_max_version: NotRequired[str] + libtmux_version_incompatible: NotRequired[t.List[str]] + tmuxp_min_version: NotRequired[str] + tmuxp_max_version: NotRequired[str] + tmuxp_version_incompatible: NotRequired[t.List[str]] diff --git a/src/tmuxp/cli/debug_info.py b/src/tmuxp/cli/debug_info.py index d72e3fc1ac..df6a65e19d 100644 --- a/src/tmuxp/cli/debug_info.py +++ b/src/tmuxp/cli/debug_info.py @@ -29,19 +29,19 @@ def command_debug_info( Print debug info to submit with Issues. """ - def prepend_tab(strings): + def prepend_tab(strings: t.List[str]) -> t.List[str]: """ Prepend tab to strings in list. """ return ["\t%s" % x for x in strings] - def output_break(): + def output_break() -> str: """ Generate output break. """ return "-" * 25 - def format_tmux_resp(std_resp): + def format_tmux_resp(std_resp: tmux_cmd) -> str: """ Format tmux command response for tmuxp stdout. """ diff --git a/src/tmuxp/cli/edit.py b/src/tmuxp/cli/edit.py index 184077c3cc..31b263dd36 100644 --- a/src/tmuxp/cli/edit.py +++ b/src/tmuxp/cli/edit.py @@ -22,7 +22,7 @@ def create_edit_subparser( def command_edit( workspace_file: t.Union[str, pathlib.Path], parser: t.Optional[argparse.ArgumentParser] = None, -): +) -> None: workspace_file = find_workspace_file(workspace_file) sys_editor = os.environ.get("EDITOR", "vim") diff --git a/src/tmuxp/cli/freeze.py b/src/tmuxp/cli/freeze.py index 3a8f050215..88a2f9751b 100644 --- a/src/tmuxp/cli/freeze.py +++ b/src/tmuxp/cli/freeze.py @@ -31,12 +31,6 @@ class CLIFreezeNamespace(argparse.Namespace): force: t.Optional[bool] -def session_completion(ctx, params, incomplete): - server = Server() - choices = [session.name for session in server.sessions] - return sorted(str(c) for c in choices if str(c).startswith(incomplete)) - - def create_freeze_subparser( parser: argparse.ArgumentParser, ) -> argparse.ArgumentParser: @@ -177,12 +171,14 @@ def extract_workspace_format( workspace_format = extract_workspace_format(dest) if not is_valid_ext(workspace_format): - workspace_format = prompt_choices( + _workspace_format = prompt_choices( "Couldn't ascertain one of [%s] from file name. Convert to" % ", ".join(valid_workspace_formats), - choices=valid_workspace_formats, + choices=t.cast(t.List[str], valid_workspace_formats), default="yaml", ) + assert is_valid_ext(_workspace_format) + workspace_format = _workspace_format if workspace_format == "yaml": workspace = configparser.dump( diff --git a/src/tmuxp/cli/import_config.py b/src/tmuxp/cli/import_config.py index 301f0dd8cf..9048331fa9 100644 --- a/src/tmuxp/cli/import_config.py +++ b/src/tmuxp/cli/import_config.py @@ -59,7 +59,7 @@ def command_import( workspace_file: str, print_list: str, parser: argparse.ArgumentParser, -): +) -> None: """Import a teamocil/tmuxinator config.""" @@ -116,9 +116,14 @@ def create_import_subparser( return parser +class ImportConfigFn(t.Protocol): + def __call__(self, workspace_dict: t.Dict[str, t.Any]) -> t.Dict[str, t.Any]: + ... + + def import_config( workspace_file: str, - importfunc: t.Callable, + importfunc: ImportConfigFn, parser: t.Optional[argparse.ArgumentParser] = None, ) -> None: existing_workspace_file = ConfigReader._from_file(pathlib.Path(workspace_file)) diff --git a/src/tmuxp/cli/load.py b/src/tmuxp/cli/load.py index 2873d764f7..e965299f90 100644 --- a/src/tmuxp/cli/load.py +++ b/src/tmuxp/cli/load.py @@ -153,7 +153,7 @@ def load_plugins(session_config: t.Dict[str, t.Any]) -> t.List[t.Any]: return plugins -def _reattach(builder: WorkspaceBuilder): +def _reattach(builder: WorkspaceBuilder) -> None: """ Reattach session (depending on env being inside tmux already or not) diff --git a/src/tmuxp/cli/utils.py b/src/tmuxp/cli/utils.py index 91f2ef165c..482f20d042 100644 --- a/src/tmuxp/cli/utils.py +++ b/src/tmuxp/cli/utils.py @@ -34,9 +34,9 @@ def tmuxp_echo( def prompt( name: str, - default: t.Any = None, + default: t.Optional[str] = None, value_proc: t.Optional[t.Callable[[str], str]] = None, -) -> t.Any: +) -> str: """Return user input from command line. :meth:`~prompt`, :meth:`~prompt_bool` and :meth:`prompt_choices` are from `flask-script`_. See the `flask-script license`_. @@ -107,15 +107,12 @@ def prompt_yes_no(name: str, default: bool = True) -> bool: return prompt_bool(name, default=default) -_C = t.TypeVar("_C") - - def prompt_choices( name: str, - choices: t.Union[t.List[_C], t.Tuple[str, _C]], - default: t.Optional[_C] = None, + choices: t.Union[t.List[str], t.Tuple[str, str]], + default: t.Optional[str] = None, no_choice: t.Sequence[str] = ("none",), -) -> t.Optional[_C]: +) -> t.Optional[str]: """Return user input from command line from set of provided choices. :param name: prompt text :param choices: list or tuple of available choices. Choices may be @@ -125,8 +122,8 @@ def prompt_choices( :rtype: str """ - _choices = [] - options = [] + _choices: t.List[str] = [] + options: t.List[str] = [] for choice in choices: if isinstance(choice, str): diff --git a/src/tmuxp/config_reader.py b/src/tmuxp/config_reader.py index 3217cbc442..6c0913d2c3 100644 --- a/src/tmuxp/config_reader.py +++ b/src/tmuxp/config_reader.py @@ -36,12 +36,15 @@ def _load(format: "FormatLiteral", content: str) -> t.Dict[str, t.Any]: {'session_name': 'my session'} """ if format == "yaml": - return yaml.load( - content, - Loader=yaml.SafeLoader, + return t.cast( + t.Dict[str, t.Any], + yaml.load( + content, + Loader=yaml.SafeLoader, + ), ) elif format == "json": - return json.loads(content) + return t.cast(t.Dict[str, t.Any], json.loads(content)) else: raise NotImplementedError(f"{format} not supported in configuration") diff --git a/src/tmuxp/exc.py b/src/tmuxp/exc.py index 5fb0d9f722..3d04c63735 100644 --- a/src/tmuxp/exc.py +++ b/src/tmuxp/exc.py @@ -80,10 +80,10 @@ class TmuxpPluginException(TmuxpException): class BeforeLoadScriptNotExists(OSError): - def __init__(self, *args, **kwargs) -> None: + def __init__(self, *args: object, **kwargs: object) -> None: super().__init__(*args, **kwargs) - self.strerror = "before_script file '%s' doesn't exist." % self.strerror + self.strerror = f"before_script file '{self.strerror}' doesn't exist." @implements_to_string @@ -106,5 +106,5 @@ def __init__( f"{self.output}" ) - def __str__(self): + def __str__(self) -> str: return self.message diff --git a/src/tmuxp/log.py b/src/tmuxp/log.py index 3a18b67916..b05b5ac7ef 100644 --- a/src/tmuxp/log.py +++ b/src/tmuxp/log.py @@ -115,8 +115,8 @@ def template( return levelname + asctime + name - def __init__(self, color: bool = True, *args, **kwargs) -> None: - logging.Formatter.__init__(self, *args, **kwargs) + def __init__(self, color: bool = True, **kwargs: t.Any) -> None: + logging.Formatter.__init__(self, **kwargs) def format(self, record: logging.LogRecord) -> str: try: @@ -125,7 +125,7 @@ def format(self, record: logging.LogRecord) -> str: record.message = f"Bad message ({e!r}): {record.__dict__!r}" date_format = "%H:%m:%S" - formatting = self.converter(record.created) # type:ignore + formatting = self.converter(record.created) record.asctime = time.strftime(date_format, formatting) prefix = self.template(record) % record.__dict__ diff --git a/src/tmuxp/plugin.py b/src/tmuxp/plugin.py index 7d56316630..f6b49432d2 100644 --- a/src/tmuxp/plugin.py +++ b/src/tmuxp/plugin.py @@ -27,7 +27,11 @@ if t.TYPE_CHECKING: - from typing_extensions import TypedDict + from libtmux.session import Session + from libtmux.window import Window + from typing_extensions import TypedDict, TypeGuard, Unpack + + from ._types import PluginConfigSchema class VersionConstraints(TypedDict): version: t.Union[Version, str] @@ -41,20 +45,52 @@ class TmuxpPluginVersionConstraints(TypedDict): libtmux: VersionConstraints +class Config(t.TypedDict): + plugin_name: str + tmux_min_version: str + tmux_max_version: t.Optional[str] + tmux_version_incompatible: t.Optional[t.List[str]] + libtmux_min_version: str + libtmux_max_version: t.Optional[str] + libtmux_version_incompatible: t.Optional[t.List[str]] + tmuxp_min_version: str + tmuxp_max_version: t.Optional[str] + tmuxp_version_incompatible: t.Optional[t.List[str]] + + +DEFAULT_CONFIG: "Config" = { + "plugin_name": "tmuxp-plugin", + "tmux_min_version": TMUX_MIN_VERSION, + "tmux_max_version": TMUX_MAX_VERSION, + "tmux_version_incompatible": None, + "libtmux_min_version": LIBTMUX_MIN_VERSION, + "libtmux_max_version": LIBTMUX_MAX_VERSION, + "libtmux_version_incompatible": None, + "tmuxp_min_version": TMUXP_MIN_VERSION, + "tmuxp_max_version": TMUXP_MAX_VERSION, + "tmuxp_version_incompatible": None, +} + + +def validate_plugin_config(config: "PluginConfigSchema") -> "TypeGuard[Config]": + return isinstance(config, dict) + + +def setup_plugin_config( + config: "PluginConfigSchema", default_config: "Config" = DEFAULT_CONFIG +) -> "Config": + new_config = config.copy() + for default_key, default_value in default_config.items(): + if default_key not in new_config: + new_config[default_key] = default_value # type:ignore + + assert validate_plugin_config(new_config) + + return new_config + + class TmuxpPlugin: - def __init__( - self, - plugin_name: str = "tmuxp-plugin", - tmux_min_version: str = TMUX_MIN_VERSION, - tmux_max_version: t.Optional[str] = TMUX_MAX_VERSION, - tmux_version_incompatible: t.Optional[t.List[str]] = None, - libtmux_min_version: str = LIBTMUX_MIN_VERSION, - libtmux_max_version: t.Optional[str] = LIBTMUX_MAX_VERSION, - libtmux_version_incompatible: t.Optional[t.List[str]] = None, - tmuxp_min_version: str = TMUXP_MIN_VERSION, - tmuxp_max_version: t.Optional[str] = TMUXP_MAX_VERSION, - tmuxp_version_incompatible: t.Optional[t.List[str]] = None, - ) -> None: + def __init__(self, **kwargs: "Unpack[PluginConfigSchema]") -> None: """ Initialize plugin. @@ -95,7 +131,8 @@ def __init__( Versions of tmuxp that are incompatible with the plugin """ - self.plugin_name = plugin_name + config = setup_plugin_config(config=kwargs) + self.plugin_name = config["plugin_name"] # Dependency versions self.tmux_version = get_version() @@ -105,26 +142,26 @@ def __init__( self.version_constraints: "TmuxpPluginVersionConstraints" = { "tmux": { "version": self.tmux_version, - "vmin": tmux_min_version, - "vmax": tmux_max_version, - "incompatible": tmux_version_incompatible - if tmux_version_incompatible + "vmin": config["tmux_min_version"], + "vmax": config["tmux_max_version"], + "incompatible": config["tmux_version_incompatible"] + if config["tmux_version_incompatible"] else [], }, "libtmux": { "version": self.libtmux_version, - "vmin": libtmux_min_version, - "vmax": libtmux_max_version, - "incompatible": libtmux_version_incompatible - if libtmux_version_incompatible + "vmin": config["libtmux_min_version"], + "vmax": config["libtmux_max_version"], + "incompatible": config["libtmux_version_incompatible"] + if config["libtmux_version_incompatible"] else [], }, "tmuxp": { "version": self.tmuxp_version, - "vmin": tmuxp_min_version, - "vmax": tmuxp_max_version, - "incompatible": tmuxp_version_incompatible - if tmuxp_version_incompatible + "vmin": config["tmuxp_min_version"], + "vmax": config["tmuxp_max_version"], + "incompatible": config["tmuxp_version_incompatible"] + if config["tmuxp_version_incompatible"] else [], }, } @@ -167,7 +204,7 @@ def _pass_version_check( return True - def before_workspace_builder(self, session): + def before_workspace_builder(self, session: "Session") -> None: """ Provide a session hook previous to creating the workspace. @@ -180,7 +217,7 @@ def before_workspace_builder(self, session): session to hook into """ - def on_window_create(self, window): + def on_window_create(self, window: "Window") -> None: """ Provide a window hook previous to doing anything with a window. @@ -192,7 +229,7 @@ def on_window_create(self, window): window to hook into """ - def after_window_finished(self, window): + def after_window_finished(self, window: "Window") -> None: """ Provide a window hook after creating the window. @@ -206,7 +243,7 @@ def after_window_finished(self, window): window to hook into """ - def before_script(self, session): + def before_script(self, session: "Session") -> None: """ Provide a session hook after the workspace has been built. @@ -234,7 +271,7 @@ def before_script(self, session): session to hook into """ - def reattach(self, session): + def reattach(self, session: "Session") -> None: """ Provide a session hook before reattaching to the session. diff --git a/src/tmuxp/shell.py b/src/tmuxp/shell.py index 1955d2bc32..bb06fb4b64 100644 --- a/src/tmuxp/shell.py +++ b/src/tmuxp/shell.py @@ -12,12 +12,35 @@ logger = logging.getLogger(__name__) if t.TYPE_CHECKING: - from typing_extensions import TypeAlias + from types import ModuleType + + from libtmux.pane import Pane + from libtmux.server import Server + from libtmux.session import Session + from libtmux.window import Window + from typing_extensions import NotRequired, TypeAlias, TypedDict, Unpack CLIShellLiteral: TypeAlias = t.Literal[ "best", "pdb", "code", "ptipython", "ptpython", "ipython", "bpython" ] + class LaunchOptionalImports(TypedDict): + server: NotRequired["Server"] + session: NotRequired["Session"] + window: NotRequired["Window"] + pane: NotRequired["Pane"] + + class LaunchImports(t.TypedDict): + libtmux: ModuleType + Server: t.Type[Server] + Session: t.Type[Session] + Window: t.Type[Window] + Pane: t.Type[Pane] + server: t.Optional["Server"] + session: t.Optional["Session"] + window: t.Optional["Window"] + pane: t.Optional["Pane"] + def has_ipython() -> bool: try: @@ -77,13 +100,15 @@ def detect_best_shell() -> "CLIShellLiteral": return "code" -def get_bpython(options, extra_args=None): +def get_bpython( + options: "LaunchOptionalImports", extra_args: t.Optional[t.Dict[str, t.Any]] = None +) -> t.Callable[[], None]: if extra_args is None: extra_args = {} from bpython import embed # F841 - def launch_bpython(): + def launch_bpython() -> None: imported_objects = get_launch_args(**options) kwargs = {} if extra_args: @@ -93,16 +118,18 @@ def launch_bpython(): return launch_bpython -def get_ipython_arguments(): +def get_ipython_arguments() -> t.List[str]: ipython_args = "IPYTHON_ARGUMENTS" return os.environ.get(ipython_args, "").split() -def get_ipython(options, **extra_args): +def get_ipython( + options: "LaunchOptionalImports", **extra_args: t.Dict[str, t.Any] +) -> t.Any: try: from IPython import start_ipython - def launch_ipython(): + def launch_ipython() -> None: imported_objects = get_launch_args(**options) ipython_arguments = extra_args or get_ipython_arguments() start_ipython(argv=ipython_arguments, user_ns=imported_objects) @@ -115,7 +142,7 @@ def launch_ipython(): # Notebook not supported for IPython < 0.11. from IPython.Shell import IPShell - def launch_ipython(): + def launch_ipython() -> None: imported_objects = get_launch_args(**options) shell = IPShell(argv=[], user_ns=imported_objects) shell.mainloop() @@ -123,13 +150,13 @@ def launch_ipython(): return launch_ipython -def get_ptpython(options, vi_mode=False): +def get_ptpython(options: "LaunchOptionalImports", vi_mode: bool = False) -> t.Any: try: from ptpython.repl import embed, run_config except ImportError: from prompt_toolkit.contrib.repl import embed, run_config - def launch_ptpython(): + def launch_ptpython() -> None: imported_objects = get_launch_args(**options) history_filename = str(pathlib.Path("~/.ptpython_history").expanduser()) embed( @@ -142,7 +169,7 @@ def launch_ptpython(): return launch_ptpython -def get_ptipython(options, vi_mode=False): +def get_ptipython(options: "LaunchOptionalImports", vi_mode: bool = False) -> t.Any: """Based on django-extensions Run renamed to launch, get_imported_objects renamed to get_launch_args @@ -155,7 +182,7 @@ def get_ptipython(options, vi_mode=False): from prompt_toolkit.contrib.ipython import embed from prompt_toolkit.contrib.repl import run_config - def launch_ptipython(): + def launch_ptipython() -> None: imported_objects = get_launch_args(**options) history_filename = str(pathlib.Path("~/.ptpython_history").expanduser()) embed( @@ -168,7 +195,7 @@ def launch_ptipython(): return launch_ptipython -def get_launch_args(**kwargs): +def get_launch_args(**kwargs: "Unpack[LaunchOptionalImports]") -> "LaunchImports": import libtmux from libtmux.pane import Pane from libtmux.server import Server @@ -188,7 +215,7 @@ def get_launch_args(**kwargs): } -def get_code(use_pythonrc, imported_objects): +def get_code(use_pythonrc: bool, imported_objects: "LaunchImports") -> t.Any: import code try: @@ -201,7 +228,11 @@ def get_code(use_pythonrc, imported_objects): # we already know 'readline' was imported successfully. import rlcompleter - readline.set_completer(rlcompleter.Completer(imported_objects).complete) + readline.set_completer( + rlcompleter.Completer( + imported_objects, # type:ignore + ).complete + ) # Enable tab completion on systems using libedit (e.g. macOS). # These lines are copied from Lib/site.py on Python 3.4. readline_doc = getattr(readline, "__doc__", "") @@ -226,9 +257,12 @@ def get_code(use_pythonrc, imported_objects): pythonrc_code = handle.read() # Match the behavior of the cpython shell where an error in # PYTHONSTARTUP prints an exception and continues. - exec(compile(pythonrc_code, pythonrc, "exec"), imported_objects) + exec( + compile(pythonrc_code, pythonrc, "exec"), + imported_objects, # type:ignore + ) - def launch_code(): + def launch_code() -> None: code.interact(local=imported_objects) return launch_code @@ -238,7 +272,7 @@ def launch( shell: t.Optional["CLIShellLiteral"] = "best", use_pythonrc: bool = False, use_vi_mode: bool = False, - **kwargs + **kwargs: "Unpack[LaunchOptionalImports]" ) -> None: # Also allowing passing shell='code' to force using code.interact imported_objects = get_launch_args(**kwargs) diff --git a/src/tmuxp/workspace/builder.py b/src/tmuxp/workspace/builder.py index b73a250fec..f149d87d4d 100644 --- a/src/tmuxp/workspace/builder.py +++ b/src/tmuxp/workspace/builder.py @@ -188,7 +188,7 @@ def __init__( pass @property - def session(self): + def session(self) -> Session: if self._session is None: raise exc.SessionMissingWorkspaceException() return self._session diff --git a/src/tmuxp/workspace/freezer.py b/src/tmuxp/workspace/freezer.py index 8f38e7be4b..0d1d14e98f 100644 --- a/src/tmuxp/workspace/freezer.py +++ b/src/tmuxp/workspace/freezer.py @@ -7,7 +7,7 @@ from libtmux.window import Window -def inline(workspace_dict): +def inline(workspace_dict: t.Dict[str, t.Any]) -> t.Any: """Return workspace with inlined shorthands. Opposite of :meth:`loader.expand`. Parameters @@ -28,7 +28,7 @@ def inline(workspace_dict): workspace_dict["shell_command"] = workspace_dict["shell_command"][0] if len(workspace_dict.keys()) == 1: - workspace_dict = workspace_dict["shell_command"] + return workspace_dict["shell_command"] if ( "shell_command_before" in workspace_dict and isinstance(workspace_dict["shell_command_before"], list) diff --git a/src/tmuxp/workspace/loader.py b/src/tmuxp/workspace/loader.py index 1668a55454..83da8deb10 100644 --- a/src/tmuxp/workspace/loader.py +++ b/src/tmuxp/workspace/loader.py @@ -7,7 +7,7 @@ import logging import os import pathlib -from typing import Dict +import typing as t logger = logging.getLogger(__name__) @@ -30,7 +30,7 @@ def expandshell(value: str) -> str: return os.path.expandvars(os.path.expanduser(value)) # NOQA: PTH111 -def expand_cmd(p: Dict) -> Dict: +def expand_cmd(p: t.Dict[str, t.Any]) -> t.Dict[str, t.Any]: if isinstance(p, str): p = {"shell_command": [p]} elif isinstance(p, list): @@ -66,7 +66,11 @@ def expand_cmd(p: Dict) -> Dict: return p -def expand(workspace_dict, cwd=None, parent=None): +def expand( + workspace_dict: t.Dict[str, t.Any], + cwd: t.Optional[t.Union[pathlib.Path, str]] = None, + parent: t.Optional[t.Any] = None, +) -> t.Dict[str, t.Any]: """Return workspace with shorthand and inline properties expanded. This is necessary to keep the code in the :class:`WorkspaceBuilder` clean @@ -185,7 +189,7 @@ def expand(workspace_dict, cwd=None, parent=None): return workspace_dict -def trickle(workspace_dict): +def trickle(workspace_dict: t.Dict[str, t.Any]) -> t.Dict[str, t.Any]: """Return a dict with "trickled down" / inherited workspace values. This will only work if workspace has been expanded to full form with

mypy: More annotations Tougher mypy typings.

# [Codecov](https://codecov.io/gh/tmux-python/tmuxp/pull/796?src=pr&el=h1&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=tmux-python) Report > Merging [#796](https://codecov.io/gh/tmux-python/tmuxp/pull/796?src=pr&el=desc&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=tmux-python) (b14fcb4) into [master](https://codecov.io/gh/tmux-python/tmuxp/commit/b14fcb4f4737b520104c5e1a4803d3cdebf5bc88?el=desc&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=tmux-python) (b14fcb4) will **not change** coverage. > The diff coverage is `n/a`. > :exclamation: Current head b14fcb4 differs from pull request most recent head 86b044f. Consider uploading reports for the commit 86b044f to get more accurate results ```diff @@ Coverage Diff @@ ## master #796 +/- ## ======================================= Coverage 71.64% 71.64% ======================================= Files 25 25 Lines 1774 1774 Branches 398 398 ======================================= Hits 1271 1271 Misses 391 391 Partials 112 112 ``` :mega: We’re building smart automated test selection to slash your CI/CD build times. [Learn more](https://about.codecov.io/iterative-testing/?utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=tmux-python)

2022-12-31T00:45:57

0.0

[]

tmux-python/tmuxp

tmux-python__tmuxp-858

bf0c47b25e2502cdcfaa7eb209d817673c45f445

diff --git a/CHANGES b/CHANGES index 6327f36f51..728719430a 100644 --- a/CHANGES +++ b/CHANGES @@ -19,6 +19,12 @@ $ pipx install --suffix=@next 'tmuxp' --pip-args '\--pre' --force  +### Internal improvements + +- CLI Tests: Refactor `tests/cli` (#858) + + - Fix resolution of direcotries + ## tmuxp 1.23.0 (_yanked_, 2022-12-28) _Yanked release: `tmuxp load` issues, see #856_

WorkspaceBuilder: Harden typings - feat(WorkspaceBuilder): Require server in constructor Related to #856

2022-12-29T20:01:14

0.0

[]

tmux-python/tmuxp

tmux-python__tmuxp-857

a7ea98c23bdd75e9115978322ddc62e9cdaba79e

diff --git a/CHANGES b/CHANGES index 3b252a3b68..a85eff48f2 100644 --- a/CHANGES +++ b/CHANGES @@ -29,6 +29,8 @@ $ pipx install --suffix=@next 'tmuxp' --pip-args '\--pre' --force - Fix resolution of direcotries +- WorkspaceBuilder: Require `Server` in constructor (#857) + ## tmuxp 1.23.0 (_yanked_, 2022-12-28) _Yanked release: `tmuxp load` issues, see #856_ diff --git a/src/tmuxp/cli/load.py b/src/tmuxp/cli/load.py index 41fbf4a869..46862bb423 100644 --- a/src/tmuxp/cli/load.py +++ b/src/tmuxp/cli/load.py @@ -107,13 +107,13 @@ def set_layout_hook(session: Session, hook_name: str) -> None: session.cmd(*cmd) -def load_plugins(sconf: t.Dict[str, t.Any]) -> t.List[t.Any]: +def load_plugins(session_config: t.Dict[str, t.Any]) -> t.List[t.Any]: """ Load and return plugins in workspace """ plugins = [] - if "plugins" in sconf: - for plugin in sconf["plugins"]: + if "plugins" in session_config: + for plugin in session_config["plugins"]: try: module_name = plugin.split(".") module_name = ".".join(module_name[:-1]) @@ -396,7 +396,9 @@ def load_workspace( try: # load WorkspaceBuilder object for tmuxp workspace / tmux server builder = WorkspaceBuilder( - sconf=expanded_workspace, plugins=load_plugins(expanded_workspace), server=t + session_config=expanded_workspace, + plugins=load_plugins(expanded_workspace), + server=t, ) except exc.EmptyWorkspaceException: tmuxp_echo("%s is empty or parsed no workspace data" % workspace_file) diff --git a/src/tmuxp/workspace/builder.py b/src/tmuxp/workspace/builder.py index 619e67f86d..1af6026d6d 100644 --- a/src/tmuxp/workspace/builder.py +++ b/src/tmuxp/workspace/builder.py @@ -10,7 +10,6 @@ from libtmux._internal.query_list import ObjectDoesNotExist from libtmux.common import has_lt_version -from libtmux.exc import TmuxSessionExists from libtmux.pane import Pane from libtmux.server import Server from libtmux.session import Session @@ -27,12 +26,10 @@ class WorkspaceBuilder: + """Load workspace from workspace :py:obj:`dict` object. - """ - Load workspace from session :py:obj:`dict`. - - Build tmux workspace from a configuration. Creates and names windows, sets - options, splits windows into panes. + Build tmux workspace from a configuration. Creates and names windows, sets options, + splits windows into panes. Examples -------- @@ -62,7 +59,7 @@ class WorkspaceBuilder: ... - cmd: htop ... ''', Loader=yaml.Loader) - >>> builder = WorkspaceBuilder(sconf=session_config, server=server) + >>> builder = WorkspaceBuilder(session_config=session_config, server=server) **New session:** @@ -102,7 +99,7 @@ class WorkspaceBuilder: 1. Load JSON / YAML file via via :class:`pathlib.Path`:: from tmuxp import config_reader - sconf = config_reader.ConfigReader._load(raw_yaml) + session_config = config_reader.ConfigReader._load(raw_yaml) The reader automatically detects the file type from :attr:`pathlib.suffix`. @@ -111,46 +108,46 @@ class WorkspaceBuilder: import pathlib from tmuxp import config_reader - sconf = config_reader.ConfigReader._from_file( + session_config = config_reader.ConfigReader._from_file( pathlib.Path('path/to/config.yaml') ) - 2. :meth:`config.expand` sconf inline shorthand:: + 2. :meth:`config.expand` session_config inline shorthand:: from tmuxp import config - sconf = config.expand(sconf) + session_config = config.expand(session_config) 3. :meth:`config.trickle` passes down default values from session -> window -> pane if applicable:: - sconf = config.trickle(sconf) + session_config = config.trickle(session_config) 4. (You are here) We will create a :class:`libtmux.Session` (a real ``tmux(1)`` session) and iterate through the list of windows, and their panes, returning full :class:`libtmux.Window` and :class:`libtmux.Pane` objects each step of the way:: - workspace = WorkspaceBuilder(sconf=sconf) + workspace = WorkspaceBuilder(session_config=session_config, server=server) It handles the magic of cases where the user may want to start a session inside tmux (when `$TMUX` is in the env variables). """ - server: t.Optional["Server"] - session: t.Optional["Session"] + server: "Server" + _session: t.Optional["Session"] + session_name: str def __init__( self, - sconf: t.Dict[str, t.Any], + session_config: t.Dict[str, t.Any], + server: Server, plugins: t.List[t.Any] = [], - server: t.Optional[Server] = None, ) -> None: - """ - Initialize workspace loading. + """Initialize workspace loading. Parameters ---------- - sconf : dict + session_config : dict session config, includes a :py:obj:`list` of ``windows``. plugins : list @@ -165,18 +162,38 @@ def __init__( ``self.session``. """ - if not sconf: - raise exc.EmptyWorkspaceException("session configuration is empty.") + if not session_config: + raise exc.EmptyWorkspaceException("Session configuration is empty.") - # validation.validate_schema(sconf) + # validation.validate_schema(session_config) - if isinstance(server, Server): - self.server = server - - self.sconf = sconf + assert isinstance(server, Server) + self.server = server + self.session_config = session_config self.plugins = plugins + if self.server is not None and self.session_exists( + session_name=self.session_config["session_name"] + ): + try: + session = self.server.sessions.get( + session_name=self.session_config["session_name"] + ) + assert session is not None + self._session = session + except ObjectDoesNotExist: + pass + + @property + def session(self): + if self._session is None: + raise ObjectDoesNotExist( + "No session object exists for WorkspaceBuilder. " + "Tip: Add session_name in constructor or run WorkspaceBuilder.build()" + ) + return self._session + def session_exists(self, session_name: str) -> bool: assert session_name is not None assert isinstance(session_name, str) @@ -192,14 +209,12 @@ def session_exists(self, session_name: str) -> bool: return True def build(self, session: t.Optional[Session] = None, append: bool = False) -> None: - """ - Build tmux workspace in session. + """Build tmux workspace in session. Optionally accepts ``session`` to build with only session object. - Without ``session``, it will use :class:`libmtux.Server` at - ``self.server`` passed in on initialization to create a new Session - object. + Without ``session``, it will use :class:`libmtux.Server` at ``self.server`` + passed in on initialization to create a new Session object. Parameters ---------- @@ -215,38 +230,25 @@ def build(self, session: t.Optional[Session] = None, append: bool = False) -> No "WorkspaceBuilder.build requires server to be passed " + "on initialization, or pass in session object to here." ) - - if self.server.has_session(self.sconf["session_name"]): - try: - self.session = self.server.sessions.get( - session_name=self.sconf["session_name"] - ) - - raise TmuxSessionExists( - "Session name %s is already running." - % self.sconf["session_name"] - ) - except ObjectDoesNotExist: - pass else: new_session_kwargs = {} - if "start_directory" in self.sconf: - new_session_kwargs["start_directory"] = self.sconf[ + if "start_directory" in self.session_config: + new_session_kwargs["start_directory"] = self.session_config[ "start_directory" ] session = self.server.new_session( - session_name=self.sconf["session_name"], + session_name=self.session_config["session_name"], **new_session_kwargs, ) assert session is not None - assert self.sconf["session_name"] == session.name - assert len(self.sconf["session_name"]) > 0 + assert self.session_config["session_name"] == session.name + assert len(self.session_config["session_name"]) > 0 assert session is not None assert session.name is not None - self.session: "Session" = session + self._session = session assert session.server is not None @@ -262,52 +264,55 @@ def build(self, session: t.Optional[Session] = None, append: bool = False) -> No focus = None - if "before_script" in self.sconf: + if "before_script" in self.session_config: try: cwd = None # we want to run the before_script file cwd'd from the # session start directory, if it exists. - if "start_directory" in self.sconf: - cwd = self.sconf["start_directory"] - run_before_script(self.sconf["before_script"], cwd=cwd) + if "start_directory" in self.session_config: + cwd = self.session_config["start_directory"] + run_before_script(self.session_config["before_script"], cwd=cwd) except Exception as e: self.session.kill_session() raise e - if "options" in self.sconf: - for option, value in self.sconf["options"].items(): + + if "options" in self.session_config: + for option, value in self.session_config["options"].items(): self.session.set_option(option, value) - if "global_options" in self.sconf: - for option, value in self.sconf["global_options"].items(): + + if "global_options" in self.session_config: + for option, value in self.session_config["global_options"].items(): self.session.set_option(option, value, _global=True) - if "environment" in self.sconf: - for option, value in self.sconf["environment"].items(): + + if "environment" in self.session_config: + for option, value in self.session_config["environment"].items(): self.session.set_environment(option, value) - for w, wconf in self.iter_create_windows(session, append): - assert isinstance(w, Window) + for window, window_config in self.iter_create_windows(session, append): + assert isinstance(window, Window) for plugin in self.plugins: - plugin.on_window_create(w) + plugin.on_window_create(window) focus_pane = None - for p, pconf in self.iter_create_panes(w, wconf): - assert isinstance(p, Pane) - p = p + for pane, pane_config in self.iter_create_panes(window, window_config): + assert isinstance(pane, Pane) + pane = pane - if "layout" in wconf: - w.select_layout(wconf["layout"]) + if "layout" in window_config: + window.select_layout(window_config["layout"]) - if "focus" in pconf and pconf["focus"]: - focus_pane = p + if "focus" in pane_config and pane_config["focus"]: + focus_pane = pane - if "focus" in wconf and wconf["focus"]: - focus = w + if "focus" in window_config and window_config["focus"]: + focus = window - self.config_after_window(w, wconf) + self.config_after_window(window, window_config) for plugin in self.plugins: - plugin.after_window_finished(w) + plugin.after_window_finished(window) if focus_pane: focus_pane.select_pane() @@ -318,11 +323,10 @@ def build(self, session: t.Optional[Session] = None, append: bool = False) -> No def iter_create_windows( self, session: Session, append: bool = False ) -> t.Iterator[t.Any]: - """ - Return :class:`libtmux.Window` iterating through session config dict. + """Return :class:`libtmux.Window` iterating through session config dict. Generator yielding :class:`libtmux.Window` by iterating through - ``sconf['windows']``. + ``session_config['windows']``. Applies ``window_options`` to window. @@ -335,172 +339,182 @@ def iter_create_windows( Returns ------- - tuple of (:class:`libtmux.Window`, ``wconf``) + tuple of (:class:`libtmux.Window`, ``window_config``) Newly created window, and the section from the tmuxp configuration that was used to create the window. """ - for i, wconf in enumerate(self.sconf["windows"], start=1): - if "window_name" not in wconf: + for window_iterator, window_config in enumerate( + self.session_config["windows"], start=1 + ): + if "window_name" not in window_config: window_name = None else: - window_name = wconf["window_name"] + window_name = window_config["window_name"] - is_first_window_pass = self.first_window_pass(i, session, append) + is_first_window_pass = self.first_window_pass( + window_iterator, session, append + ) w1 = None if is_first_window_pass: # if first window, use window 1 w1 = session.attached_window w1.move_window("99") - if "start_directory" in wconf: - sd = wconf["start_directory"] + if "start_directory" in window_config: + start_directory = window_config["start_directory"] else: - sd = None + start_directory = None # If the first pane specifies a start_directory, use that instead. - panes = wconf["panes"] + panes = window_config["panes"] if panes and "start_directory" in panes[0]: - sd = panes[0]["start_directory"] + start_directory = panes[0]["start_directory"] - if "window_shell" in wconf: - ws = wconf["window_shell"] + if "window_shell" in window_config: + window_shell = window_config["window_shell"] else: - ws = None + window_shell = None # If the first pane specifies a shell, use that instead. try: - if wconf["panes"][0]["shell"] != "": - ws = wconf["panes"][0]["shell"] + if window_config["panes"][0]["shell"] != "": + window_shell = window_config["panes"][0]["shell"] except (KeyError, IndexError): pass - environment = panes[0].get("environment", wconf.get("environment")) + environment = panes[0].get("environment", window_config.get("environment")) if environment and has_lt_version("3.0"): # Falling back to use the environment of the first pane for the window # creation is nice but yields misleading error messages. pane_env = panes[0].get("environment") - win_env = wconf.get("environment") + win_env = window_config.get("environment") if pane_env and win_env: target = "panes and windows" elif pane_env: target = "panes" else: target = "windows" - logging.warning( + logger.warning( f"Cannot set environment for new {target}. " "You need tmux 3.0 or newer for this." ) environment = None - w = session.new_window( + window = session.new_window( window_name=window_name, - start_directory=sd, + start_directory=start_directory, attach=False, # do not move to the new window - window_index=wconf.get("window_index", ""), - window_shell=ws, + window_index=window_config.get("window_index", ""), + window_shell=window_shell, environment=environment, ) + assert isinstance(window, Window) if is_first_window_pass: # if first window, use window 1 session.attached_window.kill_window() - assert isinstance(w, Window) - if "options" in wconf and isinstance(wconf["options"], dict): - for key, val in wconf["options"].items(): - w.set_window_option(key, val) + if "options" in window_config and isinstance( + window_config["options"], dict + ): + for key, val in window_config["options"].items(): + window.set_window_option(key, val) - if "focus" in wconf and wconf["focus"]: - w.select_window() + if "focus" in window_config and window_config["focus"]: + window.select_window() - yield w, wconf + yield window, window_config def iter_create_panes( - self, w: Window, wconf: t.Dict[str, t.Any] + self, window: Window, window_config: t.Dict[str, t.Any] ) -> t.Iterator[t.Any]: - """ - Return :class:`libtmux.Pane` iterating through window config dict. + """Return :class:`libtmux.Pane` iterating through window config dict. Run ``shell_command`` with ``$ tmux send-keys``. Parameters ---------- - w : :class:`libtmux.Window` + window : :class:`libtmux.Window` window to create panes for - wconf : dict + window_config : dict config section for window Returns ------- - tuple of (:class:`libtmux.Pane`, ``pconf``) + tuple of (:class:`libtmux.Pane`, ``pane_config``) Newly created pane, and the section from the tmuxp configuration that was used to create the pane. """ - assert isinstance(w, Window) + assert isinstance(window, Window) - pane_base_index_str = w.show_window_option("pane-base-index", g=True) + pane_base_index_str = window.show_window_option("pane-base-index", g=True) assert pane_base_index_str is not None pane_base_index = int(pane_base_index_str) - p = None + pane = None - for pindex, pconf in enumerate(wconf["panes"], start=pane_base_index): - if pindex == int(pane_base_index): - p = w.attached_pane + for pane_index, pane_config in enumerate( + window_config["panes"], start=pane_base_index + ): + if pane_index == int(pane_base_index): + pane = window.attached_pane else: def get_pane_start_directory(): - if "start_directory" in pconf: - return pconf["start_directory"] - elif "start_directory" in wconf: - return wconf["start_directory"] + if "start_directory" in pane_config: + return pane_config["start_directory"] + elif "start_directory" in window_config: + return window_config["start_directory"] else: return None def get_pane_shell(): - if "shell" in pconf: - return pconf["shell"] - elif "window_shell" in wconf: - return wconf["window_shell"] + if "shell" in pane_config: + return pane_config["shell"] + elif "window_shell" in window_config: + return window_config["window_shell"] else: return None - environment = pconf.get("environment", wconf.get("environment")) + environment = pane_config.get( + "environment", window_config.get("environment") + ) if environment and has_lt_version("3.0"): # Just issue a warning when the environment comes from the pane # configuration as a warning for the window was already issued when # the window was created. - if pconf.get("environment"): - logging.warning( + if pane_config.get("environment"): + logger.warning( "Cannot set environment for new panes. " "You need tmux 3.0 or newer for this." ) environment = None - assert p is not None + assert pane is not None - p = w.split_window( + pane = window.split_window( attach=True, start_directory=get_pane_start_directory(), shell=get_pane_shell(), - target=p.id, + target=pane.id, environment=environment, ) - assert isinstance(p, Pane) - if "layout" in wconf: - w.select_layout(wconf["layout"]) + assert isinstance(pane, Pane) - if "suppress_history" in pconf: - suppress = pconf["suppress_history"] - elif "suppress_history" in wconf: - suppress = wconf["suppress_history"] + if "layout" in window_config: + window.select_layout(window_config["layout"]) + + if "suppress_history" in pane_config: + suppress = pane_config["suppress_history"] + elif "suppress_history" in window_config: + suppress = window_config["suppress_history"] else: suppress = True - enter = pconf.get("enter", True) - sleep_before = pconf.get("sleep_before", None) - sleep_after = pconf.get("sleep_after", None) - for cmd in pconf["shell_command"]: + enter = pane_config.get("enter", True) + sleep_before = pane_config.get("sleep_before", None) + sleep_after = pane_config.get("sleep_after", None) + for cmd in pane_config["shell_command"]: enter = cmd.get("enter", enter) sleep_before = cmd.get("sleep_before", sleep_before) sleep_after = cmd.get("sleep_after", sleep_after) @@ -508,18 +522,20 @@ def get_pane_shell(): if sleep_before is not None: time.sleep(sleep_before) - p.send_keys(cmd["cmd"], suppress_history=suppress, enter=enter) + pane.send_keys(cmd["cmd"], suppress_history=suppress, enter=enter) if sleep_after is not None: time.sleep(sleep_after) - if "focus" in pconf and pconf["focus"]: - assert p.pane_id is not None - w.select_pane(p.pane_id) + if "focus" in pane_config and pane_config["focus"]: + assert pane.pane_id is not None + window.select_pane(pane.pane_id) - yield p, pconf + yield pane, pane_config - def config_after_window(self, w: Window, wconf: t.Dict[str, t.Any]) -> None: + def config_after_window( + self, window: Window, window_config: t.Dict[str, t.Any] + ) -> None: """Actions to apply to window after window and pane finished. When building a tmux session, sometimes its easier to postpone things @@ -528,14 +544,16 @@ def config_after_window(self, w: Window, wconf: t.Dict[str, t.Any]) -> None: Parameters ---------- - w : :class:`libtmux.Window` + window : :class:`libtmux.Window` window to create panes for - wconf : dict + window_config : dict config section for window """ - if "options_after" in wconf and isinstance(wconf["options_after"], dict): - for key, val in wconf["options_after"].items(): - w.set_window_option(key, val) + if "options_after" in window_config and isinstance( + window_config["options_after"], dict + ): + for key, val in window_config["options_after"].items(): + window.set_window_option(key, val) def find_current_attached_session(self) -> Session: assert self.server is not None diff --git a/src/tmuxp/workspace/freezer.py b/src/tmuxp/workspace/freezer.py index aed0bf4438..b9b4ab9ddb 100644 --- a/src/tmuxp/workspace/freezer.py +++ b/src/tmuxp/workspace/freezer.py @@ -1,3 +1,8 @@ +from libtmux.pane import Pane +from libtmux.session import Session +import typing as t + + def inline(workspace_dict): """Return workspace with inlined shorthands. Opposite of :meth:`loader.expand`. @@ -40,7 +45,7 @@ def inline(workspace_dict): return workspace_dict -def freeze(session): +def freeze(session: Session) -> t.Dict[str, t.Any]: """Freeze live tmux session into a tmuxp workspacee. Parameters @@ -53,53 +58,61 @@ def freeze(session): dict tmuxp compatible workspace """ - sconf = {"session_name": session.session_name, "windows": []} - - for w in session.windows: - wconf = { - "options": w.show_window_options(), - "window_name": w.name, - "layout": w.window_layout, + session_config: t.Dict[str, t.Any] = { + "session_name": session.session_name, + "windows": [], + } + + for window in session.windows: + window_config: t.Dict[str, t.Any] = { + "options": window.show_window_options(), + "window_name": window.name, + "layout": window.window_layout, "panes": [], } - if getattr(w, "window_active", "0") == "1": - wconf["focus"] = "true" + + if getattr(window, "window_active", "0") == "1": + window_config["focus"] = "true" # If all panes have same path, set 'start_directory' instead # of using 'cd' shell commands. - def pane_has_same_path(p): - return w.panes[0].pane_current_path == p.pane_current_path + def pane_has_same_path(pane: Pane) -> bool: + return window.panes[0].pane_current_path == pane.pane_current_path - if all(pane_has_same_path(p) for p in w.panes): - wconf["start_directory"] = w.panes[0].pane_current_path + if all(pane_has_same_path(pane=pane) for pane in window.panes): + window_config["start_directory"] = window.panes[0].pane_current_path - for p in w.panes: - pconf = {"shell_command": []} + for pane in window.panes: + pane_config: t.Union[str, t.Dict[str, t.Any]] = {"shell_command": []} + assert isinstance(pane_config, dict) - if "start_directory" not in wconf: - pconf["shell_command"].append("cd " + p.pane_current_path) + if "start_directory" not in window_config and pane.pane_current_path: + pane_config["shell_command"].append("cd " + pane.pane_current_path) - if getattr(p, "pane_active", "0") == "1": - pconf["focus"] = "true" + if getattr(pane, "pane_active", "0") == "1": + pane_config["focus"] = "true" - current_cmd = p.pane_current_command + current_cmd = pane.pane_current_command - def filter_interpretters_and_shells(): - return current_cmd.startswith("-") or any( - current_cmd.endswith(cmd) for cmd in ["python", "ruby", "node"] + def filter_interpretters_and_shells() -> bool: + return current_cmd is not None and ( + current_cmd.startswith("-") + or any( + current_cmd.endswith(cmd) for cmd in ["python", "ruby", "node"] + ) ) if filter_interpretters_and_shells(): current_cmd = None if current_cmd: - pconf["shell_command"].append(current_cmd) + pane_config["shell_command"].append(current_cmd) else: - if not len(pconf["shell_command"]): - pconf = "pane" + if not len(pane_config["shell_command"]): + pane_config = "pane" - wconf["panes"].append(pconf) + window_config["panes"].append(pane_config) - sconf["windows"].append(wconf) + session_config["windows"].append(window_config) - return sconf + return session_config

`Builder`: Race conditions after #796 / strict mypy strict mypy #796 makes certain uncovered loading scenarios error - `tmuxp load` when session already exists - Launching a new tmux server with default socket path and socket name <details> ``` eduflow is already running. Attach? [Y/n] y Traceback (most recent call last): File "/home/d/.cache/pypoetry/virtualenvs/tmuxp-m3jEKDEh-py3.11/bin/tmuxp", line 6, in <module> sys.exit(cli.cli()) ^^^^^^^^^ File "~/projects/python/tmuxp/src/tmuxp/cli/__init__.py", line 134, in cli command_load( File "~/projects/python/tmuxp/src/tmuxp/cli/load.py", line 640, in command_load load_workspace( File "~/projects/python/tmuxp/src/tmuxp/cli/load.py", line 416, in load_workspace _reattach(builder) File "~/projects/python/tmuxp/src/tmuxp/cli/load.py", line 161, in _reattach assert builder.session is not None ^^^^^^^^^^^^^^^ AttributeError: 'WorkspaceBuilder' object has no attribute 'session' ``` </details> Declone to loading existing session <details> ``` ❯ tmuxp load ~/work/python/libvcs [Loading] ~/projects/python/libvcs/.tmuxp.yaml Already inside TMUX, switch to session? yes/no Or (a)ppend windows in the current active session? [y/n/a] - (y, n, a): n Traceback (most recent call last): File "/home/d/.cache/pypoetry/virtualenvs/tmuxp-m3jEKDEh-py3.11/bin/tmuxp", line 6, in <module> sys.exit(cli.cli()) ^^^^^^^^^ File "~/projects/python/tmuxp/src/tmuxp/cli/__init__.py", line 134, in cli command_load( File "~/projects/python/tmuxp/src/tmuxp/cli/load.py", line 640, in command_load load_workspace( File "~/projects/python/tmuxp/src/tmuxp/cli/load.py", line 450, in load_workspace _load_detached(builder) File "~/projects/python/tmuxp/src/tmuxp/cli/load.py", line 217, in _load_detached builder.build() File "~/projects/python/tmuxp/src/tmuxp/workspace/builder.py", line 293, in build for w, wconf in self.iter_create_windows(session, append): File "~/projects/python/tmuxp/src/tmuxp/workspace/builder.py", line 416, in iter_create_windows w.set_window_option(key, val) File "~/projects/python/libtmux/src/libtmux/window.py", line 346, in set_window_option self.refresh() File "~/projects/python/libtmux/src/libtmux/window.py", line 84, in refresh return super()._refresh( ^^^^^^^^^^^^^^^^^ File "~/projects/python/libtmux/src/libtmux/neo.py", line 174, in _refresh obj = fetch_obj( ^^^^^^^^^^ File "~/projects/python/libtmux/src/libtmux/neo.py", line 242, in fetch_obj assert obj is not None AssertionError ``` </details> Intermittently during `tmuxp load` <details> ``` Done in 1.87s. Traceback (most recent call last): File "~/.local/bin/tmuxp", line 8, in <module> sys.exit(cli.cli()) ^^^^^^^^^ File "~/projects/tmuxp/src/tmuxp/cli/__init__.py", line 134, in cli command_load( File "~/projects/tmuxp/src/tmuxp/cli/load.py", line 640, in command_load load_workspace( File "~/projects/tmuxp/src/tmuxp/cli/load.py", line 452, in load_workspace _load_attached(builder, detached) File "~/projects/tmuxp/src/tmuxp/cli/load.py", line 184, in _load_attached builder.build() File "~/projects/tmuxp/src/tmuxp/workspace/builder.py", line 289, in build for w, wconf in self.iter_create_windows(session, append): File "~/projects/tmuxp/src/tmuxp/workspace/builder.py", line 411, in iter_create_windows w.set_window_option(key, val) File "~/.local/lib/python3.11/site-packages/libtmux/window.py", line 346, in set_window_option self.refresh() File "~/.local/lib/python3.11/site-packages/libtmux/window.py", line 84, in refresh return super()._refresh( ^^^^^^^^^^^^^^^^^ File "~/.local/lib/python3.11/site-packages/libtmux/neo.py", line 174, in _refresh obj = fetch_obj( ^^^^^^^^^^ File "~/.local/lib/python3.11/site-packages/libtmux/neo.py", line 242, in fetch_obj assert obj is not None AssertionError ``` </details>

2022-12-29T01:44:27

0.0

[]

tmux-python/tmuxp

tmux-python__tmuxp-845

b7e7a799f3cf9179f80be767dae279909c238017

diff --git a/CHANGES b/CHANGES index 3261ec59af..78c152ec0e 100644 --- a/CHANGES +++ b/CHANGES @@ -17,7 +17,28 @@ $ pipx install --suffix=@next 'tmuxp' --pip-args '\--pre' --force ## tmuxp 1.19.x (unreleased) -- Notes on upcoming releases will be added here +### What's new + +- #845: allow to configure window and pane specific environment variables + + Having a setup like: + ```yaml + session_name: env-demo + environment: + DATABASE_URL: "sqlite3:///default.db" + windows: + - window_name: dev + environment: + DATABASE_URL: "sqlite3:///dev-1.db" + panes: + - pane + - environment: + DATABASE_URL: "sqlite3:///dev-2.db" + ``` + will result in a window with two panes. In the first pane `$DATABASE_URL` is + `sqlite3:///dev-1.db`, while in the second pane it is `sqlite3://dev-2.db`. + Any freshly created window gets `sqlite3:///default.db` as this is what was + defined for the session.  diff --git a/docs/configuration/examples.md b/docs/configuration/examples.md index 6677c6d64f..33c8505796 100644 --- a/docs/configuration/examples.md +++ b/docs/configuration/examples.md @@ -262,7 +262,9 @@ please make a ticket on the [issue tracker][issue tracker]. ## Environment variables -tmuxp will set session environment variables. +tmuxp will set session, window and pane environment variables. Note that +setting environment variables for windows and panes requires tmuxp 1.19 or +newer. ````{tab} YAML diff --git a/examples/session-environment.json b/examples/session-environment.json index d4c5c17cf9..3c31e12ec3 100644 --- a/examples/session-environment.json +++ b/examples/session-environment.json @@ -2,15 +2,32 @@ "environment": { "EDITOR": "/usr/bin/vim", "DJANGO_SETTINGS_MODULE": "my_app.settings.local", - "SERVER_PORT": "8009", - }, + "SERVER_PORT": "8009" + }, "windows": [ { "panes": [ - "./manage.py runserver 0.0.0.0:${SERVER_PORT}" - ], + "./manage.py runserver 0.0.0.0:${SERVER_PORT}" + ], "window_name": "Django project" - }, - ], + }, + { + "environment": { + "DJANGO_SETTINGS_MODULE": "my_app.settings.local", + "SERVER_PORT": "8010" + }, + "panes": [ + "./manage.py runserver 0.0.0.0:${SERVER_PORT}", + { + "environment": { + "DJANGO_SETTINGS_MODULE": "my_app.settings.local-testing", + "SERVER_PORT": "8011" + }, + "shell_command": "./manage.py runserver 0.0.0.0:${SERVER_PORT}" + } + ], + "window_name": "Another Django project" + } + ], "session_name": "Environment variables test" -} +} \ No newline at end of file diff --git a/examples/session-environment.yaml b/examples/session-environment.yaml index 1c766b56e4..a1091e7a18 100644 --- a/examples/session-environment.yaml +++ b/examples/session-environment.yaml @@ -7,3 +7,13 @@ windows: - window_name: Django project panes: - ./manage.py runserver 0.0.0.0:${SERVER_PORT} + - window_name: Another Django project + environment: + DJANGO_SETTINGS_MODULE: my_app.settings.local + SERVER_PORT: "8010" + panes: + - ./manage.py runserver 0.0.0.0:${SERVER_PORT} + - environment: + DJANGO_SETTINGS_MODULE: my_app.settings.local-testing + SERVER_PORT: "8011" + shell_command: ./manage.py runserver 0.0.0.0:${SERVER_PORT} diff --git a/src/tmuxp/workspace/builder.py b/src/tmuxp/workspace/builder.py index 677ad9f923..b13bf4caa0 100644 --- a/src/tmuxp/workspace/builder.py +++ b/src/tmuxp/workspace/builder.py @@ -7,6 +7,7 @@ import logging import time +from libtmux.common import has_lt_version from libtmux.exc import TmuxSessionExists from libtmux.pane import Pane from libtmux.server import Server @@ -346,12 +347,31 @@ def iter_create_windows(self, session, append=False): except (KeyError, IndexError): pass + environment = panes[0].get("environment", wconf.get("environment")) + if environment and has_lt_version("3.0"): + # Falling back to use the environment of the first pane for the window + # creation is nice but yields misleading error messages. + pane_env = panes[0].get("environment") + win_env = wconf.get("environment") + if pane_env and win_env: + target = "panes and windows" + elif pane_env: + target = "panes" + else: + target = "windows" + logging.warning( + f"Cannot set environment for new {target}. " + "You need tmux 3.0 or newer for this." + ) + environment = None + w = session.new_window( window_name=window_name, start_directory=sd, attach=False, # do not move to the new window window_index=wconf.get("window_index", ""), window_shell=ws, + environment=environment, ) if is_first_window_pass: # if first window, use window 1 @@ -418,11 +438,24 @@ def get_pane_shell(): else: return None + environment = pconf.get("environment", wconf.get("environment")) + if environment and has_lt_version("3.0"): + # Just issue a warning when the environment comes from the pane + # configuration as a warning for the window was already issued when + # the window was created. + if pconf.get("environment"): + logging.warning( + "Cannot set environment for new panes. " + "You need tmux 3.0 or newer for this." + ) + environment = None + p = w.split_window( attach=True, start_directory=get_pane_start_directory(), shell=get_pane_shell(), target=p.id, + environment=environment, ) assert isinstance(p, Pane)

Per-window environment variables It would be nice if I could set different `environment` values in different windows within a session. Using `tmuxp 1.15.0, libtmux 0.15.4`, I can set environment variables for a session, e.g.: ```yaml session_name: foo environment: DATABASE_URL: "..." ``` However, what I really want to do is set different env vars in different windows, e.g.: ```yaml session_name: foo environment: DATABASE_URL: "sqlite3:///default.db" windows: - window_name: deprecated environment: DATABASE_URL: "... one test DB ..." - window_name: not_ready_yet environment: DATABASE_URL: "... another test DB ..." ``` Thanks!

@tony I could work on this issue if accepted. I could also generalize it to allow setting up pane-specific environments as well (if you agree on that). @zappolowski Go for it Just one question on the desired/expected behavior. Given this (currently hypothetical) configuration: ```yaml session_name: env-test environment: ENV_VAR: "session" windows: - panes: - "echo $ENV_VAR == window" - environment: ENV_VAR: "pane" shell_command: "echo $ENV_VAR == pane" - "echo $ENV_VAR == ???" environment: ENV_VAR: "window" - panes: - "echo $ENV_VAR == session" ``` What should be the value of `???`? Should it be `window`, i.e. pane environment falls back to window environment before using session environment, or should it be `session`, which means that it always ignores the window environment in that case?

2022-11-07T16:33:46

0.0

[]

YosefLab/Cassiopeia

YosefLab__Cassiopeia-189

feec0207170f427afa2eee5d583cb7cd5160b3af

diff --git a/cassiopeia/preprocess/cassiopeia_preprocess.py b/cassiopeia/preprocess/cassiopeia_preprocess.py index 0b6e22ec..abcc6dc6 100755 --- a/cassiopeia/preprocess/cassiopeia_preprocess.py +++ b/cassiopeia/preprocess/cassiopeia_preprocess.py @@ -12,7 +12,6 @@ import os import argparse -import configparser import logging import pandas as pd from typing import Any, Dict diff --git a/cassiopeia/preprocess/pipeline.py b/cassiopeia/preprocess/pipeline.py index 726e6d37..23e1c24d 100755 --- a/cassiopeia/preprocess/pipeline.py +++ b/cassiopeia/preprocess/pipeline.py @@ -364,7 +364,7 @@ def resolve_umi_sequence( bins=range(1, equivClass_group["grpFlag"].max()), ) plt.title("Unique Seqs per cellBC+UMI") - plt.yscale("log", basey=10) + plt.yscale("log", base=10) plt.xlabel("Number of Unique Seqs") plt.ylabel("Count (Log)") plt.savefig(os.path.join(output_directory, "seqs_per_equivClass.png")) @@ -975,8 +975,8 @@ def filter_molecule_table( plt.legend() plt.ylabel("Number of UMIs") plt.xlabel("Rank Order") - plt.xscale("log", basex=10) - plt.yscale("log", basey=10) + plt.xscale("log", base=10) + plt.yscale("log", base=10) plt.title("UMIs per CellBC") plt.savefig(os.path.join(output_directory, "umis_per_cellbc.png")) plt.close() diff --git a/cassiopeia/preprocess/utilities.py b/cassiopeia/preprocess/utilities.py index 83d0319d..9517e086 100755 --- a/cassiopeia/preprocess/utilities.py +++ b/cassiopeia/preprocess/utilities.py @@ -10,8 +10,6 @@ import warnings from collections import defaultdict, OrderedDict -import matplotlib -import matplotlib.pyplot as plt import ngs_tools as ngs import numpy as np import pandas as pd

Error in resolve_umi_sequence Hello, I was following the tutorial for the preprocessing, and when I get to the resolve_umi_sequence I get an error thrown. `__init__() got an unexpected keyword argument 'basey'` I believe it's a deprecation of the params in the new matplotlib version. Best, Chang

Hi @cnk113 , Thanks for bringing this to our attention - we'll mark this as a bug and fix this as soon as we can. Thanks, Matt @mattjones315 Hi, I met the same error, have you fix the bug?

2023-03-06T18:51:02

0.0

[]

YosefLab/Cassiopeia

YosefLab__Cassiopeia-170

ff56dcc0ce27b38808eee51901cb203b3a29888d

diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 00000000..b6115e6e --- /dev/null +++ b/.gitattributes @@ -0,0 +1,2 @@ +*.py linguist-language=python +*.ipynb linguist-documentation diff --git a/.gitignore b/.gitignore index 0b04937f..4cb7d7a2 100755 --- a/.gitignore +++ b/.gitignore @@ -163,4 +163,5 @@ build *.c stdout.log notebooks/.ipynb_checkpoints -cassiopeia/tools/branch_length_estimator/_iid_exponential_bayesian.cpp \ No newline at end of file +cassiopeia/tools/branch_length_estimator/_iid_exponential_bayesian.cpp +docs/api/reference/** \ No newline at end of file diff --git a/.readthedocs.yml b/.readthedocs.yml index b513e835..dec7d862 100755 --- a/.readthedocs.yml +++ b/.readthedocs.yml @@ -4,10 +4,9 @@ build: sphinx: configuration: docs/conf.py python: - version: 3.6 + version: 3.7 install: - method: pip path: . extra_requirements: - - docs - - requirements: docs/requirements.txt + - docs \ No newline at end of file diff --git a/Makefile b/Makefile index f416d215..aab3a5bd 100644 --- a/Makefile +++ b/Makefile @@ -18,23 +18,11 @@ clean_sdist: clean: clean_develop clean_pypi install: - - $(python) -m pip install --user . - - $(python) setup.py build - - $(python) setup.py build_ext --inplace + - $(python) -m pip install . check_build_reqs: @$(python) -c 'import pytest' \ || ( printf "$(redpip)Build requirements are missing. Run 'make prepare' to install them.$(normal)" ; false ) test: check_build_reqs - $(python) -m pytest -vv $(tests) - - -pypi: clean clean_sdist - set -x \ - && $(python) setup.py sdist bdist_wheel \ - && twine check dist/* \ - && twine upload --repository-url https://test.pypi.org/legacy/ dist/* - -clean_pypi: - - rm -rf build/ + $(python) -m pytest -vv $(tests) \ No newline at end of file diff --git a/README.md b/README.md index e406f583..1c858967 100755 --- a/README.md +++ b/README.md @@ -31,20 +31,21 @@ Installation 1. Clone the package as so: ``git clone https://github.com/YosefLab/Cassiopeia.git`` -2. Ensure you are using python>=3.7. (Python 3.6 may still work, but is not guaranteed.) +2. Ensure that you have Python >= 3.7 installed. (Python 3.6 may still work, but is not guaranteed.) We prefer using [miniconda](https://docs.conda.io/en/latest/miniconda.html). -3. Make sure that Gurobi is installed. You can follow the instructions listed [here](http://www.gurobi.com/academia/for-universities). To verify that it's working correctly, use the following tests: +3. [Optional] Make sure that Gurobi is installed. You can follow the instructions listed [here](http://www.gurobi.com/academia/for-universities). To verify that it's working correctly, use the following tests: * Run the command ``gurobi.sh`` from a terminal window * From the Gurobi installation directory (where there is a setup.py file), use ``python setup.py install --user`` - -4. Install Cassiopeia by first making sure you have stable version of `Cython` and `pytest`. Then, you can install Cassiopeia by running `make install` from the directory where you have Cassiopeia downloaded. - -To verify that it installed correctly, try running our tests with `make test`. + +4. Install Cassiopeia by first changing into the Cassiopeia directory and then `pip3 install .`. To install dev and docs requirements, you can run `pip3 install .[dev,docs]`. + +To verify that it installed correctly, try running our tests with `pytest`. Reference ---------------------- If you've found Cassiopeia useful for your research, please consider citing our paper published in Genome Biology: - +``` Matthew G Jones*, Alex Khodaverdian*, Jeffrey J Quinn*, Michelle M Chan, Jeffrey A Hussmann, Robert Wang, Chenling Xu, Jonathan S Weissman, Nir Yosef. (2020), [*Inference of single-cell phylogenies from lineage tracing data using Cassiopeia*](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02000-8), Genome Biology +``` \ No newline at end of file diff --git a/build.py b/build.py new file mode 100644 index 00000000..c714b629 --- /dev/null +++ b/build.py @@ -0,0 +1,61 @@ +import os +import shutil +from distutils.command.build_ext import build_ext +from distutils.core import Distribution, Extension + +import numpy +from Cython.Build import cythonize + +# https://github.com/mdgoldberg/poetry-cython-example + + +def build(): + extensions = [ + Extension( + "cassiopeia.preprocess.collapse_cython", + ["cassiopeia/preprocess/collapse_cython.pyx"], + ), + Extension( + "cassiopeia.solver.ilp_solver_utilities", + ["cassiopeia/solver/ilp_solver_utilities.pyx"], + include_dirs=[numpy.get_include()], + ), + Extension( + "cassiopeia.tools.branch_length_estimator._iid_exponential_bayesian", + sources=[ + "cassiopeia/tools/branch_length_estimator/_iid_exponential_bayesian.pyx", + "cassiopeia/tools/branch_length_estimator/_iid_exponential_bayesian_cpp.cpp", + ], + extra_compile_args=[ + "-std=c++17", + "-Wall", + "-Wextra", + "-pedantic", + "-O3", + ], + language="c++", + ), + ] + ext_modules = cythonize( + extensions, + compiler_directives={"language_level": 3}, + ) + + distribution = Distribution({"name": "extended", "ext_modules": ext_modules}) + distribution.package_dir = "extended" + + cmd = build_ext(distribution) + cmd.ensure_finalized() + cmd.run() + + # Copy built extensions back to the project + for output in cmd.get_outputs(): + relative_extension = os.path.relpath(output, cmd.build_lib) + shutil.copyfile(output, relative_extension) + mode = os.stat(relative_extension).st_mode + mode |= (mode & 0o444) >> 2 + os.chmod(relative_extension, mode) + + +if __name__ == "__main__": + build() \ No newline at end of file diff --git a/cassiopeia/__init__.py b/cassiopeia/__init__.py index e01f7388..18a134d8 100755 --- a/cassiopeia/__init__.py +++ b/cassiopeia/__init__.py @@ -2,15 +2,26 @@ """Top-level for Cassiopeia development.""" -package_name = "cassiopeia" -__author__ = "Matt Jones, Alex Khodaveridan, Richard Zhang, Sebastian Prillo" -__email__ = "[email protected]" -__version__ = "0.0.1" - -from . import pp +from . import preprocess as pp from . import solver -from . import pl +from . import plotting as pl from . import data from . import critique -from . import sim -from . import tl +from . import simulator as sim +from . import tools as tl + +# https://github.com/python-poetry/poetry/pull/2366#issuecomment-652418094 +# https://github.com/python-poetry/poetry/issues/144#issuecomment-623927302 +try: + import importlib.metadata as importlib_metadata +except ModuleNotFoundError: + import importlib_metadata +package_name = "cassiopeia" +__version__ = importlib_metadata.version(package_name) + +import sys + +sys.modules.update({f"{__name__}.{m}": globals()[m] for m in ["tl", "pp", "pl", "sim"]}) +del sys + +__all__ = ["pp", "solver", "pl", "data", "critique", "sim", "tl"] diff --git a/cassiopeia/critique/__init__.py b/cassiopeia/critique/__init__.py index 5fd2a7cb..f281f832 100644 --- a/cassiopeia/critique/__init__.py +++ b/cassiopeia/critique/__init__.py @@ -1,7 +1,3 @@ """Top level for the cassiopeia critique module.""" -__author__ = "Matt Jones, Richard Zhang" -__email__ = "[email protected]" -__version__ = "0.0.1" - from .compare import robinson_foulds, triplets_correct diff --git a/cassiopeia/pl.py b/cassiopeia/pl.py deleted file mode 100644 index 098fa923..00000000 --- a/cassiopeia/pl.py +++ /dev/null @@ -1,1 +0,0 @@ -from cassiopeia.plotting import * diff --git a/cassiopeia/plotting/__init__.py b/cassiopeia/plotting/__init__.py index 476ba074..70418056 100644 --- a/cassiopeia/plotting/__init__.py +++ b/cassiopeia/plotting/__init__.py @@ -4,3 +4,6 @@ from .itol_utilities import upload_and_export_itol from .local import plot_matplotlib, plot_plotly + + +__all__ = ["upload_and_export_itol", "plot_matplotlib", "plot_plotly"] diff --git a/cassiopeia/pp.py b/cassiopeia/pp.py deleted file mode 100644 index 3cc480e9..00000000 --- a/cassiopeia/pp.py +++ /dev/null @@ -1,1 +0,0 @@ -from cassiopeia.preprocess import * diff --git a/cassiopeia/preprocess/__init__.py b/cassiopeia/preprocess/__init__.py index d09bef8c..b1cda397 100755 --- a/cassiopeia/preprocess/__init__.py +++ b/cassiopeia/preprocess/__init__.py @@ -24,3 +24,25 @@ filter_umis, ) from .setup_utilities import setup + + +__all__ = [ + "align_sequences", + "call_alleles", + "collapse_umis", + "convert_fastqs_to_unmapped_bam", + "error_correct_cellbcs_to_whitelist", + "error_correct_intbcs_to_whitelist", + "error_correct_umis", + "filter_bam", + "resolve_umi_sequence", + "filter_molecule_table", + "call_lineage_groups", + "compute_empirical_indel_priors", + "convert_alleletable_to_character_matrix", + "convert_alleletable_to_lineage_profile", + "convert_lineage_profile_to_character_matrix", + "filter_cells", + "filter_umis", + "setup", +] diff --git a/cassiopeia/sim.py b/cassiopeia/sim.py deleted file mode 100644 index 47a1106b..00000000 --- a/cassiopeia/sim.py +++ /dev/null @@ -1,1 +0,0 @@ -from cassiopeia.simulator import * diff --git a/cassiopeia/simulator/__init__.py b/cassiopeia/simulator/__init__.py index 116639ad..a7118473 100755 --- a/cassiopeia/simulator/__init__.py +++ b/cassiopeia/simulator/__init__.py @@ -11,3 +11,18 @@ from .SupercellularSampler import SupercellularSampler from .TreeSimulator import TreeSimulator from .UniformLeafSubsampler import UniformLeafSubsampler + + +__all__ = [ + "BirthDeathFitnessSimulator", + "BrownianSpatialDataSimulator", + "Cas9LineageTracingDataSimulator", + "CompleteBinarySimulator", + "DataSimulator", + "LeafSubsampler", + "LineageTracingDataSimulator", + "SimpleFitSubcloneSimulator", + "SupercellularSampler", + "TreeSimulator", + "UniformLeafSubsampler", +] diff --git a/cassiopeia/solver/HybridSolver.py b/cassiopeia/solver/HybridSolver.py index 68a0d792..9a78c748 100644 --- a/cassiopeia/solver/HybridSolver.py +++ b/cassiopeia/solver/HybridSolver.py @@ -226,6 +226,9 @@ def apply_top_solver( [subtree-root, subtree-samples]. """ + if len(samples) == 1: + return samples[0], [samples], tree + clades = list( self.top_solver.perform_split( character_matrix, samples, weights, missing_state_indicator @@ -233,41 +236,37 @@ def apply_top_solver( ) root = next(node_name_generator) - tree.add_node(root) + + for clade in clades: + if len(clade) == 0: + clades.remove(clade) + if len(clades) == 1: for clade in clades[0]: tree.add_edge(root, clade) return root, [], tree - new_clades = [] subproblems = [] for clade in clades: - if len(clade) == 0: - continue - if self.assess_cutoff( clade, character_matrix, missing_state_indicator ): subproblems += [(root, clade)] - continue - - new_clades.append(clade) - - for clade in new_clades: - child, new_subproblems, tree = self.apply_top_solver( - character_matrix, - clade, - tree, - node_name_generator, - weights, - missing_state_indicator, - root, - ) - tree.add_edge(root, child) + else: + child, new_subproblems, tree = self.apply_top_solver( + character_matrix, + clade, + tree, + node_name_generator, + weights, + missing_state_indicator, + root, + ) + tree.add_edge(root, child) - subproblems += new_subproblems + subproblems += new_subproblems return root, subproblems, tree diff --git a/cassiopeia/solver/__init__.py b/cassiopeia/solver/__init__.py index 1e62d9da..d6c0146b 100755 --- a/cassiopeia/solver/__init__.py +++ b/cassiopeia/solver/__init__.py @@ -1,9 +1,5 @@ """Top level for Tree Solver development.""" -__author__ = "Matt Jones, Alex Khodaverdian, Richard Zhang, Sebastian Prillo" -__email__ = "[email protected]" -__version__ = "0.0.1" - from .HybridSolver import HybridSolver from .ILPSolver import ILPSolver from .MaxCutGreedySolver import MaxCutGreedySolver diff --git a/cassiopeia/tl.py b/cassiopeia/tl.py deleted file mode 100644 index 882c1147..00000000 --- a/cassiopeia/tl.py +++ /dev/null @@ -1,1 +0,0 @@ -from cassiopeia.tools import * diff --git a/cassiopeia/tools/__init__.py b/cassiopeia/tools/__init__.py index ece00aea..821f0771 100644 --- a/cassiopeia/tools/__init__.py +++ b/cassiopeia/tools/__init__.py @@ -14,3 +14,19 @@ calculate_likelihood_discrete, calculate_parsimony, ) + + +__all__ = [ + "calculate_likelihood_continuous", + "calculate_likelihood_discrete", + "calculate_parsimony", + "compute_morans_i", + "compute_evolutionary_coupling", + "estimate_missing_data_rates", + "estimate_mutation_rate", + "fitch_count", + "fitch_hartigan", + "score_small_parsimony", + "compute_cophenetic_correlation", + "compute_expansion_pvalues", +] diff --git a/docs/api/critique.rst b/docs/api/critique.rst index 46694706..46b46d7f 100644 --- a/docs/api/critique.rst +++ b/docs/api/critique.rst @@ -1,7 +1,6 @@ =========== Critique =========== -.. module:: cassiopeia.critique .. currentmodule:: cassiopeia Critique @@ -11,6 +10,6 @@ We support functionality for comparing trees to one another, for example when be .. autosummary:: :toctree: reference/ - + critique.robinson_foulds critique.triplets_correct \ No newline at end of file diff --git a/docs/api/data.rst b/docs/api/data.rst index 6d03519a..48a8e908 100644 --- a/docs/api/data.rst +++ b/docs/api/data.rst @@ -1,6 +1,7 @@ =========== Data =========== + .. module:: cassiopeia.data .. currentmodule:: cassiopeia @@ -11,7 +12,7 @@ The main data structure that Cassiopeia uses for all tree-based analyses is the .. autosummary:: :toctree: reference/ - + data.CassiopeiaTree Utilities diff --git a/docs/api/plotting.rst b/docs/api/plotting.rst index a938aaa3..a4258fa6 100644 --- a/docs/api/plotting.rst +++ b/docs/api/plotting.rst @@ -2,7 +2,6 @@ Plotting ========== -.. module:: cassiopeia.pl .. currentmodule:: cassiopeia Plotting diff --git a/docs/api/preprocess.rst b/docs/api/preprocess.rst index f79eb921..1b7e4432 100644 --- a/docs/api/preprocess.rst +++ b/docs/api/preprocess.rst @@ -1,7 +1,6 @@ =========== Preprocess =========== -.. module:: cassiopeia.pp .. currentmodule:: cassiopeia Data Preprocessing @@ -11,7 +10,7 @@ We have several functions that are part of our pipeline for processing sequencin .. autosummary:: :toctree: reference/ - + pp.align_sequences pp.call_alleles pp.call_lineage_groups @@ -25,8 +24,8 @@ We have several functions that are part of our pipeline for processing sequencin pp.filter_cells pp.filter_umis pp.resolve_umi_sequence - - + + Data Utilities @@ -36,7 +35,7 @@ We also have several functions that are useful for converting between data forma .. autosummary:: :toctree: reference/ - + pp.compute_empirical_indel_priors pp.convert_alleletable_to_character_matrix pp.convert_alleletable_to_lineage_profile diff --git a/docs/api/simulator.rst b/docs/api/simulator.rst index 45d16f96..dfb6b6e9 100644 --- a/docs/api/simulator.rst +++ b/docs/api/simulator.rst @@ -1,7 +1,6 @@ =========== Simulator =========== -.. module:: cassiopeia.sim .. currentmodule:: cassiopeia @@ -14,7 +13,7 @@ We have several frameworks available for simulating topologies: .. autosummary:: :toctree: reference/ - + sim.BirthDeathFitnessSimulator sim.CompleteBinarySimulator sim.SimpleFitSubcloneSimulator diff --git a/docs/api/solver.rst b/docs/api/solver.rst index 1215dac1..059a9075 100644 --- a/docs/api/solver.rst +++ b/docs/api/solver.rst @@ -1,7 +1,6 @@ =========== Solver =========== -.. module:: cassiopeia.solver .. currentmodule:: cassiopeia CassiopeiaSolvers @@ -11,7 +10,7 @@ We have several algorithms available for solving phylogenies: .. autosummary:: :toctree: reference/ - + solver.HybridSolver solver.ILPSolver solver.MaxCutSolver @@ -32,7 +31,7 @@ For use in our distance-based solver and for comparing character states, we also .. autosummary:: :toctree: reference/ - + solver.dissimilarity_functions.cluster_dissimilarity solver.dissimilarity_functions.hamming_distance solver.dissimilarity_functions.hamming_similarity_normalized_over_missing diff --git a/docs/api/tools.rst b/docs/api/tools.rst index 579cf988..10286a63 100644 --- a/docs/api/tools.rst +++ b/docs/api/tools.rst @@ -2,7 +2,6 @@ Tools ========== -.. module:: cassiopeia.tl .. currentmodule:: cassiopeia This library stores code for post-reconstruction analysis of trees. We are @@ -22,7 +21,7 @@ Branch Length Estimation (BLE) .. autosummary:: :toctree: reference/ - + tl.IIDExponentialBayesian tl.IIDExponentialMLE @@ -31,14 +30,14 @@ Coupling .. autosummary:: :toctree: reference/ - + tl.compute_evolutionary_coupling Metrics ~~~~~~~~ .. autosummary:: :toctree: reference/ - + tl.calculate_likelihood_continuous tl.calculate_likelihood_discrete tl.calculate_parsimony @@ -48,17 +47,17 @@ Parameter Estimation .. autosummary:: :toctree: reference/ - + tl.estimate_missing_data_rates tl.estimate_mutation_rate -Small-Parsimony +Small-Parsimony ~~~~~~~~~~~~~~~~~~~ .. autosummary:: :toctree: reference/ - + tl.fitch_count tl.fitch_hartigan tl.score_small_parsimony @@ -67,5 +66,5 @@ Topology ~~~~~~~~~~~~~~~~~~~ .. autosummary:: :toctree: reference/ - + tl.compute_expansion_pvalues \ No newline at end of file diff --git a/docs/conf.py b/docs/conf.py index ae662a95..fd361fdf 100755 --- a/docs/conf.py +++ b/docs/conf.py @@ -18,20 +18,14 @@ # relative to the documentation root, use os.path.abspath to make it # absolute, like shown here. # -import os import sys -import warnings from pathlib import Path HERE = Path(__file__).parent -# sys.path[:0] = [str(HERE.parent), str(HERE / "extensions")] - +sys.path[:0] = [str(HERE.parent), str(HERE / "extensions")] import cassiopeia # noqa -on_rtd = os.environ.get("READTHEDOCS") == "True" - -autodoc_mock_imports = ["gurobipy"] # -- General configuration --------------------------------------------- @@ -44,11 +38,10 @@ extensions = [ "sphinx.ext.autodoc", "sphinx.ext.intersphinx", - "sphinx.ext.mathjax", "sphinx.ext.viewcode", "nbsphinx", "nbsphinx_link", - "sphinx_gallery.load_style", + "sphinx.ext.mathjax", "sphinx.ext.napoleon", "sphinx_autodoc_typehints", # needs to be after napoleon "sphinx.ext.autosummary", @@ -56,13 +49,15 @@ "scanpydoc.definition_list_typed_field", "scanpydoc.autosummary_generate_imported", *[p.stem for p in (HERE / "extensions").glob("*.py")], - *[p.stem for p in (HERE / "extensions").glob("*.pyx")], + "sphinx_gallery.load_style", ] # nbsphinx specific settings exclude_patterns = ["_build", "**.ipynb_checkpoints"] nbsphinx_execute = "never" +autodoc_mock_imports = ["gurobipy"] + # Add any paths that contain templates here, relative to this directory. templates_path = ["_templates"] @@ -76,7 +71,7 @@ autosummary_generate = True autodoc_member_order = "bysource" napoleon_google_docstring = True # for pytorch lightning -napoleon_numpy_docstring = True +napoleon_numpy_docstring = False napoleon_include_init_with_doc = False napoleon_use_rtype = True # having a separate entry generally helps readability napoleon_use_param = True @@ -97,9 +92,9 @@ ) # General information about the project. -project = u"cassiopeia" -copyright = u"2021, Yosef Lab, UC Berkeley" -author = u"Matthew G Jones, Richard Zhang, Sebastian Prillo, Joseph Min, Jeffrey J Quinn, Alex Khodaverdian" +project = "cassiopeia" +copyright = "2022, Yosef Lab, UC Berkeley" +author = "Matthew G Jones, Richard Zhang, Sebastian Prillo, Joseph Min, Jeffrey J Quinn, Alex Khodaverdian" # The version info for the project you're documenting, acts as replacement # for |version| and |release|, also used in various other places throughout @@ -120,7 +115,7 @@ # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This patterns also effect to html_static_path and html_extra_path -exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"] +exclude_patterns = ["_build", "Thumbs.db", ".DS_Store", ".pyx"] # The name of the Pygments (syntax highlighting) style to use. pygments_style = "sphinx" @@ -172,10 +167,6 @@ } -def setup(app): - app.warningiserror = on_rtd - - # -- Options for HTMLHelp output --------------------------------------- # Output file base name for HTML help builder. @@ -191,56 +182,6 @@ def setup(app): }, } -# -- Options for LaTeX output ------------------------------------------ - -latex_elements = { - # The paper size ('letterpaper' or 'a4paper'). - # - # 'papersize': 'letterpaper', - # The font size ('10pt', '11pt' or '12pt'). - # - # 'pointsize': '10pt', - # Additional stuff for the LaTeX preamble. - # - # 'preamble': '', - # Latex figure (float) alignment - # - # 'figure_align': 'htbp', -} - -# Grouping the document tree into LaTeX files. List of tuples -# (source start file, target name, title, author, documentclass -# [howto, manual, or own class]). -# latex_documents = [ -# (master_doc, "cassiopeia.tex", u"Cassiopeia Documentation", u"Matthew Jones", "manual") -# ] - - -# -- Options for manual page output ------------------------------------ - -# One entry per manual page. List of tuples -# (source start file, name, description, authors, manual section). -man_pages = [ - (master_doc, "Cassiopeia", u"Cassiopeia Documentation", [author], 1) -] - - -# -- Options for Texinfo output ---------------------------------------- - -# Grouping the document tree into Texinfo files. List of tuples -# (source start file, target name, title, author, -# dir menu entry, description, category) -texinfo_documents = [ - ( - master_doc, - "cassiopeia", - u"Cassiopeia Documentation", - author, - "Cassiopeia", - "One line description of project.", - "Miscellaneous", - ) -] from sphinx.ext.autosummary import Autosummary from sphinx.ext.autosummary import get_documenter @@ -255,6 +196,7 @@ class AutoAutoSummary(Autosummary): option_spec = { "methods": directives.unchanged, "attributes": directives.unchanged, + "toctree": directives.unchanged, } required_arguments = 1 @@ -271,9 +213,7 @@ def get_members(obj, typ, include_public=None): continue if documenter.objtype == typ: items.append(name) - public = [ - x for x in items if x in include_public or not x.startswith("_") - ] + public = [x for x in items if x in include_public or not x.startswith("_")] return public, items def run(self): diff --git a/docs/extensions/typed_returns.py b/docs/extensions/typed_returns.py new file mode 100644 index 00000000..d4e1d267 --- /dev/null +++ b/docs/extensions/typed_returns.py @@ -0,0 +1,28 @@ +# code from https://github.com/theislab/scanpy/blob/master/docs/extensions/typed_returns.py +# with some minor adjustment +import re + +from sphinx.application import Sphinx +from sphinx.ext.napoleon import NumpyDocstring + + +def process_return(lines): + for line in lines: + m = re.fullmatch(r"(?P<param>\w+)\s+:\s+(?P<type>[\w.]+)", line) + if m: + # Once this is in scanpydoc, we can use the fancy hover stuff + yield f'-{m["param"]} (:class:`~{m["type"]}`)' + else: + yield line + + +def scanpy_parse_returns_section(self, section): + lines_raw = list(process_return(self._dedent(self._consume_to_next_section()))) + lines = self._format_block(":returns: ", lines_raw) + if lines and lines[-1]: + lines.append("") + return lines + + +def setup(app: Sphinx): + NumpyDocstring._parse_returns_section = scanpy_parse_returns_section diff --git a/docs/requirements.txt b/docs/requirements.txt deleted file mode 100755 index 5546eebb..00000000 --- a/docs/requirements.txt +++ /dev/null @@ -1,14 +0,0 @@ -sphinx_rtd_theme==0.5.0 -sphinx==4.0.0 -nbsphinx -sphinx-autodoc-typehints<1.12.0 -nbsphinx-link -sphinx_gallery -pandas -numpy>=1.9 -networkx >= 2.0 -Cython >= 0.29.2 -scanpydoc>=0.4.5 -pydata-sphinx-theme>=0.4.0 -pathlib -typing_extensions; python_version < '3.8' diff --git a/pyproject.toml b/pyproject.toml index 9420f4d3..1fa96b90 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,3 +1,102 @@ +[tool.poetry] +authors = ["Matthew Jones <[email protected]>", "Alex Khodaverdian", "Richard Zhang", "Sebastian Prillo", "Joseph Min"] +classifiers = [ + "Development Status :: 4 - Beta", + "Intended Audience :: Science/Research", + "Natural Language :: English", + "Programming Language :: Python :: 3.7", + "Programming Language :: Python :: 3.8", + "Programming Language :: Python :: 3.9", + "Operating System :: MacOS :: MacOS X", + "Operating System :: Microsoft :: Windows", + "Operating System :: POSIX :: Linux", + "Topic :: Scientific/Engineering :: Bio-Informatics", +] +description = "Single Cell Lineage Reconstruction with Cas9-Enabled Lineage Recorders" +documentation = "https://cassiopeia-lineage.readthedocs.io/" +homepage = "https://github.com/YosefLab/Cassiopeia" +keywords = ['scLT'] +license = "MIT" +name = "cassiopeia" +readme = 'README.md' +repository = "https://github.com/YosefLab/Cassiopeia" +version = "2.0.0" + +include = [ + {path = "cassiopeia/preprocess/*.so", format = "wheel"}, + {path = "cassiopeia/preprocess/*.pyx", format = "wheel"}, + {path = "cassiopeia/solver/*.so", format = "wheel"}, + {path = "cassiopeia/solver/*.pyx", format = "wheel"}, + {path = "cassiopeia/tools/branch_length_estimator/*.so", format = "wheel"}, + {path = "cassiopeia/tools/branch_length_estimator/*.pyx", format = "wheel"}, +] +packages = [ + {include = "cassiopeia"}, +] + +[tool.poetry.dependencies] +Biopython = ">=1.71" +Cython = ">=0.29.2" +PyYAML = ">=3.12" +black = {version = ">=20.8b1", optional = true} +bokeh = ">=0.12.15" +codecov = {version = ">=2.0.8", optional = true} +cvxpy = "*" +ete3 = ">=3.1.1" +hits = "*" +importlib-metadata = {version = "^1.0", python = "<3.8"} +ipython = {version = ">=7.20", optional = true, python = ">=3.7"} +isort = {version = ">=5.7", optional = true} +itolapi = "*" +jupyter = {version = ">=1.0", optional = true} +matplotlib = ">=2.2.2" +nbconvert = ">=5.4.0" +nbformat = ">=4.4.0" +nbsphinx = {version = "*", optional = true} +nbsphinx-link = {version = "*", optional = true} +networkx = ">=2.5" +ngs-tools = ">=1.5.6" +numba = ">=0.51.0" +numpy = ">=1.19.5,<1.22" +pandas = ">=1.1.4" +parameterized = "*" +plotly = ">=5.0.0" +pre-commit = {version = ">=2.7.1", optional = true} +pydata-sphinx-theme = {version = ">=0.4.3", optional = true} +pysam = ">=0.14.1" +pyseq-align = ">=1.0.2" +pytest = {version = ">=4.4", optional = true} +python = ">=3.7,<4.0" +scanpydoc = {version = ">=0.5", optional = true} +scipy = ">=1.2.0" +sphinx = {version = ">=3.4", optional = true} +sphinx-autodoc-typehints = {version = "*", optional = true} +sphinx-gallery = {version = ">0.6", optional = true} +tqdm = ">=4" +typing-extensions = ">=3.7.4" +typing_extensions = {version = "*", python = "<3.8", optional = true} + +[tool.poetry.build] +generate-setup-file = false +script = "build.py" + [build-system] -requires = ["setuptools", "wheel", "Cython", "oldest-supported-numpy"] -build-backend = "setuptools.build_meta" +build-backend = "poetry.core.masonry.api" +requires = ["poetry-core>=1.0.7", "Cython", "numpy>=1.19.5,<1.22", "setuptools", "pip>=22.0.0"] + +[tool.poetry.scripts] +cassiopeia-preprocess = 'cassiopeia.preprocess.cassiopeia_preprocess:main' + +[tool.poetry.extras] +dev = ["black", "pytest", "flake8", "codecov", "jupyter", "pre-commit", "isort"] +docs = [ + "sphinx", + "scanpydoc", + "nbsphinx", + "nbsphinx-link", + "ipython", + "pydata-sphinx-theme", + "typing_extensions", + "sphinx-autodoc-typehints", + "sphinx_gallery", +] diff --git a/setup.py b/setup.py index 7730c87b..dbb2a9e3 100755 --- a/setup.py +++ b/setup.py @@ -1,113 +1,8 @@ -#!/usr/bin/env python3.6 -# -*- coding: utf-8 -*- +#!/usr/bin/env python -from setuptools import setup, Extension, Distribution, find_packages -from setuptools import find_packages -from Cython.Build import cythonize -from Cython.Distutils import build_ext -import numpy +# This is a shim to hopefully allow Github to detect the package, build is done with poetry -with open("README.md") as readme_file: - readme = readme_file.read() +import setuptools -requirements = [ - "Biopython>=1.71", - "bokeh>=0.12.15", - "cython>=0.29.2", - "ete3>=3.1.1", - "hits", - "itolapi", - "matplotlib>=2.2.2", - "nbconvert>=5.4.0", - "nbformat>=4.4.0", - "networkx>=2.5", - "ngs-tools>=1.5.6", - "numba>=0.51.0", - "numpy>=1.19.5<1.22", - "pandas>=1.1.4", - "plotly>=5.0.0", - "pysam>=0.14.1", - "pyseq-align>=1.0.2", - "PyYAML>=3.12", - "scipy>=1.2.0", - "typing-extensions>=3.7.4", - "tqdm>=4", - "cvxpy", - "parameterized", -] - - -author = "Matthew Jones, Alex Khodaverdian, Richard Zhang, Sebastian Prillo, Joseph Min" - -cmdclass = {"build_ext": build_ext} - -# files to wrap with cython -to_cythonize = [ - Extension( - "cassiopeia.preprocess.collapse_cython", - ["cassiopeia/preprocess/collapse_cython.pyx"], - ), - Extension( - "cassiopeia.solver.ilp_solver_utilities", - ["cassiopeia/solver/ilp_solver_utilities.pyx"], - include_dirs=[numpy.get_include()], - ), -] - -extension_modules_with_custom_compile_args = [ - Extension( - "cassiopeia.tools.branch_length_estimator._iid_exponential_bayesian", - sources=[ - "cassiopeia/tools/branch_length_estimator/_iid_exponential_bayesian.pyx", - "cassiopeia/tools/branch_length_estimator/_iid_exponential_bayesian_cpp.cpp", - ], - extra_compile_args=[ - "-std=c++17", - "-Wall", - "-Wextra", - "-pedantic", - "-O3", - ], - language="c++", - ), -] - -setup( - name="cassiopeia-lineage", - python_requires=">=3.6", - ext_modules=cythonize( - to_cythonize + extension_modules_with_custom_compile_args, - compiler_directives={"language_level": "3"} - ), - # ext_modules=to_cythonize, - setup_requires=["cython", "numpy"], - cmdclass=cmdclass, - entry_points={ - "console_scripts": [ - "cassiopeia-preprocess = cassiopeia.preprocess.cassiopeia_preprocess:main" - ] - }, - author_email="[email protected]", - classifiers=[ - "Development Status :: 4 - Beta", - "Intended Audience :: Science/Research", - "License :: OSI Approved :: MIT License", - "Programming Language :: Python :: 3", - "Operating System :: MacOS :: MacOS X", - "Operating System :: Microsoft :: Windows", - "Operating System :: POSIX :: Linux", - "Topic :: Scientific/Engineering :: Bio-Informatics", - ], - long_description=readme + "\n\n", - description="Single Cell Lineage Reconstruction with Cas9-Enabled Lineage Recorders", - install_requires=requirements, - license="MIT license", - include_package_data=True, - packages=find_packages(), - keywords="scLT", - url="https://github.com/YosefLab/Cassiopeia", - version="2.0.0", - zip_safe=False, - test_suite="nose.collector", - tests_require=["nose"], -) +if __name__ == "__main__": + setuptools.setup(name="cassiopeia")

Installation instructions in readme https://github.com/YosefLab/Cassiopeia/blob/4a4ba1ac5bac1158f8ebc652ab7850c05ff95d59/README.md?plain=1#L34 This isn't required and you can't install python from pip?!

2022-02-03T18:49:43

0.0

[]

YosefLab/Cassiopeia

YosefLab__Cassiopeia-154

d13d3e72acbeabd807af01356acb7a9783346aca

diff --git a/cassiopeia/data/__init__.py b/cassiopeia/data/__init__.py index c48b9e2b..026cfd77 100644 --- a/cassiopeia/data/__init__.py +++ b/cassiopeia/data/__init__.py @@ -3,8 +3,10 @@ from .CassiopeiaTree import CassiopeiaTree from .utilities import ( compute_dissimilarity_map, + compute_inter_cluster_distances, compute_phylogenetic_weight_matrix, get_lca_characters, + net_relatedness_index, sample_bootstrap_allele_tables, sample_bootstrap_character_matrices, to_newick, diff --git a/cassiopeia/data/utilities.py b/cassiopeia/data/utilities.py index eaa06562..77e4c127 100755 --- a/cassiopeia/data/utilities.py +++ b/cassiopeia/data/utilities.py @@ -14,6 +14,7 @@ from cassiopeia.data import CassiopeiaTree from cassiopeia.mixins import CassiopeiaTreeWarning, is_ambiguous_state +from cassiopeia.mixins.errors import CassiopeiaError from cassiopeia.preprocess import utilities as preprocessing_utilities @@ -412,3 +413,97 @@ def compute_phylogenetic_weight_matrix( np.fill_diagonal(W.values, 0) return W + [email protected](nopython=True) +def net_relatedness_index( + dissimilarity_map: np.array, indices_1: np.array, indices_2: np.array +) -> float: + """Computes the net relatedness index between indices. + + Using the dissimilarity map specified and the indices of samples, compute + the net relatedness index, defined as: + + sum(distances over i,j in indices_1,indices_2) / (|indices_1| x |indices_2|) + + Args: + dissimilarity_map: Dissimilarity map between all samples. + indices_1: Indices corresponding to the first group. + indices_2: Indices corresponding to the second group. + + Returns: + The Net Relatedness Index (NRI) + """ + + nri = 0 + for i in indices_1: + for j in indices_2: + nri += dissimilarity_map[i, j] + + return nri / (len(indices_1) * len(indices_2)) + +def compute_inter_cluster_distances( + tree: CassiopeiaTree, + meta_item: Optional[str] = None, + meta_data: Optional[pd.DataFrame] = None, + dissimilarity_map: Optional[pd.DataFrame] = None, + distance_function: Callable = net_relatedness_index, + **kwargs, +) -> pd.DataFrame: + """Computes mean distance between clusters. + + Compute the mean distance between categories in a categorical variable. By + default, the phylogenetic weight matrix will be computed and used for this + distance calculation, but a user can optionally provide a dissimilarity + map instead. + + This function performs the computation in O(K^2)*O(distance_function) time + for a variable with K categories. + + Args: + tree: CassiopeiaTree + meta_item: Column in the cell meta data of the tree. If `meta_data` is + specified, this is ignored. + meta_data: Meta data to use for this calculation. This argument takes + priority over meta_item. + dissimilarity_map: Dissimilarity map to use for distances. If this is + specified, the phylogenetic weight matrix is not computed. + number_of_neighbors: Number of nearest neighbors to use for computing + the mean distances. If this is not specified, then all cells are + used. + **kwargs: Arguments to pass to the distance function. + + Returns: + A K x K distance matrix. + """ + meta_data = tree.cell_meta[meta_item] if (meta_data is None) else meta_data + + # ensure that the meta data is categorical + if not pd.api.types.is_string_dtype(meta_data): + raise CassiopeiaError("Meta data must be categorical or a string.") + + D = ( + compute_phylogenetic_weight_matrix(tree) + if (dissimilarity_map is None) + else dissimilarity_map + ) + + unique_states = meta_data.unique() + K = len(unique_states) + inter_cluster_distances = pd.DataFrame( + np.zeros((K, K)), index=unique_states, columns=unique_states + ) + + # align distance matrix and meta_data + D = D.loc[meta_data.index.values, meta_data.index.values] + + for state1 in unique_states: + indices_1 = np.where(np.array(meta_data) == state1)[0] + for state2 in unique_states: + indices_2 = np.where(np.array(meta_data) == state2)[0] + + distance = distance_function( + D.values, indices_1, indices_2, **kwargs + ) + inter_cluster_distances.loc[state1, state2] = distance + + return inter_cluster_distances diff --git a/cassiopeia/tools/__init__.py b/cassiopeia/tools/__init__.py index 792a38ce..de98134f 100644 --- a/cassiopeia/tools/__init__.py +++ b/cassiopeia/tools/__init__.py @@ -2,5 +2,6 @@ from .autocorrelation import compute_morans_i from .branch_length_estimator import IIDExponentialBayesian, IIDExponentialMLE +from .coupling import compute_evolutionary_coupling from .small_parsimony import fitch_count, fitch_hartigan, score_small_parsimony from .topology import compute_cophenetic_correlation, compute_expansion_pvalues diff --git a/cassiopeia/tools/coupling.py b/cassiopeia/tools/coupling.py new file mode 100644 index 00000000..2ba982c7 --- /dev/null +++ b/cassiopeia/tools/coupling.py @@ -0,0 +1,125 @@ +""" +File storing functionality for computing coupling statistics between meta +variables on a tree. +""" +from typing import Callable, Optional + +from collections import defaultdict +import numpy as np +import pandas as pd +from tqdm import tqdm + +from cassiopeia.data import CassiopeiaTree +from cassiopeia.data import utilities as data_utilities + + +def compute_evolutionary_coupling( + tree: CassiopeiaTree, + meta_variable: str, + minimum_proportion: float = 0.05, + number_of_shuffles: int = 500, + random_state: Optional[np.random.RandomState] = None, + dissimilarity_map: Optional[pd.DataFrame] = None, + cluster_comparison_function: Callable = data_utilities.net_relatedness_index, + **comparison_kwargs, +) -> pd.DataFrame: + """Computes Evolutionary Coupling of categorical variables. + + Using the methodology described in Yang, Jones et al, BioRxiv (2021), this + function will compute the "evolutionary coupling" statistic between values + that a categorical variable can take on with the tree. For example, this + categorical variable can be a "cell type", and this function will compute + the evolutionary couplings between all types of cell types. This indicates + how closely related these cell types are to one another. + + Briefly, this statistic is the Z-normalized mean distance between categories + in the specified categorical variable. Note that empirical nulls that have a + standard deviation of 0 lead to NaNs in the resulting evolutionary coupling + matrix. + + The computational complexity of this function is + O(n^2 log n + (B+1)(K^2 * O(distance_function)) for a tree with n leaves, a + variable with K categories, and B random shuffles. + + Args: + tree: CassiopeiaTree + meta_variable: Column in `tree.cell_meta` that stores a categorical + variable with K categories. + minimum_proportion: Minimum proportion of cells that a category needs + to appear in to be considered. + number_of_shuffles: Number of times to shuffle the data to compute the + empirical Z score. + random_state: Numpy random state to parameterize the shuffling. + dissimilarity_map: A precomputed dissimilarity map between all leaves. + cluster_comparison_function: A function for comparing the mean distance + between groups. By default, this is the Net Relatedness Index. + **comparison_kwargs: Extra arguments to pass to the cluster comparison + function. + + Returns: + A K x K evolutionary coupling dataframe. + """ + + W = ( + data_utilities.compute_phylogenetic_weight_matrix(tree) + if (dissimilarity_map is None) + else dissimilarity_map + ) + + meta_data = tree.cell_meta[meta_variable] + + # subset meta data by minimum proportion + if minimum_proportion > 0: + filter_threshold = int(len(tree.leaves) * minimum_proportion) + category_frequencies = meta_data.value_counts() + passing_categories = category_frequencies[ + category_frequencies > filter_threshold + ].index.values + meta_data = meta_data[meta_data.isin(passing_categories)] + W = W.loc[meta_data.index.values, meta_data.index.values] + + # compute inter-cluster distances + inter_cluster_distances = data_utilities.compute_inter_cluster_distances( + tree, + meta_data=meta_data, + dissimilarity_map=W, + distance_function=cluster_comparison_function, + **comparison_kwargs, + ) + + # compute background for Z-scoring + background = defaultdict(list) + for _ in tqdm( + range(number_of_shuffles), desc="Creating empirical background" + ): + permuted_assignments = meta_data.copy() + if random_state: + permuted_assignments.index = random_state.permutation( + meta_data.index.values + ) + else: + permuted_assignments.index = np.random.permutation( + meta_data.index.values + ) + background_distances = data_utilities.compute_inter_cluster_distances( + tree, + meta_data=permuted_assignments, + dissimilarity_map=W, + distance_function=cluster_comparison_function, + **comparison_kwargs, + ) + for s1 in background_distances.index: + for s2 in background_distances.columns: + background[(s1, s2)].append(background_distances.loc[s1, s2]) + + Z_scores = inter_cluster_distances.copy() + for s1 in Z_scores.index: + for s2 in Z_scores.columns: + mean = np.mean(background[(s1, s2)]) + sd = np.std(background[(s1, s2)]) + + Z_scores.loc[s1, s2] = ( + inter_cluster_distances.loc[s1, s2] - mean + ) / sd + + return Z_scores diff --git a/cassiopeia/tools/topology.py b/cassiopeia/tools/topology.py index 39f5fc9f..33af2250 100644 --- a/cassiopeia/tools/topology.py +++ b/cassiopeia/tools/topology.py @@ -9,7 +9,6 @@ import pandas as pd from scipy import spatial, stats - from cassiopeia.data import CassiopeiaTree, compute_phylogenetic_weight_matrix from cassiopeia.mixins import CassiopeiaError from cassiopeia.solver import dissimilarity_functions diff --git a/docs/api/plotting.rst b/docs/api/plotting.rst index c738dba3..bc065672 100644 --- a/docs/api/plotting.rst +++ b/docs/api/plotting.rst @@ -13,6 +13,4 @@ Currently, our plotting functionality is linked to the rich iTOL framework: .. autosummary:: :toctree: reference/ - pl.upload_and_export_itol - - \ No newline at end of file + pl.upload_and_export_itol \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.data.CassiopeiaTree.rst b/docs/api/reference/cassiopeia.data.CassiopeiaTree.rst deleted file mode 100644 index d0d90838..00000000 --- a/docs/api/reference/cassiopeia.data.CassiopeiaTree.rst +++ /dev/null @@ -1,13 +0,0 @@ -cassiopeia.data.CassiopeiaTree -============================== - -.. currentmodule:: cassiopeia.data - -.. autoclass:: CassiopeiaTree - :members: - :undoc-members: - - .. rubric:: Methods - - .. autoautosummary:: CassiopeiaTree - :methods: \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.data.sample_bootstrap_allele_tables.rst b/docs/api/reference/cassiopeia.data.sample_bootstrap_allele_tables.rst deleted file mode 100644 index 1639e437..00000000 --- a/docs/api/reference/cassiopeia.data.sample_bootstrap_allele_tables.rst +++ /dev/null @@ -1,6 +0,0 @@ -cassiopeia.data.sample\_bootstrap\_allele\_tables -================================================= - -.. currentmodule:: cassiopeia.data - -.. autofunction:: sample_bootstrap_allele_tables \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.data.sample_bootstrap_character_matrices.rst b/docs/api/reference/cassiopeia.data.sample_bootstrap_character_matrices.rst deleted file mode 100644 index 45ba28e1..00000000 --- a/docs/api/reference/cassiopeia.data.sample_bootstrap_character_matrices.rst +++ /dev/null @@ -1,6 +0,0 @@ -cassiopeia.data.sample\_bootstrap\_character\_matrices -====================================================== - -.. currentmodule:: cassiopeia.data - -.. autofunction:: sample_bootstrap_character_matrices \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.data.to_newick.rst b/docs/api/reference/cassiopeia.data.to_newick.rst deleted file mode 100644 index 6b0ff7e3..00000000 --- a/docs/api/reference/cassiopeia.data.to_newick.rst +++ /dev/null @@ -1,6 +0,0 @@ -cassiopeia.data.to\_newick -========================== - -.. currentmodule:: cassiopeia.data - -.. autofunction:: to_newick \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.pl.upload_and_export_itol.rst b/docs/api/reference/cassiopeia.pl.upload_and_export_itol.rst deleted file mode 100644 index a8e70af8..00000000 --- a/docs/api/reference/cassiopeia.pl.upload_and_export_itol.rst +++ /dev/null @@ -1,6 +0,0 @@ -cassiopeia.pl.upload\_and\_export\_itol -======================================= - -.. currentmodule:: cassiopeia.pl - -.. autofunction:: upload_and_export_itol \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.pp.align_sequences.rst b/docs/api/reference/cassiopeia.pp.align_sequences.rst deleted file mode 100644 index 11927b42..00000000 --- a/docs/api/reference/cassiopeia.pp.align_sequences.rst +++ /dev/null @@ -1,6 +0,0 @@ -cassiopeia.pp.align\_sequences -============================== - -.. currentmodule:: cassiopeia.pp - -.. autofunction:: align_sequences \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.pp.call_alleles.rst b/docs/api/reference/cassiopeia.pp.call_alleles.rst deleted file mode 100644 index 9bea85ee..00000000 --- a/docs/api/reference/cassiopeia.pp.call_alleles.rst +++ /dev/null @@ -1,6 +0,0 @@ -cassiopeia.pp.call\_alleles -=========================== - -.. currentmodule:: cassiopeia.pp - -.. autofunction:: call_alleles \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.pp.call_lineage_groups.rst b/docs/api/reference/cassiopeia.pp.call_lineage_groups.rst deleted file mode 100644 index 8df5914d..00000000 --- a/docs/api/reference/cassiopeia.pp.call_lineage_groups.rst +++ /dev/null @@ -1,6 +0,0 @@ -cassiopeia.pp.call\_lineage\_groups -=================================== - -.. currentmodule:: cassiopeia.pp - -.. autofunction:: call_lineage_groups \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.pp.collapse_umis.rst b/docs/api/reference/cassiopeia.pp.collapse_umis.rst deleted file mode 100644 index 020d2ee7..00000000 --- a/docs/api/reference/cassiopeia.pp.collapse_umis.rst +++ /dev/null @@ -1,6 +0,0 @@ -cassiopeia.pp.collapse\_umis -============================ - -.. currentmodule:: cassiopeia.pp - -.. autofunction:: collapse_umis \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.pp.compute_empirical_indel_priors.rst b/docs/api/reference/cassiopeia.pp.compute_empirical_indel_priors.rst deleted file mode 100644 index 1b0f4e5c..00000000 --- a/docs/api/reference/cassiopeia.pp.compute_empirical_indel_priors.rst +++ /dev/null @@ -1,6 +0,0 @@ -cassiopeia.pp.compute\_empirical\_indel\_priors -=============================================== - -.. currentmodule:: cassiopeia.pp - -.. autofunction:: compute_empirical_indel_priors \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.pp.convert_alleletable_to_character_matrix.rst b/docs/api/reference/cassiopeia.pp.convert_alleletable_to_character_matrix.rst deleted file mode 100644 index 5e11c83b..00000000 --- a/docs/api/reference/cassiopeia.pp.convert_alleletable_to_character_matrix.rst +++ /dev/null @@ -1,6 +0,0 @@ -cassiopeia.pp.convert\_alleletable\_to\_character\_matrix -========================================================= - -.. currentmodule:: cassiopeia.pp - -.. autofunction:: convert_alleletable_to_character_matrix \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.pp.convert_alleletable_to_lineage_profile.rst b/docs/api/reference/cassiopeia.pp.convert_alleletable_to_lineage_profile.rst deleted file mode 100644 index 370d0832..00000000 --- a/docs/api/reference/cassiopeia.pp.convert_alleletable_to_lineage_profile.rst +++ /dev/null @@ -1,6 +0,0 @@ -cassiopeia.pp.convert\_alleletable\_to\_lineage\_profile -======================================================== - -.. currentmodule:: cassiopeia.pp - -.. autofunction:: convert_alleletable_to_lineage_profile \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.pp.convert_lineage_profile_to_character_matrix.rst b/docs/api/reference/cassiopeia.pp.convert_lineage_profile_to_character_matrix.rst deleted file mode 100644 index 893a4f42..00000000 --- a/docs/api/reference/cassiopeia.pp.convert_lineage_profile_to_character_matrix.rst +++ /dev/null @@ -1,6 +0,0 @@ -cassiopeia.pp.convert\_lineage\_profile\_to\_character\_matrix -============================================================== - -.. currentmodule:: cassiopeia.pp - -.. autofunction:: convert_lineage_profile_to_character_matrix \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.pp.error_correct_umis.rst b/docs/api/reference/cassiopeia.pp.error_correct_umis.rst deleted file mode 100644 index 95101d0d..00000000 --- a/docs/api/reference/cassiopeia.pp.error_correct_umis.rst +++ /dev/null @@ -1,6 +0,0 @@ -cassiopeia.pp.error\_correct\_umis -================================== - -.. currentmodule:: cassiopeia.pp - -.. autofunction:: error_correct_umis \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.pp.filter_cells.rst b/docs/api/reference/cassiopeia.pp.filter_cells.rst deleted file mode 100644 index db1c5704..00000000 --- a/docs/api/reference/cassiopeia.pp.filter_cells.rst +++ /dev/null @@ -1,6 +0,0 @@ -cassiopeia.pp.filter\_cells -=========================== - -.. currentmodule:: cassiopeia.pp - -.. autofunction:: filter_cells \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.pp.filter_molecule_table.rst b/docs/api/reference/cassiopeia.pp.filter_molecule_table.rst deleted file mode 100644 index d062d345..00000000 --- a/docs/api/reference/cassiopeia.pp.filter_molecule_table.rst +++ /dev/null @@ -1,6 +0,0 @@ -cassiopeia.pp.filter\_molecule\_table -===================================== - -.. currentmodule:: cassiopeia.pp - -.. autofunction:: filter_molecule_table \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.pp.filter_umis.rst b/docs/api/reference/cassiopeia.pp.filter_umis.rst deleted file mode 100644 index 962eb79c..00000000 --- a/docs/api/reference/cassiopeia.pp.filter_umis.rst +++ /dev/null @@ -1,6 +0,0 @@ -cassiopeia.pp.filter\_umis -========================== - -.. currentmodule:: cassiopeia.pp - -.. autofunction:: filter_umis \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.pp.resolve_umi_sequence.rst b/docs/api/reference/cassiopeia.pp.resolve_umi_sequence.rst deleted file mode 100644 index a1a1ddc6..00000000 --- a/docs/api/reference/cassiopeia.pp.resolve_umi_sequence.rst +++ /dev/null @@ -1,6 +0,0 @@ -cassiopeia.pp.resolve\_umi\_sequence -==================================== - -.. currentmodule:: cassiopeia.pp - -.. autofunction:: resolve_umi_sequence \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.solver.HybridSolver.rst b/docs/api/reference/cassiopeia.solver.HybridSolver.rst deleted file mode 100644 index 9646d98f..00000000 --- a/docs/api/reference/cassiopeia.solver.HybridSolver.rst +++ /dev/null @@ -1,13 +0,0 @@ -cassiopeia.solver.HybridSolver -============================== - -.. currentmodule:: cassiopeia.solver - -.. autoclass:: HybridSolver - :members: - :undoc-members: - - .. rubric:: Methods - - .. autoautosummary:: HybridSolver - :methods: \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.solver.ILPSolver.rst b/docs/api/reference/cassiopeia.solver.ILPSolver.rst deleted file mode 100644 index eebcd985..00000000 --- a/docs/api/reference/cassiopeia.solver.ILPSolver.rst +++ /dev/null @@ -1,13 +0,0 @@ -cassiopeia.solver.ILPSolver -=========================== - -.. currentmodule:: cassiopeia.solver - -.. autoclass:: ILPSolver - :members: - :undoc-members: - - .. rubric:: Methods - - .. autoautosummary:: ILPSolver - :methods: \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.solver.MaxCutGreedySolver.rst b/docs/api/reference/cassiopeia.solver.MaxCutGreedySolver.rst deleted file mode 100644 index 726bb6a1..00000000 --- a/docs/api/reference/cassiopeia.solver.MaxCutGreedySolver.rst +++ /dev/null @@ -1,13 +0,0 @@ -cassiopeia.solver.MaxCutGreedySolver -==================================== - -.. currentmodule:: cassiopeia.solver - -.. autoclass:: MaxCutGreedySolver - :members: - :undoc-members: - - .. rubric:: Methods - - .. autoautosummary:: MaxCutGreedySolver - :methods: \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.solver.MaxCutSolver.rst b/docs/api/reference/cassiopeia.solver.MaxCutSolver.rst deleted file mode 100644 index 218c45fe..00000000 --- a/docs/api/reference/cassiopeia.solver.MaxCutSolver.rst +++ /dev/null @@ -1,13 +0,0 @@ -cassiopeia.solver.MaxCutSolver -============================== - -.. currentmodule:: cassiopeia.solver - -.. autoclass:: MaxCutSolver - :members: - :undoc-members: - - .. rubric:: Methods - - .. autoautosummary:: MaxCutSolver - :methods: \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.solver.NeighborJoiningSolver.rst b/docs/api/reference/cassiopeia.solver.NeighborJoiningSolver.rst deleted file mode 100644 index 17c83adf..00000000 --- a/docs/api/reference/cassiopeia.solver.NeighborJoiningSolver.rst +++ /dev/null @@ -1,13 +0,0 @@ -cassiopeia.solver.NeighborJoiningSolver -======================================= - -.. currentmodule:: cassiopeia.solver - -.. autoclass:: NeighborJoiningSolver - :members: - :undoc-members: - - .. rubric:: Methods - - .. autoautosummary:: NeighborJoiningSolver - :methods: \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.solver.PercolationSolver.rst b/docs/api/reference/cassiopeia.solver.PercolationSolver.rst deleted file mode 100644 index 8ddcc3c7..00000000 --- a/docs/api/reference/cassiopeia.solver.PercolationSolver.rst +++ /dev/null @@ -1,13 +0,0 @@ -cassiopeia.solver.PercolationSolver -=================================== - -.. currentmodule:: cassiopeia.solver - -.. autoclass:: PercolationSolver - :members: - :undoc-members: - - .. rubric:: Methods - - .. autoautosummary:: PercolationSolver - :methods: \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.solver.SharedMutationJoiningSolver.rst b/docs/api/reference/cassiopeia.solver.SharedMutationJoiningSolver.rst deleted file mode 100644 index 2b8081c3..00000000 --- a/docs/api/reference/cassiopeia.solver.SharedMutationJoiningSolver.rst +++ /dev/null @@ -1,13 +0,0 @@ -cassiopeia.solver.SharedMutationJoiningSolver -============================================= - -.. currentmodule:: cassiopeia.solver - -.. autoclass:: SharedMutationJoiningSolver - :members: - :undoc-members: - - .. rubric:: Methods - - .. autoautosummary:: SharedMutationJoiningSolver - :methods: \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.solver.SpectralGreedySolver.rst b/docs/api/reference/cassiopeia.solver.SpectralGreedySolver.rst deleted file mode 100644 index f9f4cfb6..00000000 --- a/docs/api/reference/cassiopeia.solver.SpectralGreedySolver.rst +++ /dev/null @@ -1,13 +0,0 @@ -cassiopeia.solver.SpectralGreedySolver -====================================== - -.. currentmodule:: cassiopeia.solver - -.. autoclass:: SpectralGreedySolver - :members: - :undoc-members: - - .. rubric:: Methods - - .. autoautosummary:: SpectralGreedySolver - :methods: \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.solver.SpectralSolver.rst b/docs/api/reference/cassiopeia.solver.SpectralSolver.rst deleted file mode 100644 index 7d152bf9..00000000 --- a/docs/api/reference/cassiopeia.solver.SpectralSolver.rst +++ /dev/null @@ -1,13 +0,0 @@ -cassiopeia.solver.SpectralSolver -================================ - -.. currentmodule:: cassiopeia.solver - -.. autoclass:: SpectralSolver - :members: - :undoc-members: - - .. rubric:: Methods - - .. autoautosummary:: SpectralSolver - :methods: \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.solver.UPGMASolver.rst b/docs/api/reference/cassiopeia.solver.UPGMASolver.rst deleted file mode 100644 index 691ac333..00000000 --- a/docs/api/reference/cassiopeia.solver.UPGMASolver.rst +++ /dev/null @@ -1,13 +0,0 @@ -cassiopeia.solver.UPGMASolver -============================= - -.. currentmodule:: cassiopeia.solver - -.. autoclass:: UPGMASolver - :members: - :undoc-members: - - .. rubric:: Methods - - .. autoautosummary:: UPGMASolver - :methods: \ No newline at end of file diff --git a/docs/api/reference/cassiopeia.solver.VanillaGreedySolver.rst b/docs/api/reference/cassiopeia.solver.VanillaGreedySolver.rst deleted file mode 100644 index 7e761f6f..00000000 --- a/docs/api/reference/cassiopeia.solver.VanillaGreedySolver.rst +++ /dev/null @@ -1,13 +0,0 @@ -cassiopeia.solver.VanillaGreedySolver -===================================== - -.. currentmodule:: cassiopeia.solver - -.. autoclass:: VanillaGreedySolver - :members: - :undoc-members: - - .. rubric:: Methods - - .. autoautosummary:: VanillaGreedySolver - :methods: \ No newline at end of file diff --git a/docs/api/tools.rst b/docs/api/tools.rst index 11f6b212..0ded5a5e 100644 --- a/docs/api/tools.rst +++ b/docs/api/tools.rst @@ -14,10 +14,39 @@ Small-Parsimony .. autosummary:: :toctree: reference/ - - tl.compute_cophenetic_correlation - tl.compute_expansion_pvalues - tl.compute_morans_i + tl.fitch_count tl.fitch_hartigan tl.score_small_parsimony + +Branch Length Estimation (BLE) +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autosummary:: + :toctree: reference/ + + tl.IIDExponentialBayesian + tl.IIDExponentialMLE + +Autocorrelation +~~~~~~~~~~~~~~~~~~~ + +.. autosummary:: + :toctree: reference/ + + tl.compute_morans_i + +Coupling +~~~~~~~~~~~ + +.. autosummary:: + :toctree: reference/ + + tl.compute_evolutionary_coupling + +Topology +~~~~~~~~~~~~~~~~~~~ +.. autosummary:: + :toctree: reference/ + + tl.compute_expansion_pvalues \ No newline at end of file

Implement Utilities from KP-Tracer MS Implement utilities from recent [KP-Tracer manuscript](https://www.biorxiv.org/content/10.1101/2021.10.12.464111v1) into the Cassiopeia codebase. Specifically: - Expansion index - Phylogenetic coupling - Phylotime

2021-10-27T00:04:06

0.0

[]

YosefLab/Cassiopeia

YosefLab__Cassiopeia-153

3002617a00e1281ef073a6f17b6dd55c3337bdad

diff --git a/cassiopeia/data/utilities.py b/cassiopeia/data/utilities.py index ba503d94..eaa06562 100755 --- a/cassiopeia/data/utilities.py +++ b/cassiopeia/data/utilities.py @@ -163,8 +163,7 @@ def compute_dissimilarity_map( # in a partial, which raises a TypeError when trying to numbaize. except TypeError: warnings.warn( - "Failed to numbaize dissimilarity function. " - "Falling back to Python.", + "Failed to numbaize dissimilarity function. Falling back to Python.", CassiopeiaTreeWarning, ) numbaize = False @@ -309,10 +308,8 @@ def sample_bootstrap_allele_tables( allele_table ) - lineage_profile = ( - preprocessing_utilities.convert_alleletable_to_lineage_profile( - allele_table, cut_sites - ) + lineage_profile = preprocessing_utilities.convert_alleletable_to_lineage_profile( + allele_table, cut_sites ) intbcs = allele_table["intBC"].unique() @@ -375,6 +372,7 @@ def resolve_most_abundant(state: Tuple[int, ...]) -> int: [state for state, count in most_common if count == most_common[0][1]] ) + def compute_phylogenetic_weight_matrix( tree: CassiopeiaTree, inverse: bool = False, @@ -399,10 +397,10 @@ def compute_phylogenetic_weight_matrix( An NxN phylogenetic weight matrix """ N = tree.n_cell - W = pd.DataFrame(np.zeros((N , N)), index=tree.leaves, columns=tree.leaves) + W = pd.DataFrame(np.zeros((N, N)), index=tree.leaves, columns=tree.leaves) for leaf1 in tree.leaves: - + distances = tree.get_distances(leaf1, leaves_only=True) for leaf2, _d in distances.items(): @@ -412,5 +410,5 @@ def compute_phylogenetic_weight_matrix( W.loc[leaf1, leaf2] = W.loc[leaf2, leaf1] = _d np.fill_diagonal(W.values, 0) - - return W \ No newline at end of file + + return W diff --git a/cassiopeia/tools/__init__.py b/cassiopeia/tools/__init__.py index 4c39eab6..792a38ce 100644 --- a/cassiopeia/tools/__init__.py +++ b/cassiopeia/tools/__init__.py @@ -3,4 +3,4 @@ from .autocorrelation import compute_morans_i from .branch_length_estimator import IIDExponentialBayesian, IIDExponentialMLE from .small_parsimony import fitch_count, fitch_hartigan, score_small_parsimony -from .topology import compute_expansion_pvalues \ No newline at end of file +from .topology import compute_cophenetic_correlation, compute_expansion_pvalues diff --git a/cassiopeia/tools/topology.py b/cassiopeia/tools/topology.py index 0549559e..39f5fc9f 100644 --- a/cassiopeia/tools/topology.py +++ b/cassiopeia/tools/topology.py @@ -2,14 +2,17 @@ Utilities to assess topological properties of a phylogeny, such as balance and expansion. """ -from typing import Union +from typing import Callable, Dict, Optional, Tuple, Union import math import numpy as np import pandas as pd +from scipy import spatial, stats -from cassiopeia.data import CassiopeiaTree + +from cassiopeia.data import CassiopeiaTree, compute_phylogenetic_weight_matrix from cassiopeia.mixins import CassiopeiaError +from cassiopeia.solver import dissimilarity_functions def compute_expansion_pvalues( @@ -55,14 +58,14 @@ def compute_expansion_pvalues( tree = tree.copy() if copy else tree # instantiate attributes - _depths = {} + _depths = {} for node in tree.depth_first_traverse_nodes(postorder=False): tree.set_attribute(node, "expansion_pvalue", 1.0) if tree.is_root(node): _depths[node] = 0 else: - _depths[node] = (_depths[tree.parent(node)] + 1) + _depths[node] = _depths[tree.parent(node)] + 1 for node in tree.depth_first_traverse_nodes(postorder=False): @@ -73,7 +76,7 @@ def compute_expansion_pvalues( if len(tree.leaves_in_subtree(c)) < min_clade_size: continue - + depth = _depths[c] if depth < min_depth: continue @@ -83,13 +86,79 @@ def compute_expansion_pvalues( # this value below is a simplification of the quantity: # sum[simple_coalescent_probability(n, b2, k) for \ # b2 in range(b, n - k + 2)] - p = nCk(n-b, k-1) / nCk(n-1, k-1) + p = nCk(n - b, k - 1) / nCk(n - 1, k - 1) tree.set_attribute(c, "expansion_pvalue", p) return tree if copy else None +def compute_cophenetic_correlation( + tree: CassiopeiaTree, + weights: Optional[pd.DataFrame] = None, + dissimilarity_map: Optional[pd.DataFrame] = None, + dissimilarity_function: Optional[ + Callable[[np.array, np.array, int, Dict[int, Dict[int, float]]], float] + ] = dissimilarity_functions.weighted_hamming_distance, +) -> Tuple[float, float]: + """Computes the cophenetic correlation of a lineage. + + Computes the cophenetic correlation of a lineage, which is defined as the + Pearson correlation between the phylogenetic distance and dissimilarity + between characters. + + If neither weight matrix nor the dissimilarity map are precomputed, then + this function will run in O(mn^2 + n^2logn + n^2) time, as the dissimilarity + map will take O(mn^2) time, the phylogenetic distance will take O(n^2 logn) + time, and the Pearson correlation will take O(n^2) time since it must + compare n^2 entries (n = number of leaves; m = number of characters). + + Args: + tree: CassiopeiaTree + weights: Phylogenetic weights matrix. If this is not specified, invokes + `cas.data.compute_phylogenetic_weight_matrix` + dissimilarity_map: Dissimilarity matrix between samples. If this is not + specified, then `tree.compute_dissimilarity_map` will be called. + dissimilarity_function: Dissimilarity function to use. If dissimilarity + map is not passed in, and one does not already exist in the + CassiopeiaTree, then this function will be used to compute the + dissimilarities between samples. + + Returns: + The cophenetic correlation value and significance for the tree. + """ + + # set phylogenetic weight matrix + W = ( + compute_phylogenetic_weight_matrix(tree) + if (weights is None) + else weights + ) + + # set dissimilarity map + D = ( + tree.get_dissimilarity_map() + if (dissimilarity_map is None) + else dissimilarity_map + ) + if D is None: + tree.compute_dissimilarity_map( + dissimilarity_function=dissimilarity_function + ) + D = tree.get_dissimilarity_map() + + # align matrices + cells = tree.leaves + W = W.loc[cells, cells] + D = D.loc[cells, cells] + + # convert to condensed distance matrices + Wp = spatial.distance.squareform(W) + Dp = spatial.distance.squareform(D) + + return stats.pearsonr(Wp, Dp) + + def simple_coalescent_probability(n: int, b: int, k: int) -> float: """Simple coalescent probability of imbalance. diff --git a/docs/api/tools.rst b/docs/api/tools.rst index 7352008d..11f6b212 100644 --- a/docs/api/tools.rst +++ b/docs/api/tools.rst @@ -15,6 +15,7 @@ Small-Parsimony .. autosummary:: :toctree: reference/ + tl.compute_cophenetic_correlation tl.compute_expansion_pvalues tl.compute_morans_i tl.fitch_count

Cophenetic Distance Implement utility to compute cophenetic distance for a tree. The cophenetic distance is the correlation between phylogenetic distance and some dissimilarity map between samples' character information.

2021-10-26T18:21:08

0.0

[]

YosefLab/Cassiopeia

YosefLab__Cassiopeia-150

15dc005666b0c9da627145df07a30b162136e3a7

diff --git a/cassiopeia/tools/__init__.py b/cassiopeia/tools/__init__.py index e4549f00..4c39eab6 100644 --- a/cassiopeia/tools/__init__.py +++ b/cassiopeia/tools/__init__.py @@ -3,3 +3,4 @@ from .autocorrelation import compute_morans_i from .branch_length_estimator import IIDExponentialBayesian, IIDExponentialMLE from .small_parsimony import fitch_count, fitch_hartigan, score_small_parsimony +from .topology import compute_expansion_pvalues \ No newline at end of file diff --git a/cassiopeia/tools/topology.py b/cassiopeia/tools/topology.py new file mode 100644 index 00000000..0549559e --- /dev/null +++ b/cassiopeia/tools/topology.py @@ -0,0 +1,126 @@ +""" +Utilities to assess topological properties of a phylogeny, such as balance +and expansion. +""" +from typing import Union + +import math +import numpy as np +import pandas as pd + +from cassiopeia.data import CassiopeiaTree +from cassiopeia.mixins import CassiopeiaError + + +def compute_expansion_pvalues( + tree: CassiopeiaTree, + min_clade_size: int = 10, + min_depth: int = 1, + copy: bool = False, +) -> Union[CassiopeiaTree, None]: + """Call expansion pvalues on a tree. + + Uses the methodology described in Yang, Jones et al, BioRxiv (2021) to + assess the expansion probability of a given subclade of a phylogeny. + Mathematical treatment of the coalescent probability is described in + Griffiths and Tavare, Stochastic Models (1998). + + The probability computed corresponds to the probability that, under a simple + neutral coalescent model, a given subclade contains the observed number of + cells; in other words, a one-sided p-value. Often, if the probability is + less than some threshold (e.g., 0.05), this might indicate that there exists + some subclade under this node that to which this expansion probability can + be attributed (i.e. the null hypothesis that the subclade is undergoing + neutral drift can be rejected). + + This function will add an attribute "expansion_pvalue" to the tree, and + return None unless :param:`copy` is set to True. + + On a typical balanced tree, this function will perform in O(n log n) time, + but can be up to O(n^3) on highly unbalanced trees. A future endeavor may + be to impelement the function in O(n) time. + + Args: + tree: CassiopeiaTree + min_clade_size: Minimum number of leaves in a subtree to be considered. + min_depth: Minimum depth of clade to be considered. Depth is measured + in number of nodes from the root, not branch lengths. + copy: Return copy. + + Returns: + If copy is set to False, returns the tree with attributes added + in place. Else, returns a new CassiopeiaTree. + """ + + tree = tree.copy() if copy else tree + + # instantiate attributes + _depths = {} + for node in tree.depth_first_traverse_nodes(postorder=False): + tree.set_attribute(node, "expansion_pvalue", 1.0) + + if tree.is_root(node): + _depths[node] = 0 + else: + _depths[node] = (_depths[tree.parent(node)] + 1) + + for node in tree.depth_first_traverse_nodes(postorder=False): + + n = len(tree.leaves_in_subtree(node)) + + k = len(tree.children(node)) + for c in tree.children(node): + + if len(tree.leaves_in_subtree(c)) < min_clade_size: + continue + + depth = _depths[c] + if depth < min_depth: + continue + + b = len(tree.leaves_in_subtree(c)) + + # this value below is a simplification of the quantity: + # sum[simple_coalescent_probability(n, b2, k) for \ + # b2 in range(b, n - k + 2)] + p = nCk(n-b, k-1) / nCk(n-1, k-1) + + tree.set_attribute(c, "expansion_pvalue", p) + + return tree if copy else None + + +def simple_coalescent_probability(n: int, b: int, k: int) -> float: + """Simple coalescent probability of imbalance. + + Assuming a simple coalescent model, compute the probability that a given + lineage has exactly b samples, given there are n cells and k lineages + overall. + + Args: + n: Number of leaves in subtree + b: Number of leaves in one lineage + k: Number of lineages + Returns: + Probability of observing b leaves on one lineage in a tree of n total + leaves + """ + return nCk(n - b - 1, k - 2) / nCk(n - 1, k - 1) + + +def nCk(n: int, k: int) -> float: + """Compute the quantity n choose k. + + Args: + n: Number of items total. + k: Number of items to choose. + + Returns: + The number of ways to choose k items from n. + """ + + if k > n: + raise CassiopeiaError("Argument k cannot be larger than n.") + + f = math.factorial + return f(n) // f(k) // f(n - k) diff --git a/docs/api/tools.rst b/docs/api/tools.rst index 3347e481..7352008d 100644 --- a/docs/api/tools.rst +++ b/docs/api/tools.rst @@ -15,6 +15,7 @@ Small-Parsimony .. autosummary:: :toctree: reference/ + tl.compute_expansion_pvalues tl.compute_morans_i tl.fitch_count tl.fitch_hartigan

Implement Utilities from KP-Tracer MS Implement utilities from recent [KP-Tracer manuscript](https://www.biorxiv.org/content/10.1101/2021.10.12.464111v1) into the Cassiopeia codebase. Specifically: - Expansion index - Phylogenetic coupling - Phylotime

2021-10-25T22:05:36

0.0

[]

YosefLab/Cassiopeia

YosefLab__Cassiopeia-133

670dd12d949bf466f67c1117e5f6e912ae3c860d

diff --git a/cassiopeia/preprocess/lineage_utils.py b/cassiopeia/preprocess/lineage_utils.py index e5f9ffb4..b0739155 100755 --- a/cassiopeia/preprocess/lineage_utils.py +++ b/cassiopeia/preprocess/lineage_utils.py @@ -388,9 +388,11 @@ def filtered_lineage_group_to_allele_table( Returns: final_df: A final processed DataFrame with indel information """ - final_df = pd.concat(filtered_lgs, sort=True) + if "lineageGrp" not in final_df.columns: + final_df["lineageGrp"] = 1 + grouping = [] for i in final_df.columns: if bool(re.search(r"r\d", i)): @@ -401,10 +403,6 @@ def filtered_lineage_group_to_allele_table( {"UMI": "count", "readCount": "sum"} ) - final_df["Sample"] = final_df.apply( - lambda x: x.cellBC.split(".")[0], axis=1 - ) - return final_df diff --git a/cassiopeia/preprocess/pipeline.py b/cassiopeia/preprocess/pipeline.py index d6ce0798..45f9b7c8 100755 --- a/cassiopeia/preprocess/pipeline.py +++ b/cassiopeia/preprocess/pipeline.py @@ -3,6 +3,7 @@ data into character matrices ready for phylogenetic inference. This file is mainly invoked by cassiopeia_preprocess.py. """ +import warnings from functools import partial import os @@ -21,6 +22,7 @@ from typing_extensions import Literal from cassiopeia.mixins import logger, PreprocessError +from cassiopeia.mixins.warnings import PreprocessWarning from cassiopeia.preprocess import ( alignment_utilities, constants, @@ -487,8 +489,10 @@ def align_sequences( try: from skbio import alignment except ModuleNotFoundError: - raise PreprocessError("Scikit-bio is not installed. Try pip-installing " - " first and then re-running this function.") + raise PreprocessError( + "Scikit-bio is not installed. Try pip-installing " + " first and then re-running this function." + ) if (ref is None) == (ref_filepath is None): raise PreprocessError( @@ -635,6 +639,17 @@ def call_alleles( alignments.reset_index(inplace=True) + # check cut-sites and raise a warning if any missing data is detected + cutsites = utilities.get_default_cut_site_columns(alignments) + if np.any((alignments[cutsites] == "").sum(axis=0) > 0): + warnings.warn( + "Detected missing data in alleles. You might" + " consider re-running align_sequences with a" + " lower gap-open penalty, or using a separate" + " alignment strategy.", + PreprocessWarning, + ) + return alignments diff --git a/cassiopeia/preprocess/utilities.py b/cassiopeia/preprocess/utilities.py index ddf726da..39a50d76 100755 --- a/cassiopeia/preprocess/utilities.py +++ b/cassiopeia/preprocess/utilities.py @@ -384,6 +384,7 @@ def convert_alleletable_to_character_matrix( alleletable: pd.DataFrame, ignore_intbcs: List[str] = [], allele_rep_thresh: float = 1.0, + missing_data_allele: Optional[str] = None, missing_data_state: int = -1, mutation_priors: Optional[pd.DataFrame] = None, cut_sites: Optional[List[str]] = None, @@ -404,6 +405,9 @@ def convert_alleletable_to_character_matrix( ignore_intbcs: A set of intBCs to ignore allele_rep_thresh: A threshold for removing target sites that have an allele represented by this proportion + missing_data_allele: Value in the allele table that indicates that the + cut-site is missing. This will be converted into + ``missing_data_state`` missing_data_state: A state to use for missing data. mutation_priors: A table storing the prior probability of a mutation occurring. This table is used to create a character matrix-specific @@ -416,8 +420,8 @@ def convert_alleletable_to_character_matrix( effect if there are no allele conflicts. Defaults to True. Returns: - A character matrix, a probability dictionary, and a dictionary mapping - states to the original mutation. + A character matrix, a probability dictionary, and a dictionary mapping + states to the original mutation. """ if cut_sites is None: cut_sites = get_default_cut_site_columns(alleletable) @@ -489,6 +493,11 @@ def convert_alleletable_to_character_matrix( if state == "NONE" or "None" in state: transformed_states.append(0) + elif ( + missing_data_allele is not None + and state == missing_data_allele + ): + transformed_states.append(missing_data_state) else: if state in allele_counter[c]: transformed_states.append(allele_counter[c][state]) @@ -614,6 +623,7 @@ def convert_alleletable_to_lineage_profile( def convert_lineage_profile_to_character_matrix( lineage_profile: pd.DataFrame, indel_priors: Optional[pd.DataFrame] = None, + missing_allele_indicator: Optional[str] = None, missing_state_indicator: int = -1, ) -> Tuple[ pd.DataFrame, Dict[int, Dict[int, float]], Dict[int, Dict[int, str]] @@ -633,6 +643,8 @@ def convert_lineage_profile_to_character_matrix( Args: lineage_profile: Lineage profile indel_priors: Dataframe mapping indels to prior probabilities + missing_allele_indicator: An allele that is being used to represent + missing data. missing_state_indicator: State to indicate missing data Returns: @@ -643,7 +655,13 @@ def convert_lineage_profile_to_character_matrix( prior_probs = defaultdict(dict) indel_to_charstate = defaultdict(dict) + lineage_profile = lineage_profile.copy() + lineage_profile = lineage_profile.fillna("Missing").copy() + if missing_allele_indicator: + lineage_profile.replace( + {missing_allele_indicator: "Missing"}, inplace=True + ) samples = [] diff --git a/docs/api/tools.rst b/docs/api/tools.rst index 257618fc..c162ecf5 100644 --- a/docs/api/tools.rst +++ b/docs/api/tools.rst @@ -15,5 +15,6 @@ Small-Parsimony .. autosummary:: :toctree: reference/ + tl.fitch_count tl.fitch_hartigan tl.score_small_parsimony

Global Alignment in Cassiopeia Preprocessing We've found that the local alignment strategy used in our Cassiopeia preprocessing pipeline is not ideal for some technologies like GESTALT. We'd like to implement a global alignment option in `cas.pp.align_sequences` as was described in the original GESTALT paper ([McKenna et al, 2016](https://science.sciencemag.org/content/353/6298/aaf7907)).

2021-08-06T22:21:30

0.0

[]

VNG-Realisatie/vng-api-common

VNG-Realisatie__vng-api-common-187

484d1fce7fd300dd336edce8ac88817f1e0f45a5

diff --git a/vng_api_common/notifications/api/serializers.py b/vng_api_common/notifications/api/serializers.py index dd8a12ea..0772ed8d 100644 --- a/vng_api_common/notifications/api/serializers.py +++ b/vng_api_common/notifications/api/serializers.py @@ -50,6 +50,7 @@ class NotificatieSerializer(serializers.Serializer): label=_("kenmerk"), max_length=1000, help_text=_("Een waarde behorende bij de sleutel."), + allow_blank=True, ), help_text=_( "Mapping van kenmerken (sleutel/waarde) van de notificatie. De "

Fix/lege waarde kenmerken notificatie Kenmerken in notificaties can sometimes have values that are empty (i.e., ""). Currently this isn't allowed but it should be.

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2021-12-01T15:13:14

0.0

[]

VNG-Realisatie/vng-api-common

VNG-Realisatie__vng-api-common-180

d7969a1a4ee52ef3573e5be624b8c8d198ea6a33

diff --git a/setup.cfg b/setup.cfg index 56c7bcfe..7ccc7030 100644 --- a/setup.cfg +++ b/setup.cfg @@ -43,7 +43,7 @@ install_requires = django-rest-framework-condition drf-yasg>=1.20.0 drf-nested-routers>=0.93.3 - gemma-zds-client>=0.13.0 + gemma-zds-client>=0.14.0 iso-639 isodate oyaml

Support PyJWT 2.0.0+ There's a breaking change . `encode` and `decode` no longer operate on bytestrings, but on regular strings now. It's been dealt with in gemma-zds-client already.

2021-08-30T20:50:27

0.0

[]