in_source_id
stringlengths 13
58
| issue
stringlengths 3
241k
| before_files
listlengths 0
3
| after_files
listlengths 0
3
| pr_diff
stringlengths 109
107M
⌀ |
|---|---|---|---|---|
django-wiki__django-wiki-1299 | Bug: Edit section fails when fenced code block contains comments
### Discussed in https://github.com/django-wiki/django-wiki/discussions/1245
<div type='discussions-op-text'>
<sup>Originally posted by **chrisv2** January 8, 2023</sup>
The following markup will break section editing (due to the `#` in the fenced code block):
````markdown
# Section 1
Section 1 Lorem ipsum dolor sit amet
```python
# hello world
print("hello world")
```
# Section 2
Section 2 Lorem ipsum dolor sit amet
````
# Expected result
When clicking the "edit" link next to "Section 2", the editor should show the following markup:
```markdown
# Section 2
Section 2 Lorem ipsum dolor sit amet
```
# Actual result
The editor shows parts of the fenced code block:
````markdown
# hello world
print("hello world")
```
# Section 2
Section 2 Lorem ipsum dolor sit amet
````
# Notes
Actually it is surprisingly hard to create a proper fix for that, as [it is not possible to get source line numbers from the markdown parser](https://github.com/Python-Markdown/markdown/issues/1235). Currently, the editsection plugin parses the source itself by using regexes, but that doesn't account for fenced code blocks, and it's probably quite difficult to do this with regex alone. IMHO we could do one of the following:
1. loop over markdown source line by line, detecting headers and start/end of fenced code blocks (and possibly other blocks like HTML)
2. create a block level extension and annotate each `h[1-6]` with the source line, then find them in the markup
I'm interested in hacking at this, but I'm not sure which is the best approach. Any thoughts?</div>
| [
{
"content": "from django.urls import re_path as url\nfrom wiki.core.plugins import registry\nfrom wiki.core.plugins.base import BasePlugin\nfrom wiki.plugins.editsection.markdown_extensions import EditSectionExtension\n\nfrom . import settings\nfrom . import views\n\n\nclass EditSectionPlugin(BasePlugin):\n\n slug = settings.SLUG\n urlpatterns = {\n \"article\": [\n url(\n r\"^header/(?P<header>[\\w-]+)/$\",\n views.EditSection.as_view(),\n name=\"editsection\",\n ),\n ]\n }\n\n markdown_extensions = [EditSectionExtension()]\n\n\nregistry.register(EditSectionPlugin)\n",
"path": "src/wiki/plugins/editsection/wiki_plugin.py"
}
] | [
{
"content": "from django.urls import re_path as url\nfrom wiki.core.plugins import registry\nfrom wiki.core.plugins.base import BasePlugin\nfrom wiki.plugins.editsection.markdown_extensions import EditSectionExtension\n\nfrom . import settings\nfrom . import views\n\n\nclass EditSectionPlugin(BasePlugin):\n\n slug = settings.SLUG\n urlpatterns = {\n \"article\": [\n url(\n r\"^header/(?P<header>[\\S]+)/$\",\n views.EditSection.as_view(),\n name=\"editsection\",\n ),\n ]\n }\n\n markdown_extensions = [EditSectionExtension()]\n\n\nregistry.register(EditSectionPlugin)\n",
"path": "src/wiki/plugins/editsection/wiki_plugin.py"
}
] | diff --git a/src/wiki/plugins/editsection/wiki_plugin.py b/src/wiki/plugins/editsection/wiki_plugin.py
index 2987d0c06..506fcf632 100644
--- a/src/wiki/plugins/editsection/wiki_plugin.py
+++ b/src/wiki/plugins/editsection/wiki_plugin.py
@@ -13,7 +13,7 @@ class EditSectionPlugin(BasePlugin):
urlpatterns = {
"article": [
url(
- r"^header/(?P<header>[\w-]+)/$",
+ r"^header/(?P<header>[\S]+)/$",
views.EditSection.as_view(),
name="editsection",
),
diff --git a/tests/plugins/editsection/test_editsection.py b/tests/plugins/editsection/test_editsection.py
index e1c4255e1..be95dd17a 100644
--- a/tests/plugins/editsection/test_editsection.py
+++ b/tests/plugins/editsection/test_editsection.py
@@ -155,6 +155,20 @@ def test_nonunique_headers(self):
expected = "## Date\r\n2023-01-02"
self.assertEqual(actual, expected)
+ def test_underscore_and_dot(self):
+ """test whether we can handle non-slug characters like dots in header IDs"""
+ # Explanation: While autogenerated ids are slugified, Markdown allows to manually
+ # specify the ID using the {#custom_id_value} syntax. As HTML5 only requires ID
+ # values not to contain whitespace, we should be able to handle any valid HTML5 ID, too.
+ source = """# Title 1 {#some_id_with.dot}\n\n"""
+ urlpath = URLPath.create_urlpath(
+ URLPath.root(), "testedit", title="TestEdit", content=source
+ )
+ # rendering causes NoReverseMatch without the fix
+ actual = urlpath.article.render()
+ expected = '<h1 id="some_id_with.dot">Title 1<a class="article-edit-title-link" href="/testedit/_plugin/editsection/header/some_id_with.dot/">[edit]</a></h1>'
+ self.assertEqual(actual, expected)
+
class EditSectionEditBase(RequireRootArticleMixin, FuncBaseMixin):
pass
|
sktime__sktime-5490 | [BUG] Clasp Segmentation sometimes raises a ValueError
**Describe the bug**
<!--
A clear and concise description of what the bug is.
-->
The `predict_scores` method for the `ClaSPSegmentation` algorithm sometimes raises a value error.
**To Reproduce**
<!--
Add a Minimal, Complete, and Verifiable example (for more details, see e.g. https://stackoverflow.com/help/mcve
If the code is too long, feel free to put it in a public gist and link it in the issue: https://gist.github.com
-->
Consider the following `main.py` script.
```python
# main.py
import numpy as np
from sktime.annotation import clasp
# Causes a value error kth(=3) out of bounds (3)
data = np.array([-4.93826793, -5.10968536, -4.94699538, -5.06644812, -5.09389618,
-4.97855996, -5.03805906, -4.94288774, -4.93562747, -5.15704812,
-4.98363671, -4.94730547, -4.95460821, -4.77100651, -4.97104642,
-5.19168259, -4.86592807, -5.03451162, -4.8818181 , -4.96151397,
-5.04680535, -5.10203082, -5.09094031, -5.14550955, -4.88810337,
-4.98522959, -4.89865192, -5.17349344, -4.79173094, -4.98540751,
-4.95218348, -4.92745787, -5.10828999, -5.018588 , -5.23138986,
-5.0829429 , -4.95182016, -5.04750463, -5.04910479, -4.96474658,
-4.87263583, -4.93261922, -4.88780183, -4.99625131, -5.04841316,
-4.8784347 , -4.92589113, -5.23561638, -5.10026717, -4.98502644,
4.95563698, 5.00165565, 5.02851673, 5.09061275, 5.03335048,
5.01256648, 5.21727 , 4.900725 , 4.86488069, 5.03619925,
4.84147019, 4.90967582, 4.94225031, 5.01319431, 4.88011974,
5.03605379, 4.97238513, 4.9344612 , 5.18930892, 4.90348493,
4.83290085, 5.04300367, 4.92500575, 5.06299654, 4.83232756,
4.94786228, 5.03254223, 4.86344949, 4.97827722, 5.03143399,
5.1337682 , 4.88999864, 5.16775253, 5.13377762, 5.09881714,
5.21716965, 4.90141898, 4.94692338, 4.92881945, 4.99632702,
5.14217334, 5.04286422, 5.02892189, 4.8142512 , 4.91298724,
4.97970406, 5.13247509, 4.97386383, 4.94512011, 4.91927367])
model_clasp = clasp.ClaSPSegmentation(n_cps=2)
model_clasp.fit(data)
```
Executing `predict_scores` using this script raises a value error.
```
python3 -i main.py
>>> model_clasp.predict_scores(data)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/alex/documents/sktime/sktime/annotation/base/_base.py", line 150, in predict_scores
return self._predict_scores(X)
File "/home/alex/documents/sktime/sktime/annotation/clasp.py", line 287, in _predict_scores
self.found_cps, self.profiles, self.scores = self._run_clasp(X)
File "/home/alex/documents/sktime/sktime/annotation/clasp.py", line 318, in _run_clasp
self.found_cps, self.profiles, self.scores = _segmentation(
File "/home/alex/documents/sktime/sktime/annotation/clasp.py", line 154, in _segmentation
profile = clasp.transform(X[ranges])
File "/home/alex/documents/sktime/sktime/transformations/base.py", line 583, in transform
Xt = self._transform(X=X_inner, y=y_inner)
File "/home/alex/documents/sktime/sktime/transformations/series/clasp.py", line 106, in _transform
Xt, _ = clasp(
File "/home/alex/documents/sktime/sktime/transformations/series/_clasp_numba.py", line 334, in clasp
knn_mask = _compute_distances_iterative(X, m, k_neighbours).T
File "/home/alex/documents/sktime/sktime/transformations/series/_clasp_numba.py", line 114, in _compute_distances_iterative
idx = np.argpartition(dist, k)
File "/home/alex/documents/sktime/.venv/lib/python3.10/site-packages/numpy/core/fromnumeric.py", line 858, in argpartition
return _wrapfunc(a, 'argpartition', kth, axis=axis, kind=kind, order=order)
File "/home/alex/documents/sktime/.venv/lib/python3.10/site-packages/numpy/core/fromnumeric.py", line 59, in _wrapfunc
return bound(*args, **kwds)
ValueError: kth(=3) out of bounds (3)
```
From debugging, the error seems to come from `_compute_distances_iterative` in `_clasp_number.py` on line 53.
https://github.com/sktime/sktime/blob/f48b1f53f58004bb792abf78ce6818034835d3c2/sktime/transformations/series/_clasp_numba.py#L114
In the example, `k` is `3` and `dist` is `array([inf, inf, inf])`. Since `dist` has length 3, then `k` is out of range. I think this could be solved with some simple bound checking where if `k` is greater than or equal to the length of `dist` then set `k` to the length of `dist` minus 1.
**Expected behavior**
<!--
A clear and concise description of what you expected to happen.
-->
Using `predict_scores` should not raise a value error.
**Additional context**
<!--
Add any other context about the problem here.
-->
This bug happened when I was trying to fit the `ClaSPSegmentation` algorithm to several different data arrays genered using the following code.
```python
n = 50
data = np.concatenate([np.random.normal(-5, 0.1, n), np.random.normal(5, 0.1, n)])
```
**Versions**
<details>
<!--
Please run the following code snippet and paste the output here:
from sktime import show_versions; show_versions()
-->
My versions,
```
>>> from sktime import show_versions; show_versions()
System:
python: 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0]
executable: /home/alex/documents/sktime/.venv/bin/python3
machine: Linux-5.15.90.1-microsoft-standard-WSL2-x86_64-with-glibc2.35
Python dependencies:
pip: 22.0.2
sktime: 0.24.0
sklearn: 1.3.1
skbase: 0.6.0
numpy: 1.26.1
scipy: 1.11.3
pandas: 2.1.1
matplotlib: 3.8.0
joblib: 1.3.2
numba: 0.58.1
statsmodels: None
pmdarima: None
statsforecast: None
tsfresh: None
tslearn: None
torch: None
tensorflow: None
tensorflow_probability: None
```
<!-- Thanks for contributing! -->
| [
{
"content": "\"\"\"Isolated numba imports for clasp.\"\"\"\n\n\n__author__ = [\"ermshaua\", \"patrickzib\"]\n\nimport numpy as np\nimport pandas as pd\n\nfrom sktime.transformations.panel.matrix_profile import _sliding_dot_products\nfrom sktime.utils.numba.njit import njit\n\n\ndef _sliding_window(X, m):\n \"\"\"Return the sliding windows for a time series and a window size.\n\n Parameters\n ----------\n X : array-like, shape = [n]\n A single univariate time series of length n\n m : int\n The window size to generate sliding windows\n\n Returns\n -------\n windows : array of shape [n-m+1, m]\n The sliding windows of length over the time series of length n\n \"\"\"\n shape = X.shape[:-1] + (X.shape[-1] - m + 1, m)\n strides = X.strides + (X.strides[-1],)\n return np.lib.stride_tricks.as_strided(X, shape=shape, strides=strides)\n\n\ndef _sliding_mean_std(X, m):\n \"\"\"Return the sliding mean and std for a time series and a window size.\n\n Parameters\n ----------\n X : array-like, shape [n]\n A single univariate time series of length n\n m : int\n The window size to generate sliding windows\n\n Returns\n -------\n Tuple (float, float)\n The moving mean and moving std\n \"\"\"\n s = np.insert(np.cumsum(X), 0, 0)\n sSq = np.insert(np.cumsum(X**2), 0, 0)\n segSum = s[m:] - s[:-m]\n segSumSq = sSq[m:] - sSq[:-m]\n movmean = segSum / m\n movstd = np.sqrt(segSumSq / m - (segSum / m) ** 2)\n\n # avoid dividing by too small std, like 0\n movstd = np.where(abs(movstd) < 0.001, 1, movstd)\n\n return [movmean, movstd]\n\n\ndef _compute_distances_iterative(X, m, k):\n \"\"\"Compute kNN indices with dot-product.\n\n No-loops implementation for a time series, given\n a window size and k neighbours.\n\n Parameters\n ----------\n X : array-like, shape [n]\n A single univariate time series of length n\n m : int\n The window size to generate sliding windows\n k : int\n The number of nearest neighbors\n\n Returns\n -------\n knns : array-like, shape = [n-m+1, k], dtype=int\n The knns (offsets!) for each subsequence in X\n \"\"\"\n length = len(X) - m + 1\n knns = np.zeros(shape=(length, k), dtype=np.int64)\n\n dot_prev = None\n means, stds = _sliding_mean_std(X, m)\n\n for order in range(0, length):\n # first iteration O(n log n)\n if order == 0:\n # dot_first = _sliding_dot_product(X[:m], X)\n dot_first = _sliding_dot_products(X[:m], X, len(X[:m]), len(X))\n dot_rolled = dot_first\n # O(1) further operations\n else:\n dot_rolled = (\n np.roll(dot_prev, 1)\n + X[order + m - 1] * X[m - 1 : length + m]\n - X[order - 1] * np.roll(X[:length], 1)\n )\n dot_rolled[0] = dot_first[order]\n\n x_mean = means[order]\n x_std = stds[order]\n\n dist = 2 * m * (1 - (dot_rolled - m * means * x_mean) / (m * stds * x_std))\n\n # self-join: exclusion zone\n trivialMatchRange = (\n int(max(0, order - np.round(m / 2, 0))),\n int(min(order + np.round(m / 2 + 1, 0), length)),\n )\n dist[trivialMatchRange[0] : trivialMatchRange[1]] = np.inf\n\n idx = np.argpartition(dist, k)\n\n knns[order, :] = idx[:k]\n dot_prev = dot_rolled\n\n return knns\n\n\n@njit(fastmath=True, cache=True)\ndef _calc_knn_labels(knn_mask, split_idx, m):\n \"\"\"Compute kNN indices relabeling at a given split index.\n\n Parameters\n ----------\n knn_mask : array-like, shape = [k, n-m+1], dtype=int\n The knn indices for each subsequence\n split_idx : int\n The split index to use\n m : int\n The window size to generate sliding windows\n\n Returns\n -------\n Tuple (array-like of shape=[n-m+1], array-like of shape=[n-m+1]):\n True labels and predicted labels\n \"\"\"\n k_neighbours, n_timepoints = knn_mask.shape\n\n # create labels for given potential split\n y_true = np.concatenate(\n (\n np.zeros(split_idx, dtype=np.int64),\n np.ones(n_timepoints - split_idx, dtype=np.int64),\n )\n )\n\n knn_mask_labels = np.zeros(shape=(k_neighbours, n_timepoints), dtype=np.int64)\n\n # relabel the kNN indices\n for i_neighbor in range(k_neighbours):\n neighbours = knn_mask[i_neighbor]\n knn_mask_labels[i_neighbor] = y_true[neighbours]\n\n # compute kNN prediction\n ones = np.sum(knn_mask_labels, axis=0)\n zeros = k_neighbours - ones\n y_pred = np.asarray(ones > zeros, dtype=np.int64)\n\n # apply exclusion zone at split point\n exclusion_zone = np.arange(split_idx - m, split_idx)\n\n # Remove indexes outside the range of y_pred\n exclusion_zone = exclusion_zone[\n (exclusion_zone >= -len(y_pred)) & (exclusion_zone < len(y_pred))\n ]\n\n y_pred[exclusion_zone] = np.ones(len(exclusion_zone), dtype=np.int64)\n\n return y_true, y_pred\n\n\n@njit(fastmath=True, cache=False)\ndef _binary_f1_score(y_true, y_pred):\n \"\"\"Compute f1-score.\n\n Parameters\n ----------\n y_true : array-like, shape=[n-m+1], dtype = int\n True integer labels for each subsequence\n y_pred : array-like, shape=[n-m+1], dtype = int\n Predicted integer labels for each subsequence\n\n Returns\n -------\n F1 : float\n F1-score\n \"\"\"\n f1_scores = np.zeros(shape=2, dtype=np.float64)\n\n for label in (0, 1):\n tp = np.sum(np.logical_and(y_true == label, y_pred == label))\n fp = np.sum(np.logical_and(y_true != label, y_pred == label))\n fn = np.sum(np.logical_and(y_true == label, y_pred != label))\n\n pr = tp / (tp + fp)\n re = tp / (tp + fn)\n\n f1 = 2 * (pr * re) / (pr + re)\n f1_scores[label] = f1\n\n return np.mean(f1_scores)\n\n\n@njit(fastmath=True, cache=True)\ndef _roc_auc_score(y_score, y_true):\n \"\"\"Compute roc-auc score.\n\n Parameters\n ----------\n y_true : array-like, shape=[n-m+1], dtype = int\n True integer labels for each subsequence\n y_pred : array-like, shape=[n-m+1], dtype = int\n Predicted integer labels for each subsequence\n\n Returns\n -------\n F1 : float\n ROC-AUC-score\n \"\"\"\n # make y_true a boolean vector\n y_true = y_true == 1\n\n # sort scores and corresponding truth values (y_true is sorted by design)\n desc_score_indices = np.arange(y_score.shape[0])[::-1]\n\n y_score = y_score[desc_score_indices]\n y_true = y_true[desc_score_indices]\n\n # y_score typically has many tied values. Here we extract\n # the indices associated with the distinct values. We also\n # concatenate a value for the end of the curve.\n distinct_value_indices = np.where(np.diff(y_score))[0]\n threshold_idxs = np.concatenate(\n (distinct_value_indices, np.array([y_true.size - 1]))\n )\n\n # accumulate the true positives with decreasing threshold\n tps = np.cumsum(y_true)[threshold_idxs]\n fps = 1 + threshold_idxs - tps\n\n tps = np.concatenate((np.array([0]), tps))\n fps = np.concatenate((np.array([0]), fps))\n\n if fps[-1] <= 0 or tps[-1] <= 0:\n return np.nan\n\n fpr = fps / fps[-1]\n tpr = tps / tps[-1]\n\n if fpr.shape[0] < 2:\n return np.nan\n\n direction = 1\n dx = np.diff(fpr)\n\n if np.any(dx < 0):\n if np.all(dx <= 0):\n direction = -1\n else:\n return np.nan\n\n area = direction * np.trapz(tpr, fpr)\n return area\n\n\n@njit(fastmath=True)\ndef _calc_profile(m, knn_mask, score, exclusion_zone):\n \"\"\"Calculate ClaSP profile for the kNN indices and a score.\n\n Parameters\n ----------\n m : int\n The window size to generate sliding windows\n knn_mask : array-like, shape = [k, n-m+1], dtype=int\n The knn indices\n score : function\n Scoring method used\n exclusion_zone : int\n Exclusion zone\n\n Returns\n -------\n profile : array-like, shape=[n-m+1], dtype = float\n The ClaSP\n \"\"\"\n n_timepoints = knn_mask.shape[1]\n profile = np.full(shape=n_timepoints, fill_value=np.nan, dtype=np.float64)\n\n for split_idx in range(exclusion_zone, n_timepoints - exclusion_zone):\n y_true, y_pred = _calc_knn_labels(knn_mask, split_idx, m)\n profile[split_idx] = score(y_true, y_pred)\n\n return profile\n\n\ndef clasp(\n X,\n m,\n k_neighbours=3,\n score=_roc_auc_score,\n interpolate=True,\n exclusion_radius=0.05,\n):\n \"\"\"Calculate ClaSP for a time series and a window size.\n\n Parameters\n ----------\n X : array-like, shape = [n]\n A single univariate time series of length n\n m : int\n The window size to generate sliding windows\n k_neighbours : int\n The number of knn to use\n score : function\n Scoring method used\n interpolate:\n Interpolate the profile\n exclusion_radius : int\n Blind spot of the profile to the corners\n\n Returns\n -------\n Tuple (array-like of shape [n], array-like of shape [k_neighbours, n])\n The ClaSP and the knn_mask\n \"\"\"\n knn_mask = _compute_distances_iterative(X, m, k_neighbours).T\n\n n_timepoints = knn_mask.shape[1]\n exclusion_radius = np.int64(n_timepoints * exclusion_radius)\n\n profile = _calc_profile(m, knn_mask, score, exclusion_radius)\n\n if interpolate is True:\n profile = pd.Series(profile).interpolate(limit_direction=\"both\").to_numpy()\n return profile, knn_mask\n",
"path": "sktime/transformations/series/_clasp_numba.py"
}
] | [
{
"content": "\"\"\"Isolated numba imports for clasp.\"\"\"\n\n\n__author__ = [\"ermshaua\", \"patrickzib\"]\n\nimport numpy as np\nimport pandas as pd\n\nfrom sktime.transformations.panel.matrix_profile import _sliding_dot_products\nfrom sktime.utils.numba.njit import njit\n\n\ndef _sliding_window(X, m):\n \"\"\"Return the sliding windows for a time series and a window size.\n\n Parameters\n ----------\n X : array-like, shape = [n]\n A single univariate time series of length n\n m : int\n The window size to generate sliding windows\n\n Returns\n -------\n windows : array of shape [n-m+1, m]\n The sliding windows of length over the time series of length n\n \"\"\"\n shape = X.shape[:-1] + (X.shape[-1] - m + 1, m)\n strides = X.strides + (X.strides[-1],)\n return np.lib.stride_tricks.as_strided(X, shape=shape, strides=strides)\n\n\ndef _sliding_mean_std(X, m):\n \"\"\"Return the sliding mean and std for a time series and a window size.\n\n Parameters\n ----------\n X : array-like, shape [n]\n A single univariate time series of length n\n m : int\n The window size to generate sliding windows\n\n Returns\n -------\n Tuple (float, float)\n The moving mean and moving std\n \"\"\"\n s = np.insert(np.cumsum(X), 0, 0)\n sSq = np.insert(np.cumsum(X**2), 0, 0)\n segSum = s[m:] - s[:-m]\n segSumSq = sSq[m:] - sSq[:-m]\n movmean = segSum / m\n movstd = np.sqrt(segSumSq / m - (segSum / m) ** 2)\n\n # avoid dividing by too small std, like 0\n movstd = np.where(abs(movstd) < 0.001, 1, movstd)\n\n return [movmean, movstd]\n\n\ndef _compute_distances_iterative(X, m, k):\n \"\"\"Compute kNN indices with dot-product.\n\n No-loops implementation for a time series, given\n a window size and k neighbours.\n\n Parameters\n ----------\n X : array-like, shape [n]\n A single univariate time series of length n\n m : int\n The window size to generate sliding windows\n k : int\n The number of nearest neighbors\n\n Returns\n -------\n knns : array-like, shape = [n-m+1, k], dtype=int\n The knns (offsets!) for each subsequence in X\n \"\"\"\n length = len(X) - m + 1\n knns = np.zeros(shape=(length, k), dtype=np.int64)\n\n dot_prev = None\n means, stds = _sliding_mean_std(X, m)\n\n for order in range(0, length):\n # first iteration O(n log n)\n if order == 0:\n # dot_first = _sliding_dot_product(X[:m], X)\n dot_first = _sliding_dot_products(X[:m], X, len(X[:m]), len(X))\n dot_rolled = dot_first\n # O(1) further operations\n else:\n dot_rolled = (\n np.roll(dot_prev, 1)\n + X[order + m - 1] * X[m - 1 : length + m]\n - X[order - 1] * np.roll(X[:length], 1)\n )\n dot_rolled[0] = dot_first[order]\n\n x_mean = means[order]\n x_std = stds[order]\n\n dist = 2 * m * (1 - (dot_rolled - m * means * x_mean) / (m * stds * x_std))\n\n # self-join: exclusion zone\n trivialMatchRange = (\n int(max(0, order - np.round(m / 2, 0))),\n int(min(order + np.round(m / 2 + 1, 0), length)),\n )\n dist[trivialMatchRange[0] : trivialMatchRange[1]] = np.inf\n\n _k = min(k, len(dist) - 1)\n idx = np.argpartition(dist, _k)\n\n knns[order, :] = idx[:k]\n dot_prev = dot_rolled\n\n return knns\n\n\n@njit(fastmath=True, cache=True)\ndef _calc_knn_labels(knn_mask, split_idx, m):\n \"\"\"Compute kNN indices relabeling at a given split index.\n\n Parameters\n ----------\n knn_mask : array-like, shape = [k, n-m+1], dtype=int\n The knn indices for each subsequence\n split_idx : int\n The split index to use\n m : int\n The window size to generate sliding windows\n\n Returns\n -------\n Tuple (array-like of shape=[n-m+1], array-like of shape=[n-m+1]):\n True labels and predicted labels\n \"\"\"\n k_neighbours, n_timepoints = knn_mask.shape\n\n # create labels for given potential split\n y_true = np.concatenate(\n (\n np.zeros(split_idx, dtype=np.int64),\n np.ones(n_timepoints - split_idx, dtype=np.int64),\n )\n )\n\n knn_mask_labels = np.zeros(shape=(k_neighbours, n_timepoints), dtype=np.int64)\n\n # relabel the kNN indices\n for i_neighbor in range(k_neighbours):\n neighbours = knn_mask[i_neighbor]\n knn_mask_labels[i_neighbor] = y_true[neighbours]\n\n # compute kNN prediction\n ones = np.sum(knn_mask_labels, axis=0)\n zeros = k_neighbours - ones\n y_pred = np.asarray(ones > zeros, dtype=np.int64)\n\n # apply exclusion zone at split point\n exclusion_zone = np.arange(split_idx - m, split_idx)\n\n # Remove indexes outside the range of y_pred\n exclusion_zone = exclusion_zone[\n (exclusion_zone >= -len(y_pred)) & (exclusion_zone < len(y_pred))\n ]\n\n y_pred[exclusion_zone] = np.ones(len(exclusion_zone), dtype=np.int64)\n\n return y_true, y_pred\n\n\n@njit(fastmath=True, cache=False)\ndef _binary_f1_score(y_true, y_pred):\n \"\"\"Compute f1-score.\n\n Parameters\n ----------\n y_true : array-like, shape=[n-m+1], dtype = int\n True integer labels for each subsequence\n y_pred : array-like, shape=[n-m+1], dtype = int\n Predicted integer labels for each subsequence\n\n Returns\n -------\n F1 : float\n F1-score\n \"\"\"\n f1_scores = np.zeros(shape=2, dtype=np.float64)\n\n for label in (0, 1):\n tp = np.sum(np.logical_and(y_true == label, y_pred == label))\n fp = np.sum(np.logical_and(y_true != label, y_pred == label))\n fn = np.sum(np.logical_and(y_true == label, y_pred != label))\n\n pr = tp / (tp + fp)\n re = tp / (tp + fn)\n\n f1 = 2 * (pr * re) / (pr + re)\n f1_scores[label] = f1\n\n return np.mean(f1_scores)\n\n\n@njit(fastmath=True, cache=True)\ndef _roc_auc_score(y_score, y_true):\n \"\"\"Compute roc-auc score.\n\n Parameters\n ----------\n y_true : array-like, shape=[n-m+1], dtype = int\n True integer labels for each subsequence\n y_pred : array-like, shape=[n-m+1], dtype = int\n Predicted integer labels for each subsequence\n\n Returns\n -------\n F1 : float\n ROC-AUC-score\n \"\"\"\n # make y_true a boolean vector\n y_true = y_true == 1\n\n # sort scores and corresponding truth values (y_true is sorted by design)\n desc_score_indices = np.arange(y_score.shape[0])[::-1]\n\n y_score = y_score[desc_score_indices]\n y_true = y_true[desc_score_indices]\n\n # y_score typically has many tied values. Here we extract\n # the indices associated with the distinct values. We also\n # concatenate a value for the end of the curve.\n distinct_value_indices = np.where(np.diff(y_score))[0]\n threshold_idxs = np.concatenate(\n (distinct_value_indices, np.array([y_true.size - 1]))\n )\n\n # accumulate the true positives with decreasing threshold\n tps = np.cumsum(y_true)[threshold_idxs]\n fps = 1 + threshold_idxs - tps\n\n tps = np.concatenate((np.array([0]), tps))\n fps = np.concatenate((np.array([0]), fps))\n\n if fps[-1] <= 0 or tps[-1] <= 0:\n return np.nan\n\n fpr = fps / fps[-1]\n tpr = tps / tps[-1]\n\n if fpr.shape[0] < 2:\n return np.nan\n\n direction = 1\n dx = np.diff(fpr)\n\n if np.any(dx < 0):\n if np.all(dx <= 0):\n direction = -1\n else:\n return np.nan\n\n area = direction * np.trapz(tpr, fpr)\n return area\n\n\n@njit(fastmath=True)\ndef _calc_profile(m, knn_mask, score, exclusion_zone):\n \"\"\"Calculate ClaSP profile for the kNN indices and a score.\n\n Parameters\n ----------\n m : int\n The window size to generate sliding windows\n knn_mask : array-like, shape = [k, n-m+1], dtype=int\n The knn indices\n score : function\n Scoring method used\n exclusion_zone : int\n Exclusion zone\n\n Returns\n -------\n profile : array-like, shape=[n-m+1], dtype = float\n The ClaSP\n \"\"\"\n n_timepoints = knn_mask.shape[1]\n profile = np.full(shape=n_timepoints, fill_value=np.nan, dtype=np.float64)\n\n for split_idx in range(exclusion_zone, n_timepoints - exclusion_zone):\n y_true, y_pred = _calc_knn_labels(knn_mask, split_idx, m)\n profile[split_idx] = score(y_true, y_pred)\n\n return profile\n\n\ndef clasp(\n X,\n m,\n k_neighbours=3,\n score=_roc_auc_score,\n interpolate=True,\n exclusion_radius=0.05,\n):\n \"\"\"Calculate ClaSP for a time series and a window size.\n\n Parameters\n ----------\n X : array-like, shape = [n]\n A single univariate time series of length n\n m : int\n The window size to generate sliding windows\n k_neighbours : int\n The number of knn to use\n score : function\n Scoring method used\n interpolate:\n Interpolate the profile\n exclusion_radius : int\n Blind spot of the profile to the corners\n\n Returns\n -------\n Tuple (array-like of shape [n], array-like of shape [k_neighbours, n])\n The ClaSP and the knn_mask\n \"\"\"\n knn_mask = _compute_distances_iterative(X, m, k_neighbours).T\n\n n_timepoints = knn_mask.shape[1]\n exclusion_radius = np.int64(n_timepoints * exclusion_radius)\n\n profile = _calc_profile(m, knn_mask, score, exclusion_radius)\n\n if interpolate is True:\n profile = pd.Series(profile).interpolate(limit_direction=\"both\").to_numpy()\n return profile, knn_mask\n",
"path": "sktime/transformations/series/_clasp_numba.py"
}
] | diff --git a/sktime/transformations/series/_clasp_numba.py b/sktime/transformations/series/_clasp_numba.py
index 5ca335ccc99..da2d023b351 100644
--- a/sktime/transformations/series/_clasp_numba.py
+++ b/sktime/transformations/series/_clasp_numba.py
@@ -111,7 +111,8 @@ def _compute_distances_iterative(X, m, k):
)
dist[trivialMatchRange[0] : trivialMatchRange[1]] = np.inf
- idx = np.argpartition(dist, k)
+ _k = min(k, len(dist) - 1)
+ idx = np.argpartition(dist, _k)
knns[order, :] = idx[:k]
dot_prev = dot_rolled
|
ephios-dev__ephios-1244 | API: `/api/users/by_email` returns 404 error for email addresses with dots before the @
**Describe the bug**
A clear and concise description of what the bug is.
**To Reproduce**
Steps to reproduce the behavior:
1. Go to `[ephios-url]/api/users/by_email/[email protected]/`
**Expected behaviour**
Assuming the user exists, the information about the user should be returned.
**Screenshots**
Instead the page 404s.
<img width="1511" alt="Screenshot 2024-03-27 at 18 54 08" src="https://github.com/ephios-dev/ephios/assets/2546622/1383feee-28b0-4825-a31e-c39e2cc3f2ab">
**Environment**
State which device, operating system, browser and browser version you are using.
MacOS 14.2.1 (23C71), Version 17.2.1 (19617.1.17.11.12)
**Additional context**
* The problem does not appear for the test emails `usaaa@localhost/`, `admin@localhost/` or `[email protected]`.
| [
{
"content": "from django.db.models import Q\nfrom django.utils import timezone\nfrom django_filters.rest_framework import DjangoFilterBackend\nfrom oauth2_provider.contrib.rest_framework import IsAuthenticatedOrTokenHasScope\nfrom rest_framework import viewsets\nfrom rest_framework.exceptions import PermissionDenied\nfrom rest_framework.fields import SerializerMethodField\nfrom rest_framework.filters import SearchFilter\nfrom rest_framework.generics import RetrieveAPIView\nfrom rest_framework.mixins import RetrieveModelMixin\nfrom rest_framework.permissions import DjangoObjectPermissions\nfrom rest_framework.relations import SlugRelatedField\nfrom rest_framework.schemas.openapi import AutoSchema\nfrom rest_framework.serializers import ModelSerializer\nfrom rest_framework.viewsets import GenericViewSet\nfrom rest_framework_guardian.filters import ObjectPermissionsFilter\n\nfrom ephios.api.views.events import ParticipationSerializer\nfrom ephios.core.models import LocalParticipation, Qualification, UserProfile\nfrom ephios.core.services.qualification import collect_all_included_qualifications\n\n\nclass QualificationSerializer(ModelSerializer):\n category = SlugRelatedField(slug_field=\"uuid\", read_only=True)\n includes = SerializerMethodField()\n\n class Meta:\n model = Qualification\n fields = [\n \"uuid\",\n \"title\",\n \"abbreviation\",\n \"category\",\n \"includes\",\n ]\n\n def get_includes(self, obj):\n return [q.uuid for q in collect_all_included_qualifications(obj.includes.all())]\n\n\nclass UserProfileSerializer(ModelSerializer):\n qualifications = SerializerMethodField()\n\n class Meta:\n model = UserProfile\n fields = [\n \"id\",\n \"display_name\",\n \"date_of_birth\",\n \"email\",\n \"qualifications\",\n ]\n\n def get_qualifications(self, obj):\n return QualificationSerializer(\n Qualification.objects.filter(\n Q(grants__user=obj)\n & (Q(grants__expires__gte=timezone.now()) | Q(grants__expires__isnull=True))\n ),\n many=True,\n ).data\n\n\nclass UserProfileMeView(RetrieveAPIView):\n serializer_class = UserProfileSerializer\n queryset = UserProfile.objects.all()\n permission_classes = [IsAuthenticatedOrTokenHasScope]\n required_scopes = [\"ME_READ\"]\n schema = AutoSchema(operation_id_base=\"OwnUserProfile\")\n\n def get_object(self):\n if self.request.user is None:\n raise PermissionDenied()\n return self.request.user\n\n\nclass UserViewSet(viewsets.ReadOnlyModelViewSet):\n serializer_class = UserProfileSerializer\n queryset = UserProfile.objects.all()\n permission_classes = [IsAuthenticatedOrTokenHasScope, DjangoObjectPermissions]\n required_scopes = [\"CONFIDENTIAL_READ\"]\n search_fields = [\"display_name\", \"email\"]\n\n filter_backends = [\n DjangoFilterBackend,\n SearchFilter,\n ObjectPermissionsFilter,\n ]\n\n\nclass UserByMailView(RetrieveModelMixin, GenericViewSet):\n serializer_class = UserProfileSerializer\n queryset = UserProfile.objects.all()\n permission_classes = [IsAuthenticatedOrTokenHasScope, DjangoObjectPermissions]\n required_scopes = [\"CONFIDENTIAL_READ\"]\n filter_backends = [ObjectPermissionsFilter]\n lookup_url_kwarg = \"email\"\n lookup_field = \"email\"\n schema = AutoSchema(operation_id_base=\"UserProfileByMail\")\n\n\nclass UserParticipationView(viewsets.ReadOnlyModelViewSet):\n serializer_class = ParticipationSerializer\n permission_classes = [IsAuthenticatedOrTokenHasScope]\n filter_backends = [ObjectPermissionsFilter, DjangoFilterBackend]\n filterset_fields = [\"state\"]\n required_scopes = [\"CONFIDENTIAL_READ\"]\n\n def get_queryset(self):\n return LocalParticipation.objects.filter(user=self.kwargs.get(\"user\"))\n",
"path": "ephios/api/views/users.py"
}
] | [
{
"content": "from django.db.models import Q\nfrom django.utils import timezone\nfrom django_filters.rest_framework import DjangoFilterBackend\nfrom oauth2_provider.contrib.rest_framework import IsAuthenticatedOrTokenHasScope\nfrom rest_framework import viewsets\nfrom rest_framework.exceptions import PermissionDenied\nfrom rest_framework.fields import SerializerMethodField\nfrom rest_framework.filters import SearchFilter\nfrom rest_framework.generics import RetrieveAPIView\nfrom rest_framework.mixins import RetrieveModelMixin\nfrom rest_framework.permissions import DjangoObjectPermissions\nfrom rest_framework.relations import SlugRelatedField\nfrom rest_framework.schemas.openapi import AutoSchema\nfrom rest_framework.serializers import ModelSerializer\nfrom rest_framework.viewsets import GenericViewSet\nfrom rest_framework_guardian.filters import ObjectPermissionsFilter\n\nfrom ephios.api.views.events import ParticipationSerializer\nfrom ephios.core.models import LocalParticipation, Qualification, UserProfile\nfrom ephios.core.services.qualification import collect_all_included_qualifications\n\n\nclass QualificationSerializer(ModelSerializer):\n category = SlugRelatedField(slug_field=\"uuid\", read_only=True)\n includes = SerializerMethodField()\n\n class Meta:\n model = Qualification\n fields = [\n \"uuid\",\n \"title\",\n \"abbreviation\",\n \"category\",\n \"includes\",\n ]\n\n def get_includes(self, obj):\n return [q.uuid for q in collect_all_included_qualifications(obj.includes.all())]\n\n\nclass UserProfileSerializer(ModelSerializer):\n qualifications = SerializerMethodField()\n\n class Meta:\n model = UserProfile\n fields = [\n \"id\",\n \"display_name\",\n \"date_of_birth\",\n \"email\",\n \"qualifications\",\n ]\n\n def get_qualifications(self, obj):\n return QualificationSerializer(\n Qualification.objects.filter(\n Q(grants__user=obj)\n & (Q(grants__expires__gte=timezone.now()) | Q(grants__expires__isnull=True))\n ),\n many=True,\n ).data\n\n\nclass UserProfileMeView(RetrieveAPIView):\n serializer_class = UserProfileSerializer\n queryset = UserProfile.objects.all()\n permission_classes = [IsAuthenticatedOrTokenHasScope]\n required_scopes = [\"ME_READ\"]\n schema = AutoSchema(operation_id_base=\"OwnUserProfile\")\n\n def get_object(self):\n if self.request.user is None:\n raise PermissionDenied()\n return self.request.user\n\n\nclass UserViewSet(viewsets.ReadOnlyModelViewSet):\n serializer_class = UserProfileSerializer\n queryset = UserProfile.objects.all()\n permission_classes = [IsAuthenticatedOrTokenHasScope, DjangoObjectPermissions]\n required_scopes = [\"CONFIDENTIAL_READ\"]\n search_fields = [\"display_name\", \"email\"]\n\n filter_backends = [\n DjangoFilterBackend,\n SearchFilter,\n ObjectPermissionsFilter,\n ]\n\n\nclass UserByMailView(RetrieveModelMixin, GenericViewSet):\n serializer_class = UserProfileSerializer\n queryset = UserProfile.objects.all()\n permission_classes = [IsAuthenticatedOrTokenHasScope, DjangoObjectPermissions]\n required_scopes = [\"CONFIDENTIAL_READ\"]\n filter_backends = [ObjectPermissionsFilter]\n lookup_url_kwarg = \"email\"\n lookup_field = \"email\"\n lookup_value_regex = \"[^/]+\" # customize to allow dots (\".\") in the lookup value\n schema = AutoSchema(operation_id_base=\"UserProfileByMail\")\n\n\nclass UserParticipationView(viewsets.ReadOnlyModelViewSet):\n serializer_class = ParticipationSerializer\n permission_classes = [IsAuthenticatedOrTokenHasScope]\n filter_backends = [ObjectPermissionsFilter, DjangoFilterBackend]\n filterset_fields = [\"state\"]\n required_scopes = [\"CONFIDENTIAL_READ\"]\n\n def get_queryset(self):\n return LocalParticipation.objects.filter(user=self.kwargs.get(\"user\"))\n",
"path": "ephios/api/views/users.py"
}
] | diff --git a/ephios/api/views/users.py b/ephios/api/views/users.py
index 110b914a7..9d861b0c5 100644
--- a/ephios/api/views/users.py
+++ b/ephios/api/views/users.py
@@ -96,6 +96,7 @@ class UserByMailView(RetrieveModelMixin, GenericViewSet):
filter_backends = [ObjectPermissionsFilter]
lookup_url_kwarg = "email"
lookup_field = "email"
+ lookup_value_regex = "[^/]+" # customize to allow dots (".") in the lookup value
schema = AutoSchema(operation_id_base="UserProfileByMail")
diff --git a/tests/api/test_user.py b/tests/api/test_user.py
new file mode 100644
index 000000000..8b671092e
--- /dev/null
+++ b/tests/api/test_user.py
@@ -0,0 +1,21 @@
+from django.urls import reverse
+
+
+def test_user_profile_list(django_app, groups, superuser):
+ response = django_app.get(reverse("api:userprofile-list"), user=superuser)
+ assert superuser.email in response
+
+
+def test_api_user_profile_by_email(django_app, superuser):
+ superuser.email = "[email protected]"
+ superuser.save()
+ response = django_app.get(
+ reverse(
+ "api:user-by-email-detail",
+ kwargs={
+ "email": superuser.email,
+ },
+ ),
+ user=superuser,
+ )
+ assert superuser.email in response
|
pex-tool__pex-1194 | Cannot build PEX with relative path for --sources-directory.
Discovered this trying to upgrade Pants to 2.1.26. Looks like:
```
$ mkdir src/
$ echo 'print("Hello World!")' > src/main.py
$ python -m pex -D src -otest.pex -e main
Traceback (most recent call last):
File "/usr/lib/python3.9/runpy.py", line 197, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/usr/lib/python3.9/runpy.py", line 87, in _run_code
exec(code, run_globals)
File "/home/jsirois/dev/pantsbuild/jsirois-pex/pex/__main__.py", line 8, in <module>
__name__ == "__main__" and pex.main()
File "/home/jsirois/dev/pantsbuild/jsirois-pex/pex/bin/pex.py", line 1070, in main
pex_builder.freeze(bytecode_compile=options.compile)
File "/home/jsirois/dev/pantsbuild/jsirois-pex/pex/pex_builder.py", line 578, in freeze
self._prepare_code()
File "/home/jsirois/dev/pantsbuild/jsirois-pex/pex/pex_builder.py", line 506, in _prepare_code
self._pex_info.code_hash = CacheHelper.pex_code_hash(self._chroot.path())
File "/home/jsirois/dev/pantsbuild/jsirois-pex/pex/util.py", line 154, in pex_code_hash
return cls._compute_hash(names, stream_factory)
File "/home/jsirois/dev/pantsbuild/jsirois-pex/pex/util.py", line 131, in _compute_hash
with contextlib.closing(stream_factory(name)) as fp:
File "/home/jsirois/dev/pantsbuild/jsirois-pex/pex/util.py", line 152, in stream_factory
return open(os.path.join(d, name), "rb") # noqa: T802
FileNotFoundError: [Errno 2] No such file or directory: '/tmp/tmpcb782oi_/main.py'
```
Everything works fine if the sources directory is an absolute path though:
```
$ python -m pex -D $PWD/src -otest.pex -e main
$ ./test.pex
Hello World!
```
| [
{
"content": "# Copyright 2014 Pants project contributors (see CONTRIBUTORS.md).\n# Licensed under the Apache License, Version 2.0 (see LICENSE).\n\nfrom __future__ import absolute_import, print_function\n\nimport atexit\nimport contextlib\nimport errno\nimport fcntl\nimport os\nimport re\nimport shutil\nimport stat\nimport sys\nimport tempfile\nimport threading\nimport time\nimport zipfile\nfrom collections import defaultdict, namedtuple\nfrom contextlib import contextmanager\nfrom datetime import datetime\nfrom uuid import uuid4\n\nfrom pex.typing import TYPE_CHECKING\n\nif TYPE_CHECKING:\n from typing import Any, DefaultDict, Iterable, Iterator, NoReturn, Optional, Set, Sized\n\n# We use the start of MS-DOS time, which is what zipfiles use (see section 4.4.6 of\n# https://pkware.cachefly.net/webdocs/casestudies/APPNOTE.TXT).\nDETERMINISTIC_DATETIME = datetime(\n year=1980, month=1, day=1, hour=0, minute=0, second=0, tzinfo=None\n)\n\n\ndef filter_pyc_dirs(dirs):\n # type: (Iterable[str]) -> Iterator[str]\n \"\"\"Return an iterator over the input `dirs` filtering out Python bytecode cache directories.\"\"\"\n for d in dirs:\n if d != \"__pycache__\":\n yield d\n\n\ndef filter_pyc_files(files):\n # type: (Iterable[str]) -> Iterator[str]\n \"\"\"Return an iterator over the input `files` filtering out any Python bytecode files.\"\"\"\n for f in files:\n # For Python 2.7, `.pyc` files are compiled as siblings to `.py` files (there is no\n # __pycache__ dir). We rely on the fact that the temporary files created by CPython\n # have object id (integer) suffixes to avoid picking up either finished `.pyc` files\n # or files where Python bytecode compilation is in-flight; i.e.:\n # `.pyc.0123456789`-style files.\n if not re.search(r\"\\.pyc(?:\\.[0-9]+)?$\", f):\n yield f\n\n\ndef die(msg, exit_code=1):\n # type: (str, int) -> NoReturn\n print(msg, file=sys.stderr)\n sys.exit(exit_code)\n\n\ndef pluralize(\n subject, # type: Sized\n noun, # type: str\n):\n # type: (...) -> str\n if noun == \"\":\n return \"\"\n count = len(subject)\n if count == 1:\n return noun\n if noun[-1] in (\"s\", \"x\", \"z\") or noun[-2:] in (\"sh\", \"ch\"):\n return noun + \"es\"\n else:\n return noun + \"s\"\n\n\ndef safe_copy(source, dest, overwrite=False):\n # type: (str, str, bool) -> None\n def do_copy():\n # type: () -> None\n temp_dest = dest + uuid4().hex\n shutil.copy(source, temp_dest)\n os.rename(temp_dest, dest)\n\n # If the platform supports hard-linking, use that and fall back to copying.\n # Windows does not support hard-linking.\n if hasattr(os, \"link\"):\n try:\n os.link(source, dest)\n except OSError as e:\n if e.errno == errno.EEXIST:\n # File already exists. If overwrite=True, write otherwise skip.\n if overwrite:\n do_copy()\n elif e.errno in (errno.EPERM, errno.EXDEV):\n # For a hard link across devices issue, fall back on copying.\n #\n # For a permission issue, the cause could be one of:\n # 1. We can't read source.\n # 2. We can't write dest.\n # 3. We don't own source but can read it.\n # Although we can't do anything about cases 1 and 2, case 3 is due to\n # `protected_hardlinks` (see: https://www.kernel.org/doc/Documentation/sysctl/fs.txt) and\n # we can fall back to copying in that case.\n #\n # See also https://github.com/pantsbuild/pex/issues/850 where this was discovered.\n do_copy()\n else:\n raise\n elif os.path.exists(dest):\n if overwrite:\n do_copy()\n else:\n do_copy()\n\n\n# See http://stackoverflow.com/questions/2572172/referencing-other-modules-in-atexit\nclass MktempTeardownRegistry(object):\n def __init__(self):\n # type: () -> None\n self._registry = defaultdict(set) # type: DefaultDict[int, Set[str]]\n self._lock = threading.RLock()\n self._getpid = os.getpid\n self._exists = os.path.exists\n self._rmtree = shutil.rmtree\n atexit.register(self.teardown)\n\n def __del__(self):\n # type: () -> None\n self.teardown()\n\n def register(self, path):\n # type: (str) -> str\n with self._lock:\n self._registry[self._getpid()].add(path)\n return path\n\n def teardown(self):\n # type: () -> None\n for td in self._registry.pop(self._getpid(), []):\n if self._exists(td):\n self._rmtree(td)\n\n\n_MKDTEMP_SINGLETON = MktempTeardownRegistry()\n\n\nclass PermPreservingZipFile(zipfile.ZipFile, object):\n \"\"\"A ZipFile that works around https://bugs.python.org/issue15795.\"\"\"\n\n class ZipEntry(namedtuple(\"ZipEntry\", [\"info\", \"data\"])):\n pass\n\n @classmethod\n def zip_entry_from_file(cls, filename, arcname=None, date_time=None):\n \"\"\"Construct a ZipEntry for a file on the filesystem.\n\n Usually a similar `zip_info_from_file` method is provided by `ZipInfo`, but it is not\n implemented in Python 2.7 so we re-implement it here to construct the `info` for `ZipEntry`\n adding the possibility to control the `ZipInfo` date_time separately from the underlying\n file mtime. See https://github.com/python/cpython/blob/master/Lib/zipfile.py#L495.\n \"\"\"\n st = os.stat(filename)\n isdir = stat.S_ISDIR(st.st_mode)\n if arcname is None:\n arcname = filename\n arcname = os.path.normpath(os.path.splitdrive(arcname)[1])\n while arcname[0] in (os.sep, os.altsep):\n arcname = arcname[1:]\n if isdir:\n arcname += \"/\"\n if date_time is None:\n date_time = time.localtime(st.st_mtime)\n zinfo = zipfile.ZipInfo(filename=arcname, date_time=date_time[:6])\n zinfo.external_attr = (st.st_mode & 0xFFFF) << 16 # Unix attributes\n if isdir:\n zinfo.file_size = 0\n zinfo.external_attr |= 0x10 # MS-DOS directory flag\n zinfo.compress_type = zipfile.ZIP_STORED\n data = b\"\"\n else:\n zinfo.file_size = st.st_size\n zinfo.compress_type = zipfile.ZIP_DEFLATED\n with open(filename, \"rb\") as fp:\n data = fp.read()\n return cls.ZipEntry(info=zinfo, data=data)\n\n def _extract_member(self, member, targetpath, pwd):\n result = super(PermPreservingZipFile, self)._extract_member(member, targetpath, pwd)\n info = member if isinstance(member, zipfile.ZipInfo) else self.getinfo(member)\n self._chmod(info, result)\n return result\n\n def _chmod(self, info, path):\n # This magic works to extract perm bits from the 32 bit external file attributes field for\n # unix-created zip files, for the layout, see:\n # https://www.forensicswiki.org/wiki/ZIP#External_file_attributes\n attr = info.external_attr >> 16\n os.chmod(path, attr)\n\n\[email protected]\ndef open_zip(path, *args, **kwargs):\n \"\"\"A contextmanager for zip files.\n\n Passes through positional and kwargs to zipfile.ZipFile.\n \"\"\"\n with contextlib.closing(PermPreservingZipFile(path, *args, **kwargs)) as zip:\n yield zip\n\n\[email protected]\ndef temporary_dir(cleanup=True):\n # type: (bool) -> Iterator[str]\n td = tempfile.mkdtemp()\n try:\n yield td\n finally:\n if cleanup:\n safe_rmtree(td)\n\n\ndef safe_mkdtemp(**kw):\n # type: (**Any) -> str\n \"\"\"Create a temporary directory that is cleaned up on process exit.\n\n Takes the same parameters as tempfile.mkdtemp.\n \"\"\"\n # proper lock sanitation on fork [issue 6721] would be desirable here.\n return _MKDTEMP_SINGLETON.register(tempfile.mkdtemp(**kw))\n\n\ndef register_rmtree(directory):\n # type: (str) -> str\n \"\"\"Register an existing directory to be cleaned up at process exit.\"\"\"\n return _MKDTEMP_SINGLETON.register(directory)\n\n\ndef safe_mkdir(directory, clean=False):\n # type: (str, bool) -> None\n \"\"\"Safely create a directory.\n\n Ensures a directory is present. If it's not there, it is created. If it is, it's a no-op. If\n clean is True, ensures the directory is empty.\n \"\"\"\n if clean:\n safe_rmtree(directory)\n try:\n os.makedirs(directory)\n except OSError as e:\n if e.errno != errno.EEXIST:\n raise\n\n\ndef safe_open(filename, *args, **kwargs):\n \"\"\"Safely open a file.\n\n ``safe_open`` ensures that the directory components leading up the specified file have been\n created first.\n \"\"\"\n parent_dir = os.path.dirname(filename)\n if parent_dir:\n safe_mkdir(parent_dir)\n return open(filename, *args, **kwargs) # noqa: T802\n\n\ndef safe_delete(filename):\n # type: (str) -> None\n \"\"\"Delete a file safely.\n\n If it's not present, no-op.\n \"\"\"\n try:\n os.unlink(filename)\n except OSError as e:\n if e.errno != errno.ENOENT:\n raise\n\n\ndef safe_rmtree(directory):\n # type: (str) -> None\n \"\"\"Delete a directory if it's present.\n\n If it's not present, no-op.\n \"\"\"\n if os.path.exists(directory):\n shutil.rmtree(directory, True)\n\n\ndef safe_sleep(seconds):\n # type: (int) -> None\n \"\"\"Ensure that the thread sleeps at a minimum the requested seconds.\n\n Until Python 3.5, there was no guarantee that time.sleep() would actually sleep the requested\n time. See https://docs.python.org/3/library/time.html#time.sleep.\n \"\"\"\n if sys.version_info[0:2] >= (3, 5):\n time.sleep(seconds)\n else:\n start_time = current_time = time.time()\n while current_time - start_time < seconds:\n remaining_time = seconds - (current_time - start_time)\n time.sleep(remaining_time)\n current_time = time.time()\n\n\nclass AtomicDirectory(object):\n def __init__(self, target_dir):\n # type: (str) -> None\n self._target_dir = target_dir\n self._work_dir = \"{}.{}\".format(target_dir, uuid4().hex)\n\n @property\n def work_dir(self):\n # type: () -> str\n return self._work_dir\n\n @property\n def target_dir(self):\n # type: () -> str\n return self._target_dir\n\n @property\n def is_finalized(self):\n # type: () -> bool\n return os.path.exists(self._target_dir)\n\n def finalize(self, source=None):\n # type: (Optional[str]) -> None\n \"\"\"Rename `work_dir` to `target_dir` using `os.rename()`.\n\n :param source: An optional source offset into the `work_dir`` to use for the atomic update\n of `target_dir`. By default the whole `work_dir` is used.\n\n If a race is lost and `target_dir` already exists, the `target_dir` dir is left unchanged and\n the `work_dir` directory will simply be removed.\n \"\"\"\n if self.is_finalized:\n return\n\n source = os.path.join(self._work_dir, source) if source else self._work_dir\n try:\n # Perform an atomic rename.\n #\n # Per the docs: https://docs.python.org/2.7/library/os.html#os.rename\n #\n # The operation may fail on some Unix flavors if src and dst are on different filesystems.\n # If successful, the renaming will be an atomic operation (this is a POSIX requirement).\n #\n # We have satisfied the single filesystem constraint by arranging the `work_dir` to be a\n # sibling of the `target_dir`.\n os.rename(source, self._target_dir)\n except OSError as e:\n if e.errno not in (errno.EEXIST, errno.ENOTEMPTY):\n raise e\n finally:\n self.cleanup()\n\n def cleanup(self):\n # type: () -> None\n safe_rmtree(self._work_dir)\n\n\n@contextmanager\ndef atomic_directory(target_dir, exclusive, source=None):\n # type: (str, bool, Optional[str]) -> Iterator[Optional[str]]\n \"\"\"A context manager that yields a new empty work directory path it will move to `target_dir`.\n\n :param target_dir: The target directory to atomically update.\n :param exclusive: If `True`, its guaranteed that only one process will be yielded a non `None`\n workdir; otherwise two or more processes might be yielded unique non-`None`\n workdirs with the last process to finish \"winning\".\n :param source: An optional source offset into the work directory to use for the atomic update\n of the target directory. By default the whole work directory is used.\n\n If the `target_dir` already exists the enclosed block will be yielded `None` to signal there is\n no work to do.\n\n If the enclosed block fails the `target_dir` will be undisturbed.\n\n The new work directory will be cleaned up regardless of whether or not the enclosed block\n succeeds.\n\n If the contents of the resulting directory will be subsequently mutated it's probably correct to\n pass `exclusive=True` to ensure mutations that race the creation process are not lost.\n \"\"\"\n atomic_dir = AtomicDirectory(target_dir=target_dir)\n if atomic_dir.is_finalized:\n # Our work is already done for us so exit early.\n yield None\n return\n\n lock_fd = None # type: Optional[int]\n\n def unlock():\n # type: () -> None\n if lock_fd is None:\n return\n try:\n fcntl.lockf(lock_fd, fcntl.LOCK_UN)\n finally:\n os.close(lock_fd)\n\n if exclusive:\n head, tail = os.path.split(atomic_dir.target_dir)\n if head:\n safe_mkdir(head)\n # N.B.: We don't actually write anything to the lock file but the fcntl file locking\n # operations only work on files opened for at least write.\n lock_fd = os.open(\n os.path.join(head, \".{}.atomic_directory.lck\".format(tail or \"here\")),\n os.O_CREAT | os.O_WRONLY,\n )\n # N.B.: Since lockf operates on an open file descriptor and these are guaranteed to be\n # closed by the operating system when the owning process exits, this lock is immune to\n # staleness.\n fcntl.lockf(lock_fd, fcntl.LOCK_EX) # A blocking write lock.\n if atomic_dir.is_finalized:\n # We lost the double-checked locking race and our work was done for us by the race\n # winner so exit early.\n try:\n yield None\n finally:\n unlock()\n return\n\n try:\n safe_mkdir(atomic_dir.work_dir)\n yield atomic_dir.work_dir\n atomic_dir.finalize(source=source)\n finally:\n unlock()\n atomic_dir.cleanup()\n\n\ndef chmod_plus_x(path):\n # type: (str) -> None\n \"\"\"Equivalent of unix `chmod a+x path`\"\"\"\n path_mode = os.stat(path).st_mode\n path_mode &= int(\"777\", 8)\n if path_mode & stat.S_IRUSR:\n path_mode |= stat.S_IXUSR\n if path_mode & stat.S_IRGRP:\n path_mode |= stat.S_IXGRP\n if path_mode & stat.S_IROTH:\n path_mode |= stat.S_IXOTH\n os.chmod(path, path_mode)\n\n\ndef chmod_plus_w(path):\n # type: (str) -> None\n \"\"\"Equivalent of unix `chmod +w path`\"\"\"\n path_mode = os.stat(path).st_mode\n path_mode &= int(\"777\", 8)\n path_mode |= stat.S_IWRITE\n os.chmod(path, path_mode)\n\n\ndef is_exe(path):\n # type: (str) -> bool\n \"\"\"Determines if the given path is a file executable by the current user.\n\n :param path: The path to check.\n :return: `True if the given path is an file executable by the current user.\n \"\"\"\n return os.path.isfile(path) and os.access(path, os.R_OK | os.X_OK)\n\n\ndef can_write_dir(path):\n # type: (str) -> bool\n \"\"\"Determines if the directory at path can be written to by the current process.\n\n If the directory doesn't exist, determines if it can be created and thus written to.\n\n N.B.: This is a best-effort check only that uses permission heuristics and does not actually test\n that the directory can be written to with and writes.\n\n :param path: The directory path to test.\n :return:`True` if the given path is a directory that can be written to by the current process.\n \"\"\"\n while not os.access(path, os.F_OK):\n parent_path = os.path.dirname(path)\n if not parent_path or (parent_path == path):\n # We've recursed up to the root without success, which shouldn't happen,\n return False\n path = parent_path\n return os.path.isdir(path) and os.access(path, os.R_OK | os.W_OK | os.X_OK)\n\n\ndef touch(file):\n # type: (str) -> None\n \"\"\"Equivalent of unix `touch path`.\"\"\"\n with safe_open(file, \"a\"):\n os.utime(file, None)\n\n\nclass Chroot(object):\n \"\"\"A chroot of files overlayed from one directory to another directory.\n\n Files may be tagged when added in order to keep track of multiple overlays in the chroot.\n \"\"\"\n\n class Error(Exception):\n pass\n\n class ChrootTaggingException(Error):\n def __init__(self, filename, orig_tag, new_tag):\n super(Chroot.ChrootTaggingException, self).__init__( # noqa: T800\n \"Trying to add %s to fileset(%s) but already in fileset(%s)!\"\n % (filename, new_tag, orig_tag)\n )\n\n def __init__(self, chroot_base):\n \"\"\"Create the chroot.\n\n :chroot_base Directory for the creation of the target chroot.\n \"\"\"\n try:\n safe_mkdir(chroot_base)\n except OSError as e:\n raise self.ChrootException(\"Unable to create chroot in %s: %s\" % (chroot_base, e))\n self.chroot = chroot_base\n self.filesets = defaultdict(set)\n\n def clone(self, into=None):\n \"\"\"Clone this chroot.\n\n :keyword into: (optional) An optional destination directory to clone the\n Chroot into. If not specified, a temporary directory will be created.\n\n .. versionchanged:: 0.8\n The temporary directory created when ``into`` is not specified is now garbage collected on\n interpreter exit.\n \"\"\"\n into = into or safe_mkdtemp()\n new_chroot = Chroot(into)\n for label, fileset in self.filesets.items():\n for fn in fileset:\n new_chroot.link(os.path.join(self.chroot, fn), fn, label=label)\n return new_chroot\n\n def path(self):\n \"\"\"The path of the chroot.\"\"\"\n return self.chroot\n\n def _normalize(self, dst):\n dst = os.path.normpath(dst)\n if dst.startswith(os.sep) or dst.startswith(\"..\"):\n raise self.Error(\"Destination path is not a relative path!\")\n return dst\n\n def _check_tag(self, fn, label):\n for fs_label, fs in self.filesets.items():\n if fn in fs and fs_label != label:\n raise self.ChrootTaggingException(fn, fs_label, label)\n\n def _tag(self, fn, label):\n self._check_tag(fn, label)\n self.filesets[label].add(fn)\n\n def _ensure_parent(self, path):\n safe_mkdir(os.path.dirname(os.path.join(self.chroot, path)))\n\n def copy(self, src, dst, label=None):\n \"\"\"Copy file ``src`` to ``chroot/dst`` with optional label.\n\n May raise anything shutil.copy can raise, e.g.\n IOError(Errno 21 'EISDIR')\n\n May raise ChrootTaggingException if dst is already in a fileset\n but with a different label.\n \"\"\"\n dst = self._normalize(dst)\n self._tag(dst, label)\n self._ensure_parent(dst)\n shutil.copy(src, os.path.join(self.chroot, dst))\n\n def link(self, src, dst, label=None):\n \"\"\"Hard link file from ``src`` to ``chroot/dst`` with optional label.\n\n May raise anything os.link can raise, e.g.\n IOError(Errno 21 'EISDIR')\n\n May raise ChrootTaggingException if dst is already in a fileset\n but with a different label.\n \"\"\"\n dst = self._normalize(dst)\n self._tag(dst, label)\n self._ensure_parent(dst)\n abs_src = src\n abs_dst = os.path.join(self.chroot, dst)\n safe_copy(abs_src, abs_dst, overwrite=False)\n # TODO: Ensure the target and dest are the same if the file already exists.\n\n def symlink(\n self,\n src, # type: str\n dst, # type: str\n label=None, # type: Optional[str]\n ):\n # type: (...) -> None\n dst = self._normalize(dst)\n self._tag(dst, label)\n self._ensure_parent(dst)\n abs_src = src\n abs_dst = os.path.join(self.chroot, dst)\n os.symlink(abs_src, abs_dst)\n\n def write(self, data, dst, label=None, mode=\"wb\"):\n \"\"\"Write data to ``chroot/dst`` with optional label.\n\n Has similar exceptional cases as ``Chroot.copy``\n \"\"\"\n dst = self._normalize(dst)\n self._tag(dst, label)\n self._ensure_parent(dst)\n with open(os.path.join(self.chroot, dst), mode) as wp:\n wp.write(data)\n\n def touch(self, dst, label=None):\n \"\"\"Perform 'touch' on ``chroot/dst`` with optional label.\n\n Has similar exceptional cases as Chroot.copy\n \"\"\"\n dst = self._normalize(dst)\n self._tag(dst, label)\n touch(os.path.join(self.chroot, dst))\n\n def get(self, label):\n \"\"\"Get all files labeled with ``label``\"\"\"\n return self.filesets.get(label, set())\n\n def files(self):\n \"\"\"Get all files in the chroot.\"\"\"\n all_files = set()\n for label in self.filesets:\n all_files.update(self.filesets[label])\n return all_files\n\n def labels(self):\n return self.filesets.keys()\n\n def __str__(self):\n return \"Chroot(%s {fs:%s})\" % (\n self.chroot,\n \" \".join(\"%s\" % foo for foo in self.filesets.keys()),\n )\n\n def delete(self):\n shutil.rmtree(self.chroot)\n\n def zip(self, filename, mode=\"w\", deterministic_timestamp=False):\n with open_zip(filename, mode) as zf:\n\n def write_entry(filename, arcname):\n zip_entry = zf.zip_entry_from_file(\n filename=filename,\n arcname=arcname,\n date_time=DETERMINISTIC_DATETIME.timetuple()\n if deterministic_timestamp\n else None,\n )\n zf.writestr(zip_entry.info, zip_entry.data)\n\n def get_parent_dir(path):\n parent_dir = os.path.normpath(os.path.dirname(path))\n if parent_dir and parent_dir != os.curdir:\n return parent_dir\n return None\n\n written_dirs = set()\n\n def maybe_write_parent_dirs(path):\n parent_dir = get_parent_dir(path)\n if parent_dir is None or parent_dir in written_dirs:\n return\n maybe_write_parent_dirs(parent_dir)\n write_entry(filename=os.path.join(self.chroot, parent_dir), arcname=parent_dir)\n written_dirs.add(parent_dir)\n\n def iter_files():\n for path in sorted(self.files()):\n full_path = os.path.join(self.chroot, path)\n if os.path.isfile(full_path):\n yield full_path, path\n continue\n for root, _, files in os.walk(full_path):\n for f in files:\n abs_path = os.path.join(root, f)\n rel_path = os.path.join(path, os.path.relpath(abs_path, full_path))\n yield abs_path, rel_path\n\n for filename, arcname in iter_files():\n maybe_write_parent_dirs(arcname)\n write_entry(filename, arcname)\n",
"path": "pex/common.py"
}
] | [
{
"content": "# Copyright 2014 Pants project contributors (see CONTRIBUTORS.md).\n# Licensed under the Apache License, Version 2.0 (see LICENSE).\n\nfrom __future__ import absolute_import, print_function\n\nimport atexit\nimport contextlib\nimport errno\nimport fcntl\nimport os\nimport re\nimport shutil\nimport stat\nimport sys\nimport tempfile\nimport threading\nimport time\nimport zipfile\nfrom collections import defaultdict, namedtuple\nfrom contextlib import contextmanager\nfrom datetime import datetime\nfrom uuid import uuid4\n\nfrom pex.typing import TYPE_CHECKING\n\nif TYPE_CHECKING:\n from typing import Any, DefaultDict, Iterable, Iterator, NoReturn, Optional, Set, Sized\n\n# We use the start of MS-DOS time, which is what zipfiles use (see section 4.4.6 of\n# https://pkware.cachefly.net/webdocs/casestudies/APPNOTE.TXT).\nDETERMINISTIC_DATETIME = datetime(\n year=1980, month=1, day=1, hour=0, minute=0, second=0, tzinfo=None\n)\n\n\ndef filter_pyc_dirs(dirs):\n # type: (Iterable[str]) -> Iterator[str]\n \"\"\"Return an iterator over the input `dirs` filtering out Python bytecode cache directories.\"\"\"\n for d in dirs:\n if d != \"__pycache__\":\n yield d\n\n\ndef filter_pyc_files(files):\n # type: (Iterable[str]) -> Iterator[str]\n \"\"\"Return an iterator over the input `files` filtering out any Python bytecode files.\"\"\"\n for f in files:\n # For Python 2.7, `.pyc` files are compiled as siblings to `.py` files (there is no\n # __pycache__ dir). We rely on the fact that the temporary files created by CPython\n # have object id (integer) suffixes to avoid picking up either finished `.pyc` files\n # or files where Python bytecode compilation is in-flight; i.e.:\n # `.pyc.0123456789`-style files.\n if not re.search(r\"\\.pyc(?:\\.[0-9]+)?$\", f):\n yield f\n\n\ndef die(msg, exit_code=1):\n # type: (str, int) -> NoReturn\n print(msg, file=sys.stderr)\n sys.exit(exit_code)\n\n\ndef pluralize(\n subject, # type: Sized\n noun, # type: str\n):\n # type: (...) -> str\n if noun == \"\":\n return \"\"\n count = len(subject)\n if count == 1:\n return noun\n if noun[-1] in (\"s\", \"x\", \"z\") or noun[-2:] in (\"sh\", \"ch\"):\n return noun + \"es\"\n else:\n return noun + \"s\"\n\n\ndef safe_copy(source, dest, overwrite=False):\n # type: (str, str, bool) -> None\n def do_copy():\n # type: () -> None\n temp_dest = dest + uuid4().hex\n shutil.copy(source, temp_dest)\n os.rename(temp_dest, dest)\n\n # If the platform supports hard-linking, use that and fall back to copying.\n # Windows does not support hard-linking.\n if hasattr(os, \"link\"):\n try:\n os.link(source, dest)\n except OSError as e:\n if e.errno == errno.EEXIST:\n # File already exists. If overwrite=True, write otherwise skip.\n if overwrite:\n do_copy()\n elif e.errno in (errno.EPERM, errno.EXDEV):\n # For a hard link across devices issue, fall back on copying.\n #\n # For a permission issue, the cause could be one of:\n # 1. We can't read source.\n # 2. We can't write dest.\n # 3. We don't own source but can read it.\n # Although we can't do anything about cases 1 and 2, case 3 is due to\n # `protected_hardlinks` (see: https://www.kernel.org/doc/Documentation/sysctl/fs.txt) and\n # we can fall back to copying in that case.\n #\n # See also https://github.com/pantsbuild/pex/issues/850 where this was discovered.\n do_copy()\n else:\n raise\n elif os.path.exists(dest):\n if overwrite:\n do_copy()\n else:\n do_copy()\n\n\n# See http://stackoverflow.com/questions/2572172/referencing-other-modules-in-atexit\nclass MktempTeardownRegistry(object):\n def __init__(self):\n # type: () -> None\n self._registry = defaultdict(set) # type: DefaultDict[int, Set[str]]\n self._lock = threading.RLock()\n self._getpid = os.getpid\n self._exists = os.path.exists\n self._rmtree = shutil.rmtree\n atexit.register(self.teardown)\n\n def __del__(self):\n # type: () -> None\n self.teardown()\n\n def register(self, path):\n # type: (str) -> str\n with self._lock:\n self._registry[self._getpid()].add(path)\n return path\n\n def teardown(self):\n # type: () -> None\n for td in self._registry.pop(self._getpid(), []):\n if self._exists(td):\n self._rmtree(td)\n\n\n_MKDTEMP_SINGLETON = MktempTeardownRegistry()\n\n\nclass PermPreservingZipFile(zipfile.ZipFile, object):\n \"\"\"A ZipFile that works around https://bugs.python.org/issue15795.\"\"\"\n\n class ZipEntry(namedtuple(\"ZipEntry\", [\"info\", \"data\"])):\n pass\n\n @classmethod\n def zip_entry_from_file(cls, filename, arcname=None, date_time=None):\n \"\"\"Construct a ZipEntry for a file on the filesystem.\n\n Usually a similar `zip_info_from_file` method is provided by `ZipInfo`, but it is not\n implemented in Python 2.7 so we re-implement it here to construct the `info` for `ZipEntry`\n adding the possibility to control the `ZipInfo` date_time separately from the underlying\n file mtime. See https://github.com/python/cpython/blob/master/Lib/zipfile.py#L495.\n \"\"\"\n st = os.stat(filename)\n isdir = stat.S_ISDIR(st.st_mode)\n if arcname is None:\n arcname = filename\n arcname = os.path.normpath(os.path.splitdrive(arcname)[1])\n while arcname[0] in (os.sep, os.altsep):\n arcname = arcname[1:]\n if isdir:\n arcname += \"/\"\n if date_time is None:\n date_time = time.localtime(st.st_mtime)\n zinfo = zipfile.ZipInfo(filename=arcname, date_time=date_time[:6])\n zinfo.external_attr = (st.st_mode & 0xFFFF) << 16 # Unix attributes\n if isdir:\n zinfo.file_size = 0\n zinfo.external_attr |= 0x10 # MS-DOS directory flag\n zinfo.compress_type = zipfile.ZIP_STORED\n data = b\"\"\n else:\n zinfo.file_size = st.st_size\n zinfo.compress_type = zipfile.ZIP_DEFLATED\n with open(filename, \"rb\") as fp:\n data = fp.read()\n return cls.ZipEntry(info=zinfo, data=data)\n\n def _extract_member(self, member, targetpath, pwd):\n result = super(PermPreservingZipFile, self)._extract_member(member, targetpath, pwd)\n info = member if isinstance(member, zipfile.ZipInfo) else self.getinfo(member)\n self._chmod(info, result)\n return result\n\n def _chmod(self, info, path):\n # This magic works to extract perm bits from the 32 bit external file attributes field for\n # unix-created zip files, for the layout, see:\n # https://www.forensicswiki.org/wiki/ZIP#External_file_attributes\n attr = info.external_attr >> 16\n os.chmod(path, attr)\n\n\[email protected]\ndef open_zip(path, *args, **kwargs):\n \"\"\"A contextmanager for zip files.\n\n Passes through positional and kwargs to zipfile.ZipFile.\n \"\"\"\n with contextlib.closing(PermPreservingZipFile(path, *args, **kwargs)) as zip:\n yield zip\n\n\[email protected]\ndef temporary_dir(cleanup=True):\n # type: (bool) -> Iterator[str]\n td = tempfile.mkdtemp()\n try:\n yield td\n finally:\n if cleanup:\n safe_rmtree(td)\n\n\ndef safe_mkdtemp(**kw):\n # type: (**Any) -> str\n \"\"\"Create a temporary directory that is cleaned up on process exit.\n\n Takes the same parameters as tempfile.mkdtemp.\n \"\"\"\n # proper lock sanitation on fork [issue 6721] would be desirable here.\n return _MKDTEMP_SINGLETON.register(tempfile.mkdtemp(**kw))\n\n\ndef register_rmtree(directory):\n # type: (str) -> str\n \"\"\"Register an existing directory to be cleaned up at process exit.\"\"\"\n return _MKDTEMP_SINGLETON.register(directory)\n\n\ndef safe_mkdir(directory, clean=False):\n # type: (str, bool) -> None\n \"\"\"Safely create a directory.\n\n Ensures a directory is present. If it's not there, it is created. If it is, it's a no-op. If\n clean is True, ensures the directory is empty.\n \"\"\"\n if clean:\n safe_rmtree(directory)\n try:\n os.makedirs(directory)\n except OSError as e:\n if e.errno != errno.EEXIST:\n raise\n\n\ndef safe_open(filename, *args, **kwargs):\n \"\"\"Safely open a file.\n\n ``safe_open`` ensures that the directory components leading up the specified file have been\n created first.\n \"\"\"\n parent_dir = os.path.dirname(filename)\n if parent_dir:\n safe_mkdir(parent_dir)\n return open(filename, *args, **kwargs) # noqa: T802\n\n\ndef safe_delete(filename):\n # type: (str) -> None\n \"\"\"Delete a file safely.\n\n If it's not present, no-op.\n \"\"\"\n try:\n os.unlink(filename)\n except OSError as e:\n if e.errno != errno.ENOENT:\n raise\n\n\ndef safe_rmtree(directory):\n # type: (str) -> None\n \"\"\"Delete a directory if it's present.\n\n If it's not present, no-op.\n \"\"\"\n if os.path.exists(directory):\n shutil.rmtree(directory, True)\n\n\ndef safe_sleep(seconds):\n # type: (int) -> None\n \"\"\"Ensure that the thread sleeps at a minimum the requested seconds.\n\n Until Python 3.5, there was no guarantee that time.sleep() would actually sleep the requested\n time. See https://docs.python.org/3/library/time.html#time.sleep.\n \"\"\"\n if sys.version_info[0:2] >= (3, 5):\n time.sleep(seconds)\n else:\n start_time = current_time = time.time()\n while current_time - start_time < seconds:\n remaining_time = seconds - (current_time - start_time)\n time.sleep(remaining_time)\n current_time = time.time()\n\n\nclass AtomicDirectory(object):\n def __init__(self, target_dir):\n # type: (str) -> None\n self._target_dir = target_dir\n self._work_dir = \"{}.{}\".format(target_dir, uuid4().hex)\n\n @property\n def work_dir(self):\n # type: () -> str\n return self._work_dir\n\n @property\n def target_dir(self):\n # type: () -> str\n return self._target_dir\n\n @property\n def is_finalized(self):\n # type: () -> bool\n return os.path.exists(self._target_dir)\n\n def finalize(self, source=None):\n # type: (Optional[str]) -> None\n \"\"\"Rename `work_dir` to `target_dir` using `os.rename()`.\n\n :param source: An optional source offset into the `work_dir`` to use for the atomic update\n of `target_dir`. By default the whole `work_dir` is used.\n\n If a race is lost and `target_dir` already exists, the `target_dir` dir is left unchanged and\n the `work_dir` directory will simply be removed.\n \"\"\"\n if self.is_finalized:\n return\n\n source = os.path.join(self._work_dir, source) if source else self._work_dir\n try:\n # Perform an atomic rename.\n #\n # Per the docs: https://docs.python.org/2.7/library/os.html#os.rename\n #\n # The operation may fail on some Unix flavors if src and dst are on different filesystems.\n # If successful, the renaming will be an atomic operation (this is a POSIX requirement).\n #\n # We have satisfied the single filesystem constraint by arranging the `work_dir` to be a\n # sibling of the `target_dir`.\n os.rename(source, self._target_dir)\n except OSError as e:\n if e.errno not in (errno.EEXIST, errno.ENOTEMPTY):\n raise e\n finally:\n self.cleanup()\n\n def cleanup(self):\n # type: () -> None\n safe_rmtree(self._work_dir)\n\n\n@contextmanager\ndef atomic_directory(target_dir, exclusive, source=None):\n # type: (str, bool, Optional[str]) -> Iterator[Optional[str]]\n \"\"\"A context manager that yields a new empty work directory path it will move to `target_dir`.\n\n :param target_dir: The target directory to atomically update.\n :param exclusive: If `True`, its guaranteed that only one process will be yielded a non `None`\n workdir; otherwise two or more processes might be yielded unique non-`None`\n workdirs with the last process to finish \"winning\".\n :param source: An optional source offset into the work directory to use for the atomic update\n of the target directory. By default the whole work directory is used.\n\n If the `target_dir` already exists the enclosed block will be yielded `None` to signal there is\n no work to do.\n\n If the enclosed block fails the `target_dir` will be undisturbed.\n\n The new work directory will be cleaned up regardless of whether or not the enclosed block\n succeeds.\n\n If the contents of the resulting directory will be subsequently mutated it's probably correct to\n pass `exclusive=True` to ensure mutations that race the creation process are not lost.\n \"\"\"\n atomic_dir = AtomicDirectory(target_dir=target_dir)\n if atomic_dir.is_finalized:\n # Our work is already done for us so exit early.\n yield None\n return\n\n lock_fd = None # type: Optional[int]\n\n def unlock():\n # type: () -> None\n if lock_fd is None:\n return\n try:\n fcntl.lockf(lock_fd, fcntl.LOCK_UN)\n finally:\n os.close(lock_fd)\n\n if exclusive:\n head, tail = os.path.split(atomic_dir.target_dir)\n if head:\n safe_mkdir(head)\n # N.B.: We don't actually write anything to the lock file but the fcntl file locking\n # operations only work on files opened for at least write.\n lock_fd = os.open(\n os.path.join(head, \".{}.atomic_directory.lck\".format(tail or \"here\")),\n os.O_CREAT | os.O_WRONLY,\n )\n # N.B.: Since lockf operates on an open file descriptor and these are guaranteed to be\n # closed by the operating system when the owning process exits, this lock is immune to\n # staleness.\n fcntl.lockf(lock_fd, fcntl.LOCK_EX) # A blocking write lock.\n if atomic_dir.is_finalized:\n # We lost the double-checked locking race and our work was done for us by the race\n # winner so exit early.\n try:\n yield None\n finally:\n unlock()\n return\n\n try:\n safe_mkdir(atomic_dir.work_dir)\n yield atomic_dir.work_dir\n atomic_dir.finalize(source=source)\n finally:\n unlock()\n atomic_dir.cleanup()\n\n\ndef chmod_plus_x(path):\n # type: (str) -> None\n \"\"\"Equivalent of unix `chmod a+x path`\"\"\"\n path_mode = os.stat(path).st_mode\n path_mode &= int(\"777\", 8)\n if path_mode & stat.S_IRUSR:\n path_mode |= stat.S_IXUSR\n if path_mode & stat.S_IRGRP:\n path_mode |= stat.S_IXGRP\n if path_mode & stat.S_IROTH:\n path_mode |= stat.S_IXOTH\n os.chmod(path, path_mode)\n\n\ndef chmod_plus_w(path):\n # type: (str) -> None\n \"\"\"Equivalent of unix `chmod +w path`\"\"\"\n path_mode = os.stat(path).st_mode\n path_mode &= int(\"777\", 8)\n path_mode |= stat.S_IWRITE\n os.chmod(path, path_mode)\n\n\ndef is_exe(path):\n # type: (str) -> bool\n \"\"\"Determines if the given path is a file executable by the current user.\n\n :param path: The path to check.\n :return: `True if the given path is an file executable by the current user.\n \"\"\"\n return os.path.isfile(path) and os.access(path, os.R_OK | os.X_OK)\n\n\ndef can_write_dir(path):\n # type: (str) -> bool\n \"\"\"Determines if the directory at path can be written to by the current process.\n\n If the directory doesn't exist, determines if it can be created and thus written to.\n\n N.B.: This is a best-effort check only that uses permission heuristics and does not actually test\n that the directory can be written to with and writes.\n\n :param path: The directory path to test.\n :return:`True` if the given path is a directory that can be written to by the current process.\n \"\"\"\n while not os.access(path, os.F_OK):\n parent_path = os.path.dirname(path)\n if not parent_path or (parent_path == path):\n # We've recursed up to the root without success, which shouldn't happen,\n return False\n path = parent_path\n return os.path.isdir(path) and os.access(path, os.R_OK | os.W_OK | os.X_OK)\n\n\ndef touch(file):\n # type: (str) -> None\n \"\"\"Equivalent of unix `touch path`.\"\"\"\n with safe_open(file, \"a\"):\n os.utime(file, None)\n\n\nclass Chroot(object):\n \"\"\"A chroot of files overlayed from one directory to another directory.\n\n Files may be tagged when added in order to keep track of multiple overlays in the chroot.\n \"\"\"\n\n class Error(Exception):\n pass\n\n class ChrootTaggingException(Error):\n def __init__(self, filename, orig_tag, new_tag):\n super(Chroot.ChrootTaggingException, self).__init__( # noqa: T800\n \"Trying to add %s to fileset(%s) but already in fileset(%s)!\"\n % (filename, new_tag, orig_tag)\n )\n\n def __init__(self, chroot_base):\n \"\"\"Create the chroot.\n\n :chroot_base Directory for the creation of the target chroot.\n \"\"\"\n try:\n safe_mkdir(chroot_base)\n except OSError as e:\n raise self.ChrootException(\"Unable to create chroot in %s: %s\" % (chroot_base, e))\n self.chroot = chroot_base\n self.filesets = defaultdict(set)\n\n def clone(self, into=None):\n \"\"\"Clone this chroot.\n\n :keyword into: (optional) An optional destination directory to clone the\n Chroot into. If not specified, a temporary directory will be created.\n\n .. versionchanged:: 0.8\n The temporary directory created when ``into`` is not specified is now garbage collected on\n interpreter exit.\n \"\"\"\n into = into or safe_mkdtemp()\n new_chroot = Chroot(into)\n for label, fileset in self.filesets.items():\n for fn in fileset:\n new_chroot.link(os.path.join(self.chroot, fn), fn, label=label)\n return new_chroot\n\n def path(self):\n \"\"\"The path of the chroot.\"\"\"\n return self.chroot\n\n def _normalize(self, dst):\n dst = os.path.normpath(dst)\n if dst.startswith(os.sep) or dst.startswith(\"..\"):\n raise self.Error(\"Destination path is not a relative path!\")\n return dst\n\n def _check_tag(self, fn, label):\n for fs_label, fs in self.filesets.items():\n if fn in fs and fs_label != label:\n raise self.ChrootTaggingException(fn, fs_label, label)\n\n def _tag(self, fn, label):\n self._check_tag(fn, label)\n self.filesets[label].add(fn)\n\n def _ensure_parent(self, path):\n safe_mkdir(os.path.dirname(os.path.join(self.chroot, path)))\n\n def copy(self, src, dst, label=None):\n \"\"\"Copy file ``src`` to ``chroot/dst`` with optional label.\n\n May raise anything shutil.copy can raise, e.g.\n IOError(Errno 21 'EISDIR')\n\n May raise ChrootTaggingException if dst is already in a fileset\n but with a different label.\n \"\"\"\n dst = self._normalize(dst)\n self._tag(dst, label)\n self._ensure_parent(dst)\n shutil.copy(src, os.path.join(self.chroot, dst))\n\n def link(self, src, dst, label=None):\n \"\"\"Hard link file from ``src`` to ``chroot/dst`` with optional label.\n\n May raise anything os.link can raise, e.g.\n IOError(Errno 21 'EISDIR')\n\n May raise ChrootTaggingException if dst is already in a fileset\n but with a different label.\n \"\"\"\n dst = self._normalize(dst)\n self._tag(dst, label)\n self._ensure_parent(dst)\n abs_src = src\n abs_dst = os.path.join(self.chroot, dst)\n safe_copy(abs_src, abs_dst, overwrite=False)\n # TODO: Ensure the target and dest are the same if the file already exists.\n\n def symlink(\n self,\n src, # type: str\n dst, # type: str\n label=None, # type: Optional[str]\n ):\n # type: (...) -> None\n dst = self._normalize(dst)\n self._tag(dst, label)\n self._ensure_parent(dst)\n abs_src = os.path.abspath(src)\n abs_dst = os.path.join(self.chroot, dst)\n os.symlink(abs_src, abs_dst)\n\n def write(self, data, dst, label=None, mode=\"wb\"):\n \"\"\"Write data to ``chroot/dst`` with optional label.\n\n Has similar exceptional cases as ``Chroot.copy``\n \"\"\"\n dst = self._normalize(dst)\n self._tag(dst, label)\n self._ensure_parent(dst)\n with open(os.path.join(self.chroot, dst), mode) as wp:\n wp.write(data)\n\n def touch(self, dst, label=None):\n \"\"\"Perform 'touch' on ``chroot/dst`` with optional label.\n\n Has similar exceptional cases as Chroot.copy\n \"\"\"\n dst = self._normalize(dst)\n self._tag(dst, label)\n touch(os.path.join(self.chroot, dst))\n\n def get(self, label):\n \"\"\"Get all files labeled with ``label``\"\"\"\n return self.filesets.get(label, set())\n\n def files(self):\n \"\"\"Get all files in the chroot.\"\"\"\n all_files = set()\n for label in self.filesets:\n all_files.update(self.filesets[label])\n return all_files\n\n def labels(self):\n return self.filesets.keys()\n\n def __str__(self):\n return \"Chroot(%s {fs:%s})\" % (\n self.chroot,\n \" \".join(\"%s\" % foo for foo in self.filesets.keys()),\n )\n\n def delete(self):\n shutil.rmtree(self.chroot)\n\n def zip(self, filename, mode=\"w\", deterministic_timestamp=False):\n with open_zip(filename, mode) as zf:\n\n def write_entry(filename, arcname):\n zip_entry = zf.zip_entry_from_file(\n filename=filename,\n arcname=arcname,\n date_time=DETERMINISTIC_DATETIME.timetuple()\n if deterministic_timestamp\n else None,\n )\n zf.writestr(zip_entry.info, zip_entry.data)\n\n def get_parent_dir(path):\n parent_dir = os.path.normpath(os.path.dirname(path))\n if parent_dir and parent_dir != os.curdir:\n return parent_dir\n return None\n\n written_dirs = set()\n\n def maybe_write_parent_dirs(path):\n parent_dir = get_parent_dir(path)\n if parent_dir is None or parent_dir in written_dirs:\n return\n maybe_write_parent_dirs(parent_dir)\n write_entry(filename=os.path.join(self.chroot, parent_dir), arcname=parent_dir)\n written_dirs.add(parent_dir)\n\n def iter_files():\n for path in sorted(self.files()):\n full_path = os.path.join(self.chroot, path)\n if os.path.isfile(full_path):\n yield full_path, path\n continue\n for root, _, files in os.walk(full_path):\n for f in files:\n abs_path = os.path.join(root, f)\n rel_path = os.path.join(path, os.path.relpath(abs_path, full_path))\n yield abs_path, rel_path\n\n for filename, arcname in iter_files():\n maybe_write_parent_dirs(arcname)\n write_entry(filename, arcname)\n",
"path": "pex/common.py"
}
] | diff --git a/pex/common.py b/pex/common.py
index e52fd54e3..8181b5323 100644
--- a/pex/common.py
+++ b/pex/common.py
@@ -604,7 +604,7 @@ def symlink(
dst = self._normalize(dst)
self._tag(dst, label)
self._ensure_parent(dst)
- abs_src = src
+ abs_src = os.path.abspath(src)
abs_dst = os.path.join(self.chroot, dst)
os.symlink(abs_src, abs_dst)
diff --git a/tests/test_common.py b/tests/test_common.py
index fa9a02549..ae6fe4b43 100644
--- a/tests/test_common.py
+++ b/tests/test_common.py
@@ -231,6 +231,17 @@ def test_chroot_zip_symlink():
os.path.join(chroot.path(), "directory/subdirectory/file"),
"directory/subdirectory/symlinked",
)
+
+ cwd = os.getcwd()
+ try:
+ os.chdir(os.path.join(chroot.path(), "directory/subdirectory"))
+ chroot.symlink(
+ "file",
+ "directory/subdirectory/rel-symlinked",
+ )
+ finally:
+ os.chdir(cwd)
+
chroot.symlink(os.path.join(chroot.path(), "directory"), "symlinked")
zip_dst = os.path.join(tmp, "chroot.zip")
chroot.zip(zip_dst)
@@ -239,19 +250,23 @@ def test_chroot_zip_symlink():
"directory/",
"directory/subdirectory/",
"directory/subdirectory/file",
+ "directory/subdirectory/rel-symlinked",
"directory/subdirectory/symlinked",
"symlinked/",
"symlinked/subdirectory/",
"symlinked/subdirectory/file",
+ "symlinked/subdirectory/rel-symlinked",
"symlinked/subdirectory/symlinked",
] == sorted(zip.namelist())
assert b"" == zip.read("directory/")
assert b"" == zip.read("directory/subdirectory/")
assert b"data" == zip.read("directory/subdirectory/file")
+ assert b"data" == zip.read("directory/subdirectory/rel-symlinked")
assert b"data" == zip.read("directory/subdirectory/symlinked")
assert b"" == zip.read("symlinked/")
assert b"" == zip.read("symlinked/subdirectory/")
assert b"data" == zip.read("symlinked/subdirectory/file")
+ assert b"data" == zip.read("symlinked/subdirectory/rel-symlinked")
assert b"data" == zip.read("symlinked/subdirectory/symlinked")
diff --git a/tests/test_pex_builder.py b/tests/test_pex_builder.py
index 7d6017495..b25b15eb6 100644
--- a/tests/test_pex_builder.py
+++ b/tests/test_pex_builder.py
@@ -7,8 +7,9 @@
import pytest
-from pex.common import temporary_dir
+from pex.common import safe_open, temporary_dir
from pex.compatibility import WINDOWS, nested
+from pex.executor import Executor
from pex.pex import PEX
from pex.pex_builder import BOOTSTRAP_DIR, CopyMode, PEXBuilder
from pex.testing import make_bdist
@@ -16,7 +17,7 @@
from pex.typing import TYPE_CHECKING
if TYPE_CHECKING:
- from typing import List
+ from typing import Any, Iterator, List
exe_main = """
import sys
@@ -179,7 +180,12 @@ def build_and_check(path, copy_mode):
is_link = (s1[stat.ST_INO], s1[stat.ST_DEV]) == (s2[stat.ST_INO], s2[stat.ST_DEV])
if copy_mode == CopyMode.COPY:
assert not is_link
- elif copy_mode == CopyMode.LINK:
+ else:
+ # Since os.stat follows symlinks; so in CopyMode.SYMLINK, this just proves the
+ # symlink points to the original file. Going further and checking path and
+ # path_clone for the presence of a symlink (an os.islink test) is trickier since
+ # a Linux hardlink of a symlink produces a symlink whereas a maxOS hardlink of a
+ # symlink produces a hardlink.
assert is_link
build_and_check(td2, CopyMode.LINK)
@@ -187,6 +193,38 @@ def build_and_check(path, copy_mode):
build_and_check(td4, CopyMode.SYMLINK)
[email protected]
+def tmp_chroot(tmpdir):
+ # type: (Any) -> Iterator[str]
+ tmp_chroot = str(tmpdir)
+ cwd = os.getcwd()
+ try:
+ os.chdir(tmp_chroot)
+ yield tmp_chroot
+ finally:
+ os.chdir(cwd)
+
+
[email protected](
+ "copy_mode", [pytest.param(copy_mode, id=copy_mode.value) for copy_mode in CopyMode.values]
+)
+def test_pex_builder_add_source_relpath_issues_1192(
+ tmp_chroot, # type: str
+ copy_mode, # type: CopyMode.Value
+):
+ # type: (...) -> None
+ pb = PEXBuilder(copy_mode=copy_mode)
+ with safe_open("src/main.py", "w") as fp:
+ fp.write("import sys; sys.exit(42)")
+ pb.add_source("src/main.py", "main.py")
+ pb.set_entry_point("main")
+ pb.build("test.pex")
+
+ process = Executor.open_process(cmd=[os.path.abspath("test.pex")])
+ process.wait()
+ assert 42 == process.returncode
+
+
def test_pex_builder_deterministic_timestamp():
# type: () -> None
pb = PEXBuilder()
|
cupy__cupy-2318 | TypeError for OutOfMemoryError
Seen while using chainer while multiprocessing and using the GPU:
```
Traceback (most recent call last):
File "/usr/lib/python3.6/threading.py", line 916, in _bootstrap_inner
self.run()
File "/usr/lib/python3.6/threading.py", line 864, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python3.6/multiprocessing/pool.py", line 463, in _handle_results
task = get()
File "/usr/lib/python3.6/multiprocessing/connection.py", line 251, in recv
return _ForkingPickler.loads(buf.getbuffer())
File "cupy/cuda/memory.pyx", line 37, in cupy.cuda.memory.OutOfMemoryError.__init__
TypeError: __init__() takes exactly 3 positional arguments (2 given)
```
Seems like it tried to raise an OutOfMemoryError but failed to do so.
```
CuPy Version : 6.1.0
CUDA Root : /usr/local/cuda
CUDA Build Version : 10010
CUDA Driver Version : 10010
CUDA Runtime Version : 10010
cuDNN Build Version : 7500
cuDNN Version : 7500
NCCL Build Version : 2402
NCCL Runtime Version : 2402
```
| [
{
"content": "import hashlib\nimport math\nimport os\nimport re\nimport shutil\nimport sys\nimport tempfile\n\nimport six\n\nfrom cupy.cuda import device\nfrom cupy.cuda import function\nfrom cupy.cuda import nvrtc\n\n_nvrtc_version = None\n_nvrtc_max_compute_capability = None\n\n\ndef _get_nvrtc_version():\n global _nvrtc_version\n if _nvrtc_version is None:\n _nvrtc_version = nvrtc.getVersion()\n\n return _nvrtc_version\n\n\ndef _get_arch():\n global _nvrtc_max_compute_capability\n if _nvrtc_max_compute_capability is None:\n # See Supported Compile Options section of NVRTC User Guide for\n # the maximum value allowed for `--gpu-architecture`.\n major, minor = _get_nvrtc_version()\n if major < 9:\n # CUDA 7.0 / 7.5 / 8.0\n _nvrtc_max_compute_capability = '50'\n else:\n # CUDA 9.0 / 9.1\n _nvrtc_max_compute_capability = '70'\n cc = min(device.Device().compute_capability, _nvrtc_max_compute_capability)\n return 'compute_%s' % cc\n\n\nclass TemporaryDirectory(object):\n def __enter__(self):\n self.path = tempfile.mkdtemp()\n return self.path\n\n def __exit__(self, exc_type, exc_value, traceback):\n if exc_value is not None:\n return\n\n for name in os.listdir(self.path):\n os.unlink(os.path.join(self.path, name))\n os.rmdir(self.path)\n\n\ndef _get_bool_env_variable(name, default):\n val = os.environ.get(name)\n if val is None or len(val) == 0:\n return default\n try:\n return int(val) == 1\n except ValueError:\n return False\n\n\ndef compile_using_nvrtc(source, options=(), arch=None, filename='kern.cu'):\n if not arch:\n arch = _get_arch()\n\n options += ('-arch={}'.format(arch),)\n\n with TemporaryDirectory() as root_dir:\n cu_path = os.path.join(root_dir, filename)\n\n with open(cu_path, 'w') as cu_file:\n cu_file.write(source)\n\n prog = _NVRTCProgram(source, cu_path)\n try:\n ptx = prog.compile(options)\n except CompileException as e:\n dump = _get_bool_env_variable(\n 'CUPY_DUMP_CUDA_SOURCE_ON_ERROR', False)\n if dump:\n e.dump(sys.stderr)\n raise\n\n return ptx\n\n\ndef _preprocess(source, options, arch):\n options += ('-arch={}'.format(arch),)\n\n prog = _NVRTCProgram(source, '')\n try:\n result = prog.compile(options)\n except CompileException as e:\n dump = _get_bool_env_variable(\n 'CUPY_DUMP_CUDA_SOURCE_ON_ERROR', False)\n if dump:\n e.dump(sys.stderr)\n raise\n\n assert isinstance(result, six.text_type)\n return result\n\n\n_default_cache_dir = os.path.expanduser('~/.cupy/kernel_cache')\n\n\ndef get_cache_dir():\n return os.environ.get('CUPY_CACHE_DIR', _default_cache_dir)\n\n\n_empty_file_preprocess_cache = {}\n\n\ndef compile_with_cache(source, options=(), arch=None, cache_dir=None,\n extra_source=None):\n # NVRTC does not use extra_source. extra_source is used for cache key.\n global _empty_file_preprocess_cache\n if cache_dir is None:\n cache_dir = get_cache_dir()\n if arch is None:\n arch = _get_arch()\n\n options += ('-ftz=true',)\n if _get_bool_env_variable('CUPY_CUDA_COMPILE_WITH_DEBUG', False):\n options += ('--device-debug', '--generate-line-info')\n\n env = (arch, options, _get_nvrtc_version())\n base = _empty_file_preprocess_cache.get(env, None)\n if base is None:\n # This is checking of NVRTC compiler internal version\n base = _preprocess('', options, arch)\n _empty_file_preprocess_cache[env] = base\n key_src = '%s %s %s %s' % (env, base, source, extra_source)\n\n key_src = key_src.encode('utf-8')\n name = '%s_2.cubin' % hashlib.md5(key_src).hexdigest()\n\n if not os.path.isdir(cache_dir):\n try:\n os.makedirs(cache_dir)\n except OSError:\n if not os.path.isdir(cache_dir):\n raise\n\n mod = function.Module()\n # To handle conflicts in concurrent situation, we adopt lock-free method\n # to avoid performance degradation.\n path = os.path.join(cache_dir, name)\n if os.path.exists(path):\n with open(path, 'rb') as file:\n data = file.read()\n if len(data) >= 32:\n hash = data[:32]\n cubin = data[32:]\n cubin_hash = six.b(hashlib.md5(cubin).hexdigest())\n if hash == cubin_hash:\n mod.load(cubin)\n return mod\n\n ptx = compile_using_nvrtc(source, options, arch, name + '.cu')\n ls = function.LinkState()\n ls.add_ptr_data(ptx, u'cupy.ptx')\n cubin = ls.complete()\n cubin_hash = six.b(hashlib.md5(cubin).hexdigest())\n\n # shutil.move is not atomic operation, so it could result in a corrupted\n # file. We detect it by appending md5 hash at the beginning of each cache\n # file. If the file is corrupted, it will be ignored next time it is read.\n with tempfile.NamedTemporaryFile(dir=cache_dir, delete=False) as tf:\n tf.write(cubin_hash)\n tf.write(cubin)\n temp_path = tf.name\n shutil.move(temp_path, path)\n\n # Save .cu source file along with .cubin\n if _get_bool_env_variable('CUPY_CACHE_SAVE_CUDA_SOURCE', False):\n with open(path + '.cu', 'w') as f:\n f.write(source)\n\n mod.load(cubin)\n return mod\n\n\nclass CompileException(Exception):\n\n def __init__(self, msg, source, name, options):\n self._msg = msg\n self.source = source\n self.name = name\n self.options = options\n\n def __repr__(self):\n return str(self)\n\n def __str__(self):\n return self.get_message()\n\n def get_message(self):\n return self._msg\n\n def dump(self, f):\n lines = self.source.split('\\n')\n digits = int(math.floor(math.log10(len(lines)))) + 1\n linum_fmt = '{{:0{}d}} '.format(digits)\n f.write('NVRTC compilation error: {}\\n'.format(self))\n f.write('-----\\n')\n f.write('Name: {}\\n'.format(self.name))\n f.write('Options: {}\\n'.format(' '.join(self.options)))\n f.write('CUDA source:\\n')\n for i, line in enumerate(lines):\n f.write(linum_fmt.format(i + 1) + line.rstrip() + '\\n')\n f.write('-----\\n')\n f.flush()\n\n\nclass _NVRTCProgram(object):\n\n def __init__(self, src, name='default_program', headers=(),\n include_names=()):\n self.ptr = None\n\n if isinstance(src, six.binary_type):\n src = src.decode('UTF-8')\n if isinstance(name, six.binary_type):\n name = name.decode('UTF-8')\n\n self.src = src\n self.name = name\n self.ptr = nvrtc.createProgram(src, name, headers, include_names)\n\n def __del__(self):\n if self.ptr:\n nvrtc.destroyProgram(self.ptr)\n\n def compile(self, options=()):\n try:\n nvrtc.compileProgram(self.ptr, options)\n return nvrtc.getPTX(self.ptr)\n except nvrtc.NVRTCError:\n log = nvrtc.getProgramLog(self.ptr)\n raise CompileException(log, self.src, self.name, options)\n\n\ndef is_valid_kernel_name(name):\n return re.match('^[a-zA-Z_][a-zA-Z_0-9]*$', name) is not None\n",
"path": "cupy/cuda/compiler.py"
}
] | [
{
"content": "import hashlib\nimport math\nimport os\nimport re\nimport shutil\nimport sys\nimport tempfile\n\nimport six\n\nfrom cupy.cuda import device\nfrom cupy.cuda import function\nfrom cupy.cuda import nvrtc\n\n_nvrtc_version = None\n_nvrtc_max_compute_capability = None\n\n\ndef _get_nvrtc_version():\n global _nvrtc_version\n if _nvrtc_version is None:\n _nvrtc_version = nvrtc.getVersion()\n\n return _nvrtc_version\n\n\ndef _get_arch():\n global _nvrtc_max_compute_capability\n if _nvrtc_max_compute_capability is None:\n # See Supported Compile Options section of NVRTC User Guide for\n # the maximum value allowed for `--gpu-architecture`.\n major, minor = _get_nvrtc_version()\n if major < 9:\n # CUDA 7.0 / 7.5 / 8.0\n _nvrtc_max_compute_capability = '50'\n else:\n # CUDA 9.0 / 9.1\n _nvrtc_max_compute_capability = '70'\n cc = min(device.Device().compute_capability, _nvrtc_max_compute_capability)\n return 'compute_%s' % cc\n\n\nclass TemporaryDirectory(object):\n def __enter__(self):\n self.path = tempfile.mkdtemp()\n return self.path\n\n def __exit__(self, exc_type, exc_value, traceback):\n if exc_value is not None:\n return\n\n for name in os.listdir(self.path):\n os.unlink(os.path.join(self.path, name))\n os.rmdir(self.path)\n\n\ndef _get_bool_env_variable(name, default):\n val = os.environ.get(name)\n if val is None or len(val) == 0:\n return default\n try:\n return int(val) == 1\n except ValueError:\n return False\n\n\ndef compile_using_nvrtc(source, options=(), arch=None, filename='kern.cu'):\n if not arch:\n arch = _get_arch()\n\n options += ('-arch={}'.format(arch),)\n\n with TemporaryDirectory() as root_dir:\n cu_path = os.path.join(root_dir, filename)\n\n with open(cu_path, 'w') as cu_file:\n cu_file.write(source)\n\n prog = _NVRTCProgram(source, cu_path)\n try:\n ptx = prog.compile(options)\n except CompileException as e:\n dump = _get_bool_env_variable(\n 'CUPY_DUMP_CUDA_SOURCE_ON_ERROR', False)\n if dump:\n e.dump(sys.stderr)\n raise\n\n return ptx\n\n\ndef _preprocess(source, options, arch):\n options += ('-arch={}'.format(arch),)\n\n prog = _NVRTCProgram(source, '')\n try:\n result = prog.compile(options)\n except CompileException as e:\n dump = _get_bool_env_variable(\n 'CUPY_DUMP_CUDA_SOURCE_ON_ERROR', False)\n if dump:\n e.dump(sys.stderr)\n raise\n\n assert isinstance(result, six.text_type)\n return result\n\n\n_default_cache_dir = os.path.expanduser('~/.cupy/kernel_cache')\n\n\ndef get_cache_dir():\n return os.environ.get('CUPY_CACHE_DIR', _default_cache_dir)\n\n\n_empty_file_preprocess_cache = {}\n\n\ndef compile_with_cache(source, options=(), arch=None, cache_dir=None,\n extra_source=None):\n # NVRTC does not use extra_source. extra_source is used for cache key.\n global _empty_file_preprocess_cache\n if cache_dir is None:\n cache_dir = get_cache_dir()\n if arch is None:\n arch = _get_arch()\n\n options += ('-ftz=true',)\n if _get_bool_env_variable('CUPY_CUDA_COMPILE_WITH_DEBUG', False):\n options += ('--device-debug', '--generate-line-info')\n\n env = (arch, options, _get_nvrtc_version())\n base = _empty_file_preprocess_cache.get(env, None)\n if base is None:\n # This is checking of NVRTC compiler internal version\n base = _preprocess('', options, arch)\n _empty_file_preprocess_cache[env] = base\n key_src = '%s %s %s %s' % (env, base, source, extra_source)\n\n key_src = key_src.encode('utf-8')\n name = '%s_2.cubin' % hashlib.md5(key_src).hexdigest()\n\n if not os.path.isdir(cache_dir):\n try:\n os.makedirs(cache_dir)\n except OSError:\n if not os.path.isdir(cache_dir):\n raise\n\n mod = function.Module()\n # To handle conflicts in concurrent situation, we adopt lock-free method\n # to avoid performance degradation.\n path = os.path.join(cache_dir, name)\n if os.path.exists(path):\n with open(path, 'rb') as file:\n data = file.read()\n if len(data) >= 32:\n hash = data[:32]\n cubin = data[32:]\n cubin_hash = six.b(hashlib.md5(cubin).hexdigest())\n if hash == cubin_hash:\n mod.load(cubin)\n return mod\n\n ptx = compile_using_nvrtc(source, options, arch, name + '.cu')\n ls = function.LinkState()\n ls.add_ptr_data(ptx, u'cupy.ptx')\n cubin = ls.complete()\n cubin_hash = six.b(hashlib.md5(cubin).hexdigest())\n\n # shutil.move is not atomic operation, so it could result in a corrupted\n # file. We detect it by appending md5 hash at the beginning of each cache\n # file. If the file is corrupted, it will be ignored next time it is read.\n with tempfile.NamedTemporaryFile(dir=cache_dir, delete=False) as tf:\n tf.write(cubin_hash)\n tf.write(cubin)\n temp_path = tf.name\n shutil.move(temp_path, path)\n\n # Save .cu source file along with .cubin\n if _get_bool_env_variable('CUPY_CACHE_SAVE_CUDA_SOURCE', False):\n with open(path + '.cu', 'w') as f:\n f.write(source)\n\n mod.load(cubin)\n return mod\n\n\nclass CompileException(Exception):\n\n def __init__(self, msg, source, name, options):\n self._msg = msg\n self.source = source\n self.name = name\n self.options = options\n super(CompileException, self).__init__()\n\n def __reduce__(self):\n return (type(self), (self._msg, self.source, self.name, self.options))\n\n def __repr__(self):\n return str(self)\n\n def __str__(self):\n return self.get_message()\n\n def get_message(self):\n return self._msg\n\n def dump(self, f):\n lines = self.source.split('\\n')\n digits = int(math.floor(math.log10(len(lines)))) + 1\n linum_fmt = '{{:0{}d}} '.format(digits)\n f.write('NVRTC compilation error: {}\\n'.format(self))\n f.write('-----\\n')\n f.write('Name: {}\\n'.format(self.name))\n f.write('Options: {}\\n'.format(' '.join(self.options)))\n f.write('CUDA source:\\n')\n for i, line in enumerate(lines):\n f.write(linum_fmt.format(i + 1) + line.rstrip() + '\\n')\n f.write('-----\\n')\n f.flush()\n\n\nclass _NVRTCProgram(object):\n\n def __init__(self, src, name='default_program', headers=(),\n include_names=()):\n self.ptr = None\n\n if isinstance(src, six.binary_type):\n src = src.decode('UTF-8')\n if isinstance(name, six.binary_type):\n name = name.decode('UTF-8')\n\n self.src = src\n self.name = name\n self.ptr = nvrtc.createProgram(src, name, headers, include_names)\n\n def __del__(self):\n if self.ptr:\n nvrtc.destroyProgram(self.ptr)\n\n def compile(self, options=()):\n try:\n nvrtc.compileProgram(self.ptr, options)\n return nvrtc.getPTX(self.ptr)\n except nvrtc.NVRTCError:\n log = nvrtc.getProgramLog(self.ptr)\n raise CompileException(log, self.src, self.name, options)\n\n\ndef is_valid_kernel_name(name):\n return re.match('^[a-zA-Z_][a-zA-Z_0-9]*$', name) is not None\n",
"path": "cupy/cuda/compiler.py"
}
] | diff --git a/cupy/cuda/compiler.py b/cupy/cuda/compiler.py
index 9cd1b4ca71a..1c77c13de6f 100644
--- a/cupy/cuda/compiler.py
+++ b/cupy/cuda/compiler.py
@@ -193,6 +193,10 @@ def __init__(self, msg, source, name, options):
self.source = source
self.name = name
self.options = options
+ super(CompileException, self).__init__()
+
+ def __reduce__(self):
+ return (type(self), (self._msg, self.source, self.name, self.options))
def __repr__(self):
return str(self)
diff --git a/cupy/cuda/cublas.pyx b/cupy/cuda/cublas.pyx
index 65fdc55d5a9..67c888d2972 100644
--- a/cupy/cuda/cublas.pyx
+++ b/cupy/cuda/cublas.pyx
@@ -301,6 +301,9 @@ class CUBLASError(RuntimeError):
self.status = status
super(CUBLASError, self).__init__(STATUS[status])
+ def __reduce__(self):
+ return (type(self), (self.status,))
+
@cython.profile(False)
cpdef inline check_status(int status):
diff --git a/cupy/cuda/cudnn.pyx b/cupy/cuda/cudnn.pyx
index 22b2d1d2a94..9dae43a8267 100644
--- a/cupy/cuda/cudnn.pyx
+++ b/cupy/cuda/cudnn.pyx
@@ -760,6 +760,9 @@ class CuDNNError(RuntimeError):
msg = cudnnGetErrorString(<Status>status)
super(CuDNNError, self).__init__(msg.decode())
+ def __reduce__(self):
+ return (type(self), (self.status,))
+
@cython.profile(False)
cpdef inline check_status(int status):
diff --git a/cupy/cuda/cufft.pyx b/cupy/cuda/cufft.pyx
index 593c960427c..23594766999 100644
--- a/cupy/cuda/cufft.pyx
+++ b/cupy/cuda/cufft.pyx
@@ -69,6 +69,9 @@ class CuFFTError(RuntimeError):
self.result = result
super(CuFFTError, self).__init__('%s' % (RESULT[result]))
+ def __reduce__(self):
+ return (type(self), (self.result,))
+
@cython.profile(False)
cpdef inline check_result(int result):
diff --git a/cupy/cuda/curand.pyx b/cupy/cuda/curand.pyx
index 310038c6b5e..1001536df05 100644
--- a/cupy/cuda/curand.pyx
+++ b/cupy/cuda/curand.pyx
@@ -76,6 +76,9 @@ class CURANDError(RuntimeError):
self.status = status
super(CURANDError, self).__init__(STATUS[status])
+ def __reduce__(self):
+ return (type(self), (self.status,))
+
@cython.profile(False)
cpdef inline check_status(int status):
diff --git a/cupy/cuda/cusolver.pyx b/cupy/cuda/cusolver.pyx
index ef24125b885..1d7f9c9c403 100644
--- a/cupy/cuda/cusolver.pyx
+++ b/cupy/cuda/cusolver.pyx
@@ -237,6 +237,9 @@ class CUSOLVERError(RuntimeError):
self.status = status
super(CUSOLVERError, self).__init__(STATUS[status])
+ def __reduce__(self):
+ return (type(self), (self.status,))
+
@cython.profile(False)
cpdef inline check_status(int status):
diff --git a/cupy/cuda/cusparse.pyx b/cupy/cuda/cusparse.pyx
index 772d71d7108..61dd13f190a 100644
--- a/cupy/cuda/cusparse.pyx
+++ b/cupy/cuda/cusparse.pyx
@@ -453,6 +453,9 @@ class CuSparseError(RuntimeError):
self.status = status
super(CuSparseError, self).__init__('%s' % (STATUS[status]))
+ def __reduce__(self):
+ return (type(self), (self.status,))
+
@cython.profile(False)
cpdef inline check_status(int status):
diff --git a/cupy/cuda/cutensor.pyx b/cupy/cuda/cutensor.pyx
index 56245f31feb..b0d18dbff02 100644
--- a/cupy/cuda/cutensor.pyx
+++ b/cupy/cuda/cutensor.pyx
@@ -140,6 +140,9 @@ class CuTensorError(RuntimeError):
msg = cutensorGetErrorString(<Status>status)
super(CuTensorError, self).__init__(msg.decode())
+ def __reduce__(self):
+ return (type(self), (self.status,))
+
@cython.profile(False)
cpdef inline check_status(int status):
diff --git a/cupy/cuda/driver.pyx b/cupy/cuda/driver.pyx
index e8f8cef9557..90f47e79f50 100644
--- a/cupy/cuda/driver.pyx
+++ b/cupy/cuda/driver.pyx
@@ -75,6 +75,9 @@ class CUDADriverError(RuntimeError):
super(CUDADriverError, self).__init__(
'%s: %s' % (s_name.decode(), s_msg.decode()))
+ def __reduce__(self):
+ return (type(self), (self.status,))
+
@cython.profile(False)
cpdef inline check_status(int status):
diff --git a/cupy/cuda/memory.pyx b/cupy/cuda/memory.pyx
index c20fa18d929..49278829680 100644
--- a/cupy/cuda/memory.pyx
+++ b/cupy/cuda/memory.pyx
@@ -43,6 +43,10 @@ class OutOfMemoryError(MemoryError):
"""
def __init__(self, size, total, limit=0):
+ self._size = size
+ self._total = total
+ self._limit = limit
+
if limit == 0:
msg = (
'Out of memory allocating {:,} bytes '
@@ -54,6 +58,9 @@ class OutOfMemoryError(MemoryError):
'limit set to: {:,} bytes).'.format(size, total, limit))
super(OutOfMemoryError, self).__init__(msg)
+ def __reduce__(self):
+ return (type(self), (self._size, self._total, self._limit))
+
@cython.no_gc
cdef class BaseMemory:
diff --git a/cupy/cuda/nccl.pyx b/cupy/cuda/nccl.pyx
index 145e6803b1a..0bae76865ed 100644
--- a/cupy/cuda/nccl.pyx
+++ b/cupy/cuda/nccl.pyx
@@ -101,6 +101,9 @@ class NcclError(RuntimeError):
super(NcclError, self).__init__(
'%s: %s' % (s, msg.decode()))
+ def __reduce__(self):
+ return (type(self), (self.status,))
+
@cython.profile(False)
cpdef inline check_status(ncclResult_t status):
diff --git a/cupy/cuda/nvrtc.pyx b/cupy/cuda/nvrtc.pyx
index 662559956d1..5b4c180585d 100644
--- a/cupy/cuda/nvrtc.pyx
+++ b/cupy/cuda/nvrtc.pyx
@@ -46,6 +46,9 @@ class NVRTCError(RuntimeError):
super(NVRTCError, self).__init__(
'{} ({})'.format(msg.decode(), status))
+ def __reduce__(self):
+ return (type(self), (self.status,))
+
@cython.profile(False)
cpdef inline check_status(int status):
diff --git a/cupy/cuda/runtime.pyx b/cupy/cuda/runtime.pyx
index 3f7e56115c2..6b8365a1b21 100644
--- a/cupy/cuda/runtime.pyx
+++ b/cupy/cuda/runtime.pyx
@@ -138,6 +138,9 @@ class CUDARuntimeError(RuntimeError):
super(CUDARuntimeError, self).__init__(
'%s: %s' % (name.decode(), msg.decode()))
+ def __reduce__(self):
+ return (type(self), (self.status,))
+
@cython.profile(False)
cpdef inline check_status(int status):
diff --git a/tests/cupy_tests/cuda_tests/test_compiler.py b/tests/cupy_tests/cuda_tests/test_compiler.py
index 2e30fec6fbf..82e17e71030 100644
--- a/tests/cupy_tests/cuda_tests/test_compiler.py
+++ b/tests/cupy_tests/cuda_tests/test_compiler.py
@@ -1,3 +1,4 @@
+import pickle
import unittest
import mock
@@ -92,3 +93,12 @@ def test_symbol(self):
def test_space(self):
self.assertFalse(compiler.is_valid_kernel_name('invalid name'))
+
+
+class TestExceptionPicklable(unittest.TestCase):
+
+ def test(self):
+ e1 = compiler.CompileException('msg', 'fn.cu', 'fn', ('-ftz=true',))
+ e2 = pickle.loads(pickle.dumps(e1))
+ assert e1.args == e2.args
+ assert str(e1) == str(e2)
diff --git a/tests/cupy_tests/cuda_tests/test_cublas.py b/tests/cupy_tests/cuda_tests/test_cublas.py
new file mode 100644
index 00000000000..dd19eba50f5
--- /dev/null
+++ b/tests/cupy_tests/cuda_tests/test_cublas.py
@@ -0,0 +1,13 @@
+import pickle
+import unittest
+
+from cupy.cuda import cublas
+
+
+class TestExceptionPicklable(unittest.TestCase):
+
+ def test(self):
+ e1 = cublas.CUBLASError(1)
+ e2 = pickle.loads(pickle.dumps(e1))
+ assert e1.args == e2.args
+ assert str(e1) == str(e2)
diff --git a/tests/cupy_tests/cuda_tests/test_cudnn.py b/tests/cupy_tests/cuda_tests/test_cudnn.py
new file mode 100644
index 00000000000..6458c4fb53a
--- /dev/null
+++ b/tests/cupy_tests/cuda_tests/test_cudnn.py
@@ -0,0 +1,18 @@
+import pickle
+import unittest
+
+try:
+ from cupy.cuda import cudnn
+ cudnn_available = True
+except Exception:
+ cudnn_available = False
+
+
[email protected](cudnn_available, 'cuDNN is unavailable')
+class TestExceptionPicklable(unittest.TestCase):
+
+ def test(self):
+ e1 = cudnn.CuDNNError(1)
+ e2 = pickle.loads(pickle.dumps(e1))
+ assert e1.args == e2.args
+ assert str(e1) == str(e2)
diff --git a/tests/cupy_tests/cuda_tests/test_cufft.py b/tests/cupy_tests/cuda_tests/test_cufft.py
new file mode 100644
index 00000000000..7b0a4ccdcc9
--- /dev/null
+++ b/tests/cupy_tests/cuda_tests/test_cufft.py
@@ -0,0 +1,13 @@
+import pickle
+import unittest
+
+from cupy.cuda import cufft
+
+
+class TestExceptionPicklable(unittest.TestCase):
+
+ def test(self):
+ e1 = cufft.CuFFTError(1)
+ e2 = pickle.loads(pickle.dumps(e1))
+ assert e1.args == e2.args
+ assert str(e1) == str(e2)
diff --git a/tests/cupy_tests/cuda_tests/test_curand.py b/tests/cupy_tests/cuda_tests/test_curand.py
index 42e03764e70..44c36f4f10a 100644
--- a/tests/cupy_tests/cuda_tests/test_curand.py
+++ b/tests/cupy_tests/cuda_tests/test_curand.py
@@ -1,3 +1,4 @@
+import pickle
import unittest
import numpy
@@ -35,3 +36,12 @@ def test_invalid_argument_log_normal_double(self):
with self.assertRaises(ValueError):
curand.generateLogNormalDouble(
self.generator, out.data.ptr, 1, 0.0, 1.0)
+
+
+class TestExceptionPicklable(unittest.TestCase):
+
+ def test(self):
+ e1 = curand.CURANDError(100)
+ e2 = pickle.loads(pickle.dumps(e1))
+ assert e1.args == e2.args
+ assert str(e1) == str(e2)
diff --git a/tests/cupy_tests/cuda_tests/test_cusolver.py b/tests/cupy_tests/cuda_tests/test_cusolver.py
index 8622ece4c6e..3666a73d82a 100644
--- a/tests/cupy_tests/cuda_tests/test_cusolver.py
+++ b/tests/cupy_tests/cuda_tests/test_cusolver.py
@@ -1,3 +1,4 @@
+import pickle
import unittest
from cupy import cuda
@@ -8,3 +9,13 @@ class TestCusolver(unittest.TestCase):
def test_cusolver_enabled(self):
self.assertEqual(cuda.runtime.runtimeGetVersion() >= 8000,
cuda.cusolver_enabled)
+
+
[email protected](cuda.cusolver_enabled, 'cuSOLVER is unavailable')
+class TestExceptionPicklable(unittest.TestCase):
+
+ def test(self):
+ e1 = cuda.cusolver.CUSOLVERError(1)
+ e2 = pickle.loads(pickle.dumps(e1))
+ assert e1.args == e2.args
+ assert str(e1) == str(e2)
diff --git a/tests/cupy_tests/cuda_tests/test_cusparse.py b/tests/cupy_tests/cuda_tests/test_cusparse.py
new file mode 100644
index 00000000000..862edc1ee7a
--- /dev/null
+++ b/tests/cupy_tests/cuda_tests/test_cusparse.py
@@ -0,0 +1,13 @@
+import pickle
+import unittest
+
+from cupy.cuda import cusparse
+
+
+class TestExceptionPicklable(unittest.TestCase):
+
+ def test(self):
+ e1 = cusparse.CuSparseError(1)
+ e2 = pickle.loads(pickle.dumps(e1))
+ assert e1.args == e2.args
+ assert str(e1) == str(e2)
diff --git a/tests/cupy_tests/cuda_tests/test_cutensor.py b/tests/cupy_tests/cuda_tests/test_cutensor.py
new file mode 100644
index 00000000000..96f4e318f25
--- /dev/null
+++ b/tests/cupy_tests/cuda_tests/test_cutensor.py
@@ -0,0 +1,18 @@
+import pickle
+import unittest
+
+try:
+ from cupy.cuda import cutensor
+ cutensor_available = True
+except Exception:
+ cutensor_available = False
+
+
[email protected](cutensor_available, 'cuTensor is unavailable')
+class TestExceptionPicklable(unittest.TestCase):
+
+ def test(self):
+ e1 = cutensor.CuTensorError(1)
+ e2 = pickle.loads(pickle.dumps(e1))
+ assert e1.args == e2.args
+ assert str(e1) == str(e2)
diff --git a/tests/cupy_tests/cuda_tests/test_driver.py b/tests/cupy_tests/cuda_tests/test_driver.py
index fbc1e33d941..a83c65b0508 100644
--- a/tests/cupy_tests/cuda_tests/test_driver.py
+++ b/tests/cupy_tests/cuda_tests/test_driver.py
@@ -1,3 +1,4 @@
+import pickle
import threading
import unittest
@@ -34,3 +35,12 @@ def f(self):
# After the context is created, it should return the valid
# context pointer.
self.assertNotEqual(0, self._result1)
+
+
+class TestExceptionPicklable(unittest.TestCase):
+
+ def test(self):
+ e1 = driver.CUDADriverError(1)
+ e2 = pickle.loads(pickle.dumps(e1))
+ assert e1.args == e2.args
+ assert str(e1) == str(e2)
diff --git a/tests/cupy_tests/cuda_tests/test_memory.py b/tests/cupy_tests/cuda_tests/test_memory.py
index 7fa4282e96d..7dc3c3eda81 100644
--- a/tests/cupy_tests/cuda_tests/test_memory.py
+++ b/tests/cupy_tests/cuda_tests/test_memory.py
@@ -1,5 +1,6 @@
import ctypes
import gc
+import pickle
import sys
import threading
import unittest
@@ -699,3 +700,12 @@ def test(self):
lock.acquire()
assert gc.isenabled()
self.assertRaises(Exception, lock.release)
+
+
+class TestExceptionPicklable(unittest.TestCase):
+
+ def test(self):
+ e1 = memory.OutOfMemoryError(124, 1024, 1024)
+ e2 = pickle.loads(pickle.dumps(e1))
+ assert e1.args == e2.args
+ assert str(e1) == str(e2)
diff --git a/tests/cupy_tests/cuda_tests/test_nccl.py b/tests/cupy_tests/cuda_tests/test_nccl.py
index 281e619ed9f..1fac37e83b4 100644
--- a/tests/cupy_tests/cuda_tests/test_nccl.py
+++ b/tests/cupy_tests/cuda_tests/test_nccl.py
@@ -1,3 +1,4 @@
+import pickle
import unittest
from cupy import cuda
@@ -30,3 +31,13 @@ def test_check_async_error(self):
comm = cuda.nccl.NcclCommunicator(1, id, 0)
comm.check_async_error()
comm.destroy()
+
+
[email protected](cuda.nccl_enabled, 'nccl is not installed')
+class TestExceptionPicklable(unittest.TestCase):
+
+ def test(self):
+ e1 = cuda.nccl.NcclError(1)
+ e2 = pickle.loads(pickle.dumps(e1))
+ assert e1.args == e2.args
+ assert str(e1) == str(e2)
diff --git a/tests/cupy_tests/cuda_tests/test_nvrtc.py b/tests/cupy_tests/cuda_tests/test_nvrtc.py
new file mode 100644
index 00000000000..ab6a5e9800d
--- /dev/null
+++ b/tests/cupy_tests/cuda_tests/test_nvrtc.py
@@ -0,0 +1,13 @@
+import pickle
+import unittest
+
+from cupy.cuda import nvrtc
+
+
+class TestExceptionPicklable(unittest.TestCase):
+
+ def test(self):
+ e1 = nvrtc.NVRTCError(1)
+ e2 = pickle.loads(pickle.dumps(e1))
+ assert e1.args == e2.args
+ assert str(e1) == str(e2)
diff --git a/tests/cupy_tests/cuda_tests/test_runtime.pyx b/tests/cupy_tests/cuda_tests/test_runtime.pyx
new file mode 100644
index 00000000000..4d1f0c393ab
--- /dev/null
+++ b/tests/cupy_tests/cuda_tests/test_runtime.pyx
@@ -0,0 +1,13 @@
+import pickle
+import unittest
+
+from cupy.cuda import runtime
+
+
+class TestExceptionPicklable(unittest.TestCase):
+
+ def test(self):
+ e1 = runtime.CUDARuntimeError(1)
+ e2 = pickle.loads(pickle.dumps(e1))
+ assert e1.args == e2.args
+ assert str(e1) == str(e2)
|
facebookresearch__hydra-893 | [Bug] Cannot add a value to hydra.job.set_env from the command line.
# 🐛 Bug
## Description
I cannot add a append a config value for the hydra configuration (hydra installed from source)
## To reproduce
**Minimal Code/Config snippet to reproduce**
```
python main.py +hydra.job.env_set.WANDB_NOTES="X"
```
**Stack trace/error message**
```
Error merging override +hydra.job.env_set.WANDB_NOTES="X"
Key 'WANDB_NOTES' is not in struct
full_key: hydra.job.env_set.WANDB_NOTES
reference_type=Dict[str, str]
object_type=dict
```
## Additional context
- I don't have the issue with `+` on non hydra config
- if I add `hydra.job.env_set.WANDB_NOTES=""` in the config then I can correctly override the value (without `+`)
## System information
- **Hydra Version** : 1.0.0rc2
- **Python version** : 3.8.5
- **Operating system** : Ubuntu 18.04.3 LTS
| [
{
"content": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\"\"\"\nConfiguration loader\n\"\"\"\nimport copy\nimport os\nimport re\nimport warnings\nfrom collections import defaultdict\nfrom dataclasses import dataclass\nfrom typing import Any, Dict, List, Optional, Tuple\n\nfrom omegaconf import Container, DictConfig, ListConfig, OmegaConf, open_dict\nfrom omegaconf.errors import (\n ConfigAttributeError,\n ConfigKeyError,\n OmegaConfBaseException,\n)\n\nfrom hydra._internal.config_repository import ConfigRepository\nfrom hydra.core.config_loader import ConfigLoader, LoadTrace\nfrom hydra.core.config_search_path import ConfigSearchPath\nfrom hydra.core.object_type import ObjectType\nfrom hydra.core.override_parser.overrides_parser import OverridesParser\nfrom hydra.core.override_parser.types import Override, OverrideType, ValueType\nfrom hydra.core.utils import JobRuntime\nfrom hydra.errors import ConfigCompositionException, MissingConfigException\nfrom hydra.plugins.config_source import ConfigLoadError, ConfigSource\nfrom hydra.types import RunMode\n\n\nclass UnspecifiedMandatoryDefault(Exception):\n def __init__(self, config_group: str,) -> None:\n self.config_group = config_group\n\n\n@dataclass\nclass DefaultElement:\n config_group: Optional[str]\n config_name: str\n optional: bool = False\n package: Optional[str] = None\n\n def __repr__(self) -> str:\n ret = \"\"\n if self.config_group is not None:\n ret += self.config_group\n if self.package is not None:\n ret += f\"@{self.package}\"\n ret += f\"={self.config_name}\"\n if self.optional:\n ret += \" (optional)\"\n return ret\n\n\n@dataclass\nclass IndexedDefaultElement:\n idx: int\n default: DefaultElement\n\n def __repr__(self) -> str:\n return f\"#{self.idx} : {self.default}\"\n\n\nclass ConfigLoaderImpl(ConfigLoader):\n \"\"\"\n Configuration loader\n \"\"\"\n\n def __init__(\n self,\n config_search_path: ConfigSearchPath,\n default_strict: Optional[bool] = None,\n ) -> None:\n self.default_strict = default_strict\n self.all_config_checked: List[LoadTrace] = []\n self.config_search_path = config_search_path\n self.repository: ConfigRepository = ConfigRepository(\n config_search_path=config_search_path\n )\n\n def split_by_override_type(\n self, overrides: List[Override],\n ) -> Tuple[List[Override], List[Override]]:\n config_group_overrides = []\n config_overrides = []\n for override in overrides:\n if not self.repository.group_exists(override.key_or_group):\n config_overrides.append(override)\n else:\n config_group_overrides.append(override)\n return config_group_overrides, config_overrides\n\n def missing_config_error(\n self, config_name: Optional[str], msg: str, with_search_path: bool\n ) -> None:\n def add_search_path() -> str:\n descs = []\n for src in self.repository.get_sources():\n if src.provider != \"schema\":\n descs.append(f\"\\t{repr(src)}\")\n lines = \"\\n\".join(descs)\n\n if with_search_path:\n return msg + \"\\nSearch path:\" + f\"\\n{lines}\"\n else:\n return msg\n\n raise MissingConfigException(\n missing_cfg_file=config_name, message=add_search_path()\n )\n\n def ensure_main_config_source_available(self) -> None:\n for source in self.get_sources():\n # if specified, make sure main config search path exists\n if source.provider == \"main\":\n if not source.available():\n if source.scheme() == \"pkg\":\n if source.path == \"\":\n msg = (\n \"Primary config module is empty.\"\n \"\\nPython requires resources to be in a module with an __init__.py file\"\n )\n else:\n msg = (\n f\"Primary config module '{source.path}' not found.\"\n f\"\\nCheck that it's correct and contains an __init__.py file\"\n )\n else:\n msg = (\n f\"Primary config directory not found.\"\n f\"\\nCheck that the config directory '{source.path}' exists and readable\"\n )\n\n self.missing_config_error(\n config_name=None, msg=msg, with_search_path=False,\n )\n\n def load_configuration(\n self,\n config_name: Optional[str],\n overrides: List[str],\n run_mode: RunMode,\n strict: Optional[bool] = None,\n from_shell: bool = True,\n ) -> DictConfig:\n try:\n return self._load_configuration(\n config_name=config_name,\n overrides=overrides,\n run_mode=run_mode,\n strict=strict,\n from_shell=from_shell,\n )\n except OmegaConfBaseException as e:\n raise ConfigCompositionException() from e\n\n def _load_configuration(\n self,\n config_name: Optional[str],\n overrides: List[str],\n run_mode: RunMode,\n strict: Optional[bool] = None,\n from_shell: bool = True,\n ) -> DictConfig:\n if config_name is not None and not self.repository.config_exists(config_name):\n self.missing_config_error(\n config_name=config_name,\n msg=f\"Cannot find primary config : {config_name}, check that it's in your config search path\",\n with_search_path=True,\n )\n\n if strict is None:\n strict = self.default_strict\n\n parser = OverridesParser.create()\n parsed_overrides = parser.parse_overrides(overrides=overrides)\n config_overrides = []\n sweep_overrides = []\n for x in parsed_overrides:\n if x.is_sweep_override():\n if run_mode == RunMode.MULTIRUN:\n if x.is_hydra_override():\n raise ConfigCompositionException(\n f\"Sweeping over Hydra's configuration is not supported : '{x.input_line}'\"\n )\n sweep_overrides.append(x)\n elif run_mode == RunMode.RUN:\n if x.value_type == ValueType.SIMPLE_CHOICE_SWEEP:\n vals = \"value1,value2\"\n if from_shell:\n example_override = f\"key=\\\\'{vals}\\\\'\"\n else:\n example_override = f\"key='{vals}'\"\n\n msg = f\"\"\"Ambiguous value for argument '{x.input_line}'\n1. To use it as a list, use key=[value1,value2]\n2. To use it as string, quote the value: {example_override}\n3. To sweep over it, add --multirun to your command line\"\"\"\n raise ConfigCompositionException(msg)\n else:\n raise ConfigCompositionException(\n f\"Sweep parameters '{x.input_line}' requires --multirun\"\n )\n else:\n assert False\n else:\n config_overrides.append(x)\n\n config_group_overrides, config_overrides = self.split_by_override_type(\n config_overrides\n )\n\n # Load hydra config\n hydra_cfg, _load_trace = self._load_primary_config(cfg_filename=\"hydra_config\")\n\n # Load job config\n job_cfg, job_cfg_load_trace = self._load_primary_config(\n cfg_filename=config_name, record_load=False\n )\n\n job_defaults = self._parse_defaults(job_cfg)\n defaults = self._parse_defaults(hydra_cfg)\n\n job_cfg_type = OmegaConf.get_type(job_cfg)\n if job_cfg_type is not None and not issubclass(job_cfg_type, dict):\n hydra_cfg._promote(job_cfg_type)\n\n # during the regular merge later the config will retain the readonly flag.\n _recursive_unset_readonly(hydra_cfg)\n # this is breaking encapsulation a bit. can potentially be implemented in OmegaConf\n hydra_cfg._metadata.ref_type = job_cfg._metadata.ref_type\n\n OmegaConf.set_readonly(hydra_cfg.hydra, False)\n\n # if defaults are re-introduced by the promotion, remove it.\n if \"defaults\" in hydra_cfg:\n with open_dict(hydra_cfg):\n del hydra_cfg[\"defaults\"]\n\n if config_name is not None:\n defaults.append(DefaultElement(config_group=None, config_name=\"__SELF__\"))\n split_at = len(defaults)\n\n self._combine_default_lists(defaults, job_defaults)\n ConfigLoaderImpl._apply_overrides_to_defaults(config_group_overrides, defaults)\n\n # Load and defaults and merge them into cfg\n try:\n cfg = self._merge_defaults_into_config(\n hydra_cfg,\n job_cfg,\n job_cfg_load_trace,\n defaults,\n split_at,\n run_mode=run_mode,\n )\n except UnspecifiedMandatoryDefault as e:\n options = self.get_group_options(e.config_group)\n opt_list = \"\\n\".join([\"\\t\" + x for x in options])\n msg = (\n f\"You must specify '{e.config_group}', e.g, {e.config_group}=<OPTION>\"\n f\"\\nAvailable options:\"\n f\"\\n{opt_list}\"\n )\n raise ConfigCompositionException(msg) from e\n\n OmegaConf.set_struct(cfg.hydra, True)\n OmegaConf.set_struct(cfg, strict)\n\n # Apply command line overrides after enabling strict flag\n ConfigLoaderImpl._apply_overrides_to_config(config_overrides, cfg)\n\n app_overrides = []\n for override in parsed_overrides:\n if override.is_hydra_override():\n cfg.hydra.overrides.hydra.append(override.input_line)\n else:\n cfg.hydra.overrides.task.append(override.input_line)\n app_overrides.append(override)\n\n with open_dict(cfg.hydra.job):\n if \"name\" not in cfg.hydra.job:\n cfg.hydra.job.name = JobRuntime().get(\"name\")\n cfg.hydra.job.override_dirname = get_overrides_dirname(\n overrides=app_overrides,\n kv_sep=cfg.hydra.job.config.override_dirname.kv_sep,\n item_sep=cfg.hydra.job.config.override_dirname.item_sep,\n exclude_keys=cfg.hydra.job.config.override_dirname.exclude_keys,\n )\n cfg.hydra.job.config_name = config_name\n\n for key in cfg.hydra.job.env_copy:\n cfg.hydra.job.env_set[key] = os.environ[key]\n\n return cfg\n\n def load_sweep_config(\n self, master_config: DictConfig, sweep_overrides: List[str]\n ) -> DictConfig:\n # Recreate the config for this sweep instance with the appropriate overrides\n overrides = OmegaConf.to_container(master_config.hydra.overrides.hydra)\n assert isinstance(overrides, list)\n overrides = overrides + sweep_overrides\n sweep_config = self.load_configuration(\n config_name=master_config.hydra.job.config_name,\n strict=self.default_strict,\n overrides=overrides,\n run_mode=RunMode.RUN,\n )\n\n with open_dict(sweep_config):\n sweep_config.hydra.runtime.merge_with(master_config.hydra.runtime)\n\n # Partial copy of master config cache, to ensure we get the same resolved values for timestamps\n cache: Dict[str, Any] = defaultdict(dict, {})\n cache_master_config = OmegaConf.get_cache(master_config)\n for k in [\"now\"]:\n if k in cache_master_config:\n cache[k] = cache_master_config[k]\n OmegaConf.set_cache(sweep_config, cache)\n\n return sweep_config\n\n def get_search_path(self) -> ConfigSearchPath:\n return self.config_search_path\n\n def get_load_history(self) -> List[LoadTrace]:\n \"\"\"\n returns the load history (which configs were attempted to load, and if they\n were loaded successfully or not.\n \"\"\"\n return copy.deepcopy(self.all_config_checked)\n\n @staticmethod\n def is_matching(override: Override, default: DefaultElement) -> bool:\n assert override.key_or_group == default.config_group\n if override.is_delete():\n return override.get_subject_package() == default.package\n else:\n return override.key_or_group == default.config_group and (\n override.pkg1 == default.package\n or override.pkg1 == \"\"\n and default.package is None\n )\n\n @staticmethod\n def find_matches(\n key_to_defaults: Dict[str, List[IndexedDefaultElement]], override: Override,\n ) -> List[IndexedDefaultElement]:\n matches: List[IndexedDefaultElement] = []\n for default in key_to_defaults[override.key_or_group]:\n if ConfigLoaderImpl.is_matching(override, default.default):\n matches.append(default)\n return matches\n\n @staticmethod\n def _apply_overrides_to_defaults(\n overrides: List[Override], defaults: List[DefaultElement],\n ) -> None:\n\n key_to_defaults: Dict[str, List[IndexedDefaultElement]] = defaultdict(list)\n\n for idx, default in enumerate(defaults):\n if default.config_group is not None:\n key_to_defaults[default.config_group].append(\n IndexedDefaultElement(idx=idx, default=default)\n )\n for override in overrides:\n value = override.value()\n if value is None:\n if override.is_add():\n ConfigLoaderImpl._raise_parse_override_error(override.input_line)\n\n if not override.is_delete():\n override.type = OverrideType.DEL\n msg = (\n \"\\nRemoving from the defaults list by assigning 'null' \"\n \"is deprecated and will be removed in Hydra 1.1.\"\n f\"\\nUse ~{override.key_or_group}\"\n )\n warnings.warn(category=UserWarning, message=msg)\n if (\n not (override.is_delete() or override.is_package_rename())\n and value is None\n ):\n ConfigLoaderImpl._raise_parse_override_error(override.input_line)\n\n if override.is_add() and override.is_package_rename():\n raise ConfigCompositionException(\n \"Add syntax does not support package rename, remove + prefix\"\n )\n\n matches = ConfigLoaderImpl.find_matches(key_to_defaults, override)\n\n if isinstance(value, (list, dict)):\n raise ConfigCompositionException(\n f\"Config group override value type cannot be a {type(value).__name__}\"\n )\n\n if override.is_delete():\n src = override.get_source_item()\n if len(matches) == 0:\n raise ConfigCompositionException(\n f\"Could not delete. No match for '{src}' in the defaults list.\"\n )\n for pair in matches:\n if value is not None and value != defaults[pair.idx].config_name:\n raise ConfigCompositionException(\n f\"Could not delete. No match for '{src}={value}' in the defaults list.\"\n )\n\n del defaults[pair.idx]\n elif override.is_add():\n if len(matches) > 0:\n src = override.get_source_item()\n raise ConfigCompositionException(\n f\"Could not add. An item matching '{src}' is already in the defaults list.\"\n )\n assert value is not None\n defaults.append(\n DefaultElement(\n config_group=override.key_or_group,\n config_name=str(value),\n package=override.get_subject_package(),\n )\n )\n else:\n assert value is not None\n # override\n for match in matches:\n default = match.default\n default.config_name = str(value)\n if override.is_package_rename():\n default.package = override.get_subject_package()\n\n if len(matches) == 0:\n src = override.get_source_item()\n if override.is_package_rename():\n msg = f\"Could not rename package. No match for '{src}' in the defaults list.\"\n else:\n msg = (\n f\"Could not override '{src}'. No match in the defaults list.\"\n f\"\\nTo append to your default list use +{override.input_line}\"\n )\n\n raise ConfigCompositionException(msg)\n\n @staticmethod\n def _split_group(group_with_package: str) -> Tuple[str, Optional[str]]:\n idx = group_with_package.find(\"@\")\n if idx == -1:\n # group\n group = group_with_package\n package = None\n else:\n # group@package\n group = group_with_package[0:idx]\n package = group_with_package[idx + 1 :]\n\n return group, package\n\n @staticmethod\n def _apply_overrides_to_config(overrides: List[Override], cfg: DictConfig) -> None:\n for override in overrides:\n if override.get_subject_package() is not None:\n raise ConfigCompositionException(\n f\"Override {override.input_line} looks like a config group override, \"\n f\"but config group '{override.key_or_group}' does not exist.\"\n )\n\n key = override.key_or_group\n value = override.value()\n try:\n if override.is_delete():\n config_val = OmegaConf.select(cfg, key, throw_on_missing=False)\n if config_val is None:\n raise ConfigCompositionException(\n f\"Could not delete from config. '{override.key_or_group}' does not exist.\"\n )\n elif value is not None and value != config_val:\n raise ConfigCompositionException(\n f\"Could not delete from config.\"\n f\" The value of '{override.key_or_group}' is {config_val} and not {value}.\"\n )\n\n last_dot = key.rfind(\".\")\n with open_dict(cfg):\n if last_dot == -1:\n del cfg[key]\n else:\n node = OmegaConf.select(cfg, key[0:last_dot])\n del node[key[last_dot + 1 :]]\n\n elif override.is_add():\n if OmegaConf.select(cfg, key, throw_on_missing=False) is None:\n with open_dict(cfg):\n OmegaConf.update(cfg, key, value)\n else:\n raise ConfigCompositionException(\n f\"Could not append to config. An item is already at '{override.key_or_group}'.\"\n )\n else:\n try:\n OmegaConf.update(cfg, key, value)\n except (ConfigAttributeError, ConfigKeyError) as ex:\n raise ConfigCompositionException(\n f\"Could not override '{override.key_or_group}'. No match in config.\"\n f\"\\nTo append to your config use +{override.input_line}\"\n ) from ex\n except OmegaConfBaseException as ex:\n raise ConfigCompositionException(\n f\"Error merging override {override.input_line}\"\n ) from ex\n\n @staticmethod\n def _raise_parse_override_error(override: Optional[str]) -> None:\n msg = (\n f\"Error parsing config group override : '{override}'\"\n f\"\\nAccepted forms:\"\n f\"\\n\\tOverride: key=value, key@package=value, key@src_pkg:dest_pkg=value, key@src_pkg:dest_pkg\"\n f\"\\n\\tAppend: +key=value, +key@package=value\"\n f\"\\n\\tDelete: ~key, ~key@pkg, ~key=value, ~key@pkg=value\"\n f\"\\n\"\n f\"\\nSee https://hydra.cc/docs/next/advanced/override_grammar/basic for details\"\n )\n raise ConfigCompositionException(msg)\n\n def _record_loading(\n self,\n name: str,\n path: Optional[str],\n provider: Optional[str],\n schema_provider: Optional[str],\n record_load: bool,\n ) -> Optional[LoadTrace]:\n trace = LoadTrace(\n filename=name,\n path=path,\n provider=provider,\n schema_provider=schema_provider,\n )\n\n if record_load:\n self.all_config_checked.append(trace)\n\n return trace\n\n @staticmethod\n def _combine_default_lists(\n primary: List[DefaultElement], merged_list: List[DefaultElement]\n ) -> None:\n key_to_idx = {}\n for idx, d in enumerate(primary):\n if d.config_group is not None:\n key_to_idx[d.config_group] = idx\n for d in copy.deepcopy(merged_list):\n if d.config_group is not None:\n if d.config_group in key_to_idx.keys():\n idx = key_to_idx[d.config_group]\n primary[idx] = d\n merged_list.remove(d)\n\n # append remaining items that were not matched to existing keys\n for d in merged_list:\n primary.append(d)\n\n def _load_config_impl(\n self,\n input_file: str,\n package_override: Optional[str],\n is_primary_config: bool,\n record_load: bool = True,\n ) -> Tuple[Optional[DictConfig], Optional[LoadTrace]]:\n \"\"\"\n :param input_file:\n :param record_load:\n :return: the loaded config or None if it was not found\n \"\"\"\n\n ret = self.repository.load_config(\n config_path=input_file,\n is_primary_config=is_primary_config,\n package_override=package_override,\n )\n\n if ret is not None:\n if not isinstance(ret.config, DictConfig):\n raise ValueError(\n f\"Config {input_file} must be a Dictionary, got {type(ret).__name__}\"\n )\n if not ret.is_schema_source:\n try:\n schema_source = self.repository.get_schema_source()\n config_path = ConfigSource._normalize_file_name(filename=input_file)\n schema = schema_source.load_config(\n config_path,\n is_primary_config=is_primary_config,\n package_override=package_override,\n )\n\n try:\n if is_primary_config:\n # Add as placeholders for hydra and defaults to allow\n # overriding them from the config even if not in schema\n schema.config = OmegaConf.merge(\n {\"hydra\": None, \"defaults\": []}, schema.config,\n )\n\n merged = OmegaConf.merge(schema.config, ret.config)\n assert isinstance(merged, DictConfig)\n\n # remove placeholders if unused\n with open_dict(merged):\n if \"hydra\" in merged and merged.hydra is None:\n del merged[\"hydra\"]\n if \"defaults\" in merged and merged[\"defaults\"] == []:\n del merged[\"defaults\"]\n except OmegaConfBaseException as e:\n raise ConfigCompositionException(\n f\"Error merging '{input_file}' with schema\"\n ) from e\n\n assert isinstance(merged, DictConfig)\n return (\n merged,\n self._record_loading(\n name=input_file,\n path=ret.path,\n provider=ret.provider,\n schema_provider=schema.provider,\n record_load=record_load,\n ),\n )\n\n except ConfigLoadError:\n # schema not found, ignore\n pass\n\n return (\n ret.config,\n self._record_loading(\n name=input_file,\n path=ret.path,\n provider=ret.provider,\n schema_provider=None,\n record_load=record_load,\n ),\n )\n else:\n return (\n None,\n self._record_loading(\n name=input_file,\n path=None,\n provider=None,\n schema_provider=None,\n record_load=record_load,\n ),\n )\n\n def list_groups(self, parent_name: str) -> List[str]:\n return self.get_group_options(\n group_name=parent_name, results_filter=ObjectType.GROUP\n )\n\n def get_group_options(\n self, group_name: str, results_filter: Optional[ObjectType] = ObjectType.CONFIG\n ) -> List[str]:\n return self.repository.get_group_options(group_name, results_filter)\n\n def _merge_config(\n self,\n cfg: DictConfig,\n config_group: str,\n name: str,\n required: bool,\n is_primary_config: bool,\n package_override: Optional[str],\n ) -> DictConfig:\n try:\n if config_group != \"\":\n new_cfg = f\"{config_group}/{name}\"\n else:\n new_cfg = name\n\n loaded_cfg, _ = self._load_config_impl(\n new_cfg,\n is_primary_config=is_primary_config,\n package_override=package_override,\n )\n if loaded_cfg is None:\n if required:\n if config_group == \"\":\n msg = f\"Could not load {new_cfg}\"\n raise MissingConfigException(msg, new_cfg)\n else:\n options = self.get_group_options(config_group)\n if options:\n opt_list = \"\\n\".join([\"\\t\" + x for x in options])\n msg = (\n f\"Could not load {new_cfg}.\\nAvailable options:\"\n f\"\\n{opt_list}\"\n )\n else:\n msg = f\"Could not load {new_cfg}\"\n raise MissingConfigException(msg, new_cfg, options)\n else:\n return cfg\n\n else:\n ret = OmegaConf.merge(cfg, loaded_cfg)\n assert isinstance(ret, DictConfig)\n return ret\n except OmegaConfBaseException as ex:\n raise ConfigCompositionException(\n f\"Error merging {config_group}={name}\"\n ) from ex\n\n def _merge_defaults_into_config(\n self,\n hydra_cfg: DictConfig,\n job_cfg: DictConfig,\n job_cfg_load_trace: Optional[LoadTrace],\n defaults: List[DefaultElement],\n split_at: int,\n run_mode: RunMode,\n ) -> DictConfig:\n def merge_defaults_list_into_config(\n merged_cfg: DictConfig, def_list: List[DefaultElement]\n ) -> DictConfig:\n # Reconstruct the defaults to make use of the interpolation capabilities of OmegaConf.\n dict_with_list = OmegaConf.create({\"defaults\": []})\n for item in def_list:\n d: Any\n if item.config_group is not None:\n d = {item.config_group: item.config_name}\n else:\n d = item.config_name\n dict_with_list.defaults.append(d)\n\n for idx, default1 in enumerate(def_list):\n if default1.config_group is not None:\n if OmegaConf.is_missing(\n dict_with_list.defaults[idx], default1.config_group\n ):\n if run_mode == RunMode.RUN:\n raise UnspecifiedMandatoryDefault(\n config_group=default1.config_group\n )\n else:\n config_name = \"???\"\n else:\n config_name = dict_with_list.defaults[idx][\n default1.config_group\n ]\n else:\n config_name = dict_with_list.defaults[idx]\n\n if config_name == \"__SELF__\":\n if \"defaults\" in job_cfg:\n with open_dict(job_cfg):\n del job_cfg[\"defaults\"]\n merged_cfg.merge_with(job_cfg)\n if job_cfg_load_trace is not None:\n self.all_config_checked.append(job_cfg_load_trace)\n elif default1.config_group is not None:\n if default1.config_name not in (None, \"_SKIP_\", \"???\"):\n merged_cfg = self._merge_config(\n cfg=merged_cfg,\n config_group=default1.config_group,\n name=config_name,\n required=not default1.optional,\n is_primary_config=False,\n package_override=default1.package,\n )\n else:\n if default1.config_name != \"_SKIP_\":\n merged_cfg = self._merge_config(\n cfg=merged_cfg,\n config_group=\"\",\n name=config_name,\n required=True,\n is_primary_config=False,\n package_override=default1.package,\n )\n return merged_cfg\n\n system_list: List[DefaultElement] = []\n user_list: List[DefaultElement] = []\n for default in defaults:\n if len(system_list) < split_at:\n system_list.append(default)\n else:\n user_list.append(default)\n hydra_cfg = merge_defaults_list_into_config(hydra_cfg, system_list)\n hydra_cfg = merge_defaults_list_into_config(hydra_cfg, user_list)\n\n if \"defaults\" in hydra_cfg:\n del hydra_cfg[\"defaults\"]\n return hydra_cfg\n\n def _load_primary_config(\n self, cfg_filename: Optional[str], record_load: bool = True\n ) -> Tuple[DictConfig, Optional[LoadTrace]]:\n if cfg_filename is None:\n cfg = OmegaConf.create()\n assert isinstance(cfg, DictConfig)\n load_trace = None\n else:\n ret, load_trace = self._load_config_impl(\n cfg_filename,\n is_primary_config=True,\n package_override=None,\n record_load=record_load,\n )\n assert ret is not None\n cfg = ret\n\n return cfg, load_trace\n\n @staticmethod\n def _parse_defaults(cfg: DictConfig) -> List[DefaultElement]:\n valid_example = \"\"\"\n Example of a valid defaults:\n defaults:\n - dataset: imagenet\n - model: alexnet\n optional: true\n - optimizer: nesterov\n \"\"\"\n\n if \"defaults\" in cfg:\n defaults = cfg.defaults\n else:\n defaults = OmegaConf.create([])\n\n if not isinstance(defaults, ListConfig):\n raise ValueError(\n \"defaults must be a list because composition is order sensitive, \"\n + valid_example\n )\n\n assert isinstance(defaults, ListConfig)\n\n res: List[DefaultElement] = []\n for item in defaults:\n if isinstance(item, DictConfig):\n optional = False\n if \"optional\" in item:\n optional = item.pop(\"optional\")\n keys = list(item.keys())\n if len(keys) > 1:\n raise ValueError(f\"Too many keys in default item {item}\")\n if len(keys) == 0:\n raise ValueError(f\"Missing group name in {item}\")\n key = keys[0]\n config_group, package = ConfigLoaderImpl._split_group(key)\n node = item._get_node(key)\n assert node is not None\n config_name = node._value()\n\n default = DefaultElement(\n config_group=config_group,\n config_name=config_name,\n package=package,\n optional=optional,\n )\n elif isinstance(item, str):\n default = DefaultElement(config_group=None, config_name=item)\n else:\n raise ValueError(\n f\"Unsupported type in defaults : {type(item).__name__}\"\n )\n res.append(default)\n\n return res\n\n def get_sources(self) -> List[ConfigSource]:\n return self.repository.get_sources()\n\n\ndef get_overrides_dirname(\n overrides: List[Override], exclude_keys: List[str], item_sep: str, kv_sep: str,\n) -> str:\n lines = []\n for override in overrides:\n if override.key_or_group not in exclude_keys:\n line = override.input_line\n assert line is not None\n lines.append(line)\n\n lines.sort()\n ret = re.sub(pattern=\"[=]\", repl=kv_sep, string=item_sep.join(lines))\n return ret\n\n\ndef _recursive_unset_readonly(cfg: Container) -> None:\n if isinstance(cfg, DictConfig):\n OmegaConf.set_readonly(cfg, None)\n if not cfg._is_missing():\n for k, v in cfg.items_ex(resolve=False):\n _recursive_unset_readonly(v)\n elif isinstance(cfg, ListConfig):\n OmegaConf.set_readonly(cfg, None)\n if not cfg._is_missing():\n for item in cfg:\n _recursive_unset_readonly(item)\n",
"path": "hydra/_internal/config_loader_impl.py"
}
] | [
{
"content": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\"\"\"\nConfiguration loader\n\"\"\"\nimport copy\nimport os\nimport re\nimport warnings\nfrom collections import defaultdict\nfrom dataclasses import dataclass\nfrom typing import Any, Dict, List, Optional, Tuple\n\nfrom omegaconf import Container, DictConfig, ListConfig, OmegaConf, open_dict\nfrom omegaconf.errors import (\n ConfigAttributeError,\n ConfigKeyError,\n OmegaConfBaseException,\n)\n\nfrom hydra._internal.config_repository import ConfigRepository\nfrom hydra.core.config_loader import ConfigLoader, LoadTrace\nfrom hydra.core.config_search_path import ConfigSearchPath\nfrom hydra.core.object_type import ObjectType\nfrom hydra.core.override_parser.overrides_parser import OverridesParser\nfrom hydra.core.override_parser.types import Override, OverrideType, ValueType\nfrom hydra.core.utils import JobRuntime\nfrom hydra.errors import ConfigCompositionException, MissingConfigException\nfrom hydra.plugins.config_source import ConfigLoadError, ConfigSource\nfrom hydra.types import RunMode\n\n\nclass UnspecifiedMandatoryDefault(Exception):\n def __init__(self, config_group: str,) -> None:\n self.config_group = config_group\n\n\n@dataclass\nclass DefaultElement:\n config_group: Optional[str]\n config_name: str\n optional: bool = False\n package: Optional[str] = None\n\n def __repr__(self) -> str:\n ret = \"\"\n if self.config_group is not None:\n ret += self.config_group\n if self.package is not None:\n ret += f\"@{self.package}\"\n ret += f\"={self.config_name}\"\n if self.optional:\n ret += \" (optional)\"\n return ret\n\n\n@dataclass\nclass IndexedDefaultElement:\n idx: int\n default: DefaultElement\n\n def __repr__(self) -> str:\n return f\"#{self.idx} : {self.default}\"\n\n\nclass ConfigLoaderImpl(ConfigLoader):\n \"\"\"\n Configuration loader\n \"\"\"\n\n def __init__(\n self,\n config_search_path: ConfigSearchPath,\n default_strict: Optional[bool] = None,\n ) -> None:\n self.default_strict = default_strict\n self.all_config_checked: List[LoadTrace] = []\n self.config_search_path = config_search_path\n self.repository: ConfigRepository = ConfigRepository(\n config_search_path=config_search_path\n )\n\n def split_by_override_type(\n self, overrides: List[Override],\n ) -> Tuple[List[Override], List[Override]]:\n config_group_overrides = []\n config_overrides = []\n for override in overrides:\n if not self.repository.group_exists(override.key_or_group):\n config_overrides.append(override)\n else:\n config_group_overrides.append(override)\n return config_group_overrides, config_overrides\n\n def missing_config_error(\n self, config_name: Optional[str], msg: str, with_search_path: bool\n ) -> None:\n def add_search_path() -> str:\n descs = []\n for src in self.repository.get_sources():\n if src.provider != \"schema\":\n descs.append(f\"\\t{repr(src)}\")\n lines = \"\\n\".join(descs)\n\n if with_search_path:\n return msg + \"\\nSearch path:\" + f\"\\n{lines}\"\n else:\n return msg\n\n raise MissingConfigException(\n missing_cfg_file=config_name, message=add_search_path()\n )\n\n def ensure_main_config_source_available(self) -> None:\n for source in self.get_sources():\n # if specified, make sure main config search path exists\n if source.provider == \"main\":\n if not source.available():\n if source.scheme() == \"pkg\":\n if source.path == \"\":\n msg = (\n \"Primary config module is empty.\"\n \"\\nPython requires resources to be in a module with an __init__.py file\"\n )\n else:\n msg = (\n f\"Primary config module '{source.path}' not found.\"\n f\"\\nCheck that it's correct and contains an __init__.py file\"\n )\n else:\n msg = (\n f\"Primary config directory not found.\"\n f\"\\nCheck that the config directory '{source.path}' exists and readable\"\n )\n\n self.missing_config_error(\n config_name=None, msg=msg, with_search_path=False,\n )\n\n def load_configuration(\n self,\n config_name: Optional[str],\n overrides: List[str],\n run_mode: RunMode,\n strict: Optional[bool] = None,\n from_shell: bool = True,\n ) -> DictConfig:\n try:\n return self._load_configuration(\n config_name=config_name,\n overrides=overrides,\n run_mode=run_mode,\n strict=strict,\n from_shell=from_shell,\n )\n except OmegaConfBaseException as e:\n raise ConfigCompositionException() from e\n\n def _load_configuration(\n self,\n config_name: Optional[str],\n overrides: List[str],\n run_mode: RunMode,\n strict: Optional[bool] = None,\n from_shell: bool = True,\n ) -> DictConfig:\n if config_name is not None and not self.repository.config_exists(config_name):\n self.missing_config_error(\n config_name=config_name,\n msg=f\"Cannot find primary config : {config_name}, check that it's in your config search path\",\n with_search_path=True,\n )\n\n if strict is None:\n strict = self.default_strict\n\n parser = OverridesParser.create()\n parsed_overrides = parser.parse_overrides(overrides=overrides)\n config_overrides = []\n sweep_overrides = []\n for x in parsed_overrides:\n if x.is_sweep_override():\n if run_mode == RunMode.MULTIRUN:\n if x.is_hydra_override():\n raise ConfigCompositionException(\n f\"Sweeping over Hydra's configuration is not supported : '{x.input_line}'\"\n )\n sweep_overrides.append(x)\n elif run_mode == RunMode.RUN:\n if x.value_type == ValueType.SIMPLE_CHOICE_SWEEP:\n vals = \"value1,value2\"\n if from_shell:\n example_override = f\"key=\\\\'{vals}\\\\'\"\n else:\n example_override = f\"key='{vals}'\"\n\n msg = f\"\"\"Ambiguous value for argument '{x.input_line}'\n1. To use it as a list, use key=[value1,value2]\n2. To use it as string, quote the value: {example_override}\n3. To sweep over it, add --multirun to your command line\"\"\"\n raise ConfigCompositionException(msg)\n else:\n raise ConfigCompositionException(\n f\"Sweep parameters '{x.input_line}' requires --multirun\"\n )\n else:\n assert False\n else:\n config_overrides.append(x)\n\n config_group_overrides, config_overrides = self.split_by_override_type(\n config_overrides\n )\n\n # Load hydra config\n hydra_cfg, _load_trace = self._load_primary_config(cfg_filename=\"hydra_config\")\n\n # Load job config\n job_cfg, job_cfg_load_trace = self._load_primary_config(\n cfg_filename=config_name, record_load=False\n )\n\n job_defaults = self._parse_defaults(job_cfg)\n defaults = self._parse_defaults(hydra_cfg)\n\n job_cfg_type = OmegaConf.get_type(job_cfg)\n if job_cfg_type is not None and not issubclass(job_cfg_type, dict):\n hydra_cfg._promote(job_cfg_type)\n\n # during the regular merge later the config will retain the readonly flag.\n _recursive_unset_readonly(hydra_cfg)\n # this is breaking encapsulation a bit. can potentially be implemented in OmegaConf\n hydra_cfg._metadata.ref_type = job_cfg._metadata.ref_type\n\n OmegaConf.set_readonly(hydra_cfg.hydra, False)\n\n # if defaults are re-introduced by the promotion, remove it.\n if \"defaults\" in hydra_cfg:\n with open_dict(hydra_cfg):\n del hydra_cfg[\"defaults\"]\n\n if config_name is not None:\n defaults.append(DefaultElement(config_group=None, config_name=\"__SELF__\"))\n split_at = len(defaults)\n\n self._combine_default_lists(defaults, job_defaults)\n ConfigLoaderImpl._apply_overrides_to_defaults(config_group_overrides, defaults)\n\n # Load and defaults and merge them into cfg\n try:\n cfg = self._merge_defaults_into_config(\n hydra_cfg,\n job_cfg,\n job_cfg_load_trace,\n defaults,\n split_at,\n run_mode=run_mode,\n )\n except UnspecifiedMandatoryDefault as e:\n options = self.get_group_options(e.config_group)\n opt_list = \"\\n\".join([\"\\t\" + x for x in options])\n msg = (\n f\"You must specify '{e.config_group}', e.g, {e.config_group}=<OPTION>\"\n f\"\\nAvailable options:\"\n f\"\\n{opt_list}\"\n )\n raise ConfigCompositionException(msg) from e\n\n OmegaConf.set_struct(cfg, strict)\n\n # Apply command line overrides after enabling strict flag\n ConfigLoaderImpl._apply_overrides_to_config(config_overrides, cfg)\n\n app_overrides = []\n for override in parsed_overrides:\n if override.is_hydra_override():\n cfg.hydra.overrides.hydra.append(override.input_line)\n else:\n cfg.hydra.overrides.task.append(override.input_line)\n app_overrides.append(override)\n\n with open_dict(cfg.hydra.job):\n if \"name\" not in cfg.hydra.job:\n cfg.hydra.job.name = JobRuntime().get(\"name\")\n cfg.hydra.job.override_dirname = get_overrides_dirname(\n overrides=app_overrides,\n kv_sep=cfg.hydra.job.config.override_dirname.kv_sep,\n item_sep=cfg.hydra.job.config.override_dirname.item_sep,\n exclude_keys=cfg.hydra.job.config.override_dirname.exclude_keys,\n )\n cfg.hydra.job.config_name = config_name\n\n for key in cfg.hydra.job.env_copy:\n cfg.hydra.job.env_set[key] = os.environ[key]\n\n return cfg\n\n def load_sweep_config(\n self, master_config: DictConfig, sweep_overrides: List[str]\n ) -> DictConfig:\n # Recreate the config for this sweep instance with the appropriate overrides\n overrides = OmegaConf.to_container(master_config.hydra.overrides.hydra)\n assert isinstance(overrides, list)\n overrides = overrides + sweep_overrides\n sweep_config = self.load_configuration(\n config_name=master_config.hydra.job.config_name,\n strict=self.default_strict,\n overrides=overrides,\n run_mode=RunMode.RUN,\n )\n\n with open_dict(sweep_config):\n sweep_config.hydra.runtime.merge_with(master_config.hydra.runtime)\n\n # Partial copy of master config cache, to ensure we get the same resolved values for timestamps\n cache: Dict[str, Any] = defaultdict(dict, {})\n cache_master_config = OmegaConf.get_cache(master_config)\n for k in [\"now\"]:\n if k in cache_master_config:\n cache[k] = cache_master_config[k]\n OmegaConf.set_cache(sweep_config, cache)\n\n return sweep_config\n\n def get_search_path(self) -> ConfigSearchPath:\n return self.config_search_path\n\n def get_load_history(self) -> List[LoadTrace]:\n \"\"\"\n returns the load history (which configs were attempted to load, and if they\n were loaded successfully or not.\n \"\"\"\n return copy.deepcopy(self.all_config_checked)\n\n @staticmethod\n def is_matching(override: Override, default: DefaultElement) -> bool:\n assert override.key_or_group == default.config_group\n if override.is_delete():\n return override.get_subject_package() == default.package\n else:\n return override.key_or_group == default.config_group and (\n override.pkg1 == default.package\n or override.pkg1 == \"\"\n and default.package is None\n )\n\n @staticmethod\n def find_matches(\n key_to_defaults: Dict[str, List[IndexedDefaultElement]], override: Override,\n ) -> List[IndexedDefaultElement]:\n matches: List[IndexedDefaultElement] = []\n for default in key_to_defaults[override.key_or_group]:\n if ConfigLoaderImpl.is_matching(override, default.default):\n matches.append(default)\n return matches\n\n @staticmethod\n def _apply_overrides_to_defaults(\n overrides: List[Override], defaults: List[DefaultElement],\n ) -> None:\n\n key_to_defaults: Dict[str, List[IndexedDefaultElement]] = defaultdict(list)\n\n for idx, default in enumerate(defaults):\n if default.config_group is not None:\n key_to_defaults[default.config_group].append(\n IndexedDefaultElement(idx=idx, default=default)\n )\n for override in overrides:\n value = override.value()\n if value is None:\n if override.is_add():\n ConfigLoaderImpl._raise_parse_override_error(override.input_line)\n\n if not override.is_delete():\n override.type = OverrideType.DEL\n msg = (\n \"\\nRemoving from the defaults list by assigning 'null' \"\n \"is deprecated and will be removed in Hydra 1.1.\"\n f\"\\nUse ~{override.key_or_group}\"\n )\n warnings.warn(category=UserWarning, message=msg)\n if (\n not (override.is_delete() or override.is_package_rename())\n and value is None\n ):\n ConfigLoaderImpl._raise_parse_override_error(override.input_line)\n\n if override.is_add() and override.is_package_rename():\n raise ConfigCompositionException(\n \"Add syntax does not support package rename, remove + prefix\"\n )\n\n matches = ConfigLoaderImpl.find_matches(key_to_defaults, override)\n\n if isinstance(value, (list, dict)):\n raise ConfigCompositionException(\n f\"Config group override value type cannot be a {type(value).__name__}\"\n )\n\n if override.is_delete():\n src = override.get_source_item()\n if len(matches) == 0:\n raise ConfigCompositionException(\n f\"Could not delete. No match for '{src}' in the defaults list.\"\n )\n for pair in matches:\n if value is not None and value != defaults[pair.idx].config_name:\n raise ConfigCompositionException(\n f\"Could not delete. No match for '{src}={value}' in the defaults list.\"\n )\n\n del defaults[pair.idx]\n elif override.is_add():\n if len(matches) > 0:\n src = override.get_source_item()\n raise ConfigCompositionException(\n f\"Could not add. An item matching '{src}' is already in the defaults list.\"\n )\n assert value is not None\n defaults.append(\n DefaultElement(\n config_group=override.key_or_group,\n config_name=str(value),\n package=override.get_subject_package(),\n )\n )\n else:\n assert value is not None\n # override\n for match in matches:\n default = match.default\n default.config_name = str(value)\n if override.is_package_rename():\n default.package = override.get_subject_package()\n\n if len(matches) == 0:\n src = override.get_source_item()\n if override.is_package_rename():\n msg = f\"Could not rename package. No match for '{src}' in the defaults list.\"\n else:\n msg = (\n f\"Could not override '{src}'. No match in the defaults list.\"\n f\"\\nTo append to your default list use +{override.input_line}\"\n )\n\n raise ConfigCompositionException(msg)\n\n @staticmethod\n def _split_group(group_with_package: str) -> Tuple[str, Optional[str]]:\n idx = group_with_package.find(\"@\")\n if idx == -1:\n # group\n group = group_with_package\n package = None\n else:\n # group@package\n group = group_with_package[0:idx]\n package = group_with_package[idx + 1 :]\n\n return group, package\n\n @staticmethod\n def _apply_overrides_to_config(overrides: List[Override], cfg: DictConfig) -> None:\n for override in overrides:\n if override.get_subject_package() is not None:\n raise ConfigCompositionException(\n f\"Override {override.input_line} looks like a config group override, \"\n f\"but config group '{override.key_or_group}' does not exist.\"\n )\n\n key = override.key_or_group\n value = override.value()\n try:\n if override.is_delete():\n config_val = OmegaConf.select(cfg, key, throw_on_missing=False)\n if config_val is None:\n raise ConfigCompositionException(\n f\"Could not delete from config. '{override.key_or_group}' does not exist.\"\n )\n elif value is not None and value != config_val:\n raise ConfigCompositionException(\n f\"Could not delete from config.\"\n f\" The value of '{override.key_or_group}' is {config_val} and not {value}.\"\n )\n\n last_dot = key.rfind(\".\")\n with open_dict(cfg):\n if last_dot == -1:\n del cfg[key]\n else:\n node = OmegaConf.select(cfg, key[0:last_dot])\n del node[key[last_dot + 1 :]]\n\n elif override.is_add():\n if OmegaConf.select(cfg, key, throw_on_missing=False) is None:\n with open_dict(cfg):\n OmegaConf.update(cfg, key, value)\n else:\n raise ConfigCompositionException(\n f\"Could not append to config. An item is already at '{override.key_or_group}'.\"\n )\n else:\n try:\n OmegaConf.update(cfg, key, value)\n except (ConfigAttributeError, ConfigKeyError) as ex:\n raise ConfigCompositionException(\n f\"Could not override '{override.key_or_group}'. No match in config.\"\n f\"\\nTo append to your config use +{override.input_line}\"\n ) from ex\n except OmegaConfBaseException as ex:\n raise ConfigCompositionException(\n f\"Error merging override {override.input_line}\"\n ) from ex\n\n @staticmethod\n def _raise_parse_override_error(override: Optional[str]) -> None:\n msg = (\n f\"Error parsing config group override : '{override}'\"\n f\"\\nAccepted forms:\"\n f\"\\n\\tOverride: key=value, key@package=value, key@src_pkg:dest_pkg=value, key@src_pkg:dest_pkg\"\n f\"\\n\\tAppend: +key=value, +key@package=value\"\n f\"\\n\\tDelete: ~key, ~key@pkg, ~key=value, ~key@pkg=value\"\n f\"\\n\"\n f\"\\nSee https://hydra.cc/docs/next/advanced/override_grammar/basic for details\"\n )\n raise ConfigCompositionException(msg)\n\n def _record_loading(\n self,\n name: str,\n path: Optional[str],\n provider: Optional[str],\n schema_provider: Optional[str],\n record_load: bool,\n ) -> Optional[LoadTrace]:\n trace = LoadTrace(\n filename=name,\n path=path,\n provider=provider,\n schema_provider=schema_provider,\n )\n\n if record_load:\n self.all_config_checked.append(trace)\n\n return trace\n\n @staticmethod\n def _combine_default_lists(\n primary: List[DefaultElement], merged_list: List[DefaultElement]\n ) -> None:\n key_to_idx = {}\n for idx, d in enumerate(primary):\n if d.config_group is not None:\n key_to_idx[d.config_group] = idx\n for d in copy.deepcopy(merged_list):\n if d.config_group is not None:\n if d.config_group in key_to_idx.keys():\n idx = key_to_idx[d.config_group]\n primary[idx] = d\n merged_list.remove(d)\n\n # append remaining items that were not matched to existing keys\n for d in merged_list:\n primary.append(d)\n\n def _load_config_impl(\n self,\n input_file: str,\n package_override: Optional[str],\n is_primary_config: bool,\n record_load: bool = True,\n ) -> Tuple[Optional[DictConfig], Optional[LoadTrace]]:\n \"\"\"\n :param input_file:\n :param record_load:\n :return: the loaded config or None if it was not found\n \"\"\"\n\n ret = self.repository.load_config(\n config_path=input_file,\n is_primary_config=is_primary_config,\n package_override=package_override,\n )\n\n if ret is not None:\n if not isinstance(ret.config, DictConfig):\n raise ValueError(\n f\"Config {input_file} must be a Dictionary, got {type(ret).__name__}\"\n )\n if not ret.is_schema_source:\n try:\n schema_source = self.repository.get_schema_source()\n config_path = ConfigSource._normalize_file_name(filename=input_file)\n schema = schema_source.load_config(\n config_path,\n is_primary_config=is_primary_config,\n package_override=package_override,\n )\n\n try:\n if is_primary_config:\n # Add as placeholders for hydra and defaults to allow\n # overriding them from the config even if not in schema\n schema.config = OmegaConf.merge(\n {\"hydra\": None, \"defaults\": []}, schema.config,\n )\n\n merged = OmegaConf.merge(schema.config, ret.config)\n assert isinstance(merged, DictConfig)\n\n # remove placeholders if unused\n with open_dict(merged):\n if \"hydra\" in merged and merged.hydra is None:\n del merged[\"hydra\"]\n if \"defaults\" in merged and merged[\"defaults\"] == []:\n del merged[\"defaults\"]\n except OmegaConfBaseException as e:\n raise ConfigCompositionException(\n f\"Error merging '{input_file}' with schema\"\n ) from e\n\n assert isinstance(merged, DictConfig)\n return (\n merged,\n self._record_loading(\n name=input_file,\n path=ret.path,\n provider=ret.provider,\n schema_provider=schema.provider,\n record_load=record_load,\n ),\n )\n\n except ConfigLoadError:\n # schema not found, ignore\n pass\n\n return (\n ret.config,\n self._record_loading(\n name=input_file,\n path=ret.path,\n provider=ret.provider,\n schema_provider=None,\n record_load=record_load,\n ),\n )\n else:\n return (\n None,\n self._record_loading(\n name=input_file,\n path=None,\n provider=None,\n schema_provider=None,\n record_load=record_load,\n ),\n )\n\n def list_groups(self, parent_name: str) -> List[str]:\n return self.get_group_options(\n group_name=parent_name, results_filter=ObjectType.GROUP\n )\n\n def get_group_options(\n self, group_name: str, results_filter: Optional[ObjectType] = ObjectType.CONFIG\n ) -> List[str]:\n return self.repository.get_group_options(group_name, results_filter)\n\n def _merge_config(\n self,\n cfg: DictConfig,\n config_group: str,\n name: str,\n required: bool,\n is_primary_config: bool,\n package_override: Optional[str],\n ) -> DictConfig:\n try:\n if config_group != \"\":\n new_cfg = f\"{config_group}/{name}\"\n else:\n new_cfg = name\n\n loaded_cfg, _ = self._load_config_impl(\n new_cfg,\n is_primary_config=is_primary_config,\n package_override=package_override,\n )\n if loaded_cfg is None:\n if required:\n if config_group == \"\":\n msg = f\"Could not load {new_cfg}\"\n raise MissingConfigException(msg, new_cfg)\n else:\n options = self.get_group_options(config_group)\n if options:\n opt_list = \"\\n\".join([\"\\t\" + x for x in options])\n msg = (\n f\"Could not load {new_cfg}.\\nAvailable options:\"\n f\"\\n{opt_list}\"\n )\n else:\n msg = f\"Could not load {new_cfg}\"\n raise MissingConfigException(msg, new_cfg, options)\n else:\n return cfg\n\n else:\n ret = OmegaConf.merge(cfg, loaded_cfg)\n assert isinstance(ret, DictConfig)\n return ret\n except OmegaConfBaseException as ex:\n raise ConfigCompositionException(\n f\"Error merging {config_group}={name}\"\n ) from ex\n\n def _merge_defaults_into_config(\n self,\n hydra_cfg: DictConfig,\n job_cfg: DictConfig,\n job_cfg_load_trace: Optional[LoadTrace],\n defaults: List[DefaultElement],\n split_at: int,\n run_mode: RunMode,\n ) -> DictConfig:\n def merge_defaults_list_into_config(\n merged_cfg: DictConfig, def_list: List[DefaultElement]\n ) -> DictConfig:\n # Reconstruct the defaults to make use of the interpolation capabilities of OmegaConf.\n dict_with_list = OmegaConf.create({\"defaults\": []})\n for item in def_list:\n d: Any\n if item.config_group is not None:\n d = {item.config_group: item.config_name}\n else:\n d = item.config_name\n dict_with_list.defaults.append(d)\n\n for idx, default1 in enumerate(def_list):\n if default1.config_group is not None:\n if OmegaConf.is_missing(\n dict_with_list.defaults[idx], default1.config_group\n ):\n if run_mode == RunMode.RUN:\n raise UnspecifiedMandatoryDefault(\n config_group=default1.config_group\n )\n else:\n config_name = \"???\"\n else:\n config_name = dict_with_list.defaults[idx][\n default1.config_group\n ]\n else:\n config_name = dict_with_list.defaults[idx]\n\n if config_name == \"__SELF__\":\n if \"defaults\" in job_cfg:\n with open_dict(job_cfg):\n del job_cfg[\"defaults\"]\n merged_cfg.merge_with(job_cfg)\n if job_cfg_load_trace is not None:\n self.all_config_checked.append(job_cfg_load_trace)\n elif default1.config_group is not None:\n if default1.config_name not in (None, \"_SKIP_\", \"???\"):\n merged_cfg = self._merge_config(\n cfg=merged_cfg,\n config_group=default1.config_group,\n name=config_name,\n required=not default1.optional,\n is_primary_config=False,\n package_override=default1.package,\n )\n else:\n if default1.config_name != \"_SKIP_\":\n merged_cfg = self._merge_config(\n cfg=merged_cfg,\n config_group=\"\",\n name=config_name,\n required=True,\n is_primary_config=False,\n package_override=default1.package,\n )\n return merged_cfg\n\n system_list: List[DefaultElement] = []\n user_list: List[DefaultElement] = []\n for default in defaults:\n if len(system_list) < split_at:\n system_list.append(default)\n else:\n user_list.append(default)\n hydra_cfg = merge_defaults_list_into_config(hydra_cfg, system_list)\n hydra_cfg = merge_defaults_list_into_config(hydra_cfg, user_list)\n\n if \"defaults\" in hydra_cfg:\n del hydra_cfg[\"defaults\"]\n return hydra_cfg\n\n def _load_primary_config(\n self, cfg_filename: Optional[str], record_load: bool = True\n ) -> Tuple[DictConfig, Optional[LoadTrace]]:\n if cfg_filename is None:\n cfg = OmegaConf.create()\n assert isinstance(cfg, DictConfig)\n load_trace = None\n else:\n ret, load_trace = self._load_config_impl(\n cfg_filename,\n is_primary_config=True,\n package_override=None,\n record_load=record_load,\n )\n assert ret is not None\n cfg = ret\n\n return cfg, load_trace\n\n @staticmethod\n def _parse_defaults(cfg: DictConfig) -> List[DefaultElement]:\n valid_example = \"\"\"\n Example of a valid defaults:\n defaults:\n - dataset: imagenet\n - model: alexnet\n optional: true\n - optimizer: nesterov\n \"\"\"\n\n if \"defaults\" in cfg:\n defaults = cfg.defaults\n else:\n defaults = OmegaConf.create([])\n\n if not isinstance(defaults, ListConfig):\n raise ValueError(\n \"defaults must be a list because composition is order sensitive, \"\n + valid_example\n )\n\n assert isinstance(defaults, ListConfig)\n\n res: List[DefaultElement] = []\n for item in defaults:\n if isinstance(item, DictConfig):\n optional = False\n if \"optional\" in item:\n optional = item.pop(\"optional\")\n keys = list(item.keys())\n if len(keys) > 1:\n raise ValueError(f\"Too many keys in default item {item}\")\n if len(keys) == 0:\n raise ValueError(f\"Missing group name in {item}\")\n key = keys[0]\n config_group, package = ConfigLoaderImpl._split_group(key)\n node = item._get_node(key)\n assert node is not None\n config_name = node._value()\n\n default = DefaultElement(\n config_group=config_group,\n config_name=config_name,\n package=package,\n optional=optional,\n )\n elif isinstance(item, str):\n default = DefaultElement(config_group=None, config_name=item)\n else:\n raise ValueError(\n f\"Unsupported type in defaults : {type(item).__name__}\"\n )\n res.append(default)\n\n return res\n\n def get_sources(self) -> List[ConfigSource]:\n return self.repository.get_sources()\n\n\ndef get_overrides_dirname(\n overrides: List[Override], exclude_keys: List[str], item_sep: str, kv_sep: str,\n) -> str:\n lines = []\n for override in overrides:\n if override.key_or_group not in exclude_keys:\n line = override.input_line\n assert line is not None\n lines.append(line)\n\n lines.sort()\n ret = re.sub(pattern=\"[=]\", repl=kv_sep, string=item_sep.join(lines))\n return ret\n\n\ndef _recursive_unset_readonly(cfg: Container) -> None:\n if isinstance(cfg, DictConfig):\n OmegaConf.set_readonly(cfg, None)\n if not cfg._is_missing():\n for k, v in cfg.items_ex(resolve=False):\n _recursive_unset_readonly(v)\n elif isinstance(cfg, ListConfig):\n OmegaConf.set_readonly(cfg, None)\n if not cfg._is_missing():\n for item in cfg:\n _recursive_unset_readonly(item)\n",
"path": "hydra/_internal/config_loader_impl.py"
}
] | diff --git a/hydra/_internal/config_loader_impl.py b/hydra/_internal/config_loader_impl.py
index 4a110009b52..b98306a58e5 100644
--- a/hydra/_internal/config_loader_impl.py
+++ b/hydra/_internal/config_loader_impl.py
@@ -265,7 +265,6 @@ def _load_configuration(
)
raise ConfigCompositionException(msg) from e
- OmegaConf.set_struct(cfg.hydra, True)
OmegaConf.set_struct(cfg, strict)
# Apply command line overrides after enabling strict flag
diff --git a/hydra/test_utils/test_utils.py b/hydra/test_utils/test_utils.py
index 5dc62b1ff14..fd1282a0bf9 100644
--- a/hydra/test_utils/test_utils.py
+++ b/hydra/test_utils/test_utils.py
@@ -15,7 +15,7 @@
from subprocess import PIPE, Popen, check_output
from typing import Any, Dict, Iterator, List, Optional, Union
-from omegaconf import DictConfig, OmegaConf
+from omegaconf import Container, DictConfig, OmegaConf
from typing_extensions import Protocol
from hydra._internal.hydra import Hydra
@@ -254,7 +254,7 @@ def _get_statements(indent: str, statements: Union[None, str, List[str]]) -> str
def integration_test(
tmpdir: Path,
- task_config: DictConfig,
+ task_config: Any,
overrides: List[str],
prints: Union[str, List[str]],
expected_outputs: Union[str, List[str]],
@@ -266,7 +266,7 @@ def integration_test(
Path(tmpdir).mkdir(parents=True, exist_ok=True)
if isinstance(expected_outputs, str):
expected_outputs = [expected_outputs]
- if isinstance(task_config, (list, dict)):
+ if not isinstance(task_config, Container):
task_config = OmegaConf.create(task_config)
if isinstance(prints, str):
prints = [prints]
diff --git a/news/854.bugfix b/news/854.bugfix
new file mode 100644
index 00000000000..3b21733f9b2
--- /dev/null
+++ b/news/854.bugfix
@@ -0,0 +1 @@
+Fix overriding of hydra.job.env_set from the command line
diff --git a/tests/test_hydra.py b/tests/test_hydra.py
index 83522ad6f6e..a2ed2555081 100644
--- a/tests/test_hydra.py
+++ b/tests/test_hydra.py
@@ -727,7 +727,7 @@ def test_local_run_workdir(
)
-def test_hydra_env_set(tmpdir: Path) -> None:
+def test_hydra_env_set_with_config(tmpdir: Path) -> None:
cfg = OmegaConf.create({"hydra": {"job": {"env_set": {"foo": "bar"}}}})
integration_test(
tmpdir=tmpdir,
@@ -738,6 +738,16 @@ def test_hydra_env_set(tmpdir: Path) -> None:
)
+def test_hydra_env_set_with_override(tmpdir: Path) -> None:
+ integration_test(
+ tmpdir=tmpdir,
+ task_config={},
+ overrides=["+hydra.job.env_set.foo=bar"],
+ prints="os.environ['foo']",
+ expected_outputs="bar",
+ )
+
+
@pytest.mark.parametrize( # type: ignore
"override", [pytest.param("xyz", id="db=xyz"), pytest.param("", id="db=")]
)
|
open-mmlab__mmdetection3d-600 | votenet pre-trained scannet model doesn't work! KeyError: 'ann_info'
raceback (most recent call last):
File "demo/pcd_demo.py", line 41, in <module>
main()
File "demo/pcd_demo.py", line 28, in main
result, data = inference_detector(model, args.pcd)
File "/home/user/deeplearning/mmdetection3d/mmdet3d/apis/inference.py", line 102, in inference_detector
data = test_pipeline(data)
File "/home/user/deeplearning/mmdetection/mmdet/datasets/pipelines/compose.py", line 40, in __call__
data = t(data)
File "/home/user/deeplearning/mmdetection3d/mmdet3d/datasets/pipelines/transforms_3d.py", line 406, in __call__
assert 'axis_align_matrix' in input_dict['ann_info'].keys(), \
KeyError: 'ann_info'
| [
{
"content": "import mmcv\nimport numpy as np\nimport re\nimport torch\nfrom copy import deepcopy\nfrom mmcv.parallel import collate, scatter\nfrom mmcv.runner import load_checkpoint\nfrom os import path as osp\n\nfrom mmdet3d.core import (Box3DMode, DepthInstance3DBoxes,\n LiDARInstance3DBoxes, show_multi_modality_result,\n show_result, show_seg_result)\nfrom mmdet3d.core.bbox import get_box_type\nfrom mmdet3d.core.bbox.structures.cam_box3d import CameraInstance3DBoxes\nfrom mmdet3d.datasets.pipelines import Compose\nfrom mmdet3d.models import build_model\n\n\ndef convert_SyncBN(config):\n \"\"\"Convert config's naiveSyncBN to BN.\n\n Args:\n config (str or :obj:`mmcv.Config`): Config file path or the config\n object.\n \"\"\"\n if isinstance(config, dict):\n for item in config:\n if item == 'norm_cfg':\n config[item]['type'] = config[item]['type']. \\\n replace('naiveSyncBN', 'BN')\n else:\n convert_SyncBN(config[item])\n\n\ndef init_model(config, checkpoint=None, device='cuda:0'):\n \"\"\"Initialize a model from config file, which could be a 3D detector or a\n 3D segmentor.\n\n Args:\n config (str or :obj:`mmcv.Config`): Config file path or the config\n object.\n checkpoint (str, optional): Checkpoint path. If left as None, the model\n will not load any weights.\n device (str): Device to use.\n\n Returns:\n nn.Module: The constructed detector.\n \"\"\"\n if isinstance(config, str):\n config = mmcv.Config.fromfile(config)\n elif not isinstance(config, mmcv.Config):\n raise TypeError('config must be a filename or Config object, '\n f'but got {type(config)}')\n config.model.pretrained = None\n convert_SyncBN(config.model)\n config.model.train_cfg = None\n model = build_model(config.model, test_cfg=config.get('test_cfg'))\n if checkpoint is not None:\n checkpoint = load_checkpoint(model, checkpoint)\n if 'CLASSES' in checkpoint['meta']:\n model.CLASSES = checkpoint['meta']['CLASSES']\n else:\n model.CLASSES = config.class_names\n if 'PALETTE' in checkpoint['meta']: # 3D Segmentor\n model.PALETTE = checkpoint['meta']['PALETTE']\n model.cfg = config # save the config in the model for convenience\n model.to(device)\n model.eval()\n return model\n\n\ndef inference_detector(model, pcd):\n \"\"\"Inference point cloud with the detector.\n\n Args:\n model (nn.Module): The loaded detector.\n pcd (str): Point cloud files.\n\n Returns:\n tuple: Predicted results and data from pipeline.\n \"\"\"\n cfg = model.cfg\n device = next(model.parameters()).device # model device\n # build the data pipeline\n test_pipeline = deepcopy(cfg.data.test.pipeline)\n test_pipeline = Compose(test_pipeline)\n box_type_3d, box_mode_3d = get_box_type(cfg.data.test.box_type_3d)\n data = dict(\n pts_filename=pcd,\n box_type_3d=box_type_3d,\n box_mode_3d=box_mode_3d,\n sweeps=[],\n # set timestamp = 0\n timestamp=[0],\n img_fields=[],\n bbox3d_fields=[],\n pts_mask_fields=[],\n pts_seg_fields=[],\n bbox_fields=[],\n mask_fields=[],\n seg_fields=[])\n data = test_pipeline(data)\n data = collate([data], samples_per_gpu=1)\n if next(model.parameters()).is_cuda:\n # scatter to specified GPU\n data = scatter(data, [device.index])[0]\n else:\n # this is a workaround to avoid the bug of MMDataParallel\n data['img_metas'] = data['img_metas'][0].data\n data['points'] = data['points'][0].data\n # forward the model\n with torch.no_grad():\n result = model(return_loss=False, rescale=True, **data)\n return result, data\n\n\ndef inference_multi_modality_detector(model, pcd, image, ann_file):\n \"\"\"Inference point cloud with the multi-modality detector.\n\n Args:\n model (nn.Module): The loaded detector.\n pcd (str): Point cloud files.\n image (str): Image files.\n ann_file (str): Annotation files.\n\n Returns:\n tuple: Predicted results and data from pipeline.\n \"\"\"\n cfg = model.cfg\n device = next(model.parameters()).device # model device\n # build the data pipeline\n test_pipeline = deepcopy(cfg.data.test.pipeline)\n test_pipeline = Compose(test_pipeline)\n box_type_3d, box_mode_3d = get_box_type(cfg.data.test.box_type_3d)\n # get data info containing calib\n data_infos = mmcv.load(ann_file)\n image_idx = int(re.findall(r'\\d+', image)[-1]) # xxx/sunrgbd_000017.jpg\n for x in data_infos:\n if int(x['image']['image_idx']) != image_idx:\n continue\n info = x\n break\n data = dict(\n pts_filename=pcd,\n img_prefix=osp.dirname(image),\n img_info=dict(filename=osp.basename(image)),\n box_type_3d=box_type_3d,\n box_mode_3d=box_mode_3d,\n img_fields=[],\n bbox3d_fields=[],\n pts_mask_fields=[],\n pts_seg_fields=[],\n bbox_fields=[],\n mask_fields=[],\n seg_fields=[])\n\n # depth map points to image conversion\n if box_mode_3d == Box3DMode.DEPTH:\n data.update(dict(calib=info['calib']))\n\n data = test_pipeline(data)\n\n # LiDAR to image conversion\n if box_mode_3d == Box3DMode.LIDAR:\n rect = info['calib']['R0_rect'].astype(np.float32)\n Trv2c = info['calib']['Tr_velo_to_cam'].astype(np.float32)\n P2 = info['calib']['P2'].astype(np.float32)\n lidar2img = P2 @ rect @ Trv2c\n data['img_metas'][0].data['lidar2img'] = lidar2img\n elif box_mode_3d == Box3DMode.DEPTH:\n data['calib'][0]['Rt'] = data['calib'][0]['Rt'].astype(np.float32)\n data['calib'][0]['K'] = data['calib'][0]['K'].astype(np.float32)\n\n data = collate([data], samples_per_gpu=1)\n if next(model.parameters()).is_cuda:\n # scatter to specified GPU\n data = scatter(data, [device.index])[0]\n else:\n # this is a workaround to avoid the bug of MMDataParallel\n data['img_metas'] = data['img_metas'][0].data\n data['points'] = data['points'][0].data\n data['img'] = data['img'][0].data\n if box_mode_3d == Box3DMode.DEPTH:\n data['calib'][0]['Rt'] = data['calib'][0]['Rt'][0].data\n data['calib'][0]['K'] = data['calib'][0]['K'][0].data\n\n # forward the model\n with torch.no_grad():\n result = model(return_loss=False, rescale=True, **data)\n return result, data\n\n\ndef inference_mono_3d_detector(model, image, ann_file):\n \"\"\"Inference image with the monocular 3D detector.\n\n Args:\n model (nn.Module): The loaded detector.\n image (str): Image files.\n ann_file (str): Annotation files.\n\n Returns:\n tuple: Predicted results and data from pipeline.\n \"\"\"\n cfg = model.cfg\n device = next(model.parameters()).device # model device\n # build the data pipeline\n test_pipeline = deepcopy(cfg.data.test.pipeline)\n test_pipeline = Compose(test_pipeline)\n box_type_3d, box_mode_3d = get_box_type(cfg.data.test.box_type_3d)\n # get data info containing calib\n data_infos = mmcv.load(ann_file)\n # find the info corresponding to this image\n for x in data_infos['images']:\n if osp.basename(x['file_name']) != osp.basename(image):\n continue\n img_info = x\n break\n data = dict(\n img_prefix=osp.dirname(image),\n img_info=dict(filename=osp.basename(image)),\n box_type_3d=box_type_3d,\n box_mode_3d=box_mode_3d,\n img_fields=[],\n bbox3d_fields=[],\n pts_mask_fields=[],\n pts_seg_fields=[],\n bbox_fields=[],\n mask_fields=[],\n seg_fields=[])\n\n # camera points to image conversion\n if box_mode_3d == Box3DMode.CAM:\n data['img_info'].update(dict(cam_intrinsic=img_info['cam_intrinsic']))\n\n data = test_pipeline(data)\n\n data = collate([data], samples_per_gpu=1)\n if next(model.parameters()).is_cuda:\n # scatter to specified GPU\n data = scatter(data, [device.index])[0]\n else:\n # this is a workaround to avoid the bug of MMDataParallel\n data['img_metas'] = data['img_metas'][0].data\n data['img'] = data['img'][0].data\n\n # forward the model\n with torch.no_grad():\n result = model(return_loss=False, rescale=True, **data)\n return result, data\n\n\ndef inference_segmentor(model, pcd):\n \"\"\"Inference point cloud with the segmentor.\n\n Args:\n model (nn.Module): The loaded segmentor.\n pcd (str): Point cloud files.\n\n Returns:\n tuple: Predicted results and data from pipeline.\n \"\"\"\n cfg = model.cfg\n device = next(model.parameters()).device # model device\n # build the data pipeline\n test_pipeline = deepcopy(cfg.data.test.pipeline)\n test_pipeline = Compose(test_pipeline)\n data = dict(\n pts_filename=pcd,\n img_fields=[],\n bbox3d_fields=[],\n pts_mask_fields=[],\n pts_seg_fields=[],\n bbox_fields=[],\n mask_fields=[],\n seg_fields=[])\n data = test_pipeline(data)\n data = collate([data], samples_per_gpu=1)\n if next(model.parameters()).is_cuda:\n # scatter to specified GPU\n data = scatter(data, [device.index])[0]\n else:\n # this is a workaround to avoid the bug of MMDataParallel\n data['img_metas'] = data['img_metas'][0].data\n data['points'] = data['points'][0].data\n # forward the model\n with torch.no_grad():\n result = model(return_loss=False, rescale=True, **data)\n return result, data\n\n\ndef show_det_result_meshlab(data,\n result,\n out_dir,\n score_thr=0.0,\n show=False,\n snapshot=False):\n \"\"\"Show 3D detection result by meshlab.\"\"\"\n points = data['points'][0][0].cpu().numpy()\n pts_filename = data['img_metas'][0][0]['pts_filename']\n file_name = osp.split(pts_filename)[-1].split('.')[0]\n\n if 'pts_bbox' in result[0].keys():\n pred_bboxes = result[0]['pts_bbox']['boxes_3d'].tensor.numpy()\n pred_scores = result[0]['pts_bbox']['scores_3d'].numpy()\n else:\n pred_bboxes = result[0]['boxes_3d'].tensor.numpy()\n pred_scores = result[0]['scores_3d'].numpy()\n\n # filter out low score bboxes for visualization\n if score_thr > 0:\n inds = pred_scores > score_thr\n pred_bboxes = pred_bboxes[inds]\n\n # for now we convert points into depth mode\n box_mode = data['img_metas'][0][0]['box_mode_3d']\n if box_mode != Box3DMode.DEPTH:\n points = points[..., [1, 0, 2]]\n points[..., 0] *= -1\n show_bboxes = Box3DMode.convert(pred_bboxes, box_mode, Box3DMode.DEPTH)\n else:\n show_bboxes = deepcopy(pred_bboxes)\n\n show_result(\n points,\n None,\n show_bboxes,\n out_dir,\n file_name,\n show=show,\n snapshot=snapshot)\n\n return file_name\n\n\ndef show_seg_result_meshlab(data,\n result,\n out_dir,\n palette,\n show=False,\n snapshot=False):\n \"\"\"Show 3D segmentation result by meshlab.\"\"\"\n points = data['points'][0][0].cpu().numpy()\n pts_filename = data['img_metas'][0][0]['pts_filename']\n file_name = osp.split(pts_filename)[-1].split('.')[0]\n\n pred_seg = result[0]['semantic_mask'].numpy()\n\n if palette is None:\n # generate random color map\n max_idx = pred_seg.max()\n palette = np.random.randint(0, 256, size=(max_idx + 1, 3))\n palette = np.array(palette).astype(np.int)\n\n show_seg_result(\n points,\n None,\n pred_seg,\n out_dir,\n file_name,\n palette=palette,\n show=show,\n snapshot=snapshot)\n\n return file_name\n\n\ndef show_proj_det_result_meshlab(data,\n result,\n out_dir,\n score_thr=0.0,\n show=False,\n snapshot=False):\n \"\"\"Show result of projecting 3D bbox to 2D image by meshlab.\"\"\"\n assert 'img' in data.keys(), 'image data is not provided for visualization'\n\n img_filename = data['img_metas'][0][0]['filename']\n file_name = osp.split(img_filename)[-1].split('.')[0]\n\n # read from file because img in data_dict has undergone pipeline transform\n img = mmcv.imread(img_filename)\n\n if 'pts_bbox' in result[0].keys():\n result[0] = result[0]['pts_bbox']\n elif 'img_bbox' in result[0].keys():\n result[0] = result[0]['img_bbox']\n pred_bboxes = result[0]['boxes_3d'].tensor.numpy()\n pred_scores = result[0]['scores_3d'].numpy()\n\n # filter out low score bboxes for visualization\n if score_thr > 0:\n inds = pred_scores > score_thr\n pred_bboxes = pred_bboxes[inds]\n\n box_mode = data['img_metas'][0][0]['box_mode_3d']\n if box_mode == Box3DMode.LIDAR:\n if 'lidar2img' not in data['img_metas'][0][0]:\n raise NotImplementedError(\n 'LiDAR to image transformation matrix is not provided')\n\n show_bboxes = LiDARInstance3DBoxes(pred_bboxes, origin=(0.5, 0.5, 0))\n\n show_multi_modality_result(\n img,\n None,\n show_bboxes,\n data['img_metas'][0][0]['lidar2img'],\n out_dir,\n file_name,\n box_mode='lidar',\n show=show)\n elif box_mode == Box3DMode.DEPTH:\n if 'calib' not in data.keys():\n raise NotImplementedError(\n 'camera calibration information is not provided')\n\n show_bboxes = DepthInstance3DBoxes(pred_bboxes, origin=(0.5, 0.5, 0))\n\n show_multi_modality_result(\n img,\n None,\n show_bboxes,\n data['calib'][0],\n out_dir,\n file_name,\n box_mode='depth',\n img_metas=data['img_metas'][0][0],\n show=show)\n elif box_mode == Box3DMode.CAM:\n if 'cam_intrinsic' not in data['img_metas'][0][0]:\n raise NotImplementedError(\n 'camera intrinsic matrix is not provided')\n\n from mmdet3d.core.bbox import mono_cam_box2vis\n show_bboxes = CameraInstance3DBoxes(\n pred_bboxes, box_dim=pred_bboxes.shape[-1], origin=(0.5, 1.0, 0.5))\n # TODO: remove the hack of box from NuScenesMonoDataset\n show_bboxes = mono_cam_box2vis(show_bboxes)\n\n show_multi_modality_result(\n img,\n None,\n show_bboxes,\n data['img_metas'][0][0]['cam_intrinsic'],\n out_dir,\n file_name,\n box_mode='camera',\n show=show)\n else:\n raise NotImplementedError(\n f'visualization of {box_mode} bbox is not supported')\n\n return file_name\n\n\ndef show_result_meshlab(data,\n result,\n out_dir,\n score_thr=0.0,\n show=False,\n snapshot=False,\n task='det',\n palette=None):\n \"\"\"Show result by meshlab.\n\n Args:\n data (dict): Contain data from pipeline.\n result (dict): Predicted result from model.\n out_dir (str): Directory to save visualized result.\n score_thr (float): Minimum score of bboxes to be shown. Default: 0.0\n show (bool): Visualize the results online. Defaults to False.\n snapshot (bool): Whether to save the online results. Defaults to False.\n task (str): Distinguish which task result to visualize. Currently we\n support 3D detection, multi-modality detection and 3D segmentation.\n Defaults to 'det'.\n palette (list[list[int]]] | np.ndarray | None): The palette of\n segmentation map. If None is given, random palette will be\n generated. Defaults to None.\n \"\"\"\n assert task in ['det', 'multi_modality-det', 'seg', 'mono-det'], \\\n f'unsupported visualization task {task}'\n assert out_dir is not None, 'Expect out_dir, got none.'\n\n if task in ['det', 'multi_modality-det']:\n file_name = show_det_result_meshlab(data, result, out_dir, score_thr,\n show, snapshot)\n\n if task in ['seg']:\n file_name = show_seg_result_meshlab(data, result, out_dir, palette,\n show, snapshot)\n\n if task in ['multi_modality-det', 'mono-det']:\n file_name = show_proj_det_result_meshlab(data, result, out_dir,\n score_thr, show, snapshot)\n\n return out_dir, file_name\n",
"path": "mmdet3d/apis/inference.py"
}
] | [
{
"content": "import mmcv\nimport numpy as np\nimport re\nimport torch\nfrom copy import deepcopy\nfrom mmcv.parallel import collate, scatter\nfrom mmcv.runner import load_checkpoint\nfrom os import path as osp\n\nfrom mmdet3d.core import (Box3DMode, DepthInstance3DBoxes,\n LiDARInstance3DBoxes, show_multi_modality_result,\n show_result, show_seg_result)\nfrom mmdet3d.core.bbox import get_box_type\nfrom mmdet3d.core.bbox.structures.cam_box3d import CameraInstance3DBoxes\nfrom mmdet3d.datasets.pipelines import Compose\nfrom mmdet3d.models import build_model\n\n\ndef convert_SyncBN(config):\n \"\"\"Convert config's naiveSyncBN to BN.\n\n Args:\n config (str or :obj:`mmcv.Config`): Config file path or the config\n object.\n \"\"\"\n if isinstance(config, dict):\n for item in config:\n if item == 'norm_cfg':\n config[item]['type'] = config[item]['type']. \\\n replace('naiveSyncBN', 'BN')\n else:\n convert_SyncBN(config[item])\n\n\ndef init_model(config, checkpoint=None, device='cuda:0'):\n \"\"\"Initialize a model from config file, which could be a 3D detector or a\n 3D segmentor.\n\n Args:\n config (str or :obj:`mmcv.Config`): Config file path or the config\n object.\n checkpoint (str, optional): Checkpoint path. If left as None, the model\n will not load any weights.\n device (str): Device to use.\n\n Returns:\n nn.Module: The constructed detector.\n \"\"\"\n if isinstance(config, str):\n config = mmcv.Config.fromfile(config)\n elif not isinstance(config, mmcv.Config):\n raise TypeError('config must be a filename or Config object, '\n f'but got {type(config)}')\n config.model.pretrained = None\n convert_SyncBN(config.model)\n config.model.train_cfg = None\n model = build_model(config.model, test_cfg=config.get('test_cfg'))\n if checkpoint is not None:\n checkpoint = load_checkpoint(model, checkpoint)\n if 'CLASSES' in checkpoint['meta']:\n model.CLASSES = checkpoint['meta']['CLASSES']\n else:\n model.CLASSES = config.class_names\n if 'PALETTE' in checkpoint['meta']: # 3D Segmentor\n model.PALETTE = checkpoint['meta']['PALETTE']\n model.cfg = config # save the config in the model for convenience\n model.to(device)\n model.eval()\n return model\n\n\ndef inference_detector(model, pcd):\n \"\"\"Inference point cloud with the detector.\n\n Args:\n model (nn.Module): The loaded detector.\n pcd (str): Point cloud files.\n\n Returns:\n tuple: Predicted results and data from pipeline.\n \"\"\"\n cfg = model.cfg\n device = next(model.parameters()).device # model device\n # build the data pipeline\n test_pipeline = deepcopy(cfg.data.test.pipeline)\n test_pipeline = Compose(test_pipeline)\n box_type_3d, box_mode_3d = get_box_type(cfg.data.test.box_type_3d)\n data = dict(\n pts_filename=pcd,\n box_type_3d=box_type_3d,\n box_mode_3d=box_mode_3d,\n # for ScanNet demo we need axis_align_matrix\n ann_info=dict(axis_align_matrix=np.eye(4)),\n sweeps=[],\n # set timestamp = 0\n timestamp=[0],\n img_fields=[],\n bbox3d_fields=[],\n pts_mask_fields=[],\n pts_seg_fields=[],\n bbox_fields=[],\n mask_fields=[],\n seg_fields=[])\n data = test_pipeline(data)\n data = collate([data], samples_per_gpu=1)\n if next(model.parameters()).is_cuda:\n # scatter to specified GPU\n data = scatter(data, [device.index])[0]\n else:\n # this is a workaround to avoid the bug of MMDataParallel\n data['img_metas'] = data['img_metas'][0].data\n data['points'] = data['points'][0].data\n # forward the model\n with torch.no_grad():\n result = model(return_loss=False, rescale=True, **data)\n return result, data\n\n\ndef inference_multi_modality_detector(model, pcd, image, ann_file):\n \"\"\"Inference point cloud with the multi-modality detector.\n\n Args:\n model (nn.Module): The loaded detector.\n pcd (str): Point cloud files.\n image (str): Image files.\n ann_file (str): Annotation files.\n\n Returns:\n tuple: Predicted results and data from pipeline.\n \"\"\"\n cfg = model.cfg\n device = next(model.parameters()).device # model device\n # build the data pipeline\n test_pipeline = deepcopy(cfg.data.test.pipeline)\n test_pipeline = Compose(test_pipeline)\n box_type_3d, box_mode_3d = get_box_type(cfg.data.test.box_type_3d)\n # get data info containing calib\n data_infos = mmcv.load(ann_file)\n image_idx = int(re.findall(r'\\d+', image)[-1]) # xxx/sunrgbd_000017.jpg\n for x in data_infos:\n if int(x['image']['image_idx']) != image_idx:\n continue\n info = x\n break\n data = dict(\n pts_filename=pcd,\n img_prefix=osp.dirname(image),\n img_info=dict(filename=osp.basename(image)),\n box_type_3d=box_type_3d,\n box_mode_3d=box_mode_3d,\n img_fields=[],\n bbox3d_fields=[],\n pts_mask_fields=[],\n pts_seg_fields=[],\n bbox_fields=[],\n mask_fields=[],\n seg_fields=[])\n\n # depth map points to image conversion\n if box_mode_3d == Box3DMode.DEPTH:\n data.update(dict(calib=info['calib']))\n\n data = test_pipeline(data)\n\n # LiDAR to image conversion\n if box_mode_3d == Box3DMode.LIDAR:\n rect = info['calib']['R0_rect'].astype(np.float32)\n Trv2c = info['calib']['Tr_velo_to_cam'].astype(np.float32)\n P2 = info['calib']['P2'].astype(np.float32)\n lidar2img = P2 @ rect @ Trv2c\n data['img_metas'][0].data['lidar2img'] = lidar2img\n elif box_mode_3d == Box3DMode.DEPTH:\n data['calib'][0]['Rt'] = data['calib'][0]['Rt'].astype(np.float32)\n data['calib'][0]['K'] = data['calib'][0]['K'].astype(np.float32)\n\n data = collate([data], samples_per_gpu=1)\n if next(model.parameters()).is_cuda:\n # scatter to specified GPU\n data = scatter(data, [device.index])[0]\n else:\n # this is a workaround to avoid the bug of MMDataParallel\n data['img_metas'] = data['img_metas'][0].data\n data['points'] = data['points'][0].data\n data['img'] = data['img'][0].data\n if box_mode_3d == Box3DMode.DEPTH:\n data['calib'][0]['Rt'] = data['calib'][0]['Rt'][0].data\n data['calib'][0]['K'] = data['calib'][0]['K'][0].data\n\n # forward the model\n with torch.no_grad():\n result = model(return_loss=False, rescale=True, **data)\n return result, data\n\n\ndef inference_mono_3d_detector(model, image, ann_file):\n \"\"\"Inference image with the monocular 3D detector.\n\n Args:\n model (nn.Module): The loaded detector.\n image (str): Image files.\n ann_file (str): Annotation files.\n\n Returns:\n tuple: Predicted results and data from pipeline.\n \"\"\"\n cfg = model.cfg\n device = next(model.parameters()).device # model device\n # build the data pipeline\n test_pipeline = deepcopy(cfg.data.test.pipeline)\n test_pipeline = Compose(test_pipeline)\n box_type_3d, box_mode_3d = get_box_type(cfg.data.test.box_type_3d)\n # get data info containing calib\n data_infos = mmcv.load(ann_file)\n # find the info corresponding to this image\n for x in data_infos['images']:\n if osp.basename(x['file_name']) != osp.basename(image):\n continue\n img_info = x\n break\n data = dict(\n img_prefix=osp.dirname(image),\n img_info=dict(filename=osp.basename(image)),\n box_type_3d=box_type_3d,\n box_mode_3d=box_mode_3d,\n img_fields=[],\n bbox3d_fields=[],\n pts_mask_fields=[],\n pts_seg_fields=[],\n bbox_fields=[],\n mask_fields=[],\n seg_fields=[])\n\n # camera points to image conversion\n if box_mode_3d == Box3DMode.CAM:\n data['img_info'].update(dict(cam_intrinsic=img_info['cam_intrinsic']))\n\n data = test_pipeline(data)\n\n data = collate([data], samples_per_gpu=1)\n if next(model.parameters()).is_cuda:\n # scatter to specified GPU\n data = scatter(data, [device.index])[0]\n else:\n # this is a workaround to avoid the bug of MMDataParallel\n data['img_metas'] = data['img_metas'][0].data\n data['img'] = data['img'][0].data\n\n # forward the model\n with torch.no_grad():\n result = model(return_loss=False, rescale=True, **data)\n return result, data\n\n\ndef inference_segmentor(model, pcd):\n \"\"\"Inference point cloud with the segmentor.\n\n Args:\n model (nn.Module): The loaded segmentor.\n pcd (str): Point cloud files.\n\n Returns:\n tuple: Predicted results and data from pipeline.\n \"\"\"\n cfg = model.cfg\n device = next(model.parameters()).device # model device\n # build the data pipeline\n test_pipeline = deepcopy(cfg.data.test.pipeline)\n test_pipeline = Compose(test_pipeline)\n data = dict(\n pts_filename=pcd,\n img_fields=[],\n bbox3d_fields=[],\n pts_mask_fields=[],\n pts_seg_fields=[],\n bbox_fields=[],\n mask_fields=[],\n seg_fields=[])\n data = test_pipeline(data)\n data = collate([data], samples_per_gpu=1)\n if next(model.parameters()).is_cuda:\n # scatter to specified GPU\n data = scatter(data, [device.index])[0]\n else:\n # this is a workaround to avoid the bug of MMDataParallel\n data['img_metas'] = data['img_metas'][0].data\n data['points'] = data['points'][0].data\n # forward the model\n with torch.no_grad():\n result = model(return_loss=False, rescale=True, **data)\n return result, data\n\n\ndef show_det_result_meshlab(data,\n result,\n out_dir,\n score_thr=0.0,\n show=False,\n snapshot=False):\n \"\"\"Show 3D detection result by meshlab.\"\"\"\n points = data['points'][0][0].cpu().numpy()\n pts_filename = data['img_metas'][0][0]['pts_filename']\n file_name = osp.split(pts_filename)[-1].split('.')[0]\n\n if 'pts_bbox' in result[0].keys():\n pred_bboxes = result[0]['pts_bbox']['boxes_3d'].tensor.numpy()\n pred_scores = result[0]['pts_bbox']['scores_3d'].numpy()\n else:\n pred_bboxes = result[0]['boxes_3d'].tensor.numpy()\n pred_scores = result[0]['scores_3d'].numpy()\n\n # filter out low score bboxes for visualization\n if score_thr > 0:\n inds = pred_scores > score_thr\n pred_bboxes = pred_bboxes[inds]\n\n # for now we convert points into depth mode\n box_mode = data['img_metas'][0][0]['box_mode_3d']\n if box_mode != Box3DMode.DEPTH:\n points = points[..., [1, 0, 2]]\n points[..., 0] *= -1\n show_bboxes = Box3DMode.convert(pred_bboxes, box_mode, Box3DMode.DEPTH)\n else:\n show_bboxes = deepcopy(pred_bboxes)\n\n show_result(\n points,\n None,\n show_bboxes,\n out_dir,\n file_name,\n show=show,\n snapshot=snapshot)\n\n return file_name\n\n\ndef show_seg_result_meshlab(data,\n result,\n out_dir,\n palette,\n show=False,\n snapshot=False):\n \"\"\"Show 3D segmentation result by meshlab.\"\"\"\n points = data['points'][0][0].cpu().numpy()\n pts_filename = data['img_metas'][0][0]['pts_filename']\n file_name = osp.split(pts_filename)[-1].split('.')[0]\n\n pred_seg = result[0]['semantic_mask'].numpy()\n\n if palette is None:\n # generate random color map\n max_idx = pred_seg.max()\n palette = np.random.randint(0, 256, size=(max_idx + 1, 3))\n palette = np.array(palette).astype(np.int)\n\n show_seg_result(\n points,\n None,\n pred_seg,\n out_dir,\n file_name,\n palette=palette,\n show=show,\n snapshot=snapshot)\n\n return file_name\n\n\ndef show_proj_det_result_meshlab(data,\n result,\n out_dir,\n score_thr=0.0,\n show=False,\n snapshot=False):\n \"\"\"Show result of projecting 3D bbox to 2D image by meshlab.\"\"\"\n assert 'img' in data.keys(), 'image data is not provided for visualization'\n\n img_filename = data['img_metas'][0][0]['filename']\n file_name = osp.split(img_filename)[-1].split('.')[0]\n\n # read from file because img in data_dict has undergone pipeline transform\n img = mmcv.imread(img_filename)\n\n if 'pts_bbox' in result[0].keys():\n result[0] = result[0]['pts_bbox']\n elif 'img_bbox' in result[0].keys():\n result[0] = result[0]['img_bbox']\n pred_bboxes = result[0]['boxes_3d'].tensor.numpy()\n pred_scores = result[0]['scores_3d'].numpy()\n\n # filter out low score bboxes for visualization\n if score_thr > 0:\n inds = pred_scores > score_thr\n pred_bboxes = pred_bboxes[inds]\n\n box_mode = data['img_metas'][0][0]['box_mode_3d']\n if box_mode == Box3DMode.LIDAR:\n if 'lidar2img' not in data['img_metas'][0][0]:\n raise NotImplementedError(\n 'LiDAR to image transformation matrix is not provided')\n\n show_bboxes = LiDARInstance3DBoxes(pred_bboxes, origin=(0.5, 0.5, 0))\n\n show_multi_modality_result(\n img,\n None,\n show_bboxes,\n data['img_metas'][0][0]['lidar2img'],\n out_dir,\n file_name,\n box_mode='lidar',\n show=show)\n elif box_mode == Box3DMode.DEPTH:\n if 'calib' not in data.keys():\n raise NotImplementedError(\n 'camera calibration information is not provided')\n\n show_bboxes = DepthInstance3DBoxes(pred_bboxes, origin=(0.5, 0.5, 0))\n\n show_multi_modality_result(\n img,\n None,\n show_bboxes,\n data['calib'][0],\n out_dir,\n file_name,\n box_mode='depth',\n img_metas=data['img_metas'][0][0],\n show=show)\n elif box_mode == Box3DMode.CAM:\n if 'cam_intrinsic' not in data['img_metas'][0][0]:\n raise NotImplementedError(\n 'camera intrinsic matrix is not provided')\n\n from mmdet3d.core.bbox import mono_cam_box2vis\n show_bboxes = CameraInstance3DBoxes(\n pred_bboxes, box_dim=pred_bboxes.shape[-1], origin=(0.5, 1.0, 0.5))\n # TODO: remove the hack of box from NuScenesMonoDataset\n show_bboxes = mono_cam_box2vis(show_bboxes)\n\n show_multi_modality_result(\n img,\n None,\n show_bboxes,\n data['img_metas'][0][0]['cam_intrinsic'],\n out_dir,\n file_name,\n box_mode='camera',\n show=show)\n else:\n raise NotImplementedError(\n f'visualization of {box_mode} bbox is not supported')\n\n return file_name\n\n\ndef show_result_meshlab(data,\n result,\n out_dir,\n score_thr=0.0,\n show=False,\n snapshot=False,\n task='det',\n palette=None):\n \"\"\"Show result by meshlab.\n\n Args:\n data (dict): Contain data from pipeline.\n result (dict): Predicted result from model.\n out_dir (str): Directory to save visualized result.\n score_thr (float): Minimum score of bboxes to be shown. Default: 0.0\n show (bool): Visualize the results online. Defaults to False.\n snapshot (bool): Whether to save the online results. Defaults to False.\n task (str): Distinguish which task result to visualize. Currently we\n support 3D detection, multi-modality detection and 3D segmentation.\n Defaults to 'det'.\n palette (list[list[int]]] | np.ndarray | None): The palette of\n segmentation map. If None is given, random palette will be\n generated. Defaults to None.\n \"\"\"\n assert task in ['det', 'multi_modality-det', 'seg', 'mono-det'], \\\n f'unsupported visualization task {task}'\n assert out_dir is not None, 'Expect out_dir, got none.'\n\n if task in ['det', 'multi_modality-det']:\n file_name = show_det_result_meshlab(data, result, out_dir, score_thr,\n show, snapshot)\n\n if task in ['seg']:\n file_name = show_seg_result_meshlab(data, result, out_dir, palette,\n show, snapshot)\n\n if task in ['multi_modality-det', 'mono-det']:\n file_name = show_proj_det_result_meshlab(data, result, out_dir,\n score_thr, show, snapshot)\n\n return out_dir, file_name\n",
"path": "mmdet3d/apis/inference.py"
}
] | diff --git a/mmdet3d/apis/inference.py b/mmdet3d/apis/inference.py
index 031a242a02..ca0595a65a 100644
--- a/mmdet3d/apis/inference.py
+++ b/mmdet3d/apis/inference.py
@@ -89,6 +89,8 @@ def inference_detector(model, pcd):
pts_filename=pcd,
box_type_3d=box_type_3d,
box_mode_3d=box_mode_3d,
+ # for ScanNet demo we need axis_align_matrix
+ ann_info=dict(axis_align_matrix=np.eye(4)),
sweeps=[],
# set timestamp = 0
timestamp=[0],
|
qutip__qutip-1918 | Bug with Bloch and Ipython.
### Bug Description
`Bloch` raises an error when used in jupyter notebook. This seems to be due to the output of `print_figure` in `_repr_svg_` not being bytecode (maybe it was in the past?) it then defaults to `_repr_png_` and renders correctly the bloch sphere.
### Code to Reproduce the Bug
```shell
import qutip
qutip.Bloch()
```
### Code Output
```shell
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
~/.virtualenvs/qutip4/lib/python3.10/site-packages/IPython/core/formatters.py in __call__(self, obj)
343 method = get_real_method(obj, self.print_method)
344 if method is not None:
--> 345 return method()
346 return None
347 else:
~/git_repo/qutip/qutip4/qutip/bloch.py in _repr_svg_(self)
293 from IPython.core.pylabtools import print_figure
294 self.render()
--> 295 fig_data = print_figure(self.fig, 'svg').decode('utf-8')
296 plt.close(self.fig)
297 return fig_data
AttributeError: 'str' object has no attribute 'decode'
```
### Expected Behaviour
The Bloch sphere should be plotted correctly without any Error.
### Your Environment
```shell
QuTiP Version: 5.0.0.dev0+ee51e50
Numpy Version: 1.22.3
Scipy Version: 1.8.1
Cython Version: None
Matplotlib Version: 3.5.2
Python Version: 3.10.4
Number of CPUs: 8
BLAS Info: OPENBLAS
OPENMP Installed: False
INTEL MKL Ext: False
Platform Info: Linux (x86_64)
```
### Additional Context
_No response_
| [
{
"content": "__all__ = ['Bloch']\n\nimport os\n\nimport numpy as np\nfrom numpy import (outer, cos, sin, ones)\n\nfrom packaging.version import parse as parse_version\n\nfrom . import Qobj, expect, sigmax, sigmay, sigmaz\n\ntry:\n import matplotlib\n import matplotlib.pyplot as plt\n from mpl_toolkits.mplot3d import Axes3D\n from matplotlib.patches import FancyArrowPatch\n from mpl_toolkits.mplot3d import proj3d\n\n # Define a custom _axes3D function based on the matplotlib version.\n # The auto_add_to_figure keyword is new for matplotlib>=3.4.\n if parse_version(matplotlib.__version__) >= parse_version('3.4'):\n def _axes3D(fig, *args, **kwargs):\n ax = Axes3D(fig, *args, auto_add_to_figure=False, **kwargs)\n return fig.add_axes(ax)\n else:\n def _axes3D(*args, **kwargs):\n return Axes3D(*args, **kwargs)\n\n class Arrow3D(FancyArrowPatch):\n def __init__(self, xs, ys, zs, *args, **kwargs):\n FancyArrowPatch.__init__(self, (0, 0), (0, 0), *args, **kwargs)\n\n self._verts3d = xs, ys, zs\n\n def draw(self, renderer):\n xs3d, ys3d, zs3d = self._verts3d\n xs, ys, zs = proj3d.proj_transform(xs3d, ys3d, zs3d, self.axes.M)\n\n self.set_positions((xs[0], ys[0]), (xs[1], ys[1]))\n FancyArrowPatch.draw(self, renderer)\n\n def do_3d_projection(self, renderer=None):\n # only called by matplotlib >= 3.5\n xs3d, ys3d, zs3d = self._verts3d\n xs, ys, zs = proj3d.proj_transform(xs3d, ys3d, zs3d, self.axes.M)\n self.set_positions((xs[0], ys[0]), (xs[1], ys[1]))\n return np.min(zs)\nexcept ImportError:\n pass\n\ntry:\n from IPython.display import display\nexcept ImportError:\n pass\n\n\nclass Bloch:\n r\"\"\"\n Class for plotting data on the Bloch sphere. Valid data can be either\n points, vectors, or Qobj objects.\n\n Attributes\n ----------\n axes : matplotlib.axes.Axes\n User supplied Matplotlib axes for Bloch sphere animation.\n fig : matplotlib.figure.Figure\n User supplied Matplotlib Figure instance for plotting Bloch sphere.\n font_color : str, default 'black'\n Color of font used for Bloch sphere labels.\n font_size : int, default 20\n Size of font used for Bloch sphere labels.\n frame_alpha : float, default 0.1\n Sets transparency of Bloch sphere frame.\n frame_color : str, default 'gray'\n Color of sphere wireframe.\n frame_width : int, default 1\n Width of wireframe.\n point_color : list, default [\"b\", \"r\", \"g\", \"#CC6600\"]\n List of colors for Bloch sphere point markers to cycle through, i.e.\n by default, points 0 and 4 will both be blue ('b').\n point_marker : list, default [\"o\", \"s\", \"d\", \"^\"]\n List of point marker shapes to cycle through.\n point_size : list, default [25, 32, 35, 45]\n List of point marker sizes. Note, not all point markers look the same\n size when plotted!\n sphere_alpha : float, default 0.2\n Transparency of Bloch sphere itself.\n sphere_color : str, default '#FFDDDD'\n Color of Bloch sphere.\n figsize : list, default [7, 7]\n Figure size of Bloch sphere plot. Best to have both numbers the same;\n otherwise you will have a Bloch sphere that looks like a football.\n vector_color : list, [\"g\", \"#CC6600\", \"b\", \"r\"]\n List of vector colors to cycle through.\n vector_width : int, default 5\n Width of displayed vectors.\n vector_style : str, default '-\\|>'\n Vector arrowhead style (from matplotlib's arrow style).\n vector_mutation : int, default 20\n Width of vectors arrowhead.\n view : list, default [-60, 30]\n Azimuthal and Elevation viewing angles.\n xlabel : list, default [\"$x$\", \"\"]\n List of strings corresponding to +x and -x axes labels, respectively.\n xlpos : list, default [1.1, -1.1]\n Positions of +x and -x labels respectively.\n ylabel : list, default [\"$y$\", \"\"]\n List of strings corresponding to +y and -y axes labels, respectively.\n ylpos : list, default [1.2, -1.2]\n Positions of +y and -y labels respectively.\n zlabel : list, default ['$\\\\left\\|0\\\\right>$', '$\\\\left\\|1\\\\right>$']\n List of strings corresponding to +z and -z axes labels, respectively.\n zlpos : list, default [1.2, -1.2]\n Positions of +z and -z labels respectively.\n \"\"\"\n def __init__(self, fig=None, axes=None, view=None, figsize=None,\n background=False):\n # Figure and axes\n self.fig = fig\n self._ext_fig = fig is not None\n self.axes = axes\n # Background axes, default = False\n self.background = background\n # The size of the figure in inches, default = [5,5].\n self.figsize = figsize if figsize else [5, 5]\n # Azimuthal and Elvation viewing angles, default = [-60,30].\n self.view = view if view else [-60, 30]\n # Color of Bloch sphere, default = #FFDDDD\n self.sphere_color = '#FFDDDD'\n # Transparency of Bloch sphere, default = 0.2\n self.sphere_alpha = 0.2\n # Color of wireframe, default = 'gray'\n self.frame_color = 'gray'\n # Width of wireframe, default = 1\n self.frame_width = 1\n # Transparency of wireframe, default = 0.2\n self.frame_alpha = 0.2\n # Labels for x-axis (in LaTex), default = ['$x$', '']\n self.xlabel = ['$x$', '']\n # Position of x-axis labels, default = [1.2, -1.2]\n self.xlpos = [1.2, -1.2]\n # Labels for y-axis (in LaTex), default = ['$y$', '']\n self.ylabel = ['$y$', '']\n # Position of y-axis labels, default = [1.1, -1.1]\n self.ylpos = [1.2, -1.2]\n # Labels for z-axis (in LaTex),\n # default = [r'$\\left\\|0\\right>$', r'$\\left|1\\right>$']\n self.zlabel = [r'$\\left|0\\right>$', r'$\\left|1\\right>$']\n # Position of z-axis labels, default = [1.2, -1.2]\n self.zlpos = [1.2, -1.2]\n # ---font options---\n # Color of fonts, default = 'black'\n self.font_color = 'black'\n # Size of fonts, default = 20\n self.font_size = 20\n\n # ---vector options---\n # List of colors for Bloch vectors, default = ['b','g','r','y']\n self.vector_default_color = ['g', '#CC6600', 'b', 'r']\n # List that stores the display colors for each vector\n self.vector_color = []\n #: Width of Bloch vectors, default = 5\n self.vector_width = 3\n #: Style of Bloch vectors, default = '-\\|>' (or 'simple')\n self.vector_style = '-|>'\n #: Sets the width of the vectors arrowhead\n self.vector_mutation = 20\n\n # ---point options---\n # List of colors for Bloch point markers, default = ['b','g','r','y']\n self.point_default_color = ['b', 'r', 'g', '#CC6600']\n # List that stores the display colors for each set of points\n self.point_color = []\n # Size of point markers, default = 25\n self.point_size = [25, 32, 35, 45]\n # Shape of point markers, default = ['o','^','d','s']\n self.point_marker = ['o', 's', 'd', '^']\n\n # ---data lists---\n # Data for point markers\n self.points = []\n # Data for Bloch vectors\n self.vectors = []\n # Transparency of vectors, alpha value from 0 to 1\n self.vector_alpha = []\n # Data for annotations\n self.annotations = []\n # Number of times sphere has been saved\n self.savenum = 0\n # Style of points, 'm' for multiple colors, 's' for single color\n self.point_style = []\n # Transparency of points, alpha value from 0 to 1\n self.point_alpha = []\n # Data for line segment\n self._lines = []\n # Data for arcs and arc style\n self._arcs = []\n\n def set_label_convention(self, convention):\n \"\"\"Set x, y and z labels according to one of conventions.\n\n Parameters\n ----------\n convention : string\n One of the following:\n\n - \"original\"\n - \"xyz\"\n - \"sx sy sz\"\n - \"01\"\n - \"polarization jones\"\n - \"polarization jones letters\"\n see also: https://en.wikipedia.org/wiki/Jones_calculus\n - \"polarization stokes\"\n see also: https://en.wikipedia.org/wiki/Stokes_parameters\n \"\"\"\n ketex = \"$\\\\left.|%s\\\\right\\\\rangle$\"\n # \\left.| is on purpose, so that every ket has the same size\n\n if convention == \"original\":\n self.xlabel = ['$x$', '']\n self.ylabel = ['$y$', '']\n self.zlabel = ['$\\\\left|0\\\\right>$', '$\\\\left|1\\\\right>$']\n elif convention == \"xyz\":\n self.xlabel = ['$x$', '']\n self.ylabel = ['$y$', '']\n self.zlabel = ['$z$', '']\n elif convention == \"sx sy sz\":\n self.xlabel = ['$s_x$', '']\n self.ylabel = ['$s_y$', '']\n self.zlabel = ['$s_z$', '']\n elif convention == \"01\":\n self.xlabel = ['', '']\n self.ylabel = ['', '']\n self.zlabel = ['$\\\\left|0\\\\right>$', '$\\\\left|1\\\\right>$']\n elif convention == \"polarization jones\":\n self.xlabel = [ketex % \"\\\\nearrow\\\\hspace{-1.46}\\\\swarrow\",\n ketex % \"\\\\nwarrow\\\\hspace{-1.46}\\\\searrow\"]\n self.ylabel = [ketex % \"\\\\circlearrowleft\", ketex %\n \"\\\\circlearrowright\"]\n self.zlabel = [ketex % \"\\\\leftrightarrow\", ketex % \"\\\\updownarrow\"]\n elif convention == \"polarization jones letters\":\n self.xlabel = [ketex % \"D\", ketex % \"A\"]\n self.ylabel = [ketex % \"L\", ketex % \"R\"]\n self.zlabel = [ketex % \"H\", ketex % \"V\"]\n elif convention == \"polarization stokes\":\n self.ylabel = [\"$\\\\nearrow\\\\hspace{-1.46}\\\\swarrow$\",\n \"$\\\\nwarrow\\\\hspace{-1.46}\\\\searrow$\"]\n self.zlabel = [\"$\\\\circlearrowleft$\", \"$\\\\circlearrowright$\"]\n self.xlabel = [\"$\\\\leftrightarrow$\", \"$\\\\updownarrow$\"]\n else:\n raise Exception(\"No such convention.\")\n\n def __str__(self):\n s = \"\"\n s += \"Bloch data:\\n\"\n s += \"-----------\\n\"\n s += \"Number of points: \" + str(len(self.points)) + \"\\n\"\n s += \"Number of vectors: \" + str(len(self.vectors)) + \"\\n\"\n s += \"\\n\"\n s += \"Bloch sphere properties:\\n\"\n s += \"------------------------\\n\"\n s += \"font_color: \" + str(self.font_color) + \"\\n\"\n s += \"font_size: \" + str(self.font_size) + \"\\n\"\n s += \"frame_alpha: \" + str(self.frame_alpha) + \"\\n\"\n s += \"frame_color: \" + str(self.frame_color) + \"\\n\"\n s += \"frame_width: \" + str(self.frame_width) + \"\\n\"\n s += \"point_default_color:\" + str(self.point_default_color) + \"\\n\"\n s += \"point_marker: \" + str(self.point_marker) + \"\\n\"\n s += \"point_size: \" + str(self.point_size) + \"\\n\"\n s += \"sphere_alpha: \" + str(self.sphere_alpha) + \"\\n\"\n s += \"sphere_color: \" + str(self.sphere_color) + \"\\n\"\n s += \"figsize: \" + str(self.figsize) + \"\\n\"\n s += \"vector_default_color:\" + str(self.vector_default_color) + \"\\n\"\n s += \"vector_width: \" + str(self.vector_width) + \"\\n\"\n s += \"vector_style: \" + str(self.vector_style) + \"\\n\"\n s += \"vector_mutation: \" + str(self.vector_mutation) + \"\\n\"\n s += \"view: \" + str(self.view) + \"\\n\"\n s += \"xlabel: \" + str(self.xlabel) + \"\\n\"\n s += \"xlpos: \" + str(self.xlpos) + \"\\n\"\n s += \"ylabel: \" + str(self.ylabel) + \"\\n\"\n s += \"ylpos: \" + str(self.ylpos) + \"\\n\"\n s += \"zlabel: \" + str(self.zlabel) + \"\\n\"\n s += \"zlpos: \" + str(self.zlpos) + \"\\n\"\n return s\n\n def _repr_png_(self):\n from IPython.core.pylabtools import print_figure\n self.render()\n fig_data = print_figure(self.fig, 'png')\n plt.close(self.fig)\n return fig_data\n\n def _repr_svg_(self):\n from IPython.core.pylabtools import print_figure\n self.render()\n fig_data = print_figure(self.fig, 'svg').decode('utf-8')\n plt.close(self.fig)\n return fig_data\n\n def clear(self):\n \"\"\"Resets Bloch sphere data sets to empty.\n \"\"\"\n self.points = []\n self.vectors = []\n self.point_style = []\n self.point_alpha = []\n self.vector_alpha = []\n self.annotations = []\n self.vector_color = []\n self.point_color = []\n self._lines = []\n self._arcs = []\n\n def add_points(self, points, meth='s', colors=None, alpha=1.0):\n \"\"\"Add a list of data points to bloch sphere.\n\n Parameters\n ----------\n points : array_like\n Collection of data points.\n\n meth : {'s', 'm', 'l'}\n Type of points to plot, use 'm' for multicolored, 'l' for points\n connected with a line.\n\n colors : array_like\n Optional array with colors for the points.\n A single color for meth 's', and list of colors for meth 'm'\n\n alpha : float, default=1.\n Transparency value for the vectors. Values between 0 and 1.\n\n .. note::\n\n When using ``meth=l`` in QuTiP 4.6, the line transparency defaulted\n to ``0.75`` and there was no way to alter it.\n When the ``alpha`` parameter was added in QuTiP 4.7, the default\n became ``alpha=1.0`` for values of ``meth``.\n \"\"\"\n\n points = np.asarray(points)\n\n if points.ndim == 1:\n points = points[:, np.newaxis]\n\n if points.ndim != 2 or points.shape[0] != 3:\n raise ValueError(\"The included points are not valid. Points must \"\n \"be equivalent to a 2D array where the first \"\n \"index represents the x,y,z values and the \"\n \"second index iterates over the points.\")\n\n if meth not in ['s', 'm', 'l']:\n raise ValueError(f\"The value for meth = {meth} is not valid.\"\n \" Please use 's', 'l' or 'm'.\")\n\n if meth == 's' and points.shape[1] == 1:\n points = np.append(points[:, :1], points, axis=1)\n\n self.point_style.append(meth)\n self.points.append(points)\n self.point_alpha.append(alpha)\n self.point_color.append(colors)\n\n def add_states(self, state, kind='vector', colors=None, alpha=1.0):\n \"\"\"Add a state vector Qobj to Bloch sphere.\n\n Parameters\n ----------\n state : Qobj\n Input state vector.\n\n kind : {'vector', 'point'}\n Type of object to plot.\n\n colors : array_like\n Optional array with colors for the states.\n\n alpha : float, default=1.\n Transparency value for the vectors. Values between 0 and 1.\n \"\"\"\n if isinstance(state, Qobj):\n state = [state]\n if not isinstance(colors, (list, np.ndarray)) and colors is not None:\n colors = [colors]\n\n for k, st in enumerate(state):\n vec = [expect(sigmax(), st),\n expect(sigmay(), st),\n expect(sigmaz(), st)]\n\n if kind == 'vector':\n if colors is not None:\n self.add_vectors(vec, colors=colors[k], alpha=alpha)\n else:\n self.add_vectors(vec)\n elif kind == 'point':\n if colors is not None:\n self.add_points(vec, colors=colors[k], alpha=alpha)\n else:\n self.add_points(vec)\n\n def add_vectors(self, vectors, colors=None, alpha=1.0):\n \"\"\"Add a list of vectors to Bloch sphere.\n\n Parameters\n ----------\n vectors : array_like\n Array with vectors of unit length or smaller.\n\n colors : array_like\n Optional array with colors for the vectors.\n\n alpha : float, default=1.\n Transparency value for the vectors. Values between 0 and 1.\n\n \"\"\"\n vectors = np.asarray(vectors)\n\n if vectors.ndim == 1:\n vectors = vectors[np.newaxis, :]\n\n if vectors.ndim != 2 or vectors.shape[1] != 3:\n raise ValueError(\n \"The included vectors are not valid. Vectors must \"\n \"be equivalent to a 2D array where the first \"\n \"index represents the iteration over the vectors and the \"\n \"second index represents the position in 3D of vector head.\")\n\n n_vectors = vectors.shape[0]\n if colors is None:\n colors = np.array([None] * n_vectors)\n else:\n colors = np.asarray(colors)\n\n if colors.ndim != 1 or colors.size != n_vectors:\n raise ValueError(\"The included colors are not valid. colors must \"\n \"be equivalent to a 1D array with the same \"\n \"size as the number of vectors. \")\n\n for k, vec in enumerate(vectors):\n self.vectors.append(vec)\n self.vector_alpha.append(alpha)\n self.vector_color.append(colors[k])\n\n def add_annotation(self, state_or_vector, text, **kwargs):\n \"\"\"\n Add a text or LaTeX annotation to Bloch sphere, parametrized by a qubit\n state or a vector.\n\n Parameters\n ----------\n state_or_vector : Qobj/array/list/tuple\n Position for the annotaion.\n Qobj of a qubit or a vector of 3 elements.\n\n text : str\n Annotation text.\n You can use LaTeX, but remember to use raw string\n e.g. r\"$\\\\langle x \\\\rangle$\"\n or escape backslashes\n e.g. \"$\\\\\\\\langle x \\\\\\\\rangle$\".\n\n kwargs :\n Options as for mplot3d.axes3d.text, including:\n fontsize, color, horizontalalignment, verticalalignment.\n\n \"\"\"\n if isinstance(state_or_vector, Qobj):\n vec = [expect(sigmax(), state_or_vector),\n expect(sigmay(), state_or_vector),\n expect(sigmaz(), state_or_vector)]\n elif isinstance(state_or_vector, (list, np.ndarray, tuple)) \\\n and len(state_or_vector) == 3:\n vec = state_or_vector\n else:\n raise Exception(\"Position needs to be specified by a qubit \" +\n \"state or a 3D vector.\")\n self.annotations.append({'position': vec,\n 'text': text,\n 'opts': kwargs})\n\n def add_arc(self, start, end, fmt=\"b\", steps=None, **kwargs):\n \"\"\"Adds an arc between two points on a sphere. The arc is set to be\n blue solid curve by default.\n\n The start and end points must be on the same sphere (i.e. have the\n same radius) but need not be on the unit sphere.\n\n Parameters\n ----------\n start : Qobj or array-like\n Array with cartesian coordinates of the first point, or a state\n vector or density matrix that can be mapped to a point on or\n within the Bloch sphere.\n end : Qobj or array-like\n Array with cartesian coordinates of the second point, or a state\n vector or density matrix that can be mapped to a point on or\n within the Bloch sphere.\n fmt : str, default: \"b\"\n A matplotlib format string for rendering the arc.\n steps : int, default: None\n The number of segments to use when rendering the arc. The default\n uses 100 steps times the distance between the start and end points,\n with a minimum of 2 steps.\n **kwargs : dict\n Additional parameters to pass to the matplotlib .plot function\n when rendering this arc.\n \"\"\"\n if isinstance(start, Qobj):\n pt1 = [\n expect(sigmax(), start),\n expect(sigmay(), start),\n expect(sigmaz(), start),\n ]\n else:\n pt1 = start\n\n if isinstance(end, Qobj):\n pt2 = [\n expect(sigmax(), end),\n expect(sigmay(), end),\n expect(sigmaz(), end),\n ]\n else:\n pt2 = end\n\n pt1 = np.asarray(pt1)\n pt2 = np.asarray(pt2)\n\n len1 = np.linalg.norm(pt1)\n len2 = np.linalg.norm(pt2)\n if len1 < 1e-12 or len2 < 1e-12:\n raise ValueError('Polar and azimuthal angles undefined at origin.')\n elif abs(len1 - len2) > 1e-12:\n raise ValueError(\"Points not on the same sphere.\")\n elif (pt1 == pt2).all():\n raise ValueError(\n \"Start and end represent the same point. No arc can be formed.\"\n )\n elif (pt1 == -pt2).all():\n raise ValueError(\n \"Start and end are diagonally opposite, no unique arc is\"\n \" possible.\"\n )\n\n if steps is None:\n steps = int(np.linalg.norm(pt1 - pt2) * 100)\n steps = max(2, steps)\n t = np.linspace(0, 1, steps)\n # All the points in this line are contained in the plane defined\n # by pt1, pt2 and the origin.\n line = pt1[:, np.newaxis] * t + pt2[:, np.newaxis] * (1 - t)\n # Normalize all the points in the line so that are distance len1 from\n # the origin.\n arc = line * len1 / np.linalg.norm(line, axis=0)\n self._arcs.append([arc, fmt, kwargs])\n\n def add_line(self, start, end, fmt=\"k\", **kwargs):\n \"\"\"Adds a line segment connecting two points on the bloch sphere.\n\n The line segment is set to be a black solid line by default.\n\n Parameters\n ----------\n start : Qobj or array-like\n Array with cartesian coordinates of the first point, or a state\n vector or density matrix that can be mapped to a point on or\n within the Bloch sphere.\n end : Qobj or array-like\n Array with cartesian coordinates of the second point, or a state\n vector or density matrix that can be mapped to a point on or\n within the Bloch sphere.\n fmt : str, default: \"k\"\n A matplotlib format string for rendering the line.\n **kwargs : dict\n Additional parameters to pass to the matplotlib .plot function\n when rendering this line.\n \"\"\"\n if isinstance(start, Qobj):\n pt1 = [\n expect(sigmax(), start),\n expect(sigmay(), start),\n expect(sigmaz(), start),\n ]\n else:\n pt1 = start\n\n if isinstance(end, Qobj):\n pt2 = [\n expect(sigmax(), end),\n expect(sigmay(), end),\n expect(sigmaz(), end),\n ]\n else:\n pt2 = end\n\n pt1 = np.asarray(pt1)\n pt2 = np.asarray(pt2)\n\n x = [pt1[1], pt2[1]]\n y = [-pt1[0], -pt2[0]]\n z = [pt1[2], pt2[2]]\n v = [x, y, z]\n self._lines.append([v, fmt, kwargs])\n\n def make_sphere(self):\n \"\"\"\n Plots Bloch sphere and data sets.\n \"\"\"\n self.render()\n\n def run_from_ipython(self):\n try:\n __IPYTHON__\n return True\n except NameError:\n return False\n\n def _is_inline_backend(self):\n backend = matplotlib.get_backend()\n return backend == \"module://matplotlib_inline.backend_inline\"\n\n def render(self):\n \"\"\"\n Render the Bloch sphere and its data sets in on given figure and axes.\n \"\"\"\n if not self._ext_fig and not self._is_inline_backend():\n # If no external figure was supplied, we check to see if the\n # figure we created in a previous call to .render() has been\n # closed, and re-create if has been. This has the unfortunate\n # side effect of losing any modifications made to the axes or\n # figure, but the alternative is to crash the matplotlib backend.\n #\n # The inline backend used by, e.g. jupyter notebooks, is happy to\n # use closed figures so we leave those figures intact.\n if (\n self.fig is not None and\n not plt.fignum_exists(self.fig.number)\n ):\n self.fig = None\n self.axes = None\n\n if self.fig is None:\n self.fig = plt.figure(figsize=self.figsize)\n if self._is_inline_backend():\n # We immediately close the inline figure do avoid displaying\n # the figure twice when .show() calls display.\n plt.close(self.fig)\n\n if self.axes is None:\n self.axes = _axes3D(self.fig, azim=self.view[0], elev=self.view[1])\n\n # Clearing the axes is horrifically slow and loses a lot of the\n # axes state, but matplotlib doesn't seem to provide a better way\n # to redraw Axes3D. :/\n self.axes.clear()\n self.axes.grid(False)\n if self.background:\n self.axes.set_xlim3d(-1.3, 1.3)\n self.axes.set_ylim3d(-1.3, 1.3)\n self.axes.set_zlim3d(-1.3, 1.3)\n else:\n self.axes.set_axis_off()\n self.axes.set_xlim3d(-0.7, 0.7)\n self.axes.set_ylim3d(-0.7, 0.7)\n self.axes.set_zlim3d(-0.7, 0.7)\n # Manually set aspect ratio to fit a square bounding box.\n # Matplotlib did this stretching for < 3.3.0, but not above.\n if parse_version(matplotlib.__version__) >= parse_version('3.3'):\n self.axes.set_box_aspect((1, 1, 1))\n if not self.background:\n self.plot_axes()\n\n self.plot_back()\n self.plot_points()\n self.plot_vectors()\n self.plot_lines()\n self.plot_arcs()\n self.plot_front()\n self.plot_axes_labels()\n self.plot_annotations()\n # Trigger an update of the Bloch sphere if it is already shown:\n self.fig.canvas.draw()\n\n def plot_back(self):\n # back half of sphere\n u = np.linspace(0, np.pi, 25)\n v = np.linspace(0, np.pi, 25)\n x = outer(cos(u), sin(v))\n y = outer(sin(u), sin(v))\n z = outer(ones(np.size(u)), cos(v))\n self.axes.plot_surface(x, y, z, rstride=2, cstride=2,\n color=self.sphere_color, linewidth=0,\n alpha=self.sphere_alpha)\n # wireframe\n self.axes.plot_wireframe(x, y, z, rstride=5, cstride=5,\n color=self.frame_color,\n alpha=self.frame_alpha)\n # equator\n self.axes.plot(1.0 * cos(u), 1.0 * sin(u), zs=0, zdir='z',\n lw=self.frame_width, color=self.frame_color)\n self.axes.plot(1.0 * cos(u), 1.0 * sin(u), zs=0, zdir='x',\n lw=self.frame_width, color=self.frame_color)\n\n def plot_front(self):\n # front half of sphere\n u = np.linspace(-np.pi, 0, 25)\n v = np.linspace(0, np.pi, 25)\n x = outer(cos(u), sin(v))\n y = outer(sin(u), sin(v))\n z = outer(ones(np.size(u)), cos(v))\n self.axes.plot_surface(x, y, z, rstride=2, cstride=2,\n color=self.sphere_color, linewidth=0,\n alpha=self.sphere_alpha)\n # wireframe\n self.axes.plot_wireframe(x, y, z, rstride=5, cstride=5,\n color=self.frame_color,\n alpha=self.frame_alpha)\n # equator\n self.axes.plot(1.0 * cos(u), 1.0 * sin(u),\n zs=0, zdir='z', lw=self.frame_width,\n color=self.frame_color)\n self.axes.plot(1.0 * cos(u), 1.0 * sin(u),\n zs=0, zdir='x', lw=self.frame_width,\n color=self.frame_color)\n\n def plot_axes(self):\n # axes\n span = np.linspace(-1.0, 1.0, 2)\n self.axes.plot(span, 0 * span, zs=0, zdir='z', label='X',\n lw=self.frame_width, color=self.frame_color)\n self.axes.plot(0 * span, span, zs=0, zdir='z', label='Y',\n lw=self.frame_width, color=self.frame_color)\n self.axes.plot(0 * span, span, zs=0, zdir='y', label='Z',\n lw=self.frame_width, color=self.frame_color)\n\n def plot_axes_labels(self):\n # axes labels\n opts = {'fontsize': self.font_size,\n 'color': self.font_color,\n 'horizontalalignment': 'center',\n 'verticalalignment': 'center'}\n self.axes.text(0, -self.xlpos[0], 0, self.xlabel[0], **opts)\n self.axes.text(0, -self.xlpos[1], 0, self.xlabel[1], **opts)\n\n self.axes.text(self.ylpos[0], 0, 0, self.ylabel[0], **opts)\n self.axes.text(self.ylpos[1], 0, 0, self.ylabel[1], **opts)\n\n self.axes.text(0, 0, self.zlpos[0], self.zlabel[0], **opts)\n self.axes.text(0, 0, self.zlpos[1], self.zlabel[1], **opts)\n\n for a in (self.axes.xaxis.get_ticklines() +\n self.axes.xaxis.get_ticklabels()):\n a.set_visible(False)\n for a in (self.axes.yaxis.get_ticklines() +\n self.axes.yaxis.get_ticklabels()):\n a.set_visible(False)\n for a in (self.axes.zaxis.get_ticklines() +\n self.axes.zaxis.get_ticklabels()):\n a.set_visible(False)\n\n def plot_vectors(self):\n # -X and Y data are switched for plotting purposes\n for k, vec in enumerate(self.vectors):\n\n xs3d = vec[1] * np.array([0, 1])\n ys3d = -vec[0] * np.array([0, 1])\n zs3d = vec[2] * np.array([0, 1])\n\n alpha = self.vector_alpha[k]\n color = self.vector_color[k]\n if color is None:\n idx = k % len(self.vector_default_color)\n color = self.vector_default_color[idx]\n\n if self.vector_style == '':\n # simple line style\n self.axes.plot(xs3d, ys3d, zs3d, zdir='z', label='Z',\n lw=self.vector_width, color=color,\n alpha=alpha)\n else:\n # decorated style, with arrow heads\n a = Arrow3D(xs3d, ys3d, zs3d,\n mutation_scale=self.vector_mutation,\n lw=self.vector_width,\n arrowstyle=self.vector_style,\n color=color, alpha=alpha)\n\n self.axes.add_artist(a)\n\n def plot_points(self):\n # -X and Y data are switched for plotting purposes\n for k, points in enumerate(self.points):\n points = np.asarray(points)\n num_points = points.shape[1]\n\n dist = np.linalg.norm(points, axis=0)\n if not np.allclose(dist, dist[0], rtol=1e-12):\n indperm = np.argsort(dist)\n points = points[:, indperm]\n else:\n indperm = np.arange(num_points)\n\n s = self.point_size[np.mod(k, len(self.point_size))]\n marker = self.point_marker[np.mod(k, len(self.point_marker))]\n style = self.point_style[k]\n if self.point_color[k] is not None:\n color = self.point_color[k]\n elif self.point_style[k] in ['s', 'l']:\n color = self.point_default_color[\n k % len(self.point_default_color)\n ]\n elif self.point_style[k] == 'm':\n length = np.ceil(num_points/len(self.point_default_color))\n color = np.tile(self.point_default_color, length.astype(int))\n color = color[indperm]\n\n if self.point_style[k] in ['s', 'm']:\n self.axes.scatter(np.real(points[1]),\n -np.real(points[0]),\n np.real(points[2]),\n s=s,\n marker=marker,\n color=color,\n alpha=self.point_alpha[k],\n edgecolor=None,\n zdir='z',\n )\n\n elif self.point_style[k] == 'l':\n self.axes.plot(np.real(points[1]),\n -np.real(points[0]),\n np.real(points[2]),\n color=color,\n alpha=self.point_alpha[k],\n zdir='z',\n )\n\n def plot_annotations(self):\n # -X and Y data are switched for plotting purposes\n for annotation in self.annotations:\n vec = annotation['position']\n opts = {'fontsize': self.font_size,\n 'color': self.font_color,\n 'horizontalalignment': 'center',\n 'verticalalignment': 'center'}\n opts.update(annotation['opts'])\n self.axes.text(vec[1], -vec[0], vec[2],\n annotation['text'], **opts)\n\n def plot_lines(self):\n for line, fmt, kw in self._lines:\n self.axes.plot(line[0], line[1], line[2], fmt, **kw)\n\n def plot_arcs(self):\n for arc, fmt, kw in self._arcs:\n self.axes.plot(arc[1, :], -arc[0, :], arc[2, :], fmt, **kw)\n\n def show(self):\n \"\"\"\n Display Bloch sphere and corresponding data sets.\n\n Notes\n -----\n\n When using inline plotting in Jupyter notebooks, any figure created\n in a notebook cell is displayed after the cell executes. Thus if you\n create a figure yourself and use it create a Bloch sphere with\n ``b = Bloch(..., fig=fig)`` and then call ``b.show()`` in the same\n cell, then the figure will be displayed twice. If you do create your\n own figure, the simplest solution to this is to not call ``.show()``\n in the cell you create the figure in.\n \"\"\"\n self.render()\n if self.run_from_ipython():\n display(self.fig)\n else:\n self.fig.show()\n\n def save(self, name=None, format='png', dirc=None, dpin=None):\n \"\"\"Saves Bloch sphere to file of type ``format`` in directory ``dirc``.\n\n Parameters\n ----------\n\n name : str\n Name of saved image. Must include path and format as well.\n i.e. '/Users/Paul/Desktop/bloch.png'\n This overrides the 'format' and 'dirc' arguments.\n format : str\n Format of output image.\n dirc : str\n Directory for output images. Defaults to current working directory.\n dpin : int\n Resolution in dots per inch.\n\n Returns\n -------\n File containing plot of Bloch sphere.\n\n \"\"\"\n self.render()\n # Conditional variable for first argument to savefig\n # that is set in subsequent if-elses\n complete_path = \"\"\n if dirc:\n if not os.path.isdir(os.getcwd() + \"/\" + str(dirc)):\n os.makedirs(os.getcwd() + \"/\" + str(dirc))\n if name is None:\n if dirc:\n complete_path = os.getcwd() + \"/\" + str(dirc) + '/bloch_' \\\n + str(self.savenum) + '.' + format\n else:\n complete_path = os.getcwd() + '/bloch_' + \\\n str(self.savenum) + '.' + format\n else:\n complete_path = name\n\n if dpin:\n self.fig.savefig(complete_path, dpi=dpin)\n else:\n self.fig.savefig(complete_path)\n self.savenum += 1\n if self.fig:\n plt.close(self.fig)\n\n\ndef _hide_tick_lines_and_labels(axis):\n '''\n Set visible property of ticklines and ticklabels of an axis to False\n '''\n for a in axis.get_ticklines() + axis.get_ticklabels():\n a.set_visible(False)\n",
"path": "qutip/bloch.py"
}
] | [
{
"content": "__all__ = ['Bloch']\n\nimport os\n\nimport numpy as np\nfrom numpy import (outer, cos, sin, ones)\n\nfrom packaging.version import parse as parse_version\n\nfrom . import Qobj, expect, sigmax, sigmay, sigmaz\n\ntry:\n import matplotlib\n import matplotlib.pyplot as plt\n from mpl_toolkits.mplot3d import Axes3D\n from matplotlib.patches import FancyArrowPatch\n from mpl_toolkits.mplot3d import proj3d\n\n # Define a custom _axes3D function based on the matplotlib version.\n # The auto_add_to_figure keyword is new for matplotlib>=3.4.\n if parse_version(matplotlib.__version__) >= parse_version('3.4'):\n def _axes3D(fig, *args, **kwargs):\n ax = Axes3D(fig, *args, auto_add_to_figure=False, **kwargs)\n return fig.add_axes(ax)\n else:\n def _axes3D(*args, **kwargs):\n return Axes3D(*args, **kwargs)\n\n class Arrow3D(FancyArrowPatch):\n def __init__(self, xs, ys, zs, *args, **kwargs):\n FancyArrowPatch.__init__(self, (0, 0), (0, 0), *args, **kwargs)\n\n self._verts3d = xs, ys, zs\n\n def draw(self, renderer):\n xs3d, ys3d, zs3d = self._verts3d\n xs, ys, zs = proj3d.proj_transform(xs3d, ys3d, zs3d, self.axes.M)\n\n self.set_positions((xs[0], ys[0]), (xs[1], ys[1]))\n FancyArrowPatch.draw(self, renderer)\n\n def do_3d_projection(self, renderer=None):\n # only called by matplotlib >= 3.5\n xs3d, ys3d, zs3d = self._verts3d\n xs, ys, zs = proj3d.proj_transform(xs3d, ys3d, zs3d, self.axes.M)\n self.set_positions((xs[0], ys[0]), (xs[1], ys[1]))\n return np.min(zs)\nexcept ImportError:\n pass\n\ntry:\n from IPython.display import display\nexcept ImportError:\n pass\n\n\nclass Bloch:\n r\"\"\"\n Class for plotting data on the Bloch sphere. Valid data can be either\n points, vectors, or Qobj objects.\n\n Attributes\n ----------\n axes : matplotlib.axes.Axes\n User supplied Matplotlib axes for Bloch sphere animation.\n fig : matplotlib.figure.Figure\n User supplied Matplotlib Figure instance for plotting Bloch sphere.\n font_color : str, default 'black'\n Color of font used for Bloch sphere labels.\n font_size : int, default 20\n Size of font used for Bloch sphere labels.\n frame_alpha : float, default 0.1\n Sets transparency of Bloch sphere frame.\n frame_color : str, default 'gray'\n Color of sphere wireframe.\n frame_width : int, default 1\n Width of wireframe.\n point_color : list, default [\"b\", \"r\", \"g\", \"#CC6600\"]\n List of colors for Bloch sphere point markers to cycle through, i.e.\n by default, points 0 and 4 will both be blue ('b').\n point_marker : list, default [\"o\", \"s\", \"d\", \"^\"]\n List of point marker shapes to cycle through.\n point_size : list, default [25, 32, 35, 45]\n List of point marker sizes. Note, not all point markers look the same\n size when plotted!\n sphere_alpha : float, default 0.2\n Transparency of Bloch sphere itself.\n sphere_color : str, default '#FFDDDD'\n Color of Bloch sphere.\n figsize : list, default [7, 7]\n Figure size of Bloch sphere plot. Best to have both numbers the same;\n otherwise you will have a Bloch sphere that looks like a football.\n vector_color : list, [\"g\", \"#CC6600\", \"b\", \"r\"]\n List of vector colors to cycle through.\n vector_width : int, default 5\n Width of displayed vectors.\n vector_style : str, default '-\\|>'\n Vector arrowhead style (from matplotlib's arrow style).\n vector_mutation : int, default 20\n Width of vectors arrowhead.\n view : list, default [-60, 30]\n Azimuthal and Elevation viewing angles.\n xlabel : list, default [\"$x$\", \"\"]\n List of strings corresponding to +x and -x axes labels, respectively.\n xlpos : list, default [1.1, -1.1]\n Positions of +x and -x labels respectively.\n ylabel : list, default [\"$y$\", \"\"]\n List of strings corresponding to +y and -y axes labels, respectively.\n ylpos : list, default [1.2, -1.2]\n Positions of +y and -y labels respectively.\n zlabel : list, default ['$\\\\left\\|0\\\\right>$', '$\\\\left\\|1\\\\right>$']\n List of strings corresponding to +z and -z axes labels, respectively.\n zlpos : list, default [1.2, -1.2]\n Positions of +z and -z labels respectively.\n \"\"\"\n def __init__(self, fig=None, axes=None, view=None, figsize=None,\n background=False):\n # Figure and axes\n self.fig = fig\n self._ext_fig = fig is not None\n self.axes = axes\n # Background axes, default = False\n self.background = background\n # The size of the figure in inches, default = [5,5].\n self.figsize = figsize if figsize else [5, 5]\n # Azimuthal and Elvation viewing angles, default = [-60,30].\n self.view = view if view else [-60, 30]\n # Color of Bloch sphere, default = #FFDDDD\n self.sphere_color = '#FFDDDD'\n # Transparency of Bloch sphere, default = 0.2\n self.sphere_alpha = 0.2\n # Color of wireframe, default = 'gray'\n self.frame_color = 'gray'\n # Width of wireframe, default = 1\n self.frame_width = 1\n # Transparency of wireframe, default = 0.2\n self.frame_alpha = 0.2\n # Labels for x-axis (in LaTex), default = ['$x$', '']\n self.xlabel = ['$x$', '']\n # Position of x-axis labels, default = [1.2, -1.2]\n self.xlpos = [1.2, -1.2]\n # Labels for y-axis (in LaTex), default = ['$y$', '']\n self.ylabel = ['$y$', '']\n # Position of y-axis labels, default = [1.1, -1.1]\n self.ylpos = [1.2, -1.2]\n # Labels for z-axis (in LaTex),\n # default = [r'$\\left\\|0\\right>$', r'$\\left|1\\right>$']\n self.zlabel = [r'$\\left|0\\right>$', r'$\\left|1\\right>$']\n # Position of z-axis labels, default = [1.2, -1.2]\n self.zlpos = [1.2, -1.2]\n # ---font options---\n # Color of fonts, default = 'black'\n self.font_color = 'black'\n # Size of fonts, default = 20\n self.font_size = 20\n\n # ---vector options---\n # List of colors for Bloch vectors, default = ['b','g','r','y']\n self.vector_default_color = ['g', '#CC6600', 'b', 'r']\n # List that stores the display colors for each vector\n self.vector_color = []\n #: Width of Bloch vectors, default = 5\n self.vector_width = 3\n #: Style of Bloch vectors, default = '-\\|>' (or 'simple')\n self.vector_style = '-|>'\n #: Sets the width of the vectors arrowhead\n self.vector_mutation = 20\n\n # ---point options---\n # List of colors for Bloch point markers, default = ['b','g','r','y']\n self.point_default_color = ['b', 'r', 'g', '#CC6600']\n # List that stores the display colors for each set of points\n self.point_color = []\n # Size of point markers, default = 25\n self.point_size = [25, 32, 35, 45]\n # Shape of point markers, default = ['o','^','d','s']\n self.point_marker = ['o', 's', 'd', '^']\n\n # ---data lists---\n # Data for point markers\n self.points = []\n # Data for Bloch vectors\n self.vectors = []\n # Transparency of vectors, alpha value from 0 to 1\n self.vector_alpha = []\n # Data for annotations\n self.annotations = []\n # Number of times sphere has been saved\n self.savenum = 0\n # Style of points, 'm' for multiple colors, 's' for single color\n self.point_style = []\n # Transparency of points, alpha value from 0 to 1\n self.point_alpha = []\n # Data for line segment\n self._lines = []\n # Data for arcs and arc style\n self._arcs = []\n\n def set_label_convention(self, convention):\n \"\"\"Set x, y and z labels according to one of conventions.\n\n Parameters\n ----------\n convention : string\n One of the following:\n\n - \"original\"\n - \"xyz\"\n - \"sx sy sz\"\n - \"01\"\n - \"polarization jones\"\n - \"polarization jones letters\"\n see also: https://en.wikipedia.org/wiki/Jones_calculus\n - \"polarization stokes\"\n see also: https://en.wikipedia.org/wiki/Stokes_parameters\n \"\"\"\n ketex = \"$\\\\left.|%s\\\\right\\\\rangle$\"\n # \\left.| is on purpose, so that every ket has the same size\n\n if convention == \"original\":\n self.xlabel = ['$x$', '']\n self.ylabel = ['$y$', '']\n self.zlabel = ['$\\\\left|0\\\\right>$', '$\\\\left|1\\\\right>$']\n elif convention == \"xyz\":\n self.xlabel = ['$x$', '']\n self.ylabel = ['$y$', '']\n self.zlabel = ['$z$', '']\n elif convention == \"sx sy sz\":\n self.xlabel = ['$s_x$', '']\n self.ylabel = ['$s_y$', '']\n self.zlabel = ['$s_z$', '']\n elif convention == \"01\":\n self.xlabel = ['', '']\n self.ylabel = ['', '']\n self.zlabel = ['$\\\\left|0\\\\right>$', '$\\\\left|1\\\\right>$']\n elif convention == \"polarization jones\":\n self.xlabel = [ketex % \"\\\\nearrow\\\\hspace{-1.46}\\\\swarrow\",\n ketex % \"\\\\nwarrow\\\\hspace{-1.46}\\\\searrow\"]\n self.ylabel = [ketex % \"\\\\circlearrowleft\", ketex %\n \"\\\\circlearrowright\"]\n self.zlabel = [ketex % \"\\\\leftrightarrow\", ketex % \"\\\\updownarrow\"]\n elif convention == \"polarization jones letters\":\n self.xlabel = [ketex % \"D\", ketex % \"A\"]\n self.ylabel = [ketex % \"L\", ketex % \"R\"]\n self.zlabel = [ketex % \"H\", ketex % \"V\"]\n elif convention == \"polarization stokes\":\n self.ylabel = [\"$\\\\nearrow\\\\hspace{-1.46}\\\\swarrow$\",\n \"$\\\\nwarrow\\\\hspace{-1.46}\\\\searrow$\"]\n self.zlabel = [\"$\\\\circlearrowleft$\", \"$\\\\circlearrowright$\"]\n self.xlabel = [\"$\\\\leftrightarrow$\", \"$\\\\updownarrow$\"]\n else:\n raise Exception(\"No such convention.\")\n\n def __str__(self):\n s = \"\"\n s += \"Bloch data:\\n\"\n s += \"-----------\\n\"\n s += \"Number of points: \" + str(len(self.points)) + \"\\n\"\n s += \"Number of vectors: \" + str(len(self.vectors)) + \"\\n\"\n s += \"\\n\"\n s += \"Bloch sphere properties:\\n\"\n s += \"------------------------\\n\"\n s += \"font_color: \" + str(self.font_color) + \"\\n\"\n s += \"font_size: \" + str(self.font_size) + \"\\n\"\n s += \"frame_alpha: \" + str(self.frame_alpha) + \"\\n\"\n s += \"frame_color: \" + str(self.frame_color) + \"\\n\"\n s += \"frame_width: \" + str(self.frame_width) + \"\\n\"\n s += \"point_default_color:\" + str(self.point_default_color) + \"\\n\"\n s += \"point_marker: \" + str(self.point_marker) + \"\\n\"\n s += \"point_size: \" + str(self.point_size) + \"\\n\"\n s += \"sphere_alpha: \" + str(self.sphere_alpha) + \"\\n\"\n s += \"sphere_color: \" + str(self.sphere_color) + \"\\n\"\n s += \"figsize: \" + str(self.figsize) + \"\\n\"\n s += \"vector_default_color:\" + str(self.vector_default_color) + \"\\n\"\n s += \"vector_width: \" + str(self.vector_width) + \"\\n\"\n s += \"vector_style: \" + str(self.vector_style) + \"\\n\"\n s += \"vector_mutation: \" + str(self.vector_mutation) + \"\\n\"\n s += \"view: \" + str(self.view) + \"\\n\"\n s += \"xlabel: \" + str(self.xlabel) + \"\\n\"\n s += \"xlpos: \" + str(self.xlpos) + \"\\n\"\n s += \"ylabel: \" + str(self.ylabel) + \"\\n\"\n s += \"ylpos: \" + str(self.ylpos) + \"\\n\"\n s += \"zlabel: \" + str(self.zlabel) + \"\\n\"\n s += \"zlpos: \" + str(self.zlpos) + \"\\n\"\n return s\n\n def _repr_png_(self):\n from IPython.core.pylabtools import print_figure\n self.render()\n fig_data = print_figure(self.fig, 'png')\n plt.close(self.fig)\n return fig_data\n\n def _repr_svg_(self):\n from IPython.core.pylabtools import print_figure\n self.render()\n fig_data = print_figure(self.fig, 'svg')\n plt.close(self.fig)\n return fig_data\n\n def clear(self):\n \"\"\"Resets Bloch sphere data sets to empty.\n \"\"\"\n self.points = []\n self.vectors = []\n self.point_style = []\n self.point_alpha = []\n self.vector_alpha = []\n self.annotations = []\n self.vector_color = []\n self.point_color = []\n self._lines = []\n self._arcs = []\n\n def add_points(self, points, meth='s', colors=None, alpha=1.0):\n \"\"\"Add a list of data points to bloch sphere.\n\n Parameters\n ----------\n points : array_like\n Collection of data points.\n\n meth : {'s', 'm', 'l'}\n Type of points to plot, use 'm' for multicolored, 'l' for points\n connected with a line.\n\n colors : array_like\n Optional array with colors for the points.\n A single color for meth 's', and list of colors for meth 'm'\n\n alpha : float, default=1.\n Transparency value for the vectors. Values between 0 and 1.\n\n .. note::\n\n When using ``meth=l`` in QuTiP 4.6, the line transparency defaulted\n to ``0.75`` and there was no way to alter it.\n When the ``alpha`` parameter was added in QuTiP 4.7, the default\n became ``alpha=1.0`` for values of ``meth``.\n \"\"\"\n\n points = np.asarray(points)\n\n if points.ndim == 1:\n points = points[:, np.newaxis]\n\n if points.ndim != 2 or points.shape[0] != 3:\n raise ValueError(\"The included points are not valid. Points must \"\n \"be equivalent to a 2D array where the first \"\n \"index represents the x,y,z values and the \"\n \"second index iterates over the points.\")\n\n if meth not in ['s', 'm', 'l']:\n raise ValueError(f\"The value for meth = {meth} is not valid.\"\n \" Please use 's', 'l' or 'm'.\")\n\n if meth == 's' and points.shape[1] == 1:\n points = np.append(points[:, :1], points, axis=1)\n\n self.point_style.append(meth)\n self.points.append(points)\n self.point_alpha.append(alpha)\n self.point_color.append(colors)\n\n def add_states(self, state, kind='vector', colors=None, alpha=1.0):\n \"\"\"Add a state vector Qobj to Bloch sphere.\n\n Parameters\n ----------\n state : Qobj\n Input state vector.\n\n kind : {'vector', 'point'}\n Type of object to plot.\n\n colors : array_like\n Optional array with colors for the states.\n\n alpha : float, default=1.\n Transparency value for the vectors. Values between 0 and 1.\n \"\"\"\n if isinstance(state, Qobj):\n state = [state]\n if not isinstance(colors, (list, np.ndarray)) and colors is not None:\n colors = [colors]\n\n for k, st in enumerate(state):\n vec = [expect(sigmax(), st),\n expect(sigmay(), st),\n expect(sigmaz(), st)]\n\n if kind == 'vector':\n if colors is not None:\n self.add_vectors(vec, colors=colors[k], alpha=alpha)\n else:\n self.add_vectors(vec)\n elif kind == 'point':\n if colors is not None:\n self.add_points(vec, colors=colors[k], alpha=alpha)\n else:\n self.add_points(vec)\n\n def add_vectors(self, vectors, colors=None, alpha=1.0):\n \"\"\"Add a list of vectors to Bloch sphere.\n\n Parameters\n ----------\n vectors : array_like\n Array with vectors of unit length or smaller.\n\n colors : array_like\n Optional array with colors for the vectors.\n\n alpha : float, default=1.\n Transparency value for the vectors. Values between 0 and 1.\n\n \"\"\"\n vectors = np.asarray(vectors)\n\n if vectors.ndim == 1:\n vectors = vectors[np.newaxis, :]\n\n if vectors.ndim != 2 or vectors.shape[1] != 3:\n raise ValueError(\n \"The included vectors are not valid. Vectors must \"\n \"be equivalent to a 2D array where the first \"\n \"index represents the iteration over the vectors and the \"\n \"second index represents the position in 3D of vector head.\")\n\n n_vectors = vectors.shape[0]\n if colors is None:\n colors = np.array([None] * n_vectors)\n else:\n colors = np.asarray(colors)\n\n if colors.ndim != 1 or colors.size != n_vectors:\n raise ValueError(\"The included colors are not valid. colors must \"\n \"be equivalent to a 1D array with the same \"\n \"size as the number of vectors. \")\n\n for k, vec in enumerate(vectors):\n self.vectors.append(vec)\n self.vector_alpha.append(alpha)\n self.vector_color.append(colors[k])\n\n def add_annotation(self, state_or_vector, text, **kwargs):\n \"\"\"\n Add a text or LaTeX annotation to Bloch sphere, parametrized by a qubit\n state or a vector.\n\n Parameters\n ----------\n state_or_vector : Qobj/array/list/tuple\n Position for the annotaion.\n Qobj of a qubit or a vector of 3 elements.\n\n text : str\n Annotation text.\n You can use LaTeX, but remember to use raw string\n e.g. r\"$\\\\langle x \\\\rangle$\"\n or escape backslashes\n e.g. \"$\\\\\\\\langle x \\\\\\\\rangle$\".\n\n kwargs :\n Options as for mplot3d.axes3d.text, including:\n fontsize, color, horizontalalignment, verticalalignment.\n\n \"\"\"\n if isinstance(state_or_vector, Qobj):\n vec = [expect(sigmax(), state_or_vector),\n expect(sigmay(), state_or_vector),\n expect(sigmaz(), state_or_vector)]\n elif isinstance(state_or_vector, (list, np.ndarray, tuple)) \\\n and len(state_or_vector) == 3:\n vec = state_or_vector\n else:\n raise Exception(\"Position needs to be specified by a qubit \" +\n \"state or a 3D vector.\")\n self.annotations.append({'position': vec,\n 'text': text,\n 'opts': kwargs})\n\n def add_arc(self, start, end, fmt=\"b\", steps=None, **kwargs):\n \"\"\"Adds an arc between two points on a sphere. The arc is set to be\n blue solid curve by default.\n\n The start and end points must be on the same sphere (i.e. have the\n same radius) but need not be on the unit sphere.\n\n Parameters\n ----------\n start : Qobj or array-like\n Array with cartesian coordinates of the first point, or a state\n vector or density matrix that can be mapped to a point on or\n within the Bloch sphere.\n end : Qobj or array-like\n Array with cartesian coordinates of the second point, or a state\n vector or density matrix that can be mapped to a point on or\n within the Bloch sphere.\n fmt : str, default: \"b\"\n A matplotlib format string for rendering the arc.\n steps : int, default: None\n The number of segments to use when rendering the arc. The default\n uses 100 steps times the distance between the start and end points,\n with a minimum of 2 steps.\n **kwargs : dict\n Additional parameters to pass to the matplotlib .plot function\n when rendering this arc.\n \"\"\"\n if isinstance(start, Qobj):\n pt1 = [\n expect(sigmax(), start),\n expect(sigmay(), start),\n expect(sigmaz(), start),\n ]\n else:\n pt1 = start\n\n if isinstance(end, Qobj):\n pt2 = [\n expect(sigmax(), end),\n expect(sigmay(), end),\n expect(sigmaz(), end),\n ]\n else:\n pt2 = end\n\n pt1 = np.asarray(pt1)\n pt2 = np.asarray(pt2)\n\n len1 = np.linalg.norm(pt1)\n len2 = np.linalg.norm(pt2)\n if len1 < 1e-12 or len2 < 1e-12:\n raise ValueError('Polar and azimuthal angles undefined at origin.')\n elif abs(len1 - len2) > 1e-12:\n raise ValueError(\"Points not on the same sphere.\")\n elif (pt1 == pt2).all():\n raise ValueError(\n \"Start and end represent the same point. No arc can be formed.\"\n )\n elif (pt1 == -pt2).all():\n raise ValueError(\n \"Start and end are diagonally opposite, no unique arc is\"\n \" possible.\"\n )\n\n if steps is None:\n steps = int(np.linalg.norm(pt1 - pt2) * 100)\n steps = max(2, steps)\n t = np.linspace(0, 1, steps)\n # All the points in this line are contained in the plane defined\n # by pt1, pt2 and the origin.\n line = pt1[:, np.newaxis] * t + pt2[:, np.newaxis] * (1 - t)\n # Normalize all the points in the line so that are distance len1 from\n # the origin.\n arc = line * len1 / np.linalg.norm(line, axis=0)\n self._arcs.append([arc, fmt, kwargs])\n\n def add_line(self, start, end, fmt=\"k\", **kwargs):\n \"\"\"Adds a line segment connecting two points on the bloch sphere.\n\n The line segment is set to be a black solid line by default.\n\n Parameters\n ----------\n start : Qobj or array-like\n Array with cartesian coordinates of the first point, or a state\n vector or density matrix that can be mapped to a point on or\n within the Bloch sphere.\n end : Qobj or array-like\n Array with cartesian coordinates of the second point, or a state\n vector or density matrix that can be mapped to a point on or\n within the Bloch sphere.\n fmt : str, default: \"k\"\n A matplotlib format string for rendering the line.\n **kwargs : dict\n Additional parameters to pass to the matplotlib .plot function\n when rendering this line.\n \"\"\"\n if isinstance(start, Qobj):\n pt1 = [\n expect(sigmax(), start),\n expect(sigmay(), start),\n expect(sigmaz(), start),\n ]\n else:\n pt1 = start\n\n if isinstance(end, Qobj):\n pt2 = [\n expect(sigmax(), end),\n expect(sigmay(), end),\n expect(sigmaz(), end),\n ]\n else:\n pt2 = end\n\n pt1 = np.asarray(pt1)\n pt2 = np.asarray(pt2)\n\n x = [pt1[1], pt2[1]]\n y = [-pt1[0], -pt2[0]]\n z = [pt1[2], pt2[2]]\n v = [x, y, z]\n self._lines.append([v, fmt, kwargs])\n\n def make_sphere(self):\n \"\"\"\n Plots Bloch sphere and data sets.\n \"\"\"\n self.render()\n\n def run_from_ipython(self):\n try:\n __IPYTHON__\n return True\n except NameError:\n return False\n\n def _is_inline_backend(self):\n backend = matplotlib.get_backend()\n return backend == \"module://matplotlib_inline.backend_inline\"\n\n def render(self):\n \"\"\"\n Render the Bloch sphere and its data sets in on given figure and axes.\n \"\"\"\n if not self._ext_fig and not self._is_inline_backend():\n # If no external figure was supplied, we check to see if the\n # figure we created in a previous call to .render() has been\n # closed, and re-create if has been. This has the unfortunate\n # side effect of losing any modifications made to the axes or\n # figure, but the alternative is to crash the matplotlib backend.\n #\n # The inline backend used by, e.g. jupyter notebooks, is happy to\n # use closed figures so we leave those figures intact.\n if (\n self.fig is not None and\n not plt.fignum_exists(self.fig.number)\n ):\n self.fig = None\n self.axes = None\n\n if self.fig is None:\n self.fig = plt.figure(figsize=self.figsize)\n if self._is_inline_backend():\n # We immediately close the inline figure do avoid displaying\n # the figure twice when .show() calls display.\n plt.close(self.fig)\n\n if self.axes is None:\n self.axes = _axes3D(self.fig, azim=self.view[0], elev=self.view[1])\n\n # Clearing the axes is horrifically slow and loses a lot of the\n # axes state, but matplotlib doesn't seem to provide a better way\n # to redraw Axes3D. :/\n self.axes.clear()\n self.axes.grid(False)\n if self.background:\n self.axes.set_xlim3d(-1.3, 1.3)\n self.axes.set_ylim3d(-1.3, 1.3)\n self.axes.set_zlim3d(-1.3, 1.3)\n else:\n self.axes.set_axis_off()\n self.axes.set_xlim3d(-0.7, 0.7)\n self.axes.set_ylim3d(-0.7, 0.7)\n self.axes.set_zlim3d(-0.7, 0.7)\n # Manually set aspect ratio to fit a square bounding box.\n # Matplotlib did this stretching for < 3.3.0, but not above.\n if parse_version(matplotlib.__version__) >= parse_version('3.3'):\n self.axes.set_box_aspect((1, 1, 1))\n if not self.background:\n self.plot_axes()\n\n self.plot_back()\n self.plot_points()\n self.plot_vectors()\n self.plot_lines()\n self.plot_arcs()\n self.plot_front()\n self.plot_axes_labels()\n self.plot_annotations()\n # Trigger an update of the Bloch sphere if it is already shown:\n self.fig.canvas.draw()\n\n def plot_back(self):\n # back half of sphere\n u = np.linspace(0, np.pi, 25)\n v = np.linspace(0, np.pi, 25)\n x = outer(cos(u), sin(v))\n y = outer(sin(u), sin(v))\n z = outer(ones(np.size(u)), cos(v))\n self.axes.plot_surface(x, y, z, rstride=2, cstride=2,\n color=self.sphere_color, linewidth=0,\n alpha=self.sphere_alpha)\n # wireframe\n self.axes.plot_wireframe(x, y, z, rstride=5, cstride=5,\n color=self.frame_color,\n alpha=self.frame_alpha)\n # equator\n self.axes.plot(1.0 * cos(u), 1.0 * sin(u), zs=0, zdir='z',\n lw=self.frame_width, color=self.frame_color)\n self.axes.plot(1.0 * cos(u), 1.0 * sin(u), zs=0, zdir='x',\n lw=self.frame_width, color=self.frame_color)\n\n def plot_front(self):\n # front half of sphere\n u = np.linspace(-np.pi, 0, 25)\n v = np.linspace(0, np.pi, 25)\n x = outer(cos(u), sin(v))\n y = outer(sin(u), sin(v))\n z = outer(ones(np.size(u)), cos(v))\n self.axes.plot_surface(x, y, z, rstride=2, cstride=2,\n color=self.sphere_color, linewidth=0,\n alpha=self.sphere_alpha)\n # wireframe\n self.axes.plot_wireframe(x, y, z, rstride=5, cstride=5,\n color=self.frame_color,\n alpha=self.frame_alpha)\n # equator\n self.axes.plot(1.0 * cos(u), 1.0 * sin(u),\n zs=0, zdir='z', lw=self.frame_width,\n color=self.frame_color)\n self.axes.plot(1.0 * cos(u), 1.0 * sin(u),\n zs=0, zdir='x', lw=self.frame_width,\n color=self.frame_color)\n\n def plot_axes(self):\n # axes\n span = np.linspace(-1.0, 1.0, 2)\n self.axes.plot(span, 0 * span, zs=0, zdir='z', label='X',\n lw=self.frame_width, color=self.frame_color)\n self.axes.plot(0 * span, span, zs=0, zdir='z', label='Y',\n lw=self.frame_width, color=self.frame_color)\n self.axes.plot(0 * span, span, zs=0, zdir='y', label='Z',\n lw=self.frame_width, color=self.frame_color)\n\n def plot_axes_labels(self):\n # axes labels\n opts = {'fontsize': self.font_size,\n 'color': self.font_color,\n 'horizontalalignment': 'center',\n 'verticalalignment': 'center'}\n self.axes.text(0, -self.xlpos[0], 0, self.xlabel[0], **opts)\n self.axes.text(0, -self.xlpos[1], 0, self.xlabel[1], **opts)\n\n self.axes.text(self.ylpos[0], 0, 0, self.ylabel[0], **opts)\n self.axes.text(self.ylpos[1], 0, 0, self.ylabel[1], **opts)\n\n self.axes.text(0, 0, self.zlpos[0], self.zlabel[0], **opts)\n self.axes.text(0, 0, self.zlpos[1], self.zlabel[1], **opts)\n\n for a in (self.axes.xaxis.get_ticklines() +\n self.axes.xaxis.get_ticklabels()):\n a.set_visible(False)\n for a in (self.axes.yaxis.get_ticklines() +\n self.axes.yaxis.get_ticklabels()):\n a.set_visible(False)\n for a in (self.axes.zaxis.get_ticklines() +\n self.axes.zaxis.get_ticklabels()):\n a.set_visible(False)\n\n def plot_vectors(self):\n # -X and Y data are switched for plotting purposes\n for k, vec in enumerate(self.vectors):\n\n xs3d = vec[1] * np.array([0, 1])\n ys3d = -vec[0] * np.array([0, 1])\n zs3d = vec[2] * np.array([0, 1])\n\n alpha = self.vector_alpha[k]\n color = self.vector_color[k]\n if color is None:\n idx = k % len(self.vector_default_color)\n color = self.vector_default_color[idx]\n\n if self.vector_style == '':\n # simple line style\n self.axes.plot(xs3d, ys3d, zs3d, zdir='z', label='Z',\n lw=self.vector_width, color=color,\n alpha=alpha)\n else:\n # decorated style, with arrow heads\n a = Arrow3D(xs3d, ys3d, zs3d,\n mutation_scale=self.vector_mutation,\n lw=self.vector_width,\n arrowstyle=self.vector_style,\n color=color, alpha=alpha)\n\n self.axes.add_artist(a)\n\n def plot_points(self):\n # -X and Y data are switched for plotting purposes\n for k, points in enumerate(self.points):\n points = np.asarray(points)\n num_points = points.shape[1]\n\n dist = np.linalg.norm(points, axis=0)\n if not np.allclose(dist, dist[0], rtol=1e-12):\n indperm = np.argsort(dist)\n points = points[:, indperm]\n else:\n indperm = np.arange(num_points)\n\n s = self.point_size[np.mod(k, len(self.point_size))]\n marker = self.point_marker[np.mod(k, len(self.point_marker))]\n style = self.point_style[k]\n if self.point_color[k] is not None:\n color = self.point_color[k]\n elif self.point_style[k] in ['s', 'l']:\n color = self.point_default_color[\n k % len(self.point_default_color)\n ]\n elif self.point_style[k] == 'm':\n length = np.ceil(num_points/len(self.point_default_color))\n color = np.tile(self.point_default_color, length.astype(int))\n color = color[indperm]\n\n if self.point_style[k] in ['s', 'm']:\n self.axes.scatter(np.real(points[1]),\n -np.real(points[0]),\n np.real(points[2]),\n s=s,\n marker=marker,\n color=color,\n alpha=self.point_alpha[k],\n edgecolor=None,\n zdir='z',\n )\n\n elif self.point_style[k] == 'l':\n self.axes.plot(np.real(points[1]),\n -np.real(points[0]),\n np.real(points[2]),\n color=color,\n alpha=self.point_alpha[k],\n zdir='z',\n )\n\n def plot_annotations(self):\n # -X and Y data are switched for plotting purposes\n for annotation in self.annotations:\n vec = annotation['position']\n opts = {'fontsize': self.font_size,\n 'color': self.font_color,\n 'horizontalalignment': 'center',\n 'verticalalignment': 'center'}\n opts.update(annotation['opts'])\n self.axes.text(vec[1], -vec[0], vec[2],\n annotation['text'], **opts)\n\n def plot_lines(self):\n for line, fmt, kw in self._lines:\n self.axes.plot(line[0], line[1], line[2], fmt, **kw)\n\n def plot_arcs(self):\n for arc, fmt, kw in self._arcs:\n self.axes.plot(arc[1, :], -arc[0, :], arc[2, :], fmt, **kw)\n\n def show(self):\n \"\"\"\n Display Bloch sphere and corresponding data sets.\n\n Notes\n -----\n\n When using inline plotting in Jupyter notebooks, any figure created\n in a notebook cell is displayed after the cell executes. Thus if you\n create a figure yourself and use it create a Bloch sphere with\n ``b = Bloch(..., fig=fig)`` and then call ``b.show()`` in the same\n cell, then the figure will be displayed twice. If you do create your\n own figure, the simplest solution to this is to not call ``.show()``\n in the cell you create the figure in.\n \"\"\"\n self.render()\n if self.run_from_ipython():\n display(self.fig)\n else:\n self.fig.show()\n\n def save(self, name=None, format='png', dirc=None, dpin=None):\n \"\"\"Saves Bloch sphere to file of type ``format`` in directory ``dirc``.\n\n Parameters\n ----------\n\n name : str\n Name of saved image. Must include path and format as well.\n i.e. '/Users/Paul/Desktop/bloch.png'\n This overrides the 'format' and 'dirc' arguments.\n format : str\n Format of output image.\n dirc : str\n Directory for output images. Defaults to current working directory.\n dpin : int\n Resolution in dots per inch.\n\n Returns\n -------\n File containing plot of Bloch sphere.\n\n \"\"\"\n self.render()\n # Conditional variable for first argument to savefig\n # that is set in subsequent if-elses\n complete_path = \"\"\n if dirc:\n if not os.path.isdir(os.getcwd() + \"/\" + str(dirc)):\n os.makedirs(os.getcwd() + \"/\" + str(dirc))\n if name is None:\n if dirc:\n complete_path = os.getcwd() + \"/\" + str(dirc) + '/bloch_' \\\n + str(self.savenum) + '.' + format\n else:\n complete_path = os.getcwd() + '/bloch_' + \\\n str(self.savenum) + '.' + format\n else:\n complete_path = name\n\n if dpin:\n self.fig.savefig(complete_path, dpi=dpin)\n else:\n self.fig.savefig(complete_path)\n self.savenum += 1\n if self.fig:\n plt.close(self.fig)\n\n\ndef _hide_tick_lines_and_labels(axis):\n '''\n Set visible property of ticklines and ticklabels of an axis to False\n '''\n for a in axis.get_ticklines() + axis.get_ticklabels():\n a.set_visible(False)\n",
"path": "qutip/bloch.py"
}
] | diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 45ea35ea53..8111466dbc 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -100,7 +100,7 @@ jobs:
# rather than in the GitHub Actions file directly, because bash gives us
# a proper programming language to use.
run: |
- QUTIP_TARGET="tests,graphics,semidefinite"
+ QUTIP_TARGET="tests,graphics,semidefinite,ipython"
if [[ -z "${{ matrix.nocython }}" ]]; then
QUTIP_TARGET="$QUTIP_TARGET,runtime_compilation"
fi
diff --git a/qutip/bloch.py b/qutip/bloch.py
index 858b3dd3c4..f525dd0ce0 100644
--- a/qutip/bloch.py
+++ b/qutip/bloch.py
@@ -294,7 +294,7 @@ def _repr_png_(self):
def _repr_svg_(self):
from IPython.core.pylabtools import print_figure
self.render()
- fig_data = print_figure(self.fig, 'svg').decode('utf-8')
+ fig_data = print_figure(self.fig, 'svg')
plt.close(self.fig)
return fig_data
diff --git a/qutip/tests/test_bloch.py b/qutip/tests/test_bloch.py
index 6e8680dd65..f736090c2e 100644
--- a/qutip/tests/test_bloch.py
+++ b/qutip/tests/test_bloch.py
@@ -471,3 +471,10 @@ def test_vector_errors_color_length(self, vectors, colors):
"be equivalent to a 1D array with the same "
"size as the number of vectors. ")
assert str(err.value) == err_msg
+
+
+def test_repr_svg():
+ svg = Bloch()._repr_svg_()
+ assert isinstance(svg, str)
+ assert svg.startswith("<?xml")
+ assert svg.endswith("</svg>\n")
diff --git a/setup.cfg b/setup.cfg
index 5fc2d8202b..c8746a8dc0 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -52,6 +52,8 @@ semidefinite =
tests =
pytest>=5.2
pytest-rerunfailures
+ipython =
+ ipython
; This uses ConfigParser's string interpolation to include all the above
; dependencies into one single target, convenient for testing full builds.
full =
@@ -59,3 +61,4 @@ full =
%(runtime_compilation)s
%(semidefinite)s
%(tests)s
+ %(ipython)s
|
coala__coala-3608 | Remove call_without_output from Shell.py L7
This line was used by the requirement classes, it isnt used anymore as they use sarge, so it should be removed.
difficulty/newcomer
| [
{
"content": "from contextlib import contextmanager\nimport functools\nimport shlex\nfrom subprocess import PIPE, Popen, call, DEVNULL\n\n\ncall_without_output = functools.partial(call, stdout=DEVNULL, stderr=DEVNULL)\n\"\"\"\nUses subprocess.call to execute a command, but suppresses the output and\nthe errors.\n\"\"\"\n\n\n@contextmanager\ndef run_interactive_shell_command(command, **kwargs):\n \"\"\"\n Runs a single command in shell and provides stdout, stderr and stdin\n streams.\n\n This function creates a context manager that sets up the process (using\n ``subprocess.Popen()``), returns to caller and waits for process to exit on\n leaving.\n\n By default the process is opened in ``universal_newlines`` mode and creates\n pipes for all streams (stdout, stderr and stdin) using ``subprocess.PIPE``\n special value. These pipes are closed automatically, so if you want to get\n the contents of the streams you should retrieve them before the context\n manager exits.\n\n >>> with run_interactive_shell_command([\"echo\", \"TEXT\"]) as p:\n ... stdout = p.stdout\n ... stdout_text = stdout.read()\n >>> stdout_text\n 'TEXT\\\\n'\n >>> stdout.closed\n True\n\n Custom streams provided are not closed except of ``subprocess.PIPE``.\n\n >>> from tempfile import TemporaryFile\n >>> stream = TemporaryFile()\n >>> with run_interactive_shell_command([\"echo\", \"TEXT\"],\n ... stdout=stream) as p:\n ... stderr = p.stderr\n >>> stderr.closed\n True\n >>> stream.closed\n False\n\n :param command: The command to run on shell. This parameter can either\n be a sequence of arguments that are directly passed to\n the process or a string. A string gets splitted beforehand\n using ``shlex.split()``. If providing ``shell=True`` as a\n keyword-argument, no ``shlex.split()`` is performed and the\n command string goes directly to ``subprocess.Popen()``.\n :param kwargs: Additional keyword arguments to pass to\n ``subprocess.Popen`` that are used to spawn the process.\n :return: A context manager yielding the process started from the\n command.\n \"\"\"\n if not kwargs.get('shell', False) and isinstance(command, str):\n command = shlex.split(command)\n\n args = {'stdout': PIPE,\n 'stderr': PIPE,\n 'stdin': PIPE,\n 'universal_newlines': True}\n args.update(kwargs)\n\n process = Popen(command, **args)\n try:\n yield process\n finally:\n if args['stdout'] is PIPE:\n process.stdout.close()\n if args['stderr'] is PIPE:\n process.stderr.close()\n if args['stdin'] is PIPE:\n process.stdin.close()\n\n process.wait()\n\n\ndef run_shell_command(command, stdin=None, **kwargs):\n \"\"\"\n Runs a single command in shell and returns the read stdout and stderr data.\n\n This function waits for the process (created using ``subprocess.Popen()``)\n to exit. Effectively it wraps ``run_interactive_shell_command()`` and uses\n ``communicate()`` on the process.\n\n See also ``run_interactive_shell_command()``.\n\n :param command: The command to run on shell. This parameter can either\n be a sequence of arguments that are directly passed to\n the process or a string. A string gets splitted beforehand\n using ``shlex.split()``.\n :param stdin: Initial input to send to the process.\n :param kwargs: Additional keyword arguments to pass to\n ``subprocess.Popen`` that is used to spawn the process.\n :return: A tuple with ``(stdoutstring, stderrstring)``.\n \"\"\"\n with run_interactive_shell_command(command, **kwargs) as p:\n ret = p.communicate(stdin)\n return ret\n\n\ndef get_shell_type(): # pragma: no cover\n \"\"\"\n Finds the current shell type based on the outputs of common pre-defined\n variables in them. This is useful to identify which sort of escaping\n is required for strings.\n\n :return: The shell type. This can be either \"powershell\" if Windows\n Powershell is detected, \"cmd\" if command prompt is been\n detected or \"sh\" if it's neither of these.\n \"\"\"\n out = run_shell_command('echo $host.name', shell=True)[0]\n if out.strip() == 'ConsoleHost':\n return 'powershell'\n out = run_shell_command('echo $0', shell=True)[0]\n if out.strip() == '$0':\n return 'cmd'\n return 'sh'\n",
"path": "coalib/misc/Shell.py"
}
] | [
{
"content": "from contextlib import contextmanager\nimport shlex\nfrom subprocess import PIPE, Popen\n\n\n@contextmanager\ndef run_interactive_shell_command(command, **kwargs):\n \"\"\"\n Runs a single command in shell and provides stdout, stderr and stdin\n streams.\n\n This function creates a context manager that sets up the process (using\n ``subprocess.Popen()``), returns to caller and waits for process to exit on\n leaving.\n\n By default the process is opened in ``universal_newlines`` mode and creates\n pipes for all streams (stdout, stderr and stdin) using ``subprocess.PIPE``\n special value. These pipes are closed automatically, so if you want to get\n the contents of the streams you should retrieve them before the context\n manager exits.\n\n >>> with run_interactive_shell_command([\"echo\", \"TEXT\"]) as p:\n ... stdout = p.stdout\n ... stdout_text = stdout.read()\n >>> stdout_text\n 'TEXT\\\\n'\n >>> stdout.closed\n True\n\n Custom streams provided are not closed except of ``subprocess.PIPE``.\n\n >>> from tempfile import TemporaryFile\n >>> stream = TemporaryFile()\n >>> with run_interactive_shell_command([\"echo\", \"TEXT\"],\n ... stdout=stream) as p:\n ... stderr = p.stderr\n >>> stderr.closed\n True\n >>> stream.closed\n False\n\n :param command: The command to run on shell. This parameter can either\n be a sequence of arguments that are directly passed to\n the process or a string. A string gets splitted beforehand\n using ``shlex.split()``. If providing ``shell=True`` as a\n keyword-argument, no ``shlex.split()`` is performed and the\n command string goes directly to ``subprocess.Popen()``.\n :param kwargs: Additional keyword arguments to pass to\n ``subprocess.Popen`` that are used to spawn the process.\n :return: A context manager yielding the process started from the\n command.\n \"\"\"\n if not kwargs.get('shell', False) and isinstance(command, str):\n command = shlex.split(command)\n\n args = {'stdout': PIPE,\n 'stderr': PIPE,\n 'stdin': PIPE,\n 'universal_newlines': True}\n args.update(kwargs)\n\n process = Popen(command, **args)\n try:\n yield process\n finally:\n if args['stdout'] is PIPE:\n process.stdout.close()\n if args['stderr'] is PIPE:\n process.stderr.close()\n if args['stdin'] is PIPE:\n process.stdin.close()\n\n process.wait()\n\n\ndef run_shell_command(command, stdin=None, **kwargs):\n \"\"\"\n Runs a single command in shell and returns the read stdout and stderr data.\n\n This function waits for the process (created using ``subprocess.Popen()``)\n to exit. Effectively it wraps ``run_interactive_shell_command()`` and uses\n ``communicate()`` on the process.\n\n See also ``run_interactive_shell_command()``.\n\n :param command: The command to run on shell. This parameter can either\n be a sequence of arguments that are directly passed to\n the process or a string. A string gets splitted beforehand\n using ``shlex.split()``.\n :param stdin: Initial input to send to the process.\n :param kwargs: Additional keyword arguments to pass to\n ``subprocess.Popen`` that is used to spawn the process.\n :return: A tuple with ``(stdoutstring, stderrstring)``.\n \"\"\"\n with run_interactive_shell_command(command, **kwargs) as p:\n ret = p.communicate(stdin)\n return ret\n\n\ndef get_shell_type(): # pragma: no cover\n \"\"\"\n Finds the current shell type based on the outputs of common pre-defined\n variables in them. This is useful to identify which sort of escaping\n is required for strings.\n\n :return: The shell type. This can be either \"powershell\" if Windows\n Powershell is detected, \"cmd\" if command prompt is been\n detected or \"sh\" if it's neither of these.\n \"\"\"\n out = run_shell_command('echo $host.name', shell=True)[0]\n if out.strip() == 'ConsoleHost':\n return 'powershell'\n out = run_shell_command('echo $0', shell=True)[0]\n if out.strip() == '$0':\n return 'cmd'\n return 'sh'\n",
"path": "coalib/misc/Shell.py"
}
] | diff --git a/coalib/misc/Shell.py b/coalib/misc/Shell.py
index ec44e45f0b..0bd22886dd 100644
--- a/coalib/misc/Shell.py
+++ b/coalib/misc/Shell.py
@@ -1,14 +1,6 @@
from contextlib import contextmanager
-import functools
import shlex
-from subprocess import PIPE, Popen, call, DEVNULL
-
-
-call_without_output = functools.partial(call, stdout=DEVNULL, stderr=DEVNULL)
-"""
-Uses subprocess.call to execute a command, but suppresses the output and
-the errors.
-"""
+from subprocess import PIPE, Popen
@contextmanager
|
litestar-org__litestar-1641 | Bug: StorageObject doesn't return < 0 when using expiry
### Description
When the stored value is expired, the returned interval is set to 86400 and will therefore not expire.
### URL to code causing the issue
https://github.com/litestar-org/litestar/blob/main/litestar/stores/base.py#L122
### MCVE
```python
from pathlib import Path
from litestar.stores.file import FileStore
store = FileStore(path=Path("test.db"))
async def setstore() -> None:
await store.set("test", "value", expires_in=5)
return None
async def getstore() -> int:
expiry = await store.expires_in("test")
return expiry
```
### Steps to reproduce
_No response_
### Screenshots
```bash
""
```
### Logs
_No response_
### Litestar Version
`litestar==2.0.0a5`
### Platform
- [ ] Linux
- [X] Mac
- [ ] Windows
- [X] Other (Please specify in the description above)
StaticFilesConfig and virtual directories
I'm trying to write a ``FileSystemProtocol`` to load files from the package data using [importlib_resources](https://importlib-resources.readthedocs.io/en/latest/using.html#). But because ``directories`` is defined as ``DirectoryPath``, pydantic checks if the given directories exist in the local filesystem.
This is not generally true, especially in any kind of virtual filesystem (e.g. a zipped package). I think this condition should be relaxed to support virtual filesystems.
https://github.com/starlite-api/starlite/blob/9bb6dcd57c10a591377cf8e3a537e9292566d5b9/starlite/config/static_files.py#L32
| [
{
"content": "from __future__ import annotations\n\nfrom abc import ABC, abstractmethod\nfrom datetime import datetime, timedelta, timezone\nfrom typing import TYPE_CHECKING, Optional\n\nfrom msgspec import Struct\nfrom msgspec.msgpack import decode as msgpack_decode\nfrom msgspec.msgpack import encode as msgpack_encode\n\nif TYPE_CHECKING:\n from typing_extensions import Self\n\n\n__all__ = (\"Store\", \"NamespacedStore\", \"StorageObject\")\n\n\nclass Store(ABC): # pragma: no cover\n \"\"\"Thread and process safe asynchronous key/value store.\"\"\"\n\n @abstractmethod\n async def set(self, key: str, value: str | bytes, expires_in: int | timedelta | None = None) -> None:\n \"\"\"Set a value.\n\n Args:\n key: Key to associate the value with\n value: Value to store\n expires_in: Time in seconds before the key is considered expired\n\n Returns:\n ``None``\n \"\"\"\n raise NotImplementedError\n\n @abstractmethod\n async def get(self, key: str, renew_for: int | timedelta | None = None) -> bytes | None:\n \"\"\"Get a value.\n\n Args:\n key: Key associated with the value\n renew_for: If given and the value had an initial expiry time set, renew the\n expiry time for ``renew_for`` seconds. If the value has not been set\n with an expiry time this is a no-op\n\n Returns:\n The value associated with ``key`` if it exists and is not expired, else\n ``None``\n \"\"\"\n raise NotImplementedError\n\n @abstractmethod\n async def delete(self, key: str) -> None:\n \"\"\"Delete a value.\n\n If no such key exists, this is a no-op.\n\n Args:\n key: Key of the value to delete\n \"\"\"\n raise NotImplementedError\n\n @abstractmethod\n async def delete_all(self) -> None:\n \"\"\"Delete all stored values.\"\"\"\n raise NotImplementedError\n\n @abstractmethod\n async def exists(self, key: str) -> bool:\n \"\"\"Check if a given ``key`` exists.\"\"\"\n raise NotImplementedError\n\n @abstractmethod\n async def expires_in(self, key: str) -> int | None:\n \"\"\"Get the time in seconds ``key`` expires in. If no such ``key`` exists or no\n expiry time was set, return ``None``.\n \"\"\"\n raise NotImplementedError\n\n\nclass NamespacedStore(Store):\n \"\"\"A subclass of :class:`Store`, offering hierarchical namespacing.\n\n Bulk actions on a parent namespace should affect all child namespaces, whereas other operations on all namespaces\n should be isolated.\n \"\"\"\n\n @abstractmethod\n def with_namespace(self, namespace: str) -> Self:\n \"\"\"Return a new instance of :class:`NamespacedStore`, which exists in a child namespace of the current namespace.\n Bulk actions on the parent namespace should affect all child namespaces, whereas other operations on all\n namespaces should be isolated.\n \"\"\"\n\n\nclass StorageObject(Struct):\n \"\"\":class:`msgspec.Struct` to store serialized data alongside with their expiry time.\"\"\"\n\n expires_at: Optional[datetime] # noqa: UP007\n data: bytes\n\n @classmethod\n def new(cls, data: bytes, expires_in: int | timedelta | None) -> StorageObject:\n \"\"\"Construct a new :class:`StorageObject` instance.\"\"\"\n if expires_in is not None and not isinstance(expires_in, timedelta):\n expires_in = timedelta(seconds=expires_in)\n return cls(\n data=data,\n expires_at=(datetime.now(tz=timezone.utc) + expires_in) if expires_in else None,\n )\n\n @property\n def expired(self) -> bool:\n \"\"\"Return if the :class:`StorageObject` is expired\"\"\"\n return self.expires_at is not None and datetime.now(tz=timezone.utc) >= self.expires_at\n\n @property\n def expires_in(self) -> int:\n \"\"\"Return the expiry time of this ``StorageObject`` in seconds. If no expiry time\n was set, return ``-1``.\n \"\"\"\n if self.expires_at:\n return (self.expires_at - datetime.now(tz=timezone.utc)).seconds\n return -1\n\n def to_bytes(self) -> bytes:\n \"\"\"Encode the instance to bytes\"\"\"\n return msgpack_encode(self)\n\n @classmethod\n def from_bytes(cls, raw: bytes) -> StorageObject:\n \"\"\"Load a previously encoded with :meth:`StorageObject.to_bytes`\"\"\"\n return msgpack_decode(raw, type=cls)\n",
"path": "litestar/stores/base.py"
}
] | [
{
"content": "from __future__ import annotations\n\nfrom abc import ABC, abstractmethod\nfrom datetime import datetime, timedelta, timezone\nfrom typing import TYPE_CHECKING, Optional\n\nfrom msgspec import Struct\nfrom msgspec.msgpack import decode as msgpack_decode\nfrom msgspec.msgpack import encode as msgpack_encode\n\nif TYPE_CHECKING:\n from typing_extensions import Self\n\n\n__all__ = (\"Store\", \"NamespacedStore\", \"StorageObject\")\n\n\nclass Store(ABC): # pragma: no cover\n \"\"\"Thread and process safe asynchronous key/value store.\"\"\"\n\n @abstractmethod\n async def set(self, key: str, value: str | bytes, expires_in: int | timedelta | None = None) -> None:\n \"\"\"Set a value.\n\n Args:\n key: Key to associate the value with\n value: Value to store\n expires_in: Time in seconds before the key is considered expired\n\n Returns:\n ``None``\n \"\"\"\n raise NotImplementedError\n\n @abstractmethod\n async def get(self, key: str, renew_for: int | timedelta | None = None) -> bytes | None:\n \"\"\"Get a value.\n\n Args:\n key: Key associated with the value\n renew_for: If given and the value had an initial expiry time set, renew the\n expiry time for ``renew_for`` seconds. If the value has not been set\n with an expiry time this is a no-op\n\n Returns:\n The value associated with ``key`` if it exists and is not expired, else\n ``None``\n \"\"\"\n raise NotImplementedError\n\n @abstractmethod\n async def delete(self, key: str) -> None:\n \"\"\"Delete a value.\n\n If no such key exists, this is a no-op.\n\n Args:\n key: Key of the value to delete\n \"\"\"\n raise NotImplementedError\n\n @abstractmethod\n async def delete_all(self) -> None:\n \"\"\"Delete all stored values.\"\"\"\n raise NotImplementedError\n\n @abstractmethod\n async def exists(self, key: str) -> bool:\n \"\"\"Check if a given ``key`` exists.\"\"\"\n raise NotImplementedError\n\n @abstractmethod\n async def expires_in(self, key: str) -> int | None:\n \"\"\"Get the time in seconds ``key`` expires in. If no such ``key`` exists or no\n expiry time was set, return ``None``.\n \"\"\"\n raise NotImplementedError\n\n\nclass NamespacedStore(Store):\n \"\"\"A subclass of :class:`Store`, offering hierarchical namespacing.\n\n Bulk actions on a parent namespace should affect all child namespaces, whereas other operations on all namespaces\n should be isolated.\n \"\"\"\n\n @abstractmethod\n def with_namespace(self, namespace: str) -> Self:\n \"\"\"Return a new instance of :class:`NamespacedStore`, which exists in a child namespace of the current namespace.\n Bulk actions on the parent namespace should affect all child namespaces, whereas other operations on all\n namespaces should be isolated.\n \"\"\"\n\n\nclass StorageObject(Struct):\n \"\"\":class:`msgspec.Struct` to store serialized data alongside with their expiry time.\"\"\"\n\n expires_at: Optional[datetime] # noqa: UP007\n data: bytes\n\n @classmethod\n def new(cls, data: bytes, expires_in: int | timedelta | None) -> StorageObject:\n \"\"\"Construct a new :class:`StorageObject` instance.\"\"\"\n if expires_in is not None and not isinstance(expires_in, timedelta):\n expires_in = timedelta(seconds=expires_in)\n return cls(\n data=data,\n expires_at=(datetime.now(tz=timezone.utc) + expires_in) if expires_in else None,\n )\n\n @property\n def expired(self) -> bool:\n \"\"\"Return if the :class:`StorageObject` is expired\"\"\"\n return self.expires_at is not None and datetime.now(tz=timezone.utc) >= self.expires_at\n\n @property\n def expires_in(self) -> int:\n \"\"\"Return the expiry time of this ``StorageObject`` in seconds. If no expiry time\n was set, return ``-1``.\n \"\"\"\n if self.expires_at:\n return int(self.expires_at.timestamp() - datetime.now(tz=timezone.utc).timestamp())\n return -1\n\n def to_bytes(self) -> bytes:\n \"\"\"Encode the instance to bytes\"\"\"\n return msgpack_encode(self)\n\n @classmethod\n def from_bytes(cls, raw: bytes) -> StorageObject:\n \"\"\"Load a previously encoded with :meth:`StorageObject.to_bytes`\"\"\"\n return msgpack_decode(raw, type=cls)\n",
"path": "litestar/stores/base.py"
}
] | diff --git a/litestar/stores/base.py b/litestar/stores/base.py
index 0cb90b8ccd..b748cf1764 100644
--- a/litestar/stores/base.py
+++ b/litestar/stores/base.py
@@ -119,7 +119,7 @@ def expires_in(self) -> int:
was set, return ``-1``.
"""
if self.expires_at:
- return (self.expires_at - datetime.now(tz=timezone.utc)).seconds
+ return int(self.expires_at.timestamp() - datetime.now(tz=timezone.utc).timestamp())
return -1
def to_bytes(self) -> bytes:
diff --git a/tests/test_stores.py b/tests/test_stores.py
index daefa5a44f..9d630c250b 100644
--- a/tests/test_stores.py
+++ b/tests/test_stores.py
@@ -150,7 +150,11 @@ async def test_delete_all(store: Store) -> None:
assert not any([await store.get(key) for key in keys])
-async def test_expires_in(store: Store) -> None:
[email protected]("patch_storage_obj_frozen_datetime")
+async def test_expires_in(store: Store, frozen_datetime: FrozenDateTimeFactory) -> None:
+ if not isinstance(store, RedisStore):
+ pytest.xfail("bug in FileStore and MemoryStore")
+
assert await store.expires_in("foo") is None
await store.set("foo", "bar")
@@ -159,6 +163,9 @@ async def test_expires_in(store: Store) -> None:
await store.set("foo", "bar", expires_in=10)
assert math.ceil(await store.expires_in("foo") / 10) * 10 == 10 # type: ignore[operator]
+ frozen_datetime.tick(12)
+ assert await store.expires_in("foo") is None
+
@patch("litestar.stores.redis.Redis")
@patch("litestar.stores.redis.ConnectionPool.from_url")
|
gratipay__gratipay.com-3087 | Charges higher than total gifts
For the past three weeks our charges have been about a thousand dollars higher than total gifts.
Charges higher than total gifts
For the past three weeks our charges have been about a thousand dollars higher than total gifts.
| [
{
"content": "\"\"\"This is Gratipay's payday algorithm.\n\nExchanges (moving money between Gratipay and the outside world) and transfers\n(moving money amongst Gratipay users) happen within an isolated event called\npayday. This event has duration (it's not punctiliar).\n\nPayday is designed to be crash-resistant. Everything that can be rolled back\nhappens inside a single DB transaction. Exchanges cannot be rolled back, so they\nimmediately affect the participant's balance.\n\n\"\"\"\nfrom __future__ import unicode_literals\n\nimport itertools\nfrom multiprocessing.dummy import Pool as ThreadPool\n\nfrom balanced import CardHold\n\nimport aspen.utils\nfrom aspen import log\nfrom gratipay.billing.exchanges import (\n ach_credit, cancel_card_hold, capture_card_hold, create_card_hold, upcharge\n)\nfrom gratipay.exceptions import NegativeBalance\nfrom gratipay.models import check_db\nfrom psycopg2 import IntegrityError\n\n\nwith open('fake_payday.sql') as f:\n FAKE_PAYDAY = f.read()\n\n\nclass ExceptionWrapped(Exception): pass\n\n\ndef threaded_map(func, iterable, threads=5):\n pool = ThreadPool(threads)\n def g(*a, **kw):\n # Without this wrapper we get a traceback from inside multiprocessing.\n try:\n return func(*a, **kw)\n except Exception as e:\n import traceback\n raise ExceptionWrapped(e, traceback.format_exc())\n try:\n r = pool.map(g, iterable)\n except ExceptionWrapped as e:\n print(e.args[1])\n raise e.args[0]\n pool.close()\n pool.join()\n return r\n\n\nclass NoPayday(Exception):\n __str__ = lambda self: \"No payday found where one was expected.\"\n\n\nclass Payday(object):\n \"\"\"Represent an abstract event during which money is moved.\n\n On Payday, we want to use a participant's Gratipay balance to settle their\n tips due (pulling in more money via credit card as needed), but we only\n want to use their balance at the start of Payday. Balance changes should be\n atomic globally per-Payday.\n\n Here's the call structure of the Payday.run method:\n\n run\n payin\n prepare\n create_card_holds\n transfer_tips\n transfer_takes\n settle_card_holds\n update_balances\n take_over_balances\n payout\n update_stats\n update_cached_amounts\n end\n\n \"\"\"\n\n\n @classmethod\n def start(cls):\n \"\"\"Try to start a new Payday.\n\n If there is a Payday that hasn't finished yet, then the UNIQUE\n constraint on ts_end will kick in and notify us of that. In that case\n we load the existing Payday and work on it some more. We use the start\n time of the current Payday to synchronize our work.\n\n \"\"\"\n try:\n d = cls.db.one(\"\"\"\n INSERT INTO paydays DEFAULT VALUES\n RETURNING id, (ts_start AT TIME ZONE 'UTC') AS ts_start, stage\n \"\"\", back_as=dict)\n log(\"Starting a new payday.\")\n except IntegrityError: # Collision, we have a Payday already.\n d = cls.db.one(\"\"\"\n SELECT id, (ts_start AT TIME ZONE 'UTC') AS ts_start, stage\n FROM paydays\n WHERE ts_end='1970-01-01T00:00:00+00'::timestamptz\n \"\"\", back_as=dict)\n log(\"Picking up with an existing payday.\")\n\n d['ts_start'] = d['ts_start'].replace(tzinfo=aspen.utils.utc)\n\n log(\"Payday started at %s.\" % d['ts_start'])\n\n payday = Payday()\n payday.__dict__.update(d)\n return payday\n\n\n def run(self):\n \"\"\"This is the starting point for payday.\n\n This method runs every Thursday. It is structured such that it can be\n run again safely (with a newly-instantiated Payday object) if it\n crashes.\n\n \"\"\"\n self.db.self_check()\n\n _start = aspen.utils.utcnow()\n log(\"Greetings, program! It's PAYDAY!!!!\")\n\n if self.stage < 1:\n self.payin()\n self.mark_stage_done()\n if self.stage < 2:\n self.payout()\n self.mark_stage_done()\n if self.stage < 3:\n self.update_stats()\n self.update_cached_amounts()\n self.mark_stage_done()\n\n self.end()\n\n _end = aspen.utils.utcnow()\n _delta = _end - _start\n fmt_past = \"Script ran for %%(age)s (%s).\" % _delta\n log(aspen.utils.to_age(_start, fmt_past=fmt_past))\n\n\n def payin(self):\n \"\"\"The first stage of payday where we charge credit cards and transfer\n money internally between participants.\n \"\"\"\n with self.db.get_cursor() as cursor:\n self.prepare(cursor, self.ts_start)\n holds = self.create_card_holds(cursor)\n self.transfer_tips(cursor)\n self.transfer_takes(cursor, self.ts_start)\n transfers = cursor.all(\"\"\"\n SELECT * FROM transfers WHERE \"timestamp\" > %s\n \"\"\", (self.ts_start,))\n try:\n self.settle_card_holds(cursor, holds)\n self.update_balances(cursor)\n check_db(cursor)\n except:\n # Dump transfers for debugging\n import csv\n from time import time\n with open('%s_transfers.csv' % time(), 'wb') as f:\n csv.writer(f).writerows(transfers)\n raise\n self.take_over_balances()\n # Clean up leftover functions\n self.db.run(\"\"\"\n DROP FUNCTION process_take();\n DROP FUNCTION process_tip();\n DROP FUNCTION settle_tip_graph();\n DROP FUNCTION transfer(text, text, numeric, context_type);\n \"\"\")\n\n\n @staticmethod\n def prepare(cursor, ts_start):\n \"\"\"Prepare the DB: we need temporary tables with indexes and triggers.\n \"\"\"\n cursor.run(\"\"\"\n\n -- Create the necessary temporary tables and indexes\n\n CREATE TEMPORARY TABLE payday_participants ON COMMIT DROP AS\n SELECT id\n , username\n , claimed_time\n , balance AS old_balance\n , balance AS new_balance\n , balanced_customer_href\n , last_bill_result\n , is_suspicious\n , goal\n , false AS card_hold_ok\n FROM participants\n WHERE is_suspicious IS NOT true\n AND claimed_time < %(ts_start)s\n ORDER BY claimed_time;\n\n CREATE UNIQUE INDEX ON payday_participants (id);\n CREATE UNIQUE INDEX ON payday_participants (username);\n\n CREATE TEMPORARY TABLE payday_transfers_done ON COMMIT DROP AS\n SELECT *\n FROM transfers t\n WHERE t.timestamp > %(ts_start)s;\n\n CREATE TEMPORARY TABLE payday_tips ON COMMIT DROP AS\n SELECT tipper, tippee, amount\n FROM ( SELECT DISTINCT ON (tipper, tippee) *\n FROM tips\n WHERE mtime < %(ts_start)s\n ORDER BY tipper, tippee, mtime DESC\n ) t\n JOIN payday_participants p ON p.username = t.tipper\n JOIN payday_participants p2 ON p2.username = t.tippee\n WHERE t.amount > 0\n AND (p2.goal IS NULL or p2.goal >= 0)\n AND ( SELECT id\n FROM payday_transfers_done t2\n WHERE t.tipper = t2.tipper\n AND t.tippee = t2.tippee\n AND context = 'tip'\n ) IS NULL\n ORDER BY p.claimed_time ASC, t.ctime ASC;\n\n CREATE INDEX ON payday_tips (tipper);\n CREATE INDEX ON payday_tips (tippee);\n ALTER TABLE payday_tips ADD COLUMN is_funded boolean;\n\n ALTER TABLE payday_participants ADD COLUMN giving_today numeric(35,2);\n UPDATE payday_participants\n SET giving_today = COALESCE((\n SELECT sum(amount)\n FROM payday_tips\n WHERE tipper = username\n ), 0);\n\n CREATE TEMPORARY TABLE payday_takes\n ( team text\n , member text\n , amount numeric(35,2)\n ) ON COMMIT DROP;\n\n CREATE TEMPORARY TABLE payday_transfers\n ( timestamp timestamptz DEFAULT now()\n , tipper text\n , tippee text\n , amount numeric(35,2)\n , context context_type\n ) ON COMMIT DROP;\n\n\n -- Prepare a statement that makes and records a transfer\n\n CREATE OR REPLACE FUNCTION transfer(text, text, numeric, context_type)\n RETURNS void AS $$\n BEGIN\n IF ($3 = 0) THEN RETURN; END IF;\n UPDATE payday_participants\n SET new_balance = (new_balance - $3)\n WHERE username = $1;\n UPDATE payday_participants\n SET new_balance = (new_balance + $3)\n WHERE username = $2;\n INSERT INTO payday_transfers\n (tipper, tippee, amount, context)\n VALUES ( ( SELECT p.username\n FROM participants p\n JOIN payday_participants p2 ON p.id = p2.id\n WHERE p2.username = $1 )\n , ( SELECT p.username\n FROM participants p\n JOIN payday_participants p2 ON p.id = p2.id\n WHERE p2.username = $2 )\n , $3\n , $4\n );\n END;\n $$ LANGUAGE plpgsql;\n\n\n -- Create a trigger to process tips\n\n CREATE OR REPLACE FUNCTION process_tip() RETURNS trigger AS $$\n DECLARE\n tipper payday_participants;\n BEGIN\n tipper := (\n SELECT p.*::payday_participants\n FROM payday_participants p\n WHERE username = NEW.tipper\n );\n IF (NEW.amount <= tipper.new_balance OR tipper.card_hold_ok) THEN\n EXECUTE transfer(NEW.tipper, NEW.tippee, NEW.amount, 'tip');\n RETURN NEW;\n END IF;\n RETURN NULL;\n END;\n $$ LANGUAGE plpgsql;\n\n CREATE TRIGGER process_tip BEFORE UPDATE OF is_funded ON payday_tips\n FOR EACH ROW\n WHEN (NEW.is_funded IS true AND OLD.is_funded IS NOT true)\n EXECUTE PROCEDURE process_tip();\n\n\n -- Create a trigger to process takes\n\n CREATE OR REPLACE FUNCTION process_take() RETURNS trigger AS $$\n DECLARE\n actual_amount numeric(35,2);\n team_balance numeric(35,2);\n BEGIN\n team_balance := (\n SELECT new_balance\n FROM payday_participants\n WHERE username = NEW.team\n );\n IF (team_balance <= 0) THEN RETURN NULL; END IF;\n actual_amount := NEW.amount;\n IF (team_balance < NEW.amount) THEN\n actual_amount := team_balance;\n END IF;\n EXECUTE transfer(NEW.team, NEW.member, actual_amount, 'take');\n RETURN NULL;\n END;\n $$ LANGUAGE plpgsql;\n\n CREATE TRIGGER process_take AFTER INSERT ON payday_takes\n FOR EACH ROW EXECUTE PROCEDURE process_take();\n\n\n -- Create a function to settle whole tip graph\n\n CREATE OR REPLACE FUNCTION settle_tip_graph() RETURNS void AS $$\n DECLARE\n count integer NOT NULL DEFAULT 0;\n i integer := 0;\n BEGIN\n LOOP\n i := i + 1;\n WITH updated_rows AS (\n UPDATE payday_tips\n SET is_funded = true\n WHERE is_funded IS NOT true\n RETURNING *\n )\n SELECT COUNT(*) FROM updated_rows INTO count;\n IF (count = 0) THEN\n EXIT;\n END IF;\n IF (i > 50) THEN\n RAISE 'Reached the maximum number of iterations';\n END IF;\n END LOOP;\n END;\n $$ LANGUAGE plpgsql;\n\n\n -- Save the stats we already have\n\n UPDATE paydays\n SET nparticipants = (SELECT count(*) FROM payday_participants)\n , ncc_missing = (\n SELECT count(*)\n FROM payday_participants\n WHERE old_balance < giving_today\n AND ( balanced_customer_href IS NULL\n OR\n last_bill_result IS NULL\n )\n )\n WHERE ts_end='1970-01-01T00:00:00+00'::timestamptz;\n\n \"\"\", dict(ts_start=ts_start))\n log('Prepared the DB.')\n\n\n @staticmethod\n def fetch_card_holds(participant_ids):\n holds = {}\n for hold in CardHold.query.filter(CardHold.f.meta.state == 'new'):\n state = 'new'\n if hold.failure_reason:\n state = 'failed'\n elif hold.voided_at:\n state = 'cancelled'\n elif getattr(hold, 'debit_href', None):\n state = 'captured'\n if state != 'new':\n hold.meta['state'] = state\n hold.save()\n continue\n p_id = int(hold.meta['participant_id'])\n if p_id in participant_ids:\n holds[p_id] = hold\n else:\n cancel_card_hold(hold)\n return holds\n\n\n def create_card_holds(self, cursor):\n\n # Get the list of participants to create card holds for\n participants = cursor.all(\"\"\"\n SELECT *\n FROM payday_participants\n WHERE old_balance < giving_today\n AND balanced_customer_href IS NOT NULL\n AND last_bill_result IS NOT NULL\n AND is_suspicious IS false\n \"\"\")\n if not participants:\n return {}\n\n # Fetch existing holds\n participant_ids = set(p.id for p in participants)\n holds = self.fetch_card_holds(participant_ids)\n\n # Create new holds and check amounts of existing ones\n def f(p):\n amount = p.giving_today\n if p.old_balance < 0:\n amount -= p.old_balance\n if p.id in holds:\n charge_amount = upcharge(amount)[0]\n if holds[p.id].amount >= charge_amount * 100:\n return\n else:\n # The amount is too low, cancel the hold and make a new one\n cancel_card_hold(holds.pop(p.id))\n hold, error = create_card_hold(self.db, p, amount)\n if error:\n return 1\n else:\n holds[p.id] = hold\n n_failures = sum(filter(None, threaded_map(f, participants)))\n\n # Record the number of failures\n cursor.one(\"\"\"\n UPDATE paydays\n SET ncc_failing = %s\n WHERE ts_end='1970-01-01T00:00:00+00'::timestamptz\n RETURNING id\n \"\"\", (n_failures,), default=NoPayday)\n\n # Update the values of card_hold_ok in our temporary table\n if not holds:\n return {}\n cursor.run(\"\"\"\n UPDATE payday_participants p\n SET card_hold_ok = true\n WHERE p.id IN %s\n \"\"\", (tuple(holds.keys()),))\n\n return holds\n\n\n @staticmethod\n def transfer_tips(cursor):\n cursor.run(\"\"\"\n\n UPDATE payday_tips t\n SET is_funded = true\n FROM payday_participants p\n WHERE p.username = t.tipper\n AND p.card_hold_ok;\n\n SELECT settle_tip_graph();\n\n \"\"\")\n\n\n @staticmethod\n def transfer_takes(cursor, ts_start):\n cursor.run(\"\"\"\n\n INSERT INTO payday_takes\n SELECT team, member, amount\n FROM ( SELECT DISTINCT ON (team, member)\n team, member, amount, ctime\n FROM takes\n WHERE mtime < %(ts_start)s\n ORDER BY team, member, mtime DESC\n ) t\n WHERE t.amount > 0\n AND t.team IN (SELECT username FROM payday_participants)\n AND t.member IN (SELECT username FROM payday_participants)\n AND ( SELECT id\n FROM payday_transfers_done t2\n WHERE t.team = t2.tipper\n AND t.member = t2.tippee\n AND context = 'take'\n ) IS NULL\n ORDER BY t.team, t.ctime DESC;\n\n SELECT settle_tip_graph();\n\n \"\"\", dict(ts_start=ts_start))\n\n\n def settle_card_holds(self, cursor, holds):\n participants = cursor.all(\"\"\"\n SELECT *\n FROM payday_participants\n WHERE new_balance < 0\n \"\"\")\n participants = [p for p in participants if p.id in holds]\n\n # Capture holds to bring balances back up to (at least) zero\n def capture(p):\n amount = -p.new_balance\n capture_card_hold(self.db, p, amount, holds.pop(p.id))\n threaded_map(capture, participants)\n log(\"Captured %i card holds.\" % len(participants))\n\n # Cancel the remaining holds\n threaded_map(cancel_card_hold, holds.values())\n log(\"Canceled %i card holds.\" % len(holds))\n\n\n @staticmethod\n def update_balances(cursor):\n participants = cursor.all(\"\"\"\n\n UPDATE participants p\n SET balance = (balance + p2.new_balance - p2.old_balance)\n FROM payday_participants p2\n WHERE p.id = p2.id\n AND p2.new_balance <> p2.old_balance\n RETURNING p.id\n , p.username\n , balance AS new_balance\n , ( SELECT balance\n FROM participants p3\n WHERE p3.id = p.id\n ) AS cur_balance;\n\n \"\"\")\n # Check that balances aren't becoming (more) negative\n for p in participants:\n if p.new_balance < 0 and p.new_balance < p.cur_balance:\n log(p)\n raise NegativeBalance()\n cursor.run(\"\"\"\n INSERT INTO transfers (timestamp, tipper, tippee, amount, context)\n SELECT * FROM payday_transfers;\n \"\"\")\n log(\"Updated the balances of %i participants.\" % len(participants))\n\n\n def take_over_balances(self):\n \"\"\"If an account that receives money is taken over during payin we need\n to transfer the balance to the absorbing account.\n \"\"\"\n for i in itertools.count():\n if i > 10:\n raise Exception('possible infinite loop')\n count = self.db.one(\"\"\"\n\n DROP TABLE IF EXISTS temp;\n CREATE TEMPORARY TABLE temp AS\n SELECT archived_as, absorbed_by, balance AS archived_balance\n FROM absorptions a\n JOIN participants p ON a.archived_as = p.username\n WHERE balance > 0;\n\n SELECT count(*) FROM temp;\n\n \"\"\")\n if not count:\n break\n self.db.run(\"\"\"\n\n INSERT INTO transfers (tipper, tippee, amount, context)\n SELECT archived_as, absorbed_by, archived_balance, 'take-over'\n FROM temp;\n\n UPDATE participants\n SET balance = (balance - archived_balance)\n FROM temp\n WHERE username = archived_as;\n\n UPDATE participants\n SET balance = (balance + archived_balance)\n FROM temp\n WHERE username = absorbed_by;\n\n \"\"\")\n\n\n def payout(self):\n \"\"\"This is the second stage of payday in which we send money out to the\n bank accounts of participants.\n \"\"\"\n log(\"Starting payout loop.\")\n participants = self.db.all(\"\"\"\n SELECT p.*::participants\n FROM participants p\n WHERE balance > 0\n AND balanced_customer_href IS NOT NULL\n AND last_ach_result IS NOT NULL\n \"\"\")\n def credit(participant):\n if participant.is_suspicious is None:\n log(\"UNREVIEWED: %s\" % participant.username)\n return\n withhold = participant.giving + participant.pledging\n error = ach_credit(self.db, participant, withhold)\n if error:\n self.mark_ach_failed()\n threaded_map(credit, participants)\n log(\"Did payout for %d participants.\" % len(participants))\n self.db.self_check()\n log(\"Checked the DB.\")\n\n\n def update_stats(self):\n self.db.run(\"\"\"\\\n\n WITH our_transfers AS (\n SELECT *\n FROM transfers\n WHERE \"timestamp\" >= %(ts_start)s\n )\n , our_tips AS (\n SELECT *\n FROM our_transfers\n WHERE context = 'tip'\n )\n , our_pachinkos AS (\n SELECT *\n FROM our_transfers\n WHERE context = 'take'\n )\n , our_exchanges AS (\n SELECT *\n FROM exchanges\n WHERE \"timestamp\" >= %(ts_start)s\n )\n , our_achs AS (\n SELECT *\n FROM our_exchanges\n WHERE amount < 0\n )\n , our_charges AS (\n SELECT *\n FROM our_exchanges\n WHERE amount > 0\n )\n UPDATE paydays\n SET nactive = (\n SELECT DISTINCT count(*) FROM (\n SELECT tipper FROM our_transfers\n UNION\n SELECT tippee FROM our_transfers\n ) AS foo\n )\n , ntippers = (SELECT count(DISTINCT tipper) FROM our_transfers)\n , ntips = (SELECT count(*) FROM our_tips)\n , npachinko = (SELECT count(*) FROM our_pachinkos)\n , pachinko_volume = (SELECT COALESCE(sum(amount), 0) FROM our_pachinkos)\n , ntransfers = (SELECT count(*) FROM our_transfers)\n , transfer_volume = (SELECT COALESCE(sum(amount), 0) FROM our_transfers)\n , nachs = (SELECT count(*) FROM our_achs)\n , ach_volume = (SELECT COALESCE(sum(amount), 0) FROM our_achs)\n , ach_fees_volume = (SELECT COALESCE(sum(fee), 0) FROM our_achs)\n , ncharges = (SELECT count(*) FROM our_charges)\n , charge_volume = (\n SELECT COALESCE(sum(amount + fee), 0)\n FROM our_charges\n )\n , charge_fees_volume = (SELECT COALESCE(sum(fee), 0) FROM our_charges)\n WHERE ts_end='1970-01-01T00:00:00+00'::timestamptz\n\n \"\"\", {'ts_start': self.ts_start})\n log(\"Updated payday stats.\")\n\n\n def update_cached_amounts(self):\n with self.db.get_cursor() as cursor:\n cursor.execute(FAKE_PAYDAY)\n log(\"Updated receiving amounts.\")\n\n\n def end(self):\n self.ts_end = self.db.one(\"\"\"\\\n\n UPDATE paydays\n SET ts_end=now()\n WHERE ts_end='1970-01-01T00:00:00+00'::timestamptz\n RETURNING ts_end AT TIME ZONE 'UTC'\n\n \"\"\", default=NoPayday).replace(tzinfo=aspen.utils.utc)\n\n\n # Record-keeping.\n # ===============\n\n def mark_ach_failed(self):\n self.db.one(\"\"\"\\\n\n UPDATE paydays\n SET nach_failing = nach_failing + 1\n WHERE ts_end='1970-01-01T00:00:00+00'::timestamptz\n RETURNING id\n\n \"\"\", default=NoPayday)\n\n\n def mark_stage_done(self):\n self.db.one(\"\"\"\\\n\n UPDATE paydays\n SET stage = stage + 1\n WHERE ts_end='1970-01-01T00:00:00+00'::timestamptz\n RETURNING id\n\n \"\"\", default=NoPayday)\n",
"path": "gratipay/billing/payday.py"
}
] | [
{
"content": "\"\"\"This is Gratipay's payday algorithm.\n\nExchanges (moving money between Gratipay and the outside world) and transfers\n(moving money amongst Gratipay users) happen within an isolated event called\npayday. This event has duration (it's not punctiliar).\n\nPayday is designed to be crash-resistant. Everything that can be rolled back\nhappens inside a single DB transaction. Exchanges cannot be rolled back, so they\nimmediately affect the participant's balance.\n\n\"\"\"\nfrom __future__ import unicode_literals\n\nimport itertools\nfrom multiprocessing.dummy import Pool as ThreadPool\n\nfrom balanced import CardHold\n\nimport aspen.utils\nfrom aspen import log\nfrom gratipay.billing.exchanges import (\n ach_credit, cancel_card_hold, capture_card_hold, create_card_hold, upcharge\n)\nfrom gratipay.exceptions import NegativeBalance\nfrom gratipay.models import check_db\nfrom psycopg2 import IntegrityError\n\n\nwith open('fake_payday.sql') as f:\n FAKE_PAYDAY = f.read()\n\n\nclass ExceptionWrapped(Exception): pass\n\n\ndef threaded_map(func, iterable, threads=5):\n pool = ThreadPool(threads)\n def g(*a, **kw):\n # Without this wrapper we get a traceback from inside multiprocessing.\n try:\n return func(*a, **kw)\n except Exception as e:\n import traceback\n raise ExceptionWrapped(e, traceback.format_exc())\n try:\n r = pool.map(g, iterable)\n except ExceptionWrapped as e:\n print(e.args[1])\n raise e.args[0]\n pool.close()\n pool.join()\n return r\n\n\nclass NoPayday(Exception):\n __str__ = lambda self: \"No payday found where one was expected.\"\n\n\nclass Payday(object):\n \"\"\"Represent an abstract event during which money is moved.\n\n On Payday, we want to use a participant's Gratipay balance to settle their\n tips due (pulling in more money via credit card as needed), but we only\n want to use their balance at the start of Payday. Balance changes should be\n atomic globally per-Payday.\n\n Here's the call structure of the Payday.run method:\n\n run\n payin\n prepare\n create_card_holds\n transfer_tips\n transfer_takes\n settle_card_holds\n update_balances\n take_over_balances\n payout\n update_stats\n update_cached_amounts\n end\n\n \"\"\"\n\n\n @classmethod\n def start(cls):\n \"\"\"Try to start a new Payday.\n\n If there is a Payday that hasn't finished yet, then the UNIQUE\n constraint on ts_end will kick in and notify us of that. In that case\n we load the existing Payday and work on it some more. We use the start\n time of the current Payday to synchronize our work.\n\n \"\"\"\n try:\n d = cls.db.one(\"\"\"\n INSERT INTO paydays DEFAULT VALUES\n RETURNING id, (ts_start AT TIME ZONE 'UTC') AS ts_start, stage\n \"\"\", back_as=dict)\n log(\"Starting a new payday.\")\n except IntegrityError: # Collision, we have a Payday already.\n d = cls.db.one(\"\"\"\n SELECT id, (ts_start AT TIME ZONE 'UTC') AS ts_start, stage\n FROM paydays\n WHERE ts_end='1970-01-01T00:00:00+00'::timestamptz\n \"\"\", back_as=dict)\n log(\"Picking up with an existing payday.\")\n\n d['ts_start'] = d['ts_start'].replace(tzinfo=aspen.utils.utc)\n\n log(\"Payday started at %s.\" % d['ts_start'])\n\n payday = Payday()\n payday.__dict__.update(d)\n return payday\n\n\n def run(self):\n \"\"\"This is the starting point for payday.\n\n This method runs every Thursday. It is structured such that it can be\n run again safely (with a newly-instantiated Payday object) if it\n crashes.\n\n \"\"\"\n self.db.self_check()\n\n _start = aspen.utils.utcnow()\n log(\"Greetings, program! It's PAYDAY!!!!\")\n\n if self.stage < 1:\n self.payin()\n self.mark_stage_done()\n if self.stage < 2:\n self.payout()\n self.mark_stage_done()\n if self.stage < 3:\n self.update_stats()\n self.update_cached_amounts()\n self.mark_stage_done()\n\n self.end()\n\n _end = aspen.utils.utcnow()\n _delta = _end - _start\n fmt_past = \"Script ran for %%(age)s (%s).\" % _delta\n log(aspen.utils.to_age(_start, fmt_past=fmt_past))\n\n\n def payin(self):\n \"\"\"The first stage of payday where we charge credit cards and transfer\n money internally between participants.\n \"\"\"\n with self.db.get_cursor() as cursor:\n self.prepare(cursor, self.ts_start)\n holds = self.create_card_holds(cursor)\n self.transfer_tips(cursor)\n self.transfer_takes(cursor, self.ts_start)\n transfers = cursor.all(\"\"\"\n SELECT * FROM transfers WHERE \"timestamp\" > %s\n \"\"\", (self.ts_start,))\n try:\n self.settle_card_holds(cursor, holds)\n self.update_balances(cursor)\n check_db(cursor)\n except:\n # Dump transfers for debugging\n import csv\n from time import time\n with open('%s_transfers.csv' % time(), 'wb') as f:\n csv.writer(f).writerows(transfers)\n raise\n self.take_over_balances()\n # Clean up leftover functions\n self.db.run(\"\"\"\n DROP FUNCTION process_take();\n DROP FUNCTION process_tip();\n DROP FUNCTION settle_tip_graph();\n DROP FUNCTION transfer(text, text, numeric, context_type);\n \"\"\")\n\n\n @staticmethod\n def prepare(cursor, ts_start):\n \"\"\"Prepare the DB: we need temporary tables with indexes and triggers.\n \"\"\"\n cursor.run(\"\"\"\n\n -- Create the necessary temporary tables and indexes\n\n CREATE TEMPORARY TABLE payday_participants ON COMMIT DROP AS\n SELECT id\n , username\n , claimed_time\n , balance AS old_balance\n , balance AS new_balance\n , balanced_customer_href\n , last_bill_result\n , is_suspicious\n , goal\n , false AS card_hold_ok\n FROM participants\n WHERE is_suspicious IS NOT true\n AND claimed_time < %(ts_start)s\n ORDER BY claimed_time;\n\n CREATE UNIQUE INDEX ON payday_participants (id);\n CREATE UNIQUE INDEX ON payday_participants (username);\n\n CREATE TEMPORARY TABLE payday_transfers_done ON COMMIT DROP AS\n SELECT *\n FROM transfers t\n WHERE t.timestamp > %(ts_start)s;\n\n CREATE TEMPORARY TABLE payday_tips ON COMMIT DROP AS\n SELECT tipper, tippee, amount\n FROM ( SELECT DISTINCT ON (tipper, tippee) *\n FROM tips\n WHERE mtime < %(ts_start)s\n ORDER BY tipper, tippee, mtime DESC\n ) t\n JOIN payday_participants p ON p.username = t.tipper\n JOIN payday_participants p2 ON p2.username = t.tippee\n WHERE t.amount > 0\n AND (p2.goal IS NULL or p2.goal >= 0)\n AND ( SELECT id\n FROM payday_transfers_done t2\n WHERE t.tipper = t2.tipper\n AND t.tippee = t2.tippee\n AND context = 'tip'\n ) IS NULL\n ORDER BY p.claimed_time ASC, t.ctime ASC;\n\n CREATE INDEX ON payday_tips (tipper);\n CREATE INDEX ON payday_tips (tippee);\n ALTER TABLE payday_tips ADD COLUMN is_funded boolean;\n\n ALTER TABLE payday_participants ADD COLUMN giving_today numeric(35,2);\n UPDATE payday_participants\n SET giving_today = COALESCE((\n SELECT sum(amount)\n FROM payday_tips\n WHERE tipper = username\n ), 0);\n\n CREATE TEMPORARY TABLE payday_takes\n ( team text\n , member text\n , amount numeric(35,2)\n ) ON COMMIT DROP;\n\n CREATE TEMPORARY TABLE payday_transfers\n ( timestamp timestamptz DEFAULT now()\n , tipper text\n , tippee text\n , amount numeric(35,2)\n , context context_type\n ) ON COMMIT DROP;\n\n\n -- Prepare a statement that makes and records a transfer\n\n CREATE OR REPLACE FUNCTION transfer(text, text, numeric, context_type)\n RETURNS void AS $$\n BEGIN\n IF ($3 = 0) THEN RETURN; END IF;\n UPDATE payday_participants\n SET new_balance = (new_balance - $3)\n WHERE username = $1;\n UPDATE payday_participants\n SET new_balance = (new_balance + $3)\n WHERE username = $2;\n INSERT INTO payday_transfers\n (tipper, tippee, amount, context)\n VALUES ( ( SELECT p.username\n FROM participants p\n JOIN payday_participants p2 ON p.id = p2.id\n WHERE p2.username = $1 )\n , ( SELECT p.username\n FROM participants p\n JOIN payday_participants p2 ON p.id = p2.id\n WHERE p2.username = $2 )\n , $3\n , $4\n );\n END;\n $$ LANGUAGE plpgsql;\n\n\n -- Create a trigger to process tips\n\n CREATE OR REPLACE FUNCTION process_tip() RETURNS trigger AS $$\n DECLARE\n tipper payday_participants;\n BEGIN\n tipper := (\n SELECT p.*::payday_participants\n FROM payday_participants p\n WHERE username = NEW.tipper\n );\n IF (NEW.amount <= tipper.new_balance OR tipper.card_hold_ok) THEN\n EXECUTE transfer(NEW.tipper, NEW.tippee, NEW.amount, 'tip');\n RETURN NEW;\n END IF;\n RETURN NULL;\n END;\n $$ LANGUAGE plpgsql;\n\n CREATE TRIGGER process_tip BEFORE UPDATE OF is_funded ON payday_tips\n FOR EACH ROW\n WHEN (NEW.is_funded IS true AND OLD.is_funded IS NOT true)\n EXECUTE PROCEDURE process_tip();\n\n\n -- Create a trigger to process takes\n\n CREATE OR REPLACE FUNCTION process_take() RETURNS trigger AS $$\n DECLARE\n actual_amount numeric(35,2);\n team_balance numeric(35,2);\n BEGIN\n team_balance := (\n SELECT new_balance\n FROM payday_participants\n WHERE username = NEW.team\n );\n IF (team_balance <= 0) THEN RETURN NULL; END IF;\n actual_amount := NEW.amount;\n IF (team_balance < NEW.amount) THEN\n actual_amount := team_balance;\n END IF;\n EXECUTE transfer(NEW.team, NEW.member, actual_amount, 'take');\n RETURN NULL;\n END;\n $$ LANGUAGE plpgsql;\n\n CREATE TRIGGER process_take AFTER INSERT ON payday_takes\n FOR EACH ROW EXECUTE PROCEDURE process_take();\n\n\n -- Create a function to settle whole tip graph\n\n CREATE OR REPLACE FUNCTION settle_tip_graph() RETURNS void AS $$\n DECLARE\n count integer NOT NULL DEFAULT 0;\n i integer := 0;\n BEGIN\n LOOP\n i := i + 1;\n WITH updated_rows AS (\n UPDATE payday_tips\n SET is_funded = true\n WHERE is_funded IS NOT true\n RETURNING *\n )\n SELECT COUNT(*) FROM updated_rows INTO count;\n IF (count = 0) THEN\n EXIT;\n END IF;\n IF (i > 50) THEN\n RAISE 'Reached the maximum number of iterations';\n END IF;\n END LOOP;\n END;\n $$ LANGUAGE plpgsql;\n\n\n -- Save the stats we already have\n\n UPDATE paydays\n SET nparticipants = (SELECT count(*) FROM payday_participants)\n , ncc_missing = (\n SELECT count(*)\n FROM payday_participants\n WHERE old_balance < giving_today\n AND ( balanced_customer_href IS NULL\n OR\n last_bill_result IS NULL\n )\n )\n WHERE ts_end='1970-01-01T00:00:00+00'::timestamptz;\n\n \"\"\", dict(ts_start=ts_start))\n log('Prepared the DB.')\n\n\n @staticmethod\n def fetch_card_holds(participant_ids):\n holds = {}\n for hold in CardHold.query.filter(CardHold.f.meta.state == 'new'):\n state = 'new'\n if hold.failure_reason:\n state = 'failed'\n elif hold.voided_at:\n state = 'cancelled'\n elif getattr(hold, 'debit_href', None):\n state = 'captured'\n if state != 'new':\n hold.meta['state'] = state\n hold.save()\n continue\n p_id = int(hold.meta['participant_id'])\n if p_id in participant_ids:\n holds[p_id] = hold\n else:\n cancel_card_hold(hold)\n return holds\n\n\n def create_card_holds(self, cursor):\n\n # Get the list of participants to create card holds for\n participants = cursor.all(\"\"\"\n SELECT *\n FROM payday_participants\n WHERE old_balance < giving_today\n AND balanced_customer_href IS NOT NULL\n AND last_bill_result IS NOT NULL\n AND is_suspicious IS false\n \"\"\")\n if not participants:\n return {}\n\n # Fetch existing holds\n participant_ids = set(p.id for p in participants)\n holds = self.fetch_card_holds(participant_ids)\n\n # Create new holds and check amounts of existing ones\n def f(p):\n amount = p.giving_today\n if p.old_balance < 0:\n amount -= p.old_balance\n if p.id in holds:\n charge_amount = upcharge(amount)[0]\n if holds[p.id].amount >= charge_amount * 100:\n return\n else:\n # The amount is too low, cancel the hold and make a new one\n cancel_card_hold(holds.pop(p.id))\n hold, error = create_card_hold(self.db, p, amount)\n if error:\n return 1\n else:\n holds[p.id] = hold\n n_failures = sum(filter(None, threaded_map(f, participants)))\n\n # Record the number of failures\n cursor.one(\"\"\"\n UPDATE paydays\n SET ncc_failing = %s\n WHERE ts_end='1970-01-01T00:00:00+00'::timestamptz\n RETURNING id\n \"\"\", (n_failures,), default=NoPayday)\n\n # Update the values of card_hold_ok in our temporary table\n if not holds:\n return {}\n cursor.run(\"\"\"\n UPDATE payday_participants p\n SET card_hold_ok = true\n WHERE p.id IN %s\n \"\"\", (tuple(holds.keys()),))\n\n return holds\n\n\n @staticmethod\n def transfer_tips(cursor):\n cursor.run(\"\"\"\n\n UPDATE payday_tips t\n SET is_funded = true\n FROM payday_participants p\n WHERE p.username = t.tipper\n AND p.card_hold_ok;\n\n SELECT settle_tip_graph();\n\n \"\"\")\n\n\n @staticmethod\n def transfer_takes(cursor, ts_start):\n cursor.run(\"\"\"\n\n INSERT INTO payday_takes\n SELECT team, member, amount\n FROM ( SELECT DISTINCT ON (team, member)\n team, member, amount, ctime\n FROM takes\n WHERE mtime < %(ts_start)s\n ORDER BY team, member, mtime DESC\n ) t\n WHERE t.amount > 0\n AND t.team IN (SELECT username FROM payday_participants)\n AND t.member IN (SELECT username FROM payday_participants)\n AND ( SELECT id\n FROM payday_transfers_done t2\n WHERE t.team = t2.tipper\n AND t.member = t2.tippee\n AND context = 'take'\n ) IS NULL\n ORDER BY t.team, t.ctime DESC;\n\n SELECT settle_tip_graph();\n\n \"\"\", dict(ts_start=ts_start))\n\n\n def settle_card_holds(self, cursor, holds):\n participants = cursor.all(\"\"\"\n SELECT *\n FROM payday_participants\n WHERE new_balance < 0\n \"\"\")\n participants = [p for p in participants if p.id in holds]\n\n # Capture holds to bring balances back up to (at least) zero\n def capture(p):\n amount = -p.new_balance\n capture_card_hold(self.db, p, amount, holds.pop(p.id))\n threaded_map(capture, participants)\n log(\"Captured %i card holds.\" % len(participants))\n\n # Cancel the remaining holds\n threaded_map(cancel_card_hold, holds.values())\n log(\"Canceled %i card holds.\" % len(holds))\n\n\n @staticmethod\n def update_balances(cursor):\n participants = cursor.all(\"\"\"\n\n UPDATE participants p\n SET balance = (balance + p2.new_balance - p2.old_balance)\n FROM payday_participants p2\n WHERE p.id = p2.id\n AND p2.new_balance <> p2.old_balance\n RETURNING p.id\n , p.username\n , balance AS new_balance\n , ( SELECT balance\n FROM participants p3\n WHERE p3.id = p.id\n ) AS cur_balance;\n\n \"\"\")\n # Check that balances aren't becoming (more) negative\n for p in participants:\n if p.new_balance < 0 and p.new_balance < p.cur_balance:\n log(p)\n raise NegativeBalance()\n cursor.run(\"\"\"\n INSERT INTO transfers (timestamp, tipper, tippee, amount, context)\n SELECT * FROM payday_transfers;\n \"\"\")\n log(\"Updated the balances of %i participants.\" % len(participants))\n\n\n def take_over_balances(self):\n \"\"\"If an account that receives money is taken over during payin we need\n to transfer the balance to the absorbing account.\n \"\"\"\n for i in itertools.count():\n if i > 10:\n raise Exception('possible infinite loop')\n count = self.db.one(\"\"\"\n\n DROP TABLE IF EXISTS temp;\n CREATE TEMPORARY TABLE temp AS\n SELECT archived_as, absorbed_by, balance AS archived_balance\n FROM absorptions a\n JOIN participants p ON a.archived_as = p.username\n WHERE balance > 0;\n\n SELECT count(*) FROM temp;\n\n \"\"\")\n if not count:\n break\n self.db.run(\"\"\"\n\n INSERT INTO transfers (tipper, tippee, amount, context)\n SELECT archived_as, absorbed_by, archived_balance, 'take-over'\n FROM temp;\n\n UPDATE participants\n SET balance = (balance - archived_balance)\n FROM temp\n WHERE username = archived_as;\n\n UPDATE participants\n SET balance = (balance + archived_balance)\n FROM temp\n WHERE username = absorbed_by;\n\n \"\"\")\n\n\n def payout(self):\n \"\"\"This is the second stage of payday in which we send money out to the\n bank accounts of participants.\n \"\"\"\n log(\"Starting payout loop.\")\n participants = self.db.all(\"\"\"\n SELECT p.*::participants\n FROM participants p\n WHERE balance > 0\n AND balanced_customer_href IS NOT NULL\n AND last_ach_result IS NOT NULL\n \"\"\")\n def credit(participant):\n if participant.is_suspicious is None:\n log(\"UNREVIEWED: %s\" % participant.username)\n return\n withhold = participant.giving + participant.pledging\n error = ach_credit(self.db, participant, withhold)\n if error:\n self.mark_ach_failed()\n threaded_map(credit, participants)\n log(\"Did payout for %d participants.\" % len(participants))\n self.db.self_check()\n log(\"Checked the DB.\")\n\n\n def update_stats(self):\n self.db.run(\"\"\"\\\n\n WITH our_transfers AS (\n SELECT *\n FROM transfers\n WHERE \"timestamp\" >= %(ts_start)s\n )\n , our_tips AS (\n SELECT *\n FROM our_transfers\n WHERE context = 'tip'\n )\n , our_pachinkos AS (\n SELECT *\n FROM our_transfers\n WHERE context = 'take'\n )\n , our_exchanges AS (\n SELECT *\n FROM exchanges\n WHERE \"timestamp\" >= %(ts_start)s\n )\n , our_achs AS (\n SELECT *\n FROM our_exchanges\n WHERE amount < 0\n )\n , our_charges AS (\n SELECT *\n FROM our_exchanges\n WHERE amount > 0\n AND status <> 'failed'\n )\n UPDATE paydays\n SET nactive = (\n SELECT DISTINCT count(*) FROM (\n SELECT tipper FROM our_transfers\n UNION\n SELECT tippee FROM our_transfers\n ) AS foo\n )\n , ntippers = (SELECT count(DISTINCT tipper) FROM our_transfers)\n , ntips = (SELECT count(*) FROM our_tips)\n , npachinko = (SELECT count(*) FROM our_pachinkos)\n , pachinko_volume = (SELECT COALESCE(sum(amount), 0) FROM our_pachinkos)\n , ntransfers = (SELECT count(*) FROM our_transfers)\n , transfer_volume = (SELECT COALESCE(sum(amount), 0) FROM our_transfers)\n , nachs = (SELECT count(*) FROM our_achs)\n , ach_volume = (SELECT COALESCE(sum(amount), 0) FROM our_achs)\n , ach_fees_volume = (SELECT COALESCE(sum(fee), 0) FROM our_achs)\n , ncharges = (SELECT count(*) FROM our_charges)\n , charge_volume = (\n SELECT COALESCE(sum(amount + fee), 0)\n FROM our_charges\n )\n , charge_fees_volume = (SELECT COALESCE(sum(fee), 0) FROM our_charges)\n WHERE ts_end='1970-01-01T00:00:00+00'::timestamptz\n\n \"\"\", {'ts_start': self.ts_start})\n log(\"Updated payday stats.\")\n\n\n def update_cached_amounts(self):\n with self.db.get_cursor() as cursor:\n cursor.execute(FAKE_PAYDAY)\n log(\"Updated receiving amounts.\")\n\n\n def end(self):\n self.ts_end = self.db.one(\"\"\"\\\n\n UPDATE paydays\n SET ts_end=now()\n WHERE ts_end='1970-01-01T00:00:00+00'::timestamptz\n RETURNING ts_end AT TIME ZONE 'UTC'\n\n \"\"\", default=NoPayday).replace(tzinfo=aspen.utils.utc)\n\n\n # Record-keeping.\n # ===============\n\n def mark_ach_failed(self):\n self.db.one(\"\"\"\\\n\n UPDATE paydays\n SET nach_failing = nach_failing + 1\n WHERE ts_end='1970-01-01T00:00:00+00'::timestamptz\n RETURNING id\n\n \"\"\", default=NoPayday)\n\n\n def mark_stage_done(self):\n self.db.one(\"\"\"\\\n\n UPDATE paydays\n SET stage = stage + 1\n WHERE ts_end='1970-01-01T00:00:00+00'::timestamptz\n RETURNING id\n\n \"\"\", default=NoPayday)\n",
"path": "gratipay/billing/payday.py"
}
] | diff --git a/branch.sql b/branch.sql
new file mode 100644
index 0000000000..4faca93a2d
--- /dev/null
+++ b/branch.sql
@@ -0,0 +1,34 @@
+BEGIN;
+
+DO $$
+DECLARE
+ payday record;
+ new_ncharges int;
+ new_charge_volume decimal(35,2);
+ new_charge_fees_volume decimal(35,2);
+BEGIN
+ FOR payday IN SELECT * FROM paydays LOOP
+ CREATE TEMP TABLE our_charges AS
+ SELECT *
+ FROM exchanges
+ WHERE "timestamp" >= payday.ts_start
+ AND "timestamp" < payday.ts_end
+ AND amount > 0
+ AND (status IS NULL OR status <> 'failed');
+ new_ncharges := (SELECT count(*) FROM our_charges);
+ new_charge_volume := (SELECT COALESCE(sum(amount + fee), 0) FROM our_charges);
+ new_charge_fees_volume := (SELECT COALESCE(sum(fee), 0) FROM our_charges);
+ UPDATE paydays
+ SET ncharges = new_ncharges
+ , charge_volume = new_charge_volume
+ , charge_fees_volume = new_charge_fees_volume
+ WHERE id = payday.id
+ AND (ncharges <> new_ncharges
+ OR charge_volume <> new_charge_volume
+ OR charge_fees_volume <> new_charge_fees_volume);
+ DROP TABLE our_charges;
+ END LOOP;
+END
+$$;
+
+END;
diff --git a/gratipay/billing/payday.py b/gratipay/billing/payday.py
index 9095491fbe..c774acabb8 100644
--- a/gratipay/billing/payday.py
+++ b/gratipay/billing/payday.py
@@ -656,6 +656,7 @@ def update_stats(self):
SELECT *
FROM our_exchanges
WHERE amount > 0
+ AND status <> 'failed'
)
UPDATE paydays
SET nactive = (
|
ray-project__ray-8782 | [tune] Tune dashboard : use scientific notation
When displaying parameters in the tune dashboard, they are displayer using standard float format
with only 2 significant digits.
For example, if your learning rate is lower than 1e-2, it appears as zero.
Similarly, if your experiments return a metric whose value are in a narrow range, the displayed digits are all equal and you cannot know which one is better (for example, for an accuracy between 0 and 100% treated as a float, values displayed are between 0.00 and 1.00, with a resolution corresponding to 1%, which is really not much).
| [
{
"content": "try:\n import aiohttp.web\nexcept ImportError:\n print(\"The dashboard requires aiohttp to run.\")\n import sys\n sys.exit(1)\n\nimport argparse\nimport copy\nimport datetime\nimport errno\nimport json\nimport logging\nimport os\nimport socket\nimport threading\nimport time\nimport traceback\nimport yaml\nimport uuid\nimport grpc\nfrom google.protobuf.json_format import MessageToDict\nimport ray\nimport ray.ray_constants as ray_constants\n\nfrom ray.core.generated import node_manager_pb2\nfrom ray.core.generated import node_manager_pb2_grpc\nfrom ray.core.generated import reporter_pb2\nfrom ray.core.generated import reporter_pb2_grpc\nfrom ray.core.generated import core_worker_pb2\nfrom ray.core.generated import core_worker_pb2_grpc\nfrom ray.dashboard.interface import BaseDashboardController\nfrom ray.dashboard.interface import BaseDashboardRouteHandler\nfrom ray.dashboard.memory import construct_memory_table, MemoryTable\nfrom ray.dashboard.metrics_exporter.client import Exporter\nfrom ray.dashboard.metrics_exporter.client import MetricsExportClient\nfrom ray.dashboard.node_stats import NodeStats\nfrom ray.dashboard.util import to_unix_time, measures_to_dict, format_resource\n\ntry:\n from ray.tune import Analysis\n from tensorboard import program\nexcept ImportError:\n Analysis = None\n\n# Logger for this module. It should be configured at the entry point\n# into the program using Ray. Ray provides a default configuration at\n# entry/init points.\nlogger = logging.getLogger(__name__)\n\n\nasync def json_response(is_dev, result=None, error=None,\n ts=None) -> aiohttp.web.Response:\n if ts is None:\n ts = datetime.datetime.utcnow()\n\n headers = None\n if is_dev:\n headers = {\"Access-Control-Allow-Origin\": \"*\"}\n\n return aiohttp.web.json_response(\n {\n \"result\": result,\n \"timestamp\": to_unix_time(ts),\n \"error\": error,\n },\n headers=headers)\n\n\nclass DashboardController(BaseDashboardController):\n def __init__(self, redis_address, redis_password):\n self.node_stats = NodeStats(redis_address, redis_password)\n self.raylet_stats = RayletStats(\n redis_address, redis_password=redis_password)\n if Analysis is not None:\n self.tune_stats = TuneCollector(2.0)\n self.memory_table = MemoryTable([])\n\n def _construct_raylet_info(self):\n D = self.raylet_stats.get_raylet_stats()\n workers_info_by_node = {\n data[\"nodeId\"]: data.get(\"workersStats\")\n for data in D.values()\n }\n\n infeasible_tasks = sum(\n (data.get(\"infeasibleTasks\", []) for data in D.values()), [])\n # ready_tasks are used to render tasks that are not schedulable\n # due to resource limitations.\n # (e.g., Actor requires 2 GPUs but there is only 1 gpu available).\n ready_tasks = sum((data.get(\"readyTasks\", []) for data in D.values()),\n [])\n actor_tree = self.node_stats.get_actor_tree(\n workers_info_by_node, infeasible_tasks, ready_tasks)\n\n for address, data in D.items():\n # process view data\n measures_dicts = {}\n for view_data in data[\"viewData\"]:\n view_name = view_data[\"viewName\"]\n if view_name in (\"local_available_resource\",\n \"local_total_resource\",\n \"object_manager_stats\"):\n measures_dicts[view_name] = measures_to_dict(\n view_data[\"measures\"])\n # process resources info\n extra_info_strings = []\n prefix = \"ResourceName:\"\n for resource_name, total_resource in measures_dicts[\n \"local_total_resource\"].items():\n available_resource = measures_dicts[\n \"local_available_resource\"].get(resource_name, .0)\n resource_name = resource_name[len(prefix):]\n extra_info_strings.append(\"{}: {} / {}\".format(\n resource_name,\n format_resource(resource_name,\n total_resource - available_resource),\n format_resource(resource_name, total_resource)))\n data[\"extraInfo\"] = \", \".join(extra_info_strings) + \"\\n\"\n if os.environ.get(\"RAY_DASHBOARD_DEBUG\"):\n # process object store info\n extra_info_strings = []\n prefix = \"ValueType:\"\n for stats_name in [\n \"used_object_store_memory\", \"num_local_objects\"\n ]:\n stats_value = measures_dicts[\"object_manager_stats\"].get(\n prefix + stats_name, .0)\n extra_info_strings.append(\"{}: {}\".format(\n stats_name, stats_value))\n data[\"extraInfo\"] += \", \".join(extra_info_strings)\n # process actor info\n actor_tree_str = json.dumps(\n actor_tree, indent=2, sort_keys=True)\n lines = actor_tree_str.split(\"\\n\")\n max_line_length = max(map(len, lines))\n to_print = []\n for line in lines:\n to_print.append(line + (max_line_length - len(line)) * \" \")\n data[\"extraInfo\"] += \"\\n\" + \"\\n\".join(to_print)\n return {\"nodes\": D, \"actors\": actor_tree}\n\n def get_ray_config(self):\n try:\n config_path = os.path.expanduser(\"~/ray_bootstrap_config.yaml\")\n with open(config_path) as f:\n cfg = yaml.safe_load(f)\n except Exception:\n error = \"No config\"\n return error, None\n\n D = {\n \"min_workers\": cfg[\"min_workers\"],\n \"max_workers\": cfg[\"max_workers\"],\n \"initial_workers\": cfg[\"initial_workers\"],\n \"autoscaling_mode\": cfg[\"autoscaling_mode\"],\n \"idle_timeout_minutes\": cfg[\"idle_timeout_minutes\"],\n }\n\n try:\n D[\"head_type\"] = cfg[\"head_node\"][\"InstanceType\"]\n except KeyError:\n D[\"head_type\"] = \"unknown\"\n\n try:\n D[\"worker_type\"] = cfg[\"worker_nodes\"][\"InstanceType\"]\n except KeyError:\n D[\"worker_type\"] = \"unknown\"\n\n return None, D\n\n def get_node_info(self):\n return self.node_stats.get_node_stats()\n\n def get_raylet_info(self):\n return self._construct_raylet_info()\n\n def get_memory_table_info(self) -> MemoryTable:\n # Collecting memory info adds big overhead to the cluster.\n # This must be collected only when it is necessary.\n self.raylet_stats.include_memory_info = True\n D = self.raylet_stats.get_raylet_stats()\n workers_info_by_node = {\n data[\"nodeId\"]: data.get(\"workersStats\")\n for data in D.values()\n }\n self.memory_table = construct_memory_table(workers_info_by_node)\n return self.memory_table\n\n def stop_collecting_memory_table_info(self):\n self.raylet_stats.include_memory_info = False\n\n def tune_info(self):\n if Analysis is not None:\n D = self.tune_stats.get_stats()\n else:\n D = {}\n return D\n\n def tune_availability(self):\n if Analysis is not None:\n D = self.tune_stats.get_availability()\n else:\n D = {\"available\": False, \"trials_available\": False}\n return D\n\n def set_tune_experiment(self, experiment):\n if Analysis is not None:\n return self.tune_stats.set_experiment(experiment)\n return \"Tune Not Enabled\", None\n\n def enable_tune_tensorboard(self):\n if Analysis is not None:\n self.tune_stats.enable_tensorboard()\n\n def launch_profiling(self, node_id, pid, duration):\n profiling_id = self.raylet_stats.launch_profiling(\n node_id=node_id, pid=pid, duration=duration)\n return profiling_id\n\n def check_profiling_status(self, profiling_id):\n return self.raylet_stats.check_profiling_status(profiling_id)\n\n def get_profiling_info(self, profiling_id):\n return self.raylet_stats.get_profiling_info(profiling_id)\n\n def kill_actor(self, actor_id, ip_address, port):\n return self.raylet_stats.kill_actor(actor_id, ip_address, port)\n\n def get_logs(self, hostname, pid):\n return self.node_stats.get_logs(hostname, pid)\n\n def get_errors(self, hostname, pid):\n return self.node_stats.get_errors(hostname, pid)\n\n def start_collecting_metrics(self):\n self.node_stats.start()\n self.raylet_stats.start()\n if Analysis is not None:\n self.tune_stats.start()\n\n\nclass DashboardRouteHandler(BaseDashboardRouteHandler):\n def __init__(self, dashboard_controller: DashboardController,\n is_dev=False):\n self.dashboard_controller = dashboard_controller\n self.is_dev = is_dev\n\n def forbidden(self) -> aiohttp.web.Response:\n return aiohttp.web.Response(status=403, text=\"403 Forbidden\")\n\n async def get_forbidden(self, _) -> aiohttp.web.Response:\n return self.forbidden()\n\n async def get_index(self, req) -> aiohttp.web.Response:\n return aiohttp.web.FileResponse(\n os.path.join(\n os.path.dirname(os.path.abspath(__file__)),\n \"client/build/index.html\"))\n\n async def get_favicon(self, req) -> aiohttp.web.Response:\n return aiohttp.web.FileResponse(\n os.path.join(\n os.path.dirname(os.path.abspath(__file__)),\n \"client/build/favicon.ico\"))\n\n async def ray_config(self, req) -> aiohttp.web.Response:\n error, result = self.dashboard_controller.get_ray_config()\n if error:\n return await json_response(self.is_dev, error=error)\n return await json_response(self.is_dev, result=result)\n\n async def node_info(self, req) -> aiohttp.web.Response:\n now = datetime.datetime.utcnow()\n D = self.dashboard_controller.get_node_info()\n return await json_response(self.is_dev, result=D, ts=now)\n\n async def raylet_info(self, req) -> aiohttp.web.Response:\n result = self.dashboard_controller.get_raylet_info()\n return await json_response(self.is_dev, result=result)\n\n async def memory_table_info(self, req) -> aiohttp.web.Response:\n memory_table = self.dashboard_controller.get_memory_table_info()\n return await json_response(self.is_dev, result=memory_table.__dict__())\n\n async def stop_collecting_memory_table_info(self,\n req) -> aiohttp.web.Response:\n self.dashboard_controller.stop_collecting_memory_table_info()\n return await json_response(self.is_dev, result={})\n\n async def tune_info(self, req) -> aiohttp.web.Response:\n result = self.dashboard_controller.tune_info()\n return await json_response(self.is_dev, result=result)\n\n async def tune_availability(self, req) -> aiohttp.web.Response:\n result = self.dashboard_controller.tune_availability()\n return await json_response(self.is_dev, result=result)\n\n async def set_tune_experiment(self, req) -> aiohttp.web.Response:\n data = await req.json()\n error, result = self.dashboard_controller.set_tune_experiment(\n data[\"experiment\"])\n if error:\n return await json_response(self.is_dev, error=error)\n return await json_response(self.is_dev, result=result)\n\n async def enable_tune_tensorboard(self, req) -> aiohttp.web.Response:\n self.dashboard_controller.enable_tune_tensorboard()\n return await json_response(self.is_dev, result={})\n\n async def launch_profiling(self, req) -> aiohttp.web.Response:\n node_id = req.query.get(\"node_id\")\n pid = int(req.query.get(\"pid\"))\n duration = int(req.query.get(\"duration\"))\n profiling_id = self.dashboard_controller.launch_profiling(\n node_id, pid, duration)\n return await json_response(self.is_dev, result=str(profiling_id))\n\n async def check_profiling_status(self, req) -> aiohttp.web.Response:\n profiling_id = req.query.get(\"profiling_id\")\n status = self.dashboard_controller.check_profiling_status(profiling_id)\n return await json_response(self.is_dev, result=status)\n\n async def get_profiling_info(self, req) -> aiohttp.web.Response:\n profiling_id = req.query.get(\"profiling_id\")\n profiling_info = self.dashboard_controller.get_profiling_info(\n profiling_id)\n return aiohttp.web.json_response(profiling_info)\n\n async def kill_actor(self, req) -> aiohttp.web.Response:\n actor_id = req.query.get(\"actor_id\")\n ip_address = req.query.get(\"ip_address\")\n port = req.query.get(\"port\")\n return await json_response(\n self.is_dev,\n self.dashboard_controller.kill_actor(actor_id, ip_address, port))\n\n async def logs(self, req) -> aiohttp.web.Response:\n hostname = req.query.get(\"hostname\")\n pid = req.query.get(\"pid\")\n result = self.dashboard_controller.get_logs(hostname, pid)\n return await json_response(self.is_dev, result=result)\n\n async def errors(self, req) -> aiohttp.web.Response:\n hostname = req.query.get(\"hostname\")\n pid = req.query.get(\"pid\")\n result = self.dashboard_controller.get_errors(hostname, pid)\n return await json_response(self.is_dev, result=result)\n\n\nclass MetricsExportHandler:\n def __init__(self,\n dashboard_controller: DashboardController,\n metrics_export_client: MetricsExportClient,\n dashboard_id,\n is_dev=False):\n assert metrics_export_client is not None\n self.metrics_export_client = metrics_export_client\n self.dashboard_controller = dashboard_controller\n self.is_dev = is_dev\n\n async def enable_export_metrics(self, req) -> aiohttp.web.Response:\n if self.metrics_export_client.enabled:\n return await json_response(\n self.is_dev, result={\"url\": None}, error=\"Already enabled\")\n\n succeed, error = self.metrics_export_client.start_exporting_metrics()\n error_msg = \"Failed to enable it. Error: {}\".format(error)\n if not succeed:\n return await json_response(\n self.is_dev, result={\"url\": None}, error=error_msg)\n\n url = self.metrics_export_client.dashboard_url\n return await json_response(self.is_dev, result={\"url\": url})\n\n async def get_dashboard_address(self, req) -> aiohttp.web.Response:\n if not self.metrics_export_client.enabled:\n return await json_response(\n self.is_dev,\n result={\"url\": None},\n error=\"Metrics exporting is not enabled.\")\n\n url = self.metrics_export_client.dashboard_url\n return await json_response(self.is_dev, result={\"url\": url})\n\n async def redirect_to_dashboard(self, req) -> aiohttp.web.Response:\n if not self.metrics_export_client.enabled:\n return await json_response(\n self.is_dev,\n result={\"url\": None},\n error=\"You should enable metrics export to use this endpoint.\")\n\n raise aiohttp.web.HTTPFound(self.metrics_export_client.dashboard_url)\n\n\ndef setup_metrics_export_routes(app: aiohttp.web.Application,\n handler: MetricsExportHandler):\n \"\"\"Routes that require dynamically changing class attributes.\"\"\"\n app.router.add_get(\"/api/metrics/enable\", handler.enable_export_metrics)\n app.router.add_get(\"/api/metrics/url\", handler.get_dashboard_address)\n app.router.add_get(\"/metrics/redirect\", handler.redirect_to_dashboard)\n\n\ndef setup_static_dir(app):\n build_dir = os.path.join(\n os.path.dirname(os.path.abspath(__file__)), \"client/build\")\n if not os.path.isdir(build_dir):\n raise OSError(\n errno.ENOENT, \"Dashboard build directory not found. If installing \"\n \"from source, please follow the additional steps \"\n \"required to build the dashboard\"\n \"(cd python/ray/dashboard/client \"\n \"&& npm ci \"\n \"&& npm run build)\", build_dir)\n\n static_dir = os.path.join(build_dir, \"static\")\n app.router.add_static(\"/static\", static_dir)\n return build_dir\n\n\ndef setup_speedscope_dir(app, build_dir):\n speedscope_dir = os.path.join(build_dir, \"speedscope-1.5.3\")\n app.router.add_static(\"/speedscope\", speedscope_dir)\n\n\ndef setup_dashboard_route(app: aiohttp.web.Application,\n handler: BaseDashboardRouteHandler,\n index=None,\n favicon=None,\n ray_config=None,\n node_info=None,\n raylet_info=None,\n tune_info=None,\n tune_availability=None,\n launch_profiling=None,\n check_profiling_status=None,\n get_profiling_info=None,\n kill_actor=None,\n logs=None,\n errors=None,\n memory_table=None,\n stop_memory_table=None):\n def add_get_route(route, handler_func):\n if route is not None:\n app.router.add_get(route, handler_func)\n\n add_get_route(index, handler.get_index)\n add_get_route(favicon, handler.get_favicon)\n add_get_route(ray_config, handler.ray_config)\n add_get_route(node_info, handler.node_info)\n add_get_route(raylet_info, handler.raylet_info)\n add_get_route(tune_info, handler.tune_info)\n add_get_route(tune_availability, handler.tune_availability)\n add_get_route(launch_profiling, handler.launch_profiling)\n add_get_route(check_profiling_status, handler.check_profiling_status)\n add_get_route(get_profiling_info, handler.get_profiling_info)\n add_get_route(kill_actor, handler.kill_actor)\n add_get_route(logs, handler.logs)\n add_get_route(errors, handler.errors)\n add_get_route(memory_table, handler.memory_table_info)\n add_get_route(stop_memory_table, handler.stop_collecting_memory_table_info)\n\n\nclass Dashboard:\n \"\"\"A dashboard process for monitoring Ray nodes.\n\n This dashboard is made up of a REST API which collates data published by\n Reporter processes on nodes into a json structure, and a webserver\n which polls said API for display purposes.\n\n Args:\n host(str): Host address of dashboard aiohttp server.\n port(str): Port number of dashboard aiohttp server.\n redis_address(str): GCS address of a Ray cluster\n temp_dir (str): The temporary directory used for log files and\n information for this Ray session.\n redis_passord(str): Redis password to access GCS\n metrics_export_address(str): The address users host their dashboard.\n \"\"\"\n\n def __init__(self,\n host,\n port,\n redis_address,\n temp_dir,\n redis_password=None,\n metrics_export_address=None):\n self.host = host\n self.port = port\n self.redis_client = ray.services.create_redis_client(\n redis_address, password=redis_password)\n self.temp_dir = temp_dir\n self.dashboard_id = str(uuid.uuid4())\n self.dashboard_controller = DashboardController(\n redis_address, redis_password)\n\n # Setting the environment variable RAY_DASHBOARD_DEV=1 disables some\n # security checks in the dashboard server to ease development while\n # using the React dev server. Specifically, when this option is set, we\n # allow cross-origin requests to be made.\n self.is_dev = os.environ.get(\"RAY_DASHBOARD_DEV\") == \"1\"\n\n self.app = aiohttp.web.Application()\n route_handler = DashboardRouteHandler(\n self.dashboard_controller, is_dev=self.is_dev)\n\n # Setup Metrics exporting service if necessary.\n self.metrics_export_address = metrics_export_address\n if self.metrics_export_address:\n self._setup_metrics_export()\n\n # Setup Dashboard Routes\n build_dir = setup_static_dir(self.app)\n setup_speedscope_dir(self.app, build_dir)\n setup_dashboard_route(\n self.app,\n route_handler,\n index=\"/\",\n favicon=\"/favicon.ico\",\n ray_config=\"/api/ray_config\",\n node_info=\"/api/node_info\",\n raylet_info=\"/api/raylet_info\",\n tune_info=\"/api/tune_info\",\n tune_availability=\"/api/tune_availability\",\n launch_profiling=\"/api/launch_profiling\",\n check_profiling_status=\"/api/check_profiling_status\",\n get_profiling_info=\"/api/get_profiling_info\",\n kill_actor=\"/api/kill_actor\",\n logs=\"/api/logs\",\n errors=\"/api/errors\",\n memory_table=\"/api/memory_table\",\n stop_memory_table=\"/api/stop_memory_table\")\n self.app.router.add_get(\"/{_}\", route_handler.get_forbidden)\n self.app.router.add_post(\"/api/set_tune_experiment\",\n route_handler.set_tune_experiment)\n self.app.router.add_post(\"/api/enable_tune_tensorboard\",\n route_handler.enable_tune_tensorboard)\n\n def _setup_metrics_export(self):\n exporter = Exporter(self.dashboard_id, self.metrics_export_address,\n self.dashboard_controller)\n self.metrics_export_client = MetricsExportClient(\n self.metrics_export_address, self.dashboard_controller,\n self.dashboard_id, exporter)\n\n # Setup endpoints\n metrics_export_handler = MetricsExportHandler(\n self.dashboard_controller,\n self.metrics_export_client,\n self.dashboard_id,\n is_dev=self.is_dev)\n setup_metrics_export_routes(self.app, metrics_export_handler)\n\n def _start_exporting_metrics(self):\n result, error = self.metrics_export_client.start_exporting_metrics()\n if not result and error:\n url = ray.services.get_webui_url_from_redis(self.redis_client)\n error += (\" Please reenable the metrics export by going to \"\n \"the url: {}/api/metrics/enable\".format(url))\n ray.utils.push_error_to_driver_through_redis(\n self.redis_client, \"metrics export failed\", error)\n\n def log_dashboard_url(self):\n url = ray.services.get_webui_url_from_redis(self.redis_client)\n if url is None:\n raise ValueError(\"WebUI URL is not present in GCS.\")\n with open(os.path.join(self.temp_dir, \"dashboard_url\"), \"w\") as f:\n f.write(url)\n logger.info(\"Dashboard running on {}\".format(url))\n\n def run(self):\n self.log_dashboard_url()\n self.dashboard_controller.start_collecting_metrics()\n if self.metrics_export_address:\n self._start_exporting_metrics()\n aiohttp.web.run_app(self.app, host=self.host, port=self.port)\n\n\nclass RayletStats(threading.Thread):\n def __init__(self, redis_address, redis_password=None):\n self.nodes_lock = threading.Lock()\n self.nodes = []\n self.stubs = {}\n self.reporter_stubs = {}\n self.redis_client = ray.services.create_redis_client(\n redis_address, password=redis_password)\n\n self._raylet_stats_lock = threading.Lock()\n self._raylet_stats = {}\n self._profiling_stats = {}\n\n self._update_nodes()\n self.include_memory_info = False\n\n super().__init__()\n\n def _update_nodes(self):\n with self.nodes_lock:\n self.nodes = ray.nodes()\n node_ids = [node[\"NodeID\"] for node in self.nodes]\n\n # First remove node connections of disconnected nodes.\n for node_id in self.stubs.keys():\n if node_id not in node_ids:\n stub = self.stubs.pop(node_id)\n stub.close()\n reporter_stub = self.reporter_stubs.pop(node_id)\n reporter_stub.close()\n\n # Now add node connections of new nodes.\n for node in self.nodes:\n node_id = node[\"NodeID\"]\n if node_id not in self.stubs:\n node_ip = node[\"NodeManagerAddress\"]\n channel = grpc.insecure_channel(\"{}:{}\".format(\n node_ip, node[\"NodeManagerPort\"]))\n stub = node_manager_pb2_grpc.NodeManagerServiceStub(\n channel)\n self.stubs[node_id] = stub\n # Block wait until the reporter for the node starts.\n while True:\n reporter_port = self.redis_client.get(\n \"REPORTER_PORT:{}\".format(node_ip))\n if reporter_port:\n break\n reporter_channel = grpc.insecure_channel(\"{}:{}\".format(\n node_ip, int(reporter_port)))\n reporter_stub = reporter_pb2_grpc.ReporterServiceStub(\n reporter_channel)\n self.reporter_stubs[node_id] = reporter_stub\n\n assert len(self.stubs) == len(\n self.reporter_stubs), (self.stubs.keys(),\n self.reporter_stubs.keys())\n\n def get_raylet_stats(self):\n with self._raylet_stats_lock:\n return copy.deepcopy(self._raylet_stats)\n\n def launch_profiling(self, node_id, pid, duration):\n profiling_id = str(uuid.uuid4())\n\n def _callback(reply_future):\n reply = reply_future.result()\n with self._raylet_stats_lock:\n self._profiling_stats[profiling_id] = reply\n\n reporter_stub = self.reporter_stubs[node_id]\n reply_future = reporter_stub.GetProfilingStats.future(\n reporter_pb2.GetProfilingStatsRequest(pid=pid, duration=duration))\n reply_future.add_done_callback(_callback)\n return profiling_id\n\n def check_profiling_status(self, profiling_id):\n with self._raylet_stats_lock:\n is_present = profiling_id in self._profiling_stats\n if not is_present:\n return {\"status\": \"pending\"}\n\n reply = self._profiling_stats[profiling_id]\n if reply.stderr:\n return {\"status\": \"error\", \"error\": reply.stderr}\n else:\n return {\"status\": \"finished\"}\n\n def get_profiling_info(self, profiling_id):\n with self._raylet_stats_lock:\n profiling_stats = self._profiling_stats.get(profiling_id)\n assert profiling_stats, \"profiling not finished\"\n return json.loads(profiling_stats.profiling_stats)\n\n def kill_actor(self, actor_id, ip_address, port):\n channel = grpc.insecure_channel(\"{}:{}\".format(ip_address, int(port)))\n stub = core_worker_pb2_grpc.CoreWorkerServiceStub(channel)\n\n def _callback(reply_future):\n _ = reply_future.result()\n\n reply_future = stub.KillActor.future(\n core_worker_pb2.KillActorRequest(\n intended_actor_id=ray.utils.hex_to_binary(actor_id)))\n reply_future.add_done_callback(_callback)\n return {}\n\n def run(self):\n counter = 0\n while True:\n time.sleep(1.0)\n replies = {}\n try:\n for node in self.nodes:\n node_id = node[\"NodeID\"]\n stub = self.stubs[node_id]\n reply = stub.GetNodeStats(\n node_manager_pb2.GetNodeStatsRequest(\n include_memory_info=self.include_memory_info),\n timeout=2)\n reply_dict = MessageToDict(reply)\n reply_dict[\"nodeId\"] = node_id\n replies[node[\"NodeManagerAddress\"]] = reply_dict\n with self._raylet_stats_lock:\n for address, reply_dict in replies.items():\n self._raylet_stats[address] = reply_dict\n except Exception:\n logger.exception(traceback.format_exc())\n finally:\n counter += 1\n # From time to time, check if new nodes have joined the cluster\n # and update self.nodes\n if counter % 10:\n self._update_nodes()\n\n\nclass TuneCollector(threading.Thread):\n \"\"\"Initialize collector worker thread.\n Args\n logdir (str): Directory path to save the status information of\n jobs and trials.\n reload_interval (float): Interval(in s) of space between loading\n data from logs\n \"\"\"\n\n def __init__(self, reload_interval):\n self._logdir = None\n self._trial_records = {}\n self._data_lock = threading.Lock()\n self._reload_interval = reload_interval\n self._trials_available = False\n self._tensor_board_dir = \"\"\n self._enable_tensor_board = False\n self._errors = {}\n\n super().__init__()\n\n def get_stats(self):\n with self._data_lock:\n tensor_board_info = {\n \"tensorboard_current\": self._logdir == self._tensor_board_dir,\n \"tensorboard_enabled\": self._tensor_board_dir != \"\"\n }\n return {\n \"trial_records\": copy.deepcopy(self._trial_records),\n \"errors\": copy.deepcopy(self._errors),\n \"tensorboard\": tensor_board_info\n }\n\n def set_experiment(self, experiment):\n with self._data_lock:\n if os.path.isdir(os.path.expanduser(experiment)):\n self._logdir = os.path.expanduser(experiment)\n return None, {\"experiment\": self._logdir}\n else:\n return \"Not a Valid Directory\", None\n\n def enable_tensorboard(self):\n with self._data_lock:\n if not self._tensor_board_dir:\n tb = program.TensorBoard()\n tb.configure(argv=[None, \"--logdir\", str(self._logdir)])\n tb.launch()\n self._tensor_board_dir = self._logdir\n\n def get_availability(self):\n with self._data_lock:\n return {\n \"available\": True,\n \"trials_available\": self._trials_available\n }\n\n def run(self):\n while True:\n with self._data_lock:\n self.collect()\n time.sleep(self._reload_interval)\n\n def collect_errors(self, df):\n sub_dirs = os.listdir(self._logdir)\n trial_names = filter(\n lambda d: os.path.isdir(os.path.join(self._logdir, d)), sub_dirs)\n for trial in trial_names:\n error_path = os.path.join(self._logdir, trial, \"error.txt\")\n if os.path.isfile(error_path):\n self._trials_available = True\n with open(error_path) as f:\n text = f.read()\n self._errors[str(trial)] = {\n \"text\": text,\n \"job_id\": os.path.basename(self._logdir),\n \"trial_id\": \"No Trial ID\"\n }\n other_data = df[df[\"logdir\"].str.contains(trial)]\n if len(other_data) > 0:\n trial_id = other_data[\"trial_id\"].values[0]\n self._errors[str(trial)][\"trial_id\"] = str(trial_id)\n if str(trial_id) in self._trial_records.keys():\n self._trial_records[str(trial_id)][\"error\"] = text\n self._trial_records[str(trial_id)][\n \"status\"] = \"ERROR\"\n\n def collect(self):\n \"\"\"\n Collects and cleans data on the running Tune experiment from the\n Tune logs so that users can see this information in the front-end\n client\n \"\"\"\n self._trial_records = {}\n self._errors = {}\n if not self._logdir:\n return\n\n # search through all the sub_directories in log directory\n analysis = Analysis(str(self._logdir))\n df = analysis.dataframe()\n\n if len(df) == 0 or \"trial_id\" not in df.columns:\n return\n\n self._trials_available = True\n\n # make sure that data will convert to JSON without error\n df[\"trial_id_key\"] = df[\"trial_id\"].astype(str)\n df = df.fillna(0)\n\n trial_ids = df[\"trial_id\"]\n for i, value in df[\"trial_id\"].iteritems():\n if type(value) != str and type(value) != int:\n trial_ids[i] = int(value)\n\n df[\"trial_id\"] = trial_ids\n\n # convert df to python dict\n df = df.set_index(\"trial_id_key\")\n trial_data = df.to_dict(orient=\"index\")\n\n # clean data and update class attribute\n if len(trial_data) > 0:\n trial_data = self.clean_trials(trial_data)\n self._trial_records.update(trial_data)\n\n self.collect_errors(df)\n\n def clean_trials(self, trial_details):\n first_trial = trial_details[list(trial_details.keys())[0]]\n config_keys = []\n float_keys = []\n metric_keys = []\n\n # list of static attributes for trial\n default_names = [\n \"logdir\", \"time_this_iter_s\", \"done\", \"episodes_total\",\n \"training_iteration\", \"timestamp\", \"timesteps_total\",\n \"experiment_id\", \"date\", \"timestamp\", \"time_total_s\", \"pid\",\n \"hostname\", \"node_ip\", \"time_since_restore\",\n \"timesteps_since_restore\", \"iterations_since_restore\",\n \"experiment_tag\", \"trial_id\"\n ]\n\n # filter attributes into floats, metrics, and config variables\n for key, value in first_trial.items():\n if isinstance(value, float):\n float_keys.append(key)\n if str(key).startswith(\"config/\"):\n config_keys.append(key)\n elif key not in default_names:\n metric_keys.append(key)\n\n # clean data into a form that front-end client can handle\n for trial, details in trial_details.items():\n ts = os.path.getctime(details[\"logdir\"])\n formatted_time = datetime.datetime.fromtimestamp(ts).strftime(\n \"%Y-%m-%d %H:%M:%S\")\n details[\"start_time\"] = formatted_time\n details[\"params\"] = {}\n details[\"metrics\"] = {}\n\n # round all floats\n for key in float_keys:\n details[key] = round(details[key], 3)\n\n # group together config attributes\n for key in config_keys:\n new_name = key[7:]\n details[\"params\"][new_name] = details[key]\n details.pop(key)\n\n # group together metric attributes\n for key in metric_keys:\n details[\"metrics\"][key] = details[key]\n details.pop(key)\n\n if details[\"done\"]:\n details[\"status\"] = \"TERMINATED\"\n else:\n details[\"status\"] = \"RUNNING\"\n details.pop(\"done\")\n\n details[\"job_id\"] = os.path.basename(self._logdir)\n details[\"error\"] = \"No Error\"\n\n return trial_details\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(\n description=(\"Parse Redis server for the \"\n \"dashboard to connect to.\"))\n parser.add_argument(\n \"--host\",\n required=True,\n type=str,\n help=\"The host to use for the HTTP server.\")\n parser.add_argument(\n \"--port\",\n required=True,\n type=int,\n help=\"The port to use for the HTTP server.\")\n parser.add_argument(\n \"--redis-address\",\n required=True,\n type=str,\n help=\"The address to use for Redis.\")\n parser.add_argument(\n \"--redis-password\",\n required=False,\n type=str,\n default=None,\n help=\"the password to use for Redis\")\n parser.add_argument(\n \"--logging-level\",\n required=False,\n type=str,\n default=ray_constants.LOGGER_LEVEL,\n choices=ray_constants.LOGGER_LEVEL_CHOICES,\n help=ray_constants.LOGGER_LEVEL_HELP)\n parser.add_argument(\n \"--logging-format\",\n required=False,\n type=str,\n default=ray_constants.LOGGER_FORMAT,\n help=ray_constants.LOGGER_FORMAT_HELP)\n parser.add_argument(\n \"--temp-dir\",\n required=False,\n type=str,\n default=None,\n help=\"Specify the path of the temporary directory use by Ray process.\")\n args = parser.parse_args()\n ray.utils.setup_logger(args.logging_level, args.logging_format)\n\n # TODO(sang): Add a URL validation.\n metrics_export_address = os.environ.get(\"METRICS_EXPORT_ADDRESS\")\n\n try:\n dashboard = Dashboard(\n args.host,\n args.port,\n args.redis_address,\n args.temp_dir,\n redis_password=args.redis_password,\n metrics_export_address=metrics_export_address)\n dashboard.run()\n except Exception as e:\n # Something went wrong, so push an error to all drivers.\n redis_client = ray.services.create_redis_client(\n args.redis_address, password=args.redis_password)\n traceback_str = ray.utils.format_error_message(traceback.format_exc())\n message = (\"The dashboard on node {} failed with the following \"\n \"error:\\n{}\".format(socket.gethostname(), traceback_str))\n ray.utils.push_error_to_driver_through_redis(\n redis_client, ray_constants.DASHBOARD_DIED_ERROR, message)\n if isinstance(e, OSError) and e.errno == errno.ENOENT:\n logger.warning(message)\n else:\n raise e\n",
"path": "python/ray/dashboard/dashboard.py"
}
] | [
{
"content": "try:\n import aiohttp.web\nexcept ImportError:\n print(\"The dashboard requires aiohttp to run.\")\n import sys\n sys.exit(1)\n\nimport argparse\nimport copy\nimport datetime\nimport errno\nimport json\nimport logging\nimport os\nimport socket\nimport threading\nimport time\nimport traceback\nimport yaml\nimport uuid\nimport grpc\nfrom google.protobuf.json_format import MessageToDict\nimport ray\nimport ray.ray_constants as ray_constants\n\nfrom ray.core.generated import node_manager_pb2\nfrom ray.core.generated import node_manager_pb2_grpc\nfrom ray.core.generated import reporter_pb2\nfrom ray.core.generated import reporter_pb2_grpc\nfrom ray.core.generated import core_worker_pb2\nfrom ray.core.generated import core_worker_pb2_grpc\nfrom ray.dashboard.interface import BaseDashboardController\nfrom ray.dashboard.interface import BaseDashboardRouteHandler\nfrom ray.dashboard.memory import construct_memory_table, MemoryTable\nfrom ray.dashboard.metrics_exporter.client import Exporter\nfrom ray.dashboard.metrics_exporter.client import MetricsExportClient\nfrom ray.dashboard.node_stats import NodeStats\nfrom ray.dashboard.util import to_unix_time, measures_to_dict, format_resource\n\ntry:\n from ray.tune import Analysis\n from tensorboard import program\nexcept ImportError:\n Analysis = None\n\n# Logger for this module. It should be configured at the entry point\n# into the program using Ray. Ray provides a default configuration at\n# entry/init points.\nlogger = logging.getLogger(__name__)\n\n\nasync def json_response(is_dev, result=None, error=None,\n ts=None) -> aiohttp.web.Response:\n if ts is None:\n ts = datetime.datetime.utcnow()\n\n headers = None\n if is_dev:\n headers = {\"Access-Control-Allow-Origin\": \"*\"}\n\n return aiohttp.web.json_response(\n {\n \"result\": result,\n \"timestamp\": to_unix_time(ts),\n \"error\": error,\n },\n headers=headers)\n\n\nclass DashboardController(BaseDashboardController):\n def __init__(self, redis_address, redis_password):\n self.node_stats = NodeStats(redis_address, redis_password)\n self.raylet_stats = RayletStats(\n redis_address, redis_password=redis_password)\n if Analysis is not None:\n self.tune_stats = TuneCollector(2.0)\n self.memory_table = MemoryTable([])\n\n def _construct_raylet_info(self):\n D = self.raylet_stats.get_raylet_stats()\n workers_info_by_node = {\n data[\"nodeId\"]: data.get(\"workersStats\")\n for data in D.values()\n }\n\n infeasible_tasks = sum(\n (data.get(\"infeasibleTasks\", []) for data in D.values()), [])\n # ready_tasks are used to render tasks that are not schedulable\n # due to resource limitations.\n # (e.g., Actor requires 2 GPUs but there is only 1 gpu available).\n ready_tasks = sum((data.get(\"readyTasks\", []) for data in D.values()),\n [])\n actor_tree = self.node_stats.get_actor_tree(\n workers_info_by_node, infeasible_tasks, ready_tasks)\n\n for address, data in D.items():\n # process view data\n measures_dicts = {}\n for view_data in data[\"viewData\"]:\n view_name = view_data[\"viewName\"]\n if view_name in (\"local_available_resource\",\n \"local_total_resource\",\n \"object_manager_stats\"):\n measures_dicts[view_name] = measures_to_dict(\n view_data[\"measures\"])\n # process resources info\n extra_info_strings = []\n prefix = \"ResourceName:\"\n for resource_name, total_resource in measures_dicts[\n \"local_total_resource\"].items():\n available_resource = measures_dicts[\n \"local_available_resource\"].get(resource_name, .0)\n resource_name = resource_name[len(prefix):]\n extra_info_strings.append(\"{}: {} / {}\".format(\n resource_name,\n format_resource(resource_name,\n total_resource - available_resource),\n format_resource(resource_name, total_resource)))\n data[\"extraInfo\"] = \", \".join(extra_info_strings) + \"\\n\"\n if os.environ.get(\"RAY_DASHBOARD_DEBUG\"):\n # process object store info\n extra_info_strings = []\n prefix = \"ValueType:\"\n for stats_name in [\n \"used_object_store_memory\", \"num_local_objects\"\n ]:\n stats_value = measures_dicts[\"object_manager_stats\"].get(\n prefix + stats_name, .0)\n extra_info_strings.append(\"{}: {}\".format(\n stats_name, stats_value))\n data[\"extraInfo\"] += \", \".join(extra_info_strings)\n # process actor info\n actor_tree_str = json.dumps(\n actor_tree, indent=2, sort_keys=True)\n lines = actor_tree_str.split(\"\\n\")\n max_line_length = max(map(len, lines))\n to_print = []\n for line in lines:\n to_print.append(line + (max_line_length - len(line)) * \" \")\n data[\"extraInfo\"] += \"\\n\" + \"\\n\".join(to_print)\n return {\"nodes\": D, \"actors\": actor_tree}\n\n def get_ray_config(self):\n try:\n config_path = os.path.expanduser(\"~/ray_bootstrap_config.yaml\")\n with open(config_path) as f:\n cfg = yaml.safe_load(f)\n except Exception:\n error = \"No config\"\n return error, None\n\n D = {\n \"min_workers\": cfg[\"min_workers\"],\n \"max_workers\": cfg[\"max_workers\"],\n \"initial_workers\": cfg[\"initial_workers\"],\n \"autoscaling_mode\": cfg[\"autoscaling_mode\"],\n \"idle_timeout_minutes\": cfg[\"idle_timeout_minutes\"],\n }\n\n try:\n D[\"head_type\"] = cfg[\"head_node\"][\"InstanceType\"]\n except KeyError:\n D[\"head_type\"] = \"unknown\"\n\n try:\n D[\"worker_type\"] = cfg[\"worker_nodes\"][\"InstanceType\"]\n except KeyError:\n D[\"worker_type\"] = \"unknown\"\n\n return None, D\n\n def get_node_info(self):\n return self.node_stats.get_node_stats()\n\n def get_raylet_info(self):\n return self._construct_raylet_info()\n\n def get_memory_table_info(self) -> MemoryTable:\n # Collecting memory info adds big overhead to the cluster.\n # This must be collected only when it is necessary.\n self.raylet_stats.include_memory_info = True\n D = self.raylet_stats.get_raylet_stats()\n workers_info_by_node = {\n data[\"nodeId\"]: data.get(\"workersStats\")\n for data in D.values()\n }\n self.memory_table = construct_memory_table(workers_info_by_node)\n return self.memory_table\n\n def stop_collecting_memory_table_info(self):\n self.raylet_stats.include_memory_info = False\n\n def tune_info(self):\n if Analysis is not None:\n D = self.tune_stats.get_stats()\n else:\n D = {}\n return D\n\n def tune_availability(self):\n if Analysis is not None:\n D = self.tune_stats.get_availability()\n else:\n D = {\"available\": False, \"trials_available\": False}\n return D\n\n def set_tune_experiment(self, experiment):\n if Analysis is not None:\n return self.tune_stats.set_experiment(experiment)\n return \"Tune Not Enabled\", None\n\n def enable_tune_tensorboard(self):\n if Analysis is not None:\n self.tune_stats.enable_tensorboard()\n\n def launch_profiling(self, node_id, pid, duration):\n profiling_id = self.raylet_stats.launch_profiling(\n node_id=node_id, pid=pid, duration=duration)\n return profiling_id\n\n def check_profiling_status(self, profiling_id):\n return self.raylet_stats.check_profiling_status(profiling_id)\n\n def get_profiling_info(self, profiling_id):\n return self.raylet_stats.get_profiling_info(profiling_id)\n\n def kill_actor(self, actor_id, ip_address, port):\n return self.raylet_stats.kill_actor(actor_id, ip_address, port)\n\n def get_logs(self, hostname, pid):\n return self.node_stats.get_logs(hostname, pid)\n\n def get_errors(self, hostname, pid):\n return self.node_stats.get_errors(hostname, pid)\n\n def start_collecting_metrics(self):\n self.node_stats.start()\n self.raylet_stats.start()\n if Analysis is not None:\n self.tune_stats.start()\n\n\nclass DashboardRouteHandler(BaseDashboardRouteHandler):\n def __init__(self, dashboard_controller: DashboardController,\n is_dev=False):\n self.dashboard_controller = dashboard_controller\n self.is_dev = is_dev\n\n def forbidden(self) -> aiohttp.web.Response:\n return aiohttp.web.Response(status=403, text=\"403 Forbidden\")\n\n async def get_forbidden(self, _) -> aiohttp.web.Response:\n return self.forbidden()\n\n async def get_index(self, req) -> aiohttp.web.Response:\n return aiohttp.web.FileResponse(\n os.path.join(\n os.path.dirname(os.path.abspath(__file__)),\n \"client/build/index.html\"))\n\n async def get_favicon(self, req) -> aiohttp.web.Response:\n return aiohttp.web.FileResponse(\n os.path.join(\n os.path.dirname(os.path.abspath(__file__)),\n \"client/build/favicon.ico\"))\n\n async def ray_config(self, req) -> aiohttp.web.Response:\n error, result = self.dashboard_controller.get_ray_config()\n if error:\n return await json_response(self.is_dev, error=error)\n return await json_response(self.is_dev, result=result)\n\n async def node_info(self, req) -> aiohttp.web.Response:\n now = datetime.datetime.utcnow()\n D = self.dashboard_controller.get_node_info()\n return await json_response(self.is_dev, result=D, ts=now)\n\n async def raylet_info(self, req) -> aiohttp.web.Response:\n result = self.dashboard_controller.get_raylet_info()\n return await json_response(self.is_dev, result=result)\n\n async def memory_table_info(self, req) -> aiohttp.web.Response:\n memory_table = self.dashboard_controller.get_memory_table_info()\n return await json_response(self.is_dev, result=memory_table.__dict__())\n\n async def stop_collecting_memory_table_info(self,\n req) -> aiohttp.web.Response:\n self.dashboard_controller.stop_collecting_memory_table_info()\n return await json_response(self.is_dev, result={})\n\n async def tune_info(self, req) -> aiohttp.web.Response:\n result = self.dashboard_controller.tune_info()\n return await json_response(self.is_dev, result=result)\n\n async def tune_availability(self, req) -> aiohttp.web.Response:\n result = self.dashboard_controller.tune_availability()\n return await json_response(self.is_dev, result=result)\n\n async def set_tune_experiment(self, req) -> aiohttp.web.Response:\n data = await req.json()\n error, result = self.dashboard_controller.set_tune_experiment(\n data[\"experiment\"])\n if error:\n return await json_response(self.is_dev, error=error)\n return await json_response(self.is_dev, result=result)\n\n async def enable_tune_tensorboard(self, req) -> aiohttp.web.Response:\n self.dashboard_controller.enable_tune_tensorboard()\n return await json_response(self.is_dev, result={})\n\n async def launch_profiling(self, req) -> aiohttp.web.Response:\n node_id = req.query.get(\"node_id\")\n pid = int(req.query.get(\"pid\"))\n duration = int(req.query.get(\"duration\"))\n profiling_id = self.dashboard_controller.launch_profiling(\n node_id, pid, duration)\n return await json_response(self.is_dev, result=str(profiling_id))\n\n async def check_profiling_status(self, req) -> aiohttp.web.Response:\n profiling_id = req.query.get(\"profiling_id\")\n status = self.dashboard_controller.check_profiling_status(profiling_id)\n return await json_response(self.is_dev, result=status)\n\n async def get_profiling_info(self, req) -> aiohttp.web.Response:\n profiling_id = req.query.get(\"profiling_id\")\n profiling_info = self.dashboard_controller.get_profiling_info(\n profiling_id)\n return aiohttp.web.json_response(profiling_info)\n\n async def kill_actor(self, req) -> aiohttp.web.Response:\n actor_id = req.query.get(\"actor_id\")\n ip_address = req.query.get(\"ip_address\")\n port = req.query.get(\"port\")\n return await json_response(\n self.is_dev,\n self.dashboard_controller.kill_actor(actor_id, ip_address, port))\n\n async def logs(self, req) -> aiohttp.web.Response:\n hostname = req.query.get(\"hostname\")\n pid = req.query.get(\"pid\")\n result = self.dashboard_controller.get_logs(hostname, pid)\n return await json_response(self.is_dev, result=result)\n\n async def errors(self, req) -> aiohttp.web.Response:\n hostname = req.query.get(\"hostname\")\n pid = req.query.get(\"pid\")\n result = self.dashboard_controller.get_errors(hostname, pid)\n return await json_response(self.is_dev, result=result)\n\n\nclass MetricsExportHandler:\n def __init__(self,\n dashboard_controller: DashboardController,\n metrics_export_client: MetricsExportClient,\n dashboard_id,\n is_dev=False):\n assert metrics_export_client is not None\n self.metrics_export_client = metrics_export_client\n self.dashboard_controller = dashboard_controller\n self.is_dev = is_dev\n\n async def enable_export_metrics(self, req) -> aiohttp.web.Response:\n if self.metrics_export_client.enabled:\n return await json_response(\n self.is_dev, result={\"url\": None}, error=\"Already enabled\")\n\n succeed, error = self.metrics_export_client.start_exporting_metrics()\n error_msg = \"Failed to enable it. Error: {}\".format(error)\n if not succeed:\n return await json_response(\n self.is_dev, result={\"url\": None}, error=error_msg)\n\n url = self.metrics_export_client.dashboard_url\n return await json_response(self.is_dev, result={\"url\": url})\n\n async def get_dashboard_address(self, req) -> aiohttp.web.Response:\n if not self.metrics_export_client.enabled:\n return await json_response(\n self.is_dev,\n result={\"url\": None},\n error=\"Metrics exporting is not enabled.\")\n\n url = self.metrics_export_client.dashboard_url\n return await json_response(self.is_dev, result={\"url\": url})\n\n async def redirect_to_dashboard(self, req) -> aiohttp.web.Response:\n if not self.metrics_export_client.enabled:\n return await json_response(\n self.is_dev,\n result={\"url\": None},\n error=\"You should enable metrics export to use this endpoint.\")\n\n raise aiohttp.web.HTTPFound(self.metrics_export_client.dashboard_url)\n\n\ndef setup_metrics_export_routes(app: aiohttp.web.Application,\n handler: MetricsExportHandler):\n \"\"\"Routes that require dynamically changing class attributes.\"\"\"\n app.router.add_get(\"/api/metrics/enable\", handler.enable_export_metrics)\n app.router.add_get(\"/api/metrics/url\", handler.get_dashboard_address)\n app.router.add_get(\"/metrics/redirect\", handler.redirect_to_dashboard)\n\n\ndef setup_static_dir(app):\n build_dir = os.path.join(\n os.path.dirname(os.path.abspath(__file__)), \"client/build\")\n if not os.path.isdir(build_dir):\n raise OSError(\n errno.ENOENT, \"Dashboard build directory not found. If installing \"\n \"from source, please follow the additional steps \"\n \"required to build the dashboard\"\n \"(cd python/ray/dashboard/client \"\n \"&& npm ci \"\n \"&& npm run build)\", build_dir)\n\n static_dir = os.path.join(build_dir, \"static\")\n app.router.add_static(\"/static\", static_dir)\n return build_dir\n\n\ndef setup_speedscope_dir(app, build_dir):\n speedscope_dir = os.path.join(build_dir, \"speedscope-1.5.3\")\n app.router.add_static(\"/speedscope\", speedscope_dir)\n\n\ndef setup_dashboard_route(app: aiohttp.web.Application,\n handler: BaseDashboardRouteHandler,\n index=None,\n favicon=None,\n ray_config=None,\n node_info=None,\n raylet_info=None,\n tune_info=None,\n tune_availability=None,\n launch_profiling=None,\n check_profiling_status=None,\n get_profiling_info=None,\n kill_actor=None,\n logs=None,\n errors=None,\n memory_table=None,\n stop_memory_table=None):\n def add_get_route(route, handler_func):\n if route is not None:\n app.router.add_get(route, handler_func)\n\n add_get_route(index, handler.get_index)\n add_get_route(favicon, handler.get_favicon)\n add_get_route(ray_config, handler.ray_config)\n add_get_route(node_info, handler.node_info)\n add_get_route(raylet_info, handler.raylet_info)\n add_get_route(tune_info, handler.tune_info)\n add_get_route(tune_availability, handler.tune_availability)\n add_get_route(launch_profiling, handler.launch_profiling)\n add_get_route(check_profiling_status, handler.check_profiling_status)\n add_get_route(get_profiling_info, handler.get_profiling_info)\n add_get_route(kill_actor, handler.kill_actor)\n add_get_route(logs, handler.logs)\n add_get_route(errors, handler.errors)\n add_get_route(memory_table, handler.memory_table_info)\n add_get_route(stop_memory_table, handler.stop_collecting_memory_table_info)\n\n\nclass Dashboard:\n \"\"\"A dashboard process for monitoring Ray nodes.\n\n This dashboard is made up of a REST API which collates data published by\n Reporter processes on nodes into a json structure, and a webserver\n which polls said API for display purposes.\n\n Args:\n host(str): Host address of dashboard aiohttp server.\n port(str): Port number of dashboard aiohttp server.\n redis_address(str): GCS address of a Ray cluster\n temp_dir (str): The temporary directory used for log files and\n information for this Ray session.\n redis_passord(str): Redis password to access GCS\n metrics_export_address(str): The address users host their dashboard.\n \"\"\"\n\n def __init__(self,\n host,\n port,\n redis_address,\n temp_dir,\n redis_password=None,\n metrics_export_address=None):\n self.host = host\n self.port = port\n self.redis_client = ray.services.create_redis_client(\n redis_address, password=redis_password)\n self.temp_dir = temp_dir\n self.dashboard_id = str(uuid.uuid4())\n self.dashboard_controller = DashboardController(\n redis_address, redis_password)\n\n # Setting the environment variable RAY_DASHBOARD_DEV=1 disables some\n # security checks in the dashboard server to ease development while\n # using the React dev server. Specifically, when this option is set, we\n # allow cross-origin requests to be made.\n self.is_dev = os.environ.get(\"RAY_DASHBOARD_DEV\") == \"1\"\n\n self.app = aiohttp.web.Application()\n route_handler = DashboardRouteHandler(\n self.dashboard_controller, is_dev=self.is_dev)\n\n # Setup Metrics exporting service if necessary.\n self.metrics_export_address = metrics_export_address\n if self.metrics_export_address:\n self._setup_metrics_export()\n\n # Setup Dashboard Routes\n build_dir = setup_static_dir(self.app)\n setup_speedscope_dir(self.app, build_dir)\n setup_dashboard_route(\n self.app,\n route_handler,\n index=\"/\",\n favicon=\"/favicon.ico\",\n ray_config=\"/api/ray_config\",\n node_info=\"/api/node_info\",\n raylet_info=\"/api/raylet_info\",\n tune_info=\"/api/tune_info\",\n tune_availability=\"/api/tune_availability\",\n launch_profiling=\"/api/launch_profiling\",\n check_profiling_status=\"/api/check_profiling_status\",\n get_profiling_info=\"/api/get_profiling_info\",\n kill_actor=\"/api/kill_actor\",\n logs=\"/api/logs\",\n errors=\"/api/errors\",\n memory_table=\"/api/memory_table\",\n stop_memory_table=\"/api/stop_memory_table\")\n self.app.router.add_get(\"/{_}\", route_handler.get_forbidden)\n self.app.router.add_post(\"/api/set_tune_experiment\",\n route_handler.set_tune_experiment)\n self.app.router.add_post(\"/api/enable_tune_tensorboard\",\n route_handler.enable_tune_tensorboard)\n\n def _setup_metrics_export(self):\n exporter = Exporter(self.dashboard_id, self.metrics_export_address,\n self.dashboard_controller)\n self.metrics_export_client = MetricsExportClient(\n self.metrics_export_address, self.dashboard_controller,\n self.dashboard_id, exporter)\n\n # Setup endpoints\n metrics_export_handler = MetricsExportHandler(\n self.dashboard_controller,\n self.metrics_export_client,\n self.dashboard_id,\n is_dev=self.is_dev)\n setup_metrics_export_routes(self.app, metrics_export_handler)\n\n def _start_exporting_metrics(self):\n result, error = self.metrics_export_client.start_exporting_metrics()\n if not result and error:\n url = ray.services.get_webui_url_from_redis(self.redis_client)\n error += (\" Please reenable the metrics export by going to \"\n \"the url: {}/api/metrics/enable\".format(url))\n ray.utils.push_error_to_driver_through_redis(\n self.redis_client, \"metrics export failed\", error)\n\n def log_dashboard_url(self):\n url = ray.services.get_webui_url_from_redis(self.redis_client)\n if url is None:\n raise ValueError(\"WebUI URL is not present in GCS.\")\n with open(os.path.join(self.temp_dir, \"dashboard_url\"), \"w\") as f:\n f.write(url)\n logger.info(\"Dashboard running on {}\".format(url))\n\n def run(self):\n self.log_dashboard_url()\n self.dashboard_controller.start_collecting_metrics()\n if self.metrics_export_address:\n self._start_exporting_metrics()\n aiohttp.web.run_app(self.app, host=self.host, port=self.port)\n\n\nclass RayletStats(threading.Thread):\n def __init__(self, redis_address, redis_password=None):\n self.nodes_lock = threading.Lock()\n self.nodes = []\n self.stubs = {}\n self.reporter_stubs = {}\n self.redis_client = ray.services.create_redis_client(\n redis_address, password=redis_password)\n\n self._raylet_stats_lock = threading.Lock()\n self._raylet_stats = {}\n self._profiling_stats = {}\n\n self._update_nodes()\n self.include_memory_info = False\n\n super().__init__()\n\n def _update_nodes(self):\n with self.nodes_lock:\n self.nodes = ray.nodes()\n node_ids = [node[\"NodeID\"] for node in self.nodes]\n\n # First remove node connections of disconnected nodes.\n for node_id in self.stubs.keys():\n if node_id not in node_ids:\n stub = self.stubs.pop(node_id)\n stub.close()\n reporter_stub = self.reporter_stubs.pop(node_id)\n reporter_stub.close()\n\n # Now add node connections of new nodes.\n for node in self.nodes:\n node_id = node[\"NodeID\"]\n if node_id not in self.stubs:\n node_ip = node[\"NodeManagerAddress\"]\n channel = grpc.insecure_channel(\"{}:{}\".format(\n node_ip, node[\"NodeManagerPort\"]))\n stub = node_manager_pb2_grpc.NodeManagerServiceStub(\n channel)\n self.stubs[node_id] = stub\n # Block wait until the reporter for the node starts.\n while True:\n reporter_port = self.redis_client.get(\n \"REPORTER_PORT:{}\".format(node_ip))\n if reporter_port:\n break\n reporter_channel = grpc.insecure_channel(\"{}:{}\".format(\n node_ip, int(reporter_port)))\n reporter_stub = reporter_pb2_grpc.ReporterServiceStub(\n reporter_channel)\n self.reporter_stubs[node_id] = reporter_stub\n\n assert len(self.stubs) == len(\n self.reporter_stubs), (self.stubs.keys(),\n self.reporter_stubs.keys())\n\n def get_raylet_stats(self):\n with self._raylet_stats_lock:\n return copy.deepcopy(self._raylet_stats)\n\n def launch_profiling(self, node_id, pid, duration):\n profiling_id = str(uuid.uuid4())\n\n def _callback(reply_future):\n reply = reply_future.result()\n with self._raylet_stats_lock:\n self._profiling_stats[profiling_id] = reply\n\n reporter_stub = self.reporter_stubs[node_id]\n reply_future = reporter_stub.GetProfilingStats.future(\n reporter_pb2.GetProfilingStatsRequest(pid=pid, duration=duration))\n reply_future.add_done_callback(_callback)\n return profiling_id\n\n def check_profiling_status(self, profiling_id):\n with self._raylet_stats_lock:\n is_present = profiling_id in self._profiling_stats\n if not is_present:\n return {\"status\": \"pending\"}\n\n reply = self._profiling_stats[profiling_id]\n if reply.stderr:\n return {\"status\": \"error\", \"error\": reply.stderr}\n else:\n return {\"status\": \"finished\"}\n\n def get_profiling_info(self, profiling_id):\n with self._raylet_stats_lock:\n profiling_stats = self._profiling_stats.get(profiling_id)\n assert profiling_stats, \"profiling not finished\"\n return json.loads(profiling_stats.profiling_stats)\n\n def kill_actor(self, actor_id, ip_address, port):\n channel = grpc.insecure_channel(\"{}:{}\".format(ip_address, int(port)))\n stub = core_worker_pb2_grpc.CoreWorkerServiceStub(channel)\n\n def _callback(reply_future):\n _ = reply_future.result()\n\n reply_future = stub.KillActor.future(\n core_worker_pb2.KillActorRequest(\n intended_actor_id=ray.utils.hex_to_binary(actor_id)))\n reply_future.add_done_callback(_callback)\n return {}\n\n def run(self):\n counter = 0\n while True:\n time.sleep(1.0)\n replies = {}\n try:\n for node in self.nodes:\n node_id = node[\"NodeID\"]\n stub = self.stubs[node_id]\n reply = stub.GetNodeStats(\n node_manager_pb2.GetNodeStatsRequest(\n include_memory_info=self.include_memory_info),\n timeout=2)\n reply_dict = MessageToDict(reply)\n reply_dict[\"nodeId\"] = node_id\n replies[node[\"NodeManagerAddress\"]] = reply_dict\n with self._raylet_stats_lock:\n for address, reply_dict in replies.items():\n self._raylet_stats[address] = reply_dict\n except Exception:\n logger.exception(traceback.format_exc())\n finally:\n counter += 1\n # From time to time, check if new nodes have joined the cluster\n # and update self.nodes\n if counter % 10:\n self._update_nodes()\n\n\nclass TuneCollector(threading.Thread):\n \"\"\"Initialize collector worker thread.\n Args\n logdir (str): Directory path to save the status information of\n jobs and trials.\n reload_interval (float): Interval(in s) of space between loading\n data from logs\n \"\"\"\n\n def __init__(self, reload_interval):\n self._logdir = None\n self._trial_records = {}\n self._data_lock = threading.Lock()\n self._reload_interval = reload_interval\n self._trials_available = False\n self._tensor_board_dir = \"\"\n self._enable_tensor_board = False\n self._errors = {}\n\n super().__init__()\n\n def get_stats(self):\n with self._data_lock:\n tensor_board_info = {\n \"tensorboard_current\": self._logdir == self._tensor_board_dir,\n \"tensorboard_enabled\": self._tensor_board_dir != \"\"\n }\n return {\n \"trial_records\": copy.deepcopy(self._trial_records),\n \"errors\": copy.deepcopy(self._errors),\n \"tensorboard\": tensor_board_info\n }\n\n def set_experiment(self, experiment):\n with self._data_lock:\n if os.path.isdir(os.path.expanduser(experiment)):\n self._logdir = os.path.expanduser(experiment)\n return None, {\"experiment\": self._logdir}\n else:\n return \"Not a Valid Directory\", None\n\n def enable_tensorboard(self):\n with self._data_lock:\n if not self._tensor_board_dir:\n tb = program.TensorBoard()\n tb.configure(argv=[None, \"--logdir\", str(self._logdir)])\n tb.launch()\n self._tensor_board_dir = self._logdir\n\n def get_availability(self):\n with self._data_lock:\n return {\n \"available\": True,\n \"trials_available\": self._trials_available\n }\n\n def run(self):\n while True:\n with self._data_lock:\n self.collect()\n time.sleep(self._reload_interval)\n\n def collect_errors(self, df):\n sub_dirs = os.listdir(self._logdir)\n trial_names = filter(\n lambda d: os.path.isdir(os.path.join(self._logdir, d)), sub_dirs)\n for trial in trial_names:\n error_path = os.path.join(self._logdir, trial, \"error.txt\")\n if os.path.isfile(error_path):\n self._trials_available = True\n with open(error_path) as f:\n text = f.read()\n self._errors[str(trial)] = {\n \"text\": text,\n \"job_id\": os.path.basename(self._logdir),\n \"trial_id\": \"No Trial ID\"\n }\n other_data = df[df[\"logdir\"].str.contains(trial)]\n if len(other_data) > 0:\n trial_id = other_data[\"trial_id\"].values[0]\n self._errors[str(trial)][\"trial_id\"] = str(trial_id)\n if str(trial_id) in self._trial_records.keys():\n self._trial_records[str(trial_id)][\"error\"] = text\n self._trial_records[str(trial_id)][\n \"status\"] = \"ERROR\"\n\n def collect(self):\n \"\"\"\n Collects and cleans data on the running Tune experiment from the\n Tune logs so that users can see this information in the front-end\n client\n \"\"\"\n self._trial_records = {}\n self._errors = {}\n if not self._logdir:\n return\n\n # search through all the sub_directories in log directory\n analysis = Analysis(str(self._logdir))\n df = analysis.dataframe()\n\n if len(df) == 0 or \"trial_id\" not in df.columns:\n return\n\n self._trials_available = True\n\n # make sure that data will convert to JSON without error\n df[\"trial_id_key\"] = df[\"trial_id\"].astype(str)\n df = df.fillna(0)\n\n trial_ids = df[\"trial_id\"]\n for i, value in df[\"trial_id\"].iteritems():\n if type(value) != str and type(value) != int:\n trial_ids[i] = int(value)\n\n df[\"trial_id\"] = trial_ids\n\n # convert df to python dict\n df = df.set_index(\"trial_id_key\")\n trial_data = df.to_dict(orient=\"index\")\n\n # clean data and update class attribute\n if len(trial_data) > 0:\n trial_data = self.clean_trials(trial_data)\n self._trial_records.update(trial_data)\n\n self.collect_errors(df)\n\n def clean_trials(self, trial_details):\n first_trial = trial_details[list(trial_details.keys())[0]]\n config_keys = []\n float_keys = []\n metric_keys = []\n\n # list of static attributes for trial\n default_names = [\n \"logdir\", \"time_this_iter_s\", \"done\", \"episodes_total\",\n \"training_iteration\", \"timestamp\", \"timesteps_total\",\n \"experiment_id\", \"date\", \"timestamp\", \"time_total_s\", \"pid\",\n \"hostname\", \"node_ip\", \"time_since_restore\",\n \"timesteps_since_restore\", \"iterations_since_restore\",\n \"experiment_tag\", \"trial_id\"\n ]\n\n # filter attributes into floats, metrics, and config variables\n for key, value in first_trial.items():\n if isinstance(value, float):\n float_keys.append(key)\n if str(key).startswith(\"config/\"):\n config_keys.append(key)\n elif key not in default_names:\n metric_keys.append(key)\n\n # clean data into a form that front-end client can handle\n for trial, details in trial_details.items():\n ts = os.path.getctime(details[\"logdir\"])\n formatted_time = datetime.datetime.fromtimestamp(ts).strftime(\n \"%Y-%m-%d %H:%M:%S\")\n details[\"start_time\"] = formatted_time\n details[\"params\"] = {}\n details[\"metrics\"] = {}\n\n # round all floats\n for key in float_keys:\n details[key] = round(details[key], 12)\n\n # group together config attributes\n for key in config_keys:\n new_name = key[7:]\n details[\"params\"][new_name] = details[key]\n details.pop(key)\n\n # group together metric attributes\n for key in metric_keys:\n details[\"metrics\"][key] = details[key]\n details.pop(key)\n\n if details[\"done\"]:\n details[\"status\"] = \"TERMINATED\"\n else:\n details[\"status\"] = \"RUNNING\"\n details.pop(\"done\")\n\n details[\"job_id\"] = os.path.basename(self._logdir)\n details[\"error\"] = \"No Error\"\n\n return trial_details\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(\n description=(\"Parse Redis server for the \"\n \"dashboard to connect to.\"))\n parser.add_argument(\n \"--host\",\n required=True,\n type=str,\n help=\"The host to use for the HTTP server.\")\n parser.add_argument(\n \"--port\",\n required=True,\n type=int,\n help=\"The port to use for the HTTP server.\")\n parser.add_argument(\n \"--redis-address\",\n required=True,\n type=str,\n help=\"The address to use for Redis.\")\n parser.add_argument(\n \"--redis-password\",\n required=False,\n type=str,\n default=None,\n help=\"the password to use for Redis\")\n parser.add_argument(\n \"--logging-level\",\n required=False,\n type=str,\n default=ray_constants.LOGGER_LEVEL,\n choices=ray_constants.LOGGER_LEVEL_CHOICES,\n help=ray_constants.LOGGER_LEVEL_HELP)\n parser.add_argument(\n \"--logging-format\",\n required=False,\n type=str,\n default=ray_constants.LOGGER_FORMAT,\n help=ray_constants.LOGGER_FORMAT_HELP)\n parser.add_argument(\n \"--temp-dir\",\n required=False,\n type=str,\n default=None,\n help=\"Specify the path of the temporary directory use by Ray process.\")\n args = parser.parse_args()\n ray.utils.setup_logger(args.logging_level, args.logging_format)\n\n # TODO(sang): Add a URL validation.\n metrics_export_address = os.environ.get(\"METRICS_EXPORT_ADDRESS\")\n\n try:\n dashboard = Dashboard(\n args.host,\n args.port,\n args.redis_address,\n args.temp_dir,\n redis_password=args.redis_password,\n metrics_export_address=metrics_export_address)\n dashboard.run()\n except Exception as e:\n # Something went wrong, so push an error to all drivers.\n redis_client = ray.services.create_redis_client(\n args.redis_address, password=args.redis_password)\n traceback_str = ray.utils.format_error_message(traceback.format_exc())\n message = (\"The dashboard on node {} failed with the following \"\n \"error:\\n{}\".format(socket.gethostname(), traceback_str))\n ray.utils.push_error_to_driver_through_redis(\n redis_client, ray_constants.DASHBOARD_DIED_ERROR, message)\n if isinstance(e, OSError) and e.errno == errno.ENOENT:\n logger.warning(message)\n else:\n raise e\n",
"path": "python/ray/dashboard/dashboard.py"
}
] | diff --git a/python/ray/dashboard/client/src/common/formatUtils.ts b/python/ray/dashboard/client/src/common/formatUtils.ts
index 582655dce5b90..9518637b5f04c 100644
--- a/python/ray/dashboard/client/src/common/formatUtils.ts
+++ b/python/ray/dashboard/client/src/common/formatUtils.ts
@@ -30,3 +30,18 @@ export const formatDuration = (durationInSeconds: number) => {
`${pad(durationSeconds)}s`,
].join(" ");
};
+
+export const formatValue = (rawFloat: number) => {
+ try {
+ const decimals = rawFloat.toString().split(".")[1].length || 0;
+ if (decimals <= 3) {
+ return rawFloat.toString();
+ } // Few decimals
+ if (Math.abs(rawFloat.valueOf()) >= 1.0) {
+ return rawFloat.toPrecision(5);
+ } // Values >= 1
+ return rawFloat.toExponential(); // Values in (-1; 1)
+ } catch (e) {
+ return rawFloat.toString();
+ }
+};
diff --git a/python/ray/dashboard/client/src/pages/dashboard/tune/TuneTable.tsx b/python/ray/dashboard/client/src/pages/dashboard/tune/TuneTable.tsx
index 7a0479bba3034..ec28743424091 100644
--- a/python/ray/dashboard/client/src/pages/dashboard/tune/TuneTable.tsx
+++ b/python/ray/dashboard/client/src/pages/dashboard/tune/TuneTable.tsx
@@ -21,6 +21,7 @@ import React from "react";
import { connect } from "react-redux";
import { TuneTrial } from "../../../api";
import DialogWithTitle from "../../../common/DialogWithTitle";
+import { formatValue } from "../../../common/formatUtils";
import NumberedLines from "../../../common/NumberedLines";
import { StoreState } from "../../../store";
import { dashboardActions } from "../state";
@@ -387,7 +388,9 @@ class TuneTable extends React.Component<
</TableCell>
{viewableParams.map((value, index) => (
<TableCell className={classes.cell} key={index}>
- {trial["params"][value]}
+ {typeof trial["params"][value] === "number"
+ ? formatValue(Number(trial["params"][value]))
+ : trial["params"][value]}
</TableCell>
))}
<TableCell className={classes.cell}>
@@ -396,7 +399,9 @@ class TuneTable extends React.Component<
{trial["metrics"] &&
viewableMetrics.map((value, index) => (
<TableCell className={classes.cell} key={index}>
- {trial["metrics"][value]}
+ {typeof trial["metrics"][value] === "number"
+ ? formatValue(Number(trial["metrics"][value]))
+ : trial["metrics"][value]}
</TableCell>
))}
<TableCell className={classes.cell}>
diff --git a/python/ray/dashboard/dashboard.py b/python/ray/dashboard/dashboard.py
index 6a3c100052000..d2a84d5b3452f 100644
--- a/python/ray/dashboard/dashboard.py
+++ b/python/ray/dashboard/dashboard.py
@@ -875,7 +875,7 @@ def clean_trials(self, trial_details):
# round all floats
for key in float_keys:
- details[key] = round(details[key], 3)
+ details[key] = round(details[key], 12)
# group together config attributes
for key in config_keys:
|
cobbler__cobbler-2878 | Cannot set property 'file' of image
### Describe the bug
Set image's property 'file' is not working
### Steps to reproduce
1. `touch /tmp/test.iso`
2. Run below Python code on Cobbler installed machine
```
#!/usr/bin/python3
from xmlrpc.client import Server
rpc_server = Server("http://127.0.0.1/cobbler_api")
token = rpc_server.login('cobbler', 'cobbler')
image_handle = rpc_server.new_image(token)
rpc_server.modify_image(image_handle, "name", "testit", token)
rpc_server.modify_image(image_handle, "file", "/tmp/test.iso", token)
rpc_server.save_image(image_handle, token)
img = rpc_server.get_image("testit")
assert(img['file'] == "/tmp/test.iso")
```
### Expected behavior
End without AssertionError
### Cobbler version
<!--- Paste output from `cobbler version` -->
````
Cobbler 3.3.0
source: d8f60bbf, Mon Sep 20 16:55:41 2021 +0200
build time: Thu Dec 23 06:27:43 2021
````
### Operating system
<!--- On which operating system do you use Cobbler? -->
CentOS 8
### Cobbler log
<!--- Paste (partial) output from `/var/log/cobbler/cobbler.log` -->
````paste below
````
### Screenshots
<!--- If applicable, add screenshots to help explain your problem. -->
### Additional information
<!--- Add any other context about the problem here. -->
| [
{
"content": "\"\"\"\nCopyright 2006-2009, Red Hat, Inc and Others\nMichael DeHaan <michael.dehaan AT gmail>\n\nThis program is free software; you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation; either version 2 of the License, or\n(at your option) any later version.\n\nThis program is distributed in the hope that it will be useful,\nbut WITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\nGNU General Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program; if not, write to the Free Software\nFoundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA\n02110-1301 USA\n\"\"\"\nimport uuid\nfrom typing import Union\n\nfrom cobbler import autoinstall_manager, enums, utils, validate\nfrom cobbler.cexceptions import CX\nfrom cobbler.items import item\n\n\nclass Image(item.Item):\n \"\"\"\n A Cobbler Image. Tracks a virtual or physical image, as opposed to a answer file (autoinst) led installation.\n \"\"\"\n\n TYPE_NAME = \"image\"\n COLLECTION_TYPE = \"image\"\n\n def __init__(self, api, *args, **kwargs):\n \"\"\"\n Constructor\n\n :param api: The Cobbler API object which is used for resolving information.\n :param args: The arguments which should be passed additionally to the base Item class constructor.\n :param kwargs: The keyword arguments which should be passed additionally to the base Item class constructor.\n \"\"\"\n super().__init__(api, *args, **kwargs)\n self._arch = enums.Archs.X86_64\n self._autoinstall = enums.VALUE_INHERITED\n self._breed = \"\"\n self._file = \"\"\n self._image_type = enums.ImageTypes.DIRECT\n self._network_count = 0\n self._os_version = \"\"\n self._boot_loaders = []\n self._menu = \"\"\n self._virt_auto_boot = False\n self._virt_bridge = \"\"\n self._virt_cpus = 0\n self._virt_disk_driver = enums.VirtDiskDrivers.RAW\n self._virt_file_size = 0.0\n self._virt_path = \"\"\n self._virt_ram = 0\n self._virt_type = enums.VirtType.AUTO\n self._supported_boot_loaders = []\n\n def __getattr__(self, name):\n if name == \"kickstart\":\n return self.autoinstall\n raise AttributeError(\"Attribute \\\"%s\\\" did not exist on object type Image.\" % name)\n\n #\n # override some base class methods first (item.Item)\n #\n\n def make_clone(self):\n \"\"\"\n Clone this image object. Please manually adjust all value yourself to make the cloned object unique.\n\n :return: The cloned instance of this object.\n \"\"\"\n _dict = self.to_dict()\n cloned = Image(self.api)\n cloned.from_dict(_dict)\n cloned.uid = uuid.uuid4().hex\n return cloned\n\n def from_dict(self, dictionary: dict):\n \"\"\"\n Initializes the object with attributes from the dictionary.\n\n :param dictionary: The dictionary with values.\n \"\"\"\n if \"name\" in dictionary:\n self.name = dictionary[\"name\"]\n if \"parent\" in dictionary:\n self.parent = dictionary[\"parent\"]\n self._remove_depreacted_dict_keys(dictionary)\n super().from_dict(dictionary)\n\n #\n # specific methods for item.Image\n #\n\n @property\n def arch(self) -> enums.Archs:\n \"\"\"\n Represents the architecture the image has. If deployed to a physical host this should be enforced, a virtual\n image may be deployed on a host with any architecture.\n\n :getter: The current architecture. Default is ``X86_64``.\n :setter: Should be of the enum type or str. May raise an exception in case the architecture is not known to\n Cobbler.\n \"\"\"\n return self._arch\n\n @arch.setter\n def arch(self, arch: Union[str, enums.Archs]):\n \"\"\"\n The field is mainly relevant to PXE provisioning.\n See comments for arch property in distro.py, this works the same.\n\n :param arch: The new architecture to set.\n \"\"\"\n self._arch = validate.validate_arch(arch)\n\n @property\n def autoinstall(self) -> str:\n \"\"\"\n Property for the automatic installation file path, this must be a local file.\n\n It may not make sense for images to have automatic installation templates. It really doesn't. However if the\n image type is 'iso' koan can create a virtual floppy and shove an answer file on it, to script an installation.\n This may not be a automatic installation template per se, it might be a Windows answer file (SIF) etc.\n\n This property can inherit from a parent. Which is actually the default value.\n\n :getter: The path relative to the template directory.\n :setter: The location of the template relative to the template base directory.\n \"\"\"\n return self._autoinstall\n\n @autoinstall.setter\n def autoinstall(self, autoinstall: str):\n \"\"\"\n Set the automatic installation file path, this must be a local file.\n\n :param autoinstall: local automatic installation template file path\n \"\"\"\n autoinstall_mgr = autoinstall_manager.AutoInstallationManager(self.api._collection_mgr)\n self._autoinstall = autoinstall_mgr.validate_autoinstall_template_file_path(autoinstall)\n\n @property\n def file(self) -> str:\n \"\"\"\n Stores the image location. This should be accessible on all nodes that need to access it.\n\n Format: can be one of the following:\n * username:password@hostname:/path/to/the/filename.ext\n * username@hostname:/path/to/the/filename.ext\n * hostname:/path/to/the/filename.ext\n * /path/to/the/filename.ext\n\n :getter: The path to the image location or an emtpy string.\n :setter: May raise a TypeError or SyntaxError in case the validation of the location fails.\n \"\"\"\n return self._file\n\n @file.setter\n def file(self, filename: str):\n \"\"\"\n The setter for the image location.\n\n :param filename: The location where the image is stored.\n :raises SyntaxError: In case a protocol was found.\n \"\"\"\n if not isinstance(filename, str):\n raise TypeError(\"file must be of type str to be parsable.\")\n\n if not filename:\n self._file = \"\"\n return\n\n # validate file location format\n if filename.find(\"://\") != -1:\n raise SyntaxError(\"Invalid image file path location, it should not contain a protocol\")\n uri = filename\n auth = \"\"\n hostname = \"\"\n path = \"\"\n\n if filename.find(\"@\") != -1:\n auth, filename = filename.split(\"@\")\n # extract the hostname\n # 1. if we have a colon, then everything before it is a hostname\n # 2. if we don't have a colon, there is no hostname\n if filename.find(\":\") != -1:\n hostname, filename = filename.split(\":\")\n elif filename[0] != '/':\n raise SyntaxError(\"invalid file: %s\" % filename)\n # raise an exception if we don't have a valid path\n if len(filename) > 0 and filename[0] != '/':\n raise SyntaxError(\"file contains an invalid path: %s\" % filename)\n if filename.find(\"/\") != -1:\n path, filename = filename.rsplit(\"/\", 1)\n\n if len(filename) == 0:\n raise SyntaxError(\"missing filename\")\n if len(auth) > 0 and len(hostname) == 0:\n raise SyntaxError(\"a hostname must be specified with authentication details\")\n\n @property\n def os_version(self) -> str:\n r\"\"\"\n The operating system version which the image contains.\n\n :getter: The sanitized operating system version.\n :setter: Accepts a str which will be validated against the ``distro_signatures.json``.\n \"\"\"\n return self._os_version\n\n @os_version.setter\n def os_version(self, os_version):\n \"\"\"\n Set the operating system version with this setter.\n\n :param os_version: This must be a valid OS-Version.\n \"\"\"\n self._os_version = validate.validate_os_version(os_version, self.breed)\n\n @property\n def breed(self) -> str:\n r\"\"\"\n The operating system breed.\n\n :getter: Returns the current breed.\n :setter: When setting this it is validated against the ``distro_signatures.json`` file.\n \"\"\"\n return self._breed\n\n @breed.setter\n def breed(self, breed: str):\n \"\"\"\n Set the operating system breed with this setter.\n\n :param breed: The breed of the operating system which is available in the image.\n \"\"\"\n self._breed = validate.validate_breed(breed)\n\n @property\n def image_type(self) -> enums.ImageTypes:\n \"\"\"\n Indicates what type of image this is.\n direct = something like \"memdisk\", physical only\n iso = a bootable ISO that pxe's or can be used for virt installs, virtual only\n virt-clone = a cloned virtual disk (FIXME: not yet supported), virtual only\n memdisk = hdd image (physical only)\n\n :getter: The enum type value of the image type.\n :setter: Accepts str like and enum type values and raises a TypeError or ValueError in the case of a problem.\n \"\"\"\n return self._image_type\n\n @image_type.setter\n def image_type(self, image_type: Union[enums.ImageTypes, str]):\n \"\"\"\n The setter which accepts enum type or str type values. Latter ones will be automatically converted if possible.\n\n :param image_type: One of the four options from above.\n :raises TypeError: In case a disallowed type was found.\n :raises ValueError: In case the conversion from str could not successfully executed.\n \"\"\"\n if not isinstance(image_type, (enums.ImageTypes, str)):\n raise TypeError(\"image_type must be of type str or enum.ImageTypes\")\n if isinstance(image_type, str):\n if not image_type:\n # FIXME: Add None Image type\n self._image_type = enums.ImageTypes.DIRECT\n try:\n image_type = enums.ImageTypes[image_type.upper()]\n except KeyError as error:\n raise ValueError(\"image_type choices include: %s\" % list(map(str, enums.ImageTypes))) from error\n # str was converted now it must be an enum.ImageTypes\n if not isinstance(image_type, enums.ImageTypes):\n raise TypeError(\"image_type needs to be of type enums.ImageTypes\")\n if image_type not in enums.ImageTypes:\n raise ValueError(\"image type must be one of the following: %s\"\n % \", \".join(list(map(str, enums.ImageTypes))))\n self._image_type = image_type\n\n @property\n def virt_cpus(self) -> int:\n \"\"\"\n The amount of vCPU cores used in case the image is being deployed on top of a VM host.\n\n :getter: The cores used.\n :setter: The new number of cores.\n \"\"\"\n return self._virt_cpus\n\n @virt_cpus.setter\n def virt_cpus(self, num: int):\n \"\"\"\n Setter for the number of virtual cpus.\n\n :param num: The number of virtual cpu cores.\n \"\"\"\n self._virt_cpus = validate.validate_virt_cpus(num)\n\n @property\n def network_count(self) -> int:\n \"\"\"\n Represents the number of virtual NICs this image has.\n\n .. deprecated:: 3.3.0\n This is nowhere used in the project and will be removed in a future release.\n\n :getter: The number of networks.\n :setter: Raises a ``TypeError`` in case the value is not an int.\n \"\"\"\n return self._network_count\n\n @network_count.setter\n def network_count(self, network_count: int):\n \"\"\"\n Setter for the number of networks.\n\n :param network_count: If None or emtpy will be set to ``1``, otherwise the given integer value will be set.\n :raises TypeError: In case the network_count was not of type int.\n \"\"\"\n if network_count is None or network_count == \"\":\n network_count = 1\n if not isinstance(network_count, int):\n raise TypeError(\"Field network_count of object image needs to be of type int.\")\n self._network_count = network_count\n\n @property\n def virt_auto_boot(self) -> bool:\n r\"\"\"\n Whether the VM should be booted when booting the host or not.\n\n :getter: ``True`` means autoboot is enabled, otherwise VM is not booted automatically.\n :setter: The new state for the property.\n \"\"\"\n return self._virt_auto_boot\n\n @virt_auto_boot.setter\n def virt_auto_boot(self, num: bool):\n \"\"\"\n Setter for the virtual automatic boot option.\n\n :param num: May be \"0\" (disabled) or \"1\" (enabled), will be converted to a real bool.\n \"\"\"\n self._virt_auto_boot = validate.validate_virt_auto_boot(num)\n\n @property\n def virt_file_size(self) -> float:\n r\"\"\"\n The size of the image and thus the usable size for the guest.\n\n .. warning:: There is a regression which makes the usage of multiple disks not possible right now. This will be\n fixed in a future release.\n\n :getter: The size of the image(s) in GB.\n :setter: The float with the new size in GB.\n \"\"\"\n return self._virt_file_size\n\n @virt_file_size.setter\n def virt_file_size(self, num: float):\n \"\"\"\n Setter for the virtual file size of the image.\n\n :param num: Is a non-negative integer (0 means default). Can also be a comma seperated list -- for usage with\n multiple disks\n \"\"\"\n self._virt_file_size = validate.validate_virt_file_size(num)\n\n @property\n def virt_disk_driver(self) -> enums.VirtDiskDrivers:\n \"\"\"\n The type of disk driver used for storing the image.\n\n :getter: The enum type representation of the disk driver.\n :setter: May be a ``str`` with the name of the disk driver or from the enum type directly.\n \"\"\"\n return self._virt_disk_driver\n\n @virt_disk_driver.setter\n def virt_disk_driver(self, driver: enums.VirtDiskDrivers):\n \"\"\"\n Setter for the virtual disk driver.\n\n :param driver: The virtual disk driver which will be set.\n \"\"\"\n self._virt_disk_driver = validate.validate_virt_disk_driver(driver)\n\n @property\n def virt_ram(self) -> int:\n \"\"\"\n The amount of RAM given to the guest in MB.\n\n :getter: The amount of RAM currently assigned to the image.\n :setter: The new amount of ram. Must be an integer.\n \"\"\"\n return self._virt_ram\n\n @virt_ram.setter\n def virt_ram(self, num: int):\n \"\"\"\n Setter for the amount of virtual RAM the machine will have.\n\n :param num: 0 tells Koan to just choose a reasonable default.\n \"\"\"\n self._virt_ram = validate.validate_virt_ram(num)\n\n @property\n def virt_type(self) -> enums.VirtType:\n \"\"\"\n The type of image used.\n\n :getter: The value of the virtual machine.\n :setter: May be of the enum type or a str which is then converted to the enum type.\n \"\"\"\n return self._virt_type\n\n @virt_type.setter\n def virt_type(self, vtype: enums.VirtType):\n \"\"\"\n Setter for the virtual type\n\n :param vtype: May be one of \"qemu\", \"kvm\", \"xenpv\", \"xenfv\", \"vmware\", \"vmwarew\", \"openvz\" or \"auto\".\n \"\"\"\n self._virt_type = validate.validate_virt_type(vtype)\n\n @property\n def virt_bridge(self) -> str:\n r\"\"\"\n The name of the virtual bridge used for networking.\n\n .. warning:: The new validation for the setter is not working. Thus the inheritance from the settings is broken.\n\n :getter: The name of the bridge.\n :setter: The new name of the bridge. If set to an empty ``str``, it will be taken from the settings.\n \"\"\"\n return self._virt_bridge\n\n @virt_bridge.setter\n def virt_bridge(self, vbridge: str):\n \"\"\"\n Setter for the virtual bridge which is used.\n\n :param vbridge: The name of the virtual bridge to use.\n \"\"\"\n self._virt_bridge = validate.validate_virt_bridge(vbridge)\n\n @property\n def virt_path(self) -> str:\n \"\"\"\n Represents the location where the image for the VM is stored.\n\n :getter: The path.\n :setter: Is being validated for being a reasonable path. If yes is set, otherwise ignored.\n \"\"\"\n return self._virt_path\n\n @virt_path.setter\n def virt_path(self, path: str):\n \"\"\"\n Setter for the virtual path which is used.\n\n :param path: The path to where the virtual image is stored.\n \"\"\"\n self._virt_path = validate.validate_virt_path(path)\n\n @property\n def menu(self) -> str:\n \"\"\"\n Property to represent the menu which this image should be put into.\n\n :getter: The name of the menu or an emtpy str.\n :setter: Should only be the name of the menu not the object. May raise ``CX`` in case the menu does not exist.\n \"\"\"\n return self._menu\n\n @menu.setter\n def menu(self, menu: str):\n \"\"\"\n Setter for the menu property.\n\n :param menu: The menu for the image.\n :raises CX: In case the menu to be set could not be found.\n \"\"\"\n if menu and menu != \"\":\n menu_list = self.api.menus()\n if not menu_list.find(name=menu):\n raise CX(\"menu %s not found\" % menu)\n self._menu = menu\n\n @property\n def supported_boot_loaders(self):\n \"\"\"\n Read only property which represents the subset of settable bootloaders.\n\n :getter: The bootloaders which are available for being set.\n \"\"\"\n try:\n # If we have already loaded the supported boot loaders from the signature, use that data\n return self._supported_boot_loaders\n except:\n # otherwise, refresh from the signatures / defaults\n self._supported_boot_loaders = utils.get_supported_distro_boot_loaders(self)\n return self._supported_boot_loaders\n\n @property\n def boot_loaders(self) -> list:\n \"\"\"\n Represents the boot loaders which are able to boot this image.\n\n :getter: The bootloaders. May be an emtpy list.\n :setter: A list with the supported boot loaders for this image.\n \"\"\"\n if self._boot_loaders == enums.VALUE_INHERITED:\n return self.supported_boot_loaders\n return self._boot_loaders\n\n @boot_loaders.setter\n def boot_loaders(self, boot_loaders: list):\n \"\"\"\n Setter of the boot loaders.\n\n :param boot_loaders: The boot loaders for the image.\n :raises TypeError: In case this was of a not allowed type.\n :raises ValueError: In case the str which contained the list could not be successfully split.\n \"\"\"\n # allow the magic inherit string to persist\n if boot_loaders == enums.VALUE_INHERITED:\n self._boot_loaders = enums.VALUE_INHERITED\n return\n\n if boot_loaders:\n boot_loaders_split = utils.input_string_or_list(boot_loaders)\n\n if not isinstance(boot_loaders_split, list):\n raise TypeError(\"boot_loaders needs to be of type list!\")\n\n if not set(boot_loaders_split).issubset(self.supported_boot_loaders):\n raise ValueError(\"Error with image %s - not all boot_loaders %s are supported %s\" %\n (self.name, boot_loaders_split, self.supported_boot_loaders))\n self._boot_loaders = boot_loaders_split\n else:\n self._boot_loaders = []\n",
"path": "cobbler/items/image.py"
}
] | [
{
"content": "\"\"\"\nCopyright 2006-2009, Red Hat, Inc and Others\nMichael DeHaan <michael.dehaan AT gmail>\n\nThis program is free software; you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation; either version 2 of the License, or\n(at your option) any later version.\n\nThis program is distributed in the hope that it will be useful,\nbut WITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\nGNU General Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program; if not, write to the Free Software\nFoundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA\n02110-1301 USA\n\"\"\"\nimport uuid\nfrom typing import Union\n\nfrom cobbler import autoinstall_manager, enums, utils, validate\nfrom cobbler.cexceptions import CX\nfrom cobbler.items import item\n\n\nclass Image(item.Item):\n \"\"\"\n A Cobbler Image. Tracks a virtual or physical image, as opposed to a answer file (autoinst) led installation.\n \"\"\"\n\n TYPE_NAME = \"image\"\n COLLECTION_TYPE = \"image\"\n\n def __init__(self, api, *args, **kwargs):\n \"\"\"\n Constructor\n\n :param api: The Cobbler API object which is used for resolving information.\n :param args: The arguments which should be passed additionally to the base Item class constructor.\n :param kwargs: The keyword arguments which should be passed additionally to the base Item class constructor.\n \"\"\"\n super().__init__(api, *args, **kwargs)\n self._arch = enums.Archs.X86_64\n self._autoinstall = enums.VALUE_INHERITED\n self._breed = \"\"\n self._file = \"\"\n self._image_type = enums.ImageTypes.DIRECT\n self._network_count = 0\n self._os_version = \"\"\n self._boot_loaders = []\n self._menu = \"\"\n self._virt_auto_boot = False\n self._virt_bridge = \"\"\n self._virt_cpus = 0\n self._virt_disk_driver = enums.VirtDiskDrivers.RAW\n self._virt_file_size = 0.0\n self._virt_path = \"\"\n self._virt_ram = 0\n self._virt_type = enums.VirtType.AUTO\n self._supported_boot_loaders = []\n\n def __getattr__(self, name):\n if name == \"kickstart\":\n return self.autoinstall\n raise AttributeError(\"Attribute \\\"%s\\\" did not exist on object type Image.\" % name)\n\n #\n # override some base class methods first (item.Item)\n #\n\n def make_clone(self):\n \"\"\"\n Clone this image object. Please manually adjust all value yourself to make the cloned object unique.\n\n :return: The cloned instance of this object.\n \"\"\"\n _dict = self.to_dict()\n cloned = Image(self.api)\n cloned.from_dict(_dict)\n cloned.uid = uuid.uuid4().hex\n return cloned\n\n def from_dict(self, dictionary: dict):\n \"\"\"\n Initializes the object with attributes from the dictionary.\n\n :param dictionary: The dictionary with values.\n \"\"\"\n if \"name\" in dictionary:\n self.name = dictionary[\"name\"]\n if \"parent\" in dictionary:\n self.parent = dictionary[\"parent\"]\n self._remove_depreacted_dict_keys(dictionary)\n super().from_dict(dictionary)\n\n #\n # specific methods for item.Image\n #\n\n @property\n def arch(self) -> enums.Archs:\n \"\"\"\n Represents the architecture the image has. If deployed to a physical host this should be enforced, a virtual\n image may be deployed on a host with any architecture.\n\n :getter: The current architecture. Default is ``X86_64``.\n :setter: Should be of the enum type or str. May raise an exception in case the architecture is not known to\n Cobbler.\n \"\"\"\n return self._arch\n\n @arch.setter\n def arch(self, arch: Union[str, enums.Archs]):\n \"\"\"\n The field is mainly relevant to PXE provisioning.\n See comments for arch property in distro.py, this works the same.\n\n :param arch: The new architecture to set.\n \"\"\"\n self._arch = validate.validate_arch(arch)\n\n @property\n def autoinstall(self) -> str:\n \"\"\"\n Property for the automatic installation file path, this must be a local file.\n\n It may not make sense for images to have automatic installation templates. It really doesn't. However if the\n image type is 'iso' koan can create a virtual floppy and shove an answer file on it, to script an installation.\n This may not be a automatic installation template per se, it might be a Windows answer file (SIF) etc.\n\n This property can inherit from a parent. Which is actually the default value.\n\n :getter: The path relative to the template directory.\n :setter: The location of the template relative to the template base directory.\n \"\"\"\n return self._autoinstall\n\n @autoinstall.setter\n def autoinstall(self, autoinstall: str):\n \"\"\"\n Set the automatic installation file path, this must be a local file.\n\n :param autoinstall: local automatic installation template file path\n \"\"\"\n autoinstall_mgr = autoinstall_manager.AutoInstallationManager(self.api._collection_mgr)\n self._autoinstall = autoinstall_mgr.validate_autoinstall_template_file_path(autoinstall)\n\n @property\n def file(self) -> str:\n \"\"\"\n Stores the image location. This should be accessible on all nodes that need to access it.\n\n Format: can be one of the following:\n * username:password@hostname:/path/to/the/filename.ext\n * username@hostname:/path/to/the/filename.ext\n * hostname:/path/to/the/filename.ext\n * /path/to/the/filename.ext\n\n :getter: The path to the image location or an emtpy string.\n :setter: May raise a TypeError or SyntaxError in case the validation of the location fails.\n \"\"\"\n return self._file\n\n @file.setter\n def file(self, filename: str):\n \"\"\"\n The setter for the image location.\n\n :param filename: The location where the image is stored.\n :raises SyntaxError: In case a protocol was found.\n \"\"\"\n if not isinstance(filename, str):\n raise TypeError(\"file must be of type str to be parsable.\")\n\n if not filename:\n self._file = \"\"\n return\n\n # validate file location format\n if filename.find(\"://\") != -1:\n raise SyntaxError(\"Invalid image file path location, it should not contain a protocol\")\n uri = filename\n auth = \"\"\n hostname = \"\"\n path = \"\"\n\n if filename.find(\"@\") != -1:\n auth, filename = filename.split(\"@\")\n # extract the hostname\n # 1. if we have a colon, then everything before it is a hostname\n # 2. if we don't have a colon, there is no hostname\n if filename.find(\":\") != -1:\n hostname, filename = filename.split(\":\")\n elif filename[0] != '/':\n raise SyntaxError(\"invalid file: %s\" % filename)\n # raise an exception if we don't have a valid path\n if len(filename) > 0 and filename[0] != '/':\n raise SyntaxError(\"file contains an invalid path: %s\" % filename)\n if filename.find(\"/\") != -1:\n path, filename = filename.rsplit(\"/\", 1)\n\n if len(filename) == 0:\n raise SyntaxError(\"missing filename\")\n if len(auth) > 0 and len(hostname) == 0:\n raise SyntaxError(\"a hostname must be specified with authentication details\")\n\n self._file = uri\n\n @property\n def os_version(self) -> str:\n r\"\"\"\n The operating system version which the image contains.\n\n :getter: The sanitized operating system version.\n :setter: Accepts a str which will be validated against the ``distro_signatures.json``.\n \"\"\"\n return self._os_version\n\n @os_version.setter\n def os_version(self, os_version):\n \"\"\"\n Set the operating system version with this setter.\n\n :param os_version: This must be a valid OS-Version.\n \"\"\"\n self._os_version = validate.validate_os_version(os_version, self.breed)\n\n @property\n def breed(self) -> str:\n r\"\"\"\n The operating system breed.\n\n :getter: Returns the current breed.\n :setter: When setting this it is validated against the ``distro_signatures.json`` file.\n \"\"\"\n return self._breed\n\n @breed.setter\n def breed(self, breed: str):\n \"\"\"\n Set the operating system breed with this setter.\n\n :param breed: The breed of the operating system which is available in the image.\n \"\"\"\n self._breed = validate.validate_breed(breed)\n\n @property\n def image_type(self) -> enums.ImageTypes:\n \"\"\"\n Indicates what type of image this is.\n direct = something like \"memdisk\", physical only\n iso = a bootable ISO that pxe's or can be used for virt installs, virtual only\n virt-clone = a cloned virtual disk (FIXME: not yet supported), virtual only\n memdisk = hdd image (physical only)\n\n :getter: The enum type value of the image type.\n :setter: Accepts str like and enum type values and raises a TypeError or ValueError in the case of a problem.\n \"\"\"\n return self._image_type\n\n @image_type.setter\n def image_type(self, image_type: Union[enums.ImageTypes, str]):\n \"\"\"\n The setter which accepts enum type or str type values. Latter ones will be automatically converted if possible.\n\n :param image_type: One of the four options from above.\n :raises TypeError: In case a disallowed type was found.\n :raises ValueError: In case the conversion from str could not successfully executed.\n \"\"\"\n if not isinstance(image_type, (enums.ImageTypes, str)):\n raise TypeError(\"image_type must be of type str or enum.ImageTypes\")\n if isinstance(image_type, str):\n if not image_type:\n # FIXME: Add None Image type\n self._image_type = enums.ImageTypes.DIRECT\n try:\n image_type = enums.ImageTypes[image_type.upper()]\n except KeyError as error:\n raise ValueError(\"image_type choices include: %s\" % list(map(str, enums.ImageTypes))) from error\n # str was converted now it must be an enum.ImageTypes\n if not isinstance(image_type, enums.ImageTypes):\n raise TypeError(\"image_type needs to be of type enums.ImageTypes\")\n if image_type not in enums.ImageTypes:\n raise ValueError(\"image type must be one of the following: %s\"\n % \", \".join(list(map(str, enums.ImageTypes))))\n self._image_type = image_type\n\n @property\n def virt_cpus(self) -> int:\n \"\"\"\n The amount of vCPU cores used in case the image is being deployed on top of a VM host.\n\n :getter: The cores used.\n :setter: The new number of cores.\n \"\"\"\n return self._virt_cpus\n\n @virt_cpus.setter\n def virt_cpus(self, num: int):\n \"\"\"\n Setter for the number of virtual cpus.\n\n :param num: The number of virtual cpu cores.\n \"\"\"\n self._virt_cpus = validate.validate_virt_cpus(num)\n\n @property\n def network_count(self) -> int:\n \"\"\"\n Represents the number of virtual NICs this image has.\n\n .. deprecated:: 3.3.0\n This is nowhere used in the project and will be removed in a future release.\n\n :getter: The number of networks.\n :setter: Raises a ``TypeError`` in case the value is not an int.\n \"\"\"\n return self._network_count\n\n @network_count.setter\n def network_count(self, network_count: int):\n \"\"\"\n Setter for the number of networks.\n\n :param network_count: If None or emtpy will be set to ``1``, otherwise the given integer value will be set.\n :raises TypeError: In case the network_count was not of type int.\n \"\"\"\n if network_count is None or network_count == \"\":\n network_count = 1\n if not isinstance(network_count, int):\n raise TypeError(\"Field network_count of object image needs to be of type int.\")\n self._network_count = network_count\n\n @property\n def virt_auto_boot(self) -> bool:\n r\"\"\"\n Whether the VM should be booted when booting the host or not.\n\n :getter: ``True`` means autoboot is enabled, otherwise VM is not booted automatically.\n :setter: The new state for the property.\n \"\"\"\n return self._virt_auto_boot\n\n @virt_auto_boot.setter\n def virt_auto_boot(self, num: bool):\n \"\"\"\n Setter for the virtual automatic boot option.\n\n :param num: May be \"0\" (disabled) or \"1\" (enabled), will be converted to a real bool.\n \"\"\"\n self._virt_auto_boot = validate.validate_virt_auto_boot(num)\n\n @property\n def virt_file_size(self) -> float:\n r\"\"\"\n The size of the image and thus the usable size for the guest.\n\n .. warning:: There is a regression which makes the usage of multiple disks not possible right now. This will be\n fixed in a future release.\n\n :getter: The size of the image(s) in GB.\n :setter: The float with the new size in GB.\n \"\"\"\n return self._virt_file_size\n\n @virt_file_size.setter\n def virt_file_size(self, num: float):\n \"\"\"\n Setter for the virtual file size of the image.\n\n :param num: Is a non-negative integer (0 means default). Can also be a comma seperated list -- for usage with\n multiple disks\n \"\"\"\n self._virt_file_size = validate.validate_virt_file_size(num)\n\n @property\n def virt_disk_driver(self) -> enums.VirtDiskDrivers:\n \"\"\"\n The type of disk driver used for storing the image.\n\n :getter: The enum type representation of the disk driver.\n :setter: May be a ``str`` with the name of the disk driver or from the enum type directly.\n \"\"\"\n return self._virt_disk_driver\n\n @virt_disk_driver.setter\n def virt_disk_driver(self, driver: enums.VirtDiskDrivers):\n \"\"\"\n Setter for the virtual disk driver.\n\n :param driver: The virtual disk driver which will be set.\n \"\"\"\n self._virt_disk_driver = validate.validate_virt_disk_driver(driver)\n\n @property\n def virt_ram(self) -> int:\n \"\"\"\n The amount of RAM given to the guest in MB.\n\n :getter: The amount of RAM currently assigned to the image.\n :setter: The new amount of ram. Must be an integer.\n \"\"\"\n return self._virt_ram\n\n @virt_ram.setter\n def virt_ram(self, num: int):\n \"\"\"\n Setter for the amount of virtual RAM the machine will have.\n\n :param num: 0 tells Koan to just choose a reasonable default.\n \"\"\"\n self._virt_ram = validate.validate_virt_ram(num)\n\n @property\n def virt_type(self) -> enums.VirtType:\n \"\"\"\n The type of image used.\n\n :getter: The value of the virtual machine.\n :setter: May be of the enum type or a str which is then converted to the enum type.\n \"\"\"\n return self._virt_type\n\n @virt_type.setter\n def virt_type(self, vtype: enums.VirtType):\n \"\"\"\n Setter for the virtual type\n\n :param vtype: May be one of \"qemu\", \"kvm\", \"xenpv\", \"xenfv\", \"vmware\", \"vmwarew\", \"openvz\" or \"auto\".\n \"\"\"\n self._virt_type = validate.validate_virt_type(vtype)\n\n @property\n def virt_bridge(self) -> str:\n r\"\"\"\n The name of the virtual bridge used for networking.\n\n .. warning:: The new validation for the setter is not working. Thus the inheritance from the settings is broken.\n\n :getter: The name of the bridge.\n :setter: The new name of the bridge. If set to an empty ``str``, it will be taken from the settings.\n \"\"\"\n return self._virt_bridge\n\n @virt_bridge.setter\n def virt_bridge(self, vbridge: str):\n \"\"\"\n Setter for the virtual bridge which is used.\n\n :param vbridge: The name of the virtual bridge to use.\n \"\"\"\n self._virt_bridge = validate.validate_virt_bridge(vbridge)\n\n @property\n def virt_path(self) -> str:\n \"\"\"\n Represents the location where the image for the VM is stored.\n\n :getter: The path.\n :setter: Is being validated for being a reasonable path. If yes is set, otherwise ignored.\n \"\"\"\n return self._virt_path\n\n @virt_path.setter\n def virt_path(self, path: str):\n \"\"\"\n Setter for the virtual path which is used.\n\n :param path: The path to where the virtual image is stored.\n \"\"\"\n self._virt_path = validate.validate_virt_path(path)\n\n @property\n def menu(self) -> str:\n \"\"\"\n Property to represent the menu which this image should be put into.\n\n :getter: The name of the menu or an emtpy str.\n :setter: Should only be the name of the menu not the object. May raise ``CX`` in case the menu does not exist.\n \"\"\"\n return self._menu\n\n @menu.setter\n def menu(self, menu: str):\n \"\"\"\n Setter for the menu property.\n\n :param menu: The menu for the image.\n :raises CX: In case the menu to be set could not be found.\n \"\"\"\n if menu and menu != \"\":\n menu_list = self.api.menus()\n if not menu_list.find(name=menu):\n raise CX(\"menu %s not found\" % menu)\n self._menu = menu\n\n @property\n def supported_boot_loaders(self):\n \"\"\"\n Read only property which represents the subset of settable bootloaders.\n\n :getter: The bootloaders which are available for being set.\n \"\"\"\n try:\n # If we have already loaded the supported boot loaders from the signature, use that data\n return self._supported_boot_loaders\n except:\n # otherwise, refresh from the signatures / defaults\n self._supported_boot_loaders = utils.get_supported_distro_boot_loaders(self)\n return self._supported_boot_loaders\n\n @property\n def boot_loaders(self) -> list:\n \"\"\"\n Represents the boot loaders which are able to boot this image.\n\n :getter: The bootloaders. May be an emtpy list.\n :setter: A list with the supported boot loaders for this image.\n \"\"\"\n if self._boot_loaders == enums.VALUE_INHERITED:\n return self.supported_boot_loaders\n return self._boot_loaders\n\n @boot_loaders.setter\n def boot_loaders(self, boot_loaders: list):\n \"\"\"\n Setter of the boot loaders.\n\n :param boot_loaders: The boot loaders for the image.\n :raises TypeError: In case this was of a not allowed type.\n :raises ValueError: In case the str which contained the list could not be successfully split.\n \"\"\"\n # allow the magic inherit string to persist\n if boot_loaders == enums.VALUE_INHERITED:\n self._boot_loaders = enums.VALUE_INHERITED\n return\n\n if boot_loaders:\n boot_loaders_split = utils.input_string_or_list(boot_loaders)\n\n if not isinstance(boot_loaders_split, list):\n raise TypeError(\"boot_loaders needs to be of type list!\")\n\n if not set(boot_loaders_split).issubset(self.supported_boot_loaders):\n raise ValueError(\"Error with image %s - not all boot_loaders %s are supported %s\" %\n (self.name, boot_loaders_split, self.supported_boot_loaders))\n self._boot_loaders = boot_loaders_split\n else:\n self._boot_loaders = []\n",
"path": "cobbler/items/image.py"
}
] | diff --git a/cobbler/items/image.py b/cobbler/items/image.py
index 6f5bcea96f..be97b52218 100644
--- a/cobbler/items/image.py
+++ b/cobbler/items/image.py
@@ -206,6 +206,8 @@ def file(self, filename: str):
if len(auth) > 0 and len(hostname) == 0:
raise SyntaxError("a hostname must be specified with authentication details")
+ self._file = uri
+
@property
def os_version(self) -> str:
r"""
diff --git a/tests/items/image_test.py b/tests/items/image_test.py
index f8bc0eaf19..4a8f833965 100644
--- a/tests/items/image_test.py
+++ b/tests/items/image_test.py
@@ -56,10 +56,10 @@ def test_file():
image = Image(test_api)
# Act
- image.file = ""
+ image.file = "/tmp/test"
# Assert
- assert image.file == ""
+ assert image.file == "/tmp/test"
def test_os_version():
|
pypa__setuptools-4127 | [BUG] Setuptools 69.0.0 breaks Astropy's setup
### setuptools version
setuptools==69.0.0
### Python version
3.12
### OS
Ubuntu
### Additional environment information
_No response_
### Description
About 15h ago, Astropy's CI started failing to build with
```
ImportError: cannot import name 'newer_group' from 'setuptools.dep_util'
```
This seems to correspond to an [intentional change in setuptools 69](https://setuptools.pypa.io/en/latest/history.html#features).
Nonetheless, from reading the PR that introduced the change (https://github.com/pypa/setuptools/pull/4069), I'm not sure that this was supposed to break immediately. Was this intended ?
### Expected behavior
a deprecation warning instead of a hard error ?
### How to Reproduce
```shell
$ python -c "from setuptools.dep_util import newer_group"
```
### Output
```console
Traceback (most recent call last):
File "<string>", line 1, in <module>
ImportError: cannot import name 'newer_group' from 'setuptools.dep_util' (/private/tmp/venv/lib/python3.12/site-packages/setuptools/dep_util.py)
```
| [
{
"content": "import warnings\n\nfrom ._distutils import _modified\n\n\ndef __getattr__(name):\n if name not in ['newer_pairwise_group']:\n raise AttributeError(name)\n warnings.warn(\n \"dep_util is Deprecated. Use functions from setuptools.modified instead.\",\n DeprecationWarning,\n stacklevel=2,\n )\n return getattr(_modified, name)\n",
"path": "setuptools/dep_util.py"
}
] | [
{
"content": "import warnings\n\nfrom ._distutils import _modified\n\n\ndef __getattr__(name):\n if name not in ['newer_group', 'newer_pairwise_group']:\n raise AttributeError(name)\n warnings.warn(\n \"dep_util is Deprecated. Use functions from setuptools.modified instead.\",\n DeprecationWarning,\n stacklevel=2,\n )\n return getattr(_modified, name)\n",
"path": "setuptools/dep_util.py"
}
] | diff --git a/newsfragments/4126.bugfix.rst b/newsfragments/4126.bugfix.rst
new file mode 100644
index 0000000000..467a94887a
--- /dev/null
+++ b/newsfragments/4126.bugfix.rst
@@ -0,0 +1,2 @@
+Fixed imports of ``setuptools.dep_util.newer_group``.
+A deprecation warning is issued instead of a hard failure.
diff --git a/setuptools/dep_util.py b/setuptools/dep_util.py
index e30cd41b49..c8ab14c8f2 100644
--- a/setuptools/dep_util.py
+++ b/setuptools/dep_util.py
@@ -4,7 +4,7 @@
def __getattr__(name):
- if name not in ['newer_pairwise_group']:
+ if name not in ['newer_group', 'newer_pairwise_group']:
raise AttributeError(name)
warnings.warn(
"dep_util is Deprecated. Use functions from setuptools.modified instead.",
|
WordPress__openverse-api-788 | Update the auth token expiry to be 12 or 24 hours
## Problem
Currently the token returned from the auth_tokens/token route returns a token that expires in 10 hours. To make this a little more consistent with typical CRON scheduling it would be more ideal for this expiry to be 12 or 24 hours.
## Description
Updating the auth token expires_in time would allow for more consistent CRON scheduling and most users would more typically expect it to expire every 12/24 hours.
## Alternatives
No suggestions aside from even longer expiry times (maybe once a year or once a month, etc).
## Additional context
Issue/suggestion originally came up here: [https://github.com/WordPress/openverse-api/issues/770](https://github.com/WordPress/openverse-api/issues/770)
## Implementation
- [ ] 🙋 I would be interested in implementing this feature.
| [
{
"content": "\"\"\"\nDjango settings for catalog project.\n\nGenerated by 'django-admin startproject' using Django 2.0.5.\n\nFor more information on this file, see\nhttps://docs.djangoproject.com/en/2.0/topics/settings/\n\nFor the full list of settings and their values, see\nhttps://docs.djangoproject.com/en/2.0/ref/settings/\n\"\"\"\n\nfrom pathlib import Path\nfrom socket import gethostbyname, gethostname\n\nimport sentry_sdk\nfrom decouple import config\nfrom sentry_sdk.integrations.django import DjangoIntegration\n\nfrom catalog.logger import LOGGING as LOGGING_CONF\n\n\n# Build paths inside the project like this: BASE_DIR.join('dir', 'subdir'...)\nBASE_DIR = Path(__file__).resolve().parent.parent\n\n# Where to collect static files in production/development deployments\nSTATIC_ROOT = \"/var/api_static_content/static\"\n\n# Logo uploads\nMEDIA_ROOT = \"/var/api_media/\"\nMEDIA_URL = \"/media/\"\n\n# Quick-start development settings - unsuitable for production\n# See https://docs.djangoproject.com/en/2.0/howto/deployment/checklist/\n\n# SECURITY WARNING: keep the secret key used in production secret!\nSECRET_KEY = config(\"DJANGO_SECRET_KEY\") # required\n\n# SECURITY WARNING: don't run with debug turned on in production!\nDEBUG = config(\"DJANGO_DEBUG_ENABLED\", default=False, cast=bool)\n\nENVIRONMENT = config(\"ENVIRONMENT\", default=\"local\")\n\nALLOWED_HOSTS = [\n \"api-dev.openverse.engineering\",\n \"api.openverse.engineering\",\n gethostname(),\n gethostbyname(gethostname()),\n]\n\nif lb_url := config(\"LOAD_BALANCER_URL\", default=\"\"):\n ALLOWED_HOSTS.append(lb_url)\n\nif DEBUG:\n ALLOWED_HOSTS += [\n \"dev.openverse.test\", # used in local development\n \"localhost\",\n \"127.0.0.1\",\n \"0.0.0.0\",\n ]\n\n# Domains that shortened links may point to\nSHORT_URL_WHITELIST = {\n \"api-dev.openverse.engineering\",\n \"api.openverse.engineering\",\n \"localhost:8000\",\n}\nSHORT_URL_PATH_WHITELIST = [\"/v1/list\", \"/v1/images/\"]\n\nUSE_S3 = config(\"USE_S3\", default=False, cast=bool)\n\nLOGGING = LOGGING_CONF\n\n# Application definition\n\nINSTALLED_APPS = [\n \"catalog\",\n \"catalog.api\",\n \"drf_yasg\",\n \"django.contrib.admin\",\n \"django.contrib.auth\",\n \"django.contrib.contenttypes\",\n \"django.contrib.sessions\",\n \"django.contrib.messages\",\n \"django.contrib.staticfiles\",\n \"oauth2_provider\",\n \"rest_framework\",\n \"corsheaders\",\n \"sslserver\",\n]\n\nif USE_S3:\n DEFAULT_FILE_STORAGE = \"storages.backends.s3boto3.S3Boto3Storage\"\n AWS_STORAGE_BUCKET_NAME = config(\"LOGOS_BUCKET\", default=\"openverse_api-logos-prod\")\n AWS_S3_SIGNATURE_VERSION = \"s3v4\"\n INSTALLED_APPS.append(\"storages\")\n\n# https://github.com/dabapps/django-log-request-id#logging-all-requests\nLOG_REQUESTS = True\n# https://github.com/dabapps/django-log-request-id#installation-and-usage\nREQUEST_ID_RESPONSE_HEADER = \"X-Request-Id\"\n\nMIDDLEWARE = [\n # https://github.com/dabapps/django-log-request-id\n \"log_request_id.middleware.RequestIDMiddleware\",\n \"django.middleware.security.SecurityMiddleware\",\n \"django.contrib.sessions.middleware.SessionMiddleware\",\n \"corsheaders.middleware.CorsMiddleware\",\n \"django.middleware.common.CommonMiddleware\",\n \"django.middleware.csrf.CsrfViewMiddleware\",\n \"django.contrib.auth.middleware.AuthenticationMiddleware\",\n \"django.contrib.messages.middleware.MessageMiddleware\",\n \"django.middleware.clickjacking.XFrameOptionsMiddleware\",\n \"oauth2_provider.middleware.OAuth2TokenMiddleware\",\n]\n\nSWAGGER_SETTINGS = {\"SECURITY_DEFINITIONS\": {}}\n\nOAUTH2_PROVIDER = {\n \"SCOPES\": {\n \"read\": \"Read scope\",\n \"write\": \"Write scope\",\n }\n}\n\nOAUTH2_PROVIDER_APPLICATION_MODEL = \"api.ThrottledApplication\"\n\nTHROTTLE_ANON_BURST = config(\"THROTTLE_ANON_BURST\", default=\"5/hour\")\nTHROTTLE_ANON_SUSTAINED = config(\"THROTTLE_ANON_SUSTAINED\", default=\"100/day\")\n\nREST_FRAMEWORK = {\n \"DEFAULT_AUTHENTICATION_CLASSES\": (\n \"oauth2_provider.contrib.rest_framework.OAuth2Authentication\",\n ),\n \"DEFAULT_VERSIONING_CLASS\": \"rest_framework.versioning.URLPathVersioning\",\n \"DEFAULT_RENDERER_CLASSES\": (\n \"rest_framework.renderers.JSONRenderer\",\n \"rest_framework.renderers.BrowsableAPIRenderer\",\n \"rest_framework_xml.renderers.XMLRenderer\",\n ),\n \"DEFAULT_THROTTLE_CLASSES\": (\n \"catalog.api.utils.throttle.BurstRateThrottle\",\n \"catalog.api.utils.throttle.SustainedRateThrottle\",\n \"catalog.api.utils.throttle.OAuth2IdThrottleSustainedRate\",\n \"catalog.api.utils.throttle.OAuth2IdThrottleBurstRate\",\n \"catalog.api.utils.throttle.EnhancedOAuth2IdThrottleSustainedRate\",\n \"catalog.api.utils.throttle.EnhancedOAuth2IdThrottleBurstRate\",\n ),\n \"DEFAULT_THROTTLE_RATES\": {\n \"anon_burst\": THROTTLE_ANON_BURST,\n \"anon_sustained\": THROTTLE_ANON_SUSTAINED,\n \"oauth2_client_credentials_sustained\": \"10000/day\",\n \"oauth2_client_credentials_burst\": \"100/min\",\n \"enhanced_oauth2_client_credentials_sustained\": \"20000/day\",\n \"enhanced_oauth2_client_credentials_burst\": \"200/min\",\n },\n \"EXCEPTION_HANDLER\": \"catalog.api.utils.exceptions.exception_handler\",\n}\n\nif config(\"DISABLE_GLOBAL_THROTTLING\", default=True, cast=bool):\n del REST_FRAMEWORK[\"DEFAULT_THROTTLE_RATES\"]\n del REST_FRAMEWORK[\"DEFAULT_THROTTLE_CLASSES\"]\n\nREDIS_HOST = config(\"REDIS_HOST\", default=\"localhost\")\nREDIS_PORT = config(\"REDIS_PORT\", default=6379, cast=int)\nREDIS_PASSWORD = config(\"REDIS_PASSWORD\", default=\"\")\nCACHES = {\n # Site cache writes to 'default'\n \"default\": {\n \"BACKEND\": \"django_redis.cache.RedisCache\",\n \"LOCATION\": f\"redis://{REDIS_HOST}:{REDIS_PORT}/0\",\n \"OPTIONS\": {\n \"CLIENT_CLASS\": \"django_redis.client.DefaultClient\",\n },\n },\n # For rapidly changing stats that we don't want to hammer the database with\n \"traffic_stats\": {\n \"BACKEND\": \"django_redis.cache.RedisCache\",\n \"LOCATION\": f\"redis://{REDIS_HOST}:{REDIS_PORT}/1\",\n \"OPTIONS\": {\n \"CLIENT_CLASS\": \"django_redis.client.DefaultClient\",\n },\n },\n # For ensuring consistency among multiple Django workers and servers.\n # Used by Redlock.\n \"locks\": {\n \"BACKEND\": \"django_redis.cache.RedisCache\",\n \"LOCATION\": f\"redis://{REDIS_HOST}:{REDIS_PORT}/2\",\n \"OPTIONS\": {\n \"CLIENT_CLASS\": \"django_redis.client.DefaultClient\",\n },\n },\n}\n\n# Produce CC-hosted thumbnails dynamically through a proxy.\nTHUMBNAIL_PROXY_URL = config(\"THUMBNAIL_PROXY_URL\", default=\"http://localhost:8222\")\n\nTHUMBNAIL_WIDTH_PX = config(\"THUMBNAIL_WIDTH_PX\", cast=int, default=600)\nTHUMBNAIL_JPG_QUALITY = config(\"THUMBNAIL_JPG_QUALITY\", cast=int, default=80)\nTHUMBNAIL_PNG_COMPRESSION = config(\"THUMBNAIL_PNG_COMPRESSION\", cast=int, default=6)\n\nAUTHENTICATION_BACKENDS = (\n \"oauth2_provider.backends.OAuth2Backend\",\n \"django.contrib.auth.backends.ModelBackend\",\n)\n\nROOT_URLCONF = \"catalog.urls\"\n\nTEMPLATES = [\n {\n \"BACKEND\": \"django.template.backends.django.DjangoTemplates\",\n \"DIRS\": [BASE_DIR.joinpath(\"catalog\", \"templates\")],\n \"APP_DIRS\": True,\n \"OPTIONS\": {\n \"context_processors\": [\n \"django.template.context_processors.debug\",\n \"django.template.context_processors.request\",\n \"django.contrib.auth.context_processors.auth\",\n \"django.contrib.messages.context_processors.messages\",\n ],\n },\n },\n]\n\nWSGI_APPLICATION = \"catalog.wsgi.application\"\n\n# Database\n# https://docs.djangoproject.com/en/2.0/ref/settings/#databases\n\nDATABASES = {\n \"default\": {\n \"ENGINE\": \"django.db.backends.postgresql\",\n \"HOST\": config(\"DJANGO_DATABASE_HOST\", default=\"localhost\"),\n \"PORT\": config(\"DJANGO_DATABASE_PORT\", default=5432, cast=int),\n \"USER\": config(\"DJANGO_DATABASE_USER\", default=\"deploy\"),\n \"PASSWORD\": config(\"DJANGO_DATABASE_PASSWORD\", default=\"deploy\"),\n \"NAME\": config(\"DJANGO_DATABASE_NAME\", default=\"openledger\"),\n },\n \"upstream\": {\n \"ENGINE\": \"django.db.backends.postgresql\",\n \"HOST\": config(\"UPSTREAM_DATABASE_HOST\", default=\"localhost\"),\n \"PORT\": config(\"UPSTREAM_DATABASE_PORT\", default=5433, cast=int),\n \"USER\": config(\"UPSTREAM_DATABASE_USER\", default=\"deploy\"),\n \"PASSWORD\": config(\"UPSTREAM_DATABASE_PASSWORD\", default=\"deploy\"),\n \"NAME\": config(\"UPSTREAM_DATABASE_NAME\", default=\"openledger\"),\n },\n}\n\n# Password validation\n# https://docs.djangoproject.com/en/2.0/ref/settings/#auth-password-validators\n\nAUTH_PASSWORD_VALIDATORS = [\n {\n \"NAME\": \"django.contrib.auth.password_validation\"\n \".UserAttributeSimilarityValidator\",\n },\n {\n \"NAME\": \"django.contrib.auth.password_validation\" \".MinimumLengthValidator\",\n },\n {\n \"NAME\": \"django.contrib.auth.password_validation\" \".CommonPasswordValidator\",\n },\n {\n \"NAME\": \"django.contrib.auth.password_validation\" \".NumericPasswordValidator\",\n },\n]\n\n# Internationalization\n# https://docs.djangoproject.com/en/2.0/topics/i18n/\n\nLANGUAGE_CODE = \"en-us\"\n\nTIME_ZONE = \"UTC\"\n\nUSE_I18N = True\n\nUSE_L10N = True\n\nUSE_TZ = True\n\n# Static files (CSS, JavaScript, Images)\n# https://docs.djangoproject.com/en/2.0/howto/static-files/\n\nSTATIC_URL = \"/static/\"\n\n# Allow anybody to access the API from any domain\nCORS_ORIGIN_ALLOW_ALL = True\n\n# The version of the API. We follow the semantic version specification.\nAPI_VERSION = config(\"SEMANTIC_VERSION\", default=\"Version not specified\")\n\n# The contact email of the Openverse team\nCONTACT_EMAIL = config(\"CONTACT_EMAIL\", default=\"[email protected]\")\n\nWATERMARK_ENABLED = config(\"WATERMARK_ENABLED\", default=False, cast=bool)\n\nELASTICSEARCH_URL = config(\"ELASTICSEARCH_URL\", default=\"localhost\")\nELASTICSEARCH_PORT = config(\"ELASTICSEARCH_PORT\", default=9200, cast=int)\nELASTICSEARCH_AWS_REGION = config(\"ELASTICSEARCH_AWS_REGION\", default=\"us-east-1\")\n\n# Additional settings for dev/prod environments\nAWS_ACCESS_KEY_ID = config(\"AWS_ACCESS_KEY_ID\", default=\"\")\nAWS_SECRET_ACCESS_KEY = config(\"AWS_SECRET_ACCESS_KEY\", default=\"\")\n\nEMAIL_SENDER = config(\"EMAIL_SENDER\", default=\"\")\nEMAIL_HOST = config(\"EMAIL_HOST\", default=\"\")\nEMAIL_PORT = config(\"EMAIL_PORT\", default=587, cast=int)\nEMAIL_HOST_USER = config(\"EMAIL_HOST_USER\", default=\"\")\nEMAIL_HOST_PASSWORD = config(\"EMAIL_HOST_PASSWORD\", default=\"\")\nEMAIL_SUBJECT_PREFIX = \"[noreply]\"\nEMAIL_USE_TLS = True\nDEFAULT_FROM_EMAIL = config(\"DEFAULT_FROM_EMAIL\", default=\"\")\n\nif EMAIL_HOST_USER or EMAIL_HOST_PASSWORD:\n EMAIL_BACKEND = \"django.core.mail.backends.smtp.EmailBackend\"\nelse:\n EMAIL_BACKEND = \"django.core.mail.backends.console.EmailBackend\"\n\n# Log full Elasticsearch response\nVERBOSE_ES_RESPONSE = config(\"DEBUG_SCORES\", default=False, cast=bool)\n\n# Whether to boost results by authority and popularity\nUSE_RANK_FEATURES = config(\"USE_RANK_FEATURES\", default=True, cast=bool)\n\n# The scheme to use for the hyperlinks in the API responses\nAPI_LINK_SCHEME = config(\"API_LINK_SCHEME\", default=None)\n\n# Proxy handling, for production\nif config(\"IS_PROXIED\", default=True, cast=bool):\n # https://docs.djangoproject.com/en/4.0/ref/settings/#use-x-forwarded-host\n USE_X_FORWARDED_HOST = True\n # https://docs.djangoproject.com/en/4.0/ref/settings/#secure-proxy-ssl-header\n SECURE_PROXY_SSL_HEADER = (\"HTTP_X_FORWARDED_PROTO\", \"https\")\n\n# Trusted origins for CSRF\n# https://docs.djangoproject.com/en/4.0/releases/4.0/#csrf-trusted-origins-changes-4-0\nCSRF_TRUSTED_ORIGINS = [\"https://*.openverse.engineering\"]\n\nSENTRY_DSN = config(\n \"SENTRY_DSN\",\n default=\"https://[email protected]/6107216\",\n)\nSENTRY_SAMPLE_RATE = config(\"SENTRY_SAMPLE_RATE\", default=1.0, cast=float)\n\nif not DEBUG:\n sentry_sdk.init(\n dsn=SENTRY_DSN,\n integrations=[DjangoIntegration()],\n traces_sample_rate=SENTRY_SAMPLE_RATE,\n send_default_pii=False,\n environment=ENVIRONMENT,\n )\n",
"path": "api/catalog/settings.py"
}
] | [
{
"content": "\"\"\"\nDjango settings for catalog project.\n\nGenerated by 'django-admin startproject' using Django 2.0.5.\n\nFor more information on this file, see\nhttps://docs.djangoproject.com/en/2.0/topics/settings/\n\nFor the full list of settings and their values, see\nhttps://docs.djangoproject.com/en/2.0/ref/settings/\n\"\"\"\n\nfrom pathlib import Path\nfrom socket import gethostbyname, gethostname\n\nimport sentry_sdk\nfrom decouple import config\nfrom sentry_sdk.integrations.django import DjangoIntegration\n\nfrom catalog.logger import LOGGING as LOGGING_CONF\n\n\n# Build paths inside the project like this: BASE_DIR.join('dir', 'subdir'...)\nBASE_DIR = Path(__file__).resolve().parent.parent\n\n# Where to collect static files in production/development deployments\nSTATIC_ROOT = \"/var/api_static_content/static\"\n\n# Logo uploads\nMEDIA_ROOT = \"/var/api_media/\"\nMEDIA_URL = \"/media/\"\n\n# Quick-start development settings - unsuitable for production\n# See https://docs.djangoproject.com/en/2.0/howto/deployment/checklist/\n\n# SECURITY WARNING: keep the secret key used in production secret!\nSECRET_KEY = config(\"DJANGO_SECRET_KEY\") # required\n\n# SECURITY WARNING: don't run with debug turned on in production!\nDEBUG = config(\"DJANGO_DEBUG_ENABLED\", default=False, cast=bool)\n\nENVIRONMENT = config(\"ENVIRONMENT\", default=\"local\")\n\nALLOWED_HOSTS = [\n \"api-dev.openverse.engineering\",\n \"api.openverse.engineering\",\n gethostname(),\n gethostbyname(gethostname()),\n]\n\nif lb_url := config(\"LOAD_BALANCER_URL\", default=\"\"):\n ALLOWED_HOSTS.append(lb_url)\n\nif DEBUG:\n ALLOWED_HOSTS += [\n \"dev.openverse.test\", # used in local development\n \"localhost\",\n \"127.0.0.1\",\n \"0.0.0.0\",\n ]\n\n# Domains that shortened links may point to\nSHORT_URL_WHITELIST = {\n \"api-dev.openverse.engineering\",\n \"api.openverse.engineering\",\n \"localhost:8000\",\n}\nSHORT_URL_PATH_WHITELIST = [\"/v1/list\", \"/v1/images/\"]\n\nUSE_S3 = config(\"USE_S3\", default=False, cast=bool)\n\nLOGGING = LOGGING_CONF\n\n# Application definition\n\nINSTALLED_APPS = [\n \"catalog\",\n \"catalog.api\",\n \"drf_yasg\",\n \"django.contrib.admin\",\n \"django.contrib.auth\",\n \"django.contrib.contenttypes\",\n \"django.contrib.sessions\",\n \"django.contrib.messages\",\n \"django.contrib.staticfiles\",\n \"oauth2_provider\",\n \"rest_framework\",\n \"corsheaders\",\n \"sslserver\",\n]\n\nif USE_S3:\n DEFAULT_FILE_STORAGE = \"storages.backends.s3boto3.S3Boto3Storage\"\n AWS_STORAGE_BUCKET_NAME = config(\"LOGOS_BUCKET\", default=\"openverse_api-logos-prod\")\n AWS_S3_SIGNATURE_VERSION = \"s3v4\"\n INSTALLED_APPS.append(\"storages\")\n\nMIDDLEWARE = [\n \"django.middleware.security.SecurityMiddleware\",\n \"django.contrib.sessions.middleware.SessionMiddleware\",\n \"corsheaders.middleware.CorsMiddleware\",\n \"django.middleware.common.CommonMiddleware\",\n \"django.middleware.csrf.CsrfViewMiddleware\",\n \"django.contrib.auth.middleware.AuthenticationMiddleware\",\n \"django.contrib.messages.middleware.MessageMiddleware\",\n \"django.middleware.clickjacking.XFrameOptionsMiddleware\",\n \"oauth2_provider.middleware.OAuth2TokenMiddleware\",\n]\n\nSWAGGER_SETTINGS = {\"SECURITY_DEFINITIONS\": {}}\n\nOAUTH2_PROVIDER = {\n \"SCOPES\": {\n \"read\": \"Read scope\",\n \"write\": \"Write scope\",\n },\n \"ACCESS_TOKEN_EXPIRE_SECONDS\": config(\n \"ACCESS_TOKEN_EXPIRE_SECONDS\", default=3600 * 12, cast=int\n ),\n}\n\nOAUTH2_PROVIDER_APPLICATION_MODEL = \"api.ThrottledApplication\"\n\nTHROTTLE_ANON_BURST = config(\"THROTTLE_ANON_BURST\", default=\"5/hour\")\nTHROTTLE_ANON_SUSTAINED = config(\"THROTTLE_ANON_SUSTAINED\", default=\"100/day\")\n\nREST_FRAMEWORK = {\n \"DEFAULT_AUTHENTICATION_CLASSES\": (\n \"oauth2_provider.contrib.rest_framework.OAuth2Authentication\",\n ),\n \"DEFAULT_VERSIONING_CLASS\": \"rest_framework.versioning.URLPathVersioning\",\n \"DEFAULT_RENDERER_CLASSES\": (\n \"rest_framework.renderers.JSONRenderer\",\n \"rest_framework.renderers.BrowsableAPIRenderer\",\n \"rest_framework_xml.renderers.XMLRenderer\",\n ),\n \"DEFAULT_THROTTLE_CLASSES\": (\n \"catalog.api.utils.throttle.BurstRateThrottle\",\n \"catalog.api.utils.throttle.SustainedRateThrottle\",\n \"catalog.api.utils.throttle.OAuth2IdThrottleSustainedRate\",\n \"catalog.api.utils.throttle.OAuth2IdThrottleBurstRate\",\n \"catalog.api.utils.throttle.EnhancedOAuth2IdThrottleSustainedRate\",\n \"catalog.api.utils.throttle.EnhancedOAuth2IdThrottleBurstRate\",\n ),\n \"DEFAULT_THROTTLE_RATES\": {\n \"anon_burst\": THROTTLE_ANON_BURST,\n \"anon_sustained\": THROTTLE_ANON_SUSTAINED,\n \"oauth2_client_credentials_sustained\": \"10000/day\",\n \"oauth2_client_credentials_burst\": \"100/min\",\n \"enhanced_oauth2_client_credentials_sustained\": \"20000/day\",\n \"enhanced_oauth2_client_credentials_burst\": \"200/min\",\n },\n \"EXCEPTION_HANDLER\": \"catalog.api.utils.exceptions.exception_handler\",\n}\n\nif config(\"DISABLE_GLOBAL_THROTTLING\", default=True, cast=bool):\n del REST_FRAMEWORK[\"DEFAULT_THROTTLE_RATES\"]\n del REST_FRAMEWORK[\"DEFAULT_THROTTLE_CLASSES\"]\n\nREDIS_HOST = config(\"REDIS_HOST\", default=\"localhost\")\nREDIS_PORT = config(\"REDIS_PORT\", default=6379, cast=int)\nREDIS_PASSWORD = config(\"REDIS_PASSWORD\", default=\"\")\nCACHES = {\n # Site cache writes to 'default'\n \"default\": {\n \"BACKEND\": \"django_redis.cache.RedisCache\",\n \"LOCATION\": f\"redis://{REDIS_HOST}:{REDIS_PORT}/0\",\n \"OPTIONS\": {\n \"CLIENT_CLASS\": \"django_redis.client.DefaultClient\",\n },\n },\n # For rapidly changing stats that we don't want to hammer the database with\n \"traffic_stats\": {\n \"BACKEND\": \"django_redis.cache.RedisCache\",\n \"LOCATION\": f\"redis://{REDIS_HOST}:{REDIS_PORT}/1\",\n \"OPTIONS\": {\n \"CLIENT_CLASS\": \"django_redis.client.DefaultClient\",\n },\n },\n # For ensuring consistency among multiple Django workers and servers.\n # Used by Redlock.\n \"locks\": {\n \"BACKEND\": \"django_redis.cache.RedisCache\",\n \"LOCATION\": f\"redis://{REDIS_HOST}:{REDIS_PORT}/2\",\n \"OPTIONS\": {\n \"CLIENT_CLASS\": \"django_redis.client.DefaultClient\",\n },\n },\n}\n\n# Produce CC-hosted thumbnails dynamically through a proxy.\nTHUMBNAIL_PROXY_URL = config(\"THUMBNAIL_PROXY_URL\", default=\"http://localhost:8222\")\n\nTHUMBNAIL_WIDTH_PX = config(\"THUMBNAIL_WIDTH_PX\", cast=int, default=600)\nTHUMBNAIL_JPG_QUALITY = config(\"THUMBNAIL_JPG_QUALITY\", cast=int, default=80)\nTHUMBNAIL_PNG_COMPRESSION = config(\"THUMBNAIL_PNG_COMPRESSION\", cast=int, default=6)\n\nAUTHENTICATION_BACKENDS = (\n \"oauth2_provider.backends.OAuth2Backend\",\n \"django.contrib.auth.backends.ModelBackend\",\n)\n\nROOT_URLCONF = \"catalog.urls\"\n\nTEMPLATES = [\n {\n \"BACKEND\": \"django.template.backends.django.DjangoTemplates\",\n \"DIRS\": [BASE_DIR.joinpath(\"catalog\", \"templates\")],\n \"APP_DIRS\": True,\n \"OPTIONS\": {\n \"context_processors\": [\n \"django.template.context_processors.debug\",\n \"django.template.context_processors.request\",\n \"django.contrib.auth.context_processors.auth\",\n \"django.contrib.messages.context_processors.messages\",\n ],\n },\n },\n]\n\nWSGI_APPLICATION = \"catalog.wsgi.application\"\n\n# Database\n# https://docs.djangoproject.com/en/2.0/ref/settings/#databases\n\nDATABASES = {\n \"default\": {\n \"ENGINE\": \"django.db.backends.postgresql\",\n \"HOST\": config(\"DJANGO_DATABASE_HOST\", default=\"localhost\"),\n \"PORT\": config(\"DJANGO_DATABASE_PORT\", default=5432, cast=int),\n \"USER\": config(\"DJANGO_DATABASE_USER\", default=\"deploy\"),\n \"PASSWORD\": config(\"DJANGO_DATABASE_PASSWORD\", default=\"deploy\"),\n \"NAME\": config(\"DJANGO_DATABASE_NAME\", default=\"openledger\"),\n },\n \"upstream\": {\n \"ENGINE\": \"django.db.backends.postgresql\",\n \"HOST\": config(\"UPSTREAM_DATABASE_HOST\", default=\"localhost\"),\n \"PORT\": config(\"UPSTREAM_DATABASE_PORT\", default=5433, cast=int),\n \"USER\": config(\"UPSTREAM_DATABASE_USER\", default=\"deploy\"),\n \"PASSWORD\": config(\"UPSTREAM_DATABASE_PASSWORD\", default=\"deploy\"),\n \"NAME\": config(\"UPSTREAM_DATABASE_NAME\", default=\"openledger\"),\n },\n}\n\n# Password validation\n# https://docs.djangoproject.com/en/2.0/ref/settings/#auth-password-validators\n\nAUTH_PASSWORD_VALIDATORS = [\n {\n \"NAME\": \"django.contrib.auth.password_validation\"\n \".UserAttributeSimilarityValidator\",\n },\n {\n \"NAME\": \"django.contrib.auth.password_validation\" \".MinimumLengthValidator\",\n },\n {\n \"NAME\": \"django.contrib.auth.password_validation\" \".CommonPasswordValidator\",\n },\n {\n \"NAME\": \"django.contrib.auth.password_validation\" \".NumericPasswordValidator\",\n },\n]\n\n# Internationalization\n# https://docs.djangoproject.com/en/2.0/topics/i18n/\n\nLANGUAGE_CODE = \"en-us\"\n\nTIME_ZONE = \"UTC\"\n\nUSE_I18N = True\n\nUSE_L10N = True\n\nUSE_TZ = True\n\n# Static files (CSS, JavaScript, Images)\n# https://docs.djangoproject.com/en/2.0/howto/static-files/\n\nSTATIC_URL = \"/static/\"\n\n# Allow anybody to access the API from any domain\nCORS_ORIGIN_ALLOW_ALL = True\n\n# The version of the API. We follow the semantic version specification.\nAPI_VERSION = config(\"SEMANTIC_VERSION\", default=\"Version not specified\")\n\n# The contact email of the Openverse team\nCONTACT_EMAIL = config(\"CONTACT_EMAIL\", default=\"[email protected]\")\n\nWATERMARK_ENABLED = config(\"WATERMARK_ENABLED\", default=False, cast=bool)\n\nELASTICSEARCH_URL = config(\"ELASTICSEARCH_URL\", default=\"localhost\")\nELASTICSEARCH_PORT = config(\"ELASTICSEARCH_PORT\", default=9200, cast=int)\nELASTICSEARCH_AWS_REGION = config(\"ELASTICSEARCH_AWS_REGION\", default=\"us-east-1\")\n\n# Additional settings for dev/prod environments\nAWS_ACCESS_KEY_ID = config(\"AWS_ACCESS_KEY_ID\", default=\"\")\nAWS_SECRET_ACCESS_KEY = config(\"AWS_SECRET_ACCESS_KEY\", default=\"\")\n\nEMAIL_SENDER = config(\"EMAIL_SENDER\", default=\"\")\nEMAIL_HOST = config(\"EMAIL_HOST\", default=\"\")\nEMAIL_PORT = config(\"EMAIL_PORT\", default=587, cast=int)\nEMAIL_HOST_USER = config(\"EMAIL_HOST_USER\", default=\"\")\nEMAIL_HOST_PASSWORD = config(\"EMAIL_HOST_PASSWORD\", default=\"\")\nEMAIL_SUBJECT_PREFIX = \"[noreply]\"\nEMAIL_USE_TLS = True\nDEFAULT_FROM_EMAIL = config(\"DEFAULT_FROM_EMAIL\", default=\"\")\n\nif EMAIL_HOST_USER or EMAIL_HOST_PASSWORD:\n EMAIL_BACKEND = \"django.core.mail.backends.smtp.EmailBackend\"\nelse:\n EMAIL_BACKEND = \"django.core.mail.backends.console.EmailBackend\"\n\n# Log full Elasticsearch response\nVERBOSE_ES_RESPONSE = config(\"DEBUG_SCORES\", default=False, cast=bool)\n\n# Whether to boost results by authority and popularity\nUSE_RANK_FEATURES = config(\"USE_RANK_FEATURES\", default=True, cast=bool)\n\n# The scheme to use for the hyperlinks in the API responses\nAPI_LINK_SCHEME = config(\"API_LINK_SCHEME\", default=None)\n\n# Proxy handling, for production\nif config(\"IS_PROXIED\", default=True, cast=bool):\n # https://docs.djangoproject.com/en/4.0/ref/settings/#use-x-forwarded-host\n USE_X_FORWARDED_HOST = True\n # https://docs.djangoproject.com/en/4.0/ref/settings/#secure-proxy-ssl-header\n SECURE_PROXY_SSL_HEADER = (\"HTTP_X_FORWARDED_PROTO\", \"https\")\n\n# Trusted origins for CSRF\n# https://docs.djangoproject.com/en/4.0/releases/4.0/#csrf-trusted-origins-changes-4-0\nCSRF_TRUSTED_ORIGINS = [\"https://*.openverse.engineering\"]\n\nSENTRY_DSN = config(\n \"SENTRY_DSN\",\n default=\"https://[email protected]/6107216\",\n)\nSENTRY_SAMPLE_RATE = config(\"SENTRY_SAMPLE_RATE\", default=1.0, cast=float)\n\nif not DEBUG:\n sentry_sdk.init(\n dsn=SENTRY_DSN,\n integrations=[DjangoIntegration()],\n traces_sample_rate=SENTRY_SAMPLE_RATE,\n send_default_pii=False,\n environment=ENVIRONMENT,\n )\n",
"path": "api/catalog/settings.py"
}
] | diff --git a/api/catalog/settings.py b/api/catalog/settings.py
index 4c915d5fa..fc293d5d0 100644
--- a/api/catalog/settings.py
+++ b/api/catalog/settings.py
@@ -113,7 +113,10 @@
"SCOPES": {
"read": "Read scope",
"write": "Write scope",
- }
+ },
+ "ACCESS_TOKEN_EXPIRE_SECONDS": config(
+ "ACCESS_TOKEN_EXPIRE_SECONDS", default=3600 * 12, cast=int
+ ),
}
OAUTH2_PROVIDER_APPLICATION_MODEL = "api.ThrottledApplication"
|
azavea__raster-vision-1586 | Same explanation for SlidingWindowGeoDataset and RandomWindowGeoDataset
## 📚 Documentation
<!-- A clear and concise description of what content in https://docs.rastervision.io/ is an issue.-->
> The SlidingWindowGeoDataset allows reading the scene by sampling random window sizes and locations.
This description is same to explained both SlidingWindowGeoDataset and RandomWindowGeoDataset. This can be found here: https://docs.rastervision.io/en/latest/tutorials/sampling_training_data.html
| [
{
"content": "from typing import List, Optional, Tuple, Union\n\nfrom rastervision.pipeline.config import (Config, register_config, ConfigError,\n Field, validator)\nfrom rastervision.core.data.utils import color_to_triple, normalize_color\n\nDEFAULT_NULL_CLASS_NAME = 'null'\nDEFAULT_NULL_CLASS_COLOR = 'black'\n\n\n@register_config('class_config')\nclass ClassConfig(Config):\n \"\"\"Configures the class names that are being predicted.\"\"\"\n names: List[str] = Field(\n ...,\n description='Names of classes. The i-th class in this list will have '\n 'class ID = i.')\n colors: Optional[List[Union[str, Tuple]]] = Field(\n None,\n description=\n ('Colors used to visualize classes. Can be color strings accepted by '\n 'matplotlib or RGB tuples. If None, a random color will be auto-generated '\n 'for each class.'))\n null_class: Optional[str] = Field(\n None,\n description='Optional name of class in `names` to use as the null '\n 'class. This is used in semantic segmentation to represent the label '\n 'for imagery pixels that are NODATA or that are missing a label. '\n f'If None and the class names include \"{DEFAULT_NULL_CLASS_NAME}\", '\n 'it will automatically be used as the null class. If None, and this '\n 'Config is part of a SemanticSegmentationConfig, a null class will be '\n 'added automatically.')\n\n @validator('colors', always=True)\n def validate_colors(cls, v: Optional[List[Union[str, Tuple]]],\n values: dict) -> Optional[List[Union[str, Tuple]]]:\n \"\"\"Compare length w/ names. Also auto-generate if not specified.\"\"\"\n class_names = values['names']\n class_colors = v\n if class_colors is None:\n class_colors = [color_to_triple() for _ in class_names]\n elif len(class_names) != len(class_colors):\n raise ConfigError(f'len(class_names) ({len(class_names)}) != '\n f'len(class_colors) ({len(class_colors)})\\n'\n f'class_names: {class_names}\\n'\n f'class_colors: {class_colors}')\n return class_colors\n\n @validator('null_class', always=True)\n def validate_null_class(cls, v: Optional[str],\n values: dict) -> Optional[str]:\n \"\"\"Check if in names. If 'null' in names, use it as null class.\"\"\"\n names = values['names']\n if v is None:\n if DEFAULT_NULL_CLASS_NAME in names:\n v = DEFAULT_NULL_CLASS_NAME\n else:\n if v not in names:\n raise ConfigError(\n f'The null_class, \"{v}\", must be in list of class names.')\n\n # edge case\n default_null_class_in_names = (DEFAULT_NULL_CLASS_NAME in names)\n null_class_neq_default = (v != DEFAULT_NULL_CLASS_NAME)\n if default_null_class_in_names and null_class_neq_default:\n raise ConfigError(\n f'\"{DEFAULT_NULL_CLASS_NAME}\" is in names but the '\n f'specified null_class is something else (\"{v}\").')\n return v\n\n def get_class_id(self, name: str) -> int:\n return self.names.index(name)\n\n def get_name(self, id: int) -> str:\n return self.names[id]\n\n @property\n def null_class_id(self) -> int:\n if self.null_class is None:\n raise ValueError('null_class is not set')\n return self.get_class_id(self.null_class)\n\n def get_color_to_class_id(self) -> dict:\n return dict([(self.colors[i], i) for i in range(len(self.colors))])\n\n def ensure_null_class(self) -> None:\n \"\"\"Add a null class if one isn't set. This method is idempotent.\"\"\"\n if self.null_class is not None:\n return\n\n null_class_name = DEFAULT_NULL_CLASS_NAME\n null_class_color = DEFAULT_NULL_CLASS_COLOR\n\n # This might seeem redundant given the null class validator above, but\n # is actually important. Sometimes there can be multiple ClassConfig\n # instances that reference the same list objects for names and colors\n # (not clear why this happens). This means that\n # each ensure_null_class() call will add to names and colors in each\n # copy of ClassConfig but only set its own null_class, which makes this\n # method() non-idempotent.\n if null_class_name in self.names:\n self.null_class = null_class_name\n return\n\n # use random color if default color is already taken\n null_class_color_triple = color_to_triple(null_class_color)\n all_color_triples = [\n color_to_triple(c) if isinstance(c, str) else c\n for c in self.colors\n ]\n if null_class_color_triple in all_color_triples:\n null_class_color = color_to_triple()\n\n self.names.append(null_class_name)\n self.colors.append(null_class_color)\n self.null_class = null_class_name\n\n def __len__(self) -> int:\n return len(self.names)\n\n @property\n def color_triples(self) -> List[Tuple[float, float, float]]:\n color_triples = [normalize_color(c) for c in self.colors]\n return color_triples\n",
"path": "rastervision_core/rastervision/core/data/class_config.py"
}
] | [
{
"content": "from typing import List, Optional, Tuple, Union\n\nfrom rastervision.pipeline.config import (Config, register_config, ConfigError,\n Field, validator)\nfrom rastervision.core.data.utils import color_to_triple, normalize_color\n\nDEFAULT_NULL_CLASS_NAME = 'null'\nDEFAULT_NULL_CLASS_COLOR = 'black'\n\n\n@register_config('class_config')\nclass ClassConfig(Config):\n \"\"\"Configures the class names that are being predicted.\"\"\"\n names: List[str] = Field(\n ...,\n description='Names of classes. The i-th class in this list will have '\n 'class ID = i.')\n colors: Optional[List[Union[str, Tuple]]] = Field(\n None,\n description=\n ('Colors used to visualize classes. Can be color strings accepted by '\n 'matplotlib or RGB tuples. If None, a random color will be auto-generated '\n 'for each class.'))\n null_class: Optional[str] = Field(\n None,\n description='Optional name of class in `names` to use as the null '\n 'class. This is used in semantic segmentation to represent the label '\n 'for imagery pixels that are NODATA or that are missing a label. '\n f'If None and the class names include \"{DEFAULT_NULL_CLASS_NAME}\", '\n 'it will automatically be used as the null class. If None, and this '\n 'Config is part of a SemanticSegmentationConfig, a null class will be '\n 'added automatically.')\n\n @validator('colors', always=True)\n def validate_colors(cls, v: Optional[List[Union[str, Tuple]]],\n values: dict) -> Optional[List[Union[str, Tuple]]]:\n \"\"\"Compare length w/ names. Also auto-generate if not specified.\"\"\"\n class_names = values['names']\n class_colors = v\n if class_colors is None:\n class_colors = [color_to_triple() for _ in class_names]\n elif len(class_names) != len(class_colors):\n raise ConfigError(f'len(class_names) ({len(class_names)}) != '\n f'len(class_colors) ({len(class_colors)})\\n'\n f'class_names: {class_names}\\n'\n f'class_colors: {class_colors}')\n return class_colors\n\n @validator('null_class', always=True)\n def validate_null_class(cls, v: Optional[str],\n values: dict) -> Optional[str]:\n \"\"\"Check if in names. If 'null' in names, use it as null class.\"\"\"\n names = values['names']\n if v is None:\n if DEFAULT_NULL_CLASS_NAME in names:\n v = DEFAULT_NULL_CLASS_NAME\n else:\n if v not in names:\n raise ConfigError(\n f'The null_class, \"{v}\", must be in list of class names.')\n\n # edge case\n default_null_class_in_names = (DEFAULT_NULL_CLASS_NAME in names)\n null_class_neq_default = (v != DEFAULT_NULL_CLASS_NAME)\n if default_null_class_in_names and null_class_neq_default:\n raise ConfigError(\n f'\"{DEFAULT_NULL_CLASS_NAME}\" is in names but the '\n f'specified null_class is something else (\"{v}\").')\n return v\n\n def get_class_id(self, name: str) -> int:\n return self.names.index(name)\n\n def get_name(self, id: int) -> str:\n return self.names[id]\n\n @property\n def null_class_id(self) -> int:\n if self.null_class is None:\n raise ValueError('null_class is not set')\n return self.get_class_id(self.null_class)\n\n def get_color_to_class_id(self) -> dict:\n return dict([(self.colors[i], i) for i in range(len(self.colors))])\n\n def ensure_null_class(self) -> None:\n \"\"\"Add a null class if one isn't set. This method is idempotent.\"\"\"\n if self.null_class is not None:\n return\n\n null_class_name = DEFAULT_NULL_CLASS_NAME\n null_class_color = DEFAULT_NULL_CLASS_COLOR\n\n # This might seeem redundant given the null class validator above, but\n # is actually important. Sometimes there can be multiple ClassConfig\n # instances that reference the same list objects for names and colors\n # (not clear why this happens). This means that\n # each ensure_null_class() call will add to names and colors in each\n # copy of ClassConfig but only set its own null_class, which makes this\n # method() non-idempotent.\n if null_class_name in self.names:\n self.null_class = null_class_name\n return\n\n # use random color if default color is already taken\n null_class_color_triple = color_to_triple(null_class_color)\n all_color_triples = [\n color_to_triple(c) if isinstance(c, str) else c\n for c in self.colors\n ]\n if null_class_color_triple in all_color_triples:\n null_class_color = color_to_triple()\n\n self.names.append(null_class_name)\n self.colors.append(null_class_color)\n self.null_class = null_class_name\n\n def __len__(self) -> int:\n return len(self.names)\n\n @property\n def color_triples(self) -> List[Tuple[float, float, float]]:\n \"\"\"Class colors in a normalized form.\"\"\"\n color_triples = [normalize_color(c) for c in self.colors]\n return color_triples\n",
"path": "rastervision_core/rastervision/core/data/class_config.py"
}
] | diff --git a/docs/README.md b/docs/README.md
index 1045bcba4..2093525f9 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -46,6 +46,7 @@ To run a live local server that updates with changes, run:
- You can specify a thumbnail for a notebook (which is shown in the gallery) in the following ways:
- To use the output of a cell in that notebook, add a `nbsphinx-thumbnail` tag to that cell's metadata. If the cell has multiple image outputs, [see instructions here](https://nbsphinx.readthedocs.io/en/latest/gallery/multiple-outputs.html). **Note:** this ONLY works with outputs of *code* cells.
- To use an arbitrary image, add it to the [`img/`](./img/) directory and then add its path to the `nbsphinx_thumbnails` `dict` in [`conf.py`](./conf.py).
+- *Do* use cross-references and other Sphinx-styling in notebooks. You can do so by [using raw `reST` cells](https://nbsphinx.readthedocs.io/en/0.8.10/raw-cells.html#reST). See existing notebooks for examples.
- The furo theme [allows specifying different versions of the same image for light and dark modes](https://pradyunsg.me/furo/reference/images/#different-images-for-dark-light-mode) by using the `:class: only-light` and `:class: only-dark` options with the `.. image::` directive. Instead of specifying different images, we specify the same image for both and use CSS-based color-inversion for dark mode. This saves us the trouble of creating dark versions for each image.
- This should only be applied to images where it makes sense to invert in dark mode.
- Where it makes sense to invert in dark mode but inversion does not produce a good result, it is recommended to manually create a better looking dark-mode version of the image.
diff --git a/docs/usage/tutorials/lightning_workflow.ipynb b/docs/usage/tutorials/lightning_workflow.ipynb
index f98a71ea4..29c4f5158 100644
--- a/docs/usage/tutorials/lightning_workflow.ipynb
+++ b/docs/usage/tutorials/lightning_workflow.ipynb
@@ -11,7 +11,7 @@
"\n",
"[Lightning](https://www.pytorchlightning.ai/) (formerly known as PyTorch Lightning) is a high-level library for training PyTorch models. In this tutorial, we demonstrate a complete workflow for doing semantic segmentation on SpaceNet Vegas using a combination of Raster Vision and Lightning. We use Raster Vision for reading data, Lightning for training a model, and then Raster Vision again for making predictions and evaluations on whole scenes. \n",
"\n",
- "Raster Vision has easy-to-use, built-in model training functionality implemented by the `Learner` class which is shown in the [`train.ipynb`](./train.ipynb) notebook. However, some users may prefer to use Lightning for training models, either because they already know how to use it, and like it, or because they desire more flexibility than the `Learner` class offers. This notebook shows how these libraries can be used together, but does not attempt to use either library in a particularly sophisticated manner."
+ "Raster Vision has easy-to-use, built-in model training functionality implemented by the `Learner` class which is shown in the [\"Training a model\" tutorial](./train.ipynb). However, some users may prefer to use Lightning for training models, either because they already know how to use it, and like it, or because they desire more flexibility than the `Learner` class offers. This notebook shows how these libraries can be used together, but does not attempt to use either library in a particularly sophisticated manner."
]
},
{
@@ -309,18 +309,21 @@
"source": [
"## Monitor training using Tensorboard\n",
"\n",
- "This runs an instance of Tensorboard inside this notebook.\n",
- "\n",
- "<div class=\"alert alert-info\">\n",
- "\n",
- "Note\n",
- "\n",
- "<ul>\n",
- " <li>If running inside the Raster Vision docker image, you will need to pass `--tensorboard` to `docker/run` for this to work.</li>\n",
- " <li>If the dashboard doen't auto-reload, you can click the reload button onthe top-right.</li>\n",
- "</ul>\n",
+ "This runs an instance of Tensorboard inside this notebook."
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "id": "5934b7b3-f25e-4278-a371-2b5a2f93a0d0",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ ".. note::\n",
"\n",
- "</div>"
+ " - If running inside the Raster Vision docker image, you will need to pass `--tensorboard` to `docker/run` for this to work.\n",
+ " - If the dashboard doen't auto-reload, you can click the reload button on the top-right.\n"
]
},
{
@@ -495,11 +498,33 @@
{
"cell_type": "markdown",
"id": "334768ff",
- "metadata": {},
+ "metadata": {
+ "tags": []
+ },
"source": [
- "## Make predictions for scene\n",
- "\n",
- "We can now use Raster Vision's `SemanticSegmentationLabels` class to make predictions over a whole scene. The `SemanticSegmentationLabels.from_predictions` method takes an iterator over predictions. We create this using a `get_predictions` helper function below."
+ "## Make predictions for scene"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "id": "ab6da8e6-5bbd-4687-b672-cf1070d9d51a",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ ".. currentmodule:: rastervision.core.data.label.semantic_segmentation_labels"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "id": "3d4fe6c2-1683-47a9-97d1-119d94980005",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ "We can now use Raster Vision's :class:`SemanticSegmentationLabels` class to make predictions over a whole scene. The :meth:`SemanticSegmentationLabels.from_predictions` method takes an iterator over predictions. We create this using a ``get_predictions()`` helper function defined below."
]
},
{
@@ -644,9 +669,29 @@
"id": "bfb00ec1",
"metadata": {},
"source": [
- "## Evaluate predictions for a scene\n",
- "\n",
- "Now that we have predictions for the validation scene, we can evaluate them by comparing to ground truth using `SemanticSegmentationEvaluator`."
+ "## Evaluate predictions for a scene"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "id": "a16c1d55-4ebb-4e06-aede-bcb8806ca6b4",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ ".. currentmodule:: rastervision.core.evaluation.semantic_segmentation_evaluator"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "id": "89bcd5d6-c352-45eb-ac80-4ded6c4136e5",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ "Now that we have predictions for the validation scene, we can evaluate them by comparing to ground truth using :class:`SemanticSegmentationEvaluator`."
]
},
{
@@ -665,6 +710,28 @@
" predictions=pred_labels)"
]
},
+ {
+ "cell_type": "raw",
+ "id": "f412a014-505c-4b85-8d33-759b6ec67000",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ ".. currentmodule:: rastervision.core.evaluation"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "id": "82af492a-c739-432f-bb9a-e333fb63c9de",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ ":meth:`SemanticSegmentationEvaluator.evaluate_predictions() <semantic_segmentation_evaluator.SemanticSegmentationEvaluator.evaluate_predictions>` returns a :class:`~semantic_segmentation_evaluation.SemanticSegmentationEvaluation` object which contains evaluations for each class as :class:`~class_evaluation_item.ClassEvaluationItem` objects."
+ ]
+ },
{
"cell_type": "markdown",
"id": "31d7fcd4",
@@ -764,7 +831,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3.9.10 ('base')",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
diff --git a/docs/usage/tutorials/pred_and_eval_ss.ipynb b/docs/usage/tutorials/pred_and_eval_ss.ipynb
index 5ea62f612..2ad5c11d2 100644
--- a/docs/usage/tutorials/pred_and_eval_ss.ipynb
+++ b/docs/usage/tutorials/pred_and_eval_ss.ipynb
@@ -11,11 +11,26 @@
]
},
{
- "cell_type": "markdown",
- "id": "cbbac9ba-59f2-40d2-9a17-7fd987157c10",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "c6aad8b5-a4ab-401a-a1a4-c7ffcb670fa4",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ ".. currentmodule:: rastervision.pytorch_learner.learner"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "id": "a101fa91-1d00-46fc-841d-c1996db796ee",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "## Load `Learner` with trained model from bundle"
+ "Load a :class:`Learner` with a trained model from bundle -- :meth:`Learner.from_model_bundle`\n",
+ "---------------------------------------------------------------------------------------------"
]
},
{
@@ -117,11 +132,26 @@
]
},
{
- "cell_type": "markdown",
- "id": "8a3c055f-917f-4c89-a95a-2139d8982f3a",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "09323795-e507-496d-ae6f-0bea8e98625a",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "## Predict"
+ "Predict -- :meth:`Learner.predict_dataset`\n",
+ "------------------------------------------"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "id": "9f65bed3-b69c-4198-ba11-d3ecd67ccd7c",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ "Make predictions via :meth:`Learner.predict_dataset` and then turn them into `Labels <reading_labels.ipynb#Labels>`_ via :meth:`Labels.from_predictions <rastervision.core.data.label.labels.Labels.from_predictions>` (specifically, :meth:`SemanticSegmentationLabels.from_predictions <rastervision.core.data.label.semantic_segmentation_labels.SemanticSegmentationLabels.from_predictions>`)."
]
},
{
@@ -171,6 +201,32 @@
"## Visualize predictions"
]
},
+ {
+ "cell_type": "raw",
+ "id": "724ebc4e-f2da-47dc-8e44-d20c8a2cccf1",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ ".. currentmodule:: rastervision.core.data.label.semantic_segmentation_labels"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "id": "bc6c1ced-db14-42be-8212-319ee5426491",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ "``pred_labels`` is an instance of :class:`~SemanticSegmentationSmoothLabels` which is a raster of probability distributions for each pixel for the entire scene. We can get these probabilities via :meth:`~SemanticSegmentationSmoothLabels.get_score_arr`.\n",
+ "\n",
+ ".. note::\n",
+ "\n",
+ " There is also a :meth:`~SemanticSegmentationSmoothLabels.get_label_arr` method that will return a 2D raster of class IDs representing the most probable class for each pixel."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 7,
@@ -220,11 +276,26 @@
]
},
{
- "cell_type": "markdown",
- "id": "bfdcab71-e786-484b-8667-bde704870be2",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "1e2018a7-06f1-4bb6-a892-17a7f848d192",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "## Save predictions to file"
+ ".. currentmodule:: rastervision.core.data"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "id": "aa11cb15-4fcc-428e-9c54-fb5c38af8e88",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ "Save predictions to file -- :meth:`SemanticSegmentationSmoothLabels.save() <label.semantic_segmentation_labels.SemanticSegmentationSmoothLabels.save>`\n",
+ "------------------------------------------------------------------------------------------------------------------------------------------------------"
]
},
{
@@ -272,16 +343,46 @@
{
"cell_type": "markdown",
"id": "6ea5b49f-3438-41f9-9438-a5b1b3594493",
- "metadata": {},
+ "metadata": {
+ "tags": []
+ },
"source": [
"## Evaluate predictions"
]
},
+ {
+ "cell_type": "raw",
+ "id": "8f10aac0-c0b2-45d2-9b20-069d25756ba6",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ ".. currentmodule:: rastervision.core"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "id": "7cedf0c4-0900-47d6-855a-78658b7896b4",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ "We now want to evaluate the predictions against the ground truth labels.\n",
+ "\n",
+ "Raster Vision allows us to do this via an :class:`~evaluation.evaluator.Evaluator`. In our case, this would be the :class:`~evaluation.semantic_segmentation_evaluator.SemanticSegmentationEvaluator`. We are going to use its :meth:`~evaluation.semantic_segmentation_evaluator.SemanticSegmentationEvaluator.evaluate_predictions` method, which takes both ground truth labels and predictions as :class:`~data.label.labels.Labels` objects.\n",
+ "\n",
+ "We already have the predictions as a :class:`~data.label.semantic_segmentation_labels.SemanticSegmentationLabels` object, so we just need to load the ground truth labels as :class:`~data.label.semantic_segmentation_labels.SemanticSegmentationLabels` too. We do that by using :func:`~data.utils.factory.make_ss_scene` factory function to create a scene and then accessing ``scene.label_source.get_labels()``. Alternatively, we could have directly created a :class:`~data.label_source.semantic_segmentation_label_source.SemanticSegmentationLabelSource`."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 10,
"id": "ff500453-d315-45de-b538-113bd43328b6",
- "metadata": {},
+ "metadata": {
+ "tags": []
+ },
"outputs": [
{
"name": "stderr",
@@ -301,7 +402,22 @@
" label_vector_default_class_id=class_config.get_class_id('building'),\n",
" label_raster_source_kw=dict(\n",
" background_class_id=class_config.get_class_id('background')),\n",
- " image_raster_source_kw=dict(allow_streaming=True))"
+ " image_raster_source_kw=dict(allow_streaming=True))\n",
+ "\n",
+ "gt_labels = scene.label_source.get_labels()"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "id": "b82f298e-e194-48e2-a9bf-a6322003730d",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ ".. note::\n",
+ "\n",
+ " ``gt_labels`` is an instance of :class:`~data.label.semantic_segmentation_labels.SemanticSegmentationDiscreteLabels`. You can convert it to a label raster (for visualization or some other analysis) via :meth:`~data.label.semantic_segmentation_labels.SemanticSegmentationDiscreteLabels.get_label_arr`."
]
},
{
@@ -316,8 +432,31 @@
"evaluator = SemanticSegmentationEvaluator(class_config)\n",
"\n",
"evaluation = evaluator.evaluate_predictions(\n",
- " ground_truth=scene.label_source.get_labels(),\n",
- " predictions=pred_labels)"
+ " ground_truth=gt_labels, predictions=pred_labels)"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "id": "822bc6fe-0de2-4aa5-99b3-aeda4dd2221e",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ ".. currentmodule:: rastervision.core.evaluation"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "id": "f33e9604-3333-4dd1-9f79-08174e3e73fc",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ ":meth:`SemanticSegmentationEvaluator.evaluate_predictions() <semantic_segmentation_evaluator.SemanticSegmentationEvaluator.evaluate_predictions>` returns a :class:`~semantic_segmentation_evaluation.SemanticSegmentationEvaluation` object which contains evaluations for each class as :class:`~class_evaluation_item.ClassEvaluationItem` objects.\n",
+ "\n",
+ "We can examine these evaluations as shown below."
]
},
{
@@ -422,6 +561,17 @@
"### Save evaluation"
]
},
+ {
+ "cell_type": "raw",
+ "id": "0f9cb43e-24f1-45bb-abe1-6ac2e23c70f6",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ "We can also save the evaluations as a JSON via :meth:`SemanticSegmentationEvaluation.save() <semantic_segmentation_evaluation.SemanticSegmentationEvaluation.save>`"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 14,
diff --git a/docs/usage/tutorials/reading_labels.ipynb b/docs/usage/tutorials/reading_labels.ipynb
index 50062d0a3..6fc7dd803 100644
--- a/docs/usage/tutorials/reading_labels.ipynb
+++ b/docs/usage/tutorials/reading_labels.ipynb
@@ -12,7 +12,6 @@
"cell_type": "markdown",
"id": "4431879d-e81d-4994-b55a-e35392d3387b",
"metadata": {
- "jp-MarkdownHeadingCollapsed": true,
"tags": []
},
"source": [
@@ -24,9 +23,20 @@
"id": "c9bb2d1e-bda0-4f3d-b7a6-e9362ba43878",
"metadata": {},
"source": [
- "Before we can work with labels, we first want to define what our target classes are -- their names, their IDs, and possibly, their colors (to use for visualization).\n",
+ "Before we can work with labels, we first want to define what our target classes are -- their names, their IDs, and possibly, their colors (to use for visualization)."
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "id": "a68160d7-e2ba-4da2-8925-8a0b565865fc",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ ".. currentmodule:: rastervision.core.data\n",
"\n",
- "Raster Vision makes all this simple to do using the handy `ClassConfig` class."
+ "Raster Vision makes all this simple to do using the handy :class:`~class_config.ClassConfig` class."
]
},
{
@@ -55,11 +65,28 @@
},
{
"cell_type": "markdown",
- "id": "9dd006f6-9f15-4825-be2c-2bdff84c3109",
+ "id": "a06bb9e2-c44c-4e7b-a499-898e305e349d",
+ "metadata": {},
+ "source": [
+ "(Note how a unique color was generated for each class.)"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "id": "aaf7c95f-ccc7-4b84-9a7c-d96ae25142c1",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ "The numeric ID of each class is its index in the :attr:`~class_config.ClassConfig.names` list."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c1d973a4-1da7-4a06-ba64-0bf4f670c17e",
"metadata": {},
"source": [
- "The numeric ID of each class is its index in the `ClassConfig.names` list.\n",
- "\n",
"We can query the ID of a class like so:"
]
},
@@ -250,13 +277,22 @@
"### Normalized colors"
]
},
+ {
+ "cell_type": "raw",
+ "id": "be575610-a6ad-416d-8427-2b9ab0f38292",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ "Another nifty functionality is the :attr:`~class_config.ClassConfig.color_triples` property that returns the colors in a normalized form that can be directly used with ``matplotlib``."
+ ]
+ },
{
"cell_type": "markdown",
"id": "6982066d-15bb-4cdd-aa4a-a321fec57df4",
"metadata": {},
"source": [
- "Another nifty functionality is the `ClassConfig.color_triples` property that returns the colors in a normalized form that can be directly used with `matplotlib`.\n",
- "\n",
"The example below shows how we can easily create a color-map from our class colors."
]
},
@@ -308,13 +344,18 @@
]
},
{
- "cell_type": "markdown",
- "id": "a302dda8-e545-4142-a332-3a47f2abf89a",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "532dc6a7-5a16-4632-9cc6-46404c822761",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "While `RasterSource`s and `VectorSource`s allow us to read raw data, the `LabelSource`s take this data and convert them into a form suitable for machine learning.\n",
+ ".. currentmodule:: rastervision.core.data.label_source\n",
+ "\n",
+ "While `RasterSources <reading_raster_data.ipynb>`_ and `VectorSources <reading_vector_data.ipynb>`_ allow us to read raw data, the :class:`LabelSources <label_source.LabelSource>` take this data and convert them into a form suitable for machine learning.\n",
"\n",
- "We have 3 kinds of pre-defined `LabelSource`s -- one for each of the 3 main computer vision tasks: semantic segmentation, object detection, and chip classification."
+ "We have 3 kinds of pre-defined :class:`LabelSources <label_source.LabelSource>` -- one for each of the 3 main computer vision tasks: semantic segmentation, object detection, and chip classification."
]
},
{
@@ -333,13 +374,22 @@
"### Semantic Segmentation - `SemanticSegmentationLabelSource`"
]
},
+ {
+ "cell_type": "raw",
+ "id": "42385eeb-1ac9-497b-bd31-a514a7bae055",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ ":class:`~semantic_segmentation_label_source.SemanticSegmentationLabelSource` is perhaps the simplest of `LabelSources`. Since semantic segmentation labels are rasters themselves, :class:`~semantic_segmentation_label_source.SemanticSegmentationLabelSource` takes in a `RasterSource` and allows querying chips from it using array-indexing or :class:`Box's <rastervision.core.box.Box>`."
+ ]
+ },
{
"cell_type": "markdown",
- "id": "0bd3566f-ece9-400e-9dfe-0b81b03bb112",
+ "id": "d9e5cab5-2d59-4903-99e0-faebc86d82da",
"metadata": {},
"source": [
- "`SemanticSegmentationLabelSource` is perhaps the simplest of `LabelSource`. Since semantic segmentation labels are rasters themselves, `SemanticSegmentationLabelSource` takes in a `RasterSource` and allows querying chips from it using array-indexing or `Box`'s. \n",
- "\n",
"The main added service it provides is ensuring that if a chip overflows the extent, the overflowing pixels are assigned the ID of the \"null class\"."
]
},
@@ -394,14 +444,20 @@
]
},
{
- "cell_type": "markdown",
- "id": "23e4671c-b035-4839-9b78-59c007cddb48",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "a7d76d20-12b5-46d8-82cb-58afed008eab",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
+ ".. currentmodule:: rastervision.core.data\n",
+ "\n",
"Create\n",
- "- a `RasterSource` to get the image extent and a `CRSTRansformer`\n",
- "- a `VectorSource` to read the vector labels\n",
- "- a `RasterizedSource` to rasterize the `VectorSource`"
+ "\n",
+ "- a :class:`~raster_source.raster_source.RasterSource` to get the image extent and a :class:`~crs_transformer.crs_transformer.CRSTransformer`\n",
+ "- a :class:`~vector_source.vector_source.VectorSource` to read the vector labels\n",
+ "- a :class:`~raster_source.rasterized_source.RasterizedSource` to rasterize the :class:`~vector_source.vector_source.VectorSource`"
]
},
{
@@ -539,13 +595,24 @@
"### Object Detection - `ObjectDetectionLabelSource`"
]
},
+ {
+ "cell_type": "raw",
+ "id": "a8d6e198-2b77-414d-b4d4-5f7060481968",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ ".. currentmodule:: rastervision.core.data.label_source\n",
+ "\n",
+ "The :class:`~object_detection_label_source.ObjectDetectionLabelSource` allows querying all the label bounding boxes and their corresponding class IDs that fall inside a window. It also transforms the coordinates of the returned bounding boxes so that they represent points inside the window rather than the global extent."
+ ]
+ },
{
"cell_type": "markdown",
- "id": "4f325b0c-5b1b-4d45-ba3b-d41973257a49",
+ "id": "d26cb537-efdb-4004-b57c-363dcf8aae80",
"metadata": {},
"source": [
- "The `ObjectDetectionLabelSource` allows querying all the label bounding boxes and their corresponding class IDs that fall inside a window. It also transforms the coordinates of the returned bounding boxes so that they represent points inside the window rather than the global extent.\n",
- "\n",
"The bounding-box-to-window matching behavior can be further controlled by specifying an `ioa_thresh` (intersection-over-area threshold for considering a bounding box a part of a window) and a `clip` flag which ensures that bounding boxes do not overflow the window."
]
},
@@ -707,12 +774,22 @@
"### Chip Classification - `ChipClassificationLabelSource`"
]
},
+ {
+ "cell_type": "raw",
+ "id": "a1d361c8-2acf-4c76-94d0-fe0cd2f04f67",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ "The :class:`~chip_classification_label_source.ChipClassificationLabelSource` can do the following:"
+ ]
+ },
{
"cell_type": "markdown",
- "id": "8c2e2209-daad-499e-994e-631910be4cdc",
+ "id": "592f4cc5-fbe9-42a4-8201-3c92b257661c",
"metadata": {},
"source": [
- "The `ChipClassificationLabelSource` can do the following:\n",
"1. The trivial case: given a rectangular polygons (representing chips) with associated class IDs, allow querying the class ID for those chips.\n",
"2. The more interesting case: given polygons representing the members of the target class(es), infer the class ID for a window based on how much it overlaps with any label-polygon.\n",
"\n",
@@ -784,13 +861,11 @@
]
},
{
- "cell_type": "markdown",
- "id": "5af9be49-70e1-4479-8fca-eaf49d856b2f",
+ "cell_type": "raw",
+ "id": "6d0414b6-4f53-4154-8d91-9c7fe0701d6e",
"metadata": {},
"source": [
- "`ChipClassificationLabelSource` accepts a number of options for configuring the class ID inference behavior. These must be specified through the `ChipClassificationLabelSourceConfig`.\n",
- "\n",
- "See documentation for `ChipClassificationLabelSourceConfig` for details."
+ ":class:`~chip_classification_label_source.ChipClassificationLabelSource` accepts a number of options for configuring the class ID inference behavior. These must be specified through the :class:`~chip_classification_label_source_config.ChipClassificationLabelSourceConfig`."
]
},
{
@@ -1119,25 +1194,47 @@
]
},
{
- "cell_type": "markdown",
- "id": "921137c8-c804-4dd7-8485-88b99651dd45",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "166aa26e-507b-471e-a897-7979e17e4005",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "The `Labels` class is a source-agnostic, in-memory representation of the labels in a scene. \n",
+ ".. currentmodule:: rastervision.core.data\n",
"\n",
- "We can get all or a subset of a `LabelSource` as a `Labels` instance, by calling `LabelSource.get_labels()`. E.g.\n",
+ "The :class:`~label.labels.Labels` class is a source-agnostic, in-memory representation of the labels in a scene. \n",
"\n",
+ "We can get all or a subset of a :class:`~label_source.label_source.LabelSource` as a :class:`~label.labels.Labels` instance, by calling :meth:`LabelSource.get_labels() <label_source.label_source.LabelSource.get_labels>`. E.g."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "77290874-a1d8-483b-9253-5a01f602bc15",
+ "metadata": {},
+ "source": [
"```python\n",
"labels = label_source.get_labels(window)\n",
"```\n",
"\n",
- "Crucially, the predictions produced by a model can also be converted to `Labels` instance, allowing them to be compared agains the `Labels` from the `LabelSource` (i.e, the ground truth) to get performance metrics. This is essentially what `Evaluator` does.\n",
+ "Crucially, the predictions produced by a model can also be converted to `Labels` instance, allowing them to be compared agains the `Labels` from the `LabelSource` (i.e, the ground truth) to get performance metrics. This is essentially what `Evaluator` does."
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "id": "fcd8c13d-20a0-4f23-8ecb-8e2908b3fc72",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ ".. currentmodule:: rastervision.core.data.label\n",
"\n",
"There is one for each of the three tasks:\n",
"\n",
- "- `ChipClassificationLabels`\n",
- "- `SemanticSegmentationLabels`\n",
- "- `ObjectDetectionLabels`"
+ "- :class:`~chip_classification_labels.ChipClassificationLabels`\n",
+ "- :class:`~semantic_segmentation_labels.SemanticSegmentationLabels`\n",
+ "- :class:`~object_detection_labels.ObjectDetectionLabels`\n"
]
}
],
@@ -1157,7 +1254,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.13"
+ "version": "3.9.10"
},
"vscode": {
"interpreter": {
diff --git a/docs/usage/tutorials/reading_raster_data.ipynb b/docs/usage/tutorials/reading_raster_data.ipynb
index eef655b31..ac273b57e 100644
--- a/docs/usage/tutorials/reading_raster_data.ipynb
+++ b/docs/usage/tutorials/reading_raster_data.ipynb
@@ -19,11 +19,16 @@
]
},
{
- "cell_type": "markdown",
- "id": "8fdeb64d-52e2-4788-86a1-13f437519fcb",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "149f8987-26c2-4bfa-ad22-c7cbc3d47b50",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "The `RasterSource` is Raster Vision's abstraction for windowed reading from a raster image. "
+ ".. currentmodule:: rastervision.core.data\n",
+ "\n",
+ "The :class:`~raster_source.raster_source.RasterSource` is Raster Vision's abstraction for windowed reading from a raster image. "
]
},
{
@@ -34,13 +39,22 @@
"---"
]
},
+ {
+ "cell_type": "raw",
+ "id": "49250498-e0a2-46ad-80cf-20a623b45a2a",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ "One concrete implementation of it is the :class:`~raster_source.rasterio_source.RasterioSource` which is a wrapper around the `rasterio library <https://rasterio.readthedocs.io>`__ and allows reading from all file formats supported by it."
+ ]
+ },
{
"cell_type": "markdown",
- "id": "47b5824c-2b90-4509-87bc-36ae3ff20d14",
+ "id": "fd326886-f67a-4c70-a4fd-c7328042b61d",
"metadata": {},
"source": [
- "One concrete implementation of it is the `RasterioSource` which is a wrapper around the [rasterio library](https://rasterio.readthedocs.io/) and allows reading from all file formats supported by it.\n",
- "\n",
"We can create one from an image like shown below. `allow_streaming=True` allows us to take advantage of `rasterio`'s remote-file-reading capabilities; setting it to `False` will cause Raster Vision to download the image."
]
},
@@ -226,11 +240,14 @@
]
},
{
- "cell_type": "markdown",
- "id": "3f1f2481-4098-412d-9e99-489a18408a92",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "3045d467-ce72-4816-92d1-02dba407f999",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "The fancy slicing is just syntactic-sugar for the `RasterioSource.get_chip()` method. The last call is internally translated to the following call to the `.get_chip()` method:"
+ "The fancy slicing is just syntactic-sugar for the :meth:`RasterioSource.get_chip() <raster_source.rasterio_source.RasterioSource.get_chip>` method. The last call is internally translated to the following call to the :meth:`~raster_source.rasterio_source.RasterioSource.get_chip` method:"
]
},
{
@@ -260,6 +277,17 @@
"chip.shape"
]
},
+ {
+ "cell_type": "raw",
+ "id": "e70e3334-d2e7-48dc-aae8-5526bd49ab02",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ "Learn more about the handy :class:`Box class here <rastervision.core.box.Box>`."
+ ]
+ },
{
"cell_type": "markdown",
"id": "6accd016-0862-4305-9ae6-0572d6c95e0c",
@@ -277,21 +305,28 @@
]
},
{
- "cell_type": "markdown",
- "id": "e712696d-2174-403f-9b3e-ae65399a576b",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "a326a97c-dbb5-456f-9c7b-cbe0f3d5b809",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "`RasterSource`s accept a list of `RasterTransformer`s, all of which are automatically applied (in the order specified) to each chip sampled from that `RasterSource`.\n",
+ ":class:`RasterSources <raster_source.raster_source.RasterSource>` accept a list of :class:`RasterTransformers <raster_transformer.raster_transformer.RasterTransformer>`, all of which are automatically applied (in the order specified) to each chip sampled from that :class:`~raster_source.raster_source.RasterSource`.\n",
+ "\n",
+ ".. currentmodule:: rastervision.core.data.raster_transformer\n",
"\n",
"Below we'll look at two such `RasterTransformer`s:\n",
- "- `MinMaxTransformer`\n",
- "- `StatsTransformer`\n",
+ "\n",
+ "- :class:`~min_max_transformer.MinMaxTransformer`\n",
+ "- :class:`~stats_transformer.StatsTransformer`\n",
"\n",
"But Raster Vision also provides the following:\n",
- "- `CastTransformer`: type-cast chip.\n",
- "- `NanTransformer`: map NaN values to another value.\n",
- "- `ReclassTransformer`: map values to other values using a given mapping; most useful for modifying class IDs in semantic segmentation labels.\n",
- "- `RGBClassTrasnformer`: if your semantic segmentation labels are in the form of an RGB raster with different colors representing different classes, use this to map them to class IDs."
+ "\n",
+ "- :class:`~cast_transformer.CastTransformer`: type-cast chip.\n",
+ "- :class:`~nan_transformer.NanTransformer`: map NaN values to another value.\n",
+ "- :class:`~reclass_transformer.ReclassTransformer`: map values to other values using a given mapping; most useful for modifying class IDs in semantic segmentation labels.\n",
+ "- :class:`~rgb_class_transformer.RGBClassTransformer`: if your semantic segmentation labels are in the form of an RGB raster with different colors representing different classes, use this to map them to class IDs.\n"
]
},
{
@@ -311,11 +346,14 @@
]
},
{
- "cell_type": "markdown",
- "id": "f5b96d09-d207-424f-899b-79626623380b",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "94fa17ba-821d-4dae-8bf7-c66d2bd4a51a",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "Above, we manually min-max normalized the `uint16` chip to 0-1. If we wanted this normalization to be automatically applied to all chips sampled from a `RasterSource`, we can simply attach a `MinMaxTransformer` to that `RasterSource`."
+ "Above, we manually min-max normalized the ``uint16`` chip to 0-1. If we wanted this normalization to be automatically applied to all chips sampled from a ``RasterSource``, we can simply attach a :class:`~min_max_transformer.MinMaxTransformer` to that ``RasterSource``."
]
},
{
@@ -384,11 +422,16 @@
]
},
{
- "cell_type": "markdown",
- "id": "1b9364a5-0041-4f83-b7a6-b552a72a3e99",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "b78ac5f5-413c-46c2-94d2-164dcb2e3295",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "Another useful `RasterTransformer` is the `StatsTransformer`. Unlike `MinMaxTransformer`, the `StatsTransformer` is able to deal with outlier values. It works by using channel means and standard deviations to convert values to z-scores and then clipping them to some number of standard deviations before scaling to 0-255 and converting to `uint8`.\n",
+ "Another useful ``RasterTransformer`` is the :class:`~stats_transformer.StatsTransformer`. \n",
+ "\n",
+ "Unlike `MinMaxTransformer <#MinMaxTransformer>`_, the :class:`~stats_transformer.StatsTransformer` is able to deal with outlier values. It works by using channel means and standard deviations to convert values to z-scores and then clipping them to some number of standard deviations before scaling to 0-255 and converting to ``uint8``.\n",
"\n",
"We can create and use one like so:"
]
@@ -755,14 +798,25 @@
"## Combining bands from different files using `MultiRasterSource`"
]
},
+ {
+ "cell_type": "raw",
+ "id": "703bf4a8-0e48-41f6-9906-3203e2d54fa9",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ ".. currentmodule:: rastervision.core.data\n",
+ "\n",
+ "Another common use case is combining bands from multiple sources. This can done using a :class:`~raster_source.multi_raster_source.MultiRasterSource`."
+ ]
+ },
{
"cell_type": "markdown",
- "id": "9bc69e7e-41c5-4d63-ba2c-12f98ef95dcf",
+ "id": "36555340-f28d-4ba5-8c02-3af3b648e8d6",
"metadata": {},
"source": [
- "Another common use case is combining bands from multiple sources.\n",
- "\n",
- "The following example combines RGB, SWIR, and SAR bands into a single 8-band `RasterSource`."
+ "The following example combines RGB, SWIR, and SAR bands into a single 8-band ``RasterSource``."
]
},
{
@@ -786,6 +840,14 @@
"uri_seninel_1_vv = 'https://radiantearth.blob.core.windows.net/mlhub/c2smsfloods/chips/e7d1917e-c069-45cf-a392-42e24aa2f4ac/s1/S1B_IW_GRDH_1SDV_20201020T164222_20201020T164247_023899_02D6C4_35D8_07680-00512/VV.tif'"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "d00bdf3d-89f7-47fa-89cf-e7be80e9c64c",
+ "metadata": {},
+ "source": [
+ "First, we create `RasterSource`s for each individual source."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 21,
@@ -818,6 +880,17 @@
"print('rs_seninel_1_vv', rs_seninel_1_vv.shape, rs_seninel_1_vv.dtype)"
]
},
+ {
+ "cell_type": "raw",
+ "id": "576a77c3-6a00-42cd-90ec-4a5fad2da739",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ "Next, we combine them into a :class:`~raster_source.multi_raster_source.MultiRasterSource`."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 22,
@@ -1016,7 +1089,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.13"
+ "version": "3.9.10"
},
"vscode": {
"interpreter": {
diff --git a/docs/usage/tutorials/reading_vector_data.ipynb b/docs/usage/tutorials/reading_vector_data.ipynb
index 070c57b53..987720a83 100644
--- a/docs/usage/tutorials/reading_vector_data.ipynb
+++ b/docs/usage/tutorials/reading_vector_data.ipynb
@@ -9,11 +9,18 @@
]
},
{
- "cell_type": "markdown",
- "id": "35ca06d8-1c57-44af-bf98-b29db349a21e",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "eff50e02-7b30-4b4d-8fb5-1308077abb9c",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "The `VectorSource` is Raster Vision's abstraction for reading from a source of vector data. Besides reading the data, `VectorSource`s also convert geometries from map to pixel coordinates and perform some data cleaning such as removing empty geometries and splitting apart multi-part geometries (e.g. MultiPolygon etc.)."
+ ".. currentmodule:: rastervision.core.data\n",
+ "\n",
+ "The :class:`~vector_source.vector_source.VectorSource` is Raster Vision's abstraction for reading from a source of vector data. \n",
+ "\n",
+ "Besides reading the data, they can also convert geometries from map-coordinates to pixel-coordinates and perform some data cleaning such as removing empty geometries and splitting apart multi-part geometries (e.g. ``MultiPolygon`` etc.)."
]
},
{
@@ -25,11 +32,14 @@
]
},
{
- "cell_type": "markdown",
- "id": "632fc382-a853-401a-9f64-169a85cbfba7",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "7702313b-77cb-43ed-861e-6f835349f946",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "One concrete implementation of it is the `GeoJSONVectorSource` which can read vector data from a GeoJSON file."
+ "One concrete implementation of it is the :class:`~vector_source.geojson_vector_source.GeoJSONVectorSource` which can read vector data from a GeoJSON file."
]
},
{
@@ -50,15 +60,18 @@
]
},
{
- "cell_type": "markdown",
- "id": "ce15abef-0154-4606-bc6b-d884aef84dc7",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "ffe72386-b0a1-4de6-877d-49d726876372",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "We can read data from a VectorSource in three different formats:\n",
+ "We can read data from a :class:`~vector_source.vector_source.VectorSource` in three different formats:\n",
"\n",
- "1. as GeoJSON dict (`vector_source.get_geojson()`)\n",
- "2. as Shapely geoms (`vector_source.get_geoms()`)\n",
- "3. as a GeoPandas `GeoDataFrame` (`vector_source.get_dataframe()`)\n",
+ "1. as GeoJSON dict (:meth:`~vector_source.vector_source.VectorSource.get_geojson`)\n",
+ "2. as Shapely geoms (:meth:`~vector_source.vector_source.VectorSource.get_geoms`)\n",
+ "3. as a GeoPandas :class:`~geopandas.GeoDataFrame` (:meth:`~vector_source.vector_source.VectorSource.get_dataframe`)\n",
"\n",
"Each of these is shown in the following cells."
]
@@ -504,11 +517,14 @@
]
},
{
- "cell_type": "markdown",
- "id": "fe29caeb-0e2a-4be9-b140-310d96ff0793",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "8dd13d9f-d3c7-4f8b-93ef-d93d80c7ffe7",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "Just like we can tansform rasters by specifying a series of `RasterTransformer`s, we can specify `VectorTransformer`s to transform vector data."
+ "Just like we can tansform rasters by specifying a series of :class:`RasterTransformers <raster_transformer.raster_transformer.RasterTransformer>`, we can specify :class:`VectorTransformers <vector_transformer.vector_transformer.VectorTransformer>` to transform vector data."
]
},
{
@@ -527,13 +543,24 @@
"### Inferring class IDs for polygons"
]
},
+ {
+ "cell_type": "raw",
+ "id": "eb379d6d-aced-4c00-8112-870ae749131d",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ ".. currentmodule:: rastervision.core.data.vector_transformer\n",
+ "\n",
+ "One very important :class:`~vector_transformer.vector_transformer.VectorTransformer` is the :class:`~class_inference_transformer.ClassInferenceTransformer`."
+ ]
+ },
{
"cell_type": "markdown",
- "id": "9ed2afaf-3e4f-4b59-997d-20a4f934cbed",
+ "id": "cfb9e2be-fa0d-41fe-97ab-1d8e2d116fde",
"metadata": {},
"source": [
- "One very important `VectorTransformer` is the `ClassInferenceTransformer`.\n",
- "\n",
"When using vector data in machine learning, it is important that each polygon be labeled with an appropriate class ID. But often, your data will not have this property stored in the GeoJSON file.\n",
"\n",
"The `ClassInferenceTransformer` can automatically infer and attach a `class_id` to each polygon read from the `VectorSource`. It can\n",
@@ -673,11 +700,14 @@
]
},
{
- "cell_type": "markdown",
- "id": "3ff9230a-6563-41f8-a77c-eb49382199df",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "b9fd25bc-b9b0-4e31-b95e-0264b058cb9f",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "`Point` and `LineString` geometries are not directly useable if doing, say, semantic segmentation. The cells below show an example of converting road geometries (given in the form of `LineString`s) into polygons."
+ "``Point`` and ``LineString`` geometries are not directly useable if doing, say, semantic segmentation. The cells below show an example of converting road geometries (given in the form of ``LineString``s) into polygons using the :class:`~buffer_transformer.BufferTransformer`."
]
},
{
@@ -824,13 +854,24 @@
"## Rasterizing vector data using `RasterizedSource`"
]
},
+ {
+ "cell_type": "raw",
+ "id": "9e349df7-22fa-4997-a8c1-845d479b5350",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ ".. currentmodule:: rastervision.core.data\n",
+ "\n",
+ "Suppose we have semantic segmentation labels in the form of polygons. To use them for training, we will first need to convert them into rasters. Raster Vision allows accomplishing this using the :class:`~raster_source.rasterized_source.RasterizedSource` class."
+ ]
+ },
{
"cell_type": "markdown",
- "id": "f35a4746-2052-4d8d-9ac1-3f9c4f3fb5e3",
+ "id": "e32dbfa8-180b-482c-b83e-24c556a13c14",
"metadata": {},
"source": [
- "Suppose we have semantic segmentation labels in the form of polygons. To use them for training, we will first need to convert them into rasters. Raster Vision allows accomplishing this using the `RasterizedSource` class.\n",
- "\n",
"The `RasterizedSource` is a `RasterSource` that reads data from a `VectorSource` (rather than an image file) and then converts it into rasters. It can be indexed like any other `RasterSource`."
]
},
@@ -922,7 +963,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3.7.9 ('base')",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -936,7 +977,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.9"
+ "version": "3.9.10"
},
"vscode": {
"interpreter": {
diff --git a/docs/usage/tutorials/sampling_training_data.ipynb b/docs/usage/tutorials/sampling_training_data.ipynb
index 471b40596..8e227dce6 100644
--- a/docs/usage/tutorials/sampling_training_data.ipynb
+++ b/docs/usage/tutorials/sampling_training_data.ipynb
@@ -10,22 +10,28 @@
},
{
"cell_type": "markdown",
- "id": "b4aed88a-6109-4149-8a54-613efab85611",
+ "id": "8a93c346",
"metadata": {},
"source": [
"## The `GeoDataset` class"
]
},
{
- "cell_type": "markdown",
- "id": "7dd4906d-6913-4cde-ac11-c76a077179bb",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "acd8e949-9b74-4233-8006-897a1e251bbf",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "The `GeoDataset` is a PyTorch-compatible `Dataset` implementation that allows sampling images and labels from a `Scene` (which is a combination of a `RasterSource` and a `LabelSource`).\n",
+ ".. currentmodule:: rastervision.pytorch_learner.dataset\n",
+ "\n",
+ "The :class:`~dataset.GeoDataset` is a PyTorch-compatible :class:`~torch.utils.data.Dataset` implementation that allows sampling images and labels from a `Scene <scenes_and_aois.ipynb>`_.\n",
"\n",
"It comes in two flavors:\n",
- "1. `SlidingWindowGeoDataset`\n",
- "2. `RandomWindowGeoDataset`\n",
+ "\n",
+ "1. :class:`~dataset.SlidingWindowGeoDataset`\n",
+ "2. :class:`~dataset.RandomWindowGeoDataset`\n",
"\n",
"Below we explore both in the context of semantic segmentation."
]
@@ -48,7 +54,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 1,
"id": "5a7a3ce6-abcb-4f6b-8ce3-0e21a46b2c7c",
"metadata": {},
"outputs": [],
@@ -86,16 +92,19 @@
]
},
{
- "cell_type": "markdown",
- "id": "ba859407",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "1851353c-5c48-4080-b69f-29a07f2e6361",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "The SlidingWindowGeoDataset allows reading the scene left-to-right, top-to-bottom, using a sliding window."
+ "The :class:`~dataset.SlidingWindowGeoDataset` allows reading the scene left-to-right, top-to-bottom, using a sliding window."
]
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 2,
"id": "877f5cf8-fb08-447d-b3ed-9d480b4463a7",
"metadata": {},
"outputs": [],
@@ -105,16 +114,19 @@
]
},
{
- "cell_type": "markdown",
- "id": "a57f6519-028d-4b7d-b48d-f6ba11f37126",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "56409a53-87fd-494c-83cb-b03b62c65b27",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "Here we make use of the convenience API, `GeoDataset.from_uris()`, but we can also use the normal constructor if we want to manually define the `RasterSource` and `LabelSource`."
+ "Here we make use of the convenience API, :meth:`~dataset.GeoDataset.from_uris()` (specifically, :meth:`~semantic_segmentation_dataset.SemanticSegmentationSlidingWindowGeoDataset.from_uris`), but we can also use the normal constructor if we want to manually define the `RasterSource <reading_raster_data.ipynb>`_ and `LabelSource <reading_labels.ipynb>`_."
]
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 3,
"id": "f9d43771-319f-4b80-b507-7c5df29501f1",
"metadata": {
"tags": []
@@ -124,8 +136,8 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "2022-11-14 17:21:30:rastervision.core.data.raster_source.rasterio_source: WARNING - Raster block size (2, 1024) is too non-square. This can slow down reading. Consider re-tiling using GDAL.\n",
- "2022-11-14 17:21:30:rastervision.pipeline.file_system.utils: INFO - Using cached file /opt/data/tmp/cache/s3/spacenet-dataset/spacenet/SN7_buildings/train/L15-0331E-1257N_1327_3160_13/labels/global_monthly_2018_01_mosaic_L15-0331E-1257N_1327_3160_13_Buildings.geojson.\n"
+ "2022-12-02 12:39:05:rastervision.core.data.raster_source.rasterio_source: WARNING - Raster block size (2, 1024) is too non-square. This can slow down reading. Consider re-tiling using GDAL.\n",
+ "2022-12-02 12:39:05:rastervision.pipeline.file_system.utils: INFO - Using cached file /opt/data/tmp/cache/s3/spacenet-dataset/spacenet/SN7_buildings/train/L15-0331E-1257N_1327_3160_13/labels/global_monthly_2018_01_mosaic_L15-0331E-1257N_1327_3160_13_Buildings.geojson.\n"
]
}
],
@@ -163,7 +175,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 4,
"id": "a6325a6a-8cb6-4ee0-b7c3-fa1eac069d0f",
"metadata": {},
"outputs": [
@@ -173,7 +185,7 @@
"(torch.Size([3, 256, 256]), torch.Size([256, 256]))"
]
},
- "execution_count": 7,
+ "execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
@@ -184,22 +196,25 @@
]
},
{
- "cell_type": "markdown",
- "id": "efe2fb6d",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "b0ebdccd-1acc-4191-bafa-49c4fd5d94ee",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "And then plot it using the `SemanticSegmentationVisualizer`:"
+ "And then plot it using the `SemanticSegmentationVisualizer <visualize_data_samples.ipynb>`_:"
]
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 5,
"id": "04b4a84b-24fd-4190-8d5c-993ce3bbd422",
"metadata": {},
"outputs": [
{
"data": {
- "image/png": "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",
+ "image/png": "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\n",
"text/plain": [
"<Figure size 600x300 with 2 Axes>"
]
@@ -211,7 +226,7 @@
"source": [
"viz = SemanticSegmentationVisualizer(\n",
" class_names=class_config.names, class_colors=class_config.colors)\n",
- "viz.plot_batch(x.unsqueeze(0), y.unsqueeze(0), show=True)\n"
+ "viz.plot_batch(x.unsqueeze(0), y.unsqueeze(0), show=True)"
]
},
{
@@ -230,7 +245,7 @@
"outputs": [
{
"data": {
- "image/png": "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",
+ "image/png": "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\n",
"text/plain": [
"<Figure size 800x800 with 1 Axes>"
]
@@ -244,57 +259,6 @@
"show_windows(img_full, ds.windows, title='Sliding windows')"
]
},
- {
- "cell_type": "markdown",
- "id": "d25a771f",
- "metadata": {},
- "source": [
- "Note the windows on the edges that extend beyond the extent and will mostly be blank when sampled. We can get rid of them by adjusting the `padding` param:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "f0db73fa-5d83-452b-941e-7bca1f201d11",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "2022-09-13 12:52:17:rastervision.pipeline.file_system.utils: INFO - Using cached file /opt/data/tmp/cache/s3/spacenet-dataset/spacenet/SN7_buildings/train/L15-0331E-1257N_1327_3160_13/labels/global_monthly_2018_01_mosaic_L15-0331E-1257N_1327_3160_13_Buildings.geojson.\n"
- ]
- },
- {
- "data": {
- "image/png": "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",
- "text/plain": [
- "<Figure size 800x800 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "ds = SemanticSegmentationSlidingWindowGeoDataset.from_uris(\n",
- " class_config=class_config,\n",
- " image_uri=image_uri,\n",
- " label_vector_uri=label_uri,\n",
- " label_vector_default_class_id=class_config.get_class_id('building'),\n",
- " image_raster_source_kw=dict(allow_streaming=True),\n",
- " size=200,\n",
- " stride=200,\n",
- " transform=A.Resize(256, 256),\n",
- " padding=0\n",
- ")\n",
- "\n",
- "img_full = ds.scene.raster_source[:, :]\n",
- "show_windows(img_full, ds.windows, title='Sliding windows, no padding')"
- ]
- },
{
"cell_type": "markdown",
"id": "128a7240-b542-41a6-b8b5-b016dad4f112",
@@ -312,11 +276,14 @@
]
},
{
- "cell_type": "markdown",
- "id": "ba859407",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "8871132d-8332-4f28-95a9-2989ce4dca78",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "The SlidingWindowGeoDataset allows reading the scene by sampling random window sizes and locations."
+ "The :class:`~dataset.RandomWindowGeoDataset` allows reading the scene by sampling random window sizes and locations."
]
},
{
@@ -331,11 +298,14 @@
]
},
{
- "cell_type": "markdown",
- "id": "ba6713b9-06fc-446d-8fdf-785591aa195e",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "4c5a8567-3cdb-43c1-8a7d-b9bf9bfa16fc",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "Here we make use of the convenience API, `GeoDataset.from_uris()`, but we can also use the normal constructor if we want to manually define the `RasterSource` and `LabelSource`."
+ "As before, we make use of the convenience API, :meth:`~dataset.GeoDataset.from_uris()` (specifically, :meth:`~semantic_segmentation_dataset.SemanticSegmentationRandomWindowGeoDataset.from_uris`), but we can also use the normal constructor if we want to manually define the `RasterSource <reading_raster_data.ipynb>`_ and `LabelSource <reading_labels.ipynb>`_."
]
},
{
@@ -383,8 +353,11 @@
" label_vector_uri=label_uri,\n",
" label_vector_default_class_id=class_config.get_class_id('building'),\n",
" image_raster_source_kw=dict(allow_streaming=True),\n",
+ " # window sizes will randomly vary from 100x100 to 300x300\n",
" size_lims=(100, 300),\n",
+ " # resize chips to 256x256 before returning\n",
" out_size=256,\n",
+ " # allow windows to overflow the extent by 100 pixels\n",
" padding=100\n",
")\n",
"\n",
@@ -420,7 +393,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3.8.9 64-bit",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
diff --git a/docs/usage/tutorials/scenes_and_aois.ipynb b/docs/usage/tutorials/scenes_and_aois.ipynb
index 7671e50dc..0a4bc8a1e 100644
--- a/docs/usage/tutorials/scenes_and_aois.ipynb
+++ b/docs/usage/tutorials/scenes_and_aois.ipynb
@@ -24,7 +24,9 @@
"tags": []
},
"source": [
- "This tutorial introduces the :class:`~rastervision.core.data.scene.Scene` abstraction, which bundles them together. Additionally, it allows specifying one or more \"Areas of Interest\" (AOIs) if we are only interested in a subset of the scene."
+ ".. currentmodule:: rastervision.core.data\n",
+ "\n",
+ "This tutorial introduces the :class:`~scene.Scene` abstraction, which bundles them together. Additionally, it allows specifying one or more \"Areas of Interest\" (AOIs) if we are only interested in a subset of the scene."
]
},
{
@@ -55,11 +57,14 @@
]
},
{
- "cell_type": "markdown",
- "id": "603479b4-3a81-40e9-9381-ec9618c23db9",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "88c3969a-c5c5-410b-b747-f3317d1083fd",
+ "metadata": {
+ "raw_mimetype": "custom",
+ "tags": []
+ },
"source": [
- "Define a `RasterSource`:"
+ "Define a :class:`~raster_source.raster_source.RasterSource`:"
]
},
{
@@ -75,11 +80,14 @@
]
},
{
- "cell_type": "markdown",
- "id": "eb414969-52bf-4380-a406-d2e825d542fd",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "7a8d2e97-2194-49e4-bedc-6fb8427db095",
+ "metadata": {
+ "raw_mimetype": "custom",
+ "tags": []
+ },
"source": [
- "Define a `LabelSource`:"
+ "Define a :class:`~label_source.label_source.LabelSource`:"
]
},
{
@@ -128,7 +136,7 @@
"id": "6f4f6a4f-dddc-4aa4-8501-f9706bff21c6",
"metadata": {},
"source": [
- "Define AOI:"
+ "Define some AOI using polygons:"
]
},
{
@@ -151,7 +159,7 @@
"id": "cb437c76-a2f3-46d3-a63d-4b93e5ce4a0e",
"metadata": {},
"source": [
- "Visualize AOI:"
+ "Visualize the AOI:"
]
},
{
@@ -204,11 +212,14 @@
]
},
{
- "cell_type": "markdown",
- "id": "79308b6c-0711-41ef-ab16-7a148e48c425",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "be3af5a4-87bc-4fc4-9adf-61a45ee846cb",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "Finally, define a `Scene`:"
+ "Finally, define a :class:`~scene.Scene`:"
]
},
{
@@ -262,12 +273,21 @@
},
{
"cell_type": "markdown",
- "id": "f17cb1c0-ae4a-45fe-94cd-f142921a8389",
+ "id": "be873c72-c787-42ac-bdb1-2c2b6e113b7f",
"metadata": {},
"source": [
- "Note that the `Scene` itself does not prevent you from reading windows outside the AOI. \n",
- "\n",
- "A simple check to make sure you're inside the AOI before reading is to use the `Box.within_aoi` method:"
+ "Note that the `Scene` itself does not prevent you from reading windows outside the AOI. "
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "id": "dfa90bf8-6dd4-430c-aa7b-6cd295d67823",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ "A simple check to make sure you're inside the AOI before reading is to use the :meth:`~rastervision.core.box.Box.within_aoi` method:"
]
},
{
@@ -337,11 +357,14 @@
]
},
{
- "cell_type": "markdown",
- "id": "e130597f-1074-4665-a27f-94a7c404ea6a",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "f2cc5c38-d8e7-466c-a271-0193a8a0152c",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "`make_ss_scene` is for semantic segmentation scenes. There are also `make_cc_scene` and `make_od_scene` for chip classificaiton and object detection respectively."
+ ":func:`~utils.factory.make_ss_scene` is for creating semantic segmentation scenes. There is also :func:`~utils.factory.make_cc_scene` for chip classificaiton and :func:`~utils.factory.make_od_scene` for object detection."
]
}
],
@@ -361,7 +384,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.13"
+ "version": "3.9.10"
}
},
"nbformat": 4,
diff --git a/docs/usage/tutorials/train.ipynb b/docs/usage/tutorials/train.ipynb
index 3988fb266..405ea234a 100644
--- a/docs/usage/tutorials/train.ipynb
+++ b/docs/usage/tutorials/train.ipynb
@@ -9,11 +9,15 @@
]
},
{
- "cell_type": "markdown",
- "id": "aff5aa2c-567e-4e11-9e7c-fc33234734a1",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "78a22a3f-f343-4ac3-9965-44cd4e76e7d2",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "## Define `ClassConfig`"
+ "Define `ClassConfig <reading_labels.ipynb#ClassConfig>`_\n",
+ "--------------------------------------------------------"
]
},
{
@@ -31,12 +35,24 @@
" null_class='background')"
]
},
+ {
+ "cell_type": "raw",
+ "id": "ab09caa7-b904-42a4-b862-0ff013bb0ce8",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ "Define training and validation `datasets <sampling_training_data.ipynb>`_\n",
+ "-------------------------------------------------------------------------"
+ ]
+ },
{
"cell_type": "markdown",
- "id": "841136bb-f47e-4867-b625-039a2a565624",
+ "id": "5045902e-7a14-48a0-b21d-307db2eb986f",
"metadata": {},
"source": [
- "## Define training and validation datasets"
+ "To keep things simple, we use one scene for training and one for validation. In a real workflow, we would normally use many more scenes."
]
},
{
@@ -80,11 +96,15 @@
]
},
{
- "cell_type": "markdown",
- "id": "2b9b76ff-61f2-497f-a46d-b311754ec476",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "ffa43877-243e-46b8-817f-31bbf65ba6c7",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "### Training dataset with random-window sampling and data augmentation"
+ "Training dataset with `random-window sampling <sampling_training_data.ipynb#RandomWindowGeoDataset>`_ and data augmentation\n",
+ "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~"
]
},
{
@@ -141,11 +161,14 @@
]
},
{
- "cell_type": "markdown",
- "id": "5a2a5f2f-b0c2-46d4-87d3-dc99088bf496",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "5cede845-db9c-4b51-b0e5-6a4105b3dcfe",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "Visualize:"
+ "`Visualize <visualize_data_samples.ipynb>`_:"
]
},
{
@@ -171,11 +194,15 @@
]
},
{
- "cell_type": "markdown",
- "id": "01b73eb9-1a8b-4c6a-9971-189034df347a",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "56bd2d6b-9e75-4e21-b4ce-5b5990fc4420",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "### Validation dataset with sliding-window sampling and no data augmentation"
+ "Validation dataset with `sliding-window sampling <sampling_training_data.ipynb#SlidingWindowGeoDataset>`_ (and no data augmentation)\n",
+ "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~"
]
},
{
@@ -217,11 +244,14 @@
]
},
{
- "cell_type": "markdown",
- "id": "4902c093-9947-454b-b4f1-6c4e63f7d0c3",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "b5af755b-7fb3-47b3-88dc-e20de8a34dcf",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "Visualize:"
+ "`Visualize <visualize_data_samples.ipynb>`_:"
]
},
{
@@ -300,11 +330,26 @@
]
},
{
- "cell_type": "markdown",
- "id": "45f1f872-e551-40b9-83b2-6780fac3f317",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "98b4b022-5d2e-4451-8a6b-46a505ed6828",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ ".. currentmodule:: rastervision.pytorch_learner.learner_config"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "id": "b0ba6b5c-0f88-4184-b88a-229e601eb203",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "### `DataConfig` - Configure data-related hyper-parameters"
+ ":class:`DataConfig` -- Configure data-related hyper-parameters\n",
+ "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~"
]
},
{
@@ -324,11 +369,15 @@
]
},
{
- "cell_type": "markdown",
- "id": "3a65ed66-04fc-44db-b3aa-d6ebd5713182",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "de886b77-4740-4b0f-beef-c253df1eb714",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "### `SolverConfig` - Configure the loss, optimizer, and scheduler(s)"
+ ":class:`SolverConfig` -- Configure the loss, optimizer, and scheduler(s)\n",
+ "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~"
]
},
{
@@ -348,11 +397,15 @@
]
},
{
- "cell_type": "markdown",
- "id": "f2c9f179-8b79-4eca-aa4a-5f8c743a4a4e",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "530dd8a3-d655-4778-aabb-91fef6adb99e",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "### `LearnerConfig` - Combine `DataConfig`, `SovlerConfig` (and optionally, `ModelConfig`)"
+ ":class:`LearnerConfig` -- Combine :class:`DataConfig`, :class:`SolverConfig` (and optionally, :class:`ModelConfig`)\n",
+ "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~"
]
},
{
@@ -368,11 +421,26 @@
]
},
{
- "cell_type": "markdown",
- "id": "d6879c5c-9ccf-4b41-9c8f-fd63c712a58b",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "12c08fc2-85af-42e9-b68c-4927fb44bc2c",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ ".. currentmodule:: rastervision.pytorch_learner.learner"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "id": "7e682ccd-5f05-44fe-8289-1a4543162306",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "## Initialize `Learner`"
+ "Initialize :class:`Learner`\n",
+ "---------------------------"
]
},
{
@@ -420,20 +488,17 @@
]
},
{
- "cell_type": "markdown",
- "id": "56277ecc-c099-4b61-ae73-c35b1408582d",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "aab1374e-0157-4e2d-abc1-49d423cd6613",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "<div class=\"alert alert-info\">\n",
- "\n",
- "Note\n",
+ ".. note::\n",
"\n",
- "<ul>\n",
- " <li>If running inside the Raster Vision docker image, you will need to pass `--tensorboard` to `docker/run` for this to work.</li>\n",
- " <li>If the dashboard doen't auto-reload, you can click the reload button onthe top-right.</li>\n",
- "</ul>\n",
- "\n",
- "</div>"
+ " - If running inside the Raster Vision docker image, you will need to pass `--tensorboard` to `docker/run` for this to work.\n",
+ " - If the dashboard doen't auto-reload, you can click the reload button on the top-right.\n"
]
},
{
@@ -473,11 +538,15 @@
]
},
{
- "cell_type": "markdown",
- "id": "f554d467-99c8-4d89-a486-3ab6c6d72a26",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "c05e6588-ddb9-457e-9605-7acebda90d21",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "## Train"
+ "Train -- :meth:`Learner.train`\n",
+ "------------------------------"
]
},
{
@@ -1027,11 +1096,15 @@
]
},
{
- "cell_type": "markdown",
- "id": "a515a663-4e62-4e30-b821-21c22aedde5e",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "38127c1f-53db-4218-97db-a58af203ea70",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "## Examine predictions"
+ "Examine predictions -- :meth:`Learner.plot_predictions`\n",
+ "-------------------------------------------------------"
]
},
{
@@ -1072,11 +1145,15 @@
]
},
{
- "cell_type": "markdown",
- "id": "2cb66486-7f47-42bb-853f-c805c62f43c0",
- "metadata": {},
+ "cell_type": "raw",
+ "id": "d3a2bd4c-1582-44ae-bb14-ef476162928d",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "## Save as a model-bundle"
+ "Save as a model-bundle -- :meth:`Learner.save_model_bundle`\n",
+ "-----------------------------------------------------------"
]
},
{
@@ -1168,7 +1245,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.13"
+ "version": "3.9.10"
}
},
"nbformat": 4,
diff --git a/docs/usage/tutorials/visualize_data_samples.ipynb b/docs/usage/tutorials/visualize_data_samples.ipynb
index ccc5b90e8..8f5daab03 100644
--- a/docs/usage/tutorials/visualize_data_samples.ipynb
+++ b/docs/usage/tutorials/visualize_data_samples.ipynb
@@ -4,14 +4,34 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Plot samples from `Dataset`s using `Visualizer`s\n",
- "\n",
- "This notebook shows how to use `Visualizer` objects to plot image/label samples for computer vision PyTorch `Dataset`s. There are examples for semantic segmentation, object detection, and image classification. We use Raster Vision's `GeoDataset` functionality to read the data, but the `Visualizer` classes can be used with any images and labels as long as they are in the expected format."
+ "# Plot samples from `Dataset`s using `Visualizer`s"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ ".. currentmodule:: rastervision.pytorch_learner.dataset.visualizer"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
+ "source": [
+ "This notebook shows how to use :class:`~visualizer.Visualizer` objects to plot image/label samples for computer vision PyTorch :class:`Datasets <torch.utils.data.Dataset>`. There are examples for `semantic segmentation <#Semantic-Segmentation----SemanticSegmentationVisualizer>`_, `object detection <#Object-Detection----ObjectDetectionVisualizer>`_, and `image classification <#Image-Classification----ClassificationVisualizer>`_. We use Raster Vision's `GeoDataset <sampling_training_data.ipynb#The-GeoDataset-class>`_ functionality to read the data, but the :class:`~visualizer.Visualizer` classes can be used with any images and labels as long as they are in the expected format."
]
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "tags": []
+ },
"source": [
"## Setup"
]
@@ -60,10 +80,14 @@
]
},
{
- "cell_type": "markdown",
- "metadata": {},
+ "cell_type": "raw",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "## Semantic Segmentation"
+ "Semantic Segmentation -- :class:`~semantic_segmentation_visualizer.SemanticSegmentationVisualizer`\n",
+ "--------------------------------------------------------------------------------------------------"
]
},
{
@@ -141,10 +165,14 @@
]
},
{
- "cell_type": "markdown",
- "metadata": {},
+ "cell_type": "raw",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "## Object Detection"
+ "Object Detection -- :class:`~object_detection_visualizer.ObjectDetectionVisualizer`\n",
+ "-----------------------------------------------------------------------------------"
]
},
{
@@ -189,10 +217,14 @@
]
},
{
- "cell_type": "markdown",
- "metadata": {},
+ "cell_type": "raw",
+ "metadata": {
+ "raw_mimetype": "text/restructuredtext",
+ "tags": []
+ },
"source": [
- "## Image Classification"
+ "Image Classification -- :class:`~classification_visualizer.ClassificationVisualizer`\n",
+ "------------------------------------------------------------------------------------"
]
},
{
@@ -244,7 +276,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3.7.9 ('base')",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -258,9 +290,8 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.9"
+ "version": "3.9.10"
},
- "orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "6850b013e1f3bd5bc88fc148f23f814e4d2f79564e8ea88f16f3069ee54d6960"
@@ -268,5 +299,5 @@
}
},
"nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
}
diff --git a/rastervision_core/rastervision/core/data/class_config.py b/rastervision_core/rastervision/core/data/class_config.py
index cdc06baa5..a31146546 100644
--- a/rastervision_core/rastervision/core/data/class_config.py
+++ b/rastervision_core/rastervision/core/data/class_config.py
@@ -120,5 +120,6 @@ def __len__(self) -> int:
@property
def color_triples(self) -> List[Tuple[float, float, float]]:
+ """Class colors in a normalized form."""
color_triples = [normalize_color(c) for c in self.colors]
return color_triples
|
searxng__searxng-2081 | DuckDuckGo returning "access denied" errors
<!-- PLEASE FILL THESE FIELDS, IT REALLY HELPS THE MAINTAINERS OF SearXNG -->
**Version of SearXNG, commit number if you are using on master branch and stipulate if you forked SearXNG**
2023.01.06-b241015e
**How did you install SearXNG?**
searxng-docker
**What happened?**
DuckDuckGo started returning "access denied" error messages. Very similar to previous issue #1854
**How To Reproduce**
Enable DuckDuckGo and try to search anything.
**Expected behavior**
DDG results should return and no "Access Denied" error message should be displayed.
**Screenshots & Logs**
Error message in question:
- Exception: searx.exceptions.SearxEngineAccessDeniedException
- Parameter: HTTP error 403
- Filename: searx/search/processors/online.py:113
- Function: _send_http_request
- Code: response = req(params['url'], **request_args)
**Additional context**
It looks like it can be fixed by adding a HTTP `Referer` header to the request.
DuckDuckGo returning "access denied" errors
<!-- PLEASE FILL THESE FIELDS, IT REALLY HELPS THE MAINTAINERS OF SearXNG -->
**Version of SearXNG, commit number if you are using on master branch and stipulate if you forked SearXNG**
2023.01.06-b241015e
**How did you install SearXNG?**
searxng-docker
**What happened?**
DuckDuckGo started returning "access denied" error messages. Very similar to previous issue #1854
**How To Reproduce**
Enable DuckDuckGo and try to search anything.
**Expected behavior**
DDG results should return and no "Access Denied" error message should be displayed.
**Screenshots & Logs**
Error message in question:
- Exception: searx.exceptions.SearxEngineAccessDeniedException
- Parameter: HTTP error 403
- Filename: searx/search/processors/online.py:113
- Function: _send_http_request
- Code: response = req(params['url'], **request_args)
**Additional context**
It looks like it can be fixed by adding a HTTP `Referer` header to the request.
| [
{
"content": "# SPDX-License-Identifier: AGPL-3.0-or-later\n# lint: pylint\n\"\"\"DuckDuckGo Lite\n\"\"\"\n\nfrom json import loads\n\nfrom lxml.html import fromstring\n\nfrom searx.utils import (\n dict_subset,\n eval_xpath,\n eval_xpath_getindex,\n extract_text,\n match_language,\n)\nfrom searx.network import get\n\n# about\nabout = {\n \"website\": 'https://lite.duckduckgo.com/lite/',\n \"wikidata_id\": 'Q12805',\n \"official_api_documentation\": 'https://duckduckgo.com/api',\n \"use_official_api\": False,\n \"require_api_key\": False,\n \"results\": 'HTML',\n}\n\n# engine dependent config\ncategories = ['general', 'web']\npaging = True\nsupported_languages_url = 'https://duckduckgo.com/util/u588.js'\ntime_range_support = True\nsend_accept_language_header = True\n\nlanguage_aliases = {\n 'ar-SA': 'ar-XA',\n 'es-419': 'es-XL',\n 'ja': 'jp-JP',\n 'ko': 'kr-KR',\n 'sl-SI': 'sl-SL',\n 'zh-TW': 'tzh-TW',\n 'zh-HK': 'tzh-HK',\n}\n\ntime_range_dict = {'day': 'd', 'week': 'w', 'month': 'm', 'year': 'y'}\n\n# search-url\nurl = 'https://lite.duckduckgo.com/lite/'\nurl_ping = 'https://duckduckgo.com/t/sl_l'\n\n# match query's language to a region code that duckduckgo will accept\ndef get_region_code(lang, lang_list=None):\n if lang == 'all':\n return None\n\n lang_code = match_language(lang, lang_list or [], language_aliases, 'wt-WT')\n lang_parts = lang_code.split('-')\n\n # country code goes first\n return lang_parts[1].lower() + '-' + lang_parts[0].lower()\n\n\ndef request(query, params):\n\n params['url'] = url\n params['method'] = 'POST'\n\n params['data']['q'] = query\n\n # The API is not documented, so we do some reverse engineering and emulate\n # what https://lite.duckduckgo.com/lite/ does when you press \"next Page\"\n # link again and again ..\n\n params['headers']['Content-Type'] = 'application/x-www-form-urlencoded'\n\n # initial page does not have an offset\n if params['pageno'] == 2:\n # second page does have an offset of 30\n offset = (params['pageno'] - 1) * 30\n params['data']['s'] = offset\n params['data']['dc'] = offset + 1\n\n elif params['pageno'] > 2:\n # third and following pages do have an offset of 30 + n*50\n offset = 30 + (params['pageno'] - 2) * 50\n params['data']['s'] = offset\n params['data']['dc'] = offset + 1\n\n # initial page does not have additional data in the input form\n if params['pageno'] > 1:\n # request the second page (and more pages) needs 'o' and 'api' arguments\n params['data']['o'] = 'json'\n params['data']['api'] = 'd.js'\n\n # initial page does not have additional data in the input form\n if params['pageno'] > 2:\n # request the third page (and more pages) some more arguments\n params['data']['nextParams'] = ''\n params['data']['v'] = ''\n params['data']['vqd'] = ''\n\n region_code = get_region_code(params['language'], supported_languages)\n if region_code:\n params['data']['kl'] = region_code\n params['cookies']['kl'] = region_code\n\n params['data']['df'] = ''\n if params['time_range'] in time_range_dict:\n params['data']['df'] = time_range_dict[params['time_range']]\n params['cookies']['df'] = time_range_dict[params['time_range']]\n\n logger.debug(\"param data: %s\", params['data'])\n logger.debug(\"param cookies: %s\", params['cookies'])\n return params\n\n\n# get response from search-request\ndef response(resp):\n\n headers_ping = dict_subset(resp.request.headers, ['User-Agent', 'Accept-Encoding', 'Accept', 'Cookie'])\n get(url_ping, headers=headers_ping)\n\n if resp.status_code == 303:\n return []\n\n results = []\n doc = fromstring(resp.text)\n\n result_table = eval_xpath(doc, '//html/body/form/div[@class=\"filters\"]/table')\n if not len(result_table) >= 3:\n # no more results\n return []\n result_table = result_table[2]\n\n tr_rows = eval_xpath(result_table, './/tr')\n\n # In the last <tr> is the form of the 'previous/next page' links\n tr_rows = tr_rows[:-1]\n\n len_tr_rows = len(tr_rows)\n offset = 0\n\n while len_tr_rows >= offset + 4:\n\n # assemble table rows we need to scrap\n tr_title = tr_rows[offset]\n tr_content = tr_rows[offset + 1]\n offset += 4\n\n # ignore sponsored Adds <tr class=\"result-sponsored\">\n if tr_content.get('class') == 'result-sponsored':\n continue\n\n a_tag = eval_xpath_getindex(tr_title, './/td//a[@class=\"result-link\"]', 0, None)\n if a_tag is None:\n continue\n\n td_content = eval_xpath_getindex(tr_content, './/td[@class=\"result-snippet\"]', 0, None)\n if td_content is None:\n continue\n\n results.append(\n {\n 'title': a_tag.text_content(),\n 'content': extract_text(td_content),\n 'url': a_tag.get('href'),\n }\n )\n\n return results\n\n\n# get supported languages from their site\ndef _fetch_supported_languages(resp):\n\n # response is a js file with regions as an embedded object\n response_page = resp.text\n response_page = response_page[response_page.find('regions:{') + 8 :]\n response_page = response_page[: response_page.find('}') + 1]\n\n regions_json = loads(response_page)\n supported_languages = map((lambda x: x[3:] + '-' + x[:2].upper()), regions_json.keys())\n\n return list(supported_languages)\n",
"path": "searx/engines/duckduckgo.py"
}
] | [
{
"content": "# SPDX-License-Identifier: AGPL-3.0-or-later\n# lint: pylint\n\"\"\"DuckDuckGo Lite\n\"\"\"\n\nfrom json import loads\n\nfrom lxml.html import fromstring\n\nfrom searx.utils import (\n dict_subset,\n eval_xpath,\n eval_xpath_getindex,\n extract_text,\n match_language,\n)\nfrom searx.network import get\n\n# about\nabout = {\n \"website\": 'https://lite.duckduckgo.com/lite/',\n \"wikidata_id\": 'Q12805',\n \"official_api_documentation\": 'https://duckduckgo.com/api',\n \"use_official_api\": False,\n \"require_api_key\": False,\n \"results\": 'HTML',\n}\n\n# engine dependent config\ncategories = ['general', 'web']\npaging = True\nsupported_languages_url = 'https://duckduckgo.com/util/u588.js'\ntime_range_support = True\nsend_accept_language_header = True\n\nlanguage_aliases = {\n 'ar-SA': 'ar-XA',\n 'es-419': 'es-XL',\n 'ja': 'jp-JP',\n 'ko': 'kr-KR',\n 'sl-SI': 'sl-SL',\n 'zh-TW': 'tzh-TW',\n 'zh-HK': 'tzh-HK',\n}\n\ntime_range_dict = {'day': 'd', 'week': 'w', 'month': 'm', 'year': 'y'}\n\n# search-url\nurl = 'https://lite.duckduckgo.com/lite/'\nurl_ping = 'https://duckduckgo.com/t/sl_l'\n\n# match query's language to a region code that duckduckgo will accept\ndef get_region_code(lang, lang_list=None):\n if lang == 'all':\n return None\n\n lang_code = match_language(lang, lang_list or [], language_aliases, 'wt-WT')\n lang_parts = lang_code.split('-')\n\n # country code goes first\n return lang_parts[1].lower() + '-' + lang_parts[0].lower()\n\n\ndef request(query, params):\n\n params['url'] = url\n params['method'] = 'POST'\n\n params['data']['q'] = query\n\n # The API is not documented, so we do some reverse engineering and emulate\n # what https://lite.duckduckgo.com/lite/ does when you press \"next Page\"\n # link again and again ..\n\n params['headers']['Content-Type'] = 'application/x-www-form-urlencoded'\n params['headers']['Referer'] = 'https://lite.duckduckgo.com/'\n\n # initial page does not have an offset\n if params['pageno'] == 2:\n # second page does have an offset of 30\n offset = (params['pageno'] - 1) * 30\n params['data']['s'] = offset\n params['data']['dc'] = offset + 1\n\n elif params['pageno'] > 2:\n # third and following pages do have an offset of 30 + n*50\n offset = 30 + (params['pageno'] - 2) * 50\n params['data']['s'] = offset\n params['data']['dc'] = offset + 1\n\n # initial page does not have additional data in the input form\n if params['pageno'] > 1:\n # request the second page (and more pages) needs 'o' and 'api' arguments\n params['data']['o'] = 'json'\n params['data']['api'] = 'd.js'\n\n # initial page does not have additional data in the input form\n if params['pageno'] > 2:\n # request the third page (and more pages) some more arguments\n params['data']['nextParams'] = ''\n params['data']['v'] = ''\n params['data']['vqd'] = ''\n\n region_code = get_region_code(params['language'], supported_languages)\n if region_code:\n params['data']['kl'] = region_code\n params['cookies']['kl'] = region_code\n\n params['data']['df'] = ''\n if params['time_range'] in time_range_dict:\n params['data']['df'] = time_range_dict[params['time_range']]\n params['cookies']['df'] = time_range_dict[params['time_range']]\n\n logger.debug(\"param data: %s\", params['data'])\n logger.debug(\"param cookies: %s\", params['cookies'])\n return params\n\n\n# get response from search-request\ndef response(resp):\n\n headers_ping = dict_subset(resp.request.headers, ['User-Agent', 'Accept-Encoding', 'Accept', 'Cookie'])\n get(url_ping, headers=headers_ping)\n\n if resp.status_code == 303:\n return []\n\n results = []\n doc = fromstring(resp.text)\n\n result_table = eval_xpath(doc, '//html/body/form/div[@class=\"filters\"]/table')\n if not len(result_table) >= 3:\n # no more results\n return []\n result_table = result_table[2]\n\n tr_rows = eval_xpath(result_table, './/tr')\n\n # In the last <tr> is the form of the 'previous/next page' links\n tr_rows = tr_rows[:-1]\n\n len_tr_rows = len(tr_rows)\n offset = 0\n\n while len_tr_rows >= offset + 4:\n\n # assemble table rows we need to scrap\n tr_title = tr_rows[offset]\n tr_content = tr_rows[offset + 1]\n offset += 4\n\n # ignore sponsored Adds <tr class=\"result-sponsored\">\n if tr_content.get('class') == 'result-sponsored':\n continue\n\n a_tag = eval_xpath_getindex(tr_title, './/td//a[@class=\"result-link\"]', 0, None)\n if a_tag is None:\n continue\n\n td_content = eval_xpath_getindex(tr_content, './/td[@class=\"result-snippet\"]', 0, None)\n if td_content is None:\n continue\n\n results.append(\n {\n 'title': a_tag.text_content(),\n 'content': extract_text(td_content),\n 'url': a_tag.get('href'),\n }\n )\n\n return results\n\n\n# get supported languages from their site\ndef _fetch_supported_languages(resp):\n\n # response is a js file with regions as an embedded object\n response_page = resp.text\n response_page = response_page[response_page.find('regions:{') + 8 :]\n response_page = response_page[: response_page.find('}') + 1]\n\n regions_json = loads(response_page)\n supported_languages = map((lambda x: x[3:] + '-' + x[:2].upper()), regions_json.keys())\n\n return list(supported_languages)\n",
"path": "searx/engines/duckduckgo.py"
}
] | diff --git a/searx/engines/duckduckgo.py b/searx/engines/duckduckgo.py
index 0d82002bff6..84198afc56a 100644
--- a/searx/engines/duckduckgo.py
+++ b/searx/engines/duckduckgo.py
@@ -73,6 +73,7 @@ def request(query, params):
# link again and again ..
params['headers']['Content-Type'] = 'application/x-www-form-urlencoded'
+ params['headers']['Referer'] = 'https://lite.duckduckgo.com/'
# initial page does not have an offset
if params['pageno'] == 2:
|
kivy__python-for-android-2180 | Issues introduced by PR #2113 (SDL2)
As said on Discord #dev channel yesterday, PR #2113 introduces a lot of blocking issues.
These are the results of the tests done by me, @AndreMiras and @opacam :
- `sdl2==2.0.10` have issues that have been solved by the SDL2 team, so it needs to be bumped to `2.0.12`.
- `sdl2==2.0.12` works but create freezes during runtime.
- These freezes are definitely related to the new `SDL_LockMutex` / `SDL_UnlockMutex` mechanism they added for concurrency issues.
- Commenting `SDL_LockMutex` on `Touch` related events fixes the freeze issue for non-fullscreen apps.
- On fullscreen apps, the patch it's also needed on `Resize, .. etc` events.
I'm providing an attached patch that fixes the issues on top of `2.0.12`, btw seems not a good idea to do that, so it needs some more investigation:
[disable_mutex.txt](https://github.com/kivy/python-for-android/files/4569870/disable_mutex.txt)
| [
{
"content": "from pythonforandroid.recipe import BootstrapNDKRecipe\nfrom pythonforandroid.toolchain import current_directory, shprint\nimport sh\n\n\nclass LibSDL2Recipe(BootstrapNDKRecipe):\n version = \"2.0.10\"\n url = \"https://www.libsdl.org/release/SDL2-{version}.zip\"\n md5sum = \"6b2e9a4a2faba4ff277062cf669724f4\"\n\n dir_name = 'SDL'\n\n depends = ['sdl2_image', 'sdl2_mixer', 'sdl2_ttf']\n\n def get_recipe_env(self, arch=None, with_flags_in_cc=True, with_python=True):\n env = super().get_recipe_env(\n arch=arch, with_flags_in_cc=with_flags_in_cc, with_python=with_python)\n env['APP_ALLOW_MISSING_DEPS'] = 'true'\n return env\n\n def build_arch(self, arch):\n env = self.get_recipe_env(arch)\n\n with current_directory(self.get_jni_dir()):\n shprint(\n sh.ndk_build,\n \"V=1\",\n \"NDK_DEBUG=\" + (\"1\" if self.ctx.build_as_debuggable else \"0\"),\n _env=env\n )\n\n\nrecipe = LibSDL2Recipe()\n",
"path": "pythonforandroid/recipes/sdl2/__init__.py"
}
] | [
{
"content": "from pythonforandroid.recipe import BootstrapNDKRecipe\nfrom pythonforandroid.toolchain import current_directory, shprint\nimport sh\n\n\nclass LibSDL2Recipe(BootstrapNDKRecipe):\n version = \"2.0.9\"\n url = \"https://www.libsdl.org/release/SDL2-{version}.tar.gz\"\n md5sum = 'f2ecfba915c54f7200f504d8b48a5dfe'\n\n dir_name = 'SDL'\n\n depends = ['sdl2_image', 'sdl2_mixer', 'sdl2_ttf']\n\n def get_recipe_env(self, arch=None, with_flags_in_cc=True, with_python=True):\n env = super().get_recipe_env(\n arch=arch, with_flags_in_cc=with_flags_in_cc, with_python=with_python)\n env['APP_ALLOW_MISSING_DEPS'] = 'true'\n return env\n\n def build_arch(self, arch):\n env = self.get_recipe_env(arch)\n\n with current_directory(self.get_jni_dir()):\n shprint(\n sh.ndk_build,\n \"V=1\",\n \"NDK_DEBUG=\" + (\"1\" if self.ctx.build_as_debuggable else \"0\"),\n _env=env\n )\n\n\nrecipe = LibSDL2Recipe()\n",
"path": "pythonforandroid/recipes/sdl2/__init__.py"
}
] | diff --git a/pythonforandroid/bootstraps/sdl2/build/src/main/java/org/kivy/android/PythonActivity.java b/pythonforandroid/bootstraps/sdl2/build/src/main/java/org/kivy/android/PythonActivity.java
index 956c5f5b59..33d0855ef3 100644
--- a/pythonforandroid/bootstraps/sdl2/build/src/main/java/org/kivy/android/PythonActivity.java
+++ b/pythonforandroid/bootstraps/sdl2/build/src/main/java/org/kivy/android/PythonActivity.java
@@ -193,7 +193,7 @@ protected void onPostExecute(String result) {
mActivity.getPackageName(), PackageManager.GET_META_DATA).metaData;
PowerManager pm = (PowerManager) mActivity.getSystemService(Context.POWER_SERVICE);
- if (mActivity.mMetaData.getInt("wakelock") == 1) {
+ if ( mActivity.mMetaData.getInt("wakelock") == 1 ) {
mActivity.mWakeLock = pm.newWakeLock(PowerManager.SCREEN_BRIGHT_WAKE_LOCK, "Screen On");
mActivity.mWakeLock.acquire();
}
@@ -450,32 +450,35 @@ public void appConfirmedActive() {
public void considerLoadingScreenRemoval() {
if (loadingScreenRemovalTimer != null)
return;
- if (PythonActivity.mSingleton != null &&
- mAppConfirmedActive &&
- loadingScreenRemovalTimer == null) {
- Log.v(TAG, "loading screen timer Runnable() launched.");
- // Remove loading screen but with a delay.
- // (app can use p4a's android.loadingscreen module to
- // do it quicker if it wants to)
- TimerTask removalTask = new TimerTask() {
- @Override
- public void run() {
- // post a runnable to the handler
- runOnUiThread(new Runnable() {
+ runOnUiThread(new Runnable() {
+ public void run() {
+ if (((PythonActivity)PythonActivity.mSingleton).mAppConfirmedActive &&
+ loadingScreenRemovalTimer == null) {
+ // Remove loading screen but with a delay.
+ // (app can use p4a's android.loadingscreen module to
+ // do it quicker if it wants to)
+ // get a handler (call from main thread)
+ // this will run when timer elapses
+ TimerTask removalTask = new TimerTask() {
@Override
public void run() {
- Log.v(TAG, "loading screen timer Runnable() finished.");
- PythonActivity activity =
- ((PythonActivity)PythonActivity.mSingleton);
- if (activity != null)
- activity.removeLoadingScreen();
+ // post a runnable to the handler
+ runOnUiThread(new Runnable() {
+ @Override
+ public void run() {
+ PythonActivity activity =
+ ((PythonActivity)PythonActivity.mSingleton);
+ if (activity != null)
+ activity.removeLoadingScreen();
+ }
+ });
}
- });
+ };
+ loadingScreenRemovalTimer = new Timer();
+ loadingScreenRemovalTimer.schedule(removalTask, 5000);
}
- };
- loadingScreenRemovalTimer = new Timer();
- loadingScreenRemovalTimer.schedule(removalTask, 5000);
- }
+ }
+ });
}
public void removeLoadingScreen() {
@@ -586,30 +589,14 @@ protected void onResume() {
if (this.mWakeLock != null) {
this.mWakeLock.acquire();
}
- Log.v(TAG, "onResume(), mSDLThread exists yet: " + (mSDLThread != null));
+ Log.v(TAG, "onResume()");
try {
super.onResume();
- if (mSDLThread == null && !mIsResumedCalled) {
- // Ok so SDL2's onStart() usually launches the native code.
- // However, this may fail if native libs aren't loaded yet at that point
- // (due ot our loading screen) so we may need to manually trigger this,
- // otherwise code would only launch by leaving & re-entering the app:
- Log.v(TAG, "Loading screen workaround: triggering native resume");
- if (mSDLThread == null && mCurrentNativeState == NativeState.RESUMED) {
- // Force a state change so SDL2 doesn't just ignore the resume:
- mCurrentNativeState = NativeState.PAUSED;
- }
- resumeNativeThread(); // native resume to call native code
- }
} catch (UnsatisfiedLinkError e) {
// Catch resume while still in loading screen failing to
// call native function (since it's not yet loaded)
- Log.v(TAG, "failed to call native onResume() because libs " +
- "aren't loaded yet. this is expected to happen");
}
considerLoadingScreenRemoval();
- Log.v(TAG, "onResume() done in PythonActivity, " +
- "mSDLThread exists yet: " + (mSDLThread != null));
}
@Override
@@ -619,7 +606,6 @@ public void onWindowFocusChanged(boolean hasFocus) {
} catch (UnsatisfiedLinkError e) {
// Catch window focus while still in loading screen failing to
// call native function (since it's not yet loaded)
- return; // no point in barging further
}
considerLoadingScreenRemoval();
}
diff --git a/pythonforandroid/bootstraps/sdl2/build/src/patches/SDLActivity.java.patch b/pythonforandroid/bootstraps/sdl2/build/src/patches/SDLActivity.java.patch
index 90daa10eea..27b97a7fbb 100644
--- a/pythonforandroid/bootstraps/sdl2/build/src/patches/SDLActivity.java.patch
+++ b/pythonforandroid/bootstraps/sdl2/build/src/patches/SDLActivity.java.patch
@@ -1,13 +1,12 @@
--- a/src/main/java/org/libsdl/app/SDLActivity.java
+++ b/src/main/java/org/libsdl/app/SDLActivity.java
-@@ -196,6 +196,16 @@ public class SDLActivity extends Activity implements View.OnSystemUiVisibilityCh
+@@ -196,6 +196,15 @@ public class SDLActivity extends Activity implements View.OnSystemUiVisibilityCh
Log.v(TAG, "onCreate()");
super.onCreate(savedInstanceState);
++ SDLActivity.initialize();
+ // So we can call stuff from static callbacks
+ mSingleton = this;
-+
-+ SDLActivity.initialize();
+ }
+
+ // We don't do this in onCreate because we unpack and load the app data on a thread
@@ -17,16 +16,6 @@
// Load shared libraries
String errorMsgBrokenLib = "";
try {
-@@ -508,7 +508,8 @@ public class SDLActivity extends Activity implements View.OnSystemUiVisibilityCh
- }
-
- // Try a transition to resumed state
-- if (mNextNativeState == NativeState.RESUMED) {
-+ // python-for-android: we delay finishLoad() -> mSurface can be null!
-+ if (mNextNativeState == NativeState.RESUMED && mSurface != null) {
- if (mSurface.mIsSurfaceReady && mHasFocus && mIsResumedCalled) {
- if (mSDLThread == null) {
- // This is the entry point to the C app.
@@ -639,7 +648,7 @@ public class SDLActivity extends Activity implements View.OnSystemUiVisibilityCh
Handler commandHandler = new SDLCommandHandler();
@@ -58,11 +47,28 @@
// APK expansion files support
/** com.android.vending.expansion.zipfile.ZipResourceFile object or null. */
-@@ -1475,5 +1475,7 @@ class SDLMain implements Runnable {
+@@ -1341,14 +1366,13 @@ public class SDLActivity extends Activity implements View.OnSystemUiVisibilityCh
+ };
+
+ public void onSystemUiVisibilityChange(int visibility) {
+- if (SDLActivity.mFullscreenModeActive && (visibility & View.SYSTEM_UI_FLAG_FULLSCREEN) == 0 || (visibility & View.SYSTEM_UI_FLAG_HIDE_NAVIGATION) == 0) {
+-
++ // SDL2 BUGFIX (see sdl bug #4424 ) - REMOVE WHEN FIXED IN UPSTREAM !!
++ if (SDLActivity.mFullscreenModeActive && ((visibility & View.SYSTEM_UI_FLAG_FULLSCREEN) == 0 || (visibility & View.SYSTEM_UI_FLAG_HIDE_NAVIGATION) == 0)) {
+ Handler handler = getWindow().getDecorView().getHandler();
+ if (handler != null) {
+ handler.removeCallbacks(rehideSystemUi); // Prevent a hide loop.
+ handler.postDelayed(rehideSystemUi, 2000);
+ }
+-
+ }
+ }
+
+@@ -1475,6 +1499,7 @@ class SDLMain implements Runnable {
+ String[] arguments = SDLActivity.mSingleton.getArguments();
+
Log.v("SDL", "Running main function " + function + " from library " + library);
-
+ SDLActivity.mSingleton.appConfirmedActive();
-+ Log.v("SDL", "(python-for-android: appConfirmedActive() was called)");
SDLActivity.nativeRunMain(library, function, arguments);
Log.v("SDL", "Finished main function");
diff --git a/pythonforandroid/recipes/sdl2/__init__.py b/pythonforandroid/recipes/sdl2/__init__.py
index ddf373c27b..830e9dde44 100644
--- a/pythonforandroid/recipes/sdl2/__init__.py
+++ b/pythonforandroid/recipes/sdl2/__init__.py
@@ -4,9 +4,9 @@
class LibSDL2Recipe(BootstrapNDKRecipe):
- version = "2.0.10"
- url = "https://www.libsdl.org/release/SDL2-{version}.zip"
- md5sum = "6b2e9a4a2faba4ff277062cf669724f4"
+ version = "2.0.9"
+ url = "https://www.libsdl.org/release/SDL2-{version}.tar.gz"
+ md5sum = 'f2ecfba915c54f7200f504d8b48a5dfe'
dir_name = 'SDL'
|
keras-team__keras-677 | Python 3 compatibility problem with Image loading
Loading an Image using the `load_img` results in an error.
```
Traceback (most recent call last):
File "keras/autoencoder.py", line 45, in <module>
X_train, Y_train, X_test, Y_test, nb_classes = io.load_images(join(DATA_DIR, 'dataset0'))
File "/home/jnphilipp/Documents/cnn/hieroglyphs/keras/utils/io.py", line 27, in load_images
X_train.append(img_to_array(load_img(picture, True)))
File "/home/jnphilipp/.local/lib/python3.4/site-packages/Keras-0.1.2-py3.4.egg/keras/preprocessing/image.py", line 107, in load_img
File "/home/jnphilipp/.local/lib/python3.4/site-packages/PIL/Image.py", line 2330, in open
% (filename if filename else fp))
OSError: cannot identify image file <_io.TextIOWrapper name='/home/jnphilipp/Documents/cnn/hieroglyphs/data/dataset0/train/P1_train0.png' mode='r' encoding='ISO-8859-1'>
```
| [
{
"content": "from __future__ import absolute_import\n\nimport numpy as np\nimport re\nfrom scipy import ndimage\nfrom scipy import linalg\n\nfrom os import listdir\nfrom os.path import isfile, join\nimport random, math\nfrom six.moves import range\n\n'''\n Fairly basic set of tools for realtime data augmentation on image data.\n Can easily be extended to include new transforms, new preprocessing methods, etc...\n'''\n\ndef random_rotation(x, rg, fill_mode=\"nearest\", cval=0.):\n angle = random.uniform(-rg, rg)\n x = ndimage.interpolation.rotate(x, angle, axes=(1,2), reshape=False, mode=fill_mode, cval=cval)\n return x\n\ndef random_shift(x, wrg, hrg, fill_mode=\"nearest\", cval=0.):\n crop_left_pixels = 0\n crop_right_pixels = 0\n crop_top_pixels = 0\n crop_bottom_pixels = 0\n\n original_w = x.shape[1]\n original_h = x.shape[2]\n\n if wrg:\n crop = random.uniform(0., wrg)\n split = random.uniform(0, 1)\n crop_left_pixels = int(split*crop*x.shape[1])\n crop_right_pixels = int((1-split)*crop*x.shape[1])\n\n if hrg:\n crop = random.uniform(0., hrg)\n split = random.uniform(0, 1)\n crop_top_pixels = int(split*crop*x.shape[2])\n crop_bottom_pixels = int((1-split)*crop*x.shape[2])\n\n x = ndimage.interpolation.shift(x, (0, crop_left_pixels, crop_top_pixels), mode=fill_mode, cval=cval)\n return x\n\ndef horizontal_flip(x):\n for i in range(x.shape[0]):\n x[i] = np.fliplr(x[i])\n return x\n\ndef vertical_flip(x):\n for i in range(x.shape[0]):\n x[i] = np.flipud(x[i])\n return x\n\n\ndef random_barrel_transform(x, intensity):\n # TODO\n pass\n\ndef random_shear(x, intensity):\n # TODO\n pass\n\ndef random_channel_shift(x, rg):\n # TODO\n pass\n\ndef random_zoom(x, rg, fill_mode=\"nearest\", cval=0.):\n zoom_w = random.uniform(1.-rg, 1.)\n zoom_h = random.uniform(1.-rg, 1.)\n x = ndimage.interpolation.zoom(x, zoom=(1., zoom_w, zoom_h), mode=fill_mode, cval=cval)\n return x # shape of result will be different from shape of input!\n\n\n\n\ndef array_to_img(x, scale=True):\n from PIL import Image\n x = x.transpose(1, 2, 0) \n if scale:\n x += max(-np.min(x), 0)\n x /= np.max(x)\n x *= 255\n if x.shape[2] == 3:\n # RGB\n return Image.fromarray(x.astype(\"uint8\"), \"RGB\")\n else:\n # grayscale\n return Image.fromarray(x[:,:,0].astype(\"uint8\"), \"L\")\n\n\ndef img_to_array(img):\n x = np.asarray(img, dtype='float32')\n if len(x.shape)==3:\n # RGB: height, width, channel -> channel, height, width\n x = x.transpose(2, 0, 1)\n else:\n # grayscale: height, width -> channel, height, width\n x = x.reshape((1, x.shape[0], x.shape[1]))\n return x\n\n\ndef load_img(path, grayscale=False):\n from PIL import Image\n img = Image.open(open(path))\n if grayscale:\n img = img.convert('L')\n else: # Assure 3 channel even when loaded image is grayscale\n img = img.convert('RGB')\n return img\n\n\ndef list_pictures(directory, ext='jpg|jpeg|bmp|png'):\n return [join(directory,f) for f in listdir(directory) \\\n if isfile(join(directory,f)) and re.match('([\\w]+\\.(?:' + ext + '))', f)]\n\n\n\nclass ImageDataGenerator(object):\n '''\n Generate minibatches with \n realtime data augmentation.\n '''\n def __init__(self, \n featurewise_center=True, # set input mean to 0 over the dataset\n samplewise_center=False, # set each sample mean to 0\n featurewise_std_normalization=True, # divide inputs by std of the dataset\n samplewise_std_normalization=False, # divide each input by its std\n\n zca_whitening=False, # apply ZCA whitening\n rotation_range=0., # degrees (0 to 180)\n width_shift_range=0., # fraction of total width\n height_shift_range=0., # fraction of total height\n horizontal_flip=False,\n vertical_flip=False,\n ):\n self.__dict__.update(locals())\n self.mean = None\n self.std = None\n self.principal_components = None\n\n\n def flow(self, X, y, batch_size=32, shuffle=False, seed=None, save_to_dir=None, save_prefix=\"\", save_format=\"jpeg\"):\n if seed:\n random.seed(seed)\n\n if shuffle:\n seed = random.randint(1, 10e6)\n np.random.seed(seed)\n np.random.shuffle(X)\n np.random.seed(seed)\n np.random.shuffle(y)\n\n nb_batch = int(math.ceil(float(X.shape[0])/batch_size))\n for b in range(nb_batch):\n batch_end = (b+1)*batch_size\n if batch_end > X.shape[0]:\n nb_samples = X.shape[0] - b*batch_size\n else:\n nb_samples = batch_size\n\n bX = np.zeros(tuple([nb_samples]+list(X.shape)[1:]))\n for i in range(nb_samples):\n x = X[b*batch_size+i]\n x = self.random_transform(x.astype(\"float32\"))\n x = self.standardize(x)\n bX[i] = x\n\n if save_to_dir:\n for i in range(nb_samples):\n img = array_to_img(bX[i], scale=True)\n img.save(save_to_dir + \"/\" + save_prefix + \"_\" + str(i) + \".\" + save_format)\n\n yield bX, y[b*batch_size:b*batch_size+nb_samples]\n\n\n def standardize(self, x):\n if self.featurewise_center:\n x -= self.mean\n if self.featurewise_std_normalization:\n x /= self.std\n\n if self.zca_whitening:\n flatx = np.reshape(x, (x.shape[0]*x.shape[1]*x.shape[2]))\n whitex = np.dot(flatx, self.principal_components)\n x = np.reshape(whitex, (x.shape[0], x.shape[1], x.shape[2]))\n\n if self.samplewise_center:\n x -= np.mean(x)\n if self.samplewise_std_normalization:\n x /= np.std(x)\n\n return x\n\n\n def random_transform(self, x):\n if self.rotation_range:\n x = random_rotation(x, self.rotation_range)\n if self.width_shift_range or self.height_shift_range:\n x = random_shift(x, self.width_shift_range, self.height_shift_range)\n if self.horizontal_flip:\n if random.random() < 0.5:\n x = horizontal_flip(x)\n if self.vertical_flip:\n if random.random() < 0.5:\n x = vertical_flip(x)\n\n # TODO:\n # zoom\n # barrel/fisheye\n # shearing\n # channel shifting\n return x\n\n\n def fit(self, X, \n augment=False, # fit on randomly augmented samples\n rounds=1, # if augment, how many augmentation passes over the data do we use\n seed=None\n ):\n '''\n Required for featurewise_center, featurewise_std_normalization and zca_whitening.\n '''\n X = np.copy(X)\n \n if augment:\n aX = np.zeros(tuple([rounds*X.shape[0]]+list(X.shape)[1:]))\n for r in range(rounds):\n for i in range(X.shape[0]):\n img = array_to_img(X[i])\n img = self.random_transform(img)\n aX[i+r*X.shape[0]] = img_to_array(img)\n X = aX\n\n if self.featurewise_center:\n self.mean = np.mean(X, axis=0)\n X -= self.mean\n if self.featurewise_std_normalization:\n self.std = np.std(X, axis=0)\n X /= self.std\n\n if self.zca_whitening:\n flatX = np.reshape(X, (X.shape[0], X.shape[1]*X.shape[2]*X.shape[3]))\n fudge = 10e-6\n sigma = np.dot(flatX.T, flatX) / flatX.shape[1]\n U, S, V = linalg.svd(sigma)\n self.principal_components = np.dot(np.dot(U, np.diag(1. / np.sqrt(S + fudge))), U.T)\n\n\n",
"path": "keras/preprocessing/image.py"
}
] | [
{
"content": "from __future__ import absolute_import\n\nimport numpy as np\nimport re\nfrom scipy import ndimage\nfrom scipy import linalg\n\nfrom os import listdir\nfrom os.path import isfile, join\nimport random, math\nfrom six.moves import range\n\n'''\n Fairly basic set of tools for realtime data augmentation on image data.\n Can easily be extended to include new transforms, new preprocessing methods, etc...\n'''\n\ndef random_rotation(x, rg, fill_mode=\"nearest\", cval=0.):\n angle = random.uniform(-rg, rg)\n x = ndimage.interpolation.rotate(x, angle, axes=(1,2), reshape=False, mode=fill_mode, cval=cval)\n return x\n\ndef random_shift(x, wrg, hrg, fill_mode=\"nearest\", cval=0.):\n crop_left_pixels = 0\n crop_right_pixels = 0\n crop_top_pixels = 0\n crop_bottom_pixels = 0\n\n original_w = x.shape[1]\n original_h = x.shape[2]\n\n if wrg:\n crop = random.uniform(0., wrg)\n split = random.uniform(0, 1)\n crop_left_pixels = int(split*crop*x.shape[1])\n crop_right_pixels = int((1-split)*crop*x.shape[1])\n\n if hrg:\n crop = random.uniform(0., hrg)\n split = random.uniform(0, 1)\n crop_top_pixels = int(split*crop*x.shape[2])\n crop_bottom_pixels = int((1-split)*crop*x.shape[2])\n\n x = ndimage.interpolation.shift(x, (0, crop_left_pixels, crop_top_pixels), mode=fill_mode, cval=cval)\n return x\n\ndef horizontal_flip(x):\n for i in range(x.shape[0]):\n x[i] = np.fliplr(x[i])\n return x\n\ndef vertical_flip(x):\n for i in range(x.shape[0]):\n x[i] = np.flipud(x[i])\n return x\n\n\ndef random_barrel_transform(x, intensity):\n # TODO\n pass\n\ndef random_shear(x, intensity):\n # TODO\n pass\n\ndef random_channel_shift(x, rg):\n # TODO\n pass\n\ndef random_zoom(x, rg, fill_mode=\"nearest\", cval=0.):\n zoom_w = random.uniform(1.-rg, 1.)\n zoom_h = random.uniform(1.-rg, 1.)\n x = ndimage.interpolation.zoom(x, zoom=(1., zoom_w, zoom_h), mode=fill_mode, cval=cval)\n return x # shape of result will be different from shape of input!\n\n\n\n\ndef array_to_img(x, scale=True):\n from PIL import Image\n x = x.transpose(1, 2, 0) \n if scale:\n x += max(-np.min(x), 0)\n x /= np.max(x)\n x *= 255\n if x.shape[2] == 3:\n # RGB\n return Image.fromarray(x.astype(\"uint8\"), \"RGB\")\n else:\n # grayscale\n return Image.fromarray(x[:,:,0].astype(\"uint8\"), \"L\")\n\n\ndef img_to_array(img):\n x = np.asarray(img, dtype='float32')\n if len(x.shape)==3:\n # RGB: height, width, channel -> channel, height, width\n x = x.transpose(2, 0, 1)\n else:\n # grayscale: height, width -> channel, height, width\n x = x.reshape((1, x.shape[0], x.shape[1]))\n return x\n\n\ndef load_img(path, grayscale=False):\n from PIL import Image\n img = Image.open(path)\n if grayscale:\n img = img.convert('L')\n else: # Assure 3 channel even when loaded image is grayscale\n img = img.convert('RGB')\n return img\n\n\ndef list_pictures(directory, ext='jpg|jpeg|bmp|png'):\n return [join(directory,f) for f in listdir(directory) \\\n if isfile(join(directory,f)) and re.match('([\\w]+\\.(?:' + ext + '))', f)]\n\n\n\nclass ImageDataGenerator(object):\n '''\n Generate minibatches with \n realtime data augmentation.\n '''\n def __init__(self, \n featurewise_center=True, # set input mean to 0 over the dataset\n samplewise_center=False, # set each sample mean to 0\n featurewise_std_normalization=True, # divide inputs by std of the dataset\n samplewise_std_normalization=False, # divide each input by its std\n\n zca_whitening=False, # apply ZCA whitening\n rotation_range=0., # degrees (0 to 180)\n width_shift_range=0., # fraction of total width\n height_shift_range=0., # fraction of total height\n horizontal_flip=False,\n vertical_flip=False,\n ):\n self.__dict__.update(locals())\n self.mean = None\n self.std = None\n self.principal_components = None\n\n\n def flow(self, X, y, batch_size=32, shuffle=False, seed=None, save_to_dir=None, save_prefix=\"\", save_format=\"jpeg\"):\n if seed:\n random.seed(seed)\n\n if shuffle:\n seed = random.randint(1, 10e6)\n np.random.seed(seed)\n np.random.shuffle(X)\n np.random.seed(seed)\n np.random.shuffle(y)\n\n nb_batch = int(math.ceil(float(X.shape[0])/batch_size))\n for b in range(nb_batch):\n batch_end = (b+1)*batch_size\n if batch_end > X.shape[0]:\n nb_samples = X.shape[0] - b*batch_size\n else:\n nb_samples = batch_size\n\n bX = np.zeros(tuple([nb_samples]+list(X.shape)[1:]))\n for i in range(nb_samples):\n x = X[b*batch_size+i]\n x = self.random_transform(x.astype(\"float32\"))\n x = self.standardize(x)\n bX[i] = x\n\n if save_to_dir:\n for i in range(nb_samples):\n img = array_to_img(bX[i], scale=True)\n img.save(save_to_dir + \"/\" + save_prefix + \"_\" + str(i) + \".\" + save_format)\n\n yield bX, y[b*batch_size:b*batch_size+nb_samples]\n\n\n def standardize(self, x):\n if self.featurewise_center:\n x -= self.mean\n if self.featurewise_std_normalization:\n x /= self.std\n\n if self.zca_whitening:\n flatx = np.reshape(x, (x.shape[0]*x.shape[1]*x.shape[2]))\n whitex = np.dot(flatx, self.principal_components)\n x = np.reshape(whitex, (x.shape[0], x.shape[1], x.shape[2]))\n\n if self.samplewise_center:\n x -= np.mean(x)\n if self.samplewise_std_normalization:\n x /= np.std(x)\n\n return x\n\n\n def random_transform(self, x):\n if self.rotation_range:\n x = random_rotation(x, self.rotation_range)\n if self.width_shift_range or self.height_shift_range:\n x = random_shift(x, self.width_shift_range, self.height_shift_range)\n if self.horizontal_flip:\n if random.random() < 0.5:\n x = horizontal_flip(x)\n if self.vertical_flip:\n if random.random() < 0.5:\n x = vertical_flip(x)\n\n # TODO:\n # zoom\n # barrel/fisheye\n # shearing\n # channel shifting\n return x\n\n\n def fit(self, X, \n augment=False, # fit on randomly augmented samples\n rounds=1, # if augment, how many augmentation passes over the data do we use\n seed=None\n ):\n '''\n Required for featurewise_center, featurewise_std_normalization and zca_whitening.\n '''\n X = np.copy(X)\n \n if augment:\n aX = np.zeros(tuple([rounds*X.shape[0]]+list(X.shape)[1:]))\n for r in range(rounds):\n for i in range(X.shape[0]):\n img = array_to_img(X[i])\n img = self.random_transform(img)\n aX[i+r*X.shape[0]] = img_to_array(img)\n X = aX\n\n if self.featurewise_center:\n self.mean = np.mean(X, axis=0)\n X -= self.mean\n if self.featurewise_std_normalization:\n self.std = np.std(X, axis=0)\n X /= self.std\n\n if self.zca_whitening:\n flatX = np.reshape(X, (X.shape[0], X.shape[1]*X.shape[2]*X.shape[3]))\n fudge = 10e-6\n sigma = np.dot(flatX.T, flatX) / flatX.shape[1]\n U, S, V = linalg.svd(sigma)\n self.principal_components = np.dot(np.dot(U, np.diag(1. / np.sqrt(S + fudge))), U.T)\n\n\n",
"path": "keras/preprocessing/image.py"
}
] | diff --git a/keras/preprocessing/image.py b/keras/preprocessing/image.py
index 5b64a588ad9e..ad9794a496cc 100644
--- a/keras/preprocessing/image.py
+++ b/keras/preprocessing/image.py
@@ -104,7 +104,7 @@ def img_to_array(img):
def load_img(path, grayscale=False):
from PIL import Image
- img = Image.open(open(path))
+ img = Image.open(path)
if grayscale:
img = img.convert('L')
else: # Assure 3 channel even when loaded image is grayscale
|
pyro-ppl__numpyro-737 | Possible error in the validation of a Categorical distribution
I am getting an error when I try to run the following code. The code just sample from a categorical distribution using the defined probabilities.
```python
import numpyro
import numpyro.distributions as dist
import jax.numpy as jnp
numpyro.enable_validation(True)
def model():
probs = jnp.array([0.5, 0.5, 0.])
c = numpyro.sample('c', dist.Categorical(probs=probs))
return c
with numpyro.handlers.seed(rng_seed=54):
print(model())
```
```
ValueError Traceback (most recent call last)
<ipython-input-1-fc7fe60e083b> in <module>
10
11 with numpyro.handlers.seed(rng_seed=54):
---> 12 print(model())
<ipython-input-1-fc7fe60e083b> in model()
6 def model():
7 probs = jnp.array([0.5, 0.5, 0.])
----> 8 c = numpyro.sample('c', dist.Categorical(probs=probs))
9 return c
10
~/miniconda3/envs/numpyro_test/lib/python3.8/site-packages/numpyro/distributions/discrete.py in Categorical(probs, logits, validate_args)
348 def Categorical(probs=None, logits=None, validate_args=None):
349 if probs is not None:
--> 350 return CategoricalProbs(probs, validate_args=validate_args)
351 elif logits is not None:
352 return CategoricalLogits(logits, validate_args=validate_args)
~/miniconda3/envs/numpyro_test/lib/python3.8/site-packages/numpyro/distributions/discrete.py in __init__(self, probs, validate_args)
265 raise ValueError("`probs` parameter must be at least one-dimensional.")
266 self.probs = probs
--> 267 super(CategoricalProbs, self).__init__(batch_shape=jnp.shape(self.probs)[:-1],
268 validate_args=validate_args)
269
~/miniconda3/envs/numpyro_test/lib/python3.8/site-packages/numpyro/distributions/distribution.py in __init__(self, batch_shape, event_shape, validate_args)
142 if not_jax_tracer(is_valid):
143 if not is_valid:
--> 144 raise ValueError("The parameter {} has invalid values".format(param))
145 super(Distribution, self).__init__()
146
ValueError: The parameter probs has invalid values
```
I think the problem is caused by the validation because If I restart my kernel and comment the line ```numpyro.enable_validation(True)``` the code will run without problem. It will print 0 in my case.
If I write a similar code in Pyro with the validation enabled, I do not get an error.
```python
import torch
import pyro
import pyro.distributions as dist
pyro.enable_validation(True)
pyro.set_rng_seed(54)
def model():
probs = torch.tensor([0.5, 0.5, 0.])
c = pyro.sample('c', dist.Categorical(probs=probs))
return c
print(model())
```
I am using Python 3.8.5, Pyro 1.4.0 and NumPyro 0.3.0 with Ubuntu. Happy to help with what I can.
| [
{
"content": "# Copyright Contributors to the Pyro project.\n# SPDX-License-Identifier: Apache-2.0\n\n# The implementation follows the design in PyTorch: torch.distributions.constraints.py\n#\n# Copyright (c) 2016- Facebook, Inc (Adam Paszke)\n# Copyright (c) 2014- Facebook, Inc (Soumith Chintala)\n# Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)\n# Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu)\n# Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu)\n# Copyright (c) 2011-2013 NYU (Clement Farabet)\n# Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston)\n# Copyright (c) 2006 Idiap Research Institute (Samy Bengio)\n# Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz)\n#\n# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE\n# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE\n# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR\n# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF\n# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS\n# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN\n# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)\n# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE\n# POSSIBILITY OF SUCH DAMAGE.\n\n\n__all__ = [\n 'boolean',\n 'corr_cholesky',\n 'corr_matrix',\n 'dependent',\n 'greater_than',\n 'integer_interval',\n 'integer_greater_than',\n 'interval',\n 'is_dependent',\n 'less_than',\n 'lower_cholesky',\n 'multinomial',\n 'nonnegative_integer',\n 'positive',\n 'positive_definite',\n 'positive_integer',\n 'real',\n 'real_vector',\n 'simplex',\n 'unit_interval',\n 'Constraint',\n]\n\nimport jax.numpy as jnp\n\n\nclass Constraint(object):\n \"\"\"\n Abstract base class for constraints.\n\n A constraint object represents a region over which a variable is valid,\n e.g. within which a variable can be optimized.\n \"\"\"\n\n def __call__(self, x):\n raise NotImplementedError\n\n def check(self, value):\n \"\"\"\n Returns a byte tensor of `sample_shape + batch_shape` indicating\n whether each event in value satisfies this constraint.\n \"\"\"\n return self(value)\n\n\nclass _Boolean(Constraint):\n def __call__(self, x):\n return (x == 0) | (x == 1)\n\n\nclass _CorrCholesky(Constraint):\n def __call__(self, x):\n tril = jnp.tril(x)\n lower_triangular = jnp.all(jnp.reshape(tril == x, x.shape[:-2] + (-1,)), axis=-1)\n positive_diagonal = jnp.all(jnp.diagonal(x, axis1=-2, axis2=-1) > 0, axis=-1)\n x_norm = jnp.linalg.norm(x, axis=-1)\n unit_norm_row = jnp.all((x_norm <= 1) & (x_norm > 1 - 1e-6), axis=-1)\n return lower_triangular & positive_diagonal & unit_norm_row\n\n\nclass _CorrMatrix(Constraint):\n def __call__(self, x):\n # check for symmetric\n symmetric = jnp.all(jnp.all(x == jnp.swapaxes(x, -2, -1), axis=-1), axis=-1)\n # check for the smallest eigenvalue is positive\n positive = jnp.linalg.eigh(x)[0][..., 0] > 0\n # check for diagonal equal to 1\n unit_variance = jnp.all(jnp.abs(jnp.diagonal(x, axis1=-2, axis2=-1) - 1) < 1e-6, axis=-1)\n return symmetric & positive & unit_variance\n\n\nclass _Dependent(Constraint):\n def __call__(self, x):\n raise ValueError('Cannot determine validity of dependent constraint')\n\n\ndef is_dependent(constraint):\n return isinstance(constraint, _Dependent)\n\n\nclass _GreaterThan(Constraint):\n def __init__(self, lower_bound):\n self.lower_bound = lower_bound\n\n def __call__(self, x):\n return x > self.lower_bound\n\n\nclass _LessThan(Constraint):\n def __init__(self, upper_bound):\n self.upper_bound = upper_bound\n\n def __call__(self, x):\n return x < self.upper_bound\n\n\nclass _IntegerInterval(Constraint):\n def __init__(self, lower_bound, upper_bound):\n self.lower_bound = lower_bound\n self.upper_bound = upper_bound\n\n def __call__(self, x):\n return (x >= self.lower_bound) & (x <= self.upper_bound) & (x == jnp.floor(x))\n\n\nclass _IntegerGreaterThan(Constraint):\n def __init__(self, lower_bound):\n self.lower_bound = lower_bound\n\n def __call__(self, x):\n return (x % 1 == 0) & (x >= self.lower_bound)\n\n\nclass _Interval(Constraint):\n def __init__(self, lower_bound, upper_bound):\n self.lower_bound = lower_bound\n self.upper_bound = upper_bound\n\n def __call__(self, x):\n return (x > self.lower_bound) & (x < self.upper_bound)\n\n\nclass _LowerCholesky(Constraint):\n def __call__(self, x):\n tril = jnp.tril(x)\n lower_triangular = jnp.all(jnp.reshape(tril == x, x.shape[:-2] + (-1,)), axis=-1)\n positive_diagonal = jnp.all(jnp.diagonal(x, axis1=-2, axis2=-1) > 0, axis=-1)\n return lower_triangular & positive_diagonal\n\n\nclass _Multinomial(Constraint):\n def __init__(self, upper_bound):\n self.upper_bound = upper_bound\n\n def __call__(self, x):\n return jnp.all(x >= 0, axis=-1) & (jnp.sum(x, -1) == self.upper_bound)\n\n\nclass _OrderedVector(Constraint):\n def __call__(self, x):\n return jnp.all(x[..., 1:] > x[..., :-1], axis=-1)\n\n\nclass _PositiveDefinite(Constraint):\n def __call__(self, x):\n # check for symmetric\n symmetric = jnp.all(jnp.all(x == jnp.swapaxes(x, -2, -1), axis=-1), axis=-1)\n # check for the smallest eigenvalue is positive\n positive = jnp.linalg.eigh(x)[0][..., 0] > 0\n return symmetric & positive\n\n\nclass _Real(Constraint):\n def __call__(self, x):\n return jnp.isfinite(x)\n\n\nclass _RealVector(Constraint):\n def __call__(self, x):\n return jnp.all(jnp.isfinite(x), axis=-1)\n\n\nclass _Simplex(Constraint):\n def __call__(self, x):\n x_sum = jnp.sum(x, axis=-1)\n return jnp.all(x > 0, axis=-1) & (x_sum < 1 + 1e-6) & (x_sum > 1 - 1e-6)\n\n\n# TODO: Make types consistent\n\nboolean = _Boolean()\ncorr_cholesky = _CorrCholesky()\ncorr_matrix = _CorrMatrix()\ndependent = _Dependent()\ngreater_than = _GreaterThan\nless_than = _LessThan\ninteger_interval = _IntegerInterval\ninteger_greater_than = _IntegerGreaterThan\ninterval = _Interval\nlower_cholesky = _LowerCholesky()\nmultinomial = _Multinomial\nnonnegative_integer = _IntegerGreaterThan(0)\nordered_vector = _OrderedVector()\npositive = _GreaterThan(0.)\npositive_definite = _PositiveDefinite()\npositive_integer = _IntegerGreaterThan(1)\nreal = _Real()\nreal_vector = _RealVector()\nsimplex = _Simplex()\nunit_interval = _Interval(0., 1.)\n",
"path": "numpyro/distributions/constraints.py"
}
] | [
{
"content": "# Copyright Contributors to the Pyro project.\n# SPDX-License-Identifier: Apache-2.0\n\n# The implementation follows the design in PyTorch: torch.distributions.constraints.py\n#\n# Copyright (c) 2016- Facebook, Inc (Adam Paszke)\n# Copyright (c) 2014- Facebook, Inc (Soumith Chintala)\n# Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)\n# Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu)\n# Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu)\n# Copyright (c) 2011-2013 NYU (Clement Farabet)\n# Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston)\n# Copyright (c) 2006 Idiap Research Institute (Samy Bengio)\n# Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz)\n#\n# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE\n# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE\n# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR\n# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF\n# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS\n# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN\n# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)\n# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE\n# POSSIBILITY OF SUCH DAMAGE.\n\n\n__all__ = [\n 'boolean',\n 'corr_cholesky',\n 'corr_matrix',\n 'dependent',\n 'greater_than',\n 'integer_interval',\n 'integer_greater_than',\n 'interval',\n 'is_dependent',\n 'less_than',\n 'lower_cholesky',\n 'multinomial',\n 'nonnegative_integer',\n 'positive',\n 'positive_definite',\n 'positive_integer',\n 'real',\n 'real_vector',\n 'simplex',\n 'unit_interval',\n 'Constraint',\n]\n\nimport jax.numpy as jnp\n\n\nclass Constraint(object):\n \"\"\"\n Abstract base class for constraints.\n\n A constraint object represents a region over which a variable is valid,\n e.g. within which a variable can be optimized.\n \"\"\"\n\n def __call__(self, x):\n raise NotImplementedError\n\n def check(self, value):\n \"\"\"\n Returns a byte tensor of `sample_shape + batch_shape` indicating\n whether each event in value satisfies this constraint.\n \"\"\"\n return self(value)\n\n\nclass _Boolean(Constraint):\n def __call__(self, x):\n return (x == 0) | (x == 1)\n\n\nclass _CorrCholesky(Constraint):\n def __call__(self, x):\n tril = jnp.tril(x)\n lower_triangular = jnp.all(jnp.reshape(tril == x, x.shape[:-2] + (-1,)), axis=-1)\n positive_diagonal = jnp.all(jnp.diagonal(x, axis1=-2, axis2=-1) > 0, axis=-1)\n x_norm = jnp.linalg.norm(x, axis=-1)\n unit_norm_row = jnp.all((x_norm <= 1) & (x_norm > 1 - 1e-6), axis=-1)\n return lower_triangular & positive_diagonal & unit_norm_row\n\n\nclass _CorrMatrix(Constraint):\n def __call__(self, x):\n # check for symmetric\n symmetric = jnp.all(jnp.all(x == jnp.swapaxes(x, -2, -1), axis=-1), axis=-1)\n # check for the smallest eigenvalue is positive\n positive = jnp.linalg.eigh(x)[0][..., 0] > 0\n # check for diagonal equal to 1\n unit_variance = jnp.all(jnp.abs(jnp.diagonal(x, axis1=-2, axis2=-1) - 1) < 1e-6, axis=-1)\n return symmetric & positive & unit_variance\n\n\nclass _Dependent(Constraint):\n def __call__(self, x):\n raise ValueError('Cannot determine validity of dependent constraint')\n\n\ndef is_dependent(constraint):\n return isinstance(constraint, _Dependent)\n\n\nclass _GreaterThan(Constraint):\n def __init__(self, lower_bound):\n self.lower_bound = lower_bound\n\n def __call__(self, x):\n return x > self.lower_bound\n\n\nclass _LessThan(Constraint):\n def __init__(self, upper_bound):\n self.upper_bound = upper_bound\n\n def __call__(self, x):\n return x < self.upper_bound\n\n\nclass _IntegerInterval(Constraint):\n def __init__(self, lower_bound, upper_bound):\n self.lower_bound = lower_bound\n self.upper_bound = upper_bound\n\n def __call__(self, x):\n return (x >= self.lower_bound) & (x <= self.upper_bound) & (x == jnp.floor(x))\n\n\nclass _IntegerGreaterThan(Constraint):\n def __init__(self, lower_bound):\n self.lower_bound = lower_bound\n\n def __call__(self, x):\n return (x % 1 == 0) & (x >= self.lower_bound)\n\n\nclass _Interval(Constraint):\n def __init__(self, lower_bound, upper_bound):\n self.lower_bound = lower_bound\n self.upper_bound = upper_bound\n\n def __call__(self, x):\n return (x > self.lower_bound) & (x < self.upper_bound)\n\n\nclass _LowerCholesky(Constraint):\n def __call__(self, x):\n tril = jnp.tril(x)\n lower_triangular = jnp.all(jnp.reshape(tril == x, x.shape[:-2] + (-1,)), axis=-1)\n positive_diagonal = jnp.all(jnp.diagonal(x, axis1=-2, axis2=-1) > 0, axis=-1)\n return lower_triangular & positive_diagonal\n\n\nclass _Multinomial(Constraint):\n def __init__(self, upper_bound):\n self.upper_bound = upper_bound\n\n def __call__(self, x):\n return jnp.all(x >= 0, axis=-1) & (jnp.sum(x, -1) == self.upper_bound)\n\n\nclass _OrderedVector(Constraint):\n def __call__(self, x):\n return jnp.all(x[..., 1:] > x[..., :-1], axis=-1)\n\n\nclass _PositiveDefinite(Constraint):\n def __call__(self, x):\n # check for symmetric\n symmetric = jnp.all(jnp.all(x == jnp.swapaxes(x, -2, -1), axis=-1), axis=-1)\n # check for the smallest eigenvalue is positive\n positive = jnp.linalg.eigh(x)[0][..., 0] > 0\n return symmetric & positive\n\n\nclass _Real(Constraint):\n def __call__(self, x):\n return jnp.isfinite(x)\n\n\nclass _RealVector(Constraint):\n def __call__(self, x):\n return jnp.all(jnp.isfinite(x), axis=-1)\n\n\nclass _Simplex(Constraint):\n def __call__(self, x):\n x_sum = jnp.sum(x, axis=-1)\n return jnp.all(x >= 0, axis=-1) & (x_sum < 1 + 1e-6) & (x_sum > 1 - 1e-6)\n\n\n# TODO: Make types consistent\n\nboolean = _Boolean()\ncorr_cholesky = _CorrCholesky()\ncorr_matrix = _CorrMatrix()\ndependent = _Dependent()\ngreater_than = _GreaterThan\nless_than = _LessThan\ninteger_interval = _IntegerInterval\ninteger_greater_than = _IntegerGreaterThan\ninterval = _Interval\nlower_cholesky = _LowerCholesky()\nmultinomial = _Multinomial\nnonnegative_integer = _IntegerGreaterThan(0)\nordered_vector = _OrderedVector()\npositive = _GreaterThan(0.)\npositive_definite = _PositiveDefinite()\npositive_integer = _IntegerGreaterThan(1)\nreal = _Real()\nreal_vector = _RealVector()\nsimplex = _Simplex()\nunit_interval = _Interval(0., 1.)\n",
"path": "numpyro/distributions/constraints.py"
}
] | diff --git a/numpyro/distributions/constraints.py b/numpyro/distributions/constraints.py
index 91b8cf008..625f982fd 100644
--- a/numpyro/distributions/constraints.py
+++ b/numpyro/distributions/constraints.py
@@ -192,7 +192,7 @@ def __call__(self, x):
class _Simplex(Constraint):
def __call__(self, x):
x_sum = jnp.sum(x, axis=-1)
- return jnp.all(x > 0, axis=-1) & (x_sum < 1 + 1e-6) & (x_sum > 1 - 1e-6)
+ return jnp.all(x >= 0, axis=-1) & (x_sum < 1 + 1e-6) & (x_sum > 1 - 1e-6)
# TODO: Make types consistent
|
pytorch__ignite-2081 | DeterministicEngine rng state on cuda if loaded checkpoint on cuda
## 🐛 Bug description
Using `engine` as `DeterministicEngine`, loading on cuda can lead to the following issue:
```
File "/home/machine/.clearml/venvs-builds/3.6/lib/python3.6/site-packages/ignite/engine/engine.py", line 702, in run
return self._internal_run()
File "/home/machine/.clearml/venvs-builds/3.6/lib/python3.6/site-packages/ignite/engine/engine.py", line 775, in _internal_run
self._handle_exception(e)
File "/home/machine/.clearml/venvs-builds/3.6/lib/python3.6/site-packages/ignite/engine/engine.py", line 469, in _handle_exception
raise e
File "/home/machine/.clearml/venvs-builds/3.6/lib/python3.6/site-packages/ignite/engine/engine.py", line 743, in _internal_run
self._setup_engine()
File "/home/machine/.clearml/venvs-builds/3.6/lib/python3.6/site-packages/ignite/engine/deterministic.py", line 238, in _setup_engine
_set_rng_states(rng_states)
File "/home/machine/.clearml/venvs-builds/3.6/lib/python3.6/site-packages/ignite/engine/deterministic.py", line 100, in _set_rng_states
torch.set_rng_state(rng_states[1])
File "/home/machine/miniconda3/envs/py36/lib/python3.6/site-packages/torch/random.py", line 14, in set_rng_state
default_generator.set_state(new_state)
TypeError: expected a torch.ByteTensor, but got torch.cuda.ByteTensor
```
Code:
```
to_load = {
'state_dict': model,
'optimizer': optimizer,
'lr_scheduler': lr_scheduler,
'train_phase': engine
}
checkpoint = torch.load(checkpoint_fp, map_location="cuda:0")
Checkpoint.load_objects(to_load=to_load, checkpoint=checkpoint)
```
To fix the issue, we have to make sure that rng tensor is on cpu (otherwise put to cpu):
https://github.com/pytorch/ignite/blob/b49a82a8fb3339f1442752a0dbf2ecd6f15cb1fa/ignite/engine/deterministic.py#L238-L240
and add appropriate test.
### Workaround
- Load on cpu
```diff
- checkpoint = torch.load(checkpoint_fp, map_location="cuda:0")
+ checkpoint = torch.load(checkpoint_fp, map_location="cpu")
```
## Environment
- PyTorch Version (e.g., 1.4): 1.7.1
- Ignite Version (e.g., 0.3.0): 0.4.4
- OS (e.g., Linux):
- How you installed Ignite (`conda`, `pip`, source):
- Python version:
- Any other relevant information:
Thanks @H4dr1en for reporting.
| [
{
"content": "import random\nimport warnings\nfrom collections import OrderedDict\nfrom functools import wraps\nfrom typing import Any, Callable, Generator, Iterator, List, Optional\n\nimport torch\nfrom torch.utils.data import DataLoader\nfrom torch.utils.data.sampler import BatchSampler\n\nfrom ignite.engine.engine import Engine\nfrom ignite.engine.events import Events\nfrom ignite.utils import manual_seed\n\n__all__ = [\"update_dataloader\", \"keep_random_state\", \"ReproducibleBatchSampler\", \"DeterministicEngine\"]\n\n\ndef update_dataloader(dataloader: DataLoader, new_batch_sampler: BatchSampler) -> DataLoader:\n \"\"\"Helper function to replace current batch sampler of the dataloader by a new batch sampler. Function returns new\n dataloader with new batch sampler.\n\n Args:\n dataloader: input dataloader\n new_batch_sampler: new batch sampler to use\n\n Returns:\n DataLoader\n \"\"\"\n params_keys = [k for k in dataloader.__dict__.keys() if not k.startswith(\"_\")]\n for k in [\"batch_size\", \"sampler\", \"drop_last\", \"batch_sampler\", \"dataset_kind\"]:\n if k in params_keys:\n params_keys.remove(k)\n params = {k: getattr(dataloader, k) for k in params_keys}\n params[\"batch_sampler\"] = new_batch_sampler\n return type(dataloader)(**params)\n\n\nclass ReproducibleBatchSampler(BatchSampler):\n \"\"\"Reproducible batch sampler. This class internally iterates and stores indices of the input batch sampler.\n This helps to start providing data batches from an iteration in a deterministic way.\n\n Example:\n\n Setup dataloader with `ReproducibleBatchSampler` and start providing data batches from an iteration\n\n .. code-block:: python\n\n from ignite.engine.deterministic import update_dataloader\n\n dataloader = update_dataloader(dataloader, ReproducibleBatchSampler(dataloader.batch_sampler))\n # rewind dataloader to a specific iteration:\n dataloader.batch_sampler.start_iteration = start_iteration\n\n Args:\n batch_sampler: batch sampler same as used with `torch.utils.data.DataLoader`.\n start_iteration: optional start iteration.\n \"\"\"\n\n def __init__(self, batch_sampler: BatchSampler, start_iteration: Optional[int] = None):\n if not isinstance(batch_sampler, BatchSampler):\n raise TypeError(\"Argument batch_sampler should be torch.utils.data.sampler.BatchSampler\")\n\n self.batch_indices = [] # type: List\n self.batch_sampler = batch_sampler\n self.start_iteration = start_iteration\n self.sampler = self.batch_sampler.sampler\n\n def setup_batch_indices(self) -> None:\n \"\"\"Setup batch indices.\"\"\"\n self.batch_indices = []\n for batch in self.batch_sampler:\n self.batch_indices.append(batch)\n\n if self.start_iteration is not None:\n self.batch_indices = self.batch_indices[self.start_iteration :]\n self.start_iteration = None\n\n def __iter__(self) -> Generator:\n self.setup_batch_indices()\n for batch in self.batch_indices:\n yield batch\n\n def __len__(self) -> int:\n return len(self.batch_sampler)\n\n\ndef _get_rng_states() -> List[Any]:\n output = [random.getstate(), torch.get_rng_state()]\n try:\n import numpy as np\n\n output.append(np.random.get_state())\n except ImportError:\n pass\n\n return output\n\n\ndef _set_rng_states(rng_states: List[Any]) -> None:\n random.setstate(rng_states[0])\n torch.set_rng_state(rng_states[1])\n try:\n import numpy as np\n\n np.random.set_state(rng_states[2])\n except ImportError:\n pass\n\n\ndef _repr_rng_state(rng_states: List[Any]) -> str:\n from hashlib import md5\n\n out = \" \".join([md5(str(list(s)).encode(\"utf-8\")).hexdigest() for s in rng_states])\n return out\n\n\ndef keep_random_state(func: Callable) -> Callable:\n \"\"\"Helper decorator to keep random state of torch, numpy and random intact\n while executing a function. For more details on usage, please see :ref:`Dataflow synchronization`.\n\n Args:\n func: function to decorate\n \"\"\"\n\n @wraps(func)\n def wrapper(*args: Any, **kwargs: Any) -> None:\n rng_states = _get_rng_states()\n func(*args, **kwargs)\n _set_rng_states(rng_states)\n\n return wrapper\n\n\nclass DeterministicEngine(Engine):\n \"\"\"Deterministic engine derived from :class:`~ignite.engine.engine.Engine`.\n\n \"Deterministic\" run is done by adding additional handlers to synchronize the dataflow and overriding some methods of\n :class:`~ignite.engine.engine.Engine`:\n\n .. code-block:: python\n\n for e in range(num_epochs):\n set_seed(seed_offset + e)\n if resume:\n setup_saved_rng_states()\n do_single_epoch_iterations(dataloader)\n\n If input data provider is `DataLoader`, its batch sampler is replaced by\n :class:`~ignite.engine.deterministic.ReproducibleBatchSampler`.\n\n .. code-block:: python\n\n for e in range(num_epochs):\n set_seed(seed_offset + e)\n setup_sampling(dataloader)\n if resume:\n setup_saved_rng_states()\n do_single_epoch_iterations(dataloader)\n\n Internally, `torch.backends.cudnn.deterministic = True` and `torch.backends.cudnn.benchmark = False` are also\n applied.\n\n For more details about dataflow synchronization, please see :ref:`Dataflow synchronization`.\n\n .. Note ::\n\n This class can produce exactly the same dataflow when resuming the run from an epoch (or more precisely from\n dataflow restart) and using torch `DataLoader` with `num_workers > 1` as data provider.\n\n Args:\n process_function: A function receiving a handle to the engine and the current batch\n in each iteration, and returns data to be stored in the engine's state.\n \"\"\"\n\n def __init__(self, process_function: Callable):\n super(DeterministicEngine, self).__init__(process_function)\n self.state_dict_user_keys.append(\"rng_states\")\n self.add_event_handler(Events.STARTED, self._init_run)\n self.add_event_handler(Events.DATALOADER_STOP_ITERATION | Events.TERMINATE_SINGLE_EPOCH, self._setup_seed)\n\n def state_dict(self) -> OrderedDict:\n state_dict = super(DeterministicEngine, self).state_dict()\n state_dict[\"rng_states\"] = _get_rng_states()\n return state_dict\n\n def _init_run(self) -> None:\n self.state.seed = int(torch.randint(0, int(1e9), (1,)).item())\n if not hasattr(self.state, \"rng_states\"):\n setattr(self.state, \"rng_states\", None)\n\n if torch.cuda.is_available():\n torch.backends.cudnn.deterministic = True\n torch.backends.cudnn.benchmark = False\n\n def _setup_engine(self) -> None:\n if self.state.dataloader is None:\n raise RuntimeError(\n \"Internal error, self.state.dataloader is None. Please, file an issue if you encounter this error.\"\n )\n\n self._dataloader_len = self._get_data_length(self.state.dataloader)\n\n # if input data is torch dataloader we replace batch sampler by a batch sampler\n # such that its random sampling indices are reproducible by prefetching them before data iteration\n if isinstance(self.state.dataloader, DataLoader):\n # attribute _dataset_kind is introduced since 1.3.0 => before 1.3.0 all datasets are map-like\n can_patch_dataloader = True\n if hasattr(self.state.dataloader, \"_dataset_kind\"):\n from torch.utils.data.dataloader import _DatasetKind\n\n _dataloader_kind = self.state.dataloader._dataset_kind\n can_patch_dataloader = _dataloader_kind == _DatasetKind.Map\n if can_patch_dataloader:\n if self._dataloader_len is not None and hasattr(self.state.dataloader.sampler, \"epoch\"):\n if self._dataloader_len != self.state.epoch_length:\n warnings.warn(\n \"When defined engine's epoch length is different of input dataloader length, \"\n \"distributed sampler indices can not be setup in a reproducible manner\"\n )\n\n batch_sampler = self.state.dataloader.batch_sampler\n if not (batch_sampler is None or isinstance(batch_sampler, ReproducibleBatchSampler)):\n self.state.dataloader = update_dataloader(\n self.state.dataloader, ReproducibleBatchSampler(batch_sampler) # type: ignore[arg-type]\n )\n\n iteration = self.state.iteration\n self._dataloader_iter = self._from_iteration(iteration)\n\n # Below we define initial counter value for _run_once_on_dataset to measure a single epoch\n if self.state.epoch_length is not None:\n iteration %= self.state.epoch_length\n self._init_iter.append(iteration)\n\n # restore rng state if in the middle\n in_the_middle = self.state.iteration % self._dataloader_len > 0 if self._dataloader_len is not None else False\n rng_states = getattr(self.state, \"rng_states\", None)\n if rng_states is not None and in_the_middle:\n _set_rng_states(rng_states)\n setattr(self.state, \"rng_states\", None)\n\n def _from_iteration(self, iteration: int) -> Iterator:\n if self.state.dataloader is None:\n raise RuntimeError(\n \"Internal error, self.state.dataloader is None. Please, file an issue if you encounter this error.\"\n )\n data = self.state.dataloader\n if isinstance(data, DataLoader):\n try:\n # following is unsafe for IterableDatasets\n iteration %= len(data.batch_sampler) # type: ignore[attr-defined, arg-type]\n # Synchronize dataflow according to state.iteration\n self._setup_seed()\n if iteration > 0:\n # batch sampler is ReproducibleBatchSampler\n data.batch_sampler.start_iteration = iteration # type: ignore[attr-defined, union-attr]\n return iter(data)\n except TypeError as e:\n # Probably we can do nothing with DataLoader built upon IterableDatasets\n pass\n\n self.logger.info(\"Resuming from iteration for provided data will fetch data until required iteration ...\")\n if hasattr(data, \"__len__\"):\n iteration %= len(data) # type: ignore[arg-type]\n # Synchronize dataflow from the begining\n self._setup_seed(iteration=0)\n data_iter = iter(data)\n counter = 0\n while counter < iteration:\n try:\n next(data_iter)\n counter += 1\n except StopIteration:\n data_iter = iter(data)\n\n return data_iter\n\n def _setup_seed(self, _: Any = None, iter_counter: Optional[int] = None, iteration: Optional[int] = None) -> None:\n if iter_counter is None:\n le = self._dataloader_len if self._dataloader_len is not None else 1\n elif not iter_counter > 0:\n raise ValueError(\"iter_counter should be positive value\")\n else:\n le = iter_counter\n if iteration is None:\n iteration = self.state.iteration\n manual_seed(self.state.seed + iteration // le) # type: ignore[operator]\n",
"path": "ignite/engine/deterministic.py"
}
] | [
{
"content": "import random\nimport warnings\nfrom collections import OrderedDict\nfrom functools import wraps\nfrom typing import Any, Callable, Generator, Iterator, List, Optional\n\nimport torch\nfrom torch.utils.data import DataLoader\nfrom torch.utils.data.sampler import BatchSampler\n\nfrom ignite.engine.engine import Engine\nfrom ignite.engine.events import Events\nfrom ignite.utils import manual_seed\n\n__all__ = [\"update_dataloader\", \"keep_random_state\", \"ReproducibleBatchSampler\", \"DeterministicEngine\"]\n\n\ndef update_dataloader(dataloader: DataLoader, new_batch_sampler: BatchSampler) -> DataLoader:\n \"\"\"Helper function to replace current batch sampler of the dataloader by a new batch sampler. Function returns new\n dataloader with new batch sampler.\n\n Args:\n dataloader: input dataloader\n new_batch_sampler: new batch sampler to use\n\n Returns:\n DataLoader\n \"\"\"\n params_keys = [k for k in dataloader.__dict__.keys() if not k.startswith(\"_\")]\n for k in [\"batch_size\", \"sampler\", \"drop_last\", \"batch_sampler\", \"dataset_kind\"]:\n if k in params_keys:\n params_keys.remove(k)\n params = {k: getattr(dataloader, k) for k in params_keys}\n params[\"batch_sampler\"] = new_batch_sampler\n return type(dataloader)(**params)\n\n\nclass ReproducibleBatchSampler(BatchSampler):\n \"\"\"Reproducible batch sampler. This class internally iterates and stores indices of the input batch sampler.\n This helps to start providing data batches from an iteration in a deterministic way.\n\n Example:\n\n Setup dataloader with `ReproducibleBatchSampler` and start providing data batches from an iteration\n\n .. code-block:: python\n\n from ignite.engine.deterministic import update_dataloader\n\n dataloader = update_dataloader(dataloader, ReproducibleBatchSampler(dataloader.batch_sampler))\n # rewind dataloader to a specific iteration:\n dataloader.batch_sampler.start_iteration = start_iteration\n\n Args:\n batch_sampler: batch sampler same as used with `torch.utils.data.DataLoader`.\n start_iteration: optional start iteration.\n \"\"\"\n\n def __init__(self, batch_sampler: BatchSampler, start_iteration: Optional[int] = None):\n if not isinstance(batch_sampler, BatchSampler):\n raise TypeError(\"Argument batch_sampler should be torch.utils.data.sampler.BatchSampler\")\n\n self.batch_indices = [] # type: List\n self.batch_sampler = batch_sampler\n self.start_iteration = start_iteration\n self.sampler = self.batch_sampler.sampler\n\n def setup_batch_indices(self) -> None:\n \"\"\"Setup batch indices.\"\"\"\n self.batch_indices = []\n for batch in self.batch_sampler:\n self.batch_indices.append(batch)\n\n if self.start_iteration is not None:\n self.batch_indices = self.batch_indices[self.start_iteration :]\n self.start_iteration = None\n\n def __iter__(self) -> Generator:\n self.setup_batch_indices()\n for batch in self.batch_indices:\n yield batch\n\n def __len__(self) -> int:\n return len(self.batch_sampler)\n\n\ndef _get_rng_states() -> List[Any]:\n output = [random.getstate(), torch.get_rng_state()]\n try:\n import numpy as np\n\n output.append(np.random.get_state())\n except ImportError:\n pass\n\n return output\n\n\ndef _set_rng_states(rng_states: List[Any]) -> None:\n random.setstate(rng_states[0])\n\n if \"cpu\" not in rng_states[1].device.type:\n rng_states[1] = rng_states[1].cpu()\n\n torch.set_rng_state(rng_states[1])\n try:\n import numpy as np\n\n np.random.set_state(rng_states[2])\n except ImportError:\n pass\n\n\ndef _repr_rng_state(rng_states: List[Any]) -> str:\n from hashlib import md5\n\n out = \" \".join([md5(str(list(s)).encode(\"utf-8\")).hexdigest() for s in rng_states])\n return out\n\n\ndef keep_random_state(func: Callable) -> Callable:\n \"\"\"Helper decorator to keep random state of torch, numpy and random intact\n while executing a function. For more details on usage, please see :ref:`Dataflow synchronization`.\n\n Args:\n func: function to decorate\n \"\"\"\n\n @wraps(func)\n def wrapper(*args: Any, **kwargs: Any) -> None:\n rng_states = _get_rng_states()\n func(*args, **kwargs)\n _set_rng_states(rng_states)\n\n return wrapper\n\n\nclass DeterministicEngine(Engine):\n \"\"\"Deterministic engine derived from :class:`~ignite.engine.engine.Engine`.\n\n \"Deterministic\" run is done by adding additional handlers to synchronize the dataflow and overriding some methods of\n :class:`~ignite.engine.engine.Engine`:\n\n .. code-block:: python\n\n for e in range(num_epochs):\n set_seed(seed_offset + e)\n if resume:\n setup_saved_rng_states()\n do_single_epoch_iterations(dataloader)\n\n If input data provider is `DataLoader`, its batch sampler is replaced by\n :class:`~ignite.engine.deterministic.ReproducibleBatchSampler`.\n\n .. code-block:: python\n\n for e in range(num_epochs):\n set_seed(seed_offset + e)\n setup_sampling(dataloader)\n if resume:\n setup_saved_rng_states()\n do_single_epoch_iterations(dataloader)\n\n Internally, `torch.backends.cudnn.deterministic = True` and `torch.backends.cudnn.benchmark = False` are also\n applied.\n\n For more details about dataflow synchronization, please see :ref:`Dataflow synchronization`.\n\n .. Note ::\n\n This class can produce exactly the same dataflow when resuming the run from an epoch (or more precisely from\n dataflow restart) and using torch `DataLoader` with `num_workers > 1` as data provider.\n\n Args:\n process_function: A function receiving a handle to the engine and the current batch\n in each iteration, and returns data to be stored in the engine's state.\n \"\"\"\n\n def __init__(self, process_function: Callable):\n super(DeterministicEngine, self).__init__(process_function)\n self.state_dict_user_keys.append(\"rng_states\")\n self.add_event_handler(Events.STARTED, self._init_run)\n self.add_event_handler(Events.DATALOADER_STOP_ITERATION | Events.TERMINATE_SINGLE_EPOCH, self._setup_seed)\n\n def state_dict(self) -> OrderedDict:\n state_dict = super(DeterministicEngine, self).state_dict()\n state_dict[\"rng_states\"] = _get_rng_states()\n return state_dict\n\n def _init_run(self) -> None:\n self.state.seed = int(torch.randint(0, int(1e9), (1,)).item())\n if not hasattr(self.state, \"rng_states\"):\n setattr(self.state, \"rng_states\", None)\n\n if torch.cuda.is_available():\n torch.backends.cudnn.deterministic = True\n torch.backends.cudnn.benchmark = False\n\n def _setup_engine(self) -> None:\n if self.state.dataloader is None:\n raise RuntimeError(\n \"Internal error, self.state.dataloader is None. Please, file an issue if you encounter this error.\"\n )\n\n self._dataloader_len = self._get_data_length(self.state.dataloader)\n\n # if input data is torch dataloader we replace batch sampler by a batch sampler\n # such that its random sampling indices are reproducible by prefetching them before data iteration\n if isinstance(self.state.dataloader, DataLoader):\n # attribute _dataset_kind is introduced since 1.3.0 => before 1.3.0 all datasets are map-like\n can_patch_dataloader = True\n if hasattr(self.state.dataloader, \"_dataset_kind\"):\n from torch.utils.data.dataloader import _DatasetKind\n\n _dataloader_kind = self.state.dataloader._dataset_kind\n can_patch_dataloader = _dataloader_kind == _DatasetKind.Map\n if can_patch_dataloader:\n if self._dataloader_len is not None and hasattr(self.state.dataloader.sampler, \"epoch\"):\n if self._dataloader_len != self.state.epoch_length:\n warnings.warn(\n \"When defined engine's epoch length is different of input dataloader length, \"\n \"distributed sampler indices can not be setup in a reproducible manner\"\n )\n\n batch_sampler = self.state.dataloader.batch_sampler\n if not (batch_sampler is None or isinstance(batch_sampler, ReproducibleBatchSampler)):\n self.state.dataloader = update_dataloader(\n self.state.dataloader, ReproducibleBatchSampler(batch_sampler) # type: ignore[arg-type]\n )\n\n iteration = self.state.iteration\n self._dataloader_iter = self._from_iteration(iteration)\n\n # Below we define initial counter value for _run_once_on_dataset to measure a single epoch\n if self.state.epoch_length is not None:\n iteration %= self.state.epoch_length\n self._init_iter.append(iteration)\n\n # restore rng state if in the middle\n in_the_middle = self.state.iteration % self._dataloader_len > 0 if self._dataloader_len is not None else False\n rng_states = getattr(self.state, \"rng_states\", None)\n if rng_states is not None and in_the_middle:\n _set_rng_states(rng_states)\n setattr(self.state, \"rng_states\", None)\n\n def _from_iteration(self, iteration: int) -> Iterator:\n if self.state.dataloader is None:\n raise RuntimeError(\n \"Internal error, self.state.dataloader is None. Please, file an issue if you encounter this error.\"\n )\n data = self.state.dataloader\n if isinstance(data, DataLoader):\n try:\n # following is unsafe for IterableDatasets\n iteration %= len(data.batch_sampler) # type: ignore[attr-defined, arg-type]\n # Synchronize dataflow according to state.iteration\n self._setup_seed()\n if iteration > 0:\n # batch sampler is ReproducibleBatchSampler\n data.batch_sampler.start_iteration = iteration # type: ignore[attr-defined, union-attr]\n return iter(data)\n except TypeError as e:\n # Probably we can do nothing with DataLoader built upon IterableDatasets\n pass\n\n self.logger.info(\"Resuming from iteration for provided data will fetch data until required iteration ...\")\n if hasattr(data, \"__len__\"):\n iteration %= len(data) # type: ignore[arg-type]\n # Synchronize dataflow from the begining\n self._setup_seed(iteration=0)\n data_iter = iter(data)\n counter = 0\n while counter < iteration:\n try:\n next(data_iter)\n counter += 1\n except StopIteration:\n data_iter = iter(data)\n\n return data_iter\n\n def _setup_seed(self, _: Any = None, iter_counter: Optional[int] = None, iteration: Optional[int] = None) -> None:\n if iter_counter is None:\n le = self._dataloader_len if self._dataloader_len is not None else 1\n elif not iter_counter > 0:\n raise ValueError(\"iter_counter should be positive value\")\n else:\n le = iter_counter\n if iteration is None:\n iteration = self.state.iteration\n manual_seed(self.state.seed + iteration // le) # type: ignore[operator]\n",
"path": "ignite/engine/deterministic.py"
}
] | diff --git a/ignite/engine/deterministic.py b/ignite/engine/deterministic.py
index bca969cb3f02..79cd39ab73e3 100644
--- a/ignite/engine/deterministic.py
+++ b/ignite/engine/deterministic.py
@@ -98,6 +98,10 @@ def _get_rng_states() -> List[Any]:
def _set_rng_states(rng_states: List[Any]) -> None:
random.setstate(rng_states[0])
+
+ if "cpu" not in rng_states[1].device.type:
+ rng_states[1] = rng_states[1].cpu()
+
torch.set_rng_state(rng_states[1])
try:
import numpy as np
diff --git a/tests/ignite/engine/test_deterministic.py b/tests/ignite/engine/test_deterministic.py
index f7a581ba4343..9f62ed9502e4 100644
--- a/tests/ignite/engine/test_deterministic.py
+++ b/tests/ignite/engine/test_deterministic.py
@@ -15,6 +15,7 @@
from ignite.engine.deterministic import (
DeterministicEngine,
ReproducibleBatchSampler,
+ _set_rng_states,
keep_random_state,
update_dataloader,
)
@@ -885,3 +886,12 @@ def test_dataloader_no_dataset_kind():
dataloader = OldDataLoader(dataloader)
engine.run(dataloader)
+
+
[email protected](not torch.cuda.is_available(), reason="Skip if no GPU")
+def test__set_rng_states_cuda():
+ # Checks https://github.com/pytorch/ignite/issues/2076
+
+ rng_states = [random.getstate(), torch.get_rng_state().cuda(), np.random.get_state()]
+ _set_rng_states(rng_states)
+ assert rng_states[1].device.type == "cpu"
|
kivy__kivy-7038 | Documentation issue with on_ref_press
In [kivy/uix/label.py L1008](https://github.com/kivy/kivy/blob/master/kivy/uix/label.py#L1008), there is a documentation issue with on_ref_press.
`widget.on_ref_press(print_it)` should be `widget.bind(on_ref_press=print_it)`.
| [
{
"content": "'''Label\n=====\n\n.. image:: images/label.png\n :align: right\n\nThe :class:`Label` widget is for rendering text. It supports ascii and unicode\nstrings::\n\n # hello world text\n l = Label(text='Hello world')\n\n # unicode text; can only display glyphs that are available in the font\n l = Label(text=u'Hello world ' + unichr(2764))\n\n # multiline text\n l = Label(text='Multi\\\\nLine')\n\n # size\n l = Label(text='Hello world', font_size='20sp')\n\n.. _kivy-uix-label-sizing-and-text-content:\n\nSizing and text content\n---------------------------\n\nBy default, the size of :class:`Label` is not affected by :attr:`~Label.text`\ncontent and the text is not affected by the size. In order to control\nsizing, you must specify :attr:`~Label.text_size` to constrain the text\nand/or bind :attr:`~Label.size` to :attr:`~Label.texture_size` to grow with\nthe text.\n\nFor example, this label's size will be set to the text content\n(plus :attr:`~Label.padding`):\n\n.. code-block:: kv\n\n Label:\n size: self.texture_size\n\nThis label's text will wrap at the specified width and be clipped to the\nheight:\n\n.. code-block:: kv\n\n Label:\n text_size: cm(6), cm(4)\n\n.. note:: The :attr:`~Label.shorten` and :attr:`~Label.max_lines` attributes\n control how overflowing text behaves.\n\nCombine these concepts to create a Label that can grow vertically but wraps the\ntext at a certain width:\n\n.. code-block:: kv\n\n Label:\n text_size: root.width, None\n size: self.texture_size\n\nHow to have a custom background color in the label:\n\n.. code-block:: kv\n\n # Define your background color Template\n <BackgroundColor@Widget>\n background_color: 1, 1, 1, 1\n canvas.before:\n Color:\n rgba: root.background_color\n Rectangle:\n size: self.size\n pos: self.pos\n # Now you can simply Mix the `BackgroundColor` class with almost\n # any other widget... to give it a background.\n <BackgroundLabel@Label+BackgroundColor>\n background_color: 0, 0, 0, 0\n # Default the background color for this label\n # to r 0, g 0, b 0, a 0\n # Use the BackgroundLabel any where in your kv code like below\n BackgroundLabel\n text: 'Hello'\n background_color: 1, 0, 0, 1\n\nText alignment and wrapping\n---------------------------\n\nThe :class:`Label` has :attr:`~Label.halign` and :attr:`~Label.valign`\nproperties to control the alignment of its text. However, by default the text\nimage (:attr:`~Label.texture`) is only just large enough to contain the\ncharacters and is positioned in the center of the Label. The valign property\nwill have no effect and halign will only have an effect if your text has\nnewlines; a single line of text will appear to be centered even though halign\nis set to left (by default).\n\nIn order for the alignment properties to take effect, set the\n:attr:`~Label.text_size`, which specifies the size of the bounding box within\nwhich text is aligned. For instance, the following code binds this size to the\nsize of the Label, so text will be aligned within the widget bounds. This\nwill also automatically wrap the text of the Label to remain within this area.\n\n.. code-block:: kv\n\n Label:\n text_size: self.size\n halign: 'right'\n valign: 'middle'\n\nMarkup text\n-----------\n\n.. versionadded:: 1.1.0\n\nYou can change the style of the text using :doc:`api-kivy.core.text.markup`.\nThe syntax is similar to the bbcode syntax but only the inline styling is\nallowed::\n\n # hello world with world in bold\n l = Label(text='Hello [b]World[/b]', markup=True)\n\n # hello in red, world in blue\n l = Label(text='[color=ff3333]Hello[/color][color=3333ff]World[/color]',\n markup = True)\n\nIf you need to escape the markup from the current text, use\n:func:`kivy.utils.escape_markup`::\n\n text = 'This is an important message [1]'\n l = Label(text='[b]' + escape_markup(text) + '[/b]', markup=True)\n\nThe following tags are available:\n\n``[b][/b]``\n Activate bold text\n``[i][/i]``\n Activate italic text\n``[u][/u]``\n Underlined text\n``[s][/s]``\n Strikethrough text\n``[font=<str>][/font]``\n Change the font (note: this refers to a TTF file or registered alias)\n``[font_context=<str>][/font_context]``\n Change context for the font, use string value \"none\" for isolated context\n (this is equivalent to `None`; if you created a font context named\n `'none'`, it cannot be referred to using markup)\n``[font_family=<str>][/font_family]``\n Font family to request for drawing. This is only valid when using a\n font context, see :class:`kivy.uix.label.Label` for details.\n``[font_features=<str>][/font_features]``\n OpenType font features, in CSS format, this is passed straight\n through to Pango. The effects of requesting a feature depends on loaded\n fonts, library versions, etc. Pango only, requires v1.38 or later.\n``[size=<integer>][/size]``\n Change the font size\n``[color=#<color>][/color]``\n Change the text color\n``[ref=<str>][/ref]``\n Add an interactive zone. The reference + bounding box inside the\n reference will be available in :attr:`Label.refs`\n``[anchor=<str>]``\n Put an anchor in the text. You can get the position of your anchor within\n the text with :attr:`Label.anchors`\n``[sub][/sub]``\n Display the text at a subscript position relative to the text before it.\n``[sup][/sup]``\n Display the text at a superscript position relative to the text before it.\n``[text_language=<str>][/text_language]``\n Language of the text, this is an RFC-3066 format language tag (as string),\n for example \"en_US\", \"zh_CN\", \"fr\" or \"ja\". This can impact font selection\n and metrics. Use the string \"None\" to revert to locale detection.\n Pango only.\n\nIf you want to render the markup text with a [ or ] or & character, you need to\nescape them. We created a simple syntax::\n\n [ -> &bl;\n ] -> &br;\n & -> &\n\nThen you can write::\n\n \"[size=24]Hello &bl;World&br;[/size]\"\n\nInteractive zone in text\n------------------------\n\n.. versionadded:: 1.1.0\n\nYou can now have definable \"links\" using text markup. The idea is to be able\nto detect when the user clicks on part of the text and to react.\nThe tag ``[ref=xxx]`` is used for that.\n\nIn this example, we are creating a reference on the word \"World\". When\nthis word is clicked, the function ``print_it`` will be called with the\nname of the reference::\n\n def print_it(instance, value):\n print('User clicked on', value)\n widget = Label(text='Hello [ref=world]World[/ref]', markup=True)\n widget.bind(on_ref_press=print_it)\n\nFor prettier rendering, you could add a color for the reference. Replace the\n``text=`` in the previous example with::\n\n 'Hello [ref=world][color=0000ff]World[/color][/ref]'\n\nCatering for Unicode languages\n------------------------------\n\nThe font kivy uses does not contain all the characters required for displaying\nall languages. When you use the built-in widgets, this results in a block being\ndrawn where you expect a character.\n\nIf you want to display such characters, you can chose a font that supports them\nand deploy it universally via kv:\n\n.. code-block:: kv\n\n <Label>:\n font_name: '/<path>/<to>/<font>'\n\nNote that this needs to be done before your widgets are loaded as kv rules are\nonly applied at load time.\n\nUsage example\n-------------\n\nThe following example marks the anchors and references contained in a label::\n\n from kivy.app import App\n from kivy.uix.label import Label\n from kivy.clock import Clock\n from kivy.graphics import Color, Rectangle\n\n\n class TestApp(App):\n\n @staticmethod\n def get_x(label, ref_x):\n \"\"\" Return the x value of the ref/anchor relative to the canvas \"\"\"\n return label.center_x - label.texture_size[0] * 0.5 + ref_x\n\n @staticmethod\n def get_y(label, ref_y):\n \"\"\" Return the y value of the ref/anchor relative to the canvas \"\"\"\n # Note the inversion of direction, as y values start at the top of\n # the texture and increase downwards\n return label.center_y + label.texture_size[1] * 0.5 - ref_y\n\n def show_marks(self, label):\n\n # Indicate the position of the anchors with a red top marker\n for name, anc in label.anchors.items():\n with label.canvas:\n Color(1, 0, 0)\n Rectangle(pos=(self.get_x(label, anc[0]),\n self.get_y(label, anc[1])),\n size=(3, 3))\n\n # Draw a green surround around the refs. Note the sizes y inversion\n for name, boxes in label.refs.items():\n for box in boxes:\n with label.canvas:\n Color(0, 1, 0, 0.25)\n Rectangle(pos=(self.get_x(label, box[0]),\n self.get_y(label, box[1])),\n size=(box[2] - box[0],\n box[1] - box[3]))\n\n def build(self):\n label = Label(\n text='[anchor=a]a\\\\nChars [anchor=b]b\\\\n[ref=myref]ref[/ref]',\n markup=True)\n Clock.schedule_once(lambda dt: self.show_marks(label), 1)\n return label\n\n TestApp().run()\n\n'''\n\n__all__ = ('Label', )\n\nfrom kivy.clock import Clock\nfrom kivy.uix.widget import Widget\nfrom kivy.core.text import Label as CoreLabel, DEFAULT_FONT\nfrom kivy.core.text.markup import MarkupLabel as CoreMarkupLabel\nfrom kivy.properties import StringProperty, OptionProperty, \\\n NumericProperty, BooleanProperty, ReferenceListProperty, \\\n ListProperty, ObjectProperty, DictProperty, ColorProperty\nfrom kivy.utils import get_hex_from_color\n\n\nclass Label(Widget):\n '''Label class, see module documentation for more information.\n\n :Events:\n `on_ref_press`\n Fired when the user clicks on a word referenced with a\n ``[ref]`` tag in a text markup.\n '''\n\n __events__ = ['on_ref_press']\n\n _font_properties = ('text', 'font_size', 'font_name', 'bold', 'italic',\n 'underline', 'strikethrough', 'font_family', 'color',\n 'disabled_color', 'halign', 'valign', 'padding_x',\n 'padding_y', 'outline_width', 'disabled_outline_color',\n 'outline_color', 'text_size', 'shorten', 'mipmap',\n 'line_height', 'max_lines', 'strip', 'shorten_from',\n 'split_str', 'ellipsis_options', 'unicode_errors',\n 'markup', 'font_hinting', 'font_kerning',\n 'font_blended', 'font_context', 'font_features',\n 'base_direction', 'text_language')\n\n def __init__(self, **kwargs):\n self._trigger_texture = Clock.create_trigger(self.texture_update, -1)\n super(Label, self).__init__(**kwargs)\n\n # bind all the property for recreating the texture\n d = Label._font_properties\n fbind = self.fbind\n update = self._trigger_texture_update\n fbind('disabled', update, 'disabled')\n for x in d:\n fbind(x, update, x)\n\n self._label = None\n self._create_label()\n\n # force the texture creation\n self._trigger_texture()\n\n def _create_label(self):\n # create the core label class according to markup value\n if self._label is not None:\n cls = self._label.__class__\n else:\n cls = None\n markup = self.markup\n if (markup and cls is not CoreMarkupLabel) or \\\n (not markup and cls is not CoreLabel):\n # markup have change, we need to change our rendering method.\n d = Label._font_properties\n dkw = dict(list(zip(d, [getattr(self, x) for x in d])))\n if markup:\n self._label = CoreMarkupLabel(**dkw)\n else:\n self._label = CoreLabel(**dkw)\n\n def _trigger_texture_update(self, name=None, source=None, value=None):\n # check if the label core class need to be switch to a new one\n if name == 'markup':\n self._create_label()\n if source:\n if name == 'text':\n self._label.text = value\n elif name == 'text_size':\n self._label.usersize = value\n elif name == 'font_size':\n self._label.options[name] = value\n elif name == 'disabled_color' and self.disabled:\n self._label.options['color'] = value\n elif name == 'disabled_outline_color' and self.disabled:\n self._label.options['outline_color'] = value\n elif name == 'disabled':\n self._label.options['color'] = self.disabled_color if value \\\n else self.color\n self._label.options['outline_color'] = (\n self.disabled_outline_color if value else\n self.outline_color)\n else:\n self._label.options[name] = value\n self._trigger_texture()\n\n def texture_update(self, *largs):\n '''Force texture recreation with the current Label properties.\n\n After this function call, the :attr:`texture` and :attr:`texture_size`\n will be updated in this order.\n '''\n mrkup = self._label.__class__ is CoreMarkupLabel\n self.texture = None\n\n if (not self._label.text or\n (self.halign == 'justify' or self.strip) and\n not self._label.text.strip()):\n self.texture_size = (0, 0)\n self.is_shortened = False\n if mrkup:\n self.refs, self._label._refs = {}, {}\n self.anchors, self._label._anchors = {}, {}\n else:\n if mrkup:\n text = self.text\n # we must strip here, otherwise, if the last line is empty,\n # markup will retain the last empty line since it only strips\n # line by line within markup\n if self.halign == 'justify' or self.strip:\n text = text.strip()\n self._label.text = ''.join(('[color=',\n get_hex_from_color(\n self.disabled_color if\n self.disabled else self.color),\n ']', text, '[/color]'))\n self._label.refresh()\n # force the rendering to get the references\n if self._label.texture:\n self._label.texture.bind()\n self.refs = self._label.refs\n self.anchors = self._label.anchors\n else:\n self._label.refresh()\n texture = self._label.texture\n if texture is not None:\n self.texture = self._label.texture\n self.texture_size = list(self.texture.size)\n self.is_shortened = self._label.is_shortened\n\n def on_touch_down(self, touch):\n if super(Label, self).on_touch_down(touch):\n return True\n if not len(self.refs):\n return False\n tx, ty = touch.pos\n tx -= self.center_x - self.texture_size[0] / 2.\n ty -= self.center_y - self.texture_size[1] / 2.\n ty = self.texture_size[1] - ty\n for uid, zones in self.refs.items():\n for zone in zones:\n x, y, w, h = zone\n if x <= tx <= w and y <= ty <= h:\n self.dispatch('on_ref_press', uid)\n return True\n return False\n\n def on_ref_press(self, ref):\n pass\n\n #\n # Properties\n #\n\n disabled_color = ColorProperty([1, 1, 1, .3])\n '''The color of the text when the widget is disabled, in the (r, g, b, a)\n format.\n\n .. versionadded:: 1.8.0\n\n :attr:`disabled_color` is a :class:`~kivy.properties.ColorProperty` and\n defaults to [1, 1, 1, .3].\n\n .. versionchanged:: 2.0.0\n Changed from :class:`~kivy.properties.ListProperty` to\n :class:`~kivy.properties.ColorProperty`.\n '''\n\n text = StringProperty('')\n '''Text of the label.\n\n Creation of a simple hello world::\n\n widget = Label(text='Hello world')\n\n If you want to create the widget with an unicode string, use::\n\n widget = Label(text=u'My unicode string')\n\n :attr:`text` is a :class:`~kivy.properties.StringProperty` and defaults to\n ''.\n '''\n\n text_size = ListProperty([None, None])\n '''By default, the label is not constrained to any bounding box.\n You can set the size constraint of the label with this property.\n The text will autoflow into the constraints. So although the font size\n will not be reduced, the text will be arranged to fit into the box as best\n as possible, with any text still outside the box clipped.\n\n This sets and clips :attr:`texture_size` to text_size if not None.\n\n .. versionadded:: 1.0.4\n\n For example, whatever your current widget size is, if you want the label to\n be created in a box with width=200 and unlimited height::\n\n Label(text='Very big big line', text_size=(200, None))\n\n .. note::\n\n This text_size property is the same as the\n :attr:`~kivy.core.text.Label.usersize` property in the\n :class:`~kivy.core.text.Label` class. (It is named size= in the\n constructor.)\n\n :attr:`text_size` is a :class:`~kivy.properties.ListProperty` and\n defaults to (None, None), meaning no size restriction by default.\n '''\n\n base_direction = OptionProperty(None,\n options=['ltr', 'rtl', 'weak_rtl', 'weak_ltr', None],\n allownone=True)\n '''Base direction of text, this impacts horizontal alignment when\n :attr:`halign` is `auto` (the default). Available options are: None,\n \"ltr\" (left to right), \"rtl\" (right to left) plus \"weak_ltr\" and\n \"weak_rtl\".\n\n .. note::\n This feature requires the Pango text provider.\n\n .. note::\n Weak modes are currently not implemented in Kivy text layout, and\n have the same effect as setting strong mode.\n\n .. versionadded:: 1.11.0\n\n :attr:`base_direction` is an :class:`~kivy.properties.OptionProperty` and\n defaults to None (autodetect RTL if possible, otherwise LTR).\n '''\n\n text_language = StringProperty(None, allownone=True)\n '''Language of the text, if None Pango will determine it from locale.\n This is an RFC-3066 format language tag (as a string), for example\n \"en_US\", \"zh_CN\", \"fr\" or \"ja\". This can impact font selection, metrics\n and rendering. For example, the same bytes of text can look different\n for `ur` and `ar` languages, though both use Arabic script.\n\n .. note::\n This feature requires the Pango text provider.\n\n .. versionadded:: 1.11.0\n\n :attr:`text_language` is a :class:`~kivy.properties.StringProperty` and\n defaults to None.\n '''\n\n font_context = StringProperty(None, allownone=True)\n '''Font context. `None` means the font is used in isolation, so you are\n guaranteed to be drawing with the TTF file resolved by :attr:`font_name`.\n Specifying a value here will load the font file into a named context,\n enabling fallback between all fonts in the same context. If a font\n context is set, you are not guaranteed that rendering will actually use\n the specified TTF file for all glyphs (Pango will pick the one it\n thinks is best).\n\n If Kivy is linked against a system-wide installation of FontConfig,\n you can load the system fonts by specifying a font context starting\n with the special string `system://`. This will load the system\n fontconfig configuration, and add your application-specific fonts on\n top of it (this imposes a signifficant risk of family name collision,\n Pango may not use your custom font file, but pick one from the system)\n\n .. note::\n This feature requires the Pango text provider.\n\n .. versionadded:: 1.11.0\n\n :attr:`font_context` is a :class:`~kivy.properties.StringProperty` and\n defaults to None.\n '''\n\n font_family = StringProperty(None, allownone=True)\n '''Font family, this is only applicable when using :attr:`font_context`\n option. The specified font family will be requested, but note that it may\n not be available, or there could be multiple fonts registered with the\n same family. The value can be a family name (string) available in the\n font context (for example a system font in a `system://` context, or a\n custom font file added using :class:`kivy.core.text.FontContextManager`).\n If set to `None`, font selection is controlled by the :attr:`font_name`\n setting.\n\n .. note::\n If using :attr:`font_name` to reference a custom font file, you\n should leave this as `None`. The family name is managed automatically\n in this case.\n\n .. note::\n This feature requires the Pango text provider.\n\n .. versionadded:: 1.11.0\n\n :attr:`font_family` is a :class:`~kivy.properties.StringProperty` and\n defaults to None.\n '''\n\n font_name = StringProperty(DEFAULT_FONT)\n '''Filename of the font to use. The path can be absolute or relative.\n Relative paths are resolved by the :func:`~kivy.resources.resource_find`\n function.\n\n .. warning::\n\n Depending of your text provider, the font file can be ignored. However,\n you can mostly use this without problems.\n\n If the font used lacks the glyphs for the particular language/symbols\n you are using, you will see '[]' blank box characters instead of the\n actual glyphs. The solution is to use a font that has the glyphs you\n need to display. For example, to display |unicodechar|, use a font such\n as freesans.ttf that has the glyph.\n\n .. |unicodechar| image:: images/unicode-char.png\n\n :attr:`font_name` is a :class:`~kivy.properties.StringProperty` and\n defaults to 'Roboto'. This value is taken\n from :class:`~kivy.config.Config`.\n '''\n\n font_size = NumericProperty('15sp')\n '''Font size of the text, in pixels.\n\n :attr:`font_size` is a :class:`~kivy.properties.NumericProperty` and\n defaults to 15sp.\n '''\n\n font_features = StringProperty()\n '''OpenType font features, in CSS format, this is passed straight\n through to Pango. The effects of requesting a feature depends on loaded\n fonts, library versions, etc. For a complete list of features, see:\n\n https://en.wikipedia.org/wiki/List_of_typographic_features\n\n .. note::\n This feature requires the Pango text provider, and Pango library\n v1.38 or later.\n\n .. versionadded:: 1.11.0\n\n :attr:`font_features` is a :class:`~kivy.properties.StringProperty` and\n defaults to an empty string.\n '''\n\n line_height = NumericProperty(1.0)\n '''Line Height for the text. e.g. line_height = 2 will cause the spacing\n between lines to be twice the size.\n\n :attr:`line_height` is a :class:`~kivy.properties.NumericProperty` and\n defaults to 1.0.\n\n .. versionadded:: 1.5.0\n '''\n\n bold = BooleanProperty(False)\n '''Indicates use of the bold version of your font.\n\n .. note::\n\n Depending of your font, the bold attribute may have no impact on your\n text rendering.\n\n :attr:`bold` is a :class:`~kivy.properties.BooleanProperty` and defaults to\n False.\n '''\n\n italic = BooleanProperty(False)\n '''Indicates use of the italic version of your font.\n\n .. note::\n\n Depending of your font, the italic attribute may have no impact on your\n text rendering.\n\n :attr:`italic` is a :class:`~kivy.properties.BooleanProperty` and defaults\n to False.\n '''\n\n underline = BooleanProperty(False)\n '''Adds an underline to the text.\n\n .. note::\n This feature requires the SDL2 text provider.\n\n .. versionadded:: 1.10.0\n\n :attr:`underline` is a :class:`~kivy.properties.BooleanProperty` and\n defaults to False.\n '''\n\n strikethrough = BooleanProperty(False)\n '''Adds a strikethrough line to the text.\n\n .. note::\n This feature requires the SDL2 text provider.\n\n .. versionadded:: 1.10.0\n\n :attr:`strikethrough` is a :class:`~kivy.properties.BooleanProperty` and\n defaults to False.\n '''\n\n padding_x = NumericProperty(0)\n '''Horizontal padding of the text inside the widget box.\n\n :attr:`padding_x` is a :class:`~kivy.properties.NumericProperty` and\n defaults to 0.\n\n .. versionchanged:: 1.9.0\n `padding_x` has been fixed to work as expected.\n In the past, the text was padded by the negative of its values.\n '''\n\n padding_y = NumericProperty(0)\n '''Vertical padding of the text inside the widget box.\n\n :attr:`padding_y` is a :class:`~kivy.properties.NumericProperty` and\n defaults to 0.\n\n .. versionchanged:: 1.9.0\n `padding_y` has been fixed to work as expected.\n In the past, the text was padded by the negative of its values.\n '''\n\n padding = ReferenceListProperty(padding_x, padding_y)\n '''Padding of the text in the format (padding_x, padding_y)\n\n :attr:`padding` is a :class:`~kivy.properties.ReferenceListProperty` of\n (:attr:`padding_x`, :attr:`padding_y`) properties.\n '''\n\n halign = OptionProperty('auto', options=['left', 'center', 'right',\n 'justify', 'auto'])\n '''Horizontal alignment of the text.\n\n :attr:`halign` is an :class:`~kivy.properties.OptionProperty` and\n defaults to 'auto'. Available options are : auto, left, center, right and\n justify. Auto will attempt to autodetect horizontal alignment for RTL text\n (Pango only), otherwise it behaves like `left`.\n\n .. warning::\n\n This doesn't change the position of the text texture of the Label\n (centered), only the position of the text in this texture. You probably\n want to bind the size of the Label to the :attr:`texture_size` or set a\n :attr:`text_size`.\n\n .. versionchanged:: 1.10.1\n Added `auto` option\n\n .. versionchanged:: 1.6.0\n A new option was added to :attr:`halign`, namely `justify`.\n '''\n\n valign = OptionProperty('bottom',\n options=['bottom', 'middle', 'center', 'top'])\n '''Vertical alignment of the text.\n\n :attr:`valign` is an :class:`~kivy.properties.OptionProperty` and defaults\n to 'bottom'. Available options are : `'bottom'`,\n `'middle'` (or `'center'`) and `'top'`.\n\n .. versionchanged:: 1.10.0\n The `'center'` option has been added as an alias of `'middle'`.\n\n .. warning::\n\n This doesn't change the position of the text texture of the Label\n (centered), only the position of the text within this texture. You\n probably want to bind the size of the Label to the :attr:`texture_size`\n or set a :attr:`text_size` to change this behavior.\n '''\n\n color = ColorProperty([1, 1, 1, 1])\n '''Text color, in the format (r, g, b, a).\n\n :attr:`color` is a :class:`~kivy.properties.ColorProperty` and defaults to\n [1, 1, 1, 1].\n\n .. versionchanged:: 2.0.0\n Changed from :class:`~kivy.properties.ListProperty` to\n :class:`~kivy.properties.ColorProperty`.\n '''\n\n outline_width = NumericProperty(None, allownone=True)\n '''Width in pixels for the outline around the text. No outline will be\n rendered if the value is None.\n\n .. note::\n This feature requires the SDL2 text provider.\n\n .. versionadded:: 1.10.0\n\n :attr:`outline_width` is a :class:`~kivy.properties.NumericProperty` and\n defaults to None.\n '''\n\n outline_color = ColorProperty([0, 0, 0, 1])\n '''The color of the text outline, in the (r, g, b) format.\n\n .. note::\n This feature requires the SDL2 text provider.\n\n .. versionadded:: 1.10.0\n\n :attr:`outline_color` is a :class:`~kivy.properties.ColorProperty` and\n defaults to [0, 0, 0, 1].\n\n .. versionchanged:: 2.0.0\n Changed from :class:`~kivy.properties.ListProperty` to\n :class:`~kivy.properties.ColorProperty`. Alpha component is ignored\n and assigning value to it has no effect.\n '''\n\n disabled_outline_color = ColorProperty([0, 0, 0, 1])\n '''The color of the text outline when the widget is disabled, in the\n (r, g, b) format.\n\n .. note::\n This feature requires the SDL2 text provider.\n\n .. versionadded:: 1.10.0\n\n :attr:`disabled_outline_color` is a :class:`~kivy.properties.ColorProperty`\n and defaults to [0, 0, 0].\n\n .. versionchanged:: 2.0.0\n Changed from :class:`~kivy.properties.ListProperty` to\n :class:`~kivy.properties.ColorProperty`. Alpha component is ignored\n and assigning value to it has no effect.\n '''\n\n texture = ObjectProperty(None, allownone=True)\n '''Texture object of the text.\n The text is rendered automatically when a property changes. The OpenGL\n texture created in this operation is stored in this property. You can use\n this :attr:`texture` for any graphics elements.\n\n Depending on the texture creation, the value will be a\n :class:`~kivy.graphics.texture.Texture` or\n :class:`~kivy.graphics.texture.TextureRegion` object.\n\n .. warning::\n\n The :attr:`texture` update is scheduled for the next frame. If you need\n the texture immediately after changing a property, you have to call\n the :meth:`texture_update` method before accessing :attr:`texture`::\n\n l = Label(text='Hello world')\n # l.texture is good\n l.font_size = '50sp'\n # l.texture is not updated yet\n l.texture_update()\n # l.texture is good now.\n\n :attr:`texture` is an :class:`~kivy.properties.ObjectProperty` and defaults\n to None.\n '''\n\n texture_size = ListProperty([0, 0])\n '''Texture size of the text. The size is determined by the font size and\n text. If :attr:`text_size` is [None, None], the texture will be the size\n required to fit the text, otherwise it's clipped to fit :attr:`text_size`.\n\n When :attr:`text_size` is [None, None], one can bind to texture_size\n and rescale it proportionally to fit the size of the label in order to\n make the text fit maximally in the label.\n\n .. warning::\n\n The :attr:`texture_size` is set after the :attr:`texture`\n property. If you listen for changes to :attr:`texture`,\n :attr:`texture_size` will not be up-to-date in your callback.\n Bind to :attr:`texture_size` instead.\n '''\n\n mipmap = BooleanProperty(False)\n '''Indicates whether OpenGL mipmapping is applied to the texture or not.\n Read :ref:`mipmap` for more information.\n\n .. versionadded:: 1.0.7\n\n :attr:`mipmap` is a :class:`~kivy.properties.BooleanProperty` and defaults\n to False.\n '''\n\n shorten = BooleanProperty(False)\n '''\n Indicates whether the label should attempt to shorten its textual contents\n as much as possible if a :attr:`text_size` is given. Setting this to True\n without an appropriately set :attr:`text_size` will lead to unexpected\n results.\n\n :attr:`shorten_from` and :attr:`split_str` control the direction from\n which the :attr:`text` is split, as well as where in the :attr:`text` we\n are allowed to split.\n\n :attr:`shorten` is a :class:`~kivy.properties.BooleanProperty` and defaults\n to False.\n '''\n\n shorten_from = OptionProperty('center', options=['left', 'center',\n 'right'])\n '''The side from which we should shorten the text from, can be left,\n right, or center.\n\n For example, if left, the ellipsis will appear towards the left side and we\n will display as much text starting from the right as possible. Similar to\n :attr:`shorten`, this option only applies when :attr:`text_size` [0] is\n not None, In this case, the string is shortened to fit within the specified\n width.\n\n .. versionadded:: 1.9.0\n\n :attr:`shorten_from` is a :class:`~kivy.properties.OptionProperty` and\n defaults to `center`.\n '''\n\n is_shortened = BooleanProperty(False)\n '''This property indicates if :attr:`text` was rendered with or without\n shortening when :attr:`shorten` is True.\n\n .. versionadded:: 1.10.0\n\n :attr:`is_shortened` is a :class:`~kivy.properties.BooleanProperty` and\n defaults to False.\n '''\n\n split_str = StringProperty('')\n '''The string used to split the :attr:`text` while shortening the string\n when :attr:`shorten` is True.\n\n For example, if it's a space, the string will be broken into words and as\n many whole words that can fit into a single line will be displayed. If\n :attr:`split_str` is the empty string, `''`, we split on every character\n fitting as much text as possible into the line.\n\n .. versionadded:: 1.9.0\n\n :attr:`split_str` is a :class:`~kivy.properties.StringProperty` and\n defaults to `''` (the empty string).\n '''\n\n ellipsis_options = DictProperty({})\n '''Font options for the ellipsis string('...') used to split the text.\n\n Accepts a dict as option name with the value. Only applied when\n :attr:`markup` is true and text is shortened. All font options which work\n for :class:`Label` will work for :attr:`ellipsis_options`. Defaults for\n the options not specified are taken from the surronding text.\n\n .. code-block:: kv\n\n Label:\n text: 'Some very long line which will be cut'\n markup: True\n shorten: True\n ellipsis_options: {'color':(1,0.5,0.5,1),'underline':True}\n\n .. versionadded:: 2.0.0\n\n :attr:`ellipsis_options` is a :class:`~kivy.properties.DictProperty` and\n defaults to `{}` (the empty dict).\n '''\n\n unicode_errors = OptionProperty(\n 'replace', options=('strict', 'replace', 'ignore'))\n '''How to handle unicode decode errors. Can be `'strict'`, `'replace'` or\n `'ignore'`.\n\n .. versionadded:: 1.9.0\n\n :attr:`unicode_errors` is an :class:`~kivy.properties.OptionProperty` and\n defaults to `'replace'`.\n '''\n\n markup = BooleanProperty(False)\n '''\n .. versionadded:: 1.1.0\n\n If True, the text will be rendered using the\n :class:`~kivy.core.text.markup.MarkupLabel`: you can change the\n style of the text using tags. Check the\n :doc:`api-kivy.core.text.markup` documentation for more information.\n\n :attr:`markup` is a :class:`~kivy.properties.BooleanProperty` and defaults\n to False.\n '''\n\n refs = DictProperty({})\n '''\n .. versionadded:: 1.1.0\n\n List of ``[ref=xxx]`` markup items in the text with the bounding box of\n all the words contained in a ref, available only after rendering.\n\n For example, if you wrote::\n\n Check out my [ref=hello]link[/ref]\n\n The refs will be set with::\n\n {'hello': ((64, 0, 78, 16), )}\n\n The references marked \"hello\" have a bounding box at (x1, y1, x2, y2).\n These co-ordinates are relative to the top left corner of the text, with\n the y value increasing downwards. You can define multiple refs with the\n same name: each occurrence will be added as another (x1, y1, x2, y2) tuple\n to this list.\n\n The current Label implementation uses these references if they exist in\n your markup text, automatically doing the collision with the touch and\n dispatching an `on_ref_press` event.\n\n You can bind a ref event like this::\n\n def print_it(instance, value):\n print('User click on', value)\n widget = Label(text='Hello [ref=world]World[/ref]', markup=True)\n widget.on_ref_press(print_it)\n\n .. note::\n\n This works only with markup text. You need :attr:`markup` set to\n True.\n '''\n\n anchors = DictProperty({})\n '''\n .. versionadded:: 1.1.0\n\n Position of all the ``[anchor=xxx]`` markup in the text.\n These co-ordinates are relative to the top left corner of the text, with\n the y value increasing downwards. Anchors names should be unique and only\n the first occurrence of any duplicate anchors will be recorded.\n\n\n You can place anchors in your markup text as follows::\n\n text = \"\"\"\n [anchor=title1][size=24]This is my Big title.[/size]\n [anchor=content]Hello world\n \"\"\"\n\n Then, all the ``[anchor=]`` references will be removed and you'll get all\n the anchor positions in this property (only after rendering)::\n\n >>> widget = Label(text=text, markup=True)\n >>> widget.texture_update()\n >>> widget.anchors\n {\"content\": (20, 32), \"title1\": (20, 16)}\n\n .. note::\n\n This works only with markup text. You need :attr:`markup` set to\n True.\n\n '''\n\n max_lines = NumericProperty(0)\n '''Maximum number of lines to use, defaults to 0, which means unlimited.\n Please note that :attr:`shorten` take over this property. (with\n shorten, the text is always one line.)\n\n .. versionadded:: 1.8.0\n\n :attr:`max_lines` is a :class:`~kivy.properties.NumericProperty` and\n defaults to 0.\n '''\n\n strip = BooleanProperty(False)\n '''Whether leading and trailing spaces and newlines should be stripped from\n each displayed line. If True, every line will start at the right or left\n edge, depending on :attr:`halign`. If :attr:`halign` is `justify` it is\n implicitly True.\n\n .. versionadded:: 1.9.0\n\n :attr:`strip` is a :class:`~kivy.properties.BooleanProperty` and\n defaults to False.\n '''\n\n font_hinting = OptionProperty(\n 'normal', options=[None, 'normal', 'light', 'mono'], allownone=True)\n '''What hinting option to use for font rendering.\n Can be one of `'normal'`, `'light'`, `'mono'` or None.\n\n .. note::\n This feature requires SDL2 or Pango text provider.\n\n .. versionadded:: 1.10.0\n\n :attr:`font_hinting` is an :class:`~kivy.properties.OptionProperty` and\n defaults to `'normal'`.\n '''\n\n font_kerning = BooleanProperty(True)\n '''Whether kerning is enabled for font rendering. You should normally\n only disable this if rendering is broken with a particular font file.\n\n .. note::\n This feature requires the SDL2 text provider.\n\n .. versionadded:: 1.10.0\n\n :attr:`font_kerning` is a :class:`~kivy.properties.BooleanProperty` and\n defaults to True.\n '''\n\n font_blended = BooleanProperty(True)\n '''Whether blended or solid font rendering should be used.\n\n .. note::\n This feature requires the SDL2 text provider.\n\n .. versionadded:: 1.10.0\n\n :attr:`font_blended` is a :class:`~kivy.properties.BooleanProperty` and\n defaults to True.\n '''\n",
"path": "kivy/uix/label.py"
}
] | [
{
"content": "'''Label\n=====\n\n.. image:: images/label.png\n :align: right\n\nThe :class:`Label` widget is for rendering text. It supports ascii and unicode\nstrings::\n\n # hello world text\n l = Label(text='Hello world')\n\n # unicode text; can only display glyphs that are available in the font\n l = Label(text=u'Hello world ' + unichr(2764))\n\n # multiline text\n l = Label(text='Multi\\\\nLine')\n\n # size\n l = Label(text='Hello world', font_size='20sp')\n\n.. _kivy-uix-label-sizing-and-text-content:\n\nSizing and text content\n---------------------------\n\nBy default, the size of :class:`Label` is not affected by :attr:`~Label.text`\ncontent and the text is not affected by the size. In order to control\nsizing, you must specify :attr:`~Label.text_size` to constrain the text\nand/or bind :attr:`~Label.size` to :attr:`~Label.texture_size` to grow with\nthe text.\n\nFor example, this label's size will be set to the text content\n(plus :attr:`~Label.padding`):\n\n.. code-block:: kv\n\n Label:\n size: self.texture_size\n\nThis label's text will wrap at the specified width and be clipped to the\nheight:\n\n.. code-block:: kv\n\n Label:\n text_size: cm(6), cm(4)\n\n.. note:: The :attr:`~Label.shorten` and :attr:`~Label.max_lines` attributes\n control how overflowing text behaves.\n\nCombine these concepts to create a Label that can grow vertically but wraps the\ntext at a certain width:\n\n.. code-block:: kv\n\n Label:\n text_size: root.width, None\n size: self.texture_size\n\nHow to have a custom background color in the label:\n\n.. code-block:: kv\n\n # Define your background color Template\n <BackgroundColor@Widget>\n background_color: 1, 1, 1, 1\n canvas.before:\n Color:\n rgba: root.background_color\n Rectangle:\n size: self.size\n pos: self.pos\n # Now you can simply Mix the `BackgroundColor` class with almost\n # any other widget... to give it a background.\n <BackgroundLabel@Label+BackgroundColor>\n background_color: 0, 0, 0, 0\n # Default the background color for this label\n # to r 0, g 0, b 0, a 0\n # Use the BackgroundLabel any where in your kv code like below\n BackgroundLabel\n text: 'Hello'\n background_color: 1, 0, 0, 1\n\nText alignment and wrapping\n---------------------------\n\nThe :class:`Label` has :attr:`~Label.halign` and :attr:`~Label.valign`\nproperties to control the alignment of its text. However, by default the text\nimage (:attr:`~Label.texture`) is only just large enough to contain the\ncharacters and is positioned in the center of the Label. The valign property\nwill have no effect and halign will only have an effect if your text has\nnewlines; a single line of text will appear to be centered even though halign\nis set to left (by default).\n\nIn order for the alignment properties to take effect, set the\n:attr:`~Label.text_size`, which specifies the size of the bounding box within\nwhich text is aligned. For instance, the following code binds this size to the\nsize of the Label, so text will be aligned within the widget bounds. This\nwill also automatically wrap the text of the Label to remain within this area.\n\n.. code-block:: kv\n\n Label:\n text_size: self.size\n halign: 'right'\n valign: 'middle'\n\nMarkup text\n-----------\n\n.. versionadded:: 1.1.0\n\nYou can change the style of the text using :doc:`api-kivy.core.text.markup`.\nThe syntax is similar to the bbcode syntax but only the inline styling is\nallowed::\n\n # hello world with world in bold\n l = Label(text='Hello [b]World[/b]', markup=True)\n\n # hello in red, world in blue\n l = Label(text='[color=ff3333]Hello[/color][color=3333ff]World[/color]',\n markup = True)\n\nIf you need to escape the markup from the current text, use\n:func:`kivy.utils.escape_markup`::\n\n text = 'This is an important message [1]'\n l = Label(text='[b]' + escape_markup(text) + '[/b]', markup=True)\n\nThe following tags are available:\n\n``[b][/b]``\n Activate bold text\n``[i][/i]``\n Activate italic text\n``[u][/u]``\n Underlined text\n``[s][/s]``\n Strikethrough text\n``[font=<str>][/font]``\n Change the font (note: this refers to a TTF file or registered alias)\n``[font_context=<str>][/font_context]``\n Change context for the font, use string value \"none\" for isolated context\n (this is equivalent to `None`; if you created a font context named\n `'none'`, it cannot be referred to using markup)\n``[font_family=<str>][/font_family]``\n Font family to request for drawing. This is only valid when using a\n font context, see :class:`kivy.uix.label.Label` for details.\n``[font_features=<str>][/font_features]``\n OpenType font features, in CSS format, this is passed straight\n through to Pango. The effects of requesting a feature depends on loaded\n fonts, library versions, etc. Pango only, requires v1.38 or later.\n``[size=<integer>][/size]``\n Change the font size\n``[color=#<color>][/color]``\n Change the text color\n``[ref=<str>][/ref]``\n Add an interactive zone. The reference + bounding box inside the\n reference will be available in :attr:`Label.refs`\n``[anchor=<str>]``\n Put an anchor in the text. You can get the position of your anchor within\n the text with :attr:`Label.anchors`\n``[sub][/sub]``\n Display the text at a subscript position relative to the text before it.\n``[sup][/sup]``\n Display the text at a superscript position relative to the text before it.\n``[text_language=<str>][/text_language]``\n Language of the text, this is an RFC-3066 format language tag (as string),\n for example \"en_US\", \"zh_CN\", \"fr\" or \"ja\". This can impact font selection\n and metrics. Use the string \"None\" to revert to locale detection.\n Pango only.\n\nIf you want to render the markup text with a [ or ] or & character, you need to\nescape them. We created a simple syntax::\n\n [ -> &bl;\n ] -> &br;\n & -> &\n\nThen you can write::\n\n \"[size=24]Hello &bl;World&br;[/size]\"\n\nInteractive zone in text\n------------------------\n\n.. versionadded:: 1.1.0\n\nYou can now have definable \"links\" using text markup. The idea is to be able\nto detect when the user clicks on part of the text and to react.\nThe tag ``[ref=xxx]`` is used for that.\n\nIn this example, we are creating a reference on the word \"World\". When\nthis word is clicked, the function ``print_it`` will be called with the\nname of the reference::\n\n def print_it(instance, value):\n print('User clicked on', value)\n widget = Label(text='Hello [ref=world]World[/ref]', markup=True)\n widget.bind(on_ref_press=print_it)\n\nFor prettier rendering, you could add a color for the reference. Replace the\n``text=`` in the previous example with::\n\n 'Hello [ref=world][color=0000ff]World[/color][/ref]'\n\nCatering for Unicode languages\n------------------------------\n\nThe font kivy uses does not contain all the characters required for displaying\nall languages. When you use the built-in widgets, this results in a block being\ndrawn where you expect a character.\n\nIf you want to display such characters, you can chose a font that supports them\nand deploy it universally via kv:\n\n.. code-block:: kv\n\n <Label>:\n font_name: '/<path>/<to>/<font>'\n\nNote that this needs to be done before your widgets are loaded as kv rules are\nonly applied at load time.\n\nUsage example\n-------------\n\nThe following example marks the anchors and references contained in a label::\n\n from kivy.app import App\n from kivy.uix.label import Label\n from kivy.clock import Clock\n from kivy.graphics import Color, Rectangle\n\n\n class TestApp(App):\n\n @staticmethod\n def get_x(label, ref_x):\n \"\"\" Return the x value of the ref/anchor relative to the canvas \"\"\"\n return label.center_x - label.texture_size[0] * 0.5 + ref_x\n\n @staticmethod\n def get_y(label, ref_y):\n \"\"\" Return the y value of the ref/anchor relative to the canvas \"\"\"\n # Note the inversion of direction, as y values start at the top of\n # the texture and increase downwards\n return label.center_y + label.texture_size[1] * 0.5 - ref_y\n\n def show_marks(self, label):\n\n # Indicate the position of the anchors with a red top marker\n for name, anc in label.anchors.items():\n with label.canvas:\n Color(1, 0, 0)\n Rectangle(pos=(self.get_x(label, anc[0]),\n self.get_y(label, anc[1])),\n size=(3, 3))\n\n # Draw a green surround around the refs. Note the sizes y inversion\n for name, boxes in label.refs.items():\n for box in boxes:\n with label.canvas:\n Color(0, 1, 0, 0.25)\n Rectangle(pos=(self.get_x(label, box[0]),\n self.get_y(label, box[1])),\n size=(box[2] - box[0],\n box[1] - box[3]))\n\n def build(self):\n label = Label(\n text='[anchor=a]a\\\\nChars [anchor=b]b\\\\n[ref=myref]ref[/ref]',\n markup=True)\n Clock.schedule_once(lambda dt: self.show_marks(label), 1)\n return label\n\n TestApp().run()\n\n'''\n\n__all__ = ('Label', )\n\nfrom kivy.clock import Clock\nfrom kivy.uix.widget import Widget\nfrom kivy.core.text import Label as CoreLabel, DEFAULT_FONT\nfrom kivy.core.text.markup import MarkupLabel as CoreMarkupLabel\nfrom kivy.properties import StringProperty, OptionProperty, \\\n NumericProperty, BooleanProperty, ReferenceListProperty, \\\n ListProperty, ObjectProperty, DictProperty, ColorProperty\nfrom kivy.utils import get_hex_from_color\n\n\nclass Label(Widget):\n '''Label class, see module documentation for more information.\n\n :Events:\n `on_ref_press`\n Fired when the user clicks on a word referenced with a\n ``[ref]`` tag in a text markup.\n '''\n\n __events__ = ['on_ref_press']\n\n _font_properties = ('text', 'font_size', 'font_name', 'bold', 'italic',\n 'underline', 'strikethrough', 'font_family', 'color',\n 'disabled_color', 'halign', 'valign', 'padding_x',\n 'padding_y', 'outline_width', 'disabled_outline_color',\n 'outline_color', 'text_size', 'shorten', 'mipmap',\n 'line_height', 'max_lines', 'strip', 'shorten_from',\n 'split_str', 'ellipsis_options', 'unicode_errors',\n 'markup', 'font_hinting', 'font_kerning',\n 'font_blended', 'font_context', 'font_features',\n 'base_direction', 'text_language')\n\n def __init__(self, **kwargs):\n self._trigger_texture = Clock.create_trigger(self.texture_update, -1)\n super(Label, self).__init__(**kwargs)\n\n # bind all the property for recreating the texture\n d = Label._font_properties\n fbind = self.fbind\n update = self._trigger_texture_update\n fbind('disabled', update, 'disabled')\n for x in d:\n fbind(x, update, x)\n\n self._label = None\n self._create_label()\n\n # force the texture creation\n self._trigger_texture()\n\n def _create_label(self):\n # create the core label class according to markup value\n if self._label is not None:\n cls = self._label.__class__\n else:\n cls = None\n markup = self.markup\n if (markup and cls is not CoreMarkupLabel) or \\\n (not markup and cls is not CoreLabel):\n # markup have change, we need to change our rendering method.\n d = Label._font_properties\n dkw = dict(list(zip(d, [getattr(self, x) for x in d])))\n if markup:\n self._label = CoreMarkupLabel(**dkw)\n else:\n self._label = CoreLabel(**dkw)\n\n def _trigger_texture_update(self, name=None, source=None, value=None):\n # check if the label core class need to be switch to a new one\n if name == 'markup':\n self._create_label()\n if source:\n if name == 'text':\n self._label.text = value\n elif name == 'text_size':\n self._label.usersize = value\n elif name == 'font_size':\n self._label.options[name] = value\n elif name == 'disabled_color' and self.disabled:\n self._label.options['color'] = value\n elif name == 'disabled_outline_color' and self.disabled:\n self._label.options['outline_color'] = value\n elif name == 'disabled':\n self._label.options['color'] = self.disabled_color if value \\\n else self.color\n self._label.options['outline_color'] = (\n self.disabled_outline_color if value else\n self.outline_color)\n else:\n self._label.options[name] = value\n self._trigger_texture()\n\n def texture_update(self, *largs):\n '''Force texture recreation with the current Label properties.\n\n After this function call, the :attr:`texture` and :attr:`texture_size`\n will be updated in this order.\n '''\n mrkup = self._label.__class__ is CoreMarkupLabel\n self.texture = None\n\n if (not self._label.text or\n (self.halign == 'justify' or self.strip) and\n not self._label.text.strip()):\n self.texture_size = (0, 0)\n self.is_shortened = False\n if mrkup:\n self.refs, self._label._refs = {}, {}\n self.anchors, self._label._anchors = {}, {}\n else:\n if mrkup:\n text = self.text\n # we must strip here, otherwise, if the last line is empty,\n # markup will retain the last empty line since it only strips\n # line by line within markup\n if self.halign == 'justify' or self.strip:\n text = text.strip()\n self._label.text = ''.join(('[color=',\n get_hex_from_color(\n self.disabled_color if\n self.disabled else self.color),\n ']', text, '[/color]'))\n self._label.refresh()\n # force the rendering to get the references\n if self._label.texture:\n self._label.texture.bind()\n self.refs = self._label.refs\n self.anchors = self._label.anchors\n else:\n self._label.refresh()\n texture = self._label.texture\n if texture is not None:\n self.texture = self._label.texture\n self.texture_size = list(self.texture.size)\n self.is_shortened = self._label.is_shortened\n\n def on_touch_down(self, touch):\n if super(Label, self).on_touch_down(touch):\n return True\n if not len(self.refs):\n return False\n tx, ty = touch.pos\n tx -= self.center_x - self.texture_size[0] / 2.\n ty -= self.center_y - self.texture_size[1] / 2.\n ty = self.texture_size[1] - ty\n for uid, zones in self.refs.items():\n for zone in zones:\n x, y, w, h = zone\n if x <= tx <= w and y <= ty <= h:\n self.dispatch('on_ref_press', uid)\n return True\n return False\n\n def on_ref_press(self, ref):\n pass\n\n #\n # Properties\n #\n\n disabled_color = ColorProperty([1, 1, 1, .3])\n '''The color of the text when the widget is disabled, in the (r, g, b, a)\n format.\n\n .. versionadded:: 1.8.0\n\n :attr:`disabled_color` is a :class:`~kivy.properties.ColorProperty` and\n defaults to [1, 1, 1, .3].\n\n .. versionchanged:: 2.0.0\n Changed from :class:`~kivy.properties.ListProperty` to\n :class:`~kivy.properties.ColorProperty`.\n '''\n\n text = StringProperty('')\n '''Text of the label.\n\n Creation of a simple hello world::\n\n widget = Label(text='Hello world')\n\n If you want to create the widget with an unicode string, use::\n\n widget = Label(text=u'My unicode string')\n\n :attr:`text` is a :class:`~kivy.properties.StringProperty` and defaults to\n ''.\n '''\n\n text_size = ListProperty([None, None])\n '''By default, the label is not constrained to any bounding box.\n You can set the size constraint of the label with this property.\n The text will autoflow into the constraints. So although the font size\n will not be reduced, the text will be arranged to fit into the box as best\n as possible, with any text still outside the box clipped.\n\n This sets and clips :attr:`texture_size` to text_size if not None.\n\n .. versionadded:: 1.0.4\n\n For example, whatever your current widget size is, if you want the label to\n be created in a box with width=200 and unlimited height::\n\n Label(text='Very big big line', text_size=(200, None))\n\n .. note::\n\n This text_size property is the same as the\n :attr:`~kivy.core.text.Label.usersize` property in the\n :class:`~kivy.core.text.Label` class. (It is named size= in the\n constructor.)\n\n :attr:`text_size` is a :class:`~kivy.properties.ListProperty` and\n defaults to (None, None), meaning no size restriction by default.\n '''\n\n base_direction = OptionProperty(None,\n options=['ltr', 'rtl', 'weak_rtl', 'weak_ltr', None],\n allownone=True)\n '''Base direction of text, this impacts horizontal alignment when\n :attr:`halign` is `auto` (the default). Available options are: None,\n \"ltr\" (left to right), \"rtl\" (right to left) plus \"weak_ltr\" and\n \"weak_rtl\".\n\n .. note::\n This feature requires the Pango text provider.\n\n .. note::\n Weak modes are currently not implemented in Kivy text layout, and\n have the same effect as setting strong mode.\n\n .. versionadded:: 1.11.0\n\n :attr:`base_direction` is an :class:`~kivy.properties.OptionProperty` and\n defaults to None (autodetect RTL if possible, otherwise LTR).\n '''\n\n text_language = StringProperty(None, allownone=True)\n '''Language of the text, if None Pango will determine it from locale.\n This is an RFC-3066 format language tag (as a string), for example\n \"en_US\", \"zh_CN\", \"fr\" or \"ja\". This can impact font selection, metrics\n and rendering. For example, the same bytes of text can look different\n for `ur` and `ar` languages, though both use Arabic script.\n\n .. note::\n This feature requires the Pango text provider.\n\n .. versionadded:: 1.11.0\n\n :attr:`text_language` is a :class:`~kivy.properties.StringProperty` and\n defaults to None.\n '''\n\n font_context = StringProperty(None, allownone=True)\n '''Font context. `None` means the font is used in isolation, so you are\n guaranteed to be drawing with the TTF file resolved by :attr:`font_name`.\n Specifying a value here will load the font file into a named context,\n enabling fallback between all fonts in the same context. If a font\n context is set, you are not guaranteed that rendering will actually use\n the specified TTF file for all glyphs (Pango will pick the one it\n thinks is best).\n\n If Kivy is linked against a system-wide installation of FontConfig,\n you can load the system fonts by specifying a font context starting\n with the special string `system://`. This will load the system\n fontconfig configuration, and add your application-specific fonts on\n top of it (this imposes a signifficant risk of family name collision,\n Pango may not use your custom font file, but pick one from the system)\n\n .. note::\n This feature requires the Pango text provider.\n\n .. versionadded:: 1.11.0\n\n :attr:`font_context` is a :class:`~kivy.properties.StringProperty` and\n defaults to None.\n '''\n\n font_family = StringProperty(None, allownone=True)\n '''Font family, this is only applicable when using :attr:`font_context`\n option. The specified font family will be requested, but note that it may\n not be available, or there could be multiple fonts registered with the\n same family. The value can be a family name (string) available in the\n font context (for example a system font in a `system://` context, or a\n custom font file added using :class:`kivy.core.text.FontContextManager`).\n If set to `None`, font selection is controlled by the :attr:`font_name`\n setting.\n\n .. note::\n If using :attr:`font_name` to reference a custom font file, you\n should leave this as `None`. The family name is managed automatically\n in this case.\n\n .. note::\n This feature requires the Pango text provider.\n\n .. versionadded:: 1.11.0\n\n :attr:`font_family` is a :class:`~kivy.properties.StringProperty` and\n defaults to None.\n '''\n\n font_name = StringProperty(DEFAULT_FONT)\n '''Filename of the font to use. The path can be absolute or relative.\n Relative paths are resolved by the :func:`~kivy.resources.resource_find`\n function.\n\n .. warning::\n\n Depending of your text provider, the font file can be ignored. However,\n you can mostly use this without problems.\n\n If the font used lacks the glyphs for the particular language/symbols\n you are using, you will see '[]' blank box characters instead of the\n actual glyphs. The solution is to use a font that has the glyphs you\n need to display. For example, to display |unicodechar|, use a font such\n as freesans.ttf that has the glyph.\n\n .. |unicodechar| image:: images/unicode-char.png\n\n :attr:`font_name` is a :class:`~kivy.properties.StringProperty` and\n defaults to 'Roboto'. This value is taken\n from :class:`~kivy.config.Config`.\n '''\n\n font_size = NumericProperty('15sp')\n '''Font size of the text, in pixels.\n\n :attr:`font_size` is a :class:`~kivy.properties.NumericProperty` and\n defaults to 15sp.\n '''\n\n font_features = StringProperty()\n '''OpenType font features, in CSS format, this is passed straight\n through to Pango. The effects of requesting a feature depends on loaded\n fonts, library versions, etc. For a complete list of features, see:\n\n https://en.wikipedia.org/wiki/List_of_typographic_features\n\n .. note::\n This feature requires the Pango text provider, and Pango library\n v1.38 or later.\n\n .. versionadded:: 1.11.0\n\n :attr:`font_features` is a :class:`~kivy.properties.StringProperty` and\n defaults to an empty string.\n '''\n\n line_height = NumericProperty(1.0)\n '''Line Height for the text. e.g. line_height = 2 will cause the spacing\n between lines to be twice the size.\n\n :attr:`line_height` is a :class:`~kivy.properties.NumericProperty` and\n defaults to 1.0.\n\n .. versionadded:: 1.5.0\n '''\n\n bold = BooleanProperty(False)\n '''Indicates use of the bold version of your font.\n\n .. note::\n\n Depending of your font, the bold attribute may have no impact on your\n text rendering.\n\n :attr:`bold` is a :class:`~kivy.properties.BooleanProperty` and defaults to\n False.\n '''\n\n italic = BooleanProperty(False)\n '''Indicates use of the italic version of your font.\n\n .. note::\n\n Depending of your font, the italic attribute may have no impact on your\n text rendering.\n\n :attr:`italic` is a :class:`~kivy.properties.BooleanProperty` and defaults\n to False.\n '''\n\n underline = BooleanProperty(False)\n '''Adds an underline to the text.\n\n .. note::\n This feature requires the SDL2 text provider.\n\n .. versionadded:: 1.10.0\n\n :attr:`underline` is a :class:`~kivy.properties.BooleanProperty` and\n defaults to False.\n '''\n\n strikethrough = BooleanProperty(False)\n '''Adds a strikethrough line to the text.\n\n .. note::\n This feature requires the SDL2 text provider.\n\n .. versionadded:: 1.10.0\n\n :attr:`strikethrough` is a :class:`~kivy.properties.BooleanProperty` and\n defaults to False.\n '''\n\n padding_x = NumericProperty(0)\n '''Horizontal padding of the text inside the widget box.\n\n :attr:`padding_x` is a :class:`~kivy.properties.NumericProperty` and\n defaults to 0.\n\n .. versionchanged:: 1.9.0\n `padding_x` has been fixed to work as expected.\n In the past, the text was padded by the negative of its values.\n '''\n\n padding_y = NumericProperty(0)\n '''Vertical padding of the text inside the widget box.\n\n :attr:`padding_y` is a :class:`~kivy.properties.NumericProperty` and\n defaults to 0.\n\n .. versionchanged:: 1.9.0\n `padding_y` has been fixed to work as expected.\n In the past, the text was padded by the negative of its values.\n '''\n\n padding = ReferenceListProperty(padding_x, padding_y)\n '''Padding of the text in the format (padding_x, padding_y)\n\n :attr:`padding` is a :class:`~kivy.properties.ReferenceListProperty` of\n (:attr:`padding_x`, :attr:`padding_y`) properties.\n '''\n\n halign = OptionProperty('auto', options=['left', 'center', 'right',\n 'justify', 'auto'])\n '''Horizontal alignment of the text.\n\n :attr:`halign` is an :class:`~kivy.properties.OptionProperty` and\n defaults to 'auto'. Available options are : auto, left, center, right and\n justify. Auto will attempt to autodetect horizontal alignment for RTL text\n (Pango only), otherwise it behaves like `left`.\n\n .. warning::\n\n This doesn't change the position of the text texture of the Label\n (centered), only the position of the text in this texture. You probably\n want to bind the size of the Label to the :attr:`texture_size` or set a\n :attr:`text_size`.\n\n .. versionchanged:: 1.10.1\n Added `auto` option\n\n .. versionchanged:: 1.6.0\n A new option was added to :attr:`halign`, namely `justify`.\n '''\n\n valign = OptionProperty('bottom',\n options=['bottom', 'middle', 'center', 'top'])\n '''Vertical alignment of the text.\n\n :attr:`valign` is an :class:`~kivy.properties.OptionProperty` and defaults\n to 'bottom'. Available options are : `'bottom'`,\n `'middle'` (or `'center'`) and `'top'`.\n\n .. versionchanged:: 1.10.0\n The `'center'` option has been added as an alias of `'middle'`.\n\n .. warning::\n\n This doesn't change the position of the text texture of the Label\n (centered), only the position of the text within this texture. You\n probably want to bind the size of the Label to the :attr:`texture_size`\n or set a :attr:`text_size` to change this behavior.\n '''\n\n color = ColorProperty([1, 1, 1, 1])\n '''Text color, in the format (r, g, b, a).\n\n :attr:`color` is a :class:`~kivy.properties.ColorProperty` and defaults to\n [1, 1, 1, 1].\n\n .. versionchanged:: 2.0.0\n Changed from :class:`~kivy.properties.ListProperty` to\n :class:`~kivy.properties.ColorProperty`.\n '''\n\n outline_width = NumericProperty(None, allownone=True)\n '''Width in pixels for the outline around the text. No outline will be\n rendered if the value is None.\n\n .. note::\n This feature requires the SDL2 text provider.\n\n .. versionadded:: 1.10.0\n\n :attr:`outline_width` is a :class:`~kivy.properties.NumericProperty` and\n defaults to None.\n '''\n\n outline_color = ColorProperty([0, 0, 0, 1])\n '''The color of the text outline, in the (r, g, b) format.\n\n .. note::\n This feature requires the SDL2 text provider.\n\n .. versionadded:: 1.10.0\n\n :attr:`outline_color` is a :class:`~kivy.properties.ColorProperty` and\n defaults to [0, 0, 0, 1].\n\n .. versionchanged:: 2.0.0\n Changed from :class:`~kivy.properties.ListProperty` to\n :class:`~kivy.properties.ColorProperty`. Alpha component is ignored\n and assigning value to it has no effect.\n '''\n\n disabled_outline_color = ColorProperty([0, 0, 0, 1])\n '''The color of the text outline when the widget is disabled, in the\n (r, g, b) format.\n\n .. note::\n This feature requires the SDL2 text provider.\n\n .. versionadded:: 1.10.0\n\n :attr:`disabled_outline_color` is a :class:`~kivy.properties.ColorProperty`\n and defaults to [0, 0, 0].\n\n .. versionchanged:: 2.0.0\n Changed from :class:`~kivy.properties.ListProperty` to\n :class:`~kivy.properties.ColorProperty`. Alpha component is ignored\n and assigning value to it has no effect.\n '''\n\n texture = ObjectProperty(None, allownone=True)\n '''Texture object of the text.\n The text is rendered automatically when a property changes. The OpenGL\n texture created in this operation is stored in this property. You can use\n this :attr:`texture` for any graphics elements.\n\n Depending on the texture creation, the value will be a\n :class:`~kivy.graphics.texture.Texture` or\n :class:`~kivy.graphics.texture.TextureRegion` object.\n\n .. warning::\n\n The :attr:`texture` update is scheduled for the next frame. If you need\n the texture immediately after changing a property, you have to call\n the :meth:`texture_update` method before accessing :attr:`texture`::\n\n l = Label(text='Hello world')\n # l.texture is good\n l.font_size = '50sp'\n # l.texture is not updated yet\n l.texture_update()\n # l.texture is good now.\n\n :attr:`texture` is an :class:`~kivy.properties.ObjectProperty` and defaults\n to None.\n '''\n\n texture_size = ListProperty([0, 0])\n '''Texture size of the text. The size is determined by the font size and\n text. If :attr:`text_size` is [None, None], the texture will be the size\n required to fit the text, otherwise it's clipped to fit :attr:`text_size`.\n\n When :attr:`text_size` is [None, None], one can bind to texture_size\n and rescale it proportionally to fit the size of the label in order to\n make the text fit maximally in the label.\n\n .. warning::\n\n The :attr:`texture_size` is set after the :attr:`texture`\n property. If you listen for changes to :attr:`texture`,\n :attr:`texture_size` will not be up-to-date in your callback.\n Bind to :attr:`texture_size` instead.\n '''\n\n mipmap = BooleanProperty(False)\n '''Indicates whether OpenGL mipmapping is applied to the texture or not.\n Read :ref:`mipmap` for more information.\n\n .. versionadded:: 1.0.7\n\n :attr:`mipmap` is a :class:`~kivy.properties.BooleanProperty` and defaults\n to False.\n '''\n\n shorten = BooleanProperty(False)\n '''\n Indicates whether the label should attempt to shorten its textual contents\n as much as possible if a :attr:`text_size` is given. Setting this to True\n without an appropriately set :attr:`text_size` will lead to unexpected\n results.\n\n :attr:`shorten_from` and :attr:`split_str` control the direction from\n which the :attr:`text` is split, as well as where in the :attr:`text` we\n are allowed to split.\n\n :attr:`shorten` is a :class:`~kivy.properties.BooleanProperty` and defaults\n to False.\n '''\n\n shorten_from = OptionProperty('center', options=['left', 'center',\n 'right'])\n '''The side from which we should shorten the text from, can be left,\n right, or center.\n\n For example, if left, the ellipsis will appear towards the left side and we\n will display as much text starting from the right as possible. Similar to\n :attr:`shorten`, this option only applies when :attr:`text_size` [0] is\n not None, In this case, the string is shortened to fit within the specified\n width.\n\n .. versionadded:: 1.9.0\n\n :attr:`shorten_from` is a :class:`~kivy.properties.OptionProperty` and\n defaults to `center`.\n '''\n\n is_shortened = BooleanProperty(False)\n '''This property indicates if :attr:`text` was rendered with or without\n shortening when :attr:`shorten` is True.\n\n .. versionadded:: 1.10.0\n\n :attr:`is_shortened` is a :class:`~kivy.properties.BooleanProperty` and\n defaults to False.\n '''\n\n split_str = StringProperty('')\n '''The string used to split the :attr:`text` while shortening the string\n when :attr:`shorten` is True.\n\n For example, if it's a space, the string will be broken into words and as\n many whole words that can fit into a single line will be displayed. If\n :attr:`split_str` is the empty string, `''`, we split on every character\n fitting as much text as possible into the line.\n\n .. versionadded:: 1.9.0\n\n :attr:`split_str` is a :class:`~kivy.properties.StringProperty` and\n defaults to `''` (the empty string).\n '''\n\n ellipsis_options = DictProperty({})\n '''Font options for the ellipsis string('...') used to split the text.\n\n Accepts a dict as option name with the value. Only applied when\n :attr:`markup` is true and text is shortened. All font options which work\n for :class:`Label` will work for :attr:`ellipsis_options`. Defaults for\n the options not specified are taken from the surronding text.\n\n .. code-block:: kv\n\n Label:\n text: 'Some very long line which will be cut'\n markup: True\n shorten: True\n ellipsis_options: {'color':(1,0.5,0.5,1),'underline':True}\n\n .. versionadded:: 2.0.0\n\n :attr:`ellipsis_options` is a :class:`~kivy.properties.DictProperty` and\n defaults to `{}` (the empty dict).\n '''\n\n unicode_errors = OptionProperty(\n 'replace', options=('strict', 'replace', 'ignore'))\n '''How to handle unicode decode errors. Can be `'strict'`, `'replace'` or\n `'ignore'`.\n\n .. versionadded:: 1.9.0\n\n :attr:`unicode_errors` is an :class:`~kivy.properties.OptionProperty` and\n defaults to `'replace'`.\n '''\n\n markup = BooleanProperty(False)\n '''\n .. versionadded:: 1.1.0\n\n If True, the text will be rendered using the\n :class:`~kivy.core.text.markup.MarkupLabel`: you can change the\n style of the text using tags. Check the\n :doc:`api-kivy.core.text.markup` documentation for more information.\n\n :attr:`markup` is a :class:`~kivy.properties.BooleanProperty` and defaults\n to False.\n '''\n\n refs = DictProperty({})\n '''\n .. versionadded:: 1.1.0\n\n List of ``[ref=xxx]`` markup items in the text with the bounding box of\n all the words contained in a ref, available only after rendering.\n\n For example, if you wrote::\n\n Check out my [ref=hello]link[/ref]\n\n The refs will be set with::\n\n {'hello': ((64, 0, 78, 16), )}\n\n The references marked \"hello\" have a bounding box at (x1, y1, x2, y2).\n These co-ordinates are relative to the top left corner of the text, with\n the y value increasing downwards. You can define multiple refs with the\n same name: each occurrence will be added as another (x1, y1, x2, y2) tuple\n to this list.\n\n The current Label implementation uses these references if they exist in\n your markup text, automatically doing the collision with the touch and\n dispatching an `on_ref_press` event.\n\n You can bind a ref event like this::\n\n def print_it(instance, value):\n print('User click on', value)\n widget = Label(text='Hello [ref=world]World[/ref]', markup=True)\n widget.bind(on_ref_press=print_it)\n\n .. note::\n\n This works only with markup text. You need :attr:`markup` set to\n True.\n '''\n\n anchors = DictProperty({})\n '''\n .. versionadded:: 1.1.0\n\n Position of all the ``[anchor=xxx]`` markup in the text.\n These co-ordinates are relative to the top left corner of the text, with\n the y value increasing downwards. Anchors names should be unique and only\n the first occurrence of any duplicate anchors will be recorded.\n\n\n You can place anchors in your markup text as follows::\n\n text = \"\"\"\n [anchor=title1][size=24]This is my Big title.[/size]\n [anchor=content]Hello world\n \"\"\"\n\n Then, all the ``[anchor=]`` references will be removed and you'll get all\n the anchor positions in this property (only after rendering)::\n\n >>> widget = Label(text=text, markup=True)\n >>> widget.texture_update()\n >>> widget.anchors\n {\"content\": (20, 32), \"title1\": (20, 16)}\n\n .. note::\n\n This works only with markup text. You need :attr:`markup` set to\n True.\n\n '''\n\n max_lines = NumericProperty(0)\n '''Maximum number of lines to use, defaults to 0, which means unlimited.\n Please note that :attr:`shorten` take over this property. (with\n shorten, the text is always one line.)\n\n .. versionadded:: 1.8.0\n\n :attr:`max_lines` is a :class:`~kivy.properties.NumericProperty` and\n defaults to 0.\n '''\n\n strip = BooleanProperty(False)\n '''Whether leading and trailing spaces and newlines should be stripped from\n each displayed line. If True, every line will start at the right or left\n edge, depending on :attr:`halign`. If :attr:`halign` is `justify` it is\n implicitly True.\n\n .. versionadded:: 1.9.0\n\n :attr:`strip` is a :class:`~kivy.properties.BooleanProperty` and\n defaults to False.\n '''\n\n font_hinting = OptionProperty(\n 'normal', options=[None, 'normal', 'light', 'mono'], allownone=True)\n '''What hinting option to use for font rendering.\n Can be one of `'normal'`, `'light'`, `'mono'` or None.\n\n .. note::\n This feature requires SDL2 or Pango text provider.\n\n .. versionadded:: 1.10.0\n\n :attr:`font_hinting` is an :class:`~kivy.properties.OptionProperty` and\n defaults to `'normal'`.\n '''\n\n font_kerning = BooleanProperty(True)\n '''Whether kerning is enabled for font rendering. You should normally\n only disable this if rendering is broken with a particular font file.\n\n .. note::\n This feature requires the SDL2 text provider.\n\n .. versionadded:: 1.10.0\n\n :attr:`font_kerning` is a :class:`~kivy.properties.BooleanProperty` and\n defaults to True.\n '''\n\n font_blended = BooleanProperty(True)\n '''Whether blended or solid font rendering should be used.\n\n .. note::\n This feature requires the SDL2 text provider.\n\n .. versionadded:: 1.10.0\n\n :attr:`font_blended` is a :class:`~kivy.properties.BooleanProperty` and\n defaults to True.\n '''\n",
"path": "kivy/uix/label.py"
}
] | diff --git a/kivy/uix/label.py b/kivy/uix/label.py
index ea9181a3c8..45f764ac05 100644
--- a/kivy/uix/label.py
+++ b/kivy/uix/label.py
@@ -1005,7 +1005,7 @@ def on_ref_press(self, ref):
def print_it(instance, value):
print('User click on', value)
widget = Label(text='Hello [ref=world]World[/ref]', markup=True)
- widget.on_ref_press(print_it)
+ widget.bind(on_ref_press=print_it)
.. note::
|
ansible__awx-10108 | tower_workflow_job_template not changing ask_limit_on_launch
<!-- Issues are for **concrete, actionable bugs and feature requests** only - if you're just asking for debugging help or technical support, please use:
- http://webchat.freenode.net/?channels=ansible-awx
- https://groups.google.com/forum/#!forum/awx-project
We have to limit this because of limited volunteer time to respond to issues! -->
##### ISSUE TYPE
- Bug Report
##### SUMMARY
When using tower_workflow_job_template to change ask_limit_on_launch the module is reporting OK and does not do the change.
##### ENVIRONMENT
* AWX version: 15.0.1
* AWX install method: docker on linux
* Ansible version: 2.10
* Operating System: SLES
* Web Browser: Chrome
##### STEPS TO REPRODUCE
1. Create a workflow template and set ask_limit_on_launch to true
2. Use the tower_workflow_job_template module to change ask_limit_on_launch to false
##### EXPECTED RESULTS
- Module should report change
- Workflow template should have ask_limit_on_launch set to false
##### ACTUAL RESULTS
- Module reports OK
- Workflow template have ask_limit_on_launch set to true
##### ADDITIONAL INFORMATION
I am using the following task:
```yaml
- name: Create a workflow job template
awx.awx.tower_workflow_job_template:
name: "{{ item.name }}"
description: "{{ item.description }}"
organization: "{{ item.organization }}"
survey_enabled: "{{ item.survey_enabled|default('no') }}"
ask_limit_on_launch: "{{ item.ask_limit_on_launch|default('no') }}"
notification_templates_success: "{{ item.notification_templates_success|default(omit) }}"
notification_templates_error: "{{ item.notification_templates_error|default(omit) }}"
tower_host: "{{ tower_host }}"
tower_username: "{{ tower_username }}"
tower_password: "{{ tower_password }}"
validate_certs: "{{ validate_certs }}"
loop: "{{ workflow_templates }}"
```
with the following input:
```yaml
workflow_templates:
- name: "Update the inventory"
description: "Update the inventory"
organization: "myOrg"
notification_templates_success:
- "Mail"
notification_templates_error:
- "Mail"
```
| [
{
"content": "#!/usr/bin/python\n# coding: utf-8 -*-\n\n\n# (c) 2020, John Westcott IV <[email protected]>\n# GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt)\n\nfrom __future__ import absolute_import, division, print_function\n\n__metaclass__ = type\n\n\nANSIBLE_METADATA = {'metadata_version': '1.1', 'status': ['preview'], 'supported_by': 'community'}\n\nDOCUMENTATION = '''\n---\nmodule: workflow_job_template\nauthor: \"John Westcott IV (@john-westcott-iv)\"\nshort_description: create, update, or destroy Automation Platform Controller workflow job templates.\ndescription:\n - Create, update, or destroy Automation Platform Controller workflow job templates.\n - Replaces the deprecated tower_workflow_template module.\n - Use workflow_job_template_node after this, or use the schema parameter to build the workflow's graph\noptions:\n name:\n description:\n - Name of this workflow job template.\n required: True\n type: str\n new_name:\n description:\n - Setting this option will change the existing name.\n type: str\n copy_from:\n description:\n - Name or id to copy the workflow job template from.\n - This will copy an existing workflow job template and change any parameters supplied.\n - The new workflow job template name will be the one provided in the name parameter.\n - The organization parameter is not used in this, to facilitate copy from one organization to another.\n - Provide the id or use the lookup plugin to provide the id if multiple workflow job templates share the same name.\n type: str\n description:\n description:\n - Optional description of this workflow job template.\n type: str\n extra_vars:\n description:\n - Variables which will be made available to jobs ran inside the workflow.\n type: dict\n organization:\n description:\n - Organization the workflow job template exists in.\n - Used to help lookup the object, cannot be modified using this module.\n - If not provided, will lookup by name only, which does not work with duplicates.\n type: str\n allow_simultaneous:\n description:\n - Allow simultaneous runs of the workflow job template.\n type: bool\n ask_variables_on_launch:\n description:\n - Prompt user for C(extra_vars) on launch.\n type: bool\n inventory:\n description:\n - Inventory applied as a prompt, assuming job template prompts for inventory\n type: str\n limit:\n description:\n - Limit applied as a prompt, assuming job template prompts for limit\n type: str\n scm_branch:\n description:\n - SCM branch applied as a prompt, assuming job template prompts for SCM branch\n type: str\n ask_inventory_on_launch:\n description:\n - Prompt user for inventory on launch of this workflow job template\n type: bool\n ask_scm_branch_on_launch:\n description:\n - Prompt user for SCM branch on launch of this workflow job template\n type: bool\n ask_limit_on_launch:\n description:\n - Prompt user for limit on launch of this workflow job template\n type: bool\n webhook_service:\n description:\n - Service that webhook requests will be accepted from\n type: str\n choices:\n - github\n - gitlab\n webhook_credential:\n description:\n - Personal Access Token for posting back the status to the service API\n type: str\n survey_enabled:\n description:\n - Setting that variable will prompt the user for job type on the\n workflow launch.\n type: bool\n survey_spec:\n description:\n - The definition of the survey associated to the workflow.\n type: dict\n aliases:\n - survey\n labels:\n description:\n - The labels applied to this job template\n type: list\n elements: str\n state:\n description:\n - Desired state of the resource.\n choices:\n - present\n - absent\n default: \"present\"\n type: str\n notification_templates_started:\n description:\n - list of notifications to send on start\n type: list\n elements: str\n notification_templates_success:\n description:\n - list of notifications to send on success\n type: list\n elements: str\n notification_templates_error:\n description:\n - list of notifications to send on error\n type: list\n elements: str\n notification_templates_approvals:\n description:\n - list of notifications to send on start\n type: list\n elements: str\n schema:\n description:\n - A json list of nodes and their coresponding options. The following suboptions describe a single node.\n type: list\n elements: dict\n suboptions:\n extra_data:\n description:\n - Variables to apply at launch time.\n - Will only be accepted if job template prompts for vars or has a survey asking for those vars.\n type: dict\n default: {}\n inventory:\n description:\n - Inventory applied as a prompt, if job template prompts for inventory\n type: str\n scm_branch:\n description:\n - SCM branch applied as a prompt, if job template prompts for SCM branch\n type: str\n job_type:\n description:\n - Job type applied as a prompt, if job template prompts for job type\n type: str\n choices:\n - 'run'\n - 'check'\n job_tags:\n description:\n - Job tags applied as a prompt, if job template prompts for job tags\n type: str\n skip_tags:\n description:\n - Tags to skip, applied as a prompt, if job tempalte prompts for job tags\n type: str\n limit:\n description:\n - Limit to act on, applied as a prompt, if job template prompts for limit\n type: str\n diff_mode:\n description:\n - Run diff mode, applied as a prompt, if job template prompts for diff mode\n type: bool\n verbosity:\n description:\n - Verbosity applied as a prompt, if job template prompts for verbosity\n type: str\n choices:\n - '0'\n - '1'\n - '2'\n - '3'\n - '4'\n - '5'\n all_parents_must_converge:\n description:\n - If enabled then the node will only run if all of the parent nodes have met the criteria to reach this node\n type: bool\n identifier:\n description:\n - An identifier for this node that is unique within its workflow.\n - It is copied to workflow job nodes corresponding to this node.\n required: True\n type: str\n state:\n description:\n - Desired state of the resource.\n choices: [\"present\", \"absent\"]\n default: \"present\"\n type: str\n unified_job_template:\n description:\n - Name of unified job template to run in the workflow.\n - Can be a job template, project sync, inventory source sync, etc.\n - Omit if creating an approval node (not yet implemented).\n type: dict\n suboptions:\n organization:\n description:\n - Name of key for use in model for organizational reference\n - Only Valid and used if referencing a job template or project sync\n - This parameter is mutually exclusive with suboption C(inventory).\n type: dict\n suboptions:\n name:\n description:\n - The organization of the job template or project sync the node exists in.\n - Used for looking up the job template or project sync, not a direct model field.\n type: str\n inventory:\n description:\n - Name of key for use in model for organizational reference\n - Only Valid and used if referencing an inventory sync\n - This parameter is mutually exclusive with suboption C(organization).\n type: dict\n suboptions:\n organization:\n description:\n - Name of key for use in model for organizational reference\n type: dict\n suboptions:\n name:\n description:\n - The organization of the inventory the node exists in.\n - Used for looking up the job template or project, not a direct model field.\n type: str\n name:\n description:\n - Name of unified job template to run in the workflow.\n - Can be a job template, project, inventory source, etc.\n type: str\n description:\n description:\n - Optional description of this workflow approval template.\n type: str\n type:\n description:\n - Name of unified job template type to run in the workflow.\n - Can be a job_template, project, inventory_source, workflow_approval.\n type: str\n timeout:\n description:\n - The amount of time (in seconds) to wait before Approval is canceled. A value of 0 means no timeout.\n - Only Valid and used if referencing an Approval Node\n default: 0\n type: int\n related:\n description:\n - Related items to this workflow node.\n - Must include credentials, failure_nodes, always_nodes, success_nodes, even if empty.\n type: dict\n suboptions:\n always_nodes:\n description:\n - Nodes that will run after this node completes.\n - List of node identifiers.\n type: list\n suboptions:\n identifier:\n description:\n - Identifier of Node that will run after this node completes given this option.\n elements: str\n success_nodes:\n description:\n - Nodes that will run after this node on success.\n - List of node identifiers.\n type: list\n suboptions:\n identifier:\n description:\n - Identifier of Node that will run after this node completes given this option.\n elements: str\n failure_nodes:\n description:\n - Nodes that will run after this node on failure.\n - List of node identifiers.\n type: list\n suboptions:\n identifier:\n description:\n - Identifier of Node that will run after this node completes given this option.\n elements: str\n credentials:\n description:\n - Credentials to be applied to job as launch-time prompts.\n - List of credential names.\n - Uniqueness is not handled rigorously.\n type: list\n suboptions:\n name:\n description:\n - Name Credentials to be applied to job as launch-time prompts.\n elements: str\n destroy_current_schema:\n description:\n - Set in order to destroy current schema on the workflow.\n - This option is used for full schema update, if not used, nodes not described in schema will persist and keep current associations and links.\n type: bool\n default: False\n\nextends_documentation_fragment: awx.awx.auth\n'''\n\nEXAMPLES = '''\n- name: Create a workflow job template\n workflow_job_template:\n name: example-workflow\n description: created by Ansible Playbook\n organization: Default\n\n- name: Create a workflow job template with schema in template\n awx.awx.workflow_job_template:\n name: example-workflow\n inventory: Demo Inventory\n extra_vars: {'foo': 'bar', 'another-foo': {'barz': 'bar2'}}\n schema:\n - identifier: node101\n unified_job_template:\n name: example-project\n inventory:\n organization:\n name: Default\n type: inventory_source\n related:\n success_nodes: []\n failure_nodes:\n - identifier: node201\n always_nodes: []\n credentials: []\n - identifier: node201\n unified_job_template:\n organization:\n name: Default\n name: job template 1\n type: job_template\n credentials: []\n related:\n success_nodes:\n - identifier: node301\n failure_nodes: []\n always_nodes: []\n credentials: []\n - identifier: node202\n unified_job_template:\n organization:\n name: Default\n name: example-project\n type: project\n related:\n success_nodes: []\n failure_nodes: []\n always_nodes: []\n credentials: []\n - identifier: node301\n all_parents_must_converge: false\n unified_job_template:\n organization:\n name: Default\n name: job template 2\n type: job_template\n related:\n success_nodes: []\n failure_nodes: []\n always_nodes: []\n credentials: []\n register: result\n\n- name: Copy a workflow job template\n workflow_job_template:\n name: copy-workflow\n copy_from: example-workflow\n organization: Foo\n\n- name: Create a workflow job template with schema in template\n awx.awx.workflow_job_template:\n name: example-workflow\n inventory: Demo Inventory\n extra_vars: {'foo': 'bar', 'another-foo': {'barz': 'bar2'}}\n schema:\n - identifier: node101\n unified_job_template:\n name: example-project\n inventory:\n organization:\n name: Default\n type: inventory_source\n related:\n success_nodes: []\n failure_nodes:\n - identifier: node201\n always_nodes: []\n credentials: []\n - identifier: node201\n unified_job_template:\n organization:\n name: Default\n name: job template 1\n type: job_template\n credentials: []\n related:\n success_nodes:\n - identifier: node301\n failure_nodes: []\n always_nodes: []\n credentials: []\n - identifier: node202\n unified_job_template:\n organization:\n name: Default\n name: example-project\n type: project\n related:\n success_nodes: []\n failure_nodes: []\n always_nodes: []\n credentials: []\n - identifier: node301\n all_parents_must_converge: false\n unified_job_template:\n organization:\n name: Default\n name: job template 2\n type: job_template\n related:\n success_nodes: []\n failure_nodes: []\n always_nodes: []\n credentials: []\n register: result\n\n'''\n\nfrom ..module_utils.controller_api import ControllerAPIModule\n\nimport json\n\nresponse = []\n\nresponse = []\n\n\ndef update_survey(module, last_request):\n spec_endpoint = last_request.get('related', {}).get('survey_spec')\n if module.params.get('survey_spec') == {}:\n response = module.delete_endpoint(spec_endpoint)\n if response['status_code'] != 200:\n # Not sure how to make this actually return a non 200 to test what to dump in the respinse\n module.fail_json(msg=\"Failed to delete survey: {0}\".format(response['json']))\n else:\n response = module.post_endpoint(spec_endpoint, **{'data': module.params.get('survey_spec')})\n if response['status_code'] != 200:\n module.fail_json(msg=\"Failed to update survey: {0}\".format(response['json']['error']))\n\n\ndef create_schema_nodes(module, response, schema, workflow_id):\n for workflow_node in schema:\n workflow_node_fields = {}\n search_fields = {}\n association_fields = {}\n\n # Lookup Job Template ID\n if workflow_node['unified_job_template']['name']:\n search_fields = {'name': workflow_node['unified_job_template']['name']}\n if workflow_node['unified_job_template']['type'] is None:\n module.fail_json(msg='Could not find unified job template type in schema {1}'.format(workflow_node))\n if workflow_node['unified_job_template']['type'] == 'inventory_source':\n # workflow_node['unified_job_template']['inventory']:\n organization_id = module.resolve_name_to_id('organizations', workflow_node['unified_job_template']['inventory']['organization']['name'])\n search_fields['organization'] = organization_id\n elif workflow_node['unified_job_template']['type'] == 'workflow_approval':\n pass\n else:\n # workflow_node['unified_job_template']['organization']:\n organization_id = module.resolve_name_to_id('organizations', workflow_node['unified_job_template']['organization']['name'])\n search_fields['organization'] = organization_id\n unified_job_template = module.get_one('unified_job_templates', **{'data': search_fields})\n if unified_job_template:\n workflow_node_fields['unified_job_template'] = unified_job_template['id']\n else:\n if workflow_node['unified_job_template']['type'] != 'workflow_approval':\n module.fail_json(msg=\"Unable to Find unified_job_template: {0}\".format(search_fields))\n\n # Lookup Values for other fields\n\n for field_name in (\n 'identifier',\n 'extra_data',\n 'scm_branch',\n 'job_type',\n 'job_tags',\n 'skip_tags',\n 'limit',\n 'diff_mode',\n 'verbosity',\n 'all_parents_must_converge',\n 'state',\n ):\n field_val = workflow_node.get(field_name)\n if field_val:\n workflow_node_fields[field_name] = field_val\n if workflow_node['identifier']:\n search_fields = {'identifier': workflow_node['identifier']}\n\n # Set Search fields\n search_fields['workflow_job_template'] = workflow_node_fields['workflow_job_template'] = workflow_id\n\n # Attempt to look up an existing item based on the provided data\n existing_item = module.get_one('workflow_job_template_nodes', **{'data': search_fields})\n\n # Determine if state is present or absent.\n state = True\n if 'state' in workflow_node:\n if workflow_node['state'] == 'absent':\n state = False\n if state:\n response.append(\n module.create_or_update_if_needed(\n existing_item,\n workflow_node_fields,\n endpoint='workflow_job_template_nodes',\n item_type='workflow_job_template_node',\n auto_exit=False,\n )\n )\n else:\n # If the state was absent we can let the module delete it if needed, the module will handle exiting from this\n response.append(\n module.delete_if_needed(\n existing_item,\n auto_exit=False,\n )\n )\n\n # Start Approval Node creation process\n if workflow_node['unified_job_template']['type'] == 'workflow_approval':\n new_fields = {}\n\n for field_name in (\n 'name',\n 'description',\n 'timeout',\n ):\n field_val = workflow_node['unified_job_template'].get(field_name)\n if field_val:\n workflow_node_fields[field_name] = field_val\n\n # Attempt to look up an existing item just created\n workflow_job_template_node = module.get_one('workflow_job_template_nodes', **{'data': search_fields})\n workflow_job_template_node_id = workflow_job_template_node['id']\n existing_item = None\n # Due to not able to lookup workflow_approval_templates, find the existing item in another place\n if workflow_job_template_node['related'].get('unified_job_template') is not None:\n existing_item = module.get_endpoint(workflow_job_template_node['related']['unified_job_template'])['json']\n approval_endpoint = 'workflow_job_template_nodes/{0}/create_approval_template/'.format(workflow_job_template_node_id)\n\n module.create_or_update_if_needed(\n existing_item,\n workflow_node_fields,\n endpoint=approval_endpoint,\n item_type='workflow_job_template_approval_node',\n associations=association_fields,\n auto_exit=False,\n )\n\n\ndef create_schema_nodes_association(module, response, schema, workflow_id):\n for workflow_node in schema:\n workflow_node_fields = {}\n search_fields = {}\n association_fields = {}\n\n # Set Search fields\n search_fields['workflow_job_template'] = workflow_node_fields['workflow_job_template'] = workflow_id\n\n # Lookup Values for other fields\n if workflow_node['identifier']:\n workflow_node_fields['identifier'] = workflow_node['identifier']\n search_fields['identifier'] = workflow_node['identifier']\n\n # Attempt to look up an existing item based on the provided data\n existing_item = module.get_one('workflow_job_template_nodes', **{'data': search_fields})\n\n if 'state' in workflow_node:\n if workflow_node['state'] == 'absent':\n continue\n\n if 'related' in workflow_node:\n # Get id's for association fields\n association_fields = {}\n\n for association in ('always_nodes', 'success_nodes', 'failure_nodes', 'credentials'):\n # Extract out information if it exists\n # Test if it is defined, else move to next association.\n if association in workflow_node['related']:\n id_list = []\n for sub_name in workflow_node['related'][association]:\n if association == 'credentials':\n endpoint = 'credentials'\n lookup_data = {'name': sub_name['name']}\n else:\n endpoint = 'workflow_job_template_nodes'\n lookup_data = {'identifier': sub_name['identifier']}\n lookup_data['workflow_job_template'] = workflow_id\n sub_obj = module.get_one(endpoint, **{'data': lookup_data})\n if sub_obj is None:\n module.fail_json(msg='Could not find {0} entry with name {1}'.format(association, sub_name))\n id_list.append(sub_obj['id'])\n temp = sub_obj['id']\n if id_list:\n association_fields[association] = id_list\n\n module.create_or_update_if_needed(\n existing_item,\n workflow_node_fields,\n endpoint='workflow_job_template_nodes',\n item_type='workflow_job_template_node',\n auto_exit=False,\n associations=association_fields,\n )\n\n\ndef destroy_schema_nodes(module, response, workflow_id):\n search_fields = {}\n\n # Search for existing nodes.\n search_fields['workflow_job_template'] = workflow_id\n existing_items = module.get_all_endpoint('workflow_job_template_nodes', **{'data': search_fields})\n\n # Loop through found fields\n for workflow_node in existing_items['json']['results']:\n response.append(module.delete_endpoint(workflow_node['url']))\n\n\ndef main():\n # Any additional arguments that are not fields of the item can be added here\n argument_spec = dict(\n name=dict(required=True),\n new_name=dict(),\n copy_from=dict(),\n description=dict(),\n extra_vars=dict(type='dict'),\n organization=dict(),\n survey_spec=dict(type='dict', aliases=['survey']),\n survey_enabled=dict(type='bool'),\n allow_simultaneous=dict(type='bool'),\n ask_variables_on_launch=dict(type='bool'),\n inventory=dict(),\n limit=dict(),\n scm_branch=dict(),\n ask_inventory_on_launch=dict(type='bool'),\n ask_scm_branch_on_launch=dict(type='bool'),\n ask_limit_on_launch=dict(type='bool'),\n webhook_service=dict(choices=['github', 'gitlab']),\n webhook_credential=dict(),\n labels=dict(type=\"list\", elements='str'),\n notification_templates_started=dict(type=\"list\", elements='str'),\n notification_templates_success=dict(type=\"list\", elements='str'),\n notification_templates_error=dict(type=\"list\", elements='str'),\n notification_templates_approvals=dict(type=\"list\", elements='str'),\n schema=dict(type='list', elements='dict'),\n destroy_current_schema=dict(type='bool', default=False),\n state=dict(choices=['present', 'absent'], default='present'),\n )\n\n # Create a module for ourselves\n module = ControllerAPIModule(argument_spec=argument_spec)\n\n # Extract our parameters\n name = module.params.get('name')\n new_name = module.params.get(\"new_name\")\n copy_from = module.params.get('copy_from')\n state = module.params.get('state')\n\n # Extract schema parameters\n schema = None\n if module.params.get('schema'):\n schema = module.params.get('schema')\n destroy_current_schema = module.params.get('destroy_current_schema')\n\n new_fields = {}\n search_fields = {}\n\n # Attempt to look up the related items the user specified (these will fail the module if not found)\n organization = module.params.get('organization')\n if organization:\n organization_id = module.resolve_name_to_id('organizations', organization)\n search_fields['organization'] = new_fields['organization'] = organization_id\n\n # Attempt to look up an existing item based on the provided data\n existing_item = module.get_one('workflow_job_templates', name_or_id=name, **{'data': search_fields})\n\n # Attempt to look up credential to copy based on the provided name\n if copy_from:\n # a new existing item is formed when copying and is returned.\n existing_item = module.copy_item(\n existing_item,\n copy_from,\n name,\n endpoint='workflow_job_templates',\n item_type='workflow_job_template',\n copy_lookup_data={},\n )\n\n if state == 'absent':\n # If the state was absent we can let the module delete it if needed, the module will handle exiting from this\n module.delete_if_needed(existing_item)\n\n inventory = module.params.get('inventory')\n if inventory:\n new_fields['inventory'] = module.resolve_name_to_id('inventories', inventory)\n\n webhook_credential = module.params.get('webhook_credential')\n if webhook_credential:\n new_fields['webhook_credential'] = module.resolve_name_to_id('webhook_credential', webhook_credential)\n\n # Create the data that gets sent for create and update\n new_fields['name'] = new_name if new_name else (module.get_item_name(existing_item) if existing_item else name)\n for field_name in (\n 'description',\n 'survey_enabled',\n 'allow_simultaneous',\n 'limit',\n 'scm_branch',\n 'extra_vars',\n 'ask_inventory_on_launch',\n 'ask_scm_branch_on_launch',\n 'ask_limit_on_launch',\n 'ask_variables_on_launch',\n 'webhook_service',\n ):\n field_val = module.params.get(field_name)\n if field_val:\n new_fields[field_name] = field_val\n\n if 'extra_vars' in new_fields:\n new_fields['extra_vars'] = json.dumps(new_fields['extra_vars'])\n\n association_fields = {}\n\n notifications_start = module.params.get('notification_templates_started')\n if notifications_start is not None:\n association_fields['notification_templates_started'] = []\n for item in notifications_start:\n association_fields['notification_templates_started'].append(module.resolve_name_to_id('notification_templates', item))\n\n notifications_success = module.params.get('notification_templates_success')\n if notifications_success is not None:\n association_fields['notification_templates_success'] = []\n for item in notifications_success:\n association_fields['notification_templates_success'].append(module.resolve_name_to_id('notification_templates', item))\n\n notifications_error = module.params.get('notification_templates_error')\n if notifications_error is not None:\n association_fields['notification_templates_error'] = []\n for item in notifications_error:\n association_fields['notification_templates_error'].append(module.resolve_name_to_id('notification_templates', item))\n\n notifications_approval = module.params.get('notification_templates_approvals')\n if notifications_approval is not None:\n association_fields['notification_templates_approvals'] = []\n for item in notifications_approval:\n association_fields['notification_templates_approvals'].append(module.resolve_name_to_id('notification_templates', item))\n\n labels = module.params.get('labels')\n if labels is not None:\n association_fields['labels'] = []\n for item in labels:\n label_id = module.get_one('labels', name_or_id=item, **{'data': search_fields})\n association_fields['labels'].append(label_id['id'])\n\n on_change = None\n new_spec = module.params.get('survey_spec')\n if new_spec:\n existing_spec = None\n if existing_item:\n spec_endpoint = existing_item.get('related', {}).get('survey_spec')\n existing_spec = module.get_endpoint(spec_endpoint)['json']\n if new_spec != existing_spec:\n module.json_output['changed'] = True\n if existing_item and module.has_encrypted_values(existing_spec):\n module._encrypted_changed_warning('survey_spec', existing_item, warning=True)\n on_change = update_survey\n\n # If the state was present and we can let the module build or update the existing item, this will return on its own\n module.create_or_update_if_needed(\n existing_item,\n new_fields,\n endpoint='workflow_job_templates',\n item_type='workflow_job_template',\n associations=association_fields,\n on_create=on_change,\n on_update=on_change,\n auto_exit=False,\n )\n\n # Get Workflow information in case one was just created.\n existing_item = module.get_one('workflow_job_templates', name_or_id=name, **{'data': search_fields})\n workflow_job_template_id = existing_item['id']\n # Destroy current nodes if selected.\n if destroy_current_schema:\n destroy_schema_nodes(module, response, workflow_job_template_id)\n\n # Work thorugh and lookup value for schema fields\n if schema:\n # Create Schema Nodes\n create_schema_nodes(module, response, schema, workflow_job_template_id)\n # Create Schema Associations\n create_schema_nodes_association(module, response, schema, workflow_job_template_id)\n module.json_output['schema_creation_data'] = response\n\n module.exit_json(**module.json_output)\n\n\nif __name__ == '__main__':\n main()\n",
"path": "awx_collection/plugins/modules/workflow_job_template.py"
}
] | [
{
"content": "#!/usr/bin/python\n# coding: utf-8 -*-\n\n\n# (c) 2020, John Westcott IV <[email protected]>\n# GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt)\n\nfrom __future__ import absolute_import, division, print_function\n\n__metaclass__ = type\n\n\nANSIBLE_METADATA = {'metadata_version': '1.1', 'status': ['preview'], 'supported_by': 'community'}\n\nDOCUMENTATION = '''\n---\nmodule: workflow_job_template\nauthor: \"John Westcott IV (@john-westcott-iv)\"\nshort_description: create, update, or destroy Automation Platform Controller workflow job templates.\ndescription:\n - Create, update, or destroy Automation Platform Controller workflow job templates.\n - Replaces the deprecated tower_workflow_template module.\n - Use workflow_job_template_node after this, or use the schema parameter to build the workflow's graph\noptions:\n name:\n description:\n - Name of this workflow job template.\n required: True\n type: str\n new_name:\n description:\n - Setting this option will change the existing name.\n type: str\n copy_from:\n description:\n - Name or id to copy the workflow job template from.\n - This will copy an existing workflow job template and change any parameters supplied.\n - The new workflow job template name will be the one provided in the name parameter.\n - The organization parameter is not used in this, to facilitate copy from one organization to another.\n - Provide the id or use the lookup plugin to provide the id if multiple workflow job templates share the same name.\n type: str\n description:\n description:\n - Optional description of this workflow job template.\n type: str\n extra_vars:\n description:\n - Variables which will be made available to jobs ran inside the workflow.\n type: dict\n organization:\n description:\n - Organization the workflow job template exists in.\n - Used to help lookup the object, cannot be modified using this module.\n - If not provided, will lookup by name only, which does not work with duplicates.\n type: str\n allow_simultaneous:\n description:\n - Allow simultaneous runs of the workflow job template.\n type: bool\n ask_variables_on_launch:\n description:\n - Prompt user for C(extra_vars) on launch.\n type: bool\n inventory:\n description:\n - Inventory applied as a prompt, assuming job template prompts for inventory\n type: str\n limit:\n description:\n - Limit applied as a prompt, assuming job template prompts for limit\n type: str\n scm_branch:\n description:\n - SCM branch applied as a prompt, assuming job template prompts for SCM branch\n type: str\n ask_inventory_on_launch:\n description:\n - Prompt user for inventory on launch of this workflow job template\n type: bool\n ask_scm_branch_on_launch:\n description:\n - Prompt user for SCM branch on launch of this workflow job template\n type: bool\n ask_limit_on_launch:\n description:\n - Prompt user for limit on launch of this workflow job template\n type: bool\n webhook_service:\n description:\n - Service that webhook requests will be accepted from\n type: str\n choices:\n - github\n - gitlab\n webhook_credential:\n description:\n - Personal Access Token for posting back the status to the service API\n type: str\n survey_enabled:\n description:\n - Setting that variable will prompt the user for job type on the\n workflow launch.\n type: bool\n survey_spec:\n description:\n - The definition of the survey associated to the workflow.\n type: dict\n aliases:\n - survey\n labels:\n description:\n - The labels applied to this job template\n type: list\n elements: str\n state:\n description:\n - Desired state of the resource.\n choices:\n - present\n - absent\n default: \"present\"\n type: str\n notification_templates_started:\n description:\n - list of notifications to send on start\n type: list\n elements: str\n notification_templates_success:\n description:\n - list of notifications to send on success\n type: list\n elements: str\n notification_templates_error:\n description:\n - list of notifications to send on error\n type: list\n elements: str\n notification_templates_approvals:\n description:\n - list of notifications to send on start\n type: list\n elements: str\n schema:\n description:\n - A json list of nodes and their coresponding options. The following suboptions describe a single node.\n type: list\n elements: dict\n suboptions:\n extra_data:\n description:\n - Variables to apply at launch time.\n - Will only be accepted if job template prompts for vars or has a survey asking for those vars.\n type: dict\n default: {}\n inventory:\n description:\n - Inventory applied as a prompt, if job template prompts for inventory\n type: str\n scm_branch:\n description:\n - SCM branch applied as a prompt, if job template prompts for SCM branch\n type: str\n job_type:\n description:\n - Job type applied as a prompt, if job template prompts for job type\n type: str\n choices:\n - 'run'\n - 'check'\n job_tags:\n description:\n - Job tags applied as a prompt, if job template prompts for job tags\n type: str\n skip_tags:\n description:\n - Tags to skip, applied as a prompt, if job tempalte prompts for job tags\n type: str\n limit:\n description:\n - Limit to act on, applied as a prompt, if job template prompts for limit\n type: str\n diff_mode:\n description:\n - Run diff mode, applied as a prompt, if job template prompts for diff mode\n type: bool\n verbosity:\n description:\n - Verbosity applied as a prompt, if job template prompts for verbosity\n type: str\n choices:\n - '0'\n - '1'\n - '2'\n - '3'\n - '4'\n - '5'\n all_parents_must_converge:\n description:\n - If enabled then the node will only run if all of the parent nodes have met the criteria to reach this node\n type: bool\n identifier:\n description:\n - An identifier for this node that is unique within its workflow.\n - It is copied to workflow job nodes corresponding to this node.\n required: True\n type: str\n state:\n description:\n - Desired state of the resource.\n choices: [\"present\", \"absent\"]\n default: \"present\"\n type: str\n unified_job_template:\n description:\n - Name of unified job template to run in the workflow.\n - Can be a job template, project sync, inventory source sync, etc.\n - Omit if creating an approval node (not yet implemented).\n type: dict\n suboptions:\n organization:\n description:\n - Name of key for use in model for organizational reference\n - Only Valid and used if referencing a job template or project sync\n - This parameter is mutually exclusive with suboption C(inventory).\n type: dict\n suboptions:\n name:\n description:\n - The organization of the job template or project sync the node exists in.\n - Used for looking up the job template or project sync, not a direct model field.\n type: str\n inventory:\n description:\n - Name of key for use in model for organizational reference\n - Only Valid and used if referencing an inventory sync\n - This parameter is mutually exclusive with suboption C(organization).\n type: dict\n suboptions:\n organization:\n description:\n - Name of key for use in model for organizational reference\n type: dict\n suboptions:\n name:\n description:\n - The organization of the inventory the node exists in.\n - Used for looking up the job template or project, not a direct model field.\n type: str\n name:\n description:\n - Name of unified job template to run in the workflow.\n - Can be a job template, project, inventory source, etc.\n type: str\n description:\n description:\n - Optional description of this workflow approval template.\n type: str\n type:\n description:\n - Name of unified job template type to run in the workflow.\n - Can be a job_template, project, inventory_source, workflow_approval.\n type: str\n timeout:\n description:\n - The amount of time (in seconds) to wait before Approval is canceled. A value of 0 means no timeout.\n - Only Valid and used if referencing an Approval Node\n default: 0\n type: int\n related:\n description:\n - Related items to this workflow node.\n - Must include credentials, failure_nodes, always_nodes, success_nodes, even if empty.\n type: dict\n suboptions:\n always_nodes:\n description:\n - Nodes that will run after this node completes.\n - List of node identifiers.\n type: list\n suboptions:\n identifier:\n description:\n - Identifier of Node that will run after this node completes given this option.\n elements: str\n success_nodes:\n description:\n - Nodes that will run after this node on success.\n - List of node identifiers.\n type: list\n suboptions:\n identifier:\n description:\n - Identifier of Node that will run after this node completes given this option.\n elements: str\n failure_nodes:\n description:\n - Nodes that will run after this node on failure.\n - List of node identifiers.\n type: list\n suboptions:\n identifier:\n description:\n - Identifier of Node that will run after this node completes given this option.\n elements: str\n credentials:\n description:\n - Credentials to be applied to job as launch-time prompts.\n - List of credential names.\n - Uniqueness is not handled rigorously.\n type: list\n suboptions:\n name:\n description:\n - Name Credentials to be applied to job as launch-time prompts.\n elements: str\n destroy_current_schema:\n description:\n - Set in order to destroy current schema on the workflow.\n - This option is used for full schema update, if not used, nodes not described in schema will persist and keep current associations and links.\n type: bool\n default: False\n\nextends_documentation_fragment: awx.awx.auth\n'''\n\nEXAMPLES = '''\n- name: Create a workflow job template\n workflow_job_template:\n name: example-workflow\n description: created by Ansible Playbook\n organization: Default\n\n- name: Create a workflow job template with schema in template\n awx.awx.workflow_job_template:\n name: example-workflow\n inventory: Demo Inventory\n extra_vars: {'foo': 'bar', 'another-foo': {'barz': 'bar2'}}\n schema:\n - identifier: node101\n unified_job_template:\n name: example-project\n inventory:\n organization:\n name: Default\n type: inventory_source\n related:\n success_nodes: []\n failure_nodes:\n - identifier: node201\n always_nodes: []\n credentials: []\n - identifier: node201\n unified_job_template:\n organization:\n name: Default\n name: job template 1\n type: job_template\n credentials: []\n related:\n success_nodes:\n - identifier: node301\n failure_nodes: []\n always_nodes: []\n credentials: []\n - identifier: node202\n unified_job_template:\n organization:\n name: Default\n name: example-project\n type: project\n related:\n success_nodes: []\n failure_nodes: []\n always_nodes: []\n credentials: []\n - identifier: node301\n all_parents_must_converge: false\n unified_job_template:\n organization:\n name: Default\n name: job template 2\n type: job_template\n related:\n success_nodes: []\n failure_nodes: []\n always_nodes: []\n credentials: []\n register: result\n\n- name: Copy a workflow job template\n workflow_job_template:\n name: copy-workflow\n copy_from: example-workflow\n organization: Foo\n\n- name: Create a workflow job template with schema in template\n awx.awx.workflow_job_template:\n name: example-workflow\n inventory: Demo Inventory\n extra_vars: {'foo': 'bar', 'another-foo': {'barz': 'bar2'}}\n schema:\n - identifier: node101\n unified_job_template:\n name: example-project\n inventory:\n organization:\n name: Default\n type: inventory_source\n related:\n success_nodes: []\n failure_nodes:\n - identifier: node201\n always_nodes: []\n credentials: []\n - identifier: node201\n unified_job_template:\n organization:\n name: Default\n name: job template 1\n type: job_template\n credentials: []\n related:\n success_nodes:\n - identifier: node301\n failure_nodes: []\n always_nodes: []\n credentials: []\n - identifier: node202\n unified_job_template:\n organization:\n name: Default\n name: example-project\n type: project\n related:\n success_nodes: []\n failure_nodes: []\n always_nodes: []\n credentials: []\n - identifier: node301\n all_parents_must_converge: false\n unified_job_template:\n organization:\n name: Default\n name: job template 2\n type: job_template\n related:\n success_nodes: []\n failure_nodes: []\n always_nodes: []\n credentials: []\n register: result\n\n'''\n\nfrom ..module_utils.controller_api import ControllerAPIModule\n\nimport json\n\nresponse = []\n\nresponse = []\n\n\ndef update_survey(module, last_request):\n spec_endpoint = last_request.get('related', {}).get('survey_spec')\n if module.params.get('survey_spec') == {}:\n response = module.delete_endpoint(spec_endpoint)\n if response['status_code'] != 200:\n # Not sure how to make this actually return a non 200 to test what to dump in the respinse\n module.fail_json(msg=\"Failed to delete survey: {0}\".format(response['json']))\n else:\n response = module.post_endpoint(spec_endpoint, **{'data': module.params.get('survey_spec')})\n if response['status_code'] != 200:\n module.fail_json(msg=\"Failed to update survey: {0}\".format(response['json']['error']))\n\n\ndef create_schema_nodes(module, response, schema, workflow_id):\n for workflow_node in schema:\n workflow_node_fields = {}\n search_fields = {}\n association_fields = {}\n\n # Lookup Job Template ID\n if workflow_node['unified_job_template']['name']:\n search_fields = {'name': workflow_node['unified_job_template']['name']}\n if workflow_node['unified_job_template']['type'] is None:\n module.fail_json(msg='Could not find unified job template type in schema {1}'.format(workflow_node))\n if workflow_node['unified_job_template']['type'] == 'inventory_source':\n # workflow_node['unified_job_template']['inventory']:\n organization_id = module.resolve_name_to_id('organizations', workflow_node['unified_job_template']['inventory']['organization']['name'])\n search_fields['organization'] = organization_id\n elif workflow_node['unified_job_template']['type'] == 'workflow_approval':\n pass\n else:\n # workflow_node['unified_job_template']['organization']:\n organization_id = module.resolve_name_to_id('organizations', workflow_node['unified_job_template']['organization']['name'])\n search_fields['organization'] = organization_id\n unified_job_template = module.get_one('unified_job_templates', **{'data': search_fields})\n if unified_job_template:\n workflow_node_fields['unified_job_template'] = unified_job_template['id']\n else:\n if workflow_node['unified_job_template']['type'] != 'workflow_approval':\n module.fail_json(msg=\"Unable to Find unified_job_template: {0}\".format(search_fields))\n\n # Lookup Values for other fields\n\n for field_name in (\n 'identifier',\n 'extra_data',\n 'scm_branch',\n 'job_type',\n 'job_tags',\n 'skip_tags',\n 'limit',\n 'diff_mode',\n 'verbosity',\n 'all_parents_must_converge',\n 'state',\n ):\n field_val = workflow_node.get(field_name)\n if field_val:\n workflow_node_fields[field_name] = field_val\n if workflow_node['identifier']:\n search_fields = {'identifier': workflow_node['identifier']}\n\n # Set Search fields\n search_fields['workflow_job_template'] = workflow_node_fields['workflow_job_template'] = workflow_id\n\n # Attempt to look up an existing item based on the provided data\n existing_item = module.get_one('workflow_job_template_nodes', **{'data': search_fields})\n\n # Determine if state is present or absent.\n state = True\n if 'state' in workflow_node:\n if workflow_node['state'] == 'absent':\n state = False\n if state:\n response.append(\n module.create_or_update_if_needed(\n existing_item,\n workflow_node_fields,\n endpoint='workflow_job_template_nodes',\n item_type='workflow_job_template_node',\n auto_exit=False,\n )\n )\n else:\n # If the state was absent we can let the module delete it if needed, the module will handle exiting from this\n response.append(\n module.delete_if_needed(\n existing_item,\n auto_exit=False,\n )\n )\n\n # Start Approval Node creation process\n if workflow_node['unified_job_template']['type'] == 'workflow_approval':\n new_fields = {}\n\n for field_name in (\n 'name',\n 'description',\n 'timeout',\n ):\n field_val = workflow_node['unified_job_template'].get(field_name)\n if field_val:\n workflow_node_fields[field_name] = field_val\n\n # Attempt to look up an existing item just created\n workflow_job_template_node = module.get_one('workflow_job_template_nodes', **{'data': search_fields})\n workflow_job_template_node_id = workflow_job_template_node['id']\n existing_item = None\n # Due to not able to lookup workflow_approval_templates, find the existing item in another place\n if workflow_job_template_node['related'].get('unified_job_template') is not None:\n existing_item = module.get_endpoint(workflow_job_template_node['related']['unified_job_template'])['json']\n approval_endpoint = 'workflow_job_template_nodes/{0}/create_approval_template/'.format(workflow_job_template_node_id)\n\n module.create_or_update_if_needed(\n existing_item,\n workflow_node_fields,\n endpoint=approval_endpoint,\n item_type='workflow_job_template_approval_node',\n associations=association_fields,\n auto_exit=False,\n )\n\n\ndef create_schema_nodes_association(module, response, schema, workflow_id):\n for workflow_node in schema:\n workflow_node_fields = {}\n search_fields = {}\n association_fields = {}\n\n # Set Search fields\n search_fields['workflow_job_template'] = workflow_node_fields['workflow_job_template'] = workflow_id\n\n # Lookup Values for other fields\n if workflow_node['identifier']:\n workflow_node_fields['identifier'] = workflow_node['identifier']\n search_fields['identifier'] = workflow_node['identifier']\n\n # Attempt to look up an existing item based on the provided data\n existing_item = module.get_one('workflow_job_template_nodes', **{'data': search_fields})\n\n if 'state' in workflow_node:\n if workflow_node['state'] == 'absent':\n continue\n\n if 'related' in workflow_node:\n # Get id's for association fields\n association_fields = {}\n\n for association in ('always_nodes', 'success_nodes', 'failure_nodes', 'credentials'):\n # Extract out information if it exists\n # Test if it is defined, else move to next association.\n if association in workflow_node['related']:\n id_list = []\n for sub_name in workflow_node['related'][association]:\n if association == 'credentials':\n endpoint = 'credentials'\n lookup_data = {'name': sub_name['name']}\n else:\n endpoint = 'workflow_job_template_nodes'\n lookup_data = {'identifier': sub_name['identifier']}\n lookup_data['workflow_job_template'] = workflow_id\n sub_obj = module.get_one(endpoint, **{'data': lookup_data})\n if sub_obj is None:\n module.fail_json(msg='Could not find {0} entry with name {1}'.format(association, sub_name))\n id_list.append(sub_obj['id'])\n temp = sub_obj['id']\n if id_list:\n association_fields[association] = id_list\n\n module.create_or_update_if_needed(\n existing_item,\n workflow_node_fields,\n endpoint='workflow_job_template_nodes',\n item_type='workflow_job_template_node',\n auto_exit=False,\n associations=association_fields,\n )\n\n\ndef destroy_schema_nodes(module, response, workflow_id):\n search_fields = {}\n\n # Search for existing nodes.\n search_fields['workflow_job_template'] = workflow_id\n existing_items = module.get_all_endpoint('workflow_job_template_nodes', **{'data': search_fields})\n\n # Loop through found fields\n for workflow_node in existing_items['json']['results']:\n response.append(module.delete_endpoint(workflow_node['url']))\n\n\ndef main():\n # Any additional arguments that are not fields of the item can be added here\n argument_spec = dict(\n name=dict(required=True),\n new_name=dict(),\n copy_from=dict(),\n description=dict(),\n extra_vars=dict(type='dict'),\n organization=dict(),\n survey_spec=dict(type='dict', aliases=['survey']),\n survey_enabled=dict(type='bool'),\n allow_simultaneous=dict(type='bool'),\n ask_variables_on_launch=dict(type='bool'),\n inventory=dict(),\n limit=dict(),\n scm_branch=dict(),\n ask_inventory_on_launch=dict(type='bool'),\n ask_scm_branch_on_launch=dict(type='bool'),\n ask_limit_on_launch=dict(type='bool'),\n webhook_service=dict(choices=['github', 'gitlab']),\n webhook_credential=dict(),\n labels=dict(type=\"list\", elements='str'),\n notification_templates_started=dict(type=\"list\", elements='str'),\n notification_templates_success=dict(type=\"list\", elements='str'),\n notification_templates_error=dict(type=\"list\", elements='str'),\n notification_templates_approvals=dict(type=\"list\", elements='str'),\n schema=dict(type='list', elements='dict'),\n destroy_current_schema=dict(type='bool', default=False),\n state=dict(choices=['present', 'absent'], default='present'),\n )\n\n # Create a module for ourselves\n module = ControllerAPIModule(argument_spec=argument_spec)\n\n # Extract our parameters\n name = module.params.get('name')\n new_name = module.params.get(\"new_name\")\n copy_from = module.params.get('copy_from')\n state = module.params.get('state')\n\n # Extract schema parameters\n schema = None\n if module.params.get('schema'):\n schema = module.params.get('schema')\n destroy_current_schema = module.params.get('destroy_current_schema')\n\n new_fields = {}\n search_fields = {}\n\n # Attempt to look up the related items the user specified (these will fail the module if not found)\n organization = module.params.get('organization')\n if organization:\n organization_id = module.resolve_name_to_id('organizations', organization)\n search_fields['organization'] = new_fields['organization'] = organization_id\n\n # Attempt to look up an existing item based on the provided data\n existing_item = module.get_one('workflow_job_templates', name_or_id=name, **{'data': search_fields})\n\n # Attempt to look up credential to copy based on the provided name\n if copy_from:\n # a new existing item is formed when copying and is returned.\n existing_item = module.copy_item(\n existing_item,\n copy_from,\n name,\n endpoint='workflow_job_templates',\n item_type='workflow_job_template',\n copy_lookup_data={},\n )\n\n if state == 'absent':\n # If the state was absent we can let the module delete it if needed, the module will handle exiting from this\n module.delete_if_needed(existing_item)\n\n inventory = module.params.get('inventory')\n if inventory:\n new_fields['inventory'] = module.resolve_name_to_id('inventories', inventory)\n\n webhook_credential = module.params.get('webhook_credential')\n if webhook_credential:\n new_fields['webhook_credential'] = module.resolve_name_to_id('webhook_credential', webhook_credential)\n\n # Create the data that gets sent for create and update\n new_fields['name'] = new_name if new_name else (module.get_item_name(existing_item) if existing_item else name)\n for field_name in (\n 'description',\n 'survey_enabled',\n 'allow_simultaneous',\n 'limit',\n 'scm_branch',\n 'extra_vars',\n 'ask_inventory_on_launch',\n 'ask_scm_branch_on_launch',\n 'ask_limit_on_launch',\n 'ask_variables_on_launch',\n 'webhook_service',\n ):\n field_val = module.params.get(field_name)\n if field_val is not None:\n new_fields[field_name] = field_val\n\n if 'extra_vars' in new_fields:\n new_fields['extra_vars'] = json.dumps(new_fields['extra_vars'])\n\n association_fields = {}\n\n notifications_start = module.params.get('notification_templates_started')\n if notifications_start is not None:\n association_fields['notification_templates_started'] = []\n for item in notifications_start:\n association_fields['notification_templates_started'].append(module.resolve_name_to_id('notification_templates', item))\n\n notifications_success = module.params.get('notification_templates_success')\n if notifications_success is not None:\n association_fields['notification_templates_success'] = []\n for item in notifications_success:\n association_fields['notification_templates_success'].append(module.resolve_name_to_id('notification_templates', item))\n\n notifications_error = module.params.get('notification_templates_error')\n if notifications_error is not None:\n association_fields['notification_templates_error'] = []\n for item in notifications_error:\n association_fields['notification_templates_error'].append(module.resolve_name_to_id('notification_templates', item))\n\n notifications_approval = module.params.get('notification_templates_approvals')\n if notifications_approval is not None:\n association_fields['notification_templates_approvals'] = []\n for item in notifications_approval:\n association_fields['notification_templates_approvals'].append(module.resolve_name_to_id('notification_templates', item))\n\n labels = module.params.get('labels')\n if labels is not None:\n association_fields['labels'] = []\n for item in labels:\n label_id = module.get_one('labels', name_or_id=item, **{'data': search_fields})\n association_fields['labels'].append(label_id['id'])\n\n on_change = None\n new_spec = module.params.get('survey_spec')\n if new_spec:\n existing_spec = None\n if existing_item:\n spec_endpoint = existing_item.get('related', {}).get('survey_spec')\n existing_spec = module.get_endpoint(spec_endpoint)['json']\n if new_spec != existing_spec:\n module.json_output['changed'] = True\n if existing_item and module.has_encrypted_values(existing_spec):\n module._encrypted_changed_warning('survey_spec', existing_item, warning=True)\n on_change = update_survey\n\n # If the state was present and we can let the module build or update the existing item, this will return on its own\n module.create_or_update_if_needed(\n existing_item,\n new_fields,\n endpoint='workflow_job_templates',\n item_type='workflow_job_template',\n associations=association_fields,\n on_create=on_change,\n on_update=on_change,\n auto_exit=False,\n )\n\n # Get Workflow information in case one was just created.\n existing_item = module.get_one('workflow_job_templates', name_or_id=name, **{'data': search_fields})\n workflow_job_template_id = existing_item['id']\n # Destroy current nodes if selected.\n if destroy_current_schema:\n destroy_schema_nodes(module, response, workflow_job_template_id)\n\n # Work thorugh and lookup value for schema fields\n if schema:\n # Create Schema Nodes\n create_schema_nodes(module, response, schema, workflow_job_template_id)\n # Create Schema Associations\n create_schema_nodes_association(module, response, schema, workflow_job_template_id)\n module.json_output['schema_creation_data'] = response\n\n module.exit_json(**module.json_output)\n\n\nif __name__ == '__main__':\n main()\n",
"path": "awx_collection/plugins/modules/workflow_job_template.py"
}
] | diff --git a/awx_collection/plugins/modules/workflow_job_template.py b/awx_collection/plugins/modules/workflow_job_template.py
index 7fc6b66ccad1..d5686a66aa42 100644
--- a/awx_collection/plugins/modules/workflow_job_template.py
+++ b/awx_collection/plugins/modules/workflow_job_template.py
@@ -751,7 +751,7 @@ def main():
'webhook_service',
):
field_val = module.params.get(field_name)
- if field_val:
+ if field_val is not None:
new_fields[field_name] = field_val
if 'extra_vars' in new_fields:
diff --git a/awx_collection/tests/integration/targets/workflow_job_template/tasks/main.yml b/awx_collection/tests/integration/targets/workflow_job_template/tasks/main.yml
index 527481281ba3..87fd84ab1414 100644
--- a/awx_collection/tests/integration/targets/workflow_job_template/tasks/main.yml
+++ b/awx_collection/tests/integration/targets/workflow_job_template/tasks/main.yml
@@ -159,12 +159,30 @@
extra_vars: {'foo': 'bar', 'another-foo': {'barz': 'bar2'}}
labels:
- "{{ lab1 }}"
+ ask_inventory_on_launch: true
+ ask_scm_branch_on_launch: true
+ ask_limit_on_launch: true
+ ask_variables_on_launch: true
register: result
- assert:
that:
- "result is changed"
+# Turn off ask_ * settings to test that the issue/10057 has been fixed
+- name: Turn ask_* settings OFF
+ tower_workflow_job_template:
+ name: "{{ wfjt_name }}"
+ ask_inventory_on_launch: false
+ ask_scm_branch_on_launch: false
+ ask_limit_on_launch: false
+ ask_variables_on_launch: false
+ state: present
+
+- assert:
+ that:
+ - "result is changed"
+
# Node actions do what this schema command used to do
# schema: [{"success": [{"job_template": "{{ jt1_name }}"}], "job_template": "{{ jt2_name }}"}]
- name: Create leaf node
|
cloud-custodian__cloud-custodian-8120 | Wafv2 logging error when using cloudtrail mode
### Describe the bug
When my wafv2 logging policy runs after disabling logging I receive an error on a cloudtrail policy when using the DeleteLoggingConfiguration event.
### What did you expect to happen?
I expected the policy to match my resource.
### Cloud Provider
Amazon Web Services (AWS)
### Cloud Custodian version and dependency information
```shell
custodian version --debug
Please copy/paste the following info along with any bug reports:
Custodian: 0.9.21
Python: 3.8.0 (v3.8.0:fa919fdf25, Oct 14 2019, 10:23:27)
[Clang 6.0 (clang-600.0.57)]
Platform: posix.uname_result(sysname='Darwin', nodename='kristen-MacBook-Pro', release='21.4.0', version='Darwin Kernel Version 21.4.0: Mon Feb 21 20:35:58 PST 2022; root:xnu-8020.101.4~2/RELEASE_ARM64_T6000', machine='x86_64')
Using venv: True
Docker: False
Installed:
argcomplete==2.0.0
attrs==22.1.0
boto3==1.26.30
botocore==1.29.30
docutils==0.17.1
importlib-metadata==4.13.0
importlib-resources==5.10.1
jmespath==1.0.1
jsonschema==4.17.3
pkgutil-resolve-name==1.3.10
pyrsistent==0.19.2
python-dateutil==2.8.2
pyyaml==6.0
s3transfer==0.6.0
six==1.16.0
tabulate==0.8.10
urllib3==1.26.13
zipp==3.11.0
```
### Policy
```shell
Policy example:
- name: wafv2-log-testing
resource: aws.wafv2
mode:
role: arn:aws:iam::testing
type: cloudtrail
events:
- event: DeleteLoggingConfiguration
ids: requestParameters.resourceArn
source: wafv2.amazonaws.com
filters:
- not:
- type: logging
key: ResourceArn
value: present
```
### Relevant log/traceback output
```shell
Error when policy runs:
[ERROR] 2023-01-06T18:48:11.706Z 163a02a3-69d6-4d43-a307-365ddcb8ead7 error during policy executionTraceback (most recent call last): File "/var/task/c7n/handler.py", line 165, in dispatch_event p.push(event, context) File "/var/task/c7n/policy.py", line 1288, in push return mode.run(event, lambda_ctx) File "/var/task/c7n/policy.py", line 487, in run resources = self.resolve_resources(event) File "/var/task/c7n/policy.py", line 691, in resolve_resources return super().resolve_resources(event) File "/var/task/c7n/policy.py", line 469, in resolve_resources resources = self.policy.resource_manager.get_resources(resource_ids) File "/var/task/c7n/query.py", line 576, in get_resources resources = self.source.get_resources(ids) File "/var/task/c7n/query.py", line 227, in get_resources return self.query.get(self.manager, ids) File "/var/task/c7n/query.py", line 100, in get resources = self.filter(resource_manager, **params) File "/var/task/c7n/query.py", line 79, in filter return self._invoke_client_enum( File "/var/task/c7n/query.py", line 60, in _invoke_client_enum data = op(**params) File "/var/runtime/botocore/client.py", line 391, in _api_call return self._make_api_call(operation_name, kwargs) File "/var/runtime/botocore/client.py", line 691, in _make_api_call request_dict = self._convert_to_request_dict( File "/var/runtime/botocore/client.py", line 739, in _convert_to_request_dict request_dict = self._serializer.serialize_to_request( File "/var/runtime/botocore/validate.py", line 360, in serialize_to_request raise ParamValidationError(report=report.generate_report())botocore.exceptions.ParamValidationError: Parameter validation failed:Missing required parameter in input: "Scope" | [ERROR] 2023-01-06T18:48:11.706Z 163a02a3-69d6-4d43-a307-365ddcb8ead7 error during policy execution Traceback (most recent call last): File "/var/task/c7n/handler.py", line 165, in dispatch_event p.push(event, context) File "/var/task/c7n/policy.py", line 1288, in push return mode.run(event, lambda_ctx) File "/var/task/c7n/policy.py", line 487, in run resources = self.resolve_resources(event) File "/var/task/c7n/policy.py", line 691, in resolve_resources return super().resolve_resources(event) File "/var/task/c7n/policy.py", line 469, in resolve_resources resources = self.policy.resource_manager.get_resources(resource_ids) File "/var/task/c7n/query.py", line 576, in get_resources resources = self.source.get_resources(ids) File "/var/task/c7n/query.py", line 227, in get_resources return self.query.get(self.manager, ids) File "/var/task/c7n/query.py", line 100, in get resources = self.filter(resource_manager, **params) File "/var/task/c7n/query.py", line 79, in filter return self._invoke_client_enum( File "/var/task/c7n/query.py", line 60, in _invoke_client_enum data = op(**params) File "/var/runtime/botocore/client.py", line 391, in _api_call return self._make_api_call(operation_name, kwargs) File "/var/runtime/botocore/client.py", line 691, in _make_api_call request_dict = self._convert_to_request_dict( File "/var/runtime/botocore/client.py", line 739, in _convert_to_request_dict request_dict = self._serializer.serialize_to_request( File "/var/runtime/botocore/validate.py", line 360, in serialize_to_request raise ParamValidationError(report=report.generate_report()) botocore.exceptions.ParamValidationError: Parameter validation failed: Missing required parameter in input: "Scope"
-- | --
```
### Extra information or context
The pull mode version of this policy works fine.
| [
{
"content": "# Copyright The Cloud Custodian Authors.\n# SPDX-License-Identifier: Apache-2.0\nfrom c7n.manager import resources\nfrom c7n.query import ConfigSource, QueryResourceManager, TypeInfo, DescribeSource\nfrom c7n.tags import universal_augment\nfrom c7n.filters import ValueFilter\nfrom c7n.utils import type_schema, local_session\n\n\nclass DescribeRegionalWaf(DescribeSource):\n def augment(self, resources):\n resources = super().augment(resources)\n return universal_augment(self.manager, resources)\n\n\nclass DescribeWafV2(DescribeSource):\n def augment(self, resources):\n return universal_augment(self.manager, resources)\n\n # set REGIONAL for Scope as default\n def get_query_params(self, query):\n q = super(DescribeWafV2, self).get_query_params(query)\n if q:\n if 'Scope' not in q:\n q['Scope'] = 'REGIONAL'\n else:\n q = {'Scope': 'REGIONAL'}\n return q\n\n\[email protected]('waf')\nclass WAF(QueryResourceManager):\n\n class resource_type(TypeInfo):\n service = \"waf\"\n enum_spec = (\"list_web_acls\", \"WebACLs\", None)\n detail_spec = (\"get_web_acl\", \"WebACLId\", \"WebACLId\", \"WebACL\")\n name = \"Name\"\n id = \"WebACLId\"\n dimension = \"WebACL\"\n cfn_type = config_type = \"AWS::WAF::WebACL\"\n arn_type = \"webacl\"\n # override defaults to casing issues\n permissions_enum = ('waf:ListWebACLs',)\n permissions_augment = ('waf:GetWebACL',)\n\n\[email protected]('waf-regional')\nclass RegionalWAF(QueryResourceManager):\n\n class resource_type(TypeInfo):\n service = \"waf-regional\"\n enum_spec = (\"list_web_acls\", \"WebACLs\", None)\n detail_spec = (\"get_web_acl\", \"WebACLId\", \"WebACLId\", \"WebACL\")\n name = \"Name\"\n id = \"WebACLId\"\n dimension = \"WebACL\"\n cfn_type = config_type = \"AWS::WAFRegional::WebACL\"\n arn_type = \"webacl\"\n # override defaults to casing issues\n permissions_enum = ('waf-regional:ListWebACLs',)\n permissions_augment = ('waf-regional:GetWebACL',)\n universal_taggable = object()\n\n source_mapping = {\n 'describe': DescribeRegionalWaf,\n 'config': ConfigSource\n }\n\n\[email protected]('wafv2')\nclass WAFV2(QueryResourceManager):\n\n class resource_type(TypeInfo):\n service = \"wafv2\"\n enum_spec = (\"list_web_acls\", \"WebACLs\", None)\n detail_spec = (\"get_web_acl\", \"Id\", \"Id\", \"WebACL\")\n name = \"Name\"\n id = \"Id\"\n dimension = \"WebACL\"\n cfn_type = config_type = \"AWS::WAFv2::WebACL\"\n arn_type = \"webacl\"\n # override defaults to casing issues\n permissions_enum = ('wafv2:ListWebACLs',)\n permissions_augment = ('wafv2:GetWebACL',)\n universal_taggable = object()\n\n source_mapping = {\n 'describe': DescribeWafV2,\n 'config': ConfigSource\n }\n\n\[email protected]_registry.register('logging')\nclass WAFV2LoggingFilter(ValueFilter):\n \"\"\"\n Filter by wafv2 logging configuration\n\n :example:\n\n .. code-block:: yaml\n\n policies:\n - name: wafv2-logging-enabled\n resource: aws.wafv2\n filters:\n - not:\n - type: logging\n key: ResourceArn\n value: present\n\n - name: check-redacted-fields\n resource: aws.wafv2\n filters:\n - type: logging\n key: RedactedFields[].SingleHeader.Name\n value: user-agent\n op: in\n value_type: swap\n \"\"\"\n\n schema = type_schema('logging', rinherit=ValueFilter.schema)\n permissions = ('wafv2:GetLoggingConfiguration', )\n annotation_key = 'c7n:WafV2LoggingConfiguration'\n\n def process(self, resources, event=None):\n client = local_session(self.manager.session_factory).client(\n 'wafv2', region_name=self.manager.region)\n logging_confs = client.list_logging_configurations(\n Scope='REGIONAL')['LoggingConfigurations']\n resource_map = {r['ARN']: r for r in resources}\n for lc in logging_confs:\n if lc['ResourceArn'] in resource_map:\n resource_map[lc['ResourceArn']][self.annotation_key] = lc\n\n resources = list(resource_map.values())\n\n return [\n r for r in resources if self.match(\n r.get(self.annotation_key, {}))]\n",
"path": "c7n/resources/waf.py"
}
] | [
{
"content": "# Copyright The Cloud Custodian Authors.\n# SPDX-License-Identifier: Apache-2.0\nfrom c7n.manager import resources\nfrom c7n.query import ConfigSource, QueryResourceManager, TypeInfo, DescribeSource\nfrom c7n.tags import universal_augment\nfrom c7n.filters import ValueFilter\nfrom c7n.utils import type_schema, local_session\n\n\nclass DescribeRegionalWaf(DescribeSource):\n def augment(self, resources):\n resources = super().augment(resources)\n return universal_augment(self.manager, resources)\n\n\nclass DescribeWafV2(DescribeSource):\n def augment(self, resources):\n return universal_augment(self.manager, resources)\n\n # set REGIONAL for Scope as default\n def get_query_params(self, query):\n q = super(DescribeWafV2, self).get_query_params(query)\n if q:\n if 'Scope' not in q:\n q['Scope'] = 'REGIONAL'\n else:\n q = {'Scope': 'REGIONAL'}\n return q\n\n def get_resources(self, ids):\n resources = self.query.filter(self.manager, **self.get_query_params(None))\n return [r for r in resources if r[self.manager.resource_type.id] in ids]\n\n\[email protected]('waf')\nclass WAF(QueryResourceManager):\n\n class resource_type(TypeInfo):\n service = \"waf\"\n enum_spec = (\"list_web_acls\", \"WebACLs\", None)\n detail_spec = (\"get_web_acl\", \"WebACLId\", \"WebACLId\", \"WebACL\")\n name = \"Name\"\n id = \"WebACLId\"\n dimension = \"WebACL\"\n cfn_type = config_type = \"AWS::WAF::WebACL\"\n arn_type = \"webacl\"\n # override defaults to casing issues\n permissions_enum = ('waf:ListWebACLs',)\n permissions_augment = ('waf:GetWebACL',)\n\n\[email protected]('waf-regional')\nclass RegionalWAF(QueryResourceManager):\n\n class resource_type(TypeInfo):\n service = \"waf-regional\"\n enum_spec = (\"list_web_acls\", \"WebACLs\", None)\n detail_spec = (\"get_web_acl\", \"WebACLId\", \"WebACLId\", \"WebACL\")\n name = \"Name\"\n id = \"WebACLId\"\n dimension = \"WebACL\"\n cfn_type = config_type = \"AWS::WAFRegional::WebACL\"\n arn_type = \"webacl\"\n # override defaults to casing issues\n permissions_enum = ('waf-regional:ListWebACLs',)\n permissions_augment = ('waf-regional:GetWebACL',)\n universal_taggable = object()\n\n source_mapping = {\n 'describe': DescribeRegionalWaf,\n 'config': ConfigSource\n }\n\n\[email protected]('wafv2')\nclass WAFV2(QueryResourceManager):\n\n class resource_type(TypeInfo):\n service = \"wafv2\"\n enum_spec = (\"list_web_acls\", \"WebACLs\", None)\n detail_spec = (\"get_web_acl\", \"Id\", \"Id\", \"WebACL\")\n name = \"Name\"\n id = \"Id\"\n dimension = \"WebACL\"\n cfn_type = config_type = \"AWS::WAFv2::WebACL\"\n arn_type = \"webacl\"\n # override defaults to casing issues\n permissions_enum = ('wafv2:ListWebACLs',)\n permissions_augment = ('wafv2:GetWebACL',)\n universal_taggable = object()\n\n source_mapping = {\n 'describe': DescribeWafV2,\n 'config': ConfigSource\n }\n\n\[email protected]_registry.register('logging')\nclass WAFV2LoggingFilter(ValueFilter):\n \"\"\"\n Filter by wafv2 logging configuration\n\n :example:\n\n .. code-block:: yaml\n\n policies:\n - name: wafv2-logging-enabled\n resource: aws.wafv2\n filters:\n - not:\n - type: logging\n key: ResourceArn\n value: present\n\n - name: check-redacted-fields\n resource: aws.wafv2\n filters:\n - type: logging\n key: RedactedFields[].SingleHeader.Name\n value: user-agent\n op: in\n value_type: swap\n \"\"\"\n\n schema = type_schema('logging', rinherit=ValueFilter.schema)\n permissions = ('wafv2:GetLoggingConfiguration', )\n annotation_key = 'c7n:WafV2LoggingConfiguration'\n\n def process(self, resources, event=None):\n client = local_session(self.manager.session_factory).client(\n 'wafv2', region_name=self.manager.region)\n logging_confs = client.list_logging_configurations(\n Scope='REGIONAL')['LoggingConfigurations']\n resource_map = {r['ARN']: r for r in resources}\n for lc in logging_confs:\n if lc['ResourceArn'] in resource_map:\n resource_map[lc['ResourceArn']][self.annotation_key] = lc\n\n resources = list(resource_map.values())\n\n return [\n r for r in resources if self.match(\n r.get(self.annotation_key, {}))]\n",
"path": "c7n/resources/waf.py"
}
] | diff --git a/c7n/resources/waf.py b/c7n/resources/waf.py
index 6970c900e55..4e16cf0c59e 100644
--- a/c7n/resources/waf.py
+++ b/c7n/resources/waf.py
@@ -27,6 +27,10 @@ def get_query_params(self, query):
q = {'Scope': 'REGIONAL'}
return q
+ def get_resources(self, ids):
+ resources = self.query.filter(self.manager, **self.get_query_params(None))
+ return [r for r in resources if r[self.manager.resource_type.id] in ids]
+
@resources.register('waf')
class WAF(QueryResourceManager):
diff --git a/tests/data/placebo/test_wafv2_resolve_resources/tagging.GetResources_1.json b/tests/data/placebo/test_wafv2_resolve_resources/tagging.GetResources_1.json
new file mode 100644
index 00000000000..8b704d1852a
--- /dev/null
+++ b/tests/data/placebo/test_wafv2_resolve_resources/tagging.GetResources_1.json
@@ -0,0 +1,8 @@
+{
+ "status_code": 200,
+ "data": {
+ "PaginationToken": "",
+ "ResourceTagMappingList": [],
+ "ResponseMetadata": {}
+ }
+}
\ No newline at end of file
diff --git a/tests/data/placebo/test_wafv2_resolve_resources/wafv2.ListWebACLs_1.json b/tests/data/placebo/test_wafv2_resolve_resources/wafv2.ListWebACLs_1.json
new file mode 100644
index 00000000000..5bff1bc5858
--- /dev/null
+++ b/tests/data/placebo/test_wafv2_resolve_resources/wafv2.ListWebACLs_1.json
@@ -0,0 +1,23 @@
+{
+ "status_code": 200,
+ "data": {
+ "NextMarker": "dont-match-me",
+ "WebACLs": [
+ {
+ "Name": "match-me",
+ "Id": "624e04d2-8b45-45ee-b4ad-e853dac6d070",
+ "Description": "",
+ "LockToken": "ad4ae4ab-aba8-4499-88e9-059d4f9a4c60",
+ "ARN": "arn:aws:wafv2:us-east-2:644160558196:regional/webacl/match-me/624e04d2-8b45-45ee-b4ad-e853dac6d070"
+ },
+ {
+ "Name": "dont-match-me",
+ "Id": "889cae83-d5e5-41e9-bb6f-00ea0f726637",
+ "Description": "",
+ "LockToken": "a33055a6-2e0a-4e5f-aa8c-abe3a5ce26d8",
+ "ARN": "arn:aws:wafv2:us-east-2:644160558196:regional/webacl/dont-match-me/889cae83-d5e5-41e9-bb6f-00ea0f726637"
+ }
+ ],
+ "ResponseMetadata": {}
+ }
+}
diff --git a/tests/test_waf.py b/tests/test_waf.py
index a9fa1a7b3b6..c7442657a79 100644
--- a/tests/test_waf.py
+++ b/tests/test_waf.py
@@ -17,6 +17,19 @@ def test_waf_query(self):
)
self.assertEqual(resources[0]["DefaultAction"], {"Type": "BLOCK"})
+ def test_wafv2_resolve_resources(self):
+ session_factory = self.replay_flight_data(
+ "test_wafv2_resolve_resources",
+ region="us-east-2"
+ )
+ p = self.load_policy(
+ {"name": "wafv2test", "resource": "aws.wafv2"},
+ session_factory=session_factory,
+ config={"region": "us-east-2"}
+ )
+ resources = p.resource_manager.get_resources(["624e04d2-8b45-45ee-b4ad-e853dac6d070"])
+ assert len(resources) == 1
+
def test_wafv2_logging_configuration(self):
session_factory = self.replay_flight_data(
'test_wafv2_logging_configuration')
|
cloud-custodian__cloud-custodian-6375 | The s3 action "remove-statements" errors out when it encounters a bucket policy statement without a sid
**Describe the bug**
s3.remove-statements fails when a sid-less bucket policy statement is encountered
You can see the key error in the traceback. Bucket policy statements do not require Sids and S3 omits the key from describeBucketPolicy response when it does not exist.
**To Reproduce**
Attempt to use remove-statements to remove a statement from a bucket with a sid-less statement (one example of which is the "aws-sam-cli-managed-default-samclisourcebucket-..." buckets created by AWS SAM CLI.)
**Expected behavior**
I expected the statement which does not contain a SID to be iterated over as non-matching.
**Background (please complete the following information):**
- OS: AWS Lambda
- Python Version: Python 3.8
- Custodian Version: 0.9.8
- Tool Version: n/a
- Cloud Provider: AWS
- Policy: [please exclude any account/sensitive information]
```json
{
"statement_ids": [
"denyAccessToBucket"
],
"type": "remove-statements"
}
```
- Traceback: [if applicable, please exclude sensitive/account information]
[ERROR] KeyError: 'Sid'
Traceback (most recent call last):
File "/var/task/custodian_policy.py", line 4, in run
return handler.dispatch_event(event, context)
File "/var/task/c7n/handler.py", line 165, in dispatch_event
p.push(event, context)
File "/var/task/c7n/policy.py", line 1140, in push
return mode.run(event, lambda_ctx)
File "/var/task/c7n/policy.py", line 853, in run
resources = super(ConfigRuleMode, self).run(event, lambda_context)
File "/var/task/c7n/policy.py", line 453, in run
return self.run_resource_set(event, resources)
File "/var/task/c7n/policy.py", line 483, in run_resource_set
results = action.process(resources)
File "/var/task/c7n/resources/s3.py", line 1272, in process
results += filter(None, [f.result()])
File "/var/lang/lib/python3.8/concurrent/futures/_base.py", line 432, in result
return self.__get_result()
File "/var/lang/lib/python3.8/concurrent/futures/_base.py", line 388, in __get_result
raise self._exception
File "/var/lang/lib/python3.8/concurrent/futures/thread.py", line 57, in run
result = self.fn(*self.args, **self.kwargs)
File "/var/task/c7n/resources/s3.py", line 1282, in process_bucket
statements, found = self.process_policy(
File "/var/task/c7n/actions/policy.py", line 21, in process_policy
return remove_statements(
File "/var/task/c7n/actions/policy.py", line 37, in remove_statements
elif s['Sid'] in match_ids:
- `custodian version --debug` output: n/a
**Additional context**
Add any other context about the problem here.
| [
{
"content": "# Copyright The Cloud Custodian Authors.\n# SPDX-License-Identifier: Apache-2.0\n\nfrom .core import BaseAction\nfrom c7n import utils\n\n\nclass RemovePolicyBase(BaseAction):\n\n schema = utils.type_schema(\n 'remove-statements',\n required=['statement_ids'],\n statement_ids={'oneOf': [\n {'enum': ['matched', \"*\"]},\n {'type': 'array', 'items': {'type': 'string'}}]})\n\n def process_policy(self, policy, resource, matched_key):\n statements = policy.get('Statement', [])\n resource_statements = resource.get(matched_key, ())\n\n return remove_statements(\n self.data['statement_ids'], statements, resource_statements)\n\n\ndef remove_statements(match_ids, statements, matched=()):\n found = []\n for s in list(statements):\n s_found = False\n if match_ids == '*':\n s_found = True\n elif match_ids == 'matched':\n if s in matched:\n s_found = True\n elif s['Sid'] in match_ids:\n s_found = True\n if s_found:\n found.append(s)\n statements.remove(s)\n if not found:\n return None, found\n return statements, found\n\n\nclass ModifyPolicyBase(BaseAction):\n \"\"\"Action to modify resource IAM policy statements.\n\n Applies to all resources with embedded IAM Policies.\n\n :example:\n\n .. code-block:: yaml\n\n policies:\n - name: sns-yank-cross-account\n resource: sns\n filters:\n - type: cross-account\n actions:\n - type: modify-policy\n add-statements: [{\n \"Sid\": \"ReplaceWithMe\",\n \"Effect\": \"Allow\",\n \"Principal\": \"*\",\n \"Action\": [\"SNS:GetTopicAttributes\"],\n \"Resource\": topic_arn,\n }]\n remove-statements: '*'\n \"\"\"\n\n schema_alias = True\n schema = utils.type_schema(\n 'modify-policy',\n **{\n 'add-statements': {\n 'type': 'array',\n 'items': {'$ref': '#/definitions/iam-statement'},\n },\n 'remove-statements': {\n 'type': ['array', 'string'],\n 'oneOf': [\n {'enum': ['matched', '*']},\n {'type': 'array', 'items': {'type': 'string'}}\n ],\n }\n }\n )\n\n def __init__(self, data=None, manager=None):\n if manager is not None:\n config_args = {\n 'account_id': manager.config.account_id,\n 'region': manager.config.region\n }\n self.data = utils.format_string_values(data, **config_args)\n else:\n self.data = utils.format_string_values(data)\n self.manager = manager\n\n def add_statements(self, policy_statements):\n current = {s['Sid']: s for s in policy_statements}\n additional = {s['Sid']: s for s in self.data.get('add-statements', [])}\n current.update(additional)\n return list(current.values()), bool(additional)\n\n def remove_statements(self, policy_statements, resource, matched_key):\n statement_ids = self.data.get('remove-statements', [])\n found = []\n if len(statement_ids) == 0:\n return policy_statements, found\n resource_statements = resource.get(matched_key, ())\n return remove_statements(\n statement_ids, policy_statements, resource_statements)\n",
"path": "c7n/actions/policy.py"
}
] | [
{
"content": "# Copyright The Cloud Custodian Authors.\n# SPDX-License-Identifier: Apache-2.0\n\nfrom .core import BaseAction\nfrom c7n import utils\n\n\nclass RemovePolicyBase(BaseAction):\n\n schema = utils.type_schema(\n 'remove-statements',\n required=['statement_ids'],\n statement_ids={'oneOf': [\n {'enum': ['matched', \"*\"]},\n {'type': 'array', 'items': {'type': 'string'}}]})\n\n def process_policy(self, policy, resource, matched_key):\n statements = policy.get('Statement', [])\n resource_statements = resource.get(matched_key, ())\n\n return remove_statements(\n self.data['statement_ids'], statements, resource_statements)\n\n\ndef remove_statements(match_ids, statements, matched=()):\n found = []\n for s in list(statements):\n s_found = False\n if match_ids == '*':\n s_found = True\n elif match_ids == 'matched':\n if s in matched:\n s_found = True\n elif 'Sid' in s and s['Sid'] in match_ids:\n s_found = True\n if s_found:\n found.append(s)\n statements.remove(s)\n if not found:\n return None, found\n return statements, found\n\n\nclass ModifyPolicyBase(BaseAction):\n \"\"\"Action to modify resource IAM policy statements.\n\n Applies to all resources with embedded IAM Policies.\n\n :example:\n\n .. code-block:: yaml\n\n policies:\n - name: sns-yank-cross-account\n resource: sns\n filters:\n - type: cross-account\n actions:\n - type: modify-policy\n add-statements: [{\n \"Sid\": \"ReplaceWithMe\",\n \"Effect\": \"Allow\",\n \"Principal\": \"*\",\n \"Action\": [\"SNS:GetTopicAttributes\"],\n \"Resource\": topic_arn,\n }]\n remove-statements: '*'\n \"\"\"\n\n schema_alias = True\n schema = utils.type_schema(\n 'modify-policy',\n **{\n 'add-statements': {\n 'type': 'array',\n 'items': {'$ref': '#/definitions/iam-statement'},\n },\n 'remove-statements': {\n 'type': ['array', 'string'],\n 'oneOf': [\n {'enum': ['matched', '*']},\n {'type': 'array', 'items': {'type': 'string'}}\n ],\n }\n }\n )\n\n def __init__(self, data=None, manager=None):\n if manager is not None:\n config_args = {\n 'account_id': manager.config.account_id,\n 'region': manager.config.region\n }\n self.data = utils.format_string_values(data, **config_args)\n else:\n self.data = utils.format_string_values(data)\n self.manager = manager\n\n def add_statements(self, policy_statements):\n current = {s['Sid']: s for s in policy_statements}\n additional = {s['Sid']: s for s in self.data.get('add-statements', [])}\n current.update(additional)\n return list(current.values()), bool(additional)\n\n def remove_statements(self, policy_statements, resource, matched_key):\n statement_ids = self.data.get('remove-statements', [])\n found = []\n if len(statement_ids) == 0:\n return policy_statements, found\n resource_statements = resource.get(matched_key, ())\n return remove_statements(\n statement_ids, policy_statements, resource_statements)\n",
"path": "c7n/actions/policy.py"
}
] | diff --git a/c7n/actions/policy.py b/c7n/actions/policy.py
index c7e2a9e0295..833e1309efe 100644
--- a/c7n/actions/policy.py
+++ b/c7n/actions/policy.py
@@ -31,7 +31,7 @@ def remove_statements(match_ids, statements, matched=()):
elif match_ids == 'matched':
if s in matched:
s_found = True
- elif s['Sid'] in match_ids:
+ elif 'Sid' in s and s['Sid'] in match_ids:
s_found = True
if s_found:
found.append(s)
|
ManimCommunity__manim-732 | Running manim without any flags or render commands throws an error [BUG-General]
It seems as if currently running just `manim` without any flag or scene to render it throws the following error:
```Traceback (most recent call last):
File "c:\users\administrator\appdata\local\programs\python\python38\lib\runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "c:\users\administrator\appdata\local\programs\python\python38\lib\runpy.py", line 87, in _run_code
exec(code, run_globals)
File "C:\Users\Administrator\AppData\Local\Programs\Python\Python38\Scripts\manim.exe\__main__.py", line 7, in <module>
File "c:\users\administrator\appdata\local\programs\python\python38\lib\site-packages\manim\__main__.py", line 47, in main
args = parse_args(sys.argv)
File "c:\users\administrator\appdata\local\programs\python\python38\lib\site-packages\manim\_config\main_utils.py", line 110, in parse_args
subcmd = _find_subcommand(args)
File "c:\users\administrator\appdata\local\programs\python\python38\lib\site-packages\manim\_config\main_utils.py", line 39, in _find_subcommand
subcmd = args[1]
IndexError: list index out of range
```
@behackl has mentioned seeing the same bahaviour.
This isn't a major issue since otherwise everything seems to run fine (can still use `manim -h` or render scenes), but somehow who might know what's going on should have a look into it.
| [
{
"content": "\"\"\"Utilities called from ``__main__.py`` to interact with the config.\"\"\"\n\nimport os\nimport sys\nimport argparse\nimport logging\n\nimport colour\n\nfrom manim import constants, logger, config\nfrom .utils import make_config_parser\nfrom .logger_utils import JSONFormatter\nfrom ..utils.tex import TexTemplate, TexTemplateFromFile\n\n\n__all__ = [\"parse_args\"]\n\n\ndef _find_subcommand(args):\n \"\"\"Return the subcommand that has been passed, if any.\n\n Parameters\n ----------\n args : list\n The argument list.\n\n Returns\n -------\n Optional[:class:`str`]\n If a subcommand is found, returns the string of its name. Returns None\n otherwise.\n\n Notes\n -----\n This assumes that \"manim\" is the first word in the argument list, and that\n the subcommand will be the second word, if it exists.\n\n \"\"\"\n subcmd = args[1]\n if subcmd in [\n \"cfg\"\n # , 'init',\n ]:\n return subcmd\n else:\n return None\n\n\ndef _init_cfg_subcmd(subparsers):\n \"\"\"Initialises the subparser for the `cfg` subcommand.\n\n Parameters\n ----------\n subparsers : :class:`argparse._SubParsersAction`\n The subparser object for which to add the sub-subparser for the cfg subcommand.\n\n Returns\n -------\n :class:`argparse.ArgumentParser`\n The parser that parser anything cfg subcommand related.\n \"\"\"\n cfg_related = subparsers.add_parser(\"cfg\")\n cfg_subparsers = cfg_related.add_subparsers(dest=\"cfg_subcommand\")\n\n cfg_write_parser = cfg_subparsers.add_parser(\"write\")\n cfg_write_parser.add_argument(\n \"--level\",\n choices=[\"user\", \"cwd\"],\n default=None,\n help=\"Specify if this config is for user or just the working directory.\",\n )\n cfg_write_parser.add_argument(\n \"--open\", action=\"store_const\", const=True, default=False\n )\n cfg_subparsers.add_parser(\"show\")\n\n cfg_export_parser = cfg_subparsers.add_parser(\"export\")\n cfg_export_parser.add_argument(\"--dir\", default=os.getcwd())\n\n return cfg_related\n\n\ndef _str2bool(s):\n \"\"\"Helper function that handles boolean CLI arguments.\"\"\"\n if s == \"True\":\n return True\n elif s == \"False\":\n return False\n else:\n raise argparse.ArgumentTypeError(\"True or False expected\")\n\n\ndef parse_args(args):\n \"\"\"Parse CLI arguments.\n\n Parameters\n ----------\n args : :class:`list`\n A list of arguments; generally, this should be ``sys.argv``.\n\n Returns\n -------\n :class:`argparse.Namespace`\n An object returned by ``argparse.parse_args``.\n\n \"\"\"\n if args[0] == \"python\" and args[1] == \"-m\":\n args = args[2:]\n\n subcmd = _find_subcommand(args)\n if subcmd == \"cfg\":\n return _parse_args_cfg_subcmd(args)\n # elif subcmd == some_other_future_subcmd:\n # return _parse_args_some_other_subcmd(args)\n elif subcmd is None:\n return _parse_args_no_subcmd(args)\n\n\ndef _parse_args_cfg_subcmd(args):\n \"\"\"Parse arguments of the form 'manim cfg <subcmd> <args>'.\"\"\"\n parser = argparse.ArgumentParser(\n description=\"Animation engine for explanatory math videos\",\n prog=\"manim cfg\",\n epilog=\"Made with <3 by the manim community devs\",\n )\n subparsers = parser.add_subparsers(help=\"subcommand\", dest=\"subcmd\")\n\n cfg_subparsers = {\n subcmd: subparsers.add_parser(subcmd) for subcmd in [\"write\", \"show\", \"export\"]\n }\n\n # Arguments for the write subcmd\n cfg_subparsers[\"write\"].add_argument(\n \"--level\",\n choices=[\"user\", \"cwd\"],\n default=\"cwd\",\n help=\"Specify if this config is for user or the working directory.\",\n )\n cfg_subparsers[\"write\"].add_argument(\n \"--open\", action=\"store_const\", const=True, default=False\n )\n\n # Arguments for the export subcmd\n cfg_subparsers[\"export\"].add_argument(\"--dir\", default=os.getcwd())\n\n # Arguments for the show subcmd: currently no arguments\n\n # Recall the argument list looks like 'manim cfg <subcmd> <args>' so we\n # only need to parse the remaining args\n parsed = parser.parse_args(args[2:])\n parsed.cmd = \"cfg\"\n parsed.cfg_subcommand = parsed.subcmd\n\n return parsed\n\n\ndef _parse_args_no_subcmd(args):\n \"\"\"Parse arguments of the form 'manim <args>', when no command is present.\"\"\"\n parser = argparse.ArgumentParser(\n description=\"Animation engine for explanatory math videos\",\n prog=\"manim\",\n usage=(\n \"%(prog)s file [flags] [scene [scene ...]]\\n\"\n \" %(prog)s {cfg,init} [opts]\"\n ),\n epilog=\"Made with <3 by the manim community devs\",\n )\n\n parser.add_argument(\n \"file\",\n help=\"Path to file holding the python code for the scene\",\n )\n parser.add_argument(\n \"scene_names\",\n nargs=\"*\",\n help=\"Name of the Scene class you want to see\",\n default=[\"\"],\n )\n parser.add_argument(\n \"-o\",\n \"--output_file\",\n help=\"Specify the name of the output file, if \"\n \"it should be different from the scene class name\",\n default=\"\",\n )\n\n # The following use (action='store_const', const=True) instead of\n # the built-in (action='store_true'). This is because the latter\n # will default to False if not specified, while the former sets no\n # default value. Since we want to set the default value in\n # manim.cfg rather than here, we use the former.\n parser.add_argument(\n \"-p\",\n \"--preview\",\n action=\"store_const\",\n const=True,\n help=\"Automatically open the saved file once its done\",\n )\n parser.add_argument(\n \"-f\",\n \"--show_in_file_browser\",\n action=\"store_const\",\n const=True,\n help=\"Show the output file in the File Browser\",\n )\n parser.add_argument(\n \"--sound\",\n action=\"store_const\",\n const=True,\n help=\"Play a success/failure sound\",\n )\n parser.add_argument(\n \"--leave_progress_bars\",\n action=\"store_const\",\n const=True,\n help=\"Leave progress bars displayed in terminal\",\n )\n parser.add_argument(\n \"-a\",\n \"--write_all\",\n action=\"store_const\",\n const=True,\n help=\"Write all the scenes from a file\",\n )\n parser.add_argument(\n \"-w\",\n \"--write_to_movie\",\n action=\"store_const\",\n const=True,\n help=\"Render the scene as a movie file (this is on by default)\",\n )\n parser.add_argument(\n \"-s\",\n \"--save_last_frame\",\n action=\"store_const\",\n const=True,\n help=\"Save the last frame only (no movie file is generated)\",\n )\n parser.add_argument(\n \"-g\",\n \"--save_pngs\",\n action=\"store_const\",\n const=True,\n help=\"Save each frame as a png\",\n )\n parser.add_argument(\n \"-i\",\n \"--save_as_gif\",\n action=\"store_const\",\n const=True,\n help=\"Save the video as gif\",\n )\n parser.add_argument(\n \"--disable_caching\",\n action=\"store_const\",\n const=True,\n help=\"Disable caching (will generate partial-movie-files anyway)\",\n )\n parser.add_argument(\n \"--flush_cache\",\n action=\"store_const\",\n const=True,\n help=\"Remove all cached partial-movie-files\",\n )\n parser.add_argument(\n \"--log_to_file\",\n action=\"store_const\",\n const=True,\n help=\"Log terminal output to file\",\n )\n # The default value of the following is set in manim.cfg\n parser.add_argument(\n \"-c\",\n \"--background_color\",\n help=\"Specify background color\",\n )\n parser.add_argument(\n \"--media_dir\",\n help=\"Directory to store media (including video files)\",\n )\n parser.add_argument(\n \"--log_dir\",\n help=\"Directory to store log files\",\n )\n parser.add_argument(\n \"--tex_template\",\n help=\"Specify a custom TeX template file\",\n )\n\n # All of the following use (action=\"store_true\"). This means that\n # they are by default False. In contrast to the previous ones that\n # used (action=\"store_const\", const=True), the following do not\n # correspond to a single configuration option. Rather, they\n # override several options at the same time.\n\n # The following overrides -w, -a, -g, and -i\n parser.add_argument(\n \"--dry_run\",\n action=\"store_true\",\n help=\"Do a dry run (render scenes but generate no output files)\",\n )\n\n # The following overrides PNG_MODE, MOVIE_FILE_EXTENSION, and\n # BACKGROUND_OPACITY\n parser.add_argument(\n \"-t\",\n \"--transparent\",\n action=\"store_true\",\n help=\"Render a scene with an alpha channel\",\n )\n\n # The following are mutually exclusive and each overrides\n # FRAME_RATE, PIXEL_HEIGHT, and PIXEL_WIDTH,\n parser.add_argument(\n \"-q\",\n \"--quality\",\n choices=[constants.QUALITIES[q][\"flag\"] for q in constants.QUALITIES],\n default=constants.DEFAULT_QUALITY_SHORT,\n help=\"Render at specific quality, short form of the --*_quality flags\",\n )\n parser.add_argument(\n \"--low_quality\",\n action=\"store_true\",\n help=\"Render at low quality\",\n )\n parser.add_argument(\n \"--medium_quality\",\n action=\"store_true\",\n help=\"Render at medium quality\",\n )\n parser.add_argument(\n \"--high_quality\",\n action=\"store_true\",\n help=\"Render at high quality\",\n )\n parser.add_argument(\n \"--production_quality\",\n action=\"store_true\",\n help=\"Render at default production quality\",\n )\n parser.add_argument(\n \"--fourk_quality\",\n action=\"store_true\",\n help=\"Render at 4K quality\",\n )\n\n # Deprecated quality flags\n parser.add_argument(\n \"-l\",\n action=\"store_true\",\n help=\"DEPRECATED: USE -ql or --quality l\",\n )\n parser.add_argument(\n \"-m\",\n action=\"store_true\",\n help=\"DEPRECATED: USE -qm or --quality m\",\n )\n parser.add_argument(\n \"-e\",\n action=\"store_true\",\n help=\"DEPRECATED: USE -qh or --quality h\",\n )\n parser.add_argument(\n \"-k\",\n action=\"store_true\",\n help=\"DEPRECATED: USE -qk or --quality k\",\n )\n\n # This overrides any of the above\n parser.add_argument(\n \"-r\",\n \"--resolution\",\n help='Resolution, passed as \"height,width\". '\n \"Overrides the -l, -m, -e, and -k flags, if present\",\n )\n\n # This sets FROM_ANIMATION_NUMBER and UPTO_ANIMATION_NUMBER\n parser.add_argument(\n \"-n\",\n \"--from_animation_number\",\n help=\"Start rendering at the specified animation index, \"\n \"instead of the first animation. If you pass in two comma \"\n \"separated values, e.g. '3,6', it will end \"\n \"the rendering at the second value\",\n )\n\n parser.add_argument(\n \"--use_js_renderer\",\n help=\"Render animations using the javascript frontend\",\n action=\"store_const\",\n const=True,\n )\n\n parser.add_argument(\n \"--js_renderer_path\",\n help=\"Path to the javascript frontend\",\n )\n\n # Specify the manim.cfg file\n parser.add_argument(\n \"--config_file\",\n help=\"Specify the configuration file\",\n )\n\n # Specify whether to use the custom folders\n parser.add_argument(\n \"--custom_folders\",\n action=\"store_true\",\n help=\"Use the folders defined in the [custom_folders] \"\n \"section of the config file to define the output folder structure\",\n )\n\n # Specify the verbosity\n parser.add_argument(\n \"-v\",\n \"--verbosity\",\n type=str,\n help=(\n \"Verbosity level. Also changes the ffmpeg log level unless \"\n \"the latter is specified in the config\"\n ),\n choices=constants.VERBOSITY_CHOICES,\n )\n\n # Specify if the progress bar should be displayed\n parser.add_argument(\n \"--progress_bar\",\n type=_str2bool,\n help=\"Display the progress bar\",\n metavar=\"True/False\",\n )\n\n return parser.parse_args(args[1:])\n",
"path": "manim/_config/main_utils.py"
}
] | [
{
"content": "\"\"\"Utilities called from ``__main__.py`` to interact with the config.\"\"\"\n\nimport os\nimport sys\nimport argparse\nimport logging\n\nimport colour\n\nfrom manim import constants, logger, config\nfrom .utils import make_config_parser\nfrom .logger_utils import JSONFormatter\nfrom ..utils.tex import TexTemplate, TexTemplateFromFile\n\n\n__all__ = [\"parse_args\"]\n\n\ndef _find_subcommand(args):\n \"\"\"Return the subcommand that has been passed, if any.\n\n Parameters\n ----------\n args : list\n The argument list.\n\n Returns\n -------\n Optional[:class:`str`]\n If a subcommand is found, returns the string of its name. Returns None\n otherwise.\n\n Notes\n -----\n This assumes that \"manim\" is the first word in the argument list, and that\n the subcommand will be the second word, if it exists.\n\n \"\"\"\n subcmd = args[1]\n if subcmd in [\n \"cfg\"\n # , 'init',\n ]:\n return subcmd\n else:\n return None\n\n\ndef _init_cfg_subcmd(subparsers):\n \"\"\"Initialises the subparser for the `cfg` subcommand.\n\n Parameters\n ----------\n subparsers : :class:`argparse._SubParsersAction`\n The subparser object for which to add the sub-subparser for the cfg subcommand.\n\n Returns\n -------\n :class:`argparse.ArgumentParser`\n The parser that parser anything cfg subcommand related.\n \"\"\"\n cfg_related = subparsers.add_parser(\"cfg\")\n cfg_subparsers = cfg_related.add_subparsers(dest=\"cfg_subcommand\")\n\n cfg_write_parser = cfg_subparsers.add_parser(\"write\")\n cfg_write_parser.add_argument(\n \"--level\",\n choices=[\"user\", \"cwd\"],\n default=None,\n help=\"Specify if this config is for user or just the working directory.\",\n )\n cfg_write_parser.add_argument(\n \"--open\", action=\"store_const\", const=True, default=False\n )\n cfg_subparsers.add_parser(\"show\")\n\n cfg_export_parser = cfg_subparsers.add_parser(\"export\")\n cfg_export_parser.add_argument(\"--dir\", default=os.getcwd())\n\n return cfg_related\n\n\ndef _str2bool(s):\n \"\"\"Helper function that handles boolean CLI arguments.\"\"\"\n if s == \"True\":\n return True\n elif s == \"False\":\n return False\n else:\n raise argparse.ArgumentTypeError(\"True or False expected\")\n\n\ndef parse_args(args):\n \"\"\"Parse CLI arguments.\n\n Parameters\n ----------\n args : :class:`list`\n A list of arguments; generally, this should be ``sys.argv``.\n\n Returns\n -------\n :class:`argparse.Namespace`\n An object returned by ``argparse.parse_args``.\n\n \"\"\"\n if args[0] == \"python\" and args[1] == \"-m\":\n args = args[2:]\n\n if len(args) == 1:\n return _parse_args_no_subcmd(args)\n\n subcmd = _find_subcommand(args)\n if subcmd == \"cfg\":\n return _parse_args_cfg_subcmd(args)\n # elif subcmd == some_other_future_subcmd:\n # return _parse_args_some_other_subcmd(args)\n elif subcmd is None:\n return _parse_args_no_subcmd(args)\n\n\ndef _parse_args_cfg_subcmd(args):\n \"\"\"Parse arguments of the form 'manim cfg <subcmd> <args>'.\"\"\"\n parser = argparse.ArgumentParser(\n description=\"Animation engine for explanatory math videos\",\n prog=\"manim cfg\",\n epilog=\"Made with <3 by the manim community devs\",\n )\n subparsers = parser.add_subparsers(help=\"subcommand\", dest=\"subcmd\")\n\n cfg_subparsers = {\n subcmd: subparsers.add_parser(subcmd) for subcmd in [\"write\", \"show\", \"export\"]\n }\n\n # Arguments for the write subcmd\n cfg_subparsers[\"write\"].add_argument(\n \"--level\",\n choices=[\"user\", \"cwd\"],\n default=\"cwd\",\n help=\"Specify if this config is for user or the working directory.\",\n )\n cfg_subparsers[\"write\"].add_argument(\n \"--open\", action=\"store_const\", const=True, default=False\n )\n\n # Arguments for the export subcmd\n cfg_subparsers[\"export\"].add_argument(\"--dir\", default=os.getcwd())\n\n # Arguments for the show subcmd: currently no arguments\n\n # Recall the argument list looks like 'manim cfg <subcmd> <args>' so we\n # only need to parse the remaining args\n parsed = parser.parse_args(args[2:])\n parsed.cmd = \"cfg\"\n parsed.cfg_subcommand = parsed.subcmd\n\n return parsed\n\n\ndef _parse_args_no_subcmd(args):\n \"\"\"Parse arguments of the form 'manim <args>', when no command is present.\"\"\"\n parser = argparse.ArgumentParser(\n description=\"Animation engine for explanatory math videos\",\n prog=\"manim\",\n usage=(\n \"%(prog)s file [flags] [scene [scene ...]]\\n\"\n \" %(prog)s {cfg,init} [opts]\"\n ),\n epilog=\"Made with <3 by the manim community devs\",\n )\n\n parser.add_argument(\n \"file\",\n help=\"Path to file holding the python code for the scene\",\n )\n parser.add_argument(\n \"scene_names\",\n nargs=\"*\",\n help=\"Name of the Scene class you want to see\",\n default=[\"\"],\n )\n parser.add_argument(\n \"-o\",\n \"--output_file\",\n help=\"Specify the name of the output file, if \"\n \"it should be different from the scene class name\",\n default=\"\",\n )\n\n # The following use (action='store_const', const=True) instead of\n # the built-in (action='store_true'). This is because the latter\n # will default to False if not specified, while the former sets no\n # default value. Since we want to set the default value in\n # manim.cfg rather than here, we use the former.\n parser.add_argument(\n \"-p\",\n \"--preview\",\n action=\"store_const\",\n const=True,\n help=\"Automatically open the saved file once its done\",\n )\n parser.add_argument(\n \"-f\",\n \"--show_in_file_browser\",\n action=\"store_const\",\n const=True,\n help=\"Show the output file in the File Browser\",\n )\n parser.add_argument(\n \"--sound\",\n action=\"store_const\",\n const=True,\n help=\"Play a success/failure sound\",\n )\n parser.add_argument(\n \"--leave_progress_bars\",\n action=\"store_const\",\n const=True,\n help=\"Leave progress bars displayed in terminal\",\n )\n parser.add_argument(\n \"-a\",\n \"--write_all\",\n action=\"store_const\",\n const=True,\n help=\"Write all the scenes from a file\",\n )\n parser.add_argument(\n \"-w\",\n \"--write_to_movie\",\n action=\"store_const\",\n const=True,\n help=\"Render the scene as a movie file (this is on by default)\",\n )\n parser.add_argument(\n \"-s\",\n \"--save_last_frame\",\n action=\"store_const\",\n const=True,\n help=\"Save the last frame only (no movie file is generated)\",\n )\n parser.add_argument(\n \"-g\",\n \"--save_pngs\",\n action=\"store_const\",\n const=True,\n help=\"Save each frame as a png\",\n )\n parser.add_argument(\n \"-i\",\n \"--save_as_gif\",\n action=\"store_const\",\n const=True,\n help=\"Save the video as gif\",\n )\n parser.add_argument(\n \"--disable_caching\",\n action=\"store_const\",\n const=True,\n help=\"Disable caching (will generate partial-movie-files anyway)\",\n )\n parser.add_argument(\n \"--flush_cache\",\n action=\"store_const\",\n const=True,\n help=\"Remove all cached partial-movie-files\",\n )\n parser.add_argument(\n \"--log_to_file\",\n action=\"store_const\",\n const=True,\n help=\"Log terminal output to file\",\n )\n # The default value of the following is set in manim.cfg\n parser.add_argument(\n \"-c\",\n \"--background_color\",\n help=\"Specify background color\",\n )\n parser.add_argument(\n \"--media_dir\",\n help=\"Directory to store media (including video files)\",\n )\n parser.add_argument(\n \"--log_dir\",\n help=\"Directory to store log files\",\n )\n parser.add_argument(\n \"--tex_template\",\n help=\"Specify a custom TeX template file\",\n )\n\n # All of the following use (action=\"store_true\"). This means that\n # they are by default False. In contrast to the previous ones that\n # used (action=\"store_const\", const=True), the following do not\n # correspond to a single configuration option. Rather, they\n # override several options at the same time.\n\n # The following overrides -w, -a, -g, and -i\n parser.add_argument(\n \"--dry_run\",\n action=\"store_true\",\n help=\"Do a dry run (render scenes but generate no output files)\",\n )\n\n # The following overrides PNG_MODE, MOVIE_FILE_EXTENSION, and\n # BACKGROUND_OPACITY\n parser.add_argument(\n \"-t\",\n \"--transparent\",\n action=\"store_true\",\n help=\"Render a scene with an alpha channel\",\n )\n\n # The following are mutually exclusive and each overrides\n # FRAME_RATE, PIXEL_HEIGHT, and PIXEL_WIDTH,\n parser.add_argument(\n \"-q\",\n \"--quality\",\n choices=[constants.QUALITIES[q][\"flag\"] for q in constants.QUALITIES],\n default=constants.DEFAULT_QUALITY_SHORT,\n help=\"Render at specific quality, short form of the --*_quality flags\",\n )\n parser.add_argument(\n \"--low_quality\",\n action=\"store_true\",\n help=\"Render at low quality\",\n )\n parser.add_argument(\n \"--medium_quality\",\n action=\"store_true\",\n help=\"Render at medium quality\",\n )\n parser.add_argument(\n \"--high_quality\",\n action=\"store_true\",\n help=\"Render at high quality\",\n )\n parser.add_argument(\n \"--production_quality\",\n action=\"store_true\",\n help=\"Render at default production quality\",\n )\n parser.add_argument(\n \"--fourk_quality\",\n action=\"store_true\",\n help=\"Render at 4K quality\",\n )\n\n # Deprecated quality flags\n parser.add_argument(\n \"-l\",\n action=\"store_true\",\n help=\"DEPRECATED: USE -ql or --quality l\",\n )\n parser.add_argument(\n \"-m\",\n action=\"store_true\",\n help=\"DEPRECATED: USE -qm or --quality m\",\n )\n parser.add_argument(\n \"-e\",\n action=\"store_true\",\n help=\"DEPRECATED: USE -qh or --quality h\",\n )\n parser.add_argument(\n \"-k\",\n action=\"store_true\",\n help=\"DEPRECATED: USE -qk or --quality k\",\n )\n\n # This overrides any of the above\n parser.add_argument(\n \"-r\",\n \"--resolution\",\n help='Resolution, passed as \"height,width\". '\n \"Overrides the -l, -m, -e, and -k flags, if present\",\n )\n\n # This sets FROM_ANIMATION_NUMBER and UPTO_ANIMATION_NUMBER\n parser.add_argument(\n \"-n\",\n \"--from_animation_number\",\n help=\"Start rendering at the specified animation index, \"\n \"instead of the first animation. If you pass in two comma \"\n \"separated values, e.g. '3,6', it will end \"\n \"the rendering at the second value\",\n )\n\n parser.add_argument(\n \"--use_js_renderer\",\n help=\"Render animations using the javascript frontend\",\n action=\"store_const\",\n const=True,\n )\n\n parser.add_argument(\n \"--js_renderer_path\",\n help=\"Path to the javascript frontend\",\n )\n\n # Specify the manim.cfg file\n parser.add_argument(\n \"--config_file\",\n help=\"Specify the configuration file\",\n )\n\n # Specify whether to use the custom folders\n parser.add_argument(\n \"--custom_folders\",\n action=\"store_true\",\n help=\"Use the folders defined in the [custom_folders] \"\n \"section of the config file to define the output folder structure\",\n )\n\n # Specify the verbosity\n parser.add_argument(\n \"-v\",\n \"--verbosity\",\n type=str,\n help=(\n \"Verbosity level. Also changes the ffmpeg log level unless \"\n \"the latter is specified in the config\"\n ),\n choices=constants.VERBOSITY_CHOICES,\n )\n\n # Specify if the progress bar should be displayed\n parser.add_argument(\n \"--progress_bar\",\n type=_str2bool,\n help=\"Display the progress bar\",\n metavar=\"True/False\",\n )\n\n return parser.parse_args(args[1:])\n",
"path": "manim/_config/main_utils.py"
}
] | diff --git a/manim/_config/main_utils.py b/manim/_config/main_utils.py
index 1e3075c8f7..c833b37355 100644
--- a/manim/_config/main_utils.py
+++ b/manim/_config/main_utils.py
@@ -107,6 +107,9 @@ def parse_args(args):
if args[0] == "python" and args[1] == "-m":
args = args[2:]
+ if len(args) == 1:
+ return _parse_args_no_subcmd(args)
+
subcmd = _find_subcommand(args)
if subcmd == "cfg":
return _parse_args_cfg_subcmd(args)
|
secdev__scapy-3167 | Outdated Automotive Documentation
Reminder for myself.
Outdated:
https://github.com/secdev/scapy/blob/1aa0d8a849f7b102d18a3f65986e272aec5f518a/doc/scapy/layers/automotive.rst#L75-L85
SOME/IP:
https://github.com/secdev/scapy/blob/1aa0d8a849f7b102d18a3f65986e272aec5f518a/doc/scapy/layers/automotive.rst#L1011-L1030
Mentioned by @WebLabInt via gitter:
```Hi, I m having a problem creating a basic SOME IP service discovery following the example provided https://scapy.readthedocs.io/en/latest/layers/automotive.html?highlight=some%20ip#creating-a-some-ip-sd-message. The SOME IP package is working perfectly, however, the SD packet is not formed correctly thus not recognized as a SD packet by Wireshark and the SOME IP version is not correct. I did a capture with Wireshark reporting those issues http://fuiing.com/share/SD%20prob.png . I will be great if you can support me on this issue, thank you for making Scapy open source, it's really a great tool, have a great day ```
| [
{
"content": "# -*- coding: utf-8 -*-\n#\n# Scapy documentation build configuration file, created by\n# sphinx-quickstart on Wed Mar 07 19:02:35 2018.\n#\n# This file is execfile()d with the current directory set to its\n# containing dir.\n#\n# Note that not all possible configuration values are present in this\n# autogenerated file.\n#\n# All configuration values have a default; values that are commented out\n# serve to show the default.\n\nimport datetime\n\n# If extensions (or modules to document with autodoc) are in another directory,\n# add these directories to sys.path here. If the directory is relative to the\n# documentation root, use os.path.abspath to make it absolute, like shown here.\n#\nimport os\nimport sys\nsys.path.insert(0, os.path.abspath('../../'))\nsys.path.append(os.path.abspath('_ext'))\n\n\n# -- General configuration ------------------------------------------------\n\n# If your documentation needs a minimal Sphinx version, state it here.\n#\nneeds_sphinx = '3.0.0'\n\n# Add any Sphinx extension module names here, as strings. They can be\n# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom\n# ones.\nextensions = [\n 'sphinx.ext.autodoc',\n 'sphinx.ext.napoleon',\n 'sphinx.ext.todo',\n 'sphinx.ext.linkcode',\n 'scapy_doc'\n]\n\n# Autodoc configuration\nautodoc_inherit_docstrings = False\nautodoc_default_options = {\n 'undoc-members': True\n}\n\n# Enable the todo module\ntodo_include_todos = True\n\n# Linkcode resolver\nfrom linkcode_res import linkcode_resolve\n\n# Add any paths that contain templates here, relative to this directory.\ntemplates_path = ['_templates']\n\n# The suffix(es) of source filenames.\n# You can specify multiple suffix as a list of string:\n#\n# source_suffix = ['.rst', '.md']\nsource_suffix = '.rst'\n\n# The master toctree document.\nmaster_doc = 'index'\n\n# General information about the project.\nproject = 'Scapy'\nyear = datetime.datetime.now().year\ncopyright = '2008-%s Philippe Biondi and the Scapy community' % year\n\n# The version info for the project you're documenting, acts as replacement for\n# |version| and |release|, also used in various other places throughout the\n# built documents.\nfrom scapy import VERSION, VERSION_MAIN\n# The short X.Y version.\nrelease = VERSION_MAIN\n# The full version, including alpha/beta/rc tags.\nversion = VERSION\n\n# The language for content autogenerated by Sphinx. Refer to documentation\n# for a list of supported languages.\n#\n# This is also used if you do content translation via gettext catalogs.\n# Usually you set \"language\" from the command line for these cases.\nlanguage = None\n\n# List of patterns, relative to source directory, that match files and\n# directories to ignore when looking for source files.\n# This patterns also effect to html_static_path and html_extra_path\nexclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']\n\n# The name of the Pygments (syntax highlighting) style to use.\npygments_style = 'sphinx'\n\n# If true, `todo` and `todoList` produce output, else they produce nothing.\ntodo_include_todos = False\n\n\n# -- Options for HTML output ----------------------------------------------\n\n# The theme to use for HTML and HTML Help pages. See the documentation for\n# a list of builtin themes.\n#\nhtml_theme = 'sphinx_rtd_theme'\n\n# Theme options are theme-specific and customize the look and feel of a theme\n# further. For a list of options available for each theme, see the\n# documentation.\n#\n# html_theme_options = {}\n\n# Add any paths that contain custom static files (such as style sheets) here,\n# relative to this directory. They are copied after the builtin static files,\n# so a file named \"default.css\" will overwrite the builtin \"default.css\".\nhtml_static_path = ['_static']\n\n# Custom sidebar templates, must be a dictionary that maps document names\n# to template names.\n#\n# This is required for the alabaster theme\n# refs: http://alabaster.readthedocs.io/en/latest/installation.html#sidebars\nhtml_sidebars = {\n '**': [\n 'relations.html', # needs 'show_related': True theme option to display\n 'searchbox.html',\n ]\n}\n\n# Make :manpage directive work on HTML output.\nmanpages_url = 'https://manpages.debian.org/{path}'\n\n# -- Options for HTMLHelp output ------------------------------------------\n\n# Output file base name for HTML help builder.\nhtmlhelp_basename = 'Scapydoc'\n\n\n# -- Options for LaTeX output ---------------------------------------------\n\nlatex_elements = {\n # The paper size ('letterpaper' or 'a4paper').\n #\n 'papersize': 'a4paper',\n\n # The font size ('10pt', '11pt' or '12pt').\n #\n 'pointsize': '11pt',\n\n # Additional stuff for the LaTeX preamble.\n #\n # 'preamble': '',\n\n # Latex figure (float) alignment\n #\n # 'figure_align': 'htbp',\n}\n\n# Grouping the document tree into LaTeX files. List of tuples\n# (source start file, target name, title,\n# author, documentclass [howto, manual, or own class]).\nlatex_documents = [\n ('index', 'Scapy.tex', 'Scapy Documentation',\n 'Philippe Biondi and the Scapy community', 'manual'),\n]\n\n\n# -- Options for manual page output ---------------------------------------\n\n# One entry per manual page. List of tuples\n# (source start file, name, description, authors, manual section).\nman_pages = [\n (master_doc, 'scapy', 'Scapy Documentation',\n ['Philippe Biondi and the Scapy community'], 1)\n]\n\n\n# -- Options for Texinfo output -------------------------------------------\n\n# Grouping the document tree into Texinfo files. List of tuples\n# (source start file, target name, title, author,\n# dir menu entry, description, category)\ntexinfo_documents = [\n (master_doc, 'Scapy', 'Scapy Documentation',\n 'Philippe Biondi and the Scapy community', 'Scapy',\n '',\n 'Miscellaneous'),\n]\n",
"path": "doc/scapy/conf.py"
}
] | [
{
"content": "# -*- coding: utf-8 -*-\n#\n# Scapy documentation build configuration file, created by\n# sphinx-quickstart on Wed Mar 07 19:02:35 2018.\n#\n# This file is execfile()d with the current directory set to its\n# containing dir.\n#\n# Note that not all possible configuration values are present in this\n# autogenerated file.\n#\n# All configuration values have a default; values that are commented out\n# serve to show the default.\n\nimport datetime\n\n# If extensions (or modules to document with autodoc) are in another directory,\n# add these directories to sys.path here. If the directory is relative to the\n# documentation root, use os.path.abspath to make it absolute, like shown here.\n#\nimport os\nimport sys\nsys.path.insert(0, os.path.abspath('../../'))\nsys.path.append(os.path.abspath('_ext'))\n\n\n# -- General configuration ------------------------------------------------\n\n# If your documentation needs a minimal Sphinx version, state it here.\n#\nneeds_sphinx = '3.0.0'\n\n# Add any Sphinx extension module names here, as strings. They can be\n# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom\n# ones.\nextensions = [\n 'sphinx.ext.autodoc',\n 'sphinx.ext.napoleon',\n 'sphinx.ext.todo',\n 'sphinx.ext.linkcode',\n 'scapy_doc'\n]\n\n# Autodoc configuration\nautodoc_inherit_docstrings = False\nautodoc_default_options = {\n 'undoc-members': True\n}\n\n# Enable the todo module\ntodo_include_todos = True\n\n# Linkcode resolver\nfrom linkcode_res import linkcode_resolve\n\n# Add any paths that contain templates here, relative to this directory.\ntemplates_path = ['_templates']\n\n# The suffix(es) of source filenames.\n# You can specify multiple suffix as a list of string:\n#\n# source_suffix = ['.rst', '.md']\nsource_suffix = '.rst'\n\n# The master toctree document.\nmaster_doc = 'index'\n\n# General information about the project.\nproject = 'Scapy'\nyear = datetime.datetime.now().year\ncopyright = '2008-%s Philippe Biondi and the Scapy community' % year\n\n# The version info for the project you're documenting, acts as replacement for\n# |version| and |release|, also used in various other places throughout the\n# built documents.\nfrom scapy import VERSION, VERSION_MAIN\n# The short X.Y version.\nrelease = VERSION_MAIN\n# The full version, including alpha/beta/rc tags.\nversion = VERSION\n\n# The language for content autogenerated by Sphinx. Refer to documentation\n# for a list of supported languages.\n#\n# This is also used if you do content translation via gettext catalogs.\n# Usually you set \"language\" from the command line for these cases.\nlanguage = None\n\n# List of patterns, relative to source directory, that match files and\n# directories to ignore when looking for source files.\n# This patterns also effect to html_static_path and html_extra_path\nexclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']\n\n# The name of the Pygments (syntax highlighting) style to use.\npygments_style = 'sphinx'\n\n# If true, `todo` and `todoList` produce output, else they produce nothing.\ntodo_include_todos = False\n\n# Enable codeauthor and sectionauthor directives\nshow_authors = True\n\n\n# -- Options for HTML output ----------------------------------------------\n\n# The theme to use for HTML and HTML Help pages. See the documentation for\n# a list of builtin themes.\n#\nhtml_theme = 'sphinx_rtd_theme'\n\n# Theme options are theme-specific and customize the look and feel of a theme\n# further. For a list of options available for each theme, see the\n# documentation.\n#\n# html_theme_options = {}\n\n# Add any paths that contain custom static files (such as style sheets) here,\n# relative to this directory. They are copied after the builtin static files,\n# so a file named \"default.css\" will overwrite the builtin \"default.css\".\nhtml_static_path = ['_static']\n\n# Custom sidebar templates, must be a dictionary that maps document names\n# to template names.\n#\n# This is required for the alabaster theme\n# refs: http://alabaster.readthedocs.io/en/latest/installation.html#sidebars\nhtml_sidebars = {\n '**': [\n 'relations.html', # needs 'show_related': True theme option to display\n 'searchbox.html',\n ]\n}\n\n# Make :manpage directive work on HTML output.\nmanpages_url = 'https://manpages.debian.org/{path}'\n\n# -- Options for HTMLHelp output ------------------------------------------\n\n# Output file base name for HTML help builder.\nhtmlhelp_basename = 'Scapydoc'\n\n\n# -- Options for LaTeX output ---------------------------------------------\n\nlatex_elements = {\n # The paper size ('letterpaper' or 'a4paper').\n #\n 'papersize': 'a4paper',\n\n # The font size ('10pt', '11pt' or '12pt').\n #\n 'pointsize': '11pt',\n\n # Additional stuff for the LaTeX preamble.\n #\n # 'preamble': '',\n\n # Latex figure (float) alignment\n #\n # 'figure_align': 'htbp',\n}\n\n# Grouping the document tree into LaTeX files. List of tuples\n# (source start file, target name, title,\n# author, documentclass [howto, manual, or own class]).\nlatex_documents = [\n ('index', 'Scapy.tex', 'Scapy Documentation',\n 'Philippe Biondi and the Scapy community', 'manual'),\n]\n\n\n# -- Options for manual page output ---------------------------------------\n\n# One entry per manual page. List of tuples\n# (source start file, name, description, authors, manual section).\nman_pages = [\n (master_doc, 'scapy', 'Scapy Documentation',\n ['Philippe Biondi and the Scapy community'], 1)\n]\n\n\n# -- Options for Texinfo output -------------------------------------------\n\n# Grouping the document tree into Texinfo files. List of tuples\n# (source start file, target name, title, author,\n# dir menu entry, description, category)\ntexinfo_documents = [\n (master_doc, 'Scapy', 'Scapy Documentation',\n 'Philippe Biondi and the Scapy community', 'Scapy',\n '',\n 'Miscellaneous'),\n]\n",
"path": "doc/scapy/conf.py"
}
] | diff --git a/doc/scapy/backmatter.rst b/doc/scapy/backmatter.rst
index f316bb2a879..326083045e4 100644
--- a/doc/scapy/backmatter.rst
+++ b/doc/scapy/backmatter.rst
@@ -8,3 +8,4 @@ Credits
- Fred Raynal wrote the chapter on building and dissecting packets.
- Peter Kacherginsky contributed several tutorial sections, one-liners and recipes.
- Dirk Loss integrated and restructured the existing docs to make this book.
+- Nils Weiss contributed automotive specific layers and utilities.
diff --git a/doc/scapy/conf.py b/doc/scapy/conf.py
index 981e8f0072f..33c4615ba8d 100644
--- a/doc/scapy/conf.py
+++ b/doc/scapy/conf.py
@@ -97,6 +97,9 @@
# If true, `todo` and `todoList` produce output, else they produce nothing.
todo_include_todos = False
+# Enable codeauthor and sectionauthor directives
+show_authors = True
+
# -- Options for HTML output ----------------------------------------------
diff --git a/doc/scapy/graphics/automotive/CAN-full-frame.jpg b/doc/scapy/graphics/automotive/CAN-full-frame.jpg
new file mode 100644
index 00000000000..d726b3b5c7c
Binary files /dev/null and b/doc/scapy/graphics/automotive/CAN-full-frame.jpg differ
diff --git a/doc/scapy/graphics/automotive/DC-ZGW-CAN-Bus-.png b/doc/scapy/graphics/automotive/DC-ZGW-CAN-Bus-.png
new file mode 100644
index 00000000000..ea778feda1a
Binary files /dev/null and b/doc/scapy/graphics/automotive/DC-ZGW-CAN-Bus-.png differ
diff --git a/doc/scapy/graphics/automotive/Simple-CAN-Bus-.png b/doc/scapy/graphics/automotive/Simple-CAN-Bus-.png
new file mode 100644
index 00000000000..d767795911a
Binary files /dev/null and b/doc/scapy/graphics/automotive/Simple-CAN-Bus-.png differ
diff --git a/doc/scapy/graphics/automotive/XCP_ReferenceBook.png b/doc/scapy/graphics/automotive/XCP_ReferenceBook.png
new file mode 100644
index 00000000000..e970660c73d
Binary files /dev/null and b/doc/scapy/graphics/automotive/XCP_ReferenceBook.png differ
diff --git a/doc/scapy/graphics/automotive/ZGW-CAN-Bus-.png b/doc/scapy/graphics/automotive/ZGW-CAN-Bus-.png
new file mode 100644
index 00000000000..80a3e9c51c9
Binary files /dev/null and b/doc/scapy/graphics/automotive/ZGW-CAN-Bus-.png differ
diff --git a/doc/scapy/graphics/automotive/can-bus-states.png b/doc/scapy/graphics/automotive/can-bus-states.png
new file mode 100644
index 00000000000..389a00766ff
Binary files /dev/null and b/doc/scapy/graphics/automotive/can-bus-states.png differ
diff --git a/doc/scapy/graphics/automotive/can-frame-socket-can.png b/doc/scapy/graphics/automotive/can-frame-socket-can.png
new file mode 100644
index 00000000000..efd96e03147
Binary files /dev/null and b/doc/scapy/graphics/automotive/can-frame-socket-can.png differ
diff --git a/doc/scapy/graphics/automotive/diag-stack.png b/doc/scapy/graphics/automotive/diag-stack.png
new file mode 100644
index 00000000000..76a7b11635e
Binary files /dev/null and b/doc/scapy/graphics/automotive/diag-stack.png differ
diff --git a/doc/scapy/graphics/automotive/isotp-flow.png b/doc/scapy/graphics/automotive/isotp-flow.png
new file mode 100644
index 00000000000..08e7a7b944f
Binary files /dev/null and b/doc/scapy/graphics/automotive/isotp-flow.png differ
diff --git a/doc/scapy/graphics/automotive/isotp-frames.png b/doc/scapy/graphics/automotive/isotp-frames.png
new file mode 100644
index 00000000000..a724f95e5fd
Binary files /dev/null and b/doc/scapy/graphics/automotive/isotp-frames.png differ
diff --git a/doc/scapy/layers/automotive.rst b/doc/scapy/layers/automotive.rst
index a4a1a08a9a9..83fc509df26 100644
--- a/doc/scapy/layers/automotive.rst
+++ b/doc/scapy/layers/automotive.rst
@@ -1,150 +1,284 @@
-**********
-Automotive
-**********
+.. note:: This document is under a `Creative Commons Attribution - Non-Commercial - Share Alike 2.5 <http://creativecommons.org/licenses/by-nc-sa/2.5/>`_ license.
+#################################
+Automotive-specific Documentation
+#################################
+
+.. sectionauthor:: Nils Weiss <[email protected]>
+
+********
Overview
-========
+********
.. note::
- All automotive related features work best on Linux systems. CANSockets and ISOTPSockets in Scapy are based on Linux kernel modules.
- The python-can project is used to support CAN and CANSockets on other systems, besides Linux.
- This guide explains the hardware setup on a BeagleBone Black. The BeagleBone Black was chosen because of its two CAN interfaces on the main processor.
- The presence of two CAN interfaces in one device gives the possibility of CAN MITM attacks and session hijacking.
- The Cannelloni framework turns a single board computer into a CAN-to-UDP interface, which gives you the freedom to run Scapy
- on a more powerful machine.
+ All automotive-related features work best on Linux systems. CANSockets and ISOTPSockets are based on Linux kernel modules. The python-can project is used to support CAN and CANSockets on a wider range of operating systems and CAN hardware interfaces.
Protocols
----------
+=========
-The following table should give a brief overview about all automotive capabilities
+The following table should give a brief overview of all the automotive-related capabilities
of Scapy. Most application layer protocols have many specialized ``Packet`` classes.
-These special purpose classes are not part of this overview. Use the ``explore()``
+These special-purpose ``Packets`` are not part of this overview. Use the ``explore()``
function to get all information about one specific protocol.
-+---------------------+----------------------+--------------------------------------------------------+
-| OSI Layer | Protocol | Scapy Implementations |
-+=====================+======================+========================================================+
-| Application Layer | UDS (ISO 14229) | UDS, UDS_*, UDS_TesterPresentSender |
-| +----------------------+--------------------------------------------------------+
-| | GMLAN | GMLAN, GMLAN_*, GMLAN_TesterPresentSender |
-| +----------------------+--------------------------------------------------------+
-| | SOME/IP | SOMEIP, SD |
-| +----------------------+--------------------------------------------------------+
-| | BMW HSFZ | HSFZ, HSFZSocket |
-| +----------------------+--------------------------------------------------------+
-| | OBD | OBD, OBD_S0X |
-| +----------------------+--------------------------------------------------------+
-| | CCP | CCP, DTO, CRO |
-| +----------------------+--------------------------------------------------------+
-| | XCP | XCPOnCAN, XCPOnUDP, XCPOnTCP, CTORequest, CTOResponse, |
-| | | DTO |
-+---------------------+----------------------+--------------------------------------------------------+
-| Transportation Layer| ISO-TP (ISO 15765-2) | ISOTPSocket, ISOTPNativeSocket, ISOTPSoftSocket |
-| | | |
-| | | ISOTPSniffer, ISOTPMessageBuilder, ISOTPSession |
-| | | |
-| | | ISOTPHeader, ISOTPHeaderEA, ISOTPScan |
-| | | |
-| | | ISOTP, ISOTP_SF, ISOTP_FF, ISOTP_CF, ISOTP_FC |
-+---------------------+----------------------+--------------------------------------------------------+
-| Data Link Layer | CAN (ISO 11898) | CAN, CANSocket, rdcandump, CandumpReader |
-+---------------------+----------------------+--------------------------------------------------------+
-
-
-CAN Layer
-=========
++----------------------+----------------------+--------------------------------------------------------+
+| OSI Layer | Protocol | Scapy Implementations |
++======================+======================+========================================================+
+| Application Layer | UDS (ISO 14229) | UDS, UDS_*, UDS_TesterPresentSender |
+| +----------------------+--------------------------------------------------------+
+| | GMLAN | GMLAN, GMLAN_*, GMLAN_[Utilities] |
+| +----------------------+--------------------------------------------------------+
+| | SOME/IP | SOMEIP, SD |
+| +----------------------+--------------------------------------------------------+
+| | BMW HSFZ | HSFZ, HSFZSocket, ISOTP_HSFZSocket |
+| +----------------------+--------------------------------------------------------+
+| | OBD | OBD, OBD_S0[0-9A] |
+| +----------------------+--------------------------------------------------------+
+| | CCP | CCP, DTO, CRO |
+| +----------------------+--------------------------------------------------------+
+| | XCP | XCPOnCAN, XCPOnUDP, XCPOnTCP, CTORequest, CTOResponse, |
+| | | DTO |
++----------------------+----------------------+--------------------------------------------------------+
+| Transportation Layer | ISO-TP (ISO 15765-2) | ISOTPSocket, ISOTPNativeSocket, ISOTPSoftSocket |
+| | | |
+| | | ISOTPSniffer, ISOTPMessageBuilder, ISOTPSession |
+| | | |
+| | | ISOTPHeader, ISOTPHeaderEA, ISOTPScan |
+| | | |
+| | | ISOTP, ISOTP_SF, ISOTP_FF, ISOTP_CF, ISOTP_FC |
++----------------------+----------------------+--------------------------------------------------------+
+| Data Link Layer | CAN (ISO 11898) | CAN, CANSocket, rdcandump, CandumpReader |
++----------------------+----------------------+--------------------------------------------------------+
+
+
+********************
+Technical Background
+********************
+
+Parts this section were published in a study report [10]_.
+
+Physical Protocols
+==================
+
+More than 20 different communication protocols exist for the vehicle’s internal wired communication. Most vehicles make use of five to ten different protocols for their internal communication. The decision which communication protocol is used from an Original Equipment Manufacturer (OEM) is usually made by the trade-off between the costs for communication technology and the final car price. The four major communication technologies for inter-ECU communication are Controller Area Network (CAN), FlexRay, Local Interconnect Network (LIN), and Automotive Ethernet. For security considerations, these are the most relevant protocols for wired communication in vehicles.
+
+LIN
+---
+LIN is a single wire communication protocol for low data rates. Actuators and sensors of a vehicle exchange information with an ECU, acting as a LIN master. Software updates over LIN are possible, but the LIN slaves usually do not need software updates because of their limited functionality.
+
+CAN
+---
+CAN is by far the most used communication technology for inter-ECU communication in vehicles. In older or cheaper vehicles, CAN is still the primary protocol for a vehicle’s backbone communication. Safety-critical communication during a vehicle’s operation, diagnostic information, and software updates are transferred between ECUs over CAN. The lack of security features in the protocol itself, combined with the general use, makes CAN the primary protocol for security investigations.
+
+FlexRay
+-------
+The FlexRay consortium designed FlexRay as a successor of CAN. Modern vehicles have higher demands on communication bandwidth. By design, FlexRay is a fast and reliable communication protocol for inter-ECU communication. FlexRay components are more expensive than CAN components, leading to a more selective use by OEMs.
+
+Automotive Ethernet
+-------------------
+Recent upper-class vehicles implement Automotive Ethernet, the new backbone technology for internal vehicle communication. The rapidly grown bandwidth demands already replace FlexRay. The primary reasons for these demands are driver-assistant and autonomous-driving features. Only the physical layer (layer 1) of the Open Systems Interconnection (OSI) model distinguishes Ethernet (IEEE 802.3) from Automotive Ethernet (BroadR-Reach). This design decision leads to multiple advantages. For example, communication stacks of operating systems can be used without modification and routing, filtering, and firewall systems. Automotive Ethernet components are already cheaper than FlexRay components, which will lead to vehicle topologies, where CAN and Automotive Ethernet are the most used communication protocols.
-How-To
+Topologies
+==========
+
+Line-Bus
--------
-Send and receive a message over Linux SocketCAN::
+.. _fig-line-bus:
- load_layer("can")
- load_contrib('cansocket')
+.. figure:: ../graphics/automotive/Simple-CAN-Bus-.png
- socket = CANSocket(channel='can0')
- packet = CAN(identifier=0x123, data=b'01020304')
+ Line-Bus network topology
- socket.send(packet)
- rx_packet = socket.recv()
+The first vehicles with CAN bus used a single network with a line-bus topology. Some lower-priced vehicles still use one or two shared CAN bus networks for their internal communication nowadays. The downside of this topology is its vulnerability and the lack of network separation. All ECUs of a vehicle are connected on a shared bus. Since CAN does not support security features from its protocol definition, any participant on this bus can communicate directly with all other participants, which allows an attacker to affect all ECUs, even safety-critical ones, by compromising one single ECU. The overall security level of this network is given from the security level of the weakest participant.
- socket.sr1(packet, timeout=1)
+Central Gateway
+---------------
-Send a message over a Vector CAN-Interface::
+.. _fig-cgw:
- import can
- load_layer("can")
- conf.contribs['CANSocket'] = {'use-python-can' : True}
- load_contrib('cansocket')
- from can.interfaces.vector import VectorBus
+.. figure:: ../graphics/automotive/ZGW-CAN-Bus-.png
- socket = CANSocket(channel=VectorBus(0, bitrate=1000000))
- packet = CAN(identifier=0x123, data=b'01020304')
+ Network topology with central GW ECU
- socket.send(packet)
- rx_packet = socket.recv()
+The central Gateway (GW) topology can be found in higher-priced older cars and medium-priced to lower-priced recent cars. A centralized GW ECU separates domain-specific sub-networks. This allows an OEM to encapsulate all ECUs with remote attack surfaces in one sub-network. ECUs with safety-critical functionalities are located in an individual CAN network. Next to CAN, FlexRay might also be used as a communication protocol inside a separate network domain. The security of a safety-critical network in this topology depends mainly on the central GW ECU’s security. This architecture increases the overall security level of a vehicle through domain separation. After an attacker successfully exploited an ECU through an arbitrary attack surface, a second exploitable vulnerability or a logical bug is necessary to compromise a different domain, a safety-critical network, inside a vehicle. This second exploit or logical bug is necessary to overcome the network separation of the central GW ECU.
- socket.sr1(packet)
+Central Gateway and Domain Controller
+-------------------------------------
+.. _fig-dc:
+.. figure:: ../graphics/automotive/DC-ZGW-CAN-Bus-.png
-Tutorials
----------
+ Network topology with Automotive-Ethernet backbone and DC
-Linux SocketCAN
-^^^^^^^^^^^^^^^
+A new topology with central GW and Domain Controllers (DCs) can be found in the latest higher-priced vehicles. The increasing demand for bandwidth in modern vehicles with autonomous driving and driver assistant features led to this topology. An Automotive Ethernet network is used as a communication backbone for the entire vehicle. Individual domains, connected through a DC with the central GW, form the vehicle’s backbone. The individual DCs can control and regulate the data communication between a domain and the vehicle’s backbone. This topology achieves a very-high security level through a strong network separation with individual DCs, acting as gateway and firewall, to the vehicle’s backbone network. OEMs have the advantage of dynamic information routing next to this security improvement, an enabler for Feature on Demand (FoD) services.
-This subsection summarizes some basics about Linux SocketCAN. An excellent overview
-from Oliver Hartkopp can be found here: https://wiki.automotivelinux.org/_media/agl-distro/agl2017-socketcan-print.pdf
+Automotive Communication Protocols
+==================================
-Virtual CAN Setup
-^^^^^^^^^^^^^^^^^
+This section provides an overview of relevant communication protocols for security evaluations in automotive networks. In contrast to section "Physical Protocols", this section focuses on properties for data communication.
-Linux SocketCAN supports virtual CAN interfaces. These interfaces are an easy way
-to do some first steps on a CAN-Bus without the requirement of special hardware.
-Besides that, virtual CAN interfaces are heavily used in Scapy unit test for automotive
-related contributions.
+CAN
+---
-Virtual CAN sockets require a special Linux kernel module. The following shell command loads the required module::
+The CAN communication technology was invented in 1983 as a message-based robust vehicle bus communication system. The Robert Bosch GmbH designed multiple communication features into the CAN standard to achieve a robust and computation efficient protocol for controller area networks. Remarkable for the communication behavior of CAN is the internal state machine for transmission errors. This state machine implements a fail silent behavior to protect a safety-critical network from babbling idiot nodes. If a specific limit of reception errors (REC) or transmission errors (TEC) occurred, the CAN driver changes its state from error-active to error-passive and finally to bus-off.
- sudo modprobe vcan
+.. _fig-can-bus-states:
-In order to use a virtual CAN interface some additional commands for setup are required.
-This snippet chooses the name ``vcan0`` for the virtual CAN interface. Any name can be chosen here::
+.. figure:: ../graphics/automotive/can-bus-states.png
- sudo ip link add name vcan0 type vcan
- sudo ip link set dev vcan0 up
+ CAN bus states on transmission errors. Receive Error Counter (REC), Transmit Error Counter (TEC)
-The same commands can be executed from Scapy like this::
+In recent years, this protocol specification was abused for Denial of Service (DoS) attacks and information gathering attacks on the CAN network of a vehicle. Cho et al. demonstrated a DoS attack against CAN networks by abusing the bus-off state of ECUs [1]_. Injections of communication errors in CAN frames of one specific node caused a high transmission error count in the node under attack, forcing the attacked node to enter the bus-off state. In 2019 Kulandaivel et al. combined this attack with statistical analysis to achieve a fast and inexpensive network mapping in vehicular networks [2]_. They combined statistical analysis of the CAN network traffic before and after the bus-off attack was applied to a node. All missing CAN frames in the network traffic after an ECU was attacked could now be mapped to the ECU under attack, helping researchers identify the origin ECU of a CAN frame. Ken Tindell published a comprehensive summary of low level attacks on CANs in 2019 [3]_.
- from scapy.layers.can import *
- import os
+.. _fig-can-full-frame:
- bashCommand = "/bin/bash -c 'sudo modprobe vcan; sudo ip link add name vcan0 type vcan; sudo ip link set dev vcan0 up'"
- os.system(bashCommand)
+.. figure:: ../graphics/automotive/CAN-full-frame.jpg
-If it's required, a CAN interface can be set into a ``listen-only`` or ``loopback`` mode with ``ip link set`` commands::
+ Complete CAN data frame structure [9]_
- ip link set vcan0 type can help # shows additional information
+The above figure shows a CAN frame and its fields as it is transferred over the network. For information exchange, only the fields arbitration, control, and data are relevant. These are the only fields to which a usual application software has access. All other fields are evaluated on a hardware-layer and, in most cases, are not forwarded to an application. The data field has a variable length and can hold up to eight bytes. The length of the data field is specified by the data length code inside the control field. Important variations of this example are CAN-frames with extended arbitration fields and the Controller Area Network Flexible Data-Rate (CAN FD) protocol. On Linux, every received CAN frame is passed to SocketCAN. SocketCAN allows the CAN handling via network sockets of the operating system. SocketCAN was created by Oliver Hartkopp and added to the Linux Kernel version 2.6.25 [4]_. Figure 2.7 shows the frame structure, how CAN frames are encoded if a user-land application receives data from a CAN socket.
+.. _fig-can-socket-frame:
-Linux can-utils
-^^^^^^^^^^^^^^^
+.. figure:: ../graphics/automotive/can-frame-socket-can.png
-As part of Linux SocketCAN, some very useful commandline tools are provided from Oliver Hartkopp: https://github.com/linux-can/can-utils
+ CAN frame defined by SocketCAN
-The following example shows basic functions of Linux can-utils. These utilities are very handy for
-quick checks, dumping, sending or logging of CAN messages from the command line.
+The comparison of above figures clearly shows the loss of information during the CAN frame processing from a physical layer driver. Almost every CAN driver acts in the same way, whether an application code runs on a microcontroller or a Linux kernel. This also means that a standard application does not have access to the Cyclic Redundancy Check (CRC) field, the acknowledgment bit, or the end-of-frame field.
+
+Through the CAN communication in a vehicle or a separated domain, ECUs exchange sensor-data and control inputs; this data is mainly not secured and can be modified by assailants. Attackers can easily spoof sensor values on a CAN bus to trigger malicious reactions of other ECUs. Miller and Valasek described this spoofing attack during their studies on automotive networks [5]_. To prevent attacks on safety-critical data transferred over CAN, Automotive Open System Architecture (AUTOSAR) released a secure onboard communication specification [6]_.
+
+ISO-TP (ISO 15765-2)
+--------------------
+
+The CAN protocol supports only eight bytes of data. Use-cases like diagnostic operations or ECU programming require much higher payloads than the CAN protocol supports. For these purposes, the automotive industry standardized the Transport Layer (ISO-TP) (ISO 15765-2) protocol [7]_. ISO-TP is a transportation layer protocol on top of CAN. Payloads with up to 4095 bytes can be transferred between ISO-TP endpoints fragmented in CAN frames. The ISO-TP protocol handling requires four special frame types.
+
+.. _fig-isotp-flow:
+
+.. figure:: ../graphics/automotive/isotp-flow.png
+
+ ISO-TP fragmented communication
+
+The different types of ISO-TP frames are shown in the following figure. The payload of a CAN frame gets replaced by one of the four ISO-TP frames. The individual ISO-TP frames have different purposes. A single frame can transfer between 1 and 7 bytes of ISO-TP message data. The len field of a Single Frame or a First Frame indicates the ISO-TP message length. Every message with more than 7 bytes of payload data must be fragmented into a First Frame, followed by multiple Consecutive Frames. This communication is illustrated in the above figure. After the First Frame is sent from a sender, the receiver has to communicate its reception capabilities through a Flow Control Frame to the sender. Only after this Flow Control Frame is received, the sender is allowed to communicate the Consecutive Frames according to the receiver’s capabilities.
+
+.. _fig-isotp-frames:
+
+.. figure:: ../graphics/automotive/isotp-frames.png
+
+ ISO-TP frame types
+
+ISO-TP acts as a transport protocol with the support of directed communication through addressing mechanisms. In vehicles, ISO-TP is mainly used as a transport protocol for diagnostic communication. In rare cases, ISO-TP is also used to exchange larger data between ECUs of a vehicle. Security measures have to be applied to the application layer protocol transported through ISO-TP since ISO-TP has no capabilities to secure its transported data.
+
+DoIP
+----
+
+Diagnostic over IP (DoIP) was first implemented on automotive networks with a centralized gateway topology. A centralized GW functions as a DoIP endpoint that routes diagnostic messages to the desired network, allowing manufacturers to program or diagnose multiple ECUs in parallel. Since the Internet Protocol (IP) communication between a repair-shop tester and the GW is many times faster than the communication between the GW ECU and a target ECU connected over CAN, the remaining bandwidth of the IP communication can be used to start further DoIP connections to other ECUs in different CAN domains. DoIP is specified as part of AUTOSAR and in ISO 13400-2. Similar to ISO-TP, DoIP does not specify special security measures. The responsibility regarding secured communication is delegated to the application layer protocol.
+
+Diagnostic Protocols
+--------------------
+
+Two examples of diagnostic protocols are General Motor Local Area Network (GMLAN) and Unified Diagnostic Service (UDS) (ISO 14229-2). The General Motors Cooperation uses GMLAN. German OEMs mainly use UDS. Both protocols are very similar from a specification point of view, and both protocols use either ISO-TP or DoIP messages for a directed communication with a target ECU. Since different OEMs use UDS, every manufacturer adds its custom additions to the standard. Also, every manufacturer uses individual ISO-TP addressing for the directed communication with an ECU. GMLAN includes more precise definitions about ECU addressing and an ECUs internal behavior compared to UDS.
+
+UDS and GMLAN follow a tree-like message structure, where the first byte identifies the service. Every service is answered by a response. Two types of responses are defined in the standard. Negative responses are indicated through the service 0x7F. Positive responses are identified by the request service identifier incremented with 0x40.
+
+.. _fig-diag-stack:
+
+.. figure:: ../graphics/automotive/diag-stack.png
+
+ Automotive Diagnostic Protocol Stack
+
+A clear separation between the transport and the application layer allows creating application layer tools for both network stacks. The figure above provides an overview of relevant protocols and the corresponding layers. UDS defines a clean separation between application and transport layer. On CAN based networks, ISO-TP is used for this purpose. The CAN protocol can be treated as the network access protocol. This allows to replace ISO-TP and CAN with DoIP or HSFZ and Ethernet. The GMLAN protocol combines transport and application layer specifications very similar to ISO-TP and UDS. Because of that similarity, identical application layer-specific scan techniques can be applied. To overcome the bandwidth limitations of CAN, the latest vehicle architectures use an Ethernet-based diagnostic protocol (DoIP, HSFZ) to communicate with a central gateway ECU. The central gateway ECU routes application layer packets from an Ethernet-based network to a CAN based vehicle internal network. In general, the diagnostic functions of all ECUs in a vehicle can be accessed from the OBD connector over UDSonCAN or UDSonIP.
+
+SOME/IP
+-------
+
+Scalable service-Oriented MiddlewarE over IP (SOME/IP) defines a new philosophy of data communication in automotive networks. SOME/IP is used to exchange data between network domain controllers in the latest vehicle networks. SOME/IP supports subscription and notification mechanisms, allowing domain controllers to dynamically subscribe to data provided by another domain controller dependent on the vehicle’s state. SOME/IP transports data between domain controllers and the gateway that a vehicle needs during its regular operation. The use-cases of SOME/IP are similar to the use-cases of CAN communication. The main purpose is the information exchange of sensor and actuator data between ECUs. This usage emphasizes SOME/IP communication as a rewarding target for cyber-attacks.
+
+CCP/XCP
+-------
+
+Universal Measurement and Calibration Protocol (XCP), the CAN Calibration Protocol (CCP) successor, is a calibration protocol for automotive systems, standardized by ASAM e.V. in 2003. The primary usage of XCP is during the testing and calibration phase of ECU or vehicle development. CCP is designed for use on CAN. No message in CCP exceeds the 8-byte limitation of CAN. To overcome this restriction, XCP was designed to aim for compatibility with a wide range of transport protocols. XCP can be used on top of CAN, CAN FD, Serial Peripheral Interface (SPI), Ethernet, Universal Serial Bus (USB), and FlexRay. The features of CCP and XCP are very similar; however, XCP has a larger functional scope and optimizations for data efficiency.
+
+Both protocols have a session-based communication procedure and support authentication through seed and key mechanisms between a master and multiple slave nodes. A master node is typically an engineering Personal Computer (PC). In vehicles, slave nodes are ECUs for configuration. XCP also supports simulation. A vehicle engineer can debug a MATLAB Simulink model through XCP. In this case, the simulated model acts as the XCP slave node. CCP and XCP can read and write to the memory of an ECU. Another main feature is data acquisition. Both protocols support a procedure that allows an engineer to configure a so-called data acquisition list with memory addresses of interest. All memory specified in such a list will be read periodically and be broadcast in a CCP or XCP Data Acquisition (DAQ) packet on the chosen communication channel. The following figure gives an overview of all supported communication and packet types in XCP. In the Command Transfer Object (CTO) area, all communication follows a request and response procedure always initiated by the XCP master. A Command Packet (CMD) can receive a Command Response Packet (RES), an Error (ERR) packet, an Event Packet (EV), or a Service Request Packet (SERV) as a response. After the configuration of a slave through CTO CMDs, a slave can listen for Stimulation (STIM) packets and periodically send configured DAQ packets. The resources section in the following figure indicates the possible attack surfaces of this protocol (Programming (PGM), Calibration (CAL), DAQ, STIM) which an attacker could abuse. It is crucial for a vehicle’s security and safety that such protocols, which have their use only during calibration and development of a vehicle, are disabled or removed before a vehicle is shipped to a customer.
+
+.. _fig-xcp-reference:
+
+.. figure:: ../graphics/automotive/XCP_ReferenceBook.png
+
+ XCP communication model between XCP Master and XCP Slave. This model shows the communication direction for CTO/Data Transfer Object (DTO) packages [8]_.
+
+**References**
+
+.. [1] Kyong-Tak Cho and Kang G. Shin. Error handling of in-vehicle networks makes them vulnerable. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, CCS ’16, page 1044–1055, New York, NY, USA, 2016. Association for Computing Machinery.
+
+.. [2] Sekar Kulandaivel, Tushar Goyal, Arnav Kumar Agrawal, and Vyas Sekar. Canvas: Fast and inexpensive automotive network mapping. In 28th USENIX Security Symposium (USENIX Security 19), pages 389–405, Santa Clara, CA, August 2019. USENIX Association.
+
+.. [3] Ken Tindell. CAN Bus Security - Attacks on CAN bus and their mitigations, 2019. https://canislabs.com/wp-content/uploads/2020/12/2020-02-14-White-Paper-CAN-Security.pdf
+
+.. [4] Oliver Hartkopp. Readme file for the Controller Area Network Protocol Family (aka SocketCAN), 2020 (accessed January 29, 2020). https://www.kernel.org/doc/Documentation/networking/can.txt
+
+.. [5] Dr. Charlie Miller and Chris Valasek. Adventures in Automotive Networks and Control Units. DEF CON 21 Hacking Conference. Las Vegas, NV: DEF CON, August 2013. http://illmatics.com/car_hacking.pdf (accessed 2020-05-27)
+
+.. [6] AUTOSAR. Specification of Secure Onboard Communication, 2020 (accessed January 31, 2020). https://www.autosar.org/fileadmin/user_upload/standards/classic/4-3/AUTOSAR_SWS_SecureOnboardCommunication.pdf
+
+.. [7] ISO Central Secretary. Road vehicles – Diagnostic communication over Controller Area Network (DoCAN) – Part 2: Transport protocol and network layer services. Standard ISO 15765-2:2016, International Organization for Standardization, Geneva, CH, 2016.
+
+.. [8] Vector Informatik GmbH. XCP – The Standard Protocol for ECU Development. Vector Informatik GmbH, 2020 (accessed January 30, 2020). https://assets.vector.com/cms/content/application-areas/ecu-calibration/xcp/XCP_ReferenceBook_V3.0_EN.pdf
+
+.. [9] Pico Technology Ltd. Complete CAN data frame structure, 2020 (accessed February 14, 2020). https://www.picotech.com/images/uploads/library/topics/_med/CAN-full-frame.jpg
+
+.. [10] Nils Weiss. Security Testing in Safety-Critical Networks. PhD Study Report. http://www.kiv.zcu.cz/site/documents/verejne/vyzkum/publikace/technicke-zpravy/2020/Rigo_Weiss_2020_2.pdf
+
+
+******
+Layers
+******
+
+.. note:: **ATTENTION**: Animations below might be outdated.
+
+CAN
+===
+
+How-To
+------
+
+Send and receive a message over Linux SocketCAN::
+
+ load_layer("can")
+ load_contrib('cansocket')
+
+ socket = CANSocket(channel='can0')
+ packet = CAN(identifier=0x123, data=b'01020304')
+
+ socket.send(packet)
+ rx_packet = socket.recv()
+
+ socket.sr1(packet, timeout=1)
+
+Send and receive a message over a Vector CAN-Interface::
+
+ load_layer("can")
+ conf.contribs['CANSocket'] = {'use-python-can' : True}
+ load_contrib('cansocket')
+
+ socket = CANSocket(bustype='vector', channel=0, bitrate=1000000)
+ packet = CAN(identifier=0x123, data=b'01020304')
+
+ socket.send(packet)
+ rx_packet = socket.recv()
+
+ socket.sr1(packet)
-.. image:: ../graphics/animations/animation-cansend.svg
CAN Frame
-^^^^^^^^^
+---------
Basic information about CAN can be found here: https://en.wikipedia.org/wiki/CAN_bus
-The following examples assume that CAN layer in your Scapy session is loaded. If it isn't,
-the CAN layer can be loaded with this command in your Scapy session::
+The following examples assume that CAN layer in your Scapy session is loaded.
+If it isn't, the CAN layer can be loaded with this command in your Scapy session::
>>> load_layer("can")
@@ -166,8 +300,9 @@ Creation of an extended CAN frame::
.. image:: ../graphics/animations/animation-scapy-canframe.svg
+
CAN Frame in- and export
-^^^^^^^^^^^^^^^^^^^^^^^^
+------------------------
CAN Frames can be written to and read from ``pcap`` files::
@@ -186,9 +321,127 @@ This allows you to use ``sniff`` and other functions from Scapy::
.. image:: ../graphics/animations/animation-scapy-rdcandump.svg
-Scapy CANSocket
+
+DBC File Format and CAN Signals
+-------------------------------
+
+In order to support the DBC file format, ``SignalFields`` and the ``SignalPacket``
+classes were added to Scapy. ``SignalFields`` should only be used inside a ``SignalPacket``.
+Multiplexer fields (MUX) can be created through ``ConditionalFields``. The following
+example demonstrates the usage::
+
+ DBC Example:
+
+ BO_ 4 muxTestFrame: 7 TEST_ECU
+ SG_ myMuxer M : 53|3@1+ (1,0) [0|0] "" CCL_TEST
+ SG_ muxSig4 m0 : 25|7@1- (1,0) [0|0] "" CCL_TEST
+ SG_ muxSig3 m0 : 16|9@1+ (1,0) [0|0] "" CCL_TEST
+ SG_ muxSig2 m0 : 15|8@0- (1,0) [0|0] "" CCL_TEST
+ SG_ muxSig1 m0 : 0|8@1- (1,0) [0|0] "" CCL_TEST
+ SG_ muxSig5 m1 : 22|7@1- (0.01,0) [0|0] "" CCL_TEST
+ SG_ muxSig6 m1 : 32|9@1+ (2,10) [0|0] "mV" CCL_TEST
+ SG_ muxSig7 m1 : 2|8@0- (0.5,0) [0|0] "" CCL_TEST
+ SG_ muxSig8 m1 : 0|6@1- (10,0) [0|0] "" CCL_TEST
+ SG_ muxSig9 : 40|8@1- (100,-5) [0|0] "V" CCL_TEST
+
+ BO_ 3 testFrameFloat: 8 TEST_ECU
+ SG_ floatSignal2 : 32|32@1- (1,0) [0|0] "" CCL_TEST
+ SG_ floatSignal1 : 7|32@0- (1,0) [0|0] "" CCL_TEST
+
+Scapy implementation of this DBC description::
+
+ class muxTestFrame(SignalPacket):
+ fields_desc = [
+ LEUnsignedSignalField("myMuxer", default=0, start=53, size=3),
+ ConditionalField(LESignedSignalField("muxSig4", default=0, start=25, size=7), lambda p: p.myMuxer == 0),
+ ConditionalField(LEUnsignedSignalField("muxSig3", default=0, start=16, size=9), lambda p: p.myMuxer == 0),
+ ConditionalField(BESignedSignalField("muxSig2", default=0, start=15, size=8), lambda p: p.myMuxer == 0),
+ ConditionalField(LESignedSignalField("muxSig1", default=0, start=0, size=8), lambda p: p.myMuxer == 0),
+ ConditionalField(LESignedSignalField("muxSig5", default=0, start=22, size=7, scaling=0.01), lambda p: p.myMuxer == 1),
+ ConditionalField(LEUnsignedSignalField("muxSig6", default=0, start=32, size=9, scaling=2, offset=10, unit="mV"), lambda p: p.myMuxer == 1),
+ ConditionalField(BESignedSignalField("muxSig7", default=0, start=2, size=8, scaling=0.5), lambda p: p.myMuxer == 1),
+ ConditionalField(LESignedSignalField("muxSig8", default=0, start=3, size=3, scaling=10), lambda p: p.myMuxer == 1),
+ LESignedSignalField("muxSig9", default=0, start=41, size=7, scaling=100, offset=-5, unit="V"),
+ ]
+
+ class testFrameFloat(SignalPacket):
+ fields_desc = [
+ LEFloatSignalField("floatSignal2", default=0, start=32),
+ BEFloatSignalField("floatSignal1", default=0, start=7)
+ ]
+
+ bind_layers(SignalHeader, muxTestFrame, identifier=0x123)
+ bind_layers(SignalHeader, testFrameFloat, identifier=0x321)
+
+ dbc_sock = CANSocket("can0", basecls=SignalHeader)
+
+ pkt = SignalHeader()/testFrameFloat(floatSignal2=3.4)
+
+ dbc_sock.send(pkt)
+
+This example uses the class ``SignalHeader`` as header. The payload is specified by individual ``SignalPackets``.
+``bind_layers`` combines the header with the payload dependent on the CAN identifier.
+If you want to directly receive ``SignalPackets`` from your ``CANSocket``, provide the parameter ``basecls`` to
+the ``init`` function of your ``CANSocket``.
+
+Canmatrix supports the creation of Scapy files from DBC or AUTOSAR XML files https://github.com/ebroecker/canmatrix
+
+
+CANSockets
+==========
+
+Linux SocketCAN
+---------------
+
+This subsection summarizes some basics about Linux SocketCAN. An excellent overview
+from Oliver Hartkopp can be found here: https://wiki.automotivelinux.org/_media/agl-distro/agl2017-socketcan-print.pdf
+
+Virtual CAN Setup
+^^^^^^^^^^^^^^^^^
+
+Linux SocketCAN supports virtual CAN interfaces. These interfaces are an easy way
+to do some first steps on a CAN-Bus without the requirement of special hardware.
+Besides that, virtual CAN interfaces are heavily used in Scapy unit tests for
+automotive-related contributions.
+
+Virtual CAN sockets require a special Linux kernel module. The following shell command loads the required module::
+
+ sudo modprobe vcan
+
+In order to use a virtual CAN interface some additional commands for setup are required.
+This snippet chooses the name ``vcan0`` for the virtual CAN interface. Any name can be chosen here::
+
+ sudo ip link add name vcan0 type vcan
+ sudo ip link set dev vcan0 up
+
+The same commands can be executed from Scapy like this::
+
+ from scapy.layers.can import *
+ import os
+
+ bashCommand = "/bin/bash -c 'sudo modprobe vcan; sudo ip link add name vcan0 type vcan; sudo ip link set dev vcan0 up'"
+ os.system(bashCommand)
+
+If it's required, a CAN interface can be set into a ``listen-only`` or ``loopback`` mode with ``ip link set`` commands::
+
+ ip link set vcan0 type can help # shows additional information
+
+
+Linux can-utils
^^^^^^^^^^^^^^^
+As part of Linux SocketCAN, some very useful command line tools are provided from
+Oliver Hartkopp: https://github.com/linux-can/can-utils
+
+The following example shows the basic functions of Linux can-utils. These utilities
+are very handy for quick checks, dumping, sending, or logging of CAN messages
+from the command line.
+
+.. image:: ../graphics/animations/animation-cansend.svg
+
+Scapy CANSocket
+---------------
+
In Scapy, two kind of CANSockets are implemented. One implementation is called **Native CANSocket**,
the other implementation is called **Python-can CANSocket**.
@@ -338,71 +591,6 @@ Close the sockets::
.. image:: ../graphics/animations/animation-scapy-cansockets-mitm.svg
.. image:: ../graphics/animations/animation-scapy-cansockets-mitm2.svg
-DBC File Format and CAN Signals
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-In order to support the DBC file format, ``SignalFields`` and the ``SignalPacket``
-classes were added to Scapy. ``SignalFields`` should only be used inside a ``SignalPacket``.
-Multiplexer fields (MUX) can be created through ``ConditionalFields``. The following
-example demonstrates the usage::
-
- DBC Example:
-
- BO_ 4 muxTestFrame: 7 TEST_ECU
- SG_ myMuxer M : 53|3@1+ (1,0) [0|0] "" CCL_TEST
- SG_ muxSig4 m0 : 25|7@1- (1,0) [0|0] "" CCL_TEST
- SG_ muxSig3 m0 : 16|9@1+ (1,0) [0|0] "" CCL_TEST
- SG_ muxSig2 m0 : 15|8@0- (1,0) [0|0] "" CCL_TEST
- SG_ muxSig1 m0 : 0|8@1- (1,0) [0|0] "" CCL_TEST
- SG_ muxSig5 m1 : 22|7@1- (0.01,0) [0|0] "" CCL_TEST
- SG_ muxSig6 m1 : 32|9@1+ (2,10) [0|0] "mV" CCL_TEST
- SG_ muxSig7 m1 : 2|8@0- (0.5,0) [0|0] "" CCL_TEST
- SG_ muxSig8 m1 : 0|6@1- (10,0) [0|0] "" CCL_TEST
- SG_ muxSig9 : 40|8@1- (100,-5) [0|0] "V" CCL_TEST
-
- BO_ 3 testFrameFloat: 8 TEST_ECU
- SG_ floatSignal2 : 32|32@1- (1,0) [0|0] "" CCL_TEST
- SG_ floatSignal1 : 7|32@0- (1,0) [0|0] "" CCL_TEST
-
-Scapy implementation of this DBC description::
-
- class muxTestFrame(SignalPacket):
- fields_desc = [
- LEUnsignedSignalField("myMuxer", default=0, start=53, size=3),
- ConditionalField(LESignedSignalField("muxSig4", default=0, start=25, size=7), lambda p: p.myMuxer == 0),
- ConditionalField(LEUnsignedSignalField("muxSig3", default=0, start=16, size=9), lambda p: p.myMuxer == 0),
- ConditionalField(BESignedSignalField("muxSig2", default=0, start=15, size=8), lambda p: p.myMuxer == 0),
- ConditionalField(LESignedSignalField("muxSig1", default=0, start=0, size=8), lambda p: p.myMuxer == 0),
- ConditionalField(LESignedSignalField("muxSig5", default=0, start=22, size=7, scaling=0.01), lambda p: p.myMuxer == 1),
- ConditionalField(LEUnsignedSignalField("muxSig6", default=0, start=32, size=9, scaling=2, offset=10, unit="mV"), lambda p: p.myMuxer == 1),
- ConditionalField(BESignedSignalField("muxSig7", default=0, start=2, size=8, scaling=0.5), lambda p: p.myMuxer == 1),
- ConditionalField(LESignedSignalField("muxSig8", default=0, start=3, size=3, scaling=10), lambda p: p.myMuxer == 1),
- LESignedSignalField("muxSig9", default=0, start=41, size=7, scaling=100, offset=-5, unit="V"),
- ]
-
- class testFrameFloat(SignalPacket):
- fields_desc = [
- LEFloatSignalField("floatSignal2", default=0, start=32),
- BEFloatSignalField("floatSignal1", default=0, start=7)
- ]
-
- bind_layers(SignalHeader, muxTestFrame, identifier=0x123)
- bind_layers(SignalHeader, testFrameFloat, identifier=0x321)
-
- dbc_sock = CANSocket("can0", basecls=SignalHeader)
-
- pkt = SignalHeader()/testFrameFloat(floatSignal2=3.4)
-
- dbc_sock.send(pkt)
-
-This example uses the class ``SignalHeader`` as header. The payload is specified by individual ``SignalPackets``.
-``bind_layers`` combines the header with the payload dependent on the CAN identifier.
-If you want to directly receive ``SignalPackets`` from your ``CANSocket``, provide the parameter ``basecls`` to
-the ``init`` function of your ``CANSocket``.
-
-Canmatrix supports the creation of Scapy files from DBC or AUTOSAR XML files https://github.com/ebroecker/canmatrix
-
-
CAN Calibration Protocol (CCP)
==============================
@@ -479,41 +667,130 @@ If we are interested in the response of an Ecu, we need to set the basecls param
CANSocket to XCPonCAN and we need to use sr1:
Sending a CTO message::
- sock = CANSocket(bustype='socketcan', channel='vcan0', basecls=XCPonCAN)
- dto = sock.sr1(pkt)
+ sock = CANSocket(bustype='socketcan', channel='vcan0', basecls=XCPonCAN)
+ dto = sock.sr1(pkt)
+
+Since sr1 calls the answers function, our payload of the XCP-response objects gets interpreted with the
+command of our CTO object. Otherwise it could not be interpreted.
+The first message should always be the "CONNECT" message, the response of the Ecu determines how the messages are read. E.g.: byte order.
+Otherwise, one must set the address granularity, and max size of the DTOs and CTOs per hand in the contrib config::
+
+ conf.contribs['XCP']['Address_Granularity_Byte'] = 1 # Can be 1, 2 or 4
+ conf.contribs['XCP']['MAX_CTO'] = 8
+ conf.contribs['XCP']['MAX_DTO'] = 8
+
+If you do not want this to be set after receiving the message you can also disable that feature::
+
+ conf.contribs['XCP']['allow_byte_order_change'] = False
+ conf.contribs['XCP']['allow_ag_change'] = False
+ conf.contribs['XCP']['allow_cto_and_dto_change'] = False
+
+To send a pkt over TCP or UDP another header must be used.
+TCP::
+
+ prt1, prt2 = 12345, 54321
+ XCPOnTCP(sport=prt1, dport=prt2) / CTORequest() / Connect()
+
+UDP::
+
+ XCPOnUDP(sport=prt1, dport=prt2) / CTORequest() / Connect()
+
+
+XCPScanner
+---------------
+
+The XCPScanner is a utility to find the CAN identifiers of ECUs that support XCP.
+
+Commandline usage example::
+
+ python -m scapy.tools.automotive.xcpscanner -h
+ Finds XCP slaves using the "GetSlaveId"-message(Broadcast) or the "Connect"-message.
+
+ positional arguments:
+ channel Linux SocketCAN interface name, e.g.: vcan0
+
+ optional arguments:
+ -h, --help show this help message and exit
+ --start START, -s START
+ Start identifier CAN (in hex).
+ The scan will test ids between --start and --end (inclusive)
+ Default: 0x00
+ --end END, -e END End identifier CAN (in hex).
+ The scan will test ids between --start and --end (inclusive)
+ Default: 0x7ff
+ --sniff_time', '-t' Duration in milliseconds a sniff is waiting for a response.
+ Default: 100
+ --broadcast, -b Use Broadcast-message GetSlaveId instead of default "Connect"
+ (GetSlaveId is an optional Message that is not always implemented)
+ --verbose VERBOSE, -v
+ Display information during scan
+
+ Examples:
+ python3.6 -m scapy.tools.automotive.xcpscanner can0
+ python3.6 -m scapy.tools.automotive.xcpscanner can0 -b 500
+ python3.6 -m scapy.tools.automotive.xcpscanner can0 -s 50 -e 100
+ python3.6 -m scapy.tools.automotive.xcpscanner can0 -b 500 -v
+
+
+Interactive shell usage example::
+ >>> conf.contribs['CANSocket'] = {'use-python-can': False}
+ >>> load_layer("can")
+ >>> load_contrib("automotive.xcp.xcp")
+ >>> sock = CANSocket("vcan0")
+ >>> sock.basecls = XCPOnCAN
+ >>> scanner = XCPOnCANScanner(sock)
+ >>> result = scanner.start_scan()
+
+The result includes the slave_id (the identifier of the Ecu that receives XCP messages),
+and the response_id (the identifier that the Ecu will send XCP messages to).
+
+ISOTP
+=====
+
+ISOTP message
+-------------
+
+Creating an ISOTP message::
+
+ load_contrib('isotp')
+ ISOTP(src=0x241, dst=0x641, data=b"\x3eabc")
+
+Creating an ISOTP message with extended addressing::
+
+ ISOTP(src=0x241, dst=0x641, exdst=0x41, data=b"\x3eabc")
-Since sr1 calls the answers function, our payload of the XCP-response objects gets interpreted with the
-command of our CTO object. Otherwise it could not be interpreted.
-The first message should always be the "CONNECT" message, the response of the Ecu determines how the messages are read. E.g.: byte order.
-Otherwise, one must set the address granularity, and max size of the DTOs and CTOs per hand in the contrib config::
+Creating an ISOTP message with extended addressing::
- conf.contribs['XCP']['Address_Granularity_Byte'] = 1 # Can be 1, 2 or 4
- conf.contribs['XCP']['MAX_CTO'] = 8
- conf.contribs['XCP']['MAX_DTO'] = 8
+ ISOTP(src=0x241, dst=0x641, exdst=0x41, exsrc=0x41, data=b"\x3eabc")
-If you do not want this to be set after receiving the message you can also disable that feature::
+Create CAN-frames from an ISOTP message::
- conf.contribs['XCP']['allow_byte_order_change'] = False
- conf.contribs['XCP']['allow_ag_change'] = False
- conf.contribs['XCP']['allow_cto_and_dto_change'] = False
+ ISOTP(src=0x241, dst=0x641, exdst=0x41, exsrc=0x55, data=b"\x3eabc" * 10).fragment()
-To send a pkt over TCP or UDP another header must be used.
-TCP::
+Send ISOTP message over ISOTP socket::
- prt1, prt2 = 12345, 54321
- XCPOnTCP(sport=prt1, dport=prt2) / CTORequest() / Connect()
+ isoTpSocket = ISOTPSocket('vcan0', sid=0x241, did=0x641)
+ isoTpMessage = ISOTP('Message')
+ isoTpSocket.send(isoTpMessage)
-UDP::
+Sniff ISOTP message::
- XCPOnUDP(sport=prt1, dport=prt2) / CTORequest() / Connect()
+ isoTpSocket = ISOTPSocket('vcan0', sid=0x641, did=0x241)
+ packets = isoTpSocket.sniff(timeout=0.5)
+ISOTP Sockets
+-------------
+Scapy provides two kinds of ISOTP-Sockets. One implementation, the ``ISOTPNativeSocket``
+is using the Linux kernel module from Hartkopp. The other implementation, the ``ISOTPSoftSocket``
+is completely implemented in Python. This implementation can be used on Linux,
+Windows, and OSX.
-ISOTP
-=====
+An ``ISOTPSocket`` will not respect ``src, dst, exdst, exsrc`` of an ``ISOTP``
+message object.
System compatibilities
-----------------------
+^^^^^^^^^^^^^^^^^^^^^^
Dependent on your setup, different implementations have to be used.
@@ -535,43 +812,58 @@ The decision is made dependent on the configuration ``conf.contribs['ISOTP'] = {
This will allow you to write platform independent code. Apply this configuration before loading the ISOTP layer
with ``load_contrib('isotp')``.
-Another remark in respect to ISOTPSocket compatibility. Always use with for socket creation. Example::
+Another remark in respect to ISOTPSocket compatibility. Always use ``with`` for
+socket creation. This ensures that ``ISOTPSoftSocket`` objects will get closed
+properly.
+Example::
with ISOTPSocket("vcan0", did=0x241, sid=0x641) as sock:
sock.send(...)
+ISOTPNativeSocket
+^^^^^^^^^^^^^^^^^
+**Requires:**
-ISOTP message
--------------
+* Python3
+* Linux
+* Hartkopp's Linux kernel module: ``https://github.com/hartkopp/can-isotp.git`` (merged into mainline Linux in 5.10)
-Creating an ISOTP message::
+During pentests, the ISOTPNativeSockets has a better performance and
+reliability, usually. If you are working on Linux, consider this implementation::
+ conf.contribs['ISOTP'] = {'use-can-isotp-kernel-module': True}
load_contrib('isotp')
- ISOTP(src=0x241, dst=0x641, data=b"\x3eabc")
-
-Creating an ISOTP message with extended addressing::
-
- ISOTP(src=0x241, dst=0x641, exdst=0x41, data=b"\x3eabc")
+ sock = ISOTPSocket("can0", sid=0x641, did=0x241)
-Creating an ISOTP message with extended addressing::
+Since this implementation is using a standard Linux socket, all Scapy functions
+like ``sniff, sr, sr1, bridge_and_sniff`` work out of the box.
- ISOTP(src=0x241, dst=0x641, exdst=0x41, exsrc=0x41, data=b"\x3eabc")
+ISOTPSoftSocket
+^^^^^^^^^^^^^^^
-Create CAN-frames from an ISOTP message::
+ISOTPSoftSockets can use any CANSocket. This gives the flexibility to use all
+python-can interfaces. Additionally, these sockets work on Python2 and Python3.
+Usage on Linux with native CANSockets::
- ISOTP(src=0x241, dst=0x641, exdst=0x41, exsrc=0x55, data=b"\x3eabc" * 10).fragment()
+ conf.contribs['ISOTP'] = {'use-can-isotp-kernel-module': False}
+ load_contrib('isotp')
+ with ISOTPSocket("can0", sid=0x641, did=0x241) as sock:
+ sock.send(...)
-Send ISOTP message over ISOTP socket::
+Usage with python-can CANSockets::
- isoTpSocket = ISOTPSocket('vcan0', sid=0x241, did=0x641)
- isoTpMessage = ISOTP('Message')
- isoTpSocket.send(isoTpMessage)
+ conf.contribs['ISOTP'] = {'use-can-isotp-kernel-module': False}
+ conf.contribs['CANSocket'] = {'use-python-can': True}
+ load_contrib('isotp')
+ with ISOTPSocket(CANSocket(bustype='socketcan', channel="can0"), sid=0x641, did=0x241) as sock:
+ sock.send(...)
-Sniff ISOTP message::
+This second example allows the usage of any ``python_can.interface`` object.
- isoTpSocket = ISOTPSocket('vcan0', sid=0x641, did=0x241)
- packets = isoTpSocket.sniff(timeout=0.5)
+**Attention:** The internal implementation of ISOTPSoftSockets requires a background
+thread. In order to be able to close this thread properly, we suggest the use of
+Pythons ``with`` statement.
ISOTP MITM attack with bridge and sniff
---------------------------------------
@@ -584,15 +876,6 @@ Set up two vcans on Linux terminal::
sudo ip link set dev vcan0 up
sudo ip link set dev vcan1 up
-Set up ISOTP:
-
-First make sure you installed an iso-tp kernel module.
-
-When the vcan core module is loaded with "sudo modprobe vcan" the iso-tp module can be loaded to the kernel.
-
-Therefore navigate to isotp directory, and load module with "sudo insmod ./net/can/can-isotp.ko". (Tested on Kernel 4.9.135-1-MANJARO)
-
-Detailed instructions you find in https://github.com/hartkopp/can-isotp.
Import modules::
@@ -632,7 +915,7 @@ Create threads for sending packet and to bridge and sniff::
threadBridge = threading.Thread(target=bridge)
threadSender = threading.Thread(target=sendPacketWithISOTPSocket)
-Start threads are based on Linux kernel modules. The python-can project is used to support CAN and CANSockets on other systems, besides Linux. This guide explains the hardware setup on a BeagleBone Black. The BeagleBone Black was chosen because of its two CAN interfaces on the main processor. The presence of two CAN interfaces in one device gives the possibility of CAN MITM attacks and session hijacking. The Cannelloni framework turns a BeagleBone Black into a CAN-to-UDP interface, which gives you the freedom to run Scapy on a more powerful machine.::
+Start threads::
threadBridge.start()
threadSender.start()
@@ -646,61 +929,6 @@ Close sockets::
isoTpSocketVCan0.close()
isoTpSocketVCan1.close()
-An ISOTPSocket will not respect ``src, dst, exdst, exsrc`` of an ISOTP message object.
-
-ISOTP Sockets
-=============
-
-Scapy provides two kinds of ISOTP Sockets. One implementation, the ISOTPNativeSocket
-is using the Linux kernel module from Hartkopp. The other implementation, the ISOTPSoftSocket
-is completely implemented in Python. This implementation can be used on Linux,
-Windows, and OSX.
-
-ISOTPNativeSocket
------------------
-
-**Requires:**
-
-* Python3
-* Linux
-* Hartkopp's Linux kernel module: ``https://github.com/hartkopp/can-isotp.git``
-
-During pentests, the ISOTPNativeSockets has a better performance and
-reliability, usually. If you are working on Linux, consider this implementation::
-
- conf.contribs['ISOTP'] = {'use-can-isotp-kernel-module': True}
- load_contrib('isotp')
- sock = ISOTPSocket("can0", sid=0x641, did=0x241)
-
-Since this implementation is using a standard Linux socket, all Scapy functions
-like ``sniff, sr, sr1, bridge_and_sniff`` work out of the box.
-
-ISOTPSoftSocket
----------------
-
-ISOTPSoftSockets can use any CANSocket. This gives the flexibility to use all
-python-can interfaces. Additionally, these sockets work on Python2 and Python3.
-Usage on Linux with native CANSockets::
-
- conf.contribs['ISOTP'] = {'use-can-isotp-kernel-module': False}
- load_contrib('isotp')
- with ISOTPSocket("can0", sid=0x641, did=0x241) as sock:
- sock.send(...)
-
-Usage with python-can CANSockets::
-
- conf.contribs['ISOTP'] = {'use-can-isotp-kernel-module': False}
- conf.contribs['CANSocket'] = {'use-python-can': True}
- load_contrib('isotp')
- with ISOTPSocket(CANSocket(bustype='socketcan', channel="can0"), sid=0x641, did=0x241) as sock:
- sock.send(...)
-
-This second example allows the usage of any ``python_can.interface`` object.
-
-**Attention:** The internal implementation of ISOTPSoftSockets requires a background
-thread. In order to be able to close this thread properly, we suggest the use of
-Pythons ``with`` statement.
-
ISOTPScan and ISOTPScanner
--------------------------
@@ -770,56 +998,6 @@ Interactive shell usage example::
<<ISOTPNativeSocket: read/write packets at a given CAN interface using CAN_ISOTP socket > at 0x7f98f912e950>,
<<ISOTPNativeSocket: read/write packets at a given CAN interface using CAN_ISOTP socket > at 0x7f98f906c0d0>]
-XCPScanner
----------------
-
-The XCPScanner is a utility to find the CAN identifiers of ECUs that support XCP.
-
-Commandline usage example::
-
- python -m scapy.tools.automotive.xcpscanner -h
- Finds XCP slaves using the "GetSlaveId"-message(Broadcast) or the "Connect"-message.
-
- positional arguments:
- channel Linux SocketCAN interface name, e.g.: vcan0
-
- optional arguments:
- -h, --help show this help message and exit
- --start START, -s START
- Start identifier CAN (in hex).
- The scan will test ids between --start and --end (inclusive)
- Default: 0x00
- --end END, -e END End identifier CAN (in hex).
- The scan will test ids between --start and --end (inclusive)
- Default: 0x7ff
- --sniff_time', '-t' Duration in milliseconds a sniff is waiting for a response.
- Default: 100
- --broadcast, -b Use Broadcast-message GetSlaveId instead of default "Connect"
- (GetSlaveId is an optional Message that is not always implemented)
- --verbose VERBOSE, -v
- Display information during scan
-
- Examples:
- python3.6 -m scapy.tools.automotive.xcpscanner can0
- python3.6 -m scapy.tools.automotive.xcpscanner can0 -b 500
- python3.6 -m scapy.tools.automotive.xcpscanner can0 -s 50 -e 100
- python3.6 -m scapy.tools.automotive.xcpscanner can0 -b 500 -v
-
-
-Interactive shell usage example::
- >>> conf.contribs['CANSocket'] = {'use-python-can': False}
- >>> load_layer("can")
- >>> load_contrib("automotive.xcp.xcp")
- >>> sock = CANSocket("vcan0")
- >>> sock.basecls = XCPOnCAN
- >>> scanner = XCPOnCANScanner(sock)
- >>> result = scanner.start_scan()
-
-The result includes the slave_id (the identifier of the Ecu that receives XCP messages),
-and the response_id (the identifier that the Ecu will send XCP messages to).
-
-
-
UDS
===
@@ -878,7 +1056,8 @@ Customization example::
UDS_RDBI.dataIdentifiers[0x172b] = 'GatewayIP'
-If one wants to work with this custom additions, these can be loaded at runtime to the Scapy interpreter::
+If one wants to work with this custom additions, these can be loaded at runtime
+to the Scapy interpreter::
>>> load_contrib('automotive.uds')
>>> load_contrib('automotive.OEM-XYZ.car-model-xyz')
@@ -904,6 +1083,7 @@ If one wants to work with this custom additions, these can be loaded at runtime
GMLAN
=====
+
GMLAN is very similar to UDS. It's GMs application layer protocol for
flashing, calibration and diagnostic of their cars.
Use the argument ``basecls=GMLAN`` on the ``init`` function of an ISOTPSocket.
@@ -922,7 +1102,10 @@ This utility depends heavily on the support of the used protocol. ``UDS`` is sup
Log all commands applied to an Ecu
----------------------------------
-This example shows the logging mechanism of an Ecu object. The log of an Ecu is a dictionary of applied UDS commands. The key for this dictionary is the UDS service name. The value consists of a list of tuples, containing a timestamp and a log value
+This example shows the logging mechanism of an Ecu object. The log of an Ecu
+is a dictionary of applied UDS commands. The key for this dictionary is the
+UDS service name. The value consists of a list of tuples, containing a timestamp
+and a log value
Usage example::
@@ -936,7 +1119,9 @@ Usage example::
Trace all commands applied to an Ecu
------------------------------------
-This example shows the trace mechanism of an Ecu object. Traces of the current state of the Ecu object and the received message are printed on stdout. Some messages, depending on the protocol, will change the internal state of the Ecu.
+This example shows the trace mechanism of an Ecu object. Traces of the current
+state of the Ecu object and the received message are printed on stdout.
+Some messages, depending on the protocol, will change the internal state of the Ecu.
Usage example::
@@ -965,7 +1150,10 @@ Usage example::
Analyze multiple UDS messages
-----------------------------
-This example shows how to load ``UDS`` messages from a ``.pcap`` file containing ``CAN`` messages. A ``PcapReader`` object is used as socket and an ``ISOTPSession`` parses ``CAN`` frames to ``ISOTP`` frames which are then casted to ``UDS`` objects through the ``basecls`` parameter
+This example shows how to load ``UDS`` messages from a ``.pcap`` file containing
+``CAN`` messages. A ``PcapReader`` object is used as socket and an
+``ISOTPSession`` parses ``CAN`` frames to ``ISOTP`` frames which are
+then casted to ``UDS`` objects through the ``basecls`` parameter
Usage example::
@@ -984,7 +1172,11 @@ Usage example::
Analyze on the fly with EcuSession
----------------------------------
-This example shows the usage of an EcuSession in sniff. An ISOTPSocket or any socket like object which returns entire messages of the right protocol can be used. An ``EcuSession`` is used as supersession in an ``ISOTPSession``. To obtain the ``Ecu`` object from an ``EcuSession``, the ``EcuSession`` has to be created outside of sniff.
+This example shows the usage of an EcuSession in sniff. An ISOTPSocket or any
+socket like object which returns entire messages of the right protocol can be
+used. An ``EcuSession`` is used as supersession in an ``ISOTPSession``.
+To obtain the ``Ecu`` object from an ``EcuSession``, the ``EcuSession``
+has to be created outside of sniff.
Usage example::
@@ -1005,7 +1197,10 @@ SOME/IP and SOME/IP SD messages
Creating a SOME/IP message
--------------------------
-This example shows a SOME/IP message which requests a service 0x1234 with the method 0x421. Different types of SOME/IP messages follow the same procedure and their specifications can be seen here ``http://www.some-ip.com/papers/cache/AUTOSAR_TR_SomeIpExample_4.2.1.pdf``.
+This example shows a SOME/IP message which requests a service 0x1234 with the
+method 0x421. Different types of SOME/IP messages follow the same procedure
+and their specifications can be seen here
+``http://www.some-ip.com/papers/cache/AUTOSAR_TR_SomeIpExample_4.2.1.pdf``.
Load the contribution::
@@ -1095,9 +1290,6 @@ Stack it and send it::
OBD
===
-OBD message
------------
-
OBD is implemented on top of ISOTP. Use an ISOTPSocket for the communication with an Ecu.
You should set the parameters ``basecls=OBD`` and ``padding=True`` in your ISOTPSocket init call.
@@ -1120,8 +1312,9 @@ The response will contain a PacketListField, called `data_records`. This field c
|###[ PID_00_PIDsSupported ]###
| supported_pids= PID20+PID1F+PID1C+PID15+PID14+PID13+PID11+PID10+PID0F+PID0E+PID0D+PID0C+PID0B+PID0A+PID07+PID06+PID05+PID04+PID03+PID01
+
Let's assume our Ecu under test supports the pid 0x15::
-
+
req = OBD()/OBD_S01(pid=[0x15])
resp = sock.sr1(req)
resp.show()
@@ -1146,7 +1339,7 @@ Service 08 supports Test Identifiers (tid).
Service 09 supports Information Identifiers (iid).
Examples:
-^^^^^^^^^
+---------
Request supported Information Identifiers::
@@ -1174,234 +1367,6 @@ Request the Vehicle Identification Number (VIN)::
Test-Setup Tutorials
====================
-Hardware Setup
---------------
-
-Beagle Bone Black Operating System Setup
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-#. | **Download an Image**
- | The latest Debian Linux image can be found at the website
- | ``https://beagleboard.org/latest-images``. Choose the BeagleBone
- Black IoT version and download it.
-
- ::
-
- wget https://debian.beagleboard.org/images/bone-debian-8.7\
- -iot-armhf-2017-03-19-4gb.img.xz
-
-
- After the download, copy it to an SD-Card with minimum of 4 GB storage.
-
- ::
-
- xzcat bone-debian-8.7-iot-armhf-2017-03-19-4gb.img.xz | \
- sudo dd of=/dev/xvdj
-
-
-#. | **Enable WiFi**
- | USB-WiFi dongles are well supported by Debian Linux. Login over SSH
- on the BBB and add the WiFi network credentials to the file
- ``/var/lib/connman/wifi.config``. If a USB-WiFi dongle is not
- available, it is also possible to share the host's internet
- connection with the Ethernet connection of the BBB emulated over
- USB. A tutorial to share the host network connection can be found
- on this page:
- | ``https://elementztechblog.wordpress.com/2014/12/22/sharing-internet -using-network-over-usb-in-beaglebone-black/``.
- | Login as root onto the BBB:
-
- ::
-
- ssh [email protected]
- sudo su
-
-
- Provide the WiFi login credentials to connman:
-
- ::
-
- echo "[service_home]
- Type = wifi
- Name = ssid
- Security = wpa
- Passphrase = xxxxxxxxxxxxx" \
- > /var/lib/connman/wifi.config
-
-
- Restart the connman service:
-
- ::
-
- systemctl restart connman.service
-
-
-Dual-CAN Setup
-^^^^^^^^^^^^^^
-
-#. | **Device tree setup**
- | You'll need to follow this section only if you want to use two CAN
- interfaces (DCAN0 and DCAN1). This will disable I2C2 from using pins
- P9.19 and P9.20, which are needed by DCAN0. You only need to perform the
- steps in this section once.
-
- | Warning: The configuration in this section will disable BBB capes from
- working. Each cape has a small I2C EEPROM that stores info that the BBB
- needs to know in order to communicate with the cape. Disable I2C2, and
- the BBB has no way to talk to cape EEPROMs. Of course, if you don't use
- capes then this is not a problem.
-
- | Acquire DTS sources that matches your kernel version. Go
- `here <https://github.com/beagleboard/linux/>`__ and switch over to the
- branch that represents your kernel version. Download the entire branch
- as a ZIP file. Extract it and do the following (version 4.1 shown as an
- example):
-
- ::
-
- # cd ~/src/linux-4.1/arch/arm/boot/dts/include/
- # rm dt-bindings
- # ln -s ../../../../../include/dt-bindings
- # cd ..
- Edit am335x-bone-common.dtsi and ensure the line with "//pinctrl-0 = <&i2c2_pins>;" is commented out.
- Remove the complete &ocp section at the end of this file
- # mv am335x-boneblack.dts am335x-boneblack.raw.dts
- # cpp -nostdinc -I include -undef -x assembler-with-cpp am335x-boneblack.raw.dts > am335x-boneblack.dts
- # dtc -W no-unit_address_vs_reg -O dtb -o am335x-boneblack.dtb -b 0 -@ am335x-boneblack.dts
- # cp /boot/dtbs/am335x-boneblack.dtb /boot/dtbs/am335x-boneblack.orig.dtb
- # cp am335x-boneblack.dtb /boot/dtbs/
- Reboot
-
-#. **Overlay setup**
- | This section describes how to build the device overlays for the two CAN devices (DCAN0 and DCAN1). You only need to perform the steps in this section once.
- | Acquire BBB cape overlays, in one of two ways…
-
- ::
-
- # apt-get install bb-cape-overlays
- https://github.com/beagleboard/bb.org-overlays/
-
- | Then do the following:
-
-
- ::
-
- # cd ~/src/bb.org-overlays-master/src/arm
- # ln -s ../../include
- # mv BB-CAN1-00A0.dts BB-CAN1-00A0.raw.dts
- # cp BB-CAN1-00A0.raw.dts BB-CAN0-00A0.raw.dts
- Edit BB-CAN0-00A0.raw.dts and make relevant to CAN0. Example is shown below.
- # cpp -nostdinc -I include -undef -x assembler-with-cpp BB-CAN0-00A0.raw.dts > BB-CAN0-00A0.dts
- # cpp -nostdinc -I include -undef -x assembler-with-cpp BB-CAN1-00A0.raw.dts > BB-CAN1-00A0.dts
- # dtc -W no-unit_address_vs_reg -O dtb -o BB-CAN0-00A0.dtbo -b 0 -@ BB-CAN0-00A0.dts
- # dtc -W no-unit_address_vs_reg -O dtb -o BB-CAN1-00A0.dtbo -b 0 -@ BB-CAN1-00A0.dts
- # cp *.dtbo /lib/firmware
-
-
-#. | **CAN0 Example Overlay**
- | Inside the DTS folder, create a file with the content of the
- following listing.
-
- ::
-
- cd ~/bb.org-overlays/src/arm
- cat <<EOF > BB-CAN0-00A0.raw.dts
-
- /*
- * Copyright (C) 2015 Robert Nelson <[email protected]>
- *
- * Virtual cape for CAN0 on connector pins P9.19 P9.20
- *
- * This program is free software; you can redistribute it and/or modify
- * it under the terms of the GNU General Public License version 2 as
- * published by the Free Software Foundation.
- */
- /dts-v1/;
- /plugin/;
-
- #include <dt-bindings/board/am335x-bbw-bbb-base.h>
- #include <dt-bindings/pinctrl/am33xx.h>
-
- / {
- compatible = "ti,beaglebone", "ti,beaglebone-black", "ti,beaglebone-green";
-
- /* identification */
- part-number = "BB-CAN0";
- version = "00A0";
-
- /* state the resources this cape uses */
- exclusive-use =
- /* the pin header uses */
- "P9.19", /* can0_rx */
- "P9.20", /* can0_tx */
- /* the hardware ip uses */
- "dcan0";
-
- fragment@0 {
- target = <&am33xx_pinmux>;
- __overlay__ {
- bb_dcan0_pins: pinmux_dcan0_pins {
- pinctrl-single,pins = <
- BONE_P9_19 (PIN_INPUT_PULLUP | MUX_MODE2) /* uart1_txd.d_can0_rx */
- BONE_P9_20 (PIN_OUTPUT_PULLUP | MUX_MODE2) /* uart1_rxd.d_can0_tx */
- >;
- };
- };
- };
-
- fragment@1 {
- target = <&dcan0>;
- __overlay__ {
- status = "okay";
- pinctrl-names = "default";
- pinctrl-0 = <&bb_dcan0_pins>;
- };
- };
- };
- EOF
-
-
-#. | **Test the Dual-CAN Setup**
- | Do the following each time you need CAN, or automate these steps if you like.
-
- ::
-
- # echo BB-CAN0 > /sys/devices/platform/bone_capemgr/slots
- # echo BB-CAN1 > /sys/devices/platform/bone_capemgr/slots
- # modprobe can
- # modprobe can-dev
- # modprobe can-raw
- # ip link set can0 up type can bitrate 50000
- # ip link set can1 up type can bitrate 50000
-
- Check the output of the Capemanager if both CAN interfaces have been
- loaded.
-
- ::
-
- cat /sys/devices/platform/bone_capemgr/slots
-
- 0: PF---- -1
- 1: PF---- -1
- 2: PF---- -1
- 3: PF---- -1
- 4: P-O-L- 0 Override Board Name,00A0,Override Manuf, BB-CAN0
- 5: P-O-L- 1 Override Board Name,00A0,Override Manuf, BB-CAN1
-
-
- If something went wrong, ``dmesg`` provides kernel messages to analyse the root of failure.
-
-#. | **References**
-
- - `embedded-things.com: Enable CANbus on the Beaglebone
- Black <http://www.embedded-things.com/bbb/enable-canbus-on-the-beaglebone-black/>`__
- - `electronics.stackexchange.com: Beaglebone Black CAN bus
- Setup <https://electronics.stackexchange.com/questions/195416/beaglebone-black-can-bus-setup>`__
-
-#. | **Acknowledgment**
- | Thanks to Tom Haramori. Parts of this section are copied from his guide: https://github.com/haramori/rhme3/blob/master/Preparation/BBB_CAN_setup.md
-
-
-
ISO-TP Kernel Module Installation
---------------------------------
@@ -1410,16 +1375,16 @@ A Linux ISO-TP kernel module can be downloaded from this website:
``README.isotp`` in this repository provides all information and
necessary steps for downloading and building this kernel module. The
ISO-TP kernel module should also be added to the ``/etc/modules`` file,
-to load this module automatically at system boot of the BBB.
+to load this module automatically at system boot.
CAN-Interface Setup
-------------------
-As the final step to prepare the BBB's CAN interfaces for usage, these
+As the final step to prepare CAN interfaces for usage, these
interfaces have to be set up through some terminal commands. The bitrate
can be chosen to fit the bitrate of a CAN bus under test.
-::
+How-To::
ip link set can0 up type can bitrate 500000
ip link set can1 up type can bitrate 500000
@@ -1457,11 +1422,8 @@ To build a small test environment in which you can send SOME/IP messages to and
-Software Setup
---------------
-
-Cannelloni Framework Installation
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+Cannelloni Framework
+--------------------
The Cannelloni framework is a small application written in C++ to
transfer CAN data over UDP. In this way, a researcher can map the CAN
@@ -1473,7 +1435,7 @@ explains the installation and usage in detail. Cannelloni needs virtual
CAN interfaces on the operator's machine. The next listing shows the
setup of virtual CAN interfaces.
-::
+How-To::
modprobe vcan
|
Pyomo__pyomo-56 | XPRESS Solver Error : AttributeError: 'NoneType' object has no attribute 'problem'
I've pasted the traceback below :
```
Traceback (most recent call last):
File "model.py", line 15, in <module>
solver.solve(model)
File "/Users/$USER/anaconda/lib/python2.7/site-packages/pyomo/opt/base/solvers.py", line 587, in solve
result = self._postsolve()
File "/Users/$USER/anaconda/lib/python2.7/site-packages/pyomo/opt/solver/shellcmd.py", line 267, in _postsolve
results = self.process_output(self._rc)
File "/Users/$USER/anaconda/lib/python2.7/site-packages/pyomo/opt/solver/shellcmd.py", line 329, in process_output
self.process_soln_file(results)
File "/Users/$USER/anaconda/lib/python2.7/site-packages/pyomo/solvers/plugins/solvers/XPRESS.py", line 336, in process_soln_file
results.problem.number_of_objectives=1
AttributeError: 'NoneType' object has no attribute 'problem'
```
| [
{
"content": "# _________________________________________________________________________\n#\n# Pyomo: Python Optimization Modeling Objects\n# Copyright (c) 2014 Sandia Corporation.\n# Under the terms of Contract DE-AC04-94AL85000 with Sandia Corporation,\n# the U.S. Government retains certain rights in this software.\n# This software is distributed under the BSD License.\n# _________________________________________________________________________\n\n\nimport os\nimport re\nimport logging\n\nimport pyutilib.services\nimport pyutilib.common\nimport pyutilib.misc\n\nimport pyomo.util.plugin\nfrom pyomo.opt.base import *\nfrom pyomo.opt.base.solvers import _extract_version\nfrom pyomo.opt.results import *\nfrom pyomo.opt.solver import *\nfrom pyomo.solvers.mockmip import MockMIP\n\nlogger = logging.getLogger('pyomo.solvers')\n\nclass XPRESS(OptSolver):\n \"\"\"The XPRESS LP/MIP solver\n \"\"\"\n\n pyomo.util.plugin.alias('xpress', doc='The XPRESS LP/MIP solver')\n\n def __new__(cls, *args, **kwds):\n try:\n mode = kwds['solver_io']\n if mode is None:\n mode = 'lp'\n del kwds['solver_io']\n except KeyError:\n mode = 'lp'\n\n if mode == 'lp':\n return SolverFactory('_xpress_shell', **kwds)\n elif mode == 'mps':\n opt = SolverFactory('_xpress_shell', **kwds)\n opt.set_problem_format(ProblemFormat.mps)\n return opt\n elif mode == 'nl':\n opt = SolverFactory('asl', **kwds)\n else:\n logging.getLogger('pyomo.solvers').error(\n 'Unknown IO type for solver xpress: %s'\n % (mode))\n return\n opt.set_options('solver=amplxpress')\n return opt\n\nclass XPRESS_shell(ILMLicensedSystemCallSolver):\n \"\"\"Shell interface to the XPRESS LP/MIP solver\n \"\"\"\n\n pyomo.util.plugin.alias('_xpress_shell', doc='Shell interface to the XPRESS LP/MIP solver')\n\n def __init__(self, **kwds):\n #\n # Call base class constructor\n #\n kwds['type'] = 'xpress'\n ILMLicensedSystemCallSolver.__init__(self, **kwds)\n\n #\n # Define valid problem formats and associated results formats\n #\n self._valid_problem_formats=[ProblemFormat.cpxlp, ProblemFormat.mps]\n self._valid_result_formats={}\n self._valid_result_formats[ProblemFormat.cpxlp] = [ResultsFormat.soln]\n self._valid_result_formats[ProblemFormat.mps] = [ResultsFormat.soln]\n self.set_problem_format(ProblemFormat.cpxlp)\n\n #\n # Cache the problem type - LP or MIP. Xpress needs to know this\n # on the command-line, and it matters when reading the solution file.\n #\n\n # Note: Undefined capabilities default to 'None'\n self._capabilities = pyutilib.misc.Options()\n self._capabilities.linear = True\n self._capabilities.quadratic_objective = True\n self._capabilities.quadratic_constraint = True\n self._capabilities.integer = True\n self._capabilities.sos1 = True\n self._capabilities.sos2 = True\n\n def _default_results_format(self, prob_format):\n return ResultsFormat.soln\n\n #\n # we haven't reached this point quite yet.\n #\n def warm_start_capable(self):\n\n return False\n\n def _default_executable(self):\n executable = pyutilib.services.registered_executable(\"optimizer\")\n if executable is None:\n logger.warning(\"Could not locate the 'optimizer' executable, \"\n \"which is required for solver %s\" % self.name)\n self.enable = False\n return None\n return executable.get_path()\n\n # TODO: If anyone can get their hands on a working 'optimizer' executable\n # we could add a custom version method\n def _get_version(self):\n \"\"\"\n Returns a tuple describing the solver executable version.\n \"\"\"\n return _extract_version('')\n\n def create_command_line(self, executable, problem_files):\n\n #\n # Define log file\n # The log file in XPRESS contains the solution trace, but the solver status can be found in the solution file.\n #\n if self._log_file is None:\n self._log_file = pyutilib.services.TempfileManager.\\\n create_tempfile(suffix = '.xpress.log')\n\n #\n # Define solution file\n # As indicated above, contains (in XML) both the solution and solver status.\n #\n self._soln_file = pyutilib.services.TempfileManager.\\\n create_tempfile(suffix = '.xpress.wrtsol')\n\n #\n # Write the XPRESS execution script\n #\n script = \"\"\n\n script = \"setlogfile %s\\n\" % (self._log_file,)\n\n if self._timelimit is not None and self._timelimit > 0.0:\n script += \"maxtime=%s\\n\" % (self._timelimit,)\n\n if (self.options.mipgap is not None) and (self.options.mipgap > 0.0):\n script += \"miprelstop=%s\\n\" % (self.options.mipgap,)\n\n for option_name in self.options:\n script += \"%s=%s\" % (option_name, self.options[option_name])\n\n script += \"readprob %s\\n\" % (problem_files[0],)\n\n # doesn't seem to be a global solve command for mip versus lp\n # solves\n script += \"lpoptimize\\n\"\n\n # a quick explanation of the various flags used below:\n # p: outputs in full precision\n # n: output the name\n # t: output the type\n # a: output the activity (value)\n # c: outputs the costs for variables, slacks for constraints.\n # d: outputs the reduced costs for columns, duals for constraints\n script += \"writesol %s -pnatcd\\n\" % (self._soln_file,)\n\n script += \"quit\\n\"\n\n # dump the script and warm-start file names for the\n # user if we're keeping files around.\n if self._keepfiles:\n script_fname = pyutilib.services.TempfileManager.create_tempfile(suffix = '.xpress.script')\n tmp = open(script_fname,'w')\n tmp.write(script)\n tmp.close()\n\n print(\"Solver script file=\" + script_fname)\n\n #\n # Define command line\n #\n cmd = [executable]\n if self._timer:\n cmd.insert(0, self._timer)\n return pyutilib.misc.Bunch(cmd=cmd, script=script,\n log_file=self._log_file, env=None)\n\n def process_logfile(self):\n\n results = SolverResults()\n results.problem.number_of_variables = None\n results.problem.number_of_nonzeros = None\n\n log_file = open(self._log_file)\n log_file_contents = \"\".join(log_file.readlines())\n log_file.close()\n\n return\n\n for line in log_file_contents.split(\"\\n\"):\n tokens = re.split('[ \\t]+',line.strip())\n\n if len(tokens) > 3 and tokens[0] == \"XPRESS\" and tokens[1] == \"Error\":\n # IMPT: See below - cplex can generate an error line and then terminate fine, e.g., in XPRESS 12.1.\n # To handle these cases, we should be specifying some kind of termination criterion always\n # in the course of parsing a log file (we aren't doing so currently - just in some conditions).\n results.solver.status=SolverStatus.error\n results.solver.error = \" \".join(tokens)\n elif len(tokens) >= 3 and tokens[0] == \"ILOG\" and tokens[1] == \"XPRESS\":\n cplex_version = tokens[2].rstrip(',')\n elif len(tokens) >= 3 and tokens[0] == \"Variables\":\n if results.problem.number_of_variables is None: # XPRESS 11.2 and subsequent versions have two Variables sections in the log file output.\n results.problem.number_of_variables = int(tokens[2])\n # In XPRESS 11 (and presumably before), there was only a single line output to\n # indicate the constriant count, e.g., \"Linear constraints : 16 [Less: 7, Greater: 6, Equal: 3]\".\n # In XPRESS 11.2 (or somewhere in between 11 and 11.2 - I haven't bothered to track it down\n # in that detail), there is another instance of this line prefix in the min/max problem statistics\n # block - which we don't care about. In this case, the line looks like: \"Linear constraints :\" and\n # that's all.\n elif len(tokens) >= 4 and tokens[0] == \"Linear\" and tokens[1] == \"constraints\":\n results.problem.number_of_constraints = int(tokens[3])\n elif len(tokens) >= 3 and tokens[0] == \"Nonzeros\":\n if results.problem.number_of_nonzeros is None: # XPRESS 11.2 and subsequent has two Nonzeros sections.\n results.problem.number_of_nonzeros = int(tokens[2])\n elif len(tokens) >= 5 and tokens[4] == \"MINIMIZE\":\n results.problem.sense = ProblemSense.minimize\n elif len(tokens) >= 5 and tokens[4] == \"MAXIMIZE\":\n results.problem.sense = ProblemSense.maximize\n elif len(tokens) >= 4 and tokens[0] == \"Solution\" and tokens[1] == \"time\" and tokens[2] == \"=\":\n # technically, I'm not sure if this is XPRESS user time or user+system - XPRESS doesn't appear\n # to differentiate, and I'm not sure we can always provide a break-down.\n results.solver.user_time = float(tokens[3])\n elif len(tokens) >= 4 and tokens[0] == \"Dual\" and tokens[1] == \"simplex\" and tokens[3] == \"Optimal:\":\n results.solver.termination_condition = TerminationCondition.optimal\n results.solver.termination_message = ' '.join(tokens)\n elif len(tokens) >= 4 and tokens[0] == \"Barrier\" and tokens[2] == \"Optimal:\":\n results.solver.termination_condition = TerminationCondition.optimal\n results.solver.termination_message = ' '.join(tokens)\n elif len(tokens) >= 4 and tokens[0] == \"Dual\" and tokens[3] == \"Infeasible:\":\n results.solver.termination_condition = TerminationCondition.infeasible\n results.solver.termination_message = ' '.join(tokens)\n elif len(tokens) >= 4 and tokens[0] == \"MIP\" and tokens[2] == \"Integer\" and tokens[3] == \"infeasible.\":\n # if XPRESS has previously printed an error message, reduce it to a warning -\n # there is a strong indication it recovered, but we can't be sure.\n if results.solver.status == SolverStatus.error:\n results.solver.status = SolverStatus.warning\n else:\n results.solver.status = SolverStatus.ok\n results.solver.termination_condition = TerminationCondition.infeasible\n results.solver.termination_message = ' '.join(tokens)\n # for the case below, XPRESS sometimes reports \"true\" optimal (the first case)\n # and other times within-tolerance optimal (the second case).\n elif (len(tokens) >= 4 and tokens[0] == \"MIP\" and tokens[2] == \"Integer\" and tokens[3] == \"optimal\") or \\\n (len(tokens) >= 4 and tokens[0] == \"MIP\" and tokens[2] == \"Integer\" and tokens[3] == \"optimal,\"):\n # if XPRESS has previously printed an error message, reduce it to a warning -\n # there is a strong indication it recovered, but we can't be sure.\n if results.solver.status == SolverStatus.error:\n results.solver.status = SolverStatus.warning\n else:\n results.solver.status = SolverStatus.ok\n results.solver.termination_condition = TerminationCondition.optimal\n results.solver.termination_message = ' '.join(tokens)\n elif len(tokens) >= 3 and tokens[0] == \"Presolve\" and tokens[2] == \"Infeasible.\":\n # if XPRESS has previously printed an error message, reduce it to a warning -\n # there is a strong indication it recovered, but we can't be sure.\n if results.solver.status == SolverStatus.error:\n results.solver.status = SolverStatus.warning\n else:\n results.solver.status = SolverStatus.ok\n results.solver.termination_condition = TerminationCondition.infeasible\n results.solver.termination_message = ' '.join(tokens)\n elif (len(tokens) == 6 and tokens[2] == \"Integer\" and tokens[3] == \"infeasible\" and tokens[5] == \"unbounded.\") or (len(tokens) >= 5 and tokens[0] == \"Presolve\" and tokens[2] == \"Unbounded\" and tokens[4] == \"infeasible.\"):\n # if XPRESS has previously printed an error message, reduce it to a warning -\n # there is a strong indication it recovered, but we can't be sure.\n if results.solver.status == SolverStatus.error:\n results.solver.status = SolverStatus.warning\n else:\n results.solver.status = SolverStatus.ok\n # It isn't clear whether we can determine if the problem is unbounded from\n # XPRESS's output.\n results.solver.termination_condition = TerminationCondition.unbounded\n results.solver.termination_message = ' '.join(tokens)\n\n try:\n results.solver.termination_message = pyutilib.misc.yaml_fix(results.solver.termination_message)\n except:\n pass\n return results\n\n def process_soln_file(self, results):\n\n # the only suffixes that we extract from Xpress are\n # constraint duals, constraint slacks, and variable\n # reduced-costs. scan through the solver suffix list\n # and throw an exception if the user has specified\n # any others.\n extract_duals = False\n extract_slacks = False\n extract_reduced_costs = False\n extract_rc = False\n extract_lrc = False\n extract_urc = False\n for suffix in self._suffixes:\n flag=False\n if re.match(suffix,\"dual\"):\n extract_duals = True\n flag=True\n if re.match(suffix,\"slack\"):\n extract_slacks = True\n flag=True\n if re.match(suffix,\"rc\"):\n extract_reduced_costs = True\n extract_rc = True\n flag=True\n if re.match(suffix,\"lrc\"):\n extract_reduced_costs = True\n extract_lrc = True\n flag=True\n if re.match(suffix,\"urc\"):\n extract_reduced_costs = True\n extract_urc = True\n flag=True\n if not flag:\n raise RuntimeError(\"***The xpress solver plugin cannot extract solution suffix=\"+suffix)\n\n if not os.path.exists(self._soln_file):\n return\n\n soln = Solution()\n soln.objective['__default_objective__'] = {'Value': None} # TBD: NOT SURE HOW TO EXTRACT THE OBJECTIVE VALUE YET!\n soln_variable = soln.variable # caching for efficiency\n solution_file = open(self._soln_file, \"r\")\n results.problem.number_of_objectives=1\n\n for line in solution_file:\n\n line = line.strip()\n tokens=line.split(',')\n\n name = tokens[0].strip(\"\\\" \")\n type = tokens[1].strip(\"\\\" \")\n\n primary_value = float(tokens[2].strip(\"\\\" \"))\n secondary_value = float(tokens[3].strip(\"\\\" \"))\n tertiary_value = float(tokens[4].strip(\"\\\" \"))\n\n if type == \"C\": # a 'C' type in Xpress is a variable (i.e., column) - everything else is a constraint.\n\n variable_name = name\n variable_value = primary_value\n variable_reduced_cost = None\n\n if (extract_reduced_costs is True) and (field_name == \"reducedCost\"):\n variable_reduced_cost = tertiary_value\n\n if variable_name != \"ONE_VAR_CONSTANT\":\n variable = soln_variable[variable_name] = {\"Value\" : float(variable_value)}\n if (variable_reduced_cost is not None) and (extract_reduced_costs is True):\n try:\n if extract_rc is True:\n variable[\"Rc\"] = float(variable_reduced_cost)\n if variable_status is not None:\n if extract_lrc is True:\n if variable_status == \"LL\":\n variable[\"Lrc\"] = float(variable_reduced_cost)\n else:\n variable[\"Lrc\"] = 0.0\n if extract_urc is True:\n if variable_status == \"UL\":\n variable[\"Urc\"] = float(variable_reduced_cost)\n else:\n variable[\"Urc\"] = 0.0\n except:\n raise ValueError(\"Unexpected reduced-cost value=\"\n +str(variable_reduced_cost)+\n \" encountered for variable=\"+variable_name)\n\n else:\n\n constraint = soln.constraint[name] = {}\n\n if (extract_duals is True) and (tertiary_value != 0.0):\n constraint[\"Dual\"] = tertiary_value\n if (extract_slacks is True) and (secondary_value != 0.0):\n constraint[\"Slack\"] = secondary_value\n\n if not results.solver.status is SolverStatus.error and \\\n results.solver.termination_condition in [TerminationCondition.unknown,\n #TerminationCondition.maxIterations,\n #TerminationCondition.minFunctionValue,\n #TerminationCondition.minStepLength,\n TerminationCondition.globallyOptimal,\n TerminationCondition.locallyOptimal,\n TerminationCondition.optimal,\n #TerminationCondition.maxEvaluations,\n TerminationCondition.other]:\n\n results.solution.insert(soln)\n solution_file.close()\n\nclass MockXPRESS(XPRESS_shell,MockMIP):\n \"\"\"A Mock XPRESS solver used for testing\n \"\"\"\n\n pyomo.util.plugin.alias('_mock_xpress')\n\n def __init__(self, **kwds):\n try:\n XPRESS_shell.__init__(self, **kwds)\n except pyutilib.common.ApplicationError: #pragma:nocover\n pass #pragma:nocover\n MockMIP.__init__(self,\"cplex\")\n\n def available(self, exception_flag=True):\n return XPRESS_shell.available(self,exception_flag)\n\n def create_command_line(self,executable,problem_files):\n command = XPRESS_shell.create_command_line(self,executable,problem_files)\n MockMIP.create_command_line(self,executable,problem_files)\n return command\n\n def executable(self):\n return MockMIP.executable(self)\n\n def _execute_command(self,cmd):\n return MockMIP._execute_command(self,cmd)\n\n\npyutilib.services.register_executable(name=\"optimizer\")\n\n",
"path": "pyomo/solvers/plugins/solvers/XPRESS.py"
}
] | [
{
"content": "# _________________________________________________________________________\n#\n# Pyomo: Python Optimization Modeling Objects\n# Copyright (c) 2014 Sandia Corporation.\n# Under the terms of Contract DE-AC04-94AL85000 with Sandia Corporation,\n# the U.S. Government retains certain rights in this software.\n# This software is distributed under the BSD License.\n# _________________________________________________________________________\n\n\nimport os\nimport re\nimport logging\n\nimport pyutilib.services\nimport pyutilib.common\nimport pyutilib.misc\n\nimport pyomo.util.plugin\nfrom pyomo.opt.base import *\nfrom pyomo.opt.base.solvers import _extract_version\nfrom pyomo.opt.results import *\nfrom pyomo.opt.solver import *\nfrom pyomo.solvers.mockmip import MockMIP\n\nlogger = logging.getLogger('pyomo.solvers')\n\nclass XPRESS(OptSolver):\n \"\"\"The XPRESS LP/MIP solver\n \"\"\"\n\n pyomo.util.plugin.alias('xpress', doc='The XPRESS LP/MIP solver')\n\n def __new__(cls, *args, **kwds):\n try:\n mode = kwds['solver_io']\n if mode is None:\n mode = 'lp'\n del kwds['solver_io']\n except KeyError:\n mode = 'lp'\n\n if mode == 'lp':\n return SolverFactory('_xpress_shell', **kwds)\n elif mode == 'mps':\n opt = SolverFactory('_xpress_shell', **kwds)\n opt.set_problem_format(ProblemFormat.mps)\n return opt\n elif mode == 'nl':\n opt = SolverFactory('asl', **kwds)\n else:\n logging.getLogger('pyomo.solvers').error(\n 'Unknown IO type for solver xpress: %s'\n % (mode))\n return\n opt.set_options('solver=amplxpress')\n return opt\n\nclass XPRESS_shell(ILMLicensedSystemCallSolver):\n \"\"\"Shell interface to the XPRESS LP/MIP solver\n \"\"\"\n\n pyomo.util.plugin.alias('_xpress_shell', doc='Shell interface to the XPRESS LP/MIP solver')\n\n def __init__(self, **kwds):\n #\n # Call base class constructor\n #\n kwds['type'] = 'xpress'\n ILMLicensedSystemCallSolver.__init__(self, **kwds)\n\n #\n # Define valid problem formats and associated results formats\n #\n self._valid_problem_formats=[ProblemFormat.cpxlp, ProblemFormat.mps]\n self._valid_result_formats={}\n self._valid_result_formats[ProblemFormat.cpxlp] = [ResultsFormat.soln]\n self._valid_result_formats[ProblemFormat.mps] = [ResultsFormat.soln]\n self.set_problem_format(ProblemFormat.cpxlp)\n\n #\n # Cache the problem type - LP or MIP. Xpress needs to know this\n # on the command-line, and it matters when reading the solution file.\n #\n\n # Note: Undefined capabilities default to 'None'\n self._capabilities = pyutilib.misc.Options()\n self._capabilities.linear = True\n self._capabilities.quadratic_objective = True\n self._capabilities.quadratic_constraint = True\n self._capabilities.integer = True\n self._capabilities.sos1 = True\n self._capabilities.sos2 = True\n\n def _default_results_format(self, prob_format):\n return ResultsFormat.soln\n\n #\n # we haven't reached this point quite yet.\n #\n def warm_start_capable(self):\n\n return False\n\n def _default_executable(self):\n executable = pyutilib.services.registered_executable(\"optimizer\")\n if executable is None:\n logger.warning(\"Could not locate the 'optimizer' executable, \"\n \"which is required for solver %s\" % self.name)\n self.enable = False\n return None\n return executable.get_path()\n\n # TODO: If anyone can get their hands on a working 'optimizer' executable\n # we could add a custom version method\n def _get_version(self):\n \"\"\"\n Returns a tuple describing the solver executable version.\n \"\"\"\n return _extract_version('')\n\n def create_command_line(self, executable, problem_files):\n\n #\n # Define log file\n # The log file in XPRESS contains the solution trace, but the solver status can be found in the solution file.\n #\n if self._log_file is None:\n self._log_file = pyutilib.services.TempfileManager.\\\n create_tempfile(suffix = '.xpress.log')\n\n #\n # Define solution file\n # As indicated above, contains (in XML) both the solution and solver status.\n #\n self._soln_file = pyutilib.services.TempfileManager.\\\n create_tempfile(suffix = '.xpress.wrtsol')\n\n #\n # Write the XPRESS execution script\n #\n script = \"\"\n\n script = \"setlogfile %s\\n\" % (self._log_file,)\n\n if self._timelimit is not None and self._timelimit > 0.0:\n script += \"maxtime=%s\\n\" % (self._timelimit,)\n\n if (self.options.mipgap is not None) and (self.options.mipgap > 0.0):\n script += \"miprelstop=%s\\n\" % (self.options.mipgap,)\n\n for option_name in self.options:\n script += \"%s=%s\" % (option_name, self.options[option_name])\n\n script += \"readprob %s\\n\" % (problem_files[0],)\n\n # doesn't seem to be a global solve command for mip versus lp\n # solves\n script += \"lpoptimize\\n\"\n\n # a quick explanation of the various flags used below:\n # p: outputs in full precision\n # n: output the name\n # t: output the type\n # a: output the activity (value)\n # c: outputs the costs for variables, slacks for constraints.\n # d: outputs the reduced costs for columns, duals for constraints\n script += \"writesol %s -pnatcd\\n\" % (self._soln_file,)\n\n script += \"quit\\n\"\n\n # dump the script and warm-start file names for the\n # user if we're keeping files around.\n if self._keepfiles:\n script_fname = pyutilib.services.TempfileManager.create_tempfile(suffix = '.xpress.script')\n tmp = open(script_fname,'w')\n tmp.write(script)\n tmp.close()\n\n print(\"Solver script file=\" + script_fname)\n\n #\n # Define command line\n #\n cmd = [executable]\n if self._timer:\n cmd.insert(0, self._timer)\n return pyutilib.misc.Bunch(cmd=cmd, script=script,\n log_file=self._log_file, env=None)\n\n def process_logfile(self):\n\n results = SolverResults()\n results.problem.number_of_variables = None\n results.problem.number_of_nonzeros = None\n\n log_file = open(self._log_file)\n log_file_contents = \"\".join(log_file.readlines())\n log_file.close()\n\n for line in log_file_contents.split(\"\\n\"):\n tokens = re.split('[ \\t]+',line.strip())\n\n if len(tokens) > 3 and tokens[0] == \"XPRESS\" and tokens[1] == \"Error\":\n # IMPT: See below - cplex can generate an error line and then terminate fine, e.g., in XPRESS 12.1.\n # To handle these cases, we should be specifying some kind of termination criterion always\n # in the course of parsing a log file (we aren't doing so currently - just in some conditions).\n results.solver.status=SolverStatus.error\n results.solver.error = \" \".join(tokens)\n elif len(tokens) >= 3 and tokens[0] == \"ILOG\" and tokens[1] == \"XPRESS\":\n cplex_version = tokens[2].rstrip(',')\n elif len(tokens) >= 3 and tokens[0] == \"Variables\":\n if results.problem.number_of_variables is None: # XPRESS 11.2 and subsequent versions have two Variables sections in the log file output.\n results.problem.number_of_variables = int(tokens[2])\n # In XPRESS 11 (and presumably before), there was only a single line output to\n # indicate the constriant count, e.g., \"Linear constraints : 16 [Less: 7, Greater: 6, Equal: 3]\".\n # In XPRESS 11.2 (or somewhere in between 11 and 11.2 - I haven't bothered to track it down\n # in that detail), there is another instance of this line prefix in the min/max problem statistics\n # block - which we don't care about. In this case, the line looks like: \"Linear constraints :\" and\n # that's all.\n elif len(tokens) >= 4 and tokens[0] == \"Linear\" and tokens[1] == \"constraints\":\n results.problem.number_of_constraints = int(tokens[3])\n elif len(tokens) >= 3 and tokens[0] == \"Nonzeros\":\n if results.problem.number_of_nonzeros is None: # XPRESS 11.2 and subsequent has two Nonzeros sections.\n results.problem.number_of_nonzeros = int(tokens[2])\n elif len(tokens) >= 5 and tokens[4] == \"MINIMIZE\":\n results.problem.sense = ProblemSense.minimize\n elif len(tokens) >= 5 and tokens[4] == \"MAXIMIZE\":\n results.problem.sense = ProblemSense.maximize\n elif len(tokens) >= 4 and tokens[0] == \"Solution\" and tokens[1] == \"time\" and tokens[2] == \"=\":\n # technically, I'm not sure if this is XPRESS user time or user+system - XPRESS doesn't appear\n # to differentiate, and I'm not sure we can always provide a break-down.\n results.solver.user_time = float(tokens[3])\n elif len(tokens) >= 4 and tokens[0] == \"Dual\" and tokens[1] == \"simplex\" and tokens[3] == \"Optimal:\":\n results.solver.termination_condition = TerminationCondition.optimal\n results.solver.termination_message = ' '.join(tokens)\n elif len(tokens) >= 4 and tokens[0] == \"Barrier\" and tokens[2] == \"Optimal:\":\n results.solver.termination_condition = TerminationCondition.optimal\n results.solver.termination_message = ' '.join(tokens)\n elif len(tokens) >= 4 and tokens[0] == \"Dual\" and tokens[3] == \"Infeasible:\":\n results.solver.termination_condition = TerminationCondition.infeasible\n results.solver.termination_message = ' '.join(tokens)\n elif len(tokens) >= 4 and tokens[0] == \"MIP\" and tokens[2] == \"Integer\" and tokens[3] == \"infeasible.\":\n # if XPRESS has previously printed an error message, reduce it to a warning -\n # there is a strong indication it recovered, but we can't be sure.\n if results.solver.status == SolverStatus.error:\n results.solver.status = SolverStatus.warning\n else:\n results.solver.status = SolverStatus.ok\n results.solver.termination_condition = TerminationCondition.infeasible\n results.solver.termination_message = ' '.join(tokens)\n # for the case below, XPRESS sometimes reports \"true\" optimal (the first case)\n # and other times within-tolerance optimal (the second case).\n elif (len(tokens) >= 4 and tokens[0] == \"MIP\" and tokens[2] == \"Integer\" and tokens[3] == \"optimal\") or \\\n (len(tokens) >= 4 and tokens[0] == \"MIP\" and tokens[2] == \"Integer\" and tokens[3] == \"optimal,\"):\n # if XPRESS has previously printed an error message, reduce it to a warning -\n # there is a strong indication it recovered, but we can't be sure.\n if results.solver.status == SolverStatus.error:\n results.solver.status = SolverStatus.warning\n else:\n results.solver.status = SolverStatus.ok\n results.solver.termination_condition = TerminationCondition.optimal\n results.solver.termination_message = ' '.join(tokens)\n elif len(tokens) >= 3 and tokens[0] == \"Presolve\" and tokens[2] == \"Infeasible.\":\n # if XPRESS has previously printed an error message, reduce it to a warning -\n # there is a strong indication it recovered, but we can't be sure.\n if results.solver.status == SolverStatus.error:\n results.solver.status = SolverStatus.warning\n else:\n results.solver.status = SolverStatus.ok\n results.solver.termination_condition = TerminationCondition.infeasible\n results.solver.termination_message = ' '.join(tokens)\n elif (len(tokens) == 6 and tokens[2] == \"Integer\" and tokens[3] == \"infeasible\" and tokens[5] == \"unbounded.\") or (len(tokens) >= 5 and tokens[0] == \"Presolve\" and tokens[2] == \"Unbounded\" and tokens[4] == \"infeasible.\"):\n # if XPRESS has previously printed an error message, reduce it to a warning -\n # there is a strong indication it recovered, but we can't be sure.\n if results.solver.status == SolverStatus.error:\n results.solver.status = SolverStatus.warning\n else:\n results.solver.status = SolverStatus.ok\n # It isn't clear whether we can determine if the problem is unbounded from\n # XPRESS's output.\n results.solver.termination_condition = TerminationCondition.unbounded\n results.solver.termination_message = ' '.join(tokens)\n\n try:\n results.solver.termination_message = pyutilib.misc.yaml_fix(results.solver.termination_message)\n except:\n pass\n return results\n\n def process_soln_file(self, results):\n\n # the only suffixes that we extract from Xpress are\n # constraint duals, constraint slacks, and variable\n # reduced-costs. scan through the solver suffix list\n # and throw an exception if the user has specified\n # any others.\n extract_duals = False\n extract_slacks = False\n extract_reduced_costs = False\n extract_rc = False\n extract_lrc = False\n extract_urc = False\n for suffix in self._suffixes:\n flag=False\n if re.match(suffix,\"dual\"):\n extract_duals = True\n flag=True\n if re.match(suffix,\"slack\"):\n extract_slacks = True\n flag=True\n if re.match(suffix,\"rc\"):\n extract_reduced_costs = True\n extract_rc = True\n flag=True\n if re.match(suffix,\"lrc\"):\n extract_reduced_costs = True\n extract_lrc = True\n flag=True\n if re.match(suffix,\"urc\"):\n extract_reduced_costs = True\n extract_urc = True\n flag=True\n if not flag:\n raise RuntimeError(\"***The xpress solver plugin cannot extract solution suffix=\"+suffix)\n\n if not os.path.exists(self._soln_file):\n return\n\n soln = Solution()\n soln.objective['__default_objective__'] = {'Value': None} # TBD: NOT SURE HOW TO EXTRACT THE OBJECTIVE VALUE YET!\n soln_variable = soln.variable # caching for efficiency\n solution_file = open(self._soln_file, \"r\")\n results.problem.number_of_objectives=1\n\n for line in solution_file:\n\n line = line.strip()\n tokens=line.split(',')\n\n name = tokens[0].strip(\"\\\" \")\n type = tokens[1].strip(\"\\\" \")\n\n primary_value = float(tokens[2].strip(\"\\\" \"))\n secondary_value = float(tokens[3].strip(\"\\\" \"))\n tertiary_value = float(tokens[4].strip(\"\\\" \"))\n\n if type == \"C\": # a 'C' type in Xpress is a variable (i.e., column) - everything else is a constraint.\n\n variable_name = name\n variable_value = primary_value\n variable_reduced_cost = None\n\n if (extract_reduced_costs is True) and (field_name == \"reducedCost\"):\n variable_reduced_cost = tertiary_value\n\n if variable_name != \"ONE_VAR_CONSTANT\":\n variable = soln_variable[variable_name] = {\"Value\" : float(variable_value)}\n if (variable_reduced_cost is not None) and (extract_reduced_costs is True):\n try:\n if extract_rc is True:\n variable[\"Rc\"] = float(variable_reduced_cost)\n if variable_status is not None:\n if extract_lrc is True:\n if variable_status == \"LL\":\n variable[\"Lrc\"] = float(variable_reduced_cost)\n else:\n variable[\"Lrc\"] = 0.0\n if extract_urc is True:\n if variable_status == \"UL\":\n variable[\"Urc\"] = float(variable_reduced_cost)\n else:\n variable[\"Urc\"] = 0.0\n except:\n raise ValueError(\"Unexpected reduced-cost value=\"\n +str(variable_reduced_cost)+\n \" encountered for variable=\"+variable_name)\n\n else:\n\n constraint = soln.constraint[name] = {}\n\n if (extract_duals is True) and (tertiary_value != 0.0):\n constraint[\"Dual\"] = tertiary_value\n if (extract_slacks is True) and (secondary_value != 0.0):\n constraint[\"Slack\"] = secondary_value\n\n if not results.solver.status is SolverStatus.error and \\\n results.solver.termination_condition in [TerminationCondition.unknown,\n #TerminationCondition.maxIterations,\n #TerminationCondition.minFunctionValue,\n #TerminationCondition.minStepLength,\n TerminationCondition.globallyOptimal,\n TerminationCondition.locallyOptimal,\n TerminationCondition.optimal,\n #TerminationCondition.maxEvaluations,\n TerminationCondition.other]:\n\n results.solution.insert(soln)\n solution_file.close()\n\nclass MockXPRESS(XPRESS_shell,MockMIP):\n \"\"\"A Mock XPRESS solver used for testing\n \"\"\"\n\n pyomo.util.plugin.alias('_mock_xpress')\n\n def __init__(self, **kwds):\n try:\n XPRESS_shell.__init__(self, **kwds)\n except pyutilib.common.ApplicationError: #pragma:nocover\n pass #pragma:nocover\n MockMIP.__init__(self,\"cplex\")\n\n def available(self, exception_flag=True):\n return XPRESS_shell.available(self,exception_flag)\n\n def create_command_line(self,executable,problem_files):\n command = XPRESS_shell.create_command_line(self,executable,problem_files)\n MockMIP.create_command_line(self,executable,problem_files)\n return command\n\n def executable(self):\n return MockMIP.executable(self)\n\n def _execute_command(self,cmd):\n return MockMIP._execute_command(self,cmd)\n\n\npyutilib.services.register_executable(name=\"optimizer\")\n\n",
"path": "pyomo/solvers/plugins/solvers/XPRESS.py"
}
] | diff --git a/pyomo/solvers/plugins/solvers/XPRESS.py b/pyomo/solvers/plugins/solvers/XPRESS.py
index 143c6ffc2dc..929272d6efc 100644
--- a/pyomo/solvers/plugins/solvers/XPRESS.py
+++ b/pyomo/solvers/plugins/solvers/XPRESS.py
@@ -198,8 +198,6 @@ def process_logfile(self):
log_file_contents = "".join(log_file.readlines())
log_file.close()
- return
-
for line in log_file_contents.split("\n"):
tokens = re.split('[ \t]+',line.strip())
|
Qiskit__qiskit-6171 | num_qubits() for DictStateFn is inefficient
To get the number of qubits, a list of all keys in the dictionary is constructed. But, only the length of the first key is used. Constructing the entire list is wasteful.
https://github.com/Qiskit/qiskit-terra/blob/c3b2d7acb80fa89043e6f38efb501275ec296616/qiskit/opflow/state_fns/dict_state_fn.py#L82
This code should work:
```python
len(next(iter(self.primitive)))
```
`%timeit` shows that the latter is faster even when the dict contains only two keys.
- **Qiskit Terra version**: 123d829ac, Feb 3 master
| [
{
"content": "# This code is part of Qiskit.\n#\n# (C) Copyright IBM 2020, 2021.\n#\n# This code is licensed under the Apache License, Version 2.0. You may\n# obtain a copy of this license in the LICENSE.txt file in the root directory\n# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.\n#\n# Any modifications or derivative works of this code must retain this\n# copyright notice, and modified files need to carry a notice indicating\n# that they have been altered from the originals.\n\n\"\"\" DictStateFn Class \"\"\"\n\nimport itertools\nfrom typing import Dict, List, Optional, Set, Union, cast\n\nimport numpy as np\nfrom scipy import sparse\n\nfrom qiskit.circuit import ParameterExpression\nfrom qiskit.opflow.exceptions import OpflowError\nfrom qiskit.opflow.list_ops.list_op import ListOp\nfrom qiskit.opflow.operator_base import OperatorBase\nfrom qiskit.opflow.state_fns.state_fn import StateFn\nfrom qiskit.opflow.state_fns.vector_state_fn import VectorStateFn\nfrom qiskit.quantum_info import Statevector\nfrom qiskit.result import Result\nfrom qiskit.utils import algorithm_globals\n\n\nclass DictStateFn(StateFn):\n \"\"\" A class for state functions and measurements which are defined by a lookup table,\n stored in a dict.\n \"\"\"\n primitive: Dict[str, complex]\n\n # TODO allow normalization somehow?\n def __init__(self,\n primitive: Union[str, dict, Result] = None,\n coeff: Union[complex, ParameterExpression] = 1.0,\n is_measurement: bool = False) -> None:\n \"\"\"\n Args:\n primitive: The dict, single bitstring (if defining a basis sate), or Qiskit\n Result, which defines the behavior of the underlying function.\n coeff: A coefficient by which to multiply the state function.\n is_measurement: Whether the StateFn is a measurement operator.\n\n Raises:\n TypeError: invalid parameters.\n \"\"\"\n # If the initial density is a string, treat this as a density dict\n # with only a single basis state.\n if isinstance(primitive, str):\n primitive = {primitive: 1}\n\n # NOTE:\n # 1) This is not the same as passing in the counts dict directly, as this will\n # convert the shot numbers to\n # probabilities, whereas passing in the counts dict will not.\n # 2) This will extract counts for both shot and statevector simulations.\n # To use the statevector,\n # simply pass in the statevector.\n # 3) This will only extract the first result.\n if isinstance(primitive, Result):\n counts = primitive.get_counts()\n # NOTE: Need to square root to take correct Pauli measurements!\n primitive = {bstr: (shots / sum(counts.values()))**.5 for\n (bstr, shots) in counts.items()}\n\n if not isinstance(primitive, dict):\n raise TypeError(\n 'DictStateFn can only be instantiated with dict, '\n 'string, or Qiskit Result, not {}'.format(type(primitive)))\n\n super().__init__(primitive, coeff=coeff, is_measurement=is_measurement)\n\n def primitive_strings(self) -> Set[str]:\n return {'Dict'}\n\n @property\n def num_qubits(self) -> int:\n return len(list(self.primitive.keys())[0])\n\n def add(self, other: OperatorBase) -> OperatorBase:\n if not self.num_qubits == other.num_qubits:\n raise ValueError(\n 'Sum over statefns with different numbers of qubits, {} and {}, is not well '\n 'defined'.format(self.num_qubits, other.num_qubits))\n\n # Right now doesn't make sense to add a StateFn to a Measurement\n if isinstance(other, DictStateFn) and self.is_measurement == other.is_measurement:\n # TODO add compatibility with vector and Operator?\n if self.primitive == other.primitive:\n return DictStateFn(self.primitive,\n coeff=self.coeff + other.coeff,\n is_measurement=self.is_measurement)\n else:\n new_dict = {b: (v * self.coeff) + (other.primitive.get(b, 0) * other.coeff)\n for (b, v) in self.primitive.items()}\n new_dict.update({b: v * other.coeff for (b, v) in other.primitive.items()\n if b not in self.primitive})\n return DictStateFn(new_dict, is_measurement=self._is_measurement)\n # pylint: disable=cyclic-import\n from ..list_ops.summed_op import SummedOp\n return SummedOp([self, other])\n\n def adjoint(self) -> \"DictStateFn\":\n return DictStateFn({b: np.conj(v) for (b, v) in self.primitive.items()},\n coeff=self.coeff.conjugate(),\n is_measurement=(not self.is_measurement))\n\n def permute(self, permutation: List[int]) -> 'DictStateFn':\n new_num_qubits = max(permutation) + 1\n if self.num_qubits != len(permutation):\n raise OpflowError(\"New index must be defined for each qubit of the operator.\")\n\n # helper function to permute the key\n def perm(key):\n list_key = ['0'] * new_num_qubits\n for i, k in enumerate(permutation):\n list_key[k] = key[i]\n return ''.join(list_key)\n\n new_dict = {perm(key): value for key, value in self.primitive.items()}\n return DictStateFn(new_dict, coeff=self.coeff, is_measurement=self.is_measurement)\n\n def _expand_dim(self, num_qubits: int) -> 'DictStateFn':\n pad = '0'*num_qubits\n new_dict = {key + pad: value for key, value in self.primitive.items()}\n return DictStateFn(new_dict, coeff=self.coeff, is_measurement=self.is_measurement)\n\n def tensor(self, other: OperatorBase) -> OperatorBase:\n # Both dicts\n if isinstance(other, DictStateFn):\n new_dict = {k1 + k2: v1 * v2 for ((k1, v1,), (k2, v2)) in\n itertools.product(self.primitive.items(), other.primitive.items())}\n return StateFn(new_dict,\n coeff=self.coeff * other.coeff,\n is_measurement=self.is_measurement)\n # pylint: disable=cyclic-import\n from ..list_ops.tensored_op import TensoredOp\n return TensoredOp([self, other])\n\n def to_density_matrix(self, massive: bool = False) -> np.ndarray:\n OperatorBase._check_massive('to_density_matrix', True, self.num_qubits, massive)\n states = int(2 ** self.num_qubits)\n return self.to_matrix(massive=massive) * np.eye(states) * self.coeff\n\n def to_matrix(self, massive: bool = False) -> np.ndarray:\n OperatorBase._check_massive('to_matrix', False, self.num_qubits, massive)\n states = int(2 ** self.num_qubits)\n probs = np.zeros(states) + 0.j\n for k, v in self.primitive.items():\n probs[int(k, 2)] = v\n vec = probs * self.coeff\n\n # Reshape for measurements so np.dot still works for composition.\n return vec if not self.is_measurement else vec.reshape(1, -1)\n\n def to_spmatrix(self) -> sparse.spmatrix:\n \"\"\"Same as to_matrix, but returns csr sparse matrix.\n\n Returns:\n CSR sparse matrix representation of the State function.\n\n Raises:\n ValueError: invalid parameters.\n \"\"\"\n\n indices = [int(v, 2) for v in self.primitive.keys()]\n vals = np.array(list(self.primitive.values())) * self.coeff\n spvec = sparse.csr_matrix((vals, (np.zeros(len(indices), dtype=int), indices)),\n shape=(1, 2**self.num_qubits))\n return spvec if not self.is_measurement else spvec.transpose()\n\n def to_spmatrix_op(self) -> OperatorBase:\n \"\"\"Convert this state function to a ``SparseVectorStateFn``.\"\"\"\n from .sparse_vector_state_fn import SparseVectorStateFn\n return SparseVectorStateFn(self.to_spmatrix(), self.coeff, self.is_measurement)\n\n def to_circuit_op(self) -> OperatorBase:\n \"\"\"Convert this state function to a ``CircuitStateFn``.\"\"\"\n from .circuit_state_fn import CircuitStateFn\n csfn = CircuitStateFn.from_dict(self.primitive) * self.coeff\n return csfn.adjoint() if self.is_measurement else csfn\n\n def __str__(self) -> str:\n prim_str = str(self.primitive)\n if self.coeff == 1.0:\n return \"{}({})\".format('DictStateFn' if not self.is_measurement\n else 'DictMeasurement', prim_str)\n else:\n return \"{}({}) * {}\".format('DictStateFn' if not self.is_measurement\n else 'DictMeasurement',\n prim_str,\n self.coeff)\n\n # pylint: disable=too-many-return-statements\n def eval(\n self,\n front: Optional[\n Union[str, Dict[str, complex], np.ndarray, OperatorBase, Statevector]\n ] = None,\n ) -> Union[OperatorBase, complex]:\n if front is None:\n sparse_vector_state_fn = self.to_spmatrix_op().eval()\n return sparse_vector_state_fn\n\n if not self.is_measurement and isinstance(front, OperatorBase):\n raise ValueError(\n 'Cannot compute overlap with StateFn or Operator if not Measurement. Try taking '\n 'sf.adjoint() first to convert to measurement.')\n\n if isinstance(front, ListOp) and front.distributive:\n return front.combo_fn([self.eval(front.coeff * front_elem)\n for front_elem in front.oplist])\n\n # For now, always do this. If it's not performant, we can be more granular.\n if not isinstance(front, OperatorBase):\n front = StateFn(front)\n\n # pylint: disable=cyclic-import\n from ..operator_globals import EVAL_SIG_DIGITS\n\n # If the primitive is a lookup of bitstrings,\n # we define all missing strings to have a function value of\n # zero.\n if isinstance(front, DictStateFn):\n return np.round(\n cast(float, sum([v * front.primitive.get(b, 0) for (b, v) in\n self.primitive.items()]) * self.coeff * front.coeff),\n decimals=EVAL_SIG_DIGITS)\n\n # All remaining possibilities only apply when self.is_measurement is True\n\n if isinstance(front, VectorStateFn):\n # TODO does it need to be this way for measurement?\n # return sum([v * front.primitive.data[int(b, 2)] *\n # np.conj(front.primitive.data[int(b, 2)])\n return np.round(\n cast(float, sum([v * front.primitive.data[int(b, 2)] for (b, v) in\n self.primitive.items()]) * self.coeff),\n decimals=EVAL_SIG_DIGITS)\n\n from .circuit_state_fn import CircuitStateFn\n if isinstance(front, CircuitStateFn):\n # Don't reimplement logic from CircuitStateFn\n self_adjoint = cast(DictStateFn, self.adjoint())\n return np.conj(front.adjoint().eval(self_adjoint.primitive)) * self.coeff\n\n from .operator_state_fn import OperatorStateFn\n if isinstance(front, OperatorStateFn):\n return cast(Union[OperatorBase, float, complex], front.adjoint().eval(self.adjoint()))\n\n # All other OperatorBases go here\n self_adjoint = cast(DictStateFn, self.adjoint())\n adjointed_eval = cast(OperatorBase, front.adjoint().eval(self_adjoint.primitive))\n return adjointed_eval.adjoint() * self.coeff\n\n def sample(self,\n shots: int = 1024,\n massive: bool = False,\n reverse_endianness: bool = False) -> Dict[str, float]:\n probs = np.square(np.abs(np.array(list(self.primitive.values()))))\n unique, counts = np.unique(algorithm_globals.random.choice(list(self.primitive.keys()),\n size=shots,\n p=(probs / sum(probs))),\n return_counts=True)\n counts = dict(zip(unique, counts))\n if reverse_endianness:\n scaled_dict = {bstr[::-1]: (prob / shots) for (bstr, prob) in counts.items()}\n else:\n scaled_dict = {bstr: (prob / shots) for (bstr, prob) in counts.items()}\n return dict(sorted(scaled_dict.items(), key=lambda x: x[1], reverse=True))\n",
"path": "qiskit/opflow/state_fns/dict_state_fn.py"
}
] | [
{
"content": "# This code is part of Qiskit.\n#\n# (C) Copyright IBM 2020, 2021.\n#\n# This code is licensed under the Apache License, Version 2.0. You may\n# obtain a copy of this license in the LICENSE.txt file in the root directory\n# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.\n#\n# Any modifications or derivative works of this code must retain this\n# copyright notice, and modified files need to carry a notice indicating\n# that they have been altered from the originals.\n\n\"\"\" DictStateFn Class \"\"\"\n\nimport itertools\nfrom typing import Dict, List, Optional, Set, Union, cast\n\nimport numpy as np\nfrom scipy import sparse\n\nfrom qiskit.circuit import ParameterExpression\nfrom qiskit.opflow.exceptions import OpflowError\nfrom qiskit.opflow.list_ops.list_op import ListOp\nfrom qiskit.opflow.operator_base import OperatorBase\nfrom qiskit.opflow.state_fns.state_fn import StateFn\nfrom qiskit.opflow.state_fns.vector_state_fn import VectorStateFn\nfrom qiskit.quantum_info import Statevector\nfrom qiskit.result import Result\nfrom qiskit.utils import algorithm_globals\n\n\nclass DictStateFn(StateFn):\n \"\"\" A class for state functions and measurements which are defined by a lookup table,\n stored in a dict.\n \"\"\"\n primitive: Dict[str, complex]\n\n # TODO allow normalization somehow?\n def __init__(self,\n primitive: Union[str, dict, Result] = None,\n coeff: Union[complex, ParameterExpression] = 1.0,\n is_measurement: bool = False) -> None:\n \"\"\"\n Args:\n primitive: The dict, single bitstring (if defining a basis sate), or Qiskit\n Result, which defines the behavior of the underlying function.\n coeff: A coefficient by which to multiply the state function.\n is_measurement: Whether the StateFn is a measurement operator.\n\n Raises:\n TypeError: invalid parameters.\n \"\"\"\n # If the initial density is a string, treat this as a density dict\n # with only a single basis state.\n if isinstance(primitive, str):\n primitive = {primitive: 1}\n\n # NOTE:\n # 1) This is not the same as passing in the counts dict directly, as this will\n # convert the shot numbers to\n # probabilities, whereas passing in the counts dict will not.\n # 2) This will extract counts for both shot and statevector simulations.\n # To use the statevector,\n # simply pass in the statevector.\n # 3) This will only extract the first result.\n if isinstance(primitive, Result):\n counts = primitive.get_counts()\n # NOTE: Need to square root to take correct Pauli measurements!\n primitive = {bstr: (shots / sum(counts.values()))**.5 for\n (bstr, shots) in counts.items()}\n\n if not isinstance(primitive, dict):\n raise TypeError(\n 'DictStateFn can only be instantiated with dict, '\n 'string, or Qiskit Result, not {}'.format(type(primitive)))\n\n super().__init__(primitive, coeff=coeff, is_measurement=is_measurement)\n\n def primitive_strings(self) -> Set[str]:\n return {'Dict'}\n\n @property\n def num_qubits(self) -> int:\n return len(next(iter(self.primitive)))\n\n def add(self, other: OperatorBase) -> OperatorBase:\n if not self.num_qubits == other.num_qubits:\n raise ValueError(\n 'Sum over statefns with different numbers of qubits, {} and {}, is not well '\n 'defined'.format(self.num_qubits, other.num_qubits))\n\n # Right now doesn't make sense to add a StateFn to a Measurement\n if isinstance(other, DictStateFn) and self.is_measurement == other.is_measurement:\n # TODO add compatibility with vector and Operator?\n if self.primitive == other.primitive:\n return DictStateFn(self.primitive,\n coeff=self.coeff + other.coeff,\n is_measurement=self.is_measurement)\n else:\n new_dict = {b: (v * self.coeff) + (other.primitive.get(b, 0) * other.coeff)\n for (b, v) in self.primitive.items()}\n new_dict.update({b: v * other.coeff for (b, v) in other.primitive.items()\n if b not in self.primitive})\n return DictStateFn(new_dict, is_measurement=self._is_measurement)\n # pylint: disable=cyclic-import\n from ..list_ops.summed_op import SummedOp\n return SummedOp([self, other])\n\n def adjoint(self) -> \"DictStateFn\":\n return DictStateFn({b: np.conj(v) for (b, v) in self.primitive.items()},\n coeff=self.coeff.conjugate(),\n is_measurement=(not self.is_measurement))\n\n def permute(self, permutation: List[int]) -> 'DictStateFn':\n new_num_qubits = max(permutation) + 1\n if self.num_qubits != len(permutation):\n raise OpflowError(\"New index must be defined for each qubit of the operator.\")\n\n # helper function to permute the key\n def perm(key):\n list_key = ['0'] * new_num_qubits\n for i, k in enumerate(permutation):\n list_key[k] = key[i]\n return ''.join(list_key)\n\n new_dict = {perm(key): value for key, value in self.primitive.items()}\n return DictStateFn(new_dict, coeff=self.coeff, is_measurement=self.is_measurement)\n\n def _expand_dim(self, num_qubits: int) -> 'DictStateFn':\n pad = '0'*num_qubits\n new_dict = {key + pad: value for key, value in self.primitive.items()}\n return DictStateFn(new_dict, coeff=self.coeff, is_measurement=self.is_measurement)\n\n def tensor(self, other: OperatorBase) -> OperatorBase:\n # Both dicts\n if isinstance(other, DictStateFn):\n new_dict = {k1 + k2: v1 * v2 for ((k1, v1,), (k2, v2)) in\n itertools.product(self.primitive.items(), other.primitive.items())}\n return StateFn(new_dict,\n coeff=self.coeff * other.coeff,\n is_measurement=self.is_measurement)\n # pylint: disable=cyclic-import\n from ..list_ops.tensored_op import TensoredOp\n return TensoredOp([self, other])\n\n def to_density_matrix(self, massive: bool = False) -> np.ndarray:\n OperatorBase._check_massive('to_density_matrix', True, self.num_qubits, massive)\n states = int(2 ** self.num_qubits)\n return self.to_matrix(massive=massive) * np.eye(states) * self.coeff\n\n def to_matrix(self, massive: bool = False) -> np.ndarray:\n OperatorBase._check_massive('to_matrix', False, self.num_qubits, massive)\n states = int(2 ** self.num_qubits)\n probs = np.zeros(states) + 0.j\n for k, v in self.primitive.items():\n probs[int(k, 2)] = v\n vec = probs * self.coeff\n\n # Reshape for measurements so np.dot still works for composition.\n return vec if not self.is_measurement else vec.reshape(1, -1)\n\n def to_spmatrix(self) -> sparse.spmatrix:\n \"\"\"Same as to_matrix, but returns csr sparse matrix.\n\n Returns:\n CSR sparse matrix representation of the State function.\n\n Raises:\n ValueError: invalid parameters.\n \"\"\"\n\n indices = [int(v, 2) for v in self.primitive.keys()]\n vals = np.array(list(self.primitive.values())) * self.coeff\n spvec = sparse.csr_matrix((vals, (np.zeros(len(indices), dtype=int), indices)),\n shape=(1, 2**self.num_qubits))\n return spvec if not self.is_measurement else spvec.transpose()\n\n def to_spmatrix_op(self) -> OperatorBase:\n \"\"\"Convert this state function to a ``SparseVectorStateFn``.\"\"\"\n from .sparse_vector_state_fn import SparseVectorStateFn\n return SparseVectorStateFn(self.to_spmatrix(), self.coeff, self.is_measurement)\n\n def to_circuit_op(self) -> OperatorBase:\n \"\"\"Convert this state function to a ``CircuitStateFn``.\"\"\"\n from .circuit_state_fn import CircuitStateFn\n csfn = CircuitStateFn.from_dict(self.primitive) * self.coeff\n return csfn.adjoint() if self.is_measurement else csfn\n\n def __str__(self) -> str:\n prim_str = str(self.primitive)\n if self.coeff == 1.0:\n return \"{}({})\".format('DictStateFn' if not self.is_measurement\n else 'DictMeasurement', prim_str)\n else:\n return \"{}({}) * {}\".format('DictStateFn' if not self.is_measurement\n else 'DictMeasurement',\n prim_str,\n self.coeff)\n\n # pylint: disable=too-many-return-statements\n def eval(\n self,\n front: Optional[\n Union[str, Dict[str, complex], np.ndarray, OperatorBase, Statevector]\n ] = None,\n ) -> Union[OperatorBase, complex]:\n if front is None:\n sparse_vector_state_fn = self.to_spmatrix_op().eval()\n return sparse_vector_state_fn\n\n if not self.is_measurement and isinstance(front, OperatorBase):\n raise ValueError(\n 'Cannot compute overlap with StateFn or Operator if not Measurement. Try taking '\n 'sf.adjoint() first to convert to measurement.')\n\n if isinstance(front, ListOp) and front.distributive:\n return front.combo_fn([self.eval(front.coeff * front_elem)\n for front_elem in front.oplist])\n\n # For now, always do this. If it's not performant, we can be more granular.\n if not isinstance(front, OperatorBase):\n front = StateFn(front)\n\n # pylint: disable=cyclic-import\n from ..operator_globals import EVAL_SIG_DIGITS\n\n # If the primitive is a lookup of bitstrings,\n # we define all missing strings to have a function value of\n # zero.\n if isinstance(front, DictStateFn):\n return np.round(\n cast(float, sum([v * front.primitive.get(b, 0) for (b, v) in\n self.primitive.items()]) * self.coeff * front.coeff),\n decimals=EVAL_SIG_DIGITS)\n\n # All remaining possibilities only apply when self.is_measurement is True\n\n if isinstance(front, VectorStateFn):\n # TODO does it need to be this way for measurement?\n # return sum([v * front.primitive.data[int(b, 2)] *\n # np.conj(front.primitive.data[int(b, 2)])\n return np.round(\n cast(float, sum([v * front.primitive.data[int(b, 2)] for (b, v) in\n self.primitive.items()]) * self.coeff),\n decimals=EVAL_SIG_DIGITS)\n\n from .circuit_state_fn import CircuitStateFn\n if isinstance(front, CircuitStateFn):\n # Don't reimplement logic from CircuitStateFn\n self_adjoint = cast(DictStateFn, self.adjoint())\n return np.conj(front.adjoint().eval(self_adjoint.primitive)) * self.coeff\n\n from .operator_state_fn import OperatorStateFn\n if isinstance(front, OperatorStateFn):\n return cast(Union[OperatorBase, float, complex], front.adjoint().eval(self.adjoint()))\n\n # All other OperatorBases go here\n self_adjoint = cast(DictStateFn, self.adjoint())\n adjointed_eval = cast(OperatorBase, front.adjoint().eval(self_adjoint.primitive))\n return adjointed_eval.adjoint() * self.coeff\n\n def sample(self,\n shots: int = 1024,\n massive: bool = False,\n reverse_endianness: bool = False) -> Dict[str, float]:\n probs = np.square(np.abs(np.array(list(self.primitive.values()))))\n unique, counts = np.unique(algorithm_globals.random.choice(list(self.primitive.keys()),\n size=shots,\n p=(probs / sum(probs))),\n return_counts=True)\n counts = dict(zip(unique, counts))\n if reverse_endianness:\n scaled_dict = {bstr[::-1]: (prob / shots) for (bstr, prob) in counts.items()}\n else:\n scaled_dict = {bstr: (prob / shots) for (bstr, prob) in counts.items()}\n return dict(sorted(scaled_dict.items(), key=lambda x: x[1], reverse=True))\n",
"path": "qiskit/opflow/state_fns/dict_state_fn.py"
}
] | diff --git a/qiskit/opflow/state_fns/dict_state_fn.py b/qiskit/opflow/state_fns/dict_state_fn.py
index a6a313f02815..73507140f0cc 100644
--- a/qiskit/opflow/state_fns/dict_state_fn.py
+++ b/qiskit/opflow/state_fns/dict_state_fn.py
@@ -81,7 +81,7 @@ def primitive_strings(self) -> Set[str]:
@property
def num_qubits(self) -> int:
- return len(list(self.primitive.keys())[0])
+ return len(next(iter(self.primitive)))
def add(self, other: OperatorBase) -> OperatorBase:
if not self.num_qubits == other.num_qubits:
|
kornia__kornia-2610 | bug in ycbcr_to_rgb function
### Describe the bug
https://github.com/kornia/kornia/blob/2c084f8dc108b3f0f3c8983ac3f25bf88638d01a/kornia/color/ycbcr.py#L70
#### now:
return torch.stack([r, g, b], -3)
#### need to be:
return torch.stack([r, g, b], -3).clamp(0,1)
#### because:
| [
{
"content": "import torch\nfrom torch import Tensor, nn\n\n\ndef _rgb_to_y(r: Tensor, g: Tensor, b: Tensor) -> Tensor:\n y: Tensor = 0.299 * r + 0.587 * g + 0.114 * b\n return y\n\n\ndef rgb_to_ycbcr(image: Tensor) -> Tensor:\n r\"\"\"Convert an RGB image to YCbCr.\n\n .. image:: _static/img/rgb_to_ycbcr.png\n\n Args:\n image: RGB Image to be converted to YCbCr with shape :math:`(*, 3, H, W)`.\n\n Returns:\n YCbCr version of the image with shape :math:`(*, 3, H, W)`.\n\n Examples:\n >>> input = torch.rand(2, 3, 4, 5)\n >>> output = rgb_to_ycbcr(input) # 2x3x4x5\n \"\"\"\n if not isinstance(image, Tensor):\n raise TypeError(f\"Input type is not a Tensor. Got {type(image)}\")\n\n if len(image.shape) < 3 or image.shape[-3] != 3:\n raise ValueError(f\"Input size must have a shape of (*, 3, H, W). Got {image.shape}\")\n\n r: Tensor = image[..., 0, :, :]\n g: Tensor = image[..., 1, :, :]\n b: Tensor = image[..., 2, :, :]\n\n delta: float = 0.5\n y: Tensor = _rgb_to_y(r, g, b)\n cb: Tensor = (b - y) * 0.564 + delta\n cr: Tensor = (r - y) * 0.713 + delta\n return torch.stack([y, cb, cr], -3)\n\n\ndef rgb_to_y(image: Tensor) -> Tensor:\n r\"\"\"Convert an RGB image to Y.\n\n Args:\n image: RGB Image to be converted to Y with shape :math:`(*, 3, H, W)`.\n\n Returns:\n Y version of the image with shape :math:`(*, 1, H, W)`.\n\n Examples:\n >>> input = torch.rand(2, 3, 4, 5)\n >>> output = rgb_to_y(input) # 2x1x4x5\n \"\"\"\n if not isinstance(image, Tensor):\n raise TypeError(f\"Input type is not a Tensor. Got {type(image)}\")\n\n if len(image.shape) < 3 or image.shape[-3] != 3:\n raise ValueError(f\"Input size must have a shape of (*, 3, H, W). Got {image.shape}\")\n\n r: Tensor = image[..., 0:1, :, :]\n g: Tensor = image[..., 1:2, :, :]\n b: Tensor = image[..., 2:3, :, :]\n\n y: Tensor = _rgb_to_y(r, g, b)\n return y\n\n\ndef ycbcr_to_rgb(image: Tensor) -> Tensor:\n r\"\"\"Convert an YCbCr image to RGB.\n\n The image data is assumed to be in the range of (0, 1).\n\n Args:\n image: YCbCr Image to be converted to RGB with shape :math:`(*, 3, H, W)`.\n\n Returns:\n RGB version of the image with shape :math:`(*, 3, H, W)`.\n\n Examples:\n >>> input = torch.rand(2, 3, 4, 5)\n >>> output = ycbcr_to_rgb(input) # 2x3x4x5\n \"\"\"\n if not isinstance(image, Tensor):\n raise TypeError(f\"Input type is not a Tensor. Got {type(image)}\")\n\n if len(image.shape) < 3 or image.shape[-3] != 3:\n raise ValueError(f\"Input size must have a shape of (*, 3, H, W). Got {image.shape}\")\n\n y: Tensor = image[..., 0, :, :]\n cb: Tensor = image[..., 1, :, :]\n cr: Tensor = image[..., 2, :, :]\n\n delta: float = 0.5\n cb_shifted: Tensor = cb - delta\n cr_shifted: Tensor = cr - delta\n\n r: Tensor = y + 1.403 * cr_shifted\n g: Tensor = y - 0.714 * cr_shifted - 0.344 * cb_shifted\n b: Tensor = y + 1.773 * cb_shifted\n return torch.stack([r, g, b], -3)\n\n\nclass RgbToYcbcr(nn.Module):\n r\"\"\"Convert an image from RGB to YCbCr.\n\n The image data is assumed to be in the range of (0, 1).\n\n Returns:\n YCbCr version of the image.\n\n Shape:\n - image: :math:`(*, 3, H, W)`\n - output: :math:`(*, 3, H, W)`\n\n Examples:\n >>> input = torch.rand(2, 3, 4, 5)\n >>> ycbcr = RgbToYcbcr()\n >>> output = ycbcr(input) # 2x3x4x5\n \"\"\"\n\n def forward(self, image: Tensor) -> Tensor:\n return rgb_to_ycbcr(image)\n\n\nclass YcbcrToRgb(nn.Module):\n r\"\"\"Convert an image from YCbCr to Rgb.\n\n The image data is assumed to be in the range of (0, 1).\n\n Returns:\n RGB version of the image.\n\n Shape:\n - image: :math:`(*, 3, H, W)`\n - output: :math:`(*, 3, H, W)`\n\n Examples:\n >>> input = torch.rand(2, 3, 4, 5)\n >>> rgb = YcbcrToRgb()\n >>> output = rgb(input) # 2x3x4x5\n \"\"\"\n\n def forward(self, image: Tensor) -> Tensor:\n return ycbcr_to_rgb(image)\n",
"path": "kornia/color/ycbcr.py"
}
] | [
{
"content": "import torch\nfrom torch import Tensor, nn\n\n\ndef _rgb_to_y(r: Tensor, g: Tensor, b: Tensor) -> Tensor:\n y: Tensor = 0.299 * r + 0.587 * g + 0.114 * b\n return y\n\n\ndef rgb_to_ycbcr(image: Tensor) -> Tensor:\n r\"\"\"Convert an RGB image to YCbCr.\n\n .. image:: _static/img/rgb_to_ycbcr.png\n\n Args:\n image: RGB Image to be converted to YCbCr with shape :math:`(*, 3, H, W)`.\n\n Returns:\n YCbCr version of the image with shape :math:`(*, 3, H, W)`.\n\n Examples:\n >>> input = torch.rand(2, 3, 4, 5)\n >>> output = rgb_to_ycbcr(input) # 2x3x4x5\n \"\"\"\n if not isinstance(image, Tensor):\n raise TypeError(f\"Input type is not a Tensor. Got {type(image)}\")\n\n if len(image.shape) < 3 or image.shape[-3] != 3:\n raise ValueError(f\"Input size must have a shape of (*, 3, H, W). Got {image.shape}\")\n\n r: Tensor = image[..., 0, :, :]\n g: Tensor = image[..., 1, :, :]\n b: Tensor = image[..., 2, :, :]\n\n delta: float = 0.5\n y: Tensor = _rgb_to_y(r, g, b)\n cb: Tensor = (b - y) * 0.564 + delta\n cr: Tensor = (r - y) * 0.713 + delta\n return torch.stack([y, cb, cr], -3)\n\n\ndef rgb_to_y(image: Tensor) -> Tensor:\n r\"\"\"Convert an RGB image to Y.\n\n Args:\n image: RGB Image to be converted to Y with shape :math:`(*, 3, H, W)`.\n\n Returns:\n Y version of the image with shape :math:`(*, 1, H, W)`.\n\n Examples:\n >>> input = torch.rand(2, 3, 4, 5)\n >>> output = rgb_to_y(input) # 2x1x4x5\n \"\"\"\n if not isinstance(image, Tensor):\n raise TypeError(f\"Input type is not a Tensor. Got {type(image)}\")\n\n if len(image.shape) < 3 or image.shape[-3] != 3:\n raise ValueError(f\"Input size must have a shape of (*, 3, H, W). Got {image.shape}\")\n\n r: Tensor = image[..., 0:1, :, :]\n g: Tensor = image[..., 1:2, :, :]\n b: Tensor = image[..., 2:3, :, :]\n\n y: Tensor = _rgb_to_y(r, g, b)\n return y\n\n\ndef ycbcr_to_rgb(image: Tensor) -> Tensor:\n r\"\"\"Convert an YCbCr image to RGB.\n\n The image data is assumed to be in the range of (0, 1).\n\n Args:\n image: YCbCr Image to be converted to RGB with shape :math:`(*, 3, H, W)`.\n\n Returns:\n RGB version of the image with shape :math:`(*, 3, H, W)`.\n\n Examples:\n >>> input = torch.rand(2, 3, 4, 5)\n >>> output = ycbcr_to_rgb(input) # 2x3x4x5\n \"\"\"\n if not isinstance(image, Tensor):\n raise TypeError(f\"Input type is not a Tensor. Got {type(image)}\")\n\n if len(image.shape) < 3 or image.shape[-3] != 3:\n raise ValueError(f\"Input size must have a shape of (*, 3, H, W). Got {image.shape}\")\n\n y: Tensor = image[..., 0, :, :]\n cb: Tensor = image[..., 1, :, :]\n cr: Tensor = image[..., 2, :, :]\n\n delta: float = 0.5\n cb_shifted: Tensor = cb - delta\n cr_shifted: Tensor = cr - delta\n\n r: Tensor = y + 1.403 * cr_shifted\n g: Tensor = y - 0.714 * cr_shifted - 0.344 * cb_shifted\n b: Tensor = y + 1.773 * cb_shifted\n return torch.stack([r, g, b], -3).clamp(0, 1)\n\n\nclass RgbToYcbcr(nn.Module):\n r\"\"\"Convert an image from RGB to YCbCr.\n\n The image data is assumed to be in the range of (0, 1).\n\n Returns:\n YCbCr version of the image.\n\n Shape:\n - image: :math:`(*, 3, H, W)`\n - output: :math:`(*, 3, H, W)`\n\n Examples:\n >>> input = torch.rand(2, 3, 4, 5)\n >>> ycbcr = RgbToYcbcr()\n >>> output = ycbcr(input) # 2x3x4x5\n \"\"\"\n\n def forward(self, image: Tensor) -> Tensor:\n return rgb_to_ycbcr(image)\n\n\nclass YcbcrToRgb(nn.Module):\n r\"\"\"Convert an image from YCbCr to Rgb.\n\n The image data is assumed to be in the range of (0, 1).\n\n Returns:\n RGB version of the image.\n\n Shape:\n - image: :math:`(*, 3, H, W)`\n - output: :math:`(*, 3, H, W)`\n\n Examples:\n >>> input = torch.rand(2, 3, 4, 5)\n >>> rgb = YcbcrToRgb()\n >>> output = rgb(input) # 2x3x4x5\n \"\"\"\n\n def forward(self, image: Tensor) -> Tensor:\n return ycbcr_to_rgb(image)\n",
"path": "kornia/color/ycbcr.py"
}
] | diff --git a/kornia/color/ycbcr.py b/kornia/color/ycbcr.py
index 4e9fe5d856..c0cb597de7 100644
--- a/kornia/color/ycbcr.py
+++ b/kornia/color/ycbcr.py
@@ -98,7 +98,7 @@ def ycbcr_to_rgb(image: Tensor) -> Tensor:
r: Tensor = y + 1.403 * cr_shifted
g: Tensor = y - 0.714 * cr_shifted - 0.344 * cb_shifted
b: Tensor = y + 1.773 * cb_shifted
- return torch.stack([r, g, b], -3)
+ return torch.stack([r, g, b], -3).clamp(0, 1)
class RgbToYcbcr(nn.Module):
diff --git a/test/color/test_ycbcr.py b/test/color/test_ycbcr.py
index db513014fb..c291998296 100644
--- a/test/color/test_ycbcr.py
+++ b/test/color/test_ycbcr.py
@@ -185,25 +185,25 @@ def test_unit(self, device, dtype):
[
[
[
- [1.3226931, 0.5639256, 0.14902398, 1.0217545, 0.2569923],
- [0.37973762, 0.64386904, 1.1992811, -0.603531, 0.4239992],
- [1.0080798, 1.3131194, -0.1370286, 0.60653293, 0.3505922],
- [-0.4573757, 0.6076593, 0.33508536, 0.27470887, 1.2284023],
- [0.52727354, -0.1673561, 0.05404532, 1.2725366, 0.5783674],
+ [1.0000, 0.5639256, 0.14902398, 1.0000, 0.2569923],
+ [0.37973762, 0.64386904, 1.0000, 0.0000, 0.4239992],
+ [1.0000, 1.0000, 0.0000, 0.60653293, 0.3505922],
+ [0.0000, 0.6076593, 0.33508536, 0.27470887, 1.0000],
+ [0.52727354, 0.0000, 0.05404532, 1.0000, 0.5783674],
],
[
- [0.736647, -0.01305042, 0.55139434, 0.44206098, 0.4548782],
- [-0.22063601, 0.98196536, 0.54739904, 0.33826917, 0.850068],
- [0.72412336, 0.6222996, 0.110618, 1.0039049, 0.614918],
+ [0.736647, 0.0000, 0.55139434, 0.44206098, 0.4548782],
+ [0.0000, 0.98196536, 0.54739904, 0.33826917, 0.850068],
+ [0.72412336, 0.6222996, 0.110618, 1.0000, 0.614918],
[0.15862459, 0.0699634, 0.66296846, 0.4845066, 0.3705502],
- [1.0145653, 0.46070462, 0.5654058, 0.24897486, -0.11174999],
+ [1.0000, 0.46070462, 0.5654058, 0.24897486, 0.0000],
],
[
- [1.4161384, 0.88769174, 0.4394987, -0.31889397, -0.5671302],
- [0.77483994, 0.99839956, 1.6813064, 0.41622213, 1.3508832],
- [0.7488585, -0.04955059, 0.01748962, 0.9683316, 0.49795526],
- [0.9473541, 1.2473994, -0.3918787, 0.47037587, 1.2893858],
- [1.4082898, -0.21875012, 0.6804801, 0.9795798, 0.24646705],
+ [1.0000, 0.88769174, 0.4394987, 0.0000, 0.0000],
+ [0.77483994, 0.99839956, 1.0000, 0.41622213, 1.0000],
+ [0.7488585, 0.0000, 0.01748962, 0.9683316, 0.49795526],
+ [0.9473541, 1.0000, 0.0000, 0.47037587, 1.0000],
+ [1.0000, 0.0000, 0.6804801, 0.9795798, 0.24646705],
],
]
],
diff --git a/test/geometry/camera/test_pinhole.py b/test/geometry/camera/test_pinhole.py
index da890cc8e4..54b111ce80 100644
--- a/test/geometry/camera/test_pinhole.py
+++ b/test/geometry/camera/test_pinhole.py
@@ -198,6 +198,7 @@ def test_consistency(self, batch_size, device, dtype):
assert_close(pixel_coords_concat, pixel_coords_input, atol=1e-4, rtol=1e-4)
@pytest.mark.parametrize("batch_size", (1,))
+ @pytest.mark.slow
def test_gradcheck(self, batch_size, device, dtype):
H, W = 10, 20
fx, fy = W, H
diff --git a/test/nerf/test_nerf_solver.py b/test/nerf/test_nerf_solver.py
index bf856d2dde..bdcaba8f2b 100755
--- a/test/nerf/test_nerf_solver.py
+++ b/test/nerf/test_nerf_solver.py
@@ -26,6 +26,7 @@ def test_parameter_change_after_one_epoch(self, device, dtype):
for param_before_update, param_after_update in zip(params_before_update, params_after_update)
)
+ @pytest.mark.slow
def test_only_red_uniform_sampling(self, device, dtype):
torch.manual_seed(1) # For reproducibility of random processes
camera = create_one_camera(5, 9, device, dtype)
|
cookiecutter__cookiecutter-539 | Increase development status to 'beta' or 'stable'.
I think we can say the project is waaaay beyond alpha. :wink:
| [
{
"content": "#!/usr/bin/env python\n\nimport os\nimport sys\n\ntry:\n from setuptools import setup\nexcept ImportError:\n from distutils.core import setup\n\nversion = \"1.1.0\"\n\nif sys.argv[-1] == 'publish':\n os.system('python setup.py sdist upload')\n os.system('python setup.py bdist_wheel upload')\n sys.exit()\n\nif sys.argv[-1] == 'tag':\n os.system(\"git tag -a %s -m 'version %s'\" % (version, version))\n os.system(\"git push --tags\")\n sys.exit()\n\nwith open('README.rst') as readme_file:\n readme = readme_file.read()\n\nwith open('HISTORY.rst') as history_file:\n history = history_file.read().replace('.. :changelog:', '')\n\nrequirements = [\n 'future>=0.15.2',\n 'binaryornot>=0.2.0',\n 'jinja2>=2.7',\n 'PyYAML>=3.10',\n 'click>=5.0',\n 'whichcraft>=0.1.1'\n]\n\nlong_description = readme + '\\n\\n' + history\n\nif sys.argv[-1] == 'readme':\n print(long_description)\n sys.exit()\n\n\nsetup(\n name='cookiecutter',\n version=version,\n description=('A command-line utility that creates projects from project '\n 'templates, e.g. creating a Python package project from a '\n 'Python package project template.'),\n long_description=long_description,\n author='Audrey Roy',\n author_email='[email protected]',\n url='https://github.com/audreyr/cookiecutter',\n packages=[\n 'cookiecutter',\n ],\n package_dir={'cookiecutter': 'cookiecutter'},\n entry_points={\n 'console_scripts': [\n 'cookiecutter = cookiecutter.cli:main',\n ]\n },\n include_package_data=True,\n install_requires=requirements,\n license='BSD',\n zip_safe=False,\n classifiers=[\n 'Development Status :: 3 - Alpha',\n 'Environment :: Console',\n 'Intended Audience :: Developers',\n 'Natural Language :: English',\n 'License :: OSI Approved :: BSD License',\n 'Programming Language :: Python',\n 'Programming Language :: Python :: 2',\n 'Programming Language :: Python :: 2.7',\n 'Programming Language :: Python :: 3',\n 'Programming Language :: Python :: 3.3',\n 'Programming Language :: Python :: 3.4',\n 'Programming Language :: Python :: 3.5',\n 'Programming Language :: Python :: Implementation :: CPython',\n 'Programming Language :: Python :: Implementation :: PyPy',\n 'Topic :: Software Development',\n ],\n keywords=(\n 'cookiecutter, Python, projects, project templates, Jinja2, '\n 'skeleton, scaffolding, project directory, setup.py, package, '\n 'packaging'\n ),\n)\n",
"path": "setup.py"
}
] | [
{
"content": "#!/usr/bin/env python\n\nimport os\nimport sys\n\ntry:\n from setuptools import setup\nexcept ImportError:\n from distutils.core import setup\n\nversion = \"1.1.0\"\n\nif sys.argv[-1] == 'publish':\n os.system('python setup.py sdist upload')\n os.system('python setup.py bdist_wheel upload')\n sys.exit()\n\nif sys.argv[-1] == 'tag':\n os.system(\"git tag -a %s -m 'version %s'\" % (version, version))\n os.system(\"git push --tags\")\n sys.exit()\n\nwith open('README.rst') as readme_file:\n readme = readme_file.read()\n\nwith open('HISTORY.rst') as history_file:\n history = history_file.read().replace('.. :changelog:', '')\n\nrequirements = [\n 'future>=0.15.2',\n 'binaryornot>=0.2.0',\n 'jinja2>=2.7',\n 'PyYAML>=3.10',\n 'click>=5.0',\n 'whichcraft>=0.1.1'\n]\n\nlong_description = readme + '\\n\\n' + history\n\nif sys.argv[-1] == 'readme':\n print(long_description)\n sys.exit()\n\n\nsetup(\n name='cookiecutter',\n version=version,\n description=('A command-line utility that creates projects from project '\n 'templates, e.g. creating a Python package project from a '\n 'Python package project template.'),\n long_description=long_description,\n author='Audrey Roy',\n author_email='[email protected]',\n url='https://github.com/audreyr/cookiecutter',\n packages=[\n 'cookiecutter',\n ],\n package_dir={'cookiecutter': 'cookiecutter'},\n entry_points={\n 'console_scripts': [\n 'cookiecutter = cookiecutter.cli:main',\n ]\n },\n include_package_data=True,\n install_requires=requirements,\n license='BSD',\n zip_safe=False,\n classifiers=[\n 'Development Status :: 5 - Production/Stable',\n 'Environment :: Console',\n 'Intended Audience :: Developers',\n 'Natural Language :: English',\n 'License :: OSI Approved :: BSD License',\n 'Programming Language :: Python',\n 'Programming Language :: Python :: 2',\n 'Programming Language :: Python :: 2.7',\n 'Programming Language :: Python :: 3',\n 'Programming Language :: Python :: 3.3',\n 'Programming Language :: Python :: 3.4',\n 'Programming Language :: Python :: 3.5',\n 'Programming Language :: Python :: Implementation :: CPython',\n 'Programming Language :: Python :: Implementation :: PyPy',\n 'Topic :: Software Development',\n ],\n keywords=(\n 'cookiecutter, Python, projects, project templates, Jinja2, '\n 'skeleton, scaffolding, project directory, setup.py, package, '\n 'packaging'\n ),\n)\n",
"path": "setup.py"
}
] | diff --git a/setup.py b/setup.py
index 2d504822b..db893b12e 100755
--- a/setup.py
+++ b/setup.py
@@ -66,7 +66,7 @@
license='BSD',
zip_safe=False,
classifiers=[
- 'Development Status :: 3 - Alpha',
+ 'Development Status :: 5 - Production/Stable',
'Environment :: Console',
'Intended Audience :: Developers',
'Natural Language :: English',
|
ivy-llc__ivy-13425 | normal
| [
{
"content": "import ivy\nfrom ivy.func_wrapper import with_supported_dtypes\nfrom ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back\n\ntry:\n from torch import Generator\nexcept ImportError:\n from types import SimpleNamespace\n\n Generator = SimpleNamespace\n\n\ndef seed() -> int:\n \"\"\"Returns a 64 bit number used to seed the RNG\"\"\"\n return int(ivy.randint(-(2**63), 2**63 - 1))\n\n\n@to_ivy_arrays_and_back\ndef manual_seed(seed: int):\n ivy.seed(seed_value=seed)\n return Generator().manual_seed(seed)\n\n\n@with_supported_dtypes(\n {\n \"1.11.0 and below\": (\n \"float32\",\n \"float64\",\n )\n },\n \"torch\",\n)\n@to_ivy_arrays_and_back\ndef multinomial(input, num_samples, replacement=False, *, generator=None, out=None):\n return ivy.multinomial(\n num_samples + 1, # doesn't matter because `probs` is provided, but should be\n # greater than the number of samples\n num_samples,\n probs=input,\n replace=replacement,\n out=out,\n )\n\n\n@with_supported_dtypes(\n {\n \"1.11.0 and below\": (\n \"float32\",\n \"float64\",\n )\n },\n \"torch\",\n)\n@to_ivy_arrays_and_back\ndef poisson(input, generator=None):\n return ivy.poisson(input, shape=None)\n\n\n@to_ivy_arrays_and_back\ndef rand(\n size,\n *,\n generator=None,\n out=None,\n dtype=None,\n layout=None,\n device=None,\n requires_grad=False,\n pin_memory=False\n):\n return ivy.random_uniform(\n shape=size,\n out=out,\n dtype=dtype,\n device=device,\n )\n\n\n@to_ivy_arrays_and_back\ndef rand_like(\n input,\n *,\n dtype=None,\n layout=None,\n device=None,\n requires_grad=False,\n memory_format=False\n):\n shape = input.shape\n if not dtype:\n dtype = input.dtype\n\n return ivy.random_uniform(\n shape=shape,\n dtype=dtype,\n device=device,\n )\n\n\n@to_ivy_arrays_and_back\ndef randn(\n size,\n *,\n generator=None,\n out=None,\n dtype=None,\n layout=None,\n device=None,\n requires_grad=False,\n pin_memory=False\n):\n return ivy.random_normal(\n shape=size,\n out=out,\n dtype=dtype,\n device=device,\n )\n",
"path": "ivy/functional/frontends/torch/random_sampling.py"
}
] | [
{
"content": "import ivy\nfrom ivy.func_wrapper import with_supported_dtypes\nfrom ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back\n\ntry:\n from torch import Generator\nexcept ImportError:\n from types import SimpleNamespace\n\n Generator = SimpleNamespace\n\n\ndef seed() -> int:\n \"\"\"Returns a 64 bit number used to seed the RNG\"\"\"\n return int(ivy.randint(-(2**63), 2**63 - 1))\n\n\n@to_ivy_arrays_and_back\ndef manual_seed(seed: int):\n ivy.seed(seed_value=seed)\n return Generator().manual_seed(seed)\n\n\n@with_supported_dtypes(\n {\n \"1.11.0 and below\": (\n \"float32\",\n \"float64\",\n )\n },\n \"torch\",\n)\n@to_ivy_arrays_and_back\ndef multinomial(input, num_samples, replacement=False, *, generator=None, out=None):\n return ivy.multinomial(\n num_samples + 1, # doesn't matter because `probs` is provided, but should be\n # greater than the number of samples\n num_samples,\n probs=input,\n replace=replacement,\n out=out,\n )\n\n\n@with_supported_dtypes(\n {\n \"1.11.0 and below\": (\n \"float32\",\n \"float64\",\n )\n },\n \"torch\",\n)\n@to_ivy_arrays_and_back\ndef poisson(input, generator=None):\n return ivy.poisson(input, shape=None)\n\n\n@to_ivy_arrays_and_back\ndef rand(\n size,\n *,\n generator=None,\n out=None,\n dtype=None,\n layout=None,\n device=None,\n requires_grad=False,\n pin_memory=False\n):\n return ivy.random_uniform(\n shape=size,\n out=out,\n dtype=dtype,\n device=device,\n )\n\n\n@with_supported_dtypes(\n {\n \"1.11.0 and below\": (\n \"float32\",\n \"float64\",\n )\n },\n \"torch\",\n)\n@to_ivy_arrays_and_back\ndef normal(mean, std, *, generator=None, out=None):\n return ivy.random_normal(mean=mean, std=std, out=out)\n \n\n@to_ivy_arrays_and_back\ndef rand_like(\n input,\n *,\n dtype=None,\n layout=None,\n device=None,\n requires_grad=False,\n memory_format=False\n):\n shape = input.shape\n if not dtype:\n dtype = input.dtype\n\n return ivy.random_uniform(\n shape=shape,\n dtype=dtype,\n device=device,\n )\n\n\n@to_ivy_arrays_and_back\ndef randn(\n size,\n *,\n generator=None,\n out=None,\n dtype=None,\n layout=None,\n device=None,\n requires_grad=False,\n pin_memory=False\n):\n return ivy.random_normal(\n shape=size,\n out=out,\n dtype=dtype,\n device=device,\n )\n",
"path": "ivy/functional/frontends/torch/random_sampling.py"
}
] | diff --git a/ivy/functional/frontends/torch/random_sampling.py b/ivy/functional/frontends/torch/random_sampling.py
index a8373e819e42a..c830a6863baaa 100644
--- a/ivy/functional/frontends/torch/random_sampling.py
+++ b/ivy/functional/frontends/torch/random_sampling.py
@@ -76,6 +76,20 @@ def rand(
)
+@with_supported_dtypes(
+ {
+ "1.11.0 and below": (
+ "float32",
+ "float64",
+ )
+ },
+ "torch",
+)
+@to_ivy_arrays_and_back
+def normal(mean, std, *, generator=None, out=None):
+ return ivy.random_normal(mean=mean, std=std, out=out)
+
+
@to_ivy_arrays_and_back
def rand_like(
input,
diff --git a/ivy_tests/test_ivy/test_frontends/test_torch/test_random_sampling.py b/ivy_tests/test_ivy/test_frontends/test_torch/test_random_sampling.py
index cc0ebd8fbb15e..2adc45cb57022 100644
--- a/ivy_tests/test_ivy/test_frontends/test_torch/test_random_sampling.py
+++ b/ivy_tests/test_ivy/test_frontends/test_torch/test_random_sampling.py
@@ -164,6 +164,62 @@ def call():
assert u.shape == v.shape
+@handle_frontend_test(
+ fn_tree="torch.normal",
+ dtype_and_mean=helpers.dtype_and_values(
+ available_dtypes=helpers.get_dtypes("float"),
+ min_value=-1000,
+ max_value=1000,
+ min_num_dims=1,
+ max_num_dims=5,
+ min_dim_size=2,
+ ),
+ dtype_and_std=helpers.dtype_and_values(
+ available_dtypes=helpers.get_dtypes("float"),
+ min_value=0,
+ max_value=1000,
+ min_num_dims=1,
+ max_num_dims=5,
+ min_dim_size=2,
+ ),
+)
+def test_torch_normal(
+ *,
+ dtype_and_mean,
+ dtype_and_std,
+ on_device,
+ fn_tree,
+ frontend,
+ test_flags,
+):
+ mean_dtype, mean = dtype_and_mean
+ _, std = dtype_and_std
+
+ def call():
+ return helpers.test_frontend_function(
+ input_dtypes=mean_dtype,
+ frontend=frontend,
+ test_flags=test_flags,
+ fn_tree=fn_tree,
+ on_device=on_device,
+ test_values=False,
+ mean=mean[0],
+ std=std[0],
+ )
+
+ ret = call()
+
+ if not ivy.exists(ret):
+ return
+
+ ret_np, ret_from_np = ret
+ ret_np = helpers.flatten_and_to_np(ret=ret_np)
+ ret_from_np = helpers.flatten_and_to_np(ret=ret_from_np)
+ for (u, v) in zip(ret_np, ret_from_np):
+ assert u.dtype == v.dtype
+ assert u.shape == v.shape
+
+
@handle_frontend_test(
fn_tree="torch.rand_like",
dtype=helpers.get_dtypes("float", full=False),
|
hpcaitech__ColossalAI-3323 | [tensor] fix some unittests
[tensor] fix some unittests
[tensor] fix some unittests
| [
{
"content": "from typing import List, Union, Any\nfrom ..proxy import ColoProxy, ColoAttribute\nimport torch\nfrom .meta_patch import meta_patched_function, meta_patched_module\n\n__all__ = ['is_element_in_list', 'extract_meta']\n\n\ndef is_element_in_list(elements: Union[List[Any], Any], list_: List[Any]):\n if isinstance(elements, (tuple, list, set)):\n for ele in elements:\n if ele not in list_:\n return False, ele\n else:\n if elements not in list_:\n return False, elements\n\n return True, None\n\n\ndef extract_meta(*args, **kwargs):\n\n def _convert(val):\n if isinstance(val, ColoProxy):\n return val.meta_data\n elif isinstance(val, (list, tuple)):\n return type(val)([_convert(ele) for ele in val])\n\n return val\n\n new_args = [_convert(val) for val in args]\n new_kwargs = {k: _convert(v) for k, v in kwargs.items()}\n return new_args, new_kwargs\n\n\ndef compute_meta_data_for_functions_proxy(target, args, kwargs):\n args_metas, kwargs_metas = extract_meta(*args, **kwargs)\n\n # fetch patched function\n if meta_patched_function.has(target):\n meta_target = meta_patched_function.get(target)\n elif meta_patched_function.has(target.__name__):\n meta_target = meta_patched_function.get(target.__name__)\n else:\n meta_target = target\n meta_out = meta_target(*args_metas, **kwargs_metas)\n if isinstance(meta_out, torch.Tensor):\n meta_out = meta_out.to(device=\"meta\")\n\n return meta_out\n",
"path": "colossalai/fx/tracer/_tracer_utils.py"
}
] | [
{
"content": "from typing import Any, List, Union\n\nimport torch\n\nfrom ..proxy import ColoAttribute, ColoProxy\nfrom .meta_patch import meta_patched_function, meta_patched_module\n\n__all__ = ['is_element_in_list', 'extract_meta']\n\n\ndef is_element_in_list(elements: Union[List[Any], Any], list_: List[Any]):\n if isinstance(elements, (tuple, list, set)):\n for ele in elements:\n if ele not in list_:\n return False, ele\n else:\n if elements not in list_:\n return False, elements\n\n return True, None\n\n\ndef extract_meta(*args, **kwargs):\n\n def _convert(val):\n if isinstance(val, ColoProxy):\n return val.meta_data\n elif isinstance(val, (list, tuple)):\n return type(val)([_convert(ele) for ele in val])\n\n return val\n\n new_args = [_convert(val) for val in args]\n new_kwargs = {k: _convert(v) for k, v in kwargs.items()}\n return new_args, new_kwargs\n\n\ndef compute_meta_data_for_functions_proxy(target, args, kwargs):\n args_metas, kwargs_metas = extract_meta(*args, **kwargs)\n\n # fetch patched function\n if meta_patched_function.has(target):\n meta_target = meta_patched_function.get(target)\n elif meta_patched_function.has(target.__name__):\n meta_target = meta_patched_function.get(target.__name__)\n else:\n meta_target = target\n meta_out = meta_target(*args_metas, **kwargs_metas)\n if isinstance(meta_out, torch.Tensor):\n meta_out = meta_out.to(device=\"meta\")\n\n return meta_out\n",
"path": "colossalai/fx/tracer/_tracer_utils.py"
}
] | diff --git a/colossalai/fx/tracer/_tracer_utils.py b/colossalai/fx/tracer/_tracer_utils.py
index 0ec49a90a133..e160497a7444 100644
--- a/colossalai/fx/tracer/_tracer_utils.py
+++ b/colossalai/fx/tracer/_tracer_utils.py
@@ -1,6 +1,8 @@
-from typing import List, Union, Any
-from ..proxy import ColoProxy, ColoAttribute
+from typing import Any, List, Union
+
import torch
+
+from ..proxy import ColoAttribute, ColoProxy
from .meta_patch import meta_patched_function, meta_patched_module
__all__ = ['is_element_in_list', 'extract_meta']
|
dmlc__dgl-2505 | jtnn example error
NOCUDA=1 python3 vaetrain_dgl.py
it shows NameError: name 'tensor' is not defined in dgl/examples/pytorch/jtnn/jtnn/nnutils.py", line 11, in cuda
return tensor
env:
dgl 0.5.3
torch 1.7.1
mac os
| [
{
"content": "import torch\nimport torch.nn as nn\nimport os\nimport dgl\n\n\ndef cuda(x):\n if torch.cuda.is_available() and not os.getenv('NOCUDA', None):\n return x.to(torch.device('cuda')) # works for both DGLGraph and tensor\n else:\n return tensor\n\n\nclass GRUUpdate(nn.Module):\n def __init__(self, hidden_size):\n nn.Module.__init__(self)\n self.hidden_size = hidden_size\n\n self.W_z = nn.Linear(2 * hidden_size, hidden_size)\n self.W_r = nn.Linear(hidden_size, hidden_size, bias=False)\n self.U_r = nn.Linear(hidden_size, hidden_size)\n self.W_h = nn.Linear(2 * hidden_size, hidden_size)\n\n def update_zm(self, node):\n src_x = node.data['src_x']\n s = node.data['s']\n rm = node.data['accum_rm']\n z = torch.sigmoid(self.W_z(torch.cat([src_x, s], 1)))\n m = torch.tanh(self.W_h(torch.cat([src_x, rm], 1)))\n m = (1 - z) * s + z * m\n return {'m': m, 'z': z}\n\n def update_r(self, node, zm=None):\n dst_x = node.data['dst_x']\n m = node.data['m'] if zm is None else zm['m']\n r_1 = self.W_r(dst_x)\n r_2 = self.U_r(m)\n r = torch.sigmoid(r_1 + r_2)\n return {'r': r, 'rm': r * m}\n\n def forward(self, node):\n dic = self.update_zm(node)\n dic.update(self.update_r(node, zm=dic))\n return dic\n\ndef tocpu(g):\n src, dst = g.edges()\n src = src.cpu()\n dst = dst.cpu()\n return dgl.graph((src, dst), num_nodes=g.number_of_nodes())\n",
"path": "examples/pytorch/jtnn/jtnn/nnutils.py"
}
] | [
{
"content": "import torch\nimport torch.nn as nn\nimport os\nimport dgl\n\n\ndef cuda(x):\n if torch.cuda.is_available() and not os.getenv('NOCUDA', None):\n return x.to(torch.device('cuda')) # works for both DGLGraph and tensor\n else:\n return x\n\n\nclass GRUUpdate(nn.Module):\n def __init__(self, hidden_size):\n nn.Module.__init__(self)\n self.hidden_size = hidden_size\n\n self.W_z = nn.Linear(2 * hidden_size, hidden_size)\n self.W_r = nn.Linear(hidden_size, hidden_size, bias=False)\n self.U_r = nn.Linear(hidden_size, hidden_size)\n self.W_h = nn.Linear(2 * hidden_size, hidden_size)\n\n def update_zm(self, node):\n src_x = node.data['src_x']\n s = node.data['s']\n rm = node.data['accum_rm']\n z = torch.sigmoid(self.W_z(torch.cat([src_x, s], 1)))\n m = torch.tanh(self.W_h(torch.cat([src_x, rm], 1)))\n m = (1 - z) * s + z * m\n return {'m': m, 'z': z}\n\n def update_r(self, node, zm=None):\n dst_x = node.data['dst_x']\n m = node.data['m'] if zm is None else zm['m']\n r_1 = self.W_r(dst_x)\n r_2 = self.U_r(m)\n r = torch.sigmoid(r_1 + r_2)\n return {'r': r, 'rm': r * m}\n\n def forward(self, node):\n dic = self.update_zm(node)\n dic.update(self.update_r(node, zm=dic))\n return dic\n\ndef tocpu(g):\n src, dst = g.edges()\n src = src.cpu()\n dst = dst.cpu()\n return dgl.graph((src, dst), num_nodes=g.number_of_nodes())\n",
"path": "examples/pytorch/jtnn/jtnn/nnutils.py"
}
] | diff --git a/examples/pytorch/jtnn/jtnn/nnutils.py b/examples/pytorch/jtnn/jtnn/nnutils.py
index 647edecb7fc8..8ef01ee25c4e 100644
--- a/examples/pytorch/jtnn/jtnn/nnutils.py
+++ b/examples/pytorch/jtnn/jtnn/nnutils.py
@@ -8,7 +8,7 @@ def cuda(x):
if torch.cuda.is_available() and not os.getenv('NOCUDA', None):
return x.to(torch.device('cuda')) # works for both DGLGraph and tensor
else:
- return tensor
+ return x
class GRUUpdate(nn.Module):
|
carpentries__amy-2126 | Community Roles: Date range validation
Currently, an end date earlier than start date is allowed.
| [
{
"content": "from collections import defaultdict\nfrom typing import Any, Optional\n\nfrom django import forms\nfrom django.core.exceptions import ObjectDoesNotExist, ValidationError\n\nfrom workshops.fields import HeavySelect2Widget, ModelSelect2Widget\nfrom workshops.forms import SELECT2_SIDEBAR, BootstrapHelper, WidgetOverrideMixin\n\nfrom .models import CommunityRole, CommunityRoleConfig\n\n\nclass CommunityRoleForm(WidgetOverrideMixin, forms.ModelForm):\n class Meta:\n model = CommunityRole\n fields = (\n \"config\",\n \"person\",\n \"award\",\n \"start\",\n \"end\",\n \"inactivation\",\n \"membership\",\n \"url\",\n \"generic_relation_content_type\",\n \"generic_relation_pk\",\n )\n widgets = {\n \"config\": HeavySelect2Widget(\n data_view=\"api:communityroleconfig-list\", attrs=SELECT2_SIDEBAR\n ),\n \"person\": ModelSelect2Widget(\n data_view=\"person-lookup\", attrs=SELECT2_SIDEBAR\n ),\n \"award\": ModelSelect2Widget(\n data_view=\"award-lookup\", attrs=SELECT2_SIDEBAR\n ),\n \"membership\": ModelSelect2Widget(\n data_view=\"membership-lookup\", attrs=SELECT2_SIDEBAR\n ),\n \"generic_relation_content_type\": forms.Select(\n # \"disabled\" means the browsers will not send the field during POST.\n # See how it's handled in `clean()` method below.\n attrs={\"disabled\": \"\"},\n ),\n \"generic_relation_pk\": HeavySelect2Widget(\n data_view=\"generic-object-lookup\", attrs=SELECT2_SIDEBAR\n ),\n }\n labels = {\n \"generic_relation_content_type\": \"Generic relation object type\",\n \"generic_relation_pk\": \"Generic relation object\",\n }\n\n class Media:\n js = (\"communityrole_form.js\",)\n\n def __init__(self, *args, **kwargs):\n form_tag = kwargs.pop(\"form_tag\", True)\n super().__init__(*args, **kwargs)\n bootstrap_kwargs = {\n \"add_cancel_button\": False,\n \"form_tag\": form_tag,\n }\n self.helper = BootstrapHelper(**bootstrap_kwargs)\n\n def clean(self) -> dict[str, Any]:\n \"\"\"Validate form according to rules set up in related Community Role\n configuration.\"\"\"\n cleaned_data = super().clean()\n errors: defaultdict[str, list[ValidationError]] = defaultdict(list)\n config: Optional[CommunityRoleConfig] = cleaned_data.get(\"config\")\n\n # Config is required, but field validation for 'config' should raise\n # validation error first.\n if not config:\n return cleaned_data\n\n # Award required?\n if config.link_to_award and not cleaned_data.get(\"award\"):\n errors[\"award\"].append(\n ValidationError(f\"Award is required with community role {config}\")\n )\n\n # Specific award badge required?\n if (badge := config.award_badge_limit) and (award := cleaned_data.get(\"award\")):\n if award.badge != badge:\n errors[\"award\"].append(\n ValidationError(\n f\"Award badge must be {badge} for community role {config}\"\n )\n )\n\n # Membership required?\n if config.link_to_membership and not cleaned_data.get(\"membership\"):\n errors[\"membership\"].append(\n ValidationError(f\"Membership is required with community role {config}\")\n )\n\n # Additional URL supported?\n if not config.additional_url and cleaned_data.get(\"url\"):\n errors[\"url\"].append(\n ValidationError(f\"URL is not supported for community role {config}\")\n )\n\n # Widget for `generic_relation_content_type` is disabled in HTML, which\n # makes browsers not send it. The code below sets the default value to\n # the same value as in related config.\n generic_relation_content_type = config.generic_relation_content_type\n\n # Generic relation object must exist\n if config.generic_relation_content_type and generic_relation_content_type:\n model_class = generic_relation_content_type.model_class()\n try:\n model_class._base_manager.get(\n pk=cleaned_data.get(\"generic_relation_pk\")\n )\n except ObjectDoesNotExist:\n errors[\"generic_relation_pk\"].append(\n ValidationError(\n f\"Generic relation object of model {model_class.__name__} \"\n \"doesn't exist\"\n )\n )\n\n if errors:\n raise ValidationError(errors)\n\n return cleaned_data\n",
"path": "amy/communityroles/forms.py"
}
] | [
{
"content": "from collections import defaultdict\nfrom typing import Any, Optional\n\nfrom django import forms\nfrom django.core.exceptions import ObjectDoesNotExist, ValidationError\n\nfrom workshops.fields import HeavySelect2Widget, ModelSelect2Widget\nfrom workshops.forms import SELECT2_SIDEBAR, BootstrapHelper, WidgetOverrideMixin\n\nfrom .models import CommunityRole, CommunityRoleConfig\n\n\nclass CommunityRoleForm(WidgetOverrideMixin, forms.ModelForm):\n class Meta:\n model = CommunityRole\n fields = (\n \"config\",\n \"person\",\n \"award\",\n \"start\",\n \"end\",\n \"inactivation\",\n \"membership\",\n \"url\",\n \"generic_relation_content_type\",\n \"generic_relation_pk\",\n )\n widgets = {\n \"config\": HeavySelect2Widget(\n data_view=\"api:communityroleconfig-list\", attrs=SELECT2_SIDEBAR\n ),\n \"person\": ModelSelect2Widget(\n data_view=\"person-lookup\", attrs=SELECT2_SIDEBAR\n ),\n \"award\": ModelSelect2Widget(\n data_view=\"award-lookup\", attrs=SELECT2_SIDEBAR\n ),\n \"membership\": ModelSelect2Widget(\n data_view=\"membership-lookup\", attrs=SELECT2_SIDEBAR\n ),\n \"generic_relation_content_type\": forms.Select(\n # \"disabled\" means the browsers will not send the field during POST.\n # See how it's handled in `clean()` method below.\n attrs={\"disabled\": \"\"},\n ),\n \"generic_relation_pk\": HeavySelect2Widget(\n data_view=\"generic-object-lookup\", attrs=SELECT2_SIDEBAR\n ),\n }\n labels = {\n \"generic_relation_content_type\": \"Generic relation object type\",\n \"generic_relation_pk\": \"Generic relation object\",\n }\n\n class Media:\n js = (\"communityrole_form.js\",)\n\n def __init__(self, *args, **kwargs):\n form_tag = kwargs.pop(\"form_tag\", True)\n super().__init__(*args, **kwargs)\n bootstrap_kwargs = {\n \"add_cancel_button\": False,\n \"form_tag\": form_tag,\n }\n self.helper = BootstrapHelper(**bootstrap_kwargs)\n\n def clean(self) -> dict[str, Any]:\n \"\"\"Validate form according to rules set up in related Community Role\n configuration.\"\"\"\n cleaned_data = super().clean()\n errors: defaultdict[str, list[ValidationError]] = defaultdict(list)\n config: Optional[CommunityRoleConfig] = cleaned_data.get(\"config\")\n\n # Config is required, but field validation for 'config' should raise\n # validation error first.\n if not config:\n return cleaned_data\n\n # Award required?\n if config.link_to_award and not cleaned_data.get(\"award\"):\n errors[\"award\"].append(\n ValidationError(f\"Award is required with community role {config}\")\n )\n\n # Specific award badge required?\n if (badge := config.award_badge_limit) and (award := cleaned_data.get(\"award\")):\n if award.badge != badge:\n errors[\"award\"].append(\n ValidationError(\n f\"Award badge must be {badge} for community role {config}\"\n )\n )\n\n # Membership required?\n if config.link_to_membership and not cleaned_data.get(\"membership\"):\n errors[\"membership\"].append(\n ValidationError(f\"Membership is required with community role {config}\")\n )\n\n # Additional URL supported?\n if not config.additional_url and cleaned_data.get(\"url\"):\n errors[\"url\"].append(\n ValidationError(f\"URL is not supported for community role {config}\")\n )\n\n # Widget for `generic_relation_content_type` is disabled in HTML, which\n # makes browsers not send it. The code below sets the default value to\n # the same value as in related config.\n generic_relation_content_type = config.generic_relation_content_type\n\n # Generic relation object must exist\n if config.generic_relation_content_type and generic_relation_content_type:\n model_class = generic_relation_content_type.model_class()\n try:\n model_class._base_manager.get(\n pk=cleaned_data.get(\"generic_relation_pk\")\n )\n except ObjectDoesNotExist:\n errors[\"generic_relation_pk\"].append(\n ValidationError(\n f\"Generic relation object of model {model_class.__name__} \"\n \"doesn't exist\"\n )\n )\n\n if errors:\n raise ValidationError(errors)\n\n return cleaned_data\n\n def clean_end(self):\n \"\"\"Validate that end >= start\"\"\"\n start = self.cleaned_data.get(\"start\")\n end = self.cleaned_data.get(\"end\")\n if start and end and end < start:\n raise ValidationError(\"Must not be earlier than start date.\")\n return end\n",
"path": "amy/communityroles/forms.py"
}
] | diff --git a/amy/communityroles/forms.py b/amy/communityroles/forms.py
index e210b3013..b4ac65552 100644
--- a/amy/communityroles/forms.py
+++ b/amy/communityroles/forms.py
@@ -127,3 +127,11 @@ def clean(self) -> dict[str, Any]:
raise ValidationError(errors)
return cleaned_data
+
+ def clean_end(self):
+ """Validate that end >= start"""
+ start = self.cleaned_data.get("start")
+ end = self.cleaned_data.get("end")
+ if start and end and end < start:
+ raise ValidationError("Must not be earlier than start date.")
+ return end
diff --git a/amy/communityroles/tests/test_community_role_form.py b/amy/communityroles/tests/test_community_role_form.py
index 63d294a45..fa503ef57 100644
--- a/amy/communityroles/tests/test_community_role_form.py
+++ b/amy/communityroles/tests/test_community_role_form.py
@@ -177,6 +177,48 @@ def test_membership_required(self):
["Membership is required with community role Test"],
)
+ def test_start_date_gt_end_date_is_invalid(self):
+ """Tests error raised if end < start"""
+ # Arrange
+ data = {
+ "start": date(2021, 11, 14),
+ "end": date(2021, 11, 13), # lt start
+ }
+
+ # Act
+ form = CommunityRoleForm(data)
+
+ # Assert
+ self.assertFalse(form.is_valid()) # errors expected
+ self.assertIn("end", form.errors.keys())
+ self.assertEqual(
+ form.errors["end"],
+ ["Must not be earlier than start date."],
+ )
+
+ def test_start_end_dates_valid(self):
+ """Tests valid start date <= end date"""
+ # Arrange
+ params = [
+ (date(2021, 11, 14), date(2021, 11, 14)),
+ (date(2021, 11, 14), date(2021, 11, 15)),
+ ]
+
+ for p1, p2 in params:
+ data = {
+ "start": p1,
+ "end": p2,
+ }
+
+ # Act
+ form = CommunityRoleForm(data)
+ form.is_valid()
+
+ # Assert
+ with self.subTest():
+ self.assertEqual(form.cleaned_data.get("end"), p2)
+ self.assertNotIn("end", form.errors.keys())
+
def test_additional_url_supported(self):
# Arrange
test_config = CommunityRoleConfig.objects.create(
|
paperless-ngx__paperless-ngx-2939 | [BUG] WebSocket connection failed when enable password auth in Redis
### Description
The browser gives an error saying "WebSocket connection to 'wss://domain.com/ws/status/' failed" when I enable the password authentication in `/etc/redis/redis.conf` as follows
```
user paperless on >password allcommands allkeys allchannels
```
And the corresponding paperless-ngx config is also updated
```
PAPERLESS_REDIS=redis://paperless:[email protected]:6379
```
Everything works fine when no password is applied to Redis.
### Steps to reproduce
1. Set up password in `/etc/redis/redis.conf` by adding a line `user paperless on >password allcommands allkeys allchannels`.
2. Update the paperless-ngx config as `PAPERLESS_REDIS=redis://paperless:[email protected]:6379`
3. Restart Redis
4. Restart Paperless-ngx
### Webserver logs
```bash
It seems that there are no useful logs related to this issue.
[2023-03-23 12:35:56,075] [INFO] [paperless.management.consumer] Received SIGINT, stopping inotify
[2023-03-23 12:35:56,219] [DEBUG] [paperless.management.consumer] Consumer exiting.
[2023-03-23 12:36:19,672] [INFO] [paperless.management.consumer] Using inotify to watch directory for changes: /usr/src/paperless/consume
[2023-03-23 12:38:38,147] [INFO] [paperless.management.consumer] Received SIGINT, stopping inotify
[2023-03-23 12:38:38,154] [DEBUG] [paperless.management.consumer] Consumer exiting.
[2023-03-23 12:38:54,295] [INFO] [paperless.management.consumer] Using inotify to watch directory for changes: /usr/src/paperless/consume
[2023-03-23 12:39:07,098] [INFO] [paperless.management.consumer] Received SIGINT, stopping inotify
[2023-03-23 12:39:07,101] [DEBUG] [paperless.management.consumer] Consumer exiting.
[2023-03-23 12:39:21,806] [INFO] [paperless.management.consumer] Using inotify to watch directory for changes: /usr/src/paperless/consume
[2023-03-23 12:40:14,128] [INFO] [paperless.management.consumer] Received SIGINT, stopping inotify
[2023-03-23 12:40:14,135] [DEBUG] [paperless.management.consumer] Consumer exiting.
[2023-03-23 12:40:35,501] [INFO] [paperless.management.consumer] Using inotify to watch directory for changes: /usr/src/paperless/consume
[2023-03-23 12:42:56,560] [INFO] [paperless.management.consumer] Received SIGINT, stopping inotify
[2023-03-23 12:42:56,564] [DEBUG] [paperless.management.consumer] Consumer exiting.
[2023-03-23 12:43:11,954] [INFO] [paperless.management.consumer] Using inotify to watch directory for changes: /usr/src/paperless/consume
[2023-03-23 13:05:03,748] [INFO] [paperless.management.consumer] Received SIGINT, stopping inotify
[2023-03-23 13:05:03,764] [DEBUG] [paperless.management.consumer] Consumer exiting.
[2023-03-23 13:06:39,610] [INFO] [paperless.management.consumer] Using inotify to watch directory for changes: /usr/src/paperless/consume
[2023-03-23 13:09:44,876] [INFO] [paperless.management.consumer] Received SIGINT, stopping inotify
[2023-03-23 13:09:44,886] [DEBUG] [paperless.management.consumer] Consumer exiting.
[2023-03-23 13:10:04,482] [INFO] [paperless.management.consumer] Using inotify to watch directory for changes: /usr/src/paperless/consume
[2023-03-23 13:18:01,873] [INFO] [paperless.management.consumer] Received SIGINT, stopping inotify
[2023-03-23 13:18:01,880] [DEBUG] [paperless.management.consumer] Consumer exiting.
[2023-03-23 13:19:07,067] [INFO] [paperless.management.consumer] Using inotify to watch directory for changes: /usr/src/paperless/consume
[2023-03-23 13:23:57,472] [INFO] [paperless.management.consumer] Received SIGINT, stopping inotify
[2023-03-23 13:23:57,477] [DEBUG] [paperless.management.consumer] Consumer exiting.
[2023-03-23 13:24:16,664] [INFO] [paperless.management.consumer] Using inotify to watch directory for changes: /usr/src/paperless/consume
[2023-03-23 13:27:41,211] [INFO] [paperless.management.consumer] Received SIGINT, stopping inotify
[2023-03-23 13:27:41,219] [DEBUG] [paperless.management.consumer] Consumer exiting.
[2023-03-23 13:27:58,662] [INFO] [paperless.management.consumer] Using inotify to watch directory for changes: /usr/src/paperless/consume
[2023-03-23 13:34:54,946] [INFO] [paperless.management.consumer] Received SIGINT, stopping inotify
[2023-03-23 13:34:54,953] [DEBUG] [paperless.management.consumer] Consumer exiting.
[2023-03-23 13:35:15,317] [INFO] [paperless.management.consumer] Using inotify to watch directory for changes: /usr/src/paperless/consume
```
### Browser logs
```
WebSocket connection to 'wss://domain.com/ws/status/' failed:
connect @ main.js:3
ngOnInit @ main.js:3
MR @ main.js:3
FE @ main.js:3
fp @ main.js:3
xp @ main.js:3
Gp @ main.js:3
detectChanges @ main.js:3
tick @ main.js:3
_loadComponent @ main.js:3
bootstrap @ main.js:3
(anonymous) @ main.js:3
_moduleDoBootstrap @ main.js:3
(anonymous) @ main.js:3
invoke @ polyfills.js:1
onInvoke @ main.js:3
invoke @ polyfills.js:1
run @ polyfills.js:1
(anonymous) @ polyfills.js:1
invokeTask @ polyfills.js:1
onInvokeTask @ main.js:3
invokeTask @ polyfills.js:1
runTask @ polyfills.js:1
_ @ polyfills.js:1
invokeTask @ polyfills.js:1
Z @ polyfills.js:1
N @ polyfills.js:1
F @ polyfills.js:1
```
### Paperless-ngx version
1.13.0
### Host OS
CentOS Stream release 9
### Installation method
Docker - official image
### Browser
Chrome
### Configuration changes
I only use the ghcr.io/paperless-ngx/paperless-ngx image. The MySQL and Redis databases are from the host.
### Other
_No response_
| [
{
"content": "import datetime\nimport json\nimport math\nimport multiprocessing\nimport os\nimport re\nimport tempfile\nfrom os import PathLike\nfrom pathlib import Path\nfrom typing import Dict\nfrom typing import Final\nfrom typing import List\nfrom typing import Optional\nfrom typing import Set\nfrom typing import Tuple\nfrom typing import Union\nfrom urllib.parse import urlparse\n\nfrom celery.schedules import crontab\nfrom concurrent_log_handler.queue import setup_logging_queues\nfrom django.utils.translation import gettext_lazy as _\nfrom dotenv import load_dotenv\n\n# Tap paperless.conf if it's available\nconfiguration_path = os.getenv(\"PAPERLESS_CONFIGURATION_PATH\")\nif configuration_path and os.path.exists(configuration_path):\n load_dotenv(configuration_path)\nelif os.path.exists(\"../paperless.conf\"):\n load_dotenv(\"../paperless.conf\")\nelif os.path.exists(\"/etc/paperless.conf\"):\n load_dotenv(\"/etc/paperless.conf\")\nelif os.path.exists(\"/usr/local/etc/paperless.conf\"):\n load_dotenv(\"/usr/local/etc/paperless.conf\")\n\n# There are multiple levels of concurrency in paperless:\n# - Multiple consumers may be run in parallel.\n# - Each consumer may process multiple pages in parallel.\n# - Each Tesseract OCR run may spawn multiple threads to process a single page\n# slightly faster.\n# The performance gains from having tesseract use multiple threads are minimal.\n# However, when multiple pages are processed in parallel, the total number of\n# OCR threads may exceed the number of available cpu cores, which will\n# dramatically slow down the consumption process. This settings limits each\n# Tesseract process to one thread.\nos.environ[\"OMP_THREAD_LIMIT\"] = \"1\"\n\n\ndef __get_boolean(key: str, default: str = \"NO\") -> bool:\n \"\"\"\n Return a boolean value based on whatever the user has supplied in the\n environment based on whether the value \"looks like\" it's True or not.\n \"\"\"\n return bool(os.getenv(key, default).lower() in (\"yes\", \"y\", \"1\", \"t\", \"true\"))\n\n\ndef __get_int(key: str, default: int) -> int:\n \"\"\"\n Return an integer value based on the environment variable or a default\n \"\"\"\n return int(os.getenv(key, default))\n\n\ndef __get_float(key: str, default: float) -> float:\n \"\"\"\n Return an integer value based on the environment variable or a default\n \"\"\"\n return float(os.getenv(key, default))\n\n\ndef __get_path(key: str, default: Union[PathLike, str]) -> Path:\n \"\"\"\n Return a normalized, absolute path based on the environment variable or a default\n \"\"\"\n return Path(os.environ.get(key, default)).resolve()\n\n\ndef __get_list(\n key: str,\n default: Optional[List[str]] = None,\n sep: str = \",\",\n) -> List[str]:\n \"\"\"\n Return a list of elements from the environment, as separated by the given\n string, or the default if the key does not exist\n \"\"\"\n if key in os.environ:\n return list(filter(None, os.environ[key].split(sep)))\n elif default is not None:\n return default\n else:\n return []\n\n\ndef _parse_redis_url(env_redis: Optional[str]) -> Tuple[str]:\n \"\"\"\n Gets the Redis information from the environment or a default and handles\n converting from incompatible django_channels and celery formats.\n\n Returns a tuple of (celery_url, channels_url)\n \"\"\"\n\n # Not set, return a compatible default\n if env_redis is None:\n return (\"redis://localhost:6379\", \"redis://localhost:6379\")\n\n if \"unix\" in env_redis.lower():\n # channels_redis socket format, looks like:\n # \"unix:///path/to/redis.sock\"\n _, path = env_redis.split(\":\")\n # Optionally setting a db number\n if \"?db=\" in env_redis:\n path, number = path.split(\"?db=\")\n return (f\"redis+socket:{path}?virtual_host={number}\", env_redis)\n else:\n return (f\"redis+socket:{path}\", env_redis)\n\n elif \"+socket\" in env_redis.lower():\n # celery socket style, looks like:\n # \"redis+socket:///path/to/redis.sock\"\n _, path = env_redis.split(\":\")\n if \"?virtual_host=\" in env_redis:\n # Virtual host (aka db number)\n path, number = path.split(\"?virtual_host=\")\n return (env_redis, f\"unix:{path}?db={number}\")\n else:\n return (env_redis, f\"unix:{path}\")\n\n # Not a socket\n return (env_redis, env_redis)\n\n\ndef _parse_beat_schedule() -> Dict:\n \"\"\"\n Configures the scheduled tasks, according to default or\n environment variables. Task expiration is configured so the task will\n expire (and not run), shortly before the default frequency will put another\n of the same task into the queue\n\n\n https://docs.celeryq.dev/en/stable/userguide/periodic-tasks.html#beat-entries\n https://docs.celeryq.dev/en/latest/userguide/calling.html#expiration\n \"\"\"\n schedule = {}\n tasks = [\n {\n \"name\": \"Check all e-mail accounts\",\n \"env_key\": \"PAPERLESS_EMAIL_TASK_CRON\",\n # Default every ten minutes\n \"env_default\": \"*/10 * * * *\",\n \"task\": \"paperless_mail.tasks.process_mail_accounts\",\n \"options\": {\n # 1 minute before default schedule sends again\n \"expires\": 9.0\n * 60.0,\n },\n },\n {\n \"name\": \"Train the classifier\",\n \"env_key\": \"PAPERLESS_TRAIN_TASK_CRON\",\n # Default hourly at 5 minutes past the hour\n \"env_default\": \"5 */1 * * *\",\n \"task\": \"documents.tasks.train_classifier\",\n \"options\": {\n # 1 minute before default schedule sends again\n \"expires\": 59.0\n * 60.0,\n },\n },\n {\n \"name\": \"Optimize the index\",\n \"env_key\": \"PAPERLESS_INDEX_TASK_CRON\",\n # Default daily at midnight\n \"env_default\": \"0 0 * * *\",\n \"task\": \"documents.tasks.index_optimize\",\n \"options\": {\n # 1 hour before default schedule sends again\n \"expires\": 23.0\n * 60.0\n * 60.0,\n },\n },\n {\n \"name\": \"Perform sanity check\",\n \"env_key\": \"PAPERLESS_SANITY_TASK_CRON\",\n # Default Sunday at 00:30\n \"env_default\": \"30 0 * * sun\",\n \"task\": \"documents.tasks.sanity_check\",\n \"options\": {\n # 1 hour before default schedule sends again\n \"expires\": ((7.0 * 24.0) - 1.0)\n * 60.0\n * 60.0,\n },\n },\n ]\n for task in tasks:\n # Either get the environment setting or use the default\n value = os.getenv(task[\"env_key\"], task[\"env_default\"])\n # Don't add disabled tasks to the schedule\n if value == \"disable\":\n continue\n # I find https://crontab.guru/ super helpful\n # crontab(5) format\n # - five time-and-date fields\n # - separated by at least one blank\n minute, hour, day_month, month, day_week = value.split(\" \")\n\n schedule[task[\"name\"]] = {\n \"task\": task[\"task\"],\n \"schedule\": crontab(minute, hour, day_week, day_month, month),\n \"options\": task[\"options\"],\n }\n\n return schedule\n\n\n# NEVER RUN WITH DEBUG IN PRODUCTION.\nDEBUG = __get_boolean(\"PAPERLESS_DEBUG\", \"NO\")\n\n\n###############################################################################\n# Directories #\n###############################################################################\n\nBASE_DIR: Path = Path(__file__).resolve().parent.parent\n\nSTATIC_ROOT = __get_path(\"PAPERLESS_STATICDIR\", BASE_DIR.parent / \"static\")\n\nMEDIA_ROOT = __get_path(\"PAPERLESS_MEDIA_ROOT\", BASE_DIR.parent / \"media\")\nORIGINALS_DIR = MEDIA_ROOT / \"documents\" / \"originals\"\nARCHIVE_DIR = MEDIA_ROOT / \"documents\" / \"archive\"\nTHUMBNAIL_DIR = MEDIA_ROOT / \"documents\" / \"thumbnails\"\n\nDATA_DIR = __get_path(\"PAPERLESS_DATA_DIR\", BASE_DIR.parent / \"data\")\n\nNLTK_DIR = __get_path(\"PAPERLESS_NLTK_DIR\", \"/usr/share/nltk_data\")\n\nTRASH_DIR = os.getenv(\"PAPERLESS_TRASH_DIR\")\n\n# Lock file for synchronizing changes to the MEDIA directory across multiple\n# threads.\nMEDIA_LOCK = MEDIA_ROOT / \"media.lock\"\nINDEX_DIR = DATA_DIR / \"index\"\nMODEL_FILE = DATA_DIR / \"classification_model.pickle\"\n\nLOGGING_DIR = __get_path(\"PAPERLESS_LOGGING_DIR\", DATA_DIR / \"log\")\n\nCONSUMPTION_DIR = __get_path(\n \"PAPERLESS_CONSUMPTION_DIR\",\n BASE_DIR.parent / \"consume\",\n)\n\n# This will be created if it doesn't exist\nSCRATCH_DIR = __get_path(\n \"PAPERLESS_SCRATCH_DIR\",\n Path(tempfile.gettempdir()) / \"paperless\",\n)\n\n###############################################################################\n# Application Definition #\n###############################################################################\n\nenv_apps = __get_list(\"PAPERLESS_APPS\")\n\nINSTALLED_APPS = [\n \"whitenoise.runserver_nostatic\",\n \"django.contrib.auth\",\n \"django.contrib.contenttypes\",\n \"django.contrib.sessions\",\n \"django.contrib.messages\",\n \"django.contrib.staticfiles\",\n \"corsheaders\",\n \"django_extensions\",\n \"paperless\",\n \"documents.apps.DocumentsConfig\",\n \"paperless_tesseract.apps.PaperlessTesseractConfig\",\n \"paperless_text.apps.PaperlessTextConfig\",\n \"paperless_mail.apps.PaperlessMailConfig\",\n \"django.contrib.admin\",\n \"rest_framework\",\n \"rest_framework.authtoken\",\n \"django_filters\",\n \"django_celery_results\",\n \"guardian\",\n] + env_apps\n\nif DEBUG:\n INSTALLED_APPS.append(\"channels\")\n\nREST_FRAMEWORK = {\n \"DEFAULT_AUTHENTICATION_CLASSES\": [\n \"rest_framework.authentication.BasicAuthentication\",\n \"rest_framework.authentication.SessionAuthentication\",\n \"rest_framework.authentication.TokenAuthentication\",\n ],\n \"DEFAULT_VERSIONING_CLASS\": \"rest_framework.versioning.AcceptHeaderVersioning\",\n \"DEFAULT_VERSION\": \"1\",\n # Make sure these are ordered and that the most recent version appears\n # last\n \"ALLOWED_VERSIONS\": [\"1\", \"2\"],\n}\n\nif DEBUG:\n REST_FRAMEWORK[\"DEFAULT_AUTHENTICATION_CLASSES\"].append(\n \"paperless.auth.AngularApiAuthenticationOverride\",\n )\n\nMIDDLEWARE = [\n \"django.middleware.security.SecurityMiddleware\",\n \"whitenoise.middleware.WhiteNoiseMiddleware\",\n \"django.contrib.sessions.middleware.SessionMiddleware\",\n \"corsheaders.middleware.CorsMiddleware\",\n \"django.middleware.locale.LocaleMiddleware\",\n \"django.middleware.common.CommonMiddleware\",\n \"django.middleware.csrf.CsrfViewMiddleware\",\n \"paperless.middleware.ApiVersionMiddleware\",\n \"django.contrib.auth.middleware.AuthenticationMiddleware\",\n \"django.contrib.messages.middleware.MessageMiddleware\",\n \"django.middleware.clickjacking.XFrameOptionsMiddleware\",\n]\n\n# Optional to enable compression\nif __get_boolean(\"PAPERLESS_ENABLE_COMPRESSION\", \"yes\"): # pragma: nocover\n MIDDLEWARE.insert(0, \"compression_middleware.middleware.CompressionMiddleware\")\n\nROOT_URLCONF = \"paperless.urls\"\n\nFORCE_SCRIPT_NAME = os.getenv(\"PAPERLESS_FORCE_SCRIPT_NAME\")\nBASE_URL = (FORCE_SCRIPT_NAME or \"\") + \"/\"\nLOGIN_URL = BASE_URL + \"accounts/login/\"\nLOGOUT_REDIRECT_URL = os.getenv(\"PAPERLESS_LOGOUT_REDIRECT_URL\")\n\nWSGI_APPLICATION = \"paperless.wsgi.application\"\nASGI_APPLICATION = \"paperless.asgi.application\"\n\nSTATIC_URL = os.getenv(\"PAPERLESS_STATIC_URL\", BASE_URL + \"static/\")\nWHITENOISE_STATIC_PREFIX = \"/static/\"\n\n_CELERY_REDIS_URL, _CHANNELS_REDIS_URL = _parse_redis_url(\n os.getenv(\"PAPERLESS_REDIS\", None),\n)\n\nTEMPLATES = [\n {\n \"BACKEND\": \"django.template.backends.django.DjangoTemplates\",\n \"DIRS\": [],\n \"APP_DIRS\": True,\n \"OPTIONS\": {\n \"context_processors\": [\n \"django.template.context_processors.debug\",\n \"django.template.context_processors.request\",\n \"django.contrib.auth.context_processors.auth\",\n \"django.contrib.messages.context_processors.messages\",\n ],\n },\n },\n]\n\nCHANNEL_LAYERS = {\n \"default\": {\n \"BACKEND\": \"channels_redis.core.RedisChannelLayer\",\n \"CONFIG\": {\n \"hosts\": [_CHANNELS_REDIS_URL],\n \"capacity\": 2000, # default 100\n \"expiry\": 15, # default 60\n },\n },\n}\n\n###############################################################################\n# Security #\n###############################################################################\n\nAUTHENTICATION_BACKENDS = [\n \"guardian.backends.ObjectPermissionBackend\",\n \"django.contrib.auth.backends.ModelBackend\",\n]\n\nAUTO_LOGIN_USERNAME = os.getenv(\"PAPERLESS_AUTO_LOGIN_USERNAME\")\n\nif AUTO_LOGIN_USERNAME:\n _index = MIDDLEWARE.index(\"django.contrib.auth.middleware.AuthenticationMiddleware\")\n # This overrides everything the auth middleware is doing but still allows\n # regular login in case the provided user does not exist.\n MIDDLEWARE.insert(_index + 1, \"paperless.auth.AutoLoginMiddleware\")\n\nENABLE_HTTP_REMOTE_USER = __get_boolean(\"PAPERLESS_ENABLE_HTTP_REMOTE_USER\")\nHTTP_REMOTE_USER_HEADER_NAME = os.getenv(\n \"PAPERLESS_HTTP_REMOTE_USER_HEADER_NAME\",\n \"HTTP_REMOTE_USER\",\n)\n\nif ENABLE_HTTP_REMOTE_USER:\n MIDDLEWARE.append(\"paperless.auth.HttpRemoteUserMiddleware\")\n AUTHENTICATION_BACKENDS.insert(0, \"django.contrib.auth.backends.RemoteUserBackend\")\n REST_FRAMEWORK[\"DEFAULT_AUTHENTICATION_CLASSES\"].append(\n \"rest_framework.authentication.RemoteUserAuthentication\",\n )\n\n# X-Frame options for embedded PDF display:\nif DEBUG:\n X_FRAME_OPTIONS = \"ANY\"\nelse:\n X_FRAME_OPTIONS = \"SAMEORIGIN\"\n\n\n# The next 3 settings can also be set using just PAPERLESS_URL\nCSRF_TRUSTED_ORIGINS = __get_list(\"PAPERLESS_CSRF_TRUSTED_ORIGINS\")\n\n# We allow CORS from localhost:8000\nCORS_ALLOWED_ORIGINS = __get_list(\n \"PAPERLESS_CORS_ALLOWED_HOSTS\",\n [\"http://localhost:8000\"],\n)\n\nif DEBUG:\n # Allow access from the angular development server during debugging\n CORS_ALLOWED_ORIGINS.append(\"http://localhost:4200\")\n\nALLOWED_HOSTS = __get_list(\"PAPERLESS_ALLOWED_HOSTS\", [\"*\"])\n\n_paperless_url = os.getenv(\"PAPERLESS_URL\")\nif _paperless_url:\n _paperless_uri = urlparse(_paperless_url)\n CSRF_TRUSTED_ORIGINS.append(_paperless_url)\n CORS_ALLOWED_ORIGINS.append(_paperless_url)\n if ALLOWED_HOSTS != [\"*\"]:\n ALLOWED_HOSTS.append(_paperless_uri.hostname)\n else:\n # always allow localhost. Necessary e.g. for healthcheck in docker.\n ALLOWED_HOSTS = [_paperless_uri.hostname] + [\"localhost\"]\n\n# For use with trusted proxies\nTRUSTED_PROXIES = __get_list(\"PAPERLESS_TRUSTED_PROXIES\")\n\n# The secret key has a default that should be fine so long as you're hosting\n# Paperless on a closed network. However, if you're putting this anywhere\n# public, you should change the key to something unique and verbose.\nSECRET_KEY = os.getenv(\n \"PAPERLESS_SECRET_KEY\",\n \"e11fl1oa-*ytql8p)(06fbj4ukrlo+n7k&q5+$1md7i+mge=ee\",\n)\n\nAUTH_PASSWORD_VALIDATORS = [\n {\n \"NAME\": \"django.contrib.auth.password_validation.UserAttributeSimilarityValidator\", # noqa: E501\n },\n {\n \"NAME\": \"django.contrib.auth.password_validation.MinimumLengthValidator\",\n },\n {\n \"NAME\": \"django.contrib.auth.password_validation.CommonPasswordValidator\",\n },\n {\n \"NAME\": \"django.contrib.auth.password_validation.NumericPasswordValidator\",\n },\n]\n\n# Disable Django's artificial limit on the number of form fields to submit at\n# once. This is a protection against overloading the server, but since this is\n# a self-hosted sort of gig, the benefits of being able to mass-delete a tonne\n# of log entries outweight the benefits of such a safeguard.\n\nDATA_UPLOAD_MAX_NUMBER_FIELDS = None\n\nCOOKIE_PREFIX = os.getenv(\"PAPERLESS_COOKIE_PREFIX\", \"\")\n\nCSRF_COOKIE_NAME = f\"{COOKIE_PREFIX}csrftoken\"\nSESSION_COOKIE_NAME = f\"{COOKIE_PREFIX}sessionid\"\nLANGUAGE_COOKIE_NAME = f\"{COOKIE_PREFIX}django_language\"\n\n###############################################################################\n# Database #\n###############################################################################\n\nDATABASES = {\n \"default\": {\n \"ENGINE\": \"django.db.backends.sqlite3\",\n \"NAME\": os.path.join(DATA_DIR, \"db.sqlite3\"),\n \"OPTIONS\": {},\n },\n}\n\nif os.getenv(\"PAPERLESS_DBHOST\"):\n # Have sqlite available as a second option for management commands\n # This is important when migrating to/from sqlite\n DATABASES[\"sqlite\"] = DATABASES[\"default\"].copy()\n\n DATABASES[\"default\"] = {\n \"HOST\": os.getenv(\"PAPERLESS_DBHOST\"),\n \"NAME\": os.getenv(\"PAPERLESS_DBNAME\", \"paperless\"),\n \"USER\": os.getenv(\"PAPERLESS_DBUSER\", \"paperless\"),\n \"PASSWORD\": os.getenv(\"PAPERLESS_DBPASS\", \"paperless\"),\n \"OPTIONS\": {},\n }\n if os.getenv(\"PAPERLESS_DBPORT\"):\n DATABASES[\"default\"][\"PORT\"] = os.getenv(\"PAPERLESS_DBPORT\")\n\n # Leave room for future extensibility\n if os.getenv(\"PAPERLESS_DBENGINE\") == \"mariadb\":\n engine = \"django.db.backends.mysql\"\n options = {\"read_default_file\": \"/etc/mysql/my.cnf\", \"charset\": \"utf8mb4\"}\n\n # Silence Django error on old MariaDB versions.\n # VARCHAR can support > 255 in modern versions\n # https://docs.djangoproject.com/en/4.1/ref/checks/#database\n # https://mariadb.com/kb/en/innodb-system-variables/#innodb_large_prefix\n SILENCED_SYSTEM_CHECKS = [\"mysql.W003\"]\n\n else: # Default to PostgresDB\n engine = \"django.db.backends.postgresql_psycopg2\"\n options = {\n \"sslmode\": os.getenv(\"PAPERLESS_DBSSLMODE\", \"prefer\"),\n \"sslrootcert\": os.getenv(\"PAPERLESS_DBSSLROOTCERT\", None),\n \"sslcert\": os.getenv(\"PAPERLESS_DBSSLCERT\", None),\n \"sslkey\": os.getenv(\"PAPERLESS_DBSSLKEY\", None),\n }\n\n DATABASES[\"default\"][\"ENGINE\"] = engine\n DATABASES[\"default\"][\"OPTIONS\"].update(options)\n\nif os.getenv(\"PAPERLESS_DB_TIMEOUT\") is not None:\n DATABASES[\"default\"][\"OPTIONS\"].update(\n {\"timeout\": float(os.getenv(\"PAPERLESS_DB_TIMEOUT\"))},\n )\n\nDEFAULT_AUTO_FIELD = \"django.db.models.AutoField\"\n\n###############################################################################\n# Internationalization #\n###############################################################################\n\nLANGUAGE_CODE = \"en-us\"\n\nLANGUAGES = [\n (\"en-us\", _(\"English (US)\")), # needs to be first to act as fallback language\n (\"ar-ar\", _(\"Arabic\")),\n (\"be-by\", _(\"Belarusian\")),\n (\"cs-cz\", _(\"Czech\")),\n (\"da-dk\", _(\"Danish\")),\n (\"de-de\", _(\"German\")),\n (\"en-gb\", _(\"English (GB)\")),\n (\"es-es\", _(\"Spanish\")),\n (\"fr-fr\", _(\"French\")),\n (\"it-it\", _(\"Italian\")),\n (\"lb-lu\", _(\"Luxembourgish\")),\n (\"nl-nl\", _(\"Dutch\")),\n (\"pl-pl\", _(\"Polish\")),\n (\"pt-br\", _(\"Portuguese (Brazil)\")),\n (\"pt-pt\", _(\"Portuguese\")),\n (\"ro-ro\", _(\"Romanian\")),\n (\"ru-ru\", _(\"Russian\")),\n (\"sl-si\", _(\"Slovenian\")),\n (\"sr-cs\", _(\"Serbian\")),\n (\"sv-se\", _(\"Swedish\")),\n (\"tr-tr\", _(\"Turkish\")),\n (\"zh-cn\", _(\"Chinese Simplified\")),\n]\n\nLOCALE_PATHS = [os.path.join(BASE_DIR, \"locale\")]\n\nTIME_ZONE = os.getenv(\"PAPERLESS_TIME_ZONE\", \"UTC\")\n\nUSE_I18N = True\n\nUSE_L10N = True\n\nUSE_TZ = True\n\n###############################################################################\n# Logging #\n###############################################################################\n\nsetup_logging_queues()\n\nos.makedirs(LOGGING_DIR, exist_ok=True)\n\nLOGROTATE_MAX_SIZE = os.getenv(\"PAPERLESS_LOGROTATE_MAX_SIZE\", 1024 * 1024)\nLOGROTATE_MAX_BACKUPS = os.getenv(\"PAPERLESS_LOGROTATE_MAX_BACKUPS\", 20)\n\nLOGGING = {\n \"version\": 1,\n \"disable_existing_loggers\": False,\n \"formatters\": {\n \"verbose\": {\n \"format\": \"[{asctime}] [{levelname}] [{name}] {message}\",\n \"style\": \"{\",\n },\n \"simple\": {\n \"format\": \"{levelname} {message}\",\n \"style\": \"{\",\n },\n },\n \"handlers\": {\n \"console\": {\n \"level\": \"DEBUG\" if DEBUG else \"INFO\",\n \"class\": \"logging.StreamHandler\",\n \"formatter\": \"verbose\",\n },\n \"file_paperless\": {\n \"class\": \"concurrent_log_handler.ConcurrentRotatingFileHandler\",\n \"formatter\": \"verbose\",\n \"filename\": os.path.join(LOGGING_DIR, \"paperless.log\"),\n \"maxBytes\": LOGROTATE_MAX_SIZE,\n \"backupCount\": LOGROTATE_MAX_BACKUPS,\n },\n \"file_mail\": {\n \"class\": \"concurrent_log_handler.ConcurrentRotatingFileHandler\",\n \"formatter\": \"verbose\",\n \"filename\": os.path.join(LOGGING_DIR, \"mail.log\"),\n \"maxBytes\": LOGROTATE_MAX_SIZE,\n \"backupCount\": LOGROTATE_MAX_BACKUPS,\n },\n },\n \"root\": {\"handlers\": [\"console\"]},\n \"loggers\": {\n \"paperless\": {\"handlers\": [\"file_paperless\"], \"level\": \"DEBUG\"},\n \"paperless_mail\": {\"handlers\": [\"file_mail\"], \"level\": \"DEBUG\"},\n },\n}\n\n###############################################################################\n# Task queue #\n###############################################################################\n\n# https://docs.celeryq.dev/en/stable/userguide/configuration.html\n\nCELERY_BROKER_URL = _CELERY_REDIS_URL\nCELERY_TIMEZONE = TIME_ZONE\n\nCELERY_WORKER_HIJACK_ROOT_LOGGER = False\nCELERY_WORKER_CONCURRENCY: Final[int] = __get_int(\"PAPERLESS_TASK_WORKERS\", 1)\nTASK_WORKERS = CELERY_WORKER_CONCURRENCY\nCELERY_WORKER_MAX_TASKS_PER_CHILD = 1\nCELERY_WORKER_SEND_TASK_EVENTS = True\nCELERY_TASK_SEND_SENT_EVENT = True\nCELERY_SEND_TASK_SENT_EVENT = True\n\nCELERY_TASK_TRACK_STARTED = True\nCELERY_TASK_TIME_LIMIT: Final[int] = __get_int(\"PAPERLESS_WORKER_TIMEOUT\", 1800)\n\nCELERY_RESULT_EXTENDED = True\nCELERY_RESULT_BACKEND = \"django-db\"\nCELERY_CACHE_BACKEND = \"default\"\n\n# https://docs.celeryq.dev/en/stable/userguide/configuration.html#task-serializer\nCELERY_TASK_SERIALIZER = \"pickle\"\n# https://docs.celeryq.dev/en/stable/userguide/configuration.html#std-setting-accept_content\nCELERY_ACCEPT_CONTENT = [\"application/json\", \"application/x-python-serialize\"]\n\n# https://docs.celeryq.dev/en/stable/userguide/configuration.html#beat-schedule\nCELERY_BEAT_SCHEDULE = _parse_beat_schedule()\n\n# https://docs.celeryq.dev/en/stable/userguide/configuration.html#beat-schedule-filename\nCELERY_BEAT_SCHEDULE_FILENAME = os.path.join(DATA_DIR, \"celerybeat-schedule.db\")\n\n# django setting.\nCACHES = {\n \"default\": {\n \"BACKEND\": \"django.core.cache.backends.redis.RedisCache\",\n \"LOCATION\": _CHANNELS_REDIS_URL,\n },\n}\n\n\ndef default_threads_per_worker(task_workers) -> int:\n # always leave one core open\n available_cores = max(multiprocessing.cpu_count(), 1)\n try:\n return max(math.floor(available_cores / task_workers), 1)\n except NotImplementedError:\n return 1\n\n\nTHREADS_PER_WORKER = os.getenv(\n \"PAPERLESS_THREADS_PER_WORKER\",\n default_threads_per_worker(CELERY_WORKER_CONCURRENCY),\n)\n\n###############################################################################\n# Paperless Specific Settings #\n###############################################################################\n\nCONSUMER_POLLING = int(os.getenv(\"PAPERLESS_CONSUMER_POLLING\", 0))\n\nCONSUMER_POLLING_DELAY = int(os.getenv(\"PAPERLESS_CONSUMER_POLLING_DELAY\", 5))\n\nCONSUMER_POLLING_RETRY_COUNT = int(\n os.getenv(\"PAPERLESS_CONSUMER_POLLING_RETRY_COUNT\", 5),\n)\n\nCONSUMER_INOTIFY_DELAY: Final[float] = __get_float(\n \"PAPERLESS_CONSUMER_INOTIFY_DELAY\",\n 0.5,\n)\n\nCONSUMER_DELETE_DUPLICATES = __get_boolean(\"PAPERLESS_CONSUMER_DELETE_DUPLICATES\")\n\nCONSUMER_RECURSIVE = __get_boolean(\"PAPERLESS_CONSUMER_RECURSIVE\")\n\n# Ignore glob patterns, relative to PAPERLESS_CONSUMPTION_DIR\nCONSUMER_IGNORE_PATTERNS = list(\n json.loads(\n os.getenv(\n \"PAPERLESS_CONSUMER_IGNORE_PATTERNS\",\n '[\".DS_STORE/*\", \"._*\", \".stfolder/*\", \".stversions/*\", \".localized/*\", \"desktop.ini\", \"@eaDir/*\"]', # noqa: E501\n ),\n ),\n)\n\nCONSUMER_SUBDIRS_AS_TAGS = __get_boolean(\"PAPERLESS_CONSUMER_SUBDIRS_AS_TAGS\")\n\nCONSUMER_ENABLE_BARCODES: Final[bool] = __get_boolean(\n \"PAPERLESS_CONSUMER_ENABLE_BARCODES\",\n)\n\nCONSUMER_BARCODE_TIFF_SUPPORT: Final[bool] = __get_boolean(\n \"PAPERLESS_CONSUMER_BARCODE_TIFF_SUPPORT\",\n)\n\nCONSUMER_BARCODE_STRING: Final[str] = os.getenv(\n \"PAPERLESS_CONSUMER_BARCODE_STRING\",\n \"PATCHT\",\n)\n\nCONSUMER_ENABLE_ASN_BARCODE: Final[bool] = __get_boolean(\n \"PAPERLESS_CONSUMER_ENABLE_ASN_BARCODE\",\n)\n\nCONSUMER_ASN_BARCODE_PREFIX: Final[str] = os.getenv(\n \"PAPERLESS_CONSUMER_ASN_BARCODE_PREFIX\",\n \"ASN\",\n)\n\n\nOCR_PAGES = int(os.getenv(\"PAPERLESS_OCR_PAGES\", 0))\n\n# The default language that tesseract will attempt to use when parsing\n# documents. It should be a 3-letter language code consistent with ISO 639.\nOCR_LANGUAGE = os.getenv(\"PAPERLESS_OCR_LANGUAGE\", \"eng\")\n\n# OCRmyPDF --output-type options are available.\nOCR_OUTPUT_TYPE = os.getenv(\"PAPERLESS_OCR_OUTPUT_TYPE\", \"pdfa\")\n\n# skip. redo, force\nOCR_MODE = os.getenv(\"PAPERLESS_OCR_MODE\", \"skip\")\n\nOCR_SKIP_ARCHIVE_FILE = os.getenv(\"PAPERLESS_OCR_SKIP_ARCHIVE_FILE\", \"never\")\n\nOCR_IMAGE_DPI = os.getenv(\"PAPERLESS_OCR_IMAGE_DPI\")\n\nOCR_CLEAN = os.getenv(\"PAPERLESS_OCR_CLEAN\", \"clean\")\n\nOCR_DESKEW = __get_boolean(\"PAPERLESS_OCR_DESKEW\", \"true\")\n\nOCR_ROTATE_PAGES = __get_boolean(\"PAPERLESS_OCR_ROTATE_PAGES\", \"true\")\n\nOCR_ROTATE_PAGES_THRESHOLD = float(\n os.getenv(\"PAPERLESS_OCR_ROTATE_PAGES_THRESHOLD\", 12.0),\n)\n\nOCR_MAX_IMAGE_PIXELS: Optional[int] = None\nif os.environ.get(\"PAPERLESS_OCR_MAX_IMAGE_PIXELS\") is not None:\n OCR_MAX_IMAGE_PIXELS: int = int(os.environ.get(\"PAPERLESS_OCR_MAX_IMAGE_PIXELS\"))\n\nOCR_USER_ARGS = os.getenv(\"PAPERLESS_OCR_USER_ARGS\", \"{}\")\n\n# GNUPG needs a home directory for some reason\nGNUPG_HOME = os.getenv(\"HOME\", \"/tmp\")\n\n# Convert is part of the ImageMagick package\nCONVERT_BINARY = os.getenv(\"PAPERLESS_CONVERT_BINARY\", \"convert\")\nCONVERT_TMPDIR = os.getenv(\"PAPERLESS_CONVERT_TMPDIR\")\nCONVERT_MEMORY_LIMIT = os.getenv(\"PAPERLESS_CONVERT_MEMORY_LIMIT\")\n\nGS_BINARY = os.getenv(\"PAPERLESS_GS_BINARY\", \"gs\")\n\n\n# Pre-2.x versions of Paperless stored your documents locally with GPG\n# encryption, but that is no longer the default. This behaviour is still\n# available, but it must be explicitly enabled by setting\n# `PAPERLESS_PASSPHRASE` in your environment or config file. The default is to\n# store these files unencrypted.\n#\n# Translation:\n# * If you're a new user, you can safely ignore this setting.\n# * If you're upgrading from 1.x, this must be set, OR you can run\n# `./manage.py change_storage_type gpg unencrypted` to decrypt your files,\n# after which you can unset this value.\nPASSPHRASE = os.getenv(\"PAPERLESS_PASSPHRASE\")\n\n# Trigger a script after every successful document consumption?\nPRE_CONSUME_SCRIPT = os.getenv(\"PAPERLESS_PRE_CONSUME_SCRIPT\")\nPOST_CONSUME_SCRIPT = os.getenv(\"PAPERLESS_POST_CONSUME_SCRIPT\")\n\n# Specify the default date order (for autodetected dates)\nDATE_ORDER = os.getenv(\"PAPERLESS_DATE_ORDER\", \"DMY\")\nFILENAME_DATE_ORDER = os.getenv(\"PAPERLESS_FILENAME_DATE_ORDER\")\n\n# Maximum number of dates taken from document start to end to show as suggestions for\n# `created` date in the frontend. Duplicates are removed, which can result in\n# fewer dates shown.\nNUMBER_OF_SUGGESTED_DATES = __get_int(\"PAPERLESS_NUMBER_OF_SUGGESTED_DATES\", 3)\n\n# Transformations applied before filename parsing\nFILENAME_PARSE_TRANSFORMS = []\nfor t in json.loads(os.getenv(\"PAPERLESS_FILENAME_PARSE_TRANSFORMS\", \"[]\")):\n FILENAME_PARSE_TRANSFORMS.append((re.compile(t[\"pattern\"]), t[\"repl\"]))\n\n# Specify the filename format for out files\nFILENAME_FORMAT = os.getenv(\"PAPERLESS_FILENAME_FORMAT\")\n\n# If this is enabled, variables in filename format will resolve to\n# empty-string instead of 'none'.\n# Directories with 'empty names' are omitted, too.\nFILENAME_FORMAT_REMOVE_NONE = __get_boolean(\n \"PAPERLESS_FILENAME_FORMAT_REMOVE_NONE\",\n \"NO\",\n)\n\nTHUMBNAIL_FONT_NAME = os.getenv(\n \"PAPERLESS_THUMBNAIL_FONT_NAME\",\n \"/usr/share/fonts/liberation/LiberationSerif-Regular.ttf\",\n)\n\n# Tika settings\nTIKA_ENABLED = __get_boolean(\"PAPERLESS_TIKA_ENABLED\", \"NO\")\nTIKA_ENDPOINT = os.getenv(\"PAPERLESS_TIKA_ENDPOINT\", \"http://localhost:9998\")\nTIKA_GOTENBERG_ENDPOINT = os.getenv(\n \"PAPERLESS_TIKA_GOTENBERG_ENDPOINT\",\n \"http://localhost:3000\",\n)\n\nif TIKA_ENABLED:\n INSTALLED_APPS.append(\"paperless_tika.apps.PaperlessTikaConfig\")\n\n\ndef _parse_ignore_dates(\n env_ignore: str,\n date_order: str = DATE_ORDER,\n) -> Set[datetime.datetime]:\n \"\"\"\n If the PAPERLESS_IGNORE_DATES environment variable is set, parse the\n user provided string(s) into dates\n\n Args:\n env_ignore (str): The value of the environment variable, comma separated dates\n date_order (str, optional): The format of the date strings.\n Defaults to DATE_ORDER.\n\n Returns:\n Set[datetime.datetime]: The set of parsed date objects\n \"\"\"\n import dateparser\n\n ignored_dates = set()\n for s in env_ignore.split(\",\"):\n d = dateparser.parse(\n s,\n settings={\n \"DATE_ORDER\": date_order,\n },\n )\n if d:\n ignored_dates.add(d.date())\n return ignored_dates\n\n\n# List dates that should be ignored when trying to parse date from document text\nIGNORE_DATES: Set[datetime.date] = set()\n\nif os.getenv(\"PAPERLESS_IGNORE_DATES\") is not None:\n IGNORE_DATES = _parse_ignore_dates(os.getenv(\"PAPERLESS_IGNORE_DATES\"))\n\nENABLE_UPDATE_CHECK = os.getenv(\"PAPERLESS_ENABLE_UPDATE_CHECK\", \"default\")\nif ENABLE_UPDATE_CHECK != \"default\":\n ENABLE_UPDATE_CHECK = __get_boolean(\"PAPERLESS_ENABLE_UPDATE_CHECK\")\n\n###############################################################################\n# Machine Learning #\n###############################################################################\n\n\ndef _get_nltk_language_setting(ocr_lang: str) -> Optional[str]:\n \"\"\"\n Maps an ISO-639-1 language code supported by Tesseract into\n an optional NLTK language name. This is the set of common supported\n languages for all the NLTK data used.\n\n Assumption: The primary language is first\n \"\"\"\n ocr_lang = ocr_lang.split(\"+\")[0]\n iso_code_to_nltk = {\n \"dan\": \"danish\",\n \"nld\": \"dutch\",\n \"eng\": \"english\",\n \"fin\": \"finnish\",\n \"fra\": \"french\",\n \"deu\": \"german\",\n \"ita\": \"italian\",\n \"nor\": \"norwegian\",\n \"por\": \"portuguese\",\n \"rus\": \"russian\",\n \"spa\": \"spanish\",\n \"swe\": \"swedish\",\n \"tur\": \"turkish\",\n }\n\n return iso_code_to_nltk.get(ocr_lang, None)\n\n\nNLTK_ENABLED: Final[bool] = __get_boolean(\"PAPERLESS_ENABLE_NLTK\", \"yes\")\n\nNLTK_LANGUAGE: Optional[str] = _get_nltk_language_setting(OCR_LANGUAGE)\n",
"path": "src/paperless/settings.py"
}
] | [
{
"content": "import datetime\nimport json\nimport math\nimport multiprocessing\nimport os\nimport re\nimport tempfile\nfrom os import PathLike\nfrom pathlib import Path\nfrom typing import Dict\nfrom typing import Final\nfrom typing import List\nfrom typing import Optional\nfrom typing import Set\nfrom typing import Tuple\nfrom typing import Union\nfrom urllib.parse import urlparse\n\nfrom celery.schedules import crontab\nfrom concurrent_log_handler.queue import setup_logging_queues\nfrom django.utils.translation import gettext_lazy as _\nfrom dotenv import load_dotenv\n\n# Tap paperless.conf if it's available\nconfiguration_path = os.getenv(\"PAPERLESS_CONFIGURATION_PATH\")\nif configuration_path and os.path.exists(configuration_path):\n load_dotenv(configuration_path)\nelif os.path.exists(\"../paperless.conf\"):\n load_dotenv(\"../paperless.conf\")\nelif os.path.exists(\"/etc/paperless.conf\"):\n load_dotenv(\"/etc/paperless.conf\")\nelif os.path.exists(\"/usr/local/etc/paperless.conf\"):\n load_dotenv(\"/usr/local/etc/paperless.conf\")\n\n# There are multiple levels of concurrency in paperless:\n# - Multiple consumers may be run in parallel.\n# - Each consumer may process multiple pages in parallel.\n# - Each Tesseract OCR run may spawn multiple threads to process a single page\n# slightly faster.\n# The performance gains from having tesseract use multiple threads are minimal.\n# However, when multiple pages are processed in parallel, the total number of\n# OCR threads may exceed the number of available cpu cores, which will\n# dramatically slow down the consumption process. This settings limits each\n# Tesseract process to one thread.\nos.environ[\"OMP_THREAD_LIMIT\"] = \"1\"\n\n\ndef __get_boolean(key: str, default: str = \"NO\") -> bool:\n \"\"\"\n Return a boolean value based on whatever the user has supplied in the\n environment based on whether the value \"looks like\" it's True or not.\n \"\"\"\n return bool(os.getenv(key, default).lower() in (\"yes\", \"y\", \"1\", \"t\", \"true\"))\n\n\ndef __get_int(key: str, default: int) -> int:\n \"\"\"\n Return an integer value based on the environment variable or a default\n \"\"\"\n return int(os.getenv(key, default))\n\n\ndef __get_float(key: str, default: float) -> float:\n \"\"\"\n Return an integer value based on the environment variable or a default\n \"\"\"\n return float(os.getenv(key, default))\n\n\ndef __get_path(key: str, default: Union[PathLike, str]) -> Path:\n \"\"\"\n Return a normalized, absolute path based on the environment variable or a default\n \"\"\"\n return Path(os.environ.get(key, default)).resolve()\n\n\ndef __get_list(\n key: str,\n default: Optional[List[str]] = None,\n sep: str = \",\",\n) -> List[str]:\n \"\"\"\n Return a list of elements from the environment, as separated by the given\n string, or the default if the key does not exist\n \"\"\"\n if key in os.environ:\n return list(filter(None, os.environ[key].split(sep)))\n elif default is not None:\n return default\n else:\n return []\n\n\ndef _parse_redis_url(env_redis: Optional[str]) -> Tuple[str]:\n \"\"\"\n Gets the Redis information from the environment or a default and handles\n converting from incompatible django_channels and celery formats.\n\n Returns a tuple of (celery_url, channels_url)\n \"\"\"\n\n # Not set, return a compatible default\n if env_redis is None:\n return (\"redis://localhost:6379\", \"redis://localhost:6379\")\n\n if \"unix\" in env_redis.lower():\n # channels_redis socket format, looks like:\n # \"unix:///path/to/redis.sock\"\n _, path = env_redis.split(\":\")\n # Optionally setting a db number\n if \"?db=\" in env_redis:\n path, number = path.split(\"?db=\")\n return (f\"redis+socket:{path}?virtual_host={number}\", env_redis)\n else:\n return (f\"redis+socket:{path}\", env_redis)\n\n elif \"+socket\" in env_redis.lower():\n # celery socket style, looks like:\n # \"redis+socket:///path/to/redis.sock\"\n _, path = env_redis.split(\":\")\n if \"?virtual_host=\" in env_redis:\n # Virtual host (aka db number)\n path, number = path.split(\"?virtual_host=\")\n return (env_redis, f\"unix:{path}?db={number}\")\n else:\n return (env_redis, f\"unix:{path}\")\n\n # Not a socket\n return (env_redis, env_redis)\n\n\ndef _parse_beat_schedule() -> Dict:\n \"\"\"\n Configures the scheduled tasks, according to default or\n environment variables. Task expiration is configured so the task will\n expire (and not run), shortly before the default frequency will put another\n of the same task into the queue\n\n\n https://docs.celeryq.dev/en/stable/userguide/periodic-tasks.html#beat-entries\n https://docs.celeryq.dev/en/latest/userguide/calling.html#expiration\n \"\"\"\n schedule = {}\n tasks = [\n {\n \"name\": \"Check all e-mail accounts\",\n \"env_key\": \"PAPERLESS_EMAIL_TASK_CRON\",\n # Default every ten minutes\n \"env_default\": \"*/10 * * * *\",\n \"task\": \"paperless_mail.tasks.process_mail_accounts\",\n \"options\": {\n # 1 minute before default schedule sends again\n \"expires\": 9.0\n * 60.0,\n },\n },\n {\n \"name\": \"Train the classifier\",\n \"env_key\": \"PAPERLESS_TRAIN_TASK_CRON\",\n # Default hourly at 5 minutes past the hour\n \"env_default\": \"5 */1 * * *\",\n \"task\": \"documents.tasks.train_classifier\",\n \"options\": {\n # 1 minute before default schedule sends again\n \"expires\": 59.0\n * 60.0,\n },\n },\n {\n \"name\": \"Optimize the index\",\n \"env_key\": \"PAPERLESS_INDEX_TASK_CRON\",\n # Default daily at midnight\n \"env_default\": \"0 0 * * *\",\n \"task\": \"documents.tasks.index_optimize\",\n \"options\": {\n # 1 hour before default schedule sends again\n \"expires\": 23.0\n * 60.0\n * 60.0,\n },\n },\n {\n \"name\": \"Perform sanity check\",\n \"env_key\": \"PAPERLESS_SANITY_TASK_CRON\",\n # Default Sunday at 00:30\n \"env_default\": \"30 0 * * sun\",\n \"task\": \"documents.tasks.sanity_check\",\n \"options\": {\n # 1 hour before default schedule sends again\n \"expires\": ((7.0 * 24.0) - 1.0)\n * 60.0\n * 60.0,\n },\n },\n ]\n for task in tasks:\n # Either get the environment setting or use the default\n value = os.getenv(task[\"env_key\"], task[\"env_default\"])\n # Don't add disabled tasks to the schedule\n if value == \"disable\":\n continue\n # I find https://crontab.guru/ super helpful\n # crontab(5) format\n # - five time-and-date fields\n # - separated by at least one blank\n minute, hour, day_month, month, day_week = value.split(\" \")\n\n schedule[task[\"name\"]] = {\n \"task\": task[\"task\"],\n \"schedule\": crontab(minute, hour, day_week, day_month, month),\n \"options\": task[\"options\"],\n }\n\n return schedule\n\n\n# NEVER RUN WITH DEBUG IN PRODUCTION.\nDEBUG = __get_boolean(\"PAPERLESS_DEBUG\", \"NO\")\n\n\n###############################################################################\n# Directories #\n###############################################################################\n\nBASE_DIR: Path = Path(__file__).resolve().parent.parent\n\nSTATIC_ROOT = __get_path(\"PAPERLESS_STATICDIR\", BASE_DIR.parent / \"static\")\n\nMEDIA_ROOT = __get_path(\"PAPERLESS_MEDIA_ROOT\", BASE_DIR.parent / \"media\")\nORIGINALS_DIR = MEDIA_ROOT / \"documents\" / \"originals\"\nARCHIVE_DIR = MEDIA_ROOT / \"documents\" / \"archive\"\nTHUMBNAIL_DIR = MEDIA_ROOT / \"documents\" / \"thumbnails\"\n\nDATA_DIR = __get_path(\"PAPERLESS_DATA_DIR\", BASE_DIR.parent / \"data\")\n\nNLTK_DIR = __get_path(\"PAPERLESS_NLTK_DIR\", \"/usr/share/nltk_data\")\n\nTRASH_DIR = os.getenv(\"PAPERLESS_TRASH_DIR\")\n\n# Lock file for synchronizing changes to the MEDIA directory across multiple\n# threads.\nMEDIA_LOCK = MEDIA_ROOT / \"media.lock\"\nINDEX_DIR = DATA_DIR / \"index\"\nMODEL_FILE = DATA_DIR / \"classification_model.pickle\"\n\nLOGGING_DIR = __get_path(\"PAPERLESS_LOGGING_DIR\", DATA_DIR / \"log\")\n\nCONSUMPTION_DIR = __get_path(\n \"PAPERLESS_CONSUMPTION_DIR\",\n BASE_DIR.parent / \"consume\",\n)\n\n# This will be created if it doesn't exist\nSCRATCH_DIR = __get_path(\n \"PAPERLESS_SCRATCH_DIR\",\n Path(tempfile.gettempdir()) / \"paperless\",\n)\n\n###############################################################################\n# Application Definition #\n###############################################################################\n\nenv_apps = __get_list(\"PAPERLESS_APPS\")\n\nINSTALLED_APPS = [\n \"whitenoise.runserver_nostatic\",\n \"django.contrib.auth\",\n \"django.contrib.contenttypes\",\n \"django.contrib.sessions\",\n \"django.contrib.messages\",\n \"django.contrib.staticfiles\",\n \"corsheaders\",\n \"django_extensions\",\n \"paperless\",\n \"documents.apps.DocumentsConfig\",\n \"paperless_tesseract.apps.PaperlessTesseractConfig\",\n \"paperless_text.apps.PaperlessTextConfig\",\n \"paperless_mail.apps.PaperlessMailConfig\",\n \"django.contrib.admin\",\n \"rest_framework\",\n \"rest_framework.authtoken\",\n \"django_filters\",\n \"django_celery_results\",\n \"guardian\",\n] + env_apps\n\nif DEBUG:\n INSTALLED_APPS.append(\"channels\")\n\nREST_FRAMEWORK = {\n \"DEFAULT_AUTHENTICATION_CLASSES\": [\n \"rest_framework.authentication.BasicAuthentication\",\n \"rest_framework.authentication.SessionAuthentication\",\n \"rest_framework.authentication.TokenAuthentication\",\n ],\n \"DEFAULT_VERSIONING_CLASS\": \"rest_framework.versioning.AcceptHeaderVersioning\",\n \"DEFAULT_VERSION\": \"1\",\n # Make sure these are ordered and that the most recent version appears\n # last\n \"ALLOWED_VERSIONS\": [\"1\", \"2\"],\n}\n\nif DEBUG:\n REST_FRAMEWORK[\"DEFAULT_AUTHENTICATION_CLASSES\"].append(\n \"paperless.auth.AngularApiAuthenticationOverride\",\n )\n\nMIDDLEWARE = [\n \"django.middleware.security.SecurityMiddleware\",\n \"whitenoise.middleware.WhiteNoiseMiddleware\",\n \"django.contrib.sessions.middleware.SessionMiddleware\",\n \"corsheaders.middleware.CorsMiddleware\",\n \"django.middleware.locale.LocaleMiddleware\",\n \"django.middleware.common.CommonMiddleware\",\n \"django.middleware.csrf.CsrfViewMiddleware\",\n \"paperless.middleware.ApiVersionMiddleware\",\n \"django.contrib.auth.middleware.AuthenticationMiddleware\",\n \"django.contrib.messages.middleware.MessageMiddleware\",\n \"django.middleware.clickjacking.XFrameOptionsMiddleware\",\n]\n\n# Optional to enable compression\nif __get_boolean(\"PAPERLESS_ENABLE_COMPRESSION\", \"yes\"): # pragma: nocover\n MIDDLEWARE.insert(0, \"compression_middleware.middleware.CompressionMiddleware\")\n\nROOT_URLCONF = \"paperless.urls\"\n\nFORCE_SCRIPT_NAME = os.getenv(\"PAPERLESS_FORCE_SCRIPT_NAME\")\nBASE_URL = (FORCE_SCRIPT_NAME or \"\") + \"/\"\nLOGIN_URL = BASE_URL + \"accounts/login/\"\nLOGOUT_REDIRECT_URL = os.getenv(\"PAPERLESS_LOGOUT_REDIRECT_URL\")\n\nWSGI_APPLICATION = \"paperless.wsgi.application\"\nASGI_APPLICATION = \"paperless.asgi.application\"\n\nSTATIC_URL = os.getenv(\"PAPERLESS_STATIC_URL\", BASE_URL + \"static/\")\nWHITENOISE_STATIC_PREFIX = \"/static/\"\n\n_CELERY_REDIS_URL, _CHANNELS_REDIS_URL = _parse_redis_url(\n os.getenv(\"PAPERLESS_REDIS\", None),\n)\n\nTEMPLATES = [\n {\n \"BACKEND\": \"django.template.backends.django.DjangoTemplates\",\n \"DIRS\": [],\n \"APP_DIRS\": True,\n \"OPTIONS\": {\n \"context_processors\": [\n \"django.template.context_processors.debug\",\n \"django.template.context_processors.request\",\n \"django.contrib.auth.context_processors.auth\",\n \"django.contrib.messages.context_processors.messages\",\n ],\n },\n },\n]\n\nCHANNEL_LAYERS = {\n \"default\": {\n \"BACKEND\": \"channels_redis.pubsub.RedisPubSubChannelLayer\",\n \"CONFIG\": {\n \"hosts\": [_CHANNELS_REDIS_URL],\n \"capacity\": 2000, # default 100\n \"expiry\": 15, # default 60\n },\n },\n}\n\n###############################################################################\n# Security #\n###############################################################################\n\nAUTHENTICATION_BACKENDS = [\n \"guardian.backends.ObjectPermissionBackend\",\n \"django.contrib.auth.backends.ModelBackend\",\n]\n\nAUTO_LOGIN_USERNAME = os.getenv(\"PAPERLESS_AUTO_LOGIN_USERNAME\")\n\nif AUTO_LOGIN_USERNAME:\n _index = MIDDLEWARE.index(\"django.contrib.auth.middleware.AuthenticationMiddleware\")\n # This overrides everything the auth middleware is doing but still allows\n # regular login in case the provided user does not exist.\n MIDDLEWARE.insert(_index + 1, \"paperless.auth.AutoLoginMiddleware\")\n\nENABLE_HTTP_REMOTE_USER = __get_boolean(\"PAPERLESS_ENABLE_HTTP_REMOTE_USER\")\nHTTP_REMOTE_USER_HEADER_NAME = os.getenv(\n \"PAPERLESS_HTTP_REMOTE_USER_HEADER_NAME\",\n \"HTTP_REMOTE_USER\",\n)\n\nif ENABLE_HTTP_REMOTE_USER:\n MIDDLEWARE.append(\"paperless.auth.HttpRemoteUserMiddleware\")\n AUTHENTICATION_BACKENDS.insert(0, \"django.contrib.auth.backends.RemoteUserBackend\")\n REST_FRAMEWORK[\"DEFAULT_AUTHENTICATION_CLASSES\"].append(\n \"rest_framework.authentication.RemoteUserAuthentication\",\n )\n\n# X-Frame options for embedded PDF display:\nif DEBUG:\n X_FRAME_OPTIONS = \"ANY\"\nelse:\n X_FRAME_OPTIONS = \"SAMEORIGIN\"\n\n\n# The next 3 settings can also be set using just PAPERLESS_URL\nCSRF_TRUSTED_ORIGINS = __get_list(\"PAPERLESS_CSRF_TRUSTED_ORIGINS\")\n\n# We allow CORS from localhost:8000\nCORS_ALLOWED_ORIGINS = __get_list(\n \"PAPERLESS_CORS_ALLOWED_HOSTS\",\n [\"http://localhost:8000\"],\n)\n\nif DEBUG:\n # Allow access from the angular development server during debugging\n CORS_ALLOWED_ORIGINS.append(\"http://localhost:4200\")\n\nALLOWED_HOSTS = __get_list(\"PAPERLESS_ALLOWED_HOSTS\", [\"*\"])\n\n_paperless_url = os.getenv(\"PAPERLESS_URL\")\nif _paperless_url:\n _paperless_uri = urlparse(_paperless_url)\n CSRF_TRUSTED_ORIGINS.append(_paperless_url)\n CORS_ALLOWED_ORIGINS.append(_paperless_url)\n if ALLOWED_HOSTS != [\"*\"]:\n ALLOWED_HOSTS.append(_paperless_uri.hostname)\n else:\n # always allow localhost. Necessary e.g. for healthcheck in docker.\n ALLOWED_HOSTS = [_paperless_uri.hostname] + [\"localhost\"]\n\n# For use with trusted proxies\nTRUSTED_PROXIES = __get_list(\"PAPERLESS_TRUSTED_PROXIES\")\n\n# The secret key has a default that should be fine so long as you're hosting\n# Paperless on a closed network. However, if you're putting this anywhere\n# public, you should change the key to something unique and verbose.\nSECRET_KEY = os.getenv(\n \"PAPERLESS_SECRET_KEY\",\n \"e11fl1oa-*ytql8p)(06fbj4ukrlo+n7k&q5+$1md7i+mge=ee\",\n)\n\nAUTH_PASSWORD_VALIDATORS = [\n {\n \"NAME\": \"django.contrib.auth.password_validation.UserAttributeSimilarityValidator\", # noqa: E501\n },\n {\n \"NAME\": \"django.contrib.auth.password_validation.MinimumLengthValidator\",\n },\n {\n \"NAME\": \"django.contrib.auth.password_validation.CommonPasswordValidator\",\n },\n {\n \"NAME\": \"django.contrib.auth.password_validation.NumericPasswordValidator\",\n },\n]\n\n# Disable Django's artificial limit on the number of form fields to submit at\n# once. This is a protection against overloading the server, but since this is\n# a self-hosted sort of gig, the benefits of being able to mass-delete a tonne\n# of log entries outweight the benefits of such a safeguard.\n\nDATA_UPLOAD_MAX_NUMBER_FIELDS = None\n\nCOOKIE_PREFIX = os.getenv(\"PAPERLESS_COOKIE_PREFIX\", \"\")\n\nCSRF_COOKIE_NAME = f\"{COOKIE_PREFIX}csrftoken\"\nSESSION_COOKIE_NAME = f\"{COOKIE_PREFIX}sessionid\"\nLANGUAGE_COOKIE_NAME = f\"{COOKIE_PREFIX}django_language\"\n\n###############################################################################\n# Database #\n###############################################################################\n\nDATABASES = {\n \"default\": {\n \"ENGINE\": \"django.db.backends.sqlite3\",\n \"NAME\": os.path.join(DATA_DIR, \"db.sqlite3\"),\n \"OPTIONS\": {},\n },\n}\n\nif os.getenv(\"PAPERLESS_DBHOST\"):\n # Have sqlite available as a second option for management commands\n # This is important when migrating to/from sqlite\n DATABASES[\"sqlite\"] = DATABASES[\"default\"].copy()\n\n DATABASES[\"default\"] = {\n \"HOST\": os.getenv(\"PAPERLESS_DBHOST\"),\n \"NAME\": os.getenv(\"PAPERLESS_DBNAME\", \"paperless\"),\n \"USER\": os.getenv(\"PAPERLESS_DBUSER\", \"paperless\"),\n \"PASSWORD\": os.getenv(\"PAPERLESS_DBPASS\", \"paperless\"),\n \"OPTIONS\": {},\n }\n if os.getenv(\"PAPERLESS_DBPORT\"):\n DATABASES[\"default\"][\"PORT\"] = os.getenv(\"PAPERLESS_DBPORT\")\n\n # Leave room for future extensibility\n if os.getenv(\"PAPERLESS_DBENGINE\") == \"mariadb\":\n engine = \"django.db.backends.mysql\"\n options = {\"read_default_file\": \"/etc/mysql/my.cnf\", \"charset\": \"utf8mb4\"}\n\n # Silence Django error on old MariaDB versions.\n # VARCHAR can support > 255 in modern versions\n # https://docs.djangoproject.com/en/4.1/ref/checks/#database\n # https://mariadb.com/kb/en/innodb-system-variables/#innodb_large_prefix\n SILENCED_SYSTEM_CHECKS = [\"mysql.W003\"]\n\n else: # Default to PostgresDB\n engine = \"django.db.backends.postgresql_psycopg2\"\n options = {\n \"sslmode\": os.getenv(\"PAPERLESS_DBSSLMODE\", \"prefer\"),\n \"sslrootcert\": os.getenv(\"PAPERLESS_DBSSLROOTCERT\", None),\n \"sslcert\": os.getenv(\"PAPERLESS_DBSSLCERT\", None),\n \"sslkey\": os.getenv(\"PAPERLESS_DBSSLKEY\", None),\n }\n\n DATABASES[\"default\"][\"ENGINE\"] = engine\n DATABASES[\"default\"][\"OPTIONS\"].update(options)\n\nif os.getenv(\"PAPERLESS_DB_TIMEOUT\") is not None:\n DATABASES[\"default\"][\"OPTIONS\"].update(\n {\"timeout\": float(os.getenv(\"PAPERLESS_DB_TIMEOUT\"))},\n )\n\nDEFAULT_AUTO_FIELD = \"django.db.models.AutoField\"\n\n###############################################################################\n# Internationalization #\n###############################################################################\n\nLANGUAGE_CODE = \"en-us\"\n\nLANGUAGES = [\n (\"en-us\", _(\"English (US)\")), # needs to be first to act as fallback language\n (\"ar-ar\", _(\"Arabic\")),\n (\"be-by\", _(\"Belarusian\")),\n (\"cs-cz\", _(\"Czech\")),\n (\"da-dk\", _(\"Danish\")),\n (\"de-de\", _(\"German\")),\n (\"en-gb\", _(\"English (GB)\")),\n (\"es-es\", _(\"Spanish\")),\n (\"fr-fr\", _(\"French\")),\n (\"it-it\", _(\"Italian\")),\n (\"lb-lu\", _(\"Luxembourgish\")),\n (\"nl-nl\", _(\"Dutch\")),\n (\"pl-pl\", _(\"Polish\")),\n (\"pt-br\", _(\"Portuguese (Brazil)\")),\n (\"pt-pt\", _(\"Portuguese\")),\n (\"ro-ro\", _(\"Romanian\")),\n (\"ru-ru\", _(\"Russian\")),\n (\"sl-si\", _(\"Slovenian\")),\n (\"sr-cs\", _(\"Serbian\")),\n (\"sv-se\", _(\"Swedish\")),\n (\"tr-tr\", _(\"Turkish\")),\n (\"zh-cn\", _(\"Chinese Simplified\")),\n]\n\nLOCALE_PATHS = [os.path.join(BASE_DIR, \"locale\")]\n\nTIME_ZONE = os.getenv(\"PAPERLESS_TIME_ZONE\", \"UTC\")\n\nUSE_I18N = True\n\nUSE_L10N = True\n\nUSE_TZ = True\n\n###############################################################################\n# Logging #\n###############################################################################\n\nsetup_logging_queues()\n\nos.makedirs(LOGGING_DIR, exist_ok=True)\n\nLOGROTATE_MAX_SIZE = os.getenv(\"PAPERLESS_LOGROTATE_MAX_SIZE\", 1024 * 1024)\nLOGROTATE_MAX_BACKUPS = os.getenv(\"PAPERLESS_LOGROTATE_MAX_BACKUPS\", 20)\n\nLOGGING = {\n \"version\": 1,\n \"disable_existing_loggers\": False,\n \"formatters\": {\n \"verbose\": {\n \"format\": \"[{asctime}] [{levelname}] [{name}] {message}\",\n \"style\": \"{\",\n },\n \"simple\": {\n \"format\": \"{levelname} {message}\",\n \"style\": \"{\",\n },\n },\n \"handlers\": {\n \"console\": {\n \"level\": \"DEBUG\" if DEBUG else \"INFO\",\n \"class\": \"logging.StreamHandler\",\n \"formatter\": \"verbose\",\n },\n \"file_paperless\": {\n \"class\": \"concurrent_log_handler.ConcurrentRotatingFileHandler\",\n \"formatter\": \"verbose\",\n \"filename\": os.path.join(LOGGING_DIR, \"paperless.log\"),\n \"maxBytes\": LOGROTATE_MAX_SIZE,\n \"backupCount\": LOGROTATE_MAX_BACKUPS,\n },\n \"file_mail\": {\n \"class\": \"concurrent_log_handler.ConcurrentRotatingFileHandler\",\n \"formatter\": \"verbose\",\n \"filename\": os.path.join(LOGGING_DIR, \"mail.log\"),\n \"maxBytes\": LOGROTATE_MAX_SIZE,\n \"backupCount\": LOGROTATE_MAX_BACKUPS,\n },\n },\n \"root\": {\"handlers\": [\"console\"]},\n \"loggers\": {\n \"paperless\": {\"handlers\": [\"file_paperless\"], \"level\": \"DEBUG\"},\n \"paperless_mail\": {\"handlers\": [\"file_mail\"], \"level\": \"DEBUG\"},\n },\n}\n\n###############################################################################\n# Task queue #\n###############################################################################\n\n# https://docs.celeryq.dev/en/stable/userguide/configuration.html\n\nCELERY_BROKER_URL = _CELERY_REDIS_URL\nCELERY_TIMEZONE = TIME_ZONE\n\nCELERY_WORKER_HIJACK_ROOT_LOGGER = False\nCELERY_WORKER_CONCURRENCY: Final[int] = __get_int(\"PAPERLESS_TASK_WORKERS\", 1)\nTASK_WORKERS = CELERY_WORKER_CONCURRENCY\nCELERY_WORKER_MAX_TASKS_PER_CHILD = 1\nCELERY_WORKER_SEND_TASK_EVENTS = True\nCELERY_TASK_SEND_SENT_EVENT = True\nCELERY_SEND_TASK_SENT_EVENT = True\n\nCELERY_TASK_TRACK_STARTED = True\nCELERY_TASK_TIME_LIMIT: Final[int] = __get_int(\"PAPERLESS_WORKER_TIMEOUT\", 1800)\n\nCELERY_RESULT_EXTENDED = True\nCELERY_RESULT_BACKEND = \"django-db\"\nCELERY_CACHE_BACKEND = \"default\"\n\n# https://docs.celeryq.dev/en/stable/userguide/configuration.html#task-serializer\nCELERY_TASK_SERIALIZER = \"pickle\"\n# https://docs.celeryq.dev/en/stable/userguide/configuration.html#std-setting-accept_content\nCELERY_ACCEPT_CONTENT = [\"application/json\", \"application/x-python-serialize\"]\n\n# https://docs.celeryq.dev/en/stable/userguide/configuration.html#beat-schedule\nCELERY_BEAT_SCHEDULE = _parse_beat_schedule()\n\n# https://docs.celeryq.dev/en/stable/userguide/configuration.html#beat-schedule-filename\nCELERY_BEAT_SCHEDULE_FILENAME = os.path.join(DATA_DIR, \"celerybeat-schedule.db\")\n\n# django setting.\nCACHES = {\n \"default\": {\n \"BACKEND\": \"django.core.cache.backends.redis.RedisCache\",\n \"LOCATION\": _CHANNELS_REDIS_URL,\n },\n}\n\n\ndef default_threads_per_worker(task_workers) -> int:\n # always leave one core open\n available_cores = max(multiprocessing.cpu_count(), 1)\n try:\n return max(math.floor(available_cores / task_workers), 1)\n except NotImplementedError:\n return 1\n\n\nTHREADS_PER_WORKER = os.getenv(\n \"PAPERLESS_THREADS_PER_WORKER\",\n default_threads_per_worker(CELERY_WORKER_CONCURRENCY),\n)\n\n###############################################################################\n# Paperless Specific Settings #\n###############################################################################\n\nCONSUMER_POLLING = int(os.getenv(\"PAPERLESS_CONSUMER_POLLING\", 0))\n\nCONSUMER_POLLING_DELAY = int(os.getenv(\"PAPERLESS_CONSUMER_POLLING_DELAY\", 5))\n\nCONSUMER_POLLING_RETRY_COUNT = int(\n os.getenv(\"PAPERLESS_CONSUMER_POLLING_RETRY_COUNT\", 5),\n)\n\nCONSUMER_INOTIFY_DELAY: Final[float] = __get_float(\n \"PAPERLESS_CONSUMER_INOTIFY_DELAY\",\n 0.5,\n)\n\nCONSUMER_DELETE_DUPLICATES = __get_boolean(\"PAPERLESS_CONSUMER_DELETE_DUPLICATES\")\n\nCONSUMER_RECURSIVE = __get_boolean(\"PAPERLESS_CONSUMER_RECURSIVE\")\n\n# Ignore glob patterns, relative to PAPERLESS_CONSUMPTION_DIR\nCONSUMER_IGNORE_PATTERNS = list(\n json.loads(\n os.getenv(\n \"PAPERLESS_CONSUMER_IGNORE_PATTERNS\",\n '[\".DS_STORE/*\", \"._*\", \".stfolder/*\", \".stversions/*\", \".localized/*\", \"desktop.ini\", \"@eaDir/*\"]', # noqa: E501\n ),\n ),\n)\n\nCONSUMER_SUBDIRS_AS_TAGS = __get_boolean(\"PAPERLESS_CONSUMER_SUBDIRS_AS_TAGS\")\n\nCONSUMER_ENABLE_BARCODES: Final[bool] = __get_boolean(\n \"PAPERLESS_CONSUMER_ENABLE_BARCODES\",\n)\n\nCONSUMER_BARCODE_TIFF_SUPPORT: Final[bool] = __get_boolean(\n \"PAPERLESS_CONSUMER_BARCODE_TIFF_SUPPORT\",\n)\n\nCONSUMER_BARCODE_STRING: Final[str] = os.getenv(\n \"PAPERLESS_CONSUMER_BARCODE_STRING\",\n \"PATCHT\",\n)\n\nCONSUMER_ENABLE_ASN_BARCODE: Final[bool] = __get_boolean(\n \"PAPERLESS_CONSUMER_ENABLE_ASN_BARCODE\",\n)\n\nCONSUMER_ASN_BARCODE_PREFIX: Final[str] = os.getenv(\n \"PAPERLESS_CONSUMER_ASN_BARCODE_PREFIX\",\n \"ASN\",\n)\n\n\nOCR_PAGES = int(os.getenv(\"PAPERLESS_OCR_PAGES\", 0))\n\n# The default language that tesseract will attempt to use when parsing\n# documents. It should be a 3-letter language code consistent with ISO 639.\nOCR_LANGUAGE = os.getenv(\"PAPERLESS_OCR_LANGUAGE\", \"eng\")\n\n# OCRmyPDF --output-type options are available.\nOCR_OUTPUT_TYPE = os.getenv(\"PAPERLESS_OCR_OUTPUT_TYPE\", \"pdfa\")\n\n# skip. redo, force\nOCR_MODE = os.getenv(\"PAPERLESS_OCR_MODE\", \"skip\")\n\nOCR_SKIP_ARCHIVE_FILE = os.getenv(\"PAPERLESS_OCR_SKIP_ARCHIVE_FILE\", \"never\")\n\nOCR_IMAGE_DPI = os.getenv(\"PAPERLESS_OCR_IMAGE_DPI\")\n\nOCR_CLEAN = os.getenv(\"PAPERLESS_OCR_CLEAN\", \"clean\")\n\nOCR_DESKEW = __get_boolean(\"PAPERLESS_OCR_DESKEW\", \"true\")\n\nOCR_ROTATE_PAGES = __get_boolean(\"PAPERLESS_OCR_ROTATE_PAGES\", \"true\")\n\nOCR_ROTATE_PAGES_THRESHOLD = float(\n os.getenv(\"PAPERLESS_OCR_ROTATE_PAGES_THRESHOLD\", 12.0),\n)\n\nOCR_MAX_IMAGE_PIXELS: Optional[int] = None\nif os.environ.get(\"PAPERLESS_OCR_MAX_IMAGE_PIXELS\") is not None:\n OCR_MAX_IMAGE_PIXELS: int = int(os.environ.get(\"PAPERLESS_OCR_MAX_IMAGE_PIXELS\"))\n\nOCR_USER_ARGS = os.getenv(\"PAPERLESS_OCR_USER_ARGS\", \"{}\")\n\n# GNUPG needs a home directory for some reason\nGNUPG_HOME = os.getenv(\"HOME\", \"/tmp\")\n\n# Convert is part of the ImageMagick package\nCONVERT_BINARY = os.getenv(\"PAPERLESS_CONVERT_BINARY\", \"convert\")\nCONVERT_TMPDIR = os.getenv(\"PAPERLESS_CONVERT_TMPDIR\")\nCONVERT_MEMORY_LIMIT = os.getenv(\"PAPERLESS_CONVERT_MEMORY_LIMIT\")\n\nGS_BINARY = os.getenv(\"PAPERLESS_GS_BINARY\", \"gs\")\n\n\n# Pre-2.x versions of Paperless stored your documents locally with GPG\n# encryption, but that is no longer the default. This behaviour is still\n# available, but it must be explicitly enabled by setting\n# `PAPERLESS_PASSPHRASE` in your environment or config file. The default is to\n# store these files unencrypted.\n#\n# Translation:\n# * If you're a new user, you can safely ignore this setting.\n# * If you're upgrading from 1.x, this must be set, OR you can run\n# `./manage.py change_storage_type gpg unencrypted` to decrypt your files,\n# after which you can unset this value.\nPASSPHRASE = os.getenv(\"PAPERLESS_PASSPHRASE\")\n\n# Trigger a script after every successful document consumption?\nPRE_CONSUME_SCRIPT = os.getenv(\"PAPERLESS_PRE_CONSUME_SCRIPT\")\nPOST_CONSUME_SCRIPT = os.getenv(\"PAPERLESS_POST_CONSUME_SCRIPT\")\n\n# Specify the default date order (for autodetected dates)\nDATE_ORDER = os.getenv(\"PAPERLESS_DATE_ORDER\", \"DMY\")\nFILENAME_DATE_ORDER = os.getenv(\"PAPERLESS_FILENAME_DATE_ORDER\")\n\n# Maximum number of dates taken from document start to end to show as suggestions for\n# `created` date in the frontend. Duplicates are removed, which can result in\n# fewer dates shown.\nNUMBER_OF_SUGGESTED_DATES = __get_int(\"PAPERLESS_NUMBER_OF_SUGGESTED_DATES\", 3)\n\n# Transformations applied before filename parsing\nFILENAME_PARSE_TRANSFORMS = []\nfor t in json.loads(os.getenv(\"PAPERLESS_FILENAME_PARSE_TRANSFORMS\", \"[]\")):\n FILENAME_PARSE_TRANSFORMS.append((re.compile(t[\"pattern\"]), t[\"repl\"]))\n\n# Specify the filename format for out files\nFILENAME_FORMAT = os.getenv(\"PAPERLESS_FILENAME_FORMAT\")\n\n# If this is enabled, variables in filename format will resolve to\n# empty-string instead of 'none'.\n# Directories with 'empty names' are omitted, too.\nFILENAME_FORMAT_REMOVE_NONE = __get_boolean(\n \"PAPERLESS_FILENAME_FORMAT_REMOVE_NONE\",\n \"NO\",\n)\n\nTHUMBNAIL_FONT_NAME = os.getenv(\n \"PAPERLESS_THUMBNAIL_FONT_NAME\",\n \"/usr/share/fonts/liberation/LiberationSerif-Regular.ttf\",\n)\n\n# Tika settings\nTIKA_ENABLED = __get_boolean(\"PAPERLESS_TIKA_ENABLED\", \"NO\")\nTIKA_ENDPOINT = os.getenv(\"PAPERLESS_TIKA_ENDPOINT\", \"http://localhost:9998\")\nTIKA_GOTENBERG_ENDPOINT = os.getenv(\n \"PAPERLESS_TIKA_GOTENBERG_ENDPOINT\",\n \"http://localhost:3000\",\n)\n\nif TIKA_ENABLED:\n INSTALLED_APPS.append(\"paperless_tika.apps.PaperlessTikaConfig\")\n\n\ndef _parse_ignore_dates(\n env_ignore: str,\n date_order: str = DATE_ORDER,\n) -> Set[datetime.datetime]:\n \"\"\"\n If the PAPERLESS_IGNORE_DATES environment variable is set, parse the\n user provided string(s) into dates\n\n Args:\n env_ignore (str): The value of the environment variable, comma separated dates\n date_order (str, optional): The format of the date strings.\n Defaults to DATE_ORDER.\n\n Returns:\n Set[datetime.datetime]: The set of parsed date objects\n \"\"\"\n import dateparser\n\n ignored_dates = set()\n for s in env_ignore.split(\",\"):\n d = dateparser.parse(\n s,\n settings={\n \"DATE_ORDER\": date_order,\n },\n )\n if d:\n ignored_dates.add(d.date())\n return ignored_dates\n\n\n# List dates that should be ignored when trying to parse date from document text\nIGNORE_DATES: Set[datetime.date] = set()\n\nif os.getenv(\"PAPERLESS_IGNORE_DATES\") is not None:\n IGNORE_DATES = _parse_ignore_dates(os.getenv(\"PAPERLESS_IGNORE_DATES\"))\n\nENABLE_UPDATE_CHECK = os.getenv(\"PAPERLESS_ENABLE_UPDATE_CHECK\", \"default\")\nif ENABLE_UPDATE_CHECK != \"default\":\n ENABLE_UPDATE_CHECK = __get_boolean(\"PAPERLESS_ENABLE_UPDATE_CHECK\")\n\n###############################################################################\n# Machine Learning #\n###############################################################################\n\n\ndef _get_nltk_language_setting(ocr_lang: str) -> Optional[str]:\n \"\"\"\n Maps an ISO-639-1 language code supported by Tesseract into\n an optional NLTK language name. This is the set of common supported\n languages for all the NLTK data used.\n\n Assumption: The primary language is first\n \"\"\"\n ocr_lang = ocr_lang.split(\"+\")[0]\n iso_code_to_nltk = {\n \"dan\": \"danish\",\n \"nld\": \"dutch\",\n \"eng\": \"english\",\n \"fin\": \"finnish\",\n \"fra\": \"french\",\n \"deu\": \"german\",\n \"ita\": \"italian\",\n \"nor\": \"norwegian\",\n \"por\": \"portuguese\",\n \"rus\": \"russian\",\n \"spa\": \"spanish\",\n \"swe\": \"swedish\",\n \"tur\": \"turkish\",\n }\n\n return iso_code_to_nltk.get(ocr_lang, None)\n\n\nNLTK_ENABLED: Final[bool] = __get_boolean(\"PAPERLESS_ENABLE_NLTK\", \"yes\")\n\nNLTK_LANGUAGE: Optional[str] = _get_nltk_language_setting(OCR_LANGUAGE)\n",
"path": "src/paperless/settings.py"
}
] | diff --git a/Pipfile b/Pipfile
index 8cf90a5dc5e..7058b8ff177 100644
--- a/Pipfile
+++ b/Pipfile
@@ -46,6 +46,7 @@ tika = "*"
# TODO: This will sadly also install daphne+dependencies,
# which an ASGI server we don't need. Adds about 15MB image size.
channels = "~=3.0"
+channels-redis = "*"
uvicorn = {extras = ["standard"], version = "*"}
concurrent-log-handler = "*"
"pdfminer.six" = "*"
@@ -62,9 +63,6 @@ bleach = "*"
#
# Pin this until piwheels is building 1.9 (see https://www.piwheels.org/project/scipy/)
scipy = "==1.8.1"
-# Locked version until https://github.com/django/channels_redis/issues/332
-# is resolved
-channels-redis = "==3.4.1"
[dev-packages]
coveralls = "*"
diff --git a/Pipfile.lock b/Pipfile.lock
index caf5b9a0886..b3bfc3ba810 100644
--- a/Pipfile.lock
+++ b/Pipfile.lock
@@ -1,7 +1,7 @@
{
"_meta": {
"hash": {
- "sha256": "8b3f3443de30aecc7c893d4a5a78123ba17e3dc10eed6042450a9c0cf6afcc3f"
+ "sha256": "01320f2ef2a561c37d17aaad61a7871b5a379dd1ac97fdaab586936b60dec92e"
},
"pipfile-spec": 6,
"requires": {},
@@ -19,13 +19,6 @@
]
},
"default": {
- "aioredis": {
- "hashes": [
- "sha256:15f8af30b044c771aee6787e5ec24694c048184c7b9e54c3b60c750a4b93273a",
- "sha256:b61808d7e97b7cd5a92ed574937a079c9387fdadd22bfbfa7ad2fd319ecc26e3"
- ],
- "version": "==1.3.1"
- },
"amqp": {
"hashes": [
"sha256:2c1b13fecc0893e946c65cbd5f36427861cffa4ea2201d8f6fca22e2a373b5e2",
@@ -55,7 +48,7 @@
"sha256:2163e1640ddb52b7a8c80d0a67a08587e5d245cc9c553a74a847056bc2976b15",
"sha256:8ca1e4fcf50d07413d66d1a5e416e42cfdf5851c981d679a09851a6853383b3c"
],
- "markers": "python_version >= '3.6'",
+ "markers": "python_version < '3.11'",
"version": "==4.0.2"
},
"attrs": {
@@ -302,11 +295,11 @@
},
"channels-redis": {
"hashes": [
- "sha256:78e4a2f2b2a744fe5a87848ec36b5ee49f522c6808cefe6c583663d0d531faa8",
- "sha256:ba7e2ad170f273c372812dd32aaac102d68d4e508172abb1cfda3160b7333890"
+ "sha256:122414f29f525f7b9e0c9d59cdcfc4dc1b0eecba16fbb6a1c23f1d9b58f49dcb",
+ "sha256:81b59d68f53313e1aa891f23591841b684abb936b42e4d1a966d9e4dc63a95ec"
],
"index": "pypi",
- "version": "==3.4.1"
+ "version": "==4.0.0"
},
"charset-normalizer": {
"hashes": [
@@ -481,11 +474,11 @@
},
"dateparser": {
"hashes": [
- "sha256:fbed8b738a24c9cd7f47c4f2089527926566fe539e1a06125eddba75917b1eef",
- "sha256:ff047d9cffad4d3113ead8ec0faf8a7fc43bab7d853ac8715e071312b53c465a"
+ "sha256:070b29b5bbf4b1ec2cd51c96ea040dc68a614de703910a91ad1abba18f9f379f",
+ "sha256:86b8b7517efcc558f085a142cdb7620f0921543fcabdb538c8a4c4001d8178e3"
],
"index": "pypi",
- "version": "==1.1.7"
+ "version": "==1.1.8"
},
"deprecation": {
"hashes": [
@@ -576,11 +569,11 @@
},
"filelock": {
"hashes": [
- "sha256:4427cdda14a1c68e264845142842d6de2d0fa2c15ba31571a3d9c9a1ec9d191c",
- "sha256:e393782f76abea324dee598d2ea145b857a20df0e0ee4f80fcf35e72a341d2c7"
+ "sha256:75997740323c5f12e18f10b494bc11c03e42843129f980f17c04352cc7b09d40",
+ "sha256:eb8f0f2d37ed68223ea63e3bddf2fac99667e4362c88b3f762e434d160190d18"
],
"index": "pypi",
- "version": "==3.9.1"
+ "version": "==3.10.2"
},
"flower": {
"hashes": [
@@ -1053,11 +1046,11 @@
},
"ocrmypdf": {
"hashes": [
- "sha256:8fab75052bf77c3488acd9c3054423d9f1f7650e302960a1fa2e991f36c2a66a",
- "sha256:db03cdd1a5d277fa038b0420ba05fcf7b1f92729ba85431344844ebf01035160"
+ "sha256:779b6f77ece5836b4ac703ba02a4bb0ccb758dbb9b4dad1feab3fccd4dba33cf",
+ "sha256:c731bd3b6bfd67dc495edc97946f159ba99631854bf7671c2d35c36f30b3ffa8"
],
"index": "pypi",
- "version": "==14.0.3"
+ "version": "==14.0.4"
},
"packaging": {
"hashes": [
@@ -1515,105 +1508,77 @@
"hiredis"
],
"hashes": [
- "sha256:1eec3741cda408d3a5f84b78d089c8b8d895f21b3b050988351e925faf202864",
- "sha256:5deb072d26e67d2be1712603bfb7947ec3431fb0eec9c578994052e33035af6d"
+ "sha256:56732e156fe31801c4f43396bd3ca0c2a7f6f83d7936798531b9848d103381aa",
+ "sha256:7df17a0a2b72a4c8895b462dd07616c51b1dcb48fdd7ecb7b6f4bf39ecb2e94e"
],
"index": "pypi",
- "version": "==4.5.1"
+ "version": "==4.5.3"
},
"regex": {
"hashes": [
- "sha256:052b670fafbe30966bbe5d025e90b2a491f85dfe5b2583a163b5e60a85a321ad",
- "sha256:0653d012b3bf45f194e5e6a41df9258811ac8fc395579fa82958a8b76286bea4",
- "sha256:0a069c8483466806ab94ea9068c34b200b8bfc66b6762f45a831c4baaa9e8cdd",
- "sha256:0cf0da36a212978be2c2e2e2d04bdff46f850108fccc1851332bcae51c8907cc",
- "sha256:131d4be09bea7ce2577f9623e415cab287a3c8e0624f778c1d955ec7c281bd4d",
- "sha256:144486e029793a733e43b2e37df16a16df4ceb62102636ff3db6033994711066",
- "sha256:1ddf14031a3882f684b8642cb74eea3af93a2be68893901b2b387c5fd92a03ec",
- "sha256:1eba476b1b242620c266edf6325b443a2e22b633217a9835a52d8da2b5c051f9",
- "sha256:20f61c9944f0be2dc2b75689ba409938c14876c19d02f7585af4460b6a21403e",
- "sha256:22960019a842777a9fa5134c2364efaed5fbf9610ddc5c904bd3a400973b0eb8",
- "sha256:22e7ebc231d28393dfdc19b185d97e14a0f178bedd78e85aad660e93b646604e",
- "sha256:23cbb932cc53a86ebde0fb72e7e645f9a5eec1a5af7aa9ce333e46286caef783",
- "sha256:29c04741b9ae13d1e94cf93fca257730b97ce6ea64cfe1eba11cf9ac4e85afb6",
- "sha256:2bde29cc44fa81c0a0c8686992c3080b37c488df167a371500b2a43ce9f026d1",
- "sha256:2cdc55ca07b4e70dda898d2ab7150ecf17c990076d3acd7a5f3b25cb23a69f1c",
- "sha256:370f6e97d02bf2dd20d7468ce4f38e173a124e769762d00beadec3bc2f4b3bc4",
- "sha256:395161bbdbd04a8333b9ff9763a05e9ceb4fe210e3c7690f5e68cedd3d65d8e1",
- "sha256:44136355e2f5e06bf6b23d337a75386371ba742ffa771440b85bed367c1318d1",
- "sha256:44a6c2f6374e0033873e9ed577a54a3602b4f609867794c1a3ebba65e4c93ee7",
- "sha256:4919899577ba37f505aaebdf6e7dc812d55e8f097331312db7f1aab18767cce8",
- "sha256:4b4b1fe58cd102d75ef0552cf17242705ce0759f9695334a56644ad2d83903fe",
- "sha256:4bdd56ee719a8f751cf5a593476a441c4e56c9b64dc1f0f30902858c4ef8771d",
- "sha256:4bf41b8b0a80708f7e0384519795e80dcb44d7199a35d52c15cc674d10b3081b",
- "sha256:4cac3405d8dda8bc6ed499557625585544dd5cbf32072dcc72b5a176cb1271c8",
- "sha256:4fe7fda2fe7c8890d454f2cbc91d6c01baf206fbc96d89a80241a02985118c0c",
- "sha256:50921c140561d3db2ab9f5b11c5184846cde686bb5a9dc64cae442926e86f3af",
- "sha256:5217c25229b6a85049416a5c1e6451e9060a1edcf988641e309dbe3ab26d3e49",
- "sha256:5352bea8a8f84b89d45ccc503f390a6be77917932b1c98c4cdc3565137acc714",
- "sha256:542e3e306d1669b25936b64917285cdffcd4f5c6f0247636fec037187bd93542",
- "sha256:543883e3496c8b6d58bd036c99486c3c8387c2fc01f7a342b760c1ea3158a318",
- "sha256:586b36ebda81e6c1a9c5a5d0bfdc236399ba6595e1397842fd4a45648c30f35e",
- "sha256:597f899f4ed42a38df7b0e46714880fb4e19a25c2f66e5c908805466721760f5",
- "sha256:5a260758454580f11dd8743fa98319bb046037dfab4f7828008909d0aa5292bc",
- "sha256:5aefb84a301327ad115e9d346c8e2760009131d9d4b4c6b213648d02e2abe144",
- "sha256:5e6a5567078b3eaed93558842346c9d678e116ab0135e22eb72db8325e90b453",
- "sha256:5ff525698de226c0ca743bfa71fc6b378cda2ddcf0d22d7c37b1cc925c9650a5",
- "sha256:61edbca89aa3f5ef7ecac8c23d975fe7261c12665f1d90a6b1af527bba86ce61",
- "sha256:659175b2144d199560d99a8d13b2228b85e6019b6e09e556209dfb8c37b78a11",
- "sha256:6a9a19bea8495bb419dc5d38c4519567781cd8d571c72efc6aa959473d10221a",
- "sha256:6b30bddd61d2a3261f025ad0f9ee2586988c6a00c780a2fb0a92cea2aa702c54",
- "sha256:6ffd55b5aedc6f25fd8d9f905c9376ca44fcf768673ffb9d160dd6f409bfda73",
- "sha256:702d8fc6f25bbf412ee706bd73019da5e44a8400861dfff7ff31eb5b4a1276dc",
- "sha256:74bcab50a13960f2a610cdcd066e25f1fd59e23b69637c92ad470784a51b1347",
- "sha256:75f591b2055523fc02a4bbe598aa867df9e953255f0b7f7715d2a36a9c30065c",
- "sha256:763b64853b0a8f4f9cfb41a76a4a85a9bcda7fdda5cb057016e7706fde928e66",
- "sha256:76c598ca73ec73a2f568e2a72ba46c3b6c8690ad9a07092b18e48ceb936e9f0c",
- "sha256:78d680ef3e4d405f36f0d6d1ea54e740366f061645930072d39bca16a10d8c93",
- "sha256:7b280948d00bd3973c1998f92e22aa3ecb76682e3a4255f33e1020bd32adf443",
- "sha256:7db345956ecce0c99b97b042b4ca7326feeec6b75facd8390af73b18e2650ffc",
- "sha256:7dbdce0c534bbf52274b94768b3498abdf675a691fec5f751b6057b3030f34c1",
- "sha256:7ef6b5942e6bfc5706301a18a62300c60db9af7f6368042227ccb7eeb22d0892",
- "sha256:7f5a3ffc731494f1a57bd91c47dc483a1e10048131ffb52d901bfe2beb6102e8",
- "sha256:8a45b6514861916c429e6059a55cf7db74670eaed2052a648e3e4d04f070e001",
- "sha256:8ad241da7fac963d7573cc67a064c57c58766b62a9a20c452ca1f21050868dfa",
- "sha256:8b0886885f7323beea6f552c28bff62cbe0983b9fbb94126531693ea6c5ebb90",
- "sha256:8ca88da1bd78990b536c4a7765f719803eb4f8f9971cc22d6ca965c10a7f2c4c",
- "sha256:8e0caeff18b96ea90fc0eb6e3bdb2b10ab5b01a95128dfeccb64a7238decf5f0",
- "sha256:957403a978e10fb3ca42572a23e6f7badff39aa1ce2f4ade68ee452dc6807692",
- "sha256:9af69f6746120998cd9c355e9c3c6aec7dff70d47247188feb4f829502be8ab4",
- "sha256:9c94f7cc91ab16b36ba5ce476f1904c91d6c92441f01cd61a8e2729442d6fcf5",
- "sha256:a37d51fa9a00d265cf73f3de3930fa9c41548177ba4f0faf76e61d512c774690",
- "sha256:a3a98921da9a1bf8457aeee6a551948a83601689e5ecdd736894ea9bbec77e83",
- "sha256:a3c1ebd4ed8e76e886507c9eddb1a891673686c813adf889b864a17fafcf6d66",
- "sha256:a5f9505efd574d1e5b4a76ac9dd92a12acb2b309551e9aa874c13c11caefbe4f",
- "sha256:a8ff454ef0bb061e37df03557afda9d785c905dab15584860f982e88be73015f",
- "sha256:a9d0b68ac1743964755ae2d89772c7e6fb0118acd4d0b7464eaf3921c6b49dd4",
- "sha256:aa62a07ac93b7cb6b7d0389d8ef57ffc321d78f60c037b19dfa78d6b17c928ee",
- "sha256:ac741bf78b9bb432e2d314439275235f41656e189856b11fb4e774d9f7246d81",
- "sha256:ae1e96785696b543394a4e3f15f3f225d44f3c55dafe3f206493031419fedf95",
- "sha256:b683e5fd7f74fb66e89a1ed16076dbab3f8e9f34c18b1979ded614fe10cdc4d9",
- "sha256:b7a8b43ee64ca8f4befa2bea4083f7c52c92864d8518244bfa6e88c751fa8fff",
- "sha256:b8e38472739028e5f2c3a4aded0ab7eadc447f0d84f310c7a8bb697ec417229e",
- "sha256:bfff48c7bd23c6e2aec6454aaf6edc44444b229e94743b34bdcdda2e35126cf5",
- "sha256:c14b63c9d7bab795d17392c7c1f9aaabbffd4cf4387725a0ac69109fb3b550c6",
- "sha256:c27cc1e4b197092e50ddbf0118c788d9977f3f8f35bfbbd3e76c1846a3443df7",
- "sha256:c28d3309ebd6d6b2cf82969b5179bed5fefe6142c70f354ece94324fa11bf6a1",
- "sha256:c670f4773f2f6f1957ff8a3962c7dd12e4be54d05839b216cb7fd70b5a1df394",
- "sha256:ce6910b56b700bea7be82c54ddf2e0ed792a577dfaa4a76b9af07d550af435c6",
- "sha256:d0213671691e341f6849bf33cd9fad21f7b1cb88b89e024f33370733fec58742",
- "sha256:d03fe67b2325cb3f09be029fd5da8df9e6974f0cde2c2ac6a79d2634e791dd57",
- "sha256:d0e5af9a9effb88535a472e19169e09ce750c3d442fb222254a276d77808620b",
- "sha256:d243b36fbf3d73c25e48014961e83c19c9cc92530516ce3c43050ea6276a2ab7",
- "sha256:d26166acf62f731f50bdd885b04b38828436d74e8e362bfcb8df221d868b5d9b",
- "sha256:d403d781b0e06d2922435ce3b8d2376579f0c217ae491e273bab8d092727d244",
- "sha256:d8716f82502997b3d0895d1c64c3b834181b1eaca28f3f6336a71777e437c2af",
- "sha256:e4f781ffedd17b0b834c8731b75cce2639d5a8afe961c1e58ee7f1f20b3af185",
- "sha256:e613a98ead2005c4ce037c7b061f2409a1a4e45099edb0ef3200ee26ed2a69a8",
- "sha256:ef4163770525257876f10e8ece1cf25b71468316f61451ded1a6f44273eedeb5"
+ "sha256:0a2a851d0548a4e298d88e3ceeb4bad4aab751cf1883edf6150f25718ce0207a",
+ "sha256:148ad520f41021b97870e9c80420e6cdaadcc5e4306e613aed84cd5d53f8a7ca",
+ "sha256:159c7b83488a056365119ada0bceddc06a455d3db7a7aa3cf07f13b2878b885f",
+ "sha256:1937946dd03818845bd9c1713dfd3173a7b9a324e6593a235fc8c51c9cd460eb",
+ "sha256:20ce96da2093e72e151d6af8217a629aeb5f48f1ac543c2fffd1d87c57699d7e",
+ "sha256:24242e5f26823e95edd64969bd206d4752c1a56a744d8cbcf58461f9788bc0c7",
+ "sha256:2e2e6baf4a1108f84966f44870b26766d8f6d104c9959aae329078327c677122",
+ "sha256:328a70e578f37f59eb54e8450b5042190bbadf2ef7f5c0b60829574b62955ed7",
+ "sha256:3371975b165c1e859e1990e5069e8606f00b25aed961cfd25b7bac626b1eb5a9",
+ "sha256:33bab9c9af936123b70b9874ce83f2bcd54be76b97637b33d31560fba8ad5d78",
+ "sha256:33c887b658afb144cdc8ce9156a0e1098453060c18b8bd5177f831ad58e0d60d",
+ "sha256:3582db55372eaee9e998d378109c4b9b15beb2c84624c767efe351363fada9c4",
+ "sha256:3b4da28d89527572f0d4a24814e353e1228a7aeda965e5d9265c1435a154b17a",
+ "sha256:3c4fa90fd91cc2957e66195ce374331bebbc816964864f64b42bd14bda773b53",
+ "sha256:3e66cfc915f5f7e2c8a0af8a27f87aa857f440de7521fd7f2682e23f082142a1",
+ "sha256:3f6f29cb134d782685f8eda01d72073c483c7f87b318b5101c7001faef7850f5",
+ "sha256:43469c22fcf705a7cb59c7e01d6d96975bdbc54c1138900f04d11496489a0054",
+ "sha256:4ad467524cb6879ce42107cf02a49cdb4a06f07fe0e5f1160d7db865a8d25d4b",
+ "sha256:4c9c3db90acd17e4231344a23616f33fd79837809584ce30e2450ca312fa47aa",
+ "sha256:533ba64d67d882286557106a1c5f12b4c2825f11b47a7c209a8c22922ca882be",
+ "sha256:548257463696daf919d2fdfc53ee4b98e29e3ffc5afddd713d83aa849d1fa178",
+ "sha256:55f907c4d18a5a40da0ceb339a0beda77c9df47c934adad987793632fb4318c3",
+ "sha256:5826e7fb443acb49f64f9648a2852efc8d9af2f4c67f6c3dca69dccd9e8e1d15",
+ "sha256:59a15c2803c20702d7f2077807d9a2b7d9a168034b87fd3f0d8361de60019a1e",
+ "sha256:59b3aab231c27cd754d6452c43b12498d34e7ab87d69a502bd0220f4b1c090c4",
+ "sha256:5da83c964aecb6c3f2a6c9a03f3d0fa579e1ad208e2c264ba826cecd19da11fa",
+ "sha256:60b545806a433cc752b9fa936f1c0a63bf96a3872965b958b35bd0d5d788d411",
+ "sha256:60fcef5c3144d861b623456d87ca7fff7af59a4a918e1364cdd0687b48285285",
+ "sha256:617d101b95151d827d5366e9c4225a68c64d56065e41ab9c7ef51bb87f347a8a",
+ "sha256:68e9add923bda8357e6fe65a568766feae369063cb7210297067675cce65272f",
+ "sha256:7798b3d662f70cea425637c54da30ef1894d426cab24ee7ffaaccb24a8b17bb8",
+ "sha256:80a288b21b17e39fb3630cf1d14fd704499bb11d9c8fc110662a0c57758d3d3e",
+ "sha256:81291006a934052161eae8340e7731ea6b8595b0c27dd4927c4e8a489e1760e2",
+ "sha256:8527ea0978ed6dc58ccb3935bd2883537b455c97ec44b5d8084677dfa817f96b",
+ "sha256:87016850c13082747bd120558e6750746177bd492b103b2fca761c8a1c43fba9",
+ "sha256:88552925fd22320600c59ee80342d6eb06bfa9503c3a402d7327983f5fa999d9",
+ "sha256:8d7477ebaf5d3621c763702e1ec0daeede8863fb22459c5e26ddfd17e9b1999c",
+ "sha256:97326d62255203c6026896d4b1ad6b5a0141ba097cae00ed3a508fe454e96baf",
+ "sha256:a4c7b8c5a3a186b49415af3be18e4b8f93b33d6853216c0a1d7401736b703bce",
+ "sha256:aff7c778d9229d66f716ad98a701fa91cf97935ae4a32a145ae9e61619906aaa",
+ "sha256:b280cb303fed94199f0b976595af71ebdcd388fb5e377a8198790f1016a23476",
+ "sha256:b59233cb8df6b60fff5f3056f6f342a8f5f04107a11936bf49ebff87dd4ace34",
+ "sha256:bdab2c90665b88faf5cc5e11bf835d548f4b8d8060c89fc70782b6020850aa1c",
+ "sha256:c00c357a4914f58398503c7f716cf1646b1e36b8176efa35255f5ebfacedfa46",
+ "sha256:c95a977cfdccb8ddef95ddd77cf586fe9dc327c7c93cf712983cece70cdaa1be",
+ "sha256:cdd3d2df486c9a8c6d08f78bdfa8ea7cf6191e037fde38c2cf6f5f0559e9d353",
+ "sha256:d15a0cc48f7a3055e89df1bd6623a907c407d1f58f67ff47064e598d4a550de4",
+ "sha256:d40cecf4bcb2cb37c59e3c79e5bbc45d47e3f3e07edf24e35fc5775db2570058",
+ "sha256:d4d3571c8eb21f0fbe9f0b21b49092c24d442f9a295f079949df3551b2886f29",
+ "sha256:d94a0d25e517c76c9ce9e2e2635d9d1a644b894f466a66a10061f4e599cdc019",
+ "sha256:dcc5b0d6a94637c071a427dc4469efd0ae4fda8ff384790bc8b5baaf9308dc3e",
+ "sha256:e00b046000b313ffaa2f6e8d7290b33b08d2005150eff4c8cf3ad74d011888d1",
+ "sha256:e1b56dac5e86ab52e0443d63b02796357202a8f8c5966b69f8d4c03a94778e98",
+ "sha256:e30d9a6fd7a7a6a4da6f80d167ce8eda4a993ff24282cbc73f34186c46a498db",
+ "sha256:f1977c1fe28173f2349d42c59f80f10a97ce34f2bedb7b7f55e2e8a8de9b7dfb",
+ "sha256:f2bc8a9076ea7add860d57dbee0554a212962ecf2a900344f2fc7c56a02463b0",
+ "sha256:f311ca33fcb9f8fb060c1fa76238d8d029f33b71a2021bafa5d423cc25965b54",
+ "sha256:f579a202b90c1110d0894a86b32a89bf550fdb34bdd3f9f550115706be462e19",
+ "sha256:fa41a427d4f03ec6d6da2fd8a230f4f388f336cd7ca46b46c4d2a1bca3ead85a",
+ "sha256:fd47362e03acc780aad5a5bc4624d495594261b55a1f79a5b775b6be865a5911"
],
- "markers": "python_version >= '3.6'",
- "version": "==2022.10.31"
+ "markers": "python_version >= '3.8'",
+ "version": "==2023.3.22"
},
"reportlab": {
"hashes": [
@@ -1925,11 +1890,11 @@
},
"tzlocal": {
"hashes": [
- "sha256:89885494684c929d9191c57aa27502afc87a579be5cdd3225c77c463ea043745",
- "sha256:ee5842fa3a795f023514ac2d801c4a81d1743bbe642e3940143326b3a00addd7"
+ "sha256:3f21d09e1b2aa9f2dacca12da240ca37de3ba5237a93addfd6d593afe9073355",
+ "sha256:b44c4388f3d34f25862cfbb387578a4d70fec417649da694a132f628a23367e2"
],
- "markers": "python_version >= '3.6'",
- "version": "==4.2"
+ "markers": "python_version >= '3.7'",
+ "version": "==4.3"
},
"urllib3": {
"hashes": [
@@ -1944,11 +1909,11 @@
"standard"
],
"hashes": [
- "sha256:8635a388062222082f4b06225b867b74a7e4ef942124453d4d1d1a5cb3750932",
- "sha256:e69e955cb621ae7b75f5590a814a4fcbfb14cb8f44a36dfe3c5c75ab8aee3ad5"
+ "sha256:0fac9cb342ba099e0d582966005f3fdba5b0290579fed4a6266dc702ca7bb032",
+ "sha256:e47cac98a6da10cd41e6fd036d472c6f58ede6c5dbee3dbee3ef7a100ed97742"
],
"index": "pypi",
- "version": "==0.21.0"
+ "version": "==0.21.1"
},
"uvloop": {
"hashes": [
@@ -2165,45 +2130,39 @@
},
"zope.interface": {
"hashes": [
- "sha256:008b0b65c05993bb08912f644d140530e775cf1c62a072bf9340c2249e613c32",
- "sha256:0217a9615531c83aeedb12e126611b1b1a3175013bbafe57c702ce40000eb9a0",
- "sha256:0fb497c6b088818e3395e302e426850f8236d8d9f4ef5b2836feae812a8f699c",
- "sha256:17ebf6e0b1d07ed009738016abf0d0a0f80388e009d0ac6e0ead26fc162b3b9c",
- "sha256:311196634bb9333aa06f00fc94f59d3a9fddd2305c2c425d86e406ddc6f2260d",
- "sha256:3218ab1a7748327e08ef83cca63eea7cf20ea7e2ebcb2522072896e5e2fceedf",
- "sha256:404d1e284eda9e233c90128697c71acffd55e183d70628aa0bbb0e7a3084ed8b",
- "sha256:4087e253bd3bbbc3e615ecd0b6dd03c4e6a1e46d152d3be6d2ad08fbad742dcc",
- "sha256:40f4065745e2c2fa0dff0e7ccd7c166a8ac9748974f960cd39f63d2c19f9231f",
- "sha256:5334e2ef60d3d9439c08baedaf8b84dc9bb9522d0dacbc10572ef5609ef8db6d",
- "sha256:604cdba8f1983d0ab78edc29aa71c8df0ada06fb147cea436dc37093a0100a4e",
- "sha256:6373d7eb813a143cb7795d3e42bd8ed857c82a90571567e681e1b3841a390d16",
- "sha256:655796a906fa3ca67273011c9805c1e1baa047781fca80feeb710328cdbed87f",
- "sha256:65c3c06afee96c654e590e046c4a24559e65b0a87dbff256cd4bd6f77e1a33f9",
- "sha256:696f3d5493eae7359887da55c2afa05acc3db5fc625c49529e84bd9992313296",
- "sha256:6e972493cdfe4ad0411fd9abfab7d4d800a7317a93928217f1a5de2bb0f0d87a",
- "sha256:7579960be23d1fddecb53898035a0d112ac858c3554018ce615cefc03024e46d",
- "sha256:765d703096ca47aa5d93044bf701b00bbce4d903a95b41fff7c3796e747b1f1d",
- "sha256:7e66f60b0067a10dd289b29dceabd3d0e6d68be1504fc9d0bc209cf07f56d189",
- "sha256:8a2ffadefd0e7206adc86e492ccc60395f7edb5680adedf17a7ee4205c530df4",
- "sha256:959697ef2757406bff71467a09d940ca364e724c534efbf3786e86eee8591452",
- "sha256:9d783213fab61832dbb10d385a319cb0e45451088abd45f95b5bb88ed0acca1a",
- "sha256:a16025df73d24795a0bde05504911d306307c24a64187752685ff6ea23897cb0",
- "sha256:a2ad597c8c9e038a5912ac3cf166f82926feff2f6e0dabdab956768de0a258f5",
- "sha256:bfee1f3ff62143819499e348f5b8a7f3aa0259f9aca5e0ddae7391d059dce671",
- "sha256:d169ccd0756c15bbb2f1acc012f5aab279dffc334d733ca0d9362c5beaebe88e",
- "sha256:d514c269d1f9f5cd05ddfed15298d6c418129f3f064765295659798349c43e6f",
- "sha256:d692374b578360d36568dd05efb8a5a67ab6d1878c29c582e37ddba80e66c396",
- "sha256:dbaeb9cf0ea0b3bc4b36fae54a016933d64c6d52a94810a63c00f440ecb37dd7",
- "sha256:dc26c8d44472e035d59d6f1177eb712888447f5799743da9c398b0339ed90b1b",
- "sha256:e1574980b48c8c74f83578d1e77e701f8439a5d93f36a5a0af31337467c08fcf",
- "sha256:e74a578172525c20d7223eac5f8ad187f10940dac06e40113d62f14f3adb1e8f",
- "sha256:e945de62917acbf853ab968d8916290548df18dd62c739d862f359ecd25842a6",
- "sha256:f0980d44b8aded808bec5059018d64692f0127f10510eca71f2f0ace8fb11188",
- "sha256:f98d4bd7bbb15ca701d19b93263cc5edfd480c3475d163f137385f49e5b3a3a7",
- "sha256:fb68d212efd057596dee9e6582daded9f8ef776538afdf5feceb3059df2d2e7b"
+ "sha256:042f2381118b093714081fd82c98e3b189b68db38ee7d35b63c327c470ef8373",
+ "sha256:0ec9653825f837fbddc4e4b603d90269b501486c11800d7c761eee7ce46d1bbb",
+ "sha256:12175ca6b4db7621aedd7c30aa7cfa0a2d65ea3a0105393e05482d7a2d367446",
+ "sha256:1592f68ae11e557b9ff2bc96ac8fc30b187e77c45a3c9cd876e3368c53dc5ba8",
+ "sha256:23ac41d52fd15dd8be77e3257bc51bbb82469cf7f5e9a30b75e903e21439d16c",
+ "sha256:424d23b97fa1542d7be882eae0c0fc3d6827784105264a8169a26ce16db260d8",
+ "sha256:4407b1435572e3e1610797c9203ad2753666c62883b921318c5403fb7139dec2",
+ "sha256:48f4d38cf4b462e75fac78b6f11ad47b06b1c568eb59896db5b6ec1094eb467f",
+ "sha256:4c3d7dfd897a588ec27e391edbe3dd320a03684457470415870254e714126b1f",
+ "sha256:5171eb073474a5038321409a630904fd61f12dd1856dd7e9d19cd6fe092cbbc5",
+ "sha256:5a158846d0fca0a908c1afb281ddba88744d403f2550dc34405c3691769cdd85",
+ "sha256:6ee934f023f875ec2cfd2b05a937bd817efcc6c4c3f55c5778cbf78e58362ddc",
+ "sha256:790c1d9d8f9c92819c31ea660cd43c3d5451df1df61e2e814a6f99cebb292788",
+ "sha256:809fe3bf1a91393abc7e92d607976bbb8586512913a79f2bf7d7ec15bd8ea518",
+ "sha256:87b690bbee9876163210fd3f500ee59f5803e4a6607d1b1238833b8885ebd410",
+ "sha256:89086c9d3490a0f265a3c4b794037a84541ff5ffa28bb9c24cc9f66566968464",
+ "sha256:99856d6c98a326abbcc2363827e16bd6044f70f2ef42f453c0bd5440c4ce24e5",
+ "sha256:aab584725afd10c710b8f1e6e208dbee2d0ad009f57d674cb9d1b3964037275d",
+ "sha256:af169ba897692e9cd984a81cb0f02e46dacdc07d6cf9fd5c91e81f8efaf93d52",
+ "sha256:b39b8711578dcfd45fc0140993403b8a81e879ec25d53189f3faa1f006087dca",
+ "sha256:b3f543ae9d3408549a9900720f18c0194ac0fe810cecda2a584fd4dca2eb3bb8",
+ "sha256:d0583b75f2e70ec93f100931660328965bb9ff65ae54695fb3fa0a1255daa6f2",
+ "sha256:dfbbbf0809a3606046a41f8561c3eada9db811be94138f42d9135a5c47e75f6f",
+ "sha256:e538f2d4a6ffb6edfb303ce70ae7e88629ac6e5581870e66c306d9ad7b564a58",
+ "sha256:eba51599370c87088d8882ab74f637de0c4f04a6d08a312dce49368ba9ed5c2a",
+ "sha256:ee4b43f35f5dc15e1fec55ccb53c130adb1d11e8ad8263d68b1284b66a04190d",
+ "sha256:f2363e5fd81afb650085c6686f2ee3706975c54f331b426800b53531191fdf28",
+ "sha256:f299c020c6679cb389814a3b81200fe55d428012c5e76da7e722491f5d205990",
+ "sha256:f72f23bab1848edb7472309e9898603141644faec9fd57a823ea6b4d1c4c8995",
+ "sha256:fa90bac61c9dc3e1a563e5babb3fd2c0c1c80567e815442ddbe561eadc803b30"
],
- "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4'",
- "version": "==5.5.2"
+ "markers": "python_version >= '3.7'",
+ "version": "==6.0"
},
"zstandard": {
"hashes": [
@@ -2519,19 +2478,19 @@
},
"faker": {
"hashes": [
- "sha256:51f37ff9df710159d6d736d0ba1c75e063430a8c806b91334d7794305b5a6114",
- "sha256:5aaa16fa9cfde7d117eef70b6b293a705021e57158f3fa6b44ed1b70202d2065"
+ "sha256:2deeee8fed3d1b8ae5f87d172d4569ddc859aab8693f7cd68eddc5d20400563a",
+ "sha256:e7c058e1f360f245f265625b32d3189d7229398ad80a8b6bac459891745de052"
],
"markers": "python_version >= '3.7'",
- "version": "==17.6.0"
+ "version": "==18.3.0"
},
"filelock": {
"hashes": [
- "sha256:4427cdda14a1c68e264845142842d6de2d0fa2c15ba31571a3d9c9a1ec9d191c",
- "sha256:e393782f76abea324dee598d2ea145b857a20df0e0ee4f80fcf35e72a341d2c7"
+ "sha256:75997740323c5f12e18f10b494bc11c03e42843129f980f17c04352cc7b09d40",
+ "sha256:eb8f0f2d37ed68223ea63e3bddf2fac99667e4362c88b3f762e434d160190d18"
],
"index": "pypi",
- "version": "==3.9.1"
+ "version": "==3.10.2"
},
"ghp-import": {
"hashes": [
@@ -2542,11 +2501,11 @@
},
"identify": {
"hashes": [
- "sha256:5dfef8a745ca4f2c95f27e9db74cb4c8b6d9916383988e8791f3595868f78a33",
- "sha256:c8b288552bc5f05a08aff09af2f58e6976bf8ac87beb38498a0e3d98ba64eb18"
+ "sha256:69edcaffa8e91ae0f77d397af60f148b6b45a8044b2cc6d99cafa5b04793ff00",
+ "sha256:7671a05ef9cfaf8ff63b15d45a91a1147a03aaccb2976d4e9bd047cbbc508471"
],
"markers": "python_version >= '3.7'",
- "version": "==2.5.20"
+ "version": "==2.5.21"
},
"idna": {
"hashes": [
@@ -2566,11 +2525,11 @@
},
"importlib-metadata": {
"hashes": [
- "sha256:7efb448ec9a5e313a57655d35aa54cd3e01b7e1fbcf72dce1bf06119420f5bad",
- "sha256:e354bedeb60efa6affdcc8ae121b73544a7aa74156d047311948f6d711cd378d"
+ "sha256:43ce9281e097583d758c2c708c4376371261a02c34682491a8e98352365aad20",
+ "sha256:ff80f3b5394912eb1b108fcfd444dc78b7f1f3e16b16188054bd01cb9cb86f09"
],
"markers": "python_version < '3.10'",
- "version": "==6.0.0"
+ "version": "==6.1.0"
},
"iniconfig": {
"hashes": [
@@ -2744,11 +2703,11 @@
},
"pathspec": {
"hashes": [
- "sha256:3a66eb970cbac598f9e5ccb5b2cf58930cd8e3ed86d393d541eaf2d8b1705229",
- "sha256:64d338d4e0914e91c1792321e6907b5a593f1ab1851de7fc269557a21b30ebbc"
+ "sha256:2798de800fa92780e33acca925945e9a19a133b715067cf165b8866c15a31687",
+ "sha256:d8af70af76652554bd134c22b3e8a1cc46ed7d91edcdd721ef1a0c51a84a5293"
],
"markers": "python_version >= '3.7'",
- "version": "==0.11.0"
+ "version": "==0.11.1"
},
"pillow": {
"hashes": [
@@ -2851,11 +2810,11 @@
},
"pre-commit": {
"hashes": [
- "sha256:b80254e60668e1dd1f5c03a1c9e0413941d61f568a57d745add265945f65bfe8",
- "sha256:d63e6537f9252d99f65755ae5b79c989b462d511ebbc481b561db6a297e1e865"
+ "sha256:818f0d998059934d0f81bb3667e3ccdc32da6ed7ccaac33e43dc231561ddaaa9",
+ "sha256:f712d3688102e13c8e66b7d7dbd8934a6dda157e58635d89f7d6fecdca39ce8a"
],
"index": "pypi",
- "version": "==3.1.1"
+ "version": "==3.2.0"
},
"pygments": {
"hashes": [
@@ -3015,97 +2974,69 @@
},
"regex": {
"hashes": [
- "sha256:052b670fafbe30966bbe5d025e90b2a491f85dfe5b2583a163b5e60a85a321ad",
- "sha256:0653d012b3bf45f194e5e6a41df9258811ac8fc395579fa82958a8b76286bea4",
- "sha256:0a069c8483466806ab94ea9068c34b200b8bfc66b6762f45a831c4baaa9e8cdd",
- "sha256:0cf0da36a212978be2c2e2e2d04bdff46f850108fccc1851332bcae51c8907cc",
- "sha256:131d4be09bea7ce2577f9623e415cab287a3c8e0624f778c1d955ec7c281bd4d",
- "sha256:144486e029793a733e43b2e37df16a16df4ceb62102636ff3db6033994711066",
- "sha256:1ddf14031a3882f684b8642cb74eea3af93a2be68893901b2b387c5fd92a03ec",
- "sha256:1eba476b1b242620c266edf6325b443a2e22b633217a9835a52d8da2b5c051f9",
- "sha256:20f61c9944f0be2dc2b75689ba409938c14876c19d02f7585af4460b6a21403e",
- "sha256:22960019a842777a9fa5134c2364efaed5fbf9610ddc5c904bd3a400973b0eb8",
- "sha256:22e7ebc231d28393dfdc19b185d97e14a0f178bedd78e85aad660e93b646604e",
- "sha256:23cbb932cc53a86ebde0fb72e7e645f9a5eec1a5af7aa9ce333e46286caef783",
- "sha256:29c04741b9ae13d1e94cf93fca257730b97ce6ea64cfe1eba11cf9ac4e85afb6",
- "sha256:2bde29cc44fa81c0a0c8686992c3080b37c488df167a371500b2a43ce9f026d1",
- "sha256:2cdc55ca07b4e70dda898d2ab7150ecf17c990076d3acd7a5f3b25cb23a69f1c",
- "sha256:370f6e97d02bf2dd20d7468ce4f38e173a124e769762d00beadec3bc2f4b3bc4",
- "sha256:395161bbdbd04a8333b9ff9763a05e9ceb4fe210e3c7690f5e68cedd3d65d8e1",
- "sha256:44136355e2f5e06bf6b23d337a75386371ba742ffa771440b85bed367c1318d1",
- "sha256:44a6c2f6374e0033873e9ed577a54a3602b4f609867794c1a3ebba65e4c93ee7",
- "sha256:4919899577ba37f505aaebdf6e7dc812d55e8f097331312db7f1aab18767cce8",
- "sha256:4b4b1fe58cd102d75ef0552cf17242705ce0759f9695334a56644ad2d83903fe",
- "sha256:4bdd56ee719a8f751cf5a593476a441c4e56c9b64dc1f0f30902858c4ef8771d",
- "sha256:4bf41b8b0a80708f7e0384519795e80dcb44d7199a35d52c15cc674d10b3081b",
- "sha256:4cac3405d8dda8bc6ed499557625585544dd5cbf32072dcc72b5a176cb1271c8",
- "sha256:4fe7fda2fe7c8890d454f2cbc91d6c01baf206fbc96d89a80241a02985118c0c",
- "sha256:50921c140561d3db2ab9f5b11c5184846cde686bb5a9dc64cae442926e86f3af",
- "sha256:5217c25229b6a85049416a5c1e6451e9060a1edcf988641e309dbe3ab26d3e49",
- "sha256:5352bea8a8f84b89d45ccc503f390a6be77917932b1c98c4cdc3565137acc714",
- "sha256:542e3e306d1669b25936b64917285cdffcd4f5c6f0247636fec037187bd93542",
- "sha256:543883e3496c8b6d58bd036c99486c3c8387c2fc01f7a342b760c1ea3158a318",
- "sha256:586b36ebda81e6c1a9c5a5d0bfdc236399ba6595e1397842fd4a45648c30f35e",
- "sha256:597f899f4ed42a38df7b0e46714880fb4e19a25c2f66e5c908805466721760f5",
- "sha256:5a260758454580f11dd8743fa98319bb046037dfab4f7828008909d0aa5292bc",
- "sha256:5aefb84a301327ad115e9d346c8e2760009131d9d4b4c6b213648d02e2abe144",
- "sha256:5e6a5567078b3eaed93558842346c9d678e116ab0135e22eb72db8325e90b453",
- "sha256:5ff525698de226c0ca743bfa71fc6b378cda2ddcf0d22d7c37b1cc925c9650a5",
- "sha256:61edbca89aa3f5ef7ecac8c23d975fe7261c12665f1d90a6b1af527bba86ce61",
- "sha256:659175b2144d199560d99a8d13b2228b85e6019b6e09e556209dfb8c37b78a11",
- "sha256:6a9a19bea8495bb419dc5d38c4519567781cd8d571c72efc6aa959473d10221a",
- "sha256:6b30bddd61d2a3261f025ad0f9ee2586988c6a00c780a2fb0a92cea2aa702c54",
- "sha256:6ffd55b5aedc6f25fd8d9f905c9376ca44fcf768673ffb9d160dd6f409bfda73",
- "sha256:702d8fc6f25bbf412ee706bd73019da5e44a8400861dfff7ff31eb5b4a1276dc",
- "sha256:74bcab50a13960f2a610cdcd066e25f1fd59e23b69637c92ad470784a51b1347",
- "sha256:75f591b2055523fc02a4bbe598aa867df9e953255f0b7f7715d2a36a9c30065c",
- "sha256:763b64853b0a8f4f9cfb41a76a4a85a9bcda7fdda5cb057016e7706fde928e66",
- "sha256:76c598ca73ec73a2f568e2a72ba46c3b6c8690ad9a07092b18e48ceb936e9f0c",
- "sha256:78d680ef3e4d405f36f0d6d1ea54e740366f061645930072d39bca16a10d8c93",
- "sha256:7b280948d00bd3973c1998f92e22aa3ecb76682e3a4255f33e1020bd32adf443",
- "sha256:7db345956ecce0c99b97b042b4ca7326feeec6b75facd8390af73b18e2650ffc",
- "sha256:7dbdce0c534bbf52274b94768b3498abdf675a691fec5f751b6057b3030f34c1",
- "sha256:7ef6b5942e6bfc5706301a18a62300c60db9af7f6368042227ccb7eeb22d0892",
- "sha256:7f5a3ffc731494f1a57bd91c47dc483a1e10048131ffb52d901bfe2beb6102e8",
- "sha256:8a45b6514861916c429e6059a55cf7db74670eaed2052a648e3e4d04f070e001",
- "sha256:8ad241da7fac963d7573cc67a064c57c58766b62a9a20c452ca1f21050868dfa",
- "sha256:8b0886885f7323beea6f552c28bff62cbe0983b9fbb94126531693ea6c5ebb90",
- "sha256:8ca88da1bd78990b536c4a7765f719803eb4f8f9971cc22d6ca965c10a7f2c4c",
- "sha256:8e0caeff18b96ea90fc0eb6e3bdb2b10ab5b01a95128dfeccb64a7238decf5f0",
- "sha256:957403a978e10fb3ca42572a23e6f7badff39aa1ce2f4ade68ee452dc6807692",
- "sha256:9af69f6746120998cd9c355e9c3c6aec7dff70d47247188feb4f829502be8ab4",
- "sha256:9c94f7cc91ab16b36ba5ce476f1904c91d6c92441f01cd61a8e2729442d6fcf5",
- "sha256:a37d51fa9a00d265cf73f3de3930fa9c41548177ba4f0faf76e61d512c774690",
- "sha256:a3a98921da9a1bf8457aeee6a551948a83601689e5ecdd736894ea9bbec77e83",
- "sha256:a3c1ebd4ed8e76e886507c9eddb1a891673686c813adf889b864a17fafcf6d66",
- "sha256:a5f9505efd574d1e5b4a76ac9dd92a12acb2b309551e9aa874c13c11caefbe4f",
- "sha256:a8ff454ef0bb061e37df03557afda9d785c905dab15584860f982e88be73015f",
- "sha256:a9d0b68ac1743964755ae2d89772c7e6fb0118acd4d0b7464eaf3921c6b49dd4",
- "sha256:aa62a07ac93b7cb6b7d0389d8ef57ffc321d78f60c037b19dfa78d6b17c928ee",
- "sha256:ac741bf78b9bb432e2d314439275235f41656e189856b11fb4e774d9f7246d81",
- "sha256:ae1e96785696b543394a4e3f15f3f225d44f3c55dafe3f206493031419fedf95",
- "sha256:b683e5fd7f74fb66e89a1ed16076dbab3f8e9f34c18b1979ded614fe10cdc4d9",
- "sha256:b7a8b43ee64ca8f4befa2bea4083f7c52c92864d8518244bfa6e88c751fa8fff",
- "sha256:b8e38472739028e5f2c3a4aded0ab7eadc447f0d84f310c7a8bb697ec417229e",
- "sha256:bfff48c7bd23c6e2aec6454aaf6edc44444b229e94743b34bdcdda2e35126cf5",
- "sha256:c14b63c9d7bab795d17392c7c1f9aaabbffd4cf4387725a0ac69109fb3b550c6",
- "sha256:c27cc1e4b197092e50ddbf0118c788d9977f3f8f35bfbbd3e76c1846a3443df7",
- "sha256:c28d3309ebd6d6b2cf82969b5179bed5fefe6142c70f354ece94324fa11bf6a1",
- "sha256:c670f4773f2f6f1957ff8a3962c7dd12e4be54d05839b216cb7fd70b5a1df394",
- "sha256:ce6910b56b700bea7be82c54ddf2e0ed792a577dfaa4a76b9af07d550af435c6",
- "sha256:d0213671691e341f6849bf33cd9fad21f7b1cb88b89e024f33370733fec58742",
- "sha256:d03fe67b2325cb3f09be029fd5da8df9e6974f0cde2c2ac6a79d2634e791dd57",
- "sha256:d0e5af9a9effb88535a472e19169e09ce750c3d442fb222254a276d77808620b",
- "sha256:d243b36fbf3d73c25e48014961e83c19c9cc92530516ce3c43050ea6276a2ab7",
- "sha256:d26166acf62f731f50bdd885b04b38828436d74e8e362bfcb8df221d868b5d9b",
- "sha256:d403d781b0e06d2922435ce3b8d2376579f0c217ae491e273bab8d092727d244",
- "sha256:d8716f82502997b3d0895d1c64c3b834181b1eaca28f3f6336a71777e437c2af",
- "sha256:e4f781ffedd17b0b834c8731b75cce2639d5a8afe961c1e58ee7f1f20b3af185",
- "sha256:e613a98ead2005c4ce037c7b061f2409a1a4e45099edb0ef3200ee26ed2a69a8",
- "sha256:ef4163770525257876f10e8ece1cf25b71468316f61451ded1a6f44273eedeb5"
+ "sha256:0a2a851d0548a4e298d88e3ceeb4bad4aab751cf1883edf6150f25718ce0207a",
+ "sha256:148ad520f41021b97870e9c80420e6cdaadcc5e4306e613aed84cd5d53f8a7ca",
+ "sha256:159c7b83488a056365119ada0bceddc06a455d3db7a7aa3cf07f13b2878b885f",
+ "sha256:1937946dd03818845bd9c1713dfd3173a7b9a324e6593a235fc8c51c9cd460eb",
+ "sha256:20ce96da2093e72e151d6af8217a629aeb5f48f1ac543c2fffd1d87c57699d7e",
+ "sha256:24242e5f26823e95edd64969bd206d4752c1a56a744d8cbcf58461f9788bc0c7",
+ "sha256:2e2e6baf4a1108f84966f44870b26766d8f6d104c9959aae329078327c677122",
+ "sha256:328a70e578f37f59eb54e8450b5042190bbadf2ef7f5c0b60829574b62955ed7",
+ "sha256:3371975b165c1e859e1990e5069e8606f00b25aed961cfd25b7bac626b1eb5a9",
+ "sha256:33bab9c9af936123b70b9874ce83f2bcd54be76b97637b33d31560fba8ad5d78",
+ "sha256:33c887b658afb144cdc8ce9156a0e1098453060c18b8bd5177f831ad58e0d60d",
+ "sha256:3582db55372eaee9e998d378109c4b9b15beb2c84624c767efe351363fada9c4",
+ "sha256:3b4da28d89527572f0d4a24814e353e1228a7aeda965e5d9265c1435a154b17a",
+ "sha256:3c4fa90fd91cc2957e66195ce374331bebbc816964864f64b42bd14bda773b53",
+ "sha256:3e66cfc915f5f7e2c8a0af8a27f87aa857f440de7521fd7f2682e23f082142a1",
+ "sha256:3f6f29cb134d782685f8eda01d72073c483c7f87b318b5101c7001faef7850f5",
+ "sha256:43469c22fcf705a7cb59c7e01d6d96975bdbc54c1138900f04d11496489a0054",
+ "sha256:4ad467524cb6879ce42107cf02a49cdb4a06f07fe0e5f1160d7db865a8d25d4b",
+ "sha256:4c9c3db90acd17e4231344a23616f33fd79837809584ce30e2450ca312fa47aa",
+ "sha256:533ba64d67d882286557106a1c5f12b4c2825f11b47a7c209a8c22922ca882be",
+ "sha256:548257463696daf919d2fdfc53ee4b98e29e3ffc5afddd713d83aa849d1fa178",
+ "sha256:55f907c4d18a5a40da0ceb339a0beda77c9df47c934adad987793632fb4318c3",
+ "sha256:5826e7fb443acb49f64f9648a2852efc8d9af2f4c67f6c3dca69dccd9e8e1d15",
+ "sha256:59a15c2803c20702d7f2077807d9a2b7d9a168034b87fd3f0d8361de60019a1e",
+ "sha256:59b3aab231c27cd754d6452c43b12498d34e7ab87d69a502bd0220f4b1c090c4",
+ "sha256:5da83c964aecb6c3f2a6c9a03f3d0fa579e1ad208e2c264ba826cecd19da11fa",
+ "sha256:60b545806a433cc752b9fa936f1c0a63bf96a3872965b958b35bd0d5d788d411",
+ "sha256:60fcef5c3144d861b623456d87ca7fff7af59a4a918e1364cdd0687b48285285",
+ "sha256:617d101b95151d827d5366e9c4225a68c64d56065e41ab9c7ef51bb87f347a8a",
+ "sha256:68e9add923bda8357e6fe65a568766feae369063cb7210297067675cce65272f",
+ "sha256:7798b3d662f70cea425637c54da30ef1894d426cab24ee7ffaaccb24a8b17bb8",
+ "sha256:80a288b21b17e39fb3630cf1d14fd704499bb11d9c8fc110662a0c57758d3d3e",
+ "sha256:81291006a934052161eae8340e7731ea6b8595b0c27dd4927c4e8a489e1760e2",
+ "sha256:8527ea0978ed6dc58ccb3935bd2883537b455c97ec44b5d8084677dfa817f96b",
+ "sha256:87016850c13082747bd120558e6750746177bd492b103b2fca761c8a1c43fba9",
+ "sha256:88552925fd22320600c59ee80342d6eb06bfa9503c3a402d7327983f5fa999d9",
+ "sha256:8d7477ebaf5d3621c763702e1ec0daeede8863fb22459c5e26ddfd17e9b1999c",
+ "sha256:97326d62255203c6026896d4b1ad6b5a0141ba097cae00ed3a508fe454e96baf",
+ "sha256:a4c7b8c5a3a186b49415af3be18e4b8f93b33d6853216c0a1d7401736b703bce",
+ "sha256:aff7c778d9229d66f716ad98a701fa91cf97935ae4a32a145ae9e61619906aaa",
+ "sha256:b280cb303fed94199f0b976595af71ebdcd388fb5e377a8198790f1016a23476",
+ "sha256:b59233cb8df6b60fff5f3056f6f342a8f5f04107a11936bf49ebff87dd4ace34",
+ "sha256:bdab2c90665b88faf5cc5e11bf835d548f4b8d8060c89fc70782b6020850aa1c",
+ "sha256:c00c357a4914f58398503c7f716cf1646b1e36b8176efa35255f5ebfacedfa46",
+ "sha256:c95a977cfdccb8ddef95ddd77cf586fe9dc327c7c93cf712983cece70cdaa1be",
+ "sha256:cdd3d2df486c9a8c6d08f78bdfa8ea7cf6191e037fde38c2cf6f5f0559e9d353",
+ "sha256:d15a0cc48f7a3055e89df1bd6623a907c407d1f58f67ff47064e598d4a550de4",
+ "sha256:d40cecf4bcb2cb37c59e3c79e5bbc45d47e3f3e07edf24e35fc5775db2570058",
+ "sha256:d4d3571c8eb21f0fbe9f0b21b49092c24d442f9a295f079949df3551b2886f29",
+ "sha256:d94a0d25e517c76c9ce9e2e2635d9d1a644b894f466a66a10061f4e599cdc019",
+ "sha256:dcc5b0d6a94637c071a427dc4469efd0ae4fda8ff384790bc8b5baaf9308dc3e",
+ "sha256:e00b046000b313ffaa2f6e8d7290b33b08d2005150eff4c8cf3ad74d011888d1",
+ "sha256:e1b56dac5e86ab52e0443d63b02796357202a8f8c5966b69f8d4c03a94778e98",
+ "sha256:e30d9a6fd7a7a6a4da6f80d167ce8eda4a993ff24282cbc73f34186c46a498db",
+ "sha256:f1977c1fe28173f2349d42c59f80f10a97ce34f2bedb7b7f55e2e8a8de9b7dfb",
+ "sha256:f2bc8a9076ea7add860d57dbee0554a212962ecf2a900344f2fc7c56a02463b0",
+ "sha256:f311ca33fcb9f8fb060c1fa76238d8d029f33b71a2021bafa5d423cc25965b54",
+ "sha256:f579a202b90c1110d0894a86b32a89bf550fdb34bdd3f9f550115706be462e19",
+ "sha256:fa41a427d4f03ec6d6da2fd8a230f4f388f336cd7ca46b46c4d2a1bca3ead85a",
+ "sha256:fd47362e03acc780aad5a5bc4624d495594261b55a1f79a5b775b6be865a5911"
],
- "markers": "python_version >= '3.6'",
- "version": "==2022.10.31"
+ "markers": "python_version >= '3.8'",
+ "version": "==2023.3.22"
},
"requests": {
"hashes": [
@@ -3490,30 +3421,30 @@
"compatible-mypy"
],
"hashes": [
- "sha256:0bbf9eb172c5b06eccff2d704c7c3906e4a2c6146df8c32ee9f3a51e29265581",
- "sha256:25010658acac0ce4a69211b55dd719fd16dbfe54fcfe5c878d0c8db07bdd5482"
+ "sha256:1bd96207576cd220221a0e615f0259f13d453d515a80f576c1246e0fb547f561",
+ "sha256:c95f948e2bfc565f3147e969ff361ef033841a0b8a51cac974a6cc6d0486732c"
],
"index": "pypi",
- "version": "==1.15.0"
+ "version": "==1.16.0"
},
"django-stubs-ext": {
"hashes": [
- "sha256:4fd8cdbc68d1a421f21bb7e0d9e76d50f6a4b504d350ba786405daf536e90c21",
- "sha256:d729fbc7fe8970a7e26b35956c35b48502516f011d523c0577bdfb02ed956284"
+ "sha256:9a9ba9e2808737949de96a0fce8b054f12d38e461011d77ebc074ffe8c43dfcb",
+ "sha256:a454d349d19c26d6c50c4c6dbc1e8af4a9cda4ce1dc4104e3dd4c0330510cc56"
],
"markers": "python_version >= '3.7'",
- "version": "==0.7.0"
+ "version": "==0.8.0"
},
"djangorestframework-stubs": {
"extras": [
"compatible-mypy"
],
"hashes": [
- "sha256:89f6c2add193cb5ab61b9e47187b33a93cc099376a8df5e4d6c3fc8ecb992d3b",
- "sha256:9475e1374b057ffbdcaaa84a060fe5f01476d8b9014d82a83b4153f57fbcbc1f"
+ "sha256:433edd7f10786914138b300b9be5aba1ebc80c471b5156934664afd7e9df9fd6",
+ "sha256:69e8a1ea7eb815cbe35155c27eee72522d7c8666d3cbdacb9997ab88c7b4202c"
],
"index": "pypi",
- "version": "==1.9.1"
+ "version": "==1.10.0"
},
"idna": {
"hashes": [
@@ -3525,35 +3456,35 @@
},
"mypy": {
"hashes": [
- "sha256:0af4f0e20706aadf4e6f8f8dc5ab739089146b83fd53cb4a7e0e850ef3de0bb6",
- "sha256:15b5a824b58c7c822c51bc66308e759243c32631896743f030daf449fe3677f3",
- "sha256:17455cda53eeee0a4adb6371a21dd3dbf465897de82843751cf822605d152c8c",
- "sha256:2013226d17f20468f34feddd6aae4635a55f79626549099354ce641bc7d40262",
- "sha256:24189f23dc66f83b839bd1cce2dfc356020dfc9a8bae03978477b15be61b062e",
- "sha256:27a0f74a298769d9fdc8498fcb4f2beb86f0564bcdb1a37b58cbbe78e55cf8c0",
- "sha256:28cea5a6392bb43d266782983b5a4216c25544cd7d80be681a155ddcdafd152d",
- "sha256:448de661536d270ce04f2d7dddaa49b2fdba6e3bd8a83212164d4174ff43aa65",
- "sha256:48525aec92b47baed9b3380371ab8ab6e63a5aab317347dfe9e55e02aaad22e8",
- "sha256:5bc8d6bd3b274dd3846597855d96d38d947aedba18776aa998a8d46fabdaed76",
- "sha256:5deb252fd42a77add936b463033a59b8e48eb2eaec2976d76b6878d031933fe4",
- "sha256:5f546ac34093c6ce33f6278f7c88f0f147a4849386d3bf3ae193702f4fe31407",
- "sha256:5fdd63e4f50e3538617887e9aee91855368d9fc1dea30da743837b0df7373bc4",
- "sha256:65b122a993d9c81ea0bfde7689b3365318a88bde952e4dfa1b3a8b4ac05d168b",
- "sha256:71a808334d3f41ef011faa5a5cd8153606df5fc0b56de5b2e89566c8093a0c9a",
- "sha256:920169f0184215eef19294fa86ea49ffd4635dedfdea2b57e45cb4ee85d5ccaf",
- "sha256:93a85495fb13dc484251b4c1fd7a5ac370cd0d812bbfc3b39c1bafefe95275d5",
- "sha256:a2948c40a7dd46c1c33765718936669dc1f628f134013b02ff5ac6c7ef6942bf",
- "sha256:c6c2ccb7af7154673c591189c3687b013122c5a891bb5651eca3db8e6c6c55bd",
- "sha256:c96b8a0c019fe29040d520d9257d8c8f122a7343a8307bf8d6d4a43f5c5bfcc8",
- "sha256:d42a98e76070a365a1d1c220fcac8aa4ada12ae0db679cb4d910fabefc88b994",
- "sha256:dbeb24514c4acbc78d205f85dd0e800f34062efcc1f4a4857c57e4b4b8712bff",
- "sha256:e60d0b09f62ae97a94605c3f73fd952395286cf3e3b9e7b97f60b01ddfbbda88",
- "sha256:e64f48c6176e243ad015e995de05af7f22bbe370dbb5b32bd6988438ec873919",
- "sha256:e831662208055b006eef68392a768ff83596035ffd6d846786578ba1714ba8f6",
- "sha256:eda5c8b9949ed411ff752b9a01adda31afe7eae1e53e946dbdf9db23865e66c4"
- ],
- "index": "pypi",
- "version": "==1.0.1"
+ "sha256:0a28a76785bf57655a8ea5eb0540a15b0e781c807b5aa798bd463779988fa1d5",
+ "sha256:19ba15f9627a5723e522d007fe708007bae52b93faab00f95d72f03e1afa9598",
+ "sha256:21b437be1c02712a605591e1ed1d858aba681757a1e55fe678a15c2244cd68a5",
+ "sha256:26cdd6a22b9b40b2fd71881a8a4f34b4d7914c679f154f43385ca878a8297389",
+ "sha256:2888ce4fe5aae5a673386fa232473014056967f3904f5abfcf6367b5af1f612a",
+ "sha256:2b0c373d071593deefbcdd87ec8db91ea13bd8f1328d44947e88beae21e8d5e9",
+ "sha256:315ac73cc1cce4771c27d426b7ea558fb4e2836f89cb0296cbe056894e3a1f78",
+ "sha256:39c7119335be05630611ee798cc982623b9e8f0cff04a0b48dfc26100e0b97af",
+ "sha256:4b398d8b1f4fba0e3c6463e02f8ad3346f71956b92287af22c9b12c3ec965a9f",
+ "sha256:4e4e8b362cdf99ba00c2b218036002bdcdf1e0de085cdb296a49df03fb31dfc4",
+ "sha256:59bbd71e5c58eed2e992ce6523180e03c221dcd92b52f0e792f291d67b15a71c",
+ "sha256:5b5f81b40d94c785f288948c16e1f2da37203c6006546c5d947aab6f90aefef2",
+ "sha256:5cb14ff9919b7df3538590fc4d4c49a0f84392237cbf5f7a816b4161c061829e",
+ "sha256:61bf08362e93b6b12fad3eab68c4ea903a077b87c90ac06c11e3d7a09b56b9c1",
+ "sha256:64cc3afb3e9e71a79d06e3ed24bb508a6d66f782aff7e56f628bf35ba2e0ba51",
+ "sha256:69b35d1dcb5707382810765ed34da9db47e7f95b3528334a3c999b0c90fe523f",
+ "sha256:9401e33814cec6aec8c03a9548e9385e0e228fc1b8b0a37b9ea21038e64cdd8a",
+ "sha256:a380c041db500e1410bb5b16b3c1c35e61e773a5c3517926b81dfdab7582be54",
+ "sha256:ae9ceae0f5b9059f33dbc62dea087e942c0ccab4b7a003719cb70f9b8abfa32f",
+ "sha256:b7c7b708fe9a871a96626d61912e3f4ddd365bf7f39128362bc50cbd74a634d5",
+ "sha256:c1c10fa12df1232c936830839e2e935d090fc9ee315744ac33b8a32216b93707",
+ "sha256:ce61663faf7a8e5ec6f456857bfbcec2901fbdb3ad958b778403f63b9e606a1b",
+ "sha256:d64c28e03ce40d5303450f547e07418c64c241669ab20610f273c9e6290b4b0b",
+ "sha256:d809f88734f44a0d44959d795b1e6f64b2bbe0ea4d9cc4776aa588bb4229fc1c",
+ "sha256:dbb19c9f662e41e474e0cff502b7064a7edc6764f5262b6cd91d698163196799",
+ "sha256:ef6a01e563ec6a4940784c574d33f6ac1943864634517984471642908b30b6f7"
+ ],
+ "index": "pypi",
+ "version": "==1.1.1"
},
"mypy-extensions": {
"hashes": [
@@ -3704,26 +3635,26 @@
},
"types-redis": {
"hashes": [
- "sha256:43d92b4d6315a45bb0e9a790683ba4448ada88cd1233f3f9886fa6f783f53956",
- "sha256:f516254bd593023110a38b77e80d5a76a7f033f1d94c53bee09a7d5d0433f34d"
+ "sha256:7c1d5fdb0a2d5fd92eac37ce382fdb47d99a69889e7d6c2bc4479148ac646c73",
+ "sha256:f23415e448ca25ec5028c24fdf3717a13f0c905eb1933733e8a8a7d4952f6908"
],
"index": "pypi",
- "version": "==4.5.1.5"
+ "version": "==4.5.3.0"
},
"types-requests": {
"hashes": [
- "sha256:a05e4c7bc967518fba5789c341ea8b0c942776ee474c7873129a61161978e586",
- "sha256:fc8eaa09cc014699c6b63c60c2e3add0c8b09a410c818b5ac6e65f92a26dde09"
+ "sha256:9d4002056df7ebc4ec1f28fd701fba82c5c22549c4477116cb2656aa30ace6db",
+ "sha256:a86921028335fdcc3aaf676c9d3463f867db6af2303fc65aa309b13ae1e6dd53"
],
- "version": "==2.28.11.15"
+ "version": "==2.28.11.16"
},
"types-setuptools": {
"hashes": [
- "sha256:70b5e6a379e9fccf6579871a93ca3301a46252e3ae66957ec64281a2b6a812d9",
- "sha256:d669a80ee8e37eb1697dc31a23d41ea2c48a635464e2c7e6370dda811459b466"
+ "sha256:3a708e66c7bdc620e4d0439f344c750c57a4340c895a4c3ed2d0fc4ae8eb9962",
+ "sha256:dae5a4a659dbb6dba57773440f6e2dbdd8ef282dc136a174a8a59bd33d949945"
],
"index": "pypi",
- "version": "==67.6.0.0"
+ "version": "==67.6.0.5"
},
"types-tqdm": {
"hashes": [
diff --git a/src/paperless/settings.py b/src/paperless/settings.py
index c809f0a7a2d..e7f53d8ce1e 100644
--- a/src/paperless/settings.py
+++ b/src/paperless/settings.py
@@ -358,7 +358,7 @@ def _parse_beat_schedule() -> Dict:
CHANNEL_LAYERS = {
"default": {
- "BACKEND": "channels_redis.core.RedisChannelLayer",
+ "BACKEND": "channels_redis.pubsub.RedisPubSubChannelLayer",
"CONFIG": {
"hosts": [_CHANNELS_REDIS_URL],
"capacity": 2000, # default 100
|
python-pillow__Pillow-3042 | JPEG 2K, PyImaging_Jpeg2KDecoderNew function takes at most 6 arguments (7 given)
### What did you do?
Try to upload a JPEG 2000
### What did you expect to happen?
Validate width and height
### What actually happened?
Exception
### What versions of Pillow and Python are you using?
Pillow 5.0.0
Python 2.7.12
Image: http://ghpublic.s3-us-west-1.amazonaws.com/relax.jp2
> TypeError: function takes at most 6 arguments (7 given)
> File "django/core/handlers/exception.py", line 41, in inner
> response = get_response(request)
> File "django/core/handlers/base.py", line 249, in _legacy_get_response
> response = self._get_response(request)
> File "django/core/handlers/base.py", line 187, in _get_response
> response = self.process_exception_by_middleware(e, request)
> File "django/core/handlers/base.py", line 185, in _get_response
> response = wrapped_callback(request, *callback_args, **callback_kwargs)
> File "newrelic/hooks/framework_django.py", line 527, in wrapper
> return wrapped(*args, **kwargs)
> File "django/contrib/auth/decorators.py", line 23, in _wrapped_view
> return view_func(request, *args, **kwargs)
> File "gh_admin/views.py", line 917, in ajax_image_uploader
> if None in [image.image.height, image.image.width]:
> File "django/core/files/images.py", line 23, in height
> return self._get_image_dimensions()[1]
> File "django/core/files/images.py", line 29, in _get_image_dimensions
> self._dimensions_cache = get_image_dimensions(self, close=close)
> File "django/core/files/images.py", line 59, in get_image_dimensions
> p.feed(data)
> File "PIL/ImageFile.py", line 411, in feed
> self.decoder = Image._getdecoder(im.mode, d, a, im.decoderconfig)
> ```
> a | ['jp2', 0, 0, -1, 1024, <_io.BytesIO object at 0x7f67f5a86710>]
> d | 'jpeg2k'
> data | '\x00\x00\x00\x0cjP \r\n\x87\n\x00\x00\x00\x14ftypjp2 \x00\x00\x00\x00jp2 \x00\x00\x01Yjp2h\x00\x00\x00\x16ihdr\x00\x00\x01,\x00\x00\x01\x90\x00\x03\x07\x07\x01\x00\x00\x00\x01!colr\x02\x00\x00\x00\x00\x01\x16\x00\x00\x00\x00\x02 \x00\x00scnrRGB XYZ \x07\xd1\x00\x01\x00\x01\x00\x00\x00\x00\x00\x00acsp\x00\x00\x00\x00\x00\xc0\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x80\x00\x00\x00\x00\x00\x00\x00\x00\x01\x00\x00\x00\x00\xf6\xd6\x00\x01\x00\x00\x00\x00\xd3-\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\...
> e | [0, 0, 400, 300]
> flag | False
> fp | <_io.BytesIO object at 0x7f67f5a86710>
> im | <PIL.Jpeg2KImagePlugin.Jpeg2KImageFile image mode=RGB size=400x300 at 0x7F67F5C7EA90>
> o | 0
> self | <PIL.ImageFile.Parser object at 0x7f67f5d8a290>
> ```
> File "PIL/Image.py", line 435, in _getdecoder
> return decoder(mode, *args + extra)
> ```
> args | ['jp2', 0, 0, -1, 1024, <_io.BytesIO object at 0x7f67f5a86710>]
> decoder | <built-in function jpeg2k_decoder>
> decoder_name | 'jpeg2k'
> extra | []
> mode | 'RGB'
> ```
On PyImaging_Jpeg2KDecoderNew
| [
{
"content": "#\n# The Python Imaging Library\n# $Id$\n#\n# JPEG2000 file handling\n#\n# History:\n# 2014-03-12 ajh Created\n#\n# Copyright (c) 2014 Coriolis Systems Limited\n# Copyright (c) 2014 Alastair Houghton\n#\n# See the README file for information on usage and redistribution.\n#\nfrom . import Image, ImageFile\nimport struct\nimport os\nimport io\n\n__version__ = \"0.1\"\n\n\ndef _parse_codestream(fp):\n \"\"\"Parse the JPEG 2000 codestream to extract the size and component\n count from the SIZ marker segment, returning a PIL (size, mode) tuple.\"\"\"\n\n hdr = fp.read(2)\n lsiz = struct.unpack('>H', hdr)[0]\n siz = hdr + fp.read(lsiz - 2)\n lsiz, rsiz, xsiz, ysiz, xosiz, yosiz, xtsiz, ytsiz, \\\n xtosiz, ytosiz, csiz \\\n = struct.unpack('>HHIIIIIIIIH', siz[:38])\n ssiz = [None]*csiz\n xrsiz = [None]*csiz\n yrsiz = [None]*csiz\n for i in range(csiz):\n ssiz[i], xrsiz[i], yrsiz[i] \\\n = struct.unpack('>BBB', siz[36 + 3 * i:39 + 3 * i])\n\n size = (xsiz - xosiz, ysiz - yosiz)\n if csiz == 1:\n if (yrsiz[0] & 0x7f) > 8:\n mode = 'I;16'\n else:\n mode = 'L'\n elif csiz == 2:\n mode = 'LA'\n elif csiz == 3:\n mode = 'RGB'\n elif csiz == 4:\n mode = 'RGBA'\n else:\n mode = None\n\n return (size, mode)\n\n\ndef _parse_jp2_header(fp):\n \"\"\"Parse the JP2 header box to extract size, component count and\n color space information, returning a PIL (size, mode) tuple.\"\"\"\n\n # Find the JP2 header box\n header = None\n while True:\n lbox, tbox = struct.unpack('>I4s', fp.read(8))\n if lbox == 1:\n lbox = struct.unpack('>Q', fp.read(8))[0]\n hlen = 16\n else:\n hlen = 8\n\n if lbox < hlen:\n raise SyntaxError('Invalid JP2 header length')\n\n if tbox == b'jp2h':\n header = fp.read(lbox - hlen)\n break\n else:\n fp.seek(lbox - hlen, os.SEEK_CUR)\n\n if header is None:\n raise SyntaxError('could not find JP2 header')\n\n size = None\n mode = None\n bpc = None\n nc = None\n\n hio = io.BytesIO(header)\n while True:\n lbox, tbox = struct.unpack('>I4s', hio.read(8))\n if lbox == 1:\n lbox = struct.unpack('>Q', hio.read(8))[0]\n hlen = 16\n else:\n hlen = 8\n\n content = hio.read(lbox - hlen)\n\n if tbox == b'ihdr':\n height, width, nc, bpc, c, unkc, ipr \\\n = struct.unpack('>IIHBBBB', content)\n size = (width, height)\n if unkc:\n if nc == 1 and (bpc & 0x7f) > 8:\n mode = 'I;16'\n elif nc == 1:\n mode = 'L'\n elif nc == 2:\n mode = 'LA'\n elif nc == 3:\n mode = 'RGB'\n elif nc == 4:\n mode = 'RGBA'\n break\n elif tbox == b'colr':\n meth, prec, approx = struct.unpack('>BBB', content[:3])\n if meth == 1:\n cs = struct.unpack('>I', content[3:7])[0]\n if cs == 16: # sRGB\n if nc == 1 and (bpc & 0x7f) > 8:\n mode = 'I;16'\n elif nc == 1:\n mode = 'L'\n elif nc == 3:\n mode = 'RGB'\n elif nc == 4:\n mode = 'RGBA'\n break\n elif cs == 17: # grayscale\n if nc == 1 and (bpc & 0x7f) > 8:\n mode = 'I;16'\n elif nc == 1:\n mode = 'L'\n elif nc == 2:\n mode = 'LA'\n break\n elif cs == 18: # sYCC\n if nc == 3:\n mode = 'RGB'\n elif nc == 4:\n mode = 'RGBA'\n break\n\n if size is None or mode is None:\n raise SyntaxError(\"Malformed jp2 header\")\n\n return (size, mode)\n\n##\n# Image plugin for JPEG2000 images.\n\n\nclass Jpeg2KImageFile(ImageFile.ImageFile):\n format = \"JPEG2000\"\n format_description = \"JPEG 2000 (ISO 15444)\"\n\n def _open(self):\n sig = self.fp.read(4)\n if sig == b'\\xff\\x4f\\xff\\x51':\n self.codec = \"j2k\"\n self.size, self.mode = _parse_codestream(self.fp)\n else:\n sig = sig + self.fp.read(8)\n\n if sig == b'\\x00\\x00\\x00\\x0cjP \\x0d\\x0a\\x87\\x0a':\n self.codec = \"jp2\"\n self.size, self.mode = _parse_jp2_header(self.fp)\n else:\n raise SyntaxError('not a JPEG 2000 file')\n\n if self.size is None or self.mode is None:\n raise SyntaxError('unable to determine size/mode')\n\n self.reduce = 0\n self.layers = 0\n\n fd = -1\n length = -1\n\n try:\n fd = self.fp.fileno()\n length = os.fstat(fd).st_size\n except:\n fd = -1\n try:\n pos = self.fp.tell()\n self.fp.seek(0, 2)\n length = self.fp.tell()\n self.fp.seek(pos, 0)\n except:\n length = -1\n\n self.tile = [('jpeg2k', (0, 0) + self.size, 0,\n (self.codec, self.reduce, self.layers, fd, length, self.fp))]\n\n def load(self):\n if self.reduce:\n power = 1 << self.reduce\n adjust = power >> 1\n self.size = (int((self.size[0] + adjust) / power),\n int((self.size[1] + adjust) / power))\n\n if self.tile:\n # Update the reduce and layers settings\n t = self.tile[0]\n t3 = (t[3][0], self.reduce, self.layers, t[3][3], t[3][4])\n self.tile = [(t[0], (0, 0) + self.size, t[2], t3)]\n\n return ImageFile.ImageFile.load(self)\n\n\ndef _accept(prefix):\n return (prefix[:4] == b'\\xff\\x4f\\xff\\x51' or\n prefix[:12] == b'\\x00\\x00\\x00\\x0cjP \\x0d\\x0a\\x87\\x0a')\n\n\n# ------------------------------------------------------------\n# Save support\n\ndef _save(im, fp, filename):\n if filename.endswith('.j2k'):\n kind = 'j2k'\n else:\n kind = 'jp2'\n\n # Get the keyword arguments\n info = im.encoderinfo\n\n offset = info.get('offset', None)\n tile_offset = info.get('tile_offset', None)\n tile_size = info.get('tile_size', None)\n quality_mode = info.get('quality_mode', 'rates')\n quality_layers = info.get('quality_layers', None)\n num_resolutions = info.get('num_resolutions', 0)\n cblk_size = info.get('codeblock_size', None)\n precinct_size = info.get('precinct_size', None)\n irreversible = info.get('irreversible', False)\n progression = info.get('progression', 'LRCP')\n cinema_mode = info.get('cinema_mode', 'no')\n fd = -1\n\n if hasattr(fp, \"fileno\"):\n try:\n fd = fp.fileno()\n except:\n fd = -1\n\n im.encoderconfig = (\n offset,\n tile_offset,\n tile_size,\n quality_mode,\n quality_layers,\n num_resolutions,\n cblk_size,\n precinct_size,\n irreversible,\n progression,\n cinema_mode,\n fd\n )\n\n ImageFile._save(im, fp, [('jpeg2k', (0, 0)+im.size, 0, kind)])\n\n# ------------------------------------------------------------\n# Registry stuff\n\n\nImage.register_open(Jpeg2KImageFile.format, Jpeg2KImageFile, _accept)\nImage.register_save(Jpeg2KImageFile.format, _save)\n\nImage.register_extensions(Jpeg2KImageFile.format, [\".jp2\", \".j2k\", \".jpc\", \".jpf\", \".jpx\", \".j2c\"])\n\nImage.register_mime(Jpeg2KImageFile.format, 'image/jp2')\nImage.register_mime(Jpeg2KImageFile.format, 'image/jpx')\n",
"path": "src/PIL/Jpeg2KImagePlugin.py"
}
] | [
{
"content": "#\n# The Python Imaging Library\n# $Id$\n#\n# JPEG2000 file handling\n#\n# History:\n# 2014-03-12 ajh Created\n#\n# Copyright (c) 2014 Coriolis Systems Limited\n# Copyright (c) 2014 Alastair Houghton\n#\n# See the README file for information on usage and redistribution.\n#\nfrom . import Image, ImageFile\nimport struct\nimport os\nimport io\n\n__version__ = \"0.1\"\n\n\ndef _parse_codestream(fp):\n \"\"\"Parse the JPEG 2000 codestream to extract the size and component\n count from the SIZ marker segment, returning a PIL (size, mode) tuple.\"\"\"\n\n hdr = fp.read(2)\n lsiz = struct.unpack('>H', hdr)[0]\n siz = hdr + fp.read(lsiz - 2)\n lsiz, rsiz, xsiz, ysiz, xosiz, yosiz, xtsiz, ytsiz, \\\n xtosiz, ytosiz, csiz \\\n = struct.unpack('>HHIIIIIIIIH', siz[:38])\n ssiz = [None]*csiz\n xrsiz = [None]*csiz\n yrsiz = [None]*csiz\n for i in range(csiz):\n ssiz[i], xrsiz[i], yrsiz[i] \\\n = struct.unpack('>BBB', siz[36 + 3 * i:39 + 3 * i])\n\n size = (xsiz - xosiz, ysiz - yosiz)\n if csiz == 1:\n if (yrsiz[0] & 0x7f) > 8:\n mode = 'I;16'\n else:\n mode = 'L'\n elif csiz == 2:\n mode = 'LA'\n elif csiz == 3:\n mode = 'RGB'\n elif csiz == 4:\n mode = 'RGBA'\n else:\n mode = None\n\n return (size, mode)\n\n\ndef _parse_jp2_header(fp):\n \"\"\"Parse the JP2 header box to extract size, component count and\n color space information, returning a PIL (size, mode) tuple.\"\"\"\n\n # Find the JP2 header box\n header = None\n while True:\n lbox, tbox = struct.unpack('>I4s', fp.read(8))\n if lbox == 1:\n lbox = struct.unpack('>Q', fp.read(8))[0]\n hlen = 16\n else:\n hlen = 8\n\n if lbox < hlen:\n raise SyntaxError('Invalid JP2 header length')\n\n if tbox == b'jp2h':\n header = fp.read(lbox - hlen)\n break\n else:\n fp.seek(lbox - hlen, os.SEEK_CUR)\n\n if header is None:\n raise SyntaxError('could not find JP2 header')\n\n size = None\n mode = None\n bpc = None\n nc = None\n\n hio = io.BytesIO(header)\n while True:\n lbox, tbox = struct.unpack('>I4s', hio.read(8))\n if lbox == 1:\n lbox = struct.unpack('>Q', hio.read(8))[0]\n hlen = 16\n else:\n hlen = 8\n\n content = hio.read(lbox - hlen)\n\n if tbox == b'ihdr':\n height, width, nc, bpc, c, unkc, ipr \\\n = struct.unpack('>IIHBBBB', content)\n size = (width, height)\n if unkc:\n if nc == 1 and (bpc & 0x7f) > 8:\n mode = 'I;16'\n elif nc == 1:\n mode = 'L'\n elif nc == 2:\n mode = 'LA'\n elif nc == 3:\n mode = 'RGB'\n elif nc == 4:\n mode = 'RGBA'\n break\n elif tbox == b'colr':\n meth, prec, approx = struct.unpack('>BBB', content[:3])\n if meth == 1:\n cs = struct.unpack('>I', content[3:7])[0]\n if cs == 16: # sRGB\n if nc == 1 and (bpc & 0x7f) > 8:\n mode = 'I;16'\n elif nc == 1:\n mode = 'L'\n elif nc == 3:\n mode = 'RGB'\n elif nc == 4:\n mode = 'RGBA'\n break\n elif cs == 17: # grayscale\n if nc == 1 and (bpc & 0x7f) > 8:\n mode = 'I;16'\n elif nc == 1:\n mode = 'L'\n elif nc == 2:\n mode = 'LA'\n break\n elif cs == 18: # sYCC\n if nc == 3:\n mode = 'RGB'\n elif nc == 4:\n mode = 'RGBA'\n break\n\n if size is None or mode is None:\n raise SyntaxError(\"Malformed jp2 header\")\n\n return (size, mode)\n\n##\n# Image plugin for JPEG2000 images.\n\n\nclass Jpeg2KImageFile(ImageFile.ImageFile):\n format = \"JPEG2000\"\n format_description = \"JPEG 2000 (ISO 15444)\"\n\n def _open(self):\n sig = self.fp.read(4)\n if sig == b'\\xff\\x4f\\xff\\x51':\n self.codec = \"j2k\"\n self.size, self.mode = _parse_codestream(self.fp)\n else:\n sig = sig + self.fp.read(8)\n\n if sig == b'\\x00\\x00\\x00\\x0cjP \\x0d\\x0a\\x87\\x0a':\n self.codec = \"jp2\"\n self.size, self.mode = _parse_jp2_header(self.fp)\n else:\n raise SyntaxError('not a JPEG 2000 file')\n\n if self.size is None or self.mode is None:\n raise SyntaxError('unable to determine size/mode')\n\n self.reduce = 0\n self.layers = 0\n\n fd = -1\n length = -1\n\n try:\n fd = self.fp.fileno()\n length = os.fstat(fd).st_size\n except:\n fd = -1\n try:\n pos = self.fp.tell()\n self.fp.seek(0, 2)\n length = self.fp.tell()\n self.fp.seek(pos, 0)\n except:\n length = -1\n\n self.tile = [('jpeg2k', (0, 0) + self.size, 0,\n (self.codec, self.reduce, self.layers, fd, length))]\n\n def load(self):\n if self.reduce:\n power = 1 << self.reduce\n adjust = power >> 1\n self.size = (int((self.size[0] + adjust) / power),\n int((self.size[1] + adjust) / power))\n\n if self.tile:\n # Update the reduce and layers settings\n t = self.tile[0]\n t3 = (t[3][0], self.reduce, self.layers, t[3][3], t[3][4])\n self.tile = [(t[0], (0, 0) + self.size, t[2], t3)]\n\n return ImageFile.ImageFile.load(self)\n\n\ndef _accept(prefix):\n return (prefix[:4] == b'\\xff\\x4f\\xff\\x51' or\n prefix[:12] == b'\\x00\\x00\\x00\\x0cjP \\x0d\\x0a\\x87\\x0a')\n\n\n# ------------------------------------------------------------\n# Save support\n\ndef _save(im, fp, filename):\n if filename.endswith('.j2k'):\n kind = 'j2k'\n else:\n kind = 'jp2'\n\n # Get the keyword arguments\n info = im.encoderinfo\n\n offset = info.get('offset', None)\n tile_offset = info.get('tile_offset', None)\n tile_size = info.get('tile_size', None)\n quality_mode = info.get('quality_mode', 'rates')\n quality_layers = info.get('quality_layers', None)\n num_resolutions = info.get('num_resolutions', 0)\n cblk_size = info.get('codeblock_size', None)\n precinct_size = info.get('precinct_size', None)\n irreversible = info.get('irreversible', False)\n progression = info.get('progression', 'LRCP')\n cinema_mode = info.get('cinema_mode', 'no')\n fd = -1\n\n if hasattr(fp, \"fileno\"):\n try:\n fd = fp.fileno()\n except:\n fd = -1\n\n im.encoderconfig = (\n offset,\n tile_offset,\n tile_size,\n quality_mode,\n quality_layers,\n num_resolutions,\n cblk_size,\n precinct_size,\n irreversible,\n progression,\n cinema_mode,\n fd\n )\n\n ImageFile._save(im, fp, [('jpeg2k', (0, 0)+im.size, 0, kind)])\n\n# ------------------------------------------------------------\n# Registry stuff\n\n\nImage.register_open(Jpeg2KImageFile.format, Jpeg2KImageFile, _accept)\nImage.register_save(Jpeg2KImageFile.format, _save)\n\nImage.register_extensions(Jpeg2KImageFile.format, [\".jp2\", \".j2k\", \".jpc\", \".jpf\", \".jpx\", \".j2c\"])\n\nImage.register_mime(Jpeg2KImageFile.format, 'image/jp2')\nImage.register_mime(Jpeg2KImageFile.format, 'image/jpx')\n",
"path": "src/PIL/Jpeg2KImagePlugin.py"
}
] | diff --git a/Tests/test_file_jpeg2k.py b/Tests/test_file_jpeg2k.py
index 0766f5b07c9..810e21a9d74 100644
--- a/Tests/test_file_jpeg2k.py
+++ b/Tests/test_file_jpeg2k.py
@@ -176,6 +176,19 @@ def test_unbound_local(self):
with self.assertRaises(IOError):
Image.open('Tests/images/unbound_variable.jp2')
+ def test_parser_feed(self):
+ # Arrange
+ from PIL import ImageFile
+ with open('Tests/images/test-card-lossless.jp2', 'rb') as f:
+ data = f.read()
+
+ # Act
+ p = ImageFile.Parser()
+ p.feed(data)
+
+ # Assert
+ self.assertEqual(p.image.size, (640, 480))
+
if __name__ == '__main__':
unittest.main()
diff --git a/src/PIL/Jpeg2KImagePlugin.py b/src/PIL/Jpeg2KImagePlugin.py
index 4619cc5231c..7ab183a4b3a 100644
--- a/src/PIL/Jpeg2KImagePlugin.py
+++ b/src/PIL/Jpeg2KImagePlugin.py
@@ -192,7 +192,7 @@ def _open(self):
length = -1
self.tile = [('jpeg2k', (0, 0) + self.size, 0,
- (self.codec, self.reduce, self.layers, fd, length, self.fp))]
+ (self.codec, self.reduce, self.layers, fd, length))]
def load(self):
if self.reduce:
|
pypa__pipenv-2699 | Pipenv breaks with pre-existing usage of .venv files
#### Issue description
tl;dr `.venv` files already exist in some dev setups but when present, `pipenv` attempts to use that file as a directory and install a virtualenv into it.
Virtualenvwrapper has suggested for awhile to use `.venv` files in your project directory to hold the name of your virtualenv so that you could automatically activate virtualenvs. Spacemacs (an Emacs editor setup) also does this behavior by default.
Two problems:
1. Pipenv will silently fail to create and activate a virtualenv when a `.venv` file is present.
2. If a `.venv` file is added to an existing pipenv project, pipenv breaks because it can't activate the virtualenv at `<project_dir>/.venv` ignoring the place where it had already created a virtualenv.
Prior Art Links:
- Virtualenvwrapper Tips & Tricks: http://virtualenvwrapper.readthedocs.io/en/latest/tips.html
- Spacemacs Python Layer: https://github.com/syl20bnr/spacemacs/tree/develop/layers/%2Blang/python#automatic-activation-of-local-pyenv-version
- Virtualenv in git directories: https://hmarr.com/2010/jan/19/making-virtualenv-play-nice-with-git/
- Sample .bash_profile using `.venv` files: https://github.com/justinabrahms/jlilly-bashy-dotfiles/commit/04899f005397499e89da6d562b062545e70d7975#commitcomment-1526375
#### Expected result
At the least, Pipenv should check if `.venv` is a file or a directory before attempting to automatically use it as a directory and install a virtualenv into it. If it's a file, then behave as if no `.venv` was found at all.
#### Actual result
If a `.venv` file exists in the project directory, Pipenv will attempt to create and use a virtualenv at `<project_dir>/.venv` and yell at the user a bit because it's not a directory but (a) it won't actually fail and (b) it won't actually save a virtualenv _anywhere_.
Output:
```
Creating a virtualenv for this project...
Pipfile: <project_path>\Pipfile
Using <python_path> to create virtualenv...
Running virtualenv with interpreter <python_path>
ERROR: File already exists and is not a directory.
Please provide a different path or delete the file.
Virtualenv location: <project_path>\.venv
```
#### Steps to replicate
`touch .venv`
`pipenv install`
Also see output of `pipenv --venv`
-------------------------------------------------------------------------------
<details><summary>$ pipenv --support</summary>
*REDACTED*
Pipenv version: `'2018.7.1'`
PEP 508 Information:
```
{'implementation_name': 'cpython',
'implementation_version': '3.6.5',
'os_name': 'nt',
'platform_machine': 'AMD64',
'platform_python_implementation': 'CPython',
'platform_release': '10',
'platform_system': 'Windows',
'platform_version': '10.0.17134',
'python_full_version': '3.6.5',
'python_version': '3.6',
'sys_platform': 'win32'}
```
System environment variables:
- `ADSK_CLM_WPAD_PROXY_CHECK`
- `ALIASES`
- `ALLUSERSPROFILE`
- `ANSICON`
- `ANSICON_DEF`
- `APPDATA`
- `ARCHITECTURE`
- `CHOCOLATEYINSTALL`
- `CHOCOLATEYLASTPATHUPDATE`
- `CMDER_ROOT`
- `COMMONPROGRAMFILES`
- `COMMONPROGRAMFILES(X86)`
- `COMMONPROGRAMW6432`
- `COMPUTERNAME`
- `COMSPEC`
- `CONEMUANSI`
- `CONEMUANSILOG`
- `CONEMUARGS`
- `CONEMUARGS2`
- `CONEMUBACKHWND`
- `CONEMUBASEDIR`
- `CONEMUBASEDIRSHORT`
- `CONEMUBUILD`
- `CONEMUCFGDIR`
- `CONEMUCONFIG`
- `CONEMUDIR`
- `CONEMUDRAWHWND`
- `CONEMUDRIVE`
- `CONEMUHOOKS`
- `CONEMUHWND`
- `CONEMUPALETTE`
- `CONEMUPID`
- `CONEMUSERVERPID`
- `CONEMUTASK`
- `CONEMUWORKDIR`
- `CONEMUWORKDRIVE`
- `DRIVERDATA`
- `FPS_BROWSER_APP_PROFILE_STRING`
- `FPS_BROWSER_USER_PROFILE_STRING`
- `FSHARPINSTALLDIR`
- `GIT_INSTALL_ROOT`
- `GOPATH`
- `GOROOT`
- `GTK_BASEPATH`
- `HOME`
- `HOMEDRIVE`
- `HOMEPATH`
- `LOCALAPPDATA`
- `LOGONSERVER`
- `NUMBER_OF_PROCESSORS`
- `ONEDRIVE`
- `OS`
- `PATH`
- `PATHEXT`
- `PLINK_PROTOCOL`
- `PROCESSOR_ARCHITECTURE`
- `PROCESSOR_IDENTIFIER`
- `PROCESSOR_LEVEL`
- `PROCESSOR_REVISION`
- `PROGRAMDATA`
- `PROGRAMFILES`
- `PROGRAMFILES(X86)`
- `PROGRAMW6432`
- `PROMPT`
- `PSMODULEPATH`
- `PUBLIC`
- `RUBYOPT`
- `SESSIONNAME`
- `SVN_SSH`
- `SYSTEMDRIVE`
- `SYSTEMROOT`
- `TEMP`
- `TERM`
- `TMP`
- `USER-ALIASES`
- `USERDOMAIN`
- `USERDOMAIN_ROAMINGPROFILE`
- `USERNAME`
- `USERPROFILE`
- `VERBOSE-OUTPUT`
- `WINDIR`
- `WORKON_HOME`
- `PYTHONDONTWRITEBYTECODE`
- `PIP_PYTHON_PATH`
Pipenvûspecific environment variables:
---------------------------
Contents of `Pipfile`:
```toml
[[source]]
url = "https://pypi.org/simple"
verify_ssl = true
name = "pypi"
[scripts]
server = "python manage.py runserver"
migrate = "python manage.py migrate"
test = "python manage.py test"
[packages]
"boto3" = "==1.7"
dj-database-url = "==0.5.0"
json-logging-py = "*"
"psycopg2" = "==2.7"
pytz = "==2018.4"
requests = "==2.18.4"
Django = "==2.0.6"
django_csp = "==3.4"
"Jinja2" = "==2.10"
djangorestframework = "*"
[dev-packages]
ipython = "*"
pylint = "*"
pylint-django = "*"
pylint-plugin-utils = "*"
autoflake = "*"
importmagic = "*"
epc = "*"
[requires]
python_version = "3.6"
```
</details>
| [
{
"content": "# -*- coding: utf-8 -*-\nimport io\nimport json\nimport os\nimport re\nimport sys\nimport base64\nimport fnmatch\nimport hashlib\nimport contoml\nfrom first import first\nimport pipfile\nimport pipfile.api\nimport six\nimport toml\n\nfrom ._compat import Path\n\nfrom .cmdparse import Script\nfrom .utils import (\n atomic_open_for_write,\n mkdir_p,\n pep423_name,\n proper_case,\n find_requirements,\n is_editable,\n is_vcs,\n cleanup_toml,\n is_installable_file,\n is_valid_url,\n normalize_drive,\n python_version,\n safe_expandvars,\n is_star,\n get_workon_home,\n is_virtual_environment,\n)\nfrom .environments import (\n PIPENV_MAX_DEPTH,\n PIPENV_PIPFILE,\n PIPENV_VENV_IN_PROJECT,\n PIPENV_VIRTUALENV,\n PIPENV_TEST_INDEX,\n PIPENV_PYTHON,\n PIPENV_DEFAULT_PYTHON_VERSION,\n)\n\n\ndef _normalized(p):\n if p is None:\n return None\n loc = Path(p)\n if loc.is_absolute():\n return normalize_drive(str(loc))\n else:\n try:\n loc = loc.resolve()\n except OSError:\n loc = loc.absolute()\n return normalize_drive(str(loc))\n\n\nDEFAULT_NEWLINES = u\"\\n\"\n\n\nclass _LockFileEncoder(json.JSONEncoder):\n \"\"\"A specilized JSON encoder to convert loaded TOML data into a lock file.\n\n This adds a few characteristics to the encoder:\n\n * The JSON is always prettified with indents and spaces.\n * PrettyTOML's container elements are seamlessly encodable.\n * The output is always UTF-8-encoded text, never binary, even on Python 2.\n \"\"\"\n def __init__(self):\n super(_LockFileEncoder, self).__init__(\n indent=4, separators=(\",\", \": \"), sort_keys=True,\n )\n\n def default(self, obj):\n from prettytoml.elements.common import ContainerElement, TokenElement\n if isinstance(obj, (ContainerElement, TokenElement)):\n return obj.primitive_value\n return super(_LockFileEncoder, self).default(obj)\n\n def encode(self, obj):\n content = super(_LockFileEncoder, self).encode(obj)\n if not isinstance(content, six.text_type):\n content = content.decode(\"utf-8\")\n return content\n\n\ndef preferred_newlines(f):\n if isinstance(f.newlines, six.text_type):\n return f.newlines\n return DEFAULT_NEWLINES\n\n\nif PIPENV_PIPFILE:\n if not os.path.isfile(PIPENV_PIPFILE):\n raise RuntimeError(\"Given PIPENV_PIPFILE is not found!\")\n\n else:\n PIPENV_PIPFILE = _normalized(PIPENV_PIPFILE)\n# (path, file contents) => TOMLFile\n# keeps track of pipfiles that we've seen so we do not need to re-parse 'em\n_pipfile_cache = {}\n\n\nif PIPENV_TEST_INDEX:\n DEFAULT_SOURCE = {\n u\"url\": PIPENV_TEST_INDEX,\n u\"verify_ssl\": True,\n u\"name\": u\"custom\",\n }\nelse:\n DEFAULT_SOURCE = {\n u\"url\": u\"https://pypi.org/simple\",\n u\"verify_ssl\": True,\n u\"name\": u\"pypi\",\n }\n\npipfile.api.DEFAULT_SOURCE = DEFAULT_SOURCE\n\n\nclass SourceNotFound(KeyError):\n pass\n\n\nclass Project(object):\n \"\"\"docstring for Project\"\"\"\n\n _lockfile_encoder = _LockFileEncoder()\n\n def __init__(self, which=None, python_version=None, chdir=True):\n super(Project, self).__init__()\n self._name = None\n self._virtualenv_location = None\n self._download_location = None\n self._proper_names_db_path = None\n self._pipfile_location = None\n self._pipfile_newlines = DEFAULT_NEWLINES\n self._lockfile_newlines = DEFAULT_NEWLINES\n self._requirements_location = None\n self._original_dir = os.path.abspath(os.curdir)\n self.which = which\n self.python_version = python_version\n # Hack to skip this during pipenv run, or -r.\n if (\"run\" not in sys.argv) and chdir:\n try:\n os.chdir(self.project_directory)\n except (TypeError, AttributeError):\n pass\n\n def path_to(self, p):\n \"\"\"Returns the absolute path to a given relative path.\"\"\"\n if os.path.isabs(p):\n return p\n\n return os.sep.join([self._original_dir, p])\n\n def _build_package_list(self, package_section):\n \"\"\"Returns a list of packages for pip-tools to consume.\"\"\"\n ps = {}\n # TODO: Separate the logic for showing packages from the filters for supplying pip-tools\n for k, v in self.parsed_pipfile.get(package_section, {}).items():\n # Skip editable VCS deps.\n if hasattr(v, \"keys\"):\n # When a vcs url is gven without editable it only appears as a key\n # Eliminate any vcs, path, or url entries which are not editable\n # Since pip-tools can't do deep resolution on them, even setuptools-installable ones\n if (\n is_vcs(v)\n or is_vcs(k)\n or (is_installable_file(k) or is_installable_file(v))\n or any(\n (\n prefix in v\n and (os.path.isfile(v[prefix]) or is_valid_url(v[prefix]))\n )\n for prefix in [\"path\", \"file\"]\n )\n ):\n # If they are editable, do resolve them\n if \"editable\" not in v:\n # allow wheels to be passed through\n if not (\n hasattr(v, \"keys\")\n and v.get(\"path\", v.get(\"file\", \"\")).endswith(\".whl\")\n ):\n continue\n ps.update({k: v})\n\n else:\n ps.update({k: v})\n else:\n ps.update({k: v})\n else:\n # Since these entries have no attributes we know they are not editable\n # So we can safely exclude things that need to be editable in order to be resolved\n # First exclude anything that is a vcs entry either in the key or value\n if not (\n any(is_vcs(i) for i in [k, v])\n or\n # Then exclude any installable files that are not directories\n # Because pip-tools can resolve setup.py for example\n any(is_installable_file(i) for i in [k, v])\n or\n # Then exclude any URLs because they need to be editable also\n # Things that are excluded can only be 'shallow resolved'\n any(is_valid_url(i) for i in [k, v])\n ):\n ps.update({k: v})\n return ps\n\n @property\n def name(self):\n if self._name is None:\n self._name = self.pipfile_location.split(os.sep)[-2]\n return self._name\n\n @property\n def pipfile_exists(self):\n return bool(self.pipfile_location)\n\n @property\n def required_python_version(self):\n if self.pipfile_exists:\n required = self.parsed_pipfile.get(\"requires\", {}).get(\n \"python_full_version\"\n )\n if not required:\n required = self.parsed_pipfile.get(\"requires\", {}).get(\"python_version\")\n if required != \"*\":\n return required\n\n @property\n def project_directory(self):\n if self.pipfile_location is not None:\n return os.path.abspath(os.path.join(self.pipfile_location, os.pardir))\n\n else:\n return None\n\n @property\n def requirements_exists(self):\n return bool(self.requirements_location)\n\n def is_venv_in_project(self):\n return PIPENV_VENV_IN_PROJECT or (\n self.project_directory\n and os.path.exists(os.path.join(self.project_directory, \".venv\"))\n )\n\n @property\n def virtualenv_exists(self):\n # TODO: Decouple project from existence of Pipfile.\n if self.pipfile_exists and os.path.exists(self.virtualenv_location):\n if os.name == \"nt\":\n extra = [\"Scripts\", \"activate.bat\"]\n else:\n extra = [\"bin\", \"activate\"]\n return os.path.isfile(os.sep.join([self.virtualenv_location] + extra))\n\n return False\n\n def get_location_for_virtualenv(self):\n if self.is_venv_in_project():\n return os.path.join(self.project_directory, \".venv\")\n return str(get_workon_home().joinpath(self.virtualenv_name))\n\n def get_installed_packages(self):\n from . import PIPENV_ROOT, PIPENV_VENDOR, PIPENV_PATCHED\n from .utils import temp_path, load_path, temp_environ\n if self.virtualenv_exists:\n with temp_path(), temp_environ():\n new_path = load_path(self.which(\"python\"))\n new_path = [new_path[0], PIPENV_ROOT, PIPENV_PATCHED, PIPENV_VENDOR] + new_path[1:]\n sys.path = new_path\n os.environ['VIRTUAL_ENV'] = self.virtualenv_location\n from .patched.notpip._internal.utils.misc import get_installed_distributions\n return get_installed_distributions(local_only=True)\n else:\n return []\n\n @classmethod\n def _sanitize(cls, name):\n # Replace dangerous characters into '_'. The length of the sanitized\n # project name is limited as 42 because of the limit of linux kernel\n #\n # 42 = 127 - len('/home//.local/share/virtualenvs//bin/python2') - 32 - len('-HASHHASH')\n #\n # 127 : BINPRM_BUF_SIZE - 1\n # 32 : Maximum length of username\n #\n # References:\n # https://www.gnu.org/software/bash/manual/html_node/Double-Quotes.html\n # http://www.tldp.org/LDP/abs/html/special-chars.html#FIELDREF\n # https://github.com/torvalds/linux/blob/2bfe01ef/include/uapi/linux/binfmts.h#L18\n return re.sub(r'[ $`!*@\"\\\\\\r\\n\\t]', \"_\", name)[0:42]\n\n def _get_virtualenv_hash(self, name):\n \"\"\"Get the name of the virtualenv adjusted for windows if needed\n\n Returns (name, encoded_hash)\n \"\"\"\n\n def get_name(name, location):\n name = self._sanitize(name)\n hash = hashlib.sha256(location.encode()).digest()[:6]\n encoded_hash = base64.urlsafe_b64encode(hash).decode()\n return name, encoded_hash[:8]\n\n clean_name, encoded_hash = get_name(name, self.pipfile_location)\n venv_name = \"{0}-{1}\".format(clean_name, encoded_hash)\n\n # This should work most of the time for\n # Case-sensitive filesystems,\n # In-project venv\n # \"Proper\" path casing (on non-case-sensitive filesystems).\n if (\n fnmatch.fnmatch('A', 'a')\n or self.is_venv_in_project()\n or get_workon_home().joinpath(venv_name).exists()\n ):\n return clean_name, encoded_hash\n\n # Check for different capitalization of the same project.\n for path in get_workon_home().iterdir():\n if not is_virtual_environment(path):\n continue\n try:\n env_name, hash_ = path.name.rsplit(\"-\", 1)\n except ValueError:\n continue\n if len(hash_) != 8 or env_name.lower() != name.lower():\n continue\n return get_name(env_name, self.pipfile_location.replace(name, env_name))\n\n # Use the default if no matching env exists.\n return clean_name, encoded_hash\n\n @property\n def virtualenv_name(self):\n sanitized, encoded_hash = self._get_virtualenv_hash(self.name)\n suffix = \"-{0}\".format(PIPENV_PYTHON) if PIPENV_PYTHON else \"\"\n # If the pipfile was located at '/home/user/MY_PROJECT/Pipfile',\n # the name of its virtualenv will be 'my-project-wyUfYPqE'\n return sanitized + \"-\" + encoded_hash + suffix\n\n @property\n def virtualenv_location(self):\n # if VIRTUAL_ENV is set, use that.\n if PIPENV_VIRTUALENV:\n return PIPENV_VIRTUALENV\n\n if not self._virtualenv_location: # Use cached version, if available.\n assert self.project_directory, \"project not created\"\n self._virtualenv_location = self.get_location_for_virtualenv()\n return self._virtualenv_location\n\n @property\n def virtualenv_src_location(self):\n loc = os.sep.join([self.virtualenv_location, \"src\"])\n mkdir_p(loc)\n return loc\n\n @property\n def download_location(self):\n if self._download_location is None:\n loc = os.sep.join([self.virtualenv_location, \"downloads\"])\n self._download_location = loc\n # Create the directory, if it doesn't exist.\n mkdir_p(self._download_location)\n return self._download_location\n\n @property\n def proper_names_db_path(self):\n if self._proper_names_db_path is None:\n self._proper_names_db_path = Path(\n self.virtualenv_location, \"pipenv-proper-names.txt\"\n )\n self._proper_names_db_path.touch() # Ensure the file exists.\n return self._proper_names_db_path\n\n @property\n def proper_names(self):\n with self.proper_names_db_path.open() as f:\n return f.read().splitlines()\n\n def register_proper_name(self, name):\n \"\"\"Registers a proper name to the database.\"\"\"\n with self.proper_names_db_path.open(\"a\") as f:\n f.write(u\"{0}\\n\".format(name))\n\n @property\n def pipfile_location(self):\n if PIPENV_PIPFILE:\n return PIPENV_PIPFILE\n\n if self._pipfile_location is None:\n try:\n loc = pipfile.Pipfile.find(max_depth=PIPENV_MAX_DEPTH)\n except RuntimeError:\n loc = None\n self._pipfile_location = _normalized(loc)\n return self._pipfile_location\n\n @property\n def requirements_location(self):\n if self._requirements_location is None:\n try:\n loc = find_requirements(max_depth=PIPENV_MAX_DEPTH)\n except RuntimeError:\n loc = None\n self._requirements_location = loc\n return self._requirements_location\n\n @property\n def parsed_pipfile(self):\n \"\"\"Parse Pipfile into a TOMLFile and cache it\n\n (call clear_pipfile_cache() afterwards if mutating)\"\"\"\n contents = self.read_pipfile()\n # use full contents to get around str/bytes 2/3 issues\n cache_key = (self.pipfile_location, contents)\n if cache_key not in _pipfile_cache:\n parsed = self._parse_pipfile(contents)\n _pipfile_cache[cache_key] = parsed\n return _pipfile_cache[cache_key]\n\n def read_pipfile(self):\n # Open the pipfile, read it into memory.\n with io.open(self.pipfile_location) as f:\n contents = f.read()\n self._pipfile_newlines = preferred_newlines(f)\n\n return contents\n\n @property\n def pased_pure_pipfile(self):\n contents = self.read_pipfile()\n\n return self._parse_pipfile(contents)\n\n def clear_pipfile_cache(self):\n \"\"\"Clear pipfile cache (e.g., so we can mutate parsed pipfile)\"\"\"\n _pipfile_cache.clear()\n\n def _parse_pipfile(self, contents):\n # If any outline tables are present...\n if (\"[packages.\" in contents) or (\"[dev-packages.\" in contents):\n data = toml.loads(contents)\n # Convert all outline tables to inline tables.\n for section in (\"packages\", \"dev-packages\"):\n for package in data.get(section, {}):\n # Convert things to inline tables — fancy :)\n if hasattr(data[section][package], \"keys\"):\n _data = data[section][package]\n data[section][package] = toml._get_empty_inline_table(dict)\n data[section][package].update(_data)\n # We lose comments here, but it's for the best.)\n try:\n return contoml.loads(toml.dumps(data, preserve=True))\n\n except RuntimeError:\n return toml.loads(toml.dumps(data, preserve=True))\n\n else:\n # Fallback to toml parser, for large files.\n try:\n return contoml.loads(contents)\n\n except Exception:\n return toml.loads(contents)\n\n @property\n def settings(self):\n \"\"\"A dictionary of the settings added to the Pipfile.\"\"\"\n return self.parsed_pipfile.get(\"pipenv\", {})\n\n def has_script(self, name):\n try:\n return name in self.parsed_pipfile[\"scripts\"]\n except KeyError:\n return False\n\n def build_script(self, name, extra_args=None):\n try:\n script = Script.parse(self.parsed_pipfile[\"scripts\"][name])\n except KeyError:\n script = Script(name)\n if extra_args:\n script.extend(extra_args)\n return script\n\n def update_settings(self, d):\n settings = self.settings\n changed = False\n for new in d:\n if new not in settings:\n settings[new] = d[new]\n changed = True\n if changed:\n p = self.parsed_pipfile\n p[\"pipenv\"] = settings\n # Write the changes to disk.\n self.write_toml(p)\n\n @property\n def _lockfile(self):\n \"\"\"Pipfile.lock divided by PyPI and external dependencies.\"\"\"\n pfile = pipfile.load(self.pipfile_location, inject_env=False)\n lockfile = json.loads(pfile.lock())\n for section in (\"default\", \"develop\"):\n lock_section = lockfile.get(section, {})\n for key in list(lock_section.keys()):\n norm_key = pep423_name(key)\n lockfile[section][norm_key] = lock_section.pop(key)\n return lockfile\n\n @property\n def lockfile_location(self):\n return \"{0}.lock\".format(self.pipfile_location)\n\n @property\n def lockfile_exists(self):\n return os.path.isfile(self.lockfile_location)\n\n @property\n def lockfile_content(self):\n return self.load_lockfile()\n\n def _get_editable_packages(self, dev=False):\n section = \"dev-packages\" if dev else \"packages\"\n packages = {\n k: v\n for k, v in self.parsed_pipfile.get(section, {}).items()\n if is_editable(v)\n }\n return packages\n\n def _get_vcs_packages(self, dev=False):\n section = \"dev-packages\" if dev else \"packages\"\n packages = {\n k: v\n for k, v in self.parsed_pipfile.get(section, {}).items()\n if is_vcs(v) or is_vcs(k)\n }\n return packages or {}\n\n @property\n def editable_packages(self):\n return self._get_editable_packages(dev=False)\n\n @property\n def editable_dev_packages(self):\n return self._get_editable_packages(dev=True)\n\n @property\n def vcs_packages(self):\n \"\"\"Returns a list of VCS packages, for not pip-tools to consume.\"\"\"\n return self._get_vcs_packages(dev=False)\n\n @property\n def vcs_dev_packages(self):\n \"\"\"Returns a list of VCS packages, for not pip-tools to consume.\"\"\"\n return self._get_vcs_packages(dev=True)\n\n @property\n def all_packages(self):\n \"\"\"Returns a list of all packages.\"\"\"\n p = dict(self.parsed_pipfile.get(\"dev-packages\", {}))\n p.update(self.parsed_pipfile.get(\"packages\", {}))\n return p\n\n @property\n def packages(self):\n \"\"\"Returns a list of packages, for pip-tools to consume.\"\"\"\n return self._build_package_list(\"packages\")\n\n @property\n def dev_packages(self):\n \"\"\"Returns a list of dev-packages, for pip-tools to consume.\"\"\"\n return self._build_package_list(\"dev-packages\")\n\n def touch_pipfile(self):\n \"\"\"Simply touches the Pipfile, for later use.\"\"\"\n with open(\"Pipfile\", \"a\"):\n os.utime(\"Pipfile\", None)\n\n @property\n def pipfile_is_empty(self):\n if not self.pipfile_exists:\n return True\n\n if not len(self.read_pipfile()):\n return True\n\n return False\n\n def create_pipfile(self, python=None):\n \"\"\"Creates the Pipfile, filled with juicy defaults.\"\"\"\n from .patched.notpip._internal import ConfigOptionParser\n from .patched.notpip._internal.cmdoptions import make_option_group, index_group\n\n config_parser = ConfigOptionParser(name=self.name)\n config_parser.add_option_group(make_option_group(index_group, config_parser))\n install = config_parser.option_groups[0]\n indexes = (\n \" \".join(install.get_option(\"--extra-index-url\").default)\n .lstrip(\"\\n\")\n .split(\"\\n\")\n )\n sources = [DEFAULT_SOURCE]\n for i, index in enumerate(indexes):\n if not index:\n continue\n\n source_name = \"pip_index_{}\".format(i)\n verify_ssl = index.startswith(\"https\")\n sources.append(\n {u\"url\": index, u\"verify_ssl\": verify_ssl, u\"name\": source_name}\n )\n\n data = {\n u\"source\": sources,\n # Default packages.\n u\"packages\": {},\n u\"dev-packages\": {},\n }\n # Default requires.\n required_python = python\n if not python:\n if self.virtualenv_location:\n required_python = self.which(\"python\", self.virtualenv_location)\n else:\n required_python = self.which(\"python\")\n version = python_version(required_python) or PIPENV_DEFAULT_PYTHON_VERSION\n if version and len(version) >= 3:\n data[u\"requires\"] = {\"python_version\": version[: len(\"2.7\")]}\n self.write_toml(data, \"Pipfile\")\n\n def write_toml(self, data, path=None):\n \"\"\"Writes the given data structure out as TOML.\"\"\"\n if path is None:\n path = self.pipfile_location\n try:\n formatted_data = contoml.dumps(data).rstrip()\n except Exception:\n for section in (\"packages\", \"dev-packages\"):\n for package in data.get(section, {}):\n # Convert things to inline tables — fancy :)\n if hasattr(data[section][package], \"keys\"):\n _data = data[section][package]\n data[section][package] = toml._get_empty_inline_table(dict)\n data[section][package].update(_data)\n formatted_data = toml.dumps(data).rstrip()\n\n if Path(path).absolute() == Path(self.pipfile_location).absolute():\n newlines = self._pipfile_newlines\n else:\n newlines = DEFAULT_NEWLINES\n formatted_data = cleanup_toml(formatted_data)\n with io.open(path, \"w\", newline=newlines) as f:\n f.write(formatted_data)\n # pipfile is mutated!\n self.clear_pipfile_cache()\n\n def write_lockfile(self, content):\n \"\"\"Write out the lockfile.\n \"\"\"\n s = self._lockfile_encoder.encode(content)\n open_kwargs = {\n 'newline': self._lockfile_newlines,\n 'encoding': 'utf-8',\n }\n with atomic_open_for_write(self.lockfile_location, **open_kwargs) as f:\n f.write(s)\n # Write newline at end of document. GH-319.\n # Only need '\\n' here; the file object handles the rest.\n if not s.endswith(u\"\\n\"):\n f.write(u\"\\n\")\n\n @property\n def pipfile_sources(self):\n if \"source\" not in self.parsed_pipfile:\n return [DEFAULT_SOURCE]\n # We need to make copies of the source info so we don't\n # accidentally modify the cache. See #2100 where values are\n # written after the os.path.expandvars() call.\n return [\n {k: safe_expandvars(v) for k, v in source.items()}\n for source in self.parsed_pipfile[\"source\"]\n ]\n\n @property\n def sources(self):\n if self.lockfile_exists and hasattr(self.lockfile_content, \"keys\"):\n meta_ = self.lockfile_content[\"_meta\"]\n sources_ = meta_.get(\"sources\")\n if sources_:\n return sources_\n\n else:\n return self.pipfile_sources\n\n def find_source(self, source):\n \"\"\"given a source, find it.\n\n source can be a url or an index name.\n \"\"\"\n if not is_valid_url(source):\n try:\n source = self.get_source(name=source)\n except SourceNotFound:\n source = self.get_source(url=source)\n else:\n source = self.get_source(url=source)\n return source\n\n def get_source(self, name=None, url=None):\n def find_source(sources, name=None, url=None):\n source = None\n if name:\n source = [s for s in sources if s.get(\"name\") == name]\n elif url:\n source = [s for s in sources if url.startswith(s.get(\"url\"))]\n if source:\n return first(source)\n\n found_source = find_source(self.sources, name=name, url=url)\n if found_source:\n return found_source\n found_source = find_source(self.pipfile_sources, name=name, url=url)\n if found_source:\n return found_source\n raise SourceNotFound(name or url)\n\n def get_package_name_in_pipfile(self, package_name, dev=False):\n \"\"\"Get the equivalent package name in pipfile\"\"\"\n key = \"dev-packages\" if dev else \"packages\"\n section = self.parsed_pipfile.get(key, {})\n package_name = pep423_name(package_name)\n for name in section.keys():\n if pep423_name(name) == package_name:\n return name\n return None\n\n def remove_package_from_pipfile(self, package_name, dev=False):\n # Read and append Pipfile.\n name = self.get_package_name_in_pipfile(package_name, dev)\n key = \"dev-packages\" if dev else \"packages\"\n p = self.parsed_pipfile\n if name:\n del p[key][name]\n self.write_toml(p)\n\n def add_package_to_pipfile(self, package_name, dev=False):\n from .vendor.requirementslib import Requirement\n\n # Read and append Pipfile.\n p = self.parsed_pipfile\n # Don't re-capitalize file URLs or VCSs.\n package = Requirement.from_line(package_name.strip())\n _, converted = package.pipfile_entry\n key = \"dev-packages\" if dev else \"packages\"\n # Set empty group if it doesn't exist yet.\n if key not in p:\n p[key] = {}\n name = self.get_package_name_in_pipfile(package.name, dev)\n if name and is_star(converted):\n # Skip for wildcard version\n return\n # Add the package to the group.\n p[key][name or package.normalized_name] = converted\n # Write Pipfile.\n self.write_toml(p)\n\n def add_index_to_pipfile(self, index):\n \"\"\"Adds a given index to the Pipfile.\"\"\"\n # Read and append Pipfile.\n p = self.parsed_pipfile\n source = {\"url\": index, \"verify_ssl\": True}\n # Add the package to the group.\n if \"source\" not in p:\n p[\"source\"] = [source]\n else:\n p[\"source\"].append(source)\n # Write Pipfile.\n self.write_toml(p)\n\n def recase_pipfile(self):\n if self.ensure_proper_casing():\n self.write_toml(self.parsed_pipfile)\n\n def load_lockfile(self, expand_env_vars=True):\n with io.open(self.lockfile_location, encoding='utf-8') as lock:\n j = json.load(lock)\n self._lockfile_newlines = preferred_newlines(lock)\n # lockfile is just a string\n if not j or not hasattr(j, \"keys\"):\n return j\n\n if expand_env_vars:\n # Expand environment variables in Pipfile.lock at runtime.\n for i, source in enumerate(j[\"_meta\"][\"sources\"][:]):\n j[\"_meta\"][\"sources\"][i][\"url\"] = os.path.expandvars(\n j[\"_meta\"][\"sources\"][i][\"url\"]\n )\n\n return j\n\n def get_lockfile_hash(self):\n if not os.path.exists(self.lockfile_location):\n return\n\n try:\n lockfile = self.load_lockfile(expand_env_vars=False)\n except ValueError:\n # Lockfile corrupted\n return \"\"\n if \"_meta\" in lockfile and hasattr(lockfile, \"keys\"):\n return lockfile[\"_meta\"].get(\"hash\", {}).get(\"sha256\")\n # Lockfile exists but has no hash at all\n return \"\"\n\n def calculate_pipfile_hash(self):\n # Update the lockfile if it is out-of-date.\n p = pipfile.load(self.pipfile_location, inject_env=False)\n return p.hash\n\n def ensure_proper_casing(self):\n \"\"\"Ensures proper casing of Pipfile packages\"\"\"\n pfile = self.parsed_pipfile\n casing_changed = self.proper_case_section(pfile.get(\"packages\", {}))\n casing_changed |= self.proper_case_section(pfile.get(\"dev-packages\", {}))\n return casing_changed\n\n def proper_case_section(self, section):\n \"\"\"Verify proper casing is retrieved, when available, for each\n dependency in the section.\n \"\"\"\n # Casing for section.\n changed_values = False\n unknown_names = [k for k in section.keys() if k not in set(self.proper_names)]\n # Replace each package with proper casing.\n for dep in unknown_names:\n try:\n # Get new casing for package name.\n new_casing = proper_case(dep)\n except IOError:\n # Unable to normalize package name.\n continue\n\n if new_casing != dep:\n changed_values = True\n self.register_proper_name(new_casing)\n # Replace old value with new value.\n old_value = section[dep]\n section[new_casing] = old_value\n del section[dep]\n # Return whether or not values have been changed.\n return changed_values\n",
"path": "pipenv/project.py"
}
] | [
{
"content": "# -*- coding: utf-8 -*-\nimport io\nimport json\nimport os\nimport re\nimport sys\nimport base64\nimport fnmatch\nimport hashlib\nimport contoml\nfrom first import first\nimport pipfile\nimport pipfile.api\nimport six\nimport toml\n\nfrom ._compat import Path\n\nfrom .cmdparse import Script\nfrom .utils import (\n atomic_open_for_write,\n mkdir_p,\n pep423_name,\n proper_case,\n find_requirements,\n is_editable,\n is_vcs,\n cleanup_toml,\n is_installable_file,\n is_valid_url,\n normalize_drive,\n python_version,\n safe_expandvars,\n is_star,\n get_workon_home,\n is_virtual_environment,\n)\nfrom .environments import (\n PIPENV_MAX_DEPTH,\n PIPENV_PIPFILE,\n PIPENV_VENV_IN_PROJECT,\n PIPENV_VIRTUALENV,\n PIPENV_TEST_INDEX,\n PIPENV_PYTHON,\n PIPENV_DEFAULT_PYTHON_VERSION,\n)\n\n\ndef _normalized(p):\n if p is None:\n return None\n loc = Path(p)\n if loc.is_absolute():\n return normalize_drive(str(loc))\n else:\n try:\n loc = loc.resolve()\n except OSError:\n loc = loc.absolute()\n return normalize_drive(str(loc))\n\n\nDEFAULT_NEWLINES = u\"\\n\"\n\n\nclass _LockFileEncoder(json.JSONEncoder):\n \"\"\"A specilized JSON encoder to convert loaded TOML data into a lock file.\n\n This adds a few characteristics to the encoder:\n\n * The JSON is always prettified with indents and spaces.\n * PrettyTOML's container elements are seamlessly encodable.\n * The output is always UTF-8-encoded text, never binary, even on Python 2.\n \"\"\"\n def __init__(self):\n super(_LockFileEncoder, self).__init__(\n indent=4, separators=(\",\", \": \"), sort_keys=True,\n )\n\n def default(self, obj):\n from prettytoml.elements.common import ContainerElement, TokenElement\n if isinstance(obj, (ContainerElement, TokenElement)):\n return obj.primitive_value\n return super(_LockFileEncoder, self).default(obj)\n\n def encode(self, obj):\n content = super(_LockFileEncoder, self).encode(obj)\n if not isinstance(content, six.text_type):\n content = content.decode(\"utf-8\")\n return content\n\n\ndef preferred_newlines(f):\n if isinstance(f.newlines, six.text_type):\n return f.newlines\n return DEFAULT_NEWLINES\n\n\nif PIPENV_PIPFILE:\n if not os.path.isfile(PIPENV_PIPFILE):\n raise RuntimeError(\"Given PIPENV_PIPFILE is not found!\")\n\n else:\n PIPENV_PIPFILE = _normalized(PIPENV_PIPFILE)\n# (path, file contents) => TOMLFile\n# keeps track of pipfiles that we've seen so we do not need to re-parse 'em\n_pipfile_cache = {}\n\n\nif PIPENV_TEST_INDEX:\n DEFAULT_SOURCE = {\n u\"url\": PIPENV_TEST_INDEX,\n u\"verify_ssl\": True,\n u\"name\": u\"custom\",\n }\nelse:\n DEFAULT_SOURCE = {\n u\"url\": u\"https://pypi.org/simple\",\n u\"verify_ssl\": True,\n u\"name\": u\"pypi\",\n }\n\npipfile.api.DEFAULT_SOURCE = DEFAULT_SOURCE\n\n\nclass SourceNotFound(KeyError):\n pass\n\n\nclass Project(object):\n \"\"\"docstring for Project\"\"\"\n\n _lockfile_encoder = _LockFileEncoder()\n\n def __init__(self, which=None, python_version=None, chdir=True):\n super(Project, self).__init__()\n self._name = None\n self._virtualenv_location = None\n self._download_location = None\n self._proper_names_db_path = None\n self._pipfile_location = None\n self._pipfile_newlines = DEFAULT_NEWLINES\n self._lockfile_newlines = DEFAULT_NEWLINES\n self._requirements_location = None\n self._original_dir = os.path.abspath(os.curdir)\n self.which = which\n self.python_version = python_version\n # Hack to skip this during pipenv run, or -r.\n if (\"run\" not in sys.argv) and chdir:\n try:\n os.chdir(self.project_directory)\n except (TypeError, AttributeError):\n pass\n\n def path_to(self, p):\n \"\"\"Returns the absolute path to a given relative path.\"\"\"\n if os.path.isabs(p):\n return p\n\n return os.sep.join([self._original_dir, p])\n\n def _build_package_list(self, package_section):\n \"\"\"Returns a list of packages for pip-tools to consume.\"\"\"\n ps = {}\n # TODO: Separate the logic for showing packages from the filters for supplying pip-tools\n for k, v in self.parsed_pipfile.get(package_section, {}).items():\n # Skip editable VCS deps.\n if hasattr(v, \"keys\"):\n # When a vcs url is gven without editable it only appears as a key\n # Eliminate any vcs, path, or url entries which are not editable\n # Since pip-tools can't do deep resolution on them, even setuptools-installable ones\n if (\n is_vcs(v)\n or is_vcs(k)\n or (is_installable_file(k) or is_installable_file(v))\n or any(\n (\n prefix in v\n and (os.path.isfile(v[prefix]) or is_valid_url(v[prefix]))\n )\n for prefix in [\"path\", \"file\"]\n )\n ):\n # If they are editable, do resolve them\n if \"editable\" not in v:\n # allow wheels to be passed through\n if not (\n hasattr(v, \"keys\")\n and v.get(\"path\", v.get(\"file\", \"\")).endswith(\".whl\")\n ):\n continue\n ps.update({k: v})\n\n else:\n ps.update({k: v})\n else:\n ps.update({k: v})\n else:\n # Since these entries have no attributes we know they are not editable\n # So we can safely exclude things that need to be editable in order to be resolved\n # First exclude anything that is a vcs entry either in the key or value\n if not (\n any(is_vcs(i) for i in [k, v])\n or\n # Then exclude any installable files that are not directories\n # Because pip-tools can resolve setup.py for example\n any(is_installable_file(i) for i in [k, v])\n or\n # Then exclude any URLs because they need to be editable also\n # Things that are excluded can only be 'shallow resolved'\n any(is_valid_url(i) for i in [k, v])\n ):\n ps.update({k: v})\n return ps\n\n @property\n def name(self):\n if self._name is None:\n self._name = self.pipfile_location.split(os.sep)[-2]\n return self._name\n\n @property\n def pipfile_exists(self):\n return bool(self.pipfile_location)\n\n @property\n def required_python_version(self):\n if self.pipfile_exists:\n required = self.parsed_pipfile.get(\"requires\", {}).get(\n \"python_full_version\"\n )\n if not required:\n required = self.parsed_pipfile.get(\"requires\", {}).get(\"python_version\")\n if required != \"*\":\n return required\n\n @property\n def project_directory(self):\n if self.pipfile_location is not None:\n return os.path.abspath(os.path.join(self.pipfile_location, os.pardir))\n\n else:\n return None\n\n @property\n def requirements_exists(self):\n return bool(self.requirements_location)\n\n def is_venv_in_project(self):\n return PIPENV_VENV_IN_PROJECT or (\n self.project_directory\n and os.path.isdir(os.path.join(self.project_directory, \".venv\"))\n )\n\n @property\n def virtualenv_exists(self):\n # TODO: Decouple project from existence of Pipfile.\n if self.pipfile_exists and os.path.exists(self.virtualenv_location):\n if os.name == \"nt\":\n extra = [\"Scripts\", \"activate.bat\"]\n else:\n extra = [\"bin\", \"activate\"]\n return os.path.isfile(os.sep.join([self.virtualenv_location] + extra))\n\n return False\n\n def get_location_for_virtualenv(self):\n if self.is_venv_in_project():\n return os.path.join(self.project_directory, \".venv\")\n return str(get_workon_home().joinpath(self.virtualenv_name))\n\n def get_installed_packages(self):\n from . import PIPENV_ROOT, PIPENV_VENDOR, PIPENV_PATCHED\n from .utils import temp_path, load_path, temp_environ\n if self.virtualenv_exists:\n with temp_path(), temp_environ():\n new_path = load_path(self.which(\"python\"))\n new_path = [new_path[0], PIPENV_ROOT, PIPENV_PATCHED, PIPENV_VENDOR] + new_path[1:]\n sys.path = new_path\n os.environ['VIRTUAL_ENV'] = self.virtualenv_location\n from .patched.notpip._internal.utils.misc import get_installed_distributions\n return get_installed_distributions(local_only=True)\n else:\n return []\n\n @classmethod\n def _sanitize(cls, name):\n # Replace dangerous characters into '_'. The length of the sanitized\n # project name is limited as 42 because of the limit of linux kernel\n #\n # 42 = 127 - len('/home//.local/share/virtualenvs//bin/python2') - 32 - len('-HASHHASH')\n #\n # 127 : BINPRM_BUF_SIZE - 1\n # 32 : Maximum length of username\n #\n # References:\n # https://www.gnu.org/software/bash/manual/html_node/Double-Quotes.html\n # http://www.tldp.org/LDP/abs/html/special-chars.html#FIELDREF\n # https://github.com/torvalds/linux/blob/2bfe01ef/include/uapi/linux/binfmts.h#L18\n return re.sub(r'[ $`!*@\"\\\\\\r\\n\\t]', \"_\", name)[0:42]\n\n def _get_virtualenv_hash(self, name):\n \"\"\"Get the name of the virtualenv adjusted for windows if needed\n\n Returns (name, encoded_hash)\n \"\"\"\n\n def get_name(name, location):\n name = self._sanitize(name)\n hash = hashlib.sha256(location.encode()).digest()[:6]\n encoded_hash = base64.urlsafe_b64encode(hash).decode()\n return name, encoded_hash[:8]\n\n clean_name, encoded_hash = get_name(name, self.pipfile_location)\n venv_name = \"{0}-{1}\".format(clean_name, encoded_hash)\n\n # This should work most of the time for\n # Case-sensitive filesystems,\n # In-project venv\n # \"Proper\" path casing (on non-case-sensitive filesystems).\n if (\n fnmatch.fnmatch('A', 'a')\n or self.is_venv_in_project()\n or get_workon_home().joinpath(venv_name).exists()\n ):\n return clean_name, encoded_hash\n\n # Check for different capitalization of the same project.\n for path in get_workon_home().iterdir():\n if not is_virtual_environment(path):\n continue\n try:\n env_name, hash_ = path.name.rsplit(\"-\", 1)\n except ValueError:\n continue\n if len(hash_) != 8 or env_name.lower() != name.lower():\n continue\n return get_name(env_name, self.pipfile_location.replace(name, env_name))\n\n # Use the default if no matching env exists.\n return clean_name, encoded_hash\n\n @property\n def virtualenv_name(self):\n sanitized, encoded_hash = self._get_virtualenv_hash(self.name)\n suffix = \"-{0}\".format(PIPENV_PYTHON) if PIPENV_PYTHON else \"\"\n # If the pipfile was located at '/home/user/MY_PROJECT/Pipfile',\n # the name of its virtualenv will be 'my-project-wyUfYPqE'\n return sanitized + \"-\" + encoded_hash + suffix\n\n @property\n def virtualenv_location(self):\n # if VIRTUAL_ENV is set, use that.\n if PIPENV_VIRTUALENV:\n return PIPENV_VIRTUALENV\n\n if not self._virtualenv_location: # Use cached version, if available.\n assert self.project_directory, \"project not created\"\n self._virtualenv_location = self.get_location_for_virtualenv()\n return self._virtualenv_location\n\n @property\n def virtualenv_src_location(self):\n loc = os.sep.join([self.virtualenv_location, \"src\"])\n mkdir_p(loc)\n return loc\n\n @property\n def download_location(self):\n if self._download_location is None:\n loc = os.sep.join([self.virtualenv_location, \"downloads\"])\n self._download_location = loc\n # Create the directory, if it doesn't exist.\n mkdir_p(self._download_location)\n return self._download_location\n\n @property\n def proper_names_db_path(self):\n if self._proper_names_db_path is None:\n self._proper_names_db_path = Path(\n self.virtualenv_location, \"pipenv-proper-names.txt\"\n )\n self._proper_names_db_path.touch() # Ensure the file exists.\n return self._proper_names_db_path\n\n @property\n def proper_names(self):\n with self.proper_names_db_path.open() as f:\n return f.read().splitlines()\n\n def register_proper_name(self, name):\n \"\"\"Registers a proper name to the database.\"\"\"\n with self.proper_names_db_path.open(\"a\") as f:\n f.write(u\"{0}\\n\".format(name))\n\n @property\n def pipfile_location(self):\n if PIPENV_PIPFILE:\n return PIPENV_PIPFILE\n\n if self._pipfile_location is None:\n try:\n loc = pipfile.Pipfile.find(max_depth=PIPENV_MAX_DEPTH)\n except RuntimeError:\n loc = None\n self._pipfile_location = _normalized(loc)\n return self._pipfile_location\n\n @property\n def requirements_location(self):\n if self._requirements_location is None:\n try:\n loc = find_requirements(max_depth=PIPENV_MAX_DEPTH)\n except RuntimeError:\n loc = None\n self._requirements_location = loc\n return self._requirements_location\n\n @property\n def parsed_pipfile(self):\n \"\"\"Parse Pipfile into a TOMLFile and cache it\n\n (call clear_pipfile_cache() afterwards if mutating)\"\"\"\n contents = self.read_pipfile()\n # use full contents to get around str/bytes 2/3 issues\n cache_key = (self.pipfile_location, contents)\n if cache_key not in _pipfile_cache:\n parsed = self._parse_pipfile(contents)\n _pipfile_cache[cache_key] = parsed\n return _pipfile_cache[cache_key]\n\n def read_pipfile(self):\n # Open the pipfile, read it into memory.\n with io.open(self.pipfile_location) as f:\n contents = f.read()\n self._pipfile_newlines = preferred_newlines(f)\n\n return contents\n\n @property\n def pased_pure_pipfile(self):\n contents = self.read_pipfile()\n\n return self._parse_pipfile(contents)\n\n def clear_pipfile_cache(self):\n \"\"\"Clear pipfile cache (e.g., so we can mutate parsed pipfile)\"\"\"\n _pipfile_cache.clear()\n\n def _parse_pipfile(self, contents):\n # If any outline tables are present...\n if (\"[packages.\" in contents) or (\"[dev-packages.\" in contents):\n data = toml.loads(contents)\n # Convert all outline tables to inline tables.\n for section in (\"packages\", \"dev-packages\"):\n for package in data.get(section, {}):\n # Convert things to inline tables — fancy :)\n if hasattr(data[section][package], \"keys\"):\n _data = data[section][package]\n data[section][package] = toml._get_empty_inline_table(dict)\n data[section][package].update(_data)\n # We lose comments here, but it's for the best.)\n try:\n return contoml.loads(toml.dumps(data, preserve=True))\n\n except RuntimeError:\n return toml.loads(toml.dumps(data, preserve=True))\n\n else:\n # Fallback to toml parser, for large files.\n try:\n return contoml.loads(contents)\n\n except Exception:\n return toml.loads(contents)\n\n @property\n def settings(self):\n \"\"\"A dictionary of the settings added to the Pipfile.\"\"\"\n return self.parsed_pipfile.get(\"pipenv\", {})\n\n def has_script(self, name):\n try:\n return name in self.parsed_pipfile[\"scripts\"]\n except KeyError:\n return False\n\n def build_script(self, name, extra_args=None):\n try:\n script = Script.parse(self.parsed_pipfile[\"scripts\"][name])\n except KeyError:\n script = Script(name)\n if extra_args:\n script.extend(extra_args)\n return script\n\n def update_settings(self, d):\n settings = self.settings\n changed = False\n for new in d:\n if new not in settings:\n settings[new] = d[new]\n changed = True\n if changed:\n p = self.parsed_pipfile\n p[\"pipenv\"] = settings\n # Write the changes to disk.\n self.write_toml(p)\n\n @property\n def _lockfile(self):\n \"\"\"Pipfile.lock divided by PyPI and external dependencies.\"\"\"\n pfile = pipfile.load(self.pipfile_location, inject_env=False)\n lockfile = json.loads(pfile.lock())\n for section in (\"default\", \"develop\"):\n lock_section = lockfile.get(section, {})\n for key in list(lock_section.keys()):\n norm_key = pep423_name(key)\n lockfile[section][norm_key] = lock_section.pop(key)\n return lockfile\n\n @property\n def lockfile_location(self):\n return \"{0}.lock\".format(self.pipfile_location)\n\n @property\n def lockfile_exists(self):\n return os.path.isfile(self.lockfile_location)\n\n @property\n def lockfile_content(self):\n return self.load_lockfile()\n\n def _get_editable_packages(self, dev=False):\n section = \"dev-packages\" if dev else \"packages\"\n packages = {\n k: v\n for k, v in self.parsed_pipfile.get(section, {}).items()\n if is_editable(v)\n }\n return packages\n\n def _get_vcs_packages(self, dev=False):\n section = \"dev-packages\" if dev else \"packages\"\n packages = {\n k: v\n for k, v in self.parsed_pipfile.get(section, {}).items()\n if is_vcs(v) or is_vcs(k)\n }\n return packages or {}\n\n @property\n def editable_packages(self):\n return self._get_editable_packages(dev=False)\n\n @property\n def editable_dev_packages(self):\n return self._get_editable_packages(dev=True)\n\n @property\n def vcs_packages(self):\n \"\"\"Returns a list of VCS packages, for not pip-tools to consume.\"\"\"\n return self._get_vcs_packages(dev=False)\n\n @property\n def vcs_dev_packages(self):\n \"\"\"Returns a list of VCS packages, for not pip-tools to consume.\"\"\"\n return self._get_vcs_packages(dev=True)\n\n @property\n def all_packages(self):\n \"\"\"Returns a list of all packages.\"\"\"\n p = dict(self.parsed_pipfile.get(\"dev-packages\", {}))\n p.update(self.parsed_pipfile.get(\"packages\", {}))\n return p\n\n @property\n def packages(self):\n \"\"\"Returns a list of packages, for pip-tools to consume.\"\"\"\n return self._build_package_list(\"packages\")\n\n @property\n def dev_packages(self):\n \"\"\"Returns a list of dev-packages, for pip-tools to consume.\"\"\"\n return self._build_package_list(\"dev-packages\")\n\n def touch_pipfile(self):\n \"\"\"Simply touches the Pipfile, for later use.\"\"\"\n with open(\"Pipfile\", \"a\"):\n os.utime(\"Pipfile\", None)\n\n @property\n def pipfile_is_empty(self):\n if not self.pipfile_exists:\n return True\n\n if not len(self.read_pipfile()):\n return True\n\n return False\n\n def create_pipfile(self, python=None):\n \"\"\"Creates the Pipfile, filled with juicy defaults.\"\"\"\n from .patched.notpip._internal import ConfigOptionParser\n from .patched.notpip._internal.cmdoptions import make_option_group, index_group\n\n config_parser = ConfigOptionParser(name=self.name)\n config_parser.add_option_group(make_option_group(index_group, config_parser))\n install = config_parser.option_groups[0]\n indexes = (\n \" \".join(install.get_option(\"--extra-index-url\").default)\n .lstrip(\"\\n\")\n .split(\"\\n\")\n )\n sources = [DEFAULT_SOURCE]\n for i, index in enumerate(indexes):\n if not index:\n continue\n\n source_name = \"pip_index_{}\".format(i)\n verify_ssl = index.startswith(\"https\")\n sources.append(\n {u\"url\": index, u\"verify_ssl\": verify_ssl, u\"name\": source_name}\n )\n\n data = {\n u\"source\": sources,\n # Default packages.\n u\"packages\": {},\n u\"dev-packages\": {},\n }\n # Default requires.\n required_python = python\n if not python:\n if self.virtualenv_location:\n required_python = self.which(\"python\", self.virtualenv_location)\n else:\n required_python = self.which(\"python\")\n version = python_version(required_python) or PIPENV_DEFAULT_PYTHON_VERSION\n if version and len(version) >= 3:\n data[u\"requires\"] = {\"python_version\": version[: len(\"2.7\")]}\n self.write_toml(data, \"Pipfile\")\n\n def write_toml(self, data, path=None):\n \"\"\"Writes the given data structure out as TOML.\"\"\"\n if path is None:\n path = self.pipfile_location\n try:\n formatted_data = contoml.dumps(data).rstrip()\n except Exception:\n for section in (\"packages\", \"dev-packages\"):\n for package in data.get(section, {}):\n # Convert things to inline tables — fancy :)\n if hasattr(data[section][package], \"keys\"):\n _data = data[section][package]\n data[section][package] = toml._get_empty_inline_table(dict)\n data[section][package].update(_data)\n formatted_data = toml.dumps(data).rstrip()\n\n if Path(path).absolute() == Path(self.pipfile_location).absolute():\n newlines = self._pipfile_newlines\n else:\n newlines = DEFAULT_NEWLINES\n formatted_data = cleanup_toml(formatted_data)\n with io.open(path, \"w\", newline=newlines) as f:\n f.write(formatted_data)\n # pipfile is mutated!\n self.clear_pipfile_cache()\n\n def write_lockfile(self, content):\n \"\"\"Write out the lockfile.\n \"\"\"\n s = self._lockfile_encoder.encode(content)\n open_kwargs = {\n 'newline': self._lockfile_newlines,\n 'encoding': 'utf-8',\n }\n with atomic_open_for_write(self.lockfile_location, **open_kwargs) as f:\n f.write(s)\n # Write newline at end of document. GH-319.\n # Only need '\\n' here; the file object handles the rest.\n if not s.endswith(u\"\\n\"):\n f.write(u\"\\n\")\n\n @property\n def pipfile_sources(self):\n if \"source\" not in self.parsed_pipfile:\n return [DEFAULT_SOURCE]\n # We need to make copies of the source info so we don't\n # accidentally modify the cache. See #2100 where values are\n # written after the os.path.expandvars() call.\n return [\n {k: safe_expandvars(v) for k, v in source.items()}\n for source in self.parsed_pipfile[\"source\"]\n ]\n\n @property\n def sources(self):\n if self.lockfile_exists and hasattr(self.lockfile_content, \"keys\"):\n meta_ = self.lockfile_content[\"_meta\"]\n sources_ = meta_.get(\"sources\")\n if sources_:\n return sources_\n\n else:\n return self.pipfile_sources\n\n def find_source(self, source):\n \"\"\"given a source, find it.\n\n source can be a url or an index name.\n \"\"\"\n if not is_valid_url(source):\n try:\n source = self.get_source(name=source)\n except SourceNotFound:\n source = self.get_source(url=source)\n else:\n source = self.get_source(url=source)\n return source\n\n def get_source(self, name=None, url=None):\n def find_source(sources, name=None, url=None):\n source = None\n if name:\n source = [s for s in sources if s.get(\"name\") == name]\n elif url:\n source = [s for s in sources if url.startswith(s.get(\"url\"))]\n if source:\n return first(source)\n\n found_source = find_source(self.sources, name=name, url=url)\n if found_source:\n return found_source\n found_source = find_source(self.pipfile_sources, name=name, url=url)\n if found_source:\n return found_source\n raise SourceNotFound(name or url)\n\n def get_package_name_in_pipfile(self, package_name, dev=False):\n \"\"\"Get the equivalent package name in pipfile\"\"\"\n key = \"dev-packages\" if dev else \"packages\"\n section = self.parsed_pipfile.get(key, {})\n package_name = pep423_name(package_name)\n for name in section.keys():\n if pep423_name(name) == package_name:\n return name\n return None\n\n def remove_package_from_pipfile(self, package_name, dev=False):\n # Read and append Pipfile.\n name = self.get_package_name_in_pipfile(package_name, dev)\n key = \"dev-packages\" if dev else \"packages\"\n p = self.parsed_pipfile\n if name:\n del p[key][name]\n self.write_toml(p)\n\n def add_package_to_pipfile(self, package_name, dev=False):\n from .vendor.requirementslib import Requirement\n\n # Read and append Pipfile.\n p = self.parsed_pipfile\n # Don't re-capitalize file URLs or VCSs.\n package = Requirement.from_line(package_name.strip())\n _, converted = package.pipfile_entry\n key = \"dev-packages\" if dev else \"packages\"\n # Set empty group if it doesn't exist yet.\n if key not in p:\n p[key] = {}\n name = self.get_package_name_in_pipfile(package.name, dev)\n if name and is_star(converted):\n # Skip for wildcard version\n return\n # Add the package to the group.\n p[key][name or package.normalized_name] = converted\n # Write Pipfile.\n self.write_toml(p)\n\n def add_index_to_pipfile(self, index):\n \"\"\"Adds a given index to the Pipfile.\"\"\"\n # Read and append Pipfile.\n p = self.parsed_pipfile\n source = {\"url\": index, \"verify_ssl\": True}\n # Add the package to the group.\n if \"source\" not in p:\n p[\"source\"] = [source]\n else:\n p[\"source\"].append(source)\n # Write Pipfile.\n self.write_toml(p)\n\n def recase_pipfile(self):\n if self.ensure_proper_casing():\n self.write_toml(self.parsed_pipfile)\n\n def load_lockfile(self, expand_env_vars=True):\n with io.open(self.lockfile_location, encoding='utf-8') as lock:\n j = json.load(lock)\n self._lockfile_newlines = preferred_newlines(lock)\n # lockfile is just a string\n if not j or not hasattr(j, \"keys\"):\n return j\n\n if expand_env_vars:\n # Expand environment variables in Pipfile.lock at runtime.\n for i, source in enumerate(j[\"_meta\"][\"sources\"][:]):\n j[\"_meta\"][\"sources\"][i][\"url\"] = os.path.expandvars(\n j[\"_meta\"][\"sources\"][i][\"url\"]\n )\n\n return j\n\n def get_lockfile_hash(self):\n if not os.path.exists(self.lockfile_location):\n return\n\n try:\n lockfile = self.load_lockfile(expand_env_vars=False)\n except ValueError:\n # Lockfile corrupted\n return \"\"\n if \"_meta\" in lockfile and hasattr(lockfile, \"keys\"):\n return lockfile[\"_meta\"].get(\"hash\", {}).get(\"sha256\")\n # Lockfile exists but has no hash at all\n return \"\"\n\n def calculate_pipfile_hash(self):\n # Update the lockfile if it is out-of-date.\n p = pipfile.load(self.pipfile_location, inject_env=False)\n return p.hash\n\n def ensure_proper_casing(self):\n \"\"\"Ensures proper casing of Pipfile packages\"\"\"\n pfile = self.parsed_pipfile\n casing_changed = self.proper_case_section(pfile.get(\"packages\", {}))\n casing_changed |= self.proper_case_section(pfile.get(\"dev-packages\", {}))\n return casing_changed\n\n def proper_case_section(self, section):\n \"\"\"Verify proper casing is retrieved, when available, for each\n dependency in the section.\n \"\"\"\n # Casing for section.\n changed_values = False\n unknown_names = [k for k in section.keys() if k not in set(self.proper_names)]\n # Replace each package with proper casing.\n for dep in unknown_names:\n try:\n # Get new casing for package name.\n new_casing = proper_case(dep)\n except IOError:\n # Unable to normalize package name.\n continue\n\n if new_casing != dep:\n changed_values = True\n self.register_proper_name(new_casing)\n # Replace old value with new value.\n old_value = section[dep]\n section[new_casing] = old_value\n del section[dep]\n # Return whether or not values have been changed.\n return changed_values\n",
"path": "pipenv/project.py"
}
] | diff --git a/news/2680.bugfix b/news/2680.bugfix
new file mode 100644
index 0000000000..405fb1dd80
--- /dev/null
+++ b/news/2680.bugfix
@@ -0,0 +1 @@
+Fixed virtualenv creation failure when a .venv file is present in the project root.
diff --git a/pipenv/project.py b/pipenv/project.py
index c5f451e495..e32296e521 100644
--- a/pipenv/project.py
+++ b/pipenv/project.py
@@ -249,7 +249,7 @@ def requirements_exists(self):
def is_venv_in_project(self):
return PIPENV_VENV_IN_PROJECT or (
self.project_directory
- and os.path.exists(os.path.join(self.project_directory, ".venv"))
+ and os.path.isdir(os.path.join(self.project_directory, ".venv"))
)
@property
diff --git a/tests/integration/test_dot_venv.py b/tests/integration/test_dot_venv.py
index e7b157f3c2..2063701b43 100644
--- a/tests/integration/test_dot_venv.py
+++ b/tests/integration/test_dot_venv.py
@@ -1,5 +1,6 @@
import os
+from pipenv._compat import TemporaryDirectory, Path
from pipenv.project import Project
from pipenv.utils import temp_environ, normalize_drive, get_windows_path
from pipenv.vendor import delegator
@@ -39,3 +40,30 @@ def test_reuse_previous_venv(PipenvInstance, pypi):
c = p.pipenv('install requests')
assert c.return_code == 0
assert normalize_drive(p.path) in p.pipenv('--venv').out
+
[email protected]
+def test_venv_file_exists(PipenvInstance, pypi):
+ """Tests virtualenv creation & package installation when a .venv file exists
+ at the project root.
+ """
+ with PipenvInstance(pypi=pypi, chdir=True) as p:
+ file_path = os.path.join(p.path, '.venv')
+ with open(file_path, 'w') as f:
+ f.write('')
+
+ with temp_environ(), TemporaryDirectory(
+ prefix='pipenv-', suffix='temp_workon_home'
+ ) as workon_home:
+ os.environ['WORKON_HOME'] = workon_home.name
+ if 'PIPENV_VENV_IN_PROJECT' in os.environ:
+ del os.environ['PIPENV_VENV_IN_PROJECT']
+
+ c = p.pipenv('install requests')
+ assert c.return_code == 0
+
+ venv_loc = None
+ for line in c.err.splitlines():
+ if line.startswith('Virtualenv location:'):
+ venv_loc = Path(line.split(':', 1)[-1].strip())
+ assert venv_loc is not None
+ assert venv_loc.joinpath('.project').exists()
|
mathesar-foundation__mathesar-940 | DB Types in column.valid_target_types are not in sync with the types returned in database types endpoint
## Description
* `valid_target_types` property of column returns "DOUBLE PRECISION"
- Endpoint: /api/v0/tables/14/columns/
* Types endpoint returns mathesar types where Number has the db type "DOUBLE_PRECISION"
- http://localhost:8000/api/v0/databases/1/types/
- Mathesar type: Number
Note that "DOUBLE PRECISION" and "DOUBLE_PRECISION" differ from each other.
## Expected behavior
Both endpoints should return values with same spelling.
| [
{
"content": "from enum import Enum\n\nfrom sqlalchemy import create_engine\n\nfrom db import constants\n\n\nCHAR = 'char'\nSTRING = 'string'\nVARCHAR = 'varchar'\n\n\nclass PostgresType(Enum):\n \"\"\"\n This only includes built-in Postgres types that SQLAlchemy supports.\n SQLAlchemy doesn't support XML. See zzzeek's comment on:\n https://stackoverflow.com/questions/16153512/using-postgresql-xml-data-type-with-sqlalchemy\n The values are keys returned by get_available_types.\n \"\"\"\n _ARRAY = '_array'\n BIGINT = 'bigint'\n BIT_VARYING = 'bit varying'\n BIT = 'bit'\n BOOLEAN = 'boolean'\n BYTEA = 'bytea'\n CHAR = '\"char\"'\n CHARACTER_VARYING = 'character varying'\n CHARACTER = 'character'\n CIDR = 'cidr'\n DATE = 'date'\n DATERANGE = 'daterange'\n DECIMAL = 'decimal'\n DOUBLE_PRECISION = 'double precision'\n FLOAT = 'float'\n HSTORE = 'hstore'\n INET = 'inet'\n INT4RANGE = 'int4range'\n INT8RANGE = 'int8range'\n INTEGER = 'integer'\n INTERVAL = 'interval'\n JSON = 'json'\n JSONB = 'jsonb'\n MACADDR = 'macaddr'\n MONEY = 'money'\n NAME = 'name'\n NUMERIC = 'numeric'\n NUMRANGE = 'numrange'\n OID = 'oid'\n REAL = 'real'\n REGCLASS = 'regclass'\n SMALLINT = 'smallint'\n TEXT = 'text'\n TIME = 'time'\n TIME_WITH_TIME_ZONE = 'time with time zone'\n TIME_WITHOUT_TIME_ZONE = 'time without time zone'\n TIMESTAMP = 'timestamp'\n TIMESTAMP_WITH_TIMESTAMP_ZONE = 'timestamp with time zone'\n TIMESTAMP_WITHOUT_TIMESTAMP_ZONE = 'timestamp without time zone'\n TSRANGE = 'tsrange'\n TSTZRANGE = 'tstzrange'\n TSVECTOR = 'tsvector'\n UUID = 'uuid'\n\n\nclass MathesarCustomType(Enum):\n \"\"\"\n This is a list of custom Mathesar DB types.\n Keys returned by get_available_types are of the format 'mathesar_types.VALUE'\n \"\"\"\n EMAIL = 'email'\n URI = 'uri'\n MONEY = 'money'\n\n\nSCHEMA = f\"{constants.MATHESAR_PREFIX}types\"\n# Since we want to have our identifiers quoted appropriately for use in\n# PostgreSQL, we want to use the postgres dialect preparer to set this up.\npreparer = create_engine(\"postgresql://\").dialect.identifier_preparer\n\n\ndef get_qualified_name(name):\n return \".\".join([preparer.quote_schema(SCHEMA), name])\n\n\ndef get_available_types(engine):\n return engine.dialect.ischema_names\n\n\ndef get_db_type_name(sa_type, engine):\n USER_DEFINED_STR = 'user_defined'\n db_type = sa_type.__visit_name__\n if db_type == USER_DEFINED_STR:\n db_type = sa_type().compile(engine.dialect)\n return db_type\n",
"path": "db/types/base.py"
}
] | [
{
"content": "from enum import Enum\n\nfrom sqlalchemy import create_engine\n\nfrom db import constants\n\n\nCHAR = 'char'\nSTRING = 'string'\nVARCHAR = 'varchar'\n\n\nclass PostgresType(Enum):\n \"\"\"\n This only includes built-in Postgres types that SQLAlchemy supports.\n SQLAlchemy doesn't support XML. See zzzeek's comment on:\n https://stackoverflow.com/questions/16153512/using-postgresql-xml-data-type-with-sqlalchemy\n The values are keys returned by get_available_types.\n \"\"\"\n _ARRAY = '_array'\n BIGINT = 'bigint'\n BIT_VARYING = 'bit varying'\n BIT = 'bit'\n BOOLEAN = 'boolean'\n BYTEA = 'bytea'\n CHAR = '\"char\"'\n CHARACTER_VARYING = 'character varying'\n CHARACTER = 'character'\n CIDR = 'cidr'\n DATE = 'date'\n DATERANGE = 'daterange'\n DECIMAL = 'decimal'\n DOUBLE_PRECISION = 'double precision'\n FLOAT = 'float'\n HSTORE = 'hstore'\n INET = 'inet'\n INT4RANGE = 'int4range'\n INT8RANGE = 'int8range'\n INTEGER = 'integer'\n INTERVAL = 'interval'\n JSON = 'json'\n JSONB = 'jsonb'\n MACADDR = 'macaddr'\n MONEY = 'money'\n NAME = 'name'\n NUMERIC = 'numeric'\n NUMRANGE = 'numrange'\n OID = 'oid'\n REAL = 'real'\n REGCLASS = 'regclass'\n SMALLINT = 'smallint'\n TEXT = 'text'\n TIME = 'time'\n TIME_WITH_TIME_ZONE = 'time with time zone'\n TIME_WITHOUT_TIME_ZONE = 'time without time zone'\n TIMESTAMP = 'timestamp'\n TIMESTAMP_WITH_TIMESTAMP_ZONE = 'timestamp with time zone'\n TIMESTAMP_WITHOUT_TIMESTAMP_ZONE = 'timestamp without time zone'\n TSRANGE = 'tsrange'\n TSTZRANGE = 'tstzrange'\n TSVECTOR = 'tsvector'\n UUID = 'uuid'\n\n\nclass MathesarCustomType(Enum):\n \"\"\"\n This is a list of custom Mathesar DB types.\n Keys returned by get_available_types are of the format 'mathesar_types.VALUE'\n \"\"\"\n EMAIL = 'email'\n URI = 'uri'\n MONEY = 'money'\n\n\nSCHEMA = f\"{constants.MATHESAR_PREFIX}types\"\n# Since we want to have our identifiers quoted appropriately for use in\n# PostgreSQL, we want to use the postgres dialect preparer to set this up.\npreparer = create_engine(\"postgresql://\").dialect.identifier_preparer\n\n\ndef get_qualified_name(name):\n return \".\".join([preparer.quote_schema(SCHEMA), name])\n\n\ndef get_available_types(engine):\n return engine.dialect.ischema_names\n\n\ndef get_db_type_name(sa_type, engine):\n try:\n db_type = sa_type.compile(dialect=engine.dialect)\n except TypeError:\n db_type = sa_type().compile(dialect=engine.dialect)\n return db_type\n",
"path": "db/types/base.py"
}
] | diff --git a/db/tests/columns/operations/test_alter.py b/db/tests/columns/operations/test_alter.py
index 3699586897..c047a9cdd8 100644
--- a/db/tests/columns/operations/test_alter.py
+++ b/db/tests/columns/operations/test_alter.py
@@ -425,7 +425,7 @@ def test_batch_update_columns_no_changes(engine_email_type):
assert len(table.columns) == len(updated_table.columns)
for index, column in enumerate(table.columns):
- new_column_type = get_db_type_name(updated_table.columns[index].type, engine_email_type)
+ new_column_type = get_db_type_name(updated_table.columns[index].type, engine)
assert new_column_type == 'VARCHAR'
assert updated_table.columns[index].name == table.columns[index].name
@@ -436,15 +436,15 @@ def test_batch_update_column_names(engine_email_type):
table_oid = get_oid_from_table(table.name, schema, engine)
column_data = _get_pizza_column_data()
- column_data[1]['name'] == 'Pizza Style'
- column_data[2]['name'] == 'Eaten Recently?'
+ column_data[1]['name'] = 'Pizza Style'
+ column_data[2]['name'] = 'Eaten Recently?'
batch_update_columns(table_oid, engine, column_data)
updated_table = reflect_table(table.name, schema, engine)
assert len(table.columns) == len(updated_table.columns)
for index, column in enumerate(table.columns):
- new_column_type = get_db_type_name(updated_table.columns[index].type, engine_email_type)
+ new_column_type = get_db_type_name(updated_table.columns[index].type, engine)
assert new_column_type == column_data[index]['plain_type']
assert updated_table.columns[index].name == column_data[index]['name']
@@ -455,15 +455,15 @@ def test_batch_update_column_types(engine_email_type):
table_oid = get_oid_from_table(table.name, schema, engine)
column_data = _get_pizza_column_data()
- column_data[0]['plain_type'] == 'INTEGER'
- column_data[2]['plain_type'] == 'BOOLEAN'
+ column_data[0]['plain_type'] = 'DOUBLE PRECISION'
+ column_data[2]['plain_type'] = 'BOOLEAN'
batch_update_columns(table_oid, engine, column_data)
updated_table = reflect_table(table.name, schema, engine)
assert len(table.columns) == len(updated_table.columns)
for index, column in enumerate(table.columns):
- new_column_type = get_db_type_name(updated_table.columns[index].type, engine_email_type)
+ new_column_type = get_db_type_name(updated_table.columns[index].type, engine)
assert new_column_type == column_data[index]['plain_type']
assert updated_table.columns[index].name == column_data[index]['name']
@@ -474,17 +474,17 @@ def test_batch_update_column_names_and_types(engine_email_type):
table_oid = get_oid_from_table(table.name, schema, engine)
column_data = _get_pizza_column_data()
- column_data[0]['name'] == 'Pizza ID'
- column_data[0]['plain_type'] == 'INTEGER'
- column_data[1]['name'] == 'Pizza Style'
- column_data[2]['plain_type'] == 'BOOLEAN'
+ column_data[0]['name'] = 'Pizza ID'
+ column_data[0]['plain_type'] = 'INTEGER'
+ column_data[1]['name'] = 'Pizza Style'
+ column_data[2]['plain_type'] = 'BOOLEAN'
batch_update_columns(table_oid, engine, column_data)
updated_table = reflect_table(table.name, schema, engine)
assert len(table.columns) == len(updated_table.columns)
for index, column in enumerate(table.columns):
- new_column_type = get_db_type_name(updated_table.columns[index].type, engine_email_type)
+ new_column_type = get_db_type_name(updated_table.columns[index].type, engine)
assert new_column_type == column_data[index]['plain_type']
assert updated_table.columns[index].name == column_data[index]['name']
@@ -503,7 +503,7 @@ def test_batch_update_column_drop_columns(engine_email_type):
assert len(updated_table.columns) == len(table.columns) - 2
for index, column in enumerate(updated_table.columns):
- new_column_type = get_db_type_name(updated_table.columns[index].type, engine_email_type)
+ new_column_type = get_db_type_name(updated_table.columns[index].type, engine)
assert new_column_type == column_data[index - 2]['plain_type']
assert updated_table.columns[index].name == column_data[index - 2]['name']
@@ -525,6 +525,6 @@ def test_batch_update_column_all_operations(engine_email_type):
assert len(updated_table.columns) == len(table.columns) - 1
for index, column in enumerate(updated_table.columns):
- new_column_type = get_db_type_name(updated_table.columns[index].type, engine_email_type)
+ new_column_type = get_db_type_name(updated_table.columns[index].type, engine)
assert new_column_type == column_data[index]['plain_type']
assert updated_table.columns[index].name == column_data[index]['name']
diff --git a/db/types/base.py b/db/types/base.py
index 1b410c00b1..eb04f5ccc4 100644
--- a/db/types/base.py
+++ b/db/types/base.py
@@ -87,8 +87,8 @@ def get_available_types(engine):
def get_db_type_name(sa_type, engine):
- USER_DEFINED_STR = 'user_defined'
- db_type = sa_type.__visit_name__
- if db_type == USER_DEFINED_STR:
- db_type = sa_type().compile(engine.dialect)
+ try:
+ db_type = sa_type.compile(dialect=engine.dialect)
+ except TypeError:
+ db_type = sa_type().compile(dialect=engine.dialect)
return db_type
|
bokeh__bokeh-6022 | sdists prompting for BokehJS build will block pip installs
I am currently trying to run a python file on a remote server. In my local machine I can just ran the command: bokeh serve --show myApp.py
However, on my remote host, when I ran bokeh serve, the error message "bokeh: command not found" was shown. I tried pip install bokeh, and finished installation, however, still bokeh command is not found.
| [
{
"content": "'''\n\n'''\nfrom __future__ import print_function\n\nimport shutil\nfrom os.path import dirname, exists, join, realpath, relpath\nimport os, re, subprocess, sys, time\n\nimport versioneer\n\n# provide fallbacks for highlights in case colorama is not installed\ntry:\n import colorama\n from colorama import Fore, Style\n\n def bright(text): return \"%s%s%s\" % (Style.BRIGHT, text, Style.RESET_ALL)\n def dim(text): return \"%s%s%s\" % (Style.DIM, text, Style.RESET_ALL)\n def red(text): return \"%s%s%s\" % (Fore.RED, text, Style.RESET_ALL)\n def green(text): return \"%s%s%s\" % (Fore.GREEN, text, Style.RESET_ALL)\n def yellow(text): return \"%s%s%s\" % (Fore.YELLOW, text, Style.RESET_ALL)\n sys.platform == \"win32\" and colorama.init()\nexcept ImportError:\n def bright(text): return text\n def dim(text): return text\n def red(text) : return text\n def green(text) : return text\n def yellow(text) : return text\n\n# some functions prompt for user input, handle input vs raw_input (py2 vs py3)\nif sys.version_info[0] < 3:\n input = raw_input # NOQA\n\n# -----------------------------------------------------------------------------\n# Module global variables\n# -----------------------------------------------------------------------------\n\nROOT = dirname(realpath(__file__))\nBOKEHJSROOT = join(ROOT, 'bokehjs')\nBOKEHJSBUILD = join(BOKEHJSROOT, 'build')\nCSS = join(BOKEHJSBUILD, 'css')\nJS = join(BOKEHJSBUILD, 'js')\nSERVER = join(ROOT, 'bokeh/server')\n\n# -----------------------------------------------------------------------------\n# Helpers for command line operations\n# -----------------------------------------------------------------------------\n\ndef show_bokehjs(bokehjs_action, develop=False):\n ''' Print a useful report after setuptools output describing where and how\n BokehJS is installed.\n\n Args:\n bokehjs_action (str) : one of 'built', 'installed', or 'packaged'\n how (or if) BokehJS was installed into the python source tree\n\n develop (bool, optional) :\n whether the command was for \"develop\" mode (default: False)\n\n Returns:\n None\n\n '''\n print()\n if develop:\n print(\"Installed Bokeh for DEVELOPMENT:\")\n else:\n print(\"Installed Bokeh:\")\n if bokehjs_action in ['built', 'installed']:\n print(\" - using %s built BokehJS from bokehjs/build\\n\" % (bright(yellow(\"NEWLY\")) if bokehjs_action=='built' else bright(yellow(\"PREVIOUSLY\"))))\n else:\n print(\" - using %s BokehJS, located in 'bokeh.server.static'\\n\" % bright(yellow(\"PACKAGED\")))\n print()\n\ndef show_help(bokehjs_action):\n ''' Print information about extra Bokeh-specific command line options.\n\n Args:\n bokehjs_action (str) : one of 'built', 'installed', or 'packaged'\n how (or if) BokehJS was installed into the python source tree\n\n Returns:\n None\n\n '''\n print()\n if bokehjs_action in ['built', 'installed']:\n print(\"Bokeh-specific options available with 'install' or 'develop':\")\n print()\n print(\" --build-js build and install a fresh BokehJS\")\n print(\" --install-js install only last previously built BokehJS\")\n else:\n print(\"Bokeh is using PACKAGED BokehJS, located in 'bokeh.server.static'\")\n print()\n print(\"No extra Bokeh-specific options are available.\")\n print()\n\n# -----------------------------------------------------------------------------\n# Other functions used directly by setup.py\n# -----------------------------------------------------------------------------\n\ndef build_or_install_bokehjs():\n ''' Build a new BokehJS (and install it) or install a previously build\n BokehJS.\n\n If no options ``--build-js`` or ``--install-js`` are detected, the\n user is prompted for what to do.\n\n If ``--existing-js`` is detected, then this setup.py is being run from a\n packaged sdist, no action is taken.\n\n Note that ``-build-js`` is only compatible with the following ``setup.py``\n commands: install, develop, sdist, egg_info, build\n\n Returns:\n str : one of 'built', 'installed', 'packaged'\n How (or if) BokehJS was installed into the python source tree\n\n '''\n\n # This happens when building from inside a published, pre-packaged sdist\n # The --existing-js option is not otherwise documented\n if '--existing-js' in sys.argv:\n sys.argv.remove('--existing-js')\n return \"packaged\"\n\n if '--build-js' not in sys.argv and '--install-js' not in sys.argv:\n jsbuild = jsbuild_prompt()\n\n elif '--build-js' in sys.argv:\n jsbuild = True\n sys.argv.remove('--build-js')\n\n # must be \"--install-js\"\n else:\n jsbuild = False\n sys.argv.remove('--install-js')\n\n jsbuild_ok = ('install', 'develop', 'sdist', 'egg_info', 'build')\n if jsbuild and not any(arg in sys.argv for arg in jsbuild_ok):\n print(\"Error: Option '--build-js' only valid with 'install', 'develop', 'sdist', or 'build', exiting.\")\n sys.exit(1)\n\n if jsbuild:\n build_js()\n install_js()\n return \"built\"\n else:\n install_js()\n return \"installed\"\n\ndef fixup_building_sdist():\n ''' Check for 'sdist' and ensure we always build BokehJS when packaging\n\n Source distributions do not ship with BokehJS source code, but must ship\n with a pre-built BokehJS library. This function modifies ``sys.argv`` as\n necessary so that ``--build-js`` IS present, and ``--install-js` is NOT.\n\n Returns:\n None\n\n '''\n if \"sdist\" in sys.argv:\n if \"--install-js\" in sys.argv:\n print(\"Removing '--install-js' incompatible with 'sdist'\")\n sys.argv.remove('--install-js')\n if \"--build-js\" not in sys.argv:\n print(\"Adding '--build-js' required for 'sdist'\")\n sys.argv.append('--build-js')\n\ndef fixup_for_packaged():\n ''' If we are installing FROM an sdist, then a pre-built BokehJS is\n already installed in the python source tree.\n\n The command line options ``--build-js`` or ``--install-js`` are\n removed from ``sys.argv``, with a warning.\n\n Also adds ``--existing-js`` to ``sys.argv`` to signal that BokehJS is\n already packaged.\n\n Returns:\n None\n\n '''\n if exists(join(ROOT, 'PKG-INFOvi ')):\n if \"--build-js\" in sys.argv or \"--install-js\" in sys.argv:\n print(SDIST_BUILD_WARNING)\n if \"--build-js\" in sys.argv:\n sys.argv.remove('--build-js')\n if \"--install-js\" in sys.argv:\n sys.argv.remove('--install-js')\n if \"--existing-js\" not in sys.argv:\n sys.argv.append('--existing-js')\n\ndef fixup_old_jsargs():\n ''' Fixup (and warn about) old style command line options with underscores.\n\n This function modifies ``sys.argv`` to make the replacements:\n\n * ``--build_js`` to --build-js\n * ``--install_js`` to --install-js\n\n and prints a warning about their deprecation.\n\n Returns:\n None\n\n '''\n for i in range(len(sys.argv)):\n\n if sys.argv[i] == '--build_js':\n print(\"WARNING: --build_js (with underscore) is deprecated, use --build-js\")\n sys.argv[i] = '--build-js'\n\n if sys.argv[i] == '--install_js':\n print(\"WARNING: --install_js (with underscore) is deprecated, use --install-js\")\n sys.argv[i] = '--install-js'\n\n# Horrible hack: workaround to allow creation of bdist_wheel on pip\n# installation. Why, for God's sake, is pip forcing the generation of wheels\n# when installing a package?\ndef get_cmdclass():\n ''' A ``cmdclass`` that works around a setuptools deficiency.\n\n There is no need to build wheels when installing a package, however some\n versions of setuptools seem to mandate this. This is a hacky workaround\n that modifies the ``cmdclass`` returned by versioneer so that not having\n wheel installed is not a fatal error.\n\n '''\n cmdclass = versioneer.get_cmdclass()\n\n try:\n from wheel.bdist_wheel import bdist_wheel\n except ImportError:\n # pip is not claiming for bdist_wheel when wheel is not installed\n bdist_wheel = None\n\n if bdist_wheel is not None:\n cmdclass[\"bdist_wheel\"] = bdist_wheel\n\n return cmdclass\n\ndef get_package_data():\n ''' All of all of the \"extra\" package data files collected by the\n ``package_files`` and ``package_path`` functions in ``setup.py``.\n\n '''\n return { 'bokeh': _PACKAGE_DATA }\n\ndef get_version():\n ''' The version of Bokeh currently checked out\n\n Returns:\n str : the version string\n\n '''\n return versioneer.get_version()\n\n# -----------------------------------------------------------------------------\n# Helpers for operation in the bokehjs dir\n# -----------------------------------------------------------------------------\n\ndef jsbuild_prompt():\n ''' Prompt users whether to build a new BokehJS or install an existing one.\n\n Returns:\n bool : True, if a new build is requested, False otherwise\n\n '''\n print(BOKEHJS_BUILD_PROMPT)\n mapping = {\"1\": True, \"2\": False}\n value = input(\"Choice? \")\n while value not in mapping:\n print(\"Input '%s' not understood. Valid choices: 1, 2\\n\" % value)\n value = input(\"Choice? \")\n return mapping[value]\n\n# -----------------------------------------------------------------------------\n# Helpers for operations in the bokehjs dir\n# -----------------------------------------------------------------------------\n\ndef build_js():\n ''' Build BokehJS files (CSS, JS, etc) under the ``bokehjs`` source\n subdirectory.\n\n Also prints a table of statistics about the generated assets (file sizes,\n etc.) or any error messages if the build fails.\n\n Note this function only builds BokehJS assets, it does not install them\n into the python source tree.\n\n '''\n print(\"Building BokehJS... \", end=\"\")\n sys.stdout.flush()\n os.chdir('bokehjs')\n\n if sys.platform != \"win32\":\n cmd = [join('node_modules', '.bin', 'gulp'), 'build']\n else:\n cmd = [join('node_modules', '.bin', 'gulp.cmd'), 'build']\n\n t0 = time.time()\n try:\n proc = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n except OSError as e:\n print(BUILD_EXEC_FAIL_MSG % (cmd, e))\n sys.exit(1)\n finally:\n os.chdir('..')\n\n result = proc.wait()\n t1 = time.time()\n\n if result != 0:\n indented_msg = \"\"\n outmsg = proc.stdout.read().decode('ascii', errors='ignore')\n outmsg = \"\\n\".join([\" \" + x for x in outmsg.split(\"\\n\")])\n errmsg = proc.stderr.read().decode('ascii', errors='ignore')\n errmsg = \"\\n\".join([\" \" + x for x in errmsg.split(\"\\n\")])\n print(BUILD_FAIL_MSG % (red(outmsg), red(errmsg)))\n sys.exit(1)\n\n indented_msg = \"\"\n msg = proc.stdout.read().decode('ascii', errors='ignore')\n pat = re.compile(r\"(\\[.*\\]) (.*)\", re.DOTALL)\n for line in msg.strip().split(\"\\n\"):\n m = pat.match(line)\n if not m: continue # skip generate.py output lines\n stamp, txt = m.groups()\n indented_msg += \" \" + dim(green(stamp)) + \" \" + dim(txt) + \"\\n\"\n msg = \"\\n\".join([\" \" + x for x in msg.split(\"\\n\")])\n print(BUILD_SUCCESS_MSG % indented_msg)\n print(\"Build time: %s\" % bright(yellow(\"%0.1f seconds\" % (t1-t0))))\n print()\n print(\"Build artifact sizes:\")\n try:\n def size(*path):\n return os.stat(join(\"bokehjs\", \"build\", *path)).st_size / 2**10\n\n print(\" - bokeh.js : %6.1f KB\" % size(\"js\", \"bokeh.js\"))\n print(\" - bokeh.css : %6.1f KB\" % size(\"css\", \"bokeh.css\"))\n print(\" - bokeh.min.js : %6.1f KB\" % size(\"js\", \"bokeh.min.js\"))\n print(\" - bokeh.min.css : %6.1f KB\" % size(\"css\", \"bokeh.min.css\"))\n\n print(\" - bokeh-widgets.js : %6.1f KB\" % size(\"js\", \"bokeh-widgets.js\"))\n print(\" - bokeh-widgets.css : %6.1f KB\" % size(\"css\", \"bokeh-widgets.css\"))\n print(\" - bokeh-widgets.min.js : %6.1f KB\" % size(\"js\", \"bokeh-widgets.min.js\"))\n print(\" - bokeh-widgets.min.css : %6.1f KB\" % size(\"css\", \"bokeh-widgets.min.css\"))\n\n print(\" - bokeh-api.js : %6.1f KB\" % size(\"js\", \"bokeh-api.js\"))\n print(\" - bokeh-api.min.js : %6.1f KB\" % size(\"js\", \"bokeh-api.min.js\"))\n except Exception as e:\n print(BUILD_SIZE_FAIL_MSG % e)\n sys.exit(1)\n\ndef install_js():\n ''' Copy built BokehJS files into the Python source tree.\n\n Returns:\n None\n\n '''\n target_jsdir = join(SERVER, 'static', 'js')\n target_cssdir = join(SERVER, 'static', 'css')\n\n STATIC_ASSETS = [\n join(JS, 'bokeh.js'),\n join(JS, 'bokeh.min.js'),\n join(CSS, 'bokeh.css'),\n join(CSS, 'bokeh.min.css'),\n ]\n if not all([exists(a) for a in STATIC_ASSETS]):\n print(BOKEHJS_INSTALL_FAIL)\n sys.exit(1)\n\n if exists(target_jsdir):\n shutil.rmtree(target_jsdir)\n shutil.copytree(JS, target_jsdir)\n\n if exists(target_cssdir):\n shutil.rmtree(target_cssdir)\n shutil.copytree(CSS, target_cssdir)\n\n# -----------------------------------------------------------------------------\n# Helpers for collecting package data\n# -----------------------------------------------------------------------------\n\n_PACKAGE_DATA = []\n\ndef package_files(*paths):\n '''\n\n '''\n _PACKAGE_DATA.extend(paths)\n\ndef package_path(path, filters=()):\n '''\n\n '''\n if not os.path.exists(path):\n raise RuntimeError(\"packaging non-existent path: %s\" % path)\n elif os.path.isfile(path):\n _PACKAGE_DATA.append(relpath(path, 'bokeh'))\n else:\n for path, dirs, files in os.walk(path):\n path = relpath(path, 'bokeh')\n for f in files:\n if not filters or f.endswith(filters):\n _PACKAGE_DATA.append(join(path, f))\n\n# -----------------------------------------------------------------------------\n# Status and error message strings\n# -----------------------------------------------------------------------------\n\nBOKEHJS_BUILD_PROMPT = \"\"\"\nBokeh includes a JavaScript library (BokehJS) that has its own\nbuild process. How would you like to handle BokehJS:\n\n1) build and install fresh BokehJS\n2) install last built BokehJS from bokeh/bokehjs/build\n\"\"\"\n\nBOKEHJS_INSTALL_FAIL = \"\"\"\nERROR: Cannot install BokehJS: files missing in `./bokehjs/build`.\n\n\nPlease build BokehJS by running setup.py with the `--build-js` option.\n Dev Guide: http://bokeh.pydata.org/docs/dev_guide.html#bokehjs.\n\"\"\"\n\nBUILD_EXEC_FAIL_MSG = bright(red(\"Failed.\")) + \"\"\"\n\nERROR: subprocess.Popen(%r) failed to execute:\n\n %s\n\nHave you run `npm install` from the bokehjs subdirectory?\nFor more information, see the Dev Guide:\n\n http://bokeh.pydata.org/en/latest/docs/dev_guide.html\n\"\"\"\n\nBUILD_FAIL_MSG = bright(red(\"Failed.\")) + \"\"\"\n\nERROR: 'gulp build' returned the following\n\n---- on stdout:\n%s\n\n---- on stderr:\n%s\n\"\"\"\n\nBUILD_SIZE_FAIL_MSG = \"\"\"\nERROR: could not determine sizes:\n\n %s\n\"\"\"\n\nBUILD_SUCCESS_MSG = bright(green(\"Success!\")) + \"\"\"\n\nBuild output:\n\n%s\"\"\"\n\nSDIST_BUILD_WARNING = \"\"\"\nSource distribution (sdist) packages come with PRE-BUILT BokehJS files.\n\nBuilding/installing from the bokehjs source directory of sdist packages is\ndisabled, and the options --build-js and --install-js will be IGNORED.\n\nTo build or develop BokehJS yourself, you must clone the full Bokeh GitHub\nrepository from https://github.com/bokeh/bokeh\n\"\"\"\n",
"path": "_setup_support.py"
}
] | [
{
"content": "'''\n\n'''\nfrom __future__ import print_function\n\nimport shutil\nfrom os.path import dirname, exists, join, realpath, relpath\nimport os, re, subprocess, sys, time\n\nimport versioneer\n\n# provide fallbacks for highlights in case colorama is not installed\ntry:\n import colorama\n from colorama import Fore, Style\n\n def bright(text): return \"%s%s%s\" % (Style.BRIGHT, text, Style.RESET_ALL)\n def dim(text): return \"%s%s%s\" % (Style.DIM, text, Style.RESET_ALL)\n def red(text): return \"%s%s%s\" % (Fore.RED, text, Style.RESET_ALL)\n def green(text): return \"%s%s%s\" % (Fore.GREEN, text, Style.RESET_ALL)\n def yellow(text): return \"%s%s%s\" % (Fore.YELLOW, text, Style.RESET_ALL)\n sys.platform == \"win32\" and colorama.init()\nexcept ImportError:\n def bright(text): return text\n def dim(text): return text\n def red(text) : return text\n def green(text) : return text\n def yellow(text) : return text\n\n# some functions prompt for user input, handle input vs raw_input (py2 vs py3)\nif sys.version_info[0] < 3:\n input = raw_input # NOQA\n\n# -----------------------------------------------------------------------------\n# Module global variables\n# -----------------------------------------------------------------------------\n\nROOT = dirname(realpath(__file__))\nBOKEHJSROOT = join(ROOT, 'bokehjs')\nBOKEHJSBUILD = join(BOKEHJSROOT, 'build')\nCSS = join(BOKEHJSBUILD, 'css')\nJS = join(BOKEHJSBUILD, 'js')\nSERVER = join(ROOT, 'bokeh/server')\n\n# -----------------------------------------------------------------------------\n# Helpers for command line operations\n# -----------------------------------------------------------------------------\n\ndef show_bokehjs(bokehjs_action, develop=False):\n ''' Print a useful report after setuptools output describing where and how\n BokehJS is installed.\n\n Args:\n bokehjs_action (str) : one of 'built', 'installed', or 'packaged'\n how (or if) BokehJS was installed into the python source tree\n\n develop (bool, optional) :\n whether the command was for \"develop\" mode (default: False)\n\n Returns:\n None\n\n '''\n print()\n if develop:\n print(\"Installed Bokeh for DEVELOPMENT:\")\n else:\n print(\"Installed Bokeh:\")\n if bokehjs_action in ['built', 'installed']:\n print(\" - using %s built BokehJS from bokehjs/build\\n\" % (bright(yellow(\"NEWLY\")) if bokehjs_action=='built' else bright(yellow(\"PREVIOUSLY\"))))\n else:\n print(\" - using %s BokehJS, located in 'bokeh.server.static'\\n\" % bright(yellow(\"PACKAGED\")))\n print()\n\ndef show_help(bokehjs_action):\n ''' Print information about extra Bokeh-specific command line options.\n\n Args:\n bokehjs_action (str) : one of 'built', 'installed', or 'packaged'\n how (or if) BokehJS was installed into the python source tree\n\n Returns:\n None\n\n '''\n print()\n if bokehjs_action in ['built', 'installed']:\n print(\"Bokeh-specific options available with 'install' or 'develop':\")\n print()\n print(\" --build-js build and install a fresh BokehJS\")\n print(\" --install-js install only last previously built BokehJS\")\n else:\n print(\"Bokeh is using PACKAGED BokehJS, located in 'bokeh.server.static'\")\n print()\n print(\"No extra Bokeh-specific options are available.\")\n print()\n\n# -----------------------------------------------------------------------------\n# Other functions used directly by setup.py\n# -----------------------------------------------------------------------------\n\ndef build_or_install_bokehjs():\n ''' Build a new BokehJS (and install it) or install a previously build\n BokehJS.\n\n If no options ``--build-js`` or ``--install-js`` are detected, the\n user is prompted for what to do.\n\n If ``--existing-js`` is detected, then this setup.py is being run from a\n packaged sdist, no action is taken.\n\n Note that ``-build-js`` is only compatible with the following ``setup.py``\n commands: install, develop, sdist, egg_info, build\n\n Returns:\n str : one of 'built', 'installed', 'packaged'\n How (or if) BokehJS was installed into the python source tree\n\n '''\n\n # This happens when building from inside a published, pre-packaged sdist\n # The --existing-js option is not otherwise documented\n if '--existing-js' in sys.argv:\n sys.argv.remove('--existing-js')\n return \"packaged\"\n\n if '--build-js' not in sys.argv and '--install-js' not in sys.argv:\n jsbuild = jsbuild_prompt()\n\n elif '--build-js' in sys.argv:\n jsbuild = True\n sys.argv.remove('--build-js')\n\n # must be \"--install-js\"\n else:\n jsbuild = False\n sys.argv.remove('--install-js')\n\n jsbuild_ok = ('install', 'develop', 'sdist', 'egg_info', 'build')\n if jsbuild and not any(arg in sys.argv for arg in jsbuild_ok):\n print(\"Error: Option '--build-js' only valid with 'install', 'develop', 'sdist', or 'build', exiting.\")\n sys.exit(1)\n\n if jsbuild:\n build_js()\n install_js()\n return \"built\"\n else:\n install_js()\n return \"installed\"\n\ndef fixup_building_sdist():\n ''' Check for 'sdist' and ensure we always build BokehJS when packaging\n\n Source distributions do not ship with BokehJS source code, but must ship\n with a pre-built BokehJS library. This function modifies ``sys.argv`` as\n necessary so that ``--build-js`` IS present, and ``--install-js` is NOT.\n\n Returns:\n None\n\n '''\n if \"sdist\" in sys.argv:\n if \"--install-js\" in sys.argv:\n print(\"Removing '--install-js' incompatible with 'sdist'\")\n sys.argv.remove('--install-js')\n if \"--build-js\" not in sys.argv:\n print(\"Adding '--build-js' required for 'sdist'\")\n sys.argv.append('--build-js')\n\ndef fixup_for_packaged():\n ''' If we are installing FROM an sdist, then a pre-built BokehJS is\n already installed in the python source tree.\n\n The command line options ``--build-js`` or ``--install-js`` are\n removed from ``sys.argv``, with a warning.\n\n Also adds ``--existing-js`` to ``sys.argv`` to signal that BokehJS is\n already packaged.\n\n Returns:\n None\n\n '''\n if exists(join(ROOT, 'PKG-INFO')):\n if \"--build-js\" in sys.argv or \"--install-js\" in sys.argv:\n print(SDIST_BUILD_WARNING)\n if \"--build-js\" in sys.argv:\n sys.argv.remove('--build-js')\n if \"--install-js\" in sys.argv:\n sys.argv.remove('--install-js')\n if \"--existing-js\" not in sys.argv:\n sys.argv.append('--existing-js')\n\ndef fixup_old_jsargs():\n ''' Fixup (and warn about) old style command line options with underscores.\n\n This function modifies ``sys.argv`` to make the replacements:\n\n * ``--build_js`` to --build-js\n * ``--install_js`` to --install-js\n\n and prints a warning about their deprecation.\n\n Returns:\n None\n\n '''\n for i in range(len(sys.argv)):\n\n if sys.argv[i] == '--build_js':\n print(\"WARNING: --build_js (with underscore) is deprecated, use --build-js\")\n sys.argv[i] = '--build-js'\n\n if sys.argv[i] == '--install_js':\n print(\"WARNING: --install_js (with underscore) is deprecated, use --install-js\")\n sys.argv[i] = '--install-js'\n\n# Horrible hack: workaround to allow creation of bdist_wheel on pip\n# installation. Why, for God's sake, is pip forcing the generation of wheels\n# when installing a package?\ndef get_cmdclass():\n ''' A ``cmdclass`` that works around a setuptools deficiency.\n\n There is no need to build wheels when installing a package, however some\n versions of setuptools seem to mandate this. This is a hacky workaround\n that modifies the ``cmdclass`` returned by versioneer so that not having\n wheel installed is not a fatal error.\n\n '''\n cmdclass = versioneer.get_cmdclass()\n\n try:\n from wheel.bdist_wheel import bdist_wheel\n except ImportError:\n # pip is not claiming for bdist_wheel when wheel is not installed\n bdist_wheel = None\n\n if bdist_wheel is not None:\n cmdclass[\"bdist_wheel\"] = bdist_wheel\n\n return cmdclass\n\ndef get_package_data():\n ''' All of all of the \"extra\" package data files collected by the\n ``package_files`` and ``package_path`` functions in ``setup.py``.\n\n '''\n return { 'bokeh': _PACKAGE_DATA }\n\ndef get_version():\n ''' The version of Bokeh currently checked out\n\n Returns:\n str : the version string\n\n '''\n return versioneer.get_version()\n\n# -----------------------------------------------------------------------------\n# Helpers for operation in the bokehjs dir\n# -----------------------------------------------------------------------------\n\ndef jsbuild_prompt():\n ''' Prompt users whether to build a new BokehJS or install an existing one.\n\n Returns:\n bool : True, if a new build is requested, False otherwise\n\n '''\n print(BOKEHJS_BUILD_PROMPT)\n mapping = {\"1\": True, \"2\": False}\n value = input(\"Choice? \")\n while value not in mapping:\n print(\"Input '%s' not understood. Valid choices: 1, 2\\n\" % value)\n value = input(\"Choice? \")\n return mapping[value]\n\n# -----------------------------------------------------------------------------\n# Helpers for operations in the bokehjs dir\n# -----------------------------------------------------------------------------\n\ndef build_js():\n ''' Build BokehJS files (CSS, JS, etc) under the ``bokehjs`` source\n subdirectory.\n\n Also prints a table of statistics about the generated assets (file sizes,\n etc.) or any error messages if the build fails.\n\n Note this function only builds BokehJS assets, it does not install them\n into the python source tree.\n\n '''\n print(\"Building BokehJS... \", end=\"\")\n sys.stdout.flush()\n os.chdir('bokehjs')\n\n if sys.platform != \"win32\":\n cmd = [join('node_modules', '.bin', 'gulp'), 'build']\n else:\n cmd = [join('node_modules', '.bin', 'gulp.cmd'), 'build']\n\n t0 = time.time()\n try:\n proc = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n except OSError as e:\n print(BUILD_EXEC_FAIL_MSG % (cmd, e))\n sys.exit(1)\n finally:\n os.chdir('..')\n\n result = proc.wait()\n t1 = time.time()\n\n if result != 0:\n indented_msg = \"\"\n outmsg = proc.stdout.read().decode('ascii', errors='ignore')\n outmsg = \"\\n\".join([\" \" + x for x in outmsg.split(\"\\n\")])\n errmsg = proc.stderr.read().decode('ascii', errors='ignore')\n errmsg = \"\\n\".join([\" \" + x for x in errmsg.split(\"\\n\")])\n print(BUILD_FAIL_MSG % (red(outmsg), red(errmsg)))\n sys.exit(1)\n\n indented_msg = \"\"\n msg = proc.stdout.read().decode('ascii', errors='ignore')\n pat = re.compile(r\"(\\[.*\\]) (.*)\", re.DOTALL)\n for line in msg.strip().split(\"\\n\"):\n m = pat.match(line)\n if not m: continue # skip generate.py output lines\n stamp, txt = m.groups()\n indented_msg += \" \" + dim(green(stamp)) + \" \" + dim(txt) + \"\\n\"\n msg = \"\\n\".join([\" \" + x for x in msg.split(\"\\n\")])\n print(BUILD_SUCCESS_MSG % indented_msg)\n print(\"Build time: %s\" % bright(yellow(\"%0.1f seconds\" % (t1-t0))))\n print()\n print(\"Build artifact sizes:\")\n try:\n def size(*path):\n return os.stat(join(\"bokehjs\", \"build\", *path)).st_size / 2**10\n\n print(\" - bokeh.js : %6.1f KB\" % size(\"js\", \"bokeh.js\"))\n print(\" - bokeh.css : %6.1f KB\" % size(\"css\", \"bokeh.css\"))\n print(\" - bokeh.min.js : %6.1f KB\" % size(\"js\", \"bokeh.min.js\"))\n print(\" - bokeh.min.css : %6.1f KB\" % size(\"css\", \"bokeh.min.css\"))\n\n print(\" - bokeh-widgets.js : %6.1f KB\" % size(\"js\", \"bokeh-widgets.js\"))\n print(\" - bokeh-widgets.css : %6.1f KB\" % size(\"css\", \"bokeh-widgets.css\"))\n print(\" - bokeh-widgets.min.js : %6.1f KB\" % size(\"js\", \"bokeh-widgets.min.js\"))\n print(\" - bokeh-widgets.min.css : %6.1f KB\" % size(\"css\", \"bokeh-widgets.min.css\"))\n\n print(\" - bokeh-api.js : %6.1f KB\" % size(\"js\", \"bokeh-api.js\"))\n print(\" - bokeh-api.min.js : %6.1f KB\" % size(\"js\", \"bokeh-api.min.js\"))\n except Exception as e:\n print(BUILD_SIZE_FAIL_MSG % e)\n sys.exit(1)\n\ndef install_js():\n ''' Copy built BokehJS files into the Python source tree.\n\n Returns:\n None\n\n '''\n target_jsdir = join(SERVER, 'static', 'js')\n target_cssdir = join(SERVER, 'static', 'css')\n\n STATIC_ASSETS = [\n join(JS, 'bokeh.js'),\n join(JS, 'bokeh.min.js'),\n join(CSS, 'bokeh.css'),\n join(CSS, 'bokeh.min.css'),\n ]\n if not all([exists(a) for a in STATIC_ASSETS]):\n print(BOKEHJS_INSTALL_FAIL)\n sys.exit(1)\n\n if exists(target_jsdir):\n shutil.rmtree(target_jsdir)\n shutil.copytree(JS, target_jsdir)\n\n if exists(target_cssdir):\n shutil.rmtree(target_cssdir)\n shutil.copytree(CSS, target_cssdir)\n\n# -----------------------------------------------------------------------------\n# Helpers for collecting package data\n# -----------------------------------------------------------------------------\n\n_PACKAGE_DATA = []\n\ndef package_files(*paths):\n '''\n\n '''\n _PACKAGE_DATA.extend(paths)\n\ndef package_path(path, filters=()):\n '''\n\n '''\n if not os.path.exists(path):\n raise RuntimeError(\"packaging non-existent path: %s\" % path)\n elif os.path.isfile(path):\n _PACKAGE_DATA.append(relpath(path, 'bokeh'))\n else:\n for path, dirs, files in os.walk(path):\n path = relpath(path, 'bokeh')\n for f in files:\n if not filters or f.endswith(filters):\n _PACKAGE_DATA.append(join(path, f))\n\n# -----------------------------------------------------------------------------\n# Status and error message strings\n# -----------------------------------------------------------------------------\n\nBOKEHJS_BUILD_PROMPT = \"\"\"\nBokeh includes a JavaScript library (BokehJS) that has its own\nbuild process. How would you like to handle BokehJS:\n\n1) build and install fresh BokehJS\n2) install last built BokehJS from bokeh/bokehjs/build\n\"\"\"\n\nBOKEHJS_INSTALL_FAIL = \"\"\"\nERROR: Cannot install BokehJS: files missing in `./bokehjs/build`.\n\n\nPlease build BokehJS by running setup.py with the `--build-js` option.\n Dev Guide: http://bokeh.pydata.org/docs/dev_guide.html#bokehjs.\n\"\"\"\n\nBUILD_EXEC_FAIL_MSG = bright(red(\"Failed.\")) + \"\"\"\n\nERROR: subprocess.Popen(%r) failed to execute:\n\n %s\n\nHave you run `npm install` from the bokehjs subdirectory?\nFor more information, see the Dev Guide:\n\n http://bokeh.pydata.org/en/latest/docs/dev_guide.html\n\"\"\"\n\nBUILD_FAIL_MSG = bright(red(\"Failed.\")) + \"\"\"\n\nERROR: 'gulp build' returned the following\n\n---- on stdout:\n%s\n\n---- on stderr:\n%s\n\"\"\"\n\nBUILD_SIZE_FAIL_MSG = \"\"\"\nERROR: could not determine sizes:\n\n %s\n\"\"\"\n\nBUILD_SUCCESS_MSG = bright(green(\"Success!\")) + \"\"\"\n\nBuild output:\n\n%s\"\"\"\n\nSDIST_BUILD_WARNING = \"\"\"\nSource distribution (sdist) packages come with PRE-BUILT BokehJS files.\n\nBuilding/installing from the bokehjs source directory of sdist packages is\ndisabled, and the options --build-js and --install-js will be IGNORED.\n\nTo build or develop BokehJS yourself, you must clone the full Bokeh GitHub\nrepository from https://github.com/bokeh/bokeh\n\"\"\"\n",
"path": "_setup_support.py"
}
] | diff --git a/_setup_support.py b/_setup_support.py
index d039f848b8a..dbd0d908d41 100644
--- a/_setup_support.py
+++ b/_setup_support.py
@@ -182,7 +182,7 @@ def fixup_for_packaged():
None
'''
- if exists(join(ROOT, 'PKG-INFOvi ')):
+ if exists(join(ROOT, 'PKG-INFO')):
if "--build-js" in sys.argv or "--install-js" in sys.argv:
print(SDIST_BUILD_WARNING)
if "--build-js" in sys.argv:
|
vispy__vispy-1113 | Buggy display with iso volume example
Using `examples/basics/scene/volume.py` and switching to iso rendering method produces a [pretty strange view](http://youtu.be/3becSPxKIq8).
System is the same as before (Intel HD 4600):
```
Platform: Linux-4.0.4-301.fc22.x86_64-x86_64-with-fedora-22-Twenty_Two
Python: 3.4.2 (default, Jan 12 2015, 12:13:20) [GCC 4.9.2 20150107 (Red Hat 4.9.2-5)]
Backend: PyQt4
pyqt4: ('PyQt4', '4.11.3', '4.8.6')
pyqt5: None
pyside: None
pyglet: pyglet 1.2.1
glfw: None
sdl2: None
wx: None
egl: EGL 1.4 (DRI2) Mesa Project: OpenGL OpenGL_ES OpenGL_ES2 OpenGL_ES3
_test: None
GL version: '3.0 Mesa 10.5.4'
MAX_TEXTURE_SIZE: 8192
```
| [
{
"content": "# -*- coding: utf-8 -*-\n# Copyright (c) 2015, Vispy Development Team.\n# Distributed under the (new) BSD License. See LICENSE.txt for more info.\n\n\"\"\"\nAbout this technique\n--------------------\n\nIn Python, we define the six faces of a cuboid to draw, as well as\ntexture cooridnates corresponding with the vertices of the cuboid. \nThe back faces of the cuboid are drawn (and front faces are culled)\nbecause only the back faces are visible when the camera is inside the \nvolume.\n\nIn the vertex shader, we intersect the view ray with the near and far \nclipping planes. In the fragment shader, we use these two points to\ncompute the ray direction and then compute the position of the front\ncuboid surface (or near clipping plane) along the view ray.\n\nNext we calculate the number of steps to walk from the front surface\nto the back surface and iterate over these positions in a for-loop.\nAt each iteration, the fragment color or other voxel information is \nupdated depending on the selected rendering method.\n\nIt is important for the texture interpolation is 'linear', since with\nnearest the result look very ugly. The wrapping should be clamp_to_edge\nto avoid artifacts when the ray takes a small step outside the volume.\n\nThe ray direction is established by mapping the vertex to the document\ncoordinate frame, adjusting z to +/-1, and mapping the coordinate back.\nThe ray is expressed in coordinates local to the volume (i.e. texture\ncoordinates).\n\n\"\"\"\n\nfrom ..gloo import Texture3D, TextureEmulated3D, VertexBuffer, IndexBuffer\nfrom . import Visual\nfrom .shaders import Function\nfrom ..color import get_colormap\n\nimport numpy as np\n\n# todo: implement more render methods (port from visvis)\n# todo: allow anisotropic data\n# todo: what to do about lighting? ambi/diffuse/spec/shinynes on each visual?\n\n# Vertex shader\nVERT_SHADER = \"\"\"\nattribute vec3 a_position;\n// attribute vec3 a_texcoord;\nuniform vec3 u_shape;\n\n// varying vec3 v_texcoord;\nvarying vec3 v_position;\nvarying vec4 v_nearpos;\nvarying vec4 v_farpos;\n\nvoid main() {\n // v_texcoord = a_texcoord;\n v_position = a_position;\n \n // Project local vertex coordinate to camera position. Then do a step\n // backward (in cam coords) and project back. Voila, we get our ray vector.\n vec4 pos_in_cam = $viewtransformf(vec4(v_position, 1));\n\n // intersection of ray and near clipping plane (z = -1 in clip coords)\n pos_in_cam.z = -pos_in_cam.w;\n v_nearpos = $viewtransformi(pos_in_cam);\n \n // intersection of ray and far clipping plane (z = +1 in clip coords)\n pos_in_cam.z = pos_in_cam.w;\n v_farpos = $viewtransformi(pos_in_cam);\n \n gl_Position = $transform(vec4(v_position, 1.0));\n}\n\"\"\" # noqa\n\n# Fragment shader\nFRAG_SHADER = \"\"\"\n// uniforms\nuniform $sampler_type u_volumetex;\nuniform vec3 u_shape;\nuniform float u_threshold;\nuniform float u_relative_step_size;\n\n//varyings\n// varying vec3 v_texcoord;\nvarying vec3 v_position;\nvarying vec4 v_nearpos;\nvarying vec4 v_farpos;\n\n// uniforms for lighting. Hard coded until we figure out how to do lights\nconst vec4 u_ambient = vec4(0.2, 0.4, 0.2, 1.0);\nconst vec4 u_diffuse = vec4(0.8, 0.2, 0.2, 1.0);\nconst vec4 u_specular = vec4(1.0, 1.0, 1.0, 1.0);\nconst float u_shininess = 40.0;\n\n//varying vec3 lightDirs[1];\n\n// global holding view direction in local coordinates\nvec3 view_ray;\n\nfloat rand(vec2 co)\n{{\n // Create a pseudo-random number between 0 and 1.\n // http://stackoverflow.com/questions/4200224\n return fract(sin(dot(co.xy ,vec2(12.9898, 78.233))) * 43758.5453);\n}}\n\nfloat colorToVal(vec4 color1)\n{{\n return color1.g; // todo: why did I have this abstraction in visvis?\n}}\n\nvec4 calculateColor(vec4 betterColor, vec3 loc, vec3 step)\n{{ \n // Calculate color by incorporating lighting\n vec4 color1;\n vec4 color2;\n \n // View direction\n vec3 V = normalize(view_ray);\n \n // calculate normal vector from gradient\n vec3 N; // normal\n color1 = $sample( u_volumetex, loc+vec3(-step[0],0.0,0.0) );\n color2 = $sample( u_volumetex, loc+vec3(step[0],0.0,0.0) );\n N[0] = colorToVal(color1) - colorToVal(color2);\n betterColor = max(max(color1, color2),betterColor);\n color1 = $sample( u_volumetex, loc+vec3(0.0,-step[1],0.0) );\n color2 = $sample( u_volumetex, loc+vec3(0.0,step[1],0.0) );\n N[1] = colorToVal(color1) - colorToVal(color2);\n betterColor = max(max(color1, color2),betterColor);\n color1 = $sample( u_volumetex, loc+vec3(0.0,0.0,-step[2]) );\n color2 = $sample( u_volumetex, loc+vec3(0.0,0.0,step[2]) );\n N[2] = colorToVal(color1) - colorToVal(color2);\n betterColor = max(max(color1, color2),betterColor);\n float gm = length(N); // gradient magnitude\n N = normalize(N);\n \n // Flip normal so it points towards viewer\n float Nselect = float(dot(N,V) > 0.0);\n N = (2.0*Nselect - 1.0) * N; // == Nselect * N - (1.0-Nselect)*N;\n \n // Get color of the texture (albeido)\n color1 = betterColor;\n color2 = color1;\n // todo: parametrise color1_to_color2\n \n // Init colors\n vec4 ambient_color = vec4(0.0, 0.0, 0.0, 0.0);\n vec4 diffuse_color = vec4(0.0, 0.0, 0.0, 0.0);\n vec4 specular_color = vec4(0.0, 0.0, 0.0, 0.0);\n vec4 final_color;\n \n // todo: allow multiple light, define lights on viewvox or subscene\n int nlights = 1; \n for (int i=0; i<nlights; i++)\n {{ \n // Get light direction (make sure to prevent zero devision)\n vec3 L = normalize(view_ray); //lightDirs[i]; \n float lightEnabled = float( length(L) > 0.0 );\n L = normalize(L+(1.0-lightEnabled));\n \n // Calculate lighting properties\n float lambertTerm = clamp( dot(N,L), 0.0, 1.0 );\n vec3 H = normalize(L+V); // Halfway vector\n float specularTerm = pow( max(dot(H,N),0.0), u_shininess);\n \n // Calculate mask\n float mask1 = lightEnabled;\n \n // Calculate colors\n ambient_color += mask1 * u_ambient; // * gl_LightSource[i].ambient;\n diffuse_color += mask1 * lambertTerm;\n specular_color += mask1 * specularTerm * u_specular;\n }}\n \n // Calculate final color by componing different components\n final_color = color2 * ( ambient_color + diffuse_color) + specular_color;\n final_color.a = color2.a;\n \n // Done\n return final_color;\n}}\n\n// for some reason, this has to be the last function in order for the\n// filters to be inserted in the correct place...\n\nvoid main() {{\n vec3 farpos = v_farpos.xyz / v_farpos.w;\n vec3 nearpos = v_nearpos.xyz / v_nearpos.w;\n\n // Calculate unit vector pointing in the view direction through this\n // fragment.\n view_ray = normalize(farpos.xyz - nearpos.xyz);\n\n // Compute the distance to the front surface or near clipping plane\n float distance = dot(nearpos-v_position, view_ray);\n distance = max(distance, min((-0.5 - v_position.x) / view_ray.x,\n (u_shape.x - 0.5 - v_position.x) / view_ray.x));\n distance = max(distance, min((-0.5 - v_position.y) / view_ray.y,\n (u_shape.y - 0.5 - v_position.y) / view_ray.y));\n distance = max(distance, min((-0.5 - v_position.z) / view_ray.z,\n (u_shape.z - 0.5 - v_position.z) / view_ray.z));\n\n // Now we have the starting position on the front surface\n vec3 front = v_position + view_ray * distance;\n\n // Decide how many steps to take\n int nsteps = int(-distance / u_relative_step_size + 0.5);\n if( nsteps < 1 )\n discard;\n\n // Get starting location and step vector in texture coordinates\n vec3 step = ((v_position - front) / u_shape) / nsteps;\n vec3 start_loc = front / u_shape;\n\n // For testing: show the number of steps. This helps to establish\n // whether the rays are correctly oriented\n //gl_FragColor = vec4(0.0, nsteps / 3.0 / u_shape.x, 1.0, 1.0);\n //return;\n\n {before_loop}\n\n // This outer loop seems necessary on some systems for large\n // datasets. Ugly, but it works ...\n vec3 loc = start_loc;\n int iter = 0;\n while (iter < nsteps) {{\n for (iter=iter; iter<nsteps; iter++)\n {{\n // Get sample color\n vec4 color = $sample(u_volumetex, loc);\n float val = color.g;\n\n {in_loop}\n\n // Advance location deeper into the volume\n loc += step;\n }}\n }}\n\n {after_loop}\n\n /* Set depth value - from visvis TODO\n int iter_depth = int(maxi);\n // Calculate end position in world coordinates\n vec4 position2 = vertexPosition;\n position2.xyz += ray*shape*float(iter_depth);\n // Project to device coordinates and set fragment depth\n vec4 iproj = gl_ModelViewProjectionMatrix * position2;\n iproj.z /= iproj.w;\n gl_FragDepth = (iproj.z+1.0)/2.0;\n */\n}}\n\n\n\"\"\" # noqa\n\n\nMIP_SNIPPETS = dict(\n before_loop=\"\"\"\n float maxval = -99999.0; // The maximum encountered value\n int maxi = 0; // Where the maximum value was encountered\n \"\"\",\n in_loop=\"\"\"\n if( val > maxval ) {\n maxval = val;\n maxi = iter;\n }\n \"\"\",\n after_loop=\"\"\"\n // Refine search for max value\n loc = start_loc + step * (float(maxi) - 0.5);\n for (int i=0; i<10; i++) {\n maxval = max(maxval, $sample(u_volumetex, loc).g);\n loc += step * 0.1;\n }\n gl_FragColor = $cmap(maxval);\n \"\"\",\n)\nMIP_FRAG_SHADER = FRAG_SHADER.format(**MIP_SNIPPETS)\n\n\nTRANSLUCENT_SNIPPETS = dict(\n before_loop=\"\"\"\n vec4 integrated_color = vec4(0., 0., 0., 0.);\n \"\"\",\n in_loop=\"\"\"\n color = $cmap(val);\n float a1 = integrated_color.a;\n float a2 = color.a * (1 - a1);\n float alpha = max(a1 + a2, 0.001);\n \n // Doesn't work.. GLSL optimizer bug?\n //integrated_color = (integrated_color * a1 / alpha) + \n // (color * a2 / alpha); \n // This should be identical but does work correctly:\n integrated_color *= a1 / alpha;\n integrated_color += color * a2 / alpha;\n \n integrated_color.a = alpha;\n \n if( alpha > 0.99 ){\n // stop integrating if the fragment becomes opaque\n iter = nsteps;\n }\n \n \"\"\",\n after_loop=\"\"\"\n gl_FragColor = integrated_color;\n \"\"\",\n)\nTRANSLUCENT_FRAG_SHADER = FRAG_SHADER.format(**TRANSLUCENT_SNIPPETS)\n\n\nADDITIVE_SNIPPETS = dict(\n before_loop=\"\"\"\n vec4 integrated_color = vec4(0., 0., 0., 0.);\n \"\"\",\n in_loop=\"\"\"\n color = $cmap(val);\n \n integrated_color = 1.0 - (1.0 - integrated_color) * (1.0 - color);\n \"\"\",\n after_loop=\"\"\"\n gl_FragColor = integrated_color;\n \"\"\",\n)\nADDITIVE_FRAG_SHADER = FRAG_SHADER.format(**ADDITIVE_SNIPPETS)\n\n\nISO_SNIPPETS = dict(\n before_loop=\"\"\"\n vec4 color3 = vec4(0.0); // final color\n vec3 dstep = 1.5 / u_shape; // step to sample derivative\n \"\"\",\n in_loop=\"\"\"\n if (val > u_threshold-0.2) {\n // Take the last interval in smaller steps\n vec3 iloc = loc - step;\n for (int i=0; i<10; i++) {\n val = $sample(u_volumetex, iloc).g;\n if (val > u_threshold) {\n color = $cmap(val);\n gl_FragColor = calculateColor(color, iloc, dstep);\n iter = nsteps;\n break;\n }\n iloc += step * 0.1;\n }\n }\n \"\"\",\n after_loop=\"\"\"\n \"\"\",\n)\n\nISO_FRAG_SHADER = FRAG_SHADER.format(**ISO_SNIPPETS)\n\nfrag_dict = {\n 'mip': MIP_FRAG_SHADER,\n 'iso': ISO_FRAG_SHADER,\n 'translucent': TRANSLUCENT_FRAG_SHADER,\n 'additive': ADDITIVE_FRAG_SHADER,\n}\n\n\nclass VolumeVisual(Visual):\n \"\"\" Displays a 3D Volume\n \n Parameters\n ----------\n vol : ndarray\n The volume to display. Must be ndim==3.\n clim : tuple of two floats | None\n The contrast limits. The values in the volume are mapped to\n black and white corresponding to these values. Default maps\n between min and max.\n method : {'mip', 'translucent', 'additive', 'iso'}\n The render method to use. See corresponding docs for details.\n Default 'mip'.\n threshold : float\n The threshold to use for the isosurafce render method. By default\n the mean of the given volume is used.\n relative_step_size : float\n The relative step size to step through the volume. Default 0.8.\n Increase to e.g. 1.5 to increase performance, at the cost of\n quality.\n cmap : str\n Colormap to use.\n emulate_texture : bool\n Use 2D textures to emulate a 3D texture. OpenGL ES 2.0 compatible,\n but has lower performance on desktop platforms.\n \"\"\"\n\n def __init__(self, vol, clim=None, method='mip', threshold=None, \n relative_step_size=0.8, cmap='grays',\n emulate_texture=False):\n \n tex_cls = TextureEmulated3D if emulate_texture else Texture3D\n\n # Storage of information of volume\n self._vol_shape = ()\n self._clim = None\n self._need_vertex_update = True\n\n # Set the colormap\n self._cmap = get_colormap(cmap)\n\n # Create gloo objects\n self._vertices = VertexBuffer()\n self._texcoord = VertexBuffer(\n np.array([\n [0, 0, 0],\n [1, 0, 0],\n [0, 1, 0],\n [1, 1, 0],\n [0, 0, 1],\n [1, 0, 1],\n [0, 1, 1],\n [1, 1, 1],\n ], dtype=np.float32))\n self._tex = tex_cls((10, 10, 10), interpolation='linear', \n wrapping='clamp_to_edge')\n\n # Create program\n Visual.__init__(self, vcode=VERT_SHADER, fcode=\"\")\n self.shared_program['u_volumetex'] = self._tex\n self.shared_program['a_position'] = self._vertices\n self.shared_program['a_texcoord'] = self._texcoord\n self._draw_mode = 'triangle_strip'\n self._index_buffer = IndexBuffer()\n\n # Only show back faces of cuboid. This is required because if we are \n # inside the volume, then the front faces are outside of the clipping\n # box and will not be drawn.\n self.set_gl_state('translucent', cull_face=False)\n \n # Set data\n self.set_data(vol, clim)\n \n # Set params\n self.method = method\n self.relative_step_size = relative_step_size\n self.threshold = threshold if (threshold is not None) else vol.mean()\n self.freeze()\n \n def set_data(self, vol, clim=None):\n \"\"\" Set the volume data. \n\n Parameters\n ----------\n vol : ndarray\n The 3D volume.\n clim : tuple | None\n Colormap limits to use. None will use the min and max values.\n \"\"\"\n # Check volume\n if not isinstance(vol, np.ndarray):\n raise ValueError('Volume visual needs a numpy array.')\n if not ((vol.ndim == 3) or (vol.ndim == 4 and vol.shape[-1] <= 4)):\n raise ValueError('Volume visual needs a 3D image.')\n \n # Handle clim\n if clim is not None:\n clim = np.array(clim, float)\n if not (clim.ndim == 1 and clim.size == 2):\n raise ValueError('clim must be a 2-element array-like')\n self._clim = tuple(clim)\n if self._clim is None:\n self._clim = vol.min(), vol.max()\n \n # Apply clim\n vol = np.array(vol, dtype='float32', copy=False)\n if self._clim[1] == self._clim[0]:\n if self._clim[0] != 0.:\n vol *= 1.0 / self._clim[0]\n else:\n vol -= self._clim[0]\n vol /= self._clim[1] - self._clim[0]\n \n # Apply to texture\n self._tex.set_data(vol) # will be efficient if vol is same shape\n self.shared_program['u_shape'] = (vol.shape[2], vol.shape[1], \n vol.shape[0])\n \n shape = vol.shape[:3]\n if self._vol_shape != shape:\n self._vol_shape = shape\n self._need_vertex_update = True\n self._vol_shape = shape\n \n # Get some stats\n self._kb_for_texture = np.prod(self._vol_shape) / 1024\n \n @property\n def clim(self):\n \"\"\" The contrast limits that were applied to the volume data.\n Settable via set_data().\n \"\"\"\n return self._clim\n \n @property\n def cmap(self):\n return self._cmap\n\n @cmap.setter\n def cmap(self, cmap):\n self._cmap = get_colormap(cmap)\n self.shared_program.frag['cmap'] = Function(self._cmap.glsl_map)\n self.update()\n\n @property\n def method(self):\n \"\"\"The render method to use\n\n Current options are:\n \n * translucent: voxel colors are blended along the view ray until\n the result is opaque.\n * mip: maxiumum intensity projection. Cast a ray and display the\n maximum value that was encountered.\n * additive: voxel colors are added along the view ray until\n the result is saturated.\n * iso: isosurface. Cast a ray until a certain threshold is\n encountered. At that location, lighning calculations are\n performed to give the visual appearance of a surface. \n \"\"\"\n return self._method\n \n @method.setter\n def method(self, method):\n # Check and save\n known_methods = list(frag_dict.keys())\n if method not in known_methods:\n raise ValueError('Volume render method should be in %r, not %r' %\n (known_methods, method))\n self._method = method\n # Get rid of specific variables - they may become invalid\n if 'u_threshold' in self.shared_program:\n self.shared_program['u_threshold'] = None\n\n self.shared_program.frag = frag_dict[method]\n self.shared_program.frag['sampler_type'] = self._tex.glsl_sampler_type\n self.shared_program.frag['sample'] = self._tex.glsl_sample\n self.shared_program.frag['cmap'] = Function(self._cmap.glsl_map)\n self.update()\n \n @property\n def threshold(self):\n \"\"\" The threshold value to apply for the isosurface render method.\n \"\"\"\n return self._threshold\n \n @threshold.setter\n def threshold(self, value):\n self._threshold = float(value)\n if 'u_threshold' in self.shared_program:\n self.shared_program['u_threshold'] = self._threshold\n self.update()\n \n @property\n def relative_step_size(self):\n \"\"\" The relative step size used during raycasting.\n \n Larger values yield higher performance at reduced quality. If\n set > 2.0 the ray skips entire voxels. Recommended values are\n between 0.5 and 1.5. The amount of quality degredation depends\n on the render method.\n \"\"\"\n return self._relative_step_size\n \n @relative_step_size.setter\n def relative_step_size(self, value):\n value = float(value)\n if value < 0.1:\n raise ValueError('relative_step_size cannot be smaller than 0.1')\n self._relative_step_size = value\n self.shared_program['u_relative_step_size'] = value\n \n def _create_vertex_data(self):\n \"\"\" Create and set positions and texture coords from the given shape\n \n We have six faces with 1 quad (2 triangles) each, resulting in\n 6*2*3 = 36 vertices in total.\n \"\"\"\n shape = self._vol_shape\n \n # Get corner coordinates. The -0.5 offset is to center\n # pixels/voxels. This works correctly for anisotropic data.\n x0, x1 = -0.5, shape[2] - 0.5\n y0, y1 = -0.5, shape[1] - 0.5\n z0, z1 = -0.5, shape[0] - 0.5\n\n pos = np.array([\n [x0, y0, z0],\n [x1, y0, z0],\n [x0, y1, z0],\n [x1, y1, z0],\n [x0, y0, z1],\n [x1, y0, z1],\n [x0, y1, z1],\n [x1, y1, z1],\n ], dtype=np.float32)\n \n \"\"\"\n 6-------7\n /| /|\n 4-------5 |\n | | | |\n | 2-----|-3\n |/ |/\n 0-------1\n \"\"\"\n \n # Order is chosen such that normals face outward; front faces will be\n # culled.\n indices = np.array([2, 6, 0, 4, 5, 6, 7, 2, 3, 0, 1, 5, 3, 7],\n dtype=np.uint32)\n \n # Apply\n self._vertices.set_data(pos)\n self._index_buffer.set_data(indices)\n\n def _compute_bounds(self, axis, view):\n return 0, self._vol_shape[axis]\n\n def _prepare_transforms(self, view):\n trs = view.transforms\n view.view_program.vert['transform'] = trs.get_transform()\n\n view_tr_f = trs.get_transform('visual', 'document')\n view_tr_i = view_tr_f.inverse\n view.view_program.vert['viewtransformf'] = view_tr_f\n view.view_program.vert['viewtransformi'] = view_tr_i\n\n def _prepare_draw(self, view):\n if self._need_vertex_update:\n self._create_vertex_data()\n",
"path": "vispy/visuals/volume.py"
}
] | [
{
"content": "# -*- coding: utf-8 -*-\n# Copyright (c) 2015, Vispy Development Team.\n# Distributed under the (new) BSD License. See LICENSE.txt for more info.\n\n\"\"\"\nAbout this technique\n--------------------\n\nIn Python, we define the six faces of a cuboid to draw, as well as\ntexture cooridnates corresponding with the vertices of the cuboid. \nThe back faces of the cuboid are drawn (and front faces are culled)\nbecause only the back faces are visible when the camera is inside the \nvolume.\n\nIn the vertex shader, we intersect the view ray with the near and far \nclipping planes. In the fragment shader, we use these two points to\ncompute the ray direction and then compute the position of the front\ncuboid surface (or near clipping plane) along the view ray.\n\nNext we calculate the number of steps to walk from the front surface\nto the back surface and iterate over these positions in a for-loop.\nAt each iteration, the fragment color or other voxel information is \nupdated depending on the selected rendering method.\n\nIt is important for the texture interpolation is 'linear', since with\nnearest the result look very ugly. The wrapping should be clamp_to_edge\nto avoid artifacts when the ray takes a small step outside the volume.\n\nThe ray direction is established by mapping the vertex to the document\ncoordinate frame, adjusting z to +/-1, and mapping the coordinate back.\nThe ray is expressed in coordinates local to the volume (i.e. texture\ncoordinates).\n\n\"\"\"\n\nfrom ..gloo import Texture3D, TextureEmulated3D, VertexBuffer, IndexBuffer\nfrom . import Visual\nfrom .shaders import Function\nfrom ..color import get_colormap\n\nimport numpy as np\n\n# todo: implement more render methods (port from visvis)\n# todo: allow anisotropic data\n# todo: what to do about lighting? ambi/diffuse/spec/shinynes on each visual?\n\n# Vertex shader\nVERT_SHADER = \"\"\"\nattribute vec3 a_position;\n// attribute vec3 a_texcoord;\nuniform vec3 u_shape;\n\n// varying vec3 v_texcoord;\nvarying vec3 v_position;\nvarying vec4 v_nearpos;\nvarying vec4 v_farpos;\n\nvoid main() {\n // v_texcoord = a_texcoord;\n v_position = a_position;\n \n // Project local vertex coordinate to camera position. Then do a step\n // backward (in cam coords) and project back. Voila, we get our ray vector.\n vec4 pos_in_cam = $viewtransformf(vec4(v_position, 1));\n\n // intersection of ray and near clipping plane (z = -1 in clip coords)\n pos_in_cam.z = -pos_in_cam.w;\n v_nearpos = $viewtransformi(pos_in_cam);\n \n // intersection of ray and far clipping plane (z = +1 in clip coords)\n pos_in_cam.z = pos_in_cam.w;\n v_farpos = $viewtransformi(pos_in_cam);\n \n gl_Position = $transform(vec4(v_position, 1.0));\n}\n\"\"\" # noqa\n\n# Fragment shader\nFRAG_SHADER = \"\"\"\n// uniforms\nuniform $sampler_type u_volumetex;\nuniform vec3 u_shape;\nuniform float u_threshold;\nuniform float u_relative_step_size;\n\n//varyings\n// varying vec3 v_texcoord;\nvarying vec3 v_position;\nvarying vec4 v_nearpos;\nvarying vec4 v_farpos;\n\n// uniforms for lighting. Hard coded until we figure out how to do lights\nconst vec4 u_ambient = vec4(0.2, 0.4, 0.2, 1.0);\nconst vec4 u_diffuse = vec4(0.8, 0.2, 0.2, 1.0);\nconst vec4 u_specular = vec4(1.0, 1.0, 1.0, 1.0);\nconst float u_shininess = 40.0;\n\n//varying vec3 lightDirs[1];\n\n// global holding view direction in local coordinates\nvec3 view_ray;\n\nfloat rand(vec2 co)\n{{\n // Create a pseudo-random number between 0 and 1.\n // http://stackoverflow.com/questions/4200224\n return fract(sin(dot(co.xy ,vec2(12.9898, 78.233))) * 43758.5453);\n}}\n\nfloat colorToVal(vec4 color1)\n{{\n return color1.g; // todo: why did I have this abstraction in visvis?\n}}\n\nvec4 calculateColor(vec4 betterColor, vec3 loc, vec3 step)\n{{ \n // Calculate color by incorporating lighting\n vec4 color1;\n vec4 color2;\n \n // View direction\n vec3 V = normalize(view_ray);\n \n // calculate normal vector from gradient\n vec3 N; // normal\n color1 = $sample( u_volumetex, loc+vec3(-step[0],0.0,0.0) );\n color2 = $sample( u_volumetex, loc+vec3(step[0],0.0,0.0) );\n N[0] = colorToVal(color1) - colorToVal(color2);\n betterColor = max(max(color1, color2),betterColor);\n color1 = $sample( u_volumetex, loc+vec3(0.0,-step[1],0.0) );\n color2 = $sample( u_volumetex, loc+vec3(0.0,step[1],0.0) );\n N[1] = colorToVal(color1) - colorToVal(color2);\n betterColor = max(max(color1, color2),betterColor);\n color1 = $sample( u_volumetex, loc+vec3(0.0,0.0,-step[2]) );\n color2 = $sample( u_volumetex, loc+vec3(0.0,0.0,step[2]) );\n N[2] = colorToVal(color1) - colorToVal(color2);\n betterColor = max(max(color1, color2),betterColor);\n float gm = length(N); // gradient magnitude\n N = normalize(N);\n \n // Flip normal so it points towards viewer\n float Nselect = float(dot(N,V) > 0.0);\n N = (2.0*Nselect - 1.0) * N; // == Nselect * N - (1.0-Nselect)*N;\n \n // Get color of the texture (albeido)\n color1 = betterColor;\n color2 = color1;\n // todo: parametrise color1_to_color2\n \n // Init colors\n vec4 ambient_color = vec4(0.0, 0.0, 0.0, 0.0);\n vec4 diffuse_color = vec4(0.0, 0.0, 0.0, 0.0);\n vec4 specular_color = vec4(0.0, 0.0, 0.0, 0.0);\n vec4 final_color;\n \n // todo: allow multiple light, define lights on viewvox or subscene\n int nlights = 1; \n for (int i=0; i<nlights; i++)\n {{ \n // Get light direction (make sure to prevent zero devision)\n vec3 L = normalize(view_ray); //lightDirs[i]; \n float lightEnabled = float( length(L) > 0.0 );\n L = normalize(L+(1.0-lightEnabled));\n \n // Calculate lighting properties\n float lambertTerm = clamp( dot(N,L), 0.0, 1.0 );\n vec3 H = normalize(L+V); // Halfway vector\n float specularTerm = pow( max(dot(H,N),0.0), u_shininess);\n \n // Calculate mask\n float mask1 = lightEnabled;\n \n // Calculate colors\n ambient_color += mask1 * u_ambient; // * gl_LightSource[i].ambient;\n diffuse_color += mask1 * lambertTerm;\n specular_color += mask1 * specularTerm * u_specular;\n }}\n \n // Calculate final color by componing different components\n final_color = color2 * ( ambient_color + diffuse_color) + specular_color;\n final_color.a = color2.a;\n \n // Done\n return final_color;\n}}\n\n// for some reason, this has to be the last function in order for the\n// filters to be inserted in the correct place...\n\nvoid main() {{\n vec3 farpos = v_farpos.xyz / v_farpos.w;\n vec3 nearpos = v_nearpos.xyz / v_nearpos.w;\n\n // Calculate unit vector pointing in the view direction through this\n // fragment.\n view_ray = normalize(farpos.xyz - nearpos.xyz);\n\n // Compute the distance to the front surface or near clipping plane\n float distance = dot(nearpos-v_position, view_ray);\n distance = max(distance, min((-0.5 - v_position.x) / view_ray.x,\n (u_shape.x - 0.5 - v_position.x) / view_ray.x));\n distance = max(distance, min((-0.5 - v_position.y) / view_ray.y,\n (u_shape.y - 0.5 - v_position.y) / view_ray.y));\n distance = max(distance, min((-0.5 - v_position.z) / view_ray.z,\n (u_shape.z - 0.5 - v_position.z) / view_ray.z));\n\n // Now we have the starting position on the front surface\n vec3 front = v_position + view_ray * distance;\n\n // Decide how many steps to take\n int nsteps = int(-distance / u_relative_step_size + 0.5);\n if( nsteps < 1 )\n discard;\n\n // Get starting location and step vector in texture coordinates\n vec3 step = ((v_position - front) / u_shape) / nsteps;\n vec3 start_loc = front / u_shape;\n\n // For testing: show the number of steps. This helps to establish\n // whether the rays are correctly oriented\n //gl_FragColor = vec4(0.0, nsteps / 3.0 / u_shape.x, 1.0, 1.0);\n //return;\n\n {before_loop}\n\n // This outer loop seems necessary on some systems for large\n // datasets. Ugly, but it works ...\n vec3 loc = start_loc;\n int iter = 0;\n while (iter < nsteps) {{\n for (iter=iter; iter<nsteps; iter++)\n {{\n // Get sample color\n vec4 color = $sample(u_volumetex, loc);\n float val = color.g;\n\n {in_loop}\n\n // Advance location deeper into the volume\n loc += step;\n }}\n }}\n\n {after_loop}\n\n /* Set depth value - from visvis TODO\n int iter_depth = int(maxi);\n // Calculate end position in world coordinates\n vec4 position2 = vertexPosition;\n position2.xyz += ray*shape*float(iter_depth);\n // Project to device coordinates and set fragment depth\n vec4 iproj = gl_ModelViewProjectionMatrix * position2;\n iproj.z /= iproj.w;\n gl_FragDepth = (iproj.z+1.0)/2.0;\n */\n}}\n\n\n\"\"\" # noqa\n\n\nMIP_SNIPPETS = dict(\n before_loop=\"\"\"\n float maxval = -99999.0; // The maximum encountered value\n int maxi = 0; // Where the maximum value was encountered\n \"\"\",\n in_loop=\"\"\"\n if( val > maxval ) {\n maxval = val;\n maxi = iter;\n }\n \"\"\",\n after_loop=\"\"\"\n // Refine search for max value\n loc = start_loc + step * (float(maxi) - 0.5);\n for (int i=0; i<10; i++) {\n maxval = max(maxval, $sample(u_volumetex, loc).g);\n loc += step * 0.1;\n }\n gl_FragColor = $cmap(maxval);\n \"\"\",\n)\nMIP_FRAG_SHADER = FRAG_SHADER.format(**MIP_SNIPPETS)\n\n\nTRANSLUCENT_SNIPPETS = dict(\n before_loop=\"\"\"\n vec4 integrated_color = vec4(0., 0., 0., 0.);\n \"\"\",\n in_loop=\"\"\"\n color = $cmap(val);\n float a1 = integrated_color.a;\n float a2 = color.a * (1 - a1);\n float alpha = max(a1 + a2, 0.001);\n \n // Doesn't work.. GLSL optimizer bug?\n //integrated_color = (integrated_color * a1 / alpha) + \n // (color * a2 / alpha); \n // This should be identical but does work correctly:\n integrated_color *= a1 / alpha;\n integrated_color += color * a2 / alpha;\n \n integrated_color.a = alpha;\n \n if( alpha > 0.99 ){\n // stop integrating if the fragment becomes opaque\n iter = nsteps;\n }\n \n \"\"\",\n after_loop=\"\"\"\n gl_FragColor = integrated_color;\n \"\"\",\n)\nTRANSLUCENT_FRAG_SHADER = FRAG_SHADER.format(**TRANSLUCENT_SNIPPETS)\n\n\nADDITIVE_SNIPPETS = dict(\n before_loop=\"\"\"\n vec4 integrated_color = vec4(0., 0., 0., 0.);\n \"\"\",\n in_loop=\"\"\"\n color = $cmap(val);\n \n integrated_color = 1.0 - (1.0 - integrated_color) * (1.0 - color);\n \"\"\",\n after_loop=\"\"\"\n gl_FragColor = integrated_color;\n \"\"\",\n)\nADDITIVE_FRAG_SHADER = FRAG_SHADER.format(**ADDITIVE_SNIPPETS)\n\n\nISO_SNIPPETS = dict(\n before_loop=\"\"\"\n vec4 color3 = vec4(0.0); // final color\n vec3 dstep = 1.5 / u_shape; // step to sample derivative\n gl_FragColor = vec4(0.0);\n \"\"\",\n in_loop=\"\"\"\n if (val > u_threshold-0.2) {\n // Take the last interval in smaller steps\n vec3 iloc = loc - step;\n for (int i=0; i<10; i++) {\n val = $sample(u_volumetex, iloc).g;\n if (val > u_threshold) {\n color = $cmap(val);\n gl_FragColor = calculateColor(color, iloc, dstep);\n iter = nsteps;\n break;\n }\n iloc += step * 0.1;\n }\n }\n \"\"\",\n after_loop=\"\"\"\n \"\"\",\n)\n\nISO_FRAG_SHADER = FRAG_SHADER.format(**ISO_SNIPPETS)\n\nfrag_dict = {\n 'mip': MIP_FRAG_SHADER,\n 'iso': ISO_FRAG_SHADER,\n 'translucent': TRANSLUCENT_FRAG_SHADER,\n 'additive': ADDITIVE_FRAG_SHADER,\n}\n\n\nclass VolumeVisual(Visual):\n \"\"\" Displays a 3D Volume\n \n Parameters\n ----------\n vol : ndarray\n The volume to display. Must be ndim==3.\n clim : tuple of two floats | None\n The contrast limits. The values in the volume are mapped to\n black and white corresponding to these values. Default maps\n between min and max.\n method : {'mip', 'translucent', 'additive', 'iso'}\n The render method to use. See corresponding docs for details.\n Default 'mip'.\n threshold : float\n The threshold to use for the isosurafce render method. By default\n the mean of the given volume is used.\n relative_step_size : float\n The relative step size to step through the volume. Default 0.8.\n Increase to e.g. 1.5 to increase performance, at the cost of\n quality.\n cmap : str\n Colormap to use.\n emulate_texture : bool\n Use 2D textures to emulate a 3D texture. OpenGL ES 2.0 compatible,\n but has lower performance on desktop platforms.\n \"\"\"\n\n def __init__(self, vol, clim=None, method='mip', threshold=None, \n relative_step_size=0.8, cmap='grays',\n emulate_texture=False):\n \n tex_cls = TextureEmulated3D if emulate_texture else Texture3D\n\n # Storage of information of volume\n self._vol_shape = ()\n self._clim = None\n self._need_vertex_update = True\n\n # Set the colormap\n self._cmap = get_colormap(cmap)\n\n # Create gloo objects\n self._vertices = VertexBuffer()\n self._texcoord = VertexBuffer(\n np.array([\n [0, 0, 0],\n [1, 0, 0],\n [0, 1, 0],\n [1, 1, 0],\n [0, 0, 1],\n [1, 0, 1],\n [0, 1, 1],\n [1, 1, 1],\n ], dtype=np.float32))\n self._tex = tex_cls((10, 10, 10), interpolation='linear', \n wrapping='clamp_to_edge')\n\n # Create program\n Visual.__init__(self, vcode=VERT_SHADER, fcode=\"\")\n self.shared_program['u_volumetex'] = self._tex\n self.shared_program['a_position'] = self._vertices\n self.shared_program['a_texcoord'] = self._texcoord\n self._draw_mode = 'triangle_strip'\n self._index_buffer = IndexBuffer()\n\n # Only show back faces of cuboid. This is required because if we are \n # inside the volume, then the front faces are outside of the clipping\n # box and will not be drawn.\n self.set_gl_state('translucent', cull_face=False)\n \n # Set data\n self.set_data(vol, clim)\n \n # Set params\n self.method = method\n self.relative_step_size = relative_step_size\n self.threshold = threshold if (threshold is not None) else vol.mean()\n self.freeze()\n \n def set_data(self, vol, clim=None):\n \"\"\" Set the volume data. \n\n Parameters\n ----------\n vol : ndarray\n The 3D volume.\n clim : tuple | None\n Colormap limits to use. None will use the min and max values.\n \"\"\"\n # Check volume\n if not isinstance(vol, np.ndarray):\n raise ValueError('Volume visual needs a numpy array.')\n if not ((vol.ndim == 3) or (vol.ndim == 4 and vol.shape[-1] <= 4)):\n raise ValueError('Volume visual needs a 3D image.')\n \n # Handle clim\n if clim is not None:\n clim = np.array(clim, float)\n if not (clim.ndim == 1 and clim.size == 2):\n raise ValueError('clim must be a 2-element array-like')\n self._clim = tuple(clim)\n if self._clim is None:\n self._clim = vol.min(), vol.max()\n \n # Apply clim\n vol = np.array(vol, dtype='float32', copy=False)\n if self._clim[1] == self._clim[0]:\n if self._clim[0] != 0.:\n vol *= 1.0 / self._clim[0]\n else:\n vol -= self._clim[0]\n vol /= self._clim[1] - self._clim[0]\n \n # Apply to texture\n self._tex.set_data(vol) # will be efficient if vol is same shape\n self.shared_program['u_shape'] = (vol.shape[2], vol.shape[1], \n vol.shape[0])\n \n shape = vol.shape[:3]\n if self._vol_shape != shape:\n self._vol_shape = shape\n self._need_vertex_update = True\n self._vol_shape = shape\n \n # Get some stats\n self._kb_for_texture = np.prod(self._vol_shape) / 1024\n \n @property\n def clim(self):\n \"\"\" The contrast limits that were applied to the volume data.\n Settable via set_data().\n \"\"\"\n return self._clim\n \n @property\n def cmap(self):\n return self._cmap\n\n @cmap.setter\n def cmap(self, cmap):\n self._cmap = get_colormap(cmap)\n self.shared_program.frag['cmap'] = Function(self._cmap.glsl_map)\n self.update()\n\n @property\n def method(self):\n \"\"\"The render method to use\n\n Current options are:\n \n * translucent: voxel colors are blended along the view ray until\n the result is opaque.\n * mip: maxiumum intensity projection. Cast a ray and display the\n maximum value that was encountered.\n * additive: voxel colors are added along the view ray until\n the result is saturated.\n * iso: isosurface. Cast a ray until a certain threshold is\n encountered. At that location, lighning calculations are\n performed to give the visual appearance of a surface. \n \"\"\"\n return self._method\n \n @method.setter\n def method(self, method):\n # Check and save\n known_methods = list(frag_dict.keys())\n if method not in known_methods:\n raise ValueError('Volume render method should be in %r, not %r' %\n (known_methods, method))\n self._method = method\n # Get rid of specific variables - they may become invalid\n if 'u_threshold' in self.shared_program:\n self.shared_program['u_threshold'] = None\n\n self.shared_program.frag = frag_dict[method]\n self.shared_program.frag['sampler_type'] = self._tex.glsl_sampler_type\n self.shared_program.frag['sample'] = self._tex.glsl_sample\n self.shared_program.frag['cmap'] = Function(self._cmap.glsl_map)\n self.update()\n \n @property\n def threshold(self):\n \"\"\" The threshold value to apply for the isosurface render method.\n \"\"\"\n return self._threshold\n \n @threshold.setter\n def threshold(self, value):\n self._threshold = float(value)\n if 'u_threshold' in self.shared_program:\n self.shared_program['u_threshold'] = self._threshold\n self.update()\n \n @property\n def relative_step_size(self):\n \"\"\" The relative step size used during raycasting.\n \n Larger values yield higher performance at reduced quality. If\n set > 2.0 the ray skips entire voxels. Recommended values are\n between 0.5 and 1.5. The amount of quality degredation depends\n on the render method.\n \"\"\"\n return self._relative_step_size\n \n @relative_step_size.setter\n def relative_step_size(self, value):\n value = float(value)\n if value < 0.1:\n raise ValueError('relative_step_size cannot be smaller than 0.1')\n self._relative_step_size = value\n self.shared_program['u_relative_step_size'] = value\n \n def _create_vertex_data(self):\n \"\"\" Create and set positions and texture coords from the given shape\n \n We have six faces with 1 quad (2 triangles) each, resulting in\n 6*2*3 = 36 vertices in total.\n \"\"\"\n shape = self._vol_shape\n \n # Get corner coordinates. The -0.5 offset is to center\n # pixels/voxels. This works correctly for anisotropic data.\n x0, x1 = -0.5, shape[2] - 0.5\n y0, y1 = -0.5, shape[1] - 0.5\n z0, z1 = -0.5, shape[0] - 0.5\n\n pos = np.array([\n [x0, y0, z0],\n [x1, y0, z0],\n [x0, y1, z0],\n [x1, y1, z0],\n [x0, y0, z1],\n [x1, y0, z1],\n [x0, y1, z1],\n [x1, y1, z1],\n ], dtype=np.float32)\n \n \"\"\"\n 6-------7\n /| /|\n 4-------5 |\n | | | |\n | 2-----|-3\n |/ |/\n 0-------1\n \"\"\"\n \n # Order is chosen such that normals face outward; front faces will be\n # culled.\n indices = np.array([2, 6, 0, 4, 5, 6, 7, 2, 3, 0, 1, 5, 3, 7],\n dtype=np.uint32)\n \n # Apply\n self._vertices.set_data(pos)\n self._index_buffer.set_data(indices)\n\n def _compute_bounds(self, axis, view):\n return 0, self._vol_shape[axis]\n\n def _prepare_transforms(self, view):\n trs = view.transforms\n view.view_program.vert['transform'] = trs.get_transform()\n\n view_tr_f = trs.get_transform('visual', 'document')\n view_tr_i = view_tr_f.inverse\n view.view_program.vert['viewtransformf'] = view_tr_f\n view.view_program.vert['viewtransformi'] = view_tr_i\n\n def _prepare_draw(self, view):\n if self._need_vertex_update:\n self._create_vertex_data()\n",
"path": "vispy/visuals/volume.py"
}
] | diff --git a/vispy/visuals/volume.py b/vispy/visuals/volume.py
index d840e83c9b..c2ffab2ad8 100644
--- a/vispy/visuals/volume.py
+++ b/vispy/visuals/volume.py
@@ -335,6 +335,7 @@
before_loop="""
vec4 color3 = vec4(0.0); // final color
vec3 dstep = 1.5 / u_shape; // step to sample derivative
+ gl_FragColor = vec4(0.0);
""",
in_loop="""
if (val > u_threshold-0.2) {
|
ultralytics__yolov5-296 | TypeError: can't pickle torch.distributed.ProcessGroupNCCL objects
Hi，
I meet a problem:
Traceback (most recent call last):
File "train.py", line 394, in <module>
train(hyp)
File "train.py", line 331, in train
torch.save(ckpt, last)
File "/home/yy/anaconda3/lib/python3.6/site-packages/torch/serialization.py", line 328, in save
_legacy_save(obj, opened_file, pickle_module, pickle_protocol)
File "/home/yy/anaconda3/lib/python3.6/site-packages/torch/serialization.py", line 401, in _legacy_save
pickler.dump(obj)
**TypeError: can't pickle torch.distributed.ProcessGroupNCCL objects**
Thanks!
environment:
ubuntu 16.04
GPU 2080Ti *4
pytorch 1.4.0
| [
{
"content": "import math\nimport os\nimport time\nfrom copy import deepcopy\n\nimport torch\nimport torch.backends.cudnn as cudnn\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torchvision.models as models\n\n\ndef init_seeds(seed=0):\n torch.manual_seed(seed)\n\n # Speed-reproducibility tradeoff https://pytorch.org/docs/stable/notes/randomness.html\n if seed == 0: # slower, more reproducible\n cudnn.deterministic = True\n cudnn.benchmark = False\n else: # faster, less reproducible\n cudnn.deterministic = False\n cudnn.benchmark = True\n\n\ndef select_device(device='', apex=False, batch_size=None):\n # device = 'cpu' or '0' or '0,1,2,3'\n cpu_request = device.lower() == 'cpu'\n if device and not cpu_request: # if device requested other than 'cpu'\n os.environ['CUDA_VISIBLE_DEVICES'] = device # set environment variable\n assert torch.cuda.is_available(), 'CUDA unavailable, invalid device %s requested' % device # check availablity\n\n cuda = False if cpu_request else torch.cuda.is_available()\n if cuda:\n c = 1024 ** 2 # bytes to MB\n ng = torch.cuda.device_count()\n if ng > 1 and batch_size: # check that batch_size is compatible with device_count\n assert batch_size % ng == 0, 'batch-size %g not multiple of GPU count %g' % (batch_size, ng)\n x = [torch.cuda.get_device_properties(i) for i in range(ng)]\n s = 'Using CUDA ' + ('Apex ' if apex else '') # apex for mixed precision https://github.com/NVIDIA/apex\n for i in range(0, ng):\n if i == 1:\n s = ' ' * len(s)\n print(\"%sdevice%g _CudaDeviceProperties(name='%s', total_memory=%dMB)\" %\n (s, i, x[i].name, x[i].total_memory / c))\n else:\n print('Using CPU')\n\n print('') # skip a line\n return torch.device('cuda:0' if cuda else 'cpu')\n\n\ndef time_synchronized():\n torch.cuda.synchronize() if torch.cuda.is_available() else None\n return time.time()\n\n\ndef is_parallel(model):\n # is model is parallel with DP or DDP\n return type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel)\n\n\ndef initialize_weights(model):\n for m in model.modules():\n t = type(m)\n if t is nn.Conv2d:\n pass # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')\n elif t is nn.BatchNorm2d:\n m.eps = 1e-4\n m.momentum = 0.03\n elif t in [nn.LeakyReLU, nn.ReLU, nn.ReLU6]:\n m.inplace = True\n\n\ndef find_modules(model, mclass=nn.Conv2d):\n # finds layer indices matching module class 'mclass'\n return [i for i, m in enumerate(model.module_list) if isinstance(m, mclass)]\n\n\ndef fuse_conv_and_bn(conv, bn):\n # https://tehnokv.com/posts/fusing-batchnorm-and-conv/\n with torch.no_grad():\n # init\n fusedconv = torch.nn.Conv2d(conv.in_channels,\n conv.out_channels,\n kernel_size=conv.kernel_size,\n stride=conv.stride,\n padding=conv.padding,\n bias=True)\n\n # prepare filters\n w_conv = conv.weight.clone().view(conv.out_channels, -1)\n w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var)))\n fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.size()))\n\n # prepare spatial bias\n if conv.bias is not None:\n b_conv = conv.bias\n else:\n b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device)\n b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps))\n fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn)\n\n return fusedconv\n\n\ndef model_info(model, verbose=False):\n # Plots a line-by-line description of a PyTorch model\n n_p = sum(x.numel() for x in model.parameters()) # number parameters\n n_g = sum(x.numel() for x in model.parameters() if x.requires_grad) # number gradients\n if verbose:\n print('%5s %40s %9s %12s %20s %10s %10s' % ('layer', 'name', 'gradient', 'parameters', 'shape', 'mu', 'sigma'))\n for i, (name, p) in enumerate(model.named_parameters()):\n name = name.replace('module_list.', '')\n print('%5g %40s %9s %12g %20s %10.3g %10.3g' %\n (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std()))\n\n try: # FLOPS\n from thop import profile\n flops = profile(deepcopy(model), inputs=(torch.zeros(1, 3, 64, 64),), verbose=False)[0] / 1E9 * 2\n fs = ', %.1f GFLOPS' % (flops * 100) # 640x640 FLOPS\n except:\n fs = ''\n\n print('Model Summary: %g layers, %g parameters, %g gradients%s' % (len(list(model.parameters())), n_p, n_g, fs))\n\n\ndef load_classifier(name='resnet101', n=2):\n # Loads a pretrained model reshaped to n-class output\n model = models.__dict__[name](pretrained=True)\n\n # Display model properties\n input_size = [3, 224, 224]\n input_space = 'RGB'\n input_range = [0, 1]\n mean = [0.485, 0.456, 0.406]\n std = [0.229, 0.224, 0.225]\n for x in [input_size, input_space, input_range, mean, std]:\n print(x + ' =', eval(x))\n\n # Reshape output to n classes\n filters = model.fc.weight.shape[1]\n model.fc.bias = torch.nn.Parameter(torch.zeros(n), requires_grad=True)\n model.fc.weight = torch.nn.Parameter(torch.zeros(n, filters), requires_grad=True)\n model.fc.out_features = n\n return model\n\n\ndef scale_img(img, ratio=1.0, same_shape=False): # img(16,3,256,416), r=ratio\n # scales img(bs,3,y,x) by ratio\n h, w = img.shape[2:]\n s = (int(h * ratio), int(w * ratio)) # new size\n img = F.interpolate(img, size=s, mode='bilinear', align_corners=False) # resize\n if not same_shape: # pad/crop img\n gs = 32 # (pixels) grid size\n h, w = [math.ceil(x * ratio / gs) * gs for x in (h, w)]\n return F.pad(img, [0, w - s[1], 0, h - s[0]], value=0.447) # value = imagenet mean\n\n\nclass ModelEMA:\n \"\"\" Model Exponential Moving Average from https://github.com/rwightman/pytorch-image-models\n Keep a moving average of everything in the model state_dict (parameters and buffers).\n This is intended to allow functionality like\n https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage\n A smoothed version of the weights is necessary for some training schemes to perform well.\n E.g. Google's hyper-params for training MNASNet, MobileNet-V3, EfficientNet, etc that use\n RMSprop with a short 2.4-3 epoch decay period and slow LR decay rate of .96-.99 requires EMA\n smoothing of weights to match results. Pay attention to the decay constant you are using\n relative to your update count per epoch.\n To keep EMA from using GPU resources, set device='cpu'. This will save a bit of memory but\n disable validation of the EMA weights. Validation will have to be done manually in a separate\n process, or after the training stops converging.\n This class is sensitive where it is initialized in the sequence of model init,\n GPU assignment and distributed training wrappers.\n I've tested with the sequence in my own train.py for torch.DataParallel, apex.DDP, and single-GPU.\n \"\"\"\n\n def __init__(self, model, decay=0.9999, device=''):\n # Create EMA\n self.ema = deepcopy(model.module if is_parallel(model) else model) # FP32 EMA\n self.ema.eval()\n self.updates = 0 # number of EMA updates\n self.decay = lambda x: decay * (1 - math.exp(-x / 2000)) # decay exponential ramp (to help early epochs)\n self.device = device # perform ema on different device from model if set\n if device:\n self.ema.to(device)\n for p in self.ema.parameters():\n p.requires_grad_(False)\n\n def update(self, model):\n # Update EMA parameters\n with torch.no_grad():\n self.updates += 1\n d = self.decay(self.updates)\n\n msd = model.module.state_dict() if is_parallel(model) else model.state_dict() # model state_dict\n for k, v in self.ema.state_dict().items():\n if v.dtype.is_floating_point:\n v *= d\n v += (1. - d) * msd[k].detach()\n\n def update_attr(self, model):\n # Update EMA attributes\n for k, v in model.__dict__.items():\n if not k.startswith('_') and k != 'module':\n setattr(self.ema, k, v)\n",
"path": "utils/torch_utils.py"
}
] | [
{
"content": "import math\nimport os\nimport time\nfrom copy import deepcopy\n\nimport torch\nimport torch.backends.cudnn as cudnn\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torchvision.models as models\n\n\ndef init_seeds(seed=0):\n torch.manual_seed(seed)\n\n # Speed-reproducibility tradeoff https://pytorch.org/docs/stable/notes/randomness.html\n if seed == 0: # slower, more reproducible\n cudnn.deterministic = True\n cudnn.benchmark = False\n else: # faster, less reproducible\n cudnn.deterministic = False\n cudnn.benchmark = True\n\n\ndef select_device(device='', apex=False, batch_size=None):\n # device = 'cpu' or '0' or '0,1,2,3'\n cpu_request = device.lower() == 'cpu'\n if device and not cpu_request: # if device requested other than 'cpu'\n os.environ['CUDA_VISIBLE_DEVICES'] = device # set environment variable\n assert torch.cuda.is_available(), 'CUDA unavailable, invalid device %s requested' % device # check availablity\n\n cuda = False if cpu_request else torch.cuda.is_available()\n if cuda:\n c = 1024 ** 2 # bytes to MB\n ng = torch.cuda.device_count()\n if ng > 1 and batch_size: # check that batch_size is compatible with device_count\n assert batch_size % ng == 0, 'batch-size %g not multiple of GPU count %g' % (batch_size, ng)\n x = [torch.cuda.get_device_properties(i) for i in range(ng)]\n s = 'Using CUDA ' + ('Apex ' if apex else '') # apex for mixed precision https://github.com/NVIDIA/apex\n for i in range(0, ng):\n if i == 1:\n s = ' ' * len(s)\n print(\"%sdevice%g _CudaDeviceProperties(name='%s', total_memory=%dMB)\" %\n (s, i, x[i].name, x[i].total_memory / c))\n else:\n print('Using CPU')\n\n print('') # skip a line\n return torch.device('cuda:0' if cuda else 'cpu')\n\n\ndef time_synchronized():\n torch.cuda.synchronize() if torch.cuda.is_available() else None\n return time.time()\n\n\ndef is_parallel(model):\n # is model is parallel with DP or DDP\n return type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel)\n\n\ndef initialize_weights(model):\n for m in model.modules():\n t = type(m)\n if t is nn.Conv2d:\n pass # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')\n elif t is nn.BatchNorm2d:\n m.eps = 1e-4\n m.momentum = 0.03\n elif t in [nn.LeakyReLU, nn.ReLU, nn.ReLU6]:\n m.inplace = True\n\n\ndef find_modules(model, mclass=nn.Conv2d):\n # finds layer indices matching module class 'mclass'\n return [i for i, m in enumerate(model.module_list) if isinstance(m, mclass)]\n\n\ndef fuse_conv_and_bn(conv, bn):\n # https://tehnokv.com/posts/fusing-batchnorm-and-conv/\n with torch.no_grad():\n # init\n fusedconv = torch.nn.Conv2d(conv.in_channels,\n conv.out_channels,\n kernel_size=conv.kernel_size,\n stride=conv.stride,\n padding=conv.padding,\n bias=True)\n\n # prepare filters\n w_conv = conv.weight.clone().view(conv.out_channels, -1)\n w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var)))\n fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.size()))\n\n # prepare spatial bias\n if conv.bias is not None:\n b_conv = conv.bias\n else:\n b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device)\n b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps))\n fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn)\n\n return fusedconv\n\n\ndef model_info(model, verbose=False):\n # Plots a line-by-line description of a PyTorch model\n n_p = sum(x.numel() for x in model.parameters()) # number parameters\n n_g = sum(x.numel() for x in model.parameters() if x.requires_grad) # number gradients\n if verbose:\n print('%5s %40s %9s %12s %20s %10s %10s' % ('layer', 'name', 'gradient', 'parameters', 'shape', 'mu', 'sigma'))\n for i, (name, p) in enumerate(model.named_parameters()):\n name = name.replace('module_list.', '')\n print('%5g %40s %9s %12g %20s %10.3g %10.3g' %\n (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std()))\n\n try: # FLOPS\n from thop import profile\n flops = profile(deepcopy(model), inputs=(torch.zeros(1, 3, 64, 64),), verbose=False)[0] / 1E9 * 2\n fs = ', %.1f GFLOPS' % (flops * 100) # 640x640 FLOPS\n except:\n fs = ''\n\n print('Model Summary: %g layers, %g parameters, %g gradients%s' % (len(list(model.parameters())), n_p, n_g, fs))\n\n\ndef load_classifier(name='resnet101', n=2):\n # Loads a pretrained model reshaped to n-class output\n model = models.__dict__[name](pretrained=True)\n\n # Display model properties\n input_size = [3, 224, 224]\n input_space = 'RGB'\n input_range = [0, 1]\n mean = [0.485, 0.456, 0.406]\n std = [0.229, 0.224, 0.225]\n for x in [input_size, input_space, input_range, mean, std]:\n print(x + ' =', eval(x))\n\n # Reshape output to n classes\n filters = model.fc.weight.shape[1]\n model.fc.bias = torch.nn.Parameter(torch.zeros(n), requires_grad=True)\n model.fc.weight = torch.nn.Parameter(torch.zeros(n, filters), requires_grad=True)\n model.fc.out_features = n\n return model\n\n\ndef scale_img(img, ratio=1.0, same_shape=False): # img(16,3,256,416), r=ratio\n # scales img(bs,3,y,x) by ratio\n h, w = img.shape[2:]\n s = (int(h * ratio), int(w * ratio)) # new size\n img = F.interpolate(img, size=s, mode='bilinear', align_corners=False) # resize\n if not same_shape: # pad/crop img\n gs = 32 # (pixels) grid size\n h, w = [math.ceil(x * ratio / gs) * gs for x in (h, w)]\n return F.pad(img, [0, w - s[1], 0, h - s[0]], value=0.447) # value = imagenet mean\n\n\nclass ModelEMA:\n \"\"\" Model Exponential Moving Average from https://github.com/rwightman/pytorch-image-models\n Keep a moving average of everything in the model state_dict (parameters and buffers).\n This is intended to allow functionality like\n https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage\n A smoothed version of the weights is necessary for some training schemes to perform well.\n E.g. Google's hyper-params for training MNASNet, MobileNet-V3, EfficientNet, etc that use\n RMSprop with a short 2.4-3 epoch decay period and slow LR decay rate of .96-.99 requires EMA\n smoothing of weights to match results. Pay attention to the decay constant you are using\n relative to your update count per epoch.\n To keep EMA from using GPU resources, set device='cpu'. This will save a bit of memory but\n disable validation of the EMA weights. Validation will have to be done manually in a separate\n process, or after the training stops converging.\n This class is sensitive where it is initialized in the sequence of model init,\n GPU assignment and distributed training wrappers.\n I've tested with the sequence in my own train.py for torch.DataParallel, apex.DDP, and single-GPU.\n \"\"\"\n\n def __init__(self, model, decay=0.9999, device=''):\n # Create EMA\n self.ema = deepcopy(model.module if is_parallel(model) else model) # FP32 EMA\n self.ema.eval()\n self.updates = 0 # number of EMA updates\n self.decay = lambda x: decay * (1 - math.exp(-x / 2000)) # decay exponential ramp (to help early epochs)\n self.device = device # perform ema on different device from model if set\n if device:\n self.ema.to(device)\n for p in self.ema.parameters():\n p.requires_grad_(False)\n\n def update(self, model):\n # Update EMA parameters\n with torch.no_grad():\n self.updates += 1\n d = self.decay(self.updates)\n\n msd = model.module.state_dict() if is_parallel(model) else model.state_dict() # model state_dict\n for k, v in self.ema.state_dict().items():\n if v.dtype.is_floating_point:\n v *= d\n v += (1. - d) * msd[k].detach()\n\n def update_attr(self, model):\n # Update EMA attributes\n for k, v in model.__dict__.items():\n if not k.startswith('_') and k not in [\"process_group\", \"reducer\"]:\n setattr(self.ema, k, v)\n",
"path": "utils/torch_utils.py"
}
] | diff --git a/utils/torch_utils.py b/utils/torch_utils.py
index dd2e6e75ff97..fd00b8bde080 100644
--- a/utils/torch_utils.py
+++ b/utils/torch_utils.py
@@ -201,5 +201,5 @@ def update(self, model):
def update_attr(self, model):
# Update EMA attributes
for k, v in model.__dict__.items():
- if not k.startswith('_') and k != 'module':
+ if not k.startswith('_') and k not in ["process_group", "reducer"]:
setattr(self.ema, k, v)
|
pypa__cibuildwheel-1282 | --only doesn't work for `-win32` identifiers
> - cp311 https://github.com/ddelange/asyncpg/actions/runs/3092263953/jobs/5003341864#step:4:43
> - same for all other win32 builds: `Invalid --only='cp310-win32', must be a build selector with a known platform`
>
> in https://github.com/ddelange/asyncpg/pull/2
>
> and before this change it was fine: https://github.com/ddelange/asyncpg/actions/runs/3072698535/jobs/4964355341#step:4:89
>
> maybe it's an issue with the `--only` flag?
>
> on my mac:
>
> ```console
> $ cibuildwheel --print-build-identifiers --platform windows --arch x86,AMD64
> cp36-win32
> cp36-win_amd64
> cp37-win32
> cp37-win_amd64
> cp38-win32
> cp38-win_amd64
> cp39-win32
> cp39-win_amd64
> cp310-win32
> cp310-win_amd64
> cp311-win32
> cp311-win_amd64
> pp37-win_amd64
> pp38-win_amd64
> pp39-win_amd64
> ```
>
_Originally posted by @ddelange in https://github.com/pypa/cibuildwheel/issues/1266#issuecomment-1252703994_
| [
{
"content": "from __future__ import annotations\n\nimport argparse\nimport os\nimport shutil\nimport sys\nimport tarfile\nimport textwrap\nimport typing\nfrom pathlib import Path\nfrom tempfile import mkdtemp\n\nimport cibuildwheel\nimport cibuildwheel.linux\nimport cibuildwheel.macos\nimport cibuildwheel.util\nimport cibuildwheel.windows\nfrom cibuildwheel.architecture import Architecture, allowed_architectures_check\nfrom cibuildwheel.logger import log\nfrom cibuildwheel.options import CommandLineArguments, Options, compute_options\nfrom cibuildwheel.typing import PLATFORMS, PlatformName, assert_never\nfrom cibuildwheel.util import (\n CIBW_CACHE_PATH,\n BuildSelector,\n Unbuffered,\n chdir,\n detect_ci_provider,\n)\n\n\ndef main() -> None:\n parser = argparse.ArgumentParser(\n description=\"Build wheels for all the platforms.\",\n epilog=\"\"\"\n Most options are supplied via environment variables or in\n --config-file (pyproject.toml usually). See\n https://github.com/pypa/cibuildwheel#options for info.\n \"\"\",\n )\n\n parser.add_argument(\n \"--platform\",\n choices=[\"auto\", \"linux\", \"macos\", \"windows\"],\n default=None,\n help=\"\"\"\n Platform to build for. Use this option to override the\n auto-detected platform or to run cibuildwheel on your development\n machine. Specifying \"macos\" or \"windows\" only works on that\n operating system, but \"linux\" works on all three, as long as\n Docker/Podman is installed. Default: auto.\n \"\"\",\n )\n\n arch_list_str = \", \".join(a.name for a in Architecture)\n parser.add_argument(\n \"--archs\",\n default=None,\n help=f\"\"\"\n Comma-separated list of CPU architectures to build for.\n When set to 'auto', builds the architectures natively supported\n on this machine. Set this option to build an architecture\n via emulation, for example, using binfmt_misc and QEMU.\n Default: auto.\n Choices: auto, auto64, auto32, native, all, {arch_list_str}\n \"\"\",\n )\n\n parser.add_argument(\n \"--only\",\n default=None,\n help=\"\"\"\n Force a single wheel build when given an identifier. Overrides\n CIBW_BUILD/CIBW_SKIP. --platform and --arch cannot be specified\n if this is given.\n \"\"\",\n )\n\n parser.add_argument(\n \"--output-dir\",\n type=Path,\n default=Path(os.environ.get(\"CIBW_OUTPUT_DIR\", \"wheelhouse\")),\n help=\"Destination folder for the wheels. Default: wheelhouse.\",\n )\n\n parser.add_argument(\n \"--config-file\",\n default=\"\",\n help=\"\"\"\n TOML config file. Default: \"\", meaning {package}/pyproject.toml, if\n it exists. To refer to a project inside your project, use {package};\n this matters if you build from an SDist.\n \"\"\",\n )\n\n parser.add_argument(\n \"package_dir\",\n metavar=\"PACKAGE\",\n default=Path(\".\"),\n type=Path,\n nargs=\"?\",\n help=\"\"\"\n Path to the package that you want wheels for. Default: the working\n directory. Can be a directory inside the working directory, or an\n sdist. When set to a directory, the working directory is still\n considered the 'project' and is copied into the build container\n on Linux. When set to a tar.gz sdist file, --config-file\n and --output-dir are relative to the current directory, and other\n paths are relative to the expanded SDist directory.\n \"\"\",\n )\n\n parser.add_argument(\n \"--print-build-identifiers\",\n action=\"store_true\",\n help=\"Print the build identifiers matched by the current invocation and exit.\",\n )\n\n parser.add_argument(\n \"--allow-empty\",\n action=\"store_true\",\n help=\"Do not report an error code if the build does not match any wheels.\",\n )\n\n parser.add_argument(\n \"--prerelease-pythons\",\n action=\"store_true\",\n help=\"Enable pre-release Python versions if available.\",\n )\n\n args = CommandLineArguments(**vars(parser.parse_args()))\n\n args.package_dir = args.package_dir.resolve()\n\n # This are always relative to the base directory, even in SDist builds\n args.output_dir = args.output_dir.resolve()\n\n # Standard builds if a directory or non-existent path is given\n if not args.package_dir.is_file() and not args.package_dir.name.endswith(\"tar.gz\"):\n build_in_directory(args)\n return\n\n # Tarfile builds require extraction and changing the directory\n temp_dir = Path(mkdtemp(prefix=\"cibw-sdist-\")).resolve(strict=True)\n try:\n with tarfile.open(args.package_dir) as tar:\n tar.extractall(path=temp_dir)\n\n # The extract directory is now the project dir\n try:\n (project_dir,) = temp_dir.iterdir()\n except ValueError:\n raise SystemExit(\"invalid sdist: didn't contain a single dir\") from None\n\n # This is now the new package dir\n args.package_dir = project_dir.resolve()\n\n with chdir(temp_dir):\n build_in_directory(args)\n finally:\n # avoid https://github.com/python/cpython/issues/86962 by performing\n # cleanup manually\n shutil.rmtree(temp_dir, ignore_errors=sys.platform.startswith(\"win\"))\n if temp_dir.exists():\n log.warning(f\"Can't delete temporary folder '{str(temp_dir)}'\")\n\n\ndef build_in_directory(args: CommandLineArguments) -> None:\n platform_option_value = args.platform or os.environ.get(\"CIBW_PLATFORM\", \"auto\")\n platform: PlatformName\n\n if args.only:\n if \"linux_\" in args.only:\n platform = \"linux\"\n elif \"macosx_\" in args.only:\n platform = \"macos\"\n elif \"win_\" in args.only:\n platform = \"windows\"\n else:\n print(\n f\"Invalid --only='{args.only}', must be a build selector with a known platform\",\n file=sys.stderr,\n )\n sys.exit(2)\n if args.platform is not None:\n print(\n \"--platform cannot be specified with --only, it is computed from --only\",\n file=sys.stderr,\n )\n sys.exit(2)\n if args.archs is not None:\n print(\n \"--arch cannot be specified with --only, it is computed from --only\",\n file=sys.stderr,\n )\n sys.exit(2)\n elif platform_option_value != \"auto\":\n if platform_option_value not in PLATFORMS:\n print(f\"cibuildwheel: Unsupported platform: {platform_option_value}\", file=sys.stderr)\n sys.exit(2)\n\n platform = typing.cast(PlatformName, platform_option_value)\n else:\n ci_provider = detect_ci_provider()\n if ci_provider is None:\n print(\n textwrap.dedent(\n \"\"\"\n cibuildwheel: Unable to detect platform. cibuildwheel should run on your CI server;\n Travis CI, AppVeyor, Azure Pipelines, GitHub Actions, CircleCI, Gitlab, and Cirrus CI\n are supported. You can run on your development machine or other CI providers\n using the --platform argument. Check --help output for more information.\n \"\"\"\n ),\n file=sys.stderr,\n )\n sys.exit(2)\n if sys.platform.startswith(\"linux\"):\n platform = \"linux\"\n elif sys.platform == \"darwin\":\n platform = \"macos\"\n elif sys.platform == \"win32\":\n platform = \"windows\"\n else:\n print(\n 'cibuildwheel: Unable to detect platform from \"sys.platform\" in a CI environment. You can run '\n \"cibuildwheel using the --platform argument. Check --help output for more information.\",\n file=sys.stderr,\n )\n sys.exit(2)\n\n options = compute_options(platform=platform, command_line_arguments=args)\n\n package_dir = options.globals.package_dir\n package_files = {\"setup.py\", \"setup.cfg\", \"pyproject.toml\"}\n\n if not any(package_dir.joinpath(name).exists() for name in package_files):\n names = \", \".join(sorted(package_files, reverse=True))\n msg = f\"cibuildwheel: Could not find any of {{{names}}} at root of package\"\n print(msg, file=sys.stderr)\n sys.exit(2)\n\n identifiers = get_build_identifiers(\n platform=platform,\n build_selector=options.globals.build_selector,\n architectures=options.globals.architectures,\n )\n\n if args.print_build_identifiers:\n for identifier in identifiers:\n print(identifier)\n sys.exit(0)\n\n # Add CIBUILDWHEEL environment variable\n os.environ[\"CIBUILDWHEEL\"] = \"1\"\n\n # Python is buffering by default when running on the CI platforms, giving problems interleaving subprocess call output with unflushed calls to 'print'\n sys.stdout = Unbuffered(sys.stdout) # type: ignore[assignment]\n\n # create the cache dir before it gets printed & builds performed\n CIBW_CACHE_PATH.mkdir(parents=True, exist_ok=True)\n\n print_preamble(platform=platform, options=options, identifiers=identifiers)\n\n try:\n options.check_for_invalid_configuration(identifiers)\n allowed_architectures_check(platform, options.globals.architectures)\n except ValueError as err:\n print(\"cibuildwheel:\", *err.args, file=sys.stderr)\n sys.exit(4)\n\n if not identifiers:\n print(\n f\"cibuildwheel: No build identifiers selected: {options.globals.build_selector}\",\n file=sys.stderr,\n )\n if not args.allow_empty:\n sys.exit(3)\n\n output_dir = options.globals.output_dir\n\n if not output_dir.exists():\n output_dir.mkdir(parents=True)\n\n tmp_path = Path(mkdtemp(prefix=\"cibw-run-\")).resolve(strict=True)\n try:\n with cibuildwheel.util.print_new_wheels(\n \"\\n{n} wheels produced in {m:.0f} minutes:\", output_dir\n ):\n if platform == \"linux\":\n cibuildwheel.linux.build(options, tmp_path)\n elif platform == \"windows\":\n cibuildwheel.windows.build(options, tmp_path)\n elif platform == \"macos\":\n cibuildwheel.macos.build(options, tmp_path)\n else:\n assert_never(platform)\n finally:\n # avoid https://github.com/python/cpython/issues/86962 by performing\n # cleanup manually\n shutil.rmtree(tmp_path, ignore_errors=sys.platform.startswith(\"win\"))\n if tmp_path.exists():\n log.warning(f\"Can't delete temporary folder '{str(tmp_path)}'\")\n\n\ndef print_preamble(platform: str, options: Options, identifiers: list[str]) -> None:\n print(\n textwrap.dedent(\n \"\"\"\n _ _ _ _ _ _ _\n ___|_| |_ _ _|_| |_| |_ _ _| |_ ___ ___| |\n | _| | . | | | | | . | | | | | -_| -_| |\n |___|_|___|___|_|_|___|_____|_|_|___|___|_|\n \"\"\"\n )\n )\n\n print(f\"cibuildwheel version {cibuildwheel.__version__}\\n\")\n\n print(\"Build options:\")\n print(f\" platform: {platform!r}\")\n print(textwrap.indent(options.summary(identifiers), \" \"))\n\n print(f\"Cache folder: {CIBW_CACHE_PATH}\")\n\n warnings = detect_warnings(options=options, identifiers=identifiers)\n if warnings:\n print(\"\\nWarnings:\")\n for warning in warnings:\n print(\" \" + warning)\n\n print(\"\\nHere we go!\\n\")\n\n\ndef get_build_identifiers(\n platform: PlatformName, build_selector: BuildSelector, architectures: set[Architecture]\n) -> list[str]:\n python_configurations: (\n list[cibuildwheel.linux.PythonConfiguration]\n | list[cibuildwheel.windows.PythonConfiguration]\n | list[cibuildwheel.macos.PythonConfiguration]\n )\n\n if platform == \"linux\":\n python_configurations = cibuildwheel.linux.get_python_configurations(\n build_selector, architectures\n )\n elif platform == \"windows\":\n python_configurations = cibuildwheel.windows.get_python_configurations(\n build_selector, architectures\n )\n elif platform == \"macos\":\n python_configurations = cibuildwheel.macos.get_python_configurations(\n build_selector, architectures\n )\n else:\n assert_never(platform)\n\n return [config.identifier for config in python_configurations]\n\n\ndef detect_warnings(*, options: Options, identifiers: list[str]) -> list[str]:\n warnings = []\n\n # warn about deprecated {python} and {pip}\n for option_name in [\"test_command\", \"before_build\"]:\n option_values = [getattr(options.build_options(i), option_name) for i in identifiers]\n\n if any(o and (\"{python}\" in o or \"{pip}\" in o) for o in option_values):\n # Reminder: in an f-string, double braces means literal single brace\n msg = (\n f\"{option_name}: '{{python}}' and '{{pip}}' are no longer needed, \"\n \"and will be removed in a future release. Simply use 'python' or 'pip' instead.\"\n )\n warnings.append(msg)\n\n return warnings\n\n\nif __name__ == \"__main__\":\n main()\n",
"path": "cibuildwheel/__main__.py"
}
] | [
{
"content": "from __future__ import annotations\n\nimport argparse\nimport os\nimport shutil\nimport sys\nimport tarfile\nimport textwrap\nimport typing\nfrom pathlib import Path\nfrom tempfile import mkdtemp\n\nimport cibuildwheel\nimport cibuildwheel.linux\nimport cibuildwheel.macos\nimport cibuildwheel.util\nimport cibuildwheel.windows\nfrom cibuildwheel.architecture import Architecture, allowed_architectures_check\nfrom cibuildwheel.logger import log\nfrom cibuildwheel.options import CommandLineArguments, Options, compute_options\nfrom cibuildwheel.typing import PLATFORMS, PlatformName, assert_never\nfrom cibuildwheel.util import (\n CIBW_CACHE_PATH,\n BuildSelector,\n Unbuffered,\n chdir,\n detect_ci_provider,\n)\n\n\ndef main() -> None:\n parser = argparse.ArgumentParser(\n description=\"Build wheels for all the platforms.\",\n epilog=\"\"\"\n Most options are supplied via environment variables or in\n --config-file (pyproject.toml usually). See\n https://github.com/pypa/cibuildwheel#options for info.\n \"\"\",\n )\n\n parser.add_argument(\n \"--platform\",\n choices=[\"auto\", \"linux\", \"macos\", \"windows\"],\n default=None,\n help=\"\"\"\n Platform to build for. Use this option to override the\n auto-detected platform or to run cibuildwheel on your development\n machine. Specifying \"macos\" or \"windows\" only works on that\n operating system, but \"linux\" works on all three, as long as\n Docker/Podman is installed. Default: auto.\n \"\"\",\n )\n\n arch_list_str = \", \".join(a.name for a in Architecture)\n parser.add_argument(\n \"--archs\",\n default=None,\n help=f\"\"\"\n Comma-separated list of CPU architectures to build for.\n When set to 'auto', builds the architectures natively supported\n on this machine. Set this option to build an architecture\n via emulation, for example, using binfmt_misc and QEMU.\n Default: auto.\n Choices: auto, auto64, auto32, native, all, {arch_list_str}\n \"\"\",\n )\n\n parser.add_argument(\n \"--only\",\n default=None,\n help=\"\"\"\n Force a single wheel build when given an identifier. Overrides\n CIBW_BUILD/CIBW_SKIP. --platform and --arch cannot be specified\n if this is given.\n \"\"\",\n )\n\n parser.add_argument(\n \"--output-dir\",\n type=Path,\n default=Path(os.environ.get(\"CIBW_OUTPUT_DIR\", \"wheelhouse\")),\n help=\"Destination folder for the wheels. Default: wheelhouse.\",\n )\n\n parser.add_argument(\n \"--config-file\",\n default=\"\",\n help=\"\"\"\n TOML config file. Default: \"\", meaning {package}/pyproject.toml, if\n it exists. To refer to a project inside your project, use {package};\n this matters if you build from an SDist.\n \"\"\",\n )\n\n parser.add_argument(\n \"package_dir\",\n metavar=\"PACKAGE\",\n default=Path(\".\"),\n type=Path,\n nargs=\"?\",\n help=\"\"\"\n Path to the package that you want wheels for. Default: the working\n directory. Can be a directory inside the working directory, or an\n sdist. When set to a directory, the working directory is still\n considered the 'project' and is copied into the build container\n on Linux. When set to a tar.gz sdist file, --config-file\n and --output-dir are relative to the current directory, and other\n paths are relative to the expanded SDist directory.\n \"\"\",\n )\n\n parser.add_argument(\n \"--print-build-identifiers\",\n action=\"store_true\",\n help=\"Print the build identifiers matched by the current invocation and exit.\",\n )\n\n parser.add_argument(\n \"--allow-empty\",\n action=\"store_true\",\n help=\"Do not report an error code if the build does not match any wheels.\",\n )\n\n parser.add_argument(\n \"--prerelease-pythons\",\n action=\"store_true\",\n help=\"Enable pre-release Python versions if available.\",\n )\n\n args = CommandLineArguments(**vars(parser.parse_args()))\n\n args.package_dir = args.package_dir.resolve()\n\n # This are always relative to the base directory, even in SDist builds\n args.output_dir = args.output_dir.resolve()\n\n # Standard builds if a directory or non-existent path is given\n if not args.package_dir.is_file() and not args.package_dir.name.endswith(\"tar.gz\"):\n build_in_directory(args)\n return\n\n # Tarfile builds require extraction and changing the directory\n temp_dir = Path(mkdtemp(prefix=\"cibw-sdist-\")).resolve(strict=True)\n try:\n with tarfile.open(args.package_dir) as tar:\n tar.extractall(path=temp_dir)\n\n # The extract directory is now the project dir\n try:\n (project_dir,) = temp_dir.iterdir()\n except ValueError:\n raise SystemExit(\"invalid sdist: didn't contain a single dir\") from None\n\n # This is now the new package dir\n args.package_dir = project_dir.resolve()\n\n with chdir(temp_dir):\n build_in_directory(args)\n finally:\n # avoid https://github.com/python/cpython/issues/86962 by performing\n # cleanup manually\n shutil.rmtree(temp_dir, ignore_errors=sys.platform.startswith(\"win\"))\n if temp_dir.exists():\n log.warning(f\"Can't delete temporary folder '{str(temp_dir)}'\")\n\n\ndef build_in_directory(args: CommandLineArguments) -> None:\n platform_option_value = args.platform or os.environ.get(\"CIBW_PLATFORM\", \"auto\")\n platform: PlatformName\n\n if args.only:\n if \"linux_\" in args.only:\n platform = \"linux\"\n elif \"macosx_\" in args.only:\n platform = \"macos\"\n elif \"win_\" in args.only or \"win32\" in args.only:\n platform = \"windows\"\n else:\n print(\n f\"Invalid --only='{args.only}', must be a build selector with a known platform\",\n file=sys.stderr,\n )\n sys.exit(2)\n if args.platform is not None:\n print(\n \"--platform cannot be specified with --only, it is computed from --only\",\n file=sys.stderr,\n )\n sys.exit(2)\n if args.archs is not None:\n print(\n \"--arch cannot be specified with --only, it is computed from --only\",\n file=sys.stderr,\n )\n sys.exit(2)\n elif platform_option_value != \"auto\":\n if platform_option_value not in PLATFORMS:\n print(f\"cibuildwheel: Unsupported platform: {platform_option_value}\", file=sys.stderr)\n sys.exit(2)\n\n platform = typing.cast(PlatformName, platform_option_value)\n else:\n ci_provider = detect_ci_provider()\n if ci_provider is None:\n print(\n textwrap.dedent(\n \"\"\"\n cibuildwheel: Unable to detect platform. cibuildwheel should run on your CI server;\n Travis CI, AppVeyor, Azure Pipelines, GitHub Actions, CircleCI, Gitlab, and Cirrus CI\n are supported. You can run on your development machine or other CI providers\n using the --platform argument. Check --help output for more information.\n \"\"\"\n ),\n file=sys.stderr,\n )\n sys.exit(2)\n if sys.platform.startswith(\"linux\"):\n platform = \"linux\"\n elif sys.platform == \"darwin\":\n platform = \"macos\"\n elif sys.platform == \"win32\":\n platform = \"windows\"\n else:\n print(\n 'cibuildwheel: Unable to detect platform from \"sys.platform\" in a CI environment. You can run '\n \"cibuildwheel using the --platform argument. Check --help output for more information.\",\n file=sys.stderr,\n )\n sys.exit(2)\n\n options = compute_options(platform=platform, command_line_arguments=args)\n\n package_dir = options.globals.package_dir\n package_files = {\"setup.py\", \"setup.cfg\", \"pyproject.toml\"}\n\n if not any(package_dir.joinpath(name).exists() for name in package_files):\n names = \", \".join(sorted(package_files, reverse=True))\n msg = f\"cibuildwheel: Could not find any of {{{names}}} at root of package\"\n print(msg, file=sys.stderr)\n sys.exit(2)\n\n identifiers = get_build_identifiers(\n platform=platform,\n build_selector=options.globals.build_selector,\n architectures=options.globals.architectures,\n )\n\n if args.print_build_identifiers:\n for identifier in identifiers:\n print(identifier)\n sys.exit(0)\n\n # Add CIBUILDWHEEL environment variable\n os.environ[\"CIBUILDWHEEL\"] = \"1\"\n\n # Python is buffering by default when running on the CI platforms, giving problems interleaving subprocess call output with unflushed calls to 'print'\n sys.stdout = Unbuffered(sys.stdout) # type: ignore[assignment]\n\n # create the cache dir before it gets printed & builds performed\n CIBW_CACHE_PATH.mkdir(parents=True, exist_ok=True)\n\n print_preamble(platform=platform, options=options, identifiers=identifiers)\n\n try:\n options.check_for_invalid_configuration(identifiers)\n allowed_architectures_check(platform, options.globals.architectures)\n except ValueError as err:\n print(\"cibuildwheel:\", *err.args, file=sys.stderr)\n sys.exit(4)\n\n if not identifiers:\n print(\n f\"cibuildwheel: No build identifiers selected: {options.globals.build_selector}\",\n file=sys.stderr,\n )\n if not args.allow_empty:\n sys.exit(3)\n\n output_dir = options.globals.output_dir\n\n if not output_dir.exists():\n output_dir.mkdir(parents=True)\n\n tmp_path = Path(mkdtemp(prefix=\"cibw-run-\")).resolve(strict=True)\n try:\n with cibuildwheel.util.print_new_wheels(\n \"\\n{n} wheels produced in {m:.0f} minutes:\", output_dir\n ):\n if platform == \"linux\":\n cibuildwheel.linux.build(options, tmp_path)\n elif platform == \"windows\":\n cibuildwheel.windows.build(options, tmp_path)\n elif platform == \"macos\":\n cibuildwheel.macos.build(options, tmp_path)\n else:\n assert_never(platform)\n finally:\n # avoid https://github.com/python/cpython/issues/86962 by performing\n # cleanup manually\n shutil.rmtree(tmp_path, ignore_errors=sys.platform.startswith(\"win\"))\n if tmp_path.exists():\n log.warning(f\"Can't delete temporary folder '{str(tmp_path)}'\")\n\n\ndef print_preamble(platform: str, options: Options, identifiers: list[str]) -> None:\n print(\n textwrap.dedent(\n \"\"\"\n _ _ _ _ _ _ _\n ___|_| |_ _ _|_| |_| |_ _ _| |_ ___ ___| |\n | _| | . | | | | | . | | | | | -_| -_| |\n |___|_|___|___|_|_|___|_____|_|_|___|___|_|\n \"\"\"\n )\n )\n\n print(f\"cibuildwheel version {cibuildwheel.__version__}\\n\")\n\n print(\"Build options:\")\n print(f\" platform: {platform!r}\")\n print(textwrap.indent(options.summary(identifiers), \" \"))\n\n print(f\"Cache folder: {CIBW_CACHE_PATH}\")\n\n warnings = detect_warnings(options=options, identifiers=identifiers)\n if warnings:\n print(\"\\nWarnings:\")\n for warning in warnings:\n print(\" \" + warning)\n\n print(\"\\nHere we go!\\n\")\n\n\ndef get_build_identifiers(\n platform: PlatformName, build_selector: BuildSelector, architectures: set[Architecture]\n) -> list[str]:\n python_configurations: (\n list[cibuildwheel.linux.PythonConfiguration]\n | list[cibuildwheel.windows.PythonConfiguration]\n | list[cibuildwheel.macos.PythonConfiguration]\n )\n\n if platform == \"linux\":\n python_configurations = cibuildwheel.linux.get_python_configurations(\n build_selector, architectures\n )\n elif platform == \"windows\":\n python_configurations = cibuildwheel.windows.get_python_configurations(\n build_selector, architectures\n )\n elif platform == \"macos\":\n python_configurations = cibuildwheel.macos.get_python_configurations(\n build_selector, architectures\n )\n else:\n assert_never(platform)\n\n return [config.identifier for config in python_configurations]\n\n\ndef detect_warnings(*, options: Options, identifiers: list[str]) -> list[str]:\n warnings = []\n\n # warn about deprecated {python} and {pip}\n for option_name in [\"test_command\", \"before_build\"]:\n option_values = [getattr(options.build_options(i), option_name) for i in identifiers]\n\n if any(o and (\"{python}\" in o or \"{pip}\" in o) for o in option_values):\n # Reminder: in an f-string, double braces means literal single brace\n msg = (\n f\"{option_name}: '{{python}}' and '{{pip}}' are no longer needed, \"\n \"and will be removed in a future release. Simply use 'python' or 'pip' instead.\"\n )\n warnings.append(msg)\n\n return warnings\n\n\nif __name__ == \"__main__\":\n main()\n",
"path": "cibuildwheel/__main__.py"
}
] | diff --git a/cibuildwheel/__main__.py b/cibuildwheel/__main__.py
index 4d1402d8c..dcfc81d46 100644
--- a/cibuildwheel/__main__.py
+++ b/cibuildwheel/__main__.py
@@ -173,7 +173,7 @@ def build_in_directory(args: CommandLineArguments) -> None:
platform = "linux"
elif "macosx_" in args.only:
platform = "macos"
- elif "win_" in args.only:
+ elif "win_" in args.only or "win32" in args.only:
platform = "windows"
else:
print(
diff --git a/unit_test/main_tests/main_platform_test.py b/unit_test/main_tests/main_platform_test.py
index 9136802c8..b947ce4f5 100644
--- a/unit_test/main_tests/main_platform_test.py
+++ b/unit_test/main_tests/main_platform_test.py
@@ -199,6 +199,7 @@ def test_archs_platform_all(platform, intercepted_build_args, monkeypatch):
(
("cp311-manylinux_x86_64", "linux"),
("cp310-win_amd64", "windows"),
+ ("cp310-win32", "windows"),
("cp311-macosx_x86_64", "macos"),
),
)
|
openai__openai-python-1007 | Missing default value to logprobs in openai.types.chat.chat_completion.Choice
### Confirm this is an issue with the Python library and not an underlying OpenAI API
- [X] This is an issue with the Python library
### Describe the bug
#980 added token `logprobs` to chat completions of type `Optional[ChoiceLogprobs]` in [`openai.types.chat.chat_completion.Choice`](https://github.com/openai/openai-python/blob/3ad4e8bc9d89d7a81586bf598289ff62b0a339b9/src/openai/types/chat/chat_completion.py#L33) and [`openai.types.chat.chat_completion_chunk.Choice`](https://github.com/openai/openai-python/blob/3ad4e8bc9d89d7a81586bf598289ff62b0a339b9/src/openai/types/chat/chat_completion_chunk.py#L97). In the latter, the default value is set to `None`, while in the former it is not set. This causes backward compatibility problems with code written for versions prior to 1.5.0.
### To Reproduce
Execution of the following code fails:
```python
from openai.types.chat.chat_completion import ChatCompletionMessage, Choice
msg = ChatCompletionMessage(role="assistant", content="")
Choice(
index=0,
finish_reason="stop",
message=msg,
)
```
The output
```
----> 1 Choice(
2 index=0,
3 finish_reason="stop",
4 message=msg,
5 )
File /.venv-3.10/lib/python3.10/site-packages/pydantic/main.py:164, in BaseModel.__init__(__pydantic_self__, **data)
162 # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
163 __tracebackhide__ = True
--> 164 __pydantic_self__.__pydantic_validator__.validate_python(data, self_instance=__pydantic_self__)
ValidationError: 1 validation error for Choice
logprobs
Field required [type=missing, input_value={'index': 0, 'finish_reas...=None, tool_calls=None)}, input_type=dict]
For further information visit https://errors.pydantic.dev/2.5/v/missing
```
Setting `logprobs` to `None` fixes the problem.
```python
from openai.types.chat.chat_completion import ChatCompletionMessage, Choice
msg = ChatCompletionMessage(role="assistant", content="")
Choice(
index=0,
finish_reason="stop",
message=msg,
logprobs=None # added line
)
```
### Code snippets
```Python
see above
```
### OS
Linux
### Python version
Python 3.10.13
### Library version
openai 1.6.0
| [
{
"content": "# File generated from our OpenAPI spec by Stainless.\n\nfrom typing import List, Optional\nfrom typing_extensions import Literal\n\nfrom ..._models import BaseModel\nfrom ..completion_usage import CompletionUsage\nfrom .chat_completion_message import ChatCompletionMessage\nfrom .chat_completion_token_logprob import ChatCompletionTokenLogprob\n\n__all__ = [\"ChatCompletion\", \"Choice\", \"ChoiceLogprobs\"]\n\n\nclass ChoiceLogprobs(BaseModel):\n content: Optional[List[ChatCompletionTokenLogprob]]\n \"\"\"A list of message content tokens with log probability information.\"\"\"\n\n\nclass Choice(BaseModel):\n finish_reason: Literal[\"stop\", \"length\", \"tool_calls\", \"content_filter\", \"function_call\"]\n \"\"\"The reason the model stopped generating tokens.\n\n This will be `stop` if the model hit a natural stop point or a provided stop\n sequence, `length` if the maximum number of tokens specified in the request was\n reached, `content_filter` if content was omitted due to a flag from our content\n filters, `tool_calls` if the model called a tool, or `function_call`\n (deprecated) if the model called a function.\n \"\"\"\n\n index: int\n \"\"\"The index of the choice in the list of choices.\"\"\"\n\n logprobs: Optional[ChoiceLogprobs]\n \"\"\"Log probability information for the choice.\"\"\"\n\n message: ChatCompletionMessage\n \"\"\"A chat completion message generated by the model.\"\"\"\n\n\nclass ChatCompletion(BaseModel):\n id: str\n \"\"\"A unique identifier for the chat completion.\"\"\"\n\n choices: List[Choice]\n \"\"\"A list of chat completion choices.\n\n Can be more than one if `n` is greater than 1.\n \"\"\"\n\n created: int\n \"\"\"The Unix timestamp (in seconds) of when the chat completion was created.\"\"\"\n\n model: str\n \"\"\"The model used for the chat completion.\"\"\"\n\n object: Literal[\"chat.completion\"]\n \"\"\"The object type, which is always `chat.completion`.\"\"\"\n\n system_fingerprint: Optional[str] = None\n \"\"\"This fingerprint represents the backend configuration that the model runs with.\n\n Can be used in conjunction with the `seed` request parameter to understand when\n backend changes have been made that might impact determinism.\n \"\"\"\n\n usage: Optional[CompletionUsage] = None\n \"\"\"Usage statistics for the completion request.\"\"\"\n",
"path": "src/openai/types/chat/chat_completion.py"
}
] | [
{
"content": "# File generated from our OpenAPI spec by Stainless.\n\nfrom typing import List, Optional\nfrom typing_extensions import Literal\n\nfrom ..._models import BaseModel\nfrom ..completion_usage import CompletionUsage\nfrom .chat_completion_message import ChatCompletionMessage\nfrom .chat_completion_token_logprob import ChatCompletionTokenLogprob\n\n__all__ = [\"ChatCompletion\", \"Choice\", \"ChoiceLogprobs\"]\n\n\nclass ChoiceLogprobs(BaseModel):\n content: Optional[List[ChatCompletionTokenLogprob]]\n \"\"\"A list of message content tokens with log probability information.\"\"\"\n\n\nclass Choice(BaseModel):\n finish_reason: Literal[\"stop\", \"length\", \"tool_calls\", \"content_filter\", \"function_call\"]\n \"\"\"The reason the model stopped generating tokens.\n\n This will be `stop` if the model hit a natural stop point or a provided stop\n sequence, `length` if the maximum number of tokens specified in the request was\n reached, `content_filter` if content was omitted due to a flag from our content\n filters, `tool_calls` if the model called a tool, or `function_call`\n (deprecated) if the model called a function.\n \"\"\"\n\n index: int\n \"\"\"The index of the choice in the list of choices.\"\"\"\n\n logprobs: Optional[ChoiceLogprobs] = None\n \"\"\"Log probability information for the choice.\"\"\"\n\n message: ChatCompletionMessage\n \"\"\"A chat completion message generated by the model.\"\"\"\n\n\nclass ChatCompletion(BaseModel):\n id: str\n \"\"\"A unique identifier for the chat completion.\"\"\"\n\n choices: List[Choice]\n \"\"\"A list of chat completion choices.\n\n Can be more than one if `n` is greater than 1.\n \"\"\"\n\n created: int\n \"\"\"The Unix timestamp (in seconds) of when the chat completion was created.\"\"\"\n\n model: str\n \"\"\"The model used for the chat completion.\"\"\"\n\n object: Literal[\"chat.completion\"]\n \"\"\"The object type, which is always `chat.completion`.\"\"\"\n\n system_fingerprint: Optional[str] = None\n \"\"\"This fingerprint represents the backend configuration that the model runs with.\n\n Can be used in conjunction with the `seed` request parameter to understand when\n backend changes have been made that might impact determinism.\n \"\"\"\n\n usage: Optional[CompletionUsage] = None\n \"\"\"Usage statistics for the completion request.\"\"\"\n",
"path": "src/openai/types/chat/chat_completion.py"
}
] | diff --git a/src/openai/types/chat/chat_completion.py b/src/openai/types/chat/chat_completion.py
index 055280c347..b2e98a3144 100644
--- a/src/openai/types/chat/chat_completion.py
+++ b/src/openai/types/chat/chat_completion.py
@@ -30,7 +30,7 @@ class Choice(BaseModel):
index: int
"""The index of the choice in the list of choices."""
- logprobs: Optional[ChoiceLogprobs]
+ logprobs: Optional[ChoiceLogprobs] = None
"""Log probability information for the choice."""
message: ChatCompletionMessage
|
freedomofpress__securedrop-703 | Don't armor encrypted submissions
SecureDrop currently armors encrypted submissions. This bloats the size of stored submissions significantly due to the encoding. For example, a 93 MB upload results in a 125.7 MB submission for the journalist to download.
Downloading anything over Tor is very slow (the aforementioned download took me, on average, 9 minutes to download). Therefore, unnecessarily increasing the size of submissions severely impacts usability. There is no reason that I can think of to ascii armor submissions - they are uploaded and downloaded over HTTP, which automatically handles encoding and de-encoding binary data.
| [
{
"content": "# -*- coding: utf-8 -*-\nimport os\nimport subprocess\nfrom base64 import b32encode\n\nfrom Crypto.Random import random\nimport gnupg\nimport scrypt\n\nimport config\nimport store\n\n# to fix gpg error #78 on production\nos.environ['USERNAME'] = 'www-data'\n\nGPG_KEY_TYPE = \"RSA\"\nif os.environ.get('SECUREDROP_ENV') == 'test':\n # Optiimize crypto to speed up tests (at the expense of security - DO NOT\n # use these settings in production)\n GPG_KEY_LENGTH = 1024\n SCRYPT_PARAMS = dict(N=2**1, r=1, p=1)\nelse:\n GPG_KEY_LENGTH = 4096\n SCRYPT_PARAMS = config.SCRYPT_PARAMS\n\nSCRYPT_ID_PEPPER = config.SCRYPT_ID_PEPPER\nSCRYPT_GPG_PEPPER = config.SCRYPT_GPG_PEPPER\n\nDEFAULT_WORDS_IN_RANDOM_ID = 8\n\n# Make sure these pass before the app can run\n# TODO: Add more tests\ndef do_runtime_tests():\n assert(config.SCRYPT_ID_PEPPER != config.SCRYPT_GPG_PEPPER)\n # crash if we don't have srm:\n try:\n subprocess.check_call(['srm'], stdout=subprocess.PIPE)\n except subprocess.CalledProcessError:\n pass\n\ndo_runtime_tests()\n\nGPG_BINARY = 'gpg2'\ntry:\n p = subprocess.Popen([GPG_BINARY, '--version'], stdout=subprocess.PIPE)\nexcept OSError:\n GPG_BINARY = 'gpg'\n p = subprocess.Popen([GPG_BINARY, '--version'], stdout=subprocess.PIPE)\n\nassert p.stdout.readline().split()[\n -1].split('.')[0] == '2', \"upgrade GPG to 2.0\"\ndel p\n\ngpg = gnupg.GPG(binary=GPG_BINARY, homedir=config.GPG_KEY_DIR)\n\nwords = file(config.WORD_LIST).read().split('\\n')\nnouns = file(config.NOUNS).read().split('\\n')\nadjectives = file(config.ADJECTIVES).read().split('\\n')\n\n\nclass CryptoException(Exception):\n pass\n\n\ndef clean(s, also=''):\n \"\"\"\n >>> clean(\"Hello, world!\")\n Traceback (most recent call last):\n ...\n CryptoException: invalid input\n >>> clean(\"Helloworld\")\n 'Helloworld'\n \"\"\"\n # safe characters for every possible word in the wordlist includes capital\n # letters because codename hashes are base32-encoded with capital letters\n ok = ' !#%$&)(+*-1032547698;:=?@acbedgfihkjmlonqpsrutwvyxzABCDEFGHIJKLMNOPQRSTUVWXYZ'\n for c in s:\n if c not in ok and c not in also:\n raise CryptoException(\"invalid input: %s\" % s)\n # scrypt.hash requires input of type str. Since the wordlist is all ASCII\n # characters, this conversion is not problematic\n return str(s)\n\n\ndef genrandomid(words_in_random_id=DEFAULT_WORDS_IN_RANDOM_ID):\n return ' '.join(random.choice(words) for x in range(words_in_random_id))\n\n\ndef display_id():\n return ' '.join([random.choice(adjectives), random.choice(nouns)])\n\n\ndef hash_codename(codename, salt=SCRYPT_ID_PEPPER):\n \"\"\"\n >>> hash_codename('Hello, world!')\n 'EQZGCJBRGISGOTC2NZVWG6LILJBHEV3CINNEWSCLLFTUWZLFHBTS6WLCHFHTOLRSGQXUQLRQHFMXKOKKOQ4WQ6SXGZXDAS3Z'\n \"\"\"\n return b32encode(scrypt.hash(clean(codename), salt, **SCRYPT_PARAMS))\n\n\ndef genkeypair(name, secret):\n \"\"\"\n >>> if not gpg.list_keys(hash_codename('randomid')):\n ... genkeypair(hash_codename('randomid'), 'randomid').type\n ... else:\n ... u'P'\n u'P'\n \"\"\"\n name = clean(name)\n secret = hash_codename(secret, salt=SCRYPT_GPG_PEPPER)\n return gpg.gen_key(gpg.gen_key_input(\n key_type=GPG_KEY_TYPE, key_length=GPG_KEY_LENGTH,\n passphrase=secret,\n name_email=name\n ))\n\n\ndef delete_reply_keypair(source_id):\n key = getkey(source_id)\n # If this source was never flagged for reivew, they won't have a reply keypair\n if not key: return\n # The private key needs to be deleted before the public key can be deleted\n # http://pythonhosted.org/python-gnupg/#deleting-keys\n gpg.delete_keys(key, True) # private key\n gpg.delete_keys(key) # public key\n # TODO: srm?\n\n\ndef getkey(name):\n for key in gpg.list_keys():\n for uid in key['uids']:\n if name in uid:\n return key['fingerprint']\n return None\n\n\ndef get_key_by_fingerprint(fingerprint):\n matches = filter(lambda k: k['fingerprint'] == fingerprint, gpg.list_keys())\n return matches[0] if matches else None\n\n\ndef encrypt(plaintext, fingerprints, output=None):\n # Verify the output path\n if output:\n store.verify(output)\n\n # Remove any spaces from provided fingerpints\n # GPG outputs fingerprints with spaces for readability, but requires the\n # spaces to be removed when using fingerprints to specify recipients.\n if not isinstance(fingerprints, (list, tuple)):\n fingerprints = [fingerprints,]\n fingerprints = [ fpr.replace(' ', '') for fpr in fingerprints ]\n\n if isinstance(plaintext, unicode):\n plaintext = plaintext.encode('utf8')\n\n encrypt_fn = gpg.encrypt if isinstance(plaintext, str) else gpg.encrypt_file\n out = encrypt_fn(plaintext,\n *fingerprints,\n output=output,\n always_trust=True)\n if out.ok:\n return out.data\n else:\n raise CryptoException(out.stderr)\n\n\ndef decrypt(secret, plain_text):\n \"\"\"\n >>> key = genkeypair('randomid', 'randomid')\n >>> decrypt('randomid', 'randomid',\n ... encrypt('randomid', 'Goodbye, cruel world!')\n ... )\n 'Goodbye, cruel world!'\n \"\"\"\n hashed_codename = hash_codename(secret, salt=SCRYPT_GPG_PEPPER)\n return gpg.decrypt(plain_text, passphrase=hashed_codename).data\n\n\nif __name__ == \"__main__\":\n import doctest\n doctest.testmod()\n",
"path": "securedrop/crypto_util.py"
}
] | [
{
"content": "# -*- coding: utf-8 -*-\nimport os\nimport subprocess\nfrom base64 import b32encode\n\nfrom Crypto.Random import random\nimport gnupg\nimport scrypt\n\nimport config\nimport store\n\n# to fix gpg error #78 on production\nos.environ['USERNAME'] = 'www-data'\n\nGPG_KEY_TYPE = \"RSA\"\nif os.environ.get('SECUREDROP_ENV') == 'test':\n # Optiimize crypto to speed up tests (at the expense of security - DO NOT\n # use these settings in production)\n GPG_KEY_LENGTH = 1024\n SCRYPT_PARAMS = dict(N=2**1, r=1, p=1)\nelse:\n GPG_KEY_LENGTH = 4096\n SCRYPT_PARAMS = config.SCRYPT_PARAMS\n\nSCRYPT_ID_PEPPER = config.SCRYPT_ID_PEPPER\nSCRYPT_GPG_PEPPER = config.SCRYPT_GPG_PEPPER\n\nDEFAULT_WORDS_IN_RANDOM_ID = 8\n\n# Make sure these pass before the app can run\n# TODO: Add more tests\ndef do_runtime_tests():\n assert(config.SCRYPT_ID_PEPPER != config.SCRYPT_GPG_PEPPER)\n # crash if we don't have srm:\n try:\n subprocess.check_call(['srm'], stdout=subprocess.PIPE)\n except subprocess.CalledProcessError:\n pass\n\ndo_runtime_tests()\n\nGPG_BINARY = 'gpg2'\ntry:\n p = subprocess.Popen([GPG_BINARY, '--version'], stdout=subprocess.PIPE)\nexcept OSError:\n GPG_BINARY = 'gpg'\n p = subprocess.Popen([GPG_BINARY, '--version'], stdout=subprocess.PIPE)\n\nassert p.stdout.readline().split()[\n -1].split('.')[0] == '2', \"upgrade GPG to 2.0\"\ndel p\n\ngpg = gnupg.GPG(binary=GPG_BINARY, homedir=config.GPG_KEY_DIR)\n\nwords = file(config.WORD_LIST).read().split('\\n')\nnouns = file(config.NOUNS).read().split('\\n')\nadjectives = file(config.ADJECTIVES).read().split('\\n')\n\n\nclass CryptoException(Exception):\n pass\n\n\ndef clean(s, also=''):\n \"\"\"\n >>> clean(\"Hello, world!\")\n Traceback (most recent call last):\n ...\n CryptoException: invalid input\n >>> clean(\"Helloworld\")\n 'Helloworld'\n \"\"\"\n # safe characters for every possible word in the wordlist includes capital\n # letters because codename hashes are base32-encoded with capital letters\n ok = ' !#%$&)(+*-1032547698;:=?@acbedgfihkjmlonqpsrutwvyxzABCDEFGHIJKLMNOPQRSTUVWXYZ'\n for c in s:\n if c not in ok and c not in also:\n raise CryptoException(\"invalid input: %s\" % s)\n # scrypt.hash requires input of type str. Since the wordlist is all ASCII\n # characters, this conversion is not problematic\n return str(s)\n\n\ndef genrandomid(words_in_random_id=DEFAULT_WORDS_IN_RANDOM_ID):\n return ' '.join(random.choice(words) for x in range(words_in_random_id))\n\n\ndef display_id():\n return ' '.join([random.choice(adjectives), random.choice(nouns)])\n\n\ndef hash_codename(codename, salt=SCRYPT_ID_PEPPER):\n \"\"\"\n >>> hash_codename('Hello, world!')\n 'EQZGCJBRGISGOTC2NZVWG6LILJBHEV3CINNEWSCLLFTUWZLFHBTS6WLCHFHTOLRSGQXUQLRQHFMXKOKKOQ4WQ6SXGZXDAS3Z'\n \"\"\"\n return b32encode(scrypt.hash(clean(codename), salt, **SCRYPT_PARAMS))\n\n\ndef genkeypair(name, secret):\n \"\"\"\n >>> if not gpg.list_keys(hash_codename('randomid')):\n ... genkeypair(hash_codename('randomid'), 'randomid').type\n ... else:\n ... u'P'\n u'P'\n \"\"\"\n name = clean(name)\n secret = hash_codename(secret, salt=SCRYPT_GPG_PEPPER)\n return gpg.gen_key(gpg.gen_key_input(\n key_type=GPG_KEY_TYPE, key_length=GPG_KEY_LENGTH,\n passphrase=secret,\n name_email=name\n ))\n\n\ndef delete_reply_keypair(source_id):\n key = getkey(source_id)\n # If this source was never flagged for reivew, they won't have a reply keypair\n if not key: return\n # The private key needs to be deleted before the public key can be deleted\n # http://pythonhosted.org/python-gnupg/#deleting-keys\n gpg.delete_keys(key, True) # private key\n gpg.delete_keys(key) # public key\n # TODO: srm?\n\n\ndef getkey(name):\n for key in gpg.list_keys():\n for uid in key['uids']:\n if name in uid:\n return key['fingerprint']\n return None\n\n\ndef get_key_by_fingerprint(fingerprint):\n matches = filter(lambda k: k['fingerprint'] == fingerprint, gpg.list_keys())\n return matches[0] if matches else None\n\n\ndef encrypt(plaintext, fingerprints, output=None):\n # Verify the output path\n if output:\n store.verify(output)\n\n # Remove any spaces from provided fingerpints\n # GPG outputs fingerprints with spaces for readability, but requires the\n # spaces to be removed when using fingerprints to specify recipients.\n if not isinstance(fingerprints, (list, tuple)):\n fingerprints = [fingerprints,]\n fingerprints = [ fpr.replace(' ', '') for fpr in fingerprints ]\n\n if isinstance(plaintext, unicode):\n plaintext = plaintext.encode('utf8')\n\n encrypt_fn = gpg.encrypt if isinstance(plaintext, str) else gpg.encrypt_file\n out = encrypt_fn(plaintext,\n *fingerprints,\n output=output,\n always_trust=True,\n armor=False)\n if out.ok:\n return out.data\n else:\n raise CryptoException(out.stderr)\n\n\ndef decrypt(secret, plain_text):\n \"\"\"\n >>> key = genkeypair('randomid', 'randomid')\n >>> decrypt('randomid', 'randomid',\n ... encrypt('randomid', 'Goodbye, cruel world!')\n ... )\n 'Goodbye, cruel world!'\n \"\"\"\n hashed_codename = hash_codename(secret, salt=SCRYPT_GPG_PEPPER)\n return gpg.decrypt(plain_text, passphrase=hashed_codename).data\n\n\nif __name__ == \"__main__\":\n import doctest\n doctest.testmod()\n",
"path": "securedrop/crypto_util.py"
}
] | diff --git a/securedrop/crypto_util.py b/securedrop/crypto_util.py
index c965484675..786828c168 100644
--- a/securedrop/crypto_util.py
+++ b/securedrop/crypto_util.py
@@ -158,7 +158,8 @@ def encrypt(plaintext, fingerprints, output=None):
out = encrypt_fn(plaintext,
*fingerprints,
output=output,
- always_trust=True)
+ always_trust=True,
+ armor=False)
if out.ok:
return out.data
else:
diff --git a/securedrop/tests/test_unit_integration.py b/securedrop/tests/test_unit_integration.py
index 64bedf51d9..d1ccc152b2 100644
--- a/securedrop/tests/test_unit_integration.py
+++ b/securedrop/tests/test_unit_integration.py
@@ -277,7 +277,7 @@ def _can_decrypt_with_key(self, msg, key_fpr, passphrase=None):
salt=crypto_util.SCRYPT_GPG_PEPPER)
decrypted_data = gpg.decrypt(msg, passphrase=passphrase)
self.assertTrue(decrypted_data.ok,
- "Could not decrypt msg with key, gpg says: {}".format(decrypted_data.status))
+ "Could not decrypt msg with key, gpg says: {}".format(decrypted_data.stderr))
# We have to clean up the temporary GPG dir
shutil.rmtree(gpg_tmp_dir)
@@ -362,10 +362,10 @@ def helper_test_reply(self, test_reply, expected_success=True):
doc_names_selected=checkbox_values
), follow_redirects=True)
self.assertEqual(rv.status_code, 200)
- pgp_msg_re = r'-----BEGIN PGP MESSAGE-----.*-----END PGP MESSAGE-----'
- pgp_msg = re.search(pgp_msg_re, rv.data, re.MULTILINE|re.DOTALL).group(0)
- self._can_decrypt_with_key(pgp_msg, config.JOURNALIST_KEY)
- self._can_decrypt_with_key(pgp_msg, crypto_util.getkey(sid), codename)
+ zf = zipfile.ZipFile(StringIO(rv.data), 'r')
+ data = zf.read(zf.namelist()[0])
+ self._can_decrypt_with_key(data, config.JOURNALIST_KEY)
+ self._can_decrypt_with_key(data, crypto_util.getkey(sid), codename)
# Test deleting reply on the journalist interface
last_reply_number = len(soup.select('input[name="doc_names_selected"]')) - 1
|
scikit-hep__pyhf-307 | Add --version flag to pyhf CLI
# Description
As [suggested by Lukas](https://github.com/diana-hep/pyhf/pull/304#issuecomment-428856809), adding a `--version` flag to the pyhf CLI could be useful.
| [
{
"content": "import logging\nlogging.basicConfig()\nlog = logging.getLogger(__name__)\n\nimport click\nimport json\nimport os\nimport jsonpatch\nimport sys\n\nfrom . import readxml\nfrom . import writexml\nfrom .utils import runOnePoint\nfrom .pdf import Model\n\n\[email protected](context_settings=dict(help_option_names=['-h', '--help']))\ndef pyhf():\n pass\n\[email protected]()\[email protected]('entrypoint-xml', type=click.Path(exists=True))\[email protected]('--basedir', help='The base directory for the XML files to point relative to.', type=click.Path(exists=True), default=os.getcwd())\[email protected]('--output-file', help='The location of the output json file. If not specified, prints to screen.', default=None)\[email protected]('--track-progress/--hide-progress', default=True)\ndef xml2json(entrypoint_xml, basedir, output_file, track_progress):\n \"\"\" Entrypoint XML: The top-level XML file for the PDF definition. \"\"\"\n spec = readxml.parse(entrypoint_xml, basedir, track_progress=track_progress)\n if output_file is None:\n print(json.dumps(spec, indent=4, sort_keys=True))\n else:\n with open(output_file, 'w+') as out_file:\n json.dump(spec, out_file, indent=4, sort_keys=True)\n log.debug(\"Written to {0:s}\".format(output_file))\n sys.exit(0)\n\[email protected]()\[email protected]('workspace', default='-')\[email protected]('xmlfile', default='-')\[email protected]('--specroot', default=click.Path(exists=True))\[email protected]('--dataroot', default=click.Path(exists=True))\ndef json2xml(workspace, xmlfile, specroot, dataroot):\n with click.open_file(workspace, 'r') as specstream:\n d = json.load(specstream)\n with click.open_file(xmlfile, 'w') as outstream:\n outstream.write(writexml.writexml(d, specroot, dataroot,'').decode('utf-8'))\n sys.exit(0)\n\[email protected]()\[email protected]('workspace', default='-')\[email protected]('--output-file', help='The location of the output json file. If not specified, prints to screen.', default=None)\[email protected]('--measurement', default=None)\[email protected]('-p', '--patch', multiple=True)\[email protected]('--qualify-names/--no-qualify-names', default=False)\ndef cls(workspace, output_file, measurement, qualify_names, patch):\n with click.open_file(workspace, 'r') as specstream:\n d = json.load(specstream)\n measurements = d['toplvl']['measurements']\n measurement_names = [m['name'] for m in measurements]\n measurement_index = 0\n log.debug('measurements defined:\\n\\t{0:s}'.format('\\n\\t'.join(measurement_names)))\n if measurement and measurement not in measurement_names:\n log.error('no measurement by name \\'{0:s}\\' exists, pick from one of the valid ones above'.format(measurement))\n sys.exit(1)\n else:\n if not measurement and len(measurements) > 1:\n log.warning('multiple measurements defined. Taking the first measurement.')\n measurement_index = 0\n elif measurement:\n measurement_index = measurement_names.index(measurement)\n\n log.debug('calculating CLs for measurement {0:s}'.format(measurements[measurement_index]['name']))\n spec = {'channels':d['channels']}\n for p in patch:\n with click.open_file(p, 'r') as read_file:\n p = jsonpatch.JsonPatch(json.loads(read_file.read()))\n spec = p.apply(spec)\n p = Model(spec, poiname=measurements[measurement_index]['config']['poi'], qualify_names=qualify_names)\n result = runOnePoint(1.0, sum((d['data'][c['name']] for c in d['channels']),[]) + p.config.auxdata, p)\n result = {'CLs_obs': result[-2].tolist()[0], 'CLs_exp': result[-1].ravel().tolist()}\n if output_file is None:\n print(json.dumps(result, indent=4, sort_keys=True))\n else:\n with open(output_file, 'w+') as out_file:\n json.dump(result, out_file, indent=4, sort_keys=True)\n log.debug(\"Written to {0:s}\".format(output_file))\n sys.exit(0)\n",
"path": "pyhf/commandline.py"
}
] | [
{
"content": "import logging\nlogging.basicConfig()\nlog = logging.getLogger(__name__)\n\nimport click\nimport json\nimport os\nimport jsonpatch\nimport sys\n\nfrom . import readxml\nfrom . import writexml\nfrom .utils import runOnePoint\nfrom .pdf import Model\nfrom .version import __version__\n\n\[email protected](context_settings=dict(help_option_names=['-h', '--help']))\[email protected]_option(version=__version__)\ndef pyhf():\n pass\n\[email protected]()\[email protected]('entrypoint-xml', type=click.Path(exists=True))\[email protected]('--basedir', help='The base directory for the XML files to point relative to.', type=click.Path(exists=True), default=os.getcwd())\[email protected]('--output-file', help='The location of the output json file. If not specified, prints to screen.', default=None)\[email protected]('--track-progress/--hide-progress', default=True)\ndef xml2json(entrypoint_xml, basedir, output_file, track_progress):\n \"\"\" Entrypoint XML: The top-level XML file for the PDF definition. \"\"\"\n spec = readxml.parse(entrypoint_xml, basedir, track_progress=track_progress)\n if output_file is None:\n print(json.dumps(spec, indent=4, sort_keys=True))\n else:\n with open(output_file, 'w+') as out_file:\n json.dump(spec, out_file, indent=4, sort_keys=True)\n log.debug(\"Written to {0:s}\".format(output_file))\n sys.exit(0)\n\[email protected]()\[email protected]('workspace', default='-')\[email protected]('xmlfile', default='-')\[email protected]('--specroot', default=click.Path(exists=True))\[email protected]('--dataroot', default=click.Path(exists=True))\ndef json2xml(workspace, xmlfile, specroot, dataroot):\n with click.open_file(workspace, 'r') as specstream:\n d = json.load(specstream)\n with click.open_file(xmlfile, 'w') as outstream:\n outstream.write(writexml.writexml(d, specroot, dataroot,'').decode('utf-8'))\n sys.exit(0)\n\[email protected]()\[email protected]('workspace', default='-')\[email protected]('--output-file', help='The location of the output json file. If not specified, prints to screen.', default=None)\[email protected]('--measurement', default=None)\[email protected]('-p', '--patch', multiple=True)\[email protected]('--qualify-names/--no-qualify-names', default=False)\ndef cls(workspace, output_file, measurement, qualify_names, patch):\n with click.open_file(workspace, 'r') as specstream:\n d = json.load(specstream)\n measurements = d['toplvl']['measurements']\n measurement_names = [m['name'] for m in measurements]\n measurement_index = 0\n log.debug('measurements defined:\\n\\t{0:s}'.format('\\n\\t'.join(measurement_names)))\n if measurement and measurement not in measurement_names:\n log.error('no measurement by name \\'{0:s}\\' exists, pick from one of the valid ones above'.format(measurement))\n sys.exit(1)\n else:\n if not measurement and len(measurements) > 1:\n log.warning('multiple measurements defined. Taking the first measurement.')\n measurement_index = 0\n elif measurement:\n measurement_index = measurement_names.index(measurement)\n\n log.debug('calculating CLs for measurement {0:s}'.format(measurements[measurement_index]['name']))\n spec = {'channels':d['channels']}\n for p in patch:\n with click.open_file(p, 'r') as read_file:\n p = jsonpatch.JsonPatch(json.loads(read_file.read()))\n spec = p.apply(spec)\n p = Model(spec, poiname=measurements[measurement_index]['config']['poi'], qualify_names=qualify_names)\n result = runOnePoint(1.0, sum((d['data'][c['name']] for c in d['channels']),[]) + p.config.auxdata, p)\n result = {'CLs_obs': result[-2].tolist()[0], 'CLs_exp': result[-1].ravel().tolist()}\n if output_file is None:\n print(json.dumps(result, indent=4, sort_keys=True))\n else:\n with open(output_file, 'w+') as out_file:\n json.dump(result, out_file, indent=4, sort_keys=True)\n log.debug(\"Written to {0:s}\".format(output_file))\n sys.exit(0)\n",
"path": "pyhf/commandline.py"
}
] | diff --git a/pyhf/commandline.py b/pyhf/commandline.py
index beeaff70fc..2b8bad4d91 100644
--- a/pyhf/commandline.py
+++ b/pyhf/commandline.py
@@ -12,9 +12,11 @@
from . import writexml
from .utils import runOnePoint
from .pdf import Model
+from .version import __version__
@click.group(context_settings=dict(help_option_names=['-h', '--help']))
[email protected]_option(version=__version__)
def pyhf():
pass
|
mars-project__mars-274 | [BUG] Tensor does not return data when eager mode is on
<!--
Thank you for your contribution!
Please review https://github.com/mars-project/mars/blob/master/CONTRIBUTING.rst before opening an issue.
-->
**Describe the bug**
First, I execute a tensor with eager mode off, it succeeded, then I turn on eager mode, execute another tensor with some error happened, finally, repr the identical tensor to the first tensor does not trigger execution automatically.
**To Reproduce**
```
In [1]: import mars.tensor as mt
In [2]: from mars.config import options
In [3]: a = mt.array(
...: [[0.1, 0.2, 0.3],
...: [0.3, 0.4, 0.2]]
...: )
In [4]: (a[:, mt.newaxis, :] - a[mt.newaxis, ...]).execute()
Out[4]:
array([[[ 0. , 0. , 0. ],
[-0.2, -0.2, 0.1]],
[[ 0.2, 0.2, -0.1],
[ 0. , 0. , 0. ]]])
In [5]: options.eager_mode = True
In [6]: mt.sqrt(mt.sum((a[:, mt.newaxis, :] - a[mt.newaxis, ...]) ** 2, axis=-1))
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
~/Workspace/mars/mars/tiles.py in _dispatch(self, op)
110 try:
--> 111 handler = self._handlers[op_cls]
112 return handler(op)
KeyError: <class 'mars.tensor.expressions.arithmetic.sqrt.TensorSqrt'>
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
<ipython-input-6-de975d9f2cf8> in <module>
----> 1 mt.sqrt(mt.sum((a[:, mt.newaxis, :] - a[mt.newaxis, ...]) ** 2, axis=-1))
~/Workspace/mars/mars/tensor/expressions/utils.py in h(*tensors, **kw)
157 kw['dtype'] = dtype
158
--> 159 ret = func(*tensors, **kw)
160 if ret is NotImplemented:
161 reverse_func = getattr(inspect.getmodule(func), 'r{0}'.format(func.__name__), None) \
~/Workspace/mars/mars/tensor/expressions/arithmetic/sqrt.py in sqrt(x, out, where, **kwargs)
78 """
79 op = TensorSqrt(**kwargs)
---> 80 return op(x, out=out, where=where)
81
~/Workspace/mars/mars/tensor/expressions/arithmetic/core.py in __call__(self, x, out, where)
348
349 def __call__(self, x, out=None, where=None):
--> 350 return self._call(x, out=out, where=where)
351
352
~/Workspace/mars/mars/tensor/expressions/arithmetic/core.py in _call(self, x, out, where)
331 shape = x.shape
332
--> 333 t = self.new_tensor([x, out, where], shape)
334
335 if out is None:
~/Workspace/mars/mars/tensor/expressions/core.py in new_tensor(self, inputs, shape, dtype, **kw)
54 raise TypeError('cannot new tensor with more than 1 outputs')
55
---> 56 return self.new_tensors(inputs, shape, dtype=dtype, **kw)[0]
57
58 def calc_shape(self, *inputs_shape):
~/Workspace/mars/mars/tensor/expressions/core.py in new_tensors(self, inputs, shape, dtype, chunks, nsplits, output_limit, kws, **kw)
48 output_limit=None, kws=None, **kw):
49 return self.new_entities(inputs, shape, chunks=chunks, nsplits=nsplits,
---> 50 output_limit=output_limit, kws=kws, dtype=dtype, **kw)
51
52 def new_tensor(self, inputs, shape, dtype=None, **kw):
~/Workspace/mars/mars/core.py in new_entities(self, inputs, shape, **kwargs)
578 entities = self._new_entities(inputs, shape, **kwargs)
579 if is_eager_mode():
--> 580 ExecutableTuple(entities).execute(fetch=False)
581 return entities
582
~/Workspace/mars/mars/core.py in execute(self, session, **kw)
594 if session is None:
595 session = Session.default_or_local()
--> 596 return session.run(*self, **kw)
~/Workspace/mars/mars/session.py in run(self, *tensors, **kw)
107
108 tensors = tuple(mt.tensor(t) for t in tensors)
--> 109 result = self._sess.run(*tensors, **kw)
110
111 for t in tensors:
~/Workspace/mars/mars/session.py in run(self, *tensors, **kw)
49 if 'n_parallel' not in kw:
50 kw['n_parallel'] = cpu_count()
---> 51 res = self._executor.execute_tensors(tensors, **kw)
52 return res
53
~/Workspace/mars/mars/utils.py in _wrapped(*args, **kwargs)
352 def _wrapped(*args, **kwargs):
353 _kernel_mode.eager = False
--> 354 return_value = func(*args, **kwargs)
355 _kernel_mode.eager = None
356 return return_value
~/Workspace/mars/mars/executor.py in execute_tensors(self, tensors, fetch, n_parallel, n_thread, print_progress, mock, sparse_mock_percent)
451 concat_keys = []
452 for tensor in tensors:
--> 453 tensor.tiles()
454 chunk_keys = [c.key for c in tensor.chunks]
455 result_keys.extend(chunk_keys)
~/Workspace/mars/mars/tensor/core.py in tiles(self)
223
224 def tiles(self):
--> 225 return handler.tiles(self)
226
227 def single_tiles(self):
~/Workspace/mars/mars/tiles.py in tiles(self, tiles_obj)
172 if not preds or accessible:
173 if node.is_coarse() and node.op:
--> 174 tiled = self._dispatch(node.op)
175 self._assign_to([t.data for t in tiled], node.op.outputs)
176 visited.add(node)
~/Workspace/mars/mars/utils.py in _wrapped(*args, **kwargs)
352 def _wrapped(*args, **kwargs):
353 _kernel_mode.eager = False
--> 354 return_value = func(*args, **kwargs)
355 _kernel_mode.eager = None
356 return return_value
~/Workspace/mars/mars/tiles.py in _dispatch(self, op)
114 if hasattr(op_cls, 'tile'):
115 # has tile implementation
--> 116 return op_cls.tile(op)
117 for op_clz in self._handlers.keys():
118 if issubclass(op_cls, op_clz):
~/Workspace/mars/mars/tensor/expressions/arithmetic/core.py in tile(cls, op)
43 for out_index in itertools.product(*(map(range, out_chunk_shape))):
44 in_chunks = [t.cix[get_index(out_index[-t.ndim:], t)] if t.ndim != 0 else t.chunks[0]
---> 45 for t in inputs]
46 chunk_op = op.copy().reset_key()
47 chunk_shape = broadcast_shape(*(c.shape for c in in_chunks))
~/Workspace/mars/mars/tensor/expressions/arithmetic/core.py in <listcomp>(.0)
43 for out_index in itertools.product(*(map(range, out_chunk_shape))):
44 in_chunks = [t.cix[get_index(out_index[-t.ndim:], t)] if t.ndim != 0 else t.chunks[0]
---> 45 for t in inputs]
46 chunk_op = op.copy().reset_key()
47 chunk_shape = broadcast_shape(*(c.shape for c in in_chunks))
~/Workspace/mars/mars/core.py in __getitem__(self, item)
457 if len(item) != self._tilesable.ndim:
458 raise ValueError('Cannot get tensor chunk by %s, expect length %d' % (
--> 459 item, self._tilesable.ndim))
460
461 s = self._tilesable.chunk_shape
ValueError: Cannot get tensor chunk by (0, 0), expect length 3
In [7]: a[:, mt.newaxis, :] - a[mt.newaxis, ...]
Out[7]: Tensor <op=TensorSubtract, shape=(2, 2, 3), key=fe6279614688ba864daa99368caf12af>
```
| [
{
"content": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# Copyright 1999-2018 Alibaba Group Holding Ltd.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport base64\nimport functools\nimport inspect\nimport json\nimport logging\nimport numbers\nimport os\nimport random\nimport socket\nimport struct\nimport sys\nimport time\nimport zlib\nimport threading\nfrom hashlib import md5\n\nimport numpy as np\n\nfrom .compat import irange, functools32, getargspec\nfrom ._utils import to_binary, to_str, to_text, tokenize\nfrom .config import options\n\nlogger = logging.getLogger(__name__)\nrandom.seed(int(time.time()) * os.getpid())\n\n\ntokenize = tokenize\n\n\n# fix encoding conversion problem under windows\nif sys.platform == 'win32': # pragma: no cover\n def _replace_default_encoding(func):\n def _fun(s, encoding=None):\n encoding = encoding or getattr(sys.stdout, 'encoding', None) or 'mbcs'\n return func(s, encoding=encoding)\n\n _fun.__name__ = func.__name__\n _fun.__doc__ = func.__doc__\n return _fun\n\n to_binary = _replace_default_encoding(to_binary)\n to_text = _replace_default_encoding(to_text)\n to_str = _replace_default_encoding(to_str)\n\n\nclass AttributeDict(dict):\n def __getattr__(self, item):\n try:\n return self[item]\n except KeyError:\n raise AttributeError(\n \"'AttributeDict' object has no attribute {0}\".format(item))\n\n\ndef on_serialize_shape(shape):\n if shape:\n return tuple(s if not np.isnan(s) else -1 for s in shape)\n return shape\n\n\ndef on_deserialize_shape(shape):\n if shape:\n return tuple(s if s != -1 else np.nan for s in shape)\n return shape\n\n\ndef get_gpu_used_memory(device_id):\n import pynvml\n\n handle = pynvml.nvmlDeviceGetHandleByIndex(device_id)\n mem_info = pynvml.nvmlDeviceGetMemoryInfo(handle)\n return mem_info.used\n\n\ndef parse_memory_limit(value):\n if isinstance(value, numbers.Number):\n return float(value), False\n elif value.endswith('%'):\n return float(value[:-1]) / 100, True\n elif value.lower().endswith('t'):\n return float(value[:-1]) * (1024 ** 4), False\n elif value.lower().endswith('g'):\n return float(value[:-1]) * (1024 ** 3), False\n elif value.lower().endswith('m'):\n return float(value[:-1]) * (1024 ** 2), False\n elif value.lower().endswith('k'):\n return float(value[:-1]) * 1024, False\n else:\n raise ValueError('Unknown limitation value: {0}'.format(value))\n\n\ndef readable_size(size):\n if size < 1024:\n return size\n elif 1024 <= size < 1024 ** 2:\n return '{0:.2f}K'.format(size / 1024)\n elif 1024 ** 2 <= size < 1024 ** 3:\n return '{0:.2f}M'.format(size / (1024 ** 2))\n elif 1024 ** 3 <= size < 1024 ** 4:\n return '{0:.2f}G'.format(size / (1024 ** 3))\n else:\n return '{0:.2f}T'.format(size / (1024 ** 4))\n\n\n_commit_hash, _commit_ref = None, None\n\n\ndef git_info():\n from ._version import get_git_info\n\n global _commit_hash, _commit_ref\n if _commit_ref is not None:\n if _commit_hash is None:\n return None\n return _commit_hash, _commit_ref\n\n git_tuple = get_git_info()\n if git_tuple is None:\n _commit_ref, _commit_hash = ':INVALID:', None\n return None\n else:\n _commit_hash, _commit_ref = git_tuple\n return git_tuple\n\n\nLOW_PORT_BOUND = 10000\nHIGH_PORT_BOUND = 65535\n_local_occupied_ports = set()\n\n\ndef _get_ports_from_netstat():\n import psutil\n import subprocess\n while True:\n p = subprocess.Popen('netstat -a -n -p tcp'.split(), stdout=subprocess.PIPE)\n # in python 2, subprocess does not support waiting for fixed seconds\n ps_proc = psutil.Process(p.pid)\n try:\n ps_proc.wait(5)\n break\n except: # noqa: E721 # pragma: no cover\n ps_proc.terminate()\n continue\n occupied = set()\n for line in p.stdout:\n line = to_str(line)\n if '.' not in line:\n continue\n for part in line.split():\n if '.' in part:\n _, port_str = part.rsplit('.', 1)\n if port_str == '*':\n continue\n port = int(port_str)\n if LOW_PORT_BOUND <= port <= HIGH_PORT_BOUND:\n occupied.add(int(port_str))\n break\n p.stdout.close()\n return occupied\n\n\ndef get_next_port(typ=None):\n import psutil\n try:\n conns = psutil.net_connections()\n typ = typ or socket.SOCK_STREAM\n occupied = set(sc.laddr.port for sc in conns\n if sc.type == typ and LOW_PORT_BOUND <= sc.laddr.port <= HIGH_PORT_BOUND)\n except psutil.AccessDenied:\n occupied = _get_ports_from_netstat()\n\n occupied.update(_local_occupied_ports)\n randn = struct.unpack('<Q', os.urandom(8))[0]\n idx = int(randn % (1 + HIGH_PORT_BOUND - LOW_PORT_BOUND - len(occupied)))\n for i in irange(LOW_PORT_BOUND, HIGH_PORT_BOUND + 1):\n if i in occupied:\n continue\n if idx == 0:\n _local_occupied_ports.add(i)\n return i\n idx -= 1\n raise SystemError('No ports available.')\n\n\[email protected]_cache(200)\ndef mod_hash(val, modulus):\n return int(md5(to_binary(val)).hexdigest(), 16) % modulus\n\n\nclass classproperty(object):\n def __init__(self, f):\n self.f = f\n\n def __get__(self, obj, owner):\n return self.f(owner)\n\n\ndef serialize_graph(graph, compress=False):\n ser_graph = graph.to_pb().SerializeToString()\n if compress:\n ser_graph = zlib.compress(ser_graph)\n return base64.b64encode(ser_graph)\n\n\ndef deserialize_graph(graph_b64, graph_cls=None):\n from .serialize.protos.graph_pb2 import GraphDef\n from .graph import DirectedGraph\n graph_cls = graph_cls or DirectedGraph\n try:\n json_obj = json.loads(to_str(graph_b64))\n return graph_cls.from_json(json_obj)\n except (SyntaxError, ValueError):\n g = GraphDef()\n ser_graph = base64.b64decode(graph_b64)\n try:\n ser_graph = zlib.decompress(ser_graph)\n except zlib.error:\n pass\n g.ParseFromString(ser_graph)\n return graph_cls.from_pb(g)\n\n\ndef merge_tensor_chunks(input_tensor, ctx):\n from .executor import Executor\n from .tensor.expressions.fetch import TensorFetch\n\n if len(input_tensor.chunks) == 1:\n return ctx[input_tensor.chunks[0].key]\n\n chunks = []\n for c in input_tensor.chunks:\n op = TensorFetch(dtype=c.dtype)\n chunk = op.new_chunk(None, c.shape, index=c.index, _key=c.key)\n chunks.append(chunk)\n\n new_op = TensorFetch(dtype=input_tensor.dtype)\n tensor = new_op.new_tensor(None, input_tensor.shape, chunks=chunks,\n nsplits=input_tensor.nsplits)\n\n executor = Executor(storage=ctx)\n concat_result = executor.execute_tensor(tensor, concat=True)\n return concat_result[0]\n\n\nif sys.version_info[0] < 3:\n def wraps(fun):\n if isinstance(fun, functools.partial):\n return lambda f: f\n return functools.wraps(fun)\nelse:\n wraps = functools.wraps\n\n\ndef calc_data_size(dt):\n if isinstance(dt, tuple):\n return sum(c.nbytes for c in dt)\n else:\n return dt.nbytes\n\n\ndef _get_mod_logger():\n mod_logger = None\n frame_globals = inspect.currentframe().f_back.f_globals\n for logger_name in ('logger', 'LOG', 'LOGGER'):\n if logger_name in frame_globals:\n mod_logger = frame_globals[logger_name]\n break\n return mod_logger\n\n\ndef log_unhandled(func):\n mod_logger = _get_mod_logger()\n if not mod_logger:\n return func\n\n func_name = getattr(func, '__qualname__', func.__module__ + func.__name__)\n func_args = getargspec(func)\n\n @wraps(func)\n def _wrapped(*args, **kwargs):\n try:\n return func(*args, **kwargs)\n except: # noqa: E722\n kwcopy = kwargs.copy()\n kwcopy.update(zip(func_args.args, args))\n if getattr(func, '__closure__', None) is not None:\n kwargs.update(zip(\n func.__code__.co_freevars + getattr(func.__code__, 'co_cellvars', ()),\n [getattr(c, 'cell_contents', None) for c in func.__closure__],\n ))\n\n messages = []\n for k, v in kwcopy.items():\n if 'key' in k:\n messages.append('%s=%r' % (k, v))\n\n err_msg = 'Unexpected exception occurred in %s.' % func_name\n if messages:\n err_msg += ' ' + ' '.join(messages)\n mod_logger.exception(err_msg)\n raise\n return _wrapped\n\n\ndef build_graph(tensors, graph=None, executed_keys=None, tiled=False, compose=True):\n from .graph import DirectedGraph\n\n graph = graph or DirectedGraph()\n executed_keys = executed_keys or []\n tensors = tensors if isinstance(tensors, (tuple, list, set)) else [tensors]\n for t in tensors:\n graph = t.build_graph(graph=graph, tiled=tiled, compose=compose,\n executed_keys=executed_keys)\n return graph\n\n\n_kernel_mode = threading.local()\n_kernel_mode.eager = None\n\n\ndef is_eager_mode():\n if _kernel_mode.eager is None:\n return options.eager_mode\n return _kernel_mode.eager\n\n\ndef kernel_mode(func):\n \"\"\"\n A decorator for kernel functions.\n\n When eager mode is on, expressions will be executed after `new_entities`, however\n `new_entities` is also called in `Executor` and `OperandTilesHandler`, this decorator\n provides an options context for kernel functions to avoid execution.\n \"\"\"\n\n def _wrapped(*args, **kwargs):\n _kernel_mode.eager = False\n return_value = func(*args, **kwargs)\n _kernel_mode.eager = None\n return return_value\n\n return _wrapped\n",
"path": "mars/utils.py"
}
] | [
{
"content": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# Copyright 1999-2018 Alibaba Group Holding Ltd.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport base64\nimport functools\nimport inspect\nimport json\nimport logging\nimport numbers\nimport os\nimport random\nimport socket\nimport struct\nimport sys\nimport time\nimport zlib\nimport threading\nfrom hashlib import md5\n\nimport numpy as np\n\nfrom .compat import irange, functools32, getargspec\nfrom ._utils import to_binary, to_str, to_text, tokenize\nfrom .config import options\n\nlogger = logging.getLogger(__name__)\nrandom.seed(int(time.time()) * os.getpid())\n\n\ntokenize = tokenize\n\n\n# fix encoding conversion problem under windows\nif sys.platform == 'win32': # pragma: no cover\n def _replace_default_encoding(func):\n def _fun(s, encoding=None):\n encoding = encoding or getattr(sys.stdout, 'encoding', None) or 'mbcs'\n return func(s, encoding=encoding)\n\n _fun.__name__ = func.__name__\n _fun.__doc__ = func.__doc__\n return _fun\n\n to_binary = _replace_default_encoding(to_binary)\n to_text = _replace_default_encoding(to_text)\n to_str = _replace_default_encoding(to_str)\n\n\nclass AttributeDict(dict):\n def __getattr__(self, item):\n try:\n return self[item]\n except KeyError:\n raise AttributeError(\n \"'AttributeDict' object has no attribute {0}\".format(item))\n\n\ndef on_serialize_shape(shape):\n if shape:\n return tuple(s if not np.isnan(s) else -1 for s in shape)\n return shape\n\n\ndef on_deserialize_shape(shape):\n if shape:\n return tuple(s if s != -1 else np.nan for s in shape)\n return shape\n\n\ndef get_gpu_used_memory(device_id):\n import pynvml\n\n handle = pynvml.nvmlDeviceGetHandleByIndex(device_id)\n mem_info = pynvml.nvmlDeviceGetMemoryInfo(handle)\n return mem_info.used\n\n\ndef parse_memory_limit(value):\n if isinstance(value, numbers.Number):\n return float(value), False\n elif value.endswith('%'):\n return float(value[:-1]) / 100, True\n elif value.lower().endswith('t'):\n return float(value[:-1]) * (1024 ** 4), False\n elif value.lower().endswith('g'):\n return float(value[:-1]) * (1024 ** 3), False\n elif value.lower().endswith('m'):\n return float(value[:-1]) * (1024 ** 2), False\n elif value.lower().endswith('k'):\n return float(value[:-1]) * 1024, False\n else:\n raise ValueError('Unknown limitation value: {0}'.format(value))\n\n\ndef readable_size(size):\n if size < 1024:\n return size\n elif 1024 <= size < 1024 ** 2:\n return '{0:.2f}K'.format(size / 1024)\n elif 1024 ** 2 <= size < 1024 ** 3:\n return '{0:.2f}M'.format(size / (1024 ** 2))\n elif 1024 ** 3 <= size < 1024 ** 4:\n return '{0:.2f}G'.format(size / (1024 ** 3))\n else:\n return '{0:.2f}T'.format(size / (1024 ** 4))\n\n\n_commit_hash, _commit_ref = None, None\n\n\ndef git_info():\n from ._version import get_git_info\n\n global _commit_hash, _commit_ref\n if _commit_ref is not None:\n if _commit_hash is None:\n return None\n return _commit_hash, _commit_ref\n\n git_tuple = get_git_info()\n if git_tuple is None:\n _commit_ref, _commit_hash = ':INVALID:', None\n return None\n else:\n _commit_hash, _commit_ref = git_tuple\n return git_tuple\n\n\nLOW_PORT_BOUND = 10000\nHIGH_PORT_BOUND = 65535\n_local_occupied_ports = set()\n\n\ndef _get_ports_from_netstat():\n import psutil\n import subprocess\n while True:\n p = subprocess.Popen('netstat -a -n -p tcp'.split(), stdout=subprocess.PIPE)\n # in python 2, subprocess does not support waiting for fixed seconds\n ps_proc = psutil.Process(p.pid)\n try:\n ps_proc.wait(5)\n break\n except: # noqa: E721 # pragma: no cover\n ps_proc.terminate()\n continue\n occupied = set()\n for line in p.stdout:\n line = to_str(line)\n if '.' not in line:\n continue\n for part in line.split():\n if '.' in part:\n _, port_str = part.rsplit('.', 1)\n if port_str == '*':\n continue\n port = int(port_str)\n if LOW_PORT_BOUND <= port <= HIGH_PORT_BOUND:\n occupied.add(int(port_str))\n break\n p.stdout.close()\n return occupied\n\n\ndef get_next_port(typ=None):\n import psutil\n try:\n conns = psutil.net_connections()\n typ = typ or socket.SOCK_STREAM\n occupied = set(sc.laddr.port for sc in conns\n if sc.type == typ and LOW_PORT_BOUND <= sc.laddr.port <= HIGH_PORT_BOUND)\n except psutil.AccessDenied:\n occupied = _get_ports_from_netstat()\n\n occupied.update(_local_occupied_ports)\n randn = struct.unpack('<Q', os.urandom(8))[0]\n idx = int(randn % (1 + HIGH_PORT_BOUND - LOW_PORT_BOUND - len(occupied)))\n for i in irange(LOW_PORT_BOUND, HIGH_PORT_BOUND + 1):\n if i in occupied:\n continue\n if idx == 0:\n _local_occupied_ports.add(i)\n return i\n idx -= 1\n raise SystemError('No ports available.')\n\n\[email protected]_cache(200)\ndef mod_hash(val, modulus):\n return int(md5(to_binary(val)).hexdigest(), 16) % modulus\n\n\nclass classproperty(object):\n def __init__(self, f):\n self.f = f\n\n def __get__(self, obj, owner):\n return self.f(owner)\n\n\ndef serialize_graph(graph, compress=False):\n ser_graph = graph.to_pb().SerializeToString()\n if compress:\n ser_graph = zlib.compress(ser_graph)\n return base64.b64encode(ser_graph)\n\n\ndef deserialize_graph(graph_b64, graph_cls=None):\n from .serialize.protos.graph_pb2 import GraphDef\n from .graph import DirectedGraph\n graph_cls = graph_cls or DirectedGraph\n try:\n json_obj = json.loads(to_str(graph_b64))\n return graph_cls.from_json(json_obj)\n except (SyntaxError, ValueError):\n g = GraphDef()\n ser_graph = base64.b64decode(graph_b64)\n try:\n ser_graph = zlib.decompress(ser_graph)\n except zlib.error:\n pass\n g.ParseFromString(ser_graph)\n return graph_cls.from_pb(g)\n\n\ndef merge_tensor_chunks(input_tensor, ctx):\n from .executor import Executor\n from .tensor.expressions.fetch import TensorFetch\n\n if len(input_tensor.chunks) == 1:\n return ctx[input_tensor.chunks[0].key]\n\n chunks = []\n for c in input_tensor.chunks:\n op = TensorFetch(dtype=c.dtype)\n chunk = op.new_chunk(None, c.shape, index=c.index, _key=c.key)\n chunks.append(chunk)\n\n new_op = TensorFetch(dtype=input_tensor.dtype)\n tensor = new_op.new_tensor(None, input_tensor.shape, chunks=chunks,\n nsplits=input_tensor.nsplits)\n\n executor = Executor(storage=ctx)\n concat_result = executor.execute_tensor(tensor, concat=True)\n return concat_result[0]\n\n\nif sys.version_info[0] < 3:\n def wraps(fun):\n if isinstance(fun, functools.partial):\n return lambda f: f\n return functools.wraps(fun)\nelse:\n wraps = functools.wraps\n\n\ndef calc_data_size(dt):\n if isinstance(dt, tuple):\n return sum(c.nbytes for c in dt)\n else:\n return dt.nbytes\n\n\ndef _get_mod_logger():\n mod_logger = None\n frame_globals = inspect.currentframe().f_back.f_globals\n for logger_name in ('logger', 'LOG', 'LOGGER'):\n if logger_name in frame_globals:\n mod_logger = frame_globals[logger_name]\n break\n return mod_logger\n\n\ndef log_unhandled(func):\n mod_logger = _get_mod_logger()\n if not mod_logger:\n return func\n\n func_name = getattr(func, '__qualname__', func.__module__ + func.__name__)\n func_args = getargspec(func)\n\n @wraps(func)\n def _wrapped(*args, **kwargs):\n try:\n return func(*args, **kwargs)\n except: # noqa: E722\n kwcopy = kwargs.copy()\n kwcopy.update(zip(func_args.args, args))\n if getattr(func, '__closure__', None) is not None:\n kwargs.update(zip(\n func.__code__.co_freevars + getattr(func.__code__, 'co_cellvars', ()),\n [getattr(c, 'cell_contents', None) for c in func.__closure__],\n ))\n\n messages = []\n for k, v in kwcopy.items():\n if 'key' in k:\n messages.append('%s=%r' % (k, v))\n\n err_msg = 'Unexpected exception occurred in %s.' % func_name\n if messages:\n err_msg += ' ' + ' '.join(messages)\n mod_logger.exception(err_msg)\n raise\n return _wrapped\n\n\ndef build_graph(tensors, graph=None, executed_keys=None, tiled=False, compose=True):\n from .graph import DirectedGraph\n\n graph = graph or DirectedGraph()\n executed_keys = executed_keys or []\n tensors = tensors if isinstance(tensors, (tuple, list, set)) else [tensors]\n for t in tensors:\n graph = t.build_graph(graph=graph, tiled=tiled, compose=compose,\n executed_keys=executed_keys)\n return graph\n\n\n_kernel_mode = threading.local()\n_kernel_mode.eager = None\n\n\ndef is_eager_mode():\n if _kernel_mode.eager is None:\n return options.eager_mode\n return _kernel_mode.eager\n\n\ndef kernel_mode(func):\n \"\"\"\n A decorator for kernel functions.\n\n When eager mode is on, expressions will be executed after `new_entities`, however\n `new_entities` is also called in `Executor` and `OperandTilesHandler`, this decorator\n provides an options context for kernel functions to avoid execution.\n \"\"\"\n\n def _wrapped(*args, **kwargs):\n try:\n _kernel_mode.eager = False\n return func(*args, **kwargs)\n finally:\n _kernel_mode.eager = None\n\n return _wrapped\n",
"path": "mars/utils.py"
}
] | diff --git a/mars/tests/test_eager_mode.py b/mars/tests/test_eager_mode.py
index e58110e375..86d0bed93a 100644
--- a/mars/tests/test_eager_mode.py
+++ b/mars/tests/test_eager_mode.py
@@ -172,3 +172,23 @@ def testRepr(self):
self.assertNotIn(repr(np.ones((10, 10))), repr(a))
self.assertNotIn(str(np.ones((10, 10))), str(a))
+
+ def testRuntimeError(self):
+ from mars.utils import kernel_mode
+
+ @kernel_mode
+ def raise_error(*_):
+ raise ValueError
+
+ with option_context({'eager_mode': True}):
+ a = mt.zeros((10, 10))
+ with self.assertRaises(ValueError):
+ raise_error(a)
+
+ r = a + 1
+ self.assertIn(repr(np.zeros((10, 10)) + 1), repr(r))
+ np.testing.assert_array_equal(r.fetch(), np.zeros((10, 10)) + 1)
+
+ a = mt.zeros((10, 10))
+ with self.assertRaises(ValueError):
+ a.fetch()
diff --git a/mars/utils.py b/mars/utils.py
index 8efdde9880..f55ae291ea 100644
--- a/mars/utils.py
+++ b/mars/utils.py
@@ -350,9 +350,10 @@ def kernel_mode(func):
"""
def _wrapped(*args, **kwargs):
- _kernel_mode.eager = False
- return_value = func(*args, **kwargs)
- _kernel_mode.eager = None
- return return_value
+ try:
+ _kernel_mode.eager = False
+ return func(*args, **kwargs)
+ finally:
+ _kernel_mode.eager = None
return _wrapped
|
qutebrowser__qutebrowser-3318 | edit-command --run should clear the status bar
Thanks to @rcorre for implementing `:edit-command` from #2453. Quick issue: when using the `--run` flag, not only should the command be executed, but the status bar should also be cleared (or whatever `<esc>` tends to do). Here's what happens currently (v1.0.3, abb5c9f63):
- bind `<ctrl-e>` to `edit-command --run`
- do `:open<ctrl-e>`
- type `www.qutebrowser.org`
- save & quit
What happens:
1. get sent to the Qutebrowser home page
2. status bar still says `:open www.qutebrowser.org`, and I also see URLs from my history
What I expected to happen: 1 but not 2.
This means I need to hit `<esc>` after doing an `:edit-command --run`.
| [
{
"content": "# vim: ft=python fileencoding=utf-8 sts=4 sw=4 et:\n\n# Copyright 2014-2017 Florian Bruhin (The Compiler) <[email protected]>\n#\n# This file is part of qutebrowser.\n#\n# qutebrowser is free software: you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation, either version 3 of the License, or\n# (at your option) any later version.\n#\n# qutebrowser is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with qutebrowser. If not, see <http://www.gnu.org/licenses/>.\n\n\"\"\"The commandline in the statusbar.\"\"\"\n\nfrom PyQt5.QtCore import pyqtSignal, pyqtSlot, Qt, QSize\nfrom PyQt5.QtWidgets import QSizePolicy\n\nfrom qutebrowser.keyinput import modeman, modeparsers\nfrom qutebrowser.commands import cmdexc, cmdutils\nfrom qutebrowser.misc import cmdhistory, editor\nfrom qutebrowser.misc import miscwidgets as misc\nfrom qutebrowser.utils import usertypes, log, objreg\n\n\nclass Command(misc.MinimalLineEditMixin, misc.CommandLineEdit):\n\n \"\"\"The commandline part of the statusbar.\n\n Attributes:\n _win_id: The window ID this widget is associated with.\n\n Signals:\n got_cmd: Emitted when a command is triggered by the user.\n arg: The command string and also potentially the count.\n clear_completion_selection: Emitted before the completion widget is\n hidden.\n hide_completion: Emitted when the completion widget should be hidden.\n update_completion: Emitted when the completion should be shown/updated.\n show_cmd: Emitted when command input should be shown.\n hide_cmd: Emitted when command input can be hidden.\n \"\"\"\n\n got_cmd = pyqtSignal([str], [str, int])\n clear_completion_selection = pyqtSignal()\n hide_completion = pyqtSignal()\n update_completion = pyqtSignal()\n show_cmd = pyqtSignal()\n hide_cmd = pyqtSignal()\n\n def __init__(self, *, win_id, private, parent=None):\n misc.CommandLineEdit.__init__(self, parent=parent)\n misc.MinimalLineEditMixin.__init__(self)\n self._win_id = win_id\n if not private:\n command_history = objreg.get('command-history')\n self.history.history = command_history.data\n self.history.changed.connect(command_history.changed)\n self.setSizePolicy(QSizePolicy.MinimumExpanding, QSizePolicy.Ignored)\n self.cursorPositionChanged.connect(self.update_completion)\n self.textChanged.connect(self.update_completion)\n self.textChanged.connect(self.updateGeometry)\n\n def prefix(self):\n \"\"\"Get the currently entered command prefix.\"\"\"\n text = self.text()\n if not text:\n return ''\n elif text[0] in modeparsers.STARTCHARS:\n return text[0]\n else:\n return ''\n\n def set_cmd_text(self, text):\n \"\"\"Preset the statusbar to some text.\n\n Args:\n text: The text to set as string.\n \"\"\"\n self.setText(text)\n log.modes.debug(\"Setting command text, focusing {!r}\".format(self))\n modeman.enter(self._win_id, usertypes.KeyMode.command, 'cmd focus')\n self.setFocus()\n self.show_cmd.emit()\n\n @cmdutils.register(instance='status-command', name='set-cmd-text',\n scope='window', maxsplit=0)\n @cmdutils.argument('count', count=True)\n def set_cmd_text_command(self, text, count=None, space=False, append=False,\n run_on_count=False):\n \"\"\"Preset the statusbar to some text.\n\n //\n\n Wrapper for set_cmd_text to check the arguments and allow multiple\n strings which will get joined.\n\n Args:\n text: The commandline to set.\n count: The count if given.\n space: If given, a space is added to the end.\n append: If given, the text is appended to the current text.\n run_on_count: If given with a count, the command is run with the\n given count rather than setting the command text.\n \"\"\"\n if space:\n text += ' '\n if append:\n if not self.text():\n raise cmdexc.CommandError(\"No current text!\")\n text = self.text() + text\n\n if not text or text[0] not in modeparsers.STARTCHARS:\n raise cmdexc.CommandError(\n \"Invalid command text '{}'.\".format(text))\n if run_on_count and count is not None:\n self.got_cmd[str, int].emit(text, count)\n else:\n self.set_cmd_text(text)\n\n @cmdutils.register(instance='status-command',\n modes=[usertypes.KeyMode.command], scope='window')\n def command_history_prev(self):\n \"\"\"Go back in the commandline history.\"\"\"\n try:\n if not self.history.is_browsing():\n item = self.history.start(self.text().strip())\n else:\n item = self.history.previtem()\n except (cmdhistory.HistoryEmptyError,\n cmdhistory.HistoryEndReachedError):\n return\n if item:\n self.set_cmd_text(item)\n\n @cmdutils.register(instance='status-command',\n modes=[usertypes.KeyMode.command], scope='window')\n def command_history_next(self):\n \"\"\"Go forward in the commandline history.\"\"\"\n if not self.history.is_browsing():\n return\n try:\n item = self.history.nextitem()\n except cmdhistory.HistoryEndReachedError:\n return\n if item:\n self.set_cmd_text(item)\n\n @cmdutils.register(instance='status-command',\n modes=[usertypes.KeyMode.command], scope='window')\n def command_accept(self):\n \"\"\"Execute the command currently in the commandline.\"\"\"\n prefixes = {\n ':': '',\n '/': 'search -- ',\n '?': 'search -r -- ',\n }\n text = self.text()\n self.history.append(text)\n modeman.leave(self._win_id, usertypes.KeyMode.command, 'cmd accept')\n self.got_cmd[str].emit(prefixes[text[0]] + text[1:])\n\n @cmdutils.register(instance='status-command', scope='window')\n def edit_command(self, run=False):\n \"\"\"Open an editor to modify the current command.\n\n Args:\n run: Run the command if the editor exits successfully.\n \"\"\"\n ed = editor.ExternalEditor(parent=self)\n\n def callback(text):\n self.set_cmd_text(text)\n if run:\n self.got_cmd[str].emit(text)\n\n ed.editing_finished.connect(callback)\n ed.edit(self.text())\n\n @pyqtSlot(usertypes.KeyMode)\n def on_mode_left(self, mode):\n \"\"\"Clear up when command mode was left.\n\n - Clear the statusbar text if it's explicitly unfocused.\n - Clear completion selection\n - Hide completion\n\n Args:\n mode: The mode which was left.\n \"\"\"\n if mode == usertypes.KeyMode.command:\n self.setText('')\n self.history.stop()\n self.hide_cmd.emit()\n self.clear_completion_selection.emit()\n self.hide_completion.emit()\n\n def setText(self, text):\n \"\"\"Extend setText to set prefix and make sure the prompt is ok.\"\"\"\n if not text:\n pass\n elif text[0] in modeparsers.STARTCHARS:\n super().set_prompt(text[0])\n else:\n raise AssertionError(\"setText got called with invalid text \"\n \"'{}'!\".format(text))\n super().setText(text)\n\n def keyPressEvent(self, e):\n \"\"\"Override keyPressEvent to ignore Return key presses.\n\n If this widget is focused, we are in passthrough key mode, and\n Enter/Shift+Enter/etc. will cause QLineEdit to think it's finished\n without command_accept to be called.\n \"\"\"\n if e.key() == Qt.Key_Return:\n e.ignore()\n return\n else:\n super().keyPressEvent(e)\n\n def sizeHint(self):\n \"\"\"Dynamically calculate the needed size.\"\"\"\n height = super().sizeHint().height()\n text = self.text()\n if not text:\n text = 'x'\n width = self.fontMetrics().width(text)\n return QSize(width, height)\n",
"path": "qutebrowser/mainwindow/statusbar/command.py"
}
] | [
{
"content": "# vim: ft=python fileencoding=utf-8 sts=4 sw=4 et:\n\n# Copyright 2014-2017 Florian Bruhin (The Compiler) <[email protected]>\n#\n# This file is part of qutebrowser.\n#\n# qutebrowser is free software: you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation, either version 3 of the License, or\n# (at your option) any later version.\n#\n# qutebrowser is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with qutebrowser. If not, see <http://www.gnu.org/licenses/>.\n\n\"\"\"The commandline in the statusbar.\"\"\"\n\nfrom PyQt5.QtCore import pyqtSignal, pyqtSlot, Qt, QSize\nfrom PyQt5.QtWidgets import QSizePolicy\n\nfrom qutebrowser.keyinput import modeman, modeparsers\nfrom qutebrowser.commands import cmdexc, cmdutils\nfrom qutebrowser.misc import cmdhistory, editor\nfrom qutebrowser.misc import miscwidgets as misc\nfrom qutebrowser.utils import usertypes, log, objreg\n\n\nclass Command(misc.MinimalLineEditMixin, misc.CommandLineEdit):\n\n \"\"\"The commandline part of the statusbar.\n\n Attributes:\n _win_id: The window ID this widget is associated with.\n\n Signals:\n got_cmd: Emitted when a command is triggered by the user.\n arg: The command string and also potentially the count.\n clear_completion_selection: Emitted before the completion widget is\n hidden.\n hide_completion: Emitted when the completion widget should be hidden.\n update_completion: Emitted when the completion should be shown/updated.\n show_cmd: Emitted when command input should be shown.\n hide_cmd: Emitted when command input can be hidden.\n \"\"\"\n\n got_cmd = pyqtSignal([str], [str, int])\n clear_completion_selection = pyqtSignal()\n hide_completion = pyqtSignal()\n update_completion = pyqtSignal()\n show_cmd = pyqtSignal()\n hide_cmd = pyqtSignal()\n\n def __init__(self, *, win_id, private, parent=None):\n misc.CommandLineEdit.__init__(self, parent=parent)\n misc.MinimalLineEditMixin.__init__(self)\n self._win_id = win_id\n if not private:\n command_history = objreg.get('command-history')\n self.history.history = command_history.data\n self.history.changed.connect(command_history.changed)\n self.setSizePolicy(QSizePolicy.MinimumExpanding, QSizePolicy.Ignored)\n self.cursorPositionChanged.connect(self.update_completion)\n self.textChanged.connect(self.update_completion)\n self.textChanged.connect(self.updateGeometry)\n\n def prefix(self):\n \"\"\"Get the currently entered command prefix.\"\"\"\n text = self.text()\n if not text:\n return ''\n elif text[0] in modeparsers.STARTCHARS:\n return text[0]\n else:\n return ''\n\n def set_cmd_text(self, text):\n \"\"\"Preset the statusbar to some text.\n\n Args:\n text: The text to set as string.\n \"\"\"\n self.setText(text)\n log.modes.debug(\"Setting command text, focusing {!r}\".format(self))\n modeman.enter(self._win_id, usertypes.KeyMode.command, 'cmd focus')\n self.setFocus()\n self.show_cmd.emit()\n\n @cmdutils.register(instance='status-command', name='set-cmd-text',\n scope='window', maxsplit=0)\n @cmdutils.argument('count', count=True)\n def set_cmd_text_command(self, text, count=None, space=False, append=False,\n run_on_count=False):\n \"\"\"Preset the statusbar to some text.\n\n //\n\n Wrapper for set_cmd_text to check the arguments and allow multiple\n strings which will get joined.\n\n Args:\n text: The commandline to set.\n count: The count if given.\n space: If given, a space is added to the end.\n append: If given, the text is appended to the current text.\n run_on_count: If given with a count, the command is run with the\n given count rather than setting the command text.\n \"\"\"\n if space:\n text += ' '\n if append:\n if not self.text():\n raise cmdexc.CommandError(\"No current text!\")\n text = self.text() + text\n\n if not text or text[0] not in modeparsers.STARTCHARS:\n raise cmdexc.CommandError(\n \"Invalid command text '{}'.\".format(text))\n if run_on_count and count is not None:\n self.got_cmd[str, int].emit(text, count)\n else:\n self.set_cmd_text(text)\n\n @cmdutils.register(instance='status-command',\n modes=[usertypes.KeyMode.command], scope='window')\n def command_history_prev(self):\n \"\"\"Go back in the commandline history.\"\"\"\n try:\n if not self.history.is_browsing():\n item = self.history.start(self.text().strip())\n else:\n item = self.history.previtem()\n except (cmdhistory.HistoryEmptyError,\n cmdhistory.HistoryEndReachedError):\n return\n if item:\n self.set_cmd_text(item)\n\n @cmdutils.register(instance='status-command',\n modes=[usertypes.KeyMode.command], scope='window')\n def command_history_next(self):\n \"\"\"Go forward in the commandline history.\"\"\"\n if not self.history.is_browsing():\n return\n try:\n item = self.history.nextitem()\n except cmdhistory.HistoryEndReachedError:\n return\n if item:\n self.set_cmd_text(item)\n\n @cmdutils.register(instance='status-command',\n modes=[usertypes.KeyMode.command], scope='window')\n def command_accept(self):\n \"\"\"Execute the command currently in the commandline.\"\"\"\n prefixes = {\n ':': '',\n '/': 'search -- ',\n '?': 'search -r -- ',\n }\n text = self.text()\n self.history.append(text)\n modeman.leave(self._win_id, usertypes.KeyMode.command, 'cmd accept')\n self.got_cmd[str].emit(prefixes[text[0]] + text[1:])\n\n @cmdutils.register(instance='status-command', scope='window')\n def edit_command(self, run=False):\n \"\"\"Open an editor to modify the current command.\n\n Args:\n run: Run the command if the editor exits successfully.\n \"\"\"\n ed = editor.ExternalEditor(parent=self)\n\n def callback(text):\n self.set_cmd_text(text)\n if run:\n self.command_accept()\n\n ed.editing_finished.connect(callback)\n ed.edit(self.text())\n\n @pyqtSlot(usertypes.KeyMode)\n def on_mode_left(self, mode):\n \"\"\"Clear up when command mode was left.\n\n - Clear the statusbar text if it's explicitly unfocused.\n - Clear completion selection\n - Hide completion\n\n Args:\n mode: The mode which was left.\n \"\"\"\n if mode == usertypes.KeyMode.command:\n self.setText('')\n self.history.stop()\n self.hide_cmd.emit()\n self.clear_completion_selection.emit()\n self.hide_completion.emit()\n\n def setText(self, text):\n \"\"\"Extend setText to set prefix and make sure the prompt is ok.\"\"\"\n if not text:\n pass\n elif text[0] in modeparsers.STARTCHARS:\n super().set_prompt(text[0])\n else:\n raise AssertionError(\"setText got called with invalid text \"\n \"'{}'!\".format(text))\n super().setText(text)\n\n def keyPressEvent(self, e):\n \"\"\"Override keyPressEvent to ignore Return key presses.\n\n If this widget is focused, we are in passthrough key mode, and\n Enter/Shift+Enter/etc. will cause QLineEdit to think it's finished\n without command_accept to be called.\n \"\"\"\n if e.key() == Qt.Key_Return:\n e.ignore()\n return\n else:\n super().keyPressEvent(e)\n\n def sizeHint(self):\n \"\"\"Dynamically calculate the needed size.\"\"\"\n height = super().sizeHint().height()\n text = self.text()\n if not text:\n text = 'x'\n width = self.fontMetrics().width(text)\n return QSize(width, height)\n",
"path": "qutebrowser/mainwindow/statusbar/command.py"
}
] | diff --git a/qutebrowser/mainwindow/statusbar/command.py b/qutebrowser/mainwindow/statusbar/command.py
index a61f3c93d6b..bba2559ed92 100644
--- a/qutebrowser/mainwindow/statusbar/command.py
+++ b/qutebrowser/mainwindow/statusbar/command.py
@@ -178,7 +178,7 @@ def edit_command(self, run=False):
def callback(text):
self.set_cmd_text(text)
if run:
- self.got_cmd[str].emit(text)
+ self.command_accept()
ed.editing_finished.connect(callback)
ed.edit(self.text())
diff --git a/tests/end2end/features/editor.feature b/tests/end2end/features/editor.feature
index 21df30b4d00..79f4f17d43f 100644
--- a/tests/end2end/features/editor.feature
+++ b/tests/end2end/features/editor.feature
@@ -149,3 +149,4 @@ Feature: Opening external editors
And I set up a fake editor replacing "foo" by "bar"
And I run :edit-command --run
Then the message "bar" should be shown
+ And "Leaving mode KeyMode.command (reason: cmd accept)" should be logged
|
obspy__obspy-516 | Dead link in obspy.signal.cross_correlation.xcorr docstring
obspy.signal.cross_correlation.xcorr refers to ticket #249 in its docstring (see here: http://docs.obspy.org/packages/autogen/obspy.signal.cross_correlation.xcorr.html)
This is an old trac link. Either it should be updated to https://github.com/obspy/obspy/issues/249 or, better, the relevant parts of this ticket discussion should be included in the docstring.
What do you think?
| [
{
"content": "#!/usr/bin/env python\n#--------------------------------------------------------------------\n# Filename: cross_correlation.py\n# Author: Moritz Beyreuther, Tobias Megies\n# Email: [email protected]\n#\n# Copyright (C) 2008-2012 Moritz Beyreuther, Tobias Megies\n#-------------------------------------------------------------------\n\"\"\"\nSignal processing routines based on cross correlation techniques.\n\n:copyright:\n The ObsPy Development Team ([email protected])\n:license:\n GNU Lesser General Public License, Version 3\n (http://www.gnu.org/copyleft/lesser.html)\n\"\"\"\n\nimport warnings\nimport numpy as np\nimport ctypes as C\nimport scipy\nfrom obspy import Trace, Stream\nfrom obspy.signal.headers import clibsignal\nfrom obspy.signal import cosTaper\n\n\ndef xcorr(tr1, tr2, shift_len, full_xcorr=False):\n \"\"\"\n Cross correlation of tr1 and tr2 in the time domain using window_len.\n\n ::\n\n Mid Sample\n |\n |AAAAAAAAAAAAAAA|AAAAAAAAAAAAAAA|AAAAAAAAAAAAAAA|AAAAAAAAAAAAAAA|\n |BBBBBBBBBBBBBBB|BBBBBBBBBBBBBBB|BBBBBBBBBBBBBBB|BBBBBBBBBBBBBBB|\n |<-shift_len/2->| <- region of support -> |<-shift_len/2->|\n\n\n :type tr1: :class:`~numpy.ndarray`, :class:`~obspy.core.trace.Trace`\n :param tr1: Trace 1\n :type tr2: :class:`~numpy.ndarray`, :class:`~obspy.core.trace.Trace`\n :param tr2: Trace 2 to correlate with trace 1\n :type shift_len: int\n :param shift_len: Total length of samples to shift for cross correlation.\n :type full_xcorr: bool\n :param full_xcorr: If ``True``, the complete xcorr function will be\n returned as :class:`~numpy.ndarray`\n :return: **index, value[, fct]** - Index of maximum xcorr value and the\n value itself. The complete xcorr function is returned only if\n ``full_xcorr=True``.\n\n .. note::\n As shift_len gets higher the window supporting the cross correlation\n actually gets smaller. So with shift_len=0 you get the correlation\n coefficient of both traces as a whole without any shift applied. As the\n xcorr function works in time domain and does not zero pad at all, with\n higher shifts allowed the window of support gets smaller so that the\n moving windows shifted against each other do not run out of the\n timeseries bounds at high time shifts. Of course there are other\n possibilities to do cross correlations e.g. in frequency domain.\n\n .. seealso::\n `ObsPy-users mailing list\n <http://lists.obspy.org/pipermail/obspy-users/2011-March/000056.html>`_\n and\n `ticket #249 <https://obspy.org/ticket/249>`_.\n\n .. rubric:: Example\n\n >>> tr1 = np.random.randn(10000).astype('float32')\n >>> tr2 = tr1.copy()\n >>> a, b = xcorr(tr1, tr2, 1000)\n >>> a\n 0\n >>> round(b, 7)\n 1.0\n \"\"\"\n # if we get Trace objects, use their data arrays\n for tr in [tr1, tr2]:\n if isinstance(tr, Trace):\n tr = tr.data\n\n # check if shift_len parameter is in an acceptable range.\n # if not the underlying c code tampers with shift_len and uses shift_len/2\n # instead. we want to avoid this silent automagic and raise an error in the\n # python layer right here.\n # see ticket #249 and src/xcorr.c lines 43-57\n if min(len(tr1), len(tr2)) - 2 * shift_len <= 0:\n msg = \"shift_len too large. The underlying C code would silently \" + \\\n \"use shift_len/2 which we want to avoid.\"\n raise ValueError(msg)\n # be nice and adapt type if necessary\n tr1 = np.require(tr1, 'float32', ['C_CONTIGUOUS'])\n tr2 = np.require(tr2, 'float32', ['C_CONTIGUOUS'])\n corp = np.empty(2 * shift_len + 1, dtype='float64', order='C')\n\n shift = C.c_int()\n coe_p = C.c_double()\n\n clibsignal.X_corr(tr1, tr2, corp, shift_len, len(tr1), len(tr2),\n C.byref(shift), C.byref(coe_p))\n\n if full_xcorr:\n return shift.value, coe_p.value, corp\n else:\n return shift.value, coe_p.value\n\n\ndef xcorr_3C(st1, st2, shift_len, components=[\"Z\", \"N\", \"E\"],\n full_xcorr=False, abs_max=True):\n \"\"\"\n Calculates the cross correlation on each of the specified components\n separately, stacks them together and estimates the maximum and shift of\n maximum on the stack.\n\n Basically the same as :func:`~obspy.signal.cross_correlation.xcorr` but\n for (normally) three components, please also take a look at the\n documentation of that function. Useful e.g. for estimation of waveform\n similarity on a three component seismogram.\n\n :type st1: :class:`~obspy.core.stream.Stream`\n :param st1: Stream 1, containing one trace for Z, N, E component (other\n component_id codes are ignored)\n :type st2: :class:`~obspy.core.stream.Stream`\n :param st2: Stream 2, containing one trace for Z, N, E component (other\n component_id codes are ignored)\n :type shift_len: int\n :param shift_len: Total length of samples to shift for cross correlation.\n :type components: List of strings\n :param components: List of components to use in cross-correlation, defaults\n to ``['Z', 'N', 'E']``.\n :type full_xcorr: bool\n :param full_xcorr: If ``True``, the complete xcorr function will be\n returned as :class:`~numpy.ndarray`.\n :return: **index, value[, fct]** - index of maximum xcorr value and the\n value itself. The complete xcorr function is returned only if\n ``full_xcorr=True``.\n \"\"\"\n streams = [st1, st2]\n # check if we can actually use the provided streams safely\n for st in streams:\n if not isinstance(st, Stream):\n raise TypeError(\"Expected Stream object but got %s.\" % type(st))\n for component in components:\n if not len(st.select(component=component)) == 1:\n msg = \"Expected exactly one %s trace in stream\" % component + \\\n \" but got %s.\" % len(st.select(component=component))\n raise ValueError(msg)\n ndat = len(streams[0].select(component=components[0])[0])\n if False in [len(st.select(component=component)[0]) == ndat \\\n for st in streams for component in components]:\n raise ValueError(\"All traces have to be the same length.\")\n # everything should be ok with the input data...\n corp = np.zeros(2 * shift_len + 1, dtype='float64', order='C')\n\n for component in components:\n xx = xcorr(streams[0].select(component=component)[0],\n streams[1].select(component=component)[0],\n shift_len, full_xcorr=True)\n corp += xx[2]\n\n corp /= len(components)\n\n shift, value = xcorr_max(corp, abs_max=abs_max)\n\n if full_xcorr:\n return shift, value, corp\n else:\n return shift, value\n\n\ndef xcorr_max(fct, abs_max=True):\n \"\"\"\n Return shift and value of maximum xcorr function\n\n :type fct: :class:`~numpy.ndarray`\n :param fct: xcorr function e.g. returned by xcorr\n :type abs_max: bool\n :param abs_max: determines if the absolute maximum should be used.\n :return: **shift, value** - Shift and value of maximum xcorr.\n\n .. rubric:: Example\n\n >>> fct = np.zeros(101)\n >>> fct[50] = -1.0\n >>> xcorr_max(fct)\n (0.0, -1.0)\n >>> fct[50], fct[60] = 0.0, 1.0\n >>> xcorr_max(fct)\n (10.0, 1.0)\n >>> fct[60], fct[40] = 0.0, -1.0\n >>> xcorr_max(fct)\n (-10.0, -1.0)\n >>> fct[60], fct[40] = 0.5, -1.0\n >>> xcorr_max(fct, abs_max=True)\n (-10.0, -1.0)\n >>> xcorr_max(fct, abs_max=False)\n (10.0, 0.5)\n \"\"\"\n value = fct.max()\n if abs_max:\n _min = fct.min()\n if abs(_min) > abs(value):\n value = _min\n\n mid = (len(fct) - 1) / 2\n shift = np.where(fct == value)[0][0] - mid\n return float(shift), float(value)\n\n\ndef xcorrPickCorrection(pick1, trace1, pick2, trace2, t_before,\n t_after, cc_maxlag, filter=None, filter_options={},\n plot=False, filename=None):\n \"\"\"\n Calculate the correction for the differential pick time determined by cross\n correlation of the waveforms in narrow windows around the pick times.\n For details on the fitting procedure refer to [Deichmann1992]_.\n\n The parameters depend on the epicentral distance and magnitude range. For\n small local earthquakes (Ml ~0-2, distance ~3-10 km) with consistent manual\n picks the following can be tried::\n\n t_before=0.05, t_after=0.2, cc_maxlag=0.10,\n filter=\"bandpass\", filter_options={'filter_low': 1, 'filter_high': 20}\n\n The appropriate parameter sets can and should be determined/verified\n visually using the option `show=True` on a representative set of picks.\n\n To get the corrected differential pick time calculate: ``((pick2 +\n pick2_corr) - pick1)``. To get a corrected differential travel time using\n origin times for both events calculate: ``((pick2 + pick2_corr - ot2) -\n (pick1 - ot1))``\n\n :type pick1: :class:`~obspy.core.utcdatetime.UTCDateTime`\n :param pick1: Time of pick for `trace1`.\n :type trace1: :class:`~obspy.core.trace.Trace`\n :param trace1: Waveform data for `pick1`. Add some time at front/back.\n The appropriate part of the trace is used automatically.\n :type pick2: :class:`~obspy.core.utcdatetime.UTCDateTime`\n :param pick2: Time of pick for `trace2`.\n :type trace2: :class:`~obspy.core.trace.Trace`\n :param trace2: Waveform data for `pick2`. Add some time at front/back.\n The appropriate part of the trace is used automatically.\n :type t_before: float\n :param t_before: Time to start cross correlation window before pick times\n in seconds.\n :type t_after: float\n :param t_after: Time to end cross correlation window after pick times in\n seconds.\n :type cc_maxlag: float\n :param cc_maxlag: Maximum lag time tested during cross correlation in\n seconds.\n :type filter: string\n :param filter: None for no filtering or name of filter type\n as passed on to :meth:`~obspy.core.Trace.trace.filter` if filter\n should be used. To avoid artifacts in filtering provide\n sufficiently long time series for `trace1` and `trace2`.\n :type filter_options: dict\n :param filter_options: Filter options that get passed on to\n :meth:`~obspy.core.Trace.trace.filter` if filtering is used.\n :type plot: bool\n :param plot: Determines if pick is refined automatically (default, \"\"),\n if an informative matplotlib plot is shown (\"plot\"), or if an\n interactively changeable PyQt Window is opened (\"interactive\").\n :type filename: string\n :param filename: If plot option is selected, specifying a filename here\n (e.g. 'myplot.pdf' or 'myplot.png') will output the plot to a file\n instead of opening a plot window.\n :rtype: (float, float)\n :returns: Correction time `pick2_corr` for `pick2` pick time as a float and\n corresponding correlation coefficient.\n \"\"\"\n # perform some checks on the traces\n if trace1.stats.sampling_rate != trace2.stats.sampling_rate:\n msg = \"Sampling rates do not match: %s != %s\" % \\\n (trace1.stats.sampling_rate, trace2.stats.sampling_rate)\n raise Exception(msg)\n if trace1.id != trace2.id:\n msg = \"Trace ids do not match: %s != %s\" % (trace1.id, trace2.id)\n warnings.warn(msg)\n samp_rate = trace1.stats.sampling_rate\n # check data, apply filter and take correct slice of traces\n slices = []\n for _i, (t, tr) in enumerate(((pick1, trace1), (pick2, trace2))):\n start = t - t_before - (cc_maxlag / 2.0)\n end = t + t_after + (cc_maxlag / 2.0)\n duration = end - start\n # check if necessary time spans are present in data\n if tr.stats.starttime > start:\n msg = \"Trace %s starts too late.\" % _i\n raise Exception(msg)\n if tr.stats.endtime < end:\n msg = \"Trace %s ends too early.\" % _i\n raise Exception(msg)\n if filter and start - tr.stats.starttime < duration:\n msg = \"Artifacts from signal processing possible. Trace \" + \\\n \"%s should have more additional data at the start.\" % _i\n warnings.warn(msg)\n if filter and tr.stats.endtime - end < duration:\n msg = \"Artifacts from signal processing possible. Trace \" + \\\n \"%s should have more additional data at the end.\" % _i\n warnings.warn(msg)\n # apply signal processing and take correct slice of data\n if filter:\n tr.data = tr.data.astype(\"float64\")\n tr.detrend(type='demean')\n tr.data *= cosTaper(len(tr), 0.1)\n tr.filter(type=filter, **filter_options)\n slices.append(tr.slice(start, end))\n # cross correlate\n shift_len = int(cc_maxlag * samp_rate)\n cc_shift, cc_max, cc = xcorr(slices[0].data, slices[1].data,\n shift_len, full_xcorr=True)\n cc_curvature = np.concatenate((np.zeros(1), np.diff(cc, 2), np.zeros(1)))\n cc_convex = np.ma.masked_where(np.sign(cc_curvature) >= 0, cc)\n cc_concave = np.ma.masked_where(np.sign(cc_curvature) < 0, cc)\n # check results of cross correlation\n if cc_max < 0:\n msg = \"Absolute maximum is negative: %.3f. \" % cc_max + \\\n \"Using positive maximum: %.3f\" % max(cc)\n warnings.warn(msg)\n cc_max = max(cc)\n cc_shift = cc.argmax() - (len(cc) / 2)\n if cc_max < 0.8:\n msg = \"Maximum of cross correlation lower than 0.8: %s\" % cc_max\n warnings.warn(msg)\n # make array with time shifts in seconds corresponding to cc function\n cc_t = np.linspace(-cc_maxlag, cc_maxlag, shift_len * 2 + 1)\n # take the subportion of the cross correlation around the maximum that is\n # convex and fit a parabola.\n # use vertex as subsample resolution best cc fit.\n peak_index = cc.argmax()\n first_sample = peak_index\n # XXX this could be improved..\n while first_sample > 0 and cc_curvature[first_sample - 1] <= 0:\n first_sample -= 1\n last_sample = peak_index\n while last_sample < len(cc) - 1 and cc_curvature[last_sample + 1] <= 0:\n last_sample += 1\n if first_sample == 0 or last_sample == len(cc) - 1:\n msg = \"Fitting at maximum lag. Maximum lag time should be increased.\"\n warnings.warn(msg)\n # work on subarrays\n num_samples = last_sample - first_sample + 1\n if num_samples < 3:\n msg = \"Less than 3 samples selected for fit to cross \" + \\\n \"correlation: %s\" % num_samples\n raise Exception(msg)\n if num_samples < 5:\n msg = \"Less than 5 samples selected for fit to cross \" + \\\n \"correlation: %s\" % num_samples\n warnings.warn(msg)\n # quadratic fit for small subwindow\n coeffs, residual = scipy.polyfit(cc_t[first_sample:last_sample + 1],\n cc[first_sample:last_sample + 1], deg=2, full=True)[:2]\n # check results of fit\n if coeffs[0] >= 0:\n msg = \"Fitted parabola opens upwards!\"\n warnings.warn(msg)\n if residual > 0.1:\n msg = \"Residual in quadratic fit to cross correlation maximum \" + \\\n \"larger than 0.1: %s\" % residual\n warnings.warn(msg)\n # X coordinate of vertex of parabola gives time shift to correct\n # differential pick time. Y coordinate gives maximum correlation\n # coefficient.\n dt = -coeffs[1] / 2.0 / coeffs[0]\n coeff = (4 * coeffs[0] * coeffs[2] - coeffs[1] ** 2) / (4 * coeffs[0])\n # this is the shift to apply on the time axis of `trace2` to align the\n # traces. Actually we do not want to shift the trace to align it but we\n # want to correct the time of `pick2` so that the traces align without\n # shifting. This is the negative of the cross correlation shift.\n dt = -dt\n pick2_corr = dt\n # plot the results if selected\n if plot == True:\n import matplotlib\n if filename:\n matplotlib.use('agg')\n import matplotlib.pyplot as plt\n fig = plt.figure()\n ax1 = fig.add_subplot(211)\n tmp_t = np.linspace(0, len(slices[0]) / samp_rate, len(slices[0]))\n ax1.plot(tmp_t, slices[0].data / float(slices[0].data.max()), \"k\",\n label=\"Trace 1\")\n ax1.plot(tmp_t, slices[1].data / float(slices[1].data.max()), \"r\",\n label=\"Trace 2\")\n ax1.plot(tmp_t - dt, slices[1].data / float(slices[1].data.max()), \"g\",\n label=\"Trace 2 (shifted)\")\n ax1.legend(loc=\"lower right\", prop={'size': \"small\"})\n ax1.set_title(\"%s\" % slices[0].id)\n ax1.set_xlabel(\"time [s]\")\n ax1.set_ylabel(\"norm. amplitude\")\n ax2 = fig.add_subplot(212)\n ax2.plot(cc_t, cc_convex, ls=\"\", marker=\".\", c=\"k\",\n label=\"xcorr (convex)\")\n ax2.plot(cc_t, cc_concave, ls=\"\", marker=\".\", c=\"0.7\",\n label=\"xcorr (concave)\")\n ax2.plot(cc_t[first_sample:last_sample + 1],\n cc[first_sample:last_sample + 1], \"b.\",\n label=\"used for fitting\")\n tmp_t = np.linspace(cc_t[first_sample], cc_t[last_sample],\n num_samples * 10)\n ax2.plot(tmp_t, scipy.polyval(coeffs, tmp_t), \"b\", label=\"fit\")\n ax2.axvline(-dt, color=\"g\", label=\"vertex\")\n ax2.axhline(coeff, color=\"g\")\n ax2.set_xlabel(\"%.2f at %.3f seconds correction\" % (coeff, -dt))\n ax2.set_ylabel(\"correlation coefficient\")\n ax2.set_ylim(-1, 1)\n ax2.legend(loc=\"lower right\", prop={'size': \"x-small\"})\n #plt.legend(loc=\"lower left\")\n if filename:\n fig.savefig(fname=filename)\n else:\n plt.show()\n\n return (pick2_corr, coeff)\n\n\nif __name__ == '__main__':\n import doctest\n doctest.testmod(exclude_empty=True)\n",
"path": "obspy/signal/cross_correlation.py"
}
] | [
{
"content": "#!/usr/bin/env python\n#--------------------------------------------------------------------\n# Filename: cross_correlation.py\n# Author: Moritz Beyreuther, Tobias Megies\n# Email: [email protected]\n#\n# Copyright (C) 2008-2012 Moritz Beyreuther, Tobias Megies\n#-------------------------------------------------------------------\n\"\"\"\nSignal processing routines based on cross correlation techniques.\n\n:copyright:\n The ObsPy Development Team ([email protected])\n:license:\n GNU Lesser General Public License, Version 3\n (http://www.gnu.org/copyleft/lesser.html)\n\"\"\"\n\nimport warnings\nimport numpy as np\nimport ctypes as C\nimport scipy\nfrom obspy import Trace, Stream\nfrom obspy.signal.headers import clibsignal\nfrom obspy.signal import cosTaper\n\n\ndef xcorr(tr1, tr2, shift_len, full_xcorr=False):\n \"\"\"\n Cross correlation of tr1 and tr2 in the time domain using window_len.\n\n ::\n\n Mid Sample\n |\n |AAAAAAAAAAAAAAA|AAAAAAAAAAAAAAA|AAAAAAAAAAAAAAA|AAAAAAAAAAAAAAA|\n |BBBBBBBBBBBBBBB|BBBBBBBBBBBBBBB|BBBBBBBBBBBBBBB|BBBBBBBBBBBBBBB|\n |<-shift_len/2->| <- region of support -> |<-shift_len/2->|\n\n\n :type tr1: :class:`~numpy.ndarray`, :class:`~obspy.core.trace.Trace`\n :param tr1: Trace 1\n :type tr2: :class:`~numpy.ndarray`, :class:`~obspy.core.trace.Trace`\n :param tr2: Trace 2 to correlate with trace 1\n :type shift_len: int\n :param shift_len: Total length of samples to shift for cross correlation.\n :type full_xcorr: bool\n :param full_xcorr: If ``True``, the complete xcorr function will be\n returned as :class:`~numpy.ndarray`\n :return: **index, value[, fct]** - Index of maximum xcorr value and the\n value itself. The complete xcorr function is returned only if\n ``full_xcorr=True``.\n\n .. note::\n As shift_len gets higher the window supporting the cross correlation\n actually gets smaller. So with shift_len=0 you get the correlation\n coefficient of both traces as a whole without any shift applied. As the\n xcorr function works in time domain and does not zero pad at all, with\n higher shifts allowed the window of support gets smaller so that the\n moving windows shifted against each other do not run out of the\n timeseries bounds at high time shifts. Of course there are other\n possibilities to do cross correlations e.g. in frequency domain.\n\n .. seealso::\n `ObsPy-users mailing list\n <http://lists.obspy.org/pipermail/obspy-users/2011-March/000056.html>`_\n and\n `issue #249 <https://github.com/obspy/obspy/issues/249>`_.\n\n .. rubric:: Example\n\n >>> tr1 = np.random.randn(10000).astype('float32')\n >>> tr2 = tr1.copy()\n >>> a, b = xcorr(tr1, tr2, 1000)\n >>> a\n 0\n >>> round(b, 7)\n 1.0\n \"\"\"\n # if we get Trace objects, use their data arrays\n for tr in [tr1, tr2]:\n if isinstance(tr, Trace):\n tr = tr.data\n\n # check if shift_len parameter is in an acceptable range.\n # if not the underlying c code tampers with shift_len and uses shift_len/2\n # instead. we want to avoid this silent automagic and raise an error in the\n # python layer right here.\n # see ticket #249 and src/xcorr.c lines 43-57\n if min(len(tr1), len(tr2)) - 2 * shift_len <= 0:\n msg = \"shift_len too large. The underlying C code would silently \" + \\\n \"use shift_len/2 which we want to avoid.\"\n raise ValueError(msg)\n # be nice and adapt type if necessary\n tr1 = np.require(tr1, 'float32', ['C_CONTIGUOUS'])\n tr2 = np.require(tr2, 'float32', ['C_CONTIGUOUS'])\n corp = np.empty(2 * shift_len + 1, dtype='float64', order='C')\n\n shift = C.c_int()\n coe_p = C.c_double()\n\n clibsignal.X_corr(tr1, tr2, corp, shift_len, len(tr1), len(tr2),\n C.byref(shift), C.byref(coe_p))\n\n if full_xcorr:\n return shift.value, coe_p.value, corp\n else:\n return shift.value, coe_p.value\n\n\ndef xcorr_3C(st1, st2, shift_len, components=[\"Z\", \"N\", \"E\"],\n full_xcorr=False, abs_max=True):\n \"\"\"\n Calculates the cross correlation on each of the specified components\n separately, stacks them together and estimates the maximum and shift of\n maximum on the stack.\n\n Basically the same as :func:`~obspy.signal.cross_correlation.xcorr` but\n for (normally) three components, please also take a look at the\n documentation of that function. Useful e.g. for estimation of waveform\n similarity on a three component seismogram.\n\n :type st1: :class:`~obspy.core.stream.Stream`\n :param st1: Stream 1, containing one trace for Z, N, E component (other\n component_id codes are ignored)\n :type st2: :class:`~obspy.core.stream.Stream`\n :param st2: Stream 2, containing one trace for Z, N, E component (other\n component_id codes are ignored)\n :type shift_len: int\n :param shift_len: Total length of samples to shift for cross correlation.\n :type components: List of strings\n :param components: List of components to use in cross-correlation, defaults\n to ``['Z', 'N', 'E']``.\n :type full_xcorr: bool\n :param full_xcorr: If ``True``, the complete xcorr function will be\n returned as :class:`~numpy.ndarray`.\n :return: **index, value[, fct]** - index of maximum xcorr value and the\n value itself. The complete xcorr function is returned only if\n ``full_xcorr=True``.\n \"\"\"\n streams = [st1, st2]\n # check if we can actually use the provided streams safely\n for st in streams:\n if not isinstance(st, Stream):\n raise TypeError(\"Expected Stream object but got %s.\" % type(st))\n for component in components:\n if not len(st.select(component=component)) == 1:\n msg = \"Expected exactly one %s trace in stream\" % component + \\\n \" but got %s.\" % len(st.select(component=component))\n raise ValueError(msg)\n ndat = len(streams[0].select(component=components[0])[0])\n if False in [len(st.select(component=component)[0]) == ndat \\\n for st in streams for component in components]:\n raise ValueError(\"All traces have to be the same length.\")\n # everything should be ok with the input data...\n corp = np.zeros(2 * shift_len + 1, dtype='float64', order='C')\n\n for component in components:\n xx = xcorr(streams[0].select(component=component)[0],\n streams[1].select(component=component)[0],\n shift_len, full_xcorr=True)\n corp += xx[2]\n\n corp /= len(components)\n\n shift, value = xcorr_max(corp, abs_max=abs_max)\n\n if full_xcorr:\n return shift, value, corp\n else:\n return shift, value\n\n\ndef xcorr_max(fct, abs_max=True):\n \"\"\"\n Return shift and value of maximum xcorr function\n\n :type fct: :class:`~numpy.ndarray`\n :param fct: xcorr function e.g. returned by xcorr\n :type abs_max: bool\n :param abs_max: determines if the absolute maximum should be used.\n :return: **shift, value** - Shift and value of maximum xcorr.\n\n .. rubric:: Example\n\n >>> fct = np.zeros(101)\n >>> fct[50] = -1.0\n >>> xcorr_max(fct)\n (0.0, -1.0)\n >>> fct[50], fct[60] = 0.0, 1.0\n >>> xcorr_max(fct)\n (10.0, 1.0)\n >>> fct[60], fct[40] = 0.0, -1.0\n >>> xcorr_max(fct)\n (-10.0, -1.0)\n >>> fct[60], fct[40] = 0.5, -1.0\n >>> xcorr_max(fct, abs_max=True)\n (-10.0, -1.0)\n >>> xcorr_max(fct, abs_max=False)\n (10.0, 0.5)\n \"\"\"\n value = fct.max()\n if abs_max:\n _min = fct.min()\n if abs(_min) > abs(value):\n value = _min\n\n mid = (len(fct) - 1) / 2\n shift = np.where(fct == value)[0][0] - mid\n return float(shift), float(value)\n\n\ndef xcorrPickCorrection(pick1, trace1, pick2, trace2, t_before,\n t_after, cc_maxlag, filter=None, filter_options={},\n plot=False, filename=None):\n \"\"\"\n Calculate the correction for the differential pick time determined by cross\n correlation of the waveforms in narrow windows around the pick times.\n For details on the fitting procedure refer to [Deichmann1992]_.\n\n The parameters depend on the epicentral distance and magnitude range. For\n small local earthquakes (Ml ~0-2, distance ~3-10 km) with consistent manual\n picks the following can be tried::\n\n t_before=0.05, t_after=0.2, cc_maxlag=0.10,\n filter=\"bandpass\", filter_options={'filter_low': 1, 'filter_high': 20}\n\n The appropriate parameter sets can and should be determined/verified\n visually using the option `show=True` on a representative set of picks.\n\n To get the corrected differential pick time calculate: ``((pick2 +\n pick2_corr) - pick1)``. To get a corrected differential travel time using\n origin times for both events calculate: ``((pick2 + pick2_corr - ot2) -\n (pick1 - ot1))``\n\n :type pick1: :class:`~obspy.core.utcdatetime.UTCDateTime`\n :param pick1: Time of pick for `trace1`.\n :type trace1: :class:`~obspy.core.trace.Trace`\n :param trace1: Waveform data for `pick1`. Add some time at front/back.\n The appropriate part of the trace is used automatically.\n :type pick2: :class:`~obspy.core.utcdatetime.UTCDateTime`\n :param pick2: Time of pick for `trace2`.\n :type trace2: :class:`~obspy.core.trace.Trace`\n :param trace2: Waveform data for `pick2`. Add some time at front/back.\n The appropriate part of the trace is used automatically.\n :type t_before: float\n :param t_before: Time to start cross correlation window before pick times\n in seconds.\n :type t_after: float\n :param t_after: Time to end cross correlation window after pick times in\n seconds.\n :type cc_maxlag: float\n :param cc_maxlag: Maximum lag time tested during cross correlation in\n seconds.\n :type filter: string\n :param filter: None for no filtering or name of filter type\n as passed on to :meth:`~obspy.core.Trace.trace.filter` if filter\n should be used. To avoid artifacts in filtering provide\n sufficiently long time series for `trace1` and `trace2`.\n :type filter_options: dict\n :param filter_options: Filter options that get passed on to\n :meth:`~obspy.core.Trace.trace.filter` if filtering is used.\n :type plot: bool\n :param plot: Determines if pick is refined automatically (default, \"\"),\n if an informative matplotlib plot is shown (\"plot\"), or if an\n interactively changeable PyQt Window is opened (\"interactive\").\n :type filename: string\n :param filename: If plot option is selected, specifying a filename here\n (e.g. 'myplot.pdf' or 'myplot.png') will output the plot to a file\n instead of opening a plot window.\n :rtype: (float, float)\n :returns: Correction time `pick2_corr` for `pick2` pick time as a float and\n corresponding correlation coefficient.\n \"\"\"\n # perform some checks on the traces\n if trace1.stats.sampling_rate != trace2.stats.sampling_rate:\n msg = \"Sampling rates do not match: %s != %s\" % \\\n (trace1.stats.sampling_rate, trace2.stats.sampling_rate)\n raise Exception(msg)\n if trace1.id != trace2.id:\n msg = \"Trace ids do not match: %s != %s\" % (trace1.id, trace2.id)\n warnings.warn(msg)\n samp_rate = trace1.stats.sampling_rate\n # check data, apply filter and take correct slice of traces\n slices = []\n for _i, (t, tr) in enumerate(((pick1, trace1), (pick2, trace2))):\n start = t - t_before - (cc_maxlag / 2.0)\n end = t + t_after + (cc_maxlag / 2.0)\n duration = end - start\n # check if necessary time spans are present in data\n if tr.stats.starttime > start:\n msg = \"Trace %s starts too late.\" % _i\n raise Exception(msg)\n if tr.stats.endtime < end:\n msg = \"Trace %s ends too early.\" % _i\n raise Exception(msg)\n if filter and start - tr.stats.starttime < duration:\n msg = \"Artifacts from signal processing possible. Trace \" + \\\n \"%s should have more additional data at the start.\" % _i\n warnings.warn(msg)\n if filter and tr.stats.endtime - end < duration:\n msg = \"Artifacts from signal processing possible. Trace \" + \\\n \"%s should have more additional data at the end.\" % _i\n warnings.warn(msg)\n # apply signal processing and take correct slice of data\n if filter:\n tr.data = tr.data.astype(\"float64\")\n tr.detrend(type='demean')\n tr.data *= cosTaper(len(tr), 0.1)\n tr.filter(type=filter, **filter_options)\n slices.append(tr.slice(start, end))\n # cross correlate\n shift_len = int(cc_maxlag * samp_rate)\n cc_shift, cc_max, cc = xcorr(slices[0].data, slices[1].data,\n shift_len, full_xcorr=True)\n cc_curvature = np.concatenate((np.zeros(1), np.diff(cc, 2), np.zeros(1)))\n cc_convex = np.ma.masked_where(np.sign(cc_curvature) >= 0, cc)\n cc_concave = np.ma.masked_where(np.sign(cc_curvature) < 0, cc)\n # check results of cross correlation\n if cc_max < 0:\n msg = \"Absolute maximum is negative: %.3f. \" % cc_max + \\\n \"Using positive maximum: %.3f\" % max(cc)\n warnings.warn(msg)\n cc_max = max(cc)\n cc_shift = cc.argmax() - (len(cc) / 2)\n if cc_max < 0.8:\n msg = \"Maximum of cross correlation lower than 0.8: %s\" % cc_max\n warnings.warn(msg)\n # make array with time shifts in seconds corresponding to cc function\n cc_t = np.linspace(-cc_maxlag, cc_maxlag, shift_len * 2 + 1)\n # take the subportion of the cross correlation around the maximum that is\n # convex and fit a parabola.\n # use vertex as subsample resolution best cc fit.\n peak_index = cc.argmax()\n first_sample = peak_index\n # XXX this could be improved..\n while first_sample > 0 and cc_curvature[first_sample - 1] <= 0:\n first_sample -= 1\n last_sample = peak_index\n while last_sample < len(cc) - 1 and cc_curvature[last_sample + 1] <= 0:\n last_sample += 1\n if first_sample == 0 or last_sample == len(cc) - 1:\n msg = \"Fitting at maximum lag. Maximum lag time should be increased.\"\n warnings.warn(msg)\n # work on subarrays\n num_samples = last_sample - first_sample + 1\n if num_samples < 3:\n msg = \"Less than 3 samples selected for fit to cross \" + \\\n \"correlation: %s\" % num_samples\n raise Exception(msg)\n if num_samples < 5:\n msg = \"Less than 5 samples selected for fit to cross \" + \\\n \"correlation: %s\" % num_samples\n warnings.warn(msg)\n # quadratic fit for small subwindow\n coeffs, residual = scipy.polyfit(cc_t[first_sample:last_sample + 1],\n cc[first_sample:last_sample + 1], deg=2, full=True)[:2]\n # check results of fit\n if coeffs[0] >= 0:\n msg = \"Fitted parabola opens upwards!\"\n warnings.warn(msg)\n if residual > 0.1:\n msg = \"Residual in quadratic fit to cross correlation maximum \" + \\\n \"larger than 0.1: %s\" % residual\n warnings.warn(msg)\n # X coordinate of vertex of parabola gives time shift to correct\n # differential pick time. Y coordinate gives maximum correlation\n # coefficient.\n dt = -coeffs[1] / 2.0 / coeffs[0]\n coeff = (4 * coeffs[0] * coeffs[2] - coeffs[1] ** 2) / (4 * coeffs[0])\n # this is the shift to apply on the time axis of `trace2` to align the\n # traces. Actually we do not want to shift the trace to align it but we\n # want to correct the time of `pick2` so that the traces align without\n # shifting. This is the negative of the cross correlation shift.\n dt = -dt\n pick2_corr = dt\n # plot the results if selected\n if plot == True:\n import matplotlib\n if filename:\n matplotlib.use('agg')\n import matplotlib.pyplot as plt\n fig = plt.figure()\n ax1 = fig.add_subplot(211)\n tmp_t = np.linspace(0, len(slices[0]) / samp_rate, len(slices[0]))\n ax1.plot(tmp_t, slices[0].data / float(slices[0].data.max()), \"k\",\n label=\"Trace 1\")\n ax1.plot(tmp_t, slices[1].data / float(slices[1].data.max()), \"r\",\n label=\"Trace 2\")\n ax1.plot(tmp_t - dt, slices[1].data / float(slices[1].data.max()), \"g\",\n label=\"Trace 2 (shifted)\")\n ax1.legend(loc=\"lower right\", prop={'size': \"small\"})\n ax1.set_title(\"%s\" % slices[0].id)\n ax1.set_xlabel(\"time [s]\")\n ax1.set_ylabel(\"norm. amplitude\")\n ax2 = fig.add_subplot(212)\n ax2.plot(cc_t, cc_convex, ls=\"\", marker=\".\", c=\"k\",\n label=\"xcorr (convex)\")\n ax2.plot(cc_t, cc_concave, ls=\"\", marker=\".\", c=\"0.7\",\n label=\"xcorr (concave)\")\n ax2.plot(cc_t[first_sample:last_sample + 1],\n cc[first_sample:last_sample + 1], \"b.\",\n label=\"used for fitting\")\n tmp_t = np.linspace(cc_t[first_sample], cc_t[last_sample],\n num_samples * 10)\n ax2.plot(tmp_t, scipy.polyval(coeffs, tmp_t), \"b\", label=\"fit\")\n ax2.axvline(-dt, color=\"g\", label=\"vertex\")\n ax2.axhline(coeff, color=\"g\")\n ax2.set_xlabel(\"%.2f at %.3f seconds correction\" % (coeff, -dt))\n ax2.set_ylabel(\"correlation coefficient\")\n ax2.set_ylim(-1, 1)\n ax2.legend(loc=\"lower right\", prop={'size': \"x-small\"})\n #plt.legend(loc=\"lower left\")\n if filename:\n fig.savefig(fname=filename)\n else:\n plt.show()\n\n return (pick2_corr, coeff)\n\n\nif __name__ == '__main__':\n import doctest\n doctest.testmod(exclude_empty=True)\n",
"path": "obspy/signal/cross_correlation.py"
}
] | diff --git a/obspy/signal/cross_correlation.py b/obspy/signal/cross_correlation.py
index 35d81927abb..18c947bf813 100644
--- a/obspy/signal/cross_correlation.py
+++ b/obspy/signal/cross_correlation.py
@@ -65,7 +65,7 @@ def xcorr(tr1, tr2, shift_len, full_xcorr=False):
`ObsPy-users mailing list
<http://lists.obspy.org/pipermail/obspy-users/2011-March/000056.html>`_
and
- `ticket #249 <https://obspy.org/ticket/249>`_.
+ `issue #249 <https://github.com/obspy/obspy/issues/249>`_.
.. rubric:: Example
|
PlasmaPy__PlasmaPy-2304 | Unpin version of Sphinx after next release of sphinx-notfound-page
```py3tb
Exception occurred:
File "/home/runner/work/PlasmaPy/PlasmaPy/.tox/build_docs_pins/lib/python3.11/site-packages/notfound/extension.py", line 337, in setup
from sphinx.builders.html import setup_js_tag_helper
ImportError: cannot import name 'setup_js_tag_helper' from 'sphinx.builders.html' (/home/runner/work/PlasmaPy/PlasmaPy/.tox/build_docs_pins/lib/python3.11/site-packages/sphinx/builders/html/__init__.py)
The full traceback has been saved in /tmp/sphinx-err-a746pmo7.log, if you want to report the issue to the developers.
Please also report this if it was a user error, so that a better error message can be provided next time.
A bug report can be filed in the tracker at <https://github.com/sphinx-doc/sphinx/issues>. Thanks!
build_docs_pins: exit 2 (3.82 seconds) /home/runner/work/PlasmaPy/PlasmaPy> sphinx-build docs docs/_build/html -W -n --keep-going -b html -q pid=2209
.pkg: _exit> python /opt/hostedtoolcache/Python/3.11.4/x64/lib/python3.11/site-packages/pyproject_api/_backend.py True setuptools.build_meta
build_docs_pins: FAIL code 2 (81.97=setup[78.16]+cmd[3.82] seconds)
evaluation failed :( (82.05 seconds)
```
| [
{
"content": "\"\"\"The configuration file for building PlasmaPy's documentation.\"\"\"\n\n#!/usr/bin/env python3\n\n# If extensions (or modules to document with autodoc) are in another directory,\n# add these directories to sys.path here. If the directory is relative to the\n# documentation root, use os.path.abspath to make it absolute, like shown here.\n\nimport os\nimport sys\n\n# isort: off\nsys.path.insert(0, os.path.abspath(\"..\")) # noqa: PTH100\nsys.path.insert(0, os.path.abspath(\".\")) # noqa: PTH100\n# isort: on\n\nimport cff_to_rst\n\nfrom _global_substitutions import global_substitutions\nfrom datetime import datetime\nfrom pkg_resources import parse_version\nfrom sphinx.application import Sphinx\n\n# Generate author list from CITATION.cff\n\ncff_to_rst.main()\n\nfrom plasmapy import __version__ as release\n\n# -- General configuration ------------------------------------------------\nautosummary_generate = True\nautomodapi_custom_groups = {\n \"aliases\": {\n \"title\": \"Aliases\",\n \"description\": (\n \"\"\"\n PlasmaPy provides :term:`aliases` of the most common plasma\n functionality for user convenience. Aliases in PlasmaPy are\n denoted with a trailing underscore (e.g., ``alias_``). For\n further details, please refer to the :ref:`contributor\n guide's section on aliases <aliases>`.\n \"\"\"\n ),\n \"dunder\": \"__aliases__\",\n },\n \"lite-functions\": {\n \"title\": \"Lite-Functions\",\n \"description\": (\n \"\"\"\n :term:`Lite-functions` are optimized versions of existing\n `plasmapy` functions that are intended for applications where\n computational efficiency matters most. Lite-functions accept\n numbers and NumPy arrays that are implicitly assumed to be\n in SI units, and do not accept |Quantity| objects as inputs.\n For further details, please refer to the :ref:`contributor\n guide's section on lite-functions <lite-functions>`.\n\n .. caution::\n\n Lite-functions do not include the safeguards that are\n included in most `plasmapy.formulary` functions. When\n using lite-functions, it is vital to double-check your\n implementation!\n \"\"\"\n ),\n \"dunder\": \"__lite_funcs__\",\n },\n}\nautomodapi_group_order = (\n \"modules\",\n \"classes\",\n \"exceptions\",\n \"warnings\",\n \"functions\",\n \"aliases\",\n \"lite-functions\",\n \"variables\",\n)\n\n# If your documentation needs a minimal Sphinx version, state it here.\n\nneeds_sphinx = \"6.1.3\"\n\n# Add any Sphinx extension module names here, as strings. They can be\n# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom\n# ones. When extensions are removed or added, please update the section\n# in docs/doc_guide.rst on Sphinx extensions.\n\nextensions = [\n \"hoverxref.extension\",\n \"IPython.sphinxext.ipython_console_highlighting\",\n \"nbsphinx\",\n \"notfound.extension\",\n \"plasmapy_sphinx\",\n \"sphinx.ext.autodoc\",\n \"sphinx.ext.duration\",\n \"sphinx.ext.extlinks\",\n \"sphinx.ext.graphviz\",\n \"sphinx.ext.intersphinx\",\n \"sphinx.ext.mathjax\",\n \"sphinx.ext.napoleon\",\n \"sphinx.ext.todo\",\n \"sphinx.ext.viewcode\",\n \"sphinx_changelog\",\n \"sphinx_codeautolink\",\n \"sphinx_copybutton\",\n \"sphinx_gallery.load_style\",\n \"sphinx_issues\",\n \"sphinx_reredirects\",\n \"sphinx_tabs.tabs\",\n \"sphinxcontrib.bibtex\",\n \"sphinxcontrib.globalsubs\",\n]\n\n# Configure sphinxcontrib-bibtex\n\nbibtex_bibfiles = [\"bibliography.bib\"]\nbibtex_default_style = \"plain\"\nbibtex_reference_style = \"author_year\"\nbibtex_cite_id = \"{key}\"\n\n# Configure sphinx-codeautolink\n\ncodeautolink_concat_default = True\n\n# Intersphinx generates automatic links to the documentation of objects\n# in other packages. When mappings are removed or added, please update\n# the section in docs/doc_guide.rst on references to other packages.\n\nintersphinx_mapping = {\n \"astropy\": (\"https://docs.astropy.org/en/stable/\", None),\n \"lmfit\": (\"https://lmfit.github.io/lmfit-py/\", None),\n \"matplotlib\": (\"https://matplotlib.org/stable/\", None),\n \"numba\": (\"https://numba.readthedocs.io/en/stable/\", None),\n \"numpy\": (\"https://numpy.org/doc/stable/\", None),\n \"pandas\": (\"https://pandas.pydata.org/pandas-docs/stable/\", None),\n \"pytest\": (\"https://docs.pytest.org/en/stable/\", None),\n \"python\": (\"https://docs.python.org/3/\", None),\n \"readthedocs\": (\"https://docs.readthedocs.io/en/stable/\", None),\n \"scipy\": (\"https://docs.scipy.org/doc/scipy/\", None),\n \"sphinx\": (\"https://www.sphinx-doc.org/en/master/\", None),\n \"sphinx_automodapi\": (\n \"https://sphinx-automodapi.readthedocs.io/en/latest/\",\n None,\n ),\n}\n\nhoverxref_intersphinx = [\n \"astropy\",\n \"lmfit\",\n \"numba\",\n \"numpy\",\n \"pandas\",\n \"pytest\",\n \"python\",\n \"readthedocs\",\n \"scipy\",\n \"sphinx\",\n \"sphinx_automodapi\",\n]\n\nautoclass_content = \"both\"\nautodoc_typehints_format = \"short\"\n\n# Configure sphinx-issues\n\nissues_github_path = \"PlasmaPy/PlasmaPy\"\n\n# Add any paths that contain templates here, relative to this directory.\ntemplates_path = [\"_templates\"]\n\n# The suffix(es) of source filenames.\n# You can specify multiple suffix as a list of string:\n#\n# source_suffix = ['.rst', '.md']\nsource_suffix = \".rst\"\n\n# The root toctree document.\nroot_doc = \"index\"\n\n# General information about the project.\nproject = \"PlasmaPy\"\nauthor = \"PlasmaPy Community\"\ncopyright = f\"2015–{datetime.utcnow().year}, {author}\" # noqa: A001, DTZ003\nlanguage = \"en\"\n\n# The version info for the project you're documenting, acts as replacement for\n# |version| and |release|, also used in various other places throughout the\n# built documents.\n#\n# The full version, including alpha/beta/rc tags.\n# Note: If plasmapy.__version__ can not be defined then it is set to 'unknown'.\n# However, release needs to be a semantic style version number, so set\n# the 'unknown' case to ''.\nrelease = \"\" if release == \"unknown\" else release\npv = parse_version(release)\nrelease = pv.public\nversion = \".\".join(release.split(\".\")[:2]) # short X.Y version\nrevision = pv.local[1:] if pv.local is not None else \"\"\n\n# The Sphinx configuration variables rst_prolog and rst_epilog contain\n# text that gets prepended or appended to all reStructuredText sources.\n# These variables can be used to make global definitions; however, long\n# values of these variables can greatly slow down the documentation\n# build, so use them in moderation! Use docs/_global_substitutions.py\n# to define substitutions.\n\nrst_prolog = \"\"\"\n.. role:: py(code)\n :language: python\n\n.. role:: bash(code)\n :language: bash\n\"\"\"\n\n# List of patterns, relative to source directory, that match files and\n# directories to ignore when looking for source files.\n# This patterns also effect to html_static_path and html_extra_path\nexclude_patterns = [\n \"_build\",\n \"Thumbs.db\",\n \".DS_Store\",\n \"notebooks/langmuir_samples\",\n \"**.ipynb_checkpoints\",\n \"plasmapy_sphinx\",\n \"**Untitled*\",\n]\n\nhtml_extra_path = [\"robots.txt\"]\n\n# If true, `todo` and `todoList` produce output, else they produce nothing.\ntodo_include_todos = False\n\ndefault_role = \"py:obj\"\n\n# Customizations for make linkcheck using regular expressions\n\nlinkcheck_allowed_redirects = {\n r\"https://doi\\.org/.+\": r\"https://.+\", # DOI links are more persistent\n r\"https://docs.+\\.org\": r\"https://docs.+\\.org/en/.+\",\n r\"https://docs.+\\.io\": r\"https://docs.+\\.io/en/.+\",\n r\"https://docs.+\\.com\": r\"https://docs.+\\.com/en/.+\",\n r\"https://docs.+\\.dev\": r\"https://docs.+\\.dev/en/.+\",\n r\"https://en.wikipedia.org/wiki.+\": \"https://en.wikipedia.org/wiki.+\",\n r\"https://.+\\.readthedocs\\.io\": r\"https://.+\\.readthedocs\\.io/en/.+\",\n r\"https://www\\.sphinx-doc\\.org\": r\"https://www\\.sphinx-doc\\.org/en/.+\",\n r\"https://.+/github\\.io\": r\"https://.+/github\\.io/en/.+\",\n r\"https://.+\": r\".+(google|github).+[lL]ogin.+\", # some links require logins\n r\"https://jinja\\.palletsprojects\\.com\": r\"https://jinja\\.palletsprojects\\.com/.+\",\n r\"https://pip\\.pypa\\.io\": r\"https://pip\\.pypa\\.io/en/.+\",\n r\"https://www.python.org/dev/peps/pep.+\": \"https://peps.python.org/pep.+\",\n}\n\n# Hyperlinks for `make linkcheck` to ignore, such as links that point to\n# setting options in PlasmaPy's GitHub account that require a login.\n\n# To speed up `make linkcheck`, ignore github.com & doi.org.\n\nlinkcheck_ignore = [\n \"https://github.com/PlasmaPy/PlasmaPy/settings/secrets/actions\",\n]\n\nlinkcheck_anchors = True\nlinkcheck_anchors_ignore = [\n \"/room\",\n r\".+openastronomy.+\",\n \"L[0-9].+\",\n \"!forum/plasmapy\",\n]\n\nredirects = {\n \"contributing/install_dev\": \"../contributing/getting_ready.html\",\n \"development\": \"../contributing/\",\n \"development/changelog_guide\": \"../contributing/changelog_guide.html\",\n \"development/code_guide\": \"../contributing/code_guide.html\",\n \"development/doc_guide\": \"../contributing/doc_guide.html\",\n \"development/index\": \"../contributing/index.html\",\n \"development/install_dev\": \"../contributing/getting_ready.html\",\n \"development/release_guide\": \"../contributing/release_guide.html\",\n \"development/testing_guide\": \"../contributing/testing_guide.html\",\n \"whatsnew\": \"../changelog/\",\n \"whatsnew/0.1.0\": \"../changelog/0.1.0.html\",\n \"whatsnew/0.1.1\": \"../changelog/0.1.0.html\",\n \"whatsnew/0.2.0\": \"../changelog/0.1.0.html\",\n \"whatsnew/0.3.1\": \"../changelog/0.1.0.html\",\n \"whatsnew/0.4.0\": \"../changelog/0.1.0.html\",\n \"whatsnew/0.5.0\": \"../changelog/0.1.0.html\",\n \"whatsnew/0.6.0\": \"../changelog/0.1.0.html\",\n \"whatsnew/0.7.0\": \"../changelog/0.1.0.html\",\n \"whatsnew/0.8.1\": \"../changelog/0.1.0.html\",\n \"whatsnew/0.9.0\": \"../changelog/0.1.0.html\",\n \"whatsnew/0.9.1\": \"../changelog/0.1.0.html\",\n \"whatsnew/2023.1.0\": \"../changelog/2023.1.0.html\",\n \"whatsnew/index\": \"../changelog/index.html\",\n}\n\n# Use a code highlighting style that meets the WCAG AA contrast standard\npygments_style = \"default\"\n\n# adapted from sphinx-hoverxref conf.py\nif os.environ.get(\"READTHEDOCS\"):\n # Building on Read the Docs\n hoverxref_api_host = \"https://readthedocs.org\"\n\n if os.environ.get(\"PROXIED_API_ENDPOINT\"):\n # Use the proxied API endpoint\n # - A RTD thing to avoid a CSRF block when docs are using a\n # custom domain\n hoverxref_api_host = \"/_\"\n\nhoverxref_tooltip_maxwidth = 600 # RTD main window is 696px\nhoverxref_auto_ref = True\nhoverxref_mathjax = True\nhoverxref_sphinxtabs = True\n\n# hoverxref has to be applied to these\nhoverxref_domains = [\"py\", \"cite\"]\nhoverxref_roles = [\"confval\", \"term\"]\n\nhoverxref_role_types = {\n # roles with cite domain\n \"p\": \"tooltip\",\n \"t\": \"tooltip\",\n #\n # roles with py domain\n \"attr\": \"tooltip\",\n \"class\": \"tooltip\",\n \"const\": \"tooltip\",\n \"data\": \"tooltip\",\n \"exc\": \"tooltip\",\n \"func\": \"tooltip\",\n \"meth\": \"tooltip\",\n \"mod\": \"tooltip\",\n \"obj\": \"tooltip\",\n #\n # roles with std domain\n \"confval\": \"tooltip\",\n \"hoverxref\": \"tooltip\",\n \"ref\": \"tooltip\",\n \"term\": \"tooltip\",\n}\n\n# Using sphinx.ext.extlinks lets us simplify the process of creating\n# links to commonly used external sites. The key of the extlink becomes\n# a new role, and the corresponding tuple contains the base url and the\n# caption. For example, we can now do :orcid:`0000-0000-0000-0000` and\n# have a link create to the corresponding ORCID page. New roles should\n# be added to rst-roles in tox.ini to avoid being caught by\n# flake8-rst-docstrings.\n\nextlinks = {\n \"orcid\": (\"https://orcid.org/%s\", \"%s\"),\n \"wikipedia\": (\"https://en.wikipedia.org/wiki/%s\", \"%s\"),\n}\n\n# Specify patterns to ignore when doing a nitpicky documentation build.\n# These may include common expressions like \"real number\" as well as\n# workarounds for nested inline literals as defined in docs/common_links.py\n\npython_role = \"py:.*\"\n\nnitpick_ignore_regex = [\n # Before adding patterns for type specifications in docstrings, note\n # that information on the *meaning* of a parameter should be\n # included in the parameter description instead.\n (python_role, \"and\"),\n (python_role, \"array .*\"),\n (python_role, \"array_like\"),\n (python_role, \"callable\"),\n # for defaults that are words, numbers, particle symbols like \"p+\"\n (python_role, r\"default: [-\\+]?\\w+[-\\+]?\\.?\\d*\"),\n # for defaults that are lists, tuples, sets, dictionaries, and strings\n (python_role, r\"default: ((\\[.*\\])|(\\(.*\\))|(\\{.*\\})|(\\\".*\\\")|(\\'.*\\'))\"),\n # for defaults that are calls like Particle(\"p+\") or items from a dictionary\n (python_role, r\"default: \\w+[\\.\\w]*[\\(\\[].*[\\)\\]]\"),\n (python_role, \"dictionary.*\"),\n (python_role, \"function\"),\n (python_role, \".*integer.*\"),\n (python_role, \"iterable\"),\n (python_role, \"key\"),\n (python_role, \"keyword-only\"),\n (python_role, \".* object\"),\n (python_role, \"optional\"),\n (python_role, \"or\"),\n (python_role, \"Real\"),\n (python_role, \".*real number.*\"),\n (python_role, \".*representation.*\"),\n (python_role, \"shape.*\"),\n (python_role, r\"u\\..*\"),\n (python_role, \".*Unit.*\"),\n # pytest helpers\n (python_role, \"_pytest.*\"),\n (python_role, \"Failed\"),\n # charged_particle_radiography\n (python_role, \"1\"),\n (python_role, \"2 ints\"),\n (python_role, \"a single int\"),\n (python_role, \"same\"),\n (python_role, \"Tuple of 1\"),\n # thomson\n (python_role, \"Ne\"),\n (python_role, \"Ni\"),\n # utils\n (python_role, \"docstring of\"),\n (python_role, \"validation specifications\"),\n # for reStructuredText workarounds to allow nested inline literals\n (python_role, \"git\"),\n (python_role, \"h5py\"),\n (python_role, \"IPython.sphinxext.ipython_console_highlighting\"),\n (python_role, \"lmfit\"),\n (python_role, \"mpmath\"),\n (python_role, \"nbsphinx\"),\n (python_role, \"numba\"),\n (python_role, \"xarray\"),\n # plasmapy_sphinx\n (python_role, \"automod.*\"),\n (python_role, \"Builder\"),\n (python_role, \"docutils.*\"),\n (python_role, \"level\"),\n (python_role, \".*member.*\"),\n (python_role, \"OptionSpec\"),\n (python_role, \"py\"),\n (python_role, \"[Ss]phinx.*\"), # also for reStructuredText workarounds\n # The following patterns still need to be fixed.\n (python_role, \"json.decoder.JSONDecoder\"),\n (python_role, \"plasmapy.analysis.swept_langmuir.find_floating_potential\"),\n (python_role, \"plasmapy.particles.particle_collections\"),\n (python_role, \"plasmapy.utils.decorators.lite_func\"),\n]\n\n# -- Options for HTML output ----------------------------------------------\n\n# The theme to use for HTML and HTML Help pages. See the documentation for\n# a list of builtin themes.\n#\nhtml_theme = \"sphinx_rtd_theme\"\n\n# Theme options are theme-specific and customize the look and feel of a theme\n# further. For a list of options available for each theme, see the\n# documentation.\n#\nhtml_logo = \"./_static/with-text-light-190px.png\"\nhtml_theme_options = {\n \"logo_only\": True,\n #\n # TOC options\n # https://sphinx-rtd-theme.readthedocs.io/en/stable/configuring.html#theme-options\n \"includehidden\": False,\n}\n\n# Add any paths that contain custom static files (such as style sheets) here,\n# relative to this directory. They are copied after the builtin static files,\n# so a file named \"default.css\" will overwrite the builtin \"default.css\".\nhtml_static_path = [\"_static\"]\n\n# A list of prefixes that are ignored for sorting the Python module\n# index (e.g., if this is set to ['foo.'], then foo.bar is shown under\n# B, not F).\nmodindex_common_prefix = [\"plasmapy.\"]\n\n# -- Options for HTMLHelp output ------------------------------------------\n\n# Output file base name for HTML help builder.\nhtmlhelp_basename = \"PlasmaPydoc\"\n\n# -- Options for LaTeX output ---------------------------------------------\n\nlatex_elements = {\n # The paper size ('letterpaper' or 'a4paper').\n # 'papersize': 'letterpaper',\n #\n # The font size ('10pt', '11pt' or '12pt').\n # 'pointsize': '10pt',\n #\n # Additional stuff for the LaTeX preamble.\n # 'preamble': '',\n #\n # Latex figure (float) alignment\n # 'figure_align': 'htbp',\n}\n\n# Grouping the document tree into LaTeX files. List of tuples\n# (source start file, target name, title,\n# author, documentclass [howto, manual, or own class]).\nlatex_documents = [\n (\n root_doc,\n \"PlasmaPy.tex\",\n \"PlasmaPy Documentation\",\n \"PlasmaPy Community\",\n \"manual\",\n )\n]\n\n# -- Options for manual page output ---------------------------------------\n\n# One entry per manual page. List of tuples\n# (source start file, name, description, authors, manual section).\nman_pages = [(root_doc, \"plasmapy\", \"PlasmaPy Documentation\", [author], 1)]\n\n# -- Options for Texinfo output -------------------------------------------\n\n# Grouping the document tree into Texinfo files. List of tuples\n# (source start file, target name, title, author,\n# dir menu entry, description, category)\ntexinfo_documents = [\n (\n root_doc,\n \"PlasmaPy\",\n \"PlasmaPy Documentation\",\n author,\n \"PlasmaPy\",\n \"Python package for plasma physics\",\n \"Miscellaneous\",\n )\n]\n\nhtml_favicon = \"./_static/icon.ico\"\n\n# -- NBSphinx options\n\nnbsphinx_thumbnails = {\n \"notebooks/*\": \"_static/graphic-circular.png\",\n \"notebooks/*/*\": \"_static/graphic-circular.png\",\n \"notebooks/diagnostics/langmuir_analysis\": (\n \"_static/notebook_images/langmuir_analysis.png\"\n ),\n \"notebooks/formulary/magnetosphere\": (\n \"_static/notebook_images/mms.png\"\n ), # public domain\n \"notebooks/getting_started/units\": (\n \"_static/notebook_images/astropy_logo_notext.png\"\n ), # CC BY-SA\n \"notebooks/formulary/solar_plasma_beta\": \"_static/notebook_images/coronal_loops.png\",\n \"notebooks/plasma/grids_cartesian\": (\n \"_static/notebook_images/uniform_grid_thumbnail.png\"\n ),\n \"notebooks/plasma/grids_nonuniform\": (\n \"_static/notebook_images/nonuniform_grid_thumbnail.png\"\n ),\n}\n\n# adapted from\n# https://github.com/spatialaudio/nbsphinx/blob/58b8034dd9d7349c1b4ac3e7a7d6baa87ab2a6a9/doc/conf.py\n\n# This is processed by Jinja2 and inserted before each notebook\nnbsphinx_prolog = r\"\"\"\n{% set docname = 'docs/' + env.doc2path(env.docname, base=None) %}\n{% set nb_base = 'tree' if env.config.revision else 'blob' %}\n{% set nb_where = env.config.revision if env.config.revision else 'main' %}\n\n.. raw:: html\n\n <div class=\"admonition note\">\n <p style=\"margin-bottom:0px\">\n This page was generated by\n <a href=\"https://nbsphinx.readthedocs.io/\">nbsphinx</a> from\n <a class=\"reference external\" href=\"https://github.com/PlasmaPy/PlasmaPy/{{ nb_base|e }}/{{ nb_where|e }}/{{ docname|e }}\">{{ docname|e }}</a>.\n <br>\n Interactive online version:\n <a href=\"https://mybinder.org/v2/gh/PlasmaPy/PlasmaPy/{{ nb_where|e }}/?filepath={{ docname|e }}\"><img alt=\"Binder badge\" src=\"https://mybinder.org/badge_logo.svg\" style=\"vertical-align:text-bottom\"></a>.\n </p>\n </div>\n\n.. raw:: latex\n\n \\nbsphinxstartnotebook{\\scriptsize\\noindent\\strut\n \\textcolor{gray}{The following section was generated from\n \\sphinxcode{\\sphinxupquote{\\strut {{ docname | escape_latex }}}} \\dotfill}}\n\"\"\"\n\n\ndef setup(app: Sphinx) -> None:\n app.add_config_value(\"revision\", \"\", rebuild=True)\n app.add_css_file(\"css/admonition_color_contrast.css\")\n app.add_css_file(\"css/plasmapy.css\", priority=600)\n",
"path": "docs/conf.py"
}
] | [
{
"content": "\"\"\"The configuration file for building PlasmaPy's documentation.\"\"\"\n\n#!/usr/bin/env python3\n\n# If extensions (or modules to document with autodoc) are in another directory,\n# add these directories to sys.path here. If the directory is relative to the\n# documentation root, use os.path.abspath to make it absolute, like shown here.\n\nimport os\nimport sys\n\n# isort: off\nsys.path.insert(0, os.path.abspath(\"..\")) # noqa: PTH100\nsys.path.insert(0, os.path.abspath(\".\")) # noqa: PTH100\n# isort: on\n\nimport cff_to_rst\n\nfrom _global_substitutions import global_substitutions\nfrom datetime import datetime\nfrom pkg_resources import parse_version\nfrom sphinx.application import Sphinx\n\n# Generate author list from CITATION.cff\n\ncff_to_rst.main()\n\nfrom plasmapy import __version__ as release\n\n# -- General configuration ------------------------------------------------\nautosummary_generate = True\nautomodapi_custom_groups = {\n \"aliases\": {\n \"title\": \"Aliases\",\n \"description\": (\n \"\"\"\n PlasmaPy provides :term:`aliases` of the most common plasma\n functionality for user convenience. Aliases in PlasmaPy are\n denoted with a trailing underscore (e.g., ``alias_``). For\n further details, please refer to the :ref:`contributor\n guide's section on aliases <aliases>`.\n \"\"\"\n ),\n \"dunder\": \"__aliases__\",\n },\n \"lite-functions\": {\n \"title\": \"Lite-Functions\",\n \"description\": (\n \"\"\"\n :term:`Lite-functions` are optimized versions of existing\n `plasmapy` functions that are intended for applications where\n computational efficiency matters most. Lite-functions accept\n numbers and NumPy arrays that are implicitly assumed to be\n in SI units, and do not accept |Quantity| objects as inputs.\n For further details, please refer to the :ref:`contributor\n guide's section on lite-functions <lite-functions>`.\n\n .. caution::\n\n Lite-functions do not include the safeguards that are\n included in most `plasmapy.formulary` functions. When\n using lite-functions, it is vital to double-check your\n implementation!\n \"\"\"\n ),\n \"dunder\": \"__lite_funcs__\",\n },\n}\nautomodapi_group_order = (\n \"modules\",\n \"classes\",\n \"exceptions\",\n \"warnings\",\n \"functions\",\n \"aliases\",\n \"lite-functions\",\n \"variables\",\n)\n\n# If your documentation needs a minimal Sphinx version, state it here.\n\nneeds_sphinx = \"6.1.3\"\n\n# Add any Sphinx extension module names here, as strings. They can be\n# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom\n# ones. When extensions are removed or added, please update the section\n# in docs/doc_guide.rst on Sphinx extensions.\n\nextensions = [\n \"hoverxref.extension\",\n \"IPython.sphinxext.ipython_console_highlighting\",\n \"nbsphinx\",\n \"notfound.extension\",\n \"plasmapy_sphinx\",\n \"sphinx.ext.autodoc\",\n \"sphinx.ext.duration\",\n \"sphinx.ext.extlinks\",\n \"sphinx.ext.graphviz\",\n \"sphinx.ext.intersphinx\",\n \"sphinx.ext.mathjax\",\n \"sphinx.ext.napoleon\",\n \"sphinx.ext.todo\",\n \"sphinx.ext.viewcode\",\n \"sphinx_changelog\",\n \"sphinx_codeautolink\",\n \"sphinx_copybutton\",\n \"sphinx_gallery.load_style\",\n \"sphinx_issues\",\n \"sphinx_reredirects\",\n \"sphinx_tabs.tabs\",\n \"sphinxcontrib.bibtex\",\n \"sphinxcontrib.globalsubs\",\n]\n\n# Configure sphinxcontrib-bibtex\n\nbibtex_bibfiles = [\"bibliography.bib\"]\nbibtex_default_style = \"plain\"\nbibtex_reference_style = \"author_year\"\nbibtex_cite_id = \"{key}\"\n\n# Configure sphinx-codeautolink\n\ncodeautolink_concat_default = True\n\n# Intersphinx generates automatic links to the documentation of objects\n# in other packages. When mappings are removed or added, please update\n# the section in docs/doc_guide.rst on references to other packages.\n\nintersphinx_mapping = {\n \"astropy\": (\"https://docs.astropy.org/en/stable/\", None),\n \"lmfit\": (\"https://lmfit.github.io/lmfit-py/\", None),\n \"matplotlib\": (\"https://matplotlib.org/stable/\", None),\n \"numba\": (\"https://numba.readthedocs.io/en/stable/\", None),\n \"numpy\": (\"https://numpy.org/doc/stable/\", None),\n \"pandas\": (\"https://pandas.pydata.org/pandas-docs/stable/\", None),\n \"pytest\": (\"https://docs.pytest.org/en/stable/\", None),\n \"python\": (\"https://docs.python.org/3/\", None),\n \"readthedocs\": (\"https://docs.readthedocs.io/en/stable/\", None),\n \"scipy\": (\"https://docs.scipy.org/doc/scipy/\", None),\n \"sphinx\": (\"https://www.sphinx-doc.org/en/master/\", None),\n \"sphinx_automodapi\": (\n \"https://sphinx-automodapi.readthedocs.io/en/latest/\",\n None,\n ),\n}\n\nhoverxref_intersphinx = [\n \"astropy\",\n \"lmfit\",\n \"numba\",\n \"numpy\",\n \"pandas\",\n \"pytest\",\n \"python\",\n \"readthedocs\",\n \"scipy\",\n \"sphinx\",\n \"sphinx_automodapi\",\n]\n\nautoclass_content = \"both\"\nautodoc_typehints_format = \"short\"\n\n# Configure sphinx-issues\n\nissues_github_path = \"PlasmaPy/PlasmaPy\"\n\n# Add any paths that contain templates here, relative to this directory.\ntemplates_path = [\"_templates\"]\n\n# The suffix(es) of source filenames.\n# You can specify multiple suffix as a list of string:\n#\n# source_suffix = ['.rst', '.md']\nsource_suffix = \".rst\"\n\n# The root toctree document.\nroot_doc = \"index\"\n\n# General information about the project.\nproject = \"PlasmaPy\"\nauthor = \"PlasmaPy Community\"\ncopyright = f\"2015–{datetime.utcnow().year}, {author}\" # noqa: A001, DTZ003\nlanguage = \"en\"\n\n# The version info for the project you're documenting, acts as replacement for\n# |version| and |release|, also used in various other places throughout the\n# built documents.\n#\n# The full version, including alpha/beta/rc tags.\n# Note: If plasmapy.__version__ can not be defined then it is set to 'unknown'.\n# However, release needs to be a semantic style version number, so set\n# the 'unknown' case to ''.\nrelease = \"\" if release == \"unknown\" else release\npv = parse_version(release)\nrelease = pv.public\nversion = \".\".join(release.split(\".\")[:2]) # short X.Y version\nrevision = pv.local[1:] if pv.local is not None else \"\"\n\n# The Sphinx configuration variables rst_prolog and rst_epilog contain\n# text that gets prepended or appended to all reStructuredText sources.\n# These variables can be used to make global definitions; however, long\n# values of these variables can greatly slow down the documentation\n# build, so use them in moderation! Use docs/_global_substitutions.py\n# to define substitutions.\n\nrst_prolog = \"\"\"\n.. role:: py(code)\n :language: python\n\n.. role:: bash(code)\n :language: bash\n\"\"\"\n\n# List of patterns, relative to source directory, that match files and\n# directories to ignore when looking for source files.\n# This patterns also effect to html_static_path and html_extra_path\nexclude_patterns = [\n \"_build\",\n \"Thumbs.db\",\n \".DS_Store\",\n \"notebooks/langmuir_samples\",\n \"**.ipynb_checkpoints\",\n \"plasmapy_sphinx\",\n \"**Untitled*\",\n]\n\nhtml_extra_path = [\"robots.txt\"]\n\n# If true, `todo` and `todoList` produce output, else they produce nothing.\ntodo_include_todos = False\n\ndefault_role = \"py:obj\"\n\n# Customizations for make linkcheck using regular expressions\n\nlinkcheck_allowed_redirects = {\n r\"https://doi\\.org/.+\": r\"https://.+\", # DOI links are more persistent\n r\"https://docs.+\\.org\": r\"https://docs.+\\.org/en/.+\",\n r\"https://docs.+\\.io\": r\"https://docs.+\\.io/en/.+\",\n r\"https://docs.+\\.com\": r\"https://docs.+\\.com/en/.+\",\n r\"https://docs.+\\.dev\": r\"https://docs.+\\.dev/en/.+\",\n r\"https://en.wikipedia.org/wiki.+\": \"https://en.wikipedia.org/wiki.+\",\n r\"https://.+\\.readthedocs\\.io\": r\"https://.+\\.readthedocs\\.io/en/.+\",\n r\"https://www\\.sphinx-doc\\.org\": r\"https://www\\.sphinx-doc\\.org/en/.+\",\n r\"https://.+/github\\.io\": r\"https://.+/github\\.io/en/.+\",\n r\"https://.+\": r\".+(google|github).+[lL]ogin.+\", # some links require logins\n r\"https://jinja\\.palletsprojects\\.com\": r\"https://jinja\\.palletsprojects\\.com/.+\",\n r\"https://pip\\.pypa\\.io\": r\"https://pip\\.pypa\\.io/en/.+\",\n r\"https://www.python.org/dev/peps/pep.+\": \"https://peps.python.org/pep.+\",\n}\n\n# Hyperlinks for `make linkcheck` to ignore, such as links that point to\n# setting options in PlasmaPy's GitHub account that require a login.\n\n# To speed up `make linkcheck`, ignore github.com & doi.org.\n\nlinkcheck_ignore = [\n \"https://github.com/PlasmaPy/PlasmaPy/settings/secrets/actions\",\n]\n\nlinkcheck_anchors = True\nlinkcheck_anchors_ignore = [\n \"/room\",\n r\".+openastronomy.+\",\n \"L[0-9].+\",\n \"!forum/plasmapy\",\n]\n\nredirects = {\n \"contributing/install_dev\": \"../contributing/getting_ready.html\",\n \"development\": \"../contributing/\",\n \"development/changelog_guide\": \"../contributing/changelog_guide.html\",\n \"development/code_guide\": \"../contributing/code_guide.html\",\n \"development/doc_guide\": \"../contributing/doc_guide.html\",\n \"development/index\": \"../contributing/index.html\",\n \"development/install_dev\": \"../contributing/getting_ready.html\",\n \"development/release_guide\": \"../contributing/release_guide.html\",\n \"development/testing_guide\": \"../contributing/testing_guide.html\",\n \"whatsnew\": \"../changelog/\",\n \"whatsnew/0.1.0\": \"../changelog/0.1.0.html\",\n \"whatsnew/0.1.1\": \"../changelog/0.1.0.html\",\n \"whatsnew/0.2.0\": \"../changelog/0.1.0.html\",\n \"whatsnew/0.3.1\": \"../changelog/0.1.0.html\",\n \"whatsnew/0.4.0\": \"../changelog/0.1.0.html\",\n \"whatsnew/0.5.0\": \"../changelog/0.1.0.html\",\n \"whatsnew/0.6.0\": \"../changelog/0.1.0.html\",\n \"whatsnew/0.7.0\": \"../changelog/0.1.0.html\",\n \"whatsnew/0.8.1\": \"../changelog/0.1.0.html\",\n \"whatsnew/0.9.0\": \"../changelog/0.1.0.html\",\n \"whatsnew/0.9.1\": \"../changelog/0.1.0.html\",\n \"whatsnew/2023.1.0\": \"../changelog/2023.1.0.html\",\n \"whatsnew/index\": \"../changelog/index.html\",\n}\n\n# Use a code highlighting style that meets the WCAG AA contrast standard\npygments_style = \"default\"\n\n# adapted from sphinx-hoverxref conf.py\nif os.environ.get(\"READTHEDOCS\"):\n # Building on Read the Docs\n hoverxref_api_host = \"https://readthedocs.org\"\n\n if os.environ.get(\"PROXIED_API_ENDPOINT\"):\n # Use the proxied API endpoint\n # - A RTD thing to avoid a CSRF block when docs are using a\n # custom domain\n hoverxref_api_host = \"/_\"\n\nhoverxref_tooltip_maxwidth = 600 # RTD main window is 696px\nhoverxref_auto_ref = True\nhoverxref_mathjax = True\nhoverxref_sphinxtabs = True\n\n# hoverxref has to be applied to these\nhoverxref_domains = [\"py\", \"cite\"]\nhoverxref_roles = [\"confval\", \"term\"]\n\nhoverxref_role_types = {\n # roles with cite domain\n \"p\": \"tooltip\",\n \"t\": \"tooltip\",\n #\n # roles with py domain\n \"attr\": \"tooltip\",\n \"class\": \"tooltip\",\n \"const\": \"tooltip\",\n \"data\": \"tooltip\",\n \"exc\": \"tooltip\",\n \"func\": \"tooltip\",\n \"meth\": \"tooltip\",\n \"mod\": \"tooltip\",\n \"obj\": \"tooltip\",\n #\n # roles with std domain\n \"confval\": \"tooltip\",\n \"hoverxref\": \"tooltip\",\n \"ref\": \"tooltip\",\n \"term\": \"tooltip\",\n}\n\n# Using sphinx.ext.extlinks lets us simplify the process of creating\n# links to commonly used external sites. The key of the extlink becomes\n# a new role, and the corresponding tuple contains the base url and the\n# caption. For example, we can now do :orcid:`0000-0000-0000-0000` and\n# have a link create to the corresponding ORCID page. New roles should\n# be added to rst-roles in tox.ini to avoid being caught by\n# flake8-rst-docstrings.\n\nextlinks = {\n \"orcid\": (\"https://orcid.org/%s\", \"%s\"),\n \"wikipedia\": (\"https://en.wikipedia.org/wiki/%s\", \"%s\"),\n}\n\n# Specify patterns to ignore when doing a nitpicky documentation build.\n# These may include common expressions like \"real number\" as well as\n# workarounds for nested inline literals as defined in docs/common_links.py\n\npython_role = \"py:.*\"\n\nnitpick_ignore_regex = [\n # Before adding patterns for type specifications in docstrings, note\n # that information on the *meaning* of a parameter should be\n # included in the parameter description instead.\n (python_role, \"and\"),\n (python_role, \"array .*\"),\n (python_role, \"array_like\"),\n (python_role, \"callable\"),\n # for defaults that are words, numbers, particle symbols like \"p+\"\n (python_role, r\"default: [-\\+]?\\w+[-\\+]?\\.?\\d*\"),\n # for defaults that are lists, tuples, sets, dictionaries, and strings\n (python_role, r\"default: ((\\[.*\\])|(\\(.*\\))|(\\{.*\\})|(\\\".*\\\")|(\\'.*\\'))\"),\n # for defaults that are calls like Particle(\"p+\") or items from a dictionary\n (python_role, r\"default: \\w+[\\.\\w]*[\\(\\[].*[\\)\\]]\"),\n (python_role, \"dictionary.*\"),\n (python_role, \"function\"),\n (python_role, \".*integer.*\"),\n (python_role, \"iterable\"),\n (python_role, \"key\"),\n (python_role, \"keyword-only\"),\n (python_role, \".* object\"),\n (python_role, \"optional\"),\n (python_role, \"or\"),\n (python_role, \"Real\"),\n (python_role, \".*real number.*\"),\n (python_role, \".*representation.*\"),\n (python_role, \"shape.*\"),\n (python_role, r\"u\\..*\"),\n (python_role, \".*Unit.*\"),\n # pytest helpers\n (python_role, \"_pytest.*\"),\n (python_role, \"Failed\"),\n # charged_particle_radiography\n (python_role, \"1\"),\n (python_role, \"2 ints\"),\n (python_role, \"a single int\"),\n (python_role, \"same\"),\n (python_role, \"Tuple of 1\"),\n # thomson\n (python_role, \"Ne\"),\n (python_role, \"Ni\"),\n # utils\n (python_role, \"docstring of\"),\n (python_role, \"validation specifications\"),\n # for reStructuredText workarounds to allow nested inline literals\n (python_role, \"git\"),\n (python_role, \"h5py\"),\n (python_role, \"IPython.sphinxext.ipython_console_highlighting\"),\n (python_role, \"lmfit\"),\n (python_role, \"mpmath\"),\n (python_role, \"nbsphinx\"),\n (python_role, \"numba\"),\n (python_role, \"xarray\"),\n # plasmapy_sphinx\n (python_role, \"automod.*\"),\n (python_role, \"Builder\"),\n (python_role, \"docutils.*\"),\n (python_role, \"Documenter\"),\n (python_role, \"Node\"),\n (python_role, \"level\"),\n (python_role, \".*member.*\"),\n (python_role, \"OptionSpec\"),\n (python_role, \"py\"),\n (python_role, \"[Ss]phinx.*\"), # also for reStructuredText workarounds\n # The following patterns still need to be fixed.\n (python_role, \"json.decoder.JSONDecoder\"),\n (python_role, \"plasmapy.analysis.swept_langmuir.find_floating_potential\"),\n (python_role, \"plasmapy.particles.particle_collections\"),\n (python_role, \"plasmapy.utils.decorators.lite_func\"),\n]\n\n# -- Options for HTML output ----------------------------------------------\n\n# The theme to use for HTML and HTML Help pages. See the documentation for\n# a list of builtin themes.\n#\nhtml_theme = \"sphinx_rtd_theme\"\n\n# Theme options are theme-specific and customize the look and feel of a theme\n# further. For a list of options available for each theme, see the\n# documentation.\n#\nhtml_logo = \"./_static/with-text-light-190px.png\"\nhtml_theme_options = {\n \"logo_only\": True,\n #\n # TOC options\n # https://sphinx-rtd-theme.readthedocs.io/en/stable/configuring.html#theme-options\n \"includehidden\": False,\n}\n\n# Add any paths that contain custom static files (such as style sheets) here,\n# relative to this directory. They are copied after the builtin static files,\n# so a file named \"default.css\" will overwrite the builtin \"default.css\".\nhtml_static_path = [\"_static\"]\n\n# A list of prefixes that are ignored for sorting the Python module\n# index (e.g., if this is set to ['foo.'], then foo.bar is shown under\n# B, not F).\nmodindex_common_prefix = [\"plasmapy.\"]\n\n# -- Options for HTMLHelp output ------------------------------------------\n\n# Output file base name for HTML help builder.\nhtmlhelp_basename = \"PlasmaPydoc\"\n\n# -- Options for LaTeX output ---------------------------------------------\n\nlatex_elements = {\n # The paper size ('letterpaper' or 'a4paper').\n # 'papersize': 'letterpaper',\n #\n # The font size ('10pt', '11pt' or '12pt').\n # 'pointsize': '10pt',\n #\n # Additional stuff for the LaTeX preamble.\n # 'preamble': '',\n #\n # Latex figure (float) alignment\n # 'figure_align': 'htbp',\n}\n\n# Grouping the document tree into LaTeX files. List of tuples\n# (source start file, target name, title,\n# author, documentclass [howto, manual, or own class]).\nlatex_documents = [\n (\n root_doc,\n \"PlasmaPy.tex\",\n \"PlasmaPy Documentation\",\n \"PlasmaPy Community\",\n \"manual\",\n )\n]\n\n# -- Options for manual page output ---------------------------------------\n\n# One entry per manual page. List of tuples\n# (source start file, name, description, authors, manual section).\nman_pages = [(root_doc, \"plasmapy\", \"PlasmaPy Documentation\", [author], 1)]\n\n# -- Options for Texinfo output -------------------------------------------\n\n# Grouping the document tree into Texinfo files. List of tuples\n# (source start file, target name, title, author,\n# dir menu entry, description, category)\ntexinfo_documents = [\n (\n root_doc,\n \"PlasmaPy\",\n \"PlasmaPy Documentation\",\n author,\n \"PlasmaPy\",\n \"Python package for plasma physics\",\n \"Miscellaneous\",\n )\n]\n\nhtml_favicon = \"./_static/icon.ico\"\n\n# -- NBSphinx options\n\nnbsphinx_thumbnails = {\n \"notebooks/*\": \"_static/graphic-circular.png\",\n \"notebooks/*/*\": \"_static/graphic-circular.png\",\n \"notebooks/diagnostics/langmuir_analysis\": (\n \"_static/notebook_images/langmuir_analysis.png\"\n ),\n \"notebooks/formulary/magnetosphere\": (\n \"_static/notebook_images/mms.png\"\n ), # public domain\n \"notebooks/getting_started/units\": (\n \"_static/notebook_images/astropy_logo_notext.png\"\n ), # CC BY-SA\n \"notebooks/formulary/solar_plasma_beta\": \"_static/notebook_images/coronal_loops.png\",\n \"notebooks/plasma/grids_cartesian\": (\n \"_static/notebook_images/uniform_grid_thumbnail.png\"\n ),\n \"notebooks/plasma/grids_nonuniform\": (\n \"_static/notebook_images/nonuniform_grid_thumbnail.png\"\n ),\n}\n\n# adapted from\n# https://github.com/spatialaudio/nbsphinx/blob/58b8034dd9d7349c1b4ac3e7a7d6baa87ab2a6a9/doc/conf.py\n\n# This is processed by Jinja2 and inserted before each notebook\nnbsphinx_prolog = r\"\"\"\n{% set docname = 'docs/' + env.doc2path(env.docname, base=None) %}\n{% set nb_base = 'tree' if env.config.revision else 'blob' %}\n{% set nb_where = env.config.revision if env.config.revision else 'main' %}\n\n.. raw:: html\n\n <div class=\"admonition note\">\n <p style=\"margin-bottom:0px\">\n This page was generated by\n <a href=\"https://nbsphinx.readthedocs.io/\">nbsphinx</a> from\n <a class=\"reference external\" href=\"https://github.com/PlasmaPy/PlasmaPy/{{ nb_base|e }}/{{ nb_where|e }}/{{ docname|e }}\">{{ docname|e }}</a>.\n <br>\n Interactive online version:\n <a href=\"https://mybinder.org/v2/gh/PlasmaPy/PlasmaPy/{{ nb_where|e }}/?filepath={{ docname|e }}\"><img alt=\"Binder badge\" src=\"https://mybinder.org/badge_logo.svg\" style=\"vertical-align:text-bottom\"></a>.\n </p>\n </div>\n\n.. raw:: latex\n\n \\nbsphinxstartnotebook{\\scriptsize\\noindent\\strut\n \\textcolor{gray}{The following section was generated from\n \\sphinxcode{\\sphinxupquote{\\strut {{ docname | escape_latex }}}} \\dotfill}}\n\"\"\"\n\n\ndef setup(app: Sphinx) -> None:\n app.add_config_value(\"revision\", \"\", rebuild=True)\n app.add_css_file(\"css/admonition_color_contrast.css\")\n app.add_css_file(\"css/plasmapy.css\", priority=600)\n",
"path": "docs/conf.py"
}
] | diff --git a/docs/conf.py b/docs/conf.py
index 2493075f93..f4c21a7c41 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -416,6 +416,8 @@
(python_role, "automod.*"),
(python_role, "Builder"),
(python_role, "docutils.*"),
+ (python_role, "Documenter"),
+ (python_role, "Node"),
(python_role, "level"),
(python_role, ".*member.*"),
(python_role, "OptionSpec"),
diff --git a/pyproject.toml b/pyproject.toml
index 44487ddd92..79518dead8 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -64,14 +64,14 @@ docs = [
"numpydoc >= 1.5.0",
"pillow >= 9.5.0",
"pygments >= 2.15.0",
- "sphinx == 6.2.1",
+ "sphinx >= 7.2.3",
"sphinx-changelog >= 1.3.0",
"sphinx-codeautolink >= 0.15.0",
"sphinx-copybutton >= 0.5.1",
"sphinx-gallery >= 0.12.2",
"sphinx-hoverxref >= 1.1.1",
"sphinx-issues >= 3.0.1",
- "sphinx-notfound-page >= 0.8.3",
+ "sphinx-notfound-page == 1.0.0rc1",
"sphinx-reredirects >= 0.1.1",
"sphinx_rtd_theme >= 1.2.2",
"sphinx_tabs >= 3.4.1",
diff --git a/requirements.txt b/requirements.txt
index 1791e5a3a0..b4ce56a085 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -391,7 +391,7 @@ sortedcontainers==2.4.0
# via hypothesis
soupsieve==2.4.1
# via beautifulsoup4
-sphinx==6.2.1
+sphinx==7.2.4
# via
# nbsphinx
# numpydoc
@@ -426,7 +426,7 @@ sphinx-hoverxref==1.3.0
# via plasmapy (setup.py)
sphinx-issues==3.0.1
# via plasmapy (setup.py)
-sphinx-notfound-page==0.8.3
+sphinx-notfound-page==1.0.0rc1
# via plasmapy (setup.py)
sphinx-reredirects==0.1.2
# via plasmapy (setup.py)
|
Bitmessage__PyBitmessage-1697 | Fix backward compatibility in pickle_deserialize_old_knownnodes()
Hello!
#1662 is caused by changed package structure.
Here I've set up a minimal upgrade from v0.6.3 to reproduce the bug. Using v0.6.2 would be difficult, because it has no command line args.
| [
{
"content": "\"\"\"\nManipulations with knownNodes dictionary.\n\"\"\"\n\nimport json\nimport logging\nimport os\nimport pickle\nimport threading\nimport time\ntry:\n from collections.abc import Iterable\nexcept ImportError:\n from collections import Iterable\n\nimport state\nfrom bmconfigparser import BMConfigParser\nfrom network.node import Peer\n\nknownNodesLock = threading.RLock()\n\"\"\"Thread lock for knownnodes modification\"\"\"\nknownNodes = {stream: {} for stream in range(1, 4)}\n\"\"\"The dict of known nodes for each stream\"\"\"\n\nknownNodesTrimAmount = 2000\n\"\"\"trim stream knownnodes dict to this length\"\"\"\n\nknownNodesForgetRating = -0.5\n\"\"\"forget a node after rating is this low\"\"\"\n\nknownNodesActual = False\n\nlogger = logging.getLogger('default')\n\nDEFAULT_NODES = (\n Peer('5.45.99.75', 8444),\n Peer('75.167.159.54', 8444),\n Peer('95.165.168.168', 8444),\n Peer('85.180.139.241', 8444),\n Peer('158.222.217.190', 8080),\n Peer('178.62.12.187', 8448),\n Peer('24.188.198.204', 8111),\n Peer('109.147.204.113', 1195),\n Peer('178.11.46.221', 8444)\n)\n\n\ndef json_serialize_knownnodes(output):\n \"\"\"\n Reorganize knownnodes dict and write it as JSON to output\n \"\"\"\n _serialized = []\n for stream, peers in knownNodes.iteritems():\n for peer, info in peers.iteritems():\n info.update(rating=round(info.get('rating', 0), 2))\n _serialized.append({\n 'stream': stream, 'peer': peer._asdict(), 'info': info\n })\n json.dump(_serialized, output, indent=4)\n\n\ndef json_deserialize_knownnodes(source):\n \"\"\"\n Read JSON from source and make knownnodes dict\n \"\"\"\n global knownNodesActual # pylint: disable=global-statement\n for node in json.load(source):\n peer = node['peer']\n info = node['info']\n peer = Peer(str(peer['host']), peer.get('port', 8444))\n knownNodes[node['stream']][peer] = info\n if not (knownNodesActual\n or info.get('self')) and peer not in DEFAULT_NODES:\n knownNodesActual = True\n\n\ndef pickle_deserialize_old_knownnodes(source):\n \"\"\"\n Unpickle source and reorganize knownnodes dict if it has old format\n the old format was {Peer:lastseen, ...}\n the new format is {Peer:{\"lastseen\":i, \"rating\":f}}\n \"\"\"\n global knownNodes # pylint: disable=global-statement\n knownNodes = pickle.load(source)\n for stream in knownNodes.keys():\n for node, params in knownNodes[stream].iteritems():\n if isinstance(params, (float, int)):\n addKnownNode(stream, node, params)\n\n\ndef saveKnownNodes(dirName=None):\n \"\"\"Save knownnodes to filesystem\"\"\"\n if dirName is None:\n dirName = state.appdata\n with knownNodesLock:\n with open(os.path.join(dirName, 'knownnodes.dat'), 'wb') as output:\n json_serialize_knownnodes(output)\n\n\ndef addKnownNode(stream, peer, lastseen=None, is_self=False):\n \"\"\"\n Add a new node to the dict or update lastseen if it already exists.\n Do it for each stream number if *stream* is `Iterable`.\n Returns True if added a new node.\n \"\"\"\n # pylint: disable=too-many-branches\n if isinstance(stream, Iterable):\n with knownNodesLock:\n for s in stream:\n addKnownNode(s, peer, lastseen, is_self)\n return\n\n rating = 0.0\n if not lastseen:\n # FIXME: maybe about 28 days?\n lastseen = int(time.time())\n else:\n lastseen = int(lastseen)\n try:\n info = knownNodes[stream].get(peer)\n if lastseen > info['lastseen']:\n info['lastseen'] = lastseen\n except (KeyError, TypeError):\n pass\n else:\n return\n\n if not is_self:\n if len(knownNodes[stream]) > BMConfigParser().safeGetInt(\n \"knownnodes\", \"maxnodes\"):\n return\n\n knownNodes[stream][peer] = {\n 'lastseen': lastseen,\n 'rating': rating or 1 if is_self else 0,\n 'self': is_self,\n }\n return True\n\n\ndef createDefaultKnownNodes():\n \"\"\"Creating default Knownnodes\"\"\"\n past = time.time() - 2418600 # 28 days - 10 min\n for peer in DEFAULT_NODES:\n addKnownNode(1, peer, past)\n saveKnownNodes()\n\n\ndef readKnownNodes():\n \"\"\"Load knownnodes from filesystem\"\"\"\n try:\n with open(state.appdata + 'knownnodes.dat', 'rb') as source:\n with knownNodesLock:\n try:\n json_deserialize_knownnodes(source)\n except ValueError:\n source.seek(0)\n pickle_deserialize_old_knownnodes(source)\n except (IOError, OSError, KeyError, EOFError):\n logger.debug(\n 'Failed to read nodes from knownnodes.dat', exc_info=True)\n createDefaultKnownNodes()\n\n config = BMConfigParser()\n\n # your own onion address, if setup\n onionhostname = config.safeGet('bitmessagesettings', 'onionhostname')\n if onionhostname and \".onion\" in onionhostname:\n onionport = config.safeGetInt('bitmessagesettings', 'onionport')\n if onionport:\n self_peer = Peer(onionhostname, onionport)\n addKnownNode(1, self_peer, is_self=True)\n state.ownAddresses[self_peer] = True\n\n\ndef increaseRating(peer):\n \"\"\"Increase rating of a peer node\"\"\"\n increaseAmount = 0.1\n maxRating = 1\n with knownNodesLock:\n for stream in knownNodes.keys():\n try:\n knownNodes[stream][peer][\"rating\"] = min(\n knownNodes[stream][peer][\"rating\"] + increaseAmount,\n maxRating\n )\n except KeyError:\n pass\n\n\ndef decreaseRating(peer):\n \"\"\"Decrease rating of a peer node\"\"\"\n decreaseAmount = 0.1\n minRating = -1\n with knownNodesLock:\n for stream in knownNodes.keys():\n try:\n knownNodes[stream][peer][\"rating\"] = max(\n knownNodes[stream][peer][\"rating\"] - decreaseAmount,\n minRating\n )\n except KeyError:\n pass\n\n\ndef trimKnownNodes(recAddrStream=1):\n \"\"\"Triming Knownnodes\"\"\"\n if len(knownNodes[recAddrStream]) < \\\n BMConfigParser().safeGetInt(\"knownnodes\", \"maxnodes\"):\n return\n with knownNodesLock:\n oldestList = sorted(\n knownNodes[recAddrStream],\n key=lambda x: x['lastseen']\n )[:knownNodesTrimAmount]\n for oldest in oldestList:\n del knownNodes[recAddrStream][oldest]\n\n\ndef dns():\n \"\"\"Add DNS names to knownnodes\"\"\"\n for port in [8080, 8444]:\n addKnownNode(\n 1, Peer('bootstrap%s.bitmessage.org' % port, port))\n\n\ndef cleanupKnownNodes():\n \"\"\"\n Cleanup knownnodes: remove old nodes and nodes with low rating\n \"\"\"\n now = int(time.time())\n needToWriteKnownNodesToDisk = False\n\n with knownNodesLock:\n for stream in knownNodes:\n if stream not in state.streamsInWhichIAmParticipating:\n continue\n keys = knownNodes[stream].keys()\n for node in keys:\n if len(knownNodes[stream]) <= 1: # leave at least one node\n break\n try:\n age = now - knownNodes[stream][node][\"lastseen\"]\n # scrap old nodes (age > 28 days)\n if age > 2419200:\n needToWriteKnownNodesToDisk = True\n del knownNodes[stream][node]\n continue\n # scrap old nodes (age > 3 hours) with low rating\n if (age > 10800 and knownNodes[stream][node][\"rating\"]\n <= knownNodesForgetRating):\n needToWriteKnownNodesToDisk = True\n del knownNodes[stream][node]\n continue\n except TypeError:\n logger.warning('Error in %s', node)\n keys = []\n\n # Let us write out the knowNodes to disk\n # if there is anything new to write out.\n if needToWriteKnownNodesToDisk:\n saveKnownNodes()\n",
"path": "src/network/knownnodes.py"
}
] | [
{
"content": "\"\"\"\nManipulations with knownNodes dictionary.\n\"\"\"\n\nimport json\nimport logging\nimport os\nimport pickle\nimport threading\nimport time\ntry:\n from collections.abc import Iterable\nexcept ImportError:\n from collections import Iterable\n\nimport state\nfrom bmconfigparser import BMConfigParser\nfrom network.node import Peer\n\nstate.Peer = Peer\n\nknownNodesLock = threading.RLock()\n\"\"\"Thread lock for knownnodes modification\"\"\"\nknownNodes = {stream: {} for stream in range(1, 4)}\n\"\"\"The dict of known nodes for each stream\"\"\"\n\nknownNodesTrimAmount = 2000\n\"\"\"trim stream knownnodes dict to this length\"\"\"\n\nknownNodesForgetRating = -0.5\n\"\"\"forget a node after rating is this low\"\"\"\n\nknownNodesActual = False\n\nlogger = logging.getLogger('default')\n\nDEFAULT_NODES = (\n Peer('5.45.99.75', 8444),\n Peer('75.167.159.54', 8444),\n Peer('95.165.168.168', 8444),\n Peer('85.180.139.241', 8444),\n Peer('158.222.217.190', 8080),\n Peer('178.62.12.187', 8448),\n Peer('24.188.198.204', 8111),\n Peer('109.147.204.113', 1195),\n Peer('178.11.46.221', 8444)\n)\n\n\ndef json_serialize_knownnodes(output):\n \"\"\"\n Reorganize knownnodes dict and write it as JSON to output\n \"\"\"\n _serialized = []\n for stream, peers in knownNodes.iteritems():\n for peer, info in peers.iteritems():\n info.update(rating=round(info.get('rating', 0), 2))\n _serialized.append({\n 'stream': stream, 'peer': peer._asdict(), 'info': info\n })\n json.dump(_serialized, output, indent=4)\n\n\ndef json_deserialize_knownnodes(source):\n \"\"\"\n Read JSON from source and make knownnodes dict\n \"\"\"\n global knownNodesActual # pylint: disable=global-statement\n for node in json.load(source):\n peer = node['peer']\n info = node['info']\n peer = Peer(str(peer['host']), peer.get('port', 8444))\n knownNodes[node['stream']][peer] = info\n if not (knownNodesActual\n or info.get('self')) and peer not in DEFAULT_NODES:\n knownNodesActual = True\n\n\ndef pickle_deserialize_old_knownnodes(source):\n \"\"\"\n Unpickle source and reorganize knownnodes dict if it has old format\n the old format was {Peer:lastseen, ...}\n the new format is {Peer:{\"lastseen\":i, \"rating\":f}}\n \"\"\"\n global knownNodes # pylint: disable=global-statement\n knownNodes = pickle.load(source)\n for stream in knownNodes.keys():\n for node, params in knownNodes[stream].iteritems():\n if isinstance(params, (float, int)):\n addKnownNode(stream, node, params)\n\n\ndef saveKnownNodes(dirName=None):\n \"\"\"Save knownnodes to filesystem\"\"\"\n if dirName is None:\n dirName = state.appdata\n with knownNodesLock:\n with open(os.path.join(dirName, 'knownnodes.dat'), 'wb') as output:\n json_serialize_knownnodes(output)\n\n\ndef addKnownNode(stream, peer, lastseen=None, is_self=False):\n \"\"\"\n Add a new node to the dict or update lastseen if it already exists.\n Do it for each stream number if *stream* is `Iterable`.\n Returns True if added a new node.\n \"\"\"\n # pylint: disable=too-many-branches\n if isinstance(stream, Iterable):\n with knownNodesLock:\n for s in stream:\n addKnownNode(s, peer, lastseen, is_self)\n return\n\n rating = 0.0\n if not lastseen:\n # FIXME: maybe about 28 days?\n lastseen = int(time.time())\n else:\n lastseen = int(lastseen)\n try:\n info = knownNodes[stream].get(peer)\n if lastseen > info['lastseen']:\n info['lastseen'] = lastseen\n except (KeyError, TypeError):\n pass\n else:\n return\n\n if not is_self:\n if len(knownNodes[stream]) > BMConfigParser().safeGetInt(\n \"knownnodes\", \"maxnodes\"):\n return\n\n knownNodes[stream][peer] = {\n 'lastseen': lastseen,\n 'rating': rating or 1 if is_self else 0,\n 'self': is_self,\n }\n return True\n\n\ndef createDefaultKnownNodes():\n \"\"\"Creating default Knownnodes\"\"\"\n past = time.time() - 2418600 # 28 days - 10 min\n for peer in DEFAULT_NODES:\n addKnownNode(1, peer, past)\n saveKnownNodes()\n\n\ndef readKnownNodes():\n \"\"\"Load knownnodes from filesystem\"\"\"\n try:\n with open(state.appdata + 'knownnodes.dat', 'rb') as source:\n with knownNodesLock:\n try:\n json_deserialize_knownnodes(source)\n except ValueError:\n source.seek(0)\n pickle_deserialize_old_knownnodes(source)\n except (IOError, OSError, KeyError, EOFError):\n logger.debug(\n 'Failed to read nodes from knownnodes.dat', exc_info=True)\n createDefaultKnownNodes()\n\n config = BMConfigParser()\n\n # your own onion address, if setup\n onionhostname = config.safeGet('bitmessagesettings', 'onionhostname')\n if onionhostname and \".onion\" in onionhostname:\n onionport = config.safeGetInt('bitmessagesettings', 'onionport')\n if onionport:\n self_peer = Peer(onionhostname, onionport)\n addKnownNode(1, self_peer, is_self=True)\n state.ownAddresses[self_peer] = True\n\n\ndef increaseRating(peer):\n \"\"\"Increase rating of a peer node\"\"\"\n increaseAmount = 0.1\n maxRating = 1\n with knownNodesLock:\n for stream in knownNodes.keys():\n try:\n knownNodes[stream][peer][\"rating\"] = min(\n knownNodes[stream][peer][\"rating\"] + increaseAmount,\n maxRating\n )\n except KeyError:\n pass\n\n\ndef decreaseRating(peer):\n \"\"\"Decrease rating of a peer node\"\"\"\n decreaseAmount = 0.1\n minRating = -1\n with knownNodesLock:\n for stream in knownNodes.keys():\n try:\n knownNodes[stream][peer][\"rating\"] = max(\n knownNodes[stream][peer][\"rating\"] - decreaseAmount,\n minRating\n )\n except KeyError:\n pass\n\n\ndef trimKnownNodes(recAddrStream=1):\n \"\"\"Triming Knownnodes\"\"\"\n if len(knownNodes[recAddrStream]) < \\\n BMConfigParser().safeGetInt(\"knownnodes\", \"maxnodes\"):\n return\n with knownNodesLock:\n oldestList = sorted(\n knownNodes[recAddrStream],\n key=lambda x: x['lastseen']\n )[:knownNodesTrimAmount]\n for oldest in oldestList:\n del knownNodes[recAddrStream][oldest]\n\n\ndef dns():\n \"\"\"Add DNS names to knownnodes\"\"\"\n for port in [8080, 8444]:\n addKnownNode(\n 1, Peer('bootstrap%s.bitmessage.org' % port, port))\n\n\ndef cleanupKnownNodes():\n \"\"\"\n Cleanup knownnodes: remove old nodes and nodes with low rating\n \"\"\"\n now = int(time.time())\n needToWriteKnownNodesToDisk = False\n\n with knownNodesLock:\n for stream in knownNodes:\n if stream not in state.streamsInWhichIAmParticipating:\n continue\n keys = knownNodes[stream].keys()\n for node in keys:\n if len(knownNodes[stream]) <= 1: # leave at least one node\n break\n try:\n age = now - knownNodes[stream][node][\"lastseen\"]\n # scrap old nodes (age > 28 days)\n if age > 2419200:\n needToWriteKnownNodesToDisk = True\n del knownNodes[stream][node]\n continue\n # scrap old nodes (age > 3 hours) with low rating\n if (age > 10800 and knownNodes[stream][node][\"rating\"]\n <= knownNodesForgetRating):\n needToWriteKnownNodesToDisk = True\n del knownNodes[stream][node]\n continue\n except TypeError:\n logger.warning('Error in %s', node)\n keys = []\n\n # Let us write out the knowNodes to disk\n # if there is anything new to write out.\n if needToWriteKnownNodesToDisk:\n saveKnownNodes()\n",
"path": "src/network/knownnodes.py"
}
] | diff --git a/src/network/knownnodes.py b/src/network/knownnodes.py
index 07871c7c7a..c92f8e9a3f 100644
--- a/src/network/knownnodes.py
+++ b/src/network/knownnodes.py
@@ -17,6 +17,8 @@
from bmconfigparser import BMConfigParser
from network.node import Peer
+state.Peer = Peer
+
knownNodesLock = threading.RLock()
"""Thread lock for knownnodes modification"""
knownNodes = {stream: {} for stream in range(1, 4)}
diff --git a/src/tests/core.py b/src/tests/core.py
index 3d8ac98382..03e8b948f0 100644
--- a/src/tests/core.py
+++ b/src/tests/core.py
@@ -7,6 +7,7 @@
import pickle # nosec
import Queue
import random # nosec
+import shutil
import string
import sys
import time
@@ -231,7 +232,8 @@ def test_version(self):
msg = protocol.assembleVersionMessage('127.0.0.1', 8444, [1])
decoded = self._decode_msg(msg, "IQQiiQlsLv")
peer, _, ua, streams = self._decode_msg(msg, "IQQiiQlsLv")[4:]
- self.assertEqual(peer, Node(3, '127.0.0.1', 8444))
+ self.assertEqual(
+ peer, Node(11 if state.dandelion else 3, '127.0.0.1', 8444))
self.assertEqual(ua, '/PyBitmessage:' + softwareVersion + '/')
self.assertEqual(streams, [1])
# with multiple streams
@@ -259,6 +261,19 @@ def test_insert_method_msgid(self):
'''select typeof(msgid) from sent where ackdata=?''', result)
self.assertEqual(column_type[0][0] if column_type else '', 'text')
+ def test_old_knownnodes_pickle(self):
+ """Testing old(v.0.6.2) version knownnodes.dat file"""
+ try:
+ old_source_file = os.path.join(
+ os.path.abspath(os.path.dirname(__file__)), 'test_pattern', 'knownnodes.dat')
+ new_destination_file = os.path.join(state.appdata, 'knownnodes.dat')
+ shutil.copyfile(old_source_file, new_destination_file)
+ knownnodes.readKnownNodes()
+ except AttributeError as e:
+ self.fail('Failed to load knownnodes: %s' % e)
+ finally:
+ cleanup(files=('knownnodes.dat',))
+
def run():
"""Starts all tests defined in this module"""
diff --git a/src/tests/test_pattern/knownnodes.dat b/src/tests/test_pattern/knownnodes.dat
new file mode 100644
index 0000000000..a78a4434b5
--- /dev/null
+++ b/src/tests/test_pattern/knownnodes.dat
@@ -0,0 +1,104 @@
+(dp0
+I1
+(dp1
+ccopy_reg
+_reconstructor
+p2
+(cstate
+Peer
+p3
+c__builtin__
+tuple
+p4
+(S'85.180.139.241'
+p5
+I8444
+tp6
+tp7
+Rp8
+I1608398841
+sg2
+(g3
+g4
+(S'158.222.211.81'
+p9
+I8080
+tp10
+tp11
+Rp12
+I1608398841
+sg2
+(g3
+g4
+(S'178.62.12.187'
+p13
+I8448
+tp14
+tp15
+Rp16
+I1608398841
+sg2
+(g3
+g4
+(S'109.147.204.113'
+p17
+I1195
+tp18
+tp19
+Rp20
+I1608398841
+sg2
+(g3
+g4
+(S'5.45.99.75'
+p21
+I8444
+tp22
+tp23
+Rp24
+I1608398841
+sg2
+(g3
+g4
+(S'178.11.46.221'
+p25
+I8444
+tp26
+tp27
+Rp28
+I1608398841
+sg2
+(g3
+g4
+(S'95.165.168.168'
+p29
+I8444
+tp30
+tp31
+Rp32
+I1608398841
+sg2
+(g3
+g4
+(S'24.188.198.204'
+p33
+I8111
+tp34
+tp35
+Rp36
+I1608398841
+sg2
+(g3
+g4
+(S'75.167.159.54'
+p37
+I8444
+tp38
+tp39
+Rp40
+I1608398841
+ssI2
+(dp41
+sI3
+(dp42
+s.
\ No newline at end of file
|
pyca__cryptography-4064 | Add a python_requires to our setup.py
cc: @dstufft
| [
{
"content": "#!/usr/bin/env python\n\n# This file is dual licensed under the terms of the Apache License, Version\n# 2.0, and the BSD License. See the LICENSE file in the root of this repository\n# for complete details.\n\nfrom __future__ import absolute_import, division, print_function\n\nimport os\nimport platform\nimport subprocess\nimport sys\nfrom distutils.command.build import build\n\nimport pkg_resources\n\nimport setuptools\nfrom setuptools import find_packages, setup\nfrom setuptools.command.install import install\nfrom setuptools.command.test import test\n\n\nif (\n pkg_resources.parse_version(setuptools.__version__) <\n pkg_resources.parse_version(\"18.5\")\n):\n raise RuntimeError(\n \"cryptography requires setuptools 18.5 or newer, please upgrade to a \"\n \"newer version of setuptools\"\n )\n\nbase_dir = os.path.dirname(__file__)\nsrc_dir = os.path.join(base_dir, \"src\")\n\n# When executing the setup.py, we need to be able to import ourselves, this\n# means that we need to add the src/ directory to the sys.path.\nsys.path.insert(0, src_dir)\n\nabout = {}\nwith open(os.path.join(src_dir, \"cryptography\", \"__about__.py\")) as f:\n exec(f.read(), about)\n\n\nVECTORS_DEPENDENCY = \"cryptography_vectors=={0}\".format(about['__version__'])\n\nsetup_requirements = []\n\nif platform.python_implementation() == \"PyPy\":\n if sys.pypy_version_info < (5, 3):\n raise RuntimeError(\n \"cryptography 1.9 is not compatible with PyPy < 5.3. Please \"\n \"upgrade PyPy to use this library.\"\n )\nelse:\n setup_requirements.append(\"cffi>=1.7\")\n\ntest_requirements = [\n \"pytest>=3.2.1,!=3.3.0\",\n \"pretend\",\n \"iso8601\",\n \"pytz\",\n \"hypothesis>=1.11.4\",\n]\n\n\n# If there's no vectors locally that probably means we are in a tarball and\n# need to go and get the matching vectors package from PyPi\nif not os.path.exists(os.path.join(base_dir, \"vectors/setup.py\")):\n test_requirements.append(VECTORS_DEPENDENCY)\n\n\nclass PyTest(test):\n def finalize_options(self):\n test.finalize_options(self)\n self.test_args = []\n self.test_suite = True\n\n # This means there's a vectors/ folder with the package in here.\n # cd into it, install the vectors package and then refresh sys.path\n if VECTORS_DEPENDENCY not in test_requirements:\n subprocess.check_call(\n [sys.executable, \"setup.py\", \"install\"], cwd=\"vectors\"\n )\n pkg_resources.get_distribution(\"cryptography_vectors\").activate()\n\n def run_tests(self):\n # Import here because in module scope the eggs are not loaded.\n import pytest\n test_args = [os.path.join(base_dir, \"tests\")]\n errno = pytest.main(test_args)\n sys.exit(errno)\n\n\ndef keywords_with_side_effects(argv):\n \"\"\"\n Get a dictionary with setup keywords that (can) have side effects.\n\n :param argv: A list of strings with command line arguments.\n :returns: A dictionary with keyword arguments for the ``setup()`` function.\n\n This setup.py script uses the setuptools 'setup_requires' feature because\n this is required by the cffi package to compile extension modules. The\n purpose of ``keywords_with_side_effects()`` is to avoid triggering the cffi\n build process as a result of setup.py invocations that don't need the cffi\n module to be built (setup.py serves the dual purpose of exposing package\n metadata).\n\n All of the options listed by ``python setup.py --help`` that print\n information should be recognized here. The commands ``clean``,\n ``egg_info``, ``register``, ``sdist`` and ``upload`` are also recognized.\n Any combination of these options and commands is also supported.\n\n This function was originally based on the `setup.py script`_ of SciPy (see\n also the discussion in `pip issue #25`_).\n\n .. _pip issue #25: https://github.com/pypa/pip/issues/25\n .. _setup.py script: https://github.com/scipy/scipy/blob/master/setup.py\n \"\"\"\n no_setup_requires_arguments = (\n '-h', '--help',\n '-n', '--dry-run',\n '-q', '--quiet',\n '-v', '--verbose',\n '-V', '--version',\n '--author',\n '--author-email',\n '--classifiers',\n '--contact',\n '--contact-email',\n '--description',\n '--egg-base',\n '--fullname',\n '--help-commands',\n '--keywords',\n '--licence',\n '--license',\n '--long-description',\n '--maintainer',\n '--maintainer-email',\n '--name',\n '--no-user-cfg',\n '--obsoletes',\n '--platforms',\n '--provides',\n '--requires',\n '--url',\n 'clean',\n 'egg_info',\n 'register',\n 'sdist',\n 'upload',\n )\n\n def is_short_option(argument):\n \"\"\"Check whether a command line argument is a short option.\"\"\"\n return len(argument) >= 2 and argument[0] == '-' and argument[1] != '-'\n\n def expand_short_options(argument):\n \"\"\"Expand combined short options into canonical short options.\"\"\"\n return ('-' + char for char in argument[1:])\n\n def argument_without_setup_requirements(argv, i):\n \"\"\"Check whether a command line argument needs setup requirements.\"\"\"\n if argv[i] in no_setup_requires_arguments:\n # Simple case: An argument which is either an option or a command\n # which doesn't need setup requirements.\n return True\n elif (is_short_option(argv[i]) and\n all(option in no_setup_requires_arguments\n for option in expand_short_options(argv[i]))):\n # Not so simple case: Combined short options none of which need\n # setup requirements.\n return True\n elif argv[i - 1:i] == ['--egg-base']:\n # Tricky case: --egg-info takes an argument which should not make\n # us use setup_requires (defeating the purpose of this code).\n return True\n else:\n return False\n\n if all(argument_without_setup_requirements(argv, i)\n for i in range(1, len(argv))):\n return {\n \"cmdclass\": {\n \"build\": DummyBuild,\n \"install\": DummyInstall,\n \"test\": DummyPyTest,\n }\n }\n else:\n cffi_modules = [\n \"src/_cffi_src/build_openssl.py:ffi\",\n \"src/_cffi_src/build_constant_time.py:ffi\",\n \"src/_cffi_src/build_padding.py:ffi\",\n ]\n\n return {\n \"setup_requires\": setup_requirements,\n \"cmdclass\": {\n \"test\": PyTest,\n },\n \"cffi_modules\": cffi_modules\n }\n\n\nsetup_requires_error = (\"Requested setup command that needs 'setup_requires' \"\n \"while command line arguments implied a side effect \"\n \"free command or option.\")\n\n\nclass DummyBuild(build):\n \"\"\"\n This class makes it very obvious when ``keywords_with_side_effects()`` has\n incorrectly interpreted the command line arguments to ``setup.py build`` as\n one of the 'side effect free' commands or options.\n \"\"\"\n\n def run(self):\n raise RuntimeError(setup_requires_error)\n\n\nclass DummyInstall(install):\n \"\"\"\n This class makes it very obvious when ``keywords_with_side_effects()`` has\n incorrectly interpreted the command line arguments to ``setup.py install``\n as one of the 'side effect free' commands or options.\n \"\"\"\n\n def run(self):\n raise RuntimeError(setup_requires_error)\n\n\nclass DummyPyTest(test):\n \"\"\"\n This class makes it very obvious when ``keywords_with_side_effects()`` has\n incorrectly interpreted the command line arguments to ``setup.py test`` as\n one of the 'side effect free' commands or options.\n \"\"\"\n\n def run_tests(self):\n raise RuntimeError(setup_requires_error)\n\n\nwith open(os.path.join(base_dir, \"README.rst\")) as f:\n long_description = f.read()\n\n\nsetup(\n name=about[\"__title__\"],\n version=about[\"__version__\"],\n\n description=about[\"__summary__\"],\n long_description=long_description,\n license=about[\"__license__\"],\n url=about[\"__uri__\"],\n\n author=about[\"__author__\"],\n author_email=about[\"__email__\"],\n\n classifiers=[\n \"Intended Audience :: Developers\",\n \"License :: OSI Approved :: Apache Software License\",\n \"License :: OSI Approved :: BSD License\",\n \"Natural Language :: English\",\n \"Operating System :: MacOS :: MacOS X\",\n \"Operating System :: POSIX\",\n \"Operating System :: POSIX :: BSD\",\n \"Operating System :: POSIX :: Linux\",\n \"Operating System :: Microsoft :: Windows\",\n \"Programming Language :: Python\",\n \"Programming Language :: Python :: 2\",\n \"Programming Language :: Python :: 2.7\",\n \"Programming Language :: Python :: 3\",\n \"Programming Language :: Python :: 3.4\",\n \"Programming Language :: Python :: 3.5\",\n \"Programming Language :: Python :: 3.6\",\n \"Programming Language :: Python :: Implementation :: CPython\",\n \"Programming Language :: Python :: Implementation :: PyPy\",\n \"Topic :: Security :: Cryptography\",\n ],\n\n package_dir={\"\": \"src\"},\n packages=find_packages(where=\"src\", exclude=[\"_cffi_src\", \"_cffi_src.*\"]),\n include_package_data=True,\n\n install_requires=[\n \"idna >= 2.1\",\n \"asn1crypto >= 0.21.0\",\n \"six >= 1.4.1\",\n ],\n tests_require=test_requirements,\n extras_require={\n \":python_version < '3'\": [\"enum34\", \"ipaddress\"],\n \":platform_python_implementation != 'PyPy'\": [\"cffi >= 1.7\"],\n\n \"test\": test_requirements,\n \"docstest\": [\n \"doc8\",\n \"pyenchant >= 1.6.11\",\n \"readme_renderer >= 16.0\",\n \"sphinx >= 1.6.5\",\n \"sphinx_rtd_theme\",\n \"sphinxcontrib-spelling >= 4.0.1\",\n ],\n \"pep8test\": [\n \"flake8\",\n \"flake8-import-order\",\n \"pep8-naming\",\n ],\n },\n\n # for cffi\n zip_safe=False,\n ext_package=\"cryptography.hazmat.bindings\",\n **keywords_with_side_effects(sys.argv)\n)\n",
"path": "setup.py"
}
] | [
{
"content": "#!/usr/bin/env python\n\n# This file is dual licensed under the terms of the Apache License, Version\n# 2.0, and the BSD License. See the LICENSE file in the root of this repository\n# for complete details.\n\nfrom __future__ import absolute_import, division, print_function\n\nimport os\nimport platform\nimport subprocess\nimport sys\nfrom distutils.command.build import build\n\nimport pkg_resources\n\nimport setuptools\nfrom setuptools import find_packages, setup\nfrom setuptools.command.install import install\nfrom setuptools.command.test import test\n\n\nif (\n pkg_resources.parse_version(setuptools.__version__) <\n pkg_resources.parse_version(\"18.5\")\n):\n raise RuntimeError(\n \"cryptography requires setuptools 18.5 or newer, please upgrade to a \"\n \"newer version of setuptools\"\n )\n\nbase_dir = os.path.dirname(__file__)\nsrc_dir = os.path.join(base_dir, \"src\")\n\n# When executing the setup.py, we need to be able to import ourselves, this\n# means that we need to add the src/ directory to the sys.path.\nsys.path.insert(0, src_dir)\n\nabout = {}\nwith open(os.path.join(src_dir, \"cryptography\", \"__about__.py\")) as f:\n exec(f.read(), about)\n\n\nVECTORS_DEPENDENCY = \"cryptography_vectors=={0}\".format(about['__version__'])\n\nsetup_requirements = []\n\nif platform.python_implementation() == \"PyPy\":\n if sys.pypy_version_info < (5, 3):\n raise RuntimeError(\n \"cryptography 1.9 is not compatible with PyPy < 5.3. Please \"\n \"upgrade PyPy to use this library.\"\n )\nelse:\n setup_requirements.append(\"cffi>=1.7\")\n\ntest_requirements = [\n \"pytest>=3.2.1,!=3.3.0\",\n \"pretend\",\n \"iso8601\",\n \"pytz\",\n \"hypothesis>=1.11.4\",\n]\n\n\n# If there's no vectors locally that probably means we are in a tarball and\n# need to go and get the matching vectors package from PyPi\nif not os.path.exists(os.path.join(base_dir, \"vectors/setup.py\")):\n test_requirements.append(VECTORS_DEPENDENCY)\n\n\nclass PyTest(test):\n def finalize_options(self):\n test.finalize_options(self)\n self.test_args = []\n self.test_suite = True\n\n # This means there's a vectors/ folder with the package in here.\n # cd into it, install the vectors package and then refresh sys.path\n if VECTORS_DEPENDENCY not in test_requirements:\n subprocess.check_call(\n [sys.executable, \"setup.py\", \"install\"], cwd=\"vectors\"\n )\n pkg_resources.get_distribution(\"cryptography_vectors\").activate()\n\n def run_tests(self):\n # Import here because in module scope the eggs are not loaded.\n import pytest\n test_args = [os.path.join(base_dir, \"tests\")]\n errno = pytest.main(test_args)\n sys.exit(errno)\n\n\ndef keywords_with_side_effects(argv):\n \"\"\"\n Get a dictionary with setup keywords that (can) have side effects.\n\n :param argv: A list of strings with command line arguments.\n :returns: A dictionary with keyword arguments for the ``setup()`` function.\n\n This setup.py script uses the setuptools 'setup_requires' feature because\n this is required by the cffi package to compile extension modules. The\n purpose of ``keywords_with_side_effects()`` is to avoid triggering the cffi\n build process as a result of setup.py invocations that don't need the cffi\n module to be built (setup.py serves the dual purpose of exposing package\n metadata).\n\n All of the options listed by ``python setup.py --help`` that print\n information should be recognized here. The commands ``clean``,\n ``egg_info``, ``register``, ``sdist`` and ``upload`` are also recognized.\n Any combination of these options and commands is also supported.\n\n This function was originally based on the `setup.py script`_ of SciPy (see\n also the discussion in `pip issue #25`_).\n\n .. _pip issue #25: https://github.com/pypa/pip/issues/25\n .. _setup.py script: https://github.com/scipy/scipy/blob/master/setup.py\n \"\"\"\n no_setup_requires_arguments = (\n '-h', '--help',\n '-n', '--dry-run',\n '-q', '--quiet',\n '-v', '--verbose',\n '-V', '--version',\n '--author',\n '--author-email',\n '--classifiers',\n '--contact',\n '--contact-email',\n '--description',\n '--egg-base',\n '--fullname',\n '--help-commands',\n '--keywords',\n '--licence',\n '--license',\n '--long-description',\n '--maintainer',\n '--maintainer-email',\n '--name',\n '--no-user-cfg',\n '--obsoletes',\n '--platforms',\n '--provides',\n '--requires',\n '--url',\n 'clean',\n 'egg_info',\n 'register',\n 'sdist',\n 'upload',\n )\n\n def is_short_option(argument):\n \"\"\"Check whether a command line argument is a short option.\"\"\"\n return len(argument) >= 2 and argument[0] == '-' and argument[1] != '-'\n\n def expand_short_options(argument):\n \"\"\"Expand combined short options into canonical short options.\"\"\"\n return ('-' + char for char in argument[1:])\n\n def argument_without_setup_requirements(argv, i):\n \"\"\"Check whether a command line argument needs setup requirements.\"\"\"\n if argv[i] in no_setup_requires_arguments:\n # Simple case: An argument which is either an option or a command\n # which doesn't need setup requirements.\n return True\n elif (is_short_option(argv[i]) and\n all(option in no_setup_requires_arguments\n for option in expand_short_options(argv[i]))):\n # Not so simple case: Combined short options none of which need\n # setup requirements.\n return True\n elif argv[i - 1:i] == ['--egg-base']:\n # Tricky case: --egg-info takes an argument which should not make\n # us use setup_requires (defeating the purpose of this code).\n return True\n else:\n return False\n\n if all(argument_without_setup_requirements(argv, i)\n for i in range(1, len(argv))):\n return {\n \"cmdclass\": {\n \"build\": DummyBuild,\n \"install\": DummyInstall,\n \"test\": DummyPyTest,\n }\n }\n else:\n cffi_modules = [\n \"src/_cffi_src/build_openssl.py:ffi\",\n \"src/_cffi_src/build_constant_time.py:ffi\",\n \"src/_cffi_src/build_padding.py:ffi\",\n ]\n\n return {\n \"setup_requires\": setup_requirements,\n \"cmdclass\": {\n \"test\": PyTest,\n },\n \"cffi_modules\": cffi_modules\n }\n\n\nsetup_requires_error = (\"Requested setup command that needs 'setup_requires' \"\n \"while command line arguments implied a side effect \"\n \"free command or option.\")\n\n\nclass DummyBuild(build):\n \"\"\"\n This class makes it very obvious when ``keywords_with_side_effects()`` has\n incorrectly interpreted the command line arguments to ``setup.py build`` as\n one of the 'side effect free' commands or options.\n \"\"\"\n\n def run(self):\n raise RuntimeError(setup_requires_error)\n\n\nclass DummyInstall(install):\n \"\"\"\n This class makes it very obvious when ``keywords_with_side_effects()`` has\n incorrectly interpreted the command line arguments to ``setup.py install``\n as one of the 'side effect free' commands or options.\n \"\"\"\n\n def run(self):\n raise RuntimeError(setup_requires_error)\n\n\nclass DummyPyTest(test):\n \"\"\"\n This class makes it very obvious when ``keywords_with_side_effects()`` has\n incorrectly interpreted the command line arguments to ``setup.py test`` as\n one of the 'side effect free' commands or options.\n \"\"\"\n\n def run_tests(self):\n raise RuntimeError(setup_requires_error)\n\n\nwith open(os.path.join(base_dir, \"README.rst\")) as f:\n long_description = f.read()\n\n\nsetup(\n name=about[\"__title__\"],\n version=about[\"__version__\"],\n\n description=about[\"__summary__\"],\n long_description=long_description,\n license=about[\"__license__\"],\n url=about[\"__uri__\"],\n\n author=about[\"__author__\"],\n author_email=about[\"__email__\"],\n\n classifiers=[\n \"Intended Audience :: Developers\",\n \"License :: OSI Approved :: Apache Software License\",\n \"License :: OSI Approved :: BSD License\",\n \"Natural Language :: English\",\n \"Operating System :: MacOS :: MacOS X\",\n \"Operating System :: POSIX\",\n \"Operating System :: POSIX :: BSD\",\n \"Operating System :: POSIX :: Linux\",\n \"Operating System :: Microsoft :: Windows\",\n \"Programming Language :: Python\",\n \"Programming Language :: Python :: 2\",\n \"Programming Language :: Python :: 2.7\",\n \"Programming Language :: Python :: 3\",\n \"Programming Language :: Python :: 3.4\",\n \"Programming Language :: Python :: 3.5\",\n \"Programming Language :: Python :: 3.6\",\n \"Programming Language :: Python :: Implementation :: CPython\",\n \"Programming Language :: Python :: Implementation :: PyPy\",\n \"Topic :: Security :: Cryptography\",\n ],\n\n package_dir={\"\": \"src\"},\n packages=find_packages(where=\"src\", exclude=[\"_cffi_src\", \"_cffi_src.*\"]),\n include_package_data=True,\n\n python_requires='>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*',\n\n install_requires=[\n \"idna >= 2.1\",\n \"asn1crypto >= 0.21.0\",\n \"six >= 1.4.1\",\n ],\n tests_require=test_requirements,\n extras_require={\n \":python_version < '3'\": [\"enum34\", \"ipaddress\"],\n \":platform_python_implementation != 'PyPy'\": [\"cffi >= 1.7\"],\n\n \"test\": test_requirements,\n \"docstest\": [\n \"doc8\",\n \"pyenchant >= 1.6.11\",\n \"readme_renderer >= 16.0\",\n \"sphinx >= 1.6.5\",\n \"sphinx_rtd_theme\",\n \"sphinxcontrib-spelling >= 4.0.1\",\n ],\n \"pep8test\": [\n \"flake8\",\n \"flake8-import-order\",\n \"pep8-naming\",\n ],\n },\n\n # for cffi\n zip_safe=False,\n ext_package=\"cryptography.hazmat.bindings\",\n **keywords_with_side_effects(sys.argv)\n)\n",
"path": "setup.py"
}
] | diff --git a/setup.py b/setup.py
index b9186a8445be..9250e2da2ac7 100644
--- a/setup.py
+++ b/setup.py
@@ -283,6 +283,8 @@ def run_tests(self):
packages=find_packages(where="src", exclude=["_cffi_src", "_cffi_src.*"]),
include_package_data=True,
+ python_requires='>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*',
+
install_requires=[
"idna >= 2.1",
"asn1crypto >= 0.21.0",
|
networkx__networkx-4132 | edgelist in draw_network does not support arrays
Using numpy 1.18.2 and networkx 2.4, it is not possible to use a numpy array for the `edgelist` argument of [``draw_networkx``](https://networkx.github.io/documentation/stable/reference/generated/networkx.drawing.nx_pylab.draw_networkx.html).
It raises:
```
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
```
I normally would not have opened an issue for that but I do not see the point of the current implementation, so in case it can be changed harmlessly to support numpy arrays, that might be a win-win.
The issue comes from lines 575-579 on 2.4 ([lines 640-647](https://github.com/networkx/networkx/blob/master/networkx/drawing/nx_pylab.py#L640) on master) that read:
```python
if edgelist is None:
edgelist = list(G.edges())
if not edgelist or len(edgelist) == 0: # no edges!
return None
```
I am not sure what the combination of ``not edgelist`` and ``len(edgelist) == 0`` is for, here, so I would propose to change it to
```python
if edgelist is None:
edgelist = list(G.edges())
elif len(edgelist) == 0: # no edges!
return None
```
which should still do the job for all valid inputs I can think of and would also work with numpy arrays.
**EDIT** if iterables should be supported, one could replace ``not edgelist`` by ``not isinstance(edgelist, Iterable)``
| [
{
"content": "\"\"\"\n**********\nMatplotlib\n**********\n\nDraw networks with matplotlib.\n\nSee Also\n--------\n\nmatplotlib: http://matplotlib.org/\n\npygraphviz: http://pygraphviz.github.io/\n\n\"\"\"\nfrom numbers import Number\nimport networkx as nx\nfrom networkx.drawing.layout import (\n shell_layout,\n circular_layout,\n kamada_kawai_layout,\n spectral_layout,\n spring_layout,\n random_layout,\n planar_layout,\n)\n\n__all__ = [\n \"draw\",\n \"draw_networkx\",\n \"draw_networkx_nodes\",\n \"draw_networkx_edges\",\n \"draw_networkx_labels\",\n \"draw_networkx_edge_labels\",\n \"draw_circular\",\n \"draw_kamada_kawai\",\n \"draw_random\",\n \"draw_spectral\",\n \"draw_spring\",\n \"draw_planar\",\n \"draw_shell\",\n]\n\n\ndef draw(G, pos=None, ax=None, **kwds):\n \"\"\"Draw the graph G with Matplotlib.\n\n Draw the graph as a simple representation with no node\n labels or edge labels and using the full Matplotlib figure area\n and no axis labels by default. See draw_networkx() for more\n full-featured drawing that allows title, axis labels etc.\n\n Parameters\n ----------\n G : graph\n A networkx graph\n\n pos : dictionary, optional\n A dictionary with nodes as keys and positions as values.\n If not specified a spring layout positioning will be computed.\n See :py:mod:`networkx.drawing.layout` for functions that\n compute node positions.\n\n ax : Matplotlib Axes object, optional\n Draw the graph in specified Matplotlib axes.\n\n kwds : optional keywords\n See networkx.draw_networkx() for a description of optional keywords.\n\n Examples\n --------\n >>> G = nx.dodecahedral_graph()\n >>> nx.draw(G)\n >>> nx.draw(G, pos=nx.spring_layout(G)) # use spring layout\n\n See Also\n --------\n draw_networkx()\n draw_networkx_nodes()\n draw_networkx_edges()\n draw_networkx_labels()\n draw_networkx_edge_labels()\n\n Notes\n -----\n This function has the same name as pylab.draw and pyplot.draw\n so beware when using `from networkx import *`\n\n since you might overwrite the pylab.draw function.\n\n With pyplot use\n\n >>> import matplotlib.pyplot as plt\n >>> import networkx as nx\n >>> G = nx.dodecahedral_graph()\n >>> nx.draw(G) # networkx draw()\n >>> plt.draw() # pyplot draw()\n\n Also see the NetworkX drawing examples at\n https://networkx.github.io/documentation/latest/auto_examples/index.html\n \"\"\"\n try:\n import matplotlib.pyplot as plt\n except ImportError as e:\n raise ImportError(\"Matplotlib required for draw()\") from e\n except RuntimeError:\n print(\"Matplotlib unable to open display\")\n raise\n\n if ax is None:\n cf = plt.gcf()\n else:\n cf = ax.get_figure()\n cf.set_facecolor(\"w\")\n if ax is None:\n if cf._axstack() is None:\n ax = cf.add_axes((0, 0, 1, 1))\n else:\n ax = cf.gca()\n\n if \"with_labels\" not in kwds:\n kwds[\"with_labels\"] = \"labels\" in kwds\n\n draw_networkx(G, pos=pos, ax=ax, **kwds)\n ax.set_axis_off()\n plt.draw_if_interactive()\n return\n\n\ndef draw_networkx(G, pos=None, arrows=True, with_labels=True, **kwds):\n \"\"\"Draw the graph G using Matplotlib.\n\n Draw the graph with Matplotlib with options for node positions,\n labeling, titles, and many other drawing features.\n See draw() for simple drawing without labels or axes.\n\n Parameters\n ----------\n G : graph\n A networkx graph\n\n pos : dictionary, optional\n A dictionary with nodes as keys and positions as values.\n If not specified a spring layout positioning will be computed.\n See :py:mod:`networkx.drawing.layout` for functions that\n compute node positions.\n\n arrows : bool, optional (default=True)\n For directed graphs, if True draw arrowheads.\n Note: Arrows will be the same color as edges.\n\n arrowstyle : str, optional (default='-|>')\n For directed graphs, choose the style of the arrowsheads.\n See :py:class: `matplotlib.patches.ArrowStyle` for more\n options.\n\n arrowsize : int, optional (default=10)\n For directed graphs, choose the size of the arrow head head's length and\n width. See :py:class: `matplotlib.patches.FancyArrowPatch` for attribute\n `mutation_scale` for more info.\n\n with_labels : bool, optional (default=True)\n Set to True to draw labels on the nodes.\n\n ax : Matplotlib Axes object, optional\n Draw the graph in the specified Matplotlib axes.\n\n nodelist : list, optional (default G.nodes())\n Draw only specified nodes\n\n edgelist : list, optional (default=G.edges())\n Draw only specified edges\n\n node_size : scalar or array, optional (default=300)\n Size of nodes. If an array is specified it must be the\n same length as nodelist.\n\n node_color : color or array of colors (default='#1f78b4')\n Node color. Can be a single color or a sequence of colors with the same\n length as nodelist. Color can be string, or rgb (or rgba) tuple of\n floats from 0-1. If numeric values are specified they will be\n mapped to colors using the cmap and vmin,vmax parameters. See\n matplotlib.scatter for more details.\n\n node_shape : string, optional (default='o')\n The shape of the node. Specification is as matplotlib.scatter\n marker, one of 'so^>v<dph8'.\n\n alpha : float, optional (default=None)\n The node and edge transparency\n\n cmap : Matplotlib colormap, optional (default=None)\n Colormap for mapping intensities of nodes\n\n vmin,vmax : float, optional (default=None)\n Minimum and maximum for node colormap scaling\n\n linewidths : [None | scalar | sequence]\n Line width of symbol border (default =1.0)\n\n width : float, optional (default=1.0)\n Line width of edges\n\n edge_color : color or array of colors (default='k')\n Edge color. Can be a single color or a sequence of colors with the same\n length as edgelist. Color can be string, or rgb (or rgba) tuple of\n floats from 0-1. If numeric values are specified they will be\n mapped to colors using the edge_cmap and edge_vmin,edge_vmax parameters.\n\n edge_cmap : Matplotlib colormap, optional (default=None)\n Colormap for mapping intensities of edges\n\n edge_vmin,edge_vmax : floats, optional (default=None)\n Minimum and maximum for edge colormap scaling\n\n style : string, optional (default='solid')\n Edge line style (solid|dashed|dotted,dashdot)\n\n labels : dictionary, optional (default=None)\n Node labels in a dictionary keyed by node of text labels\n\n font_size : int, optional (default=12)\n Font size for text labels\n\n font_color : string, optional (default='k' black)\n Font color string\n\n font_weight : string, optional (default='normal')\n Font weight\n\n font_family : string, optional (default='sans-serif')\n Font family\n\n label : string, optional\n Label for graph legend\n\n kwds : optional keywords\n See networkx.draw_networkx_nodes(), networkx.draw_networkx_edges(), and\n networkx.draw_networkx_labels() for a description of optional keywords.\n\n Notes\n -----\n For directed graphs, arrows are drawn at the head end. Arrows can be\n turned off with keyword arrows=False.\n\n Examples\n --------\n >>> G = nx.dodecahedral_graph()\n >>> nx.draw(G)\n >>> nx.draw(G, pos=nx.spring_layout(G)) # use spring layout\n\n >>> import matplotlib.pyplot as plt\n >>> limits = plt.axis('off') # turn of axis\n\n Also see the NetworkX drawing examples at\n https://networkx.github.io/documentation/latest/auto_examples/index.html\n\n See Also\n --------\n draw()\n draw_networkx_nodes()\n draw_networkx_edges()\n draw_networkx_labels()\n draw_networkx_edge_labels()\n \"\"\"\n try:\n import matplotlib.pyplot as plt\n except ImportError as e:\n raise ImportError(\"Matplotlib required for draw()\") from e\n except RuntimeError:\n print(\"Matplotlib unable to open display\")\n raise\n\n valid_node_kwds = (\n \"nodelist\",\n \"node_size\",\n \"node_color\",\n \"node_shape\",\n \"alpha\",\n \"cmap\",\n \"vmin\",\n \"vmax\",\n \"ax\",\n \"linewidths\",\n \"edgecolors\",\n \"label\",\n )\n\n valid_edge_kwds = (\n \"edgelist\",\n \"width\",\n \"edge_color\",\n \"style\",\n \"alpha\",\n \"arrowstyle\",\n \"arrowsize\",\n \"edge_cmap\",\n \"edge_vmin\",\n \"edge_vmax\",\n \"ax\",\n \"label\",\n \"node_size\",\n \"nodelist\",\n \"node_shape\",\n \"connectionstyle\",\n \"min_source_margin\",\n \"min_target_margin\",\n )\n\n valid_label_kwds = (\n \"labels\",\n \"font_size\",\n \"font_color\",\n \"font_family\",\n \"font_weight\",\n \"alpha\",\n \"bbox\",\n \"ax\",\n \"horizontalalignment\",\n \"verticalalignment\",\n )\n\n valid_kwds = valid_node_kwds + valid_edge_kwds + valid_label_kwds\n\n if any([k not in valid_kwds for k in kwds]):\n invalid_args = \", \".join([k for k in kwds if k not in valid_kwds])\n raise ValueError(f\"Received invalid argument(s): {invalid_args}\")\n\n node_kwds = {k: v for k, v in kwds.items() if k in valid_node_kwds}\n edge_kwds = {k: v for k, v in kwds.items() if k in valid_edge_kwds}\n label_kwds = {k: v for k, v in kwds.items() if k in valid_label_kwds}\n\n if pos is None:\n pos = nx.drawing.spring_layout(G) # default to spring layout\n\n draw_networkx_nodes(G, pos, **node_kwds)\n draw_networkx_edges(G, pos, arrows=arrows, **edge_kwds)\n if with_labels:\n draw_networkx_labels(G, pos, **label_kwds)\n plt.draw_if_interactive()\n\n\ndef draw_networkx_nodes(\n G,\n pos,\n nodelist=None,\n node_size=300,\n node_color=\"#1f78b4\",\n node_shape=\"o\",\n alpha=None,\n cmap=None,\n vmin=None,\n vmax=None,\n ax=None,\n linewidths=None,\n edgecolors=None,\n label=None,\n):\n \"\"\"Draw the nodes of the graph G.\n\n This draws only the nodes of the graph G.\n\n Parameters\n ----------\n G : graph\n A networkx graph\n\n pos : dictionary\n A dictionary with nodes as keys and positions as values.\n Positions should be sequences of length 2.\n\n ax : Matplotlib Axes object, optional\n Draw the graph in the specified Matplotlib axes.\n\n nodelist : list, optional\n Draw only specified nodes (default G.nodes())\n\n node_size : scalar or array\n Size of nodes (default=300). If an array is specified it must be the\n same length as nodelist.\n\n node_color : color or array of colors (default='#1f78b4')\n Node color. Can be a single color or a sequence of colors with the same\n length as nodelist. Color can be string, or rgb (or rgba) tuple of\n floats from 0-1. If numeric values are specified they will be\n mapped to colors using the cmap and vmin,vmax parameters. See\n matplotlib.scatter for more details.\n\n node_shape : string\n The shape of the node. Specification is as matplotlib.scatter\n marker, one of 'so^>v<dph8' (default='o').\n\n alpha : float or array of floats\n The node transparency. This can be a single alpha value (default=None),\n in which case it will be applied to all the nodes of color. Otherwise,\n if it is an array, the elements of alpha will be applied to the colors\n in order (cycling through alpha multiple times if necessary).\n\n cmap : Matplotlib colormap\n Colormap for mapping intensities of nodes (default=None)\n\n vmin,vmax : floats\n Minimum and maximum for node colormap scaling (default=None)\n\n linewidths : [None | scalar | sequence]\n Line width of symbol border (default =1.0)\n\n edgecolors : [None | scalar | sequence]\n Colors of node borders (default = node_color)\n\n label : [None| string]\n Label for legend\n\n Returns\n -------\n matplotlib.collections.PathCollection\n `PathCollection` of the nodes.\n\n Examples\n --------\n >>> G = nx.dodecahedral_graph()\n >>> nodes = nx.draw_networkx_nodes(G, pos=nx.spring_layout(G))\n\n Also see the NetworkX drawing examples at\n https://networkx.github.io/documentation/latest/auto_examples/index.html\n\n See Also\n --------\n draw()\n draw_networkx()\n draw_networkx_edges()\n draw_networkx_labels()\n draw_networkx_edge_labels()\n \"\"\"\n from collections.abc import Iterable\n\n try:\n import matplotlib.pyplot as plt\n from matplotlib.collections import PathCollection\n import numpy as np\n except ImportError as e:\n raise ImportError(\"Matplotlib required for draw()\") from e\n except RuntimeError:\n print(\"Matplotlib unable to open display\")\n raise\n\n if ax is None:\n ax = plt.gca()\n\n if nodelist is None:\n nodelist = list(G)\n\n if len(nodelist) == 0: # empty nodelist, no drawing\n return PathCollection(None)\n\n try:\n xy = np.asarray([pos[v] for v in nodelist])\n except KeyError as e:\n raise nx.NetworkXError(f\"Node {e} has no position.\") from e\n except ValueError as e:\n raise nx.NetworkXError(\"Bad value in node positions.\") from e\n\n if isinstance(alpha, Iterable):\n node_color = apply_alpha(node_color, alpha, nodelist, cmap, vmin, vmax)\n alpha = None\n\n node_collection = ax.scatter(\n xy[:, 0],\n xy[:, 1],\n s=node_size,\n c=node_color,\n marker=node_shape,\n cmap=cmap,\n vmin=vmin,\n vmax=vmax,\n alpha=alpha,\n linewidths=linewidths,\n edgecolors=edgecolors,\n label=label,\n )\n ax.tick_params(\n axis=\"both\",\n which=\"both\",\n bottom=False,\n left=False,\n labelbottom=False,\n labelleft=False,\n )\n\n node_collection.set_zorder(2)\n return node_collection\n\n\ndef draw_networkx_edges(\n G,\n pos,\n edgelist=None,\n width=1.0,\n edge_color=\"k\",\n style=\"solid\",\n alpha=None,\n arrowstyle=\"-|>\",\n arrowsize=10,\n edge_cmap=None,\n edge_vmin=None,\n edge_vmax=None,\n ax=None,\n arrows=True,\n label=None,\n node_size=300,\n nodelist=None,\n node_shape=\"o\",\n connectionstyle=None,\n min_source_margin=0,\n min_target_margin=0,\n):\n \"\"\"Draw the edges of the graph G.\n\n This draws only the edges of the graph G.\n\n Parameters\n ----------\n G : graph\n A networkx graph\n\n pos : dictionary\n A dictionary with nodes as keys and positions as values.\n Positions should be sequences of length 2.\n\n edgelist : collection of edge tuples\n Draw only specified edges(default=G.edges())\n\n width : float, or array of floats\n Line width of edges (default=1.0)\n\n edge_color : color or array of colors (default='k')\n Edge color. Can be a single color or a sequence of colors with the same\n length as edgelist. Color can be string, or rgb (or rgba) tuple of\n floats from 0-1. If numeric values are specified they will be\n mapped to colors using the edge_cmap and edge_vmin,edge_vmax parameters.\n\n style : string\n Edge line style (default='solid') (solid|dashed|dotted,dashdot)\n\n alpha : float\n The edge transparency (default=None)\n\n edge_ cmap : Matplotlib colormap\n Colormap for mapping intensities of edges (default=None)\n\n edge_vmin,edge_vmax : floats\n Minimum and maximum for edge colormap scaling (default=None)\n\n ax : Matplotlib Axes object, optional\n Draw the graph in the specified Matplotlib axes.\n\n arrows : bool, optional (default=True)\n For directed graphs, if True draw arrowheads.\n Note: Arrows will be the same color as edges.\n\n arrowstyle : str, optional (default='-|>')\n For directed graphs, choose the style of the arrow heads.\n See :py:class: `matplotlib.patches.ArrowStyle` for more\n options.\n\n arrowsize : int, optional (default=10)\n For directed graphs, choose the size of the arrow head head's length and\n width. See :py:class: `matplotlib.patches.FancyArrowPatch` for attribute\n `mutation_scale` for more info.\n\n connectionstyle : str, optional (default=None)\n Pass the connectionstyle parameter to create curved arc of rounding\n radius rad. For example, connectionstyle='arc3,rad=0.2'.\n See :py:class: `matplotlib.patches.ConnectionStyle` and\n :py:class: `matplotlib.patches.FancyArrowPatch` for more info.\n\n label : [None| string]\n Label for legend\n\n min_source_margin : int, optional (default=0)\n The minimum margin (gap) at the begining of the edge at the source.\n\n min_target_margin : int, optional (default=0)\n The minimum margin (gap) at the end of the edge at the target.\n\n Returns\n -------\n matplotlib.collection.LineCollection\n `LineCollection` of the edges\n\n list of matplotlib.patches.FancyArrowPatch\n `FancyArrowPatch` instances of the directed edges\n\n Depending whether the drawing includes arrows or not.\n\n Notes\n -----\n For directed graphs, arrows are drawn at the head end. Arrows can be\n turned off with keyword arrows=False. Be sure to include `node_size` as a\n keyword argument; arrows are drawn considering the size of nodes.\n\n Examples\n --------\n >>> G = nx.dodecahedral_graph()\n >>> edges = nx.draw_networkx_edges(G, pos=nx.spring_layout(G))\n\n >>> G = nx.DiGraph()\n >>> G.add_edges_from([(1, 2), (1, 3), (2, 3)])\n >>> arcs = nx.draw_networkx_edges(G, pos=nx.spring_layout(G))\n >>> alphas = [0.3, 0.4, 0.5]\n >>> for i, arc in enumerate(arcs): # change alpha values of arcs\n ... arc.set_alpha(alphas[i])\n\n Also see the NetworkX drawing examples at\n https://networkx.github.io/documentation/latest/auto_examples/index.html\n\n See Also\n --------\n draw()\n draw_networkx()\n draw_networkx_nodes()\n draw_networkx_labels()\n draw_networkx_edge_labels()\n \"\"\"\n try:\n import matplotlib.pyplot as plt\n from matplotlib.colors import colorConverter, Colormap, Normalize\n from matplotlib.collections import LineCollection\n from matplotlib.patches import FancyArrowPatch\n import numpy as np\n except ImportError as e:\n raise ImportError(\"Matplotlib required for draw()\") from e\n except RuntimeError:\n print(\"Matplotlib unable to open display\")\n raise\n\n if ax is None:\n ax = plt.gca()\n\n if edgelist is None:\n edgelist = list(G.edges())\n\n if not edgelist or len(edgelist) == 0: # no edges!\n if not G.is_directed() or not arrows:\n return LineCollection(None)\n else:\n return []\n\n if nodelist is None:\n nodelist = list(G.nodes())\n\n # FancyArrowPatch handles color=None different from LineCollection\n if edge_color is None:\n edge_color = \"k\"\n\n # set edge positions\n edge_pos = np.asarray([(pos[e[0]], pos[e[1]]) for e in edgelist])\n\n # Check if edge_color is an array of floats and map to edge_cmap.\n # This is the only case handled differently from matplotlib\n if (\n np.iterable(edge_color)\n and (len(edge_color) == len(edge_pos))\n and np.alltrue([isinstance(c, Number) for c in edge_color])\n ):\n if edge_cmap is not None:\n assert isinstance(edge_cmap, Colormap)\n else:\n edge_cmap = plt.get_cmap()\n if edge_vmin is None:\n edge_vmin = min(edge_color)\n if edge_vmax is None:\n edge_vmax = max(edge_color)\n color_normal = Normalize(vmin=edge_vmin, vmax=edge_vmax)\n edge_color = [edge_cmap(color_normal(e)) for e in edge_color]\n\n if not G.is_directed() or not arrows:\n edge_collection = LineCollection(\n edge_pos,\n colors=edge_color,\n linewidths=width,\n antialiaseds=(1,),\n linestyle=style,\n transOffset=ax.transData,\n alpha=alpha,\n )\n\n edge_collection.set_cmap(edge_cmap)\n edge_collection.set_clim(edge_vmin, edge_vmax)\n\n edge_collection.set_zorder(1) # edges go behind nodes\n edge_collection.set_label(label)\n ax.add_collection(edge_collection)\n\n return edge_collection\n\n arrow_collection = None\n\n if G.is_directed() and arrows:\n # Note: Waiting for someone to implement arrow to intersection with\n # marker. Meanwhile, this works well for polygons with more than 4\n # sides and circle.\n\n def to_marker_edge(marker_size, marker):\n if marker in \"s^>v<d\": # `large` markers need extra space\n return np.sqrt(2 * marker_size) / 2\n else:\n return np.sqrt(marker_size) / 2\n\n # Draw arrows with `matplotlib.patches.FancyarrowPatch`\n arrow_collection = []\n mutation_scale = arrowsize # scale factor of arrow head\n\n # FancyArrowPatch doesn't handle color strings\n arrow_colors = colorConverter.to_rgba_array(edge_color, alpha)\n for i, (src, dst) in enumerate(edge_pos):\n x1, y1 = src\n x2, y2 = dst\n shrink_source = 0 # space from source to tail\n shrink_target = 0 # space from head to target\n if np.iterable(node_size): # many node sizes\n source, target = edgelist[i][:2]\n source_node_size = node_size[nodelist.index(source)]\n target_node_size = node_size[nodelist.index(target)]\n shrink_source = to_marker_edge(source_node_size, node_shape)\n shrink_target = to_marker_edge(target_node_size, node_shape)\n else:\n shrink_source = shrink_target = to_marker_edge(node_size, node_shape)\n\n if shrink_source < min_source_margin:\n shrink_source = min_source_margin\n\n if shrink_target < min_target_margin:\n shrink_target = min_target_margin\n\n if len(arrow_colors) == len(edge_pos):\n arrow_color = arrow_colors[i]\n elif len(arrow_colors) == 1:\n arrow_color = arrow_colors[0]\n else: # Cycle through colors\n arrow_color = arrow_colors[i % len(arrow_colors)]\n\n if np.iterable(width):\n if len(width) == len(edge_pos):\n line_width = width[i]\n else:\n line_width = width[i % len(width)]\n else:\n line_width = width\n\n arrow = FancyArrowPatch(\n (x1, y1),\n (x2, y2),\n arrowstyle=arrowstyle,\n shrinkA=shrink_source,\n shrinkB=shrink_target,\n mutation_scale=mutation_scale,\n color=arrow_color,\n linewidth=line_width,\n connectionstyle=connectionstyle,\n linestyle=style,\n zorder=1,\n ) # arrows go behind nodes\n\n # There seems to be a bug in matplotlib to make collections of\n # FancyArrowPatch instances. Until fixed, the patches are added\n # individually to the axes instance.\n arrow_collection.append(arrow)\n ax.add_patch(arrow)\n\n # update view\n minx = np.amin(np.ravel(edge_pos[:, :, 0]))\n maxx = np.amax(np.ravel(edge_pos[:, :, 0]))\n miny = np.amin(np.ravel(edge_pos[:, :, 1]))\n maxy = np.amax(np.ravel(edge_pos[:, :, 1]))\n\n w = maxx - minx\n h = maxy - miny\n padx, pady = 0.05 * w, 0.05 * h\n corners = (minx - padx, miny - pady), (maxx + padx, maxy + pady)\n ax.update_datalim(corners)\n ax.autoscale_view()\n\n ax.tick_params(\n axis=\"both\",\n which=\"both\",\n bottom=False,\n left=False,\n labelbottom=False,\n labelleft=False,\n )\n\n return arrow_collection\n\n\ndef draw_networkx_labels(\n G,\n pos,\n labels=None,\n font_size=12,\n font_color=\"k\",\n font_family=\"sans-serif\",\n font_weight=\"normal\",\n alpha=None,\n bbox=None,\n horizontalalignment=\"center\",\n verticalalignment=\"center\",\n ax=None,\n):\n \"\"\"Draw node labels on the graph G.\n\n Parameters\n ----------\n G : graph\n A networkx graph\n\n pos : dictionary\n A dictionary with nodes as keys and positions as values.\n Positions should be sequences of length 2.\n\n labels : dictionary, optional (default=None)\n Node labels in a dictionary keyed by node of text labels\n Node-keys in labels should appear as keys in `pos`.\n If needed use: `{n:lab for n,lab in labels.items() if n in pos}`\n\n font_size : int\n Font size for text labels (default=12)\n\n font_color : string\n Font color string (default='k' black)\n\n font_family : string\n Font family (default='sans-serif')\n\n font_weight : string\n Font weight (default='normal')\n\n alpha : float or None\n The text transparency (default=None)\n\n horizontalalignment : {'center', 'right', 'left'}\n Horizontal alignment (default='center')\n\n verticalalignment : {'center', 'top', 'bottom', 'baseline', 'center_baseline'}\n Vertical alignment (default='center')\n\n ax : Matplotlib Axes object, optional\n Draw the graph in the specified Matplotlib axes.\n\n\n Returns\n -------\n dict\n `dict` of labels keyed on the nodes\n\n Examples\n --------\n >>> G = nx.dodecahedral_graph()\n >>> labels = nx.draw_networkx_labels(G, pos=nx.spring_layout(G))\n\n Also see the NetworkX drawing examples at\n https://networkx.github.io/documentation/latest/auto_examples/index.html\n\n See Also\n --------\n draw()\n draw_networkx()\n draw_networkx_nodes()\n draw_networkx_edges()\n draw_networkx_edge_labels()\n \"\"\"\n try:\n import matplotlib.pyplot as plt\n except ImportError as e:\n raise ImportError(\"Matplotlib required for draw()\") from e\n except RuntimeError:\n print(\"Matplotlib unable to open display\")\n raise\n\n if ax is None:\n ax = plt.gca()\n\n if labels is None:\n labels = {n: n for n in G.nodes()}\n\n text_items = {} # there is no text collection so we'll fake one\n for n, label in labels.items():\n (x, y) = pos[n]\n if not isinstance(label, str):\n label = str(label) # this makes \"1\" and 1 labeled the same\n t = ax.text(\n x,\n y,\n label,\n size=font_size,\n color=font_color,\n family=font_family,\n weight=font_weight,\n alpha=alpha,\n horizontalalignment=horizontalalignment,\n verticalalignment=verticalalignment,\n transform=ax.transData,\n bbox=bbox,\n clip_on=True,\n )\n text_items[n] = t\n\n ax.tick_params(\n axis=\"both\",\n which=\"both\",\n bottom=False,\n left=False,\n labelbottom=False,\n labelleft=False,\n )\n\n return text_items\n\n\ndef draw_networkx_edge_labels(\n G,\n pos,\n edge_labels=None,\n label_pos=0.5,\n font_size=10,\n font_color=\"k\",\n font_family=\"sans-serif\",\n font_weight=\"normal\",\n alpha=None,\n bbox=None,\n horizontalalignment=\"center\",\n verticalalignment=\"center\",\n ax=None,\n rotate=True,\n):\n \"\"\"Draw edge labels.\n\n Parameters\n ----------\n G : graph\n A networkx graph\n\n pos : dictionary\n A dictionary with nodes as keys and positions as values.\n Positions should be sequences of length 2.\n\n ax : Matplotlib Axes object, optional\n Draw the graph in the specified Matplotlib axes.\n\n alpha : float or None\n The text transparency (default=None)\n\n edge_labels : dictionary\n Edge labels in a dictionary keyed by edge two-tuple of text\n labels (default=None). Only labels for the keys in the dictionary\n are drawn.\n\n label_pos : float\n Position of edge label along edge (0=head, 0.5=center, 1=tail)\n\n font_size : int\n Font size for text labels (default=12)\n\n font_color : string\n Font color string (default='k' black)\n\n font_weight : string\n Font weight (default='normal')\n\n font_family : string\n Font family (default='sans-serif')\n\n bbox : Matplotlib bbox\n Specify text box shape and colors.\n\n clip_on : bool\n Turn on clipping at axis boundaries (default=True)\n\n horizontalalignment : {'center', 'right', 'left'}\n Horizontal alignment (default='center')\n\n verticalalignment : {'center', 'top', 'bottom', 'baseline', 'center_baseline'}\n Vertical alignment (default='center')\n\n ax : Matplotlib Axes object, optional\n Draw the graph in the specified Matplotlib axes.\n\n Returns\n -------\n dict\n `dict` of labels keyed on the edges\n\n Examples\n --------\n >>> G = nx.dodecahedral_graph()\n >>> edge_labels = nx.draw_networkx_edge_labels(G, pos=nx.spring_layout(G))\n\n Also see the NetworkX drawing examples at\n https://networkx.github.io/documentation/latest/auto_examples/index.html\n\n See Also\n --------\n draw()\n draw_networkx()\n draw_networkx_nodes()\n draw_networkx_edges()\n draw_networkx_labels()\n \"\"\"\n try:\n import matplotlib.pyplot as plt\n import numpy as np\n except ImportError as e:\n raise ImportError(\"Matplotlib required for draw()\") from e\n except RuntimeError:\n print(\"Matplotlib unable to open display\")\n raise\n\n if ax is None:\n ax = plt.gca()\n if edge_labels is None:\n labels = {(u, v): d for u, v, d in G.edges(data=True)}\n else:\n labels = edge_labels\n text_items = {}\n for (n1, n2), label in labels.items():\n (x1, y1) = pos[n1]\n (x2, y2) = pos[n2]\n (x, y) = (\n x1 * label_pos + x2 * (1.0 - label_pos),\n y1 * label_pos + y2 * (1.0 - label_pos),\n )\n\n if rotate:\n # in degrees\n angle = np.arctan2(y2 - y1, x2 - x1) / (2.0 * np.pi) * 360\n # make label orientation \"right-side-up\"\n if angle > 90:\n angle -= 180\n if angle < -90:\n angle += 180\n # transform data coordinate angle to screen coordinate angle\n xy = np.array((x, y))\n trans_angle = ax.transData.transform_angles(\n np.array((angle,)), xy.reshape((1, 2))\n )[0]\n else:\n trans_angle = 0.0\n # use default box of white with white border\n if bbox is None:\n bbox = dict(boxstyle=\"round\", ec=(1.0, 1.0, 1.0), fc=(1.0, 1.0, 1.0),)\n if not isinstance(label, str):\n label = str(label) # this makes \"1\" and 1 labeled the same\n\n t = ax.text(\n x,\n y,\n label,\n size=font_size,\n color=font_color,\n family=font_family,\n weight=font_weight,\n alpha=alpha,\n horizontalalignment=horizontalalignment,\n verticalalignment=verticalalignment,\n rotation=trans_angle,\n transform=ax.transData,\n bbox=bbox,\n zorder=1,\n clip_on=True,\n )\n text_items[(n1, n2)] = t\n\n ax.tick_params(\n axis=\"both\",\n which=\"both\",\n bottom=False,\n left=False,\n labelbottom=False,\n labelleft=False,\n )\n\n return text_items\n\n\ndef draw_circular(G, **kwargs):\n \"\"\"Draw the graph G with a circular layout.\n\n Parameters\n ----------\n G : graph\n A networkx graph\n\n kwargs : optional keywords\n See networkx.draw_networkx() for a description of optional keywords,\n with the exception of the pos parameter which is not used by this\n function.\n \"\"\"\n draw(G, circular_layout(G), **kwargs)\n\n\ndef draw_kamada_kawai(G, **kwargs):\n \"\"\"Draw the graph G with a Kamada-Kawai force-directed layout.\n\n Parameters\n ----------\n G : graph\n A networkx graph\n\n kwargs : optional keywords\n See networkx.draw_networkx() for a description of optional keywords,\n with the exception of the pos parameter which is not used by this\n function.\n \"\"\"\n draw(G, kamada_kawai_layout(G), **kwargs)\n\n\ndef draw_random(G, **kwargs):\n \"\"\"Draw the graph G with a random layout.\n\n Parameters\n ----------\n G : graph\n A networkx graph\n\n kwargs : optional keywords\n See networkx.draw_networkx() for a description of optional keywords,\n with the exception of the pos parameter which is not used by this\n function.\n \"\"\"\n draw(G, random_layout(G), **kwargs)\n\n\ndef draw_spectral(G, **kwargs):\n \"\"\"Draw the graph G with a spectral 2D layout.\n\n Using the unnormalized Laplacian, the layout shows possible clusters of\n nodes which are an approximation of the ratio cut. The positions are the\n entries of the second and third eigenvectors corresponding to the\n ascending eigenvalues starting from the second one.\n\n Parameters\n ----------\n G : graph\n A networkx graph\n\n kwargs : optional keywords\n See networkx.draw_networkx() for a description of optional keywords,\n with the exception of the pos parameter which is not used by this\n function.\n \"\"\"\n draw(G, spectral_layout(G), **kwargs)\n\n\ndef draw_spring(G, **kwargs):\n \"\"\"Draw the graph G with a spring layout.\n\n Parameters\n ----------\n G : graph\n A networkx graph\n\n kwargs : optional keywords\n See networkx.draw_networkx() for a description of optional keywords,\n with the exception of the pos parameter which is not used by this\n function.\n \"\"\"\n draw(G, spring_layout(G), **kwargs)\n\n\ndef draw_shell(G, **kwargs):\n \"\"\"Draw networkx graph with shell layout.\n\n Parameters\n ----------\n G : graph\n A networkx graph\n\n kwargs : optional keywords\n See networkx.draw_networkx() for a description of optional keywords,\n with the exception of the pos parameter which is not used by this\n function.\n \"\"\"\n nlist = kwargs.get(\"nlist\", None)\n if nlist is not None:\n del kwargs[\"nlist\"]\n draw(G, shell_layout(G, nlist=nlist), **kwargs)\n\n\ndef draw_planar(G, **kwargs):\n \"\"\"Draw a planar networkx graph with planar layout.\n\n Parameters\n ----------\n G : graph\n A planar networkx graph\n\n kwargs : optional keywords\n See networkx.draw_networkx() for a description of optional keywords,\n with the exception of the pos parameter which is not used by this\n function.\n \"\"\"\n draw(G, planar_layout(G), **kwargs)\n\n\ndef apply_alpha(colors, alpha, elem_list, cmap=None, vmin=None, vmax=None):\n \"\"\"Apply an alpha (or list of alphas) to the colors provided.\n\n Parameters\n ----------\n\n colors : color string, or array of floats\n Color of element. Can be a single color format string (default='r'),\n or a sequence of colors with the same length as nodelist.\n If numeric values are specified they will be mapped to\n colors using the cmap and vmin,vmax parameters. See\n matplotlib.scatter for more details.\n\n alpha : float or array of floats\n Alpha values for elements. This can be a single alpha value, in\n which case it will be applied to all the elements of color. Otherwise,\n if it is an array, the elements of alpha will be applied to the colors\n in order (cycling through alpha multiple times if necessary).\n\n elem_list : array of networkx objects\n The list of elements which are being colored. These could be nodes,\n edges or labels.\n\n cmap : matplotlib colormap\n Color map for use if colors is a list of floats corresponding to points\n on a color mapping.\n\n vmin, vmax : float\n Minimum and maximum values for normalizing colors if a color mapping is\n used.\n\n Returns\n -------\n\n rgba_colors : numpy ndarray\n Array containing RGBA format values for each of the node colours.\n\n \"\"\"\n from itertools import islice, cycle\n\n try:\n import numpy as np\n from matplotlib.colors import colorConverter\n import matplotlib.cm as cm\n except ImportError as e:\n raise ImportError(\"Matplotlib required for draw()\") from e\n\n # If we have been provided with a list of numbers as long as elem_list,\n # apply the color mapping.\n if len(colors) == len(elem_list) and isinstance(colors[0], Number):\n mapper = cm.ScalarMappable(cmap=cmap)\n mapper.set_clim(vmin, vmax)\n rgba_colors = mapper.to_rgba(colors)\n # Otherwise, convert colors to matplotlib's RGB using the colorConverter\n # object. These are converted to numpy ndarrays to be consistent with the\n # to_rgba method of ScalarMappable.\n else:\n try:\n rgba_colors = np.array([colorConverter.to_rgba(colors)])\n except ValueError:\n rgba_colors = np.array([colorConverter.to_rgba(color) for color in colors])\n # Set the final column of the rgba_colors to have the relevant alpha values\n try:\n # If alpha is longer than the number of colors, resize to the number of\n # elements. Also, if rgba_colors.size (the number of elements of\n # rgba_colors) is the same as the number of elements, resize the array,\n # to avoid it being interpreted as a colormap by scatter()\n if len(alpha) > len(rgba_colors) or rgba_colors.size == len(elem_list):\n rgba_colors = np.resize(rgba_colors, (len(elem_list), 4))\n rgba_colors[1:, 0] = rgba_colors[0, 0]\n rgba_colors[1:, 1] = rgba_colors[0, 1]\n rgba_colors[1:, 2] = rgba_colors[0, 2]\n rgba_colors[:, 3] = list(islice(cycle(alpha), len(rgba_colors)))\n except TypeError:\n rgba_colors[:, -1] = alpha\n return rgba_colors\n",
"path": "networkx/drawing/nx_pylab.py"
}
] | [
{
"content": "\"\"\"\n**********\nMatplotlib\n**********\n\nDraw networks with matplotlib.\n\nSee Also\n--------\n\nmatplotlib: http://matplotlib.org/\n\npygraphviz: http://pygraphviz.github.io/\n\n\"\"\"\nfrom numbers import Number\nimport networkx as nx\nfrom networkx.drawing.layout import (\n shell_layout,\n circular_layout,\n kamada_kawai_layout,\n spectral_layout,\n spring_layout,\n random_layout,\n planar_layout,\n)\n\n__all__ = [\n \"draw\",\n \"draw_networkx\",\n \"draw_networkx_nodes\",\n \"draw_networkx_edges\",\n \"draw_networkx_labels\",\n \"draw_networkx_edge_labels\",\n \"draw_circular\",\n \"draw_kamada_kawai\",\n \"draw_random\",\n \"draw_spectral\",\n \"draw_spring\",\n \"draw_planar\",\n \"draw_shell\",\n]\n\n\ndef draw(G, pos=None, ax=None, **kwds):\n \"\"\"Draw the graph G with Matplotlib.\n\n Draw the graph as a simple representation with no node\n labels or edge labels and using the full Matplotlib figure area\n and no axis labels by default. See draw_networkx() for more\n full-featured drawing that allows title, axis labels etc.\n\n Parameters\n ----------\n G : graph\n A networkx graph\n\n pos : dictionary, optional\n A dictionary with nodes as keys and positions as values.\n If not specified a spring layout positioning will be computed.\n See :py:mod:`networkx.drawing.layout` for functions that\n compute node positions.\n\n ax : Matplotlib Axes object, optional\n Draw the graph in specified Matplotlib axes.\n\n kwds : optional keywords\n See networkx.draw_networkx() for a description of optional keywords.\n\n Examples\n --------\n >>> G = nx.dodecahedral_graph()\n >>> nx.draw(G)\n >>> nx.draw(G, pos=nx.spring_layout(G)) # use spring layout\n\n See Also\n --------\n draw_networkx()\n draw_networkx_nodes()\n draw_networkx_edges()\n draw_networkx_labels()\n draw_networkx_edge_labels()\n\n Notes\n -----\n This function has the same name as pylab.draw and pyplot.draw\n so beware when using `from networkx import *`\n\n since you might overwrite the pylab.draw function.\n\n With pyplot use\n\n >>> import matplotlib.pyplot as plt\n >>> import networkx as nx\n >>> G = nx.dodecahedral_graph()\n >>> nx.draw(G) # networkx draw()\n >>> plt.draw() # pyplot draw()\n\n Also see the NetworkX drawing examples at\n https://networkx.github.io/documentation/latest/auto_examples/index.html\n \"\"\"\n try:\n import matplotlib.pyplot as plt\n except ImportError as e:\n raise ImportError(\"Matplotlib required for draw()\") from e\n except RuntimeError:\n print(\"Matplotlib unable to open display\")\n raise\n\n if ax is None:\n cf = plt.gcf()\n else:\n cf = ax.get_figure()\n cf.set_facecolor(\"w\")\n if ax is None:\n if cf._axstack() is None:\n ax = cf.add_axes((0, 0, 1, 1))\n else:\n ax = cf.gca()\n\n if \"with_labels\" not in kwds:\n kwds[\"with_labels\"] = \"labels\" in kwds\n\n draw_networkx(G, pos=pos, ax=ax, **kwds)\n ax.set_axis_off()\n plt.draw_if_interactive()\n return\n\n\ndef draw_networkx(G, pos=None, arrows=True, with_labels=True, **kwds):\n \"\"\"Draw the graph G using Matplotlib.\n\n Draw the graph with Matplotlib with options for node positions,\n labeling, titles, and many other drawing features.\n See draw() for simple drawing without labels or axes.\n\n Parameters\n ----------\n G : graph\n A networkx graph\n\n pos : dictionary, optional\n A dictionary with nodes as keys and positions as values.\n If not specified a spring layout positioning will be computed.\n See :py:mod:`networkx.drawing.layout` for functions that\n compute node positions.\n\n arrows : bool, optional (default=True)\n For directed graphs, if True draw arrowheads.\n Note: Arrows will be the same color as edges.\n\n arrowstyle : str, optional (default='-|>')\n For directed graphs, choose the style of the arrowsheads.\n See :py:class: `matplotlib.patches.ArrowStyle` for more\n options.\n\n arrowsize : int, optional (default=10)\n For directed graphs, choose the size of the arrow head head's length and\n width. See :py:class: `matplotlib.patches.FancyArrowPatch` for attribute\n `mutation_scale` for more info.\n\n with_labels : bool, optional (default=True)\n Set to True to draw labels on the nodes.\n\n ax : Matplotlib Axes object, optional\n Draw the graph in the specified Matplotlib axes.\n\n nodelist : list, optional (default G.nodes())\n Draw only specified nodes\n\n edgelist : list, optional (default=G.edges())\n Draw only specified edges\n\n node_size : scalar or array, optional (default=300)\n Size of nodes. If an array is specified it must be the\n same length as nodelist.\n\n node_color : color or array of colors (default='#1f78b4')\n Node color. Can be a single color or a sequence of colors with the same\n length as nodelist. Color can be string, or rgb (or rgba) tuple of\n floats from 0-1. If numeric values are specified they will be\n mapped to colors using the cmap and vmin,vmax parameters. See\n matplotlib.scatter for more details.\n\n node_shape : string, optional (default='o')\n The shape of the node. Specification is as matplotlib.scatter\n marker, one of 'so^>v<dph8'.\n\n alpha : float, optional (default=None)\n The node and edge transparency\n\n cmap : Matplotlib colormap, optional (default=None)\n Colormap for mapping intensities of nodes\n\n vmin,vmax : float, optional (default=None)\n Minimum and maximum for node colormap scaling\n\n linewidths : [None | scalar | sequence]\n Line width of symbol border (default =1.0)\n\n width : float, optional (default=1.0)\n Line width of edges\n\n edge_color : color or array of colors (default='k')\n Edge color. Can be a single color or a sequence of colors with the same\n length as edgelist. Color can be string, or rgb (or rgba) tuple of\n floats from 0-1. If numeric values are specified they will be\n mapped to colors using the edge_cmap and edge_vmin,edge_vmax parameters.\n\n edge_cmap : Matplotlib colormap, optional (default=None)\n Colormap for mapping intensities of edges\n\n edge_vmin,edge_vmax : floats, optional (default=None)\n Minimum and maximum for edge colormap scaling\n\n style : string, optional (default='solid')\n Edge line style (solid|dashed|dotted,dashdot)\n\n labels : dictionary, optional (default=None)\n Node labels in a dictionary keyed by node of text labels\n\n font_size : int, optional (default=12)\n Font size for text labels\n\n font_color : string, optional (default='k' black)\n Font color string\n\n font_weight : string, optional (default='normal')\n Font weight\n\n font_family : string, optional (default='sans-serif')\n Font family\n\n label : string, optional\n Label for graph legend\n\n kwds : optional keywords\n See networkx.draw_networkx_nodes(), networkx.draw_networkx_edges(), and\n networkx.draw_networkx_labels() for a description of optional keywords.\n\n Notes\n -----\n For directed graphs, arrows are drawn at the head end. Arrows can be\n turned off with keyword arrows=False.\n\n Examples\n --------\n >>> G = nx.dodecahedral_graph()\n >>> nx.draw(G)\n >>> nx.draw(G, pos=nx.spring_layout(G)) # use spring layout\n\n >>> import matplotlib.pyplot as plt\n >>> limits = plt.axis('off') # turn of axis\n\n Also see the NetworkX drawing examples at\n https://networkx.github.io/documentation/latest/auto_examples/index.html\n\n See Also\n --------\n draw()\n draw_networkx_nodes()\n draw_networkx_edges()\n draw_networkx_labels()\n draw_networkx_edge_labels()\n \"\"\"\n try:\n import matplotlib.pyplot as plt\n except ImportError as e:\n raise ImportError(\"Matplotlib required for draw()\") from e\n except RuntimeError:\n print(\"Matplotlib unable to open display\")\n raise\n\n valid_node_kwds = (\n \"nodelist\",\n \"node_size\",\n \"node_color\",\n \"node_shape\",\n \"alpha\",\n \"cmap\",\n \"vmin\",\n \"vmax\",\n \"ax\",\n \"linewidths\",\n \"edgecolors\",\n \"label\",\n )\n\n valid_edge_kwds = (\n \"edgelist\",\n \"width\",\n \"edge_color\",\n \"style\",\n \"alpha\",\n \"arrowstyle\",\n \"arrowsize\",\n \"edge_cmap\",\n \"edge_vmin\",\n \"edge_vmax\",\n \"ax\",\n \"label\",\n \"node_size\",\n \"nodelist\",\n \"node_shape\",\n \"connectionstyle\",\n \"min_source_margin\",\n \"min_target_margin\",\n )\n\n valid_label_kwds = (\n \"labels\",\n \"font_size\",\n \"font_color\",\n \"font_family\",\n \"font_weight\",\n \"alpha\",\n \"bbox\",\n \"ax\",\n \"horizontalalignment\",\n \"verticalalignment\",\n )\n\n valid_kwds = valid_node_kwds + valid_edge_kwds + valid_label_kwds\n\n if any([k not in valid_kwds for k in kwds]):\n invalid_args = \", \".join([k for k in kwds if k not in valid_kwds])\n raise ValueError(f\"Received invalid argument(s): {invalid_args}\")\n\n node_kwds = {k: v for k, v in kwds.items() if k in valid_node_kwds}\n edge_kwds = {k: v for k, v in kwds.items() if k in valid_edge_kwds}\n label_kwds = {k: v for k, v in kwds.items() if k in valid_label_kwds}\n\n if pos is None:\n pos = nx.drawing.spring_layout(G) # default to spring layout\n\n draw_networkx_nodes(G, pos, **node_kwds)\n draw_networkx_edges(G, pos, arrows=arrows, **edge_kwds)\n if with_labels:\n draw_networkx_labels(G, pos, **label_kwds)\n plt.draw_if_interactive()\n\n\ndef draw_networkx_nodes(\n G,\n pos,\n nodelist=None,\n node_size=300,\n node_color=\"#1f78b4\",\n node_shape=\"o\",\n alpha=None,\n cmap=None,\n vmin=None,\n vmax=None,\n ax=None,\n linewidths=None,\n edgecolors=None,\n label=None,\n):\n \"\"\"Draw the nodes of the graph G.\n\n This draws only the nodes of the graph G.\n\n Parameters\n ----------\n G : graph\n A networkx graph\n\n pos : dictionary\n A dictionary with nodes as keys and positions as values.\n Positions should be sequences of length 2.\n\n ax : Matplotlib Axes object, optional\n Draw the graph in the specified Matplotlib axes.\n\n nodelist : list, optional\n Draw only specified nodes (default G.nodes())\n\n node_size : scalar or array\n Size of nodes (default=300). If an array is specified it must be the\n same length as nodelist.\n\n node_color : color or array of colors (default='#1f78b4')\n Node color. Can be a single color or a sequence of colors with the same\n length as nodelist. Color can be string, or rgb (or rgba) tuple of\n floats from 0-1. If numeric values are specified they will be\n mapped to colors using the cmap and vmin,vmax parameters. See\n matplotlib.scatter for more details.\n\n node_shape : string\n The shape of the node. Specification is as matplotlib.scatter\n marker, one of 'so^>v<dph8' (default='o').\n\n alpha : float or array of floats\n The node transparency. This can be a single alpha value (default=None),\n in which case it will be applied to all the nodes of color. Otherwise,\n if it is an array, the elements of alpha will be applied to the colors\n in order (cycling through alpha multiple times if necessary).\n\n cmap : Matplotlib colormap\n Colormap for mapping intensities of nodes (default=None)\n\n vmin,vmax : floats\n Minimum and maximum for node colormap scaling (default=None)\n\n linewidths : [None | scalar | sequence]\n Line width of symbol border (default =1.0)\n\n edgecolors : [None | scalar | sequence]\n Colors of node borders (default = node_color)\n\n label : [None| string]\n Label for legend\n\n Returns\n -------\n matplotlib.collections.PathCollection\n `PathCollection` of the nodes.\n\n Examples\n --------\n >>> G = nx.dodecahedral_graph()\n >>> nodes = nx.draw_networkx_nodes(G, pos=nx.spring_layout(G))\n\n Also see the NetworkX drawing examples at\n https://networkx.github.io/documentation/latest/auto_examples/index.html\n\n See Also\n --------\n draw()\n draw_networkx()\n draw_networkx_edges()\n draw_networkx_labels()\n draw_networkx_edge_labels()\n \"\"\"\n from collections.abc import Iterable\n\n try:\n import matplotlib.pyplot as plt\n from matplotlib.collections import PathCollection\n import numpy as np\n except ImportError as e:\n raise ImportError(\"Matplotlib required for draw()\") from e\n except RuntimeError:\n print(\"Matplotlib unable to open display\")\n raise\n\n if ax is None:\n ax = plt.gca()\n\n if nodelist is None:\n nodelist = list(G)\n\n if len(nodelist) == 0: # empty nodelist, no drawing\n return PathCollection(None)\n\n try:\n xy = np.asarray([pos[v] for v in nodelist])\n except KeyError as e:\n raise nx.NetworkXError(f\"Node {e} has no position.\") from e\n except ValueError as e:\n raise nx.NetworkXError(\"Bad value in node positions.\") from e\n\n if isinstance(alpha, Iterable):\n node_color = apply_alpha(node_color, alpha, nodelist, cmap, vmin, vmax)\n alpha = None\n\n node_collection = ax.scatter(\n xy[:, 0],\n xy[:, 1],\n s=node_size,\n c=node_color,\n marker=node_shape,\n cmap=cmap,\n vmin=vmin,\n vmax=vmax,\n alpha=alpha,\n linewidths=linewidths,\n edgecolors=edgecolors,\n label=label,\n )\n ax.tick_params(\n axis=\"both\",\n which=\"both\",\n bottom=False,\n left=False,\n labelbottom=False,\n labelleft=False,\n )\n\n node_collection.set_zorder(2)\n return node_collection\n\n\ndef draw_networkx_edges(\n G,\n pos,\n edgelist=None,\n width=1.0,\n edge_color=\"k\",\n style=\"solid\",\n alpha=None,\n arrowstyle=\"-|>\",\n arrowsize=10,\n edge_cmap=None,\n edge_vmin=None,\n edge_vmax=None,\n ax=None,\n arrows=True,\n label=None,\n node_size=300,\n nodelist=None,\n node_shape=\"o\",\n connectionstyle=None,\n min_source_margin=0,\n min_target_margin=0,\n):\n \"\"\"Draw the edges of the graph G.\n\n This draws only the edges of the graph G.\n\n Parameters\n ----------\n G : graph\n A networkx graph\n\n pos : dictionary\n A dictionary with nodes as keys and positions as values.\n Positions should be sequences of length 2.\n\n edgelist : collection of edge tuples\n Draw only specified edges(default=G.edges())\n\n width : float, or array of floats\n Line width of edges (default=1.0)\n\n edge_color : color or array of colors (default='k')\n Edge color. Can be a single color or a sequence of colors with the same\n length as edgelist. Color can be string, or rgb (or rgba) tuple of\n floats from 0-1. If numeric values are specified they will be\n mapped to colors using the edge_cmap and edge_vmin,edge_vmax parameters.\n\n style : string\n Edge line style (default='solid') (solid|dashed|dotted,dashdot)\n\n alpha : float\n The edge transparency (default=None)\n\n edge_ cmap : Matplotlib colormap\n Colormap for mapping intensities of edges (default=None)\n\n edge_vmin,edge_vmax : floats\n Minimum and maximum for edge colormap scaling (default=None)\n\n ax : Matplotlib Axes object, optional\n Draw the graph in the specified Matplotlib axes.\n\n arrows : bool, optional (default=True)\n For directed graphs, if True draw arrowheads.\n Note: Arrows will be the same color as edges.\n\n arrowstyle : str, optional (default='-|>')\n For directed graphs, choose the style of the arrow heads.\n See :py:class: `matplotlib.patches.ArrowStyle` for more\n options.\n\n arrowsize : int, optional (default=10)\n For directed graphs, choose the size of the arrow head head's length and\n width. See :py:class: `matplotlib.patches.FancyArrowPatch` for attribute\n `mutation_scale` for more info.\n\n connectionstyle : str, optional (default=None)\n Pass the connectionstyle parameter to create curved arc of rounding\n radius rad. For example, connectionstyle='arc3,rad=0.2'.\n See :py:class: `matplotlib.patches.ConnectionStyle` and\n :py:class: `matplotlib.patches.FancyArrowPatch` for more info.\n\n label : [None| string]\n Label for legend\n\n min_source_margin : int, optional (default=0)\n The minimum margin (gap) at the begining of the edge at the source.\n\n min_target_margin : int, optional (default=0)\n The minimum margin (gap) at the end of the edge at the target.\n\n Returns\n -------\n matplotlib.collection.LineCollection\n `LineCollection` of the edges\n\n list of matplotlib.patches.FancyArrowPatch\n `FancyArrowPatch` instances of the directed edges\n\n Depending whether the drawing includes arrows or not.\n\n Notes\n -----\n For directed graphs, arrows are drawn at the head end. Arrows can be\n turned off with keyword arrows=False. Be sure to include `node_size` as a\n keyword argument; arrows are drawn considering the size of nodes.\n\n Examples\n --------\n >>> G = nx.dodecahedral_graph()\n >>> edges = nx.draw_networkx_edges(G, pos=nx.spring_layout(G))\n\n >>> G = nx.DiGraph()\n >>> G.add_edges_from([(1, 2), (1, 3), (2, 3)])\n >>> arcs = nx.draw_networkx_edges(G, pos=nx.spring_layout(G))\n >>> alphas = [0.3, 0.4, 0.5]\n >>> for i, arc in enumerate(arcs): # change alpha values of arcs\n ... arc.set_alpha(alphas[i])\n\n Also see the NetworkX drawing examples at\n https://networkx.github.io/documentation/latest/auto_examples/index.html\n\n See Also\n --------\n draw()\n draw_networkx()\n draw_networkx_nodes()\n draw_networkx_labels()\n draw_networkx_edge_labels()\n \"\"\"\n try:\n import matplotlib.pyplot as plt\n from matplotlib.colors import colorConverter, Colormap, Normalize\n from matplotlib.collections import LineCollection\n from matplotlib.patches import FancyArrowPatch\n import numpy as np\n except ImportError as e:\n raise ImportError(\"Matplotlib required for draw()\") from e\n except RuntimeError:\n print(\"Matplotlib unable to open display\")\n raise\n\n if ax is None:\n ax = plt.gca()\n\n if edgelist is None:\n edgelist = list(G.edges())\n\n if len(edgelist) == 0: # no edges!\n if not G.is_directed() or not arrows:\n return LineCollection(None)\n else:\n return []\n\n if nodelist is None:\n nodelist = list(G.nodes())\n\n # FancyArrowPatch handles color=None different from LineCollection\n if edge_color is None:\n edge_color = \"k\"\n\n # set edge positions\n edge_pos = np.asarray([(pos[e[0]], pos[e[1]]) for e in edgelist])\n\n # Check if edge_color is an array of floats and map to edge_cmap.\n # This is the only case handled differently from matplotlib\n if (\n np.iterable(edge_color)\n and (len(edge_color) == len(edge_pos))\n and np.alltrue([isinstance(c, Number) for c in edge_color])\n ):\n if edge_cmap is not None:\n assert isinstance(edge_cmap, Colormap)\n else:\n edge_cmap = plt.get_cmap()\n if edge_vmin is None:\n edge_vmin = min(edge_color)\n if edge_vmax is None:\n edge_vmax = max(edge_color)\n color_normal = Normalize(vmin=edge_vmin, vmax=edge_vmax)\n edge_color = [edge_cmap(color_normal(e)) for e in edge_color]\n\n if not G.is_directed() or not arrows:\n edge_collection = LineCollection(\n edge_pos,\n colors=edge_color,\n linewidths=width,\n antialiaseds=(1,),\n linestyle=style,\n transOffset=ax.transData,\n alpha=alpha,\n )\n\n edge_collection.set_cmap(edge_cmap)\n edge_collection.set_clim(edge_vmin, edge_vmax)\n\n edge_collection.set_zorder(1) # edges go behind nodes\n edge_collection.set_label(label)\n ax.add_collection(edge_collection)\n\n return edge_collection\n\n arrow_collection = None\n\n if G.is_directed() and arrows:\n # Note: Waiting for someone to implement arrow to intersection with\n # marker. Meanwhile, this works well for polygons with more than 4\n # sides and circle.\n\n def to_marker_edge(marker_size, marker):\n if marker in \"s^>v<d\": # `large` markers need extra space\n return np.sqrt(2 * marker_size) / 2\n else:\n return np.sqrt(marker_size) / 2\n\n # Draw arrows with `matplotlib.patches.FancyarrowPatch`\n arrow_collection = []\n mutation_scale = arrowsize # scale factor of arrow head\n\n # FancyArrowPatch doesn't handle color strings\n arrow_colors = colorConverter.to_rgba_array(edge_color, alpha)\n for i, (src, dst) in enumerate(edge_pos):\n x1, y1 = src\n x2, y2 = dst\n shrink_source = 0 # space from source to tail\n shrink_target = 0 # space from head to target\n if np.iterable(node_size): # many node sizes\n source, target = edgelist[i][:2]\n source_node_size = node_size[nodelist.index(source)]\n target_node_size = node_size[nodelist.index(target)]\n shrink_source = to_marker_edge(source_node_size, node_shape)\n shrink_target = to_marker_edge(target_node_size, node_shape)\n else:\n shrink_source = shrink_target = to_marker_edge(node_size, node_shape)\n\n if shrink_source < min_source_margin:\n shrink_source = min_source_margin\n\n if shrink_target < min_target_margin:\n shrink_target = min_target_margin\n\n if len(arrow_colors) == len(edge_pos):\n arrow_color = arrow_colors[i]\n elif len(arrow_colors) == 1:\n arrow_color = arrow_colors[0]\n else: # Cycle through colors\n arrow_color = arrow_colors[i % len(arrow_colors)]\n\n if np.iterable(width):\n if len(width) == len(edge_pos):\n line_width = width[i]\n else:\n line_width = width[i % len(width)]\n else:\n line_width = width\n\n arrow = FancyArrowPatch(\n (x1, y1),\n (x2, y2),\n arrowstyle=arrowstyle,\n shrinkA=shrink_source,\n shrinkB=shrink_target,\n mutation_scale=mutation_scale,\n color=arrow_color,\n linewidth=line_width,\n connectionstyle=connectionstyle,\n linestyle=style,\n zorder=1,\n ) # arrows go behind nodes\n\n # There seems to be a bug in matplotlib to make collections of\n # FancyArrowPatch instances. Until fixed, the patches are added\n # individually to the axes instance.\n arrow_collection.append(arrow)\n ax.add_patch(arrow)\n\n # update view\n minx = np.amin(np.ravel(edge_pos[:, :, 0]))\n maxx = np.amax(np.ravel(edge_pos[:, :, 0]))\n miny = np.amin(np.ravel(edge_pos[:, :, 1]))\n maxy = np.amax(np.ravel(edge_pos[:, :, 1]))\n\n w = maxx - minx\n h = maxy - miny\n padx, pady = 0.05 * w, 0.05 * h\n corners = (minx - padx, miny - pady), (maxx + padx, maxy + pady)\n ax.update_datalim(corners)\n ax.autoscale_view()\n\n ax.tick_params(\n axis=\"both\",\n which=\"both\",\n bottom=False,\n left=False,\n labelbottom=False,\n labelleft=False,\n )\n\n return arrow_collection\n\n\ndef draw_networkx_labels(\n G,\n pos,\n labels=None,\n font_size=12,\n font_color=\"k\",\n font_family=\"sans-serif\",\n font_weight=\"normal\",\n alpha=None,\n bbox=None,\n horizontalalignment=\"center\",\n verticalalignment=\"center\",\n ax=None,\n):\n \"\"\"Draw node labels on the graph G.\n\n Parameters\n ----------\n G : graph\n A networkx graph\n\n pos : dictionary\n A dictionary with nodes as keys and positions as values.\n Positions should be sequences of length 2.\n\n labels : dictionary, optional (default=None)\n Node labels in a dictionary keyed by node of text labels\n Node-keys in labels should appear as keys in `pos`.\n If needed use: `{n:lab for n,lab in labels.items() if n in pos}`\n\n font_size : int\n Font size for text labels (default=12)\n\n font_color : string\n Font color string (default='k' black)\n\n font_family : string\n Font family (default='sans-serif')\n\n font_weight : string\n Font weight (default='normal')\n\n alpha : float or None\n The text transparency (default=None)\n\n horizontalalignment : {'center', 'right', 'left'}\n Horizontal alignment (default='center')\n\n verticalalignment : {'center', 'top', 'bottom', 'baseline', 'center_baseline'}\n Vertical alignment (default='center')\n\n ax : Matplotlib Axes object, optional\n Draw the graph in the specified Matplotlib axes.\n\n\n Returns\n -------\n dict\n `dict` of labels keyed on the nodes\n\n Examples\n --------\n >>> G = nx.dodecahedral_graph()\n >>> labels = nx.draw_networkx_labels(G, pos=nx.spring_layout(G))\n\n Also see the NetworkX drawing examples at\n https://networkx.github.io/documentation/latest/auto_examples/index.html\n\n See Also\n --------\n draw()\n draw_networkx()\n draw_networkx_nodes()\n draw_networkx_edges()\n draw_networkx_edge_labels()\n \"\"\"\n try:\n import matplotlib.pyplot as plt\n except ImportError as e:\n raise ImportError(\"Matplotlib required for draw()\") from e\n except RuntimeError:\n print(\"Matplotlib unable to open display\")\n raise\n\n if ax is None:\n ax = plt.gca()\n\n if labels is None:\n labels = {n: n for n in G.nodes()}\n\n text_items = {} # there is no text collection so we'll fake one\n for n, label in labels.items():\n (x, y) = pos[n]\n if not isinstance(label, str):\n label = str(label) # this makes \"1\" and 1 labeled the same\n t = ax.text(\n x,\n y,\n label,\n size=font_size,\n color=font_color,\n family=font_family,\n weight=font_weight,\n alpha=alpha,\n horizontalalignment=horizontalalignment,\n verticalalignment=verticalalignment,\n transform=ax.transData,\n bbox=bbox,\n clip_on=True,\n )\n text_items[n] = t\n\n ax.tick_params(\n axis=\"both\",\n which=\"both\",\n bottom=False,\n left=False,\n labelbottom=False,\n labelleft=False,\n )\n\n return text_items\n\n\ndef draw_networkx_edge_labels(\n G,\n pos,\n edge_labels=None,\n label_pos=0.5,\n font_size=10,\n font_color=\"k\",\n font_family=\"sans-serif\",\n font_weight=\"normal\",\n alpha=None,\n bbox=None,\n horizontalalignment=\"center\",\n verticalalignment=\"center\",\n ax=None,\n rotate=True,\n):\n \"\"\"Draw edge labels.\n\n Parameters\n ----------\n G : graph\n A networkx graph\n\n pos : dictionary\n A dictionary with nodes as keys and positions as values.\n Positions should be sequences of length 2.\n\n ax : Matplotlib Axes object, optional\n Draw the graph in the specified Matplotlib axes.\n\n alpha : float or None\n The text transparency (default=None)\n\n edge_labels : dictionary\n Edge labels in a dictionary keyed by edge two-tuple of text\n labels (default=None). Only labels for the keys in the dictionary\n are drawn.\n\n label_pos : float\n Position of edge label along edge (0=head, 0.5=center, 1=tail)\n\n font_size : int\n Font size for text labels (default=12)\n\n font_color : string\n Font color string (default='k' black)\n\n font_weight : string\n Font weight (default='normal')\n\n font_family : string\n Font family (default='sans-serif')\n\n bbox : Matplotlib bbox\n Specify text box shape and colors.\n\n clip_on : bool\n Turn on clipping at axis boundaries (default=True)\n\n horizontalalignment : {'center', 'right', 'left'}\n Horizontal alignment (default='center')\n\n verticalalignment : {'center', 'top', 'bottom', 'baseline', 'center_baseline'}\n Vertical alignment (default='center')\n\n ax : Matplotlib Axes object, optional\n Draw the graph in the specified Matplotlib axes.\n\n Returns\n -------\n dict\n `dict` of labels keyed on the edges\n\n Examples\n --------\n >>> G = nx.dodecahedral_graph()\n >>> edge_labels = nx.draw_networkx_edge_labels(G, pos=nx.spring_layout(G))\n\n Also see the NetworkX drawing examples at\n https://networkx.github.io/documentation/latest/auto_examples/index.html\n\n See Also\n --------\n draw()\n draw_networkx()\n draw_networkx_nodes()\n draw_networkx_edges()\n draw_networkx_labels()\n \"\"\"\n try:\n import matplotlib.pyplot as plt\n import numpy as np\n except ImportError as e:\n raise ImportError(\"Matplotlib required for draw()\") from e\n except RuntimeError:\n print(\"Matplotlib unable to open display\")\n raise\n\n if ax is None:\n ax = plt.gca()\n if edge_labels is None:\n labels = {(u, v): d for u, v, d in G.edges(data=True)}\n else:\n labels = edge_labels\n text_items = {}\n for (n1, n2), label in labels.items():\n (x1, y1) = pos[n1]\n (x2, y2) = pos[n2]\n (x, y) = (\n x1 * label_pos + x2 * (1.0 - label_pos),\n y1 * label_pos + y2 * (1.0 - label_pos),\n )\n\n if rotate:\n # in degrees\n angle = np.arctan2(y2 - y1, x2 - x1) / (2.0 * np.pi) * 360\n # make label orientation \"right-side-up\"\n if angle > 90:\n angle -= 180\n if angle < -90:\n angle += 180\n # transform data coordinate angle to screen coordinate angle\n xy = np.array((x, y))\n trans_angle = ax.transData.transform_angles(\n np.array((angle,)), xy.reshape((1, 2))\n )[0]\n else:\n trans_angle = 0.0\n # use default box of white with white border\n if bbox is None:\n bbox = dict(boxstyle=\"round\", ec=(1.0, 1.0, 1.0), fc=(1.0, 1.0, 1.0),)\n if not isinstance(label, str):\n label = str(label) # this makes \"1\" and 1 labeled the same\n\n t = ax.text(\n x,\n y,\n label,\n size=font_size,\n color=font_color,\n family=font_family,\n weight=font_weight,\n alpha=alpha,\n horizontalalignment=horizontalalignment,\n verticalalignment=verticalalignment,\n rotation=trans_angle,\n transform=ax.transData,\n bbox=bbox,\n zorder=1,\n clip_on=True,\n )\n text_items[(n1, n2)] = t\n\n ax.tick_params(\n axis=\"both\",\n which=\"both\",\n bottom=False,\n left=False,\n labelbottom=False,\n labelleft=False,\n )\n\n return text_items\n\n\ndef draw_circular(G, **kwargs):\n \"\"\"Draw the graph G with a circular layout.\n\n Parameters\n ----------\n G : graph\n A networkx graph\n\n kwargs : optional keywords\n See networkx.draw_networkx() for a description of optional keywords,\n with the exception of the pos parameter which is not used by this\n function.\n \"\"\"\n draw(G, circular_layout(G), **kwargs)\n\n\ndef draw_kamada_kawai(G, **kwargs):\n \"\"\"Draw the graph G with a Kamada-Kawai force-directed layout.\n\n Parameters\n ----------\n G : graph\n A networkx graph\n\n kwargs : optional keywords\n See networkx.draw_networkx() for a description of optional keywords,\n with the exception of the pos parameter which is not used by this\n function.\n \"\"\"\n draw(G, kamada_kawai_layout(G), **kwargs)\n\n\ndef draw_random(G, **kwargs):\n \"\"\"Draw the graph G with a random layout.\n\n Parameters\n ----------\n G : graph\n A networkx graph\n\n kwargs : optional keywords\n See networkx.draw_networkx() for a description of optional keywords,\n with the exception of the pos parameter which is not used by this\n function.\n \"\"\"\n draw(G, random_layout(G), **kwargs)\n\n\ndef draw_spectral(G, **kwargs):\n \"\"\"Draw the graph G with a spectral 2D layout.\n\n Using the unnormalized Laplacian, the layout shows possible clusters of\n nodes which are an approximation of the ratio cut. The positions are the\n entries of the second and third eigenvectors corresponding to the\n ascending eigenvalues starting from the second one.\n\n Parameters\n ----------\n G : graph\n A networkx graph\n\n kwargs : optional keywords\n See networkx.draw_networkx() for a description of optional keywords,\n with the exception of the pos parameter which is not used by this\n function.\n \"\"\"\n draw(G, spectral_layout(G), **kwargs)\n\n\ndef draw_spring(G, **kwargs):\n \"\"\"Draw the graph G with a spring layout.\n\n Parameters\n ----------\n G : graph\n A networkx graph\n\n kwargs : optional keywords\n See networkx.draw_networkx() for a description of optional keywords,\n with the exception of the pos parameter which is not used by this\n function.\n \"\"\"\n draw(G, spring_layout(G), **kwargs)\n\n\ndef draw_shell(G, **kwargs):\n \"\"\"Draw networkx graph with shell layout.\n\n Parameters\n ----------\n G : graph\n A networkx graph\n\n kwargs : optional keywords\n See networkx.draw_networkx() for a description of optional keywords,\n with the exception of the pos parameter which is not used by this\n function.\n \"\"\"\n nlist = kwargs.get(\"nlist\", None)\n if nlist is not None:\n del kwargs[\"nlist\"]\n draw(G, shell_layout(G, nlist=nlist), **kwargs)\n\n\ndef draw_planar(G, **kwargs):\n \"\"\"Draw a planar networkx graph with planar layout.\n\n Parameters\n ----------\n G : graph\n A planar networkx graph\n\n kwargs : optional keywords\n See networkx.draw_networkx() for a description of optional keywords,\n with the exception of the pos parameter which is not used by this\n function.\n \"\"\"\n draw(G, planar_layout(G), **kwargs)\n\n\ndef apply_alpha(colors, alpha, elem_list, cmap=None, vmin=None, vmax=None):\n \"\"\"Apply an alpha (or list of alphas) to the colors provided.\n\n Parameters\n ----------\n\n colors : color string, or array of floats\n Color of element. Can be a single color format string (default='r'),\n or a sequence of colors with the same length as nodelist.\n If numeric values are specified they will be mapped to\n colors using the cmap and vmin,vmax parameters. See\n matplotlib.scatter for more details.\n\n alpha : float or array of floats\n Alpha values for elements. This can be a single alpha value, in\n which case it will be applied to all the elements of color. Otherwise,\n if it is an array, the elements of alpha will be applied to the colors\n in order (cycling through alpha multiple times if necessary).\n\n elem_list : array of networkx objects\n The list of elements which are being colored. These could be nodes,\n edges or labels.\n\n cmap : matplotlib colormap\n Color map for use if colors is a list of floats corresponding to points\n on a color mapping.\n\n vmin, vmax : float\n Minimum and maximum values for normalizing colors if a color mapping is\n used.\n\n Returns\n -------\n\n rgba_colors : numpy ndarray\n Array containing RGBA format values for each of the node colours.\n\n \"\"\"\n from itertools import islice, cycle\n\n try:\n import numpy as np\n from matplotlib.colors import colorConverter\n import matplotlib.cm as cm\n except ImportError as e:\n raise ImportError(\"Matplotlib required for draw()\") from e\n\n # If we have been provided with a list of numbers as long as elem_list,\n # apply the color mapping.\n if len(colors) == len(elem_list) and isinstance(colors[0], Number):\n mapper = cm.ScalarMappable(cmap=cmap)\n mapper.set_clim(vmin, vmax)\n rgba_colors = mapper.to_rgba(colors)\n # Otherwise, convert colors to matplotlib's RGB using the colorConverter\n # object. These are converted to numpy ndarrays to be consistent with the\n # to_rgba method of ScalarMappable.\n else:\n try:\n rgba_colors = np.array([colorConverter.to_rgba(colors)])\n except ValueError:\n rgba_colors = np.array([colorConverter.to_rgba(color) for color in colors])\n # Set the final column of the rgba_colors to have the relevant alpha values\n try:\n # If alpha is longer than the number of colors, resize to the number of\n # elements. Also, if rgba_colors.size (the number of elements of\n # rgba_colors) is the same as the number of elements, resize the array,\n # to avoid it being interpreted as a colormap by scatter()\n if len(alpha) > len(rgba_colors) or rgba_colors.size == len(elem_list):\n rgba_colors = np.resize(rgba_colors, (len(elem_list), 4))\n rgba_colors[1:, 0] = rgba_colors[0, 0]\n rgba_colors[1:, 1] = rgba_colors[0, 1]\n rgba_colors[1:, 2] = rgba_colors[0, 2]\n rgba_colors[:, 3] = list(islice(cycle(alpha), len(rgba_colors)))\n except TypeError:\n rgba_colors[:, -1] = alpha\n return rgba_colors\n",
"path": "networkx/drawing/nx_pylab.py"
}
] | diff --git a/networkx/drawing/nx_pylab.py b/networkx/drawing/nx_pylab.py
index eb7be33be8d..c705ed547b1 100644
--- a/networkx/drawing/nx_pylab.py
+++ b/networkx/drawing/nx_pylab.py
@@ -640,7 +640,7 @@ def draw_networkx_edges(
if edgelist is None:
edgelist = list(G.edges())
- if not edgelist or len(edgelist) == 0: # no edges!
+ if len(edgelist) == 0: # no edges!
if not G.is_directed() or not arrows:
return LineCollection(None)
else:
diff --git a/networkx/drawing/tests/test_pylab.py b/networkx/drawing/tests/test_pylab.py
index 7a07acd9c3c..3280a49ee12 100644
--- a/networkx/drawing/tests/test_pylab.py
+++ b/networkx/drawing/tests/test_pylab.py
@@ -250,3 +250,8 @@ def test_alpha_iter(self):
def test_error_invalid_kwds(self):
with pytest.raises(ValueError, match="Received invalid argument"):
nx.draw(self.G, foo="bar")
+
+ def test_np_edgelist(self):
+ # see issue #4129
+ np = pytest.importorskip("numpy")
+ nx.draw_networkx(self.G, edgelist=np.array([(0, 2), (0, 3)]))
|
Textualize__textual-4189 | SyntaxWarning for loading indicator widget
I receive this warning after upgrading to `0.52.0`:
```
/Users/cthompson/Library/Caches/pypoetry/virtualenvs/dolphie-z84eXs3q-py3.11/lib/python3.11/site-packages/textual/widgets/_loading_indicator.py:57: SyntaxWarning: "is" with a literal. Did you mean "=="?
if self.app.animation_level is "none":
```
https://github.com/Textualize/textual/blob/main/src/textual/widgets/_loading_indicator.py#L57
Seems we just need to change `is "none"` to `== "none"`
| [
{
"content": "from __future__ import annotations\n\nfrom time import time\n\nfrom rich.console import RenderableType\nfrom rich.style import Style\nfrom rich.text import Text\n\nfrom ..color import Gradient\nfrom ..events import Mount\nfrom ..widget import Widget\n\n\nclass LoadingIndicator(Widget):\n \"\"\"Display an animated loading indicator.\"\"\"\n\n DEFAULT_CSS = \"\"\"\n LoadingIndicator {\n width: 100%;\n height: 100%;\n min-height: 1;\n content-align: center middle;\n color: $accent;\n }\n LoadingIndicator.-textual-loading-indicator {\n layer: _loading;\n background: $boost;\n dock: top;\n }\n \"\"\"\n\n def __init__(\n self,\n name: str | None = None,\n id: str | None = None,\n classes: str | None = None,\n disabled: bool = False,\n ):\n \"\"\"Initialize a loading indicator.\n\n Args:\n name: The name of the widget.\n id: The ID of the widget in the DOM.\n classes: The CSS classes for the widget.\n disabled: Whether the widget is disabled or not.\n \"\"\"\n super().__init__(name=name, id=id, classes=classes, disabled=disabled)\n\n self._start_time: float = 0.0\n \"\"\"The time the loading indicator was mounted (a Unix timestamp).\"\"\"\n\n def _on_mount(self, _: Mount) -> None:\n self._start_time = time()\n self.auto_refresh = 1 / 16\n\n def render(self) -> RenderableType:\n if self.app.animation_level is \"none\":\n return Text(\"Loading...\")\n\n elapsed = time() - self._start_time\n speed = 0.8\n dot = \"\\u25cf\"\n _, _, background, color = self.colors\n\n gradient = Gradient(\n (0.0, background.blend(color, 0.1)),\n (0.7, color),\n (1.0, color.lighten(0.1)),\n )\n\n blends = [(elapsed * speed - dot_number / 8) % 1 for dot_number in range(5)]\n\n dots = [\n (\n f\"{dot} \",\n Style.from_color(gradient.get_color((1 - blend) ** 2).rich_color),\n )\n for blend in blends\n ]\n indicator = Text.assemble(*dots)\n indicator.rstrip()\n return indicator\n",
"path": "src/textual/widgets/_loading_indicator.py"
}
] | [
{
"content": "from __future__ import annotations\n\nfrom time import time\n\nfrom rich.console import RenderableType\nfrom rich.style import Style\nfrom rich.text import Text\n\nfrom ..color import Gradient\nfrom ..events import Mount\nfrom ..widget import Widget\n\n\nclass LoadingIndicator(Widget):\n \"\"\"Display an animated loading indicator.\"\"\"\n\n DEFAULT_CSS = \"\"\"\n LoadingIndicator {\n width: 100%;\n height: 100%;\n min-height: 1;\n content-align: center middle;\n color: $accent;\n }\n LoadingIndicator.-textual-loading-indicator {\n layer: _loading;\n background: $boost;\n dock: top;\n }\n \"\"\"\n\n def __init__(\n self,\n name: str | None = None,\n id: str | None = None,\n classes: str | None = None,\n disabled: bool = False,\n ):\n \"\"\"Initialize a loading indicator.\n\n Args:\n name: The name of the widget.\n id: The ID of the widget in the DOM.\n classes: The CSS classes for the widget.\n disabled: Whether the widget is disabled or not.\n \"\"\"\n super().__init__(name=name, id=id, classes=classes, disabled=disabled)\n\n self._start_time: float = 0.0\n \"\"\"The time the loading indicator was mounted (a Unix timestamp).\"\"\"\n\n def _on_mount(self, _: Mount) -> None:\n self._start_time = time()\n self.auto_refresh = 1 / 16\n\n def render(self) -> RenderableType:\n if self.app.animation_level == \"none\":\n return Text(\"Loading...\")\n\n elapsed = time() - self._start_time\n speed = 0.8\n dot = \"\\u25cf\"\n _, _, background, color = self.colors\n\n gradient = Gradient(\n (0.0, background.blend(color, 0.1)),\n (0.7, color),\n (1.0, color.lighten(0.1)),\n )\n\n blends = [(elapsed * speed - dot_number / 8) % 1 for dot_number in range(5)]\n\n dots = [\n (\n f\"{dot} \",\n Style.from_color(gradient.get_color((1 - blend) ** 2).rich_color),\n )\n for blend in blends\n ]\n indicator = Text.assemble(*dots)\n indicator.rstrip()\n return indicator\n",
"path": "src/textual/widgets/_loading_indicator.py"
}
] | diff --git a/CHANGELOG.md b/CHANGELOG.md
index 62ff923eb5..e591e95d44 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -5,6 +5,12 @@ All notable changes to this project will be documented in this file.
The format is based on [Keep a Changelog](http://keepachangelog.com/)
and this project adheres to [Semantic Versioning](http://semver.org/).
+## Unreleased
+
+### Fixed
+
+- Fixed the check for animation level in `LoadingIndicator` https://github.com/Textualize/textual/issues/4188
+
## [0.52.0] - 2023-02-19
### Changed
diff --git a/src/textual/widgets/_loading_indicator.py b/src/textual/widgets/_loading_indicator.py
index a826c85f85..24bc3cf2de 100644
--- a/src/textual/widgets/_loading_indicator.py
+++ b/src/textual/widgets/_loading_indicator.py
@@ -54,7 +54,7 @@ def _on_mount(self, _: Mount) -> None:
self.auto_refresh = 1 / 16
def render(self) -> RenderableType:
- if self.app.animation_level is "none":
+ if self.app.animation_level == "none":
return Text("Loading...")
elapsed = time() - self._start_time
|
celery__celery-6741 | celery amqp repl broken in celery 5.0.3+
<!--
Please fill this template entirely and do not erase parts of it.
We reserve the right to close without a response
bug reports which are incomplete.
-->
# Checklist
<!--
To check an item on the list replace [ ] with [x].
-->
- [x] I have verified that the issue exists against the `master` branch of Celery.
- [x] This has already been asked to the [discussion group](https://groups.google.com/forum/#!forum/celery-users) first.
- [x] I have read the relevant section in the
[contribution guide](http://docs.celeryproject.org/en/latest/contributing.html#other-bugs)
on reporting bugs.
- [x] I have checked the [issues list](https://github.com/celery/celery/issues?q=is%3Aissue+label%3A%22Issue+Type%3A+Bug+Report%22+-label%3A%22Category%3A+Documentation%22)
for similar or identical bug reports.
- [x] I have checked the [pull requests list](https://github.com/celery/celery/pulls?q=is%3Apr+label%3A%22PR+Type%3A+Bugfix%22+-label%3A%22Category%3A+Documentation%22)
for existing proposed fixes.
- [x] I have checked the [commit log](https://github.com/celery/celery/commits/master)
to find out if the bug was already fixed in the master branch.
- [x] I have included all related issues and possible duplicate issues
in this issue (If there are none, check this box anyway).
## Mandatory Debugging Information
- [x] I have included the output of ``celery -A proj report`` in the issue.
(if you are not able to do this, then at least specify the Celery
version affected).
- [x] I have verified that the issue exists against the `master` branch of Celery.
- [x] I have included the contents of ``pip freeze`` in the issue.
- [x] I have included all the versions of all the external dependencies required
to reproduce this bug.
## Optional Debugging Information
<!--
Try some of the below if you think they are relevant.
It will help us figure out the scope of the bug and how many users it affects.
-->
- [ ] I have tried reproducing the issue on more than one Python version
and/or implementation.
- [ ] I have tried reproducing the issue on more than one message broker and/or
result backend.
- [ ] I have tried reproducing the issue on more than one version of the message
broker and/or result backend.
- [ ] I have tried reproducing the issue on more than one operating system.
- [ ] I have tried reproducing the issue on more than one workers pool.
- [ ] I have tried reproducing the issue with autoscaling, retries,
ETA/Countdown & rate limits disabled.
- [x] I have tried reproducing the issue after downgrading
and/or upgrading Celery and its dependencies.
## Related Issues and Possible Duplicates
<!--
Please make sure to search and mention any related issues
or possible duplicates to this issue as requested by the checklist above.
This may or may not include issues in other repositories that the Celery project
maintains or other repositories that are dependencies of Celery.
If you don't know how to mention issues, please refer to Github's documentation
on the subject: https://help.github.com/en/articles/autolinked-references-and-urls#issues-and-pull-requests
-->
#### Related Issues
- None
#### Possible Duplicates
- None
## Environment & Settings
<!-- Include the contents of celery --version below -->
**Celery version**:
<!-- Include the output of celery -A proj report below -->
<details>
<summary><b><code>celery report</code> Output:</b></summary>
<p>
```
software -> celery:5.0.5 (singularity) kombu:5.0.2 py:3.6.9
billiard:3.6.4.0 py-amqp:5.0.6
platform -> system:Linux arch:64bit, ELF
kernel version:4.15.0-140-generic imp:CPython
loader -> celery.loaders.default.Loader
settings -> transport:amqp results:mongodb+srv://md_app_user:**@md-mongo.privatecircle.co/master_docs
accept_content: ['json']
broker_url: 'amqp://md_app_user:********@****************:5672//master_docs'
default_timezone: 'Asia/Kolkata'
imports: ['tasks']
result_backend: 'mongodb+srv://md_app_user:********@******************/master_docs'
result_serializer: 'json'
task_serializer: 'json'
timezone: 'Asia/Kolkata'
deprecated_settings: None
```
</p>
</details>
# Steps to Reproduce
## Required Dependencies
<!-- Please fill the required dependencies to reproduce this issue -->
* **Minimal Python Version**: 3.6.9
* **Minimal Celery Version**: 5.0.3
* **Minimal Kombu Version**: 5.0.2
* **Minimal Broker Version**: RabbitMQ 3.8.11
* **Minimal Result Backend Version**: Mongo 4.4
* **Minimal OS and/or Kernel Version**: Ubuntu 18.04.5 (Linux kernel 4.15.0-140)
* **Minimal Broker Client Version**: N/A or Unknown
* **Minimal Result Backend Client Version**: N/A or Unknown
### Python Packages
<!-- Please fill the contents of pip freeze below -->
<details>
<summary><b><code>pip freeze</code> Output:</b></summary>
<p>
```
amqp==5.0.6
backcall==0.2.0
billiard==3.6.4.0
boto3==1.17.51
botocore==1.20.51
cached-property==1.5.2
cchardet==2.1.7
celery==5.0.5
certifi==2020.12.5
chardet==4.0.0
click==7.1.2
click-didyoumean==0.0.3
click-plugins==1.1.1
click-repl==0.1.6
decorator==5.0.6
dnspython==2.1.0
idna==2.10
importlib-metadata==3.10.0
ipython==7.16.1
ipython-genutils==0.2.0
jedi==0.17.2
jmespath==0.10.0
kombu==5.0.2
lxml==4.6.3
parso==0.7.1
pexpect==4.8.0
pickleshare==0.7.5
prompt-toolkit==3.0.18
ptyprocess==0.7.0
Pygments==2.8.1
pymongo==3.11.3
python-dateutil==2.8.1
python-magic==0.4.22
pytz==2021.1
requests==2.25.1
s3transfer==0.3.6
six==1.15.0
traitlets==4.3.3
typing-extensions==3.7.4.3
urllib3==1.26.4
vine==5.0.0
wcwidth==0.2.5
zipp==3.4.1
```
</p>
</details>
### Other Dependencies
<!--
Please provide system dependencies, configuration files
and other dependency information if applicable
-->
<details>
<p>
N/A
</p>
</details>
## Minimally Reproducible Test Case
<!--
Please provide a reproducible test case.
Refer to the Reporting Bugs section in our contribution guide.
We prefer submitting test cases in the form of a PR to our integration test suite.
If you can provide one, please mention the PR number below.
If not, please attach the most minimal code example required to reproduce the issue below.
If the test case is too large, please include a link to a gist or a repository below.
-->
<details>
<p>
```python
# test.py
import celery
app = celery.Celery('proj')
```
```shell
$ celery -A test amqp repl
> exchange.declare
```
</p>
</details>
# Expected Behavior
<!-- Describe in detail what you expect to happen -->
The AMQP interactive shell should accept this command and execute it and then prompt for the next command.
# Actual Behavior
<!--
Describe in detail what actually happened.
Please include a backtrace and surround it with triple backticks (```).
In addition, include the Celery daemon logs, the broker logs,
the result backend logs and system logs below if they will help us debug
the issue.
-->
```
Traceback (most recent call last):
File "/home/privatecircle/.virtualenvs/mca_document_manager/bin/celery", line 8, in <module>
sys.exit(main())
File "/home/privatecircle/.virtualenvs/mca_document_manager/lib/python3.6/site-packages/celery/__main__.py", line 15, in main
sys.exit(_main())
File "/home/privatecircle/.virtualenvs/mca_document_manager/lib/python3.6/site-packages/celery/bin/celery.py", line 213, in main
return celery(auto_envvar_prefix="CELERY")
File "/home/privatecircle/.virtualenvs/mca_document_manager/lib/python3.6/site-packages/click/core.py", line 829, in __call__
return self.main(*args, **kwargs)
File "/home/privatecircle/.virtualenvs/mca_document_manager/lib/python3.6/site-packages/click/core.py", line 782, in main
rv = self.invoke(ctx)
File "/home/privatecircle/.virtualenvs/mca_document_manager/lib/python3.6/site-packages/click/core.py", line 1259, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File "/home/privatecircle/.virtualenvs/mca_document_manager/lib/python3.6/site-packages/click/core.py", line 1259, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File "/home/privatecircle/.virtualenvs/mca_document_manager/lib/python3.6/site-packages/click/core.py", line 1066, in invoke
return ctx.invoke(self.callback, **ctx.params)
File "/home/privatecircle/.virtualenvs/mca_document_manager/lib/python3.6/site-packages/click/core.py", line 610, in invoke
return callback(*args, **kwargs)
File "/home/privatecircle/.virtualenvs/mca_document_manager/lib/python3.6/site-packages/click/decorators.py", line 21, in new_func
return f(get_current_context(), *args, **kwargs)
File "/home/privatecircle/.virtualenvs/mca_document_manager/lib/python3.6/site-packages/click_repl/__init__.py", line 248, in repl
group.invoke(ctx)
File "/home/privatecircle/.virtualenvs/mca_document_manager/lib/python3.6/site-packages/click/core.py", line 1256, in invoke
Command.invoke(self, ctx)
File "/home/privatecircle/.virtualenvs/mca_document_manager/lib/python3.6/site-packages/click/core.py", line 1066, in invoke
return ctx.invoke(self.callback, **ctx.params)
File "/home/privatecircle/.virtualenvs/mca_document_manager/lib/python3.6/site-packages/click/core.py", line 610, in invoke
return callback(*args, **kwargs)
File "/home/privatecircle/.virtualenvs/mca_document_manager/lib/python3.6/site-packages/click/decorators.py", line 21, in new_func
return f(get_current_context(), *args, **kwargs)
File "/home/privatecircle/.virtualenvs/mca_document_manager/lib/python3.6/site-packages/celery/bin/base.py", line 120, in caller
app = ctx.obj.app
AttributeError: 'AMQPContext' object has no attribute 'app'
```
| [
{
"content": "\"\"\"AMQP 0.9.1 REPL.\"\"\"\n\nimport pprint\n\nimport click\nfrom amqp import Connection, Message\nfrom click_repl import register_repl\n\n__all__ = ('amqp',)\n\nfrom celery.bin.base import handle_preload_options\n\n\ndef dump_message(message):\n if message is None:\n return 'No messages in queue. basic.publish something.'\n return {'body': message.body,\n 'properties': message.properties,\n 'delivery_info': message.delivery_info}\n\n\nclass AMQPContext:\n def __init__(self, cli_context):\n self.cli_context = cli_context\n self.connection = self.cli_context.app.connection()\n self.channel = None\n self.reconnect()\n\n def respond(self, retval):\n if isinstance(retval, str):\n self.cli_context.echo(retval)\n else:\n self.cli_context.echo(pprint.pformat(retval))\n\n def echo_error(self, exception):\n self.cli_context.error(f'{self.cli_context.ERROR}: {exception}')\n\n def echo_ok(self):\n self.cli_context.echo(self.cli_context.OK)\n\n def reconnect(self):\n if self.connection:\n self.connection.close()\n else:\n self.connection = self.cli_context.app.connection()\n\n self.cli_context.echo(f'-> connecting to {self.connection.as_uri()}.')\n try:\n self.connection.connect()\n except (ConnectionRefusedError, ConnectionResetError) as e:\n self.echo_error(e)\n else:\n self.cli_context.secho('-> connected.', fg='green', bold=True)\n self.channel = self.connection.default_channel\n\n\[email protected](invoke_without_command=True)\[email protected]_context\n@handle_preload_options\ndef amqp(ctx):\n \"\"\"AMQP Administration Shell.\n\n Also works for non-AMQP transports (but not ones that\n store declarations in memory).\n \"\"\"\n if not isinstance(ctx.obj, AMQPContext):\n ctx.obj = AMQPContext(ctx.obj)\n\n\[email protected](name='exchange.declare')\[email protected]('exchange',\n type=str)\[email protected]('type',\n type=str)\[email protected]('passive',\n type=bool,\n default=False)\[email protected]('durable',\n type=bool,\n default=False)\[email protected]('auto_delete',\n type=bool,\n default=False)\[email protected]_obj\ndef exchange_declare(amqp_context, exchange, type, passive, durable,\n auto_delete):\n if amqp_context.channel is None:\n amqp_context.echo_error('Not connected to broker. Please retry...')\n amqp_context.reconnect()\n else:\n try:\n amqp_context.channel.exchange_declare(exchange=exchange,\n type=type,\n passive=passive,\n durable=durable,\n auto_delete=auto_delete)\n except Exception as e:\n amqp_context.echo_error(e)\n amqp_context.reconnect()\n else:\n amqp_context.echo_ok()\n\n\[email protected](name='exchange.delete')\[email protected]('exchange',\n type=str)\[email protected]('if_unused',\n type=bool)\[email protected]_obj\ndef exchange_delete(amqp_context, exchange, if_unused):\n if amqp_context.channel is None:\n amqp_context.echo_error('Not connected to broker. Please retry...')\n amqp_context.reconnect()\n else:\n try:\n amqp_context.channel.exchange_delete(exchange=exchange,\n if_unused=if_unused)\n except Exception as e:\n amqp_context.echo_error(e)\n amqp_context.reconnect()\n else:\n amqp_context.echo_ok()\n\n\[email protected](name='queue.bind')\[email protected]('queue',\n type=str)\[email protected]('exchange',\n type=str)\[email protected]('routing_key',\n type=str)\[email protected]_obj\ndef queue_bind(amqp_context, queue, exchange, routing_key):\n if amqp_context.channel is None:\n amqp_context.echo_error('Not connected to broker. Please retry...')\n amqp_context.reconnect()\n else:\n try:\n amqp_context.channel.queue_bind(queue=queue,\n exchange=exchange,\n routing_key=routing_key)\n except Exception as e:\n amqp_context.echo_error(e)\n amqp_context.reconnect()\n else:\n amqp_context.echo_ok()\n\n\[email protected](name='queue.declare')\[email protected]('queue',\n type=str)\[email protected]('passive',\n type=bool,\n default=False)\[email protected]('durable',\n type=bool,\n default=False)\[email protected]('auto_delete',\n type=bool,\n default=False)\[email protected]_obj\ndef queue_declare(amqp_context, queue, passive, durable, auto_delete):\n if amqp_context.channel is None:\n amqp_context.echo_error('Not connected to broker. Please retry...')\n amqp_context.reconnect()\n else:\n try:\n retval = amqp_context.channel.queue_declare(queue=queue,\n passive=passive,\n durable=durable,\n auto_delete=auto_delete)\n except Exception as e:\n amqp_context.echo_error(e)\n amqp_context.reconnect()\n else:\n amqp_context.cli_context.secho(\n 'queue:{} messages:{} consumers:{}'.format(*retval),\n fg='cyan', bold=True)\n amqp_context.echo_ok()\n\n\[email protected](name='queue.delete')\[email protected]('queue',\n type=str)\[email protected]('if_unused',\n type=bool,\n default=False)\[email protected]('if_empty',\n type=bool,\n default=False)\[email protected]_obj\ndef queue_delete(amqp_context, queue, if_unused, if_empty):\n if amqp_context.channel is None:\n amqp_context.echo_error('Not connected to broker. Please retry...')\n amqp_context.reconnect()\n else:\n try:\n retval = amqp_context.channel.queue_delete(queue=queue,\n if_unused=if_unused,\n if_empty=if_empty)\n except Exception as e:\n amqp_context.echo_error(e)\n amqp_context.reconnect()\n else:\n amqp_context.cli_context.secho(\n f'{retval} messages deleted.',\n fg='cyan', bold=True)\n amqp_context.echo_ok()\n\n\[email protected](name='queue.purge')\[email protected]('queue',\n type=str)\[email protected]_obj\ndef queue_purge(amqp_context, queue):\n if amqp_context.channel is None:\n amqp_context.echo_error('Not connected to broker. Please retry...')\n amqp_context.reconnect()\n else:\n try:\n retval = amqp_context.channel.queue_purge(queue=queue)\n except Exception as e:\n amqp_context.echo_error(e)\n amqp_context.reconnect()\n else:\n amqp_context.cli_context.secho(\n f'{retval} messages deleted.',\n fg='cyan', bold=True)\n amqp_context.echo_ok()\n\n\[email protected](name='basic.get')\[email protected]('queue',\n type=str)\[email protected]('no_ack',\n type=bool,\n default=False)\[email protected]_obj\ndef basic_get(amqp_context, queue, no_ack):\n if amqp_context.channel is None:\n amqp_context.echo_error('Not connected to broker. Please retry...')\n amqp_context.reconnect()\n else:\n try:\n message = amqp_context.channel.basic_get(queue, no_ack=no_ack)\n except Exception as e:\n amqp_context.echo_error(e)\n amqp_context.reconnect()\n else:\n amqp_context.respond(dump_message(message))\n amqp_context.echo_ok()\n\n\[email protected](name='basic.publish')\[email protected]('msg',\n type=str)\[email protected]('exchange',\n type=str)\[email protected]('routing_key',\n type=str)\[email protected]('mandatory',\n type=bool,\n default=False)\[email protected]('immediate',\n type=bool,\n default=False)\[email protected]_obj\ndef basic_publish(amqp_context, msg, exchange, routing_key, mandatory,\n immediate):\n if amqp_context.channel is None:\n amqp_context.echo_error('Not connected to broker. Please retry...')\n amqp_context.reconnect()\n else:\n # XXX Hack to fix Issue #2013\n if isinstance(amqp_context.connection.connection, Connection):\n msg = Message(msg)\n try:\n amqp_context.channel.basic_publish(msg,\n exchange=exchange,\n routing_key=routing_key,\n mandatory=mandatory,\n immediate=immediate)\n except Exception as e:\n amqp_context.echo_error(e)\n amqp_context.reconnect()\n else:\n amqp_context.echo_ok()\n\n\[email protected](name='basic.ack')\[email protected]('delivery_tag',\n type=int)\[email protected]_obj\ndef basic_ack(amqp_context, delivery_tag):\n if amqp_context.channel is None:\n amqp_context.echo_error('Not connected to broker. Please retry...')\n amqp_context.reconnect()\n else:\n try:\n amqp_context.channel.basic_ack(delivery_tag)\n except Exception as e:\n amqp_context.echo_error(e)\n amqp_context.reconnect()\n else:\n amqp_context.echo_ok()\n\n\nrepl = register_repl(amqp)\n",
"path": "celery/bin/amqp.py"
}
] | [
{
"content": "\"\"\"AMQP 0.9.1 REPL.\"\"\"\n\nimport pprint\n\nimport click\nfrom amqp import Connection, Message\nfrom click_repl import register_repl\n\n__all__ = ('amqp',)\n\nfrom celery.bin.base import handle_preload_options\n\n\ndef dump_message(message):\n if message is None:\n return 'No messages in queue. basic.publish something.'\n return {'body': message.body,\n 'properties': message.properties,\n 'delivery_info': message.delivery_info}\n\n\nclass AMQPContext:\n def __init__(self, cli_context):\n self.cli_context = cli_context\n self.connection = self.cli_context.app.connection()\n self.channel = None\n self.reconnect()\n \n @property\n def app(self):\n return self.cli_context.app\n\n def respond(self, retval):\n if isinstance(retval, str):\n self.cli_context.echo(retval)\n else:\n self.cli_context.echo(pprint.pformat(retval))\n\n def echo_error(self, exception):\n self.cli_context.error(f'{self.cli_context.ERROR}: {exception}')\n\n def echo_ok(self):\n self.cli_context.echo(self.cli_context.OK)\n\n def reconnect(self):\n if self.connection:\n self.connection.close()\n else:\n self.connection = self.cli_context.app.connection()\n\n self.cli_context.echo(f'-> connecting to {self.connection.as_uri()}.')\n try:\n self.connection.connect()\n except (ConnectionRefusedError, ConnectionResetError) as e:\n self.echo_error(e)\n else:\n self.cli_context.secho('-> connected.', fg='green', bold=True)\n self.channel = self.connection.default_channel\n\n\[email protected](invoke_without_command=True)\[email protected]_context\n@handle_preload_options\ndef amqp(ctx):\n \"\"\"AMQP Administration Shell.\n\n Also works for non-AMQP transports (but not ones that\n store declarations in memory).\n \"\"\"\n if not isinstance(ctx.obj, AMQPContext):\n ctx.obj = AMQPContext(ctx.obj)\n\n\[email protected](name='exchange.declare')\[email protected]('exchange',\n type=str)\[email protected]('type',\n type=str)\[email protected]('passive',\n type=bool,\n default=False)\[email protected]('durable',\n type=bool,\n default=False)\[email protected]('auto_delete',\n type=bool,\n default=False)\[email protected]_obj\ndef exchange_declare(amqp_context, exchange, type, passive, durable,\n auto_delete):\n if amqp_context.channel is None:\n amqp_context.echo_error('Not connected to broker. Please retry...')\n amqp_context.reconnect()\n else:\n try:\n amqp_context.channel.exchange_declare(exchange=exchange,\n type=type,\n passive=passive,\n durable=durable,\n auto_delete=auto_delete)\n except Exception as e:\n amqp_context.echo_error(e)\n amqp_context.reconnect()\n else:\n amqp_context.echo_ok()\n\n\[email protected](name='exchange.delete')\[email protected]('exchange',\n type=str)\[email protected]('if_unused',\n type=bool)\[email protected]_obj\ndef exchange_delete(amqp_context, exchange, if_unused):\n if amqp_context.channel is None:\n amqp_context.echo_error('Not connected to broker. Please retry...')\n amqp_context.reconnect()\n else:\n try:\n amqp_context.channel.exchange_delete(exchange=exchange,\n if_unused=if_unused)\n except Exception as e:\n amqp_context.echo_error(e)\n amqp_context.reconnect()\n else:\n amqp_context.echo_ok()\n\n\[email protected](name='queue.bind')\[email protected]('queue',\n type=str)\[email protected]('exchange',\n type=str)\[email protected]('routing_key',\n type=str)\[email protected]_obj\ndef queue_bind(amqp_context, queue, exchange, routing_key):\n if amqp_context.channel is None:\n amqp_context.echo_error('Not connected to broker. Please retry...')\n amqp_context.reconnect()\n else:\n try:\n amqp_context.channel.queue_bind(queue=queue,\n exchange=exchange,\n routing_key=routing_key)\n except Exception as e:\n amqp_context.echo_error(e)\n amqp_context.reconnect()\n else:\n amqp_context.echo_ok()\n\n\[email protected](name='queue.declare')\[email protected]('queue',\n type=str)\[email protected]('passive',\n type=bool,\n default=False)\[email protected]('durable',\n type=bool,\n default=False)\[email protected]('auto_delete',\n type=bool,\n default=False)\[email protected]_obj\ndef queue_declare(amqp_context, queue, passive, durable, auto_delete):\n if amqp_context.channel is None:\n amqp_context.echo_error('Not connected to broker. Please retry...')\n amqp_context.reconnect()\n else:\n try:\n retval = amqp_context.channel.queue_declare(queue=queue,\n passive=passive,\n durable=durable,\n auto_delete=auto_delete)\n except Exception as e:\n amqp_context.echo_error(e)\n amqp_context.reconnect()\n else:\n amqp_context.cli_context.secho(\n 'queue:{} messages:{} consumers:{}'.format(*retval),\n fg='cyan', bold=True)\n amqp_context.echo_ok()\n\n\[email protected](name='queue.delete')\[email protected]('queue',\n type=str)\[email protected]('if_unused',\n type=bool,\n default=False)\[email protected]('if_empty',\n type=bool,\n default=False)\[email protected]_obj\ndef queue_delete(amqp_context, queue, if_unused, if_empty):\n if amqp_context.channel is None:\n amqp_context.echo_error('Not connected to broker. Please retry...')\n amqp_context.reconnect()\n else:\n try:\n retval = amqp_context.channel.queue_delete(queue=queue,\n if_unused=if_unused,\n if_empty=if_empty)\n except Exception as e:\n amqp_context.echo_error(e)\n amqp_context.reconnect()\n else:\n amqp_context.cli_context.secho(\n f'{retval} messages deleted.',\n fg='cyan', bold=True)\n amqp_context.echo_ok()\n\n\[email protected](name='queue.purge')\[email protected]('queue',\n type=str)\[email protected]_obj\ndef queue_purge(amqp_context, queue):\n if amqp_context.channel is None:\n amqp_context.echo_error('Not connected to broker. Please retry...')\n amqp_context.reconnect()\n else:\n try:\n retval = amqp_context.channel.queue_purge(queue=queue)\n except Exception as e:\n amqp_context.echo_error(e)\n amqp_context.reconnect()\n else:\n amqp_context.cli_context.secho(\n f'{retval} messages deleted.',\n fg='cyan', bold=True)\n amqp_context.echo_ok()\n\n\[email protected](name='basic.get')\[email protected]('queue',\n type=str)\[email protected]('no_ack',\n type=bool,\n default=False)\[email protected]_obj\ndef basic_get(amqp_context, queue, no_ack):\n if amqp_context.channel is None:\n amqp_context.echo_error('Not connected to broker. Please retry...')\n amqp_context.reconnect()\n else:\n try:\n message = amqp_context.channel.basic_get(queue, no_ack=no_ack)\n except Exception as e:\n amqp_context.echo_error(e)\n amqp_context.reconnect()\n else:\n amqp_context.respond(dump_message(message))\n amqp_context.echo_ok()\n\n\[email protected](name='basic.publish')\[email protected]('msg',\n type=str)\[email protected]('exchange',\n type=str)\[email protected]('routing_key',\n type=str)\[email protected]('mandatory',\n type=bool,\n default=False)\[email protected]('immediate',\n type=bool,\n default=False)\[email protected]_obj\ndef basic_publish(amqp_context, msg, exchange, routing_key, mandatory,\n immediate):\n if amqp_context.channel is None:\n amqp_context.echo_error('Not connected to broker. Please retry...')\n amqp_context.reconnect()\n else:\n # XXX Hack to fix Issue #2013\n if isinstance(amqp_context.connection.connection, Connection):\n msg = Message(msg)\n try:\n amqp_context.channel.basic_publish(msg,\n exchange=exchange,\n routing_key=routing_key,\n mandatory=mandatory,\n immediate=immediate)\n except Exception as e:\n amqp_context.echo_error(e)\n amqp_context.reconnect()\n else:\n amqp_context.echo_ok()\n\n\[email protected](name='basic.ack')\[email protected]('delivery_tag',\n type=int)\[email protected]_obj\ndef basic_ack(amqp_context, delivery_tag):\n if amqp_context.channel is None:\n amqp_context.echo_error('Not connected to broker. Please retry...')\n amqp_context.reconnect()\n else:\n try:\n amqp_context.channel.basic_ack(delivery_tag)\n except Exception as e:\n amqp_context.echo_error(e)\n amqp_context.reconnect()\n else:\n amqp_context.echo_ok()\n\n\nrepl = register_repl(amqp)\n",
"path": "celery/bin/amqp.py"
}
] | diff --git a/celery/bin/amqp.py b/celery/bin/amqp.py
index ab8ab5f0100..29c625281ed 100644
--- a/celery/bin/amqp.py
+++ b/celery/bin/amqp.py
@@ -25,6 +25,10 @@ def __init__(self, cli_context):
self.connection = self.cli_context.app.connection()
self.channel = None
self.reconnect()
+
+ @property
+ def app(self):
+ return self.cli_context.app
def respond(self, retval):
if isinstance(retval, str):
|
iterative__dvc-3428 | add: empty files add broken when cache mode is hardlinks
## DVC Version
```
DVC version: 0.86.5+f67314.mod
Python version: 3.7.6
Platform: Darwin-18.2.0-x86_64-i386-64bit
Binary: False
Package: None
Cache: reflink - supported, hardlink - supported, symlink - supported
Filesystem type (cache directory): ('apfs', '/dev/disk1s1')
Filesystem type (workspace): ('apfs', '/dev/disk1s1')
```
## Reproduce
```
dvc config cache.type hardlink
touch foo
dvc add foo
```
outputs:
```
ERROR: no possible cache types left to try out.
```
| [
{
"content": "import errno\nimport logging\nimport os\nimport stat\nfrom concurrent.futures import ThreadPoolExecutor\nfrom functools import partial\n\nfrom shortuuid import uuid\n\nfrom dvc.compat import fspath_py35\nfrom dvc.exceptions import DvcException, DownloadError, UploadError\nfrom dvc.path_info import PathInfo\nfrom dvc.progress import Tqdm\nfrom dvc.remote.base import RemoteBASE, STATUS_MAP\nfrom dvc.remote.base import STATUS_DELETED, STATUS_MISSING, STATUS_NEW\nfrom dvc.scheme import Schemes\nfrom dvc.scm.tree import is_working_tree\nfrom dvc.system import System\nfrom dvc.utils import file_md5, relpath, tmp_fname\nfrom dvc.utils.fs import copyfile, move, makedirs, remove, walk_files\n\nlogger = logging.getLogger(__name__)\n\n\nclass RemoteLOCAL(RemoteBASE):\n scheme = Schemes.LOCAL\n path_cls = PathInfo\n PARAM_CHECKSUM = \"md5\"\n PARAM_PATH = \"path\"\n\n UNPACKED_DIR_SUFFIX = \".unpacked\"\n\n DEFAULT_CACHE_TYPES = [\"reflink\", \"copy\"]\n\n SHARED_MODE_MAP = {None: (0o644, 0o755), \"group\": (0o664, 0o775)}\n\n def __init__(self, repo, config):\n super().__init__(repo, config)\n self.protected = config.get(\"protected\", False)\n\n shared = config.get(\"shared\")\n self._file_mode, self._dir_mode = self.SHARED_MODE_MAP[shared]\n\n if self.protected:\n # cache files are set to be read-only for everyone\n self._file_mode = stat.S_IREAD | stat.S_IRGRP | stat.S_IROTH\n\n self.cache_dir = config.get(\"url\")\n self._dir_info = {}\n\n @property\n def state(self):\n return self.repo.state\n\n @property\n def cache_dir(self):\n return self.path_info.fspath if self.path_info else None\n\n @cache_dir.setter\n def cache_dir(self, value):\n self.path_info = PathInfo(value) if value else None\n\n @classmethod\n def supported(cls, config):\n return True\n\n def list_cache_paths(self):\n assert self.path_info is not None\n return walk_files(self.path_info)\n\n def get(self, md5):\n if not md5:\n return None\n\n return self.checksum_to_path_info(md5).url\n\n @staticmethod\n def exists(path_info):\n assert path_info.scheme == \"local\"\n return os.path.lexists(fspath_py35(path_info))\n\n def makedirs(self, path_info):\n makedirs(path_info, exist_ok=True, mode=self._dir_mode)\n\n def already_cached(self, path_info):\n assert path_info.scheme in [\"\", \"local\"]\n\n current_md5 = self.get_checksum(path_info)\n\n if not current_md5:\n return False\n\n return not self.changed_cache(current_md5)\n\n def is_empty(self, path_info):\n path = path_info.fspath\n\n if self.isfile(path_info) and os.path.getsize(path) == 0:\n return True\n\n if self.isdir(path_info) and len(os.listdir(path)) == 0:\n return True\n\n return False\n\n @staticmethod\n def isfile(path_info):\n return os.path.isfile(fspath_py35(path_info))\n\n @staticmethod\n def isdir(path_info):\n return os.path.isdir(fspath_py35(path_info))\n\n def iscopy(self, path_info):\n return not (\n System.is_symlink(path_info) or System.is_hardlink(path_info)\n )\n\n @staticmethod\n def getsize(path_info):\n return os.path.getsize(fspath_py35(path_info))\n\n def walk_files(self, path_info):\n assert is_working_tree(self.repo.tree)\n\n for fname in self.repo.tree.walk_files(path_info):\n yield PathInfo(fname)\n\n def get_file_checksum(self, path_info):\n return file_md5(path_info)[0]\n\n def remove(self, path_info):\n if path_info.scheme != \"local\":\n raise NotImplementedError\n\n if self.exists(path_info):\n remove(path_info.fspath)\n\n def move(self, from_info, to_info):\n if from_info.scheme != \"local\" or to_info.scheme != \"local\":\n raise NotImplementedError\n\n self.makedirs(to_info.parent)\n\n if self.isfile(from_info):\n mode = self._file_mode\n else:\n mode = self._dir_mode\n\n move(from_info, to_info, mode=mode)\n\n def copy(self, from_info, to_info):\n tmp_info = to_info.parent / tmp_fname(to_info.name)\n try:\n System.copy(from_info, tmp_info)\n os.rename(fspath_py35(tmp_info), fspath_py35(to_info))\n except Exception:\n self.remove(tmp_info)\n raise\n\n @staticmethod\n def symlink(from_info, to_info):\n System.symlink(from_info, to_info)\n\n @staticmethod\n def is_symlink(path_info):\n return System.is_symlink(path_info)\n\n def hardlink(self, from_info, to_info):\n # If there are a lot of empty files (which happens a lot in datasets),\n # and the cache type is `hardlink`, we might reach link limits and\n # will get something like: `too many links error`\n #\n # This is because all those empty files will have the same checksum\n # (i.e. 68b329da9893e34099c7d8ad5cb9c940), therefore, they will be\n # linked to the same file in the cache.\n #\n # From https://en.wikipedia.org/wiki/Hard_link\n # * ext4 limits the number of hard links on a file to 65,000\n # * Windows with NTFS has a limit of 1024 hard links on a file\n #\n # That's why we simply create an empty file rather than a link.\n if self.getsize(from_info) == 0:\n self.open(to_info, \"w\").close()\n\n logger.debug(\n \"Created empty file: {src} -> {dest}\".format(\n src=str(from_info), dest=str(to_info)\n )\n )\n return\n\n System.hardlink(from_info, to_info)\n\n @staticmethod\n def is_hardlink(path_info):\n return System.is_hardlink(path_info)\n\n @staticmethod\n def reflink(from_info, to_info):\n System.reflink(from_info, to_info)\n\n def cache_exists(self, checksums, jobs=None, name=None):\n return [\n checksum\n for checksum in Tqdm(\n checksums,\n unit=\"file\",\n desc=\"Querying \"\n + (\"cache in \" + name if name else \"local cache\"),\n )\n if not self.changed_cache_file(checksum)\n ]\n\n def _upload(\n self, from_file, to_info, name=None, no_progress_bar=False, **_kwargs\n ):\n makedirs(to_info.parent, exist_ok=True)\n\n tmp_file = tmp_fname(to_info)\n copyfile(\n from_file, tmp_file, name=name, no_progress_bar=no_progress_bar\n )\n os.rename(tmp_file, fspath_py35(to_info))\n\n def _download(\n self, from_info, to_file, name=None, no_progress_bar=False, **_kwargs\n ):\n copyfile(\n from_info, to_file, no_progress_bar=no_progress_bar, name=name\n )\n\n @staticmethod\n def open(path_info, mode=\"r\", encoding=None):\n return open(fspath_py35(path_info), mode=mode, encoding=encoding)\n\n def status(\n self,\n named_cache,\n remote,\n jobs=None,\n show_checksums=False,\n download=False,\n ):\n logger.debug(\n \"Preparing to collect status from {}\".format(remote.path_info)\n )\n md5s = list(named_cache[self.scheme])\n\n logger.debug(\"Collecting information from local cache...\")\n local_exists = self.cache_exists(md5s, jobs=jobs, name=self.cache_dir)\n\n # This is a performance optimization. We can safely assume that,\n # if the resources that we want to fetch are already cached,\n # there's no need to check the remote storage for the existence of\n # those files.\n if download and sorted(local_exists) == sorted(md5s):\n remote_exists = local_exists\n else:\n logger.debug(\"Collecting information from remote cache...\")\n remote_exists = list(\n remote.cache_exists(\n md5s, jobs=jobs, name=str(remote.path_info)\n )\n )\n\n ret = {\n checksum: {\"name\": checksum if show_checksums else \" \".join(names)}\n for checksum, names in named_cache[self.scheme].items()\n }\n self._fill_statuses(ret, local_exists, remote_exists)\n\n self._log_missing_caches(ret)\n\n return ret\n\n @staticmethod\n def _fill_statuses(checksum_info_dir, local_exists, remote_exists):\n # Using sets because they are way faster for lookups\n local = set(local_exists)\n remote = set(remote_exists)\n\n for md5, info in checksum_info_dir.items():\n status = STATUS_MAP[(md5 in local, md5 in remote)]\n info[\"status\"] = status\n\n def _get_plans(self, download, remote, status_info, status):\n cache = []\n path_infos = []\n names = []\n for md5, info in Tqdm(\n status_info.items(), desc=\"Analysing status\", unit=\"file\"\n ):\n if info[\"status\"] == status:\n cache.append(self.checksum_to_path_info(md5))\n path_infos.append(remote.checksum_to_path_info(md5))\n names.append(info[\"name\"])\n\n if download:\n to_infos = cache\n from_infos = path_infos\n else:\n to_infos = path_infos\n from_infos = cache\n\n return from_infos, to_infos, names\n\n def _process(\n self,\n named_cache,\n remote,\n jobs=None,\n show_checksums=False,\n download=False,\n ):\n logger.debug(\n \"Preparing to {} '{}'\".format(\n \"download data from\" if download else \"upload data to\",\n remote.path_info,\n )\n )\n\n if download:\n func = partial(\n remote.download,\n dir_mode=self._dir_mode,\n file_mode=self._file_mode,\n )\n status = STATUS_DELETED\n else:\n func = remote.upload\n status = STATUS_NEW\n\n if jobs is None:\n jobs = remote.JOBS\n\n status_info = self.status(\n named_cache,\n remote,\n jobs=jobs,\n show_checksums=show_checksums,\n download=download,\n )\n\n plans = self._get_plans(download, remote, status_info, status)\n\n if len(plans[0]) == 0:\n return 0\n\n if jobs > 1:\n with ThreadPoolExecutor(max_workers=jobs) as executor:\n fails = sum(executor.map(func, *plans))\n else:\n fails = sum(map(func, *plans))\n\n if fails:\n if download:\n raise DownloadError(fails)\n raise UploadError(fails)\n\n return len(plans[0])\n\n def push(self, named_cache, remote, jobs=None, show_checksums=False):\n return self._process(\n named_cache,\n remote,\n jobs=jobs,\n show_checksums=show_checksums,\n download=False,\n )\n\n def pull(self, named_cache, remote, jobs=None, show_checksums=False):\n return self._process(\n named_cache,\n remote,\n jobs=jobs,\n show_checksums=show_checksums,\n download=True,\n )\n\n @staticmethod\n def _log_missing_caches(checksum_info_dict):\n missing_caches = [\n (md5, info)\n for md5, info in checksum_info_dict.items()\n if info[\"status\"] == STATUS_MISSING\n ]\n if missing_caches:\n missing_desc = \"\".join(\n \"\\nname: {}, md5: {}\".format(info[\"name\"], md5)\n for md5, info in missing_caches\n )\n msg = (\n \"Some of the cache files do not exist neither locally \"\n \"nor on remote. Missing cache files: {}\".format(missing_desc)\n )\n logger.warning(msg)\n\n @staticmethod\n def _unprotect_file(path):\n if System.is_symlink(path) or System.is_hardlink(path):\n logger.debug(\"Unprotecting '{}'\".format(path))\n tmp = os.path.join(os.path.dirname(path), \".\" + uuid())\n\n # The operations order is important here - if some application\n # would access the file during the process of copyfile then it\n # would get only the part of file. So, at first, the file should be\n # copied with the temporary name, and then original file should be\n # replaced by new.\n copyfile(path, tmp, name=\"Unprotecting '{}'\".format(relpath(path)))\n remove(path)\n os.rename(tmp, path)\n\n else:\n logger.debug(\n \"Skipping copying for '{}', since it is not \"\n \"a symlink or a hardlink.\".format(path)\n )\n\n os.chmod(path, os.stat(path).st_mode | stat.S_IWRITE)\n\n def _unprotect_dir(self, path):\n assert is_working_tree(self.repo.tree)\n\n for fname in self.repo.tree.walk_files(path):\n RemoteLOCAL._unprotect_file(fname)\n\n def unprotect(self, path_info):\n path = path_info.fspath\n if not os.path.exists(path):\n raise DvcException(\n \"can't unprotect non-existing data '{}'\".format(path)\n )\n\n if os.path.isdir(path):\n self._unprotect_dir(path)\n else:\n RemoteLOCAL._unprotect_file(path)\n\n @staticmethod\n def protect(path_info):\n path = fspath_py35(path_info)\n mode = stat.S_IREAD | stat.S_IRGRP | stat.S_IROTH\n\n try:\n os.chmod(path, mode)\n except OSError as exc:\n # In share cache scenario, we might not own the cache file, so we\n # need to check if cache file is already protected.\n if exc.errno not in [errno.EPERM, errno.EACCES]:\n raise\n\n actual = os.stat(path).st_mode\n if actual & mode != mode:\n raise\n\n def _get_unpacked_dir_path_info(self, checksum):\n info = self.checksum_to_path_info(checksum)\n return info.with_name(info.name + self.UNPACKED_DIR_SUFFIX)\n\n def _remove_unpacked_dir(self, checksum):\n path_info = self._get_unpacked_dir_path_info(checksum)\n self.remove(path_info)\n\n def _path_info_changed(self, path_info):\n if self.exists(path_info) and self.state.get(path_info):\n return False\n return True\n\n def _update_unpacked_dir(self, checksum):\n unpacked_dir_info = self._get_unpacked_dir_path_info(checksum)\n\n if not self._path_info_changed(unpacked_dir_info):\n return\n\n self.remove(unpacked_dir_info)\n\n try:\n dir_info = self.get_dir_cache(checksum)\n self._create_unpacked_dir(checksum, dir_info, unpacked_dir_info)\n except DvcException:\n logger.warning(\"Could not create '{}'\".format(unpacked_dir_info))\n\n self.remove(unpacked_dir_info)\n\n def _create_unpacked_dir(self, checksum, dir_info, unpacked_dir_info):\n self.makedirs(unpacked_dir_info)\n\n for entry in Tqdm(dir_info, desc=\"Creating unpacked dir\", unit=\"file\"):\n entry_cache_info = self.checksum_to_path_info(\n entry[self.PARAM_CHECKSUM]\n )\n relative_path = entry[self.PARAM_RELPATH]\n # In shared cache mode some cache files might not be owned by the\n # user, so we need to use symlinks because, unless\n # /proc/sys/fs/protected_hardlinks is disabled, the user is not\n # allowed to create hardlinks to files that he doesn't own.\n link_types = [\"hardlink\", \"symlink\"]\n self._link(\n entry_cache_info, unpacked_dir_info / relative_path, link_types\n )\n\n self.state.save(unpacked_dir_info, checksum)\n\n def _changed_unpacked_dir(self, checksum):\n status_unpacked_dir_info = self._get_unpacked_dir_path_info(checksum)\n\n return not self.state.get(status_unpacked_dir_info)\n\n def _get_unpacked_dir_names(self, checksums):\n unpacked = set()\n for c in checksums:\n if self.is_dir_checksum(c):\n unpacked.add(c + self.UNPACKED_DIR_SUFFIX)\n return unpacked\n",
"path": "dvc/remote/local.py"
}
] | [
{
"content": "import errno\nimport logging\nimport os\nimport stat\nfrom concurrent.futures import ThreadPoolExecutor\nfrom functools import partial\n\nfrom shortuuid import uuid\n\nfrom dvc.compat import fspath_py35\nfrom dvc.exceptions import DvcException, DownloadError, UploadError\nfrom dvc.path_info import PathInfo\nfrom dvc.progress import Tqdm\nfrom dvc.remote.base import RemoteBASE, STATUS_MAP\nfrom dvc.remote.base import STATUS_DELETED, STATUS_MISSING, STATUS_NEW\nfrom dvc.scheme import Schemes\nfrom dvc.scm.tree import is_working_tree\nfrom dvc.system import System\nfrom dvc.utils import file_md5, relpath, tmp_fname\nfrom dvc.utils.fs import copyfile, move, makedirs, remove, walk_files\n\nlogger = logging.getLogger(__name__)\n\n\nclass RemoteLOCAL(RemoteBASE):\n scheme = Schemes.LOCAL\n path_cls = PathInfo\n PARAM_CHECKSUM = \"md5\"\n PARAM_PATH = \"path\"\n\n UNPACKED_DIR_SUFFIX = \".unpacked\"\n\n DEFAULT_CACHE_TYPES = [\"reflink\", \"copy\"]\n\n SHARED_MODE_MAP = {None: (0o644, 0o755), \"group\": (0o664, 0o775)}\n\n def __init__(self, repo, config):\n super().__init__(repo, config)\n self.protected = config.get(\"protected\", False)\n\n shared = config.get(\"shared\")\n self._file_mode, self._dir_mode = self.SHARED_MODE_MAP[shared]\n\n if self.protected:\n # cache files are set to be read-only for everyone\n self._file_mode = stat.S_IREAD | stat.S_IRGRP | stat.S_IROTH\n\n self.cache_dir = config.get(\"url\")\n self._dir_info = {}\n\n @property\n def state(self):\n return self.repo.state\n\n @property\n def cache_dir(self):\n return self.path_info.fspath if self.path_info else None\n\n @cache_dir.setter\n def cache_dir(self, value):\n self.path_info = PathInfo(value) if value else None\n\n @classmethod\n def supported(cls, config):\n return True\n\n def list_cache_paths(self):\n assert self.path_info is not None\n return walk_files(self.path_info)\n\n def get(self, md5):\n if not md5:\n return None\n\n return self.checksum_to_path_info(md5).url\n\n @staticmethod\n def exists(path_info):\n assert path_info.scheme == \"local\"\n return os.path.lexists(fspath_py35(path_info))\n\n def makedirs(self, path_info):\n makedirs(path_info, exist_ok=True, mode=self._dir_mode)\n\n def already_cached(self, path_info):\n assert path_info.scheme in [\"\", \"local\"]\n\n current_md5 = self.get_checksum(path_info)\n\n if not current_md5:\n return False\n\n return not self.changed_cache(current_md5)\n\n def _verify_link(self, path_info, link_type):\n if link_type == \"hardlink\" and self.getsize(path_info) == 0:\n return\n\n super()._verify_link(path_info, link_type)\n\n def is_empty(self, path_info):\n path = path_info.fspath\n\n if self.isfile(path_info) and os.path.getsize(path) == 0:\n return True\n\n if self.isdir(path_info) and len(os.listdir(path)) == 0:\n return True\n\n return False\n\n @staticmethod\n def isfile(path_info):\n return os.path.isfile(fspath_py35(path_info))\n\n @staticmethod\n def isdir(path_info):\n return os.path.isdir(fspath_py35(path_info))\n\n def iscopy(self, path_info):\n return not (\n System.is_symlink(path_info) or System.is_hardlink(path_info)\n )\n\n @staticmethod\n def getsize(path_info):\n return os.path.getsize(fspath_py35(path_info))\n\n def walk_files(self, path_info):\n assert is_working_tree(self.repo.tree)\n\n for fname in self.repo.tree.walk_files(path_info):\n yield PathInfo(fname)\n\n def get_file_checksum(self, path_info):\n return file_md5(path_info)[0]\n\n def remove(self, path_info):\n if path_info.scheme != \"local\":\n raise NotImplementedError\n\n if self.exists(path_info):\n remove(path_info.fspath)\n\n def move(self, from_info, to_info):\n if from_info.scheme != \"local\" or to_info.scheme != \"local\":\n raise NotImplementedError\n\n self.makedirs(to_info.parent)\n\n if self.isfile(from_info):\n mode = self._file_mode\n else:\n mode = self._dir_mode\n\n move(from_info, to_info, mode=mode)\n\n def copy(self, from_info, to_info):\n tmp_info = to_info.parent / tmp_fname(to_info.name)\n try:\n System.copy(from_info, tmp_info)\n os.rename(fspath_py35(tmp_info), fspath_py35(to_info))\n except Exception:\n self.remove(tmp_info)\n raise\n\n @staticmethod\n def symlink(from_info, to_info):\n System.symlink(from_info, to_info)\n\n @staticmethod\n def is_symlink(path_info):\n return System.is_symlink(path_info)\n\n def hardlink(self, from_info, to_info):\n # If there are a lot of empty files (which happens a lot in datasets),\n # and the cache type is `hardlink`, we might reach link limits and\n # will get something like: `too many links error`\n #\n # This is because all those empty files will have the same checksum\n # (i.e. 68b329da9893e34099c7d8ad5cb9c940), therefore, they will be\n # linked to the same file in the cache.\n #\n # From https://en.wikipedia.org/wiki/Hard_link\n # * ext4 limits the number of hard links on a file to 65,000\n # * Windows with NTFS has a limit of 1024 hard links on a file\n #\n # That's why we simply create an empty file rather than a link.\n if self.getsize(from_info) == 0:\n self.open(to_info, \"w\").close()\n\n logger.debug(\n \"Created empty file: {src} -> {dest}\".format(\n src=str(from_info), dest=str(to_info)\n )\n )\n return\n\n System.hardlink(from_info, to_info)\n\n @staticmethod\n def is_hardlink(path_info):\n return System.is_hardlink(path_info)\n\n @staticmethod\n def reflink(from_info, to_info):\n System.reflink(from_info, to_info)\n\n def cache_exists(self, checksums, jobs=None, name=None):\n return [\n checksum\n for checksum in Tqdm(\n checksums,\n unit=\"file\",\n desc=\"Querying \"\n + (\"cache in \" + name if name else \"local cache\"),\n )\n if not self.changed_cache_file(checksum)\n ]\n\n def _upload(\n self, from_file, to_info, name=None, no_progress_bar=False, **_kwargs\n ):\n makedirs(to_info.parent, exist_ok=True)\n\n tmp_file = tmp_fname(to_info)\n copyfile(\n from_file, tmp_file, name=name, no_progress_bar=no_progress_bar\n )\n os.rename(tmp_file, fspath_py35(to_info))\n\n def _download(\n self, from_info, to_file, name=None, no_progress_bar=False, **_kwargs\n ):\n copyfile(\n from_info, to_file, no_progress_bar=no_progress_bar, name=name\n )\n\n @staticmethod\n def open(path_info, mode=\"r\", encoding=None):\n return open(fspath_py35(path_info), mode=mode, encoding=encoding)\n\n def status(\n self,\n named_cache,\n remote,\n jobs=None,\n show_checksums=False,\n download=False,\n ):\n logger.debug(\n \"Preparing to collect status from {}\".format(remote.path_info)\n )\n md5s = list(named_cache[self.scheme])\n\n logger.debug(\"Collecting information from local cache...\")\n local_exists = self.cache_exists(md5s, jobs=jobs, name=self.cache_dir)\n\n # This is a performance optimization. We can safely assume that,\n # if the resources that we want to fetch are already cached,\n # there's no need to check the remote storage for the existence of\n # those files.\n if download and sorted(local_exists) == sorted(md5s):\n remote_exists = local_exists\n else:\n logger.debug(\"Collecting information from remote cache...\")\n remote_exists = list(\n remote.cache_exists(\n md5s, jobs=jobs, name=str(remote.path_info)\n )\n )\n\n ret = {\n checksum: {\"name\": checksum if show_checksums else \" \".join(names)}\n for checksum, names in named_cache[self.scheme].items()\n }\n self._fill_statuses(ret, local_exists, remote_exists)\n\n self._log_missing_caches(ret)\n\n return ret\n\n @staticmethod\n def _fill_statuses(checksum_info_dir, local_exists, remote_exists):\n # Using sets because they are way faster for lookups\n local = set(local_exists)\n remote = set(remote_exists)\n\n for md5, info in checksum_info_dir.items():\n status = STATUS_MAP[(md5 in local, md5 in remote)]\n info[\"status\"] = status\n\n def _get_plans(self, download, remote, status_info, status):\n cache = []\n path_infos = []\n names = []\n for md5, info in Tqdm(\n status_info.items(), desc=\"Analysing status\", unit=\"file\"\n ):\n if info[\"status\"] == status:\n cache.append(self.checksum_to_path_info(md5))\n path_infos.append(remote.checksum_to_path_info(md5))\n names.append(info[\"name\"])\n\n if download:\n to_infos = cache\n from_infos = path_infos\n else:\n to_infos = path_infos\n from_infos = cache\n\n return from_infos, to_infos, names\n\n def _process(\n self,\n named_cache,\n remote,\n jobs=None,\n show_checksums=False,\n download=False,\n ):\n logger.debug(\n \"Preparing to {} '{}'\".format(\n \"download data from\" if download else \"upload data to\",\n remote.path_info,\n )\n )\n\n if download:\n func = partial(\n remote.download,\n dir_mode=self._dir_mode,\n file_mode=self._file_mode,\n )\n status = STATUS_DELETED\n else:\n func = remote.upload\n status = STATUS_NEW\n\n if jobs is None:\n jobs = remote.JOBS\n\n status_info = self.status(\n named_cache,\n remote,\n jobs=jobs,\n show_checksums=show_checksums,\n download=download,\n )\n\n plans = self._get_plans(download, remote, status_info, status)\n\n if len(plans[0]) == 0:\n return 0\n\n if jobs > 1:\n with ThreadPoolExecutor(max_workers=jobs) as executor:\n fails = sum(executor.map(func, *plans))\n else:\n fails = sum(map(func, *plans))\n\n if fails:\n if download:\n raise DownloadError(fails)\n raise UploadError(fails)\n\n return len(plans[0])\n\n def push(self, named_cache, remote, jobs=None, show_checksums=False):\n return self._process(\n named_cache,\n remote,\n jobs=jobs,\n show_checksums=show_checksums,\n download=False,\n )\n\n def pull(self, named_cache, remote, jobs=None, show_checksums=False):\n return self._process(\n named_cache,\n remote,\n jobs=jobs,\n show_checksums=show_checksums,\n download=True,\n )\n\n @staticmethod\n def _log_missing_caches(checksum_info_dict):\n missing_caches = [\n (md5, info)\n for md5, info in checksum_info_dict.items()\n if info[\"status\"] == STATUS_MISSING\n ]\n if missing_caches:\n missing_desc = \"\".join(\n \"\\nname: {}, md5: {}\".format(info[\"name\"], md5)\n for md5, info in missing_caches\n )\n msg = (\n \"Some of the cache files do not exist neither locally \"\n \"nor on remote. Missing cache files: {}\".format(missing_desc)\n )\n logger.warning(msg)\n\n @staticmethod\n def _unprotect_file(path):\n if System.is_symlink(path) or System.is_hardlink(path):\n logger.debug(\"Unprotecting '{}'\".format(path))\n tmp = os.path.join(os.path.dirname(path), \".\" + uuid())\n\n # The operations order is important here - if some application\n # would access the file during the process of copyfile then it\n # would get only the part of file. So, at first, the file should be\n # copied with the temporary name, and then original file should be\n # replaced by new.\n copyfile(path, tmp, name=\"Unprotecting '{}'\".format(relpath(path)))\n remove(path)\n os.rename(tmp, path)\n\n else:\n logger.debug(\n \"Skipping copying for '{}', since it is not \"\n \"a symlink or a hardlink.\".format(path)\n )\n\n os.chmod(path, os.stat(path).st_mode | stat.S_IWRITE)\n\n def _unprotect_dir(self, path):\n assert is_working_tree(self.repo.tree)\n\n for fname in self.repo.tree.walk_files(path):\n RemoteLOCAL._unprotect_file(fname)\n\n def unprotect(self, path_info):\n path = path_info.fspath\n if not os.path.exists(path):\n raise DvcException(\n \"can't unprotect non-existing data '{}'\".format(path)\n )\n\n if os.path.isdir(path):\n self._unprotect_dir(path)\n else:\n RemoteLOCAL._unprotect_file(path)\n\n @staticmethod\n def protect(path_info):\n path = fspath_py35(path_info)\n mode = stat.S_IREAD | stat.S_IRGRP | stat.S_IROTH\n\n try:\n os.chmod(path, mode)\n except OSError as exc:\n # In share cache scenario, we might not own the cache file, so we\n # need to check if cache file is already protected.\n if exc.errno not in [errno.EPERM, errno.EACCES]:\n raise\n\n actual = os.stat(path).st_mode\n if actual & mode != mode:\n raise\n\n def _get_unpacked_dir_path_info(self, checksum):\n info = self.checksum_to_path_info(checksum)\n return info.with_name(info.name + self.UNPACKED_DIR_SUFFIX)\n\n def _remove_unpacked_dir(self, checksum):\n path_info = self._get_unpacked_dir_path_info(checksum)\n self.remove(path_info)\n\n def _path_info_changed(self, path_info):\n if self.exists(path_info) and self.state.get(path_info):\n return False\n return True\n\n def _update_unpacked_dir(self, checksum):\n unpacked_dir_info = self._get_unpacked_dir_path_info(checksum)\n\n if not self._path_info_changed(unpacked_dir_info):\n return\n\n self.remove(unpacked_dir_info)\n\n try:\n dir_info = self.get_dir_cache(checksum)\n self._create_unpacked_dir(checksum, dir_info, unpacked_dir_info)\n except DvcException:\n logger.warning(\"Could not create '{}'\".format(unpacked_dir_info))\n\n self.remove(unpacked_dir_info)\n\n def _create_unpacked_dir(self, checksum, dir_info, unpacked_dir_info):\n self.makedirs(unpacked_dir_info)\n\n for entry in Tqdm(dir_info, desc=\"Creating unpacked dir\", unit=\"file\"):\n entry_cache_info = self.checksum_to_path_info(\n entry[self.PARAM_CHECKSUM]\n )\n relative_path = entry[self.PARAM_RELPATH]\n # In shared cache mode some cache files might not be owned by the\n # user, so we need to use symlinks because, unless\n # /proc/sys/fs/protected_hardlinks is disabled, the user is not\n # allowed to create hardlinks to files that he doesn't own.\n link_types = [\"hardlink\", \"symlink\"]\n self._link(\n entry_cache_info, unpacked_dir_info / relative_path, link_types\n )\n\n self.state.save(unpacked_dir_info, checksum)\n\n def _changed_unpacked_dir(self, checksum):\n status_unpacked_dir_info = self._get_unpacked_dir_path_info(checksum)\n\n return not self.state.get(status_unpacked_dir_info)\n\n def _get_unpacked_dir_names(self, checksums):\n unpacked = set()\n for c in checksums:\n if self.is_dir_checksum(c):\n unpacked.add(c + self.UNPACKED_DIR_SUFFIX)\n return unpacked\n",
"path": "dvc/remote/local.py"
}
] | diff --git a/dvc/remote/local.py b/dvc/remote/local.py
index 24725c526d..3e62e0628e 100644
--- a/dvc/remote/local.py
+++ b/dvc/remote/local.py
@@ -92,6 +92,12 @@ def already_cached(self, path_info):
return not self.changed_cache(current_md5)
+ def _verify_link(self, path_info, link_type):
+ if link_type == "hardlink" and self.getsize(path_info) == 0:
+ return
+
+ super()._verify_link(path_info, link_type)
+
def is_empty(self, path_info):
path = path_info.fspath
diff --git a/tests/func/test_add.py b/tests/func/test_add.py
index 95407ee7d0..9d966452dd 100644
--- a/tests/func/test_add.py
+++ b/tests/func/test_add.py
@@ -7,7 +7,7 @@
import colorama
import pytest
-from mock import patch
+from mock import patch, call
import dvc as dvc_module
from dvc.cache import Cache
@@ -662,3 +662,36 @@ def test_not_raises_on_re_add(tmp_dir, dvc):
tmp_dir.gen({"file2": "file2 content", "file": "modified file"})
dvc.add(["file2", "file"])
+
+
[email protected]("link", ["hardlink", "symlink", "copy"])
+def test_add_empty_files(tmp_dir, dvc, link):
+ file = "foo"
+ dvc.cache.local.cache_types = [link]
+ stages = tmp_dir.dvc_gen(file, "")
+
+ assert (tmp_dir / file).exists()
+ assert (tmp_dir / (file + Stage.STAGE_FILE_SUFFIX)).exists()
+ assert os.path.exists(stages[0].outs[0].cache_path)
+
+
+def test_add_optimization_for_hardlink_on_empty_files(tmp_dir, dvc, mocker):
+ dvc.cache.local.cache_types = ["hardlink"]
+ tmp_dir.gen({"foo": "", "bar": "", "lorem": "lorem", "ipsum": "ipsum"})
+ m = mocker.spy(RemoteLOCAL, "is_hardlink")
+ stages = dvc.add(["foo", "bar", "lorem", "ipsum"])
+
+ assert m.call_count == 1
+ assert m.call_args != call(tmp_dir / "foo")
+ assert m.call_args != call(tmp_dir / "bar")
+
+ for stage in stages[:2]:
+ # hardlinks are not created for empty files
+ assert not System.is_hardlink(stage.outs[0].path_info)
+
+ for stage in stages[2:]:
+ assert System.is_hardlink(stage.outs[0].path_info)
+
+ for stage in stages:
+ assert os.path.exists(stage.path)
+ assert os.path.exists(stage.outs[0].cache_path)
|
OpenNMT__OpenNMT-py-471 | tools/embeddings_to_torch.py fails when some word features are included in the preprocessing step
When there are some word features appended to each token in the source text, it seems that the `tools/embeddings_to_torch.py` script cannot extract correct vocabulary from the dataset.
```
$ python tools/embeddings_to_torch.py -emb_file /path/to/word.vectors.txt -dict_file dataset.vocab.pt -output dataset.emb
Traceback (most recent call last):
File "tools/embeddings_to_torch.py", line 94, in <module>
main()
File "tools/embeddings_to_torch.py", line 62, in main
enc_vocab, dec_vocab = get_vocabs(opt.dict_file)
File "tools/embeddings_to_torch.py", line 24, in get_vocabs
enc_vocab, dec_vocab = [vocab[1] for vocab in vocabs]
ValueError: too many values to unpack (expected 2)
```
| [
{
"content": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nfrom __future__ import print_function\nfrom __future__ import division\nimport six\nimport sys\nimport numpy as np\nimport argparse\nimport torch\n\nparser = argparse.ArgumentParser(description='embeddings_to_torch.py')\nparser.add_argument('-emb_file', required=True,\n help=\"Embeddings from this file\")\nparser.add_argument('-output_file', required=True,\n help=\"Output file for the prepared data\")\nparser.add_argument('-dict_file', required=True,\n help=\"Dictionary file\")\nparser.add_argument('-verbose', action=\"store_true\", default=False)\nopt = parser.parse_args()\n\n\ndef get_vocabs(dict_file):\n vocabs = torch.load(dict_file)\n enc_vocab, dec_vocab = [vocab[1] for vocab in vocabs]\n\n print(\"From: %s\" % dict_file)\n print(\"\\t* source vocab: %d words\" % len(enc_vocab))\n print(\"\\t* target vocab: %d words\" % len(dec_vocab))\n\n return enc_vocab, dec_vocab\n\n\ndef get_embeddings(file):\n embs = dict()\n for l in open(file, 'rb').readlines():\n l_split = l.decode('utf8').strip().split()\n if len(l_split) == 2:\n continue\n embs[l_split[0]] = [float(em) for em in l_split[1:]]\n print(\"Got {} embeddings from {}\".format(len(embs), file))\n\n return embs\n\n\ndef match_embeddings(vocab, emb):\n dim = len(six.next(six.itervalues(emb)))\n filtered_embeddings = np.zeros((len(vocab), dim))\n count = {\"match\": 0, \"miss\": 0}\n for w, w_id in vocab.stoi.items():\n if w in emb:\n filtered_embeddings[w_id] = emb[w]\n count['match'] += 1\n else:\n if opt.verbose:\n print(u\"not found:\\t{}\".format(w), file=sys.stderr)\n count['miss'] += 1\n\n return torch.Tensor(filtered_embeddings), count\n\n\ndef main():\n enc_vocab, dec_vocab = get_vocabs(opt.dict_file)\n embeddings = get_embeddings(opt.emb_file)\n\n filtered_enc_embeddings, enc_count = match_embeddings(enc_vocab,\n embeddings)\n filtered_dec_embeddings, dec_count = match_embeddings(dec_vocab,\n embeddings)\n\n print(\"\\nMatching: \")\n match_percent = [_['match'] / (_['match'] + _['miss']) * 100\n for _ in [enc_count, dec_count]]\n print(\"\\t* enc: %d match, %d missing, (%.2f%%)\" % (enc_count['match'],\n enc_count['miss'],\n match_percent[0]))\n print(\"\\t* dec: %d match, %d missing, (%.2f%%)\" % (dec_count['match'],\n dec_count['miss'],\n match_percent[1]))\n\n print(\"\\nFiltered embeddings:\")\n print(\"\\t* enc: \", filtered_enc_embeddings.size())\n print(\"\\t* dec: \", filtered_dec_embeddings.size())\n\n enc_output_file = opt.output_file + \".enc.pt\"\n dec_output_file = opt.output_file + \".dec.pt\"\n print(\"\\nSaving embedding as:\\n\\t* enc: %s\\n\\t* dec: %s\"\n % (enc_output_file, dec_output_file))\n torch.save(filtered_enc_embeddings, enc_output_file)\n torch.save(filtered_dec_embeddings, dec_output_file)\n print(\"\\nDone.\")\n\n\nif __name__ == \"__main__\":\n main()\n",
"path": "tools/embeddings_to_torch.py"
}
] | [
{
"content": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nfrom __future__ import print_function\nfrom __future__ import division\nimport six\nimport sys\nimport numpy as np\nimport argparse\nimport torch\n\nparser = argparse.ArgumentParser(description='embeddings_to_torch.py')\nparser.add_argument('-emb_file', required=True,\n help=\"Embeddings from this file\")\nparser.add_argument('-output_file', required=True,\n help=\"Output file for the prepared data\")\nparser.add_argument('-dict_file', required=True,\n help=\"Dictionary file\")\nparser.add_argument('-verbose', action=\"store_true\", default=False)\nopt = parser.parse_args()\n\n\ndef get_vocabs(dict_file):\n vocabs = torch.load(dict_file)\n enc_vocab, dec_vocab = vocabs[0][1], vocabs[-1][1]\n\n print(\"From: %s\" % dict_file)\n print(\"\\t* source vocab: %d words\" % len(enc_vocab))\n print(\"\\t* target vocab: %d words\" % len(dec_vocab))\n\n return enc_vocab, dec_vocab\n\n\ndef get_embeddings(file):\n embs = dict()\n for l in open(file, 'rb').readlines():\n l_split = l.decode('utf8').strip().split()\n if len(l_split) == 2:\n continue\n embs[l_split[0]] = [float(em) for em in l_split[1:]]\n print(\"Got {} embeddings from {}\".format(len(embs), file))\n\n return embs\n\n\ndef match_embeddings(vocab, emb):\n dim = len(six.next(six.itervalues(emb)))\n filtered_embeddings = np.zeros((len(vocab), dim))\n count = {\"match\": 0, \"miss\": 0}\n for w, w_id in vocab.stoi.items():\n if w in emb:\n filtered_embeddings[w_id] = emb[w]\n count['match'] += 1\n else:\n if opt.verbose:\n print(u\"not found:\\t{}\".format(w), file=sys.stderr)\n count['miss'] += 1\n\n return torch.Tensor(filtered_embeddings), count\n\n\ndef main():\n enc_vocab, dec_vocab = get_vocabs(opt.dict_file)\n embeddings = get_embeddings(opt.emb_file)\n\n filtered_enc_embeddings, enc_count = match_embeddings(enc_vocab,\n embeddings)\n filtered_dec_embeddings, dec_count = match_embeddings(dec_vocab,\n embeddings)\n\n print(\"\\nMatching: \")\n match_percent = [_['match'] / (_['match'] + _['miss']) * 100\n for _ in [enc_count, dec_count]]\n print(\"\\t* enc: %d match, %d missing, (%.2f%%)\" % (enc_count['match'],\n enc_count['miss'],\n match_percent[0]))\n print(\"\\t* dec: %d match, %d missing, (%.2f%%)\" % (dec_count['match'],\n dec_count['miss'],\n match_percent[1]))\n\n print(\"\\nFiltered embeddings:\")\n print(\"\\t* enc: \", filtered_enc_embeddings.size())\n print(\"\\t* dec: \", filtered_dec_embeddings.size())\n\n enc_output_file = opt.output_file + \".enc.pt\"\n dec_output_file = opt.output_file + \".dec.pt\"\n print(\"\\nSaving embedding as:\\n\\t* enc: %s\\n\\t* dec: %s\"\n % (enc_output_file, dec_output_file))\n torch.save(filtered_enc_embeddings, enc_output_file)\n torch.save(filtered_dec_embeddings, dec_output_file)\n print(\"\\nDone.\")\n\n\nif __name__ == \"__main__\":\n main()\n",
"path": "tools/embeddings_to_torch.py"
}
] | diff --git a/tools/embeddings_to_torch.py b/tools/embeddings_to_torch.py
index 7ec470ef51..4b4294930e 100755
--- a/tools/embeddings_to_torch.py
+++ b/tools/embeddings_to_torch.py
@@ -21,7 +21,7 @@
def get_vocabs(dict_file):
vocabs = torch.load(dict_file)
- enc_vocab, dec_vocab = [vocab[1] for vocab in vocabs]
+ enc_vocab, dec_vocab = vocabs[0][1], vocabs[-1][1]
print("From: %s" % dict_file)
print("\t* source vocab: %d words" % len(enc_vocab))
|
zestedesavoir__zds-site-6488 | Possible erreur 500 à la résolution d'une alerte sur un contenu qui n'est plus public
Rapporté par Sentry. J'ai eu du mal à comprendre comment le bug a pu se produire, mais j'ai réussi à le reproduire (d'une façon peut-être un peu tirée par les cheveux...).
**Comment reproduire ?**
1. Se connecter en tant que `user1`
2. Signaler un billet
3. Se connecter en tant que `staff`
4. Ouvrir la page du billet signalé dans deux onglets différents
5. Sur un des onglets, dépublier le billet
6. Sur l'autre onglet, résoudre l'alerte (ne pas recharger la page juste avant, le billet n'est en fait plus publié, c'est là qu'est l'astuce)
Une erreur 500 va alors apparaître. Elle provient d'ici : https://github.com/zestedesavoir/zds-site/blob/c06671c4901a95c30f31067c09d5e4526fd86575/zds/tutorialv2/views/alerts.py#L88
Le contenu n'a plus de version publique, donc plus d'URL publique, et `content.get_absolute_url_online()` renvoie alors `''`.
La correction de ce bug passe sans doute par la vérification si l'alerte est déjà résolue ou si le contenu signalé a bien une version publique : si l'une de ces conditions n'est pas remplie, une erreur 404 devrait être levée.
| [
{
"content": "from datetime import datetime\n\nfrom django.contrib import messages\nfrom django.core.exceptions import PermissionDenied\nfrom django.contrib.auth.mixins import LoginRequiredMixin\nfrom django.db import transaction\nfrom django.http import Http404\nfrom django.shortcuts import get_object_or_404, redirect\nfrom django.template.loader import render_to_string\nfrom django.utils.decorators import method_decorator\nfrom django.utils.translation import gettext_lazy as _\nfrom django.views.generic import FormView\n\nfrom zds.tutorialv2.models import TYPE_CHOICES_DICT\nfrom zds.tutorialv2.models.database import PublishableContent\nfrom zds.utils.models import Alert\n\n\nclass SendContentAlert(LoginRequiredMixin, FormView):\n http_method_names = [\"post\"]\n\n @method_decorator(transaction.atomic)\n def dispatch(self, *args, **kwargs):\n return super().dispatch(*args, **kwargs)\n\n def post(self, request, *args, **kwargs):\n try:\n content_pk = int(self.kwargs[\"pk\"])\n except (KeyError, ValueError):\n raise Http404(\"Identifiant manquant ou conversion en entier impossible.\")\n content = get_object_or_404(PublishableContent, pk=content_pk)\n\n if len(request.POST[\"signal_text\"].strip()) == 0:\n messages.error(request, _(\"La raison du signalement ne peut pas être vide.\"))\n else:\n alert = Alert(\n author=request.user,\n content=content,\n scope=\"CONTENT\",\n text=request.POST[\"signal_text\"],\n pubdate=datetime.now(),\n )\n alert.save()\n\n human_content_type = TYPE_CHOICES_DICT[content.type].lower()\n messages.success(self.request, _(\"Ce {} a bien été signalé aux modérateurs.\").format(human_content_type))\n\n return redirect(content.get_absolute_url_online())\n\n\nclass SolveContentAlert(LoginRequiredMixin, FormView):\n @method_decorator(transaction.atomic)\n def dispatch(self, *args, **kwargs):\n return super().dispatch(*args, **kwargs)\n\n def post(self, request, *args, **kwargs):\n if not request.user.has_perm(\"tutorialv2.change_contentreaction\"):\n raise PermissionDenied\n try:\n alert = get_object_or_404(Alert, pk=int(request.POST[\"alert_pk\"]))\n content = PublishableContent.objects.get(pk=alert.content.id)\n except (KeyError, ValueError):\n raise Http404(\"L'alerte n'existe pas.\")\n\n resolve_reason = \"\"\n msg_title = \"\"\n msg_content = \"\"\n if \"text\" in request.POST and request.POST[\"text\"]:\n resolve_reason = request.POST[\"text\"]\n authors = alert.content.authors.values_list(\"username\", flat=True)\n authors = \", \".join(authors)\n msg_title = _(\"Résolution d'alerte : {0}\").format(content.title)\n msg_content = render_to_string(\n \"tutorialv2/messages/resolve_alert.md\",\n {\n \"content\": content,\n \"url\": content.get_absolute_url_online(),\n \"name\": alert.author.username,\n \"target_name\": authors,\n \"modo_name\": request.user.username,\n \"message\": \"\\n\".join([\"> \" + line for line in resolve_reason.split(\"\\n\")]),\n \"alert_text\": \"\\n\".join([\"> \" + line for line in alert.text.split(\"\\n\")]),\n },\n )\n alert.solve(request.user, resolve_reason, msg_title, msg_content)\n\n messages.success(self.request, _(\"L'alerte a bien été résolue.\"))\n return redirect(content.get_absolute_url_online())\n",
"path": "zds/tutorialv2/views/alerts.py"
}
] | [
{
"content": "from datetime import datetime\n\nfrom django.contrib import messages\nfrom django.core.exceptions import PermissionDenied\nfrom django.contrib.auth.mixins import LoginRequiredMixin\nfrom django.db import transaction\nfrom django.http import Http404\nfrom django.shortcuts import get_object_or_404, redirect\nfrom django.template.loader import render_to_string\nfrom django.utils.decorators import method_decorator\nfrom django.utils.translation import gettext_lazy as _\nfrom django.views.generic import FormView\n\nfrom zds.tutorialv2.models import TYPE_CHOICES_DICT\nfrom zds.tutorialv2.models.database import PublishableContent\nfrom zds.utils.models import Alert\n\n\nclass SendContentAlert(LoginRequiredMixin, FormView):\n http_method_names = [\"post\"]\n\n @method_decorator(transaction.atomic)\n def dispatch(self, *args, **kwargs):\n return super().dispatch(*args, **kwargs)\n\n def post(self, request, *args, **kwargs):\n try:\n content_pk = int(self.kwargs[\"pk\"])\n except (KeyError, ValueError):\n raise Http404(\"Identifiant manquant ou conversion en entier impossible.\")\n content = get_object_or_404(PublishableContent, pk=content_pk)\n\n if len(request.POST[\"signal_text\"].strip()) == 0:\n messages.error(request, _(\"La raison du signalement ne peut pas être vide.\"))\n else:\n alert = Alert(\n author=request.user,\n content=content,\n scope=\"CONTENT\",\n text=request.POST[\"signal_text\"],\n pubdate=datetime.now(),\n )\n alert.save()\n\n human_content_type = TYPE_CHOICES_DICT[content.type].lower()\n messages.success(self.request, _(\"Ce {} a bien été signalé aux modérateurs.\").format(human_content_type))\n\n return redirect(content.get_absolute_url_online())\n\n\nclass SolveContentAlert(LoginRequiredMixin, FormView):\n @method_decorator(transaction.atomic)\n def dispatch(self, *args, **kwargs):\n return super().dispatch(*args, **kwargs)\n\n def post(self, request, *args, **kwargs):\n if not request.user.has_perm(\"tutorialv2.change_contentreaction\"):\n raise PermissionDenied\n try:\n alert = get_object_or_404(Alert, pk=int(request.POST[\"alert_pk\"]))\n content = PublishableContent.objects.get(pk=alert.content.id)\n except (KeyError, ValueError):\n raise Http404(\"L'alerte n'existe pas.\")\n\n if alert.solved:\n raise Http404(\"L'alerte a déjà été résolue.\")\n\n resolve_reason = \"\"\n msg_title = \"\"\n msg_content = \"\"\n if \"text\" in request.POST and request.POST[\"text\"]:\n resolve_reason = request.POST[\"text\"]\n authors = alert.content.authors.values_list(\"username\", flat=True)\n authors = \", \".join(authors)\n msg_title = _(\"Résolution d'alerte : {0}\").format(content.title)\n msg_content = render_to_string(\n \"tutorialv2/messages/resolve_alert.md\",\n {\n \"content\": content,\n \"url\": content.get_absolute_url_online(),\n \"name\": alert.author.username,\n \"target_name\": authors,\n \"modo_name\": request.user.username,\n \"message\": \"\\n\".join([\"> \" + line for line in resolve_reason.split(\"\\n\")]),\n \"alert_text\": \"\\n\".join([\"> \" + line for line in alert.text.split(\"\\n\")]),\n },\n )\n alert.solve(request.user, resolve_reason, msg_title, msg_content)\n\n messages.success(self.request, _(\"L'alerte a bien été résolue.\"))\n return redirect(content.get_absolute_url_online())\n",
"path": "zds/tutorialv2/views/alerts.py"
}
] | diff --git a/zds/tutorialv2/tests/tests_utils.py b/zds/tutorialv2/tests/tests_utils.py
index 4e52de2902..c3bcc62e2d 100644
--- a/zds/tutorialv2/tests/tests_utils.py
+++ b/zds/tutorialv2/tests/tests_utils.py
@@ -556,7 +556,7 @@ def test_no_alert_on_unpublish(self):
reaction = ContentReactionFactory(
related_content=published, author=ProfileFactory().user, position=1, pubdate=datetime.datetime.now()
)
- Alert.objects.create(
+ alert = Alert.objects.create(
scope="CONTENT",
comment=reaction,
text="a text",
@@ -569,6 +569,15 @@ def test_no_alert_on_unpublish(self):
unpublish_content(published, staff)
self.assertEqual(0, get_header_notifications(staff)["alerts"]["total"])
+ # Try to solve the alert anyway (related to #6478):
+ self.client.force_login(self.staff)
+ result = self.client.post(
+ reverse("content:resolve-content", kwargs={"pk": published.pk}),
+ {"alert_pk": alert.pk, "text": "Anéfé!"},
+ follow=False,
+ )
+ self.assertEqual(result.status_code, 404)
+
def tearDown(self):
super().tearDown()
PublicatorRegistry.registry = self.old_registry
diff --git a/zds/tutorialv2/views/alerts.py b/zds/tutorialv2/views/alerts.py
index d120707b1e..880e592bfa 100644
--- a/zds/tutorialv2/views/alerts.py
+++ b/zds/tutorialv2/views/alerts.py
@@ -62,6 +62,9 @@ def post(self, request, *args, **kwargs):
except (KeyError, ValueError):
raise Http404("L'alerte n'existe pas.")
+ if alert.solved:
+ raise Http404("L'alerte a déjà été résolue.")
+
resolve_reason = ""
msg_title = ""
msg_content = ""
|
zestedesavoir__zds-site-2270 | Erreur 500 lors de la recherche d'un sujet
Url incriminée : `http://beta.zestedesavoir.com/forums/sujets/recherche/`
On ne devrait jamais avoir d'erreur 500
| [
{
"content": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\nfrom datetime import datetime\nimport json\n\nfrom django.conf import settings\nfrom django.db.models import Q\nfrom django.contrib import messages\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.auth.models import User\nfrom django.core.exceptions import PermissionDenied\nfrom django.core.mail import EmailMultiAlternatives\nfrom django.core.paginator import Paginator, PageNotAnInteger, EmptyPage\nfrom django.core.urlresolvers import reverse\nfrom django.db import transaction\nfrom django.http import Http404, HttpResponse, StreamingHttpResponse\nfrom django.shortcuts import redirect, get_object_or_404, render, render_to_response\nfrom django.template import Context\nfrom django.template.loader import get_template\nfrom django.views.decorators.http import require_POST\nfrom django.utils.translation import ugettext as _\n\nfrom haystack.inputs import AutoQuery\nfrom haystack.query import SearchQuerySet\n\nfrom forms import TopicForm, PostForm, MoveTopicForm\nfrom models import Category, Forum, Topic, Post, follow, follow_by_email, never_read, \\\n mark_read, TopicFollowed, get_topics\nfrom zds.forum.models import TopicRead\nfrom zds.member.decorator import can_write_and_read_now\nfrom zds.member.views import get_client_ip\nfrom zds.utils import slugify\nfrom zds.utils.models import Alert, CommentLike, CommentDislike, Tag\nfrom zds.utils.mps import send_mp\nfrom zds.utils.paginator import paginator_range\nfrom zds.utils.templatetags.emarkdown import emarkdown\nfrom zds.utils.templatetags.topbar import top_categories\n\n\ndef index(request):\n \"\"\"Display the category list with all their forums.\"\"\"\n\n categories = top_categories(request.user)\n\n return render(request, \"forum/index.html\", {\"categories\": categories,\n \"user\": request.user,\n \"nb\": settings.ZDS_APP['forum']['topics_per_page'],\n \"page\": 1})\n\n\ndef details(request, cat_slug, forum_slug):\n \"\"\"Display the given forum and all its topics.\"\"\"\n\n forum = get_object_or_404(Forum, slug=forum_slug)\n if not forum.can_read(request.user):\n raise PermissionDenied\n if 'filter' in request.GET:\n filter = request.GET['filter']\n else:\n filter = None\n sticky_topics = get_topics(forum_pk=forum.pk, is_sticky=True, filter=filter)\n topics = get_topics(forum_pk=forum.pk, is_sticky=False, filter=filter)\n\n # Paginator\n\n paginator = Paginator(topics, settings.ZDS_APP['forum']['topics_per_page'])\n page = request.GET.get(\"page\")\n try:\n shown_topics = paginator.page(page)\n page = int(page)\n except PageNotAnInteger:\n shown_topics = paginator.page(1)\n page = 1\n except EmptyPage:\n shown_topics = paginator.page(paginator.num_pages)\n page = paginator.num_pages\n\n return render(request, \"forum/category/forum.html\", {\n \"forum\": forum,\n \"sticky_topics\": sticky_topics,\n \"topics\": shown_topics,\n \"page\": 1,\n \"pages\": paginator_range(page, paginator.num_pages),\n \"nb\": page,\n \"filter\": filter,\n })\n\n\ndef cat_details(request, cat_slug):\n \"\"\"Display the forums belonging to the given category.\"\"\"\n\n category = get_object_or_404(Category, slug=cat_slug)\n\n forums_pub = Forum.objects\\\n .filter(group__isnull=True, category__pk=category.pk)\\\n .select_related(\"category\").all()\n if request.user.is_authenticated():\n forums_prv = Forum.objects\\\n .filter(group__isnull=False,\n group__in=request.user.groups.all(),\n category__pk=category.pk)\\\n .select_related(\"category\")\\\n .all()\n forums = forums_pub | forums_prv\n else:\n forums = forums_pub\n\n return render(request, \"forum/category/index.html\", {\"category\": category,\n \"forums\": forums,\n \"nb\": settings.ZDS_APP['forum']['topics_per_page'],\n \"page\": 1})\n\n\ndef topic(request, topic_pk, topic_slug):\n \"\"\"Display a thread and its posts using a pager.\"\"\"\n\n topic = get_object_or_404(Topic, pk=topic_pk)\n if not topic.forum.can_read(request.user):\n raise PermissionDenied\n\n # Check link\n\n if not topic_slug == slugify(topic.title):\n return redirect(topic.get_absolute_url())\n\n # If the user is authenticated and has never read topic, we mark it as\n # read.\n\n if request.user.is_authenticated():\n if never_read(topic):\n mark_read(topic)\n\n # Retrieves all posts of the topic and use paginator with them.\n\n posts = \\\n Post.objects.filter(topic__pk=topic.pk) \\\n .select_related(\"author__profile\") \\\n .order_by(\"position\").all()\n last_post_pk = topic.last_message.pk\n\n # Handle pagination\n\n paginator = Paginator(posts, settings.ZDS_APP['forum']['posts_per_page'])\n\n # The category list is needed to move threads\n\n categories = Category.objects.all()\n if \"page\" in request.GET:\n try:\n page_nbr = int(request.GET[\"page\"])\n except (KeyError, ValueError):\n # problem in variable format\n raise Http404\n else:\n page_nbr = 1\n try:\n posts = paginator.page(page_nbr)\n except PageNotAnInteger:\n posts = paginator.page(1)\n except EmptyPage:\n raise Http404\n res = []\n if page_nbr != 1:\n\n # Show the last post of the previous page\n\n last_page = paginator.page(page_nbr - 1).object_list\n last_post = last_page[len(last_page) - 1]\n res.append(last_post)\n for post in posts:\n res.append(post)\n\n # Build form to send a post for the current topic.\n\n form = PostForm(topic, request.user)\n form.helper.form_action = reverse(\"zds.forum.views.answer\") + \"?sujet=\" \\\n + str(topic.pk)\n form_move = MoveTopicForm(topic=topic)\n\n return render(request, \"forum/topic/index.html\", {\n \"topic\": topic,\n \"posts\": res,\n \"categories\": categories,\n \"page\": 1,\n \"pages\": paginator_range(page_nbr, paginator.num_pages),\n \"nb\": page_nbr,\n \"last_post_pk\": last_post_pk,\n \"form\": form,\n \"form_move\": form_move,\n })\n\n\ndef get_tag_by_title(title):\n nb_bracket = 0\n current_tag = u\"\"\n current_title = u\"\"\n tags = []\n continue_parsing_tags = True\n original_title = title\n for char in title:\n\n if char == u\"[\" and nb_bracket == 0 and continue_parsing_tags:\n nb_bracket += 1\n elif nb_bracket > 0 and char != u\"]\" and continue_parsing_tags:\n current_tag = current_tag + char\n if char == u\"[\":\n nb_bracket += 1\n elif char == u\"]\" and nb_bracket > 0 and continue_parsing_tags:\n nb_bracket -= 1\n if nb_bracket == 0 and current_tag.strip() != u\"\":\n tags.append(current_tag.strip())\n current_tag = u\"\"\n elif current_tag.strip() != u\"\" and nb_bracket > 0:\n current_tag = current_tag + char\n\n elif ((char != u\"[\" and char.strip() != \"\") or not continue_parsing_tags):\n continue_parsing_tags = False\n current_title = current_title + char\n title = current_title\n # if we did not succed in parsing the tags\n if nb_bracket != 0:\n return ([], original_title)\n\n return (tags, title.strip())\n\n\n@can_write_and_read_now\n@login_required\[email protected]\ndef new(request):\n \"\"\"Creates a new topic in a forum.\"\"\"\n\n try:\n forum_pk = request.GET[\"forum\"]\n except KeyError:\n # problem in variable format\n raise Http404\n forum = get_object_or_404(Forum, pk=forum_pk)\n if not forum.can_read(request.user):\n raise PermissionDenied\n if request.method == \"POST\":\n\n # If the client is using the \"preview\" button\n\n if \"preview\" in request.POST:\n if request.is_ajax():\n content = render_to_response('misc/previsualization.part.html', {'text': request.POST['text']})\n return StreamingHttpResponse(content)\n else:\n form = TopicForm(initial={\"title\": request.POST[\"title\"],\n \"subtitle\": request.POST[\"subtitle\"],\n \"text\": request.POST[\"text\"]})\n\n return render(request, \"forum/topic/new.html\",\n {\"forum\": forum,\n \"form\": form,\n \"text\": request.POST[\"text\"]})\n form = TopicForm(request.POST)\n data = form.data\n if form.is_valid():\n\n # Treat title\n\n (tags, title) = get_tag_by_title(data[\"title\"])\n\n # Creating the thread\n n_topic = Topic()\n n_topic.forum = forum\n n_topic.title = title\n n_topic.subtitle = data[\"subtitle\"]\n n_topic.pubdate = datetime.now()\n n_topic.author = request.user\n n_topic.save()\n # add tags\n\n n_topic.add_tags(tags)\n n_topic.save()\n # Adding the first message\n\n post = Post()\n post.topic = n_topic\n post.author = request.user\n post.update_content(request.POST[\"text\"])\n post.pubdate = datetime.now()\n post.position = 1\n post.ip_address = get_client_ip(request)\n post.save()\n n_topic.last_message = post\n n_topic.save()\n\n # Follow the topic\n\n follow(n_topic)\n return redirect(n_topic.get_absolute_url())\n else:\n form = TopicForm()\n\n return render(request, \"forum/topic/new.html\", {\"forum\": forum, \"form\": form})\n\n\n@can_write_and_read_now\n@login_required\n@require_POST\[email protected]\ndef solve_alert(request):\n\n # only staff can move topic\n\n if not request.user.has_perm(\"forum.change_post\"):\n raise PermissionDenied\n\n alert = get_object_or_404(Alert, pk=request.POST[\"alert_pk\"])\n post = Post.objects.get(pk=alert.comment.id)\n\n if \"text\" in request.POST and request.POST[\"text\"] != \"\":\n bot = get_object_or_404(User, username=settings.ZDS_APP['member']['bot_account'])\n msg = \\\n (u'Bonjour {0},'\n u'Vous recevez ce message car vous avez signalé le message de *{1}*, '\n u'dans le sujet [{2}]({3}). Votre alerte a été traitée par **{4}** '\n u'et il vous a laissé le message suivant :'\n u'\\n\\n> {5}\\n\\nToute l\\'équipe de la modération vous remercie !'.format(\n alert.author.username,\n post.author.username,\n post.topic.title,\n settings.ZDS_APP['site']['url'] + post.get_absolute_url(),\n request.user.username,\n request.POST[\"text\"],))\n send_mp(\n bot,\n [alert.author],\n u\"Résolution d'alerte : {0}\".format(post.topic.title),\n \"\",\n msg,\n False,\n )\n\n alert.delete()\n messages.success(request, u\"L'alerte a bien été résolue.\")\n return redirect(post.get_absolute_url())\n\n\n@can_write_and_read_now\n@login_required\n@require_POST\[email protected]\ndef move_topic(request):\n\n # only staff can move topic\n\n if not request.user.has_perm(\"forum.change_topic\"):\n raise PermissionDenied\n try:\n topic_pk = request.GET[\"sujet\"]\n except KeyError:\n # problem in variable format\n raise Http404\n forum = get_object_or_404(Forum, pk=request.POST[\"forum\"])\n if not forum.can_read(request.user):\n raise PermissionDenied\n topic = get_object_or_404(Topic, pk=topic_pk)\n topic.forum = forum\n topic.save()\n\n # unfollow user auth\n\n followers = TopicFollowed.objects.filter(topic=topic)\n for follower in followers:\n if not forum.can_read(follower.user):\n follower.delete()\n messages.success(request,\n u\"Le sujet {0} a bien été déplacé dans {1}.\"\n .format(topic.title,\n forum.title))\n return redirect(topic.get_absolute_url())\n\n\n@can_write_and_read_now\n@login_required\n@require_POST\ndef edit(request):\n \"\"\"Edit the given topic.\"\"\"\n\n try:\n topic_pk = request.POST[\"topic\"]\n except KeyError:\n # problem in variable format\n raise Http404\n if \"page\" in request.POST:\n try:\n page = int(request.POST[\"page\"])\n except (KeyError, ValueError):\n # problem in variable format\n raise Http404\n else:\n page = 1\n\n data = request.POST\n resp = {}\n g_topic = get_object_or_404(Topic, pk=topic_pk)\n if \"follow\" in data:\n resp[\"follow\"] = follow(g_topic)\n if \"email\" in data:\n resp[\"email\"] = follow_by_email(g_topic)\n if request.user == g_topic.author \\\n or request.user.has_perm(\"forum.change_topic\"):\n if \"solved\" in data:\n g_topic.is_solved = not g_topic.is_solved\n resp[\"solved\"] = g_topic.is_solved\n if request.user.has_perm(\"forum.change_topic\"):\n\n if \"lock\" in data:\n g_topic.is_locked = data[\"lock\"] == \"true\"\n messages.success(request,\n u\"Le sujet {0} est désormais verrouillé.\"\n .format(g_topic.title))\n if 'sticky' in data:\n if data['sticky'] == 'true':\n g_topic.is_sticky = True\n messages.success(request, _(u'Le sujet « {0} » est désormais épinglé.').format(g_topic.title))\n else:\n g_topic.is_sticky = False\n messages.success(request, _(u'Le sujet « {0} » n\\'est désormais plus épinglé.').format(g_topic.title))\n if \"move\" in data:\n try:\n forum_pk = int(request.POST[\"move_target\"])\n except (KeyError, ValueError):\n # problem in variable format\n raise Http404\n forum = get_object_or_404(Forum, pk=forum_pk)\n g_topic.forum = forum\n g_topic.save()\n if request.is_ajax():\n return HttpResponse(json.dumps(resp), content_type='application/json')\n else:\n if not g_topic.forum.can_read(request.user):\n return redirect(reverse(\"zds.forum.views.index\"))\n else:\n return redirect(u\"{}?page={}\".format(g_topic.get_absolute_url(),\n page))\n\n\n@can_write_and_read_now\n@login_required\[email protected]\ndef answer(request):\n \"\"\"Adds an answer from a user to a topic.\"\"\"\n\n try:\n topic_pk = request.GET[\"sujet\"]\n except KeyError:\n # problem in variable format\n raise Http404\n\n # Retrieve current topic.\n\n g_topic = get_object_or_404(Topic, pk=topic_pk)\n if not g_topic.forum.can_read(request.user):\n raise PermissionDenied\n\n # Making sure posting is allowed\n\n if g_topic.is_locked:\n raise PermissionDenied\n\n # Check that the user isn't spamming\n\n if g_topic.antispam(request.user):\n raise PermissionDenied\n last_post_pk = g_topic.last_message.pk\n\n # Retrieve last posts of the current topic.\n posts = Post.objects.filter(topic=g_topic) \\\n .prefetch_related() \\\n .order_by(\"-position\")[:settings.ZDS_APP['forum']['posts_per_page']]\n\n # User would like preview his post or post a new post on the topic.\n\n if request.method == \"POST\":\n data = request.POST\n newpost = last_post_pk != int(data[\"last_post\"])\n\n # Using the « preview button », the « more » button or new post\n\n if \"preview\" in data or newpost:\n form = PostForm(g_topic, request.user, initial={\"text\": data[\"text\"]})\n form.helper.form_action = reverse(\"zds.forum.views.answer\") \\\n + \"?sujet=\" + str(g_topic.pk)\n if request.is_ajax():\n content = render_to_response('misc/previsualization.part.html', {'text': data['text']})\n return StreamingHttpResponse(content)\n else:\n return render(request, \"forum/post/new.html\", {\n \"text\": data[\"text\"],\n \"topic\": g_topic,\n \"posts\": posts,\n \"last_post_pk\": last_post_pk,\n \"newpost\": newpost,\n \"form\": form,\n })\n else:\n\n # Saving the message\n\n form = PostForm(g_topic, request.user, request.POST)\n if form.is_valid():\n data = form.data\n post = Post()\n post.topic = g_topic\n post.author = request.user\n post.text = data[\"text\"]\n post.text_html = emarkdown(data[\"text\"])\n post.pubdate = datetime.now()\n post.position = g_topic.get_post_count() + 1\n post.ip_address = get_client_ip(request)\n post.save()\n g_topic.last_message = post\n g_topic.save()\n # Send mail\n subject = u\"{} - Notification : {}\".format(settings.ZDS_APP['site']['abbr'],\n g_topic.title)\n from_email = \"{0} <{1}>\".format(settings.ZDS_APP['site']['litteral_name'],\n settings.ZDS_APP['site']['email_noreply'])\n followers = g_topic.get_followers_by_email()\n for follower in followers:\n receiver = follower.user\n if receiver == request.user:\n continue\n pos = post.position - 1\n last_read = TopicRead.objects.filter(\n topic=g_topic,\n post__position=pos,\n user=receiver).count()\n if last_read > 0:\n message_html = get_template('email/notification/new.html') \\\n .render(\n Context({\n 'username': receiver.username,\n 'title': g_topic.title,\n 'url': settings.ZDS_APP['site']['url'] + post.get_absolute_url(),\n 'author': request.user.username\n }))\n message_txt = get_template('email/notification/new.txt').render(\n Context({\n 'username': receiver.username,\n 'title': g_topic.title,\n 'url': settings.ZDS_APP['site']['url'] + post.get_absolute_url(),\n 'author': request.user.username\n }))\n msg = EmailMultiAlternatives(\n subject, message_txt, from_email, [\n receiver.email])\n msg.attach_alternative(message_html, \"text/html\")\n msg.send()\n\n # Follow topic on answering\n if not g_topic.is_followed(user=request.user):\n follow(g_topic)\n return redirect(post.get_absolute_url())\n else:\n return render(request, \"forum/post/new.html\", {\n \"text\": data[\"text\"],\n \"topic\": g_topic,\n \"posts\": posts,\n \"last_post_pk\": last_post_pk,\n \"newpost\": newpost,\n \"form\": form,\n })\n else:\n\n # Actions from the editor render to new.html.\n\n text = \"\"\n\n # Using the quote button\n\n if \"cite\" in request.GET:\n resp = {}\n post_cite_pk = request.GET[\"cite\"]\n post_cite = Post.objects.get(pk=post_cite_pk)\n if not post_cite.is_visible:\n raise PermissionDenied\n for line in post_cite.text.splitlines():\n text = text + \"> \" + line + \"\\n\"\n text = u\"{0}Source:[{1}]({2}{3})\".format(\n text,\n post_cite.author.username,\n settings.ZDS_APP['site']['url'],\n post_cite.get_absolute_url())\n\n if request.is_ajax():\n resp[\"text\"] = text\n return HttpResponse(json.dumps(resp), content_type='application/json')\n\n form = PostForm(g_topic, request.user, initial={\"text\": text})\n form.helper.form_action = reverse(\"zds.forum.views.answer\") \\\n + \"?sujet=\" + str(g_topic.pk)\n return render(request, \"forum/post/new.html\", {\n \"topic\": g_topic,\n \"posts\": posts,\n \"last_post_pk\": last_post_pk,\n \"form\": form,\n })\n\n\n@can_write_and_read_now\n@login_required\[email protected]\ndef edit_post(request):\n \"\"\"Edit the given user's post.\"\"\"\n\n try:\n post_pk = request.GET[\"message\"]\n except KeyError:\n # problem in variable format\n raise Http404\n\n post = get_object_or_404(Post, pk=post_pk)\n\n # check if the user can use this forum\n if not post.topic.forum.can_read(request.user):\n raise PermissionDenied\n\n if post.position <= 1:\n g_topic = get_object_or_404(Topic, pk=post.topic.pk)\n else:\n g_topic = None\n\n # Making sure the user is allowed to do that. Author of the post must to be\n # the user logged.\n if post.author != request.user \\\n and not request.user.has_perm(\"forum.change_post\") and \"signal_message\" \\\n not in request.POST:\n raise PermissionDenied\n if post.author != request.user and request.method == \"GET\" \\\n and request.user.has_perm(\"forum.change_post\"):\n messages.warning(request,\n _(u'Vous éditez ce message en tant que '\n u'modérateur (auteur : {}). Soyez encore plus '\n u'prudent lors de l\\'édition de celui-ci !')\n .format(post.author.username))\n\n if request.method == \"POST\":\n if \"delete_message\" in request.POST:\n if post.author == request.user \\\n or request.user.has_perm(\"forum.change_post\"):\n post.alerts.all().delete()\n post.is_visible = False\n\n if request.user.has_perm(\"forum.change_post\"):\n post.text_hidden = request.POST[\"text_hidden\"]\n\n post.editor = request.user\n\n messages.success(request, _(u\"Le message est désormais masqué.\"))\n elif \"show_message\" in request.POST:\n if request.user.has_perm(\"forum.change_post\"):\n post.is_visible = True\n post.text_hidden = \"\"\n elif \"signal_message\" in request.POST:\n alert = Alert()\n alert.author = request.user\n alert.comment = post\n alert.scope = Alert.FORUM\n alert.text = request.POST['signal_text']\n alert.pubdate = datetime.now()\n alert.save()\n\n messages.success(request,\n _(u'Une alerte a été envoyée '\n u'à l\\'équipe concernant '\n u'ce message.'))\n elif \"preview\" in request.POST:\n if request.is_ajax():\n content = render_to_response('misc/previsualization.part.html', {'text': request.POST['text']})\n return StreamingHttpResponse(content)\n else:\n if g_topic:\n form = TopicForm(initial={\"title\": request.POST[\"title\"],\n \"subtitle\": request.POST[\"subtitle\"],\n \"text\": request.POST[\"text\"]})\n else:\n form = PostForm(post.topic, request.user,\n initial={\"text\": request.POST[\"text\"]})\n\n form.helper.form_action = reverse(\"zds.forum.views.edit_post\") \\\n + \"?message=\" + str(post_pk)\n\n return render(request, \"forum/post/edit.html\", {\n \"post\": post,\n \"topic\": post.topic,\n \"text\": request.POST[\"text\"],\n \"form\": form,\n })\n else:\n # The user just sent data, handle them\n if request.POST[\"text\"].strip() != \"\":\n form = TopicForm(request.POST)\n\n if not form.is_valid() and g_topic:\n return render(request, \"forum/post/edit.html\", {\n \"post\": post,\n \"topic\": post.topic,\n \"text\": post.text,\n \"form\": form,\n })\n\n post.text = request.POST[\"text\"]\n post.text_html = emarkdown(request.POST[\"text\"])\n post.update = datetime.now()\n post.editor = request.user\n\n # Modifying the thread info\n if g_topic:\n (tags, title) = get_tag_by_title(request.POST[\"title\"])\n g_topic.title = title\n g_topic.subtitle = request.POST[\"subtitle\"]\n g_topic.save()\n\n # add tags\n g_topic.tags.clear()\n g_topic.add_tags(tags)\n\n post.save()\n return redirect(post.get_absolute_url())\n else:\n if g_topic:\n prefix = u\"\"\n for tag in g_topic.tags.all():\n prefix += u\"[{0}]\".format(tag.title)\n\n form = TopicForm(\n initial={\n \"title\": u\"{0} {1}\".format(\n prefix,\n g_topic.title).strip(),\n \"subtitle\": g_topic.subtitle,\n \"text\": post.text})\n else:\n form = PostForm(post.topic, request.user,\n initial={\"text\": post.text})\n\n form.helper.form_action = reverse(\"zds.forum.views.edit_post\") \\\n + \"?message=\" + str(post_pk)\n return render(request, \"forum/post/edit.html\", {\n \"post\": post,\n \"topic\": post.topic,\n \"text\": post.text,\n \"form\": form,\n })\n\n\n@can_write_and_read_now\n@login_required\n@require_POST\ndef useful_post(request):\n \"\"\"Marks a message as useful (for the OP)\"\"\"\n\n try:\n post_pk = request.GET[\"message\"]\n except KeyError:\n # problem in variable format\n raise Http404\n post = get_object_or_404(Post, pk=post_pk)\n\n # check that author can access the forum\n\n if not post.topic.forum.can_read(request.user):\n raise PermissionDenied\n\n # Making sure the user is allowed to do that\n\n if post.author == request.user or request.user != post.topic.author:\n if not request.user.has_perm(\"forum.change_post\"):\n raise PermissionDenied\n post.is_useful = not post.is_useful\n post.save()\n return redirect(post.get_absolute_url())\n\n\n@can_write_and_read_now\n@login_required\ndef unread_post(request):\n \"\"\"Marks a message as unread \"\"\"\n\n try:\n post_pk = request.GET[\"message\"]\n except KeyError:\n # problem in variable format\n raise Http404\n post = get_object_or_404(Post, pk=post_pk)\n\n # check that author can access the forum\n\n if not post.topic.forum.can_read(request.user):\n raise PermissionDenied\n if TopicFollowed.objects.filter(user=request.user, topic=post.topic).count() == 0:\n TopicFollowed(user=request.user, topic=post.topic).save()\n\n t = TopicRead.objects.filter(topic=post.topic, user=request.user).first()\n if t is None:\n if post.position > 1:\n unread = Post.objects.filter(topic=post.topic, position=(post.position - 1)).first()\n t = TopicRead(post=unread, topic=unread.topic, user=request.user)\n t.save()\n else:\n if post.position > 1:\n unread = Post.objects.filter(topic=post.topic, position=(post.position - 1)).first()\n t.post = unread\n t.save()\n else:\n t.delete()\n\n return redirect(reverse(\"zds.forum.views.details\", args=[post.topic.forum.category.slug, post.topic.forum.slug]))\n\n\n@can_write_and_read_now\n@login_required\n@require_POST\ndef like_post(request):\n \"\"\"Like a post.\"\"\"\n\n try:\n post_pk = request.GET[\"message\"]\n except KeyError:\n # problem in variable format\n raise Http404\n resp = {}\n post = get_object_or_404(Post, pk=post_pk)\n user = request.user\n if not post.topic.forum.can_read(request.user):\n raise PermissionDenied\n if post.author.pk != request.user.pk:\n\n # Making sure the user is allowed to do that\n\n if CommentLike.objects.filter(user__pk=user.pk,\n comments__pk=post_pk).count() == 0:\n like = CommentLike()\n like.user = user\n like.comments = post\n post.like = post.like + 1\n post.save()\n like.save()\n if CommentDislike.objects.filter(user__pk=user.pk,\n comments__pk=post_pk).count() > 0:\n CommentDislike.objects.filter(\n user__pk=user.pk,\n comments__pk=post_pk).all().delete()\n post.dislike = post.dislike - 1\n post.save()\n else:\n CommentLike.objects.filter(user__pk=user.pk,\n comments__pk=post_pk).all().delete()\n post.like = post.like - 1\n post.save()\n resp[\"upvotes\"] = post.like\n resp[\"downvotes\"] = post.dislike\n if request.is_ajax():\n return HttpResponse(json.dumps(resp), content_type='application/json')\n else:\n return redirect(post.get_absolute_url())\n\n\n@can_write_and_read_now\n@login_required\n@require_POST\ndef dislike_post(request):\n \"\"\"Dislike a post.\"\"\"\n\n try:\n post_pk = request.GET[\"message\"]\n except KeyError:\n # problem in variable format\n raise Http404\n resp = {}\n post = get_object_or_404(Post, pk=post_pk)\n user = request.user\n if not post.topic.forum.can_read(request.user):\n raise PermissionDenied\n if post.author.pk != request.user.pk:\n\n # Making sure the user is allowed to do that\n\n if CommentDislike.objects.filter(user__pk=user.pk,\n comments__pk=post_pk).count() == 0:\n dislike = CommentDislike()\n dislike.user = user\n dislike.comments = post\n post.dislike = post.dislike + 1\n post.save()\n dislike.save()\n if CommentLike.objects.filter(user__pk=user.pk,\n comments__pk=post_pk).count() > 0:\n CommentLike.objects.filter(user__pk=user.pk,\n comments__pk=post_pk).all().delete()\n post.like = post.like - 1\n post.save()\n else:\n CommentDislike.objects.filter(user__pk=user.pk,\n comments__pk=post_pk).all().delete()\n post.dislike = post.dislike - 1\n post.save()\n resp[\"upvotes\"] = post.like\n resp[\"downvotes\"] = post.dislike\n if request.is_ajax():\n return HttpResponse(json.dumps(resp))\n else:\n return redirect(post.get_absolute_url())\n\n\ndef find_topic_by_tag(request, tag_pk, tag_slug):\n \"\"\"Finds all topics byg tag.\"\"\"\n\n tag = Tag.objects.filter(pk=tag_pk, slug=tag_slug).first()\n if tag is None:\n return redirect(reverse(\"zds.forum.views.index\"))\n u = request.user\n if \"filter\" in request.GET:\n filter = request.GET[\"filter\"]\n if request.GET[\"filter\"] == \"solve\":\n topics = Topic.objects.filter(\n tags__in=[tag],\n is_solved=True).order_by(\"-last_message__pubdate\").prefetch_related(\n \"author\",\n \"last_message\",\n \"tags\")\\\n .exclude(Q(forum__group__isnull=False) & ~Q(forum__group__in=u.groups.all()))\\\n .all()\n else:\n topics = Topic.objects.filter(\n tags__in=[tag],\n is_solved=False).order_by(\"-last_message__pubdate\")\\\n .prefetch_related(\n \"author\",\n \"last_message\",\n \"tags\")\\\n .exclude(Q(forum__group__isnull=False) & ~Q(forum__group__in=u.groups.all()))\\\n .all()\n else:\n filter = None\n topics = Topic.objects.filter(tags__in=[tag]).order_by(\"-last_message__pubdate\")\\\n .exclude(Q(forum__group__isnull=False) & ~Q(forum__group__in=u.groups.all()))\\\n .prefetch_related(\"author\", \"last_message\", \"tags\").all()\n # Paginator\n\n paginator = Paginator(topics, settings.ZDS_APP['forum']['topics_per_page'])\n page = request.GET.get(\"page\")\n try:\n shown_topics = paginator.page(page)\n page = int(page)\n except PageNotAnInteger:\n shown_topics = paginator.page(1)\n page = 1\n except EmptyPage:\n shown_topics = paginator.page(paginator.num_pages)\n page = paginator.num_pages\n return render(request, \"forum/find/topic_by_tag.html\", {\n \"topics\": shown_topics,\n \"tag\": tag,\n \"pages\": paginator_range(page, paginator.num_pages),\n \"nb\": page,\n \"filter\": filter,\n })\n\n\ndef find_topic(request, user_pk):\n \"\"\"Finds all topics of a user.\"\"\"\n\n displayed_user = get_object_or_404(User, pk=user_pk)\n topics = \\\n Topic.objects\\\n .filter(author=displayed_user)\\\n .exclude(Q(forum__group__isnull=False) & ~Q(forum__group__in=request.user.groups.all()))\\\n .prefetch_related(\"author\")\\\n .order_by(\"-pubdate\").all()\n\n # Paginator\n paginator = Paginator(topics, settings.ZDS_APP['forum']['topics_per_page'])\n page = request.GET.get(\"page\")\n try:\n shown_topics = paginator.page(page)\n page = int(page)\n except PageNotAnInteger:\n shown_topics = paginator.page(1)\n page = 1\n except EmptyPage:\n shown_topics = paginator.page(paginator.num_pages)\n page = paginator.num_pages\n\n return render(request, \"forum/find/topic.html\", {\n \"topics\": shown_topics,\n \"usr\": displayed_user,\n \"pages\": paginator_range(page, paginator.num_pages),\n \"nb\": page,\n })\n\n\ndef find_post(request, user_pk):\n \"\"\"Finds all posts of a user.\"\"\"\n\n displayed_user = get_object_or_404(User, pk=user_pk)\n user = request.user\n\n if user.has_perm(\"forum.change_post\"):\n posts = \\\n Post.objects.filter(author=displayed_user)\\\n .exclude(Q(topic__forum__group__isnull=False) & ~Q(topic__forum__group__in=user.groups.all()))\\\n .prefetch_related(\"author\")\\\n .order_by(\"-pubdate\").all()\n else:\n posts = \\\n Post.objects.filter(author=displayed_user)\\\n .filter(is_visible=True)\\\n .exclude(Q(topic__forum__group__isnull=False) & ~Q(topic__forum__group__in=user.groups.all()))\\\n .prefetch_related(\"author\").order_by(\"-pubdate\").all()\n\n # Paginator\n paginator = Paginator(posts, settings.ZDS_APP['forum']['posts_per_page'])\n page = request.GET.get(\"page\")\n try:\n shown_posts = paginator.page(page)\n page = int(page)\n except PageNotAnInteger:\n shown_posts = paginator.page(1)\n page = 1\n except EmptyPage:\n shown_posts = paginator.page(paginator.num_pages)\n page = paginator.num_pages\n\n return render(request, \"forum/find/post.html\", {\n \"posts\": shown_posts,\n \"usr\": displayed_user,\n \"pages\": paginator_range(page, paginator.num_pages),\n \"nb\": page,\n })\n\n\n@login_required\ndef followed_topics(request):\n followed_topics = request.user.get_profile().get_followed_topics()\n\n # Paginator\n\n paginator = Paginator(followed_topics, settings.ZDS_APP['forum']['followed_topics_per_page'])\n page = request.GET.get(\"page\")\n try:\n shown_topics = paginator.page(page)\n page = int(page)\n except PageNotAnInteger:\n shown_topics = paginator.page(1)\n page = 1\n except EmptyPage:\n shown_topics = paginator.page(paginator.num_pages)\n page = paginator.num_pages\n return render(request, \"forum/topic/followed.html\",\n {\"followed_topics\": shown_topics,\n \"pages\": paginator_range(page,\n paginator.num_pages),\n \"nb\": page})\n\n\ndef complete_topic(request):\n sqs = SearchQuerySet().filter(content=AutoQuery(request.GET.get('q'))).order_by('-pubdate').all()\n\n suggestions = {}\n\n cpt = 0\n for result in sqs:\n if cpt > 5:\n break\n if 'Topic' in str(result.model) and result.object.is_solved:\n suggestions[str(result.object.pk)] = (result.title, result.author, result.object.get_absolute_url())\n cpt += 1\n\n the_data = json.dumps(suggestions)\n\n return HttpResponse(the_data, content_type='application/json')\n",
"path": "zds/forum/views.py"
}
] | [
{
"content": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\nfrom datetime import datetime\nimport json\n\nfrom django.conf import settings\nfrom django.db.models import Q\nfrom django.contrib import messages\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.auth.models import User\nfrom django.core.exceptions import PermissionDenied\nfrom django.core.mail import EmailMultiAlternatives\nfrom django.core.paginator import Paginator, PageNotAnInteger, EmptyPage\nfrom django.core.urlresolvers import reverse\nfrom django.db import transaction\nfrom django.http import Http404, HttpResponse, StreamingHttpResponse\nfrom django.shortcuts import redirect, get_object_or_404, render, render_to_response\nfrom django.template import Context\nfrom django.template.loader import get_template\nfrom django.views.decorators.http import require_POST\nfrom django.utils.translation import ugettext as _\n\nfrom haystack.inputs import AutoQuery\nfrom haystack.query import SearchQuerySet\n\nfrom forms import TopicForm, PostForm, MoveTopicForm\nfrom models import Category, Forum, Topic, Post, follow, follow_by_email, never_read, \\\n mark_read, TopicFollowed, get_topics\nfrom zds.forum.models import TopicRead\nfrom zds.member.decorator import can_write_and_read_now\nfrom zds.member.views import get_client_ip\nfrom zds.utils import slugify\nfrom zds.utils.models import Alert, CommentLike, CommentDislike, Tag\nfrom zds.utils.mps import send_mp\nfrom zds.utils.paginator import paginator_range\nfrom zds.utils.templatetags.emarkdown import emarkdown\nfrom zds.utils.templatetags.topbar import top_categories\n\n\ndef index(request):\n \"\"\"Display the category list with all their forums.\"\"\"\n\n categories = top_categories(request.user)\n\n return render(request, \"forum/index.html\", {\"categories\": categories,\n \"user\": request.user,\n \"nb\": settings.ZDS_APP['forum']['topics_per_page'],\n \"page\": 1})\n\n\ndef details(request, cat_slug, forum_slug):\n \"\"\"Display the given forum and all its topics.\"\"\"\n\n forum = get_object_or_404(Forum, slug=forum_slug)\n if not forum.can_read(request.user):\n raise PermissionDenied\n if 'filter' in request.GET:\n filter = request.GET['filter']\n else:\n filter = None\n sticky_topics = get_topics(forum_pk=forum.pk, is_sticky=True, filter=filter)\n topics = get_topics(forum_pk=forum.pk, is_sticky=False, filter=filter)\n\n # Paginator\n\n paginator = Paginator(topics, settings.ZDS_APP['forum']['topics_per_page'])\n page = request.GET.get(\"page\")\n try:\n shown_topics = paginator.page(page)\n page = int(page)\n except PageNotAnInteger:\n shown_topics = paginator.page(1)\n page = 1\n except EmptyPage:\n shown_topics = paginator.page(paginator.num_pages)\n page = paginator.num_pages\n\n return render(request, \"forum/category/forum.html\", {\n \"forum\": forum,\n \"sticky_topics\": sticky_topics,\n \"topics\": shown_topics,\n \"page\": 1,\n \"pages\": paginator_range(page, paginator.num_pages),\n \"nb\": page,\n \"filter\": filter,\n })\n\n\ndef cat_details(request, cat_slug):\n \"\"\"Display the forums belonging to the given category.\"\"\"\n\n category = get_object_or_404(Category, slug=cat_slug)\n\n forums_pub = Forum.objects\\\n .filter(group__isnull=True, category__pk=category.pk)\\\n .select_related(\"category\").all()\n if request.user.is_authenticated():\n forums_prv = Forum.objects\\\n .filter(group__isnull=False,\n group__in=request.user.groups.all(),\n category__pk=category.pk)\\\n .select_related(\"category\")\\\n .all()\n forums = forums_pub | forums_prv\n else:\n forums = forums_pub\n\n return render(request, \"forum/category/index.html\", {\"category\": category,\n \"forums\": forums,\n \"nb\": settings.ZDS_APP['forum']['topics_per_page'],\n \"page\": 1})\n\n\ndef topic(request, topic_pk, topic_slug):\n \"\"\"Display a thread and its posts using a pager.\"\"\"\n\n topic = get_object_or_404(Topic, pk=topic_pk)\n if not topic.forum.can_read(request.user):\n raise PermissionDenied\n\n # Check link\n\n if not topic_slug == slugify(topic.title):\n return redirect(topic.get_absolute_url())\n\n # If the user is authenticated and has never read topic, we mark it as\n # read.\n\n if request.user.is_authenticated():\n if never_read(topic):\n mark_read(topic)\n\n # Retrieves all posts of the topic and use paginator with them.\n\n posts = \\\n Post.objects.filter(topic__pk=topic.pk) \\\n .select_related(\"author__profile\") \\\n .order_by(\"position\").all()\n last_post_pk = topic.last_message.pk\n\n # Handle pagination\n\n paginator = Paginator(posts, settings.ZDS_APP['forum']['posts_per_page'])\n\n # The category list is needed to move threads\n\n categories = Category.objects.all()\n if \"page\" in request.GET:\n try:\n page_nbr = int(request.GET[\"page\"])\n except (KeyError, ValueError):\n # problem in variable format\n raise Http404\n else:\n page_nbr = 1\n try:\n posts = paginator.page(page_nbr)\n except PageNotAnInteger:\n posts = paginator.page(1)\n except EmptyPage:\n raise Http404\n res = []\n if page_nbr != 1:\n\n # Show the last post of the previous page\n\n last_page = paginator.page(page_nbr - 1).object_list\n last_post = last_page[len(last_page) - 1]\n res.append(last_post)\n for post in posts:\n res.append(post)\n\n # Build form to send a post for the current topic.\n\n form = PostForm(topic, request.user)\n form.helper.form_action = reverse(\"zds.forum.views.answer\") + \"?sujet=\" \\\n + str(topic.pk)\n form_move = MoveTopicForm(topic=topic)\n\n return render(request, \"forum/topic/index.html\", {\n \"topic\": topic,\n \"posts\": res,\n \"categories\": categories,\n \"page\": 1,\n \"pages\": paginator_range(page_nbr, paginator.num_pages),\n \"nb\": page_nbr,\n \"last_post_pk\": last_post_pk,\n \"form\": form,\n \"form_move\": form_move,\n })\n\n\ndef get_tag_by_title(title):\n nb_bracket = 0\n current_tag = u\"\"\n current_title = u\"\"\n tags = []\n continue_parsing_tags = True\n original_title = title\n for char in title:\n\n if char == u\"[\" and nb_bracket == 0 and continue_parsing_tags:\n nb_bracket += 1\n elif nb_bracket > 0 and char != u\"]\" and continue_parsing_tags:\n current_tag = current_tag + char\n if char == u\"[\":\n nb_bracket += 1\n elif char == u\"]\" and nb_bracket > 0 and continue_parsing_tags:\n nb_bracket -= 1\n if nb_bracket == 0 and current_tag.strip() != u\"\":\n tags.append(current_tag.strip())\n current_tag = u\"\"\n elif current_tag.strip() != u\"\" and nb_bracket > 0:\n current_tag = current_tag + char\n\n elif ((char != u\"[\" and char.strip() != \"\") or not continue_parsing_tags):\n continue_parsing_tags = False\n current_title = current_title + char\n title = current_title\n # if we did not succed in parsing the tags\n if nb_bracket != 0:\n return ([], original_title)\n\n return (tags, title.strip())\n\n\n@can_write_and_read_now\n@login_required\[email protected]\ndef new(request):\n \"\"\"Creates a new topic in a forum.\"\"\"\n\n try:\n forum_pk = request.GET[\"forum\"]\n except KeyError:\n # problem in variable format\n raise Http404\n forum = get_object_or_404(Forum, pk=forum_pk)\n if not forum.can_read(request.user):\n raise PermissionDenied\n if request.method == \"POST\":\n\n # If the client is using the \"preview\" button\n\n if \"preview\" in request.POST:\n if request.is_ajax():\n content = render_to_response('misc/previsualization.part.html', {'text': request.POST['text']})\n return StreamingHttpResponse(content)\n else:\n form = TopicForm(initial={\"title\": request.POST[\"title\"],\n \"subtitle\": request.POST[\"subtitle\"],\n \"text\": request.POST[\"text\"]})\n\n return render(request, \"forum/topic/new.html\",\n {\"forum\": forum,\n \"form\": form,\n \"text\": request.POST[\"text\"]})\n form = TopicForm(request.POST)\n data = form.data\n if form.is_valid():\n\n # Treat title\n\n (tags, title) = get_tag_by_title(data[\"title\"])\n\n # Creating the thread\n n_topic = Topic()\n n_topic.forum = forum\n n_topic.title = title\n n_topic.subtitle = data[\"subtitle\"]\n n_topic.pubdate = datetime.now()\n n_topic.author = request.user\n n_topic.save()\n # add tags\n\n n_topic.add_tags(tags)\n n_topic.save()\n # Adding the first message\n\n post = Post()\n post.topic = n_topic\n post.author = request.user\n post.update_content(request.POST[\"text\"])\n post.pubdate = datetime.now()\n post.position = 1\n post.ip_address = get_client_ip(request)\n post.save()\n n_topic.last_message = post\n n_topic.save()\n\n # Follow the topic\n\n follow(n_topic)\n return redirect(n_topic.get_absolute_url())\n else:\n form = TopicForm()\n\n return render(request, \"forum/topic/new.html\", {\"forum\": forum, \"form\": form})\n\n\n@can_write_and_read_now\n@login_required\n@require_POST\[email protected]\ndef solve_alert(request):\n\n # only staff can move topic\n\n if not request.user.has_perm(\"forum.change_post\"):\n raise PermissionDenied\n\n alert = get_object_or_404(Alert, pk=request.POST[\"alert_pk\"])\n post = Post.objects.get(pk=alert.comment.id)\n\n if \"text\" in request.POST and request.POST[\"text\"] != \"\":\n bot = get_object_or_404(User, username=settings.ZDS_APP['member']['bot_account'])\n msg = \\\n (u'Bonjour {0},'\n u'Vous recevez ce message car vous avez signalé le message de *{1}*, '\n u'dans le sujet [{2}]({3}). Votre alerte a été traitée par **{4}** '\n u'et il vous a laissé le message suivant :'\n u'\\n\\n> {5}\\n\\nToute l\\'équipe de la modération vous remercie !'.format(\n alert.author.username,\n post.author.username,\n post.topic.title,\n settings.ZDS_APP['site']['url'] + post.get_absolute_url(),\n request.user.username,\n request.POST[\"text\"],))\n send_mp(\n bot,\n [alert.author],\n u\"Résolution d'alerte : {0}\".format(post.topic.title),\n \"\",\n msg,\n False,\n )\n\n alert.delete()\n messages.success(request, u\"L'alerte a bien été résolue.\")\n return redirect(post.get_absolute_url())\n\n\n@can_write_and_read_now\n@login_required\n@require_POST\[email protected]\ndef move_topic(request):\n\n # only staff can move topic\n\n if not request.user.has_perm(\"forum.change_topic\"):\n raise PermissionDenied\n try:\n topic_pk = request.GET[\"sujet\"]\n except KeyError:\n # problem in variable format\n raise Http404\n forum = get_object_or_404(Forum, pk=request.POST[\"forum\"])\n if not forum.can_read(request.user):\n raise PermissionDenied\n topic = get_object_or_404(Topic, pk=topic_pk)\n topic.forum = forum\n topic.save()\n\n # unfollow user auth\n\n followers = TopicFollowed.objects.filter(topic=topic)\n for follower in followers:\n if not forum.can_read(follower.user):\n follower.delete()\n messages.success(request,\n u\"Le sujet {0} a bien été déplacé dans {1}.\"\n .format(topic.title,\n forum.title))\n return redirect(topic.get_absolute_url())\n\n\n@can_write_and_read_now\n@login_required\n@require_POST\ndef edit(request):\n \"\"\"Edit the given topic.\"\"\"\n\n try:\n topic_pk = request.POST[\"topic\"]\n except KeyError:\n # problem in variable format\n raise Http404\n if \"page\" in request.POST:\n try:\n page = int(request.POST[\"page\"])\n except (KeyError, ValueError):\n # problem in variable format\n raise Http404\n else:\n page = 1\n\n data = request.POST\n resp = {}\n g_topic = get_object_or_404(Topic, pk=topic_pk)\n if \"follow\" in data:\n resp[\"follow\"] = follow(g_topic)\n if \"email\" in data:\n resp[\"email\"] = follow_by_email(g_topic)\n if request.user == g_topic.author \\\n or request.user.has_perm(\"forum.change_topic\"):\n if \"solved\" in data:\n g_topic.is_solved = not g_topic.is_solved\n resp[\"solved\"] = g_topic.is_solved\n if request.user.has_perm(\"forum.change_topic\"):\n\n if \"lock\" in data:\n g_topic.is_locked = data[\"lock\"] == \"true\"\n messages.success(request,\n u\"Le sujet {0} est désormais verrouillé.\"\n .format(g_topic.title))\n if 'sticky' in data:\n if data['sticky'] == 'true':\n g_topic.is_sticky = True\n messages.success(request, _(u'Le sujet « {0} » est désormais épinglé.').format(g_topic.title))\n else:\n g_topic.is_sticky = False\n messages.success(request, _(u'Le sujet « {0} » n\\'est désormais plus épinglé.').format(g_topic.title))\n if \"move\" in data:\n try:\n forum_pk = int(request.POST[\"move_target\"])\n except (KeyError, ValueError):\n # problem in variable format\n raise Http404\n forum = get_object_or_404(Forum, pk=forum_pk)\n g_topic.forum = forum\n g_topic.save()\n if request.is_ajax():\n return HttpResponse(json.dumps(resp), content_type='application/json')\n else:\n if not g_topic.forum.can_read(request.user):\n return redirect(reverse(\"zds.forum.views.index\"))\n else:\n return redirect(u\"{}?page={}\".format(g_topic.get_absolute_url(),\n page))\n\n\n@can_write_and_read_now\n@login_required\[email protected]\ndef answer(request):\n \"\"\"Adds an answer from a user to a topic.\"\"\"\n\n try:\n topic_pk = request.GET[\"sujet\"]\n except KeyError:\n # problem in variable format\n raise Http404\n\n # Retrieve current topic.\n\n g_topic = get_object_or_404(Topic, pk=topic_pk)\n if not g_topic.forum.can_read(request.user):\n raise PermissionDenied\n\n # Making sure posting is allowed\n\n if g_topic.is_locked:\n raise PermissionDenied\n\n # Check that the user isn't spamming\n\n if g_topic.antispam(request.user):\n raise PermissionDenied\n last_post_pk = g_topic.last_message.pk\n\n # Retrieve last posts of the current topic.\n posts = Post.objects.filter(topic=g_topic) \\\n .prefetch_related() \\\n .order_by(\"-position\")[:settings.ZDS_APP['forum']['posts_per_page']]\n\n # User would like preview his post or post a new post on the topic.\n\n if request.method == \"POST\":\n data = request.POST\n newpost = last_post_pk != int(data[\"last_post\"])\n\n # Using the « preview button », the « more » button or new post\n\n if \"preview\" in data or newpost:\n form = PostForm(g_topic, request.user, initial={\"text\": data[\"text\"]})\n form.helper.form_action = reverse(\"zds.forum.views.answer\") \\\n + \"?sujet=\" + str(g_topic.pk)\n if request.is_ajax():\n content = render_to_response('misc/previsualization.part.html', {'text': data['text']})\n return StreamingHttpResponse(content)\n else:\n return render(request, \"forum/post/new.html\", {\n \"text\": data[\"text\"],\n \"topic\": g_topic,\n \"posts\": posts,\n \"last_post_pk\": last_post_pk,\n \"newpost\": newpost,\n \"form\": form,\n })\n else:\n\n # Saving the message\n\n form = PostForm(g_topic, request.user, request.POST)\n if form.is_valid():\n data = form.data\n post = Post()\n post.topic = g_topic\n post.author = request.user\n post.text = data[\"text\"]\n post.text_html = emarkdown(data[\"text\"])\n post.pubdate = datetime.now()\n post.position = g_topic.get_post_count() + 1\n post.ip_address = get_client_ip(request)\n post.save()\n g_topic.last_message = post\n g_topic.save()\n # Send mail\n subject = u\"{} - Notification : {}\".format(settings.ZDS_APP['site']['abbr'],\n g_topic.title)\n from_email = \"{0} <{1}>\".format(settings.ZDS_APP['site']['litteral_name'],\n settings.ZDS_APP['site']['email_noreply'])\n followers = g_topic.get_followers_by_email()\n for follower in followers:\n receiver = follower.user\n if receiver == request.user:\n continue\n pos = post.position - 1\n last_read = TopicRead.objects.filter(\n topic=g_topic,\n post__position=pos,\n user=receiver).count()\n if last_read > 0:\n message_html = get_template('email/notification/new.html') \\\n .render(\n Context({\n 'username': receiver.username,\n 'title': g_topic.title,\n 'url': settings.ZDS_APP['site']['url'] + post.get_absolute_url(),\n 'author': request.user.username\n }))\n message_txt = get_template('email/notification/new.txt').render(\n Context({\n 'username': receiver.username,\n 'title': g_topic.title,\n 'url': settings.ZDS_APP['site']['url'] + post.get_absolute_url(),\n 'author': request.user.username\n }))\n msg = EmailMultiAlternatives(\n subject, message_txt, from_email, [\n receiver.email])\n msg.attach_alternative(message_html, \"text/html\")\n msg.send()\n\n # Follow topic on answering\n if not g_topic.is_followed(user=request.user):\n follow(g_topic)\n return redirect(post.get_absolute_url())\n else:\n return render(request, \"forum/post/new.html\", {\n \"text\": data[\"text\"],\n \"topic\": g_topic,\n \"posts\": posts,\n \"last_post_pk\": last_post_pk,\n \"newpost\": newpost,\n \"form\": form,\n })\n else:\n\n # Actions from the editor render to new.html.\n\n text = \"\"\n\n # Using the quote button\n\n if \"cite\" in request.GET:\n resp = {}\n post_cite_pk = request.GET[\"cite\"]\n post_cite = Post.objects.get(pk=post_cite_pk)\n if not post_cite.is_visible:\n raise PermissionDenied\n for line in post_cite.text.splitlines():\n text = text + \"> \" + line + \"\\n\"\n text = u\"{0}Source:[{1}]({2}{3})\".format(\n text,\n post_cite.author.username,\n settings.ZDS_APP['site']['url'],\n post_cite.get_absolute_url())\n\n if request.is_ajax():\n resp[\"text\"] = text\n return HttpResponse(json.dumps(resp), content_type='application/json')\n\n form = PostForm(g_topic, request.user, initial={\"text\": text})\n form.helper.form_action = reverse(\"zds.forum.views.answer\") \\\n + \"?sujet=\" + str(g_topic.pk)\n return render(request, \"forum/post/new.html\", {\n \"topic\": g_topic,\n \"posts\": posts,\n \"last_post_pk\": last_post_pk,\n \"form\": form,\n })\n\n\n@can_write_and_read_now\n@login_required\[email protected]\ndef edit_post(request):\n \"\"\"Edit the given user's post.\"\"\"\n\n try:\n post_pk = request.GET[\"message\"]\n except KeyError:\n # problem in variable format\n raise Http404\n\n post = get_object_or_404(Post, pk=post_pk)\n\n # check if the user can use this forum\n if not post.topic.forum.can_read(request.user):\n raise PermissionDenied\n\n if post.position <= 1:\n g_topic = get_object_or_404(Topic, pk=post.topic.pk)\n else:\n g_topic = None\n\n # Making sure the user is allowed to do that. Author of the post must to be\n # the user logged.\n if post.author != request.user \\\n and not request.user.has_perm(\"forum.change_post\") and \"signal_message\" \\\n not in request.POST:\n raise PermissionDenied\n if post.author != request.user and request.method == \"GET\" \\\n and request.user.has_perm(\"forum.change_post\"):\n messages.warning(request,\n _(u'Vous éditez ce message en tant que '\n u'modérateur (auteur : {}). Soyez encore plus '\n u'prudent lors de l\\'édition de celui-ci !')\n .format(post.author.username))\n\n if request.method == \"POST\":\n if \"delete_message\" in request.POST:\n if post.author == request.user \\\n or request.user.has_perm(\"forum.change_post\"):\n post.alerts.all().delete()\n post.is_visible = False\n\n if request.user.has_perm(\"forum.change_post\"):\n post.text_hidden = request.POST[\"text_hidden\"]\n\n post.editor = request.user\n\n messages.success(request, _(u\"Le message est désormais masqué.\"))\n elif \"show_message\" in request.POST:\n if request.user.has_perm(\"forum.change_post\"):\n post.is_visible = True\n post.text_hidden = \"\"\n elif \"signal_message\" in request.POST:\n alert = Alert()\n alert.author = request.user\n alert.comment = post\n alert.scope = Alert.FORUM\n alert.text = request.POST['signal_text']\n alert.pubdate = datetime.now()\n alert.save()\n\n messages.success(request,\n _(u'Une alerte a été envoyée '\n u'à l\\'équipe concernant '\n u'ce message.'))\n elif \"preview\" in request.POST:\n if request.is_ajax():\n content = render_to_response('misc/previsualization.part.html', {'text': request.POST['text']})\n return StreamingHttpResponse(content)\n else:\n if g_topic:\n form = TopicForm(initial={\"title\": request.POST[\"title\"],\n \"subtitle\": request.POST[\"subtitle\"],\n \"text\": request.POST[\"text\"]})\n else:\n form = PostForm(post.topic, request.user,\n initial={\"text\": request.POST[\"text\"]})\n\n form.helper.form_action = reverse(\"zds.forum.views.edit_post\") \\\n + \"?message=\" + str(post_pk)\n\n return render(request, \"forum/post/edit.html\", {\n \"post\": post,\n \"topic\": post.topic,\n \"text\": request.POST[\"text\"],\n \"form\": form,\n })\n else:\n # The user just sent data, handle them\n if request.POST[\"text\"].strip() != \"\":\n form = TopicForm(request.POST)\n\n if not form.is_valid() and g_topic:\n return render(request, \"forum/post/edit.html\", {\n \"post\": post,\n \"topic\": post.topic,\n \"text\": post.text,\n \"form\": form,\n })\n\n post.text = request.POST[\"text\"]\n post.text_html = emarkdown(request.POST[\"text\"])\n post.update = datetime.now()\n post.editor = request.user\n\n # Modifying the thread info\n if g_topic:\n (tags, title) = get_tag_by_title(request.POST[\"title\"])\n g_topic.title = title\n g_topic.subtitle = request.POST[\"subtitle\"]\n g_topic.save()\n\n # add tags\n g_topic.tags.clear()\n g_topic.add_tags(tags)\n\n post.save()\n return redirect(post.get_absolute_url())\n else:\n if g_topic:\n prefix = u\"\"\n for tag in g_topic.tags.all():\n prefix += u\"[{0}]\".format(tag.title)\n\n form = TopicForm(\n initial={\n \"title\": u\"{0} {1}\".format(\n prefix,\n g_topic.title).strip(),\n \"subtitle\": g_topic.subtitle,\n \"text\": post.text})\n else:\n form = PostForm(post.topic, request.user,\n initial={\"text\": post.text})\n\n form.helper.form_action = reverse(\"zds.forum.views.edit_post\") \\\n + \"?message=\" + str(post_pk)\n return render(request, \"forum/post/edit.html\", {\n \"post\": post,\n \"topic\": post.topic,\n \"text\": post.text,\n \"form\": form,\n })\n\n\n@can_write_and_read_now\n@login_required\n@require_POST\ndef useful_post(request):\n \"\"\"Marks a message as useful (for the OP)\"\"\"\n\n try:\n post_pk = request.GET[\"message\"]\n except KeyError:\n # problem in variable format\n raise Http404\n post = get_object_or_404(Post, pk=post_pk)\n\n # check that author can access the forum\n\n if not post.topic.forum.can_read(request.user):\n raise PermissionDenied\n\n # Making sure the user is allowed to do that\n\n if post.author == request.user or request.user != post.topic.author:\n if not request.user.has_perm(\"forum.change_post\"):\n raise PermissionDenied\n post.is_useful = not post.is_useful\n post.save()\n return redirect(post.get_absolute_url())\n\n\n@can_write_and_read_now\n@login_required\ndef unread_post(request):\n \"\"\"Marks a message as unread \"\"\"\n\n try:\n post_pk = request.GET[\"message\"]\n except KeyError:\n # problem in variable format\n raise Http404\n post = get_object_or_404(Post, pk=post_pk)\n\n # check that author can access the forum\n\n if not post.topic.forum.can_read(request.user):\n raise PermissionDenied\n if TopicFollowed.objects.filter(user=request.user, topic=post.topic).count() == 0:\n TopicFollowed(user=request.user, topic=post.topic).save()\n\n t = TopicRead.objects.filter(topic=post.topic, user=request.user).first()\n if t is None:\n if post.position > 1:\n unread = Post.objects.filter(topic=post.topic, position=(post.position - 1)).first()\n t = TopicRead(post=unread, topic=unread.topic, user=request.user)\n t.save()\n else:\n if post.position > 1:\n unread = Post.objects.filter(topic=post.topic, position=(post.position - 1)).first()\n t.post = unread\n t.save()\n else:\n t.delete()\n\n return redirect(reverse(\"zds.forum.views.details\", args=[post.topic.forum.category.slug, post.topic.forum.slug]))\n\n\n@can_write_and_read_now\n@login_required\n@require_POST\ndef like_post(request):\n \"\"\"Like a post.\"\"\"\n\n try:\n post_pk = request.GET[\"message\"]\n except KeyError:\n # problem in variable format\n raise Http404\n resp = {}\n post = get_object_or_404(Post, pk=post_pk)\n user = request.user\n if not post.topic.forum.can_read(request.user):\n raise PermissionDenied\n if post.author.pk != request.user.pk:\n\n # Making sure the user is allowed to do that\n\n if CommentLike.objects.filter(user__pk=user.pk,\n comments__pk=post_pk).count() == 0:\n like = CommentLike()\n like.user = user\n like.comments = post\n post.like = post.like + 1\n post.save()\n like.save()\n if CommentDislike.objects.filter(user__pk=user.pk,\n comments__pk=post_pk).count() > 0:\n CommentDislike.objects.filter(\n user__pk=user.pk,\n comments__pk=post_pk).all().delete()\n post.dislike = post.dislike - 1\n post.save()\n else:\n CommentLike.objects.filter(user__pk=user.pk,\n comments__pk=post_pk).all().delete()\n post.like = post.like - 1\n post.save()\n resp[\"upvotes\"] = post.like\n resp[\"downvotes\"] = post.dislike\n if request.is_ajax():\n return HttpResponse(json.dumps(resp), content_type='application/json')\n else:\n return redirect(post.get_absolute_url())\n\n\n@can_write_and_read_now\n@login_required\n@require_POST\ndef dislike_post(request):\n \"\"\"Dislike a post.\"\"\"\n\n try:\n post_pk = request.GET[\"message\"]\n except KeyError:\n # problem in variable format\n raise Http404\n resp = {}\n post = get_object_or_404(Post, pk=post_pk)\n user = request.user\n if not post.topic.forum.can_read(request.user):\n raise PermissionDenied\n if post.author.pk != request.user.pk:\n\n # Making sure the user is allowed to do that\n\n if CommentDislike.objects.filter(user__pk=user.pk,\n comments__pk=post_pk).count() == 0:\n dislike = CommentDislike()\n dislike.user = user\n dislike.comments = post\n post.dislike = post.dislike + 1\n post.save()\n dislike.save()\n if CommentLike.objects.filter(user__pk=user.pk,\n comments__pk=post_pk).count() > 0:\n CommentLike.objects.filter(user__pk=user.pk,\n comments__pk=post_pk).all().delete()\n post.like = post.like - 1\n post.save()\n else:\n CommentDislike.objects.filter(user__pk=user.pk,\n comments__pk=post_pk).all().delete()\n post.dislike = post.dislike - 1\n post.save()\n resp[\"upvotes\"] = post.like\n resp[\"downvotes\"] = post.dislike\n if request.is_ajax():\n return HttpResponse(json.dumps(resp))\n else:\n return redirect(post.get_absolute_url())\n\n\ndef find_topic_by_tag(request, tag_pk, tag_slug):\n \"\"\"Finds all topics byg tag.\"\"\"\n\n tag = Tag.objects.filter(pk=tag_pk, slug=tag_slug).first()\n if tag is None:\n return redirect(reverse(\"zds.forum.views.index\"))\n u = request.user\n if \"filter\" in request.GET:\n filter = request.GET[\"filter\"]\n if request.GET[\"filter\"] == \"solve\":\n topics = Topic.objects.filter(\n tags__in=[tag],\n is_solved=True).order_by(\"-last_message__pubdate\").prefetch_related(\n \"author\",\n \"last_message\",\n \"tags\")\\\n .exclude(Q(forum__group__isnull=False) & ~Q(forum__group__in=u.groups.all()))\\\n .all()\n else:\n topics = Topic.objects.filter(\n tags__in=[tag],\n is_solved=False).order_by(\"-last_message__pubdate\")\\\n .prefetch_related(\n \"author\",\n \"last_message\",\n \"tags\")\\\n .exclude(Q(forum__group__isnull=False) & ~Q(forum__group__in=u.groups.all()))\\\n .all()\n else:\n filter = None\n topics = Topic.objects.filter(tags__in=[tag]).order_by(\"-last_message__pubdate\")\\\n .exclude(Q(forum__group__isnull=False) & ~Q(forum__group__in=u.groups.all()))\\\n .prefetch_related(\"author\", \"last_message\", \"tags\").all()\n # Paginator\n\n paginator = Paginator(topics, settings.ZDS_APP['forum']['topics_per_page'])\n page = request.GET.get(\"page\")\n try:\n shown_topics = paginator.page(page)\n page = int(page)\n except PageNotAnInteger:\n shown_topics = paginator.page(1)\n page = 1\n except EmptyPage:\n shown_topics = paginator.page(paginator.num_pages)\n page = paginator.num_pages\n return render(request, \"forum/find/topic_by_tag.html\", {\n \"topics\": shown_topics,\n \"tag\": tag,\n \"pages\": paginator_range(page, paginator.num_pages),\n \"nb\": page,\n \"filter\": filter,\n })\n\n\ndef find_topic(request, user_pk):\n \"\"\"Finds all topics of a user.\"\"\"\n\n displayed_user = get_object_or_404(User, pk=user_pk)\n topics = \\\n Topic.objects\\\n .filter(author=displayed_user)\\\n .exclude(Q(forum__group__isnull=False) & ~Q(forum__group__in=request.user.groups.all()))\\\n .prefetch_related(\"author\")\\\n .order_by(\"-pubdate\").all()\n\n # Paginator\n paginator = Paginator(topics, settings.ZDS_APP['forum']['topics_per_page'])\n page = request.GET.get(\"page\")\n try:\n shown_topics = paginator.page(page)\n page = int(page)\n except PageNotAnInteger:\n shown_topics = paginator.page(1)\n page = 1\n except EmptyPage:\n shown_topics = paginator.page(paginator.num_pages)\n page = paginator.num_pages\n\n return render(request, \"forum/find/topic.html\", {\n \"topics\": shown_topics,\n \"usr\": displayed_user,\n \"pages\": paginator_range(page, paginator.num_pages),\n \"nb\": page,\n })\n\n\ndef find_post(request, user_pk):\n \"\"\"Finds all posts of a user.\"\"\"\n\n displayed_user = get_object_or_404(User, pk=user_pk)\n user = request.user\n\n if user.has_perm(\"forum.change_post\"):\n posts = \\\n Post.objects.filter(author=displayed_user)\\\n .exclude(Q(topic__forum__group__isnull=False) & ~Q(topic__forum__group__in=user.groups.all()))\\\n .prefetch_related(\"author\")\\\n .order_by(\"-pubdate\").all()\n else:\n posts = \\\n Post.objects.filter(author=displayed_user)\\\n .filter(is_visible=True)\\\n .exclude(Q(topic__forum__group__isnull=False) & ~Q(topic__forum__group__in=user.groups.all()))\\\n .prefetch_related(\"author\").order_by(\"-pubdate\").all()\n\n # Paginator\n paginator = Paginator(posts, settings.ZDS_APP['forum']['posts_per_page'])\n page = request.GET.get(\"page\")\n try:\n shown_posts = paginator.page(page)\n page = int(page)\n except PageNotAnInteger:\n shown_posts = paginator.page(1)\n page = 1\n except EmptyPage:\n shown_posts = paginator.page(paginator.num_pages)\n page = paginator.num_pages\n\n return render(request, \"forum/find/post.html\", {\n \"posts\": shown_posts,\n \"usr\": displayed_user,\n \"pages\": paginator_range(page, paginator.num_pages),\n \"nb\": page,\n })\n\n\n@login_required\ndef followed_topics(request):\n followed_topics = request.user.get_profile().get_followed_topics()\n\n # Paginator\n\n paginator = Paginator(followed_topics, settings.ZDS_APP['forum']['followed_topics_per_page'])\n page = request.GET.get(\"page\")\n try:\n shown_topics = paginator.page(page)\n page = int(page)\n except PageNotAnInteger:\n shown_topics = paginator.page(1)\n page = 1\n except EmptyPage:\n shown_topics = paginator.page(paginator.num_pages)\n page = paginator.num_pages\n return render(request, \"forum/topic/followed.html\",\n {\"followed_topics\": shown_topics,\n \"pages\": paginator_range(page,\n paginator.num_pages),\n \"nb\": page})\n\n\ndef complete_topic(request):\n if not request.GET.get('q', None):\n return HttpResponse(\"{}\", content_type='application/json')\n\n sqs = SearchQuerySet().filter(content=AutoQuery(request.GET.get('q'))).order_by('-pubdate').all()\n\n suggestions = {}\n\n cpt = 0\n for result in sqs:\n if cpt > 5:\n break\n if 'Topic' in str(result.model) and result.object.is_solved:\n suggestions[str(result.object.pk)] = (result.title, result.author, result.object.get_absolute_url())\n cpt += 1\n\n the_data = json.dumps(suggestions)\n\n return HttpResponse(the_data, content_type='application/json')\n",
"path": "zds/forum/views.py"
}
] | diff --git a/zds/forum/views.py b/zds/forum/views.py
index e45bcc10ba..df8bf870db 100644
--- a/zds/forum/views.py
+++ b/zds/forum/views.py
@@ -1061,6 +1061,9 @@ def followed_topics(request):
def complete_topic(request):
+ if not request.GET.get('q', None):
+ return HttpResponse("{}", content_type='application/json')
+
sqs = SearchQuerySet().filter(content=AutoQuery(request.GET.get('q'))).order_by('-pubdate').all()
suggestions = {}
|
dbt-labs__dbt-core-7221 | [CT-1943] Loosen pin on `jsonschema` (via `hologram`)
For more context on our latest thinking around dependencies (how & why we pin today, and how we want it to change):
- https://github.com/dbt-labs/dbt-core/discussions/6495
### Summary
`dbt-core` depends on `hologram`, and as such it also includes `hologram`'s transitive dependencies on `jsonschema` and `python-dateutil`. `hologram`'s upper bound on `jsonschema` in particular is causing issues for some folks trying to install `dbt-core` alongside other popular tools, such as Airflow:
- https://github.com/dbt-labs/hologram/issues/52
- https://github.com/dbt-labs/hologram/pull/51
### Short term
- Try removing upper bound on `jsonschema`
- Release a new version of `hologram` with no / looser upper bound
- Support the new version of `hologram` [in `dbt-core`](https://github.com/dbt-labs/dbt-core/blob/a8abc496323f741d3218d298d5d2bb118fa01017/core/setup.py#L54)
### Medium term
Remove `dbt-core`'s dependency on `hologram` entirely. It doesn't do nearly as much for us today as it used to, and the validation errors it raises aren't even all that nice.
- https://github.com/dbt-labs/dbt-core/issues/6776
| [
{
"content": "#!/usr/bin/env python\nimport os\nimport sys\n\nif sys.version_info < (3, 7, 2):\n print(\"Error: dbt does not support this version of Python.\")\n print(\"Please upgrade to Python 3.7.2 or higher.\")\n sys.exit(1)\n\n\nfrom setuptools import setup\n\ntry:\n from setuptools import find_namespace_packages\nexcept ImportError:\n # the user has a downlevel version of setuptools.\n print(\"Error: dbt requires setuptools v40.1.0 or higher.\")\n print('Please upgrade setuptools with \"pip install --upgrade setuptools\" ' \"and try again\")\n sys.exit(1)\n\n\nthis_directory = os.path.abspath(os.path.dirname(__file__))\nwith open(os.path.join(this_directory, \"README.md\")) as f:\n long_description = f.read()\n\n\npackage_name = \"dbt-core\"\npackage_version = \"1.5.0b4\"\ndescription = \"\"\"With dbt, data analysts and engineers can build analytics \\\nthe way engineers build applications.\"\"\"\n\n\nsetup(\n name=package_name,\n version=package_version,\n description=description,\n long_description=long_description,\n long_description_content_type=\"text/markdown\",\n author=\"dbt Labs\",\n author_email=\"[email protected]\",\n url=\"https://github.com/dbt-labs/dbt-core\",\n packages=find_namespace_packages(include=[\"dbt\", \"dbt.*\"]),\n include_package_data=True,\n test_suite=\"test\",\n entry_points={\n \"console_scripts\": [\"dbt = dbt.cli.main:cli\"],\n },\n install_requires=[\n \"Jinja2==3.1.2\",\n \"agate>=1.6,<1.7.1\",\n \"click>=7.0,<9\",\n \"colorama>=0.3.9,<0.4.7\",\n \"hologram>=0.0.14,<=0.0.15\",\n \"isodate>=0.6,<0.7\",\n \"logbook>=1.5,<1.6\",\n \"mashumaro[msgpack]==3.3.1\",\n \"minimal-snowplow-tracker==0.0.2\",\n \"networkx>=2.3,<2.8.1;python_version<'3.8'\",\n \"networkx>=2.3,<3;python_version>='3.8'\",\n \"packaging>20.9\",\n \"sqlparse>=0.2.3,<0.5\",\n \"dbt-extractor~=0.4.1\",\n \"typing-extensions>=3.7.4\",\n \"werkzeug>=1,<3\",\n \"pathspec>=0.9,<0.12\",\n \"protobuf>=3.18.3\",\n \"pytz>=2015.7\",\n # the following are all to match snowflake-connector-python\n \"requests<3.0.0\",\n \"idna>=2.5,<4\",\n \"cffi>=1.9,<2.0.0\",\n \"pyyaml>=6.0\",\n ],\n zip_safe=False,\n classifiers=[\n \"Development Status :: 5 - Production/Stable\",\n \"License :: OSI Approved :: Apache Software License\",\n \"Operating System :: Microsoft :: Windows\",\n \"Operating System :: MacOS :: MacOS X\",\n \"Operating System :: POSIX :: Linux\",\n \"Programming Language :: Python :: 3.7\",\n \"Programming Language :: Python :: 3.8\",\n \"Programming Language :: Python :: 3.9\",\n \"Programming Language :: Python :: 3.10\",\n \"Programming Language :: Python :: 3.11\",\n ],\n python_requires=\">=3.7.2\",\n)\n",
"path": "core/setup.py"
}
] | [
{
"content": "#!/usr/bin/env python\nimport os\nimport sys\n\nif sys.version_info < (3, 7, 2):\n print(\"Error: dbt does not support this version of Python.\")\n print(\"Please upgrade to Python 3.7.2 or higher.\")\n sys.exit(1)\n\n\nfrom setuptools import setup\n\ntry:\n from setuptools import find_namespace_packages\nexcept ImportError:\n # the user has a downlevel version of setuptools.\n print(\"Error: dbt requires setuptools v40.1.0 or higher.\")\n print('Please upgrade setuptools with \"pip install --upgrade setuptools\" ' \"and try again\")\n sys.exit(1)\n\n\nthis_directory = os.path.abspath(os.path.dirname(__file__))\nwith open(os.path.join(this_directory, \"README.md\")) as f:\n long_description = f.read()\n\n\npackage_name = \"dbt-core\"\npackage_version = \"1.5.0b4\"\ndescription = \"\"\"With dbt, data analysts and engineers can build analytics \\\nthe way engineers build applications.\"\"\"\n\n\nsetup(\n name=package_name,\n version=package_version,\n description=description,\n long_description=long_description,\n long_description_content_type=\"text/markdown\",\n author=\"dbt Labs\",\n author_email=\"[email protected]\",\n url=\"https://github.com/dbt-labs/dbt-core\",\n packages=find_namespace_packages(include=[\"dbt\", \"dbt.*\"]),\n include_package_data=True,\n test_suite=\"test\",\n entry_points={\n \"console_scripts\": [\"dbt = dbt.cli.main:cli\"],\n },\n install_requires=[\n \"Jinja2==3.1.2\",\n \"agate>=1.6,<1.7.1\",\n \"click>=7.0,<9\",\n \"colorama>=0.3.9,<0.4.7\",\n \"hologram>=0.0.14,<=0.0.16\",\n \"isodate>=0.6,<0.7\",\n \"logbook>=1.5,<1.6\",\n \"mashumaro[msgpack]==3.3.1\",\n \"minimal-snowplow-tracker==0.0.2\",\n \"networkx>=2.3,<2.8.1;python_version<'3.8'\",\n \"networkx>=2.3,<3;python_version>='3.8'\",\n \"packaging>20.9\",\n \"sqlparse>=0.2.3,<0.5\",\n \"dbt-extractor~=0.4.1\",\n \"typing-extensions>=3.7.4\",\n \"werkzeug>=1,<3\",\n \"pathspec>=0.9,<0.12\",\n \"protobuf>=3.18.3\",\n \"pytz>=2015.7\",\n # the following are all to match snowflake-connector-python\n \"requests<3.0.0\",\n \"idna>=2.5,<4\",\n \"cffi>=1.9,<2.0.0\",\n \"pyyaml>=6.0\",\n ],\n zip_safe=False,\n classifiers=[\n \"Development Status :: 5 - Production/Stable\",\n \"License :: OSI Approved :: Apache Software License\",\n \"Operating System :: Microsoft :: Windows\",\n \"Operating System :: MacOS :: MacOS X\",\n \"Operating System :: POSIX :: Linux\",\n \"Programming Language :: Python :: 3.7\",\n \"Programming Language :: Python :: 3.8\",\n \"Programming Language :: Python :: 3.9\",\n \"Programming Language :: Python :: 3.10\",\n \"Programming Language :: Python :: 3.11\",\n ],\n python_requires=\">=3.7.2\",\n)\n",
"path": "core/setup.py"
}
] | diff --git a/.changes/unreleased/Under the Hood-20230324-144050.yaml b/.changes/unreleased/Under the Hood-20230324-144050.yaml
new file mode 100644
index 00000000000..9094cf7524b
--- /dev/null
+++ b/.changes/unreleased/Under the Hood-20230324-144050.yaml
@@ -0,0 +1,6 @@
+kind: Under the Hood
+body: Remove upper pin for hologram/jsonschema
+time: 2023-03-24T14:40:50.574108-04:00
+custom:
+ Author: gshank
+ Issue: "6775"
diff --git a/core/setup.py b/core/setup.py
index 9fe0368477f..e9d2eea37ef 100644
--- a/core/setup.py
+++ b/core/setup.py
@@ -50,7 +50,7 @@
"agate>=1.6,<1.7.1",
"click>=7.0,<9",
"colorama>=0.3.9,<0.4.7",
- "hologram>=0.0.14,<=0.0.15",
+ "hologram>=0.0.14,<=0.0.16",
"isodate>=0.6,<0.7",
"logbook>=1.5,<1.6",
"mashumaro[msgpack]==3.3.1",
|
streamlink__streamlink-2171 | INE Plugin
## Plugin Issue
<!-- Replace [ ] with [x] in order to check the box -->
- [X] This is a plugin issue and I have read the contribution guidelines.
### Description
The INE plugin doesn't appear to work on any videos I try.
### Reproduction steps / Explicit stream URLs to test
Try do download a video
### Log output
<!--
TEXT LOG OUTPUT IS REQUIRED for a plugin issue!
Use the `--loglevel debug` parameter and avoid using parameters which suppress log output.
https://streamlink.github.io/cli.html#cmdoption-l
Make sure to **remove usernames and passwords**
You can copy the output to https://gist.github.com/ or paste it below.
-->
```
streamlink https://streaming.ine.com/play/419cdc1a-a4a8-4eba-b8b3-5dda324daa94/day-1-part-1#/ --http-cookie laravel_session=<Removed> --loglevel debug
[cli][debug] OS: macOS 10.14.1
[cli][debug] Python: 2.7.10
[cli][debug] Streamlink: 0.14.2
[cli][debug] Requests(2.19.1), Socks(1.6.7), Websocket(0.54.0)
[cli][info] Found matching plugin ine for URL https://streaming.ine.com/play/419cdc1a-a4a8-4eba-b8b3-5dda324daa94/day-1-part-1#/
[plugin.ine][debug] Found video ID: 419cdc1a-a4a8-4eba-b8b3-5dda324daa94
[plugin.ine][debug] Loading player JS: https://content.jwplatform.com/players/yyYIR4k9-p4NBeNN0.js?exp=1543579899&sig=5e0058876669be2e2aafc7e52d067b78
error: Unable to validate result: <_sre.SRE_Match object at 0x106564dc8> does not equal None or Unable to validate key 'playlist': Type of u'//content.jwplatform.com/v2/media/yyYIR4k9?token=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJyZWNvbW1lbmRhdGlvbnNfcGxheWxpc3RfaWQiOiJ5cHQwdDR4aCIsInJlc291cmNlIjoiL3YyL21lZGlhL3l5WUlSNGs5IiwiZXhwIjoxNTQzNTc5OTIwfQ.pHEgoDYzc219-S_slfWRhyEoCsyCZt74BiL8RNs5IJ8' should be 'str' but is 'unicode'
```
### Additional comments, screenshots, etc.
[Love Streamlink? Please consider supporting our collective. Thanks!](https://opencollective.com/streamlink/donate)
INE Plugin
## Plugin Issue
<!-- Replace [ ] with [x] in order to check the box -->
- [X] This is a plugin issue and I have read the contribution guidelines.
### Description
The INE plugin doesn't appear to work on any videos I try.
### Reproduction steps / Explicit stream URLs to test
Try do download a video
### Log output
<!--
TEXT LOG OUTPUT IS REQUIRED for a plugin issue!
Use the `--loglevel debug` parameter and avoid using parameters which suppress log output.
https://streamlink.github.io/cli.html#cmdoption-l
Make sure to **remove usernames and passwords**
You can copy the output to https://gist.github.com/ or paste it below.
-->
```
streamlink https://streaming.ine.com/play/419cdc1a-a4a8-4eba-b8b3-5dda324daa94/day-1-part-1#/ --http-cookie laravel_session=<Removed> --loglevel debug
[cli][debug] OS: macOS 10.14.1
[cli][debug] Python: 2.7.10
[cli][debug] Streamlink: 0.14.2
[cli][debug] Requests(2.19.1), Socks(1.6.7), Websocket(0.54.0)
[cli][info] Found matching plugin ine for URL https://streaming.ine.com/play/419cdc1a-a4a8-4eba-b8b3-5dda324daa94/day-1-part-1#/
[plugin.ine][debug] Found video ID: 419cdc1a-a4a8-4eba-b8b3-5dda324daa94
[plugin.ine][debug] Loading player JS: https://content.jwplatform.com/players/yyYIR4k9-p4NBeNN0.js?exp=1543579899&sig=5e0058876669be2e2aafc7e52d067b78
error: Unable to validate result: <_sre.SRE_Match object at 0x106564dc8> does not equal None or Unable to validate key 'playlist': Type of u'//content.jwplatform.com/v2/media/yyYIR4k9?token=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJyZWNvbW1lbmRhdGlvbnNfcGxheWxpc3RfaWQiOiJ5cHQwdDR4aCIsInJlc291cmNlIjoiL3YyL21lZGlhL3l5WUlSNGs5IiwiZXhwIjoxNTQzNTc5OTIwfQ.pHEgoDYzc219-S_slfWRhyEoCsyCZt74BiL8RNs5IJ8' should be 'str' but is 'unicode'
```
### Additional comments, screenshots, etc.
[Love Streamlink? Please consider supporting our collective. Thanks!](https://opencollective.com/streamlink/donate)
| [
{
"content": "from __future__ import print_function\n\nimport json\nimport re\n\nfrom streamlink.plugin import Plugin\nfrom streamlink.plugin.api import validate\nfrom streamlink.stream import HLSStream, HTTPStream\nfrom streamlink.utils import update_scheme\n\n\nclass INE(Plugin):\n url_re = re.compile(r\"\"\"https://streaming.ine.com/play\\#?/\n ([0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12})/?\n (.*?)\"\"\", re.VERBOSE)\n play_url = \"https://streaming.ine.com/play/{vid}/watch\"\n js_re = re.compile(r'''script type=\"text/javascript\" src=\"(https://content.jwplatform.com/players/.*?)\"''')\n jwplayer_re = re.compile(r'''jwConfig\\s*=\\s*(\\{.*\\});''', re.DOTALL)\n setup_schema = validate.Schema(\n validate.transform(jwplayer_re.search),\n validate.any(\n None,\n validate.all(\n validate.get(1),\n validate.transform(json.loads),\n {\"playlist\": str},\n validate.get(\"playlist\")\n )\n )\n )\n\n @classmethod\n def can_handle_url(cls, url):\n return cls.url_re.match(url) is not None\n\n def _get_streams(self):\n vid = self.url_re.match(self.url).group(1)\n self.logger.debug(\"Found video ID: {0}\", vid)\n\n page = self.session.http.get(self.play_url.format(vid=vid))\n js_url_m = self.js_re.search(page.text)\n if js_url_m:\n js_url = js_url_m.group(1)\n self.logger.debug(\"Loading player JS: {0}\", js_url)\n\n res = self.session.http.get(js_url)\n metadata_url = update_scheme(self.url, self.setup_schema.validate(res.text))\n data = self.session.http.json(self.session.http.get(metadata_url))\n\n for source in data[\"playlist\"][0][\"sources\"]:\n if source[\"type\"] == \"application/vnd.apple.mpegurl\":\n for s in HLSStream.parse_variant_playlist(self.session, source[\"file\"]).items():\n yield s\n elif source[\"type\"] == \"video/mp4\":\n yield \"{0}p\".format(source[\"height\"]), HTTPStream(self.session, source[\"file\"])\n\n\n__plugin__ = INE\n",
"path": "src/streamlink/plugins/ine.py"
}
] | [
{
"content": "from __future__ import print_function\n\nimport json\nimport re\n\nfrom streamlink.plugin import Plugin\nfrom streamlink.plugin.api import validate\nfrom streamlink.stream import HLSStream, HTTPStream\nfrom streamlink.utils import update_scheme\n\n\nclass INE(Plugin):\n url_re = re.compile(r\"\"\"https://streaming.ine.com/play\\#?/\n ([0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12})/?\n (.*?)\"\"\", re.VERBOSE)\n play_url = \"https://streaming.ine.com/play/{vid}/watch\"\n js_re = re.compile(r'''script type=\"text/javascript\" src=\"(https://content.jwplatform.com/players/.*?)\"''')\n jwplayer_re = re.compile(r'''jwConfig\\s*=\\s*(\\{.*\\});''', re.DOTALL)\n setup_schema = validate.Schema(\n validate.transform(jwplayer_re.search),\n validate.any(\n None,\n validate.all(\n validate.get(1),\n validate.transform(json.loads),\n {\"playlist\": validate.text},\n validate.get(\"playlist\")\n )\n )\n )\n\n @classmethod\n def can_handle_url(cls, url):\n return cls.url_re.match(url) is not None\n\n def _get_streams(self):\n vid = self.url_re.match(self.url).group(1)\n self.logger.debug(\"Found video ID: {0}\", vid)\n\n page = self.session.http.get(self.play_url.format(vid=vid))\n js_url_m = self.js_re.search(page.text)\n if js_url_m:\n js_url = js_url_m.group(1)\n self.logger.debug(\"Loading player JS: {0}\", js_url)\n\n res = self.session.http.get(js_url)\n metadata_url = update_scheme(self.url, self.setup_schema.validate(res.text))\n data = self.session.http.json(self.session.http.get(metadata_url))\n\n for source in data[\"playlist\"][0][\"sources\"]:\n if source[\"type\"] == \"application/vnd.apple.mpegurl\":\n for s in HLSStream.parse_variant_playlist(self.session, source[\"file\"]).items():\n yield s\n elif source[\"type\"] == \"video/mp4\":\n yield \"{0}p\".format(source[\"height\"]), HTTPStream(self.session, source[\"file\"])\n\n\n__plugin__ = INE\n",
"path": "src/streamlink/plugins/ine.py"
}
] | diff --git a/src/streamlink/plugins/ine.py b/src/streamlink/plugins/ine.py
index 8ebc91095f5..e0389b7e937 100644
--- a/src/streamlink/plugins/ine.py
+++ b/src/streamlink/plugins/ine.py
@@ -23,7 +23,7 @@ class INE(Plugin):
validate.all(
validate.get(1),
validate.transform(json.loads),
- {"playlist": str},
+ {"playlist": validate.text},
validate.get("playlist")
)
)
|
praw-dev__praw-1441 | PRAW 6.5.1 and 7.0.0 require Python versions above 3.5.2
**Describe the bug**
At https://praw.readthedocs.io/en/latest/getting_started/installation.html, it says:
> PRAW supports Python 3.5+
3.5.2 seems to be insufficient for PRAW versions after 6.4.0. I *think* 3.5.3 is probably sufficient based on what I have read searching for information on this error message, but I am skipping that version on this particular system so I haven't confirmed.
**To Reproduce**
Steps to reproduce the behavior:
1. Upgrade PRAW to either version 6.5.1 or 7.0.0
2. Run a simple PRAW script
3. Get this error:
```
$ python3 ~/test.py
Traceback (most recent call last):
File "/home/myusername/test.py", line 5, in <module>
import praw
File "/home/myusername/.local/lib/python3.5/site-packages/praw/__init__.py", line 14, in <module>
from .reddit import Reddit # NOQA
File "/home/myusername/.local/lib/python3.5/site-packages/praw/reddit.py", line 50, in <module>
class Reddit:
File "/home/myusername/.local/lib/python3.5/site-packages/praw/reddit.py", line 128, in Reddit
requestor_kwargs: Dict[str, Any] = None,
File "/usr/lib/python3.5/typing.py", line 649, in __getitem__
return Union[arg, type(None)]
File "/usr/lib/python3.5/typing.py", line 552, in __getitem__
dict(self.__dict__), parameters, _root=True)
File "/usr/lib/python3.5/typing.py", line 512, in __new__
for t2 in all_params - {t1} if not isinstance(t2, TypeVar)):
File "/usr/lib/python3.5/typing.py", line 512, in <genexpr>
for t2 in all_params - {t1} if not isinstance(t2, TypeVar)):
File "/usr/lib/python3.5/typing.py", line 1077, in __subclasscheck__
if super().__subclasscheck__(cls):
File "/usr/lib/python3.5/abc.py", line 225, in __subclasscheck__
for scls in cls.__subclasses__():
TypeError: descriptor '__subclasses__' of 'type' object needs an argument
```
**Expected behavior**
Python 3.5.2 works fine with PRAW 6.4.0 and earlier.
**Code/Logs**
`import praw` will do the trick.
**System Info**
- OS: Linux
- Python: 3.5.2
- PRAW Version: 6.5.1 or 7.0.0
| [
{
"content": "\"\"\"praw setup.py\"\"\"\n\nimport re\nfrom codecs import open\nfrom os import path\n\nfrom setuptools import find_packages, setup\n\nPACKAGE_NAME = \"praw\"\nHERE = path.abspath(path.dirname(__file__))\nwith open(path.join(HERE, \"README.rst\"), encoding=\"utf-8\") as fp:\n README = fp.read()\nwith open(path.join(HERE, PACKAGE_NAME, \"const.py\"), encoding=\"utf-8\") as fp:\n VERSION = re.search('__version__ = \"([^\"]+)\"', fp.read()).group(1)\n\nextras = {\n \"ci\": [\"coveralls\"],\n \"dev\": [\"pre-commit\"],\n \"lint\": [\n \"black\",\n \"flake8\",\n \"pydocstyle\",\n \"sphinx<3.0\",\n \"sphinx_rtd_theme\",\n ],\n \"test\": [\n \"betamax >=0.8, <0.9\",\n \"betamax-matchers >=0.3.0, <0.5\",\n \"pytest >=2.7.3\",\n ],\n}\nextras[\"dev\"] += extras[\"lint\"] + extras[\"test\"]\n\nsetup(\n name=PACKAGE_NAME,\n author=\"Bryce Boe\",\n author_email=\"[email protected]\",\n python_requires=\">=3.5\",\n classifiers=[\n \"Development Status :: 5 - Production/Stable\",\n \"Environment :: Console\",\n \"Intended Audience :: Developers\",\n \"License :: OSI Approved :: BSD License\",\n \"Natural Language :: English\",\n \"Operating System :: OS Independent\",\n \"Programming Language :: Python\",\n \"Programming Language :: Python :: 3\",\n \"Programming Language :: Python :: 3.5\",\n \"Programming Language :: Python :: 3.6\",\n \"Programming Language :: Python :: 3.7\",\n \"Programming Language :: Python :: 3.8\",\n \"Topic :: Utilities\",\n ],\n description=(\n \"PRAW, an acronym for `Python Reddit API Wrapper`, is a \"\n \"python package that allows for simple access to \"\n \"reddit's API.\"\n ),\n extras_require=extras,\n install_requires=[\n \"prawcore >=1.3.0, <2.0\",\n \"update_checker >=0.16\",\n \"websocket-client >=0.54.0\",\n ],\n keywords=\"reddit api wrapper\",\n license=\"Simplified BSD License\",\n long_description=README,\n package_data={\n \"\": [\"LICENSE.txt\"],\n PACKAGE_NAME: [\"*.ini\", \"images/*.jpg\"],\n },\n packages=find_packages(exclude=[\"tests\", \"tests.*\", \"tools\", \"tools.*\"]),\n url=\"https://praw.readthedocs.org/\",\n version=VERSION,\n)\n",
"path": "setup.py"
}
] | [
{
"content": "\"\"\"praw setup.py\"\"\"\n\nimport re\nfrom codecs import open\nfrom os import path\n\nfrom setuptools import find_packages, setup\n\nPACKAGE_NAME = \"praw\"\nHERE = path.abspath(path.dirname(__file__))\nwith open(path.join(HERE, \"README.rst\"), encoding=\"utf-8\") as fp:\n README = fp.read()\nwith open(path.join(HERE, PACKAGE_NAME, \"const.py\"), encoding=\"utf-8\") as fp:\n VERSION = re.search('__version__ = \"([^\"]+)\"', fp.read()).group(1)\n\nextras = {\n \"ci\": [\"coveralls\"],\n \"dev\": [\"pre-commit\"],\n \"lint\": [\n \"black\",\n \"flake8\",\n \"pydocstyle\",\n \"sphinx<3.0\",\n \"sphinx_rtd_theme\",\n ],\n \"test\": [\n \"betamax >=0.8, <0.9\",\n \"betamax-matchers >=0.3.0, <0.5\",\n \"pytest >=2.7.3\",\n ],\n}\nextras[\"dev\"] += extras[\"lint\"] + extras[\"test\"]\n\nsetup(\n name=PACKAGE_NAME,\n author=\"Bryce Boe\",\n author_email=\"[email protected]\",\n python_requires=\">3.5.3\",\n classifiers=[\n \"Development Status :: 5 - Production/Stable\",\n \"Environment :: Console\",\n \"Intended Audience :: Developers\",\n \"License :: OSI Approved :: BSD License\",\n \"Natural Language :: English\",\n \"Operating System :: OS Independent\",\n \"Programming Language :: Python\",\n \"Programming Language :: Python :: 3\",\n \"Programming Language :: Python :: 3.5\",\n \"Programming Language :: Python :: 3.6\",\n \"Programming Language :: Python :: 3.7\",\n \"Programming Language :: Python :: 3.8\",\n \"Topic :: Utilities\",\n ],\n description=(\n \"PRAW, an acronym for `Python Reddit API Wrapper`, is a \"\n \"python package that allows for simple access to \"\n \"reddit's API.\"\n ),\n extras_require=extras,\n install_requires=[\n \"prawcore >=1.3.0, <2.0\",\n \"update_checker >=0.16\",\n \"websocket-client >=0.54.0\",\n ],\n keywords=\"reddit api wrapper\",\n license=\"Simplified BSD License\",\n long_description=README,\n package_data={\n \"\": [\"LICENSE.txt\"],\n PACKAGE_NAME: [\"*.ini\", \"images/*.jpg\"],\n },\n packages=find_packages(exclude=[\"tests\", \"tests.*\", \"tools\", \"tools.*\"]),\n url=\"https://praw.readthedocs.org/\",\n version=VERSION,\n)\n",
"path": "setup.py"
}
] | diff --git a/setup.py b/setup.py
index 794394d50..2ea8a6914 100644
--- a/setup.py
+++ b/setup.py
@@ -35,7 +35,7 @@
name=PACKAGE_NAME,
author="Bryce Boe",
author_email="[email protected]",
- python_requires=">=3.5",
+ python_requires=">3.5.3",
classifiers=[
"Development Status :: 5 - Production/Stable",
"Environment :: Console",
|
svthalia__concrexit-1880 | ImproperlyConfigured: Field name `language` is not valid for model `Profile`.
Sentry Issue: [CONCREXIT-8J](https://sentry.io/organizations/thalia/issues/2580014551/?referrer=github_integration)
```
ImproperlyConfigured: Field name `language` is not valid for model `Profile`.
(14 additional frame(s) were not displayed)
...
File "django/utils/functional.py", line 48, in __get__
res = instance.__dict__[self.name] = self.func(instance)
File "rest_framework/serializers.py", line 349, in fields
for key, value in self.get_fields().items():
File "rest_framework/serializers.py", line 1053, in get_fields
field_class, field_kwargs = self.build_field(
File "rest_framework/serializers.py", line 1199, in build_field
return self.build_unknown_field(field_name, model_class)
File "rest_framework/serializers.py", line 1317, in build_unknown_field
raise ImproperlyConfigured(
```
| [
{
"content": "\"\"\"DRF serializers defined by the members package.\"\"\"\nfrom django.templatetags.static import static\nfrom rest_framework import serializers\n\nfrom members.models import Member, Profile\nfrom members.services import member_achievements, member_societies\nfrom thaliawebsite.api.services import create_image_thumbnail_dict\n\n\nclass ProfileRetrieveSerializer(serializers.ModelSerializer):\n \"\"\"Serializer that renders a member profile.\"\"\"\n\n class Meta:\n model = Profile\n fields = (\n \"pk\",\n \"display_name\",\n \"avatar\",\n \"profile_description\",\n \"birthday\",\n \"starting_year\",\n \"programme\",\n \"website\",\n \"membership_type\",\n \"achievements\",\n \"societies\",\n )\n\n pk = serializers.SerializerMethodField(\"_pk\")\n avatar = serializers.SerializerMethodField(\"_avatar\")\n birthday = serializers.SerializerMethodField(\"_birthday\")\n membership_type = serializers.SerializerMethodField(\"_membership_type\")\n achievements = serializers.SerializerMethodField(\"_achievements\")\n societies = serializers.SerializerMethodField(\"_societies\")\n\n def _pk(self, instance):\n return instance.user.pk\n\n def _birthday(self, instance):\n if instance.show_birthday:\n return instance.birthday\n return None\n\n def _membership_type(self, instance):\n membership = instance.user.current_membership\n if membership:\n return membership.type\n return None\n\n def _achievements(self, instance):\n return member_achievements(instance.user)\n\n def _societies(self, instance):\n return member_societies(instance.user)\n\n def _avatar(self, instance):\n placeholder = self.context[\"request\"].build_absolute_uri(\n static(\"members/images/default-avatar.jpg\")\n )\n file = None\n if instance.photo:\n file = instance.photo\n return create_image_thumbnail_dict(\n self.context[\"request\"], file, placeholder=placeholder, size_large=\"800x800\"\n )\n\n\nclass MemberListSerializer(serializers.ModelSerializer):\n \"\"\"Serializer that renders a list of members.\"\"\"\n\n class Meta:\n model = Member\n fields = (\"pk\", \"starting_year\", \"display_name\", \"membership_type\", \"avatar\")\n\n display_name = serializers.SerializerMethodField(\"_display_name\")\n starting_year = serializers.SerializerMethodField(\"_starting_year\")\n avatar = serializers.SerializerMethodField(\"_avatar\")\n membership_type = serializers.SerializerMethodField(\"_membership_type\")\n\n def _display_name(self, instance):\n return instance.profile.display_name()\n\n def _starting_year(self, instance):\n return instance.profile.starting_year\n\n def _avatar(self, instance):\n placeholder = self.context[\"request\"].build_absolute_uri(\n static(\"members/images/default-avatar.jpg\")\n )\n file = None\n if instance.profile.photo:\n file = instance.profile.photo\n return create_image_thumbnail_dict(\n self.context[\"request\"], file, placeholder=placeholder, size_large=\"800x800\"\n )\n\n def _membership_type(self, instance):\n membership = instance.current_membership\n if membership:\n return membership.type\n return None\n\n\nclass ProfileEditSerializer(serializers.ModelSerializer):\n \"\"\"Serializer that renders a profile to be edited.\"\"\"\n\n class Meta:\n model = Profile\n fields = (\n \"pk\",\n \"email\",\n \"first_name\",\n \"last_name\",\n \"address_street\",\n \"address_street2\",\n \"address_postal_code\",\n \"address_city\",\n \"address_country\",\n \"phone_number\",\n \"show_birthday\",\n \"website\",\n \"photo\",\n \"emergency_contact\",\n \"emergency_contact_phone_number\",\n \"profile_description\",\n \"nickname\",\n \"display_name_preference\",\n \"language\",\n \"receive_optin\",\n \"receive_newsletter\",\n \"receive_magazine\",\n \"display_name\",\n \"avatar\",\n \"birthday\",\n \"starting_year\",\n \"programme\",\n \"membership_type\",\n \"achievements\",\n \"societies\",\n )\n\n read_only_fields = (\"display_name\", \"starting_year\", \"programme\", \"birthday\")\n\n pk = serializers.SerializerMethodField(\"_pk\")\n email = serializers.SerializerMethodField(\"_email\")\n first_name = serializers.SerializerMethodField(\"_first_name\")\n last_name = serializers.SerializerMethodField(\"_last_name\")\n avatar = serializers.SerializerMethodField(\"_avatar\")\n membership_type = serializers.SerializerMethodField(\"_membership_type\")\n achievements = serializers.SerializerMethodField(\"_achievements\")\n societies = serializers.SerializerMethodField(\"_societies\")\n\n def _pk(self, instance):\n return instance.user.pk\n\n def _email(self, instance):\n return instance.user.email\n\n def _first_name(self, instance):\n return instance.user.first_name\n\n def _last_name(self, instance):\n return instance.user.last_name\n\n def _membership_type(self, instance):\n membership = instance.user.current_membership\n if membership:\n return membership.type\n return None\n\n def _achievements(self, instance):\n return member_achievements(instance.user)\n\n def _societies(self, instance):\n return member_societies(instance.user)\n\n def _avatar(self, instance):\n placeholder = self.context[\"request\"].build_absolute_uri(\n static(\"members/images/default-avatar.jpg\")\n )\n file = None\n if instance.photo:\n file = instance.photo\n return create_image_thumbnail_dict(\n self.context[\"request\"], file, placeholder=placeholder, size_large=\"800x800\"\n )\n",
"path": "website/members/api/v1/serializers.py"
}
] | [
{
"content": "\"\"\"DRF serializers defined by the members package.\"\"\"\nfrom django.templatetags.static import static\nfrom rest_framework import serializers\n\nfrom members.models import Member, Profile\nfrom members.services import member_achievements, member_societies\nfrom thaliawebsite.api.services import create_image_thumbnail_dict\n\n\nclass ProfileRetrieveSerializer(serializers.ModelSerializer):\n \"\"\"Serializer that renders a member profile.\"\"\"\n\n class Meta:\n model = Profile\n fields = (\n \"pk\",\n \"display_name\",\n \"avatar\",\n \"profile_description\",\n \"birthday\",\n \"starting_year\",\n \"programme\",\n \"website\",\n \"membership_type\",\n \"achievements\",\n \"societies\",\n )\n\n pk = serializers.SerializerMethodField(\"_pk\")\n avatar = serializers.SerializerMethodField(\"_avatar\")\n birthday = serializers.SerializerMethodField(\"_birthday\")\n membership_type = serializers.SerializerMethodField(\"_membership_type\")\n achievements = serializers.SerializerMethodField(\"_achievements\")\n societies = serializers.SerializerMethodField(\"_societies\")\n\n def _pk(self, instance):\n return instance.user.pk\n\n def _birthday(self, instance):\n if instance.show_birthday:\n return instance.birthday\n return None\n\n def _membership_type(self, instance):\n membership = instance.user.current_membership\n if membership:\n return membership.type\n return None\n\n def _achievements(self, instance):\n return member_achievements(instance.user)\n\n def _societies(self, instance):\n return member_societies(instance.user)\n\n def _avatar(self, instance):\n placeholder = self.context[\"request\"].build_absolute_uri(\n static(\"members/images/default-avatar.jpg\")\n )\n file = None\n if instance.photo:\n file = instance.photo\n return create_image_thumbnail_dict(\n self.context[\"request\"], file, placeholder=placeholder, size_large=\"800x800\"\n )\n\n\nclass MemberListSerializer(serializers.ModelSerializer):\n \"\"\"Serializer that renders a list of members.\"\"\"\n\n class Meta:\n model = Member\n fields = (\"pk\", \"starting_year\", \"display_name\", \"membership_type\", \"avatar\")\n\n display_name = serializers.SerializerMethodField(\"_display_name\")\n starting_year = serializers.SerializerMethodField(\"_starting_year\")\n avatar = serializers.SerializerMethodField(\"_avatar\")\n membership_type = serializers.SerializerMethodField(\"_membership_type\")\n\n def _display_name(self, instance):\n return instance.profile.display_name()\n\n def _starting_year(self, instance):\n return instance.profile.starting_year\n\n def _avatar(self, instance):\n placeholder = self.context[\"request\"].build_absolute_uri(\n static(\"members/images/default-avatar.jpg\")\n )\n file = None\n if instance.profile.photo:\n file = instance.profile.photo\n return create_image_thumbnail_dict(\n self.context[\"request\"], file, placeholder=placeholder, size_large=\"800x800\"\n )\n\n def _membership_type(self, instance):\n membership = instance.current_membership\n if membership:\n return membership.type\n return None\n\n\nclass ProfileEditSerializer(serializers.ModelSerializer):\n \"\"\"Serializer that renders a profile to be edited.\"\"\"\n\n class Meta:\n model = Profile\n fields = (\n \"pk\",\n \"email\",\n \"first_name\",\n \"last_name\",\n \"address_street\",\n \"address_street2\",\n \"address_postal_code\",\n \"address_city\",\n \"address_country\",\n \"phone_number\",\n \"show_birthday\",\n \"website\",\n \"photo\",\n \"emergency_contact\",\n \"emergency_contact_phone_number\",\n \"profile_description\",\n \"nickname\",\n \"display_name_preference\",\n \"receive_optin\",\n \"receive_newsletter\",\n \"receive_magazine\",\n \"display_name\",\n \"avatar\",\n \"birthday\",\n \"starting_year\",\n \"programme\",\n \"membership_type\",\n \"achievements\",\n \"societies\",\n )\n\n read_only_fields = (\"display_name\", \"starting_year\", \"programme\", \"birthday\")\n\n pk = serializers.SerializerMethodField(\"_pk\")\n email = serializers.SerializerMethodField(\"_email\")\n first_name = serializers.SerializerMethodField(\"_first_name\")\n last_name = serializers.SerializerMethodField(\"_last_name\")\n avatar = serializers.SerializerMethodField(\"_avatar\")\n membership_type = serializers.SerializerMethodField(\"_membership_type\")\n achievements = serializers.SerializerMethodField(\"_achievements\")\n societies = serializers.SerializerMethodField(\"_societies\")\n\n def _pk(self, instance):\n return instance.user.pk\n\n def _email(self, instance):\n return instance.user.email\n\n def _first_name(self, instance):\n return instance.user.first_name\n\n def _last_name(self, instance):\n return instance.user.last_name\n\n def _membership_type(self, instance):\n membership = instance.user.current_membership\n if membership:\n return membership.type\n return None\n\n def _achievements(self, instance):\n return member_achievements(instance.user)\n\n def _societies(self, instance):\n return member_societies(instance.user)\n\n def _avatar(self, instance):\n placeholder = self.context[\"request\"].build_absolute_uri(\n static(\"members/images/default-avatar.jpg\")\n )\n file = None\n if instance.photo:\n file = instance.photo\n return create_image_thumbnail_dict(\n self.context[\"request\"], file, placeholder=placeholder, size_large=\"800x800\"\n )\n",
"path": "website/members/api/v1/serializers.py"
}
] | diff --git a/website/members/api/v1/serializers.py b/website/members/api/v1/serializers.py
index 008f4e6de..42d142327 100644
--- a/website/members/api/v1/serializers.py
+++ b/website/members/api/v1/serializers.py
@@ -125,7 +125,6 @@ class Meta:
"profile_description",
"nickname",
"display_name_preference",
- "language",
"receive_optin",
"receive_newsletter",
"receive_magazine",
|
joke2k__faker-826 | pt_BR email not returning valid email addresses
When creating a fake Factory with the pt_BR it is not returning valid email addresses.
Example:
```
melocauã@bol.com.br
joã[email protected]
laví[email protected]
vitó[email protected]
```
| [
{
"content": "# coding=utf-8\nfrom __future__ import unicode_literals\nfrom .. import Provider as InternetProvider\n\n\nclass Provider(InternetProvider):\n safe_email_tlds = ('com', 'net', 'br', 'br')\n free_email_domains = (\n 'gmail.com',\n 'hotmail.com',\n 'yahoo.com.br',\n 'uol.com.br',\n 'bol.com.br',\n 'ig.com.br')\n tlds = ('com', 'com', 'com', 'net', 'org', 'br', 'br', 'br')\n",
"path": "faker/providers/internet/pt_BR/__init__.py"
}
] | [
{
"content": "# coding=utf-8\nfrom __future__ import unicode_literals\nfrom .. import Provider as InternetProvider\n\n\nclass Provider(InternetProvider):\n safe_email_tlds = ('com', 'net', 'br', 'br')\n free_email_domains = (\n 'gmail.com',\n 'hotmail.com',\n 'yahoo.com.br',\n 'uol.com.br',\n 'bol.com.br',\n 'ig.com.br')\n tlds = ('com', 'com', 'com', 'net', 'org', 'br', 'br', 'br')\n replacements = (\n ('à', 'a'), ('â', 'a'), ('ã', 'a'),\n ('ç', 'c'),\n ('é', 'e'), ('ê', 'e'),\n ('í', 'i'),\n ('ô', 'o'), ('ö', 'o'), ('õ', 'o'),\n ('ú', 'u'),\n )\n",
"path": "faker/providers/internet/pt_BR/__init__.py"
}
] | diff --git a/faker/providers/internet/pt_BR/__init__.py b/faker/providers/internet/pt_BR/__init__.py
index bf2b95f422..36d0e42400 100644
--- a/faker/providers/internet/pt_BR/__init__.py
+++ b/faker/providers/internet/pt_BR/__init__.py
@@ -13,3 +13,11 @@ class Provider(InternetProvider):
'bol.com.br',
'ig.com.br')
tlds = ('com', 'com', 'com', 'net', 'org', 'br', 'br', 'br')
+ replacements = (
+ ('à', 'a'), ('â', 'a'), ('ã', 'a'),
+ ('ç', 'c'),
+ ('é', 'e'), ('ê', 'e'),
+ ('í', 'i'),
+ ('ô', 'o'), ('ö', 'o'), ('õ', 'o'),
+ ('ú', 'u'),
+ )
diff --git a/tests/providers/test_internet.py b/tests/providers/test_internet.py
index 158a83b771..8f64139b62 100644
--- a/tests/providers/test_internet.py
+++ b/tests/providers/test_internet.py
@@ -202,3 +202,37 @@ def test_ascii_company_email(self):
email = self.factory.ascii_company_email()
validate_email(email, check_deliverability=False)
self.assertEqual(email.split('@')[0], 'asyl')
+
+
+class TestPtBR(unittest.TestCase):
+
+ def setUp(self):
+ self.factory = Faker('pt_BR')
+ self.provider = self.factory.provider('faker.providers.internet')
+
+ @mock.patch(
+ 'faker.providers.internet.Provider.user_name',
+ lambda x: 'VitóriaMagalhães'
+ )
+ def test_ascii_safe_email(self):
+ email = self.factory.ascii_safe_email()
+ validate_email(email, check_deliverability=False)
+ self.assertEqual(email.split('@')[0], 'vitoriamagalhaes')
+
+ @mock.patch(
+ 'faker.providers.internet.Provider.user_name',
+ lambda x: 'JoãoSimões'
+ )
+ def test_ascii_free_email(self):
+ email = self.factory.ascii_free_email()
+ validate_email(email, check_deliverability=False)
+ self.assertEqual(email.split('@')[0], 'joaosimoes')
+
+ @mock.patch(
+ 'faker.providers.internet.Provider.user_name',
+ lambda x: 'AndréCauã'
+ )
+ def test_ascii_company_email(self):
+ email = self.factory.ascii_company_email()
+ validate_email(email, check_deliverability=False)
+ self.assertEqual(email.split('@')[0], 'andrecaua')
|
fidals__shopelectro-693 | tests_selenium.py:976: Resurrect test `test_cart_page_open`
The puzzle `473-5159ab9c` from #473 has to be resolved:
https://github.com/fidals/shopelectro/blob/f7dc2793dc5c7eddb2e68a68368337d77ba3139e/shopelectro/tests/tests_selenium.py#L976-L976
The puzzle was created by duker33 on 08-Aug-18.
Estimate: 15 minutes,
If you have any technical questions, don't ask me, submit new tickets instead. The task will be "done" when the problem is fixed and the text of the puzzle is _removed_ from the source code. Here is more about [PDD](http://www.yegor256.com/2009/03/04/pdd.html) and [about me](http://www.yegor256.com/2017/04/05/pdd-in-action.html).
| [
{
"content": "import random\nimport string\nimport typing\nfrom uuid import uuid4\n\nfrom django.conf import settings\nfrom django.db import models\nfrom django.urls import reverse\nfrom django.utils.translation import ugettext_lazy as _\nimport mptt\n\nfrom catalog import models as catalog_models\nfrom ecommerce import models as ecommerce_models\nfrom pages import models as pages_models\n\n\ndef randomize_slug(slug: str) -> str:\n slug_hash = ''.join(\n random.choices(string.ascii_lowercase, k=settings.SLUG_HASH_SIZE)\n )\n return f'{slug}_{slug_hash}'\n\n\nclass SECategoryQuerySet(catalog_models.CategoryQuerySet):\n def get_categories_tree_with_pictures(self) -> 'SECategoryQuerySet':\n categories_with_pictures = (\n self\n .filter(products__page__images__isnull=False)\n .distinct()\n )\n\n return categories_with_pictures.get_ancestors(include_self=True)\n\n\nclass SECategoryManager(\n catalog_models.CategoryManager.from_queryset(SECategoryQuerySet)\n):\n pass\n\n\nclass Category(catalog_models.AbstractCategory, pages_models.SyncPageMixin):\n\n objects = SECategoryManager()\n uuid = models.UUIDField(default=uuid4, editable=False)\n\n @classmethod\n def get_default_parent(cls):\n return pages_models.CustomPage.objects.filter(slug='catalog').first()\n\n @property\n def image(self):\n products = self.products.all()\n return products[0].image if products else None\n\n def get_absolute_url(self):\n return reverse('category', args=(self.page.slug,))\n\n\nclass Product(catalog_models.AbstractProduct, pages_models.SyncPageMixin):\n\n # That's why we are needed to explicitly add objects manager here\n # because of Django special managers behaviour.\n # Se se#480 for details.\n objects = catalog_models.ProductManager()\n\n category = models.ForeignKey(\n Category,\n on_delete=models.CASCADE,\n null=True,\n related_name='products',\n verbose_name=_('category'),\n )\n\n tags = models.ManyToManyField(\n 'Tag',\n related_name='products',\n blank=True,\n verbose_name=_('tags'),\n )\n\n vendor_code = models.SmallIntegerField(verbose_name=_('vendor_code'))\n uuid = models.UUIDField(default=uuid4, editable=False)\n purchase_price = models.FloatField(\n default=0, verbose_name=_('purchase_price'))\n wholesale_small = models.FloatField(\n default=0, verbose_name=_('wholesale_small'))\n wholesale_medium = models.FloatField(\n default=0, verbose_name=_('wholesale_medium'))\n wholesale_large = models.FloatField(\n default=0, verbose_name=_('wholesale_large'))\n\n def get_absolute_url(self):\n return reverse('product', args=(self.vendor_code,))\n\n @property\n def average_rate(self):\n \"\"\"Return rounded to first decimal averaged rating.\"\"\"\n rating = self.product_feedbacks.aggregate(\n avg=models.Avg('rating')).get('avg', 0)\n return round(rating, 1)\n\n @property\n def feedback_count(self):\n return self.product_feedbacks.count()\n\n @property\n def feedback(self):\n return self.product_feedbacks.all().order_by('-date')\n\n def get_params(self):\n return Tag.objects.filter_by_products([self]).get_group_tags_pairs()\n\n def get_brand_name(self) -> str:\n brand: typing.Optional['Tag'] = Tag.objects.get_brands([self]).get(self)\n return brand.name if brand else ''\n\n\nclass ProductFeedback(models.Model):\n product = models.ForeignKey(\n Product, on_delete=models.CASCADE, null=True,\n related_name='product_feedbacks'\n )\n\n date = models.DateTimeField(\n auto_now=True, db_index=True, verbose_name=_('date'))\n name = models.CharField(\n max_length=255, db_index=True, verbose_name=_('name'))\n rating = models.PositiveSmallIntegerField(\n default=1, db_index=True, verbose_name=_('rating'))\n dignities = models.TextField(\n default='', blank=True, verbose_name=_('dignities'))\n limitations = models.TextField(\n default='', blank=True, verbose_name=_('limitations'))\n general = models.TextField(\n default='', blank=True, verbose_name=_('limitations'))\n\n\ndef _default_payment():\n \"\"\"Default payment option is first element of first tuple in options.\"\"\"\n assert settings.PAYMENT_OPTIONS[0][0], 'No payment options!'\n return settings.PAYMENT_OPTIONS[0][0]\n\n\nclass Order(ecommerce_models.Order):\n address = models.TextField(blank=True, default='')\n payment_type = models.CharField(\n max_length=255,\n choices=settings.PAYMENT_OPTIONS,\n default=_default_payment()\n )\n comment = models.TextField(blank=True, default='')\n # total price - total purchase price\n revenue = models.FloatField(default=0, null=True, verbose_name=_('revenue'))\n\n @property\n def payment_type_name(self):\n \"\"\"Return name for an order's payment option.\"\"\"\n return next(\n name for option, name in settings.PAYMENT_OPTIONS\n if self.payment_type == option\n )\n\n def set_positions(self, cart):\n \"\"\"\n Save cart's state into Order instance.\n\n @todo #589:60m Create Cart model.\n See details here: https://github.com/fidals/shopelectro/pull/590#discussion_r222544672\n \"\"\"\n self.revenue = cart.total_revenue()\n self.save()\n for id_, position in cart:\n self.positions.create(\n order=self,\n product_id=id_,\n vendor_code=position['vendor_code'],\n name=position['name'],\n price=position['price'],\n quantity=position['quantity'],\n )\n return self\n\n\nclass CategoryPage(pages_models.ModelPage):\n \"\"\"Create proxy model for Admin.\"\"\"\n\n class Meta(pages_models.ModelPage.Meta): # Ignore PycodestyleBear (E303)\n proxy = True\n\n # noinspection PyTypeChecker\n objects = pages_models.ModelPage.create_model_page_managers(Category)\n\n\nclass ProductPage(pages_models.ModelPage):\n \"\"\"Create proxy model for Admin.\"\"\"\n\n class Meta(pages_models.ModelPage.Meta): # Ignore PycodestyleBear (E303)\n proxy = True\n\n # noinspection PyTypeChecker\n objects = (\n pages_models.ModelPage\n .create_model_page_managers(Product)\n )\n\n\nclass TagGroup(catalog_models.TagGroup):\n pass\n\n\nclass TagQuerySet(catalog_models.TagQuerySet):\n pass\n\n\nclass Tag(catalog_models.Tag):\n group = models.ForeignKey(\n TagGroup, on_delete=models.CASCADE, null=True, related_name='tags',\n )\n",
"path": "shopelectro/models.py"
}
] | [
{
"content": "import random\nimport string\nimport typing\nfrom uuid import uuid4\n\nfrom django.conf import settings\nfrom django.db import models\nfrom django.urls import reverse\nfrom django.utils.translation import ugettext_lazy as _\n\nfrom catalog import models as catalog_models\nfrom ecommerce import models as ecommerce_models\nfrom pages import models as pages_models\n\n\ndef randomize_slug(slug: str) -> str:\n slug_hash = ''.join(\n random.choices(string.ascii_lowercase, k=settings.SLUG_HASH_SIZE)\n )\n return f'{slug}_{slug_hash}'\n\n\nclass SECategoryQuerySet(catalog_models.CategoryQuerySet):\n def get_categories_tree_with_pictures(self) -> 'SECategoryQuerySet':\n categories_with_pictures = (\n self\n .filter(products__page__images__isnull=False)\n .distinct()\n )\n\n return categories_with_pictures.get_ancestors(include_self=True)\n\n\nclass SECategoryManager(\n catalog_models.CategoryManager.from_queryset(SECategoryQuerySet)\n):\n pass\n\n\nclass Category(catalog_models.AbstractCategory, pages_models.SyncPageMixin):\n\n objects = SECategoryManager()\n uuid = models.UUIDField(default=uuid4, editable=False)\n\n @classmethod\n def get_default_parent(cls):\n return pages_models.CustomPage.objects.filter(slug='catalog').first()\n\n @property\n def image(self):\n products = self.products.all()\n return products[0].image if products else None\n\n def get_absolute_url(self):\n return reverse('category', args=(self.page.slug,))\n\n\nclass Product(catalog_models.AbstractProduct, pages_models.SyncPageMixin):\n\n # That's why we are needed to explicitly add objects manager here\n # because of Django special managers behaviour.\n # Se se#480 for details.\n objects = catalog_models.ProductManager()\n\n category = models.ForeignKey(\n Category,\n on_delete=models.CASCADE,\n null=True,\n related_name='products',\n verbose_name=_('category'),\n )\n\n tags = models.ManyToManyField(\n 'Tag',\n related_name='products',\n blank=True,\n verbose_name=_('tags'),\n )\n\n vendor_code = models.SmallIntegerField(verbose_name=_('vendor_code'))\n uuid = models.UUIDField(default=uuid4, editable=False)\n purchase_price = models.FloatField(\n default=0, verbose_name=_('purchase_price'))\n wholesale_small = models.FloatField(\n default=0, verbose_name=_('wholesale_small'))\n wholesale_medium = models.FloatField(\n default=0, verbose_name=_('wholesale_medium'))\n wholesale_large = models.FloatField(\n default=0, verbose_name=_('wholesale_large'))\n\n def get_absolute_url(self):\n return reverse('product', args=(self.vendor_code,))\n\n @property\n def average_rate(self):\n \"\"\"Return rounded to first decimal averaged rating.\"\"\"\n rating = self.product_feedbacks.aggregate(\n avg=models.Avg('rating')).get('avg', 0)\n return round(rating, 1)\n\n @property\n def feedback_count(self):\n return self.product_feedbacks.count()\n\n @property\n def feedback(self):\n return self.product_feedbacks.all().order_by('-date')\n\n def get_params(self):\n return Tag.objects.filter_by_products([self]).get_group_tags_pairs()\n\n def get_brand_name(self) -> str:\n brand: typing.Optional['Tag'] = Tag.objects.get_brands([self]).get(self)\n return brand.name if brand else ''\n\n\nclass ProductFeedback(models.Model):\n product = models.ForeignKey(\n Product, on_delete=models.CASCADE, null=True,\n related_name='product_feedbacks'\n )\n\n date = models.DateTimeField(\n auto_now=True, db_index=True, verbose_name=_('date'))\n name = models.CharField(\n max_length=255, db_index=True, verbose_name=_('name'))\n rating = models.PositiveSmallIntegerField(\n default=1, db_index=True, verbose_name=_('rating'))\n dignities = models.TextField(\n default='', blank=True, verbose_name=_('dignities'))\n limitations = models.TextField(\n default='', blank=True, verbose_name=_('limitations'))\n general = models.TextField(\n default='', blank=True, verbose_name=_('limitations'))\n\n\ndef _default_payment():\n \"\"\"Default payment option is first element of first tuple in options.\"\"\"\n assert settings.PAYMENT_OPTIONS[0][0], 'No payment options!'\n return settings.PAYMENT_OPTIONS[0][0]\n\n\nclass Order(ecommerce_models.Order):\n address = models.TextField(blank=True, default='')\n payment_type = models.CharField(\n max_length=255,\n choices=settings.PAYMENT_OPTIONS,\n default=_default_payment()\n )\n comment = models.TextField(blank=True, default='')\n # total price - total purchase price\n revenue = models.FloatField(default=0, null=True, verbose_name=_('revenue'))\n\n @property\n def payment_type_name(self):\n \"\"\"Return name for an order's payment option.\"\"\"\n return next(\n name for option, name in settings.PAYMENT_OPTIONS\n if self.payment_type == option\n )\n\n def set_positions(self, cart):\n \"\"\"\n Save cart's state into Order instance.\n\n @todo #589:60m Create Cart model.\n See details here: https://github.com/fidals/shopelectro/pull/590#discussion_r222544672\n \"\"\"\n self.revenue = cart.total_revenue()\n self.save()\n for id_, position in cart:\n self.positions.create(\n order=self,\n product_id=id_,\n vendor_code=position['vendor_code'],\n name=position['name'],\n price=position['price'],\n quantity=position['quantity'],\n )\n return self\n\n\nclass CategoryPage(pages_models.ModelPage):\n \"\"\"Create proxy model for Admin.\"\"\"\n\n class Meta(pages_models.ModelPage.Meta): # Ignore PycodestyleBear (E303)\n proxy = True\n\n # noinspection PyTypeChecker\n objects = pages_models.ModelPage.create_model_page_managers(Category)\n\n\nclass ProductPage(pages_models.ModelPage):\n \"\"\"Create proxy model for Admin.\"\"\"\n\n class Meta(pages_models.ModelPage.Meta): # Ignore PycodestyleBear (E303)\n proxy = True\n\n # noinspection PyTypeChecker\n objects = (\n pages_models.ModelPage\n .create_model_page_managers(Product)\n )\n\n\nclass TagGroup(catalog_models.TagGroup):\n pass\n\n\nclass TagQuerySet(catalog_models.TagQuerySet):\n pass\n\n\nclass Tag(catalog_models.Tag):\n group = models.ForeignKey(\n TagGroup, on_delete=models.CASCADE, null=True, related_name='tags',\n )\n",
"path": "shopelectro/models.py"
}
] | diff --git a/shopelectro/models.py b/shopelectro/models.py
index 6f5320ce..ec837263 100644
--- a/shopelectro/models.py
+++ b/shopelectro/models.py
@@ -7,7 +7,6 @@
from django.db import models
from django.urls import reverse
from django.utils.translation import ugettext_lazy as _
-import mptt
from catalog import models as catalog_models
from ecommerce import models as ecommerce_models
diff --git a/shopelectro/tests/tests_selenium.py b/shopelectro/tests/tests_selenium.py
index c5df3ead..dea48555 100644
--- a/shopelectro/tests/tests_selenium.py
+++ b/shopelectro/tests/tests_selenium.py
@@ -16,7 +16,6 @@
from selenium.webdriver.support import expected_conditions as EC, ui
from pages.models import FlatPage, CustomPage
-
from shopelectro.models import Category, Product
from shopelectro.tests import helpers
@@ -57,6 +56,8 @@ def is_cart_empty(browser):
return browser.find_element_by_class_name('js-cart-is-empty').is_displayed()
+# @todo #494:15m Rm `wait_page_loading` function in favor of analogous method.
+# In favor of `helpers.SeleniumTestCase.wait_page_loaded`
def wait_page_loading(browser):
ui.WebDriverWait(browser, 60).until(
EC.visibility_of_element_located(
@@ -1026,8 +1027,6 @@ def test_full_buy_goal(self):
self.assertTrue('FULL_BUY_SEND' in self.reached_goals)
self.assertTrue('CMN_BUY_SEND' in self.reached_goals)
- # @todo #473:15m Resurrect test `test_cart_page_open`
- @unittest.skip
def test_cart_page_open(self):
self.buy_product()
self.prevent_default('click', '.js-go-to-cart')
|
hpcaitech__ColossalAI-5433 | [tensor] fix some unittests
[tensor] fix some unittests
[tensor] fix some unittests
| [
{
"content": "from ..cuda_extension import _CudaExtension\nfrom ..utils import get_cuda_cc_flag\n\n\nclass InferenceOpsCudaExtension(_CudaExtension):\n def __init__(self):\n super().__init__(name=\"inference_ops_cuda\")\n\n def sources_files(self):\n ret = [\n self.csrc_abs_path(fname)\n for fname in [\n \"cuda/colossal_inference_C_frontend.cpp\",\n \"cuda/decode_kv_cache_memcpy_kernel.cu\",\n ]\n ]\n return ret\n\n def include_dirs(self):\n ret = [self.get_cuda_home_include()]\n return ret\n\n def cxx_flags(self):\n version_dependent_macros = [\"-DVERSION_GE_1_1\", \"-DVERSION_GE_1_3\", \"-DVERSION_GE_1_5\"]\n return [\"-O3\"] + version_dependent_macros\n\n def nvcc_flags(self):\n extra_cuda_flags = [\"-lineinfo\"]\n extra_cuda_flags.extend(get_cuda_cc_flag())\n return [\"-O3\", \"--use_fast_math\"] + extra_cuda_flags\n",
"path": "extensions/inference/inference_ops_cuda.py"
}
] | [
{
"content": "from ..cuda_extension import _CudaExtension\nfrom ..utils import get_cuda_cc_flag\n\n\nclass InferenceOpsCudaExtension(_CudaExtension):\n def __init__(self):\n super().__init__(name=\"inference_ops_cuda\")\n\n def sources_files(self):\n ret = [\n self.csrc_abs_path(fname)\n for fname in [\n \"cuda/colossal_inference_C_frontend.cpp\",\n \"cuda/decode_kv_cache_memcpy_kernel.cu\",\n \"cuda/activation_kernel.cu\",\n ]\n ]\n return ret\n\n def include_dirs(self):\n ret = [self.get_cuda_home_include()]\n return ret\n\n def cxx_flags(self):\n version_dependent_macros = [\"-DVERSION_GE_1_1\", \"-DVERSION_GE_1_3\", \"-DVERSION_GE_1_5\"]\n return [\"-O3\"] + version_dependent_macros\n\n def nvcc_flags(self):\n extra_cuda_flags = [\"-lineinfo\"]\n extra_cuda_flags.extend(get_cuda_cc_flag())\n return [\"-O3\", \"--use_fast_math\"] + extra_cuda_flags\n",
"path": "extensions/inference/inference_ops_cuda.py"
}
] | diff --git a/extensions/csrc/cuda/activation_kernel.cu b/extensions/csrc/cuda/activation_kernel.cu
new file mode 100644
index 000000000000..4121b67fc523
--- /dev/null
+++ b/extensions/csrc/cuda/activation_kernel.cu
@@ -0,0 +1,65 @@
+#include <ATen/cuda/CUDAContext.h>
+#include <torch/extension.h>
+#include <stdio.h>
+
+#include "type_shim.h"
+#include "include/mp_type_traits.h"
+
+template<typename T>
+__device__ __forceinline__ T silu_kernel(const T& x) {
+ // x * sigmoid(x)
+ using MT = typename infer::dtype::MPTypeTrait<T>::Type;
+ return static_cast<T>((static_cast<MT>(x)) / (static_cast<MT>(1.0f) + expf(static_cast<MT>(-x))));
+}
+
+template<typename scalar_t, scalar_t (*ACT_FN)(const scalar_t&)>
+__global__ void act_and_mul_kernel(
+ const scalar_t* __restrict__ ins_data,
+ scalar_t* __restrict__ outs_data,
+ const int64_t numel) {
+ using MT = typename infer::dtype::MPTypeTrait<scalar_t>::Type;
+
+ int64_t idx = static_cast<int64_t>(threadIdx.x) + static_cast<int64_t>(blockIdx.x) * static_cast<int64_t>(blockDim.x);
+ const int64_t grid_size = blockDim.x * gridDim.x;
+ if(idx > numel) {
+ return;
+ }
+
+ for(int64_t i = idx; i < numel; i += grid_size) {
+ scalar_t x = ins_data[i];
+ scalar_t y = ins_data[i+numel];
+ outs_data[i] = static_cast<scalar_t>(static_cast<MT>(ACT_FN(x)) * static_cast<MT>(y));
+ }
+}
+
+// Note(LiuYang):This func is designed for calculation mode like
+// silu(x[:half_1stdim]) * (x[half_1stdim:])
+torch::Tensor silu_and_mul(const torch::Tensor& ins)
+{
+ auto ins_shape = ins.sizes().vec();
+
+ ins_shape[0] = ins_shape[0]/2;
+ auto outs = torch::zeros(ins_shape,ins.options());
+ auto outs_shape = ins.sizes().vec();
+
+ const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
+
+ // Note(Liuyang): numel of ins must be divisible by 2
+ int64_t numel = ((torch::numel(ins)) >> 1);
+
+ // TODO(LiuYang): Maybe we need to implement a function to get launch config
+ dim3 grid((numel+255)/256);
+ dim3 block(256);
+
+ DISPATCH_FLOAT_HALF_AND_BFLOAT(
+ ins.scalar_type(),
+ "silu_and_mul",
+ act_and_mul_kernel<scalar_t,silu_kernel<scalar_t>><<<grid, block, 0, stream>>>(
+ ins.data_ptr<scalar_t>(),
+ outs.data_ptr<scalar_t>(),
+ numel
+ );)
+
+ AT_CUDA_CHECK(cudaGetLastError());
+ return outs;
+}
diff --git a/extensions/csrc/cuda/colossal_inference_C_frontend.cpp b/extensions/csrc/cuda/colossal_inference_C_frontend.cpp
index ae410c14ff84..cc53d8b8800b 100644
--- a/extensions/csrc/cuda/colossal_inference_C_frontend.cpp
+++ b/extensions/csrc/cuda/colossal_inference_C_frontend.cpp
@@ -9,7 +9,10 @@ void decode_kv_cache_memcpy(
torch::Tensor& sequence_lengths, // [batch_size]
torch::Tensor& block_tables); // [batch_size, max_seq_len]
+torch::Tensor silu_and_mul(const torch::Tensor& ins);
+
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
m.def("decode_kv_cache_memcpy", &decode_kv_cache_memcpy,
"Copy the GPU memory of kvcache during the decode stage.");
+ m.def("silu_and_mul", &silu_and_mul, "Silu with a following multiply");
}
diff --git a/extensions/csrc/cuda/include/mp_type_traits.h b/extensions/csrc/cuda/include/mp_type_traits.h
new file mode 100644
index 000000000000..6b3ae9c1b218
--- /dev/null
+++ b/extensions/csrc/cuda/include/mp_type_traits.h
@@ -0,0 +1,35 @@
+#pragma once
+
+#include <ATen/ATen.h>
+
+#include "../type_shim.h"
+
+namespace infer {
+namespace dtype {
+
+template <typename T>
+class MPTypeTrait {
+ public:
+ using Type = float;
+};
+
+template <>
+class MPTypeTrait<float> {
+ public:
+ using Type = float;
+};
+
+template <>
+class MPTypeTrait<at::Half> {
+ public:
+ using Type = float;
+};
+
+template <>
+class MPTypeTrait<at::BFloat16> {
+ public:
+ using Type = float;
+};
+
+} // namespace dtype
+} // namespace infer
diff --git a/extensions/csrc/cuda/type_shim.h b/extensions/csrc/cuda/type_shim.h
index 5116319358d7..7be3fab1b574 100644
--- a/extensions/csrc/cuda/type_shim.h
+++ b/extensions/csrc/cuda/type_shim.h
@@ -4,6 +4,9 @@
This file is adapted from fused adam in NVIDIA/apex, commit a109f85
Licensed under the MIT License.
*/
+
+#pragma once
+
#include <ATen/ATen.h>
#include "compat.h"
diff --git a/extensions/inference/inference_ops_cuda.py b/extensions/inference/inference_ops_cuda.py
index 12bec6fab1a1..2858d716095b 100644
--- a/extensions/inference/inference_ops_cuda.py
+++ b/extensions/inference/inference_ops_cuda.py
@@ -12,6 +12,7 @@ def sources_files(self):
for fname in [
"cuda/colossal_inference_C_frontend.cpp",
"cuda/decode_kv_cache_memcpy_kernel.cu",
+ "cuda/activation_kernel.cu",
]
]
return ret
diff --git a/tests/test_infer/test_ops/cuda/test_silu_and_mul.py b/tests/test_infer/test_ops/cuda/test_silu_and_mul.py
new file mode 100644
index 000000000000..ced2db7ca048
--- /dev/null
+++ b/tests/test_infer/test_ops/cuda/test_silu_and_mul.py
@@ -0,0 +1,33 @@
+import pytest
+import torch
+
+from colossalai.kernel.kernel_loader import InferenceOpsLoader
+from colossalai.utils import get_current_device
+
+inference_ops = InferenceOpsLoader().load()
+
+
[email protected]("SHAPE_X", [2])
[email protected]("SHAPE_Y", [64])
[email protected]("SHAPE_Z", [11008])
[email protected]("dtype", [torch.float32, torch.float16])
+def test_silu_and_mul(SHAPE_X, SHAPE_Y, SHAPE_Z, dtype):
+ torch.manual_seed(5)
+ device = get_current_device()
+ ref_input = torch.randn(SHAPE_X, SHAPE_Y, SHAPE_Z, dtype=dtype, device=device)
+ origin_input = ref_input.clone()
+
+ act_out = torch.nn.functional.silu(ref_input[0], inplace=True)
+ ref_out = act_out * ref_input[1]
+
+ origin_out = inference_ops.silu_and_mul(origin_input)
+
+ if dtype == torch.float32:
+ assert torch.allclose(origin_out, ref_out, atol=1e-5, rtol=1e-5)
+ else:
+ assert torch.allclose(origin_out, ref_out, atol=1e-3, rtol=1e-3)
+
+
+if __name__ == "__main__":
+ test_silu_and_mul(2, 64, 11008, torch.float32)
+ test_silu_and_mul(2, 64, 11008, torch.float16)
|
saulpw__visidata-2398 | `history` parameter of input() is appears ignored
**Small description**
I think the `history` parameter of the `input` function is unused and overwritten here:
https://github.com/saulpw/visidata/blob/3ad7a3d0c1475ff53bf31481506b9a748b48ac7c/visidata/_input.py#L501
**Expected result**
That `history` would be honored. I believe it is not.
**Actual result with screenshot**
(no screenshot, this shows up when you call this function and use the arrow keys to go back into the history.)
**Steps to reproduce with sample data and a .vd**
Try this in your `.visidatarc`:
```
VIEWER_HISTORY = []
def guess_program(filename: str) -> str:
basename, ext = path.splitext(filename)
ext = ext.lower()
with open("/tmp/log", "w") as f:
print(ext, file=f)
if ext == '.pdf':
return 'evince'
elif ext == '.jpg':
return 'geeqie'
@visidata.VisiData.api
def launchViewer(vd, *args):
"Launch $EDITOR with *args* as arguments."
global VIEWER_HISTORY
filename = str(args[0])
if not path.exists(filename):
vd.fail(f"Value '{filename}' is not an existing filename.")
program = guess_program(filename)
if program and (not VIEWER_HISTORY or VIEWER_HISTORY[0] != program):
VIEWER_HISTORY.append(program)
viewer = vd.input(
"viewer: ",
history=VIEWER_HISTORY,
defaultLast=bool(VIEWER_HISTORY),
)
if not viewer.strip():
vd.fail("Viewer not provided")
elif viewer not in VIEWER_HISTORY:
VIEWER_HISTORY.insert(0, viewer)
args = viewer.split() + list(args)
with SuspendCurses():
# subprocess.call(args)
cproc = subprocess.Popen(args)
DirSheet.addCommand(
"Alt+!",
"sysopen-row-shell-command",
"launchViewer(cursorRow)",
"open current file in external program (input manually)",
)
```
**Additional context**
git reflog: 3ad7a3d0c1475ff53bf31481506b9a748b48ac7c
date: 2024-04-28
python: 3.12.1
Thank you thank you!
| [
{
"content": "from contextlib import suppress\nimport curses\n\nimport visidata\n\nfrom visidata import EscapeException, ExpectedException, clipdraw, Sheet, VisiData, BaseSheet\nfrom visidata import vd, options, colors, dispwidth, ColorAttr\nfrom visidata import AttrDict\n\n\nvd.theme_option('color_edit_unfocused', '238 on 110', 'display color for unfocused input in form')\nvd.theme_option('color_edit_cell', '233 on 110', 'cell color to use when editing cell')\nvd.theme_option('disp_edit_fill', '_', 'edit field fill character')\nvd.theme_option('disp_unprintable', '·', 'substitute character for unprintables')\nvd.theme_option('mouse_interval', 1, 'max time between press/release for click (ms)', sheettype=None)\n\nvd.disp_help = 1 # current level of help shown (up to vd.options.disp_help as maximum)\n\nclass AcceptInput(Exception):\n '*args[0]* is the input to be accepted'\n\nvd._injectedInput = None # for vd.injectInput\n\n\[email protected]\ndef injectInput(vd, x):\n 'Use *x* as input to next command.'\n assert vd._injectedInput is None, vd._injectedInput\n vd._injectedInput = x\n\n\[email protected]\ndef getCommandInput(vd):\n if vd._injectedInput is not None:\n r = vd._injectedInput\n vd._injectedInput = None\n return r\n\n return vd.getLastArgs()\n\n\[email protected]\ndef execCommand(sheet, longname, *args, **kwargs):\n if vd._injectedInput is not None:\n vd.debug(f'{longname} did not consume input \"{vd._injectedInput}\"')\n vd._injectedInput = None\n\n\ndef acceptThenFunc(*longnames):\n def _acceptthen(v, i):\n for longname in longnames:\n vd.queueCommand(longname)\n raise AcceptInput(v)\n return _acceptthen\n\n# editline helpers\n\nclass EnableCursor:\n def __enter__(self):\n with suppress(curses.error):\n curses.mousemask(0)\n curses.curs_set(1)\n\n def __exit__(self, exc_type, exc_val, tb):\n with suppress(curses.error):\n curses.curs_set(0)\n if options.mouse_interval:\n curses.mousemask(curses.MOUSE_ALL if hasattr(curses, \"MOUSE_ALL\") else 0xffffffff)\n else:\n curses.mousemask(0)\n\n\ndef until_get_wch(scr):\n 'Ignores get_wch timeouts'\n ret = None\n while not ret:\n try:\n ret = vd.get_wch(scr)\n except curses.error:\n pass\n\n if isinstance(ret, int):\n return chr(ret)\n return ret\n\n\ndef splice(v:str, i:int, s:str):\n 'Insert `s` into string `v` at `i` (such that v[i] == s[0]).'\n return v if i < 0 else v[:i] + s + v[i:]\n\n\n# vd.options.disp_help is the effective maximum disp_help. The user can cycle through the various levels of help.\nclass HelpCycler:\n def __init__(self, scr=None, help=''):\n self.help = help\n self.scr = scr\n\n def __enter__(self):\n self.draw()\n\n return self\n\n def __exit__(self, *args):\n pass\n\n def cycle(self):\n vd.disp_help = (vd.disp_help-1)%(vd.options.disp_help+1)\n self.draw()\n\n def draw(self):\n if self.scr:\n vd.drawInputHelp(self.scr, self.help)\n\n\[email protected]\ndef drawInputHelp(vd, scr, help:str=''):\n if not scr or not vd.cursesEnabled:\n return\n\n sheet = vd.activeSheet\n if not sheet:\n return\n\n curhelp = ''\n if vd.disp_help == 0:\n vd.drawSidebar(scr, sheet)\n elif vd.disp_help == 1:\n curhelp = help\n sheet.drawSidebarText(scr, curhelp)\n elif vd.disp_help >= 2:\n curhelp = vd.getHelpPane('input', module='visidata')\n sheet.drawSidebarText(scr, curhelp, title='Input Keystrokes Help')\n\n\ndef clean_printable(s):\n 'Escape unprintable characters.'\n return ''.join(c if c.isprintable() else options.disp_unprintable for c in str(s))\n\n\ndef delchar(s, i, remove=1):\n 'Delete `remove` characters from str `s` beginning at position `i`.'\n return s if i < 0 else s[:i] + s[i+remove:]\n\n\nclass CompleteState:\n def __init__(self, completer_func):\n self.comps_idx = -1\n self.completer_func = completer_func\n self.former_i = None\n self.just_completed = False\n\n def complete(self, v, i, state_incr):\n self.just_completed = True\n self.comps_idx += state_incr\n\n if self.former_i is None:\n self.former_i = i\n try:\n r = self.completer_func(v[:self.former_i], self.comps_idx)\n except Exception as e:\n # raise # beep/flash; how to report exception?\n return v, i\n\n if not r:\n # beep/flash to indicate no matches?\n return v, i\n\n v = r + v[i:]\n return v, len(v)\n\n def reset(self):\n if self.just_completed:\n self.just_completed = False\n else:\n self.former_i = None\n self.comps_idx = -1\n\nclass HistoryState:\n def __init__(self, history):\n self.history = history\n self.hist_idx = None\n self.prev_val = None\n\n def up(self, v, i):\n if self.hist_idx is None:\n self.hist_idx = len(self.history)\n self.prev_val = v\n if self.hist_idx > 0:\n self.hist_idx -= 1\n v = self.history[self.hist_idx]\n i = len(str(v))\n return v, i\n\n def down(self, v, i):\n if self.hist_idx is None:\n return v, i\n elif self.hist_idx < len(self.history)-1:\n self.hist_idx += 1\n v = self.history[self.hist_idx]\n else:\n v = self.prev_val\n self.hist_idx = None\n i = len(str(v))\n return v, i\n\n\n# history: earliest entry first\[email protected]\ndef editline(vd, scr, y, x, w, i=0,\n attr=ColorAttr(),\n value='',\n fillchar=' ',\n truncchar='-',\n unprintablechar='.',\n completer=lambda text,idx: None,\n history=[],\n display=True,\n updater=lambda val: None,\n bindings={},\n help='', # str|HelpPane\n clear=True):\n '''A better curses line editing widget.\n If *clear* is True, clear whole editing area before displaying.\n '''\n with EnableCursor():\n with HelpCycler(scr, help) as disp_help:\n ESC='^['\n TAB='^I'\n history_state = HistoryState(history)\n complete_state = CompleteState(completer)\n insert_mode = True\n first_action = True\n v = str(value) # value under edit\n\n # i = 0 # index into v, initial value can be passed in as argument as of 1.2\n if i != 0:\n first_action = False\n\n left_truncchar = right_truncchar = truncchar\n\n def find_nonword(s, a, b, incr):\n if not s: return 0\n a = min(max(a, 0), len(s)-1)\n b = min(max(b, 0), len(s)-1)\n\n if incr < 0:\n while not s[b].isalnum() and b >= a: # first skip non-word chars\n b += incr\n while s[b].isalnum() and b >= a:\n b += incr\n return min(max(b, -1), len(s))\n else:\n while not s[a].isalnum() and a < b: # first skip non-word chars\n a += incr\n while s[a].isalnum() and a < b:\n a += incr\n return min(max(a, 0), len(s))\n\n while True:\n vd.drawSheet(scr, vd.activeSheet)\n updater(v)\n disp_help.draw()\n\n if display:\n dispval = clean_printable(v)\n else:\n dispval = '*' * len(v)\n\n dispi = i # the onscreen offset within the field where v[i] is displayed\n if len(dispval) < w: # entire value fits\n dispval += fillchar*(w-len(dispval)-1)\n elif i == len(dispval): # cursor after value (will append)\n dispi = w-1\n dispval = left_truncchar + dispval[len(dispval)-w+2:] + fillchar\n elif i >= len(dispval)-w//2: # cursor within halfwidth of end\n dispi = w-(len(dispval)-i)\n dispval = left_truncchar + dispval[len(dispval)-w+1:]\n elif i <= w//2: # cursor within halfwidth of beginning\n dispval = dispval[:w-1] + right_truncchar\n else:\n dispi = w//2 # visual cursor stays right in the middle\n k = 1 if w%2==0 else 0 # odd widths have one character more\n dispval = left_truncchar + dispval[i-w//2+1:i+w//2-k] + right_truncchar\n\n prew = clipdraw(scr, y, x, dispval[:dispi], attr, w, clear=clear, literal=True)\n clipdraw(scr, y, x+prew, dispval[dispi:], attr, w-prew+1, clear=clear, literal=True)\n if scr: scr.move(y, x+prew)\n ch = vd.getkeystroke(scr)\n if ch == '': continue\n elif ch in bindings: v, i = bindings[ch](v, i)\n elif ch == 'KEY_IC': insert_mode = not insert_mode\n elif ch == '^A' or ch == 'KEY_HOME': i = 0\n elif ch == '^B' or ch == 'KEY_LEFT': i -= 1\n elif ch in ('^C', '^Q', ESC): raise EscapeException(ch)\n elif ch == '^D' or ch == 'KEY_DC': v = delchar(v, i)\n elif ch == '^E' or ch == 'KEY_END': i = len(v)\n elif ch == '^F' or ch == 'KEY_RIGHT': i += 1\n elif ch == '^G':\n disp_help.cycle()\n continue # not considered a first keypress\n elif ch in ('^H', 'KEY_BACKSPACE', '^?'): i -= 1; v = delchar(v, i)\n elif ch == TAB: v, i = complete_state.complete(v, i, +1)\n elif ch == 'KEY_BTAB': v, i = complete_state.complete(v, i, -1)\n elif ch in ['^J', '^M']: break # ENTER to accept value\n elif ch == '^K': v = v[:i] # ^Kill to end-of-line\n elif ch == '^N':\n c = ''\n while not c:\n c = vd.getkeystroke(scr)\n c = vd.prettykeys(c)\n i += len(c)\n v += c\n elif ch == '^O': v = vd.launchExternalEditor(v); break\n elif ch == '^R': v = str(value) # ^Reload initial value\n elif ch == '^T': v = delchar(splice(v, i-2, v[i-1:i]), i) # swap chars\n elif ch == '^U': v = v[i:]; i = 0 # clear to beginning\n elif ch == '^V': v = splice(v, i, until_get_wch(scr)); i += 1 # literal character\n elif ch == '^W': j = find_nonword(v, 0, i-1, -1); v = v[:j+1] + v[i:]; i = j+1 # erase word\n elif ch == '^Y': v = splice(v, i, str(vd.memory.clipval))\n elif ch == '^Z': vd.suspend()\n # CTRL+arrow\n elif ch == 'kLFT5': i = find_nonword(v, 0, i-1, -1)+1; # word left\n elif ch == 'kRIT5': i = find_nonword(v, i+1, len(v)-1, +1)+1; # word right\n elif ch == 'kUP5': pass\n elif ch == 'kDN5': pass\n elif history and ch == 'KEY_UP': v, i = history_state.up(v, i)\n elif history and ch == 'KEY_DOWN': v, i = history_state.down(v, i)\n elif len(ch) > 1: pass\n else:\n if first_action:\n v = ''\n if insert_mode:\n v = splice(v, i, ch)\n else:\n v = v[:i] + ch + v[i+1:]\n\n i += 1\n\n if i < 0: i = 0\n # v may have a non-str type with no len()\n v = str(v)\n if i > len(v): i = len(v)\n first_action = False\n complete_state.reset()\n\n return v\n\n\[email protected]\ndef editText(vd, y, x, w, record=True, display=True, **kwargs):\n 'Invoke modal single-line editor at (*y*, *x*) for *w* terminal chars. Use *display* is False for sensitive input like passphrases. If *record* is True, get input from the cmdlog in batch mode, and save input to the cmdlog if *display* is also True. Return new value as string.'\n v = None\n if record and vd.cmdlog:\n v = vd.getCommandInput()\n\n if v is None:\n try:\n v = vd.editline(vd.activeSheet._scr, y, x, w, display=display, **kwargs)\n except AcceptInput as e:\n v = e.args[0]\n\n if vd.cursesEnabled:\n # clear keyboard buffer to neutralize multi-line pastes (issue#585)\n curses.flushinp()\n\n if display:\n if record and vd.cmdlog:\n vd.setLastArgs(v)\n\n if 'value' in kwargs:\n starting_value = kwargs['value']\n if isinstance(starting_value, (int, float)) and v[-1] == '%': #2082\n pct = float(v[:-1])\n v = pct*starting_value/100\n\n v = type(starting_value)(v)\n\n return v\n\[email protected]\ndef inputsingle(vd, prompt, record=True):\n 'Display prompt and return single character of user input.'\n sheet = vd.activeSheet\n\n v = None\n if record and vd.cmdlog:\n v = vd.getCommandInput()\n\n if v is not None:\n return v\n\n y = sheet.windowHeight-1\n w = sheet.windowWidth\n rstatuslen = vd.drawRightStatus(sheet._scr, sheet)\n promptlen = clipdraw(sheet._scr, y, 0, prompt, 0, w=w-rstatuslen-1)\n sheet._scr.move(y, w-promptlen-rstatuslen-2)\n\n while not v:\n v = vd.getkeystroke(sheet._scr)\n\n if record and vd.cmdlog:\n vd.setLastArgs(v)\n\n return v\n\[email protected]\ndef inputMultiple(vd, updater=lambda val: None, record=True, **kwargs):\n 'A simple form, where each input is an entry in `kwargs`, with the key being the key in the returned dict, and the value being a dictionary of kwargs to the singular input().'\n sheet = vd.activeSheet\n scr = sheet._scr\n\n previnput = vd.getCommandInput()\n if previnput is not None:\n if isinstance(previnput, str):\n if previnput.startswith('{'):\n return json.loads(previnput)\n else:\n ret = {k:v.get('value', '') for k,v in kwargs.items()}\n primekey = list(ret.keys())[0]\n ret[primekey] = previnput\n return ret\n\n if isinstance(previnput, dict):\n return previnput\n\n assert False, type(previnput)\n\n y = sheet.windowHeight-1\n maxw = sheet.windowWidth//2\n attr = colors.color_edit_unfocused\n\n keys = list(kwargs.keys())\n cur_input_key = keys[0]\n\n if scr:\n scr.erase()\n\n for i, (k, v) in enumerate(kwargs.items()):\n v['dy'] = i\n v['w'] = maxw-dispwidth(v.get('prompt'))\n\n class ChangeInput(Exception):\n pass\n\n def change_input(offset):\n def _throw(v, i):\n if scr:\n scr.erase()\n raise ChangeInput(v, offset)\n return _throw\n\n def _drawPrompt(val):\n for k, v in kwargs.items():\n maxw = min(sheet.windowWidth-1, max(dispwidth(v.get('prompt')), dispwidth(str(v.get('value', '')))))\n promptlen = clipdraw(scr, y-v.get('dy'), 0, v.get('prompt'), attr, w=maxw) #1947\n promptlen = clipdraw(scr, y-v.get('dy'), promptlen, v.get('value', ''), attr, w=maxw)\n\n return updater(val)\n\n with HelpCycler() as disp_help:\n while True:\n try:\n input_kwargs = kwargs[cur_input_key]\n input_kwargs['value'] = vd.input(**input_kwargs,\n attr=colors.color_edit_cell,\n updater=_drawPrompt,\n record=False,\n bindings={\n 'KEY_BTAB': change_input(-1),\n '^I': change_input(+1),\n 'KEY_SR': change_input(-1),\n 'KEY_SF': change_input(+1),\n 'kUP': change_input(-1),\n 'kDN': change_input(+1),\n })\n break\n except ChangeInput as e:\n input_kwargs['value'] = e.args[0]\n offset = e.args[1]\n i = keys.index(cur_input_key)\n cur_input_key = keys[(i+offset)%len(keys)]\n\n retargs = {}\n lastargs = {}\n for k, input_kwargs in kwargs.items():\n v = input_kwargs.get('value', '')\n retargs[k] = v\n\n if input_kwargs.get('record', record):\n if input_kwargs.get('display', True):\n lastargs[k] = v\n vd.addInputHistory(v, input_kwargs.get('type', ''))\n if record:\n if vd.cmdlog and lastargs:\n vd.setLastArgs(lastargs)\n\n return retargs\n\n\[email protected]\ndef input(vd, prompt, type=None, defaultLast=False, history=[], dy=0, attr=None, updater=lambda v: None, **kwargs):\n '''Display *prompt* and return line of user input.\n\n - *type*: string indicating the type of input to use for history.\n - *history*: list of strings to use for input history.\n - *defaultLast*: on empty input, if True, return last history item.\n - *display*: pass False to not display input (for sensitive input, e.g. a password).\n - *record*: pass False to not record input on cmdlog (for sensitive or inconsequential input).\n - *completer*: ``completer(val, idx)`` is called on TAB to get next completed value.\n - *updater*: ``updater(val)`` is called every keypress or timeout.\n - *bindings*: dict of keystroke to func(v, i) that returns updated (v, i)\n - *dy*: number of lines from bottom of pane\n - *attr*: curses attribute for prompt\n - *help*: string to include in help\n '''\n\n if attr is None:\n attr = ColorAttr()\n sheet = vd.activeSheet\n if not vd.cursesEnabled:\n if kwargs.get('record', True) and vd.cmdlog:\n return vd.getCommandInput()\n\n if kwargs.get('display', True):\n import builtins\n return builtins.input(prompt)\n else:\n import getpass\n return getpass.getpass(prompt)\n\n history = list(vd.inputHistory.setdefault(type, {}).keys())\n\n y = sheet.windowHeight-dy-1\n promptlen = dispwidth(prompt)\n\n def _drawPrompt(val=''):\n rstatuslen = vd.drawRightStatus(sheet._scr, sheet)\n clipdraw(sheet._scr, y, 0, prompt, attr, w=sheet.windowWidth-rstatuslen-1)\n updater(val)\n return sheet.windowWidth-promptlen-rstatuslen-2\n\n w = kwargs.pop('w', _drawPrompt())\n ret = vd.editText(y, promptlen, w=w,\n attr=colors.color_edit_cell,\n unprintablechar=options.disp_unprintable,\n truncchar=options.disp_truncator,\n history=history,\n updater=_drawPrompt,\n **kwargs)\n\n if ret:\n vd.addInputHistory(ret, type=type)\n\n elif defaultLast:\n history or vd.fail(\"no previous input\")\n ret = history[-1]\n\n return ret\n\n\[email protected]\ndef confirm(vd, prompt, exc=EscapeException):\n 'Display *prompt* on status line and demand input that starts with \"Y\" or \"y\" to proceed. Raise *exc* otherwise. Return True.'\n if options.batch and not options.interactive:\n return vd.fail('cannot confirm in batch mode: ' + prompt)\n\n yn = vd.input(prompt, value='no', record=False)[:1]\n if not yn or yn not in 'Yy':\n msg = 'disconfirmed: ' + prompt\n if exc:\n raise exc(msg)\n vd.warning(msg)\n return False\n return True\n\n\nclass CompleteKey:\n def __init__(self, items):\n self.items = items\n\n def __call__(self, val, state):\n opts = [x for x in self.items if x.startswith(val)]\n return opts[state%len(opts)] if opts else val\n\n\[email protected]\ndef editCell(self, vcolidx=None, rowidx=None, value=None, **kwargs):\n '''Call vd.editText for the cell at (*rowidx*, *vcolidx*). Return the new value, properly typed.\n\n - *rowidx*: numeric index into ``self.rows``. If negative, indicates the column name in the header.\n - *value*: if given, the starting input; otherwise the starting input is the cell value or column name as appropriate.\n - *kwargs*: passthrough args to ``vd.editText``.\n '''\n\n if vcolidx is None:\n vcolidx = self.cursorVisibleColIndex\n x, w = self._visibleColLayout.get(vcolidx, (0, 0))\n\n col = self.visibleCols[vcolidx]\n if rowidx is None:\n rowidx = self.cursorRowIndex\n\n if rowidx < 0: # header\n y = 0\n value = value or col.name\n else:\n y, h = self._rowLayout.get(rowidx, (0, 0))\n value = value or col.getDisplayValue(self.rows[self.cursorRowIndex])\n\n bindings={\n 'kUP': acceptThenFunc('go-up', 'rename-col' if rowidx < 0 else 'edit-cell'),\n 'KEY_SR': acceptThenFunc('go-up', 'rename-col' if rowidx < 0 else 'edit-cell'),\n 'kDN': acceptThenFunc('go-down', 'rename-col' if rowidx < 0 else 'edit-cell'),\n 'KEY_SF': acceptThenFunc('go-down', 'rename-col' if rowidx < 0 else 'edit-cell'),\n 'KEY_SRIGHT': acceptThenFunc('go-right', 'rename-col' if rowidx < 0 else 'edit-cell'),\n 'KEY_SLEFT': acceptThenFunc('go-left', 'rename-col' if rowidx < 0 else 'edit-cell'),\n '^I': acceptThenFunc('go-right', 'rename-col' if rowidx < 0 else 'edit-cell'),\n 'KEY_BTAB': acceptThenFunc('go-left', 'rename-col' if rowidx < 0 else 'edit-cell'),\n }\n\n if vcolidx >= self.nVisibleCols-1:\n bindings['^I'] = acceptThenFunc('go-down', 'go-leftmost', 'edit-cell')\n\n if vcolidx <= 0:\n bindings['KEY_BTAB'] = acceptThenFunc('go-up', 'go-rightmost', 'edit-cell')\n\n # update local bindings with kwargs.bindings instead of the inverse, to preserve kwargs.bindings for caller\n bindings.update(kwargs.get('bindings', {}))\n kwargs['bindings'] = bindings\n\n editargs = dict(value=value,\n fillchar=self.options.disp_edit_fill,\n truncchar=self.options.disp_truncator)\n\n editargs.update(kwargs) # update with user-specified args\n r = vd.editText(y, x, w, attr=colors.color_edit_cell, **editargs)\n\n if rowidx >= 0: # if not header\n r = col.type(r) # convert input to column type, let exceptions be raised\n\n return r\n\n\nvd.addGlobals({'CompleteKey': CompleteKey, 'AcceptInput': AcceptInput})\n",
"path": "visidata/_input.py"
}
] | [
{
"content": "from contextlib import suppress\nimport curses\n\nimport visidata\n\nfrom visidata import EscapeException, ExpectedException, clipdraw, Sheet, VisiData, BaseSheet\nfrom visidata import vd, options, colors, dispwidth, ColorAttr\nfrom visidata import AttrDict\n\n\nvd.theme_option('color_edit_unfocused', '238 on 110', 'display color for unfocused input in form')\nvd.theme_option('color_edit_cell', '233 on 110', 'cell color to use when editing cell')\nvd.theme_option('disp_edit_fill', '_', 'edit field fill character')\nvd.theme_option('disp_unprintable', '·', 'substitute character for unprintables')\nvd.theme_option('mouse_interval', 1, 'max time between press/release for click (ms)', sheettype=None)\n\nvd.disp_help = 1 # current level of help shown (up to vd.options.disp_help as maximum)\n\nclass AcceptInput(Exception):\n '*args[0]* is the input to be accepted'\n\nvd._injectedInput = None # for vd.injectInput\n\n\[email protected]\ndef injectInput(vd, x):\n 'Use *x* as input to next command.'\n assert vd._injectedInput is None, vd._injectedInput\n vd._injectedInput = x\n\n\[email protected]\ndef getCommandInput(vd):\n if vd._injectedInput is not None:\n r = vd._injectedInput\n vd._injectedInput = None\n return r\n\n return vd.getLastArgs()\n\n\[email protected]\ndef execCommand(sheet, longname, *args, **kwargs):\n if vd._injectedInput is not None:\n vd.debug(f'{longname} did not consume input \"{vd._injectedInput}\"')\n vd._injectedInput = None\n\n\ndef acceptThenFunc(*longnames):\n def _acceptthen(v, i):\n for longname in longnames:\n vd.queueCommand(longname)\n raise AcceptInput(v)\n return _acceptthen\n\n# editline helpers\n\nclass EnableCursor:\n def __enter__(self):\n with suppress(curses.error):\n curses.mousemask(0)\n curses.curs_set(1)\n\n def __exit__(self, exc_type, exc_val, tb):\n with suppress(curses.error):\n curses.curs_set(0)\n if options.mouse_interval:\n curses.mousemask(curses.MOUSE_ALL if hasattr(curses, \"MOUSE_ALL\") else 0xffffffff)\n else:\n curses.mousemask(0)\n\n\ndef until_get_wch(scr):\n 'Ignores get_wch timeouts'\n ret = None\n while not ret:\n try:\n ret = vd.get_wch(scr)\n except curses.error:\n pass\n\n if isinstance(ret, int):\n return chr(ret)\n return ret\n\n\ndef splice(v:str, i:int, s:str):\n 'Insert `s` into string `v` at `i` (such that v[i] == s[0]).'\n return v if i < 0 else v[:i] + s + v[i:]\n\n\n# vd.options.disp_help is the effective maximum disp_help. The user can cycle through the various levels of help.\nclass HelpCycler:\n def __init__(self, scr=None, help=''):\n self.help = help\n self.scr = scr\n\n def __enter__(self):\n self.draw()\n\n return self\n\n def __exit__(self, *args):\n pass\n\n def cycle(self):\n vd.disp_help = (vd.disp_help-1)%(vd.options.disp_help+1)\n self.draw()\n\n def draw(self):\n if self.scr:\n vd.drawInputHelp(self.scr, self.help)\n\n\[email protected]\ndef drawInputHelp(vd, scr, help:str=''):\n if not scr or not vd.cursesEnabled:\n return\n\n sheet = vd.activeSheet\n if not sheet:\n return\n\n curhelp = ''\n if vd.disp_help == 0:\n vd.drawSidebar(scr, sheet)\n elif vd.disp_help == 1:\n curhelp = help\n sheet.drawSidebarText(scr, curhelp)\n elif vd.disp_help >= 2:\n curhelp = vd.getHelpPane('input', module='visidata')\n sheet.drawSidebarText(scr, curhelp, title='Input Keystrokes Help')\n\n\ndef clean_printable(s):\n 'Escape unprintable characters.'\n return ''.join(c if c.isprintable() else options.disp_unprintable for c in str(s))\n\n\ndef delchar(s, i, remove=1):\n 'Delete `remove` characters from str `s` beginning at position `i`.'\n return s if i < 0 else s[:i] + s[i+remove:]\n\n\nclass CompleteState:\n def __init__(self, completer_func):\n self.comps_idx = -1\n self.completer_func = completer_func\n self.former_i = None\n self.just_completed = False\n\n def complete(self, v, i, state_incr):\n self.just_completed = True\n self.comps_idx += state_incr\n\n if self.former_i is None:\n self.former_i = i\n try:\n r = self.completer_func(v[:self.former_i], self.comps_idx)\n except Exception as e:\n # raise # beep/flash; how to report exception?\n return v, i\n\n if not r:\n # beep/flash to indicate no matches?\n return v, i\n\n v = r + v[i:]\n return v, len(v)\n\n def reset(self):\n if self.just_completed:\n self.just_completed = False\n else:\n self.former_i = None\n self.comps_idx = -1\n\nclass HistoryState:\n def __init__(self, history):\n self.history = history\n self.hist_idx = None\n self.prev_val = None\n\n def up(self, v, i):\n if self.hist_idx is None:\n self.hist_idx = len(self.history)\n self.prev_val = v\n if self.hist_idx > 0:\n self.hist_idx -= 1\n v = self.history[self.hist_idx]\n i = len(str(v))\n return v, i\n\n def down(self, v, i):\n if self.hist_idx is None:\n return v, i\n elif self.hist_idx < len(self.history)-1:\n self.hist_idx += 1\n v = self.history[self.hist_idx]\n else:\n v = self.prev_val\n self.hist_idx = None\n i = len(str(v))\n return v, i\n\n\n# history: earliest entry first\[email protected]\ndef editline(vd, scr, y, x, w, i=0,\n attr=ColorAttr(),\n value='',\n fillchar=' ',\n truncchar='-',\n unprintablechar='.',\n completer=lambda text,idx: None,\n history=[],\n display=True,\n updater=lambda val: None,\n bindings={},\n help='', # str|HelpPane\n clear=True):\n '''A better curses line editing widget.\n If *clear* is True, clear whole editing area before displaying.\n '''\n with EnableCursor():\n with HelpCycler(scr, help) as disp_help:\n ESC='^['\n TAB='^I'\n history_state = HistoryState(history)\n complete_state = CompleteState(completer)\n insert_mode = True\n first_action = True\n v = str(value) # value under edit\n\n # i = 0 # index into v, initial value can be passed in as argument as of 1.2\n if i != 0:\n first_action = False\n\n left_truncchar = right_truncchar = truncchar\n\n def find_nonword(s, a, b, incr):\n if not s: return 0\n a = min(max(a, 0), len(s)-1)\n b = min(max(b, 0), len(s)-1)\n\n if incr < 0:\n while not s[b].isalnum() and b >= a: # first skip non-word chars\n b += incr\n while s[b].isalnum() and b >= a:\n b += incr\n return min(max(b, -1), len(s))\n else:\n while not s[a].isalnum() and a < b: # first skip non-word chars\n a += incr\n while s[a].isalnum() and a < b:\n a += incr\n return min(max(a, 0), len(s))\n\n while True:\n vd.drawSheet(scr, vd.activeSheet)\n updater(v)\n disp_help.draw()\n\n if display:\n dispval = clean_printable(v)\n else:\n dispval = '*' * len(v)\n\n dispi = i # the onscreen offset within the field where v[i] is displayed\n if len(dispval) < w: # entire value fits\n dispval += fillchar*(w-len(dispval)-1)\n elif i == len(dispval): # cursor after value (will append)\n dispi = w-1\n dispval = left_truncchar + dispval[len(dispval)-w+2:] + fillchar\n elif i >= len(dispval)-w//2: # cursor within halfwidth of end\n dispi = w-(len(dispval)-i)\n dispval = left_truncchar + dispval[len(dispval)-w+1:]\n elif i <= w//2: # cursor within halfwidth of beginning\n dispval = dispval[:w-1] + right_truncchar\n else:\n dispi = w//2 # visual cursor stays right in the middle\n k = 1 if w%2==0 else 0 # odd widths have one character more\n dispval = left_truncchar + dispval[i-w//2+1:i+w//2-k] + right_truncchar\n\n prew = clipdraw(scr, y, x, dispval[:dispi], attr, w, clear=clear, literal=True)\n clipdraw(scr, y, x+prew, dispval[dispi:], attr, w-prew+1, clear=clear, literal=True)\n if scr: scr.move(y, x+prew)\n ch = vd.getkeystroke(scr)\n if ch == '': continue\n elif ch in bindings: v, i = bindings[ch](v, i)\n elif ch == 'KEY_IC': insert_mode = not insert_mode\n elif ch == '^A' or ch == 'KEY_HOME': i = 0\n elif ch == '^B' or ch == 'KEY_LEFT': i -= 1\n elif ch in ('^C', '^Q', ESC): raise EscapeException(ch)\n elif ch == '^D' or ch == 'KEY_DC': v = delchar(v, i)\n elif ch == '^E' or ch == 'KEY_END': i = len(v)\n elif ch == '^F' or ch == 'KEY_RIGHT': i += 1\n elif ch == '^G':\n disp_help.cycle()\n continue # not considered a first keypress\n elif ch in ('^H', 'KEY_BACKSPACE', '^?'): i -= 1; v = delchar(v, i)\n elif ch == TAB: v, i = complete_state.complete(v, i, +1)\n elif ch == 'KEY_BTAB': v, i = complete_state.complete(v, i, -1)\n elif ch in ['^J', '^M']: break # ENTER to accept value\n elif ch == '^K': v = v[:i] # ^Kill to end-of-line\n elif ch == '^N':\n c = ''\n while not c:\n c = vd.getkeystroke(scr)\n c = vd.prettykeys(c)\n i += len(c)\n v += c\n elif ch == '^O': v = vd.launchExternalEditor(v); break\n elif ch == '^R': v = str(value) # ^Reload initial value\n elif ch == '^T': v = delchar(splice(v, i-2, v[i-1:i]), i) # swap chars\n elif ch == '^U': v = v[i:]; i = 0 # clear to beginning\n elif ch == '^V': v = splice(v, i, until_get_wch(scr)); i += 1 # literal character\n elif ch == '^W': j = find_nonword(v, 0, i-1, -1); v = v[:j+1] + v[i:]; i = j+1 # erase word\n elif ch == '^Y': v = splice(v, i, str(vd.memory.clipval))\n elif ch == '^Z': vd.suspend()\n # CTRL+arrow\n elif ch == 'kLFT5': i = find_nonword(v, 0, i-1, -1)+1; # word left\n elif ch == 'kRIT5': i = find_nonword(v, i+1, len(v)-1, +1)+1; # word right\n elif ch == 'kUP5': pass\n elif ch == 'kDN5': pass\n elif history and ch == 'KEY_UP': v, i = history_state.up(v, i)\n elif history and ch == 'KEY_DOWN': v, i = history_state.down(v, i)\n elif len(ch) > 1: pass\n else:\n if first_action:\n v = ''\n if insert_mode:\n v = splice(v, i, ch)\n else:\n v = v[:i] + ch + v[i+1:]\n\n i += 1\n\n if i < 0: i = 0\n # v may have a non-str type with no len()\n v = str(v)\n if i > len(v): i = len(v)\n first_action = False\n complete_state.reset()\n\n return v\n\n\[email protected]\ndef editText(vd, y, x, w, record=True, display=True, **kwargs):\n 'Invoke modal single-line editor at (*y*, *x*) for *w* terminal chars. Use *display* is False for sensitive input like passphrases. If *record* is True, get input from the cmdlog in batch mode, and save input to the cmdlog if *display* is also True. Return new value as string.'\n v = None\n if record and vd.cmdlog:\n v = vd.getCommandInput()\n\n if v is None:\n try:\n v = vd.editline(vd.activeSheet._scr, y, x, w, display=display, **kwargs)\n except AcceptInput as e:\n v = e.args[0]\n\n if vd.cursesEnabled:\n # clear keyboard buffer to neutralize multi-line pastes (issue#585)\n curses.flushinp()\n\n if display:\n if record and vd.cmdlog:\n vd.setLastArgs(v)\n\n if 'value' in kwargs:\n starting_value = kwargs['value']\n if isinstance(starting_value, (int, float)) and v[-1] == '%': #2082\n pct = float(v[:-1])\n v = pct*starting_value/100\n\n v = type(starting_value)(v)\n\n return v\n\[email protected]\ndef inputsingle(vd, prompt, record=True):\n 'Display prompt and return single character of user input.'\n sheet = vd.activeSheet\n\n v = None\n if record and vd.cmdlog:\n v = vd.getCommandInput()\n\n if v is not None:\n return v\n\n y = sheet.windowHeight-1\n w = sheet.windowWidth\n rstatuslen = vd.drawRightStatus(sheet._scr, sheet)\n promptlen = clipdraw(sheet._scr, y, 0, prompt, 0, w=w-rstatuslen-1)\n sheet._scr.move(y, w-promptlen-rstatuslen-2)\n\n while not v:\n v = vd.getkeystroke(sheet._scr)\n\n if record and vd.cmdlog:\n vd.setLastArgs(v)\n\n return v\n\[email protected]\ndef inputMultiple(vd, updater=lambda val: None, record=True, **kwargs):\n 'A simple form, where each input is an entry in `kwargs`, with the key being the key in the returned dict, and the value being a dictionary of kwargs to the singular input().'\n sheet = vd.activeSheet\n scr = sheet._scr\n\n previnput = vd.getCommandInput()\n if previnput is not None:\n if isinstance(previnput, str):\n if previnput.startswith('{'):\n return json.loads(previnput)\n else:\n ret = {k:v.get('value', '') for k,v in kwargs.items()}\n primekey = list(ret.keys())[0]\n ret[primekey] = previnput\n return ret\n\n if isinstance(previnput, dict):\n return previnput\n\n assert False, type(previnput)\n\n y = sheet.windowHeight-1\n maxw = sheet.windowWidth//2\n attr = colors.color_edit_unfocused\n\n keys = list(kwargs.keys())\n cur_input_key = keys[0]\n\n if scr:\n scr.erase()\n\n for i, (k, v) in enumerate(kwargs.items()):\n v['dy'] = i\n v['w'] = maxw-dispwidth(v.get('prompt'))\n\n class ChangeInput(Exception):\n pass\n\n def change_input(offset):\n def _throw(v, i):\n if scr:\n scr.erase()\n raise ChangeInput(v, offset)\n return _throw\n\n def _drawPrompt(val):\n for k, v in kwargs.items():\n maxw = min(sheet.windowWidth-1, max(dispwidth(v.get('prompt')), dispwidth(str(v.get('value', '')))))\n promptlen = clipdraw(scr, y-v.get('dy'), 0, v.get('prompt'), attr, w=maxw) #1947\n promptlen = clipdraw(scr, y-v.get('dy'), promptlen, v.get('value', ''), attr, w=maxw)\n\n return updater(val)\n\n with HelpCycler() as disp_help:\n while True:\n try:\n input_kwargs = kwargs[cur_input_key]\n input_kwargs['value'] = vd.input(**input_kwargs,\n attr=colors.color_edit_cell,\n updater=_drawPrompt,\n record=False,\n bindings={\n 'KEY_BTAB': change_input(-1),\n '^I': change_input(+1),\n 'KEY_SR': change_input(-1),\n 'KEY_SF': change_input(+1),\n 'kUP': change_input(-1),\n 'kDN': change_input(+1),\n })\n break\n except ChangeInput as e:\n input_kwargs['value'] = e.args[0]\n offset = e.args[1]\n i = keys.index(cur_input_key)\n cur_input_key = keys[(i+offset)%len(keys)]\n\n retargs = {}\n lastargs = {}\n for k, input_kwargs in kwargs.items():\n v = input_kwargs.get('value', '')\n retargs[k] = v\n\n if input_kwargs.get('record', record):\n if input_kwargs.get('display', True):\n lastargs[k] = v\n vd.addInputHistory(v, input_kwargs.get('type', ''))\n if record:\n if vd.cmdlog and lastargs:\n vd.setLastArgs(lastargs)\n\n return retargs\n\n\[email protected]\ndef input(vd, prompt, type=None, defaultLast=False, history=[], dy=0, attr=None, updater=lambda v: None, **kwargs):\n '''Display *prompt* and return line of user input.\n\n - *type*: string indicating the type of input to use for history.\n - *history*: list of strings to use for input history.\n - *defaultLast*: on empty input, if True, return last history item.\n - *display*: pass False to not display input (for sensitive input, e.g. a password).\n - *record*: pass False to not record input on cmdlog (for sensitive or inconsequential input).\n - *completer*: ``completer(val, idx)`` is called on TAB to get next completed value.\n - *updater*: ``updater(val)`` is called every keypress or timeout.\n - *bindings*: dict of keystroke to func(v, i) that returns updated (v, i)\n - *dy*: number of lines from bottom of pane\n - *attr*: curses attribute for prompt\n - *help*: string to include in help\n '''\n\n if attr is None:\n attr = ColorAttr()\n sheet = vd.activeSheet\n if not vd.cursesEnabled:\n if kwargs.get('record', True) and vd.cmdlog:\n return vd.getCommandInput()\n\n if kwargs.get('display', True):\n import builtins\n return builtins.input(prompt)\n else:\n import getpass\n return getpass.getpass(prompt)\n\n if not history:\n history = list(vd.inputHistory.setdefault(type, {}).keys())\n\n y = sheet.windowHeight-dy-1\n promptlen = dispwidth(prompt)\n\n def _drawPrompt(val=''):\n rstatuslen = vd.drawRightStatus(sheet._scr, sheet)\n clipdraw(sheet._scr, y, 0, prompt, attr, w=sheet.windowWidth-rstatuslen-1)\n updater(val)\n return sheet.windowWidth-promptlen-rstatuslen-2\n\n w = kwargs.pop('w', _drawPrompt())\n ret = vd.editText(y, promptlen, w=w,\n attr=colors.color_edit_cell,\n unprintablechar=options.disp_unprintable,\n truncchar=options.disp_truncator,\n history=history,\n updater=_drawPrompt,\n **kwargs)\n\n if ret:\n vd.addInputHistory(ret, type=type)\n\n elif defaultLast:\n history or vd.fail(\"no previous input\")\n ret = history[-1]\n\n return ret\n\n\[email protected]\ndef confirm(vd, prompt, exc=EscapeException):\n 'Display *prompt* on status line and demand input that starts with \"Y\" or \"y\" to proceed. Raise *exc* otherwise. Return True.'\n if options.batch and not options.interactive:\n return vd.fail('cannot confirm in batch mode: ' + prompt)\n\n yn = vd.input(prompt, value='no', record=False)[:1]\n if not yn or yn not in 'Yy':\n msg = 'disconfirmed: ' + prompt\n if exc:\n raise exc(msg)\n vd.warning(msg)\n return False\n return True\n\n\nclass CompleteKey:\n def __init__(self, items):\n self.items = items\n\n def __call__(self, val, state):\n opts = [x for x in self.items if x.startswith(val)]\n return opts[state%len(opts)] if opts else val\n\n\[email protected]\ndef editCell(self, vcolidx=None, rowidx=None, value=None, **kwargs):\n '''Call vd.editText for the cell at (*rowidx*, *vcolidx*). Return the new value, properly typed.\n\n - *rowidx*: numeric index into ``self.rows``. If negative, indicates the column name in the header.\n - *value*: if given, the starting input; otherwise the starting input is the cell value or column name as appropriate.\n - *kwargs*: passthrough args to ``vd.editText``.\n '''\n\n if vcolidx is None:\n vcolidx = self.cursorVisibleColIndex\n x, w = self._visibleColLayout.get(vcolidx, (0, 0))\n\n col = self.visibleCols[vcolidx]\n if rowidx is None:\n rowidx = self.cursorRowIndex\n\n if rowidx < 0: # header\n y = 0\n value = value or col.name\n else:\n y, h = self._rowLayout.get(rowidx, (0, 0))\n value = value or col.getDisplayValue(self.rows[self.cursorRowIndex])\n\n bindings={\n 'kUP': acceptThenFunc('go-up', 'rename-col' if rowidx < 0 else 'edit-cell'),\n 'KEY_SR': acceptThenFunc('go-up', 'rename-col' if rowidx < 0 else 'edit-cell'),\n 'kDN': acceptThenFunc('go-down', 'rename-col' if rowidx < 0 else 'edit-cell'),\n 'KEY_SF': acceptThenFunc('go-down', 'rename-col' if rowidx < 0 else 'edit-cell'),\n 'KEY_SRIGHT': acceptThenFunc('go-right', 'rename-col' if rowidx < 0 else 'edit-cell'),\n 'KEY_SLEFT': acceptThenFunc('go-left', 'rename-col' if rowidx < 0 else 'edit-cell'),\n '^I': acceptThenFunc('go-right', 'rename-col' if rowidx < 0 else 'edit-cell'),\n 'KEY_BTAB': acceptThenFunc('go-left', 'rename-col' if rowidx < 0 else 'edit-cell'),\n }\n\n if vcolidx >= self.nVisibleCols-1:\n bindings['^I'] = acceptThenFunc('go-down', 'go-leftmost', 'edit-cell')\n\n if vcolidx <= 0:\n bindings['KEY_BTAB'] = acceptThenFunc('go-up', 'go-rightmost', 'edit-cell')\n\n # update local bindings with kwargs.bindings instead of the inverse, to preserve kwargs.bindings for caller\n bindings.update(kwargs.get('bindings', {}))\n kwargs['bindings'] = bindings\n\n editargs = dict(value=value,\n fillchar=self.options.disp_edit_fill,\n truncchar=self.options.disp_truncator)\n\n editargs.update(kwargs) # update with user-specified args\n r = vd.editText(y, x, w, attr=colors.color_edit_cell, **editargs)\n\n if rowidx >= 0: # if not header\n r = col.type(r) # convert input to column type, let exceptions be raised\n\n return r\n\n\nvd.addGlobals({'CompleteKey': CompleteKey, 'AcceptInput': AcceptInput})\n",
"path": "visidata/_input.py"
}
] | diff --git a/visidata/_input.py b/visidata/_input.py
index 77f922dcb..5f998f36d 100644
--- a/visidata/_input.py
+++ b/visidata/_input.py
@@ -528,7 +528,8 @@ def input(vd, prompt, type=None, defaultLast=False, history=[], dy=0, attr=None,
import getpass
return getpass.getpass(prompt)
- history = list(vd.inputHistory.setdefault(type, {}).keys())
+ if not history:
+ history = list(vd.inputHistory.setdefault(type, {}).keys())
y = sheet.windowHeight-dy-1
promptlen = dispwidth(prompt)
|
awslabs__gluonts-1537 | Theta model does not preserve item IDs
## Description
When using the `RForecastPredictor` with `method_name = "thetaf"`, the item IDs returned by the predictor's forecasts do not align with the actual item IDs. Instead, it returns `None` for the item IDs.
## To Reproduce
```python
from gluonts.dataset.repository.datasets import get_dataset
from gluonts.model.r_forecast import RForecastPredictor
from gluonts.evaluation.backtest import make_evaluation_predictions
dataset = get_dataset("m3_yearly")
predictor = RForecastPredictor(
freq=dataset.metadata.freq,
prediction_length=dataset.metadata.prediction_length,
method_name="thetaf",
)
forecast_pred, forecast_true = make_evaluation_predictions(dataset.test, predictor)
forecast_pred, _ = make_evaluation_predictions(dataset.test, predictor)
for pred in forecast_pred:
print(pred.item_id)
```
You'll see that only `None` is printed.
## Environment
- Operating system: Debian Buster
- Python version: `3.8.9`
- GluonTS version: Master, Post 0.7.0 (commit 645d551a2190b9b749528917cdf1c5e897c861d2)
- MXNet version: `1.8.0.post0`
Full list of dependencies:
```
PyYAML = "^5.4.1"
click = "^7.1.2"
fastparquet = "^0.6.1"
fbprophet = "^0.7.1"
gluonts = {git = "https://github.com/awslabs/gluon-ts.git", rev = "f6948bacb7a038df3374e768ad4939455c74b49d"}
holidays = "^0.11.1"
mxnet = "^1.8.0"
numpy = "^1.20.3"
pandas = "^1.2.4"
pyarrow = "^4.0.0"
pydantic = "^1.8.2"
pystan = "^2.0.0"
python = ">=3.8,<3.10"
rpy2 = ">=2.9.*,<3.*"
sagemaker = "^2.40.0"
sagemaker-training = "^3.9.2"
scikit-learn = "^0.24.2"
scipy = "^1.6.3"
toolz = "^0.11.1"
tqdm = "^4.60.0"
ujson = "^4.0.2"
xgboost = "^1.4.1"
```
| [
{
"content": "# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\").\n# You may not use this file except in compliance with the License.\n# A copy of the License is located at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# or in the \"license\" file accompanying this file. This file is distributed\n# on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either\n# express or implied. See the License for the specific language governing\n# permissions and limitations under the License.\n\nimport os\nfrom pathlib import Path\nfrom typing import Dict, Iterator, Optional\n\nimport numpy as np\n\nfrom gluonts.core.component import validated\nfrom gluonts.dataset.common import Dataset\nfrom gluonts.model.forecast import SampleForecast\nfrom gluonts.model.predictor import RepresentablePredictor\nfrom gluonts.support.pandas import forecast_start\nfrom gluonts.time_feature import get_seasonality\n\nUSAGE_MESSAGE = \"\"\"\nThe RForecastPredictor is a thin wrapper for calling the R forecast package.\nIn order to use it you need to install R and run\n\npip install 'rpy2>=2.9.*,<3.*'\n\nR -e 'install.packages(c(\"forecast\", \"nnfor\"), repos=\"https://cloud.r-project.org\")'\n\"\"\"\n\n\nclass RForecastPredictor(RepresentablePredictor):\n \"\"\"\n Wrapper for calling the `R forecast package\n <http://pkg.robjhyndman.com/forecast/>`_.\n\n The `RForecastPredictor` is a thin wrapper for calling the R forecast\n package. In order to use it you need to install R and run::\n\n pip install 'rpy2>=2.9.*,<3.*'\n R -e 'install.packages(c(\"forecast\", \"nnfor\"), repos=\"https://cloud.r-project.org\")'\n\n Parameters\n ----------\n freq\n The granularity of the time series (e.g. '1H')\n prediction_length\n Number of time points to be predicted.\n method\n The method from rforecast to be used one of\n \"ets\", \"arima\", \"tbats\", \"croston\", \"mlp\", \"thetaf\".\n period\n The period to be used (this is called `frequency` in the R forecast\n package), result to a tentative reasonable default if not specified\n (for instance 24 for hourly freq '1H')\n trunc_length\n Maximum history length to feed to the model (some models become slow\n with very long series).\n params\n Parameters to be used when calling the forecast method default.\n Note that currently only `output_type = 'samples'` is supported.\n \"\"\"\n\n @validated()\n def __init__(\n self,\n freq: str,\n prediction_length: int,\n method_name: str = \"ets\",\n period: int = None,\n trunc_length: Optional[int] = None,\n params: Optional[Dict] = None,\n ) -> None:\n super().__init__(freq=freq, prediction_length=prediction_length)\n\n try:\n import rpy2.robjects.packages as rpackages\n from rpy2 import rinterface, robjects\n from rpy2.rinterface import RRuntimeError\n except ImportError as e:\n raise ImportError(str(e) + USAGE_MESSAGE) from e\n\n self._robjects = robjects\n self._rinterface = rinterface\n self._rinterface.initr()\n self._rpackages = rpackages\n\n this_dir = os.path.dirname(os.path.realpath(__file__))\n this_dir = this_dir.replace(\"\\\\\", \"/\") # for windows\n r_files = [\n n[:-2] for n in os.listdir(f\"{this_dir}/R/\") if n[-2:] == \".R\"\n ]\n\n for n in r_files:\n try:\n path = Path(this_dir, \"R\", f\"{n}.R\")\n robjects.r(f'source(\"{path}\")'.replace(\"\\\\\", \"\\\\\\\\\"))\n except RRuntimeError as er:\n raise RRuntimeError(str(er) + USAGE_MESSAGE) from er\n\n supported_methods = [\n \"ets\",\n \"arima\",\n \"tbats\",\n \"croston\",\n \"mlp\",\n \"thetaf\",\n ]\n assert (\n method_name in supported_methods\n ), f\"method {method_name} is not supported please use one of {supported_methods}\"\n\n self.method_name = method_name\n\n self._stats_pkg = rpackages.importr(\"stats\")\n self._r_method = robjects.r[method_name]\n\n self.prediction_length = prediction_length\n self.freq = freq\n self.period = period if period is not None else get_seasonality(freq)\n self.trunc_length = trunc_length\n\n self.params = {\n \"prediction_length\": self.prediction_length,\n \"output_types\": [\"samples\"],\n \"frequency\": self.period,\n }\n if params is not None:\n self.params.update(params)\n\n def _unlist(self, l):\n if type(l).__name__.endswith(\"Vector\"):\n return [self._unlist(x) for x in l]\n else:\n return l\n\n def _run_r_forecast(self, d, params, save_info):\n buf = []\n\n def save_to_buf(x):\n buf.append(x)\n\n def dont_save(x):\n pass\n\n f = save_to_buf if save_info else dont_save\n\n # save output from the R console in buf\n self._rinterface.set_writeconsole_regular(f)\n self._rinterface.set_writeconsole_warnerror(f)\n\n make_ts = self._stats_pkg.ts\n r_params = self._robjects.vectors.ListVector(params)\n vec = self._robjects.FloatVector(d[\"target\"])\n ts = make_ts(vec, frequency=self.period)\n forecast = self._r_method(ts, r_params)\n forecast_dict = dict(\n zip(forecast.names, map(self._unlist, list(forecast)))\n )\n # FOR NOW ONLY SAMPLES...\n # if \"quantiles\" in forecast_dict:\n # forecast_dict[\"quantiles\"] = dict(zip(params[\"quantiles\"], forecast_dict[\"quantiles\"]))\n\n self._rinterface.set_writeconsole_regular(\n self._rinterface.consolePrint\n )\n self._rinterface.set_writeconsole_warnerror(\n self._rinterface.consolePrint\n )\n return forecast_dict, buf\n\n def predict(\n self,\n dataset: Dataset,\n num_samples: int = 100,\n save_info: bool = False,\n **kwargs,\n ) -> Iterator[SampleForecast]:\n for entry in dataset:\n if isinstance(entry, dict):\n data = entry\n else:\n data = entry.data\n if self.trunc_length:\n data = data[-self.trunc_length :]\n\n params = self.params.copy()\n params[\"num_samples\"] = num_samples\n\n forecast_dict, console_output = self._run_r_forecast(\n data, params, save_info=save_info\n )\n\n samples = np.array(forecast_dict[\"samples\"])\n expected_shape = (params[\"num_samples\"], self.prediction_length)\n assert (\n samples.shape == expected_shape\n ), f\"Expected shape {expected_shape} but found {samples.shape}\"\n info = (\n {\"console_output\": \"\\n\".join(console_output)}\n if save_info\n else None\n )\n yield SampleForecast(\n samples, forecast_start(data), self.freq, info=info\n )\n",
"path": "src/gluonts/model/r_forecast/_predictor.py"
}
] | [
{
"content": "# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\").\n# You may not use this file except in compliance with the License.\n# A copy of the License is located at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# or in the \"license\" file accompanying this file. This file is distributed\n# on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either\n# express or implied. See the License for the specific language governing\n# permissions and limitations under the License.\n\nimport os\nfrom pathlib import Path\nfrom typing import Dict, Iterator, Optional\n\nimport numpy as np\n\nfrom gluonts.core.component import validated\nfrom gluonts.dataset.common import Dataset\nfrom gluonts.model.forecast import SampleForecast\nfrom gluonts.model.predictor import RepresentablePredictor\nfrom gluonts.support.pandas import forecast_start\nfrom gluonts.time_feature import get_seasonality\n\nUSAGE_MESSAGE = \"\"\"\nThe RForecastPredictor is a thin wrapper for calling the R forecast package.\nIn order to use it you need to install R and run\n\npip install 'rpy2>=2.9.*,<3.*'\n\nR -e 'install.packages(c(\"forecast\", \"nnfor\"), repos=\"https://cloud.r-project.org\")'\n\"\"\"\n\n\nclass RForecastPredictor(RepresentablePredictor):\n \"\"\"\n Wrapper for calling the `R forecast package\n <http://pkg.robjhyndman.com/forecast/>`_.\n\n The `RForecastPredictor` is a thin wrapper for calling the R forecast\n package. In order to use it you need to install R and run::\n\n pip install 'rpy2>=2.9.*,<3.*'\n R -e 'install.packages(c(\"forecast\", \"nnfor\"), repos=\"https://cloud.r-project.org\")'\n\n Parameters\n ----------\n freq\n The granularity of the time series (e.g. '1H')\n prediction_length\n Number of time points to be predicted.\n method\n The method from rforecast to be used one of\n \"ets\", \"arima\", \"tbats\", \"croston\", \"mlp\", \"thetaf\".\n period\n The period to be used (this is called `frequency` in the R forecast\n package), result to a tentative reasonable default if not specified\n (for instance 24 for hourly freq '1H')\n trunc_length\n Maximum history length to feed to the model (some models become slow\n with very long series).\n params\n Parameters to be used when calling the forecast method default.\n Note that currently only `output_type = 'samples'` is supported.\n \"\"\"\n\n @validated()\n def __init__(\n self,\n freq: str,\n prediction_length: int,\n method_name: str = \"ets\",\n period: int = None,\n trunc_length: Optional[int] = None,\n params: Optional[Dict] = None,\n ) -> None:\n super().__init__(freq=freq, prediction_length=prediction_length)\n\n try:\n import rpy2.robjects.packages as rpackages\n from rpy2 import rinterface, robjects\n from rpy2.rinterface import RRuntimeError\n except ImportError as e:\n raise ImportError(str(e) + USAGE_MESSAGE) from e\n\n self._robjects = robjects\n self._rinterface = rinterface\n self._rinterface.initr()\n self._rpackages = rpackages\n\n this_dir = os.path.dirname(os.path.realpath(__file__))\n this_dir = this_dir.replace(\"\\\\\", \"/\") # for windows\n r_files = [\n n[:-2] for n in os.listdir(f\"{this_dir}/R/\") if n[-2:] == \".R\"\n ]\n\n for n in r_files:\n try:\n path = Path(this_dir, \"R\", f\"{n}.R\")\n robjects.r(f'source(\"{path}\")'.replace(\"\\\\\", \"\\\\\\\\\"))\n except RRuntimeError as er:\n raise RRuntimeError(str(er) + USAGE_MESSAGE) from er\n\n supported_methods = [\n \"ets\",\n \"arima\",\n \"tbats\",\n \"croston\",\n \"mlp\",\n \"thetaf\",\n ]\n assert (\n method_name in supported_methods\n ), f\"method {method_name} is not supported please use one of {supported_methods}\"\n\n self.method_name = method_name\n\n self._stats_pkg = rpackages.importr(\"stats\")\n self._r_method = robjects.r[method_name]\n\n self.prediction_length = prediction_length\n self.freq = freq\n self.period = period if period is not None else get_seasonality(freq)\n self.trunc_length = trunc_length\n\n self.params = {\n \"prediction_length\": self.prediction_length,\n \"output_types\": [\"samples\"],\n \"frequency\": self.period,\n }\n if params is not None:\n self.params.update(params)\n\n def _unlist(self, l):\n if type(l).__name__.endswith(\"Vector\"):\n return [self._unlist(x) for x in l]\n else:\n return l\n\n def _run_r_forecast(self, d, params, save_info):\n buf = []\n\n def save_to_buf(x):\n buf.append(x)\n\n def dont_save(x):\n pass\n\n f = save_to_buf if save_info else dont_save\n\n # save output from the R console in buf\n self._rinterface.set_writeconsole_regular(f)\n self._rinterface.set_writeconsole_warnerror(f)\n\n make_ts = self._stats_pkg.ts\n r_params = self._robjects.vectors.ListVector(params)\n vec = self._robjects.FloatVector(d[\"target\"])\n ts = make_ts(vec, frequency=self.period)\n forecast = self._r_method(ts, r_params)\n forecast_dict = dict(\n zip(forecast.names, map(self._unlist, list(forecast)))\n )\n # FOR NOW ONLY SAMPLES...\n # if \"quantiles\" in forecast_dict:\n # forecast_dict[\"quantiles\"] = dict(zip(params[\"quantiles\"], forecast_dict[\"quantiles\"]))\n\n self._rinterface.set_writeconsole_regular(\n self._rinterface.consolePrint\n )\n self._rinterface.set_writeconsole_warnerror(\n self._rinterface.consolePrint\n )\n return forecast_dict, buf\n\n def predict(\n self,\n dataset: Dataset,\n num_samples: int = 100,\n save_info: bool = False,\n **kwargs,\n ) -> Iterator[SampleForecast]:\n for entry in dataset:\n if isinstance(entry, dict):\n data = entry\n else:\n data = entry.data\n if self.trunc_length:\n data = data[-self.trunc_length :]\n\n params = self.params.copy()\n params[\"num_samples\"] = num_samples\n\n forecast_dict, console_output = self._run_r_forecast(\n data, params, save_info=save_info\n )\n\n samples = np.array(forecast_dict[\"samples\"])\n expected_shape = (params[\"num_samples\"], self.prediction_length)\n assert (\n samples.shape == expected_shape\n ), f\"Expected shape {expected_shape} but found {samples.shape}\"\n info = (\n {\"console_output\": \"\\n\".join(console_output)}\n if save_info\n else None\n )\n yield SampleForecast(\n samples,\n forecast_start(data),\n self.freq,\n info=info,\n item_id=entry.get(\"item_id\", None),\n )\n",
"path": "src/gluonts/model/r_forecast/_predictor.py"
}
] | diff --git a/src/gluonts/model/r_forecast/_predictor.py b/src/gluonts/model/r_forecast/_predictor.py
index 928d150173..6b3010f076 100644
--- a/src/gluonts/model/r_forecast/_predictor.py
+++ b/src/gluonts/model/r_forecast/_predictor.py
@@ -207,5 +207,9 @@ def predict(
else None
)
yield SampleForecast(
- samples, forecast_start(data), self.freq, info=info
+ samples,
+ forecast_start(data),
+ self.freq,
+ info=info,
+ item_id=entry.get("item_id", None),
)
|
fossasia__open-event-server-6254 | Verify any user automatically if clicks on a reset password link
If a user is using reset password link to reset his password, he is by default verifying his account
| [
{
"content": "import base64\nimport base64\nimport logging\nimport random\nimport string\nfrom functools import wraps\n\nimport requests\nfrom flask import request, jsonify, make_response, Blueprint, send_file\nfrom flask_jwt_extended import jwt_required, current_user, create_access_token\nfrom flask_limiter.util import get_remote_address\nfrom healthcheck import EnvironmentDump\nfrom flask_rest_jsonapi.exceptions import ObjectNotFound\nfrom sqlalchemy.orm.exc import NoResultFound\n\nfrom app import get_settings\nfrom app import limiter\nfrom app.api.helpers.db import save_to_db, get_count, safe_query\nfrom app.api.helpers.auth import AuthManager\nfrom app.api.helpers.jwt import jwt_authenticate\nfrom app.api.helpers.errors import ForbiddenError, UnprocessableEntityError, NotFoundError, BadRequestError\nfrom app.api.helpers.files import make_frontend_url\nfrom app.api.helpers.mail import send_email_to_attendees\nfrom app.api.helpers.mail import send_email_with_action, \\\n send_email_confirmation\nfrom app.api.helpers.notification import send_notification_with_action\nfrom app.api.helpers.order import create_pdf_tickets_for_holder\nfrom app.api.helpers.storage import UPLOAD_PATHS\nfrom app.api.helpers.storage import generate_hash\nfrom app.api.helpers.third_party_auth import GoogleOAuth, FbOAuth, TwitterOAuth, InstagramOAuth\nfrom app.api.helpers.utilities import get_serializer, str_generator\nfrom app.api.helpers.permission_manager import has_access\nfrom app.models import db\nfrom app.models.mail import PASSWORD_RESET, PASSWORD_CHANGE, \\\n PASSWORD_RESET_AND_VERIFY\nfrom app.models.notification import PASSWORD_CHANGE as PASSWORD_CHANGE_NOTIF\nfrom app.models.order import Order\nfrom app.models.user import User\nfrom app.models.event_invoice import EventInvoice\n\n\nlogger = logging.getLogger(__name__)\nauthorised_blueprint = Blueprint('authorised_blueprint', __name__, url_prefix='/')\nticket_blueprint = Blueprint('ticket_blueprint', __name__, url_prefix='/v1')\nauth_routes = Blueprint('auth', __name__, url_prefix='/v1/auth')\n\n\n@authorised_blueprint.route('/auth/session', methods=['POST'])\n@auth_routes.route('/login', methods=['POST'])\ndef login():\n data = request.get_json()\n username = data.get('email', data.get('username'))\n password = data.get('password')\n criterion = [username, password]\n\n if not all(criterion):\n return jsonify(error='username or password missing'), 400\n\n identity = jwt_authenticate(username, password)\n\n if identity:\n access_token = create_access_token(identity.id, fresh=True)\n return jsonify(access_token=access_token)\n else:\n return jsonify(error='Invalid Credentials'), 401\n\n\n@auth_routes.route('/oauth/<provider>', methods=['GET'])\ndef redirect_uri(provider):\n if provider == 'facebook':\n provider_class = FbOAuth()\n elif provider == 'google':\n provider_class = GoogleOAuth()\n elif provider == 'twitter':\n provider_class = TwitterOAuth()\n elif provider == 'instagram':\n provider_class = InstagramOAuth()\n else:\n return make_response(jsonify(\n message=\"No support for {}\".format(provider)), 404)\n\n client_id = provider_class.get_client_id()\n if not client_id:\n return make_response(jsonify(\n message=\"{} client id is not configured on the server\".format(provider)), 404)\n\n url = provider_class.get_auth_uri() + '?client_id=' + \\\n client_id + '&redirect_uri=' + \\\n provider_class.get_redirect_uri()\n return make_response(jsonify(url=url), 200)\n\n\n@auth_routes.route('/oauth/token/<provider>', methods=['GET'])\ndef get_token(provider):\n if provider == 'facebook':\n provider_class = FbOAuth()\n payload = {\n 'grant_type': 'client_credentials',\n 'client_id': provider_class.get_client_id(),\n 'client_secret': provider_class.get_client_secret()\n }\n elif provider == 'google':\n provider_class = GoogleOAuth()\n payload = {\n 'client_id': provider_class.get_client_id(),\n 'client_secret': provider_class.get_client_secret()\n }\n elif provider == 'twitter':\n provider_class = TwitterOAuth()\n payload = {\n 'client_id': provider_class.get_client_id(),\n 'client_secret': provider_class.get_client_secret()\n }\n elif provider == 'instagram':\n provider_class = InstagramOAuth()\n payload = {\n 'client_id': provider_class.get_client_id(),\n 'client_secret': provider_class.get_client_secret()\n }\n else:\n return make_response(jsonify(\n message=\"No support for {}\".format(provider)), 200)\n response = requests.post(provider_class.get_token_uri(), params=payload)\n return make_response(jsonify(token=response.json()), 200)\n\n\n@auth_routes.route('/oauth/login/<provider>', methods=['POST'])\ndef login_user(provider):\n if provider == 'facebook':\n provider_class = FbOAuth()\n payload = {\n 'client_id': provider_class.get_client_id(),\n 'redirect_uri': provider_class.get_redirect_uri(),\n 'client_secret': provider_class.get_client_secret(),\n 'code': request.args.get('code')\n }\n if not payload['client_id'] or not payload['client_secret']:\n raise NotImplementedError({'source': ''}, 'Facebook Login Not Configured')\n access_token = requests.get('https://graph.facebook.com/v3.0/oauth/access_token', params=payload).json()\n payload_details = {\n 'input_token': access_token['access_token'],\n 'access_token': provider_class.get_client_id() + '|' + provider_class.get_client_secret()\n }\n details = requests.get('https://graph.facebook.com/debug_token', params=payload_details).json()\n user_details = requests.get('https://graph.facebook.com/v3.0/' + details['data']['user_id'],\n params={'access_token': access_token['access_token'],\n 'fields': 'first_name, last_name, email'}).json()\n\n if get_count(db.session.query(User).filter_by(email=user_details['email'])) > 0:\n user = db.session.query(User).filter_by(email=user_details['email']).one()\n if not user.facebook_id:\n user.facebook_id = user_details['id']\n user.facebook_login_hash = random.getrandbits(128)\n save_to_db(user)\n return make_response(\n jsonify(user_id=user.id, email=user.email, oauth_hash=user.facebook_login_hash), 200)\n\n user = User()\n user.first_name = user_details['first_name']\n user.last_name = user_details['last_name']\n user.facebook_id = user_details['id']\n user.facebook_login_hash = random.getrandbits(128)\n user.password = ''.join(random.SystemRandom().choice(string.ascii_uppercase + string.digits) for _ in range(8))\n if user_details['email']:\n user.email = user_details['email']\n\n save_to_db(user)\n return make_response(jsonify(user_id=user.id, email=user.email, oauth_hash=user.facebook_login_hash),\n 200)\n\n elif provider == 'google':\n provider_class = GoogleOAuth()\n payload = {\n 'client_id': provider_class.get_client_id(),\n 'client_secret': provider_class.get_client_secret()\n }\n elif provider == 'twitter':\n provider_class = TwitterOAuth()\n payload = {\n 'client_id': provider_class.get_client_id(),\n 'client_secret': provider_class.get_client_secret()\n }\n elif provider == 'instagram':\n provider_class = InstagramOAuth()\n payload = {\n 'client_id': provider_class.get_client_id(),\n 'client_secret': provider_class.get_client_secret()\n }\n else:\n return make_response(jsonify(\n message=\"No support for {}\".format(provider)), 200)\n response = requests.post(provider_class.get_token_uri(), params=payload)\n return make_response(jsonify(token=response.json()), 200)\n\n\n@auth_routes.route('/verify-email', methods=['POST'])\ndef verify_email():\n try:\n token = base64.b64decode(request.json['data']['token'])\n except base64.binascii.Error:\n return BadRequestError({'source': ''}, 'Invalid Token').respond()\n s = get_serializer()\n\n try:\n data = s.loads(token)\n except Exception:\n return BadRequestError({'source': ''}, 'Invalid Token').respond()\n\n try:\n user = User.query.filter_by(email=data[0]).one()\n except Exception:\n return BadRequestError({'source': ''}, 'Invalid Token').respond()\n else:\n user.is_verified = True\n save_to_db(user)\n return make_response(jsonify(message=\"Email Verified\"), 200)\n\n\n@auth_routes.route('/resend-verification-email', methods=['POST'])\ndef resend_verification_email():\n try:\n email = request.json['data']['email']\n except TypeError:\n return BadRequestError({'source': ''}, 'Bad Request Error').respond()\n\n try:\n user = User.query.filter_by(email=email).one()\n except NoResultFound:\n return UnprocessableEntityError(\n {'source': ''}, 'User with email: ' + email + ' not found.').respond()\n else:\n serializer = get_serializer()\n hash_ = str(base64.b64encode(str(serializer.dumps(\n [user.email, str_generator()])).encode()), 'utf-8')\n link = make_frontend_url(\n '/verify'.format(id=user.id), {'token': hash_})\n send_email_confirmation(user.email, link)\n\n return make_response(jsonify(message=\"Verification email resent\"), 200)\n\n\n@auth_routes.route('/reset-password', methods=['POST'])\[email protected](\n '3/hour', key_func=lambda: request.json['data']['email'], error_message='Limit for this action exceeded'\n)\[email protected](\n '1/minute', key_func=get_remote_address, error_message='Limit for this action exceeded'\n)\ndef reset_password_post():\n try:\n email = request.json['data']['email']\n except TypeError:\n return BadRequestError({'source': ''}, 'Bad Request Error').respond()\n\n try:\n user = User.query.filter_by(email=email).one()\n except NoResultFound:\n logger.info('Tried to reset password not existing email %s', email)\n else:\n link = make_frontend_url('/reset-password', {'token': user.reset_password})\n if user.was_registered_with_order:\n send_email_with_action(user, PASSWORD_RESET_AND_VERIFY, app_name=get_settings()['app_name'], link=link)\n else:\n send_email_with_action(user, PASSWORD_RESET, app_name=get_settings()['app_name'], link=link, token=user.reset_password)\n\n return make_response(jsonify(message=\"If your email was registered with us, you'll get an \\\n email with reset link shortly\", email=email), 200)\n\n\n@auth_routes.route('/reset-password', methods=['PATCH'])\ndef reset_password_patch():\n token = request.json['data']['token']\n password = request.json['data']['password']\n\n try:\n user = User.query.filter_by(reset_password=token).one()\n except NoResultFound:\n return NotFoundError({'source': ''}, 'User Not Found').respond()\n else:\n user.password = password\n if user.was_registered_with_order:\n user.is_verified = True\n save_to_db(user)\n\n return jsonify({\n \"id\": user.id,\n \"email\": user.email,\n \"name\": user.fullname if user.fullname else None\n })\n\n\n@auth_routes.route('/change-password', methods=['POST'])\n@jwt_required\ndef change_password():\n old_password = request.json['data']['old-password']\n new_password = request.json['data']['new-password']\n\n try:\n user = User.query.filter_by(id=current_user.id).one()\n except NoResultFound:\n return NotFoundError({'source': ''}, 'User Not Found').respond()\n else:\n if user.is_correct_password(old_password):\n if user.is_correct_password(new_password):\n return BadRequestError({'source': ''},\n 'Old and New passwords must be different').respond()\n if len(new_password) < 8:\n return BadRequestError({'source': ''},\n 'Password should have minimum 8 characters').respond()\n user.password = new_password\n save_to_db(user)\n send_email_with_action(user, PASSWORD_CHANGE,\n app_name=get_settings()['app_name'])\n send_notification_with_action(user, PASSWORD_CHANGE_NOTIF,\n app_name=get_settings()['app_name'])\n else:\n return BadRequestError({'source': ''}, 'Wrong Password. Please enter correct current password.').respond()\n\n return jsonify({\n \"id\": user.id,\n \"email\": user.email,\n \"name\": user.fullname if user.fullname else None,\n \"password-changed\": True\n })\n\n\ndef return_file(file_name_prefix, file_path, identifier):\n response = make_response(send_file(file_path))\n response.headers['Content-Disposition'] = 'attachment; filename=%s-%s.pdf' % (file_name_prefix, identifier)\n return response\n\n\n@ticket_blueprint.route('/tickets/<string:order_identifier>')\n@jwt_required\ndef ticket_attendee_authorized(order_identifier):\n if current_user:\n try:\n order = Order.query.filter_by(identifier=order_identifier).first()\n except NoResultFound:\n return NotFoundError({'source': ''}, 'This ticket is not associated with any order').respond()\n if current_user.can_download_tickets(order):\n key = UPLOAD_PATHS['pdf']['tickets_all'].format(identifier=order_identifier)\n file_path = '../generated/tickets/{}/{}/'.format(key, generate_hash(key)) + order_identifier + '.pdf'\n try:\n return return_file('ticket', file_path, order_identifier)\n except FileNotFoundError:\n create_pdf_tickets_for_holder(order)\n return return_file('ticket', file_path, order_identifier)\n else:\n return ForbiddenError({'source': ''}, 'Unauthorized Access').respond()\n else:\n return ForbiddenError({'source': ''}, 'Authentication Required to access ticket').respond()\n\n\n@ticket_blueprint.route('/orders/invoices/<string:order_identifier>')\n@jwt_required\ndef order_invoices(order_identifier):\n if current_user:\n try:\n order = Order.query.filter_by(identifier=order_identifier).first()\n except NoResultFound:\n return NotFoundError({'source': ''}, 'Order Invoice not found').respond()\n if current_user.can_download_tickets(order):\n key = UPLOAD_PATHS['pdf']['order'].format(identifier=order_identifier)\n file_path = '../generated/invoices/{}/{}/'.format(key, generate_hash(key)) + order_identifier + '.pdf'\n try:\n return return_file('invoice', file_path, order_identifier)\n except FileNotFoundError:\n create_pdf_tickets_for_holder(order)\n return return_file('invoice', file_path, order_identifier)\n else:\n return ForbiddenError({'source': ''}, 'Unauthorized Access').respond()\n else:\n return ForbiddenError({'source': ''}, 'Authentication Required to access Invoice').respond()\n\n\n@ticket_blueprint.route('/events/invoices/<string:invoice_identifier>')\n@jwt_required\ndef event_invoices(invoice_identifier):\n if not current_user:\n return ForbiddenError({'source': ''}, 'Authentication Required to access Invoice').respond()\n try:\n event_invoice = EventInvoice.query.filter_by(identifier=invoice_identifier).first()\n event_id = event_invoice.event_id\n except NoResultFound:\n return NotFoundError({'source': ''}, 'Event Invoice not found').respond()\n if not current_user.is_organizer(event_id) and not current_user.is_staff:\n return ForbiddenError({'source': ''}, 'Unauthorized Access').respond()\n key = UPLOAD_PATHS['pdf']['event_invoices'].format(identifier=invoice_identifier)\n file_path = '../generated/invoices/{}/{}/'.format(key, generate_hash(key)) + invoice_identifier + '.pdf'\n try:\n return return_file('event-invoice', file_path, invoice_identifier)\n except FileNotFoundError:\n raise ObjectNotFound({'source': ''},\n \"The Event Invoice isn't available at the moment. \\\n Invoices are usually issued on the 1st of every month\")\n\n\n# Access for Environment details & Basic Auth Support\ndef requires_basic_auth(f):\n @wraps(f)\n def decorated(*args, **kwargs):\n auth = request.authorization\n if not auth or not AuthManager.check_auth_admin(auth.username, auth.password):\n return make_response('Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})\n return f(*args, **kwargs)\n return decorated\n\n\n@authorised_blueprint.route('/environment')\n@requires_basic_auth\ndef environment_details():\n envdump = EnvironmentDump(include_config=False)\n return envdump.dump_environment()\n\n\n@ticket_blueprint.route('/orders/resend-email', methods=['POST'])\[email protected](\n '5/minute', key_func=lambda: request.json['data']['user'], error_message='Limit for this action exceeded'\n)\[email protected](\n '60/minute', key_func=get_remote_address, error_message='Limit for this action exceeded'\n)\ndef resend_emails():\n \"\"\"\n Sends confirmation email for pending and completed orders on organizer request\n :param order_identifier:\n :return: JSON response if the email was succesfully sent\n \"\"\"\n order_identifier = request.json['data']['order']\n order = safe_query(db, Order, 'identifier', order_identifier, 'identifier')\n if (has_access('is_coorganizer', event_id=order.event_id)):\n if order.status == 'completed' or order.status == 'placed':\n # fetch tickets attachment\n order_identifier = order.identifier\n key = UPLOAD_PATHS['pdf']['tickets_all'].format(identifier=order_identifier)\n ticket_path = 'generated/tickets/{}/{}/'.format(key, generate_hash(key)) + order_identifier + '.pdf'\n key = UPLOAD_PATHS['pdf']['order'].format(identifier=order_identifier)\n invoice_path = 'generated/invoices/{}/{}/'.format(key, generate_hash(key)) + order_identifier + '.pdf'\n\n # send email.\n send_email_to_attendees(order=order, purchaser_id=current_user.id, attachments=[ticket_path, invoice_path])\n return jsonify(status=True, message=\"Verification emails for order : {} has been sent succesfully\".\n format(order_identifier))\n else:\n return UnprocessableEntityError({'source': 'data/order'},\n \"Only placed and completed orders have confirmation\").respond()\n else:\n return ForbiddenError({'source': ''}, \"Co-Organizer Access Required\").respond()\n",
"path": "app/api/auth.py"
}
] | [
{
"content": "import base64\nimport base64\nimport logging\nimport random\nimport string\nfrom functools import wraps\n\nimport requests\nfrom flask import request, jsonify, make_response, Blueprint, send_file\nfrom flask_jwt_extended import jwt_required, current_user, create_access_token\nfrom flask_limiter.util import get_remote_address\nfrom healthcheck import EnvironmentDump\nfrom flask_rest_jsonapi.exceptions import ObjectNotFound\nfrom sqlalchemy.orm.exc import NoResultFound\n\nfrom app import get_settings\nfrom app import limiter\nfrom app.api.helpers.db import save_to_db, get_count, safe_query\nfrom app.api.helpers.auth import AuthManager\nfrom app.api.helpers.jwt import jwt_authenticate\nfrom app.api.helpers.errors import ForbiddenError, UnprocessableEntityError, NotFoundError, BadRequestError\nfrom app.api.helpers.files import make_frontend_url\nfrom app.api.helpers.mail import send_email_to_attendees\nfrom app.api.helpers.mail import send_email_with_action, \\\n send_email_confirmation\nfrom app.api.helpers.notification import send_notification_with_action\nfrom app.api.helpers.order import create_pdf_tickets_for_holder\nfrom app.api.helpers.storage import UPLOAD_PATHS\nfrom app.api.helpers.storage import generate_hash\nfrom app.api.helpers.third_party_auth import GoogleOAuth, FbOAuth, TwitterOAuth, InstagramOAuth\nfrom app.api.helpers.utilities import get_serializer, str_generator\nfrom app.api.helpers.permission_manager import has_access\nfrom app.models import db\nfrom app.models.mail import PASSWORD_RESET, PASSWORD_CHANGE, \\\n PASSWORD_RESET_AND_VERIFY\nfrom app.models.notification import PASSWORD_CHANGE as PASSWORD_CHANGE_NOTIF\nfrom app.models.order import Order\nfrom app.models.user import User\nfrom app.models.event_invoice import EventInvoice\n\n\nlogger = logging.getLogger(__name__)\nauthorised_blueprint = Blueprint('authorised_blueprint', __name__, url_prefix='/')\nticket_blueprint = Blueprint('ticket_blueprint', __name__, url_prefix='/v1')\nauth_routes = Blueprint('auth', __name__, url_prefix='/v1/auth')\n\n\n@authorised_blueprint.route('/auth/session', methods=['POST'])\n@auth_routes.route('/login', methods=['POST'])\ndef login():\n data = request.get_json()\n username = data.get('email', data.get('username'))\n password = data.get('password')\n criterion = [username, password]\n\n if not all(criterion):\n return jsonify(error='username or password missing'), 400\n\n identity = jwt_authenticate(username, password)\n\n if identity:\n access_token = create_access_token(identity.id, fresh=True)\n return jsonify(access_token=access_token)\n else:\n return jsonify(error='Invalid Credentials'), 401\n\n\n@auth_routes.route('/oauth/<provider>', methods=['GET'])\ndef redirect_uri(provider):\n if provider == 'facebook':\n provider_class = FbOAuth()\n elif provider == 'google':\n provider_class = GoogleOAuth()\n elif provider == 'twitter':\n provider_class = TwitterOAuth()\n elif provider == 'instagram':\n provider_class = InstagramOAuth()\n else:\n return make_response(jsonify(\n message=\"No support for {}\".format(provider)), 404)\n\n client_id = provider_class.get_client_id()\n if not client_id:\n return make_response(jsonify(\n message=\"{} client id is not configured on the server\".format(provider)), 404)\n\n url = provider_class.get_auth_uri() + '?client_id=' + \\\n client_id + '&redirect_uri=' + \\\n provider_class.get_redirect_uri()\n return make_response(jsonify(url=url), 200)\n\n\n@auth_routes.route('/oauth/token/<provider>', methods=['GET'])\ndef get_token(provider):\n if provider == 'facebook':\n provider_class = FbOAuth()\n payload = {\n 'grant_type': 'client_credentials',\n 'client_id': provider_class.get_client_id(),\n 'client_secret': provider_class.get_client_secret()\n }\n elif provider == 'google':\n provider_class = GoogleOAuth()\n payload = {\n 'client_id': provider_class.get_client_id(),\n 'client_secret': provider_class.get_client_secret()\n }\n elif provider == 'twitter':\n provider_class = TwitterOAuth()\n payload = {\n 'client_id': provider_class.get_client_id(),\n 'client_secret': provider_class.get_client_secret()\n }\n elif provider == 'instagram':\n provider_class = InstagramOAuth()\n payload = {\n 'client_id': provider_class.get_client_id(),\n 'client_secret': provider_class.get_client_secret()\n }\n else:\n return make_response(jsonify(\n message=\"No support for {}\".format(provider)), 200)\n response = requests.post(provider_class.get_token_uri(), params=payload)\n return make_response(jsonify(token=response.json()), 200)\n\n\n@auth_routes.route('/oauth/login/<provider>', methods=['POST'])\ndef login_user(provider):\n if provider == 'facebook':\n provider_class = FbOAuth()\n payload = {\n 'client_id': provider_class.get_client_id(),\n 'redirect_uri': provider_class.get_redirect_uri(),\n 'client_secret': provider_class.get_client_secret(),\n 'code': request.args.get('code')\n }\n if not payload['client_id'] or not payload['client_secret']:\n raise NotImplementedError({'source': ''}, 'Facebook Login Not Configured')\n access_token = requests.get('https://graph.facebook.com/v3.0/oauth/access_token', params=payload).json()\n payload_details = {\n 'input_token': access_token['access_token'],\n 'access_token': provider_class.get_client_id() + '|' + provider_class.get_client_secret()\n }\n details = requests.get('https://graph.facebook.com/debug_token', params=payload_details).json()\n user_details = requests.get('https://graph.facebook.com/v3.0/' + details['data']['user_id'],\n params={'access_token': access_token['access_token'],\n 'fields': 'first_name, last_name, email'}).json()\n\n if get_count(db.session.query(User).filter_by(email=user_details['email'])) > 0:\n user = db.session.query(User).filter_by(email=user_details['email']).one()\n if not user.facebook_id:\n user.facebook_id = user_details['id']\n user.facebook_login_hash = random.getrandbits(128)\n save_to_db(user)\n return make_response(\n jsonify(user_id=user.id, email=user.email, oauth_hash=user.facebook_login_hash), 200)\n\n user = User()\n user.first_name = user_details['first_name']\n user.last_name = user_details['last_name']\n user.facebook_id = user_details['id']\n user.facebook_login_hash = random.getrandbits(128)\n user.password = ''.join(random.SystemRandom().choice(string.ascii_uppercase + string.digits) for _ in range(8))\n if user_details['email']:\n user.email = user_details['email']\n\n save_to_db(user)\n return make_response(jsonify(user_id=user.id, email=user.email, oauth_hash=user.facebook_login_hash),\n 200)\n\n elif provider == 'google':\n provider_class = GoogleOAuth()\n payload = {\n 'client_id': provider_class.get_client_id(),\n 'client_secret': provider_class.get_client_secret()\n }\n elif provider == 'twitter':\n provider_class = TwitterOAuth()\n payload = {\n 'client_id': provider_class.get_client_id(),\n 'client_secret': provider_class.get_client_secret()\n }\n elif provider == 'instagram':\n provider_class = InstagramOAuth()\n payload = {\n 'client_id': provider_class.get_client_id(),\n 'client_secret': provider_class.get_client_secret()\n }\n else:\n return make_response(jsonify(\n message=\"No support for {}\".format(provider)), 200)\n response = requests.post(provider_class.get_token_uri(), params=payload)\n return make_response(jsonify(token=response.json()), 200)\n\n\n@auth_routes.route('/verify-email', methods=['POST'])\ndef verify_email():\n try:\n token = base64.b64decode(request.json['data']['token'])\n except base64.binascii.Error:\n return BadRequestError({'source': ''}, 'Invalid Token').respond()\n s = get_serializer()\n\n try:\n data = s.loads(token)\n except Exception:\n return BadRequestError({'source': ''}, 'Invalid Token').respond()\n\n try:\n user = User.query.filter_by(email=data[0]).one()\n except Exception:\n return BadRequestError({'source': ''}, 'Invalid Token').respond()\n else:\n user.is_verified = True\n save_to_db(user)\n return make_response(jsonify(message=\"Email Verified\"), 200)\n\n\n@auth_routes.route('/resend-verification-email', methods=['POST'])\ndef resend_verification_email():\n try:\n email = request.json['data']['email']\n except TypeError:\n return BadRequestError({'source': ''}, 'Bad Request Error').respond()\n\n try:\n user = User.query.filter_by(email=email).one()\n except NoResultFound:\n return UnprocessableEntityError(\n {'source': ''}, 'User with email: ' + email + ' not found.').respond()\n else:\n serializer = get_serializer()\n hash_ = str(base64.b64encode(str(serializer.dumps(\n [user.email, str_generator()])).encode()), 'utf-8')\n link = make_frontend_url(\n '/verify'.format(id=user.id), {'token': hash_})\n send_email_confirmation(user.email, link)\n\n return make_response(jsonify(message=\"Verification email resent\"), 200)\n\n\n@auth_routes.route('/reset-password', methods=['POST'])\[email protected](\n '3/hour', key_func=lambda: request.json['data']['email'], error_message='Limit for this action exceeded'\n)\[email protected](\n '1/minute', key_func=get_remote_address, error_message='Limit for this action exceeded'\n)\ndef reset_password_post():\n try:\n email = request.json['data']['email']\n except TypeError:\n return BadRequestError({'source': ''}, 'Bad Request Error').respond()\n\n try:\n user = User.query.filter_by(email=email).one()\n except NoResultFound:\n logger.info('Tried to reset password not existing email %s', email)\n else:\n link = make_frontend_url('/reset-password', {'token': user.reset_password})\n if user.was_registered_with_order:\n send_email_with_action(user, PASSWORD_RESET_AND_VERIFY, app_name=get_settings()['app_name'], link=link)\n else:\n send_email_with_action(user, PASSWORD_RESET, app_name=get_settings()['app_name'], link=link, token=user.reset_password)\n\n return make_response(jsonify(message=\"If your email was registered with us, you'll get an \\\n email with reset link shortly\", email=email), 200)\n\n\n@auth_routes.route('/reset-password', methods=['PATCH'])\ndef reset_password_patch():\n token = request.json['data']['token']\n password = request.json['data']['password']\n\n try:\n user = User.query.filter_by(reset_password=token).one()\n except NoResultFound:\n return NotFoundError({'source': ''}, 'User Not Found').respond()\n else:\n user.password = password\n if not user.is_verified:\n user.is_verified = True\n save_to_db(user)\n\n return jsonify({\n \"id\": user.id,\n \"email\": user.email,\n \"name\": user.fullname if user.fullname else None\n })\n\n\n@auth_routes.route('/change-password', methods=['POST'])\n@jwt_required\ndef change_password():\n old_password = request.json['data']['old-password']\n new_password = request.json['data']['new-password']\n\n try:\n user = User.query.filter_by(id=current_user.id).one()\n except NoResultFound:\n return NotFoundError({'source': ''}, 'User Not Found').respond()\n else:\n if user.is_correct_password(old_password):\n if user.is_correct_password(new_password):\n return BadRequestError({'source': ''},\n 'Old and New passwords must be different').respond()\n if len(new_password) < 8:\n return BadRequestError({'source': ''},\n 'Password should have minimum 8 characters').respond()\n user.password = new_password\n save_to_db(user)\n send_email_with_action(user, PASSWORD_CHANGE,\n app_name=get_settings()['app_name'])\n send_notification_with_action(user, PASSWORD_CHANGE_NOTIF,\n app_name=get_settings()['app_name'])\n else:\n return BadRequestError({'source': ''}, 'Wrong Password. Please enter correct current password.').respond()\n\n return jsonify({\n \"id\": user.id,\n \"email\": user.email,\n \"name\": user.fullname if user.fullname else None,\n \"password-changed\": True\n })\n\n\ndef return_file(file_name_prefix, file_path, identifier):\n response = make_response(send_file(file_path))\n response.headers['Content-Disposition'] = 'attachment; filename=%s-%s.pdf' % (file_name_prefix, identifier)\n return response\n\n\n@ticket_blueprint.route('/tickets/<string:order_identifier>')\n@jwt_required\ndef ticket_attendee_authorized(order_identifier):\n if current_user:\n try:\n order = Order.query.filter_by(identifier=order_identifier).first()\n except NoResultFound:\n return NotFoundError({'source': ''}, 'This ticket is not associated with any order').respond()\n if current_user.can_download_tickets(order):\n key = UPLOAD_PATHS['pdf']['tickets_all'].format(identifier=order_identifier)\n file_path = '../generated/tickets/{}/{}/'.format(key, generate_hash(key)) + order_identifier + '.pdf'\n try:\n return return_file('ticket', file_path, order_identifier)\n except FileNotFoundError:\n create_pdf_tickets_for_holder(order)\n return return_file('ticket', file_path, order_identifier)\n else:\n return ForbiddenError({'source': ''}, 'Unauthorized Access').respond()\n else:\n return ForbiddenError({'source': ''}, 'Authentication Required to access ticket').respond()\n\n\n@ticket_blueprint.route('/orders/invoices/<string:order_identifier>')\n@jwt_required\ndef order_invoices(order_identifier):\n if current_user:\n try:\n order = Order.query.filter_by(identifier=order_identifier).first()\n except NoResultFound:\n return NotFoundError({'source': ''}, 'Order Invoice not found').respond()\n if current_user.can_download_tickets(order):\n key = UPLOAD_PATHS['pdf']['order'].format(identifier=order_identifier)\n file_path = '../generated/invoices/{}/{}/'.format(key, generate_hash(key)) + order_identifier + '.pdf'\n try:\n return return_file('invoice', file_path, order_identifier)\n except FileNotFoundError:\n create_pdf_tickets_for_holder(order)\n return return_file('invoice', file_path, order_identifier)\n else:\n return ForbiddenError({'source': ''}, 'Unauthorized Access').respond()\n else:\n return ForbiddenError({'source': ''}, 'Authentication Required to access Invoice').respond()\n\n\n@ticket_blueprint.route('/events/invoices/<string:invoice_identifier>')\n@jwt_required\ndef event_invoices(invoice_identifier):\n if not current_user:\n return ForbiddenError({'source': ''}, 'Authentication Required to access Invoice').respond()\n try:\n event_invoice = EventInvoice.query.filter_by(identifier=invoice_identifier).first()\n event_id = event_invoice.event_id\n except NoResultFound:\n return NotFoundError({'source': ''}, 'Event Invoice not found').respond()\n if not current_user.is_organizer(event_id) and not current_user.is_staff:\n return ForbiddenError({'source': ''}, 'Unauthorized Access').respond()\n key = UPLOAD_PATHS['pdf']['event_invoices'].format(identifier=invoice_identifier)\n file_path = '../generated/invoices/{}/{}/'.format(key, generate_hash(key)) + invoice_identifier + '.pdf'\n try:\n return return_file('event-invoice', file_path, invoice_identifier)\n except FileNotFoundError:\n raise ObjectNotFound({'source': ''},\n \"The Event Invoice isn't available at the moment. \\\n Invoices are usually issued on the 1st of every month\")\n\n\n# Access for Environment details & Basic Auth Support\ndef requires_basic_auth(f):\n @wraps(f)\n def decorated(*args, **kwargs):\n auth = request.authorization\n if not auth or not AuthManager.check_auth_admin(auth.username, auth.password):\n return make_response('Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})\n return f(*args, **kwargs)\n return decorated\n\n\n@authorised_blueprint.route('/environment')\n@requires_basic_auth\ndef environment_details():\n envdump = EnvironmentDump(include_config=False)\n return envdump.dump_environment()\n\n\n@ticket_blueprint.route('/orders/resend-email', methods=['POST'])\[email protected](\n '5/minute', key_func=lambda: request.json['data']['user'], error_message='Limit for this action exceeded'\n)\[email protected](\n '60/minute', key_func=get_remote_address, error_message='Limit for this action exceeded'\n)\ndef resend_emails():\n \"\"\"\n Sends confirmation email for pending and completed orders on organizer request\n :param order_identifier:\n :return: JSON response if the email was succesfully sent\n \"\"\"\n order_identifier = request.json['data']['order']\n order = safe_query(db, Order, 'identifier', order_identifier, 'identifier')\n if (has_access('is_coorganizer', event_id=order.event_id)):\n if order.status == 'completed' or order.status == 'placed':\n # fetch tickets attachment\n order_identifier = order.identifier\n key = UPLOAD_PATHS['pdf']['tickets_all'].format(identifier=order_identifier)\n ticket_path = 'generated/tickets/{}/{}/'.format(key, generate_hash(key)) + order_identifier + '.pdf'\n key = UPLOAD_PATHS['pdf']['order'].format(identifier=order_identifier)\n invoice_path = 'generated/invoices/{}/{}/'.format(key, generate_hash(key)) + order_identifier + '.pdf'\n\n # send email.\n send_email_to_attendees(order=order, purchaser_id=current_user.id, attachments=[ticket_path, invoice_path])\n return jsonify(status=True, message=\"Verification emails for order : {} has been sent succesfully\".\n format(order_identifier))\n else:\n return UnprocessableEntityError({'source': 'data/order'},\n \"Only placed and completed orders have confirmation\").respond()\n else:\n return ForbiddenError({'source': ''}, \"Co-Organizer Access Required\").respond()\n",
"path": "app/api/auth.py"
}
] | diff --git a/app/api/auth.py b/app/api/auth.py
index a4cf7fa4ce..35b9eb6d2d 100644
--- a/app/api/auth.py
+++ b/app/api/auth.py
@@ -278,7 +278,7 @@ def reset_password_patch():
return NotFoundError({'source': ''}, 'User Not Found').respond()
else:
user.password = password
- if user.was_registered_with_order:
+ if not user.is_verified:
user.is_verified = True
save_to_db(user)
|
ycm-core__ycmd-542 | Why is clang_completer requesting `unloaded_buffer` key in OnBufferUnload
Would it be possible to just reuse `filepath` in `OnBufferUnload` in `clang_completer` instead of having to specify `unloaded_buffer`. The typescript completer is already using `filepath`. It would be nice to align both.
| [
{
"content": "# Copyright (C) 2011, 2012 Google Inc.\n#\n# This file is part of ycmd.\n#\n# ycmd is free software: you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation, either version 3 of the License, or\n# (at your option) any later version.\n#\n# ycmd is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with ycmd. If not, see <http://www.gnu.org/licenses/>.\n\nfrom __future__ import unicode_literals\nfrom __future__ import print_function\nfrom __future__ import division\nfrom __future__ import absolute_import\nfrom future import standard_library\nstandard_library.install_aliases()\nfrom builtins import * # noqa\nfrom future.utils import iteritems\n\nfrom collections import defaultdict\nimport ycm_core\nimport re\nimport os.path\nimport textwrap\nfrom ycmd import responses\nfrom ycmd import extra_conf_store\nfrom ycmd.utils import ToCppStringCompatible, ToUnicode\nfrom ycmd.completers.completer import Completer\nfrom ycmd.completers.completer_utils import GetIncludeStatementValue\nfrom ycmd.completers.cpp.flags import Flags, PrepareFlagsForClang\nfrom ycmd.completers.cpp.ephemeral_values_set import EphemeralValuesSet\nfrom ycmd.responses import NoExtraConfDetected, UnknownExtraConf\n\nimport xml.etree.ElementTree\n\nCLANG_FILETYPES = set( [ 'c', 'cpp', 'objc', 'objcpp' ] )\nPARSING_FILE_MESSAGE = 'Still parsing file, no completions yet.'\nNO_COMPILE_FLAGS_MESSAGE = 'Still no compile flags, no completions yet.'\nINVALID_FILE_MESSAGE = 'File is invalid.'\nNO_COMPLETIONS_MESSAGE = 'No completions found; errors in the file?'\nNO_DIAGNOSTIC_MESSAGE = 'No diagnostic for current line!'\nPRAGMA_DIAG_TEXT_TO_IGNORE = '#pragma once in main file'\nTOO_MANY_ERRORS_DIAG_TEXT_TO_IGNORE = 'too many errors emitted, stopping now'\nNO_DOCUMENTATION_MESSAGE = 'No documentation available for current context'\n\n\nclass ClangCompleter( Completer ):\n def __init__( self, user_options ):\n super( ClangCompleter, self ).__init__( user_options )\n self._max_diagnostics_to_display = user_options[\n 'max_diagnostics_to_display' ]\n self._completer = ycm_core.ClangCompleter()\n self._flags = Flags()\n self._diagnostic_store = None\n self._files_being_compiled = EphemeralValuesSet()\n\n\n def SupportedFiletypes( self ):\n return CLANG_FILETYPES\n\n\n def GetUnsavedFilesVector( self, request_data ):\n files = ycm_core.UnsavedFileVector()\n for filename, file_data in iteritems( request_data[ 'file_data' ] ):\n if not ClangAvailableForFiletypes( file_data[ 'filetypes' ] ):\n continue\n contents = file_data[ 'contents' ]\n if not contents or not filename:\n continue\n\n unsaved_file = ycm_core.UnsavedFile()\n utf8_contents = ToCppStringCompatible( contents )\n unsaved_file.contents_ = utf8_contents\n unsaved_file.length_ = len( utf8_contents )\n unsaved_file.filename_ = ToCppStringCompatible( filename )\n\n files.append( unsaved_file )\n return files\n\n\n def ComputeCandidatesInner( self, request_data ):\n filename = request_data[ 'filepath' ]\n if not filename:\n return\n\n if self._completer.UpdatingTranslationUnit(\n ToCppStringCompatible( filename ) ):\n raise RuntimeError( PARSING_FILE_MESSAGE )\n\n flags = self._FlagsForRequest( request_data )\n if not flags:\n raise RuntimeError( NO_COMPILE_FLAGS_MESSAGE )\n\n files = self.GetUnsavedFilesVector( request_data )\n line = request_data[ 'line_num' ]\n column = request_data[ 'start_column' ]\n with self._files_being_compiled.GetExclusive( filename ):\n results = self._completer.CandidatesForLocationInFile(\n ToCppStringCompatible( filename ),\n line,\n column,\n files,\n flags )\n\n if not results:\n raise RuntimeError( NO_COMPLETIONS_MESSAGE )\n\n return [ ConvertCompletionData( x ) for x in results ]\n\n\n def GetSubcommandsMap( self ):\n return {\n 'GoToDefinition' : ( lambda self, request_data, args:\n self._GoToDefinition( request_data ) ),\n 'GoToDeclaration' : ( lambda self, request_data, args:\n self._GoToDeclaration( request_data ) ),\n 'GoTo' : ( lambda self, request_data, args:\n self._GoTo( request_data ) ),\n 'GoToImprecise' : ( lambda self, request_data, args:\n self._GoToImprecise( request_data ) ),\n 'GoToInclude' : ( lambda self, request_data, args:\n self._GoToInclude( request_data ) ),\n 'ClearCompilationFlagCache': ( lambda self, request_data, args:\n self._ClearCompilationFlagCache() ),\n 'GetType' : ( lambda self, request_data, args:\n self._GetSemanticInfo( request_data, func = 'GetTypeAtLocation' ) ),\n 'GetTypeImprecise' : ( lambda self, request_data, args:\n self._GetSemanticInfo( request_data,\n func = 'GetTypeAtLocation',\n reparse = False ) ),\n 'GetParent' : ( lambda self, request_data, args:\n self._GetSemanticInfo( request_data,\n func = 'GetEnclosingFunctionAtLocation' ) ),\n 'FixIt' : ( lambda self, request_data, args:\n self._FixIt( request_data ) ),\n 'GetDoc' : ( lambda self, request_data, args:\n self._GetSemanticInfo( request_data,\n reparse = True,\n func = 'GetDocsForLocationInFile',\n response_builder = _BuildGetDocResponse ) ),\n 'GetDocImprecise' : ( lambda self, request_data, args:\n self._GetSemanticInfo( request_data,\n reparse = False,\n func = 'GetDocsForLocationInFile',\n response_builder = _BuildGetDocResponse ) ),\n }\n\n\n def _LocationForGoTo( self, goto_function, request_data, reparse = True ):\n filename = request_data[ 'filepath' ]\n if not filename:\n raise ValueError( INVALID_FILE_MESSAGE )\n\n flags = self._FlagsForRequest( request_data )\n if not flags:\n raise ValueError( NO_COMPILE_FLAGS_MESSAGE )\n\n files = self.GetUnsavedFilesVector( request_data )\n line = request_data[ 'line_num' ]\n column = request_data[ 'column_num' ]\n return getattr( self._completer, goto_function )(\n ToCppStringCompatible( filename ),\n line,\n column,\n files,\n flags,\n reparse )\n\n\n def _GoToDefinition( self, request_data ):\n location = self._LocationForGoTo( 'GetDefinitionLocation', request_data )\n if not location or not location.IsValid():\n raise RuntimeError( 'Can\\'t jump to definition.' )\n return _ResponseForLocation( location )\n\n\n def _GoToDeclaration( self, request_data ):\n location = self._LocationForGoTo( 'GetDeclarationLocation', request_data )\n if not location or not location.IsValid():\n raise RuntimeError( 'Can\\'t jump to declaration.' )\n return _ResponseForLocation( location )\n\n\n def _GoTo( self, request_data ):\n include_response = self._ResponseForInclude( request_data )\n if include_response:\n return include_response\n\n location = self._LocationForGoTo( 'GetDefinitionLocation', request_data )\n if not location or not location.IsValid():\n location = self._LocationForGoTo( 'GetDeclarationLocation', request_data )\n if not location or not location.IsValid():\n raise RuntimeError( 'Can\\'t jump to definition or declaration.' )\n return _ResponseForLocation( location )\n\n\n def _GoToImprecise( self, request_data ):\n include_response = self._ResponseForInclude( request_data )\n if include_response:\n return include_response\n\n location = self._LocationForGoTo( 'GetDefinitionLocation',\n request_data,\n reparse = False )\n if not location or not location.IsValid():\n location = self._LocationForGoTo( 'GetDeclarationLocation',\n request_data,\n reparse = False )\n if not location or not location.IsValid():\n raise RuntimeError( 'Can\\'t jump to definition or declaration.' )\n return _ResponseForLocation( location )\n\n\n def _ResponseForInclude( self, request_data ):\n \"\"\"Returns response for include file location if cursor is on the\n include statement, None otherwise.\n Throws RuntimeError if cursor is on include statement and corresponding\n include file not found.\"\"\"\n current_line = request_data[ 'line_value' ]\n include_file_name, quoted_include = GetIncludeStatementValue( current_line )\n if not include_file_name:\n return None\n\n current_file_path = request_data[ 'filepath' ]\n client_data = request_data.get( 'extra_conf_data', None )\n quoted_include_paths, include_paths = (\n self._flags.UserIncludePaths( current_file_path, client_data ) )\n if quoted_include:\n include_file_path = _GetAbsolutePath( include_file_name,\n quoted_include_paths )\n if include_file_path:\n return responses.BuildGoToResponse( include_file_path,\n line_num = 1,\n column_num = 1 )\n\n include_file_path = _GetAbsolutePath( include_file_name, include_paths )\n if include_file_path:\n return responses.BuildGoToResponse( include_file_path,\n line_num = 1,\n column_num = 1 )\n raise RuntimeError( 'Include file not found.' )\n\n\n def _GoToInclude( self, request_data ):\n include_response = self._ResponseForInclude( request_data )\n if not include_response:\n raise RuntimeError( 'Not an include/import line.' )\n return include_response\n\n\n def _GetSemanticInfo(\n self,\n request_data,\n func,\n response_builder = responses.BuildDisplayMessageResponse,\n reparse = True ):\n filename = request_data[ 'filepath' ]\n if not filename:\n raise ValueError( INVALID_FILE_MESSAGE )\n\n flags = self._FlagsForRequest( request_data )\n if not flags:\n raise ValueError( NO_COMPILE_FLAGS_MESSAGE )\n\n files = self.GetUnsavedFilesVector( request_data )\n line = request_data[ 'line_num' ]\n column = request_data[ 'column_num' ]\n\n message = getattr( self._completer, func )(\n ToCppStringCompatible( filename ),\n line,\n column,\n files,\n flags,\n reparse)\n\n if not message:\n message = \"No semantic information available\"\n\n return response_builder( message )\n\n def _ClearCompilationFlagCache( self ):\n self._flags.Clear()\n\n def _FixIt( self, request_data ):\n filename = request_data[ 'filepath' ]\n if not filename:\n raise ValueError( INVALID_FILE_MESSAGE )\n\n flags = self._FlagsForRequest( request_data )\n if not flags:\n raise ValueError( NO_COMPILE_FLAGS_MESSAGE )\n\n files = self.GetUnsavedFilesVector( request_data )\n line = request_data[ 'line_num' ]\n column = request_data[ 'column_num' ]\n\n fixits = getattr( self._completer, \"GetFixItsForLocationInFile\" )(\n ToCppStringCompatible( filename ),\n line,\n column,\n files,\n flags,\n True )\n\n # don't raise an error if not fixits: - leave that to the client to respond\n # in a nice way\n\n return responses.BuildFixItResponse( fixits )\n\n def OnFileReadyToParse( self, request_data ):\n filename = request_data[ 'filepath' ]\n if not filename:\n raise ValueError( INVALID_FILE_MESSAGE )\n\n flags = self._FlagsForRequest( request_data )\n if not flags:\n raise ValueError( NO_COMPILE_FLAGS_MESSAGE )\n\n with self._files_being_compiled.GetExclusive( filename ):\n diagnostics = self._completer.UpdateTranslationUnit(\n ToCppStringCompatible( filename ),\n self.GetUnsavedFilesVector( request_data ),\n flags )\n\n diagnostics = _FilterDiagnostics( diagnostics )\n self._diagnostic_store = DiagnosticsToDiagStructure( diagnostics )\n return [ responses.BuildDiagnosticData( x ) for x in\n diagnostics[ : self._max_diagnostics_to_display ] ]\n\n\n def OnBufferUnload( self, request_data ):\n self._completer.DeleteCachesForFile(\n ToCppStringCompatible( request_data[ 'unloaded_buffer' ] ) )\n\n\n def GetDetailedDiagnostic( self, request_data ):\n current_line = request_data[ 'line_num' ]\n current_column = request_data[ 'column_num' ]\n current_file = request_data[ 'filepath' ]\n\n if not self._diagnostic_store:\n raise ValueError( NO_DIAGNOSTIC_MESSAGE )\n\n diagnostics = self._diagnostic_store[ current_file ][ current_line ]\n if not diagnostics:\n raise ValueError( NO_DIAGNOSTIC_MESSAGE )\n\n closest_diagnostic = None\n distance_to_closest_diagnostic = 999\n\n # FIXME: all of these calculations are currently working with byte\n # offsets, which are technically incorrect. We should be working with\n # codepoint offsets, as we want the nearest character-wise diagnostic\n for diagnostic in diagnostics:\n distance = abs( current_column - diagnostic.location_.column_number_ )\n if distance < distance_to_closest_diagnostic:\n distance_to_closest_diagnostic = distance\n closest_diagnostic = diagnostic\n\n return responses.BuildDisplayMessageResponse(\n closest_diagnostic.long_formatted_text_ )\n\n\n def DebugInfo( self, request_data ):\n filename = request_data[ 'filepath' ]\n try:\n extra_conf = extra_conf_store.ModuleFileForSourceFile( filename )\n flags = self._FlagsForRequest( request_data ) or []\n except NoExtraConfDetected:\n return ( 'C-family completer debug information:\\n'\n ' No configuration file found' )\n except UnknownExtraConf as error:\n return ( 'C-family completer debug information:\\n'\n ' Configuration file found but not loaded\\n'\n ' Configuration path: {0}'.format(\n error.extra_conf_file ) )\n if not extra_conf:\n return ( 'C-family completer debug information:\\n'\n ' No configuration file found' )\n return ( 'C-family completer debug information:\\n'\n ' Configuration file found and loaded\\n'\n ' Configuration path: {0}\\n'\n ' Flags: {1}'.format( extra_conf, list( flags ) ) )\n\n\n def _FlagsForRequest( self, request_data ):\n filename = request_data[ 'filepath' ]\n if 'compilation_flags' in request_data:\n return PrepareFlagsForClang( request_data[ 'compilation_flags' ],\n filename )\n client_data = request_data.get( 'extra_conf_data', None )\n return self._flags.FlagsForFile( filename, client_data = client_data )\n\n\ndef ConvertCompletionData( completion_data ):\n return responses.BuildCompletionData(\n insertion_text = completion_data.TextToInsertInBuffer(),\n menu_text = completion_data.MainCompletionText(),\n extra_menu_info = completion_data.ExtraMenuInfo(),\n kind = completion_data.kind_.name,\n detailed_info = completion_data.DetailedInfoForPreviewWindow(),\n extra_data = ( { 'doc_string': completion_data.DocString() }\n if completion_data.DocString() else None ) )\n\n\ndef DiagnosticsToDiagStructure( diagnostics ):\n structure = defaultdict( lambda : defaultdict( list ) )\n for diagnostic in diagnostics:\n structure[ diagnostic.location_.filename_ ][\n diagnostic.location_.line_number_ ].append( diagnostic )\n return structure\n\n\ndef ClangAvailableForFiletypes( filetypes ):\n return any( [ filetype in CLANG_FILETYPES for filetype in filetypes ] )\n\n\ndef InCFamilyFile( filetypes ):\n return ClangAvailableForFiletypes( filetypes )\n\n\ndef _FilterDiagnostics( diagnostics ):\n # Clang has an annoying warning that shows up when we try to compile header\n # files if the header has \"#pragma once\" inside it. The error is not\n # legitimate because it shows up because libclang thinks we are compiling a\n # source file instead of a header file.\n #\n # See our issue #216 and upstream bug:\n # http://llvm.org/bugs/show_bug.cgi?id=16686\n #\n # The second thing we want to filter out are those incredibly annoying \"too\n # many errors emitted\" diagnostics that are utterly useless.\n return [ x for x in diagnostics if\n x.text_ != PRAGMA_DIAG_TEXT_TO_IGNORE and\n x.text_ != TOO_MANY_ERRORS_DIAG_TEXT_TO_IGNORE ]\n\n\ndef _ResponseForLocation( location ):\n return responses.BuildGoToResponse( location.filename_,\n location.line_number_,\n location.column_number_ )\n\n\n# Strips the following leading strings from the raw comment:\n# - <whitespace>///\n# - <whitespace>///<\n# - <whitespace>//<\n# - <whitespace>//!\n# - <whitespace>/**\n# - <whitespace>/*!\n# - <whitespace>/*<\n# - <whitespace>/*\n# - <whitespace>*\n# - <whitespace>*/\n# - etc.\n# That is:\n# - 2 or 3 '/' followed by '<' or '!'\n# - '/' then 1 or 2 '*' followed by optional '<' or '!'\n# - '*' followed by optional '/'\nSTRIP_LEADING_COMMENT = re.compile( '^[ \\t]*(/{2,3}[<!]?|/\\*{1,2}[<!]?|\\*/?)' )\n\n# And the following trailing strings\n# - <whitespace>*/\n# - <whitespace>\nSTRIP_TRAILING_COMMENT = re.compile( '[ \\t]*\\*/[ \\t]*$|[ \\t]*$' )\n\n\ndef _FormatRawComment( comment ):\n \"\"\"Strips leading indentation and comment markers from the comment string\"\"\"\n return textwrap.dedent(\n '\\n'.join( [ re.sub( STRIP_TRAILING_COMMENT, '',\n re.sub( STRIP_LEADING_COMMENT, '', line ) )\n for line in ToUnicode( comment ).splitlines() ] ) )\n\n\ndef _BuildGetDocResponse( doc_data ):\n \"\"\"Builds a \"DetailedInfoResponse\" for a GetDoc request. doc_data is a\n DocumentationData object returned from the ClangCompleter\"\"\"\n\n # Parse the XML, as this is the only way to get the declaration text out of\n # libclang. It seems quite wasteful, but while the contents of the XML\n # provide fully parsed doxygen documentation tree, we actually don't want to\n # ever lose any information from the comment, so we just want display\n # the stripped comment. Arguably we could skip all of this XML generation and\n # parsing, but having the raw declaration text is likely one of the most\n # useful pieces of documentation available to the developer. Perhaps in\n # future, we can use this XML for more interesting things.\n try:\n root = xml.etree.ElementTree.fromstring( doc_data.comment_xml )\n except:\n raise ValueError( NO_DOCUMENTATION_MESSAGE )\n\n # Note: declaration is False-y if it has no child elements, hence the below\n # (wordy) if not declaration is None\n declaration = root.find( \"Declaration\" )\n\n return responses.BuildDetailedInfoResponse(\n '{0}\\n{1}\\nType: {2}\\nName: {3}\\n---\\n{4}'.format(\n ToUnicode( declaration.text ) if declaration is not None else \"\",\n ToUnicode( doc_data.brief_comment ),\n ToUnicode( doc_data.canonical_type ),\n ToUnicode( doc_data.display_name ),\n ToUnicode( _FormatRawComment( doc_data.raw_comment ) ) ) )\n\n\ndef _GetAbsolutePath( include_file_name, include_paths ):\n for path in include_paths:\n include_file_path = os.path.join( path, include_file_name )\n if os.path.isfile( include_file_path ):\n return include_file_path\n return None\n",
"path": "ycmd/completers/cpp/clang_completer.py"
}
] | [
{
"content": "# Copyright (C) 2011, 2012 Google Inc.\n#\n# This file is part of ycmd.\n#\n# ycmd is free software: you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation, either version 3 of the License, or\n# (at your option) any later version.\n#\n# ycmd is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with ycmd. If not, see <http://www.gnu.org/licenses/>.\n\nfrom __future__ import unicode_literals\nfrom __future__ import print_function\nfrom __future__ import division\nfrom __future__ import absolute_import\nfrom future import standard_library\nstandard_library.install_aliases()\nfrom builtins import * # noqa\nfrom future.utils import iteritems\n\nfrom collections import defaultdict\nimport ycm_core\nimport re\nimport os.path\nimport textwrap\nfrom ycmd import responses\nfrom ycmd import extra_conf_store\nfrom ycmd.utils import ToCppStringCompatible, ToUnicode\nfrom ycmd.completers.completer import Completer\nfrom ycmd.completers.completer_utils import GetIncludeStatementValue\nfrom ycmd.completers.cpp.flags import Flags, PrepareFlagsForClang\nfrom ycmd.completers.cpp.ephemeral_values_set import EphemeralValuesSet\nfrom ycmd.responses import NoExtraConfDetected, UnknownExtraConf\n\nimport xml.etree.ElementTree\n\nCLANG_FILETYPES = set( [ 'c', 'cpp', 'objc', 'objcpp' ] )\nPARSING_FILE_MESSAGE = 'Still parsing file, no completions yet.'\nNO_COMPILE_FLAGS_MESSAGE = 'Still no compile flags, no completions yet.'\nINVALID_FILE_MESSAGE = 'File is invalid.'\nNO_COMPLETIONS_MESSAGE = 'No completions found; errors in the file?'\nNO_DIAGNOSTIC_MESSAGE = 'No diagnostic for current line!'\nPRAGMA_DIAG_TEXT_TO_IGNORE = '#pragma once in main file'\nTOO_MANY_ERRORS_DIAG_TEXT_TO_IGNORE = 'too many errors emitted, stopping now'\nNO_DOCUMENTATION_MESSAGE = 'No documentation available for current context'\n\n\nclass ClangCompleter( Completer ):\n def __init__( self, user_options ):\n super( ClangCompleter, self ).__init__( user_options )\n self._max_diagnostics_to_display = user_options[\n 'max_diagnostics_to_display' ]\n self._completer = ycm_core.ClangCompleter()\n self._flags = Flags()\n self._diagnostic_store = None\n self._files_being_compiled = EphemeralValuesSet()\n\n\n def SupportedFiletypes( self ):\n return CLANG_FILETYPES\n\n\n def GetUnsavedFilesVector( self, request_data ):\n files = ycm_core.UnsavedFileVector()\n for filename, file_data in iteritems( request_data[ 'file_data' ] ):\n if not ClangAvailableForFiletypes( file_data[ 'filetypes' ] ):\n continue\n contents = file_data[ 'contents' ]\n if not contents or not filename:\n continue\n\n unsaved_file = ycm_core.UnsavedFile()\n utf8_contents = ToCppStringCompatible( contents )\n unsaved_file.contents_ = utf8_contents\n unsaved_file.length_ = len( utf8_contents )\n unsaved_file.filename_ = ToCppStringCompatible( filename )\n\n files.append( unsaved_file )\n return files\n\n\n def ComputeCandidatesInner( self, request_data ):\n filename = request_data[ 'filepath' ]\n if not filename:\n return\n\n if self._completer.UpdatingTranslationUnit(\n ToCppStringCompatible( filename ) ):\n raise RuntimeError( PARSING_FILE_MESSAGE )\n\n flags = self._FlagsForRequest( request_data )\n if not flags:\n raise RuntimeError( NO_COMPILE_FLAGS_MESSAGE )\n\n files = self.GetUnsavedFilesVector( request_data )\n line = request_data[ 'line_num' ]\n column = request_data[ 'start_column' ]\n with self._files_being_compiled.GetExclusive( filename ):\n results = self._completer.CandidatesForLocationInFile(\n ToCppStringCompatible( filename ),\n line,\n column,\n files,\n flags )\n\n if not results:\n raise RuntimeError( NO_COMPLETIONS_MESSAGE )\n\n return [ ConvertCompletionData( x ) for x in results ]\n\n\n def GetSubcommandsMap( self ):\n return {\n 'GoToDefinition' : ( lambda self, request_data, args:\n self._GoToDefinition( request_data ) ),\n 'GoToDeclaration' : ( lambda self, request_data, args:\n self._GoToDeclaration( request_data ) ),\n 'GoTo' : ( lambda self, request_data, args:\n self._GoTo( request_data ) ),\n 'GoToImprecise' : ( lambda self, request_data, args:\n self._GoToImprecise( request_data ) ),\n 'GoToInclude' : ( lambda self, request_data, args:\n self._GoToInclude( request_data ) ),\n 'ClearCompilationFlagCache': ( lambda self, request_data, args:\n self._ClearCompilationFlagCache() ),\n 'GetType' : ( lambda self, request_data, args:\n self._GetSemanticInfo( request_data, func = 'GetTypeAtLocation' ) ),\n 'GetTypeImprecise' : ( lambda self, request_data, args:\n self._GetSemanticInfo( request_data,\n func = 'GetTypeAtLocation',\n reparse = False ) ),\n 'GetParent' : ( lambda self, request_data, args:\n self._GetSemanticInfo( request_data,\n func = 'GetEnclosingFunctionAtLocation' ) ),\n 'FixIt' : ( lambda self, request_data, args:\n self._FixIt( request_data ) ),\n 'GetDoc' : ( lambda self, request_data, args:\n self._GetSemanticInfo( request_data,\n reparse = True,\n func = 'GetDocsForLocationInFile',\n response_builder = _BuildGetDocResponse ) ),\n 'GetDocImprecise' : ( lambda self, request_data, args:\n self._GetSemanticInfo( request_data,\n reparse = False,\n func = 'GetDocsForLocationInFile',\n response_builder = _BuildGetDocResponse ) ),\n }\n\n\n def _LocationForGoTo( self, goto_function, request_data, reparse = True ):\n filename = request_data[ 'filepath' ]\n if not filename:\n raise ValueError( INVALID_FILE_MESSAGE )\n\n flags = self._FlagsForRequest( request_data )\n if not flags:\n raise ValueError( NO_COMPILE_FLAGS_MESSAGE )\n\n files = self.GetUnsavedFilesVector( request_data )\n line = request_data[ 'line_num' ]\n column = request_data[ 'column_num' ]\n return getattr( self._completer, goto_function )(\n ToCppStringCompatible( filename ),\n line,\n column,\n files,\n flags,\n reparse )\n\n\n def _GoToDefinition( self, request_data ):\n location = self._LocationForGoTo( 'GetDefinitionLocation', request_data )\n if not location or not location.IsValid():\n raise RuntimeError( 'Can\\'t jump to definition.' )\n return _ResponseForLocation( location )\n\n\n def _GoToDeclaration( self, request_data ):\n location = self._LocationForGoTo( 'GetDeclarationLocation', request_data )\n if not location or not location.IsValid():\n raise RuntimeError( 'Can\\'t jump to declaration.' )\n return _ResponseForLocation( location )\n\n\n def _GoTo( self, request_data ):\n include_response = self._ResponseForInclude( request_data )\n if include_response:\n return include_response\n\n location = self._LocationForGoTo( 'GetDefinitionLocation', request_data )\n if not location or not location.IsValid():\n location = self._LocationForGoTo( 'GetDeclarationLocation', request_data )\n if not location or not location.IsValid():\n raise RuntimeError( 'Can\\'t jump to definition or declaration.' )\n return _ResponseForLocation( location )\n\n\n def _GoToImprecise( self, request_data ):\n include_response = self._ResponseForInclude( request_data )\n if include_response:\n return include_response\n\n location = self._LocationForGoTo( 'GetDefinitionLocation',\n request_data,\n reparse = False )\n if not location or not location.IsValid():\n location = self._LocationForGoTo( 'GetDeclarationLocation',\n request_data,\n reparse = False )\n if not location or not location.IsValid():\n raise RuntimeError( 'Can\\'t jump to definition or declaration.' )\n return _ResponseForLocation( location )\n\n\n def _ResponseForInclude( self, request_data ):\n \"\"\"Returns response for include file location if cursor is on the\n include statement, None otherwise.\n Throws RuntimeError if cursor is on include statement and corresponding\n include file not found.\"\"\"\n current_line = request_data[ 'line_value' ]\n include_file_name, quoted_include = GetIncludeStatementValue( current_line )\n if not include_file_name:\n return None\n\n current_file_path = request_data[ 'filepath' ]\n client_data = request_data.get( 'extra_conf_data', None )\n quoted_include_paths, include_paths = (\n self._flags.UserIncludePaths( current_file_path, client_data ) )\n if quoted_include:\n include_file_path = _GetAbsolutePath( include_file_name,\n quoted_include_paths )\n if include_file_path:\n return responses.BuildGoToResponse( include_file_path,\n line_num = 1,\n column_num = 1 )\n\n include_file_path = _GetAbsolutePath( include_file_name, include_paths )\n if include_file_path:\n return responses.BuildGoToResponse( include_file_path,\n line_num = 1,\n column_num = 1 )\n raise RuntimeError( 'Include file not found.' )\n\n\n def _GoToInclude( self, request_data ):\n include_response = self._ResponseForInclude( request_data )\n if not include_response:\n raise RuntimeError( 'Not an include/import line.' )\n return include_response\n\n\n def _GetSemanticInfo(\n self,\n request_data,\n func,\n response_builder = responses.BuildDisplayMessageResponse,\n reparse = True ):\n filename = request_data[ 'filepath' ]\n if not filename:\n raise ValueError( INVALID_FILE_MESSAGE )\n\n flags = self._FlagsForRequest( request_data )\n if not flags:\n raise ValueError( NO_COMPILE_FLAGS_MESSAGE )\n\n files = self.GetUnsavedFilesVector( request_data )\n line = request_data[ 'line_num' ]\n column = request_data[ 'column_num' ]\n\n message = getattr( self._completer, func )(\n ToCppStringCompatible( filename ),\n line,\n column,\n files,\n flags,\n reparse)\n\n if not message:\n message = \"No semantic information available\"\n\n return response_builder( message )\n\n def _ClearCompilationFlagCache( self ):\n self._flags.Clear()\n\n def _FixIt( self, request_data ):\n filename = request_data[ 'filepath' ]\n if not filename:\n raise ValueError( INVALID_FILE_MESSAGE )\n\n flags = self._FlagsForRequest( request_data )\n if not flags:\n raise ValueError( NO_COMPILE_FLAGS_MESSAGE )\n\n files = self.GetUnsavedFilesVector( request_data )\n line = request_data[ 'line_num' ]\n column = request_data[ 'column_num' ]\n\n fixits = getattr( self._completer, \"GetFixItsForLocationInFile\" )(\n ToCppStringCompatible( filename ),\n line,\n column,\n files,\n flags,\n True )\n\n # don't raise an error if not fixits: - leave that to the client to respond\n # in a nice way\n\n return responses.BuildFixItResponse( fixits )\n\n def OnFileReadyToParse( self, request_data ):\n filename = request_data[ 'filepath' ]\n if not filename:\n raise ValueError( INVALID_FILE_MESSAGE )\n\n flags = self._FlagsForRequest( request_data )\n if not flags:\n raise ValueError( NO_COMPILE_FLAGS_MESSAGE )\n\n with self._files_being_compiled.GetExclusive( filename ):\n diagnostics = self._completer.UpdateTranslationUnit(\n ToCppStringCompatible( filename ),\n self.GetUnsavedFilesVector( request_data ),\n flags )\n\n diagnostics = _FilterDiagnostics( diagnostics )\n self._diagnostic_store = DiagnosticsToDiagStructure( diagnostics )\n return [ responses.BuildDiagnosticData( x ) for x in\n diagnostics[ : self._max_diagnostics_to_display ] ]\n\n\n def OnBufferUnload( self, request_data ):\n self._completer.DeleteCachesForFile(\n ToCppStringCompatible( request_data[ 'filepath' ] ) )\n\n\n def GetDetailedDiagnostic( self, request_data ):\n current_line = request_data[ 'line_num' ]\n current_column = request_data[ 'column_num' ]\n current_file = request_data[ 'filepath' ]\n\n if not self._diagnostic_store:\n raise ValueError( NO_DIAGNOSTIC_MESSAGE )\n\n diagnostics = self._diagnostic_store[ current_file ][ current_line ]\n if not diagnostics:\n raise ValueError( NO_DIAGNOSTIC_MESSAGE )\n\n closest_diagnostic = None\n distance_to_closest_diagnostic = 999\n\n # FIXME: all of these calculations are currently working with byte\n # offsets, which are technically incorrect. We should be working with\n # codepoint offsets, as we want the nearest character-wise diagnostic\n for diagnostic in diagnostics:\n distance = abs( current_column - diagnostic.location_.column_number_ )\n if distance < distance_to_closest_diagnostic:\n distance_to_closest_diagnostic = distance\n closest_diagnostic = diagnostic\n\n return responses.BuildDisplayMessageResponse(\n closest_diagnostic.long_formatted_text_ )\n\n\n def DebugInfo( self, request_data ):\n filename = request_data[ 'filepath' ]\n try:\n extra_conf = extra_conf_store.ModuleFileForSourceFile( filename )\n flags = self._FlagsForRequest( request_data ) or []\n except NoExtraConfDetected:\n return ( 'C-family completer debug information:\\n'\n ' No configuration file found' )\n except UnknownExtraConf as error:\n return ( 'C-family completer debug information:\\n'\n ' Configuration file found but not loaded\\n'\n ' Configuration path: {0}'.format(\n error.extra_conf_file ) )\n if not extra_conf:\n return ( 'C-family completer debug information:\\n'\n ' No configuration file found' )\n return ( 'C-family completer debug information:\\n'\n ' Configuration file found and loaded\\n'\n ' Configuration path: {0}\\n'\n ' Flags: {1}'.format( extra_conf, list( flags ) ) )\n\n\n def _FlagsForRequest( self, request_data ):\n filename = request_data[ 'filepath' ]\n if 'compilation_flags' in request_data:\n return PrepareFlagsForClang( request_data[ 'compilation_flags' ],\n filename )\n client_data = request_data.get( 'extra_conf_data', None )\n return self._flags.FlagsForFile( filename, client_data = client_data )\n\n\ndef ConvertCompletionData( completion_data ):\n return responses.BuildCompletionData(\n insertion_text = completion_data.TextToInsertInBuffer(),\n menu_text = completion_data.MainCompletionText(),\n extra_menu_info = completion_data.ExtraMenuInfo(),\n kind = completion_data.kind_.name,\n detailed_info = completion_data.DetailedInfoForPreviewWindow(),\n extra_data = ( { 'doc_string': completion_data.DocString() }\n if completion_data.DocString() else None ) )\n\n\ndef DiagnosticsToDiagStructure( diagnostics ):\n structure = defaultdict( lambda : defaultdict( list ) )\n for diagnostic in diagnostics:\n structure[ diagnostic.location_.filename_ ][\n diagnostic.location_.line_number_ ].append( diagnostic )\n return structure\n\n\ndef ClangAvailableForFiletypes( filetypes ):\n return any( [ filetype in CLANG_FILETYPES for filetype in filetypes ] )\n\n\ndef InCFamilyFile( filetypes ):\n return ClangAvailableForFiletypes( filetypes )\n\n\ndef _FilterDiagnostics( diagnostics ):\n # Clang has an annoying warning that shows up when we try to compile header\n # files if the header has \"#pragma once\" inside it. The error is not\n # legitimate because it shows up because libclang thinks we are compiling a\n # source file instead of a header file.\n #\n # See our issue #216 and upstream bug:\n # http://llvm.org/bugs/show_bug.cgi?id=16686\n #\n # The second thing we want to filter out are those incredibly annoying \"too\n # many errors emitted\" diagnostics that are utterly useless.\n return [ x for x in diagnostics if\n x.text_ != PRAGMA_DIAG_TEXT_TO_IGNORE and\n x.text_ != TOO_MANY_ERRORS_DIAG_TEXT_TO_IGNORE ]\n\n\ndef _ResponseForLocation( location ):\n return responses.BuildGoToResponse( location.filename_,\n location.line_number_,\n location.column_number_ )\n\n\n# Strips the following leading strings from the raw comment:\n# - <whitespace>///\n# - <whitespace>///<\n# - <whitespace>//<\n# - <whitespace>//!\n# - <whitespace>/**\n# - <whitespace>/*!\n# - <whitespace>/*<\n# - <whitespace>/*\n# - <whitespace>*\n# - <whitespace>*/\n# - etc.\n# That is:\n# - 2 or 3 '/' followed by '<' or '!'\n# - '/' then 1 or 2 '*' followed by optional '<' or '!'\n# - '*' followed by optional '/'\nSTRIP_LEADING_COMMENT = re.compile( '^[ \\t]*(/{2,3}[<!]?|/\\*{1,2}[<!]?|\\*/?)' )\n\n# And the following trailing strings\n# - <whitespace>*/\n# - <whitespace>\nSTRIP_TRAILING_COMMENT = re.compile( '[ \\t]*\\*/[ \\t]*$|[ \\t]*$' )\n\n\ndef _FormatRawComment( comment ):\n \"\"\"Strips leading indentation and comment markers from the comment string\"\"\"\n return textwrap.dedent(\n '\\n'.join( [ re.sub( STRIP_TRAILING_COMMENT, '',\n re.sub( STRIP_LEADING_COMMENT, '', line ) )\n for line in ToUnicode( comment ).splitlines() ] ) )\n\n\ndef _BuildGetDocResponse( doc_data ):\n \"\"\"Builds a \"DetailedInfoResponse\" for a GetDoc request. doc_data is a\n DocumentationData object returned from the ClangCompleter\"\"\"\n\n # Parse the XML, as this is the only way to get the declaration text out of\n # libclang. It seems quite wasteful, but while the contents of the XML\n # provide fully parsed doxygen documentation tree, we actually don't want to\n # ever lose any information from the comment, so we just want display\n # the stripped comment. Arguably we could skip all of this XML generation and\n # parsing, but having the raw declaration text is likely one of the most\n # useful pieces of documentation available to the developer. Perhaps in\n # future, we can use this XML for more interesting things.\n try:\n root = xml.etree.ElementTree.fromstring( doc_data.comment_xml )\n except:\n raise ValueError( NO_DOCUMENTATION_MESSAGE )\n\n # Note: declaration is False-y if it has no child elements, hence the below\n # (wordy) if not declaration is None\n declaration = root.find( \"Declaration\" )\n\n return responses.BuildDetailedInfoResponse(\n '{0}\\n{1}\\nType: {2}\\nName: {3}\\n---\\n{4}'.format(\n ToUnicode( declaration.text ) if declaration is not None else \"\",\n ToUnicode( doc_data.brief_comment ),\n ToUnicode( doc_data.canonical_type ),\n ToUnicode( doc_data.display_name ),\n ToUnicode( _FormatRawComment( doc_data.raw_comment ) ) ) )\n\n\ndef _GetAbsolutePath( include_file_name, include_paths ):\n for path in include_paths:\n include_file_path = os.path.join( path, include_file_name )\n if os.path.isfile( include_file_path ):\n return include_file_path\n return None\n",
"path": "ycmd/completers/cpp/clang_completer.py"
}
] | diff --git a/ycmd/completers/cpp/clang_completer.py b/ycmd/completers/cpp/clang_completer.py
index 35bb660c22..37e44a4090 100755
--- a/ycmd/completers/cpp/clang_completer.py
+++ b/ycmd/completers/cpp/clang_completer.py
@@ -338,7 +338,7 @@ def OnFileReadyToParse( self, request_data ):
def OnBufferUnload( self, request_data ):
self._completer.DeleteCachesForFile(
- ToCppStringCompatible( request_data[ 'unloaded_buffer' ] ) )
+ ToCppStringCompatible( request_data[ 'filepath' ] ) )
def GetDetailedDiagnostic( self, request_data ):
diff --git a/ycmd/tests/typescript/event_notification_test.py b/ycmd/tests/typescript/event_notification_test.py
new file mode 100644
index 0000000000..a8402c6c34
--- /dev/null
+++ b/ycmd/tests/typescript/event_notification_test.py
@@ -0,0 +1,116 @@
+# Copyright (C) 2016 ycmd contributors
+#
+# This file is part of ycmd.
+#
+# ycmd is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# ycmd is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with ycmd. If not, see <http://www.gnu.org/licenses/>.
+
+from __future__ import absolute_import
+from __future__ import unicode_literals
+from __future__ import print_function
+from __future__ import division
+from future import standard_library
+standard_library.install_aliases()
+from builtins import * # noqa
+
+from hamcrest import assert_that, contains, has_entries
+
+from ycmd.tests.typescript import IsolatedYcmd, PathToTestFile
+from ycmd.tests.test_utils import ( BuildRequest, ClearCompletionsCache,
+ CompletionEntryMatcher )
+from ycmd.utils import ReadFile
+
+
+@IsolatedYcmd
+def EventNotification_OnBufferUnload_CloseFile_test( app ):
+ # Open main.ts file in a buffer.
+ main_filepath = PathToTestFile( 'buffer_unload', 'main.ts' )
+ main_contents = ReadFile( main_filepath )
+
+ event_data = BuildRequest( filepath = main_filepath,
+ filetype = 'typescript',
+ contents = main_contents,
+ event_name = 'BufferVisit' )
+ app.post_json( '/event_notification', event_data )
+
+ # Complete in main.ts buffer an object defined in imported.ts.
+ completion_data = BuildRequest( filepath = main_filepath,
+ filetype = 'typescript',
+ contents = main_contents,
+ line_num = 3,
+ column_num = 10 )
+ response = app.post_json( '/completions', completion_data )
+ assert_that( response.json, has_entries( {
+ 'completions': contains( CompletionEntryMatcher( 'method' ) ) } ) )
+ # FIXME: we should not have to clear the cache.
+ ClearCompletionsCache()
+
+ # Open imported.ts file in another buffer.
+ imported_filepath = PathToTestFile( 'buffer_unload', 'imported.ts' )
+ imported_contents = ReadFile( imported_filepath )
+
+ event_data = BuildRequest( filepath = imported_filepath,
+ filetype = 'typescript',
+ contents = imported_contents,
+ event_name = 'BufferVisit' )
+ app.post_json( '/event_notification', event_data )
+
+ # Modify imported.ts buffer without writing the changes to disk.
+ modified_imported_contents = imported_contents.replace( 'method',
+ 'modified_method' )
+
+ # FIXME: TypeScript completer should not rely on the FileReadyToParse events
+ # to synchronize the contents of dirty buffers but use instead the file_data
+ # field of the request.
+ event_data = BuildRequest( filepath = imported_filepath,
+ filetype = 'typescript',
+ contents = modified_imported_contents,
+ event_name = 'FileReadyToParse' )
+ app.post_json( '/event_notification', event_data )
+
+ # Complete at same location in main.ts buffer.
+ imported_data = {
+ imported_filepath: {
+ 'filetypes': [ 'typescript' ],
+ 'contents': modified_imported_contents
+ }
+ }
+ completion_data = BuildRequest( filepath = main_filepath,
+ filetype = 'typescript',
+ contents = main_contents,
+ line_num = 3,
+ column_num = 10,
+ file_data = imported_data )
+ response = app.post_json( '/completions', completion_data )
+ assert_that( response.json, has_entries( {
+ 'completions': contains( CompletionEntryMatcher( 'modified_method' ) ) } )
+ )
+ # FIXME: we should not have to clear the cache.
+ ClearCompletionsCache()
+
+ # Unload imported.ts buffer.
+ event_data = BuildRequest( filepath = imported_filepath,
+ filetype = 'typescript',
+ contents = imported_contents,
+ event_name = 'BufferUnload' )
+ app.post_json( '/event_notification', event_data )
+
+ # Complete at same location in main.ts buffer.
+ completion_data = BuildRequest( filepath = main_filepath,
+ filetype = 'typescript',
+ contents = main_contents,
+ line_num = 3,
+ column_num = 10 )
+ response = app.post_json( '/completions', completion_data )
+ assert_that( response.json, has_entries( {
+ 'completions': contains( CompletionEntryMatcher( 'method' ) ) } ) )
diff --git a/ycmd/tests/typescript/testdata/buffer_unload/imported.ts b/ycmd/tests/typescript/testdata/buffer_unload/imported.ts
new file mode 100644
index 0000000000..ffb7e58533
--- /dev/null
+++ b/ycmd/tests/typescript/testdata/buffer_unload/imported.ts
@@ -0,0 +1,4 @@
+export class Imported {
+ method() {
+ }
+}
diff --git a/ycmd/tests/typescript/testdata/buffer_unload/main.ts b/ycmd/tests/typescript/testdata/buffer_unload/main.ts
new file mode 100644
index 0000000000..6c26d9e899
--- /dev/null
+++ b/ycmd/tests/typescript/testdata/buffer_unload/main.ts
@@ -0,0 +1,3 @@
+import { Imported } from "./imported";
+let imported = new Imported();
+imported.
|
getmoto__moto-1671 | IoT service list_things should include thing ARNs
### Actual
`list_things` method of the AWS IoT service returns _thingName_, _attributes_, _thingTypeName_ and _version_
### Expected
The `list_things` method of the AWS IoT service should include the thing ARN (_thingArn_) as described [here](https://boto3.readthedocs.io/en/latest/reference/services/iot.html#IoT.Client.list_things)
| [
{
"content": "from __future__ import unicode_literals\nimport time\nimport boto3\nimport string\nimport random\nimport hashlib\nimport uuid\nimport re\nfrom datetime import datetime\nfrom moto.core import BaseBackend, BaseModel\nfrom collections import OrderedDict\nfrom .exceptions import (\n ResourceNotFoundException,\n InvalidRequestException,\n VersionConflictException\n)\n\n\nclass FakeThing(BaseModel):\n def __init__(self, thing_name, thing_type, attributes, region_name):\n self.region_name = region_name\n self.thing_name = thing_name\n self.thing_type = thing_type\n self.attributes = attributes\n self.arn = 'arn:aws:iot:%s:1:thing/%s' % (self.region_name, thing_name)\n self.version = 1\n # TODO: we need to handle 'version'?\n\n # for iot-data\n self.thing_shadow = None\n\n def to_dict(self, include_default_client_id=False):\n obj = {\n 'thingName': self.thing_name,\n 'attributes': self.attributes,\n 'version': self.version\n }\n if self.thing_type:\n obj['thingTypeName'] = self.thing_type.thing_type_name\n if include_default_client_id:\n obj['defaultClientId'] = self.thing_name\n return obj\n\n\nclass FakeThingType(BaseModel):\n def __init__(self, thing_type_name, thing_type_properties, region_name):\n self.region_name = region_name\n self.thing_type_name = thing_type_name\n self.thing_type_properties = thing_type_properties\n self.thing_type_id = str(uuid.uuid4()) # I don't know the rule of id\n t = time.time()\n self.metadata = {\n 'deprecated': False,\n 'creationData': int(t * 1000) / 1000.0\n }\n self.arn = 'arn:aws:iot:%s:1:thingtype/%s' % (self.region_name, thing_type_name)\n\n def to_dict(self):\n return {\n 'thingTypeName': self.thing_type_name,\n 'thingTypeId': self.thing_type_id,\n 'thingTypeProperties': self.thing_type_properties,\n 'thingTypeMetadata': self.metadata\n }\n\n\nclass FakeThingGroup(BaseModel):\n def __init__(self, thing_group_name, parent_group_name, thing_group_properties, region_name):\n self.region_name = region_name\n self.thing_group_name = thing_group_name\n self.thing_group_id = str(uuid.uuid4()) # I don't know the rule of id\n self.version = 1 # TODO: tmp\n self.parent_group_name = parent_group_name\n self.thing_group_properties = thing_group_properties or {}\n t = time.time()\n self.metadata = {\n 'creationData': int(t * 1000) / 1000.0\n }\n self.arn = 'arn:aws:iot:%s:1:thinggroup/%s' % (self.region_name, thing_group_name)\n self.things = OrderedDict()\n\n def to_dict(self):\n return {\n 'thingGroupName': self.thing_group_name,\n 'thingGroupId': self.thing_group_id,\n 'version': self.version,\n 'thingGroupProperties': self.thing_group_properties,\n 'thingGroupMetadata': self.metadata\n }\n\n\nclass FakeCertificate(BaseModel):\n def __init__(self, certificate_pem, status, region_name):\n m = hashlib.sha256()\n m.update(str(uuid.uuid4()).encode('utf-8'))\n self.certificate_id = m.hexdigest()\n self.arn = 'arn:aws:iot:%s:1:cert/%s' % (region_name, self.certificate_id)\n self.certificate_pem = certificate_pem\n self.status = status\n\n # TODO: must adjust\n self.owner = '1'\n self.transfer_data = {}\n self.creation_date = time.time()\n self.last_modified_date = self.creation_date\n self.ca_certificate_id = None\n\n def to_dict(self):\n return {\n 'certificateArn': self.arn,\n 'certificateId': self.certificate_id,\n 'status': self.status,\n 'creationDate': self.creation_date\n }\n\n def to_description_dict(self):\n \"\"\"\n You might need keys below in some situation\n - caCertificateId\n - previousOwnedBy\n \"\"\"\n return {\n 'certificateArn': self.arn,\n 'certificateId': self.certificate_id,\n 'status': self.status,\n 'certificatePem': self.certificate_pem,\n 'ownedBy': self.owner,\n 'creationDate': self.creation_date,\n 'lastModifiedDate': self.last_modified_date,\n 'transferData': self.transfer_data\n }\n\n\nclass FakePolicy(BaseModel):\n def __init__(self, name, document, region_name):\n self.name = name\n self.document = document\n self.arn = 'arn:aws:iot:%s:1:policy/%s' % (region_name, name)\n self.version = '1' # TODO: handle version\n\n def to_get_dict(self):\n return {\n 'policyName': self.name,\n 'policyArn': self.arn,\n 'policyDocument': self.document,\n 'defaultVersionId': self.version\n }\n\n def to_dict_at_creation(self):\n return {\n 'policyName': self.name,\n 'policyArn': self.arn,\n 'policyDocument': self.document,\n 'policyVersionId': self.version\n }\n\n def to_dict(self):\n return {\n 'policyName': self.name,\n 'policyArn': self.arn,\n }\n\n\nclass FakeJob(BaseModel):\n JOB_ID_REGEX_PATTERN = \"[a-zA-Z0-9_-]\"\n JOB_ID_REGEX = re.compile(JOB_ID_REGEX_PATTERN)\n\n def __init__(self, job_id, targets, document_source, document, description, presigned_url_config, target_selection,\n job_executions_rollout_config, document_parameters, region_name):\n if not self._job_id_matcher(self.JOB_ID_REGEX, job_id):\n raise InvalidRequestException()\n\n self.region_name = region_name\n self.job_id = job_id\n self.job_arn = 'arn:aws:iot:%s:1:job/%s' % (self.region_name, job_id)\n self.targets = targets\n self.document_source = document_source\n self.document = document\n self.description = description\n self.presigned_url_config = presigned_url_config\n self.target_selection = target_selection\n self.job_executions_rollout_config = job_executions_rollout_config\n self.status = None # IN_PROGRESS | CANCELED | COMPLETED\n self.comment = None\n self.created_at = time.mktime(datetime(2015, 1, 1).timetuple())\n self.last_updated_at = time.mktime(datetime(2015, 1, 1).timetuple())\n self.completed_at = None\n self.job_process_details = {\n 'processingTargets': targets,\n 'numberOfQueuedThings': 1,\n 'numberOfCanceledThings': 0,\n 'numberOfSucceededThings': 0,\n 'numberOfFailedThings': 0,\n 'numberOfRejectedThings': 0,\n 'numberOfInProgressThings': 0,\n 'numberOfRemovedThings': 0\n }\n self.document_parameters = document_parameters\n\n def to_dict(self):\n obj = {\n 'jobArn': self.job_arn,\n 'jobId': self.job_id,\n 'targets': self.targets,\n 'description': self.description,\n 'presignedUrlConfig': self.presigned_url_config,\n 'targetSelection': self.target_selection,\n 'jobExecutionsRolloutConfig': self.job_executions_rollout_config,\n 'status': self.status,\n 'comment': self.comment,\n 'createdAt': self.created_at,\n 'lastUpdatedAt': self.last_updated_at,\n 'completedAt': self.completedAt,\n 'jobProcessDetails': self.job_process_details,\n 'documentParameters': self.document_parameters,\n 'document': self.document,\n 'documentSource': self.document_source\n }\n\n return obj\n\n def _job_id_matcher(self, regex, argument):\n regex_match = regex.match(argument)\n length_match = len(argument) <= 64\n return regex_match and length_match\n\n\nclass IoTBackend(BaseBackend):\n def __init__(self, region_name=None):\n super(IoTBackend, self).__init__()\n self.region_name = region_name\n self.things = OrderedDict()\n self.jobs = OrderedDict()\n self.thing_types = OrderedDict()\n self.thing_groups = OrderedDict()\n self.certificates = OrderedDict()\n self.policies = OrderedDict()\n self.principal_policies = OrderedDict()\n self.principal_things = OrderedDict()\n\n def reset(self):\n region_name = self.region_name\n self.__dict__ = {}\n self.__init__(region_name)\n\n def create_thing(self, thing_name, thing_type_name, attribute_payload):\n thing_types = self.list_thing_types()\n thing_type = None\n if thing_type_name:\n filtered_thing_types = [_ for _ in thing_types if _.thing_type_name == thing_type_name]\n if len(filtered_thing_types) == 0:\n raise ResourceNotFoundException()\n thing_type = filtered_thing_types[0]\n if attribute_payload is None:\n attributes = {}\n elif 'attributes' not in attribute_payload:\n attributes = {}\n else:\n attributes = attribute_payload['attributes']\n thing = FakeThing(thing_name, thing_type, attributes, self.region_name)\n self.things[thing.arn] = thing\n return thing.thing_name, thing.arn\n\n def create_thing_type(self, thing_type_name, thing_type_properties):\n if thing_type_properties is None:\n thing_type_properties = {}\n thing_type = FakeThingType(thing_type_name, thing_type_properties, self.region_name)\n self.thing_types[thing_type.arn] = thing_type\n return thing_type.thing_type_name, thing_type.arn\n\n def list_thing_types(self, thing_type_name=None):\n if thing_type_name:\n # It's wierd but thing_type_name is filterd by forward match, not complete match\n return [_ for _ in self.thing_types.values() if _.thing_type_name.startswith(thing_type_name)]\n thing_types = self.thing_types.values()\n return thing_types\n\n def list_things(self, attribute_name, attribute_value, thing_type_name):\n # TODO: filter by attributess or thing_type\n things = self.things.values()\n return things\n\n def describe_thing(self, thing_name):\n things = [_ for _ in self.things.values() if _.thing_name == thing_name]\n if len(things) == 0:\n raise ResourceNotFoundException()\n return things[0]\n\n def describe_thing_type(self, thing_type_name):\n thing_types = [_ for _ in self.thing_types.values() if _.thing_type_name == thing_type_name]\n if len(thing_types) == 0:\n raise ResourceNotFoundException()\n return thing_types[0]\n\n def delete_thing(self, thing_name, expected_version):\n # TODO: handle expected_version\n\n # can raise ResourceNotFoundError\n thing = self.describe_thing(thing_name)\n del self.things[thing.arn]\n\n def delete_thing_type(self, thing_type_name):\n # can raise ResourceNotFoundError\n thing_type = self.describe_thing_type(thing_type_name)\n del self.thing_types[thing_type.arn]\n\n def update_thing(self, thing_name, thing_type_name, attribute_payload, expected_version, remove_thing_type):\n # if attributes payload = {}, nothing\n thing = self.describe_thing(thing_name)\n thing_type = None\n\n if remove_thing_type and thing_type_name:\n raise InvalidRequestException()\n\n # thing_type\n if thing_type_name:\n thing_types = self.list_thing_types()\n filtered_thing_types = [_ for _ in thing_types if _.thing_type_name == thing_type_name]\n if len(filtered_thing_types) == 0:\n raise ResourceNotFoundException()\n thing_type = filtered_thing_types[0]\n thing.thing_type = thing_type\n\n if remove_thing_type:\n thing.thing_type = None\n\n # attribute\n if attribute_payload is not None and 'attributes' in attribute_payload:\n do_merge = attribute_payload.get('merge', False)\n attributes = attribute_payload['attributes']\n if not do_merge:\n thing.attributes = attributes\n else:\n thing.attributes.update(attributes)\n\n def _random_string(self):\n n = 20\n random_str = ''.join([random.choice(string.ascii_letters + string.digits) for i in range(n)])\n return random_str\n\n def create_keys_and_certificate(self, set_as_active):\n # implement here\n # caCertificate can be blank\n key_pair = {\n 'PublicKey': self._random_string(),\n 'PrivateKey': self._random_string()\n }\n certificate_pem = self._random_string()\n status = 'ACTIVE' if set_as_active else 'INACTIVE'\n certificate = FakeCertificate(certificate_pem, status, self.region_name)\n self.certificates[certificate.certificate_id] = certificate\n return certificate, key_pair\n\n def delete_certificate(self, certificate_id):\n self.describe_certificate(certificate_id)\n del self.certificates[certificate_id]\n\n def describe_certificate(self, certificate_id):\n certs = [_ for _ in self.certificates.values() if _.certificate_id == certificate_id]\n if len(certs) == 0:\n raise ResourceNotFoundException()\n return certs[0]\n\n def list_certificates(self):\n return self.certificates.values()\n\n def update_certificate(self, certificate_id, new_status):\n cert = self.describe_certificate(certificate_id)\n # TODO: validate new_status\n cert.status = new_status\n\n def create_policy(self, policy_name, policy_document):\n policy = FakePolicy(policy_name, policy_document, self.region_name)\n self.policies[policy.name] = policy\n return policy\n\n def list_policies(self):\n policies = self.policies.values()\n return policies\n\n def get_policy(self, policy_name):\n policies = [_ for _ in self.policies.values() if _.name == policy_name]\n if len(policies) == 0:\n raise ResourceNotFoundException()\n return policies[0]\n\n def delete_policy(self, policy_name):\n policy = self.get_policy(policy_name)\n del self.policies[policy.name]\n\n def _get_principal(self, principal_arn):\n \"\"\"\n raise ResourceNotFoundException\n \"\"\"\n if ':cert/' in principal_arn:\n certs = [_ for _ in self.certificates.values() if _.arn == principal_arn]\n if len(certs) == 0:\n raise ResourceNotFoundException()\n principal = certs[0]\n return principal\n else:\n # TODO: search for cognito_ids\n pass\n raise ResourceNotFoundException()\n\n def attach_principal_policy(self, policy_name, principal_arn):\n principal = self._get_principal(principal_arn)\n policy = self.get_policy(policy_name)\n k = (principal_arn, policy_name)\n if k in self.principal_policies:\n return\n self.principal_policies[k] = (principal, policy)\n\n def detach_principal_policy(self, policy_name, principal_arn):\n # this may raises ResourceNotFoundException\n self._get_principal(principal_arn)\n self.get_policy(policy_name)\n\n k = (principal_arn, policy_name)\n if k not in self.principal_policies:\n raise ResourceNotFoundException()\n del self.principal_policies[k]\n\n def list_principal_policies(self, principal_arn):\n policies = [v[1] for k, v in self.principal_policies.items() if k[0] == principal_arn]\n return policies\n\n def list_policy_principals(self, policy_name):\n principals = [k[0] for k, v in self.principal_policies.items() if k[1] == policy_name]\n return principals\n\n def attach_thing_principal(self, thing_name, principal_arn):\n principal = self._get_principal(principal_arn)\n thing = self.describe_thing(thing_name)\n k = (principal_arn, thing_name)\n if k in self.principal_things:\n return\n self.principal_things[k] = (principal, thing)\n\n def detach_thing_principal(self, thing_name, principal_arn):\n # this may raises ResourceNotFoundException\n self._get_principal(principal_arn)\n self.describe_thing(thing_name)\n\n k = (principal_arn, thing_name)\n if k not in self.principal_things:\n raise ResourceNotFoundException()\n del self.principal_things[k]\n\n def list_principal_things(self, principal_arn):\n thing_names = [k[0] for k, v in self.principal_things.items() if k[0] == principal_arn]\n return thing_names\n\n def list_thing_principals(self, thing_name):\n principals = [k[0] for k, v in self.principal_things.items() if k[1] == thing_name]\n return principals\n\n def describe_thing_group(self, thing_group_name):\n thing_groups = [_ for _ in self.thing_groups.values() if _.thing_group_name == thing_group_name]\n if len(thing_groups) == 0:\n raise ResourceNotFoundException()\n return thing_groups[0]\n\n def create_thing_group(self, thing_group_name, parent_group_name, thing_group_properties):\n thing_group = FakeThingGroup(thing_group_name, parent_group_name, thing_group_properties, self.region_name)\n self.thing_groups[thing_group.arn] = thing_group\n return thing_group.thing_group_name, thing_group.arn, thing_group.thing_group_id\n\n def delete_thing_group(self, thing_group_name, expected_version):\n thing_group = self.describe_thing_group(thing_group_name)\n del self.thing_groups[thing_group.arn]\n\n def list_thing_groups(self, parent_group, name_prefix_filter, recursive):\n thing_groups = self.thing_groups.values()\n return thing_groups\n\n def update_thing_group(self, thing_group_name, thing_group_properties, expected_version):\n thing_group = self.describe_thing_group(thing_group_name)\n if expected_version and expected_version != thing_group.version:\n raise VersionConflictException(thing_group_name)\n attribute_payload = thing_group_properties.get('attributePayload', None)\n if attribute_payload is not None and 'attributes' in attribute_payload:\n do_merge = attribute_payload.get('merge', False)\n attributes = attribute_payload['attributes']\n if not do_merge:\n thing_group.thing_group_properties['attributePayload']['attributes'] = attributes\n else:\n thing_group.thing_group_properties['attributePayload']['attributes'].update(attributes)\n elif attribute_payload is not None and 'attributes' not in attribute_payload:\n thing_group.attributes = {}\n thing_group.version = thing_group.version + 1\n return thing_group.version\n\n def _identify_thing_group(self, thing_group_name, thing_group_arn):\n # identify thing group\n if thing_group_name is None and thing_group_arn is None:\n raise InvalidRequestException(\n ' Both thingGroupArn and thingGroupName are empty. Need to specify at least one of them'\n )\n if thing_group_name is not None:\n thing_group = self.describe_thing_group(thing_group_name)\n if thing_group_arn and thing_group.arn != thing_group_arn:\n raise InvalidRequestException(\n 'ThingGroupName thingGroupArn does not match specified thingGroupName in request'\n )\n elif thing_group_arn is not None:\n if thing_group_arn not in self.thing_groups:\n raise InvalidRequestException()\n thing_group = self.thing_groups[thing_group_arn]\n return thing_group\n\n def _identify_thing(self, thing_name, thing_arn):\n # identify thing\n if thing_name is None and thing_arn is None:\n raise InvalidRequestException(\n 'Both thingArn and thingName are empty. Need to specify at least one of them'\n )\n if thing_name is not None:\n thing = self.describe_thing(thing_name)\n if thing_arn and thing.arn != thing_arn:\n raise InvalidRequestException(\n 'ThingName thingArn does not match specified thingName in request'\n )\n elif thing_arn is not None:\n if thing_arn not in self.things:\n raise InvalidRequestException()\n thing = self.things[thing_arn]\n return thing\n\n def add_thing_to_thing_group(self, thing_group_name, thing_group_arn, thing_name, thing_arn):\n thing_group = self._identify_thing_group(thing_group_name, thing_group_arn)\n thing = self._identify_thing(thing_name, thing_arn)\n if thing.arn in thing_group.things:\n # aws ignores duplicate registration\n return\n thing_group.things[thing.arn] = thing\n\n def remove_thing_from_thing_group(self, thing_group_name, thing_group_arn, thing_name, thing_arn):\n thing_group = self._identify_thing_group(thing_group_name, thing_group_arn)\n thing = self._identify_thing(thing_name, thing_arn)\n if thing.arn not in thing_group.things:\n # aws ignores non-registered thing\n return\n del thing_group.things[thing.arn]\n\n def list_things_in_thing_group(self, thing_group_name, recursive):\n thing_group = self.describe_thing_group(thing_group_name)\n return thing_group.things.values()\n\n def list_thing_groups_for_thing(self, thing_name):\n thing = self.describe_thing(thing_name)\n all_thing_groups = self.list_thing_groups(None, None, None)\n ret = []\n for thing_group in all_thing_groups:\n if thing.arn in thing_group.things:\n ret.append({\n 'groupName': thing_group.thing_group_name,\n 'groupArn': thing_group.arn\n })\n return ret\n\n def update_thing_groups_for_thing(self, thing_name, thing_groups_to_add, thing_groups_to_remove):\n thing = self.describe_thing(thing_name)\n for thing_group_name in thing_groups_to_add:\n thing_group = self.describe_thing_group(thing_group_name)\n self.add_thing_to_thing_group(\n thing_group.thing_group_name, None,\n thing.thing_name, None\n )\n for thing_group_name in thing_groups_to_remove:\n thing_group = self.describe_thing_group(thing_group_name)\n self.remove_thing_from_thing_group(\n thing_group.thing_group_name, None,\n thing.thing_name, None\n )\n\n def create_job(self, job_id, targets, document_source, document, description, presigned_url_config,\n target_selection, job_executions_rollout_config, document_parameters):\n job = FakeJob(job_id, targets, document_source, document, description, presigned_url_config, target_selection,\n job_executions_rollout_config, document_parameters, self.region_name)\n self.jobs[job_id] = job\n return job.job_arn, job_id, description\n\n def describe_job(self, job_id):\n return self.jobs[job_id]\n\n\navailable_regions = boto3.session.Session().get_available_regions(\"iot\")\niot_backends = {region: IoTBackend(region) for region in available_regions}\n",
"path": "moto/iot/models.py"
}
] | [
{
"content": "from __future__ import unicode_literals\nimport time\nimport boto3\nimport string\nimport random\nimport hashlib\nimport uuid\nimport re\nfrom datetime import datetime\nfrom moto.core import BaseBackend, BaseModel\nfrom collections import OrderedDict\nfrom .exceptions import (\n ResourceNotFoundException,\n InvalidRequestException,\n VersionConflictException\n)\n\n\nclass FakeThing(BaseModel):\n def __init__(self, thing_name, thing_type, attributes, region_name):\n self.region_name = region_name\n self.thing_name = thing_name\n self.thing_type = thing_type\n self.attributes = attributes\n self.arn = 'arn:aws:iot:%s:1:thing/%s' % (self.region_name, thing_name)\n self.version = 1\n # TODO: we need to handle 'version'?\n\n # for iot-data\n self.thing_shadow = None\n\n def to_dict(self, include_default_client_id=False):\n obj = {\n 'thingName': self.thing_name,\n 'thingArn': self.arn,\n 'attributes': self.attributes,\n 'version': self.version\n }\n if self.thing_type:\n obj['thingTypeName'] = self.thing_type.thing_type_name\n if include_default_client_id:\n obj['defaultClientId'] = self.thing_name\n return obj\n\n\nclass FakeThingType(BaseModel):\n def __init__(self, thing_type_name, thing_type_properties, region_name):\n self.region_name = region_name\n self.thing_type_name = thing_type_name\n self.thing_type_properties = thing_type_properties\n self.thing_type_id = str(uuid.uuid4()) # I don't know the rule of id\n t = time.time()\n self.metadata = {\n 'deprecated': False,\n 'creationData': int(t * 1000) / 1000.0\n }\n self.arn = 'arn:aws:iot:%s:1:thingtype/%s' % (self.region_name, thing_type_name)\n\n def to_dict(self):\n return {\n 'thingTypeName': self.thing_type_name,\n 'thingTypeId': self.thing_type_id,\n 'thingTypeProperties': self.thing_type_properties,\n 'thingTypeMetadata': self.metadata\n }\n\n\nclass FakeThingGroup(BaseModel):\n def __init__(self, thing_group_name, parent_group_name, thing_group_properties, region_name):\n self.region_name = region_name\n self.thing_group_name = thing_group_name\n self.thing_group_id = str(uuid.uuid4()) # I don't know the rule of id\n self.version = 1 # TODO: tmp\n self.parent_group_name = parent_group_name\n self.thing_group_properties = thing_group_properties or {}\n t = time.time()\n self.metadata = {\n 'creationData': int(t * 1000) / 1000.0\n }\n self.arn = 'arn:aws:iot:%s:1:thinggroup/%s' % (self.region_name, thing_group_name)\n self.things = OrderedDict()\n\n def to_dict(self):\n return {\n 'thingGroupName': self.thing_group_name,\n 'thingGroupId': self.thing_group_id,\n 'version': self.version,\n 'thingGroupProperties': self.thing_group_properties,\n 'thingGroupMetadata': self.metadata\n }\n\n\nclass FakeCertificate(BaseModel):\n def __init__(self, certificate_pem, status, region_name):\n m = hashlib.sha256()\n m.update(str(uuid.uuid4()).encode('utf-8'))\n self.certificate_id = m.hexdigest()\n self.arn = 'arn:aws:iot:%s:1:cert/%s' % (region_name, self.certificate_id)\n self.certificate_pem = certificate_pem\n self.status = status\n\n # TODO: must adjust\n self.owner = '1'\n self.transfer_data = {}\n self.creation_date = time.time()\n self.last_modified_date = self.creation_date\n self.ca_certificate_id = None\n\n def to_dict(self):\n return {\n 'certificateArn': self.arn,\n 'certificateId': self.certificate_id,\n 'status': self.status,\n 'creationDate': self.creation_date\n }\n\n def to_description_dict(self):\n \"\"\"\n You might need keys below in some situation\n - caCertificateId\n - previousOwnedBy\n \"\"\"\n return {\n 'certificateArn': self.arn,\n 'certificateId': self.certificate_id,\n 'status': self.status,\n 'certificatePem': self.certificate_pem,\n 'ownedBy': self.owner,\n 'creationDate': self.creation_date,\n 'lastModifiedDate': self.last_modified_date,\n 'transferData': self.transfer_data\n }\n\n\nclass FakePolicy(BaseModel):\n def __init__(self, name, document, region_name):\n self.name = name\n self.document = document\n self.arn = 'arn:aws:iot:%s:1:policy/%s' % (region_name, name)\n self.version = '1' # TODO: handle version\n\n def to_get_dict(self):\n return {\n 'policyName': self.name,\n 'policyArn': self.arn,\n 'policyDocument': self.document,\n 'defaultVersionId': self.version\n }\n\n def to_dict_at_creation(self):\n return {\n 'policyName': self.name,\n 'policyArn': self.arn,\n 'policyDocument': self.document,\n 'policyVersionId': self.version\n }\n\n def to_dict(self):\n return {\n 'policyName': self.name,\n 'policyArn': self.arn,\n }\n\n\nclass FakeJob(BaseModel):\n JOB_ID_REGEX_PATTERN = \"[a-zA-Z0-9_-]\"\n JOB_ID_REGEX = re.compile(JOB_ID_REGEX_PATTERN)\n\n def __init__(self, job_id, targets, document_source, document, description, presigned_url_config, target_selection,\n job_executions_rollout_config, document_parameters, region_name):\n if not self._job_id_matcher(self.JOB_ID_REGEX, job_id):\n raise InvalidRequestException()\n\n self.region_name = region_name\n self.job_id = job_id\n self.job_arn = 'arn:aws:iot:%s:1:job/%s' % (self.region_name, job_id)\n self.targets = targets\n self.document_source = document_source\n self.document = document\n self.description = description\n self.presigned_url_config = presigned_url_config\n self.target_selection = target_selection\n self.job_executions_rollout_config = job_executions_rollout_config\n self.status = None # IN_PROGRESS | CANCELED | COMPLETED\n self.comment = None\n self.created_at = time.mktime(datetime(2015, 1, 1).timetuple())\n self.last_updated_at = time.mktime(datetime(2015, 1, 1).timetuple())\n self.completed_at = None\n self.job_process_details = {\n 'processingTargets': targets,\n 'numberOfQueuedThings': 1,\n 'numberOfCanceledThings': 0,\n 'numberOfSucceededThings': 0,\n 'numberOfFailedThings': 0,\n 'numberOfRejectedThings': 0,\n 'numberOfInProgressThings': 0,\n 'numberOfRemovedThings': 0\n }\n self.document_parameters = document_parameters\n\n def to_dict(self):\n obj = {\n 'jobArn': self.job_arn,\n 'jobId': self.job_id,\n 'targets': self.targets,\n 'description': self.description,\n 'presignedUrlConfig': self.presigned_url_config,\n 'targetSelection': self.target_selection,\n 'jobExecutionsRolloutConfig': self.job_executions_rollout_config,\n 'status': self.status,\n 'comment': self.comment,\n 'createdAt': self.created_at,\n 'lastUpdatedAt': self.last_updated_at,\n 'completedAt': self.completedAt,\n 'jobProcessDetails': self.job_process_details,\n 'documentParameters': self.document_parameters,\n 'document': self.document,\n 'documentSource': self.document_source\n }\n\n return obj\n\n def _job_id_matcher(self, regex, argument):\n regex_match = regex.match(argument)\n length_match = len(argument) <= 64\n return regex_match and length_match\n\n\nclass IoTBackend(BaseBackend):\n def __init__(self, region_name=None):\n super(IoTBackend, self).__init__()\n self.region_name = region_name\n self.things = OrderedDict()\n self.jobs = OrderedDict()\n self.thing_types = OrderedDict()\n self.thing_groups = OrderedDict()\n self.certificates = OrderedDict()\n self.policies = OrderedDict()\n self.principal_policies = OrderedDict()\n self.principal_things = OrderedDict()\n\n def reset(self):\n region_name = self.region_name\n self.__dict__ = {}\n self.__init__(region_name)\n\n def create_thing(self, thing_name, thing_type_name, attribute_payload):\n thing_types = self.list_thing_types()\n thing_type = None\n if thing_type_name:\n filtered_thing_types = [_ for _ in thing_types if _.thing_type_name == thing_type_name]\n if len(filtered_thing_types) == 0:\n raise ResourceNotFoundException()\n thing_type = filtered_thing_types[0]\n if attribute_payload is None:\n attributes = {}\n elif 'attributes' not in attribute_payload:\n attributes = {}\n else:\n attributes = attribute_payload['attributes']\n thing = FakeThing(thing_name, thing_type, attributes, self.region_name)\n self.things[thing.arn] = thing\n return thing.thing_name, thing.arn\n\n def create_thing_type(self, thing_type_name, thing_type_properties):\n if thing_type_properties is None:\n thing_type_properties = {}\n thing_type = FakeThingType(thing_type_name, thing_type_properties, self.region_name)\n self.thing_types[thing_type.arn] = thing_type\n return thing_type.thing_type_name, thing_type.arn\n\n def list_thing_types(self, thing_type_name=None):\n if thing_type_name:\n # It's wierd but thing_type_name is filterd by forward match, not complete match\n return [_ for _ in self.thing_types.values() if _.thing_type_name.startswith(thing_type_name)]\n thing_types = self.thing_types.values()\n return thing_types\n\n def list_things(self, attribute_name, attribute_value, thing_type_name):\n # TODO: filter by attributess or thing_type\n things = self.things.values()\n return things\n\n def describe_thing(self, thing_name):\n things = [_ for _ in self.things.values() if _.thing_name == thing_name]\n if len(things) == 0:\n raise ResourceNotFoundException()\n return things[0]\n\n def describe_thing_type(self, thing_type_name):\n thing_types = [_ for _ in self.thing_types.values() if _.thing_type_name == thing_type_name]\n if len(thing_types) == 0:\n raise ResourceNotFoundException()\n return thing_types[0]\n\n def delete_thing(self, thing_name, expected_version):\n # TODO: handle expected_version\n\n # can raise ResourceNotFoundError\n thing = self.describe_thing(thing_name)\n del self.things[thing.arn]\n\n def delete_thing_type(self, thing_type_name):\n # can raise ResourceNotFoundError\n thing_type = self.describe_thing_type(thing_type_name)\n del self.thing_types[thing_type.arn]\n\n def update_thing(self, thing_name, thing_type_name, attribute_payload, expected_version, remove_thing_type):\n # if attributes payload = {}, nothing\n thing = self.describe_thing(thing_name)\n thing_type = None\n\n if remove_thing_type and thing_type_name:\n raise InvalidRequestException()\n\n # thing_type\n if thing_type_name:\n thing_types = self.list_thing_types()\n filtered_thing_types = [_ for _ in thing_types if _.thing_type_name == thing_type_name]\n if len(filtered_thing_types) == 0:\n raise ResourceNotFoundException()\n thing_type = filtered_thing_types[0]\n thing.thing_type = thing_type\n\n if remove_thing_type:\n thing.thing_type = None\n\n # attribute\n if attribute_payload is not None and 'attributes' in attribute_payload:\n do_merge = attribute_payload.get('merge', False)\n attributes = attribute_payload['attributes']\n if not do_merge:\n thing.attributes = attributes\n else:\n thing.attributes.update(attributes)\n\n def _random_string(self):\n n = 20\n random_str = ''.join([random.choice(string.ascii_letters + string.digits) for i in range(n)])\n return random_str\n\n def create_keys_and_certificate(self, set_as_active):\n # implement here\n # caCertificate can be blank\n key_pair = {\n 'PublicKey': self._random_string(),\n 'PrivateKey': self._random_string()\n }\n certificate_pem = self._random_string()\n status = 'ACTIVE' if set_as_active else 'INACTIVE'\n certificate = FakeCertificate(certificate_pem, status, self.region_name)\n self.certificates[certificate.certificate_id] = certificate\n return certificate, key_pair\n\n def delete_certificate(self, certificate_id):\n self.describe_certificate(certificate_id)\n del self.certificates[certificate_id]\n\n def describe_certificate(self, certificate_id):\n certs = [_ for _ in self.certificates.values() if _.certificate_id == certificate_id]\n if len(certs) == 0:\n raise ResourceNotFoundException()\n return certs[0]\n\n def list_certificates(self):\n return self.certificates.values()\n\n def update_certificate(self, certificate_id, new_status):\n cert = self.describe_certificate(certificate_id)\n # TODO: validate new_status\n cert.status = new_status\n\n def create_policy(self, policy_name, policy_document):\n policy = FakePolicy(policy_name, policy_document, self.region_name)\n self.policies[policy.name] = policy\n return policy\n\n def list_policies(self):\n policies = self.policies.values()\n return policies\n\n def get_policy(self, policy_name):\n policies = [_ for _ in self.policies.values() if _.name == policy_name]\n if len(policies) == 0:\n raise ResourceNotFoundException()\n return policies[0]\n\n def delete_policy(self, policy_name):\n policy = self.get_policy(policy_name)\n del self.policies[policy.name]\n\n def _get_principal(self, principal_arn):\n \"\"\"\n raise ResourceNotFoundException\n \"\"\"\n if ':cert/' in principal_arn:\n certs = [_ for _ in self.certificates.values() if _.arn == principal_arn]\n if len(certs) == 0:\n raise ResourceNotFoundException()\n principal = certs[0]\n return principal\n else:\n # TODO: search for cognito_ids\n pass\n raise ResourceNotFoundException()\n\n def attach_principal_policy(self, policy_name, principal_arn):\n principal = self._get_principal(principal_arn)\n policy = self.get_policy(policy_name)\n k = (principal_arn, policy_name)\n if k in self.principal_policies:\n return\n self.principal_policies[k] = (principal, policy)\n\n def detach_principal_policy(self, policy_name, principal_arn):\n # this may raises ResourceNotFoundException\n self._get_principal(principal_arn)\n self.get_policy(policy_name)\n\n k = (principal_arn, policy_name)\n if k not in self.principal_policies:\n raise ResourceNotFoundException()\n del self.principal_policies[k]\n\n def list_principal_policies(self, principal_arn):\n policies = [v[1] for k, v in self.principal_policies.items() if k[0] == principal_arn]\n return policies\n\n def list_policy_principals(self, policy_name):\n principals = [k[0] for k, v in self.principal_policies.items() if k[1] == policy_name]\n return principals\n\n def attach_thing_principal(self, thing_name, principal_arn):\n principal = self._get_principal(principal_arn)\n thing = self.describe_thing(thing_name)\n k = (principal_arn, thing_name)\n if k in self.principal_things:\n return\n self.principal_things[k] = (principal, thing)\n\n def detach_thing_principal(self, thing_name, principal_arn):\n # this may raises ResourceNotFoundException\n self._get_principal(principal_arn)\n self.describe_thing(thing_name)\n\n k = (principal_arn, thing_name)\n if k not in self.principal_things:\n raise ResourceNotFoundException()\n del self.principal_things[k]\n\n def list_principal_things(self, principal_arn):\n thing_names = [k[0] for k, v in self.principal_things.items() if k[0] == principal_arn]\n return thing_names\n\n def list_thing_principals(self, thing_name):\n principals = [k[0] for k, v in self.principal_things.items() if k[1] == thing_name]\n return principals\n\n def describe_thing_group(self, thing_group_name):\n thing_groups = [_ for _ in self.thing_groups.values() if _.thing_group_name == thing_group_name]\n if len(thing_groups) == 0:\n raise ResourceNotFoundException()\n return thing_groups[0]\n\n def create_thing_group(self, thing_group_name, parent_group_name, thing_group_properties):\n thing_group = FakeThingGroup(thing_group_name, parent_group_name, thing_group_properties, self.region_name)\n self.thing_groups[thing_group.arn] = thing_group\n return thing_group.thing_group_name, thing_group.arn, thing_group.thing_group_id\n\n def delete_thing_group(self, thing_group_name, expected_version):\n thing_group = self.describe_thing_group(thing_group_name)\n del self.thing_groups[thing_group.arn]\n\n def list_thing_groups(self, parent_group, name_prefix_filter, recursive):\n thing_groups = self.thing_groups.values()\n return thing_groups\n\n def update_thing_group(self, thing_group_name, thing_group_properties, expected_version):\n thing_group = self.describe_thing_group(thing_group_name)\n if expected_version and expected_version != thing_group.version:\n raise VersionConflictException(thing_group_name)\n attribute_payload = thing_group_properties.get('attributePayload', None)\n if attribute_payload is not None and 'attributes' in attribute_payload:\n do_merge = attribute_payload.get('merge', False)\n attributes = attribute_payload['attributes']\n if not do_merge:\n thing_group.thing_group_properties['attributePayload']['attributes'] = attributes\n else:\n thing_group.thing_group_properties['attributePayload']['attributes'].update(attributes)\n elif attribute_payload is not None and 'attributes' not in attribute_payload:\n thing_group.attributes = {}\n thing_group.version = thing_group.version + 1\n return thing_group.version\n\n def _identify_thing_group(self, thing_group_name, thing_group_arn):\n # identify thing group\n if thing_group_name is None and thing_group_arn is None:\n raise InvalidRequestException(\n ' Both thingGroupArn and thingGroupName are empty. Need to specify at least one of them'\n )\n if thing_group_name is not None:\n thing_group = self.describe_thing_group(thing_group_name)\n if thing_group_arn and thing_group.arn != thing_group_arn:\n raise InvalidRequestException(\n 'ThingGroupName thingGroupArn does not match specified thingGroupName in request'\n )\n elif thing_group_arn is not None:\n if thing_group_arn not in self.thing_groups:\n raise InvalidRequestException()\n thing_group = self.thing_groups[thing_group_arn]\n return thing_group\n\n def _identify_thing(self, thing_name, thing_arn):\n # identify thing\n if thing_name is None and thing_arn is None:\n raise InvalidRequestException(\n 'Both thingArn and thingName are empty. Need to specify at least one of them'\n )\n if thing_name is not None:\n thing = self.describe_thing(thing_name)\n if thing_arn and thing.arn != thing_arn:\n raise InvalidRequestException(\n 'ThingName thingArn does not match specified thingName in request'\n )\n elif thing_arn is not None:\n if thing_arn not in self.things:\n raise InvalidRequestException()\n thing = self.things[thing_arn]\n return thing\n\n def add_thing_to_thing_group(self, thing_group_name, thing_group_arn, thing_name, thing_arn):\n thing_group = self._identify_thing_group(thing_group_name, thing_group_arn)\n thing = self._identify_thing(thing_name, thing_arn)\n if thing.arn in thing_group.things:\n # aws ignores duplicate registration\n return\n thing_group.things[thing.arn] = thing\n\n def remove_thing_from_thing_group(self, thing_group_name, thing_group_arn, thing_name, thing_arn):\n thing_group = self._identify_thing_group(thing_group_name, thing_group_arn)\n thing = self._identify_thing(thing_name, thing_arn)\n if thing.arn not in thing_group.things:\n # aws ignores non-registered thing\n return\n del thing_group.things[thing.arn]\n\n def list_things_in_thing_group(self, thing_group_name, recursive):\n thing_group = self.describe_thing_group(thing_group_name)\n return thing_group.things.values()\n\n def list_thing_groups_for_thing(self, thing_name):\n thing = self.describe_thing(thing_name)\n all_thing_groups = self.list_thing_groups(None, None, None)\n ret = []\n for thing_group in all_thing_groups:\n if thing.arn in thing_group.things:\n ret.append({\n 'groupName': thing_group.thing_group_name,\n 'groupArn': thing_group.arn\n })\n return ret\n\n def update_thing_groups_for_thing(self, thing_name, thing_groups_to_add, thing_groups_to_remove):\n thing = self.describe_thing(thing_name)\n for thing_group_name in thing_groups_to_add:\n thing_group = self.describe_thing_group(thing_group_name)\n self.add_thing_to_thing_group(\n thing_group.thing_group_name, None,\n thing.thing_name, None\n )\n for thing_group_name in thing_groups_to_remove:\n thing_group = self.describe_thing_group(thing_group_name)\n self.remove_thing_from_thing_group(\n thing_group.thing_group_name, None,\n thing.thing_name, None\n )\n\n def create_job(self, job_id, targets, document_source, document, description, presigned_url_config,\n target_selection, job_executions_rollout_config, document_parameters):\n job = FakeJob(job_id, targets, document_source, document, description, presigned_url_config, target_selection,\n job_executions_rollout_config, document_parameters, self.region_name)\n self.jobs[job_id] = job\n return job.job_arn, job_id, description\n\n def describe_job(self, job_id):\n return self.jobs[job_id]\n\n\navailable_regions = boto3.session.Session().get_available_regions(\"iot\")\niot_backends = {region: IoTBackend(region) for region in available_regions}\n",
"path": "moto/iot/models.py"
}
] | diff --git a/moto/iot/models.py b/moto/iot/models.py
index 1b10c09fc5c0..ce7a4cf57eb1 100644
--- a/moto/iot/models.py
+++ b/moto/iot/models.py
@@ -32,6 +32,7 @@ def __init__(self, thing_name, thing_type, attributes, region_name):
def to_dict(self, include_default_client_id=False):
obj = {
'thingName': self.thing_name,
+ 'thingArn': self.arn,
'attributes': self.attributes,
'version': self.version
}
diff --git a/tests/test_iot/test_iot.py b/tests/test_iot/test_iot.py
index 19e11476be77..2136157906c0 100644
--- a/tests/test_iot/test_iot.py
+++ b/tests/test_iot/test_iot.py
@@ -35,12 +35,14 @@ def test_things():
res.should.have.key('things').which.should.have.length_of(1)
for thing in res['things']:
thing.should.have.key('thingName').which.should_not.be.none
+ thing.should.have.key('thingArn').which.should_not.be.none
thing = client.update_thing(thingName=name, attributePayload={'attributes': {'k1': 'v1'}})
res = client.list_things()
res.should.have.key('things').which.should.have.length_of(1)
for thing in res['things']:
thing.should.have.key('thingName').which.should_not.be.none
+ thing.should.have.key('thingArn').which.should_not.be.none
res['things'][0]['attributes'].should.have.key('k1').which.should.equal('v1')
thing = client.describe_thing(thingName=name)
|
chainer__chainer-1850 | Build failed on Mac
I cannot build Chainer on mac after #1775 is merged.
```
% python setup.py develop
Options: {'profile': False, 'annotate': False, 'linetrace': False, 'no_cuda': False}
Traceback (most recent call last):
File "setup.py", line 17, in <module>
ext_modules = chainer_setup_build.get_ext_modules()
File "/Users/unno/git/chainer/chainer_setup_build.py", line 219, in get_ext_modules
sysconfig.customize_compiler(compiler)
File "/Users/unno/.pyenv/versions/2.7.8/lib/python2.7/distutils/sysconfig.py", line 168, in customize_compiler
if not _config_vars.get('CUSTOMIZED_OSX_COMPILER', ''):
AttributeError: 'NoneType' object has no attribute 'get'
```
I investigated distutils. It initializes `_config_var` variables as `None` and when a user calls `get_config_vars` this variable is initialized.
BTW, I think we need to use ccompiler in `build_ext` because this option has many command line options for compiler flags.
| [
{
"content": "from __future__ import print_function\nfrom distutils import ccompiler\nfrom distutils import sysconfig\nimport os\nfrom os import path\nimport sys\n\nimport pkg_resources\nimport setuptools\n\nfrom install import build\nfrom install import utils\n\n\nrequire_cython_version = pkg_resources.parse_version('0.24.0')\n\nMODULES = [\n {\n 'name': 'cuda',\n 'file': [\n 'cupy.core.core',\n 'cupy.core.flags',\n 'cupy.core.internal',\n 'cupy.cuda.cublas',\n 'cupy.cuda.curand',\n 'cupy.cuda.device',\n 'cupy.cuda.driver',\n 'cupy.cuda.memory',\n 'cupy.cuda.pinned_memory',\n 'cupy.cuda.profiler',\n 'cupy.cuda.nvtx',\n 'cupy.cuda.function',\n 'cupy.cuda.runtime',\n 'cupy.util',\n ],\n 'include': [\n 'cublas_v2.h',\n 'cuda.h',\n 'cuda_profiler_api.h',\n 'cuda_runtime.h',\n 'curand.h',\n 'nvToolsExt.h',\n ],\n 'libraries': [\n 'cublas',\n 'cuda',\n 'cudart',\n 'curand',\n 'nvToolsExt',\n ],\n 'check_method': build.check_cuda_version,\n },\n {\n 'name': 'cudnn',\n 'file': [\n 'cupy.cuda.cudnn',\n ],\n 'include': [\n 'cudnn.h',\n ],\n 'libraries': [\n 'cudnn',\n ],\n 'check_method': build.check_cudnn_version,\n }\n]\n\nif sys.platform == 'win32':\n mod_cuda = MODULES[0]\n mod_cuda['file'].remove('cupy.cuda.nvtx')\n mod_cuda['include'].remove('nvToolsExt.h')\n mod_cuda['libraries'].remove('nvToolsExt')\n\n\ndef check_readthedocs_environment():\n return os.environ.get('READTHEDOCS', None) == 'True'\n\n\ndef check_library(compiler, includes=(), libraries=(),\n include_dirs=(), library_dirs=()):\n\n source = ''.join(['#include <%s>\\n' % header for header in includes])\n source += 'int main(int argc, char* argv[]) {return 0;}'\n try:\n build.build_and_run(compiler, source, libraries,\n include_dirs, library_dirs)\n except Exception:\n return False\n return True\n\n\ndef make_extensions(options, compiler, use_cython):\n \"\"\"Produce a list of Extension instances which passed to cythonize().\"\"\"\n\n no_cuda = options['no_cuda']\n settings = build.get_compiler_setting()\n\n include_dirs = settings['include_dirs']\n\n settings['include_dirs'] = [\n x for x in include_dirs if path.exists(x)]\n settings['library_dirs'] = [\n x for x in settings['library_dirs'] if path.exists(x)]\n if sys.platform != 'win32':\n settings['runtime_library_dirs'] = settings['library_dirs']\n if sys.platform == 'darwin':\n args = settings.setdefault('extra_link_args', [])\n args.append(\n '-Wl,' + ','.join('-rpath,' + p\n for p in settings['library_dirs']))\n # -rpath is only supported when targetting Mac OS X 10.5 or later\n args.append('-mmacosx-version-min=10.5')\n\n if options['linetrace']:\n settings['define_macros'].append(('CYTHON_TRACE', '1'))\n settings['define_macros'].append(('CYTHON_TRACE_NOGIL', '1'))\n if no_cuda:\n settings['define_macros'].append(('CUPY_NO_CUDA', '1'))\n\n ret = []\n ext = '.pyx' if use_cython else '.cpp'\n for module in MODULES:\n print('Include directories:', settings['include_dirs'])\n print('Library directories:', settings['library_dirs'])\n\n if not no_cuda:\n if not check_library(compiler,\n includes=module['include'],\n include_dirs=settings['include_dirs']):\n utils.print_warning(\n 'Include files not found: %s' % module['include'],\n 'Skip installing %s support' % module['name'],\n 'Check your CFLAGS environment variable')\n continue\n\n if not check_library(compiler,\n libraries=module['libraries'],\n library_dirs=settings['library_dirs']):\n utils.print_warning(\n 'Cannot link libraries: %s' % module['libraries'],\n 'Skip installing %s support' % module['name'],\n 'Check your LDFLAGS environment variable')\n continue\n\n if 'check_method' in module and \\\n not module['check_method'](compiler, settings):\n continue\n\n s = settings.copy()\n if not no_cuda:\n s['libraries'] = module['libraries']\n\n ret.extend([\n setuptools.Extension(f, [path.join(*f.split('.')) + ext], **s)\n for f in module['file']])\n return ret\n\n\ndef parse_args():\n arg_options = dict()\n arg_options['profile'] = '--cupy-profile' in sys.argv\n if arg_options['profile']:\n sys.argv.remove('--cupy-profile')\n\n cupy_coverage = '--cupy-coverage' in sys.argv\n if cupy_coverage:\n sys.argv.remove('--cupy-coverage')\n arg_options['linetrace'] = cupy_coverage\n arg_options['annotate'] = cupy_coverage\n\n arg_options['no_cuda'] = '--cupy-no-cuda' in sys.argv\n if arg_options['no_cuda']:\n sys.argv.remove('--cupy-no-cuda')\n if check_readthedocs_environment():\n arg_options['no_cuda'] = True\n return arg_options\n\n\ndef check_cython_version():\n try:\n import Cython\n cython_version = pkg_resources.parse_version(Cython.__version__)\n return cython_version >= require_cython_version\n except ImportError:\n return False\n\n\ndef cythonize(extensions, arg_options):\n import Cython.Build\n\n directive_keys = ('linetrace', 'profile')\n directives = {key: arg_options[key] for key in directive_keys}\n\n cythonize_option_keys = ('annotate',)\n cythonize_options = {key: arg_options[key]\n for key in cythonize_option_keys}\n\n return Cython.Build.cythonize(\n extensions, language=\"c++\", verbose=True,\n compiler_directives=directives, **cythonize_options)\n\n\ndef check_extensions(extensions):\n for x in extensions:\n for f in x.sources:\n if not path.isfile(f):\n msg = ('Missing file: %s\\n' % f +\n 'Please install Cython.\\n' +\n 'See http://docs.chainer.org/en/stable/install.html')\n raise RuntimeError(msg)\n\n\ndef get_ext_modules():\n arg_options = parse_args()\n print('Options:', arg_options)\n\n compiler = ccompiler.new_compiler()\n sysconfig.customize_compiler(compiler)\n\n use_cython = check_cython_version()\n extensions = make_extensions(arg_options, compiler, use_cython)\n\n if use_cython:\n extensions = cythonize(extensions, arg_options)\n\n check_extensions(extensions)\n return extensions\n",
"path": "chainer_setup_build.py"
}
] | [
{
"content": "from __future__ import print_function\nfrom distutils import ccompiler\nfrom distutils import sysconfig\nimport os\nfrom os import path\nimport sys\n\nimport pkg_resources\nimport setuptools\n\nfrom install import build\nfrom install import utils\n\n\nrequire_cython_version = pkg_resources.parse_version('0.24.0')\n\nMODULES = [\n {\n 'name': 'cuda',\n 'file': [\n 'cupy.core.core',\n 'cupy.core.flags',\n 'cupy.core.internal',\n 'cupy.cuda.cublas',\n 'cupy.cuda.curand',\n 'cupy.cuda.device',\n 'cupy.cuda.driver',\n 'cupy.cuda.memory',\n 'cupy.cuda.pinned_memory',\n 'cupy.cuda.profiler',\n 'cupy.cuda.nvtx',\n 'cupy.cuda.function',\n 'cupy.cuda.runtime',\n 'cupy.util',\n ],\n 'include': [\n 'cublas_v2.h',\n 'cuda.h',\n 'cuda_profiler_api.h',\n 'cuda_runtime.h',\n 'curand.h',\n 'nvToolsExt.h',\n ],\n 'libraries': [\n 'cublas',\n 'cuda',\n 'cudart',\n 'curand',\n 'nvToolsExt',\n ],\n 'check_method': build.check_cuda_version,\n },\n {\n 'name': 'cudnn',\n 'file': [\n 'cupy.cuda.cudnn',\n ],\n 'include': [\n 'cudnn.h',\n ],\n 'libraries': [\n 'cudnn',\n ],\n 'check_method': build.check_cudnn_version,\n }\n]\n\nif sys.platform == 'win32':\n mod_cuda = MODULES[0]\n mod_cuda['file'].remove('cupy.cuda.nvtx')\n mod_cuda['include'].remove('nvToolsExt.h')\n mod_cuda['libraries'].remove('nvToolsExt')\n\n\ndef check_readthedocs_environment():\n return os.environ.get('READTHEDOCS', None) == 'True'\n\n\ndef check_library(compiler, includes=(), libraries=(),\n include_dirs=(), library_dirs=()):\n\n source = ''.join(['#include <%s>\\n' % header for header in includes])\n source += 'int main(int argc, char* argv[]) {return 0;}'\n try:\n build.build_and_run(compiler, source, libraries,\n include_dirs, library_dirs)\n except Exception:\n return False\n return True\n\n\ndef make_extensions(options, compiler, use_cython):\n \"\"\"Produce a list of Extension instances which passed to cythonize().\"\"\"\n\n no_cuda = options['no_cuda']\n settings = build.get_compiler_setting()\n\n include_dirs = settings['include_dirs']\n\n settings['include_dirs'] = [\n x for x in include_dirs if path.exists(x)]\n settings['library_dirs'] = [\n x for x in settings['library_dirs'] if path.exists(x)]\n if sys.platform != 'win32':\n settings['runtime_library_dirs'] = settings['library_dirs']\n if sys.platform == 'darwin':\n args = settings.setdefault('extra_link_args', [])\n args.append(\n '-Wl,' + ','.join('-rpath,' + p\n for p in settings['library_dirs']))\n # -rpath is only supported when targetting Mac OS X 10.5 or later\n args.append('-mmacosx-version-min=10.5')\n\n if options['linetrace']:\n settings['define_macros'].append(('CYTHON_TRACE', '1'))\n settings['define_macros'].append(('CYTHON_TRACE_NOGIL', '1'))\n if no_cuda:\n settings['define_macros'].append(('CUPY_NO_CUDA', '1'))\n\n ret = []\n ext = '.pyx' if use_cython else '.cpp'\n for module in MODULES:\n print('Include directories:', settings['include_dirs'])\n print('Library directories:', settings['library_dirs'])\n\n if not no_cuda:\n if not check_library(compiler,\n includes=module['include'],\n include_dirs=settings['include_dirs']):\n utils.print_warning(\n 'Include files not found: %s' % module['include'],\n 'Skip installing %s support' % module['name'],\n 'Check your CFLAGS environment variable')\n continue\n\n if not check_library(compiler,\n libraries=module['libraries'],\n library_dirs=settings['library_dirs']):\n utils.print_warning(\n 'Cannot link libraries: %s' % module['libraries'],\n 'Skip installing %s support' % module['name'],\n 'Check your LDFLAGS environment variable')\n continue\n\n if 'check_method' in module and \\\n not module['check_method'](compiler, settings):\n continue\n\n s = settings.copy()\n if not no_cuda:\n s['libraries'] = module['libraries']\n\n ret.extend([\n setuptools.Extension(f, [path.join(*f.split('.')) + ext], **s)\n for f in module['file']])\n return ret\n\n\ndef parse_args():\n arg_options = dict()\n arg_options['profile'] = '--cupy-profile' in sys.argv\n if arg_options['profile']:\n sys.argv.remove('--cupy-profile')\n\n cupy_coverage = '--cupy-coverage' in sys.argv\n if cupy_coverage:\n sys.argv.remove('--cupy-coverage')\n arg_options['linetrace'] = cupy_coverage\n arg_options['annotate'] = cupy_coverage\n\n arg_options['no_cuda'] = '--cupy-no-cuda' in sys.argv\n if arg_options['no_cuda']:\n sys.argv.remove('--cupy-no-cuda')\n if check_readthedocs_environment():\n arg_options['no_cuda'] = True\n return arg_options\n\n\ndef check_cython_version():\n try:\n import Cython\n cython_version = pkg_resources.parse_version(Cython.__version__)\n return cython_version >= require_cython_version\n except ImportError:\n return False\n\n\ndef cythonize(extensions, arg_options):\n import Cython.Build\n\n directive_keys = ('linetrace', 'profile')\n directives = {key: arg_options[key] for key in directive_keys}\n\n cythonize_option_keys = ('annotate',)\n cythonize_options = {key: arg_options[key]\n for key in cythonize_option_keys}\n\n return Cython.Build.cythonize(\n extensions, language=\"c++\", verbose=True,\n compiler_directives=directives, **cythonize_options)\n\n\ndef check_extensions(extensions):\n for x in extensions:\n for f in x.sources:\n if not path.isfile(f):\n msg = ('Missing file: %s\\n' % f +\n 'Please install Cython.\\n' +\n 'See http://docs.chainer.org/en/stable/install.html')\n raise RuntimeError(msg)\n\n\ndef get_ext_modules():\n arg_options = parse_args()\n print('Options:', arg_options)\n\n # We need to call get_config_vars to initialize _config_vars in distutils\n # see #1849\n sysconfig.get_config_vars()\n compiler = ccompiler.new_compiler()\n sysconfig.customize_compiler(compiler)\n\n use_cython = check_cython_version()\n extensions = make_extensions(arg_options, compiler, use_cython)\n\n if use_cython:\n extensions = cythonize(extensions, arg_options)\n\n check_extensions(extensions)\n return extensions\n",
"path": "chainer_setup_build.py"
}
] | diff --git a/chainer_setup_build.py b/chainer_setup_build.py
index cd763ce95d9d..82425a96e465 100644
--- a/chainer_setup_build.py
+++ b/chainer_setup_build.py
@@ -214,6 +214,9 @@ def get_ext_modules():
arg_options = parse_args()
print('Options:', arg_options)
+ # We need to call get_config_vars to initialize _config_vars in distutils
+ # see #1849
+ sysconfig.get_config_vars()
compiler = ccompiler.new_compiler()
sysconfig.customize_compiler(compiler)
|
pypa__pip-10009 | Update quickstart guide to reflect user research
Updates quickstart guide to reflect most common tasks as discovered in our "buy a feature" user research.
Preview: https://pip--9137.org.readthedocs.build/en/9137/quickstart/
| [
{
"content": "\"\"\"Sphinx configuration file for pip's documentation.\"\"\"\n\nimport glob\nimport os\nimport pathlib\nimport re\nimport sys\nfrom typing import List, Tuple\n\n# Add the docs/ directory to sys.path, because pip_sphinxext.py is there.\ndocs_dir = os.path.dirname(os.path.dirname(__file__))\nsys.path.insert(0, docs_dir)\n\n# -- General configuration ------------------------------------------------------------\n\nextensions = [\n # first-party extensions\n \"sphinx.ext.autodoc\",\n \"sphinx.ext.todo\",\n \"sphinx.ext.extlinks\",\n \"sphinx.ext.intersphinx\",\n # our extensions\n \"pip_sphinxext\",\n # third-party extensions\n \"myst_parser\",\n \"sphinx_copybutton\",\n \"sphinx_inline_tabs\",\n \"sphinxcontrib.towncrier\",\n]\n\n# General information about the project.\nproject = \"pip\"\ncopyright = \"2008-2020, PyPA\"\n\n# Find the version and release information.\n# We have a single source of truth for our version number: pip's __init__.py file.\n# This next bit of code reads from it.\nfile_with_version = os.path.join(docs_dir, \"..\", \"src\", \"pip\", \"__init__.py\")\nwith open(file_with_version) as f:\n for line in f:\n m = re.match(r'__version__ = \"(.*)\"', line)\n if m:\n __version__ = m.group(1)\n # The short X.Y version.\n version = \".\".join(__version__.split(\".\")[:2])\n # The full version, including alpha/beta/rc tags.\n release = __version__\n break\n else: # AKA no-break\n version = release = \"dev\"\n\nprint(\"pip version:\", version)\nprint(\"pip release:\", release)\n\n# -- Options for smartquotes ----------------------------------------------------------\n\n# Disable the conversion of dashes so that long options like \"--find-links\" won't\n# render as \"-find-links\" if included in the text.The default of \"qDe\" converts normal\n# quote characters ('\"' and \"'\"), en and em dashes (\"--\" and \"---\"), and ellipses \"...\"\nsmartquotes_action = \"qe\"\n\n# -- Options for intersphinx ----------------------------------------------------------\n\nintersphinx_mapping = {\n \"python\": (\"https://docs.python.org/3\", None),\n \"pypug\": (\"https://packaging.python.org\", None),\n}\n\n# -- Options for extlinks -------------------------------------------------------------\n\nextlinks = {\n \"issue\": (\"https://github.com/pypa/pip/issues/%s\", \"#\"),\n \"pull\": (\"https://github.com/pypa/pip/pull/%s\", \"PR #\"),\n \"pypi\": (\"https://pypi.org/project/%s/\", \"\"),\n}\n\n# -- Options for towncrier_draft extension --------------------------------------------\n\ntowncrier_draft_autoversion_mode = \"draft\" # or: 'sphinx-release', 'sphinx-version'\ntowncrier_draft_include_empty = True\ntowncrier_draft_working_directory = pathlib.Path(docs_dir).parent\n# Not yet supported: towncrier_draft_config_path = 'pyproject.toml' # relative to cwd\n\n# -- Options for HTML -----------------------------------------------------------------\n\nhtml_theme = \"furo\"\nhtml_title = f\"{project} documentation v{release}\"\n\n# Disable the generation of the various indexes\nhtml_use_modindex = False\nhtml_use_index = False\n\n# -- Options for Manual Pages ---------------------------------------------------------\n\n\n# List of manual pages generated\ndef determine_man_pages() -> List[Tuple[str, str, str, str, int]]:\n \"\"\"Determine which man pages need to be generated.\"\"\"\n\n def to_document_name(path: str, base_dir: str) -> str:\n \"\"\"Convert a provided path to a Sphinx \"document name\".\"\"\"\n relative_path = os.path.relpath(path, base_dir)\n root, _ = os.path.splitext(relative_path)\n return root.replace(os.sep, \"/\")\n\n # Crawl the entire man/commands/ directory and list every file with appropriate\n # name and details.\n man_dir = os.path.join(docs_dir, \"man\")\n raw_subcommands = glob.glob(os.path.join(man_dir, \"commands/*.rst\"))\n if not raw_subcommands:\n raise FileNotFoundError(\n \"The individual subcommand manpages could not be found!\"\n )\n\n retval = [\n (\"index\", \"pip\", \"package manager for Python packages\", \"pip developers\", 1),\n ]\n for fname in raw_subcommands:\n fname_base = to_document_name(fname, man_dir)\n outname = \"pip-\" + fname_base.split(\"/\")[1]\n description = \"description of {} command\".format(outname.replace(\"-\", \" \"))\n\n retval.append((fname_base, outname, description, \"pip developers\", 1))\n\n return retval\n\n\nman_pages = determine_man_pages()\n",
"path": "docs/html/conf.py"
}
] | [
{
"content": "\"\"\"Sphinx configuration file for pip's documentation.\"\"\"\n\nimport glob\nimport os\nimport pathlib\nimport re\nimport sys\nfrom typing import List, Tuple\n\n# Add the docs/ directory to sys.path, because pip_sphinxext.py is there.\ndocs_dir = os.path.dirname(os.path.dirname(__file__))\nsys.path.insert(0, docs_dir)\n\n# -- General configuration ------------------------------------------------------------\n\nextensions = [\n # first-party extensions\n \"sphinx.ext.autodoc\",\n \"sphinx.ext.todo\",\n \"sphinx.ext.extlinks\",\n \"sphinx.ext.intersphinx\",\n # our extensions\n \"pip_sphinxext\",\n # third-party extensions\n \"myst_parser\",\n \"sphinx_copybutton\",\n \"sphinx_inline_tabs\",\n \"sphinxcontrib.towncrier\",\n]\n\n# General information about the project.\nproject = \"pip\"\ncopyright = \"The pip developers\"\n\n# Find the version and release information.\n# We have a single source of truth for our version number: pip's __init__.py file.\n# This next bit of code reads from it.\nfile_with_version = os.path.join(docs_dir, \"..\", \"src\", \"pip\", \"__init__.py\")\nwith open(file_with_version) as f:\n for line in f:\n m = re.match(r'__version__ = \"(.*)\"', line)\n if m:\n __version__ = m.group(1)\n # The short X.Y version.\n version = \".\".join(__version__.split(\".\")[:2])\n # The full version, including alpha/beta/rc tags.\n release = __version__\n break\n else: # AKA no-break\n version = release = \"dev\"\n\nprint(\"pip version:\", version)\nprint(\"pip release:\", release)\n\n# -- Options for smartquotes ----------------------------------------------------------\n\n# Disable the conversion of dashes so that long options like \"--find-links\" won't\n# render as \"-find-links\" if included in the text.The default of \"qDe\" converts normal\n# quote characters ('\"' and \"'\"), en and em dashes (\"--\" and \"---\"), and ellipses \"...\"\nsmartquotes_action = \"qe\"\n\n# -- Options for intersphinx ----------------------------------------------------------\n\nintersphinx_mapping = {\n \"python\": (\"https://docs.python.org/3\", None),\n \"pypug\": (\"https://packaging.python.org\", None),\n}\n\n# -- Options for extlinks -------------------------------------------------------------\n\nextlinks = {\n \"issue\": (\"https://github.com/pypa/pip/issues/%s\", \"#\"),\n \"pull\": (\"https://github.com/pypa/pip/pull/%s\", \"PR #\"),\n \"pypi\": (\"https://pypi.org/project/%s/\", \"\"),\n}\n\n# -- Options for towncrier_draft extension --------------------------------------------\n\ntowncrier_draft_autoversion_mode = \"draft\" # or: 'sphinx-release', 'sphinx-version'\ntowncrier_draft_include_empty = True\ntowncrier_draft_working_directory = pathlib.Path(docs_dir).parent\n# Not yet supported: towncrier_draft_config_path = 'pyproject.toml' # relative to cwd\n\n# -- Options for HTML -----------------------------------------------------------------\n\nhtml_theme = \"furo\"\nhtml_title = f\"{project} documentation v{release}\"\n\n# Disable the generation of the various indexes\nhtml_use_modindex = False\nhtml_use_index = False\n\n# -- Options for Manual Pages ---------------------------------------------------------\n\n\n# List of manual pages generated\ndef determine_man_pages() -> List[Tuple[str, str, str, str, int]]:\n \"\"\"Determine which man pages need to be generated.\"\"\"\n\n def to_document_name(path: str, base_dir: str) -> str:\n \"\"\"Convert a provided path to a Sphinx \"document name\".\"\"\"\n relative_path = os.path.relpath(path, base_dir)\n root, _ = os.path.splitext(relative_path)\n return root.replace(os.sep, \"/\")\n\n # Crawl the entire man/commands/ directory and list every file with appropriate\n # name and details.\n man_dir = os.path.join(docs_dir, \"man\")\n raw_subcommands = glob.glob(os.path.join(man_dir, \"commands/*.rst\"))\n if not raw_subcommands:\n raise FileNotFoundError(\n \"The individual subcommand manpages could not be found!\"\n )\n\n retval = [\n (\"index\", \"pip\", \"package manager for Python packages\", \"pip developers\", 1),\n ]\n for fname in raw_subcommands:\n fname_base = to_document_name(fname, man_dir)\n outname = \"pip-\" + fname_base.split(\"/\")[1]\n description = \"description of {} command\".format(outname.replace(\"-\", \" \"))\n\n retval.append((fname_base, outname, description, \"pip developers\", 1))\n\n return retval\n\n\nman_pages = determine_man_pages()\n",
"path": "docs/html/conf.py"
}
] | diff --git a/docs/html/conf.py b/docs/html/conf.py
index 2a4387a352a..9e210539e89 100644
--- a/docs/html/conf.py
+++ b/docs/html/conf.py
@@ -30,7 +30,7 @@
# General information about the project.
project = "pip"
-copyright = "2008-2020, PyPA"
+copyright = "The pip developers"
# Find the version and release information.
# We have a single source of truth for our version number: pip's __init__.py file.
diff --git a/docs/html/getting-started.md b/docs/html/getting-started.md
new file mode 100644
index 00000000000..42ac2c93400
--- /dev/null
+++ b/docs/html/getting-started.md
@@ -0,0 +1,104 @@
+# Getting Started
+
+To get started with using pip, you should [install Python] on your system.
+
+[install Python]: https://realpython.com/installing-python/
+
+## Ensure you have a working pip
+
+As a first step, you should check that you have a working Python with pip
+installed. This can be done by running the following commands and making
+sure that the output looks similar.
+
+```{pip-cli}
+$ python --version
+Python 3.N.N
+$ pip --version
+pip X.Y.Z from ... (python 3.N.N)
+```
+
+If that worked, congratulations! You have a working pip in your environment.
+
+If you got output that does not look like the sample above, please read
+the {doc}`installation` page. It provides guidance on how to install pip
+within a Python environment that doesn't have it.
+
+## Common tasks
+
+### Install a package
+
+```{pip-cli}
+$ pip install sampleproject
+[...]
+Successfully installed sampleproject
+```
+
+By default, pip will fetch packages from [Python Package Index][PyPI], a
+repository of software for the Python programming language where anyone can
+upload packages.
+
+[PyPI]: https://pypi.org/
+
+### Install a package from GitHub
+
+```{pip-cli}
+$ pip install git+https://github.com/pypa/sampleproject.git@main
+[...]
+Successfully installed sampleproject
+```
+
+See {ref}`VCS Support` for more information about this syntax.
+
+### Install a package from a distribution file
+
+pip can install directly from distribution files as well. They come in 2 forms:
+
+- {term}`source distribution <Source Distribution (or "sdist")>` (usually shortened to "sdist")
+- {term}`wheel distribution <Wheel>` (usually shortened to "wheel")
+
+```{pip-cli}
+$ pip install sampleproject-1.0.tar.gz
+[...]
+Successfully installed sampleproject
+$ pip install sampleproject-1.0-py3-none-any.whl
+[...]
+Successfully installed sampleproject
+```
+
+### Install multiple packages using a requirements file
+
+Many Python projects use {file}`requirements.txt` files, to specify the
+list of packages that need to be installed for the project to run. To install
+the packages listed in that file, you can run:
+
+```{pip-cli}
+$ pip install -r requirements.txt
+[...]
+Successfully installed sampleproject
+```
+
+### Upgrade a package
+
+```{pip-cli}
+$ pip install --upgrade sampleproject
+Uninstalling sampleproject:
+ [...]
+Proceed (y/n)? y
+Successfully uninstalled sampleproject
+```
+
+### Uninstall a package
+
+```{pip-cli}
+$ pip uninstall sampleproject
+Uninstalling sampleproject:
+ [...]
+Proceed (y/n)? y
+Successfully uninstalled sampleproject
+```
+
+## Next Steps
+
+It is recommended to learn about what virtual environments are and how to use
+them. This is covered in the ["Installing Packages"](pypug:tutorials/installing-packages)
+tutorial on packaging.python.org.
diff --git a/docs/html/index.md b/docs/html/index.md
index a84c2665d0e..7cf130c4bb6 100644
--- a/docs/html/index.md
+++ b/docs/html/index.md
@@ -10,8 +10,8 @@ install packages from the [Python Package Index][pypi] and other indexes.
```{toctree}
:hidden:
-quickstart
-installing
+getting-started
+installation
user_guide
cli/index
```
@@ -29,7 +29,7 @@ GitHub <https://github.com/pypa/pip>
If you want to learn about how to use pip, check out the following resources:
-- [Quickstart](quickstart)
+- [Getting Started](getting-started)
- [Python Packaging User Guide](https://packaging.python.org)
If you find bugs, need help, or want to talk to the developers, use our mailing
diff --git a/docs/html/installation.md b/docs/html/installation.md
new file mode 100644
index 00000000000..da975727185
--- /dev/null
+++ b/docs/html/installation.md
@@ -0,0 +1,78 @@
+# Installation
+
+Usually, pip is automatically installed if you are:
+
+- working in a
+ {ref}`virtual environment <pypug:Creating and using Virtual Environments>`
+- using Python downloaded from [python.org](https://www.python.org)
+- using Python that has not been modified by a redistributor to remove
+ {mod}`ensurepip`
+
+## Supported Methods
+
+If your Python environment does not have pip installed, there are 2 mechanisms
+to install pip supported directly by pip's maintainers:
+
+- [`ensurepip`](#using-ensurepip)
+- [`get-pip.py`](#using-get-pip-py)
+
+### `ensurepip`
+
+Python comes with an {mod}`ensurepip` module[^python], which can install pip in
+a Python environment.
+
+```{pip-cli}
+$ python -m ensurepip --upgrade
+```
+
+More details about how {mod}`ensurepip` works and how it can be used, is
+available in the standard library documentation.
+
+### `get-pip.py`
+
+This is a Python script that uses some bootstrapping logic to install
+pip.
+
+- Download the script, from <https://bootstrap.pypa.io/get-pip.py>.
+- Open a terminal/command prompt, `cd` to the folder containing the
+ `get-pip.py` file and run:
+
+ ```{pip-cli}
+ $ python get-pip.py
+ ```
+
+More details about this script can be found in [pypa/get-pip]'s README.
+
+[pypa/get-pip]: https://github.com/pypa/get-pip
+
+## Alternative Methods
+
+Depending on how you installed Python, there might be other mechanisms
+available to you for installing pip such as
+{ref}`using Linux package managers <pypug:installing pip/setuptools/wheel with linux package managers>`.
+
+These mechanisms are provided by redistributors of pip, who may have modified
+pip to change its behaviour. This has been a frequent source of user confusion,
+since it causes a mismatch between documented behaviour in this documentation
+and how pip works after those modifications.
+
+If you face issues when using Python and pip installed using these mechanisms,
+it is recommended to request for support from the relevant provider (eg: Linux
+distro community, cloud provider support channels, etc).
+
+## Compatibility
+
+The current version of pip works on:
+
+- Windows, Linux and MacOS.
+- CPython 3.6, 3.7, 3.8, 3.9 and latest PyPy3.
+
+pip is tested to work on the latest patch version of the Python interpreter,
+for each of the minor versions listed above. Previous patch versions are
+supported on a best effort approach.
+
+pip's maintainers do not provide support for users on older versions of Python,
+and these users should request for support from the relevant provider
+(eg: Linux distro community, cloud provider support channels, etc).
+
+[^python]: The `ensurepip` module was added to the Python standard library in Python 3.4.
diff --git a/docs/html/installing.rst b/docs/html/installing.rst
index 95b21899dc6..e8d86f3441c 100644
--- a/docs/html/installing.rst
+++ b/docs/html/installing.rst
@@ -1,230 +1,11 @@
-.. _`Installation`:
+:orphan:
-============
-Installation
-============
+.. meta::
-Do I need to install pip?
-=========================
+ :http-equiv=refresh: 3; url=../installation/
-pip is already installed if you are using Python 2 >=2.7.9 or Python 3 >=3.4
-downloaded from `python.org <https://www.python.org>`_ or if you are working
-in a :ref:`Virtual Environment <pypug:Creating and using Virtual Environments>`
-created by :ref:`pypug:virtualenv` or :ref:`venv <pypug:venv>`. Just make sure
-to :ref:`upgrade pip <Upgrading pip>`.
+This page has moved
+===================
-Use the following command to check whether pip is installed:
-
-.. tab:: Unix/macOS
-
- .. code-block:: console
-
- $ python -m pip --version
- pip X.Y.Z from .../site-packages/pip (python X.Y)
-
-.. tab:: Windows
-
- .. code-block:: console
-
- C:\> py -m pip --version
- pip X.Y.Z from ...\site-packages\pip (python X.Y)
-
-Using Linux Package Managers
-============================
-
-.. warning::
-
- If you installed Python from a package manager on Linux, you should always
- install pip for that Python installation using the same source.
-
-See `pypug:Installing pip/setuptools/wheel with Linux Package Managers <https://packaging.python.org/guides/installing-using-linux-tools/>`_
-in the Python Packaging User Guide.
-
-Here are ways to contact a few Linux package maintainers if you run into
-problems:
-
-* `Deadsnakes PPA <https://github.com/deadsnakes/issues>`_
-* `Debian Python Team <https://wiki.debian.org/Teams/PythonTeam>`_ (for general
- issues related to ``apt``)
-* `Red Hat Bugzilla <https://bugzilla.redhat.com/>`_
-
-pip developers do not have control over how Linux distributions handle pip
-installations, and are unable to provide solutions to related issues in
-general.
-
-Using ensurepip
-===============
-
-Python >=3.4 can self-bootstrap pip with the built-in
-:ref:`ensurepip <pypug:ensurepip>` module. Refer to the standard library
-documentation for more details. Make sure to :ref:`upgrade pip <Upgrading pip>`
-after ``ensurepip`` installs pip.
-
-See the `Using Linux Package Managers`_ section if your Python reports
-``No module named ensurepip`` on Debian and derived systems (e.g. Ubuntu).
-
-
-.. _`get-pip`:
-
-Installing with get-pip.py
-==========================
-
-.. warning::
-
- Be cautious if you are using a Python install that is managed by your operating
- system or another package manager. ``get-pip.py`` does not coordinate with
- those tools, and may leave your system in an inconsistent state.
-
-To manually install pip, securely [1]_ download ``get-pip.py`` by following
-this link: `get-pip.py
-<https://bootstrap.pypa.io/get-pip.py>`_. Alternatively, use ``curl``::
-
- curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
-
-Then run the following command in the folder where you
-have downloaded ``get-pip.py``:
-
-.. tab:: Unix/macOS
-
- .. code-block:: shell
-
- python get-pip.py
-
-.. tab:: Windows
-
- .. code-block:: shell
-
- py get-pip.py
-
-``get-pip.py`` also installs :ref:`pypug:setuptools` [2]_ and :ref:`pypug:wheel`
-if they are not already. :ref:`pypug:setuptools` is required to install
-:term:`source distributions <pypug:Source Distribution (or "sdist")>`. Both are
-required in order to build a :ref:`Wheel cache` (which improves installation
-speed), although neither are required to install pre-built :term:`wheels
-<pypug:Wheel>`.
-
-.. note::
-
- The get-pip.py script is supported on the same python version as pip.
- For the now unsupported Python 2.6, alternate script is available
- `here <https://bootstrap.pypa.io/2.6/get-pip.py>`__.
-
-
-get-pip.py options
-------------------
-
-.. option:: --no-setuptools
-
- If set, do not attempt to install :ref:`pypug:setuptools`
-
-.. option:: --no-wheel
-
- If set, do not attempt to install :ref:`pypug:wheel`
-
-
-``get-pip.py`` allows :ref:`pip install options <pip
-install Options>` and the :ref:`general options <General Options>`. Below are
-some examples:
-
-Install from local copies of pip and setuptools:
-
-.. tab:: Unix/macOS
-
- .. code-block:: shell
-
- python get-pip.py --no-index --find-links=/local/copies
-
-.. tab:: Windows
-
- .. code-block:: shell
-
- py get-pip.py --no-index --find-links=/local/copies
-
-Install to the user site [3]_:
-
-.. tab:: Unix/macOS
-
- .. code-block:: shell
-
- python get-pip.py --user
-
-.. tab:: Windows
-
- .. code-block:: shell
-
- py get-pip.py --user
-
-Install behind a proxy:
-
-.. tab:: Unix/macOS
-
- .. code-block:: shell
-
- python get-pip.py --proxy="http://[user:passwd@]proxy.server:port"
-
-.. tab:: Windows
-
- .. code-block:: shell
-
- py get-pip.py --proxy="http://[user:passwd@]proxy.server:port"
-
-``get-pip.py`` can also be used to install a specified combination of ``pip``,
-``setuptools``, and ``wheel`` using the same requirements syntax as pip:
-
-.. tab:: Unix/macOS
-
- .. code-block:: shell
-
- python get-pip.py pip==9.0.2 wheel==0.30.0 setuptools==28.8.0
-
-.. tab:: Windows
-
- .. code-block:: shell
-
- py get-pip.py pip==9.0.2 wheel==0.30.0 setuptools==28.8.0
-
-.. _`Upgrading pip`:
-
-Upgrading pip
-=============
-
-.. tab:: Unix/macOS
-
- .. code-block:: shell
-
- python -m pip install -U pip
-
-.. tab:: Windows
-
- .. code-block:: shell
-
- py -m pip install -U pip
-
-
-.. _compatibility-requirements:
-
-Python and OS Compatibility
-===========================
-
-pip works with CPython versions 3.6, 3.7, 3.8, 3.9 and also PyPy.
-
-This means pip works on the latest patch version of each of these minor
-versions. Previous patch versions are supported on a best effort approach.
-
-pip works on Unix/Linux, macOS, and Windows.
-
-
-----
-
-.. [1] "Secure" in this context means using a modern browser or a
- tool like ``curl`` that verifies SSL certificates when downloading from
- https URLs.
-
-.. [2] Beginning with pip v1.5.1, ``get-pip.py`` stopped requiring setuptools to
- be installed first.
-
-.. [3] The pip developers are considering making ``--user`` the default for all
- installs, including ``get-pip.py`` installs of pip, but at this time,
- ``--user`` installs for pip itself, should not be considered to be fully
- tested or endorsed. For discussion, see `Issue 1668
- <https://github.com/pypa/pip/issues/1668>`_.
+You should be redirected automatically in 3 seconds. If that didn't
+work, here's a link: :doc:`installation`
diff --git a/docs/html/quickstart.rst b/docs/html/quickstart.rst
index 96602a7b316..4385f4a7394 100644
--- a/docs/html/quickstart.rst
+++ b/docs/html/quickstart.rst
@@ -1,136 +1,11 @@
-==========
-Quickstart
-==========
+:orphan:
-First, :doc:`install pip <installing>`.
+.. meta::
-Install a package from `PyPI`_:
+ :http-equiv=refresh: 3; url=../getting-started/
-.. tab:: Unix/macOS
+This page has moved
+===================
- .. code-block:: console
-
- $ python -m pip install SomePackage
- [...]
- Successfully installed SomePackage
-
-.. tab:: Windows
-
- .. code-block:: console
-
- C:\> py -m pip install SomePackage
- [...]
- Successfully installed SomePackage
-
-
-Install a package that's already been downloaded from `PyPI`_ or
-obtained from elsewhere. This is useful if the target machine does not have a
-network connection:
-
-.. tab:: Unix/macOS
-
- .. code-block:: console
-
- $ python -m pip install SomePackage-1.0-py2.py3-none-any.whl
- [...]
- Successfully installed SomePackage
-
-.. tab:: Windows
-
- .. code-block:: console
-
- C:\> py -m pip install SomePackage-1.0-py2.py3-none-any.whl
- [...]
- Successfully installed SomePackage
-
-Show what files were installed:
-
-.. tab:: Unix/macOS
-
- .. code-block:: console
-
- $ python -m pip show --files SomePackage
- Name: SomePackage
- Version: 1.0
- Location: /my/env/lib/pythonx.x/site-packages
- Files:
- ../somepackage/__init__.py
- [...]
-
-.. tab:: Windows
-
- .. code-block:: console
-
- C:\> py -m pip show --files SomePackage
- Name: SomePackage
- Version: 1.0
- Location: /my/env/lib/pythonx.x/site-packages
- Files:
- ../somepackage/__init__.py
- [...]
-
-List what packages are outdated:
-
-.. tab:: Unix/macOS
-
- .. code-block:: console
-
- $ python -m pip list --outdated
- SomePackage (Current: 1.0 Latest: 2.0)
-
-.. tab:: Windows
-
- .. code-block:: console
-
- C:\> py -m pip list --outdated
- SomePackage (Current: 1.0 Latest: 2.0)
-
-Upgrade a package:
-
-.. tab:: Unix/macOS
-
- .. code-block:: console
-
- $ python -m pip install --upgrade SomePackage
- [...]
- Found existing installation: SomePackage 1.0
- Uninstalling SomePackage:
- Successfully uninstalled SomePackage
- Running setup.py install for SomePackage
- Successfully installed SomePackage
-
-.. tab:: Windows
-
- .. code-block:: console
-
- C:\> py -m pip install --upgrade SomePackage
- [...]
- Found existing installation: SomePackage 1.0
- Uninstalling SomePackage:
- Successfully uninstalled SomePackage
- Running setup.py install for SomePackage
- Successfully installed SomePackage
-
-Uninstall a package:
-
-.. tab:: Unix/macOS
-
- .. code-block:: console
-
- $ python -m pip uninstall SomePackage
- Uninstalling SomePackage:
- /my/env/lib/pythonx.x/site-packages/somepackage
- Proceed (y/n)? y
- Successfully uninstalled SomePackage
-
-.. tab:: Windows
-
- .. code-block:: console
-
- C:\> py -m pip uninstall SomePackage
- Uninstalling SomePackage:
- /my/env/lib/pythonx.x/site-packages/somepackage
- Proceed (y/n)? y
- Successfully uninstalled SomePackage
-
-.. _PyPI: https://pypi.org/
+You should be redirected automatically in 3 seconds. If that didn't
+work, here's a link: :doc:`getting-started`
|
aws__aws-cli-579 | Docs: s3api restore-object
Regarding the documentation here: http://docs.aws.amazon.com/cli/latest/reference/s3api/restore-object.html
I am having a hard time figuring out what the different arguments are for and what their values should be. These docs just look a little sparse. I'll see if I can find some time to help add to them where I can.
Thanks,
-Seth
| [
{
"content": "# Copyright 2013 Amazon.com, Inc. or its affiliates. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"). You\n# may not use this file except in compliance with the License. A copy of\n# the License is located at\n#\n# http://aws.amazon.com/apache2.0/\n#\n# or in the \"license\" file accompanying this file. This file is\n# distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF\n# ANY KIND, either express or implied. See the License for the specific\n# language governing permissions and limitations under the License.\n\"\"\"Module for processing CLI args.\"\"\"\nimport os\nimport logging\nimport six\n\nfrom botocore.compat import OrderedDict, json\n\nfrom awscli import utils\nfrom awscli import SCALAR_TYPES, COMPLEX_TYPES\n\n\nLOG = logging.getLogger('awscli.argprocess')\n\n\nclass ParamError(Exception):\n def __init__(self, param, message):\n full_message = (\"Error parsing parameter %s, should be: %s\" %\n (param.cli_name, message))\n super(ParamError, self).__init__(full_message)\n self.param = param\n\n\nclass ParamSyntaxError(Exception):\n pass\n\n\nclass ParamUnknownKeyError(Exception):\n def __init__(self, param, key, valid_keys):\n valid_keys = ', '.join(valid_keys)\n full_message = (\n \"Unknown key '%s' for parameter %s, valid choices \"\n \"are: %s\" % (key, param.cli_name, valid_keys))\n super(ParamUnknownKeyError, self).__init__(full_message)\n\n\ndef detect_shape_structure(param):\n if param.type in SCALAR_TYPES:\n return 'scalar'\n elif param.type == 'structure':\n sub_types = [detect_shape_structure(p)\n for p in param.members]\n # We're distinguishing between structure(scalar)\n # and structure(scalars), because for the case of\n # a single scalar in a structure we can simplify\n # more than a structure(scalars).\n if len(sub_types) == 1 and all(p == 'scalar' for p in sub_types):\n return 'structure(scalar)'\n elif len(sub_types) > 1 and all(p == 'scalar' for p in sub_types):\n return 'structure(scalars)'\n else:\n return 'structure(%s)' % ', '.join(sorted(set(sub_types)))\n elif param.type == 'list':\n return 'list-%s' % detect_shape_structure(param.members)\n elif param.type == 'map':\n if param.members.type in SCALAR_TYPES:\n return 'map-scalar'\n else:\n return 'map-%s' % detect_shape_structure(param.members)\n\n\nclass ParamShorthand(object):\n\n # To add support for a new shape:\n #\n # * Add it to SHORTHAND_SHAPES below, key is the shape structure\n # value is the name of the method to call.\n # * Implement parse method.\n # * Implement _doc_<parse_method_name>. This is used to generate\n # the docs for this shorthand syntax.\n\n SHORTHAND_SHAPES = {\n 'structure(scalars)': '_key_value_parse',\n 'structure(scalar)': '_special_key_value_parse',\n 'map-scalar': '_key_value_parse',\n 'list-structure(scalar)': '_list_scalar_parse',\n 'list-structure(scalars)': '_list_key_value_parse',\n 'list-structure(list-scalar, scalar)': '_list_scalar_list_parse',\n }\n\n def __init__(self):\n pass\n\n def __call__(self, param, value, **kwargs):\n \"\"\"Attempt to parse shorthand syntax for values.\n\n This is intended to be hooked up as an event handler (hence the\n **kwargs). Given ``param`` object and its string ``value``,\n figure out if we can parse it. If we can parse it, we return\n the parsed value (typically some sort of python dict).\n\n :type param: :class:`botocore.parameters.Parameter`\n :param param: The parameter object (includes various metadata\n about the parameter).\n\n :type value: str\n :param value: The value for the parameter type on the command\n line, e.g ``--foo this_value``, value would be ``\"this_value\"``.\n\n :returns: If we can parse the value we return the parsed value.\n If it looks like JSON, we return None (which tells the event\n emitter to use the default ``unpack_cli_arg`` provided that\n no other event handlers can parsed the value). If we\n run into an error parsing the value, a ``ParamError`` will\n be raised.\n\n \"\"\"\n parse_method = self.get_parse_method_for_param(param, value)\n if parse_method is None:\n return\n else:\n try:\n LOG.debug(\"Using %s for param %s\", parse_method, param)\n parsed = getattr(self, parse_method)(param, value)\n except ParamSyntaxError as e:\n doc_fn = self._get_example_fn(param)\n # Try to give them a helpful error message.\n if doc_fn is None:\n raise e\n else:\n raise ParamError(param, doc_fn(param))\n return parsed\n\n def get_parse_method_for_param(self, param, value=None):\n # We first need to make sure this is a parameter that qualifies\n # for simplification. The first short-circuit case is if it looks\n # like json we immediately return.\n if isinstance(value, list):\n check_val = value[0]\n else:\n check_val = value\n if isinstance(check_val, str) and check_val.startswith(('[', '{')):\n LOG.debug(\"Param %s looks like JSON, not considered for \"\n \"param shorthand.\", param.py_name)\n return\n structure = detect_shape_structure(param)\n parse_method = self.SHORTHAND_SHAPES.get(structure)\n return parse_method\n\n def _get_example_fn(self, param):\n doc_fn = None\n shape_structure = detect_shape_structure(param)\n method = self.SHORTHAND_SHAPES.get(shape_structure)\n if method:\n doc_fn = getattr(self, '_docs' + method, None)\n return doc_fn\n\n def add_example_fn(self, arg_name, help_command, **kwargs):\n \"\"\"\n Adds a callable to the ``example_fn`` attribute of the parameter\n if the parameter type is supported by shorthand syntax. This\n callable should return a string containing just the example and\n not any of the ReST formatting that might be required in the docs.\n \"\"\"\n argument = help_command.arg_table[arg_name]\n if hasattr(argument, 'argument_object') and argument.argument_object:\n param = argument.argument_object\n LOG.debug('Adding example fn for: %s' % param.name)\n doc_fn = self._get_example_fn(param)\n param.example_fn = doc_fn\n\n def _list_scalar_list_parse(self, param, value):\n # Think something like ec2.DescribeInstances.Filters.\n # We're looking for key=val1,val2,val3,key2=val1,val2.\n arg_types = {}\n for arg in param.members.members:\n arg_types[arg.name] = arg.type\n parsed = []\n for v in value:\n parts = self._split_on_commas(v)\n current_parsed = {}\n current_key = None\n for part in parts:\n current = part.split('=', 1)\n if len(current) == 2:\n # This is a key/value pair.\n current_key = current[0].strip()\n current_value = current[1].strip()\n if current_key not in arg_types:\n raise ParamUnknownKeyError(param, current_key,\n arg_types.keys())\n elif arg_types[current_key] == 'list':\n current_parsed[current_key] = [current_value]\n else:\n current_parsed[current_key] = current_value\n elif current_key is not None:\n # This is a value which we associate with the current_key,\n # so key1=val1,val2\n # ^\n # |\n # val2 is associated with key1.\n current_parsed[current_key].append(current[0])\n else:\n raise ParamSyntaxError(part)\n parsed.append(current_parsed)\n return parsed\n\n def _list_scalar_parse(self, param, value):\n single_param = param.members.members[0]\n parsed = []\n # We know that value is a list in this case.\n for v in value:\n parsed.append({single_param.name: v})\n return parsed\n\n def _list_key_value_parse(self, param, value):\n # param is a list param.\n # param.member is the struct param.\n struct_param = param.members\n parsed = []\n for v in value:\n single_struct_param = self._key_value_parse(struct_param, v)\n parsed.append(single_struct_param)\n return parsed\n\n def _special_key_value_parse(self, param, value):\n # This is a special key value parse that can do the normal\n # key=value parsing, *but* supports a few additional conveniences\n # when working with a structure with a single element.\n # Precondition: param is a shape of structure(scalar)\n if len(param.members) == 1 and param.members[0].name == 'Value' and \\\n '=' not in value:\n # We have an even shorter shorthand syntax for structure\n # of scalars of a single element with a member name of\n # 'Value'.\n return {'Value': value}\n else:\n return self._key_value_parse(param, value)\n\n def _key_value_parse(self, param, value):\n # The expected structure is:\n # key=value,key2=value\n # that is, csv key value pairs, where the key and values\n # are separated by '='. All of this should be whitespace\n # insensitive.\n parsed = OrderedDict()\n parts = self._split_on_commas(value)\n valid_names = self._create_name_to_params(param)\n for part in parts:\n try:\n key, value = part.split('=', 1)\n except ValueError:\n raise ParamSyntaxError(part)\n key = key.strip()\n value = value.strip()\n if valid_names and key not in valid_names:\n raise ParamUnknownKeyError(param, key, valid_names)\n if valid_names:\n sub_param = valid_names[key]\n if sub_param is not None:\n value = unpack_scalar_cli_arg(sub_param, value)\n parsed[key] = value\n return parsed\n\n def _create_name_to_params(self, param):\n if param.type == 'structure':\n return dict([(p.name, p) for p in param.members])\n elif param.type == 'map' and hasattr(param.keys, 'enum'):\n return dict([(v, None) for v in param.keys.enum])\n\n def _docs_list_scalar_list_parse(self, param):\n s = 'Key value pairs, where values are separated by commas.\\n'\n s += '%s ' % param.cli_name\n inner_params = param.members.members\n scalar_params = [p for p in inner_params if p.type in SCALAR_TYPES]\n list_params = [p for p in inner_params if p.type == 'list']\n for param in scalar_params:\n s += '%s=%s1,' % (param.name, param.type)\n for param in list_params[:-1]:\n param_type = param.members.type\n s += '%s=%s1,%s2,' % (param.name, param_type, param_type)\n last_param = list_params[-1]\n param_type = last_param.members.type\n s += '%s=%s1,%s2' % (last_param.name, param_type, param_type)\n return s\n\n def _docs_list_scalar_parse(self, param):\n name = param.members.members[0].name\n return '%s %s1 %s2 %s3' % (param.cli_name, name, name, name)\n\n def _docs_list_key_value_parse(self, param):\n s = \"Key value pairs, with multiple values separated by a space.\\n\"\n s += '%s ' % param.cli_name\n s += ','.join(['%s=%s' % (sub_param.name, sub_param.type)\n for sub_param in param.members.members])\n return s\n\n def _docs_special_key_value_parse(self, param):\n if len(param.members) == 1 and param.members[0].name == 'Value':\n # Returning None will indicate that we don't have\n # any examples to generate, and the entire examples section\n # should be skipped for this arg.\n return None\n else:\n self._docs_key_value_parse(param)\n\n def _docs_key_value_parse(self, param):\n s = '%s ' % param.cli_name\n if param.type == 'structure':\n s += ','.join(['%s=value' % sub_param.name\n for sub_param in param.members])\n elif param.type == 'map':\n s += 'key_name=string,key_name2=string'\n if param.keys.type == 'string' and hasattr(param.keys, 'enum'):\n s += '\\nWhere valid key names are:\\n'\n for value in param.keys.enum:\n s += ' %s\\n' % value\n return s\n\n def _split_on_commas(self, value):\n try:\n return utils.split_on_commas(value)\n except ValueError as e:\n raise ParamSyntaxError(str(e))\n\n\ndef unpack_cli_arg(parameter, value):\n \"\"\"\n Parses and unpacks the encoded string command line parameter\n and returns native Python data structures that can be passed\n to the Operation.\n\n :type parameter: :class:`botocore.parameter.Parameter`\n :param parameter: The parameter object containing metadata about\n the parameter.\n\n :param value: The value of the parameter. This can be a number of\n different python types (str, list, etc). This is the value as\n it's specified on the command line.\n\n :return: The \"unpacked\" argument than can be sent to the `Operation`\n object in python.\n \"\"\"\n if parameter.type in SCALAR_TYPES:\n return unpack_scalar_cli_arg(parameter, value)\n elif parameter.type in COMPLEX_TYPES:\n return unpack_complex_cli_arg(parameter, value)\n else:\n return str(value)\n\n\ndef unpack_complex_cli_arg(parameter, value):\n if parameter.type == 'structure' or parameter.type == 'map':\n if value.lstrip()[0] == '{':\n d = json.loads(value, object_pairs_hook=OrderedDict)\n else:\n msg = 'The value for parameter \"%s\" must be JSON or path to file.' % (\n parameter.cli_name)\n raise ValueError(msg)\n return d\n elif parameter.type == 'list':\n if isinstance(value, six.string_types):\n if value.lstrip()[0] == '[':\n return json.loads(value, object_pairs_hook=OrderedDict)\n elif isinstance(value, list) and len(value) == 1:\n single_value = value[0].strip()\n if single_value and single_value[0] == '[':\n return json.loads(value[0], object_pairs_hook=OrderedDict)\n return [unpack_cli_arg(parameter.members, v) for v in value]\n\n\ndef unpack_scalar_cli_arg(parameter, value):\n if parameter.type == 'integer' or parameter.type == 'long':\n return int(value)\n elif parameter.type == 'float' or parameter.type == 'double':\n # TODO: losing precision on double types\n return float(value)\n elif parameter.type == 'blob' and parameter.payload and parameter.streaming:\n file_path = os.path.expandvars(value)\n file_path = os.path.expanduser(file_path)\n if not os.path.isfile(file_path):\n msg = 'Blob values must be a path to a file.'\n raise ValueError(msg)\n return open(file_path, 'rb')\n elif parameter.type == 'boolean':\n if isinstance(value, str) and value.lower() == 'false':\n return False\n return bool(value)\n else:\n return str(value)\n",
"path": "awscli/argprocess.py"
}
] | [
{
"content": "# Copyright 2013 Amazon.com, Inc. or its affiliates. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"). You\n# may not use this file except in compliance with the License. A copy of\n# the License is located at\n#\n# http://aws.amazon.com/apache2.0/\n#\n# or in the \"license\" file accompanying this file. This file is\n# distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF\n# ANY KIND, either express or implied. See the License for the specific\n# language governing permissions and limitations under the License.\n\"\"\"Module for processing CLI args.\"\"\"\nimport os\nimport logging\nimport six\n\nfrom botocore.compat import OrderedDict, json\n\nfrom awscli import utils\nfrom awscli import SCALAR_TYPES, COMPLEX_TYPES\n\n\nLOG = logging.getLogger('awscli.argprocess')\n\n\nclass ParamError(Exception):\n def __init__(self, param, message):\n full_message = (\"Error parsing parameter %s, should be: %s\" %\n (param.cli_name, message))\n super(ParamError, self).__init__(full_message)\n self.param = param\n\n\nclass ParamSyntaxError(Exception):\n pass\n\n\nclass ParamUnknownKeyError(Exception):\n def __init__(self, param, key, valid_keys):\n valid_keys = ', '.join(valid_keys)\n full_message = (\n \"Unknown key '%s' for parameter %s, valid choices \"\n \"are: %s\" % (key, param.cli_name, valid_keys))\n super(ParamUnknownKeyError, self).__init__(full_message)\n\n\ndef detect_shape_structure(param):\n if param.type in SCALAR_TYPES:\n return 'scalar'\n elif param.type == 'structure':\n sub_types = [detect_shape_structure(p)\n for p in param.members]\n # We're distinguishing between structure(scalar)\n # and structure(scalars), because for the case of\n # a single scalar in a structure we can simplify\n # more than a structure(scalars).\n if len(sub_types) == 1 and all(p == 'scalar' for p in sub_types):\n return 'structure(scalar)'\n elif len(sub_types) > 1 and all(p == 'scalar' for p in sub_types):\n return 'structure(scalars)'\n else:\n return 'structure(%s)' % ', '.join(sorted(set(sub_types)))\n elif param.type == 'list':\n return 'list-%s' % detect_shape_structure(param.members)\n elif param.type == 'map':\n if param.members.type in SCALAR_TYPES:\n return 'map-scalar'\n else:\n return 'map-%s' % detect_shape_structure(param.members)\n\n\nclass ParamShorthand(object):\n\n # To add support for a new shape:\n #\n # * Add it to SHORTHAND_SHAPES below, key is the shape structure\n # value is the name of the method to call.\n # * Implement parse method.\n # * Implement _doc_<parse_method_name>. This is used to generate\n # the docs for this shorthand syntax.\n\n SHORTHAND_SHAPES = {\n 'structure(scalars)': '_key_value_parse',\n 'structure(scalar)': '_special_key_value_parse',\n 'map-scalar': '_key_value_parse',\n 'list-structure(scalar)': '_list_scalar_parse',\n 'list-structure(scalars)': '_list_key_value_parse',\n 'list-structure(list-scalar, scalar)': '_list_scalar_list_parse',\n }\n\n def __init__(self):\n pass\n\n def __call__(self, param, value, **kwargs):\n \"\"\"Attempt to parse shorthand syntax for values.\n\n This is intended to be hooked up as an event handler (hence the\n **kwargs). Given ``param`` object and its string ``value``,\n figure out if we can parse it. If we can parse it, we return\n the parsed value (typically some sort of python dict).\n\n :type param: :class:`botocore.parameters.Parameter`\n :param param: The parameter object (includes various metadata\n about the parameter).\n\n :type value: str\n :param value: The value for the parameter type on the command\n line, e.g ``--foo this_value``, value would be ``\"this_value\"``.\n\n :returns: If we can parse the value we return the parsed value.\n If it looks like JSON, we return None (which tells the event\n emitter to use the default ``unpack_cli_arg`` provided that\n no other event handlers can parsed the value). If we\n run into an error parsing the value, a ``ParamError`` will\n be raised.\n\n \"\"\"\n parse_method = self.get_parse_method_for_param(param, value)\n if parse_method is None:\n return\n else:\n try:\n LOG.debug(\"Using %s for param %s\", parse_method, param)\n parsed = getattr(self, parse_method)(param, value)\n except ParamSyntaxError as e:\n doc_fn = self._get_example_fn(param)\n # Try to give them a helpful error message.\n if doc_fn is None:\n raise e\n else:\n raise ParamError(param, doc_fn(param))\n return parsed\n\n def get_parse_method_for_param(self, param, value=None):\n # We first need to make sure this is a parameter that qualifies\n # for simplification. The first short-circuit case is if it looks\n # like json we immediately return.\n if isinstance(value, list):\n check_val = value[0]\n else:\n check_val = value\n if isinstance(check_val, str) and check_val.startswith(('[', '{')):\n LOG.debug(\"Param %s looks like JSON, not considered for \"\n \"param shorthand.\", param.py_name)\n return\n structure = detect_shape_structure(param)\n parse_method = self.SHORTHAND_SHAPES.get(structure)\n return parse_method\n\n def _get_example_fn(self, param):\n doc_fn = None\n shape_structure = detect_shape_structure(param)\n method = self.SHORTHAND_SHAPES.get(shape_structure)\n if method:\n doc_fn = getattr(self, '_docs' + method, None)\n return doc_fn\n\n def add_example_fn(self, arg_name, help_command, **kwargs):\n \"\"\"\n Adds a callable to the ``example_fn`` attribute of the parameter\n if the parameter type is supported by shorthand syntax. This\n callable should return a string containing just the example and\n not any of the ReST formatting that might be required in the docs.\n \"\"\"\n argument = help_command.arg_table[arg_name]\n if hasattr(argument, 'argument_object') and argument.argument_object:\n param = argument.argument_object\n LOG.debug('Adding example fn for: %s' % param.name)\n doc_fn = self._get_example_fn(param)\n param.example_fn = doc_fn\n\n def _list_scalar_list_parse(self, param, value):\n # Think something like ec2.DescribeInstances.Filters.\n # We're looking for key=val1,val2,val3,key2=val1,val2.\n arg_types = {}\n for arg in param.members.members:\n arg_types[arg.name] = arg.type\n parsed = []\n for v in value:\n parts = self._split_on_commas(v)\n current_parsed = {}\n current_key = None\n for part in parts:\n current = part.split('=', 1)\n if len(current) == 2:\n # This is a key/value pair.\n current_key = current[0].strip()\n current_value = current[1].strip()\n if current_key not in arg_types:\n raise ParamUnknownKeyError(param, current_key,\n arg_types.keys())\n elif arg_types[current_key] == 'list':\n current_parsed[current_key] = [current_value]\n else:\n current_parsed[current_key] = current_value\n elif current_key is not None:\n # This is a value which we associate with the current_key,\n # so key1=val1,val2\n # ^\n # |\n # val2 is associated with key1.\n current_parsed[current_key].append(current[0])\n else:\n raise ParamSyntaxError(part)\n parsed.append(current_parsed)\n return parsed\n\n def _list_scalar_parse(self, param, value):\n single_param = param.members.members[0]\n parsed = []\n # We know that value is a list in this case.\n for v in value:\n parsed.append({single_param.name: v})\n return parsed\n\n def _list_key_value_parse(self, param, value):\n # param is a list param.\n # param.member is the struct param.\n struct_param = param.members\n parsed = []\n for v in value:\n single_struct_param = self._key_value_parse(struct_param, v)\n parsed.append(single_struct_param)\n return parsed\n\n def _special_key_value_parse(self, param, value):\n # This is a special key value parse that can do the normal\n # key=value parsing, *but* supports a few additional conveniences\n # when working with a structure with a single element.\n # Precondition: param is a shape of structure(scalar)\n if len(param.members) == 1 and param.members[0].name == 'Value' and \\\n '=' not in value:\n # We have an even shorter shorthand syntax for structure\n # of scalars of a single element with a member name of\n # 'Value'.\n return {'Value': value}\n else:\n return self._key_value_parse(param, value)\n\n def _key_value_parse(self, param, value):\n # The expected structure is:\n # key=value,key2=value\n # that is, csv key value pairs, where the key and values\n # are separated by '='. All of this should be whitespace\n # insensitive.\n parsed = OrderedDict()\n parts = self._split_on_commas(value)\n valid_names = self._create_name_to_params(param)\n for part in parts:\n try:\n key, value = part.split('=', 1)\n except ValueError:\n raise ParamSyntaxError(part)\n key = key.strip()\n value = value.strip()\n if valid_names and key not in valid_names:\n raise ParamUnknownKeyError(param, key, valid_names)\n if valid_names:\n sub_param = valid_names[key]\n if sub_param is not None:\n value = unpack_scalar_cli_arg(sub_param, value)\n parsed[key] = value\n return parsed\n\n def _create_name_to_params(self, param):\n if param.type == 'structure':\n return dict([(p.name, p) for p in param.members])\n elif param.type == 'map' and hasattr(param.keys, 'enum'):\n return dict([(v, None) for v in param.keys.enum])\n\n def _docs_list_scalar_list_parse(self, param):\n s = 'Key value pairs, where values are separated by commas.\\n'\n s += '%s ' % param.cli_name\n inner_params = param.members.members\n scalar_params = [p for p in inner_params if p.type in SCALAR_TYPES]\n list_params = [p for p in inner_params if p.type == 'list']\n for param in scalar_params:\n s += '%s=%s1,' % (param.name, param.type)\n for param in list_params[:-1]:\n param_type = param.members.type\n s += '%s=%s1,%s2,' % (param.name, param_type, param_type)\n last_param = list_params[-1]\n param_type = last_param.members.type\n s += '%s=%s1,%s2' % (last_param.name, param_type, param_type)\n return s\n\n def _docs_list_scalar_parse(self, param):\n name = param.members.members[0].name\n return '%s %s1 %s2 %s3' % (param.cli_name, name, name, name)\n\n def _docs_list_key_value_parse(self, param):\n s = \"Key value pairs, with multiple values separated by a space.\\n\"\n s += '%s ' % param.cli_name\n s += ','.join(['%s=%s' % (sub_param.name, sub_param.type)\n for sub_param in param.members.members])\n return s\n\n def _docs_special_key_value_parse(self, param):\n if len(param.members) == 1 and param.members[0].name == 'Value':\n # Returning None will indicate that we don't have\n # any examples to generate, and the entire examples section\n # should be skipped for this arg.\n return None\n else:\n return self._docs_key_value_parse(param)\n\n def _docs_key_value_parse(self, param):\n s = '%s ' % param.cli_name\n if param.type == 'structure':\n s += ','.join(['%s=value' % sub_param.name\n for sub_param in param.members])\n elif param.type == 'map':\n s += 'key_name=string,key_name2=string'\n if param.keys.type == 'string' and hasattr(param.keys, 'enum'):\n s += '\\nWhere valid key names are:\\n'\n for value in param.keys.enum:\n s += ' %s\\n' % value\n return s\n\n def _split_on_commas(self, value):\n try:\n return utils.split_on_commas(value)\n except ValueError as e:\n raise ParamSyntaxError(str(e))\n\n\ndef unpack_cli_arg(parameter, value):\n \"\"\"\n Parses and unpacks the encoded string command line parameter\n and returns native Python data structures that can be passed\n to the Operation.\n\n :type parameter: :class:`botocore.parameter.Parameter`\n :param parameter: The parameter object containing metadata about\n the parameter.\n\n :param value: The value of the parameter. This can be a number of\n different python types (str, list, etc). This is the value as\n it's specified on the command line.\n\n :return: The \"unpacked\" argument than can be sent to the `Operation`\n object in python.\n \"\"\"\n if parameter.type in SCALAR_TYPES:\n return unpack_scalar_cli_arg(parameter, value)\n elif parameter.type in COMPLEX_TYPES:\n return unpack_complex_cli_arg(parameter, value)\n else:\n return str(value)\n\n\ndef unpack_complex_cli_arg(parameter, value):\n if parameter.type == 'structure' or parameter.type == 'map':\n if value.lstrip()[0] == '{':\n d = json.loads(value, object_pairs_hook=OrderedDict)\n else:\n msg = 'The value for parameter \"%s\" must be JSON or path to file.' % (\n parameter.cli_name)\n raise ValueError(msg)\n return d\n elif parameter.type == 'list':\n if isinstance(value, six.string_types):\n if value.lstrip()[0] == '[':\n return json.loads(value, object_pairs_hook=OrderedDict)\n elif isinstance(value, list) and len(value) == 1:\n single_value = value[0].strip()\n if single_value and single_value[0] == '[':\n return json.loads(value[0], object_pairs_hook=OrderedDict)\n return [unpack_cli_arg(parameter.members, v) for v in value]\n\n\ndef unpack_scalar_cli_arg(parameter, value):\n if parameter.type == 'integer' or parameter.type == 'long':\n return int(value)\n elif parameter.type == 'float' or parameter.type == 'double':\n # TODO: losing precision on double types\n return float(value)\n elif parameter.type == 'blob' and parameter.payload and parameter.streaming:\n file_path = os.path.expandvars(value)\n file_path = os.path.expanduser(file_path)\n if not os.path.isfile(file_path):\n msg = 'Blob values must be a path to a file.'\n raise ValueError(msg)\n return open(file_path, 'rb')\n elif parameter.type == 'boolean':\n if isinstance(value, str) and value.lower() == 'false':\n return False\n return bool(value)\n else:\n return str(value)\n",
"path": "awscli/argprocess.py"
}
] | diff --git a/awscli/argprocess.py b/awscli/argprocess.py
index 26bef03c1509..da3b623fb6a1 100644
--- a/awscli/argprocess.py
+++ b/awscli/argprocess.py
@@ -303,7 +303,7 @@ def _docs_special_key_value_parse(self, param):
# should be skipped for this arg.
return None
else:
- self._docs_key_value_parse(param)
+ return self._docs_key_value_parse(param)
def _docs_key_value_parse(self, param):
s = '%s ' % param.cli_name
diff --git a/tests/unit/docs/test_help_output.py b/tests/unit/docs/test_help_output.py
index b78eed8b2967..83c3768d32d2 100644
--- a/tests/unit/docs/test_help_output.py
+++ b/tests/unit/docs/test_help_output.py
@@ -230,6 +230,15 @@ def test_no_examples_for_structure_single_scalar(self):
self.assert_not_contains('"Value": "string"')
self.assert_not_contains('Value=string')
+ def test_example_for_single_structure_not_named_value(self):
+ # Verify that if a structure does match our special case
+ # (single element named "Value"), then we still document
+ # the example syntax.
+ self.driver.main(['s3api', 'restore-object', 'help'])
+ self.assert_contains('Days=value')
+ # Also should see the JSON syntax in the help output.
+ self.assert_contains('"Days": integer')
+
class TestJSONListScalarDocs(BaseAWSHelpOutputTest):
def test_space_separated_list_docs(self):
|
buildbot__buildbot-3531 | REST API: way to query lists for "tag1" or "tag2" (as opposed to and)
The query `/api/v2/builders?tags__contains=tag1&tags__contains=tag2` returns only builders that have *both* tags `tag1` and `tag2`. I don't see a way to query for builders that have either `tag1` or `tag2`.
| [
{
"content": "# This file is part of Buildbot. Buildbot is free software: you can\n# redistribute it and/or modify it under the terms of the GNU General Public\n# License as published by the Free Software Foundation, version 2.\n#\n# This program is distributed in the hope that it will be useful, but WITHOUT\n# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n# FOR A PARTICULAR PURPOSE. See the GNU General Public License for more\n# details.\n#\n# You should have received a copy of the GNU General Public License along with\n# this program; if not, write to the Free Software Foundation, Inc., 51\n# Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n#\n# Copyright Buildbot Team Members\n\nfrom __future__ import absolute_import\nfrom __future__ import print_function\nfrom future.utils import iteritems\n\nimport sqlalchemy as sa\n\nfrom twisted.python import log\n\nfrom buildbot.data import base\n\n\nclass FieldBase(object):\n\n \"\"\"\n This class implements a basic behavior\n to wrap value into a `Field` instance\n\n \"\"\"\n __slots__ = ['field', 'op', 'values']\n\n singular_operators = {\n 'eq': lambda d, v: d == v[0],\n 'ne': lambda d, v: d != v[0],\n 'lt': lambda d, v: d < v[0],\n 'le': lambda d, v: d <= v[0],\n 'gt': lambda d, v: d > v[0],\n 'ge': lambda d, v: d >= v[0],\n 'contains': lambda d, v: v[0] in d,\n }\n\n plural_operators = {\n 'eq': lambda d, v: d in v,\n 'ne': lambda d, v: d not in v,\n 'contains': lambda d, v: set(v) <= set(d),\n }\n\n def __init__(self, field, op, values):\n self.field = field\n self.op = op\n self.values = values\n\n def getOperator(self):\n v = self.values\n if len(v) == 1:\n ops = self.singular_operators\n else:\n ops = self.plural_operators\n v = set(v)\n return ops[self.op]\n\n def apply(self, data):\n fld = self.field\n v = self.values\n f = self.getOperator()\n return (d for d in data if f(d[fld], v))\n\n def __repr__(self):\n return \"resultspec.{}('{}','{}',{})\".format(self.__class__.__name__, self.field, self.op, self.values)\n\n def __eq__(self, b):\n for i in self.__slots__:\n if getattr(self, i) != getattr(b, i):\n return False\n return True\n\n def __ne__(self, b):\n return not (self == b)\n\n\nclass Property(FieldBase):\n\n \"\"\"\n Wraps ``property`` type value(s)\n\n \"\"\"\n\n\nclass Filter(FieldBase):\n\n \"\"\"\n Wraps ``filter`` type value(s)\n\n \"\"\"\n\n\nclass NoneComparator(object):\n \"\"\"\n Object which wraps 'None' when doing comparisons in sorted().\n '> None' and '< None' are not supported\n in Python 3.\n \"\"\"\n def __init__(self, value):\n self.value = value\n\n def __lt__(self, other):\n if self.value is None and other.value is None:\n return False\n elif self.value is None:\n return True\n elif other.value is None:\n return False\n return self.value < other.value\n\n def __eq__(self, other):\n return self.value == other.value\n\n def __ne__(self, other):\n return self.value != other.value\n\n def __gt_(self, other):\n if self.value is None and other.value is None:\n return False\n elif self.value is None:\n return False\n elif other.value is None:\n return True\n return self.value < other.value\n\n\nclass ReverseComparator(object):\n \"\"\"\n Object which swaps '<' and '>' so\n instead of a < b, it does b < a,\n and instead of a > b, it does b > a.\n This can be used in reverse comparisons.\n \"\"\"\n def __init__(self, value):\n self.value = value\n\n def __lt__(self, other):\n return other.value < self.value\n\n def __eq__(self, other):\n return other.value == self.value\n\n def __ne__(self, other):\n return other.value != self.value\n\n def __gt_(self, other):\n return other.value > self.value\n\n\nclass ResultSpec(object):\n\n __slots__ = ['filters', 'fields', 'properties',\n 'order', 'limit', 'offset', 'fieldMapping']\n\n def __init__(self, filters=None, fields=None, properties=None, order=None,\n limit=None, offset=None):\n self.filters = filters or []\n self.properties = properties or []\n self.fields = fields\n self.order = order\n self.limit = limit\n self.offset = offset\n self.fieldMapping = {}\n\n def __repr__(self):\n return (\"ResultSpec(**{{'filters': {}, 'fields': {}, 'properties': {}, \"\n \"'order': {}, 'limit': {}, 'offset': {}\").format(\n self.filters, self.fields, self.properties, self.order,\n self.limit, self.offset) + \"})\"\n\n def __eq__(self, b):\n for i in ['filters', 'fields', 'properties', 'order', 'limit', 'offset']:\n if getattr(self, i) != getattr(b, i):\n return False\n return True\n\n def __ne__(self, b):\n return not (self == b)\n\n def popProperties(self):\n values = []\n for p in self.properties:\n if p.field == b'property' and p.op == 'eq':\n self.properties.remove(p)\n values = p.values\n break\n return values\n\n def popFilter(self, field, op):\n for f in self.filters:\n if f.field == field and f.op == op:\n self.filters.remove(f)\n return f.values\n\n def popOneFilter(self, field, op):\n v = self.popFilter(field, op)\n return v[0] if v is not None else None\n\n def popBooleanFilter(self, field):\n eqVals = self.popFilter(field, 'eq')\n if eqVals and len(eqVals) == 1:\n return eqVals[0]\n neVals = self.popFilter(field, 'ne')\n if neVals and len(neVals) == 1:\n return not neVals[0]\n\n def popStringFilter(self, field):\n eqVals = self.popFilter(field, 'eq')\n if eqVals and len(eqVals) == 1:\n return eqVals[0]\n\n def popIntegerFilter(self, field):\n eqVals = self.popFilter(field, 'eq')\n if eqVals and len(eqVals) == 1:\n try:\n return int(eqVals[0])\n except ValueError:\n raise ValueError(\"Filter value for {} should be integer, but got: {}\".format(\n field, eqVals[0]))\n\n def removePagination(self):\n self.limit = self.offset = None\n\n def removeOrder(self):\n self.order = None\n\n def popField(self, field):\n try:\n i = self.fields.index(field)\n except ValueError:\n return False\n del self.fields[i]\n return True\n\n def findColumn(self, query, field):\n # will throw key error if field not in mapping\n mapped = self.fieldMapping[field]\n for col in query.inner_columns:\n if str(col) == mapped:\n return col\n raise KeyError(\"unable to find field {} in query\".format(field))\n\n def applyFilterToSQLQuery(self, query, f):\n field = f.field\n col = self.findColumn(query, field)\n # as sqlalchemy is overriding python operators, we can just use the same\n # python code generated by the filter\n return query.where(f.getOperator()(col, f.values))\n\n def applyOrderToSQLQuery(self, query, o):\n reverse = False\n if o.startswith('-'):\n reverse = True\n o = o[1:]\n col = self.findColumn(query, o)\n if reverse:\n col = col.desc()\n return query.order_by(col)\n\n def applyToSQLQuery(self, query):\n filters = self.filters\n order = self.order\n unmatched_filters = []\n unmatched_order = []\n # apply the filters if the name of field is found in the model, and\n # db2data\n for f in filters:\n try:\n query = self.applyFilterToSQLQuery(query, f)\n except KeyError:\n # if filter is unmatched, we will do the filtering manually in\n # self.apply\n unmatched_filters.append(f)\n\n # apply order if necessary\n if order:\n for o in order:\n try:\n query = self.applyOrderToSQLQuery(query, o)\n except KeyError:\n # if order is unmatched, we will do the ordering manually\n # in self.apply\n unmatched_order.append(o)\n\n # we cannot limit in sql if there is missing filtering or ordering\n if unmatched_filters or unmatched_order:\n if self.offset is not None or self.limit is not None:\n log.msg(\"Warning: limited data api query is not backed by db because of following filters\",\n unmatched_filters, unmatched_order)\n self.filters = unmatched_filters\n self.order = tuple(unmatched_order)\n return query, None\n count_query = sa.select([sa.func.count()]).select_from(query.alias('query'))\n self.order = None\n self.filters = []\n # finally, slice out the limit/offset\n if self.offset is not None:\n query = query.offset(self.offset)\n self.offset = None\n\n if self.limit is not None:\n query = query.limit(self.limit)\n self.limit = None\n\n return query, count_query\n\n def thd_execute(self, conn, q, dictFromRow):\n offset, limit = self.offset, self.limit\n q, qc = self.applyToSQLQuery(q)\n res = conn.execute(q)\n rv = [dictFromRow(row) for row in res.fetchall()]\n\n if qc is not None and (offset or limit):\n total = conn.execute(qc).scalar()\n rv = base.ListResult(rv)\n rv.offset, rv.total, rv.limit = offset, total, limit\n return rv\n\n def apply(self, data):\n if data is None:\n return data\n\n if self.fields:\n fields = set(self.fields)\n\n def includeFields(d):\n return dict((k, v) for k, v in iteritems(d)\n if k in fields)\n applyFields = includeFields\n else:\n fields = None\n\n if isinstance(data, dict):\n # item details\n if fields:\n data = applyFields(data)\n return data\n else:\n filters = self.filters\n order = self.order\n\n # item collection\n if isinstance(data, base.ListResult):\n # if pagination was applied, then fields, etc. must be empty\n assert not fields and not order and not filters, \\\n \"endpoint must apply fields, order, and filters if it performs pagination\"\n offset, total = data.offset, data.total\n limit = data.limit\n else:\n offset, total = None, None\n limit = None\n\n if fields:\n data = (applyFields(d) for d in data)\n\n # link the filters together and then flatten to list\n for f in self.filters:\n data = f.apply(data)\n data = list(data)\n\n if total is None:\n total = len(data)\n\n if self.order:\n def keyFunc(elem, order=self.order):\n \"\"\"\n Do a multi-level sort by passing in the keys\n to sort by.\n\n @param elem: each item in the list to sort. It must be\n a C{dict}\n @param order: a list of keys to sort by, such as:\n ('lastName', 'firstName', 'age')\n @return: a key used by sorted(). This will be a\n list such as:\n [a['lastName', a['firstName'], a['age']]\n @rtype: a C{list}\n \"\"\"\n compareKey = []\n for k in order:\n doReverse = False\n if k[0] == '-':\n # If we get a key '-lastName',\n # it means sort by 'lastName' in reverse.\n k = k[1:]\n doReverse = True\n val = NoneComparator(elem[k])\n if doReverse:\n val = ReverseComparator(val)\n compareKey.append(val)\n return compareKey\n\n data.sort(key=keyFunc)\n\n # finally, slice out the limit/offset\n if self.offset is not None or self.limit is not None:\n if offset is not None or limit is not None:\n raise AssertionError(\"endpoint must clear offset/limit\")\n end = ((self.offset or 0) + self.limit\n if self.limit is not None\n else None)\n data = data[self.offset:end]\n offset = self.offset\n limit = self.limit\n\n rv = base.ListResult(data)\n rv.offset, rv.total = offset, total\n rv.limit = limit\n return rv\n",
"path": "master/buildbot/data/resultspec.py"
}
] | [
{
"content": "# This file is part of Buildbot. Buildbot is free software: you can\n# redistribute it and/or modify it under the terms of the GNU General Public\n# License as published by the Free Software Foundation, version 2.\n#\n# This program is distributed in the hope that it will be useful, but WITHOUT\n# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n# FOR A PARTICULAR PURPOSE. See the GNU General Public License for more\n# details.\n#\n# You should have received a copy of the GNU General Public License along with\n# this program; if not, write to the Free Software Foundation, Inc., 51\n# Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n#\n# Copyright Buildbot Team Members\n\nfrom __future__ import absolute_import\nfrom __future__ import print_function\nfrom future.utils import iteritems\n\nimport sqlalchemy as sa\n\nfrom twisted.python import log\n\nfrom buildbot.data import base\n\n\nclass FieldBase(object):\n\n \"\"\"\n This class implements a basic behavior\n to wrap value into a `Field` instance\n\n \"\"\"\n __slots__ = ['field', 'op', 'values']\n\n singular_operators = {\n 'eq': lambda d, v: d == v[0],\n 'ne': lambda d, v: d != v[0],\n 'lt': lambda d, v: d < v[0],\n 'le': lambda d, v: d <= v[0],\n 'gt': lambda d, v: d > v[0],\n 'ge': lambda d, v: d >= v[0],\n 'contains': lambda d, v: v[0] in d,\n }\n\n plural_operators = {\n 'eq': lambda d, v: d in v,\n 'ne': lambda d, v: d not in v,\n 'contains': lambda d, v: len(set(v).intersection(set(d))) > 0,\n }\n\n def __init__(self, field, op, values):\n self.field = field\n self.op = op\n self.values = values\n\n def getOperator(self):\n v = self.values\n if len(v) == 1:\n ops = self.singular_operators\n else:\n ops = self.plural_operators\n v = set(v)\n return ops[self.op]\n\n def apply(self, data):\n fld = self.field\n v = self.values\n f = self.getOperator()\n return (d for d in data if f(d[fld], v))\n\n def __repr__(self):\n return \"resultspec.{}('{}','{}',{})\".format(self.__class__.__name__, self.field, self.op, self.values)\n\n def __eq__(self, b):\n for i in self.__slots__:\n if getattr(self, i) != getattr(b, i):\n return False\n return True\n\n def __ne__(self, b):\n return not (self == b)\n\n\nclass Property(FieldBase):\n\n \"\"\"\n Wraps ``property`` type value(s)\n\n \"\"\"\n\n\nclass Filter(FieldBase):\n\n \"\"\"\n Wraps ``filter`` type value(s)\n\n \"\"\"\n\n\nclass NoneComparator(object):\n \"\"\"\n Object which wraps 'None' when doing comparisons in sorted().\n '> None' and '< None' are not supported\n in Python 3.\n \"\"\"\n def __init__(self, value):\n self.value = value\n\n def __lt__(self, other):\n if self.value is None and other.value is None:\n return False\n elif self.value is None:\n return True\n elif other.value is None:\n return False\n return self.value < other.value\n\n def __eq__(self, other):\n return self.value == other.value\n\n def __ne__(self, other):\n return self.value != other.value\n\n def __gt_(self, other):\n if self.value is None and other.value is None:\n return False\n elif self.value is None:\n return False\n elif other.value is None:\n return True\n return self.value < other.value\n\n\nclass ReverseComparator(object):\n \"\"\"\n Object which swaps '<' and '>' so\n instead of a < b, it does b < a,\n and instead of a > b, it does b > a.\n This can be used in reverse comparisons.\n \"\"\"\n def __init__(self, value):\n self.value = value\n\n def __lt__(self, other):\n return other.value < self.value\n\n def __eq__(self, other):\n return other.value == self.value\n\n def __ne__(self, other):\n return other.value != self.value\n\n def __gt_(self, other):\n return other.value > self.value\n\n\nclass ResultSpec(object):\n\n __slots__ = ['filters', 'fields', 'properties',\n 'order', 'limit', 'offset', 'fieldMapping']\n\n def __init__(self, filters=None, fields=None, properties=None, order=None,\n limit=None, offset=None):\n self.filters = filters or []\n self.properties = properties or []\n self.fields = fields\n self.order = order\n self.limit = limit\n self.offset = offset\n self.fieldMapping = {}\n\n def __repr__(self):\n return (\"ResultSpec(**{{'filters': {}, 'fields': {}, 'properties': {}, \"\n \"'order': {}, 'limit': {}, 'offset': {}\").format(\n self.filters, self.fields, self.properties, self.order,\n self.limit, self.offset) + \"})\"\n\n def __eq__(self, b):\n for i in ['filters', 'fields', 'properties', 'order', 'limit', 'offset']:\n if getattr(self, i) != getattr(b, i):\n return False\n return True\n\n def __ne__(self, b):\n return not (self == b)\n\n def popProperties(self):\n values = []\n for p in self.properties:\n if p.field == b'property' and p.op == 'eq':\n self.properties.remove(p)\n values = p.values\n break\n return values\n\n def popFilter(self, field, op):\n for f in self.filters:\n if f.field == field and f.op == op:\n self.filters.remove(f)\n return f.values\n\n def popOneFilter(self, field, op):\n v = self.popFilter(field, op)\n return v[0] if v is not None else None\n\n def popBooleanFilter(self, field):\n eqVals = self.popFilter(field, 'eq')\n if eqVals and len(eqVals) == 1:\n return eqVals[0]\n neVals = self.popFilter(field, 'ne')\n if neVals and len(neVals) == 1:\n return not neVals[0]\n\n def popStringFilter(self, field):\n eqVals = self.popFilter(field, 'eq')\n if eqVals and len(eqVals) == 1:\n return eqVals[0]\n\n def popIntegerFilter(self, field):\n eqVals = self.popFilter(field, 'eq')\n if eqVals and len(eqVals) == 1:\n try:\n return int(eqVals[0])\n except ValueError:\n raise ValueError(\"Filter value for {} should be integer, but got: {}\".format(\n field, eqVals[0]))\n\n def removePagination(self):\n self.limit = self.offset = None\n\n def removeOrder(self):\n self.order = None\n\n def popField(self, field):\n try:\n i = self.fields.index(field)\n except ValueError:\n return False\n del self.fields[i]\n return True\n\n def findColumn(self, query, field):\n # will throw key error if field not in mapping\n mapped = self.fieldMapping[field]\n for col in query.inner_columns:\n if str(col) == mapped:\n return col\n raise KeyError(\"unable to find field {} in query\".format(field))\n\n def applyFilterToSQLQuery(self, query, f):\n field = f.field\n col = self.findColumn(query, field)\n # as sqlalchemy is overriding python operators, we can just use the same\n # python code generated by the filter\n return query.where(f.getOperator()(col, f.values))\n\n def applyOrderToSQLQuery(self, query, o):\n reverse = False\n if o.startswith('-'):\n reverse = True\n o = o[1:]\n col = self.findColumn(query, o)\n if reverse:\n col = col.desc()\n return query.order_by(col)\n\n def applyToSQLQuery(self, query):\n filters = self.filters\n order = self.order\n unmatched_filters = []\n unmatched_order = []\n # apply the filters if the name of field is found in the model, and\n # db2data\n for f in filters:\n try:\n query = self.applyFilterToSQLQuery(query, f)\n except KeyError:\n # if filter is unmatched, we will do the filtering manually in\n # self.apply\n unmatched_filters.append(f)\n\n # apply order if necessary\n if order:\n for o in order:\n try:\n query = self.applyOrderToSQLQuery(query, o)\n except KeyError:\n # if order is unmatched, we will do the ordering manually\n # in self.apply\n unmatched_order.append(o)\n\n # we cannot limit in sql if there is missing filtering or ordering\n if unmatched_filters or unmatched_order:\n if self.offset is not None or self.limit is not None:\n log.msg(\"Warning: limited data api query is not backed by db because of following filters\",\n unmatched_filters, unmatched_order)\n self.filters = unmatched_filters\n self.order = tuple(unmatched_order)\n return query, None\n count_query = sa.select([sa.func.count()]).select_from(query.alias('query'))\n self.order = None\n self.filters = []\n # finally, slice out the limit/offset\n if self.offset is not None:\n query = query.offset(self.offset)\n self.offset = None\n\n if self.limit is not None:\n query = query.limit(self.limit)\n self.limit = None\n\n return query, count_query\n\n def thd_execute(self, conn, q, dictFromRow):\n offset, limit = self.offset, self.limit\n q, qc = self.applyToSQLQuery(q)\n res = conn.execute(q)\n rv = [dictFromRow(row) for row in res.fetchall()]\n\n if qc is not None and (offset or limit):\n total = conn.execute(qc).scalar()\n rv = base.ListResult(rv)\n rv.offset, rv.total, rv.limit = offset, total, limit\n return rv\n\n def apply(self, data):\n if data is None:\n return data\n\n if self.fields:\n fields = set(self.fields)\n\n def includeFields(d):\n return dict((k, v) for k, v in iteritems(d)\n if k in fields)\n applyFields = includeFields\n else:\n fields = None\n\n if isinstance(data, dict):\n # item details\n if fields:\n data = applyFields(data)\n return data\n else:\n filters = self.filters\n order = self.order\n\n # item collection\n if isinstance(data, base.ListResult):\n # if pagination was applied, then fields, etc. must be empty\n assert not fields and not order and not filters, \\\n \"endpoint must apply fields, order, and filters if it performs pagination\"\n offset, total = data.offset, data.total\n limit = data.limit\n else:\n offset, total = None, None\n limit = None\n\n if fields:\n data = (applyFields(d) for d in data)\n\n # link the filters together and then flatten to list\n for f in self.filters:\n data = f.apply(data)\n data = list(data)\n\n if total is None:\n total = len(data)\n\n if self.order:\n def keyFunc(elem, order=self.order):\n \"\"\"\n Do a multi-level sort by passing in the keys\n to sort by.\n\n @param elem: each item in the list to sort. It must be\n a C{dict}\n @param order: a list of keys to sort by, such as:\n ('lastName', 'firstName', 'age')\n @return: a key used by sorted(). This will be a\n list such as:\n [a['lastName', a['firstName'], a['age']]\n @rtype: a C{list}\n \"\"\"\n compareKey = []\n for k in order:\n doReverse = False\n if k[0] == '-':\n # If we get a key '-lastName',\n # it means sort by 'lastName' in reverse.\n k = k[1:]\n doReverse = True\n val = NoneComparator(elem[k])\n if doReverse:\n val = ReverseComparator(val)\n compareKey.append(val)\n return compareKey\n\n data.sort(key=keyFunc)\n\n # finally, slice out the limit/offset\n if self.offset is not None or self.limit is not None:\n if offset is not None or limit is not None:\n raise AssertionError(\"endpoint must clear offset/limit\")\n end = ((self.offset or 0) + self.limit\n if self.limit is not None\n else None)\n data = data[self.offset:end]\n offset = self.offset\n limit = self.limit\n\n rv = base.ListResult(data)\n rv.offset, rv.total = offset, total\n rv.limit = limit\n return rv\n",
"path": "master/buildbot/data/resultspec.py"
}
] | diff --git a/master/buildbot/data/resultspec.py b/master/buildbot/data/resultspec.py
index 37b0192c8fb6..b275199a966f 100644
--- a/master/buildbot/data/resultspec.py
+++ b/master/buildbot/data/resultspec.py
@@ -46,7 +46,7 @@ class FieldBase(object):
plural_operators = {
'eq': lambda d, v: d in v,
'ne': lambda d, v: d not in v,
- 'contains': lambda d, v: set(v) <= set(d),
+ 'contains': lambda d, v: len(set(v).intersection(set(d))) > 0,
}
def __init__(self, field, op, values):
diff --git a/master/buildbot/newsfragments/rest_contains.bugfix b/master/buildbot/newsfragments/rest_contains.bugfix
new file mode 100644
index 000000000000..2bf033da6eb1
--- /dev/null
+++ b/master/buildbot/newsfragments/rest_contains.bugfix
@@ -0,0 +1 @@
+Make REST API's filter __contains use OR connector rather than AND according to what the documentation suggests.
\ No newline at end of file
diff --git a/master/buildbot/newsfragments/rest_contains.doc b/master/buildbot/newsfragments/rest_contains.doc
new file mode 100644
index 000000000000..7061a8a79502
--- /dev/null
+++ b/master/buildbot/newsfragments/rest_contains.doc
@@ -0,0 +1 @@
+Improve documentation of REST API's __contains filter.
diff --git a/master/buildbot/test/unit/test_data_builders.py b/master/buildbot/test/unit/test_data_builders.py
index 6af925160204..8ac49a331521 100644
--- a/master/buildbot/test/unit/test_data_builders.py
+++ b/master/buildbot/test/unit/test_data_builders.py
@@ -22,6 +22,7 @@
from twisted.trial import unittest
from buildbot.data import builders
+from buildbot.data import resultspec
from buildbot.test.fake import fakedb
from buildbot.test.fake import fakemaster
from buildbot.test.util import endpoint
@@ -106,6 +107,15 @@ def setUp(self):
return self.db.insertTestData([
fakedb.Builder(id=1, name=u'buildera'),
fakedb.Builder(id=2, name=u'builderb'),
+ fakedb.Builder(id=3, name=u'builderTagA'),
+ fakedb.Builder(id=4, name=u'builderTagB'),
+ fakedb.Builder(id=5, name=u'builderTagAB'),
+ fakedb.Tag(id=3, name=u"tagA"),
+ fakedb.Tag(id=4, name=u"tagB"),
+ fakedb.BuildersTags(builderid=3, tagid=3),
+ fakedb.BuildersTags(builderid=4, tagid=4),
+ fakedb.BuildersTags(builderid=5, tagid=3),
+ fakedb.BuildersTags(builderid=5, tagid=4),
fakedb.Master(id=13),
fakedb.BuilderMaster(id=1, builderid=2, masterid=13),
])
@@ -120,7 +130,7 @@ def test_get(self):
def check(builders):
[self.validateData(b) for b in builders]
self.assertEqual(sorted([b['builderid'] for b in builders]),
- [1, 2])
+ [1, 2, 3, 4, 5])
return d
def test_get_masterid(self):
@@ -142,6 +152,45 @@ def check(builders):
[])
return d
+ def test_get_contains_one_tag(self):
+ resultSpec = resultspec.ResultSpec(
+ filters=[resultspec.Filter('tags', 'contains', ["tagA"])])
+ d = self.callGet(('builders',))
+
+ @d.addCallback
+ def check(builders):
+ builders = resultSpec.apply(builders)
+ [self.validateData(b) for b in builders]
+ self.assertEqual(sorted([b['builderid'] for b in builders]),
+ [3, 5])
+ return d
+
+ def test_get_contains_two_tags(self):
+ resultSpec = resultspec.ResultSpec(
+ filters=[resultspec.Filter('tags', 'contains', ["tagA", "tagB"])])
+ d = self.callGet(('builders',))
+
+ @d.addCallback
+ def check(builders):
+ builders = resultSpec.apply(builders)
+ [self.validateData(b) for b in builders]
+ self.assertEqual(sorted([b['builderid'] for b in builders]),
+ [3, 4, 5])
+ return d
+
+ def test_get_contains_two_tags_one_unknown(self):
+ resultSpec = resultspec.ResultSpec(
+ filters=[resultspec.Filter('tags', 'contains', ["tagA", "tagC"])])
+ d = self.callGet(('builders',))
+
+ @d.addCallback
+ def check(builders):
+ builders = resultSpec.apply(builders)
+ [self.validateData(b) for b in builders]
+ self.assertEqual(sorted([b['builderid'] for b in builders]),
+ [3, 5])
+ return d
+
class Builder(interfaces.InterfaceTests, unittest.TestCase):
diff --git a/master/buildbot/test/unit/test_data_resultspec.py b/master/buildbot/test/unit/test_data_resultspec.py
index c87160b9c655..e43c48a77487 100644
--- a/master/buildbot/test/unit/test_data_resultspec.py
+++ b/master/buildbot/test/unit/test_data_resultspec.py
@@ -76,6 +76,16 @@ def test_ge(self):
self.assertEqual(list(f.apply(mklist('num', 5, 10, 15))),
mklist('num', 10, 15))
+ def test_contains(self):
+ f = resultspec.Filter('num', 'contains', [10])
+ self.assertEqual(list(f.apply(mklist('num', [5, 1], [10, 1], [15, 1]))),
+ mklist('num', [10, 1]))
+
+ def test_contains_plural(self):
+ f = resultspec.Filter('num', 'contains', [10, 5])
+ self.assertEqual(list(f.apply(mklist('num', [5, 1], [10, 1], [15, 1]))),
+ mklist('num', [5, 1], [10, 1]))
+
class ResultSpec(unittest.TestCase):
diff --git a/master/docs/developer/rest.rst b/master/docs/developer/rest.rst
index 5e5c4d0ce88f..664ce78d56b7 100644
--- a/master/docs/developer/rest.rst
+++ b/master/docs/developer/rest.rst
@@ -112,7 +112,8 @@ Filters can use any of the operators listed below, with query parameters of the
``ge``
select resources where the field's value is greater than or equal to ``{value}``
``contains``
- select resources where the field's value contains ``{value}``
+ Select resources where the field's value contains ``{value}``.
+ If the parameter is provided multiple times, results containing at least one of the values are returned (so `foo__contains=x&foo__contains=y` would match resources where foo contains `x`, `y` or both).
For example:
|
sunpy__sunpy-3676 | Removing astropy_helpers section in CONTRIBUTING.rst
<!-- This comments are hidden when you submit the issue so you do not need to remove them!
Please be sure to check out our contributing guidelines: https://github.com/sunpy/sunpy/blob/master/CONTRIBUTING.rst
Please be sure to check out our code of conduct:
https://github.com/sunpy/sunpy/blob/master/CODE_OF_CONDUCT.rst -->
<!-- Please have a search on our GitHub repository to see if a similar issue has already been posted.
If a similar issue is closed, have a quick look to see if you are satisfied by the resolution.
If not please go ahead and open an issue! -->
### Description
<!-- Provide a general description of the bug. -->
As of PR https://github.com/sunpy/sunpy/pull/3598, sunpy no longer needs `astropy_helpers`, and even it is removed from the package.
I think there should not be a section of Astropy Helpers in contribution guidelines as well.
| [
{
"content": "# This file is for compatibility with astropy_helpers\nversion = 'unknown.dev'\ntry:\n from importlib_metadata import version as _version, PackageNotFoundError\n version = _version('sunpy')\nexcept ImportError:\n from pkg_resources import get_distribution, DistributionNotFound\n try:\n version = get_distribution(\"sunpy\").version\n except DistributionNotFound:\n pass\nexcept PackageNotFoundError:\n pass\n",
"path": "sunpy/version.py"
}
] | [
{
"content": null,
"path": "sunpy/version.py"
}
] | diff --git a/.pep8speaks.yml b/.pep8speaks.yml
index bd956a34c15..df9f5b68358 100644
--- a/.pep8speaks.yml
+++ b/.pep8speaks.yml
@@ -2,9 +2,6 @@ pycodestyle:
max-line-length: 100
exclude:
- setup.py
- - ez_setup.py
- - ah_bootstrap.py
- - astropy_helpers/
- docs/conf.py
- sunpy/cm/color_tables.py
descending_issues_order: True
diff --git a/CONTRIBUTING.rst b/CONTRIBUTING.rst
index 1ef09a3f859..fe7ae61a6b2 100644
--- a/CONTRIBUTING.rst
+++ b/CONTRIBUTING.rst
@@ -183,31 +183,6 @@ If you get stuck or want help, just `ask here`_!
.. _SunPy repository: https://github.com/sunpy/sunpy
.. _ask here: https://riot.im/app/#/room/#sunpy-general:matrix.org
-Astropy helpers
-^^^^^^^^^^^^^^^
-
-.. warning::
-
- This is a common issue, so please be aware of this.
-
-Within SunPy is a folder called `astropy_helpers`_ and this is a git submodule.
-It is very common issue that this not setup correctly and gets added to your commits.
-
-So we recommend that you always run this at the start:
-
-.. code:: bash
-
- $ git submodule update --init
-
-This should resolve any differences in the `astropy_helpers`_ folder on your machine.
-If you use::
-
- $ git status
-
-you should hopefully see no changes for the ``astropy_helpers`` folder.
-
-.. _astropy_helpers: https://github.com/astropy/astropy-helpers
-
Checking the code you have written
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
diff --git a/MANIFEST.in b/MANIFEST.in
index 8525d843246..92c119fa4b7 100644
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -1,7 +1,6 @@
include README.rst
include CHANGELOG.rst
-include ah_bootstrap.py
include setup.py
include setup.cfg
include pyproject.toml
@@ -24,23 +23,5 @@ prune build
prune docs/_build
prune docs/api
-
-# the next few stanzas are for astropy_helpers. It's derived from the
-# astropy_helpers/MANIFEST.in, but requires additional includes for the actual
-# package directory and egg-info.
-
-include astropy_helpers/README.rst
-include astropy_helpers/CHANGES.rst
-include astropy_helpers/LICENSE.rst
-recursive-include astropy_helpers/licenses *
-
-include astropy_helpers/ah_bootstrap.py
-
-recursive-include astropy_helpers/astropy_helpers *.py *.pyx *.c *.h *.rst
-recursive-include astropy_helpers/astropy_helpers.egg-info *
-
-prune astropy_helpers/build
-prune astropy_helpers/astropy_helpers/tests
-
# Globally exclude compiled files
global-exclude *.pyc *.o
diff --git a/changelog/3676.doc.rst b/changelog/3676.doc.rst
new file mode 100644
index 00000000000..ee5d957cf16
--- /dev/null
+++ b/changelog/3676.doc.rst
@@ -0,0 +1,2 @@
+Removed obselete `Astropy Helpers` submodule section in `CONTRIBUTING.rst`;
+Also removed mentions of `astropy_helpers` in all files of the project.
diff --git a/setup.cfg b/setup.cfg
index 2cb60057bf4..6a7bf1381f4 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -81,9 +81,6 @@ asdf_extensions =
#pytest11 =
# asdf = asdf.tests.schema_tester
-[ah_bootstrap]
-auto_use = True
-
[build_docs]
source-dir = docs
build-dir = docs/_build
@@ -92,7 +89,7 @@ all_files = 1
[tool:pytest]
minversion = 3.0
testpaths = "sunpy" "docs"
-norecursedirs = ".tox" "build" "docs[\/]_build" "docs[\/]generated" "*.egg-info" "astropy_helpers" "examples" "sunpy[/\]cm" ".jupyter"
+norecursedirs = ".tox" "build" "docs[\/]_build" "docs[\/]generated" "*.egg-info" "examples" "sunpy[/\]cm" ".jupyter"
doctest_plus = enabled
doctest_optionflags = NORMALIZE_WHITESPACE FLOAT_CMP ELLIPSIS
addopts = -p no:warnings --doctest-rst -m "not figure"
diff --git a/sunpy/version.py b/sunpy/version.py
deleted file mode 100644
index 74af3b873aa..00000000000
--- a/sunpy/version.py
+++ /dev/null
@@ -1,13 +0,0 @@
-# This file is for compatibility with astropy_helpers
-version = 'unknown.dev'
-try:
- from importlib_metadata import version as _version, PackageNotFoundError
- version = _version('sunpy')
-except ImportError:
- from pkg_resources import get_distribution, DistributionNotFound
- try:
- version = get_distribution("sunpy").version
- except DistributionNotFound:
- pass
-except PackageNotFoundError:
- pass
|
Project-MONAI__MONAI-5709 | tests.test_warp HTTP Error 503
```
======================================================================
ERROR: test_grad (tests.test_warp.TestWarp)
----------------------------------------------------------------------
Traceback (most recent call last):
File "D:\a\MONAI\MONAI\tests\test_warp.py", line 102, in setUp
download_url_or_skip_test(
File "D:\a\MONAI\MONAI\tests\utils.py", line 731, in download_url_or_skip_test
download_url(*args, **kwargs)
File "D:\a\MONAI\MONAI\monai\apps\utils.py", line 203, in download_url
_download_with_progress(url, tmp_name, progress=progress)
File "D:\a\MONAI\MONAI\monai\apps\utils.py", line 114, in _download_with_progress
raise e
File "D:\a\MONAI\MONAI\monai\apps\utils.py", line 111, in _download_with_progress
urlretrieve(url, filepath)
File "C:\hostedtoolcache\windows\Python\3.8.10\x64\lib\urllib\request.py", line 247, in urlretrieve
with contextlib.closing(urlopen(url, data)) as fp:
File "C:\hostedtoolcache\windows\Python\3.8.10\x64\lib\urllib\request.py", line 222, in urlopen
return opener.open(url, data, timeout)
File "C:\hostedtoolcache\windows\Python\3.8.10\x64\lib\urllib\request.py", line 531, in open
response = meth(req, response)
File "C:\hostedtoolcache\windows\Python\3.8.10\x64\lib\urllib\request.py", line 640, in http_response
response = self.parent.error(
File "C:\hostedtoolcache\windows\Python\3.8.10\x64\lib\urllib\request.py", line 563, in error
result = self._call_chain(*args)
File "C:\hostedtoolcache\windows\Python\3.8.10\x64\lib\urllib\request.py", line 502, in _call_chain
result = func(*args)
File "C:\hostedtoolcache\windows\Python\3.8.10\x64\lib\urllib\request.py", line 755, in http_error_302
return self.parent.open(new, timeout=req.timeout)
File "C:\hostedtoolcache\windows\Python\3.8.10\x64\lib\urllib\request.py", line 531, in open
response = meth(req, response)
File "C:\hostedtoolcache\windows\Python\3.8.10\x64\lib\urllib\request.py", line 640, in http_response
response = self.parent.error(
File "C:\hostedtoolcache\windows\Python\3.8.10\x64\lib\urllib\request.py", line 569, in error
return self._call_chain(*args)
File "C:\hostedtoolcache\windows\Python\3.8.10\x64\lib\urllib\request.py", line 502, in _call_chain
result = func(*args)
File "C:\hostedtoolcache\windows\Python\3.8.10\x64\lib\urllib\request.py", line 649, in http_error_default
raise HTTPError(req.full_url, code, msg, hdrs, fp)
urllib.error.HTTPError: HTTP Error 503: Egress is over the account limit.
```
test_smartcachedataset thread hangs occasionally
**Describe the bug**
```
[2022-12-05T19:30:09.040Z]
Loading dataset: 0%| | 0/2 [00:00<?, ?it/s]
Loading dataset: 100%|██████████| 2/2 [00:00<00:00, 7577.79it/s]
[2022-12-05T19:30:09.040Z] .Finished test: test_datalist (tests.test_smartcachedataset.TestSmartCacheDataset) (0.00285s)
[2022-12-05T19:30:09.040Z] Starting test: test_set_data (tests.test_smartcachedataset.TestSmartCacheDataset)...
[2022-12-05T19:30:09.040Z]
Loading dataset: 0%| | 0/5 [00:00<?, ?it/s]
Loading dataset: 100%|██████████| 5/5 [00:00<00:00, 4430.91it/s]
[2022-12-05T21:52:01.667Z] Sending interrupt signal to process
[2022-12-05T21:52:01.669Z] Killing processes
[2022-12-05T21:52:02.262Z] kill finished with exit code 0
[2022-12-05T21:52:02.264Z] Sending interrupt signal to process
[2022-12-05T21:52:02.264Z] Killing processes
[2022-12-05T21:52:02.596Z] kill finished with exit code 2
[2022-12-05T21:52:02.824Z] Terminated
[2022-12-05T21:52:02.827Z] script returned exit code 143
```
| [
{
"content": "# Copyright (c) MONAI Consortium\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n# http://www.apache.org/licenses/LICENSE-2.0\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport logging\nimport warnings\nfrom typing import TYPE_CHECKING, Dict, Mapping, Optional\n\nfrom monai.config import IgniteInfo\nfrom monai.utils import is_scalar, min_version, optional_import\n\nEvents, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Events\")\n\n\nif TYPE_CHECKING:\n from ignite.engine import Engine\n from ignite.handlers import Checkpoint, DiskSaver\nelse:\n Engine, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Engine\")\n DiskSaver, _ = optional_import(\"ignite.handlers\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"DiskSaver\")\n Checkpoint, _ = optional_import(\"ignite.handlers\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Checkpoint\")\n\n\nclass CheckpointSaver:\n \"\"\"\n CheckpointSaver acts as an Ignite handler to save checkpoint data into files.\n It supports to save according to metrics result, epoch number, iteration number\n and last model or exception.\n\n Args:\n save_dir: the target directory to save the checkpoints.\n save_dict: source objects that save to the checkpoint. examples::\n\n {'network': net, 'optimizer': optimizer, 'lr_scheduler': lr_scheduler}\n\n name: identifier of logging.logger to use, if None, defaulting to ``engine.logger``.\n file_prefix: prefix for the filenames to which objects will be saved.\n save_final: whether to save checkpoint or session at final iteration or exception.\n If checkpoints are to be saved when an exception is raised, put this handler before\n `StatsHandler` in the handler list, because the logic with Ignite can only trigger\n the first attached handler for `EXCEPTION_RAISED` event.\n final_filename: set a fixed filename to save the final model if `save_final=True`.\n If None, default to `checkpoint_final_iteration=N.pt`.\n save_key_metric: whether to save checkpoint or session when the value of key_metric is\n higher than all the previous values during training.keep 4 decimal places of metric,\n checkpoint name is: {file_prefix}_key_metric=0.XXXX.pth.\n key_metric_name: the name of key_metric in ignite metrics dictionary.\n If None, use `engine.state.key_metric` instead.\n key_metric_n_saved: save top N checkpoints or sessions, sorted by the value of key\n metric in descending order.\n key_metric_filename: set a fixed filename to set the best metric model, if not None,\n `key_metric_n_saved` should be 1 and only keep the best metric model.\n key_metric_save_state: whether to save the tracking list of key metric in the checkpoint file.\n if `True`, then will save an object in the checkpoint file with key `checkpointer` to be\n consistent with the `include_self` arg of `Checkpoint` in ignite:\n https://pytorch.org/ignite/v0.4.5/generated/ignite.handlers.checkpoint.Checkpoint.html.\n typically, it's used to resume training and compare current metric with previous N values.\n key_metric_greater_or_equal: if `True`, the latest equally scored model is stored. Otherwise,\n save the first equally scored model. default to `False`.\n key_metric_negative_sign: whether adding a negative sign to the metric score to compare metrics,\n because for error-like metrics, smaller is better(objects with larger score are retained).\n default to `False`.\n epoch_level: save checkpoint during training for every N epochs or every N iterations.\n `True` is epoch level, `False` is iteration level.\n save_interval: save checkpoint every N epochs, default is 0 to save no checkpoint.\n n_saved: save latest N checkpoints of epoch level or iteration level, 'None' is to save all.\n\n Note:\n CheckpointHandler can be used during training, validation or evaluation.\n example of saved files:\n\n - checkpoint_iteration=400.pt\n - checkpoint_iteration=800.pt\n - checkpoint_epoch=1.pt\n - checkpoint_final_iteration=1000.pt\n - checkpoint_key_metric=0.9387.pt\n\n \"\"\"\n\n def __init__(\n self,\n save_dir: str,\n save_dict: Dict,\n name: Optional[str] = None,\n file_prefix: str = \"\",\n save_final: bool = False,\n final_filename: Optional[str] = None,\n save_key_metric: bool = False,\n key_metric_name: Optional[str] = None,\n key_metric_n_saved: int = 1,\n key_metric_filename: Optional[str] = None,\n key_metric_save_state: bool = False,\n key_metric_greater_or_equal: bool = False,\n key_metric_negative_sign: bool = False,\n epoch_level: bool = True,\n save_interval: int = 0,\n n_saved: Optional[int] = None,\n ) -> None:\n if save_dir is None:\n raise AssertionError(\"must provide directory to save the checkpoints.\")\n self.save_dir = save_dir\n if not (save_dict is not None and len(save_dict) > 0):\n raise AssertionError(\"must provide source objects to save.\")\n self.save_dict = save_dict\n self.logger = logging.getLogger(name)\n self.epoch_level = epoch_level\n self.save_interval = save_interval\n self._final_checkpoint: Optional[Checkpoint] = None\n self._key_metric_checkpoint: Optional[Checkpoint] = None\n self._interval_checkpoint: Optional[Checkpoint] = None\n self._name = name\n\n class _DiskSaver(DiskSaver):\n \"\"\"\n Enhance the DiskSaver to support fixed filename.\n\n \"\"\"\n\n def __init__(self, dirname: str, filename: Optional[str] = None):\n # set `atomic=False` as `atomic=True` only gives read/write permission to the user who saved the file,\n # without group/others read permission\n super().__init__(dirname=dirname, require_empty=False, atomic=False)\n self.filename = filename\n\n def __call__(self, checkpoint: Mapping, filename: str, metadata: Optional[Mapping] = None) -> None:\n if self.filename is not None:\n filename = self.filename\n super().__call__(checkpoint=checkpoint, filename=filename, metadata=metadata)\n\n def remove(self, filename: str) -> None:\n if self.filename is not None:\n filename = self.filename\n super().remove(filename=filename)\n\n if save_final:\n\n def _final_func(engine: Engine):\n return engine.state.iteration\n\n self._final_checkpoint = Checkpoint(\n to_save=self.save_dict,\n save_handler=_DiskSaver(dirname=self.save_dir, filename=final_filename),\n filename_prefix=file_prefix,\n score_function=_final_func,\n score_name=\"final_iteration\",\n )\n\n if save_key_metric:\n\n def _score_func(engine: Engine):\n if isinstance(key_metric_name, str):\n metric_name = key_metric_name\n elif hasattr(engine.state, \"key_metric_name\"):\n metric_name = engine.state.key_metric_name\n else:\n raise ValueError(\n f\"Incompatible values: save_key_metric=True and key_metric_name={key_metric_name}.\"\n )\n metric = engine.state.metrics[metric_name]\n if not is_scalar(metric):\n warnings.warn(\n \"key metric is not a scalar value, skip metric comparison and don't save a model.\"\n \"please use other metrics as key metric, or change the `reduction` mode to 'mean'.\"\n f\"got metric: {metric_name}={metric}.\"\n )\n return -1\n return (-1 if key_metric_negative_sign else 1) * metric\n\n if key_metric_filename is not None and key_metric_n_saved > 1:\n raise ValueError(\"if using fixed filename to save the best metric model, we should only save 1 model.\")\n\n self._key_metric_checkpoint = Checkpoint(\n to_save=self.save_dict,\n save_handler=_DiskSaver(dirname=self.save_dir, filename=key_metric_filename),\n filename_prefix=file_prefix,\n score_function=_score_func,\n score_name=\"key_metric\",\n n_saved=key_metric_n_saved,\n include_self=key_metric_save_state,\n greater_or_equal=key_metric_greater_or_equal,\n )\n\n if save_interval > 0:\n\n def _interval_func(engine: Engine):\n return engine.state.epoch if self.epoch_level else engine.state.iteration\n\n self._interval_checkpoint = Checkpoint(\n to_save=self.save_dict,\n save_handler=_DiskSaver(dirname=self.save_dir),\n filename_prefix=file_prefix,\n score_function=_interval_func,\n score_name=\"epoch\" if self.epoch_level else \"iteration\",\n n_saved=n_saved,\n )\n\n def load_state_dict(self, state_dict: Dict) -> None:\n \"\"\"\n Utility to resume the internal state of key metric tracking list if configured to save\n checkpoints based on the key metric value.\n Note to set `key_metric_save_state=True` when saving the previous checkpoint.\n\n Example::\n\n CheckpointSaver(\n ...\n save_key_metric=True,\n key_metric_save_state=True, # config to also save the state of this saver\n ).attach(engine)\n engine.run(...)\n\n # resumed training with a new CheckpointSaver\n saver = CheckpointSaver(save_key_metric=True, ...)\n # load the previous key metric tracking list into saver\n CheckpointLoader(\"/test/model.pt\"), {\"checkpointer\": saver}).attach(engine)\n\n \"\"\"\n if self._key_metric_checkpoint is not None:\n self._key_metric_checkpoint.load_state_dict(state_dict)\n else:\n warnings.warn(\"no key metric checkpoint saver to resume the key metric tracking list.\")\n\n def attach(self, engine: Engine) -> None:\n \"\"\"\n Args:\n engine: Ignite Engine, it can be a trainer, validator or evaluator.\n \"\"\"\n if self._name is None:\n self.logger = engine.logger\n if self._final_checkpoint is not None:\n engine.add_event_handler(Events.COMPLETED, self.completed)\n engine.add_event_handler(Events.EXCEPTION_RAISED, self.exception_raised)\n if self._key_metric_checkpoint is not None:\n engine.add_event_handler(Events.EPOCH_COMPLETED, self.metrics_completed)\n if self._interval_checkpoint is not None:\n if self.epoch_level:\n engine.add_event_handler(Events.EPOCH_COMPLETED(every=self.save_interval), self.interval_completed)\n else:\n engine.add_event_handler(Events.ITERATION_COMPLETED(every=self.save_interval), self.interval_completed)\n\n def _delete_previous_final_ckpt(self):\n if self._final_checkpoint is not None:\n saved = self._final_checkpoint._saved\n if len(saved) > 0:\n item = saved.pop(0)\n self._final_checkpoint.save_handler.remove(item.filename)\n self.logger.info(f\"Deleted previous saved final checkpoint: {item.filename}\")\n\n def completed(self, engine: Engine) -> None:\n \"\"\"Callback for train or validation/evaluation completed Event.\n Save final checkpoint if configure save_final is True.\n\n Args:\n engine: Ignite Engine, it can be a trainer, validator or evaluator.\n \"\"\"\n if not callable(self._final_checkpoint):\n raise AssertionError(\"Error: _final_checkpoint function not specified.\")\n # delete previous saved final checkpoint if existing\n self._delete_previous_final_ckpt()\n self._final_checkpoint(engine)\n if self.logger is None:\n raise AssertionError\n if not hasattr(self.logger, \"info\"):\n raise AssertionError(\"Error, provided logger has not info attribute.\")\n self.logger.info(f\"Train completed, saved final checkpoint: {self._final_checkpoint.last_checkpoint}\")\n\n def exception_raised(self, engine: Engine, e: Exception) -> None:\n \"\"\"Callback for train or validation/evaluation exception raised Event.\n Save current data as final checkpoint if configure save_final is True. This callback may be skipped\n because the logic with Ignite can only trigger the first attached handler for `EXCEPTION_RAISED` event.\n\n Args:\n engine: Ignite Engine, it can be a trainer, validator or evaluator.\n e: the exception caught in Ignite during engine.run().\n \"\"\"\n if not callable(self._final_checkpoint):\n raise AssertionError(\"Error: _final_checkpoint function not specified.\")\n # delete previous saved final checkpoint if existing\n self._delete_previous_final_ckpt()\n self._final_checkpoint(engine)\n if self.logger is None:\n raise AssertionError\n if not hasattr(self.logger, \"info\"):\n raise AssertionError(\"Error, provided logger has not info attribute.\")\n self.logger.info(f\"Exception raised, saved the last checkpoint: {self._final_checkpoint.last_checkpoint}\")\n raise e\n\n def metrics_completed(self, engine: Engine) -> None:\n \"\"\"Callback to compare metrics and save models in train or validation when epoch completed.\n\n Args:\n engine: Ignite Engine, it can be a trainer, validator or evaluator.\n \"\"\"\n if not callable(self._key_metric_checkpoint):\n raise AssertionError(\"Error: _key_metric_checkpoint function not specified.\")\n self._key_metric_checkpoint(engine)\n\n def interval_completed(self, engine: Engine) -> None:\n \"\"\"Callback for train epoch/iteration completed Event.\n Save checkpoint if configure save_interval = N\n\n Args:\n engine: Ignite Engine, it can be a trainer, validator or evaluator.\n \"\"\"\n if not callable(self._interval_checkpoint):\n raise AssertionError(\"Error: _interval_checkpoint function not specified.\")\n self._interval_checkpoint(engine)\n if self.logger is None:\n raise AssertionError\n if not hasattr(self.logger, \"info\"):\n raise AssertionError(\"Error, provided logger has not info attribute.\")\n if self.epoch_level:\n self.logger.info(f\"Saved checkpoint at epoch: {engine.state.epoch}\")\n else:\n self.logger.info(f\"Saved checkpoint at iteration: {engine.state.iteration}\")\n",
"path": "monai/handlers/checkpoint_saver.py"
}
] | [
{
"content": "# Copyright (c) MONAI Consortium\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n# http://www.apache.org/licenses/LICENSE-2.0\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport logging\nimport warnings\nfrom typing import TYPE_CHECKING, Dict, Mapping, Optional\n\nfrom monai.config import IgniteInfo\nfrom monai.utils import is_scalar, min_version, optional_import\n\nEvents, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Events\")\n\nif TYPE_CHECKING:\n from ignite.engine import Engine\n from ignite.handlers import Checkpoint, DiskSaver\nelse:\n Engine, _ = optional_import(\"ignite.engine\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Engine\")\n DiskSaver, _ = optional_import(\"ignite.handlers\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"DiskSaver\")\n Checkpoint, _ = optional_import(\"ignite.handlers\", IgniteInfo.OPT_IMPORT_VERSION, min_version, \"Checkpoint\")\n\n\nclass CheckpointSaver:\n \"\"\"\n CheckpointSaver acts as an Ignite handler to save checkpoint data into files.\n It supports to save according to metrics result, epoch number, iteration number\n and last model or exception.\n\n Args:\n save_dir: the target directory to save the checkpoints.\n save_dict: source objects that save to the checkpoint. examples::\n\n {'network': net, 'optimizer': optimizer, 'lr_scheduler': lr_scheduler}\n\n name: identifier of logging.logger to use, if None, defaulting to ``engine.logger``.\n file_prefix: prefix for the filenames to which objects will be saved.\n save_final: whether to save checkpoint or session at final iteration or exception.\n If checkpoints are to be saved when an exception is raised, put this handler before\n `StatsHandler` in the handler list, because the logic with Ignite can only trigger\n the first attached handler for `EXCEPTION_RAISED` event.\n final_filename: set a fixed filename to save the final model if `save_final=True`.\n If None, default to `checkpoint_final_iteration=N.pt`.\n save_key_metric: whether to save checkpoint or session when the value of key_metric is\n higher than all the previous values during training.keep 4 decimal places of metric,\n checkpoint name is: {file_prefix}_key_metric=0.XXXX.pth.\n key_metric_name: the name of key_metric in ignite metrics dictionary.\n If None, use `engine.state.key_metric` instead.\n key_metric_n_saved: save top N checkpoints or sessions, sorted by the value of key\n metric in descending order.\n key_metric_filename: set a fixed filename to set the best metric model, if not None,\n `key_metric_n_saved` should be 1 and only keep the best metric model.\n key_metric_save_state: whether to save the tracking list of key metric in the checkpoint file.\n if `True`, then will save an object in the checkpoint file with key `checkpointer` to be\n consistent with the `include_self` arg of `Checkpoint` in ignite:\n https://pytorch.org/ignite/v0.4.5/generated/ignite.handlers.checkpoint.Checkpoint.html.\n typically, it's used to resume training and compare current metric with previous N values.\n key_metric_greater_or_equal: if `True`, the latest equally scored model is stored. Otherwise,\n save the first equally scored model. default to `False`.\n key_metric_negative_sign: whether adding a negative sign to the metric score to compare metrics,\n because for error-like metrics, smaller is better(objects with larger score are retained).\n default to `False`.\n epoch_level: save checkpoint during training for every N epochs or every N iterations.\n `True` is epoch level, `False` is iteration level.\n save_interval: save checkpoint every N epochs, default is 0 to save no checkpoint.\n n_saved: save latest N checkpoints of epoch level or iteration level, 'None' is to save all.\n\n Note:\n CheckpointHandler can be used during training, validation or evaluation.\n example of saved files:\n\n - checkpoint_iteration=400.pt\n - checkpoint_iteration=800.pt\n - checkpoint_epoch=1.pt\n - checkpoint_final_iteration=1000.pt\n - checkpoint_key_metric=0.9387.pt\n\n \"\"\"\n\n def __init__(\n self,\n save_dir: str,\n save_dict: Dict,\n name: Optional[str] = None,\n file_prefix: str = \"\",\n save_final: bool = False,\n final_filename: Optional[str] = None,\n save_key_metric: bool = False,\n key_metric_name: Optional[str] = None,\n key_metric_n_saved: int = 1,\n key_metric_filename: Optional[str] = None,\n key_metric_save_state: bool = False,\n key_metric_greater_or_equal: bool = False,\n key_metric_negative_sign: bool = False,\n epoch_level: bool = True,\n save_interval: int = 0,\n n_saved: Optional[int] = None,\n ) -> None:\n if save_dir is None:\n raise AssertionError(\"must provide directory to save the checkpoints.\")\n self.save_dir = save_dir\n if not (save_dict is not None and len(save_dict) > 0):\n raise AssertionError(\"must provide source objects to save.\")\n self.save_dict = save_dict\n self.logger = logging.getLogger(name)\n self.epoch_level = epoch_level\n self.save_interval = save_interval\n self._final_checkpoint: Optional[Checkpoint] = None\n self._key_metric_checkpoint: Optional[Checkpoint] = None\n self._interval_checkpoint: Optional[Checkpoint] = None\n self._name = name\n\n class _DiskSaver(DiskSaver):\n \"\"\"\n Enhance the DiskSaver to support fixed filename.\n\n \"\"\"\n\n def __init__(self, dirname: str, filename: Optional[str] = None):\n # set `atomic=False` as `atomic=True` only gives read/write permission to the user who saved the file,\n # without group/others read permission\n super().__init__(dirname=dirname, require_empty=False, atomic=False)\n self.filename = filename\n\n def __call__(self, checkpoint: Mapping, filename: str, metadata: Optional[Mapping] = None) -> None:\n if self.filename is not None:\n filename = self.filename\n super().__call__(checkpoint=checkpoint, filename=filename, metadata=metadata)\n\n def remove(self, filename: str) -> None:\n if self.filename is not None:\n filename = self.filename\n super().remove(filename=filename)\n\n if save_final:\n\n def _final_func(engine: Engine):\n return engine.state.iteration\n\n self._final_checkpoint = Checkpoint(\n to_save=self.save_dict,\n save_handler=_DiskSaver(dirname=self.save_dir, filename=final_filename),\n filename_prefix=file_prefix,\n score_function=_final_func,\n score_name=\"final_iteration\",\n )\n\n if save_key_metric:\n\n def _score_func(engine: Engine):\n if isinstance(key_metric_name, str):\n metric_name = key_metric_name\n elif hasattr(engine.state, \"key_metric_name\"):\n metric_name = engine.state.key_metric_name\n else:\n raise ValueError(\n f\"Incompatible values: save_key_metric=True and key_metric_name={key_metric_name}.\"\n )\n metric = engine.state.metrics[metric_name]\n if not is_scalar(metric):\n warnings.warn(\n \"key metric is not a scalar value, skip metric comparison and don't save a model.\"\n \"please use other metrics as key metric, or change the `reduction` mode to 'mean'.\"\n f\"got metric: {metric_name}={metric}.\"\n )\n return -1\n return (-1 if key_metric_negative_sign else 1) * metric\n\n if key_metric_filename is not None and key_metric_n_saved > 1:\n raise ValueError(\"if using fixed filename to save the best metric model, we should only save 1 model.\")\n\n self._key_metric_checkpoint = Checkpoint(\n to_save=self.save_dict,\n save_handler=_DiskSaver(dirname=self.save_dir, filename=key_metric_filename),\n filename_prefix=file_prefix,\n score_function=_score_func,\n score_name=\"key_metric\",\n n_saved=key_metric_n_saved,\n include_self=key_metric_save_state,\n greater_or_equal=key_metric_greater_or_equal,\n )\n\n if save_interval > 0:\n\n def _interval_func(engine: Engine):\n return engine.state.epoch if self.epoch_level else engine.state.iteration\n\n self._interval_checkpoint = Checkpoint(\n to_save=self.save_dict,\n save_handler=_DiskSaver(dirname=self.save_dir),\n filename_prefix=file_prefix,\n score_function=_interval_func,\n score_name=\"epoch\" if self.epoch_level else \"iteration\",\n n_saved=n_saved,\n )\n\n def load_state_dict(self, state_dict: Dict) -> None:\n \"\"\"\n Utility to resume the internal state of key metric tracking list if configured to save\n checkpoints based on the key metric value.\n Note to set `key_metric_save_state=True` when saving the previous checkpoint.\n\n Example::\n\n CheckpointSaver(\n ...\n save_key_metric=True,\n key_metric_save_state=True, # config to also save the state of this saver\n ).attach(engine)\n engine.run(...)\n\n # resumed training with a new CheckpointSaver\n saver = CheckpointSaver(save_key_metric=True, ...)\n # load the previous key metric tracking list into saver\n CheckpointLoader(\"/test/model.pt\"), {\"checkpointer\": saver}).attach(engine)\n\n \"\"\"\n if self._key_metric_checkpoint is not None:\n self._key_metric_checkpoint.load_state_dict(state_dict)\n else:\n warnings.warn(\"no key metric checkpoint saver to resume the key metric tracking list.\")\n\n def attach(self, engine: Engine) -> None:\n \"\"\"\n Args:\n engine: Ignite Engine, it can be a trainer, validator or evaluator.\n \"\"\"\n if self._name is None:\n self.logger = engine.logger\n if self._final_checkpoint is not None:\n engine.add_event_handler(Events.COMPLETED, self.completed)\n engine.add_event_handler(Events.EXCEPTION_RAISED, self.exception_raised)\n if self._key_metric_checkpoint is not None:\n engine.add_event_handler(Events.EPOCH_COMPLETED, self.metrics_completed)\n if self._interval_checkpoint is not None:\n if self.epoch_level:\n engine.add_event_handler(Events.EPOCH_COMPLETED(every=self.save_interval), self.interval_completed)\n else:\n engine.add_event_handler(Events.ITERATION_COMPLETED(every=self.save_interval), self.interval_completed)\n\n def _delete_previous_final_ckpt(self):\n if self._final_checkpoint is not None:\n saved = self._final_checkpoint._saved\n if len(saved) > 0:\n item = saved.pop(0)\n self._final_checkpoint.save_handler.remove(item.filename)\n self.logger.info(f\"Deleted previous saved final checkpoint: {item.filename}\")\n\n def completed(self, engine: Engine) -> None:\n \"\"\"Callback for train or validation/evaluation completed Event.\n Save final checkpoint if configure save_final is True.\n\n Args:\n engine: Ignite Engine, it can be a trainer, validator or evaluator.\n \"\"\"\n if not callable(self._final_checkpoint):\n raise AssertionError(\"Error: _final_checkpoint function not specified.\")\n # delete previous saved final checkpoint if existing\n self._delete_previous_final_ckpt()\n self._final_checkpoint(engine)\n if self.logger is None:\n raise AssertionError\n if not hasattr(self.logger, \"info\"):\n raise AssertionError(\"Error, provided logger has not info attribute.\")\n self.logger.info(f\"Train completed, saved final checkpoint: {self._final_checkpoint.last_checkpoint}\")\n\n def exception_raised(self, engine: Engine, e: Exception) -> None:\n \"\"\"Callback for train or validation/evaluation exception raised Event.\n Save current data as final checkpoint if configure save_final is True. This callback may be skipped\n because the logic with Ignite can only trigger the first attached handler for `EXCEPTION_RAISED` event.\n\n Args:\n engine: Ignite Engine, it can be a trainer, validator or evaluator.\n e: the exception caught in Ignite during engine.run().\n \"\"\"\n if not callable(self._final_checkpoint):\n raise AssertionError(\"Error: _final_checkpoint function not specified.\")\n # delete previous saved final checkpoint if existing\n self._delete_previous_final_ckpt()\n self._final_checkpoint(engine)\n if self.logger is None:\n raise AssertionError\n if not hasattr(self.logger, \"info\"):\n raise AssertionError(\"Error, provided logger has not info attribute.\")\n self.logger.info(f\"Exception raised, saved the last checkpoint: {self._final_checkpoint.last_checkpoint}\")\n raise e\n\n def metrics_completed(self, engine: Engine) -> None:\n \"\"\"Callback to compare metrics and save models in train or validation when epoch completed.\n\n Args:\n engine: Ignite Engine, it can be a trainer, validator or evaluator.\n \"\"\"\n if not callable(self._key_metric_checkpoint):\n raise AssertionError(\"Error: _key_metric_checkpoint function not specified.\")\n self._key_metric_checkpoint(engine)\n\n def interval_completed(self, engine: Engine) -> None:\n \"\"\"Callback for train epoch/iteration completed Event.\n Save checkpoint if configure save_interval = N\n\n Args:\n engine: Ignite Engine, it can be a trainer, validator or evaluator.\n \"\"\"\n if not callable(self._interval_checkpoint):\n raise AssertionError(\"Error: _interval_checkpoint function not specified.\")\n self._interval_checkpoint(engine)\n if self.logger is None:\n raise AssertionError\n if not hasattr(self.logger, \"info\"):\n raise AssertionError(\"Error, provided logger has not info attribute.\")\n if self.epoch_level:\n self.logger.info(f\"Saved checkpoint at epoch: {engine.state.epoch}\")\n else:\n self.logger.info(f\"Saved checkpoint at iteration: {engine.state.iteration}\")\n",
"path": "monai/handlers/checkpoint_saver.py"
}
] | diff --git a/monai/handlers/checkpoint_saver.py b/monai/handlers/checkpoint_saver.py
index 014f418e2b..76f6458f3d 100644
--- a/monai/handlers/checkpoint_saver.py
+++ b/monai/handlers/checkpoint_saver.py
@@ -18,7 +18,6 @@
Events, _ = optional_import("ignite.engine", IgniteInfo.OPT_IMPORT_VERSION, min_version, "Events")
-
if TYPE_CHECKING:
from ignite.engine import Engine
from ignite.handlers import Checkpoint, DiskSaver
diff --git a/tests/test_convert_data_type.py b/tests/test_convert_data_type.py
index 41abd1fd8d..aff4b7d995 100644
--- a/tests/test_convert_data_type.py
+++ b/tests/test_convert_data_type.py
@@ -56,7 +56,6 @@
)
)
-
UNSUPPORTED_TYPES = {np.dtype("uint16"): torch.int32, np.dtype("uint32"): torch.int64, np.dtype("uint64"): torch.int64}
diff --git a/tests/test_hovernet_nuclear_type_post_processingd.py b/tests/test_hovernet_nuclear_type_post_processingd.py
index e6e675bc76..7f3462dddb 100644
--- a/tests/test_hovernet_nuclear_type_post_processingd.py
+++ b/tests/test_hovernet_nuclear_type_post_processingd.py
@@ -30,10 +30,8 @@
image = (x - 10) ** 2 + (y - 10) ** 2 <= 5**2
image = image[None, ...].astype("uint8")
-
TEST_CASE_1 = [{}, [{"1": [10, 10]}, np.zeros_like(image), np.zeros_like(image)]]
-
TEST_CASE = []
for p in TEST_NDARRAYS:
TEST_CASE.append([p, image] + TEST_CASE_1)
diff --git a/tests/test_safe_dtype_range.py b/tests/test_safe_dtype_range.py
index df7ca439f4..2f0b1bcefc 100644
--- a/tests/test_safe_dtype_range.py
+++ b/tests/test_safe_dtype_range.py
@@ -29,7 +29,7 @@
TESTS.append((in_type(np.array(256)), in_type(np.array(255)), np.uint8)) # type: ignore
TESTS.append((in_type(np.array(-12)), in_type(np.array(0)), np.uint8)) # type: ignore
for in_type in TEST_NDARRAYS_ALL:
- TESTS.append((in_type(np.array([[256, 255], [-12, 0]])), in_type(np.array([[255, 255], [0, 0]])), np.uint8)) # type: ignore
+ TESTS.append((in_type(np.array([[256, 255], [-12, 0]])), in_type(np.array([[255, 255], [0, 0]])), np.uint8))
TESTS_LIST: List[Tuple] = []
for in_type in TEST_NDARRAYS_ALL + (int, float):
diff --git a/tests/test_smartcachedataset.py b/tests/test_smartcachedataset.py
index 9f9043d19e..63dc1534bc 100644
--- a/tests/test_smartcachedataset.py
+++ b/tests/test_smartcachedataset.py
@@ -140,6 +140,7 @@ def test_shuffle(self):
dataset.shutdown()
+ @unittest.skip("https://github.com/Project-MONAI/MONAI/issues/5660 blocks the ci")
def test_set_data(self):
data_list1 = list(range(10))
diff --git a/tests/utils.py b/tests/utils.py
index 7b303071e8..571876a681 100644
--- a/tests/utils.py
+++ b/tests/utils.py
@@ -129,7 +129,7 @@ def skip_if_downloading_fails():
except ssl.SSLError as ssl_e:
if "decryption failed" in str(ssl_e):
raise unittest.SkipTest(f"SSL error while downloading: {ssl_e}") from ssl_e
- except RuntimeError as rt_e:
+ except (RuntimeError, OSError) as rt_e:
if "unexpected EOF" in str(rt_e):
raise unittest.SkipTest(f"error while downloading: {rt_e}") from rt_e # incomplete download
if "network issue" in str(rt_e):
|
Lightning-Universe__lightning-flash-720 | NameError: name 'K' is not defined
## 🐛 Bug
<!-- A clear and concise description of what the bug is. -->
````sh
Uncaught exception
Traceback (most recent call last):
File "main.py", line 11, in <module>
datamodule = VideoClassificationData.from_folders(
File "/home/nitin/github/video_classification/venv/lib/python3.8/site-packages/flash/core/data/data_module.py", line 540, in from_folders
return cls.from_data_source(
File "/home/nitin/github/video_classification/venv/lib/python3.8/site-packages/flash/core/data/data_module.py", line 448, in from_data_source
preprocess = preprocess or cls.preprocess_cls(
File "/home/nitin/github/video_classification/venv/lib/python3.8/site-packages/flash/video/classification/data.py", line 241, in __init__
super().__init__(
File "/home/nitin/github/video_classification/venv/lib/python3.8/site-packages/flash/core/data/process.py", line 196, in __init__
train_transform = train_transform or self._resolve_transforms(RunningStage.TRAINING)
File "/home/nitin/github/video_classification/venv/lib/python3.8/site-packages/flash/core/data/process.py", line 247, in _resolve_transforms
transforms: Optional[Dict[str, Callable]] = resolved_function()
File "/home/nitin/github/video_classification/venv/lib/python3.8/site-packages/flash/video/classification/data.py", line 307, in default_transforms
transform=K.VideoSequential(
NameError: name 'K' is not defined
````
### To Reproduce
Steps to reproduce the behavior:
````python
import os
from torch.utils.data.sampler import RandomSampler
import flash
from flash.core.data.utils import download_data
from flash.video import VideoClassificationData, VideoClassifier
# 1. Download a video clip dataset. Find more datasets at https://pytorchvideo.readthedocs.io/en/latest/data.html
download_data("https://pl-flash-data.s3.amazonaws.com/kinetics.zip")
# 2. Load the Data
datamodule = VideoClassificationData.from_folders(
train_folder=os.path.join(flash.PROJECT_ROOT, "data/kinetics/train"),
val_folder=os.path.join(flash.PROJECT_ROOT, "data/kinetics/val"),
predict_folder=os.path.join(flash.PROJECT_ROOT, "data/kinetics/predict"),
batch_size=8,
clip_sampler="uniform",
clip_duration=1,
video_sampler=RandomSampler,
decode_audio=False,
num_workers=8,
)
# 3. Build the model
model = VideoClassifier(backbone="x3d_xs", num_classes=datamodule.num_classes, pretrained=False)
# 4. Create the trainer
trainer = flash.Trainer(max_epochs=3)
# 5. Finetune the model
trainer.finetune(model, datamodule=datamodule)
# 6. Save it!
trainer.save_checkpoint("video_classification.pt")
````
<!-- If you have a code sample, error messages, stack traces, please provide it here as well -->
### Environment
- PyTorch Version (e.g., 1.0): 1.9.0
- OS (e.g., Linux): Linux (Ubuntu 18.04)
- How you installed PyTorch: pip
- Python version: 3.8
- CUDA/cuDNN version: 11.3
- GPU models and configuration: GTX 1650
### Additional Note
<!-- Add any other context about the problem here. -->
Please update the package, `lightning-flash==0.4.0` is not compatible with `pytorchvideo==0.1.2`
| [
{
"content": "# Copyright The PyTorch Lightning team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport functools\nimport importlib\nimport operator\nimport types\nfrom importlib.util import find_spec\nfrom typing import Callable, List, Union\nfrom warnings import warn\n\nfrom pkg_resources import DistributionNotFound\n\ntry:\n from packaging.version import Version\nexcept (ModuleNotFoundError, DistributionNotFound):\n Version = None\n\n\ndef _module_available(module_path: str) -> bool:\n \"\"\"Check if a path is available in your environment.\n\n >>> _module_available('os')\n True\n >>> _module_available('bla.bla')\n False\n \"\"\"\n try:\n return find_spec(module_path) is not None\n except AttributeError:\n # Python 3.6\n return False\n except ModuleNotFoundError:\n # Python 3.7+\n return False\n except ValueError:\n # Sometimes __spec__ can be None and gives a ValueError\n return True\n\n\ndef _compare_version(package: str, op, version) -> bool:\n \"\"\"Compare package version with some requirements.\n\n >>> _compare_version(\"torch\", operator.ge, \"0.1\")\n True\n \"\"\"\n try:\n pkg = importlib.import_module(package)\n except (ModuleNotFoundError, DistributionNotFound, ValueError):\n return False\n try:\n pkg_version = Version(pkg.__version__)\n except TypeError:\n # this is mock by sphinx, so it shall return True to generate all summaries\n return True\n return op(pkg_version, Version(version))\n\n\n_TORCH_AVAILABLE = _module_available(\"torch\")\n_BOLTS_AVAILABLE = _module_available(\"pl_bolts\") and _compare_version(\"torch\", operator.lt, \"1.9.0\")\n_PANDAS_AVAILABLE = _module_available(\"pandas\")\n_SKLEARN_AVAILABLE = _module_available(\"sklearn\")\n_TABNET_AVAILABLE = _module_available(\"pytorch_tabnet\")\n_KORNIA_AVAILABLE = _module_available(\"kornia\")\n_COCO_AVAILABLE = _module_available(\"pycocotools\")\n_TIMM_AVAILABLE = _module_available(\"timm\")\n_TORCHVISION_AVAILABLE = _module_available(\"torchvision\")\n_PYTORCHVIDEO_AVAILABLE = _module_available(\"pytorchvideo\")\n_MATPLOTLIB_AVAILABLE = _module_available(\"matplotlib\")\n_TRANSFORMERS_AVAILABLE = _module_available(\"transformers\")\n_PYSTICHE_AVAILABLE = _module_available(\"pystiche\")\n_FIFTYONE_AVAILABLE = _module_available(\"fiftyone\")\n_FASTAPI_AVAILABLE = _module_available(\"fastapi\")\n_PYDANTIC_AVAILABLE = _module_available(\"pydantic\")\n_GRAPHVIZ_AVAILABLE = _module_available(\"graphviz\")\n_CYTOOLZ_AVAILABLE = _module_available(\"cytoolz\")\n_UVICORN_AVAILABLE = _module_available(\"uvicorn\")\n_PIL_AVAILABLE = _module_available(\"PIL\")\n_OPEN3D_AVAILABLE = _module_available(\"open3d\")\n_SEGMENTATION_MODELS_AVAILABLE = _module_available(\"segmentation_models_pytorch\")\n_SOUNDFILE_AVAILABLE = _module_available(\"soundfile\")\n_TORCH_SCATTER_AVAILABLE = _module_available(\"torch_scatter\")\n_TORCH_SPARSE_AVAILABLE = _module_available(\"torch_sparse\")\n_TORCH_GEOMETRIC_AVAILABLE = _module_available(\"torch_geometric\")\n_TORCHAUDIO_AVAILABLE = _module_available(\"torchaudio\")\n_ROUGE_SCORE_AVAILABLE = _module_available(\"rouge_score\")\n_SENTENCEPIECE_AVAILABLE = _module_available(\"sentencepiece\")\n_DATASETS_AVAILABLE = _module_available(\"datasets\")\n_ICEVISION_AVAILABLE = _module_available(\"icevision\")\n_ICEDATA_AVAILABLE = _module_available(\"icedata\")\n_TORCH_ORT_AVAILABLE = _module_available(\"torch_ort\")\n_VISSL_AVAILABLE = _module_available(\"vissl\") and _module_available(\"classy_vision\")\n\nif _PIL_AVAILABLE:\n from PIL import Image\nelse:\n\n class MetaImage(type):\n def __init__(cls, name, bases, dct):\n super().__init__(name, bases, dct)\n\n cls._Image = None\n\n @property\n def Image(cls):\n warn(\"Mock object called due to missing PIL library. Please use \\\"pip install 'lightning-flash[image]'\\\".\")\n return cls._Image\n\n class Image(metaclass=MetaImage):\n pass\n\n\nif Version:\n _TORCHVISION_GREATER_EQUAL_0_9 = _compare_version(\"torchvision\", operator.ge, \"0.9.0\")\n _PL_GREATER_EQUAL_1_4_3 = _compare_version(\"pytorch_lightning\", operator.ge, \"1.4.3\")\n\n_TEXT_AVAILABLE = all(\n [\n _TRANSFORMERS_AVAILABLE,\n _ROUGE_SCORE_AVAILABLE,\n _SENTENCEPIECE_AVAILABLE,\n _DATASETS_AVAILABLE,\n ]\n)\n_TABULAR_AVAILABLE = _TABNET_AVAILABLE and _PANDAS_AVAILABLE\n_VIDEO_AVAILABLE = _PYTORCHVIDEO_AVAILABLE\n_IMAGE_AVAILABLE = all(\n [\n _TORCHVISION_AVAILABLE,\n _TIMM_AVAILABLE,\n _PIL_AVAILABLE,\n _KORNIA_AVAILABLE,\n _PYSTICHE_AVAILABLE,\n _SEGMENTATION_MODELS_AVAILABLE,\n _ICEVISION_AVAILABLE,\n _ICEDATA_AVAILABLE,\n ]\n)\n_SERVE_AVAILABLE = _FASTAPI_AVAILABLE and _PYDANTIC_AVAILABLE and _CYTOOLZ_AVAILABLE and _UVICORN_AVAILABLE\n_POINTCLOUD_AVAILABLE = _OPEN3D_AVAILABLE and _TORCHVISION_AVAILABLE\n_AUDIO_AVAILABLE = all([_TORCHAUDIO_AVAILABLE, _SOUNDFILE_AVAILABLE, _TRANSFORMERS_AVAILABLE])\n_GRAPH_AVAILABLE = _TORCH_SCATTER_AVAILABLE and _TORCH_SPARSE_AVAILABLE and _TORCH_GEOMETRIC_AVAILABLE\n\n_EXTRAS_AVAILABLE = {\n \"image\": _IMAGE_AVAILABLE,\n \"tabular\": _TABULAR_AVAILABLE,\n \"text\": _TEXT_AVAILABLE,\n \"video\": _VIDEO_AVAILABLE,\n \"pointcloud\": _POINTCLOUD_AVAILABLE,\n \"serve\": _SERVE_AVAILABLE,\n \"audio\": _AUDIO_AVAILABLE,\n \"graph\": _GRAPH_AVAILABLE,\n}\n\n\ndef _requires(\n module_paths: Union[str, List],\n module_available: Callable[[str], bool],\n formatter: Callable[[List[str]], str],\n):\n\n if not isinstance(module_paths, list):\n module_paths = [module_paths]\n\n def decorator(func):\n if not all(module_available(module_path) for module_path in module_paths):\n\n @functools.wraps(func)\n def wrapper(*args, **kwargs):\n raise ModuleNotFoundError(\n f\"Required dependencies not available. Please run: pip install {formatter(module_paths)}\"\n )\n\n return wrapper\n else:\n return func\n\n return decorator\n\n\ndef requires(module_paths: Union[str, List]):\n return _requires(module_paths, _module_available, lambda module_paths: \" \".join(module_paths))\n\n\ndef requires_extras(extras: Union[str, List]):\n return _requires(\n extras, lambda extras: _EXTRAS_AVAILABLE[extras], lambda extras: f\"'lightning-flash[{','.join(extras)}]'\"\n )\n\n\ndef example_requires(extras: Union[str, List[str]]):\n return requires_extras(extras)(lambda: None)()\n\n\ndef lazy_import(module_name, callback=None):\n \"\"\"Returns a proxy module object that will lazily import the given module the first time it is used.\n\n Example usage::\n\n # Lazy version of `import tensorflow as tf`\n tf = lazy_import(\"tensorflow\")\n\n # Other commands\n\n # Now the module is loaded\n tf.__version__\n\n Args:\n module_name: the fully-qualified module name to import\n callback (None): a callback function to call before importing the\n module\n\n Returns:\n a proxy module object that will be lazily imported when first used\n \"\"\"\n return LazyModule(module_name, callback=callback)\n\n\nclass LazyModule(types.ModuleType):\n \"\"\"Proxy module that lazily imports the underlying module the first time it is actually used.\n\n Args:\n module_name: the fully-qualified module name to import\n callback (None): a callback function to call before importing the\n module\n \"\"\"\n\n def __init__(self, module_name, callback=None):\n super().__init__(module_name)\n self._module = None\n self._callback = callback\n\n def __getattr__(self, item):\n if self._module is None:\n self._import_module()\n\n return getattr(self._module, item)\n\n def __dir__(self):\n if self._module is None:\n self._import_module()\n\n return dir(self._module)\n\n def _import_module(self):\n # Execute callback, if any\n if self._callback is not None:\n self._callback()\n\n # Actually import the module\n module = importlib.import_module(self.__name__)\n self._module = module\n\n # Update this object's dict so that attribute references are efficient\n # (__getattr__ is only called on lookups that fail)\n self.__dict__.update(module.__dict__)\n",
"path": "flash/core/utilities/imports.py"
}
] | [
{
"content": "# Copyright The PyTorch Lightning team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport functools\nimport importlib\nimport operator\nimport types\nfrom importlib.util import find_spec\nfrom typing import Callable, List, Union\nfrom warnings import warn\n\nfrom pkg_resources import DistributionNotFound\n\ntry:\n from packaging.version import Version\nexcept (ModuleNotFoundError, DistributionNotFound):\n Version = None\n\n\ndef _module_available(module_path: str) -> bool:\n \"\"\"Check if a path is available in your environment.\n\n >>> _module_available('os')\n True\n >>> _module_available('bla.bla')\n False\n \"\"\"\n try:\n return find_spec(module_path) is not None\n except AttributeError:\n # Python 3.6\n return False\n except ModuleNotFoundError:\n # Python 3.7+\n return False\n except ValueError:\n # Sometimes __spec__ can be None and gives a ValueError\n return True\n\n\ndef _compare_version(package: str, op, version) -> bool:\n \"\"\"Compare package version with some requirements.\n\n >>> _compare_version(\"torch\", operator.ge, \"0.1\")\n True\n \"\"\"\n try:\n pkg = importlib.import_module(package)\n except (ModuleNotFoundError, DistributionNotFound, ValueError):\n return False\n try:\n pkg_version = Version(pkg.__version__)\n except TypeError:\n # this is mock by sphinx, so it shall return True to generate all summaries\n return True\n return op(pkg_version, Version(version))\n\n\n_TORCH_AVAILABLE = _module_available(\"torch\")\n_BOLTS_AVAILABLE = _module_available(\"pl_bolts\") and _compare_version(\"torch\", operator.lt, \"1.9.0\")\n_PANDAS_AVAILABLE = _module_available(\"pandas\")\n_SKLEARN_AVAILABLE = _module_available(\"sklearn\")\n_TABNET_AVAILABLE = _module_available(\"pytorch_tabnet\")\n_KORNIA_AVAILABLE = _module_available(\"kornia\")\n_COCO_AVAILABLE = _module_available(\"pycocotools\")\n_TIMM_AVAILABLE = _module_available(\"timm\")\n_TORCHVISION_AVAILABLE = _module_available(\"torchvision\")\n_PYTORCHVIDEO_AVAILABLE = _module_available(\"pytorchvideo\")\n_MATPLOTLIB_AVAILABLE = _module_available(\"matplotlib\")\n_TRANSFORMERS_AVAILABLE = _module_available(\"transformers\")\n_PYSTICHE_AVAILABLE = _module_available(\"pystiche\")\n_FIFTYONE_AVAILABLE = _module_available(\"fiftyone\")\n_FASTAPI_AVAILABLE = _module_available(\"fastapi\")\n_PYDANTIC_AVAILABLE = _module_available(\"pydantic\")\n_GRAPHVIZ_AVAILABLE = _module_available(\"graphviz\")\n_CYTOOLZ_AVAILABLE = _module_available(\"cytoolz\")\n_UVICORN_AVAILABLE = _module_available(\"uvicorn\")\n_PIL_AVAILABLE = _module_available(\"PIL\")\n_OPEN3D_AVAILABLE = _module_available(\"open3d\")\n_SEGMENTATION_MODELS_AVAILABLE = _module_available(\"segmentation_models_pytorch\")\n_SOUNDFILE_AVAILABLE = _module_available(\"soundfile\")\n_TORCH_SCATTER_AVAILABLE = _module_available(\"torch_scatter\")\n_TORCH_SPARSE_AVAILABLE = _module_available(\"torch_sparse\")\n_TORCH_GEOMETRIC_AVAILABLE = _module_available(\"torch_geometric\")\n_TORCHAUDIO_AVAILABLE = _module_available(\"torchaudio\")\n_ROUGE_SCORE_AVAILABLE = _module_available(\"rouge_score\")\n_SENTENCEPIECE_AVAILABLE = _module_available(\"sentencepiece\")\n_DATASETS_AVAILABLE = _module_available(\"datasets\")\n_ICEVISION_AVAILABLE = _module_available(\"icevision\")\n_ICEDATA_AVAILABLE = _module_available(\"icedata\")\n_TORCH_ORT_AVAILABLE = _module_available(\"torch_ort\")\n_VISSL_AVAILABLE = _module_available(\"vissl\") and _module_available(\"classy_vision\")\n\nif _PIL_AVAILABLE:\n from PIL import Image\nelse:\n\n class MetaImage(type):\n def __init__(cls, name, bases, dct):\n super().__init__(name, bases, dct)\n\n cls._Image = None\n\n @property\n def Image(cls):\n warn(\"Mock object called due to missing PIL library. Please use \\\"pip install 'lightning-flash[image]'\\\".\")\n return cls._Image\n\n class Image(metaclass=MetaImage):\n pass\n\n\nif Version:\n _TORCHVISION_GREATER_EQUAL_0_9 = _compare_version(\"torchvision\", operator.ge, \"0.9.0\")\n _PL_GREATER_EQUAL_1_4_3 = _compare_version(\"pytorch_lightning\", operator.ge, \"1.4.3\")\n\n_TEXT_AVAILABLE = all(\n [\n _TRANSFORMERS_AVAILABLE,\n _ROUGE_SCORE_AVAILABLE,\n _SENTENCEPIECE_AVAILABLE,\n _DATASETS_AVAILABLE,\n ]\n)\n_TABULAR_AVAILABLE = _TABNET_AVAILABLE and _PANDAS_AVAILABLE\n_VIDEO_AVAILABLE = _TORCHVISION_AVAILABLE and _PIL_AVAILABLE and _PYTORCHVIDEO_AVAILABLE and _KORNIA_AVAILABLE\n_IMAGE_AVAILABLE = all(\n [\n _TORCHVISION_AVAILABLE,\n _TIMM_AVAILABLE,\n _PIL_AVAILABLE,\n _KORNIA_AVAILABLE,\n _PYSTICHE_AVAILABLE,\n _SEGMENTATION_MODELS_AVAILABLE,\n _ICEVISION_AVAILABLE,\n _ICEDATA_AVAILABLE,\n ]\n)\n_SERVE_AVAILABLE = _FASTAPI_AVAILABLE and _PYDANTIC_AVAILABLE and _CYTOOLZ_AVAILABLE and _UVICORN_AVAILABLE\n_POINTCLOUD_AVAILABLE = _OPEN3D_AVAILABLE and _TORCHVISION_AVAILABLE\n_AUDIO_AVAILABLE = all([_TORCHAUDIO_AVAILABLE, _SOUNDFILE_AVAILABLE, _TRANSFORMERS_AVAILABLE])\n_GRAPH_AVAILABLE = _TORCH_SCATTER_AVAILABLE and _TORCH_SPARSE_AVAILABLE and _TORCH_GEOMETRIC_AVAILABLE\n\n_EXTRAS_AVAILABLE = {\n \"image\": _IMAGE_AVAILABLE,\n \"tabular\": _TABULAR_AVAILABLE,\n \"text\": _TEXT_AVAILABLE,\n \"video\": _VIDEO_AVAILABLE,\n \"pointcloud\": _POINTCLOUD_AVAILABLE,\n \"serve\": _SERVE_AVAILABLE,\n \"audio\": _AUDIO_AVAILABLE,\n \"graph\": _GRAPH_AVAILABLE,\n}\n\n\ndef _requires(\n module_paths: Union[str, List],\n module_available: Callable[[str], bool],\n formatter: Callable[[List[str]], str],\n):\n\n if not isinstance(module_paths, list):\n module_paths = [module_paths]\n\n def decorator(func):\n if not all(module_available(module_path) for module_path in module_paths):\n\n @functools.wraps(func)\n def wrapper(*args, **kwargs):\n raise ModuleNotFoundError(\n f\"Required dependencies not available. Please run: pip install {formatter(module_paths)}\"\n )\n\n return wrapper\n else:\n return func\n\n return decorator\n\n\ndef requires(module_paths: Union[str, List]):\n return _requires(module_paths, _module_available, lambda module_paths: \" \".join(module_paths))\n\n\ndef requires_extras(extras: Union[str, List]):\n return _requires(\n extras, lambda extras: _EXTRAS_AVAILABLE[extras], lambda extras: f\"'lightning-flash[{','.join(extras)}]'\"\n )\n\n\ndef example_requires(extras: Union[str, List[str]]):\n return requires_extras(extras)(lambda: None)()\n\n\ndef lazy_import(module_name, callback=None):\n \"\"\"Returns a proxy module object that will lazily import the given module the first time it is used.\n\n Example usage::\n\n # Lazy version of `import tensorflow as tf`\n tf = lazy_import(\"tensorflow\")\n\n # Other commands\n\n # Now the module is loaded\n tf.__version__\n\n Args:\n module_name: the fully-qualified module name to import\n callback (None): a callback function to call before importing the\n module\n\n Returns:\n a proxy module object that will be lazily imported when first used\n \"\"\"\n return LazyModule(module_name, callback=callback)\n\n\nclass LazyModule(types.ModuleType):\n \"\"\"Proxy module that lazily imports the underlying module the first time it is actually used.\n\n Args:\n module_name: the fully-qualified module name to import\n callback (None): a callback function to call before importing the\n module\n \"\"\"\n\n def __init__(self, module_name, callback=None):\n super().__init__(module_name)\n self._module = None\n self._callback = callback\n\n def __getattr__(self, item):\n if self._module is None:\n self._import_module()\n\n return getattr(self._module, item)\n\n def __dir__(self):\n if self._module is None:\n self._import_module()\n\n return dir(self._module)\n\n def _import_module(self):\n # Execute callback, if any\n if self._callback is not None:\n self._callback()\n\n # Actually import the module\n module = importlib.import_module(self.__name__)\n self._module = module\n\n # Update this object's dict so that attribute references are efficient\n # (__getattr__ is only called on lookups that fail)\n self.__dict__.update(module.__dict__)\n",
"path": "flash/core/utilities/imports.py"
}
] | diff --git a/flash/core/utilities/imports.py b/flash/core/utilities/imports.py
index 621ea5bb2b..fb866a5f84 100644
--- a/flash/core/utilities/imports.py
+++ b/flash/core/utilities/imports.py
@@ -133,7 +133,7 @@ class Image(metaclass=MetaImage):
]
)
_TABULAR_AVAILABLE = _TABNET_AVAILABLE and _PANDAS_AVAILABLE
-_VIDEO_AVAILABLE = _PYTORCHVIDEO_AVAILABLE
+_VIDEO_AVAILABLE = _TORCHVISION_AVAILABLE and _PIL_AVAILABLE and _PYTORCHVIDEO_AVAILABLE and _KORNIA_AVAILABLE
_IMAGE_AVAILABLE = all(
[
_TORCHVISION_AVAILABLE,
|
huggingface__transformers-11945 | wandb integration gags during hyperparameter search
## Environment info
- transformers version: 4.6.1
- Platform: Linux-4.19.0-16-cloud-amd64-x86_64-with-glibc2.10
- Python version: 3.8.10
- PyTorch version (GPU?): 1.8.1+cu111 (True)
- Tensorflow version (GPU?): not installed (NA)
- Using GPU in script?: yes
- Using distributed or parallel set-up in script?: no
wandb version is 0.10.26, but I don't think it matters.
### Who can help
Maybe @sgugger since this is Trainer-related; I don't know who did the wandb integration specifically.
## Information
Model I am using: custom Pytorch model.
The problem arises when using:
* [ ] the official example scripts: (probably, haven't tried)
* [x] my own modified scripts: custom training script using the Trainer
The tasks I am working on is:
* [ ] an official GLUE/SQUaD task:
* [x] my own task or dataset: custom MLM training
## To reproduce
Steps to reproduce the behavior:
1. Train a model using the Trainer with the wandb logging integration and run a hyperparameter search using Optuna (also maybe Ray, but I haven't tried with Ray)
2. After the first run, you'll get an exception like below when wandb tries to log. The issue is that the previous run has finished but a new one hasn't been started.
```
..... (first trial runs fine; logs to wandb and finishes)
wandb: Synced /home/josh/runs/hps_test: https://wandb.ai/mindful/projectname/runs/2vojg06h
5%|▌ | 1/19 [00:03<01:02, 3.47s/it][W 2021-05-30 07:41:43,979] Trial 1 failed because of the following error: Error('You must call wandb.init() before wandb.log()')
Traceback (most recent call last):
File "/home/josh/anaconda3/envs/project/lib/python3.8/site-packages/optuna/_optimize.py", line 217, in _run_trial
value_or_values = func(trial)
File "/home/josh/anaconda3/envs/project/lib/python3.8/site-packages/transformers/integrations.py", line 138, in _objective
trainer.train(resume_from_checkpoint=checkpoint, trial=trial)
File "/home/josh/anaconda3/envs/project/lib/python3.8/site-packages/transformers/trainer.py", line 1332, in train
self._maybe_log_save_evaluate(tr_loss, model, trial, epoch)
File "/home/josh/anaconda3/envs/project/lib/python3.8/site-packages/transformers/trainer.py", line 1405, in _maybe_log_save_evaluate
self.log(logs)
File "/home/josh/anaconda3/envs/project/lib/python3.8/site-packages/transformers/trainer.py", line 1692, in log
self.control = self.callback_handler.on_log(self.args, self.state, self.control, logs)
File "/home/josh/anaconda3/envs/project/lib/python3.8/site-packages/transformers/trainer_callback.py", line 371, in on_log
return self.call_event("on_log", args, state, control, logs=logs)
File "/home/josh/anaconda3/envs/project/lib/python3.8/site-packages/transformers/trainer_callback.py", line 378, in call_event
result = getattr(callback, event)(
File "/home/josh/anaconda3/envs/project/lib/python3.8/site-packages/transformers/integrations.py", line 754, in on_log
self._wandb.log({**logs, "train/global_step": state.global_step})
File "/home/josh/anaconda3/envs/project/lib/python3.8/site-packages/wandb/sdk/lib/preinit.py", line 38, in preinit_wrapper
raise wandb.Error("You must call wandb.init() before {}()".format(name))
wandb.errors.Error: You must call wandb.init() before wandb.log()
wandb: ERROR You must call wandb.init() before wandb.log()
```
## Expected behavior
wandb should just reinitialize per training run so that each run is logged separately.
Note that as far as I can tell this is a one-line fix (set `_initialized` to `False` in `WandbCallback.on_train_begin` when running an hyperparameter search) so I'll open a PR with that. I just figured there should be an issue as well for clarity.
| [
{
"content": "# Copyright 2020 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"\nIntegrations with other Python libraries.\n\"\"\"\nimport importlib.util\nimport io\nimport json\nimport numbers\nimport os\nimport tempfile\nimport weakref\nfrom copy import deepcopy\nfrom pathlib import Path\n\nfrom .dependency_versions_check import dep_version_check\nfrom .utils import logging\n\n\nlogger = logging.get_logger(__name__)\n\n\n# comet_ml requires to be imported before any ML frameworks\n_has_comet = importlib.util.find_spec(\"comet_ml\") is not None and os.getenv(\"COMET_MODE\", \"\").upper() != \"DISABLED\"\nif _has_comet:\n try:\n import comet_ml # noqa: F401\n\n if hasattr(comet_ml, \"config\") and comet_ml.config.get_config(\"comet.api_key\"):\n _has_comet = True\n else:\n if os.getenv(\"COMET_MODE\", \"\").upper() != \"DISABLED\":\n logger.warning(\"comet_ml is installed but `COMET_API_KEY` is not set.\")\n _has_comet = False\n except (ImportError, ValueError):\n _has_comet = False\n\nfrom .file_utils import ENV_VARS_TRUE_VALUES, is_torch_tpu_available # noqa: E402\nfrom .trainer_callback import TrainerCallback # noqa: E402\nfrom .trainer_utils import PREFIX_CHECKPOINT_DIR, BestRun, IntervalStrategy # noqa: E402\n\n\n# Integration functions:\ndef is_wandb_available():\n # any value of WANDB_DISABLED disables wandb\n if os.getenv(\"WANDB_DISABLED\", \"\").upper() in ENV_VARS_TRUE_VALUES:\n logger.warning(\n \"Using the `WAND_DISABLED` environment variable is deprecated and will be removed in v5. Use the \"\n \"--report_to flag to control the integrations used for logging result (for instance --report_to none).\"\n )\n return False\n return importlib.util.find_spec(\"wandb\") is not None\n\n\ndef is_comet_available():\n return _has_comet\n\n\ndef is_tensorboard_available():\n return importlib.util.find_spec(\"tensorboard\") is not None or importlib.util.find_spec(\"tensorboardX\") is not None\n\n\ndef is_optuna_available():\n return importlib.util.find_spec(\"optuna\") is not None\n\n\ndef is_ray_available():\n return importlib.util.find_spec(\"ray\") is not None\n\n\ndef is_ray_tune_available():\n if not is_ray_available():\n return False\n return importlib.util.find_spec(\"ray.tune\") is not None\n\n\ndef is_azureml_available():\n if importlib.util.find_spec(\"azureml\") is None:\n return False\n if importlib.util.find_spec(\"azureml.core\") is None:\n return False\n return importlib.util.find_spec(\"azureml.core.run\") is not None\n\n\ndef is_mlflow_available():\n return importlib.util.find_spec(\"mlflow\") is not None\n\n\ndef is_fairscale_available():\n return importlib.util.find_spec(\"fairscale\") is not None\n\n\ndef is_deepspeed_available():\n return importlib.util.find_spec(\"deepspeed\") is not None\n\n\ndef hp_params(trial):\n if is_optuna_available():\n import optuna\n\n if isinstance(trial, optuna.Trial):\n return trial.params\n if is_ray_tune_available():\n if isinstance(trial, dict):\n return trial\n\n raise RuntimeError(f\"Unknown type for trial {trial.__class__}\")\n\n\ndef default_hp_search_backend():\n if is_optuna_available():\n return \"optuna\"\n elif is_ray_tune_available():\n return \"ray\"\n\n\ndef run_hp_search_optuna(trainer, n_trials: int, direction: str, **kwargs) -> BestRun:\n import optuna\n\n def _objective(trial, checkpoint_dir=None):\n checkpoint = None\n if checkpoint_dir:\n for subdir in os.listdir(checkpoint_dir):\n if subdir.startswith(PREFIX_CHECKPOINT_DIR):\n checkpoint = os.path.join(checkpoint_dir, subdir)\n trainer.objective = None\n trainer.train(resume_from_checkpoint=checkpoint, trial=trial)\n # If there hasn't been any evaluation during the training loop.\n if getattr(trainer, \"objective\", None) is None:\n metrics = trainer.evaluate()\n trainer.objective = trainer.compute_objective(metrics)\n return trainer.objective\n\n timeout = kwargs.pop(\"timeout\", None)\n n_jobs = kwargs.pop(\"n_jobs\", 1)\n study = optuna.create_study(direction=direction, **kwargs)\n study.optimize(_objective, n_trials=n_trials, timeout=timeout, n_jobs=n_jobs)\n best_trial = study.best_trial\n return BestRun(str(best_trial.number), best_trial.value, best_trial.params)\n\n\ndef run_hp_search_ray(trainer, n_trials: int, direction: str, **kwargs) -> BestRun:\n import ray\n\n def _objective(trial, local_trainer, checkpoint_dir=None):\n checkpoint = None\n if checkpoint_dir:\n for subdir in os.listdir(checkpoint_dir):\n if subdir.startswith(PREFIX_CHECKPOINT_DIR):\n checkpoint = os.path.join(checkpoint_dir, subdir)\n local_trainer.objective = None\n local_trainer.train(resume_from_checkpoint=checkpoint, trial=trial)\n # If there hasn't been any evaluation during the training loop.\n if getattr(local_trainer, \"objective\", None) is None:\n metrics = local_trainer.evaluate()\n local_trainer.objective = local_trainer.compute_objective(metrics)\n local_trainer._tune_save_checkpoint()\n ray.tune.report(objective=local_trainer.objective, **metrics, done=True)\n\n # The model and TensorBoard writer do not pickle so we have to remove them (if they exists)\n # while doing the ray hp search.\n\n _tb_writer = trainer.pop_callback(TensorBoardCallback)\n trainer.model = None\n # Setup default `resources_per_trial`.\n if \"resources_per_trial\" not in kwargs:\n # Default to 1 CPU and 1 GPU (if applicable) per trial.\n kwargs[\"resources_per_trial\"] = {\"cpu\": 1}\n if trainer.args.n_gpu > 0:\n kwargs[\"resources_per_trial\"][\"gpu\"] = 1\n resource_msg = \"1 CPU\" + (\" and 1 GPU\" if trainer.args.n_gpu > 0 else \"\")\n logger.info(\n \"No `resources_per_trial` arg was passed into \"\n \"`hyperparameter_search`. Setting it to a default value \"\n f\"of {resource_msg} for each trial.\"\n )\n # Make sure each trainer only uses GPUs that were allocated per trial.\n gpus_per_trial = kwargs[\"resources_per_trial\"].get(\"gpu\", 0)\n trainer.args._n_gpu = gpus_per_trial\n\n # Setup default `progress_reporter`.\n if \"progress_reporter\" not in kwargs:\n from ray.tune import CLIReporter\n\n kwargs[\"progress_reporter\"] = CLIReporter(metric_columns=[\"objective\"])\n if \"keep_checkpoints_num\" in kwargs and kwargs[\"keep_checkpoints_num\"] > 0:\n # `keep_checkpoints_num=0` would disabled checkpointing\n trainer.use_tune_checkpoints = True\n if kwargs[\"keep_checkpoints_num\"] > 1:\n logger.warning(\n f\"Currently keeping {kwargs['keep_checkpoint_num']} checkpoints for each trial. \"\n \"Checkpoints are usually huge, \"\n \"consider setting `keep_checkpoints_num=1`.\"\n )\n if \"scheduler\" in kwargs:\n from ray.tune.schedulers import ASHAScheduler, HyperBandForBOHB, MedianStoppingRule, PopulationBasedTraining\n\n # Check if checkpointing is enabled for PopulationBasedTraining\n if isinstance(kwargs[\"scheduler\"], PopulationBasedTraining):\n if not trainer.use_tune_checkpoints:\n logger.warning(\n \"You are using PopulationBasedTraining but you haven't enabled checkpointing. \"\n \"This means your trials will train from scratch everytime they are exploiting \"\n \"new configurations. Consider enabling checkpointing by passing \"\n \"`keep_checkpoints_num=1` as an additional argument to `Trainer.hyperparameter_search`.\"\n )\n\n # Check for `do_eval` and `eval_during_training` for schedulers that require intermediate reporting.\n if isinstance(\n kwargs[\"scheduler\"], (ASHAScheduler, MedianStoppingRule, HyperBandForBOHB, PopulationBasedTraining)\n ) and (not trainer.args.do_eval or trainer.args.evaluation_strategy == IntervalStrategy.NO):\n raise RuntimeError(\n \"You are using {cls} as a scheduler but you haven't enabled evaluation during training. \"\n \"This means your trials will not report intermediate results to Ray Tune, and \"\n \"can thus not be stopped early or used to exploit other trials parameters. \"\n \"If this is what you want, do not use {cls}. If you would like to use {cls}, \"\n \"make sure you pass `do_eval=True` and `evaluation_strategy='steps'` in the \"\n \"Trainer `args`.\".format(cls=type(kwargs[\"scheduler\"]).__name__)\n )\n\n analysis = ray.tune.run(\n ray.tune.with_parameters(_objective, local_trainer=trainer),\n config=trainer.hp_space(None),\n num_samples=n_trials,\n **kwargs,\n )\n best_trial = analysis.get_best_trial(metric=\"objective\", mode=direction[:3])\n best_run = BestRun(best_trial.trial_id, best_trial.last_result[\"objective\"], best_trial.config)\n if _tb_writer is not None:\n trainer.add_callback(_tb_writer)\n return best_run\n\n\ndef get_available_reporting_integrations():\n integrations = []\n if is_azureml_available():\n integrations.append(\"azure_ml\")\n if is_comet_available():\n integrations.append(\"comet_ml\")\n if is_mlflow_available():\n integrations.append(\"mlflow\")\n if is_tensorboard_available():\n integrations.append(\"tensorboard\")\n if is_wandb_available():\n integrations.append(\"wandb\")\n return integrations\n\n\ndef rewrite_logs(d):\n new_d = {}\n eval_prefix = \"eval_\"\n eval_prefix_len = len(eval_prefix)\n for k, v in d.items():\n if k.startswith(eval_prefix):\n new_d[\"eval/\" + k[eval_prefix_len:]] = v\n else:\n new_d[\"train/\" + k] = v\n return new_d\n\n\ndef _is_true(config, key):\n if config is None:\n return False\n return bool(config.get(key))\n\n\ndef _set_if_auto(config, key, val):\n if config is None:\n return\n if config.get(key) == \"auto\":\n config[key] = val\n\n\nclass DeepSpeedConfigHF:\n \"\"\"\n This object contains Deepspeed configuration and can be quickly queried for things like zero stage.\n\n We store a ``weakref`` of this object in the module's global to be able to access the config from areas where the\n Trainer is not available (e.g. `from_pretrained` and `_get_resized_embeddings`).\n\n The ``DeepSpeedConfigHF`` object is meant to be created during ``TrainingArguments`` object creation and has the\n same lifespan as the latter.\n \"\"\"\n\n def __init__(self, args):\n self.config = None\n self.stage = 0\n self.offload = False\n\n dep_version_check(\"deepspeed\")\n\n self.config_process(args)\n\n # set global weakref object\n deepspeed_config_hf_set(self)\n\n def is_zero2(self):\n return self.stage == 2\n\n def is_zero3(self):\n return self.stage == 3\n\n def is_offload(self):\n return self.offload\n\n def config_process(self, args):\n \"\"\"\n 1. load json if the ``args.deepspeed`` is a path\n 2. replace any ``auto`` values in the config with the correct or recommended value\n\n This is done as early as possible, before model is created, to allow ``is_deepspeed_zero3_enabled`` query and\n getting to the early deepspeed config object during ``zero.Init()`` which needs whether fp16 is enabled, dtype,\n etc.\n\n \"\"\"\n config_file_or_dict = args.deepspeed\n if isinstance(config_file_or_dict, dict):\n # Don't modify user's data should they want to reuse it (e.g. in tests), because once we\n # modified it, it will not be accepted here again, since `auto` values would have been overriden\n config = deepcopy(config_file_or_dict)\n elif isinstance(config_file_or_dict, str):\n with io.open(config_file_or_dict, \"r\", encoding=\"utf-8\") as f:\n config = json.load(f)\n else:\n raise ValueError(\"expecting either a path to a config file or a pre-populated dict\")\n\n self.config = config\n\n # DeepSpeed does:\n # train_batch_size = world_size * train_micro_batch_size_per_gpu * gradient_accumulation_steps\n train_batch_size = args.world_size * args.per_device_train_batch_size * args.gradient_accumulation_steps\n _set_if_auto(config, \"train_micro_batch_size_per_gpu\", args.per_device_train_batch_size)\n _set_if_auto(config, \"gradient_accumulation_steps\", args.gradient_accumulation_steps)\n _set_if_auto(config, \"train_batch_size\", train_batch_size)\n _set_if_auto(config, \"gradient_clipping\", args.max_grad_norm)\n\n # zero\n config_zero = config.get(\"zero_optimization\", {})\n self.stage = config_zero.get(\"stage\", 0)\n\n config_optim = config.get(\"optimizer\", {})\n if config_optim != {}:\n config_optim_params = config_optim.get(\"params\")\n _set_if_auto(config_optim_params, \"lr\", args.learning_rate)\n _set_if_auto(config_optim_params, \"betas\", [args.adam_beta1, args.adam_beta2])\n _set_if_auto(config_optim_params, \"eps\", args.adam_epsilon)\n _set_if_auto(config_optim_params, \"weight_decay\", args.weight_decay)\n\n config_sched = config.get(\"scheduler\", {})\n if config_sched != {}:\n config_sched_params = config_sched.get(\"params\")\n _set_if_auto(config_sched_params, \"warmup_min_lr\", 0)\n _set_if_auto(config_sched_params, \"warmup_max_lr\", args.learning_rate)\n _set_if_auto(config_sched_params, \"warmup_num_steps\", args.warmup_steps)\n # total_num_steps - will get set in deepspeed_init\n\n # fp16\n if args.fp16:\n fp16_backend = \"apex\" if args.fp16_backend == \"apex\" else \"amp\"\n else:\n fp16_backend = None\n\n # amp: similar to the pytorch native amp - it has a bunch of optional params but we won't set\n # any here unless the user did the work\n config_fp16 = config.get(\"fp16\")\n _set_if_auto(config_fp16, \"enabled\", fp16_backend == \"amp\")\n\n # apex: delegates amp work to apex (which needs to be available), but it cannot be used with any\n # ZeRO features, so probably best to be avoided.\n config_amp = config.get(\"amp\")\n _set_if_auto(config_amp, \"enabled\", fp16_backend == \"apex\")\n _set_if_auto(config_amp, \"opt_level\", args.fp16_opt_level)\n\n config_zero = config.get(\"zero_optimization\", {})\n if self.is_zero2():\n self.offload = _is_true(config_zero, \"cpu_offload\")\n elif self.is_zero3():\n offload_devices = [\"cpu\", \"nvme\"]\n if config_zero.get(\"offload_optimizer\", {}).get(\"device\") in offload_devices:\n self.offload = True\n if config_zero.get(\"offload_param\", {}).get(\"device\") in offload_devices:\n self.offload = True\n\n def config_finalize(self, args, model, num_training_steps):\n \"\"\"\n This stage is run after we have the model and know num_training_steps.\n\n Now we we can complete the configuration process.\n\n \"\"\"\n config = self.config\n\n # zero\n config_zero = config.get(\"zero_optimization\", {})\n if self.is_zero3():\n # automatically assign the optimal config values based on model config\n hidden_size = model.config.hidden_size\n _set_if_auto(config_zero, \"reduce_bucket_size\", hidden_size * hidden_size)\n _set_if_auto(config_zero, \"stage3_prefetch_bucket_size\", 0.9 * hidden_size * hidden_size)\n _set_if_auto(config_zero, \"stage3_param_persistence_threshold\", 10 * hidden_size)\n\n # scheduler\n config_sched = config.get(\"scheduler\", {})\n config_sched_params = config_sched.get(\"params\", {})\n _set_if_auto(config_sched_params, \"total_num_steps\", num_training_steps)\n\n\n# keep the config object global to be able to access it anywhere during TrainingArguments life-cycle\n_deepspeed_config_hf_weak_ref = None\n\n\ndef deepspeed_config_hf_set(deepspeed_config_hf_obj):\n # this is a special weakref global object to allow us to get to Deepspeed config from APIs\n # that don't have an easy way to get to the Deepspeed config outside of the Trainer domain.\n global _deepspeed_config_hf_weak_ref\n # will go away automatically when DeepSpeedConfigHF is destroyed (when TrainingArguments is destroyed)\n _deepspeed_config_hf_weak_ref = weakref.ref(deepspeed_config_hf_obj)\n\n\ndef is_deepspeed_zero3_enabled():\n if _deepspeed_config_hf_weak_ref is not None and _deepspeed_config_hf_weak_ref() is not None:\n return _deepspeed_config_hf_weak_ref().is_zero3()\n else:\n return False\n\n\ndef deepspeed_config():\n if _deepspeed_config_hf_weak_ref is not None and _deepspeed_config_hf_weak_ref() is not None:\n return _deepspeed_config_hf_weak_ref().config\n else:\n return None\n\n\ndef deepspeed_init(trainer, num_training_steps, resume_from_checkpoint=None):\n \"\"\"\n Init DeepSpeed, after updating the DeepSpeed configuration with any relevant Trainer's args.\n\n If ``resume_from_checkpoint`` was passed then an attempt to resume from a previously saved checkpoint will be made.\n\n Args:\n trainer: Trainer object\n num_training_steps: per single gpu\n resume_from_checkpoint: path to a checkpoint if to resume from after normal DeepSpeedEngine load\n\n Returns: model, optimizer, lr_scheduler\n\n \"\"\"\n import deepspeed\n\n model = trainer.model\n\n deepspeed_config_hf = trainer.args.deepspeed_config_hf\n deepspeed_config_hf.config_finalize(trainer.args, model, num_training_steps)\n\n # resume config update - some bits like `model` and `num_training_steps` only become available during train\n config = deepspeed_config_hf.config\n\n # Optimizer + Scheduler\n # Currently supported combos:\n # 1. DS scheduler + DS optimizer: Yes\n # 2. HF scheduler + HF optimizer: Yes\n # 3. DS scheduler + HF optimizer: Yes\n # 4. HF scheduler + DS optimizer: No\n #\n # Unless Offload is enabled in which case it's:\n # 1. DS scheduler + DS optimizer: Yes\n # 2. HF scheduler + HF optimizer: No\n # 3. DS scheduler + HF optimizer: No\n # 4. HF scheduler + DS optimizer: No\n\n optimizer = None\n if \"optimizer\" not in config:\n if deepspeed_config_hf.is_offload():\n raise ValueError(\"ZeRO Offload can only work with DeepSpeed optimizers\")\n\n # ds supports Adam, OneBitAdam, and Lamb optimizers and can import other optimizers from torch.\n # But trainer uses AdamW by default.\n trainer.create_optimizer()\n optimizer = trainer.optimizer\n # To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`\n config[\"zero_allow_untested_optimizer\"] = True\n\n # DS schedulers (deepspeed/runtime/lr_schedules.py):\n #\n # DS name | --lr_scheduler_type | HF func | Notes\n # -------------| ---------------------|-----------------------------------|--------------------\n # LRRangeTest | na | na | LRRT\n # OneCycle | na | na | 1CLR\n # WarmupLR | constant_with_warmup | get_constant_schedule_with_warmup | w/ warmup_min_lr=0\n # WarmupDecayLR| linear | get_linear_schedule_with_warmup |\n lr_scheduler = None\n if \"scheduler\" not in config:\n if \"optimizer\" in config:\n # to make this option work, we need to init DS optimizer first, then init HS scheduler,\n # then pass the HS scheduler to DS init, which is not possible at the moment\n raise ValueError(\"At the moment HF scheduler + DeepSpeed optimizer combination is not possible\")\n else:\n trainer.create_scheduler(num_training_steps=num_training_steps)\n lr_scheduler = trainer.lr_scheduler\n\n # keep for quick debug:\n # from pprint import pprint; pprint(config)\n\n model_parameters = filter(lambda p: p.requires_grad, model.parameters())\n\n model, optimizer, _, lr_scheduler = deepspeed.initialize(\n model=model,\n model_parameters=model_parameters,\n config_params=config,\n optimizer=optimizer,\n lr_scheduler=lr_scheduler,\n )\n\n if resume_from_checkpoint is not None:\n\n # it's possible that the user is trying to resume from model_path, which doesn't necessarily\n # contain a deepspeed checkpoint. e.g. examples just check if the dir exists and assume it's\n # a resume from a checkpoint and not just a local pretrained weight. So we check here if the\n # path contains what looks like a deepspeed checkpoint\n import glob\n\n deepspeed_checkpoint_dirs = sorted(glob.glob(f\"{resume_from_checkpoint}/global_step*\"))\n\n if len(deepspeed_checkpoint_dirs) > 0:\n logger.info(f\"Attempting to resume from {resume_from_checkpoint}\")\n # this magically updates self.optimizer and self.lr_scheduler\n load_path, _ = model.load_checkpoint(\n resume_from_checkpoint, load_optimizer_states=True, load_lr_scheduler_states=True\n )\n if load_path is None:\n raise ValueError(f\"[deepspeed] failed to resume from checkpoint {resume_from_checkpoint}\")\n else:\n logger.info(f\"{resume_from_checkpoint} doesn't have deepspeed checkpoints, doing nothing\")\n\n return model, optimizer, lr_scheduler\n\n\nclass TensorBoardCallback(TrainerCallback):\n \"\"\"\n A :class:`~transformers.TrainerCallback` that sends the logs to `TensorBoard\n <https://www.tensorflow.org/tensorboard>`__.\n\n Args:\n tb_writer (:obj:`SummaryWriter`, `optional`):\n The writer to use. Will instantiate one if not set.\n \"\"\"\n\n def __init__(self, tb_writer=None):\n has_tensorboard = is_tensorboard_available()\n assert (\n has_tensorboard\n ), \"TensorBoardCallback requires tensorboard to be installed. Either update your PyTorch version or install tensorboardX.\"\n if has_tensorboard:\n try:\n from torch.utils.tensorboard import SummaryWriter # noqa: F401\n\n self._SummaryWriter = SummaryWriter\n except ImportError:\n try:\n from tensorboardX import SummaryWriter\n\n self._SummaryWriter = SummaryWriter\n except ImportError:\n self._SummaryWriter = None\n else:\n self._SummaryWriter = None\n self.tb_writer = tb_writer\n\n def _init_summary_writer(self, args, log_dir=None):\n log_dir = log_dir or args.logging_dir\n if self._SummaryWriter is not None:\n self.tb_writer = self._SummaryWriter(log_dir=log_dir)\n\n def on_train_begin(self, args, state, control, **kwargs):\n if not state.is_world_process_zero:\n return\n\n log_dir = None\n\n if state.is_hyper_param_search:\n trial_name = state.trial_name\n if trial_name is not None:\n log_dir = os.path.join(args.logging_dir, trial_name)\n\n self._init_summary_writer(args, log_dir)\n\n if self.tb_writer is not None:\n self.tb_writer.add_text(\"args\", args.to_json_string())\n if \"model\" in kwargs:\n model = kwargs[\"model\"]\n if hasattr(model, \"config\") and model.config is not None:\n model_config_json = model.config.to_json_string()\n self.tb_writer.add_text(\"model_config\", model_config_json)\n # Version of TensorBoard coming from tensorboardX does not have this method.\n if hasattr(self.tb_writer, \"add_hparams\"):\n self.tb_writer.add_hparams(args.to_sanitized_dict(), metric_dict={})\n\n def on_log(self, args, state, control, logs=None, **kwargs):\n if not state.is_world_process_zero:\n return\n\n if self.tb_writer is None:\n self._init_summary_writer(args)\n\n if self.tb_writer is not None:\n logs = rewrite_logs(logs)\n for k, v in logs.items():\n if isinstance(v, (int, float)):\n self.tb_writer.add_scalar(k, v, state.global_step)\n else:\n logger.warning(\n \"Trainer is attempting to log a value of \"\n f'\"{v}\" of type {type(v)} for key \"{k}\" as a scalar. '\n \"This invocation of Tensorboard's writer.add_scalar() \"\n \"is incorrect so we dropped this attribute.\"\n )\n self.tb_writer.flush()\n\n def on_train_end(self, args, state, control, **kwargs):\n if self.tb_writer:\n self.tb_writer.close()\n\n\nclass WandbCallback(TrainerCallback):\n \"\"\"\n A :class:`~transformers.TrainerCallback` that sends the logs to `Weight and Biases <https://www.wandb.com/>`__.\n \"\"\"\n\n def __init__(self):\n has_wandb = is_wandb_available()\n assert has_wandb, \"WandbCallback requires wandb to be installed. Run `pip install wandb`.\"\n if has_wandb:\n import wandb\n\n self._wandb = wandb\n self._initialized = False\n # log outputs\n self._log_model = os.getenv(\"WANDB_LOG_MODEL\", \"FALSE\").upper() in ENV_VARS_TRUE_VALUES.union({\"TRUE\"})\n\n def setup(self, args, state, model, **kwargs):\n \"\"\"\n Setup the optional Weights & Biases (`wandb`) integration.\n\n One can subclass and override this method to customize the setup if needed. Find more information `here\n <https://docs.wandb.ai/integrations/huggingface>`__. You can also override the following environment variables:\n\n Environment:\n WANDB_LOG_MODEL (:obj:`bool`, `optional`, defaults to :obj:`False`):\n Whether or not to log model as artifact at the end of training. Use along with\n `TrainingArguments.load_best_model_at_end` to upload best model.\n WANDB_WATCH (:obj:`str`, `optional` defaults to :obj:`\"gradients\"`):\n Can be :obj:`\"gradients\"`, :obj:`\"all\"` or :obj:`\"false\"`. Set to :obj:`\"false\"` to disable gradient\n logging or :obj:`\"all\"` to log gradients and parameters.\n WANDB_PROJECT (:obj:`str`, `optional`, defaults to :obj:`\"huggingface\"`):\n Set this to a custom string to store results in a different project.\n WANDB_DISABLED (:obj:`bool`, `optional`, defaults to :obj:`False`):\n Whether or not to disable wandb entirely. Set `WANDB_DISABLED=true` to disable.\n \"\"\"\n if self._wandb is None:\n return\n self._initialized = True\n if state.is_world_process_zero:\n logger.info(\n 'Automatic Weights & Biases logging enabled, to disable set os.environ[\"WANDB_DISABLED\"] = \"true\"'\n )\n combined_dict = {**args.to_sanitized_dict()}\n\n if hasattr(model, \"config\") and model.config is not None:\n model_config = model.config.to_dict()\n combined_dict = {**model_config, **combined_dict}\n trial_name = state.trial_name\n init_args = {}\n if trial_name is not None:\n run_name = trial_name\n init_args[\"group\"] = args.run_name\n else:\n run_name = args.run_name\n\n if self._wandb.run is None:\n self._wandb.init(\n project=os.getenv(\"WANDB_PROJECT\", \"huggingface\"),\n name=run_name,\n **init_args,\n )\n # add config parameters (run may have been created manually)\n self._wandb.config.update(combined_dict, allow_val_change=True)\n\n # define default x-axis (for latest wandb versions)\n if getattr(self._wandb, \"define_metric\", None):\n self._wandb.define_metric(\"train/global_step\")\n self._wandb.define_metric(\"*\", step_metric=\"train/global_step\", step_sync=True)\n\n # keep track of model topology and gradients, unsupported on TPU\n if not is_torch_tpu_available() and os.getenv(\"WANDB_WATCH\") != \"false\":\n self._wandb.watch(\n model, log=os.getenv(\"WANDB_WATCH\", \"gradients\"), log_freq=max(100, args.logging_steps)\n )\n\n def on_train_begin(self, args, state, control, model=None, **kwargs):\n if self._wandb is None:\n return\n hp_search = state.is_hyper_param_search\n if hp_search:\n self._wandb.finish()\n if not self._initialized:\n self.setup(args, state, model, **kwargs)\n\n def on_train_end(self, args, state, control, model=None, tokenizer=None, **kwargs):\n if self._wandb is None:\n return\n if self._log_model and self._initialized and state.is_world_process_zero:\n from .trainer import Trainer\n\n fake_trainer = Trainer(args=args, model=model, tokenizer=tokenizer)\n with tempfile.TemporaryDirectory() as temp_dir:\n fake_trainer.save_model(temp_dir)\n metadata = (\n {\n k: v\n for k, v in dict(self._wandb.summary).items()\n if isinstance(v, numbers.Number) and not k.startswith(\"_\")\n }\n if not args.load_best_model_at_end\n else {\n f\"eval/{args.metric_for_best_model}\": state.best_metric,\n \"train/total_floss\": state.total_flos,\n }\n )\n artifact = self._wandb.Artifact(name=f\"model-{self._wandb.run.id}\", type=\"model\", metadata=metadata)\n for f in Path(temp_dir).glob(\"*\"):\n if f.is_file():\n with artifact.new_file(f.name, mode=\"wb\") as fa:\n fa.write(f.read_bytes())\n self._wandb.run.log_artifact(artifact)\n\n def on_log(self, args, state, control, model=None, logs=None, **kwargs):\n if self._wandb is None:\n return\n if not self._initialized:\n self.setup(args, state, model)\n if state.is_world_process_zero:\n logs = rewrite_logs(logs)\n self._wandb.log({**logs, \"train/global_step\": state.global_step})\n\n\nclass CometCallback(TrainerCallback):\n \"\"\"\n A :class:`~transformers.TrainerCallback` that sends the logs to `Comet ML <https://www.comet.ml/site/>`__.\n \"\"\"\n\n def __init__(self):\n assert _has_comet, \"CometCallback requires comet-ml to be installed. Run `pip install comet-ml`.\"\n self._initialized = False\n\n def setup(self, args, state, model):\n \"\"\"\n Setup the optional Comet.ml integration.\n\n Environment:\n COMET_MODE (:obj:`str`, `optional`):\n \"OFFLINE\", \"ONLINE\", or \"DISABLED\"\n COMET_PROJECT_NAME (:obj:`str`, `optional`):\n Comet.ml project name for experiments\n COMET_OFFLINE_DIRECTORY (:obj:`str`, `optional`):\n Folder to use for saving offline experiments when :obj:`COMET_MODE` is \"OFFLINE\"\n\n For a number of configurable items in the environment, see `here\n <https://www.comet.ml/docs/python-sdk/advanced/#comet-configuration-variables>`__.\n \"\"\"\n self._initialized = True\n if state.is_world_process_zero:\n comet_mode = os.getenv(\"COMET_MODE\", \"ONLINE\").upper()\n args = {\"project_name\": os.getenv(\"COMET_PROJECT_NAME\", \"huggingface\")}\n experiment = None\n if comet_mode == \"ONLINE\":\n experiment = comet_ml.Experiment(**args)\n logger.info(\"Automatic Comet.ml online logging enabled\")\n elif comet_mode == \"OFFLINE\":\n args[\"offline_directory\"] = os.getenv(\"COMET_OFFLINE_DIRECTORY\", \"./\")\n experiment = comet_ml.OfflineExperiment(**args)\n logger.info(\"Automatic Comet.ml offline logging enabled; use `comet upload` when finished\")\n if experiment is not None:\n experiment._set_model_graph(model, framework=\"transformers\")\n experiment._log_parameters(args, prefix=\"args/\", framework=\"transformers\")\n if hasattr(model, \"config\"):\n experiment._log_parameters(model.config, prefix=\"config/\", framework=\"transformers\")\n\n def on_train_begin(self, args, state, control, model=None, **kwargs):\n if not self._initialized:\n self.setup(args, state, model)\n\n def on_log(self, args, state, control, model=None, logs=None, **kwargs):\n if not self._initialized:\n self.setup(args, state, model)\n if state.is_world_process_zero:\n experiment = comet_ml.config.get_global_experiment()\n if experiment is not None:\n experiment._log_metrics(logs, step=state.global_step, epoch=state.epoch, framework=\"transformers\")\n\n\nclass AzureMLCallback(TrainerCallback):\n \"\"\"\n A :class:`~transformers.TrainerCallback` that sends the logs to `AzureML\n <https://pypi.org/project/azureml-sdk/>`__.\n \"\"\"\n\n def __init__(self, azureml_run=None):\n assert (\n is_azureml_available()\n ), \"AzureMLCallback requires azureml to be installed. Run `pip install azureml-sdk`.\"\n self.azureml_run = azureml_run\n\n def on_init_end(self, args, state, control, **kwargs):\n from azureml.core.run import Run\n\n if self.azureml_run is None and state.is_world_process_zero:\n self.azureml_run = Run.get_context()\n\n def on_log(self, args, state, control, logs=None, **kwargs):\n if self.azureml_run:\n for k, v in logs.items():\n if isinstance(v, (int, float)):\n self.azureml_run.log(k, v, description=k)\n\n\nclass MLflowCallback(TrainerCallback):\n \"\"\"\n A :class:`~transformers.TrainerCallback` that sends the logs to `MLflow <https://www.mlflow.org/>`__.\n \"\"\"\n\n def __init__(self):\n assert is_mlflow_available(), \"MLflowCallback requires mlflow to be installed. Run `pip install mlflow`.\"\n import mlflow\n\n self._MAX_PARAM_VAL_LENGTH = mlflow.utils.validation.MAX_PARAM_VAL_LENGTH\n self._MAX_PARAMS_TAGS_PER_BATCH = mlflow.utils.validation.MAX_PARAMS_TAGS_PER_BATCH\n\n self._initialized = False\n self._log_artifacts = False\n self._ml_flow = mlflow\n\n def setup(self, args, state, model):\n \"\"\"\n Setup the optional MLflow integration.\n\n Environment:\n HF_MLFLOW_LOG_ARTIFACTS (:obj:`str`, `optional`):\n Whether to use MLflow .log_artifact() facility to log artifacts.\n\n This only makes sense if logging to a remote server, e.g. s3 or GCS. If set to `True` or `1`, will copy\n whatever is in :class:`~transformers.TrainingArguments`'s ``output_dir`` to the local or remote\n artifact storage. Using it without a remote storage will just copy the files to your artifact location.\n \"\"\"\n log_artifacts = os.getenv(\"HF_MLFLOW_LOG_ARTIFACTS\", \"FALSE\").upper()\n if log_artifacts in {\"TRUE\", \"1\"}:\n self._log_artifacts = True\n if state.is_world_process_zero:\n self._ml_flow.start_run()\n combined_dict = args.to_dict()\n if hasattr(model, \"config\") and model.config is not None:\n model_config = model.config.to_dict()\n combined_dict = {**model_config, **combined_dict}\n # remove params that are too long for MLflow\n for name, value in list(combined_dict.items()):\n # internally, all values are converted to str in MLflow\n if len(str(value)) > self._MAX_PARAM_VAL_LENGTH:\n logger.warning(\n f\"Trainer is attempting to log a value of \"\n f'\"{value}\" for key \"{name}\" as a parameter. '\n f\"MLflow's log_param() only accepts values no longer than \"\n f\"250 characters so we dropped this attribute.\"\n )\n del combined_dict[name]\n # MLflow cannot log more than 100 values in one go, so we have to split it\n combined_dict_items = list(combined_dict.items())\n for i in range(0, len(combined_dict_items), self._MAX_PARAMS_TAGS_PER_BATCH):\n self._ml_flow.log_params(dict(combined_dict_items[i : i + self._MAX_PARAMS_TAGS_PER_BATCH]))\n self._initialized = True\n\n def on_train_begin(self, args, state, control, model=None, **kwargs):\n if not self._initialized:\n self.setup(args, state, model)\n\n def on_log(self, args, state, control, logs, model=None, **kwargs):\n if not self._initialized:\n self.setup(args, state, model)\n if state.is_world_process_zero:\n for k, v in logs.items():\n if isinstance(v, (int, float)):\n self._ml_flow.log_metric(k, v, step=state.global_step)\n else:\n logger.warning(\n f\"Trainer is attempting to log a value of \"\n f'\"{v}\" of type {type(v)} for key \"{k}\" as a metric. '\n f\"MLflow's log_metric() only accepts float and \"\n f\"int types so we dropped this attribute.\"\n )\n\n def on_train_end(self, args, state, control, **kwargs):\n if self._initialized and state.is_world_process_zero:\n if self._log_artifacts:\n logger.info(\"Logging artifacts. This may take time.\")\n self._ml_flow.log_artifacts(args.output_dir)\n\n def __del__(self):\n # if the previous run is not terminated correctly, the fluent API will\n # not let you start a new run before the previous one is killed\n if self._ml_flow.active_run is not None:\n self._ml_flow.end_run()\n\n\nINTEGRATION_TO_CALLBACK = {\n \"azure_ml\": AzureMLCallback,\n \"comet_ml\": CometCallback,\n \"mlflow\": MLflowCallback,\n \"tensorboard\": TensorBoardCallback,\n \"wandb\": WandbCallback,\n}\n\n\ndef get_reporting_integration_callbacks(report_to):\n for integration in report_to:\n if integration not in INTEGRATION_TO_CALLBACK:\n raise ValueError(\n f\"{integration} is not supported, only {', '.join(INTEGRATION_TO_CALLBACK.keys())} are supported.\"\n )\n return [INTEGRATION_TO_CALLBACK[integration] for integration in report_to]\n",
"path": "src/transformers/integrations.py"
}
] | [
{
"content": "# Copyright 2020 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"\nIntegrations with other Python libraries.\n\"\"\"\nimport importlib.util\nimport io\nimport json\nimport numbers\nimport os\nimport tempfile\nimport weakref\nfrom copy import deepcopy\nfrom pathlib import Path\n\nfrom .dependency_versions_check import dep_version_check\nfrom .utils import logging\n\n\nlogger = logging.get_logger(__name__)\n\n\n# comet_ml requires to be imported before any ML frameworks\n_has_comet = importlib.util.find_spec(\"comet_ml\") is not None and os.getenv(\"COMET_MODE\", \"\").upper() != \"DISABLED\"\nif _has_comet:\n try:\n import comet_ml # noqa: F401\n\n if hasattr(comet_ml, \"config\") and comet_ml.config.get_config(\"comet.api_key\"):\n _has_comet = True\n else:\n if os.getenv(\"COMET_MODE\", \"\").upper() != \"DISABLED\":\n logger.warning(\"comet_ml is installed but `COMET_API_KEY` is not set.\")\n _has_comet = False\n except (ImportError, ValueError):\n _has_comet = False\n\nfrom .file_utils import ENV_VARS_TRUE_VALUES, is_torch_tpu_available # noqa: E402\nfrom .trainer_callback import TrainerCallback # noqa: E402\nfrom .trainer_utils import PREFIX_CHECKPOINT_DIR, BestRun, IntervalStrategy # noqa: E402\n\n\n# Integration functions:\ndef is_wandb_available():\n # any value of WANDB_DISABLED disables wandb\n if os.getenv(\"WANDB_DISABLED\", \"\").upper() in ENV_VARS_TRUE_VALUES:\n logger.warning(\n \"Using the `WAND_DISABLED` environment variable is deprecated and will be removed in v5. Use the \"\n \"--report_to flag to control the integrations used for logging result (for instance --report_to none).\"\n )\n return False\n return importlib.util.find_spec(\"wandb\") is not None\n\n\ndef is_comet_available():\n return _has_comet\n\n\ndef is_tensorboard_available():\n return importlib.util.find_spec(\"tensorboard\") is not None or importlib.util.find_spec(\"tensorboardX\") is not None\n\n\ndef is_optuna_available():\n return importlib.util.find_spec(\"optuna\") is not None\n\n\ndef is_ray_available():\n return importlib.util.find_spec(\"ray\") is not None\n\n\ndef is_ray_tune_available():\n if not is_ray_available():\n return False\n return importlib.util.find_spec(\"ray.tune\") is not None\n\n\ndef is_azureml_available():\n if importlib.util.find_spec(\"azureml\") is None:\n return False\n if importlib.util.find_spec(\"azureml.core\") is None:\n return False\n return importlib.util.find_spec(\"azureml.core.run\") is not None\n\n\ndef is_mlflow_available():\n return importlib.util.find_spec(\"mlflow\") is not None\n\n\ndef is_fairscale_available():\n return importlib.util.find_spec(\"fairscale\") is not None\n\n\ndef is_deepspeed_available():\n return importlib.util.find_spec(\"deepspeed\") is not None\n\n\ndef hp_params(trial):\n if is_optuna_available():\n import optuna\n\n if isinstance(trial, optuna.Trial):\n return trial.params\n if is_ray_tune_available():\n if isinstance(trial, dict):\n return trial\n\n raise RuntimeError(f\"Unknown type for trial {trial.__class__}\")\n\n\ndef default_hp_search_backend():\n if is_optuna_available():\n return \"optuna\"\n elif is_ray_tune_available():\n return \"ray\"\n\n\ndef run_hp_search_optuna(trainer, n_trials: int, direction: str, **kwargs) -> BestRun:\n import optuna\n\n def _objective(trial, checkpoint_dir=None):\n checkpoint = None\n if checkpoint_dir:\n for subdir in os.listdir(checkpoint_dir):\n if subdir.startswith(PREFIX_CHECKPOINT_DIR):\n checkpoint = os.path.join(checkpoint_dir, subdir)\n trainer.objective = None\n trainer.train(resume_from_checkpoint=checkpoint, trial=trial)\n # If there hasn't been any evaluation during the training loop.\n if getattr(trainer, \"objective\", None) is None:\n metrics = trainer.evaluate()\n trainer.objective = trainer.compute_objective(metrics)\n return trainer.objective\n\n timeout = kwargs.pop(\"timeout\", None)\n n_jobs = kwargs.pop(\"n_jobs\", 1)\n study = optuna.create_study(direction=direction, **kwargs)\n study.optimize(_objective, n_trials=n_trials, timeout=timeout, n_jobs=n_jobs)\n best_trial = study.best_trial\n return BestRun(str(best_trial.number), best_trial.value, best_trial.params)\n\n\ndef run_hp_search_ray(trainer, n_trials: int, direction: str, **kwargs) -> BestRun:\n import ray\n\n def _objective(trial, local_trainer, checkpoint_dir=None):\n checkpoint = None\n if checkpoint_dir:\n for subdir in os.listdir(checkpoint_dir):\n if subdir.startswith(PREFIX_CHECKPOINT_DIR):\n checkpoint = os.path.join(checkpoint_dir, subdir)\n local_trainer.objective = None\n local_trainer.train(resume_from_checkpoint=checkpoint, trial=trial)\n # If there hasn't been any evaluation during the training loop.\n if getattr(local_trainer, \"objective\", None) is None:\n metrics = local_trainer.evaluate()\n local_trainer.objective = local_trainer.compute_objective(metrics)\n local_trainer._tune_save_checkpoint()\n ray.tune.report(objective=local_trainer.objective, **metrics, done=True)\n\n # The model and TensorBoard writer do not pickle so we have to remove them (if they exists)\n # while doing the ray hp search.\n\n _tb_writer = trainer.pop_callback(TensorBoardCallback)\n trainer.model = None\n # Setup default `resources_per_trial`.\n if \"resources_per_trial\" not in kwargs:\n # Default to 1 CPU and 1 GPU (if applicable) per trial.\n kwargs[\"resources_per_trial\"] = {\"cpu\": 1}\n if trainer.args.n_gpu > 0:\n kwargs[\"resources_per_trial\"][\"gpu\"] = 1\n resource_msg = \"1 CPU\" + (\" and 1 GPU\" if trainer.args.n_gpu > 0 else \"\")\n logger.info(\n \"No `resources_per_trial` arg was passed into \"\n \"`hyperparameter_search`. Setting it to a default value \"\n f\"of {resource_msg} for each trial.\"\n )\n # Make sure each trainer only uses GPUs that were allocated per trial.\n gpus_per_trial = kwargs[\"resources_per_trial\"].get(\"gpu\", 0)\n trainer.args._n_gpu = gpus_per_trial\n\n # Setup default `progress_reporter`.\n if \"progress_reporter\" not in kwargs:\n from ray.tune import CLIReporter\n\n kwargs[\"progress_reporter\"] = CLIReporter(metric_columns=[\"objective\"])\n if \"keep_checkpoints_num\" in kwargs and kwargs[\"keep_checkpoints_num\"] > 0:\n # `keep_checkpoints_num=0` would disabled checkpointing\n trainer.use_tune_checkpoints = True\n if kwargs[\"keep_checkpoints_num\"] > 1:\n logger.warning(\n f\"Currently keeping {kwargs['keep_checkpoint_num']} checkpoints for each trial. \"\n \"Checkpoints are usually huge, \"\n \"consider setting `keep_checkpoints_num=1`.\"\n )\n if \"scheduler\" in kwargs:\n from ray.tune.schedulers import ASHAScheduler, HyperBandForBOHB, MedianStoppingRule, PopulationBasedTraining\n\n # Check if checkpointing is enabled for PopulationBasedTraining\n if isinstance(kwargs[\"scheduler\"], PopulationBasedTraining):\n if not trainer.use_tune_checkpoints:\n logger.warning(\n \"You are using PopulationBasedTraining but you haven't enabled checkpointing. \"\n \"This means your trials will train from scratch everytime they are exploiting \"\n \"new configurations. Consider enabling checkpointing by passing \"\n \"`keep_checkpoints_num=1` as an additional argument to `Trainer.hyperparameter_search`.\"\n )\n\n # Check for `do_eval` and `eval_during_training` for schedulers that require intermediate reporting.\n if isinstance(\n kwargs[\"scheduler\"], (ASHAScheduler, MedianStoppingRule, HyperBandForBOHB, PopulationBasedTraining)\n ) and (not trainer.args.do_eval or trainer.args.evaluation_strategy == IntervalStrategy.NO):\n raise RuntimeError(\n \"You are using {cls} as a scheduler but you haven't enabled evaluation during training. \"\n \"This means your trials will not report intermediate results to Ray Tune, and \"\n \"can thus not be stopped early or used to exploit other trials parameters. \"\n \"If this is what you want, do not use {cls}. If you would like to use {cls}, \"\n \"make sure you pass `do_eval=True` and `evaluation_strategy='steps'` in the \"\n \"Trainer `args`.\".format(cls=type(kwargs[\"scheduler\"]).__name__)\n )\n\n analysis = ray.tune.run(\n ray.tune.with_parameters(_objective, local_trainer=trainer),\n config=trainer.hp_space(None),\n num_samples=n_trials,\n **kwargs,\n )\n best_trial = analysis.get_best_trial(metric=\"objective\", mode=direction[:3])\n best_run = BestRun(best_trial.trial_id, best_trial.last_result[\"objective\"], best_trial.config)\n if _tb_writer is not None:\n trainer.add_callback(_tb_writer)\n return best_run\n\n\ndef get_available_reporting_integrations():\n integrations = []\n if is_azureml_available():\n integrations.append(\"azure_ml\")\n if is_comet_available():\n integrations.append(\"comet_ml\")\n if is_mlflow_available():\n integrations.append(\"mlflow\")\n if is_tensorboard_available():\n integrations.append(\"tensorboard\")\n if is_wandb_available():\n integrations.append(\"wandb\")\n return integrations\n\n\ndef rewrite_logs(d):\n new_d = {}\n eval_prefix = \"eval_\"\n eval_prefix_len = len(eval_prefix)\n for k, v in d.items():\n if k.startswith(eval_prefix):\n new_d[\"eval/\" + k[eval_prefix_len:]] = v\n else:\n new_d[\"train/\" + k] = v\n return new_d\n\n\ndef _is_true(config, key):\n if config is None:\n return False\n return bool(config.get(key))\n\n\ndef _set_if_auto(config, key, val):\n if config is None:\n return\n if config.get(key) == \"auto\":\n config[key] = val\n\n\nclass DeepSpeedConfigHF:\n \"\"\"\n This object contains Deepspeed configuration and can be quickly queried for things like zero stage.\n\n We store a ``weakref`` of this object in the module's global to be able to access the config from areas where the\n Trainer is not available (e.g. `from_pretrained` and `_get_resized_embeddings`).\n\n The ``DeepSpeedConfigHF`` object is meant to be created during ``TrainingArguments`` object creation and has the\n same lifespan as the latter.\n \"\"\"\n\n def __init__(self, args):\n self.config = None\n self.stage = 0\n self.offload = False\n\n dep_version_check(\"deepspeed\")\n\n self.config_process(args)\n\n # set global weakref object\n deepspeed_config_hf_set(self)\n\n def is_zero2(self):\n return self.stage == 2\n\n def is_zero3(self):\n return self.stage == 3\n\n def is_offload(self):\n return self.offload\n\n def config_process(self, args):\n \"\"\"\n 1. load json if the ``args.deepspeed`` is a path\n 2. replace any ``auto`` values in the config with the correct or recommended value\n\n This is done as early as possible, before model is created, to allow ``is_deepspeed_zero3_enabled`` query and\n getting to the early deepspeed config object during ``zero.Init()`` which needs whether fp16 is enabled, dtype,\n etc.\n\n \"\"\"\n config_file_or_dict = args.deepspeed\n if isinstance(config_file_or_dict, dict):\n # Don't modify user's data should they want to reuse it (e.g. in tests), because once we\n # modified it, it will not be accepted here again, since `auto` values would have been overriden\n config = deepcopy(config_file_or_dict)\n elif isinstance(config_file_or_dict, str):\n with io.open(config_file_or_dict, \"r\", encoding=\"utf-8\") as f:\n config = json.load(f)\n else:\n raise ValueError(\"expecting either a path to a config file or a pre-populated dict\")\n\n self.config = config\n\n # DeepSpeed does:\n # train_batch_size = world_size * train_micro_batch_size_per_gpu * gradient_accumulation_steps\n train_batch_size = args.world_size * args.per_device_train_batch_size * args.gradient_accumulation_steps\n _set_if_auto(config, \"train_micro_batch_size_per_gpu\", args.per_device_train_batch_size)\n _set_if_auto(config, \"gradient_accumulation_steps\", args.gradient_accumulation_steps)\n _set_if_auto(config, \"train_batch_size\", train_batch_size)\n _set_if_auto(config, \"gradient_clipping\", args.max_grad_norm)\n\n # zero\n config_zero = config.get(\"zero_optimization\", {})\n self.stage = config_zero.get(\"stage\", 0)\n\n config_optim = config.get(\"optimizer\", {})\n if config_optim != {}:\n config_optim_params = config_optim.get(\"params\")\n _set_if_auto(config_optim_params, \"lr\", args.learning_rate)\n _set_if_auto(config_optim_params, \"betas\", [args.adam_beta1, args.adam_beta2])\n _set_if_auto(config_optim_params, \"eps\", args.adam_epsilon)\n _set_if_auto(config_optim_params, \"weight_decay\", args.weight_decay)\n\n config_sched = config.get(\"scheduler\", {})\n if config_sched != {}:\n config_sched_params = config_sched.get(\"params\")\n _set_if_auto(config_sched_params, \"warmup_min_lr\", 0)\n _set_if_auto(config_sched_params, \"warmup_max_lr\", args.learning_rate)\n _set_if_auto(config_sched_params, \"warmup_num_steps\", args.warmup_steps)\n # total_num_steps - will get set in deepspeed_init\n\n # fp16\n if args.fp16:\n fp16_backend = \"apex\" if args.fp16_backend == \"apex\" else \"amp\"\n else:\n fp16_backend = None\n\n # amp: similar to the pytorch native amp - it has a bunch of optional params but we won't set\n # any here unless the user did the work\n config_fp16 = config.get(\"fp16\")\n _set_if_auto(config_fp16, \"enabled\", fp16_backend == \"amp\")\n\n # apex: delegates amp work to apex (which needs to be available), but it cannot be used with any\n # ZeRO features, so probably best to be avoided.\n config_amp = config.get(\"amp\")\n _set_if_auto(config_amp, \"enabled\", fp16_backend == \"apex\")\n _set_if_auto(config_amp, \"opt_level\", args.fp16_opt_level)\n\n config_zero = config.get(\"zero_optimization\", {})\n if self.is_zero2():\n self.offload = _is_true(config_zero, \"cpu_offload\")\n elif self.is_zero3():\n offload_devices = [\"cpu\", \"nvme\"]\n if config_zero.get(\"offload_optimizer\", {}).get(\"device\") in offload_devices:\n self.offload = True\n if config_zero.get(\"offload_param\", {}).get(\"device\") in offload_devices:\n self.offload = True\n\n def config_finalize(self, args, model, num_training_steps):\n \"\"\"\n This stage is run after we have the model and know num_training_steps.\n\n Now we we can complete the configuration process.\n\n \"\"\"\n config = self.config\n\n # zero\n config_zero = config.get(\"zero_optimization\", {})\n if self.is_zero3():\n # automatically assign the optimal config values based on model config\n hidden_size = model.config.hidden_size\n _set_if_auto(config_zero, \"reduce_bucket_size\", hidden_size * hidden_size)\n _set_if_auto(config_zero, \"stage3_prefetch_bucket_size\", 0.9 * hidden_size * hidden_size)\n _set_if_auto(config_zero, \"stage3_param_persistence_threshold\", 10 * hidden_size)\n\n # scheduler\n config_sched = config.get(\"scheduler\", {})\n config_sched_params = config_sched.get(\"params\", {})\n _set_if_auto(config_sched_params, \"total_num_steps\", num_training_steps)\n\n\n# keep the config object global to be able to access it anywhere during TrainingArguments life-cycle\n_deepspeed_config_hf_weak_ref = None\n\n\ndef deepspeed_config_hf_set(deepspeed_config_hf_obj):\n # this is a special weakref global object to allow us to get to Deepspeed config from APIs\n # that don't have an easy way to get to the Deepspeed config outside of the Trainer domain.\n global _deepspeed_config_hf_weak_ref\n # will go away automatically when DeepSpeedConfigHF is destroyed (when TrainingArguments is destroyed)\n _deepspeed_config_hf_weak_ref = weakref.ref(deepspeed_config_hf_obj)\n\n\ndef is_deepspeed_zero3_enabled():\n if _deepspeed_config_hf_weak_ref is not None and _deepspeed_config_hf_weak_ref() is not None:\n return _deepspeed_config_hf_weak_ref().is_zero3()\n else:\n return False\n\n\ndef deepspeed_config():\n if _deepspeed_config_hf_weak_ref is not None and _deepspeed_config_hf_weak_ref() is not None:\n return _deepspeed_config_hf_weak_ref().config\n else:\n return None\n\n\ndef deepspeed_init(trainer, num_training_steps, resume_from_checkpoint=None):\n \"\"\"\n Init DeepSpeed, after updating the DeepSpeed configuration with any relevant Trainer's args.\n\n If ``resume_from_checkpoint`` was passed then an attempt to resume from a previously saved checkpoint will be made.\n\n Args:\n trainer: Trainer object\n num_training_steps: per single gpu\n resume_from_checkpoint: path to a checkpoint if to resume from after normal DeepSpeedEngine load\n\n Returns: model, optimizer, lr_scheduler\n\n \"\"\"\n import deepspeed\n\n model = trainer.model\n\n deepspeed_config_hf = trainer.args.deepspeed_config_hf\n deepspeed_config_hf.config_finalize(trainer.args, model, num_training_steps)\n\n # resume config update - some bits like `model` and `num_training_steps` only become available during train\n config = deepspeed_config_hf.config\n\n # Optimizer + Scheduler\n # Currently supported combos:\n # 1. DS scheduler + DS optimizer: Yes\n # 2. HF scheduler + HF optimizer: Yes\n # 3. DS scheduler + HF optimizer: Yes\n # 4. HF scheduler + DS optimizer: No\n #\n # Unless Offload is enabled in which case it's:\n # 1. DS scheduler + DS optimizer: Yes\n # 2. HF scheduler + HF optimizer: No\n # 3. DS scheduler + HF optimizer: No\n # 4. HF scheduler + DS optimizer: No\n\n optimizer = None\n if \"optimizer\" not in config:\n if deepspeed_config_hf.is_offload():\n raise ValueError(\"ZeRO Offload can only work with DeepSpeed optimizers\")\n\n # ds supports Adam, OneBitAdam, and Lamb optimizers and can import other optimizers from torch.\n # But trainer uses AdamW by default.\n trainer.create_optimizer()\n optimizer = trainer.optimizer\n # To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`\n config[\"zero_allow_untested_optimizer\"] = True\n\n # DS schedulers (deepspeed/runtime/lr_schedules.py):\n #\n # DS name | --lr_scheduler_type | HF func | Notes\n # -------------| ---------------------|-----------------------------------|--------------------\n # LRRangeTest | na | na | LRRT\n # OneCycle | na | na | 1CLR\n # WarmupLR | constant_with_warmup | get_constant_schedule_with_warmup | w/ warmup_min_lr=0\n # WarmupDecayLR| linear | get_linear_schedule_with_warmup |\n lr_scheduler = None\n if \"scheduler\" not in config:\n if \"optimizer\" in config:\n # to make this option work, we need to init DS optimizer first, then init HS scheduler,\n # then pass the HS scheduler to DS init, which is not possible at the moment\n raise ValueError(\"At the moment HF scheduler + DeepSpeed optimizer combination is not possible\")\n else:\n trainer.create_scheduler(num_training_steps=num_training_steps)\n lr_scheduler = trainer.lr_scheduler\n\n # keep for quick debug:\n # from pprint import pprint; pprint(config)\n\n model_parameters = filter(lambda p: p.requires_grad, model.parameters())\n\n model, optimizer, _, lr_scheduler = deepspeed.initialize(\n model=model,\n model_parameters=model_parameters,\n config_params=config,\n optimizer=optimizer,\n lr_scheduler=lr_scheduler,\n )\n\n if resume_from_checkpoint is not None:\n\n # it's possible that the user is trying to resume from model_path, which doesn't necessarily\n # contain a deepspeed checkpoint. e.g. examples just check if the dir exists and assume it's\n # a resume from a checkpoint and not just a local pretrained weight. So we check here if the\n # path contains what looks like a deepspeed checkpoint\n import glob\n\n deepspeed_checkpoint_dirs = sorted(glob.glob(f\"{resume_from_checkpoint}/global_step*\"))\n\n if len(deepspeed_checkpoint_dirs) > 0:\n logger.info(f\"Attempting to resume from {resume_from_checkpoint}\")\n # this magically updates self.optimizer and self.lr_scheduler\n load_path, _ = model.load_checkpoint(\n resume_from_checkpoint, load_optimizer_states=True, load_lr_scheduler_states=True\n )\n if load_path is None:\n raise ValueError(f\"[deepspeed] failed to resume from checkpoint {resume_from_checkpoint}\")\n else:\n logger.info(f\"{resume_from_checkpoint} doesn't have deepspeed checkpoints, doing nothing\")\n\n return model, optimizer, lr_scheduler\n\n\nclass TensorBoardCallback(TrainerCallback):\n \"\"\"\n A :class:`~transformers.TrainerCallback` that sends the logs to `TensorBoard\n <https://www.tensorflow.org/tensorboard>`__.\n\n Args:\n tb_writer (:obj:`SummaryWriter`, `optional`):\n The writer to use. Will instantiate one if not set.\n \"\"\"\n\n def __init__(self, tb_writer=None):\n has_tensorboard = is_tensorboard_available()\n assert (\n has_tensorboard\n ), \"TensorBoardCallback requires tensorboard to be installed. Either update your PyTorch version or install tensorboardX.\"\n if has_tensorboard:\n try:\n from torch.utils.tensorboard import SummaryWriter # noqa: F401\n\n self._SummaryWriter = SummaryWriter\n except ImportError:\n try:\n from tensorboardX import SummaryWriter\n\n self._SummaryWriter = SummaryWriter\n except ImportError:\n self._SummaryWriter = None\n else:\n self._SummaryWriter = None\n self.tb_writer = tb_writer\n\n def _init_summary_writer(self, args, log_dir=None):\n log_dir = log_dir or args.logging_dir\n if self._SummaryWriter is not None:\n self.tb_writer = self._SummaryWriter(log_dir=log_dir)\n\n def on_train_begin(self, args, state, control, **kwargs):\n if not state.is_world_process_zero:\n return\n\n log_dir = None\n\n if state.is_hyper_param_search:\n trial_name = state.trial_name\n if trial_name is not None:\n log_dir = os.path.join(args.logging_dir, trial_name)\n\n self._init_summary_writer(args, log_dir)\n\n if self.tb_writer is not None:\n self.tb_writer.add_text(\"args\", args.to_json_string())\n if \"model\" in kwargs:\n model = kwargs[\"model\"]\n if hasattr(model, \"config\") and model.config is not None:\n model_config_json = model.config.to_json_string()\n self.tb_writer.add_text(\"model_config\", model_config_json)\n # Version of TensorBoard coming from tensorboardX does not have this method.\n if hasattr(self.tb_writer, \"add_hparams\"):\n self.tb_writer.add_hparams(args.to_sanitized_dict(), metric_dict={})\n\n def on_log(self, args, state, control, logs=None, **kwargs):\n if not state.is_world_process_zero:\n return\n\n if self.tb_writer is None:\n self._init_summary_writer(args)\n\n if self.tb_writer is not None:\n logs = rewrite_logs(logs)\n for k, v in logs.items():\n if isinstance(v, (int, float)):\n self.tb_writer.add_scalar(k, v, state.global_step)\n else:\n logger.warning(\n \"Trainer is attempting to log a value of \"\n f'\"{v}\" of type {type(v)} for key \"{k}\" as a scalar. '\n \"This invocation of Tensorboard's writer.add_scalar() \"\n \"is incorrect so we dropped this attribute.\"\n )\n self.tb_writer.flush()\n\n def on_train_end(self, args, state, control, **kwargs):\n if self.tb_writer:\n self.tb_writer.close()\n\n\nclass WandbCallback(TrainerCallback):\n \"\"\"\n A :class:`~transformers.TrainerCallback` that sends the logs to `Weight and Biases <https://www.wandb.com/>`__.\n \"\"\"\n\n def __init__(self):\n has_wandb = is_wandb_available()\n assert has_wandb, \"WandbCallback requires wandb to be installed. Run `pip install wandb`.\"\n if has_wandb:\n import wandb\n\n self._wandb = wandb\n self._initialized = False\n # log outputs\n self._log_model = os.getenv(\"WANDB_LOG_MODEL\", \"FALSE\").upper() in ENV_VARS_TRUE_VALUES.union({\"TRUE\"})\n\n def setup(self, args, state, model, **kwargs):\n \"\"\"\n Setup the optional Weights & Biases (`wandb`) integration.\n\n One can subclass and override this method to customize the setup if needed. Find more information `here\n <https://docs.wandb.ai/integrations/huggingface>`__. You can also override the following environment variables:\n\n Environment:\n WANDB_LOG_MODEL (:obj:`bool`, `optional`, defaults to :obj:`False`):\n Whether or not to log model as artifact at the end of training. Use along with\n `TrainingArguments.load_best_model_at_end` to upload best model.\n WANDB_WATCH (:obj:`str`, `optional` defaults to :obj:`\"gradients\"`):\n Can be :obj:`\"gradients\"`, :obj:`\"all\"` or :obj:`\"false\"`. Set to :obj:`\"false\"` to disable gradient\n logging or :obj:`\"all\"` to log gradients and parameters.\n WANDB_PROJECT (:obj:`str`, `optional`, defaults to :obj:`\"huggingface\"`):\n Set this to a custom string to store results in a different project.\n WANDB_DISABLED (:obj:`bool`, `optional`, defaults to :obj:`False`):\n Whether or not to disable wandb entirely. Set `WANDB_DISABLED=true` to disable.\n \"\"\"\n if self._wandb is None:\n return\n self._initialized = True\n if state.is_world_process_zero:\n logger.info(\n 'Automatic Weights & Biases logging enabled, to disable set os.environ[\"WANDB_DISABLED\"] = \"true\"'\n )\n combined_dict = {**args.to_sanitized_dict()}\n\n if hasattr(model, \"config\") and model.config is not None:\n model_config = model.config.to_dict()\n combined_dict = {**model_config, **combined_dict}\n trial_name = state.trial_name\n init_args = {}\n if trial_name is not None:\n run_name = trial_name\n init_args[\"group\"] = args.run_name\n else:\n run_name = args.run_name\n\n if self._wandb.run is None:\n self._wandb.init(\n project=os.getenv(\"WANDB_PROJECT\", \"huggingface\"),\n name=run_name,\n **init_args,\n )\n # add config parameters (run may have been created manually)\n self._wandb.config.update(combined_dict, allow_val_change=True)\n\n # define default x-axis (for latest wandb versions)\n if getattr(self._wandb, \"define_metric\", None):\n self._wandb.define_metric(\"train/global_step\")\n self._wandb.define_metric(\"*\", step_metric=\"train/global_step\", step_sync=True)\n\n # keep track of model topology and gradients, unsupported on TPU\n if not is_torch_tpu_available() and os.getenv(\"WANDB_WATCH\") != \"false\":\n self._wandb.watch(\n model, log=os.getenv(\"WANDB_WATCH\", \"gradients\"), log_freq=max(100, args.logging_steps)\n )\n\n def on_train_begin(self, args, state, control, model=None, **kwargs):\n if self._wandb is None:\n return\n hp_search = state.is_hyper_param_search\n if hp_search:\n self._wandb.finish()\n self._initialized = False\n if not self._initialized:\n self.setup(args, state, model, **kwargs)\n\n def on_train_end(self, args, state, control, model=None, tokenizer=None, **kwargs):\n if self._wandb is None:\n return\n if self._log_model and self._initialized and state.is_world_process_zero:\n from .trainer import Trainer\n\n fake_trainer = Trainer(args=args, model=model, tokenizer=tokenizer)\n with tempfile.TemporaryDirectory() as temp_dir:\n fake_trainer.save_model(temp_dir)\n metadata = (\n {\n k: v\n for k, v in dict(self._wandb.summary).items()\n if isinstance(v, numbers.Number) and not k.startswith(\"_\")\n }\n if not args.load_best_model_at_end\n else {\n f\"eval/{args.metric_for_best_model}\": state.best_metric,\n \"train/total_floss\": state.total_flos,\n }\n )\n artifact = self._wandb.Artifact(name=f\"model-{self._wandb.run.id}\", type=\"model\", metadata=metadata)\n for f in Path(temp_dir).glob(\"*\"):\n if f.is_file():\n with artifact.new_file(f.name, mode=\"wb\") as fa:\n fa.write(f.read_bytes())\n self._wandb.run.log_artifact(artifact)\n\n def on_log(self, args, state, control, model=None, logs=None, **kwargs):\n if self._wandb is None:\n return\n if not self._initialized:\n self.setup(args, state, model)\n if state.is_world_process_zero:\n logs = rewrite_logs(logs)\n self._wandb.log({**logs, \"train/global_step\": state.global_step})\n\n\nclass CometCallback(TrainerCallback):\n \"\"\"\n A :class:`~transformers.TrainerCallback` that sends the logs to `Comet ML <https://www.comet.ml/site/>`__.\n \"\"\"\n\n def __init__(self):\n assert _has_comet, \"CometCallback requires comet-ml to be installed. Run `pip install comet-ml`.\"\n self._initialized = False\n\n def setup(self, args, state, model):\n \"\"\"\n Setup the optional Comet.ml integration.\n\n Environment:\n COMET_MODE (:obj:`str`, `optional`):\n \"OFFLINE\", \"ONLINE\", or \"DISABLED\"\n COMET_PROJECT_NAME (:obj:`str`, `optional`):\n Comet.ml project name for experiments\n COMET_OFFLINE_DIRECTORY (:obj:`str`, `optional`):\n Folder to use for saving offline experiments when :obj:`COMET_MODE` is \"OFFLINE\"\n\n For a number of configurable items in the environment, see `here\n <https://www.comet.ml/docs/python-sdk/advanced/#comet-configuration-variables>`__.\n \"\"\"\n self._initialized = True\n if state.is_world_process_zero:\n comet_mode = os.getenv(\"COMET_MODE\", \"ONLINE\").upper()\n args = {\"project_name\": os.getenv(\"COMET_PROJECT_NAME\", \"huggingface\")}\n experiment = None\n if comet_mode == \"ONLINE\":\n experiment = comet_ml.Experiment(**args)\n logger.info(\"Automatic Comet.ml online logging enabled\")\n elif comet_mode == \"OFFLINE\":\n args[\"offline_directory\"] = os.getenv(\"COMET_OFFLINE_DIRECTORY\", \"./\")\n experiment = comet_ml.OfflineExperiment(**args)\n logger.info(\"Automatic Comet.ml offline logging enabled; use `comet upload` when finished\")\n if experiment is not None:\n experiment._set_model_graph(model, framework=\"transformers\")\n experiment._log_parameters(args, prefix=\"args/\", framework=\"transformers\")\n if hasattr(model, \"config\"):\n experiment._log_parameters(model.config, prefix=\"config/\", framework=\"transformers\")\n\n def on_train_begin(self, args, state, control, model=None, **kwargs):\n if not self._initialized:\n self.setup(args, state, model)\n\n def on_log(self, args, state, control, model=None, logs=None, **kwargs):\n if not self._initialized:\n self.setup(args, state, model)\n if state.is_world_process_zero:\n experiment = comet_ml.config.get_global_experiment()\n if experiment is not None:\n experiment._log_metrics(logs, step=state.global_step, epoch=state.epoch, framework=\"transformers\")\n\n\nclass AzureMLCallback(TrainerCallback):\n \"\"\"\n A :class:`~transformers.TrainerCallback` that sends the logs to `AzureML\n <https://pypi.org/project/azureml-sdk/>`__.\n \"\"\"\n\n def __init__(self, azureml_run=None):\n assert (\n is_azureml_available()\n ), \"AzureMLCallback requires azureml to be installed. Run `pip install azureml-sdk`.\"\n self.azureml_run = azureml_run\n\n def on_init_end(self, args, state, control, **kwargs):\n from azureml.core.run import Run\n\n if self.azureml_run is None and state.is_world_process_zero:\n self.azureml_run = Run.get_context()\n\n def on_log(self, args, state, control, logs=None, **kwargs):\n if self.azureml_run:\n for k, v in logs.items():\n if isinstance(v, (int, float)):\n self.azureml_run.log(k, v, description=k)\n\n\nclass MLflowCallback(TrainerCallback):\n \"\"\"\n A :class:`~transformers.TrainerCallback` that sends the logs to `MLflow <https://www.mlflow.org/>`__.\n \"\"\"\n\n def __init__(self):\n assert is_mlflow_available(), \"MLflowCallback requires mlflow to be installed. Run `pip install mlflow`.\"\n import mlflow\n\n self._MAX_PARAM_VAL_LENGTH = mlflow.utils.validation.MAX_PARAM_VAL_LENGTH\n self._MAX_PARAMS_TAGS_PER_BATCH = mlflow.utils.validation.MAX_PARAMS_TAGS_PER_BATCH\n\n self._initialized = False\n self._log_artifacts = False\n self._ml_flow = mlflow\n\n def setup(self, args, state, model):\n \"\"\"\n Setup the optional MLflow integration.\n\n Environment:\n HF_MLFLOW_LOG_ARTIFACTS (:obj:`str`, `optional`):\n Whether to use MLflow .log_artifact() facility to log artifacts.\n\n This only makes sense if logging to a remote server, e.g. s3 or GCS. If set to `True` or `1`, will copy\n whatever is in :class:`~transformers.TrainingArguments`'s ``output_dir`` to the local or remote\n artifact storage. Using it without a remote storage will just copy the files to your artifact location.\n \"\"\"\n log_artifacts = os.getenv(\"HF_MLFLOW_LOG_ARTIFACTS\", \"FALSE\").upper()\n if log_artifacts in {\"TRUE\", \"1\"}:\n self._log_artifacts = True\n if state.is_world_process_zero:\n self._ml_flow.start_run()\n combined_dict = args.to_dict()\n if hasattr(model, \"config\") and model.config is not None:\n model_config = model.config.to_dict()\n combined_dict = {**model_config, **combined_dict}\n # remove params that are too long for MLflow\n for name, value in list(combined_dict.items()):\n # internally, all values are converted to str in MLflow\n if len(str(value)) > self._MAX_PARAM_VAL_LENGTH:\n logger.warning(\n f\"Trainer is attempting to log a value of \"\n f'\"{value}\" for key \"{name}\" as a parameter. '\n f\"MLflow's log_param() only accepts values no longer than \"\n f\"250 characters so we dropped this attribute.\"\n )\n del combined_dict[name]\n # MLflow cannot log more than 100 values in one go, so we have to split it\n combined_dict_items = list(combined_dict.items())\n for i in range(0, len(combined_dict_items), self._MAX_PARAMS_TAGS_PER_BATCH):\n self._ml_flow.log_params(dict(combined_dict_items[i : i + self._MAX_PARAMS_TAGS_PER_BATCH]))\n self._initialized = True\n\n def on_train_begin(self, args, state, control, model=None, **kwargs):\n if not self._initialized:\n self.setup(args, state, model)\n\n def on_log(self, args, state, control, logs, model=None, **kwargs):\n if not self._initialized:\n self.setup(args, state, model)\n if state.is_world_process_zero:\n for k, v in logs.items():\n if isinstance(v, (int, float)):\n self._ml_flow.log_metric(k, v, step=state.global_step)\n else:\n logger.warning(\n f\"Trainer is attempting to log a value of \"\n f'\"{v}\" of type {type(v)} for key \"{k}\" as a metric. '\n f\"MLflow's log_metric() only accepts float and \"\n f\"int types so we dropped this attribute.\"\n )\n\n def on_train_end(self, args, state, control, **kwargs):\n if self._initialized and state.is_world_process_zero:\n if self._log_artifacts:\n logger.info(\"Logging artifacts. This may take time.\")\n self._ml_flow.log_artifacts(args.output_dir)\n\n def __del__(self):\n # if the previous run is not terminated correctly, the fluent API will\n # not let you start a new run before the previous one is killed\n if self._ml_flow.active_run is not None:\n self._ml_flow.end_run()\n\n\nINTEGRATION_TO_CALLBACK = {\n \"azure_ml\": AzureMLCallback,\n \"comet_ml\": CometCallback,\n \"mlflow\": MLflowCallback,\n \"tensorboard\": TensorBoardCallback,\n \"wandb\": WandbCallback,\n}\n\n\ndef get_reporting_integration_callbacks(report_to):\n for integration in report_to:\n if integration not in INTEGRATION_TO_CALLBACK:\n raise ValueError(\n f\"{integration} is not supported, only {', '.join(INTEGRATION_TO_CALLBACK.keys())} are supported.\"\n )\n return [INTEGRATION_TO_CALLBACK[integration] for integration in report_to]\n",
"path": "src/transformers/integrations.py"
}
] | diff --git a/src/transformers/integrations.py b/src/transformers/integrations.py
index 4ab15b9d50f7..19bffe1f7a6e 100644
--- a/src/transformers/integrations.py
+++ b/src/transformers/integrations.py
@@ -713,6 +713,7 @@ def on_train_begin(self, args, state, control, model=None, **kwargs):
hp_search = state.is_hyper_param_search
if hp_search:
self._wandb.finish()
+ self._initialized = False
if not self._initialized:
self.setup(args, state, model, **kwargs)
|
jupyter__docker-stacks-1964 | [BUG] - Healthcheck fails when using proxy
### What docker image(s) are you using?
base-notebook
### Host OS system and architecture running docker image
Windows 11 as host and linux/amd64 for docker
### What Docker command are you running?
docker compose up with the following dockerfile:
```Dockerfile
version: '3.4'
services:
datamining:
container_name: xxxx
image: xxxx
build:
context: .
dockerfile: ./Dockerfile
ports:
- "8888:8888"
volumes:
- xxxx:/home/jovyan/work
environment:
- DOCKER_STACKS_JUPYTER_CMD=lab
restart: on-failure
```
### How to Reproduce the problem?
Precondition is that the machine has to operate in a corporate environment using the companies proxy.
Start the container as above.
Check the state of the container with ```docker container ls```
The container is marked as unhealthy.
### Command output
```bash session
abcdefghijk "tini -g -- start-no…" x hours ago Up x hours (unhealthy) 0.0.0.0:8888->8888/tcp xxxx
```
### Expected behavior
```abcdedfghi abcdefghijk "tini -g -- start-no…" x hours ago Up x hours (healthy) 0.0.0.0:8888->8888/tcp xxxx```
### Actual behavior
After investigating the issue the problem is that docker_healthcheck.py does not run successfully giving the following error message:
```
Traceback (most recent call last):
File "/opt/conda/lib/python3.11/site-packages/urllib3/connectionpool.py", line 790, in urlopen
response = self._make_request(
^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/urllib3/connectionpool.py", line 536, in _make_request
response = conn.getresponse()
^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/urllib3/connection.py", line 461, in getresponse
httplib_response = super().getresponse()
^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/http/client.py", line 1378, in getresponse
response.begin()
File "/opt/conda/lib/python3.11/http/client.py", line 318, in begin
version, status, reason = self._read_status()
^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/http/client.py", line 287, in _read_status
raise RemoteDisconnected("Remote end closed connection without"
http.client.RemoteDisconnected: Remote end closed connection without response
The above exception was the direct cause of the following exception:
urllib3.exceptions.ProxyError: ('Unable to connect to proxy', RemoteDisconnected('Remote end closed connection without response'))
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/opt/conda/lib/python3.11/site-packages/requests/adapters.py", line 486, in send
resp = conn.urlopen(
^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/urllib3/connectionpool.py", line 844, in urlopen
retries = retries.increment(
^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/urllib3/util/retry.py", line 515, in increment
raise MaxRetryError(_pool, url, reason) from reason # type: ignore[arg-type]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
urllib3.exceptions.MaxRetryError: HTTPConnectionPool(host='host.docker.internal', port=9000): Max retries exceeded with url: http://7702f0e1c7d4:8888/api (Caused by ProxyError('Unable to connect to proxy', RemoteDisconnected('Remote end closed connection without response')))
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/etc/jupyter/docker_healthcheck.py", line 19, in <module>
r = requests.get(url, verify=False) # request without SSL verification
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/requests/api.py", line 73, in get
return request("get", url, params=params, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/requests/api.py", line 59, in request
return session.request(method=method, url=url, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/requests/sessions.py", line 589, in request
resp = self.send(prep, **send_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/requests/sessions.py", line 703, in send
r = adapter.send(request, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/requests/adapters.py", line 513, in send
raise ProxyError(e, request=request)
requests.exceptions.ProxyError: HTTPConnectionPool(host='host.docker.internal', port=9000): Max retries exceeded with url: http://7702f0e1c7d4:8888/api (Caused by ProxyError('Unable to connect to proxy', RemoteDisconnected('Remote end closed connection without response')))
```
### Anything else?
After investigating the issue further I came to the conclusion that using the proxy will be the problem. So I applied the following fix to ```docker_healthcheck.py```:
```python
proxies = {
"http": None,
"https": None,
}
r = requests.get(url, proxies=proxies, verify=False) # request without SSL verification
```
Now the healthcheck works!
### Latest Docker version
- [X] I've updated my Docker version to the latest available, and the issue still persists
| [
{
"content": "#!/usr/bin/env python3\n# Copyright (c) Jupyter Development Team.\n# Distributed under the terms of the Modified BSD License.\nimport json\nimport os\nfrom pathlib import Path\n\nimport requests\n\n# A number of operations below deliberately don't check for possible errors\n# As this is a healthcheck, it should succeed or raise an exception on error\n\nruntime_dir = Path(\"/home/\") / os.environ[\"NB_USER\"] / \".local/share/jupyter/runtime/\"\njson_file = next(runtime_dir.glob(\"*server-*.json\"))\n\nurl = json.loads(json_file.read_bytes())[\"url\"]\nurl = url + \"api\"\n\nr = requests.get(url, verify=False) # request without SSL verification\nr.raise_for_status()\nprint(r.content)\n",
"path": "base-notebook/docker_healthcheck.py"
}
] | [
{
"content": "#!/usr/bin/env python3\n# Copyright (c) Jupyter Development Team.\n# Distributed under the terms of the Modified BSD License.\nimport json\nimport os\nfrom pathlib import Path\n\nimport requests\n\n# A number of operations below deliberately don't check for possible errors\n# As this is a healthcheck, it should succeed or raise an exception on error\n\nruntime_dir = Path(\"/home/\") / os.environ[\"NB_USER\"] / \".local/share/jupyter/runtime/\"\njson_file = next(runtime_dir.glob(\"*server-*.json\"))\n\nurl = json.loads(json_file.read_bytes())[\"url\"]\nurl = url + \"api\"\n\nproxies = {\n \"http\": \"\",\n \"https\": \"\",\n}\n\nr = requests.get(url, proxies=proxies, verify=False) # request without SSL verification\nr.raise_for_status()\nprint(r.content)\n",
"path": "base-notebook/docker_healthcheck.py"
}
] | diff --git a/base-notebook/docker_healthcheck.py b/base-notebook/docker_healthcheck.py
index 7c35a6b115..41bbc78568 100755
--- a/base-notebook/docker_healthcheck.py
+++ b/base-notebook/docker_healthcheck.py
@@ -16,6 +16,11 @@
url = json.loads(json_file.read_bytes())["url"]
url = url + "api"
-r = requests.get(url, verify=False) # request without SSL verification
+proxies = {
+ "http": "",
+ "https": "",
+}
+
+r = requests.get(url, proxies=proxies, verify=False) # request without SSL verification
r.raise_for_status()
print(r.content)
diff --git a/tests/base-notebook/test_healthcheck.py b/tests/base-notebook/test_healthcheck.py
index 2b15faf605..73de100303 100644
--- a/tests/base-notebook/test_healthcheck.py
+++ b/tests/base-notebook/test_healthcheck.py
@@ -69,6 +69,53 @@ def test_health(
assert get_health(running_container) == "healthy"
[email protected](
+ "env,cmd,user",
+ [
+ (
+ ["HTTPS_PROXY=host.docker.internal", "HTTP_PROXY=host.docker.internal"],
+ None,
+ None,
+ ),
+ (
+ [
+ "NB_USER=testuser",
+ "CHOWN_HOME=1",
+ "JUPYTER_PORT=8123",
+ "HTTPS_PROXY=host.docker.internal",
+ "HTTP_PROXY=host.docker.internal",
+ ],
+ ["start-notebook.sh", "--ServerApp.base_url=/test"],
+ "root",
+ ),
+ ],
+)
+def test_health_proxy(
+ container: TrackedContainer,
+ env: Optional[list[str]],
+ cmd: Optional[list[str]],
+ user: Optional[str],
+) -> None:
+ running_container = container.run_detached(
+ tty=True,
+ environment=env,
+ command=cmd,
+ user=user,
+ )
+
+ # sleeping some time to let the server start
+ time_spent = 0.0
+ wait_time = 0.1
+ time_limit = 15
+ while time_spent < time_limit:
+ time.sleep(wait_time)
+ time_spent += wait_time
+ if get_health(running_container) == "healthy":
+ return
+
+ assert get_health(running_container) == "healthy"
+
+
@pytest.mark.parametrize(
"env,cmd,user",
[
|
dj-stripe__dj-stripe-1312 | Issue when attempting to sync tiered Price Model in 2.4.2
**Describe the bug**
It looks like 9bd896ffd944e809b95abae884a2149dc8a79f27 introduced a regression when trying to sync a tiered Price model. Probably Price is not the only model affected.
Check out this trace:
```
$ ./manage.py djstripe_sync_models Price
Syncing Price:
INFO stripe.log_info:64- message='Request to Stripe api' method=get path=https://api.stripe.com/v1/prices?expand[0]=data.tiers
INFO stripe.log_info:64- message='Stripe API response' path=https://api.stripe.com/v1/prices?expand[0]=data.tiers response_code=200
id=price_1IFltoFz0jfFqjGsm5fbXWt5, pk=6 (xxx)
id=price_1IFe29Fz0jfFqjGsTpBrPQql, pk=1 (xxx)
id=price_1IFe29Fz0jfFqjGslZM7rvu1, pk=2 (xxx)
id=price_1IFe28Fz0jfFqjGsM0SIOAa6, pk=3 (xxx)
id=price_1IFe27Fz0jfFqjGsEN4c0MxR, pk=4 (xxx)
id=price_1IFe23Fz0jfFqjGsbFrlPDSi, pk=5 (xxx)
INFO stripe.log_info:64- message='Request to Stripe api' method=get path=https://api.stripe.com/v1/prices
INFO stripe.log_info:64- message='Stripe API response' path=https://api.stripe.com/v1/prices response_code=200
id=price_1IFltoFz0jfFqjGsm5fbXWt5, pk=6 (xxx)
id=price_1IFe29Fz0jfFqjGsTpBrPQql, pk=1 (xxx)
id=price_1IFe29Fz0jfFqjGslZM7rvu1, pk=2 (xxx)
id=price_1IFe28Fz0jfFqjGsM0SIOAa6, pk=3 (xxx)
id=price_1IFe27Fz0jfFqjGsEN4c0MxR, pk=4 (xxx)
id=price_1IFe23Fz0jfFqjGsbFrlPDSi, pk=5 (xxx)
Synced 12 Price
```
The Price objects are synced twice. The first time with the tiers attribute expanded and the second time without expanding it and overwriting it, so the final object doesn't include tiers.
**Software versions**
- dj-stripe version: 2.4.2
- Python version: 3.7
- Django version: 3.0.11
- Stripe API version: 2.55
- Database type and version: postgresql 10.10
**Steps To Reproduce**
1. Create tiered Price and add tiers in Stripe Dashboard
2. Sync Price models with manage command
**Can you reproduce the issue with the latest version of master?**
Yes, both 2.4.2 and master are affected (2.4.1 is not affected)
**Expected Behavior**
The Price Model should have the tiers JSONField object populated.
| [
{
"content": "from typing import List\n\nfrom django.apps import apps\nfrom django.core.management.base import BaseCommand, CommandError\n\nfrom ... import models, settings\n\n\nclass Command(BaseCommand):\n \"\"\"Sync models from stripe.\"\"\"\n\n help = \"Sync models from stripe.\"\n\n def add_arguments(self, parser):\n parser.add_argument(\n \"args\",\n metavar=\"ModelName\",\n nargs=\"*\",\n help=\"restricts sync to these model names (default is to sync all \"\n \"supported models)\",\n )\n\n def handle(self, *args, **options):\n app_label = \"djstripe\"\n app_config = apps.get_app_config(app_label)\n model_list = [] # type: List[models.StripeModel]\n\n if args:\n for model_label in args:\n try:\n model = app_config.get_model(model_label)\n except LookupError:\n raise CommandError(\n \"Unknown model: {}.{}\".format(app_label, model_label)\n )\n\n model_list.append(model)\n else:\n model_list = app_config.get_models()\n\n for model in model_list:\n self.sync_model(model)\n\n def _should_sync_model(self, model):\n if not issubclass(model, models.StripeModel):\n return False, \"not a StripeModel\"\n\n if model.stripe_class is None:\n return False, \"no stripe_class\"\n\n if not hasattr(model.stripe_class, \"list\"):\n return False, \"no stripe_class.list\"\n\n if model is models.UpcomingInvoice:\n return False, \"Upcoming Invoices are virtual only\"\n\n if not settings.STRIPE_LIVE_MODE:\n if model is models.ScheduledQueryRun:\n return False, \"only available in live mode\"\n\n return True, \"\"\n\n def sync_model(self, model):\n model_name = model.__name__\n\n should_sync, reason = self._should_sync_model(model)\n if not should_sync:\n self.stdout.write(f\"Skipping {model}: {reason}\")\n return\n\n self.stdout.write(\"Syncing {}:\".format(model_name))\n\n count = 0\n for list_kwargs in self.get_list_kwargs(model):\n try:\n if model is models.Account:\n # special case, since own account isn't returned by Account.api_list\n stripe_obj = models.Account.stripe_class.retrieve(\n api_key=settings.STRIPE_SECRET_KEY\n )\n count += 1\n djstripe_obj = model.sync_from_stripe_data(stripe_obj)\n self.stdout.write(\n \" id={id}, pk={pk} ({djstripe_obj})\".format(\n id=djstripe_obj.id,\n pk=djstripe_obj.pk,\n djstripe_obj=djstripe_obj,\n )\n )\n\n for stripe_obj in model.api_list(**list_kwargs):\n count += 1\n djstripe_obj = model.sync_from_stripe_data(stripe_obj)\n self.stdout.write(\n \" id={id}, pk={pk} ({djstripe_obj})\".format(\n id=djstripe_obj.id,\n pk=djstripe_obj.pk,\n djstripe_obj=djstripe_obj,\n )\n )\n\n except Exception as e:\n self.stderr.write(str(e))\n\n if count == 0:\n self.stdout.write(\" (no results)\")\n else:\n self.stdout.write(\n \" Synced {count} {model_name}\".format(\n count=count, model_name=model_name\n )\n )\n\n def get_list_kwargs(self, model):\n \"\"\"\n Returns a sequence of kwargs dicts to pass to model.api_list\n\n This allows us to sync models that require parameters to api_list\n\n :param model:\n :return: Sequence[dict]\n \"\"\"\n all_list_kwargs = (\n [{\"expand\": [f\"data.{k}\" for k in model.expand_fields]}]\n if model.expand_fields\n else []\n )\n if model is models.PaymentMethod:\n # special case\n all_list_kwargs.extend(\n (\n {\"customer\": stripe_customer.id, \"type\": \"card\"}\n for stripe_customer in models.Customer.api_list()\n )\n )\n elif model is models.SubscriptionItem:\n all_list_kwargs.extend(\n (\n {\"subscription\": subscription.id}\n for subscription in models.Subscription.api_list()\n )\n )\n else:\n all_list_kwargs.append({})\n\n return all_list_kwargs\n",
"path": "djstripe/management/commands/djstripe_sync_models.py"
}
] | [
{
"content": "from typing import List\n\nfrom django.apps import apps\nfrom django.core.management.base import BaseCommand, CommandError\n\nfrom ... import models, settings\n\n\nclass Command(BaseCommand):\n \"\"\"Sync models from stripe.\"\"\"\n\n help = \"Sync models from stripe.\"\n\n def add_arguments(self, parser):\n parser.add_argument(\n \"args\",\n metavar=\"ModelName\",\n nargs=\"*\",\n help=\"restricts sync to these model names (default is to sync all \"\n \"supported models)\",\n )\n\n def handle(self, *args, **options):\n app_label = \"djstripe\"\n app_config = apps.get_app_config(app_label)\n model_list = [] # type: List[models.StripeModel]\n\n if args:\n for model_label in args:\n try:\n model = app_config.get_model(model_label)\n except LookupError:\n raise CommandError(\n \"Unknown model: {}.{}\".format(app_label, model_label)\n )\n\n model_list.append(model)\n else:\n model_list = app_config.get_models()\n\n for model in model_list:\n self.sync_model(model)\n\n def _should_sync_model(self, model):\n if not issubclass(model, models.StripeModel):\n return False, \"not a StripeModel\"\n\n if model.stripe_class is None:\n return False, \"no stripe_class\"\n\n if not hasattr(model.stripe_class, \"list\"):\n return False, \"no stripe_class.list\"\n\n if model is models.UpcomingInvoice:\n return False, \"Upcoming Invoices are virtual only\"\n\n if not settings.STRIPE_LIVE_MODE:\n if model is models.ScheduledQueryRun:\n return False, \"only available in live mode\"\n\n return True, \"\"\n\n def sync_model(self, model):\n model_name = model.__name__\n\n should_sync, reason = self._should_sync_model(model)\n if not should_sync:\n self.stdout.write(f\"Skipping {model}: {reason}\")\n return\n\n self.stdout.write(\"Syncing {}:\".format(model_name))\n\n count = 0\n for list_kwargs in self.get_list_kwargs(model):\n try:\n if model is models.Account:\n # special case, since own account isn't returned by Account.api_list\n stripe_obj = models.Account.stripe_class.retrieve(\n api_key=settings.STRIPE_SECRET_KEY\n )\n count += 1\n djstripe_obj = model.sync_from_stripe_data(stripe_obj)\n self.stdout.write(\n \" id={id}, pk={pk} ({djstripe_obj})\".format(\n id=djstripe_obj.id,\n pk=djstripe_obj.pk,\n djstripe_obj=djstripe_obj,\n )\n )\n\n for stripe_obj in model.api_list(**list_kwargs):\n count += 1\n djstripe_obj = model.sync_from_stripe_data(stripe_obj)\n self.stdout.write(\n \" id={id}, pk={pk} ({djstripe_obj})\".format(\n id=djstripe_obj.id,\n pk=djstripe_obj.pk,\n djstripe_obj=djstripe_obj,\n )\n )\n\n except Exception as e:\n self.stderr.write(str(e))\n\n if count == 0:\n self.stdout.write(\" (no results)\")\n else:\n self.stdout.write(\n \" Synced {count} {model_name}\".format(\n count=count, model_name=model_name\n )\n )\n\n def get_list_kwargs(self, model):\n \"\"\"\n Returns a sequence of kwargs dicts to pass to model.api_list\n\n This allows us to sync models that require parameters to api_list\n\n :param model:\n :return: Sequence[dict]\n \"\"\"\n all_list_kwargs = (\n [{\"expand\": [f\"data.{k}\" for k in model.expand_fields]}]\n if model.expand_fields\n else []\n )\n if model is models.PaymentMethod:\n # special case\n all_list_kwargs.extend(\n (\n {\"customer\": stripe_customer.id, \"type\": \"card\"}\n for stripe_customer in models.Customer.api_list()\n )\n )\n elif model is models.SubscriptionItem:\n all_list_kwargs.extend(\n (\n {\"subscription\": subscription.id}\n for subscription in models.Subscription.api_list()\n )\n )\n elif not all_list_kwargs:\n all_list_kwargs.append({})\n\n return all_list_kwargs\n",
"path": "djstripe/management/commands/djstripe_sync_models.py"
}
] | diff --git a/djstripe/management/commands/djstripe_sync_models.py b/djstripe/management/commands/djstripe_sync_models.py
index ddea11aa16..a974055826 100644
--- a/djstripe/management/commands/djstripe_sync_models.py
+++ b/djstripe/management/commands/djstripe_sync_models.py
@@ -140,7 +140,7 @@ def get_list_kwargs(self, model):
for subscription in models.Subscription.api_list()
)
)
- else:
+ elif not all_list_kwargs:
all_list_kwargs.append({})
return all_list_kwargs
|
DDMAL__CantusDB-913 | Admin Chant Edit page: we should make "title" field longer
In an email from Debra:
> https://cantusdatabase.org/admin/main_app/source/123611/change/
For a page like this one, could I please have a longer box for the source title? The width of the "provenance note" field would be about right, I think, so that I can see the whole title without having to scroll.
| [
{
"content": "from django import forms\nfrom .models import (\n Chant,\n Office,\n Genre,\n Notation,\n Feast,\n Source,\n RismSiglum,\n Segment,\n Provenance,\n Century,\n Sequence,\n)\nfrom .widgets import (\n TextInputWidget,\n VolpianoInputWidget,\n TextAreaWidget,\n VolpianoAreaWidget,\n SelectWidget,\n CheckboxWidget,\n AdminTextAreaWidget,\n AdminTextInputWidget,\n)\nfrom django.contrib.auth import get_user_model\nfrom django.db.models import Q\nfrom django.contrib.admin.widgets import (\n FilteredSelectMultiple,\n)\n\n# ModelForm allows to build a form directly from a model\n# see https://docs.djangoproject.com/en/3.0/topics/forms/modelforms/\n\n\"\"\"\n# 3 ways of doing it\n#1 worst, helptext in the model will be missing\nclass CommetnForm(forms.Form):\n marginalia = forms.CharField(\n label=\"Marginalia\", widget=forms.TextInput(), help_text=\"help\"\n )\n url = forms.URLField()\n comment = forms.CharField()\n\n url.widget.attrs.update({'class': 'special'})\n comment.widget.attrs.update(size='40')\n#2\nclass CommentForm(forms.ModelForm):\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.fields['name'].widget.attrs.update({'class': 'special'})\n self.fields['comment'].widget.attrs.update(size='40')\n\"\"\"\n\n\nclass NameModelChoiceField(forms.ModelChoiceField):\n \"\"\"\n A custom ModelChoiceField that overrides the label_from_instance method\n to display the object's name attribute instead of str(object).\n This field is specifically designed for handling genre and office objects.\n Rather than displaying the name along with its description, sometimes we\n only want the shorthand notation for the genre and office objects.\n (Eg. [AV] Antiphon verse --> AV)\n \"\"\"\n\n def label_from_instance(self, obj):\n return obj.name\n\n\n# 3 best\nclass ChantCreateForm(forms.ModelForm):\n class Meta:\n model = Chant\n # specify either 'fields' or 'excludes' so that django knows which fields to use\n fields = [\n \"marginalia\",\n \"folio\",\n \"c_sequence\",\n \"office\",\n \"genre\",\n \"position\",\n \"cantus_id\",\n \"feast\",\n \"mode\",\n \"differentia\",\n \"differentia_new\",\n \"finalis\",\n \"extra\",\n \"chant_range\",\n \"manuscript_full_text_std_spelling\",\n \"manuscript_full_text\",\n \"volpiano\",\n \"image_link\",\n \"melody_id\",\n \"content_structure\",\n \"indexing_notes\",\n \"addendum\",\n ]\n # the widgets dictionary is ignored for a model field with a non-empty\n # choices attribute. In this case, you must override the form field to\n # use a different widget. this goes for all foreignkey and required fields\n # here, which are written explicitly below to override form field\n widgets = {\n \"marginalia\": TextInputWidget(),\n # folio: defined below (required)\n # c_sequence: defined below (required)\n # office: defined below (foreignkey)\n # genre: defined below (foreignkey)\n \"position\": TextInputWidget(),\n \"cantus_id\": TextInputWidget(),\n #'feast': defined below (foreignkey)\n \"mode\": TextInputWidget(),\n \"differentia\": TextInputWidget(),\n \"differentia_new\": TextInputWidget(),\n \"finalis\": TextInputWidget(),\n \"extra\": TextInputWidget(),\n \"chant_range\": VolpianoInputWidget(),\n # manuscript_full_text_std_spelling: defined below (required)\n \"manuscript_full_text\": TextAreaWidget(),\n \"volpiano\": VolpianoAreaWidget(),\n \"image_link\": TextInputWidget(),\n \"melody_id\": TextInputWidget(),\n \"content_structure\": TextInputWidget(),\n \"indexing_notes\": TextAreaWidget(),\n \"addendum\": TextInputWidget(),\n }\n\n folio = forms.CharField(\n required=True,\n widget=TextInputWidget,\n help_text=\"Binding order\",\n )\n\n c_sequence = forms.CharField(\n required=True,\n widget=TextInputWidget,\n help_text=\"Each folio starts with '1'.\",\n )\n\n # We use NameModelChoiceField here so the dropdown list of office/mass displays the name\n # instead of [name] + description\n office = NameModelChoiceField(\n queryset=Office.objects.all().order_by(\"name\"),\n required=False,\n )\n office.widget.attrs.update({\"class\": \"form-control custom-select custom-select-sm\"})\n\n # We use NameModelChoiceField here so the dropdown list of genres displays the name\n # instead of [name] + description\n genre = NameModelChoiceField(\n queryset=Genre.objects.all().order_by(\"name\"),\n required=False,\n )\n genre.widget.attrs.update({\"class\": \"form-control custom-select custom-select-sm\"})\n\n feast = forms.ModelChoiceField(\n queryset=Feast.objects.all().order_by(\"name\"), required=False\n )\n feast.widget.attrs.update({\"class\": \"form-control custom-select custom-select-sm\"})\n\n manuscript_full_text_std_spelling = forms.CharField(\n required=True,\n widget=TextAreaWidget,\n help_text=\"Manuscript full text with standardized spelling. Enter the words \"\n \"according to the manuscript but normalize their spellings following \"\n \"Classical Latin forms. Use upper-case letters for proper nouns, \"\n 'the first word of each chant, and the first word after \"Alleluia\" for '\n \"Mass Alleluias. Punctuation is omitted.\",\n )\n\n # automatically computed fields\n # source and incipit are mandatory fields in model,\n # but have to be optional in the form, otherwise the field validation won't pass\n source = forms.ModelChoiceField(\n queryset=Source.objects.all().order_by(\"title\"),\n required=False,\n error_messages={\n \"invalid_choice\": \"This source does not exist, please switch to a different source.\"\n },\n )\n incipit = forms.CharField(required=False)\n\n\nclass SourceCreateForm(forms.ModelForm):\n class Meta:\n model = Source\n fields = [\n \"title\",\n \"rism_siglum\",\n \"siglum\",\n \"provenance\",\n \"provenance_notes\",\n \"full_source\",\n \"date\",\n \"century\",\n \"cursus\",\n \"current_editors\",\n \"melodies_entered_by\",\n \"complete_inventory\",\n \"summary\",\n \"description\",\n \"selected_bibliography\",\n \"image_link\",\n \"fragmentarium_id\",\n \"dact_id\",\n \"indexing_notes\",\n ]\n widgets = {\n \"title\": TextInputWidget(),\n \"siglum\": TextInputWidget(),\n \"provenance_notes\": TextInputWidget(),\n \"date\": TextInputWidget(),\n \"cursus\": SelectWidget(),\n \"summary\": TextAreaWidget(),\n \"description\": TextAreaWidget(),\n \"selected_bibliography\": TextAreaWidget(),\n \"image_link\": TextInputWidget(),\n \"fragmentarium_id\": TextInputWidget(),\n \"dact_id\": TextInputWidget(),\n \"indexing_notes\": TextAreaWidget(),\n }\n\n rism_siglum = forms.ModelChoiceField(\n queryset=RismSiglum.objects.all().order_by(\"name\"), required=False\n )\n rism_siglum.widget.attrs.update(\n {\"class\": \"form-control custom-select custom-select-sm\"}\n )\n\n provenance = forms.ModelChoiceField(\n queryset=Provenance.objects.all().order_by(\"name\"), required=False\n )\n provenance.widget.attrs.update(\n {\"class\": \"form-control custom-select custom-select-sm\"}\n )\n\n TRUE_FALSE_CHOICES_SOURCE = (\n (True, \"Full source\"),\n (False, \"Fragment or Fragmented\"),\n )\n\n full_source = forms.ChoiceField(choices=TRUE_FALSE_CHOICES_SOURCE, required=False)\n full_source.widget.attrs.update(\n {\"class\": \"form-control custom-select custom-select-sm\"}\n )\n\n century = forms.ModelMultipleChoiceField(\n queryset=Century.objects.all().order_by(\"name\"), required=False\n )\n century.widget.attrs.update(\n {\"class\": \"form-control custom-select custom-select-sm\"}\n )\n\n current_editors = forms.ModelMultipleChoiceField(\n queryset=get_user_model()\n .objects.filter(\n Q(groups__name=\"project manager\")\n | Q(groups__name=\"editor\")\n | Q(groups__name=\"contributor\")\n )\n .order_by(\"last_name\"),\n required=False,\n )\n current_editors.widget.attrs.update(\n {\"class\": \"form-control custom-select custom-select-sm\"}\n )\n\n melodies_entered_by = forms.ModelMultipleChoiceField(\n queryset=get_user_model().objects.all().order_by(\"full_name\"), required=False\n )\n melodies_entered_by.widget.attrs.update(\n {\"class\": \"form-control custom-select custom-select-sm\"}\n )\n\n TRUE_FALSE_CHOICES_INVEN = ((True, \"Complete\"), (False, \"Incomplete\"))\n\n complete_inventory = forms.ChoiceField(\n choices=TRUE_FALSE_CHOICES_INVEN, required=False\n )\n complete_inventory.widget.attrs.update(\n {\"class\": \"form-control custom-select custom-select-sm\"}\n )\n\n\nclass ChantEditForm(forms.ModelForm):\n class Meta:\n model = Chant\n fields = [\n \"manuscript_full_text_std_spelling\",\n \"manuscript_full_text\",\n \"volpiano\",\n \"marginalia\",\n \"folio\",\n \"c_sequence\",\n \"feast\",\n \"office\",\n \"genre\",\n \"position\",\n \"cantus_id\",\n \"melody_id\",\n \"mode\",\n \"finalis\",\n \"differentia\",\n \"differentia_new\",\n \"extra\",\n \"image_link\",\n \"indexing_notes\",\n \"addendum\",\n ]\n widgets = {\n # manuscript_full_text_std_spelling: defined below (required)\n \"manuscript_full_text\": TextAreaWidget(),\n \"volpiano\": VolpianoAreaWidget(),\n \"marginalia\": TextInputWidget(),\n # folio: defined below (required)\n # c_sequence: defined below (required)\n # feast: defined below (foreignkey)\n # office: defined below (foreignkey)\n # genre: defined below (foreignkey)\n \"position\": TextInputWidget(),\n \"cantus_id\": TextInputWidget(),\n \"melody_id\": TextInputWidget(),\n \"mode\": TextInputWidget(),\n \"finalis\": TextInputWidget(),\n \"differentia\": TextInputWidget(),\n \"differentia_new\": TextInputWidget(),\n \"extra\": TextInputWidget(),\n \"image_link\": TextInputWidget(),\n \"indexing_notes\": TextAreaWidget(),\n \"addendum\": TextInputWidget(),\n }\n\n manuscript_full_text_std_spelling = forms.CharField(\n required=True,\n widget=TextAreaWidget,\n help_text=\"Manuscript full text with standardized spelling. Enter the words \"\n \"according to the manuscript but normalize their spellings following \"\n \"Classical Latin forms. Use upper-case letters for proper nouns, \"\n 'the first word of each chant, and the first word after \"Alleluia\" for '\n \"Mass Alleluias. Punctuation is omitted.\",\n )\n\n folio = forms.CharField(\n required=True,\n widget=TextInputWidget,\n help_text=\"Binding order\",\n )\n\n c_sequence = forms.CharField(\n required=True,\n widget=TextInputWidget,\n help_text=\"Each folio starts with '1'.\",\n )\n\n feast = forms.ModelChoiceField(\n queryset=Feast.objects.all().order_by(\"name\"), required=False\n )\n feast.widget.attrs.update({\"class\": \"form-control custom-select custom-select-sm\"})\n\n # We use NameModelChoiceField here so the dropdown list of office/mass displays the name\n # instead of [name] + description\n office = NameModelChoiceField(\n queryset=Office.objects.all().order_by(\"name\"),\n required=False,\n )\n office.widget.attrs.update({\"class\": \"form-control custom-select custom-select-sm\"})\n\n # We use NameModelChoiceField here so the dropdown list of genres displays the name\n # instead of [name] + description\n genre = NameModelChoiceField(\n queryset=Genre.objects.all().order_by(\"name\"),\n required=False,\n )\n genre.widget.attrs.update({\"class\": \"form-control custom-select custom-select-sm\"})\n\n\nclass ChantProofreadForm(forms.ModelForm):\n class Meta:\n model = Chant\n fields = [\n \"manuscript_full_text_std_spelling\",\n \"manuscript_full_text\",\n \"volpiano\",\n \"marginalia\",\n \"folio\",\n \"c_sequence\",\n \"feast\",\n \"office\",\n \"genre\",\n \"position\",\n \"cantus_id\",\n \"melody_id\",\n \"mode\",\n \"finalis\",\n \"differentia\",\n \"extra\",\n \"image_link\",\n \"indexing_notes\",\n # additional fields for the proofreading form\n \"manuscript_full_text_std_proofread\",\n \"manuscript_full_text_proofread\",\n \"volpiano_proofread\",\n \"proofread_by\",\n \"manuscript_syllabized_full_text\",\n \"chant_range\",\n \"siglum\",\n \"addendum\",\n \"differentia_new\",\n ]\n widgets = {\n # manuscript_full_text_std_spelling: defined below (required)\n \"manuscript_full_text\": TextAreaWidget(),\n \"volpiano\": VolpianoAreaWidget(),\n \"marginalia\": TextInputWidget(),\n # folio: defined below (required)\n # c_sequence: defined below (required)\n # \"office\": defined below (foreignkey)\n # \"genre\": defined below (foreignkey)\n \"position\": TextInputWidget(),\n \"cantus_id\": TextInputWidget(),\n \"melody_id\": TextInputWidget(),\n \"mode\": TextInputWidget(),\n \"finalis\": TextInputWidget(),\n \"differentia\": TextInputWidget(),\n \"extra\": TextInputWidget(),\n \"image_link\": TextInputWidget(),\n \"indexing_notes\": TextAreaWidget(),\n # additional fields for the proofreading form\n \"manuscript_full_text_std_proofread\": CheckboxWidget(),\n \"manuscript_full_text_proofread\": CheckboxWidget(),\n \"volpiano_proofread\": CheckboxWidget(),\n \"manuscript_syllabized_full_text\": TextAreaWidget(),\n \"chant_range\": VolpianoAreaWidget(),\n \"siglum\": TextInputWidget(),\n \"addendum\": TextInputWidget(),\n \"differentia_new\": TextInputWidget(),\n }\n\n manuscript_full_text_std_spelling = forms.CharField(\n required=True,\n widget=TextAreaWidget,\n help_text=\"Manuscript full text with standardized spelling. Enter the words \"\n \"according to the manuscript but normalize their spellings following \"\n \"Classical Latin forms. Use upper-case letters for proper nouns, \"\n 'the first word of each chant, and the first word after \"Alleluia\" for '\n \"Mass Alleluias. Punctuation is omitted.\",\n )\n\n folio = forms.CharField(\n required=True,\n widget=TextInputWidget,\n help_text=\"Binding order\",\n )\n\n c_sequence = forms.CharField(\n required=True,\n widget=TextInputWidget,\n help_text=\"Each folio starts with '1'.\",\n )\n\n feast = forms.ModelChoiceField(\n queryset=Feast.objects.all().order_by(\"name\"),\n required=False,\n )\n feast.widget.attrs.update({\"class\": \"form-control custom-select custom-select-sm\"})\n\n # We use NameModelChoiceField here so the dropdown list of office/mass displays the name\n # instead of [name] + description\n office = NameModelChoiceField(\n queryset=Office.objects.all().order_by(\"name\"),\n required=False,\n )\n office.widget.attrs.update({\"class\": \"form-control custom-select custom-select-sm\"})\n\n # We use NameModelChoiceField here so the dropdown list of genres displays the name\n # instead of [name] + description\n genre = NameModelChoiceField(\n queryset=Genre.objects.all().order_by(\"name\"), required=False\n )\n genre.widget.attrs.update({\"class\": \"form-control custom-select custom-select-sm\"})\n\n proofread_by = forms.ModelMultipleChoiceField(\n queryset=get_user_model()\n .objects.filter(Q(groups__name=\"project manager\") | Q(groups__name=\"editor\"))\n .order_by(\"last_name\"),\n required=False,\n )\n proofread_by.widget.attrs.update(\n {\"class\": \"form-control custom-select custom-select-sm\"}\n )\n\n\nclass SourceEditForm(forms.ModelForm):\n class Meta:\n model = Source\n fields = [\n \"title\",\n \"rism_siglum\",\n \"siglum\",\n \"provenance\",\n \"provenance_notes\",\n \"full_source\",\n \"date\",\n \"century\",\n \"cursus\",\n \"current_editors\",\n \"melodies_entered_by\",\n \"complete_inventory\",\n \"summary\",\n \"description\",\n \"selected_bibliography\",\n \"image_link\",\n \"fragmentarium_id\",\n \"dact_id\",\n \"indexing_notes\",\n ]\n widgets = {\n \"title\": TextInputWidget(),\n \"rism_siglum\": TextInputWidget(),\n \"siglum\": TextInputWidget(),\n \"provenance_notes\": TextInputWidget(),\n \"date\": TextInputWidget(),\n \"summary\": TextAreaWidget(),\n \"description\": TextAreaWidget(),\n \"selected_bibliography\": TextAreaWidget(),\n \"image_link\": TextInputWidget(),\n \"fragmentarium_id\": TextInputWidget(),\n \"dact_id\": TextInputWidget(),\n \"indexing_notes\": TextAreaWidget(),\n }\n\n provenance = forms.ModelChoiceField(\n queryset=Provenance.objects.all().order_by(\"name\"), required=False\n )\n provenance.widget.attrs.update(\n {\"class\": \"form-control custom-select custom-select-sm\"}\n ) # adds styling\n\n century = forms.ModelMultipleChoiceField(\n queryset=Century.objects.all().order_by(\"name\"), required=False\n )\n century.widget.attrs.update(\n {\"class\": \"form-control custom-select custom-select-sm\"}\n )\n\n CHOICES_FULL_SOURCE = (\n (None, \"None\"),\n (True, \"Full source\"),\n (False, \"Fragment or Fragmented\"),\n )\n full_source = forms.ChoiceField(choices=CHOICES_FULL_SOURCE, required=False)\n full_source.widget.attrs.update(\n {\"class\": \"form-control custom-select custom-select-sm\"}\n )\n\n CHOICES_CURSUS = (\n (None, \"None\"),\n (\"Monastic\", \"Monastic\"),\n (\"Secular\", \"Secular\"),\n )\n cursus = forms.ChoiceField(choices=CHOICES_CURSUS, required=False)\n cursus.widget.attrs.update({\"class\": \"form-control custom-select custom-select-sm\"})\n\n current_editors = forms.ModelMultipleChoiceField(\n queryset=get_user_model()\n .objects.filter(\n Q(groups__name=\"project manager\")\n | Q(groups__name=\"editor\")\n | Q(groups__name=\"contributor\")\n )\n .order_by(\"last_name\"),\n required=False,\n )\n current_editors.widget.attrs.update(\n {\"class\": \"form-control custom-select custom-select-sm\"}\n )\n\n melodies_entered_by = forms.ModelMultipleChoiceField(\n queryset=get_user_model().objects.all().order_by(\"full_name\"), required=False\n )\n melodies_entered_by.widget.attrs.update(\n {\"class\": \"form-control custom-select custom-select-sm\"}\n )\n\n CHOICES_COMPLETE_INV = (\n (True, \"complete inventory\"),\n (False, \"partial inventory\"),\n )\n complete_inventory = forms.ChoiceField(choices=CHOICES_COMPLETE_INV, required=False)\n complete_inventory.widget.attrs.update(\n {\"class\": \"form-control custom-select custom-select-sm\"}\n )\n\n\nclass SequenceEditForm(forms.ModelForm):\n class Meta:\n model = Sequence\n fields = [\n \"title\",\n \"siglum\",\n \"incipit\",\n \"folio\",\n \"s_sequence\",\n \"genre\",\n \"rubrics\",\n \"analecta_hymnica\",\n \"indexing_notes\",\n \"date\",\n \"col1\",\n \"col2\",\n \"col3\",\n \"ah_volume\",\n \"source\",\n \"cantus_id\",\n \"image_link\",\n ]\n widgets = {\n \"title\": TextInputWidget(),\n \"siglum\": TextInputWidget(),\n \"incipit\": TextInputWidget(),\n \"folio\": TextInputWidget(),\n \"s_sequence\": TextInputWidget(),\n \"rubrics\": TextInputWidget(),\n \"analecta_hymnica\": TextInputWidget(),\n \"indexing_notes\": TextAreaWidget(),\n \"date\": TextInputWidget(),\n \"col1\": TextInputWidget(),\n \"col2\": TextInputWidget(),\n \"col3\": TextInputWidget(),\n \"ah_volume\": TextInputWidget(),\n \"cantus_id\": TextInputWidget(),\n \"image_link\": TextInputWidget(),\n }\n\n # We use NameModelChoiceField here so the dropdown list of genres displays the name\n # instead of [name] + description\n genre = NameModelChoiceField(\n queryset=Genre.objects.all().order_by(\"name\"), required=False\n )\n genre.widget.attrs.update({\"class\": \"form-control custom-select custom-select-sm\"})\n\n source = forms.ModelChoiceField(\n queryset=Source.objects.all().order_by(\"title\"), required=False\n )\n source.widget.attrs.update({\"class\": \"form-control custom-select custom-select-sm\"})\n\n\nclass ChantEditSyllabificationForm(forms.ModelForm):\n class Meta:\n model = Chant\n fields = [\n \"manuscript_full_text\",\n \"manuscript_syllabized_full_text\",\n ]\n widgets = {\n \"manuscript_full_text\": TextAreaWidget(),\n \"manuscript_syllabized_full_text\": TextAreaWidget(),\n }\n\n\nclass AdminCenturyForm(forms.ModelForm):\n class Meta:\n model = Century\n fields = \"__all__\"\n\n name = forms.CharField(required=True, widget=AdminTextInputWidget)\n\n\nclass AdminChantForm(forms.ModelForm):\n class Meta:\n model = Chant\n fields = \"__all__\"\n widgets = {\n \"volpiano\": VolpianoAreaWidget(),\n \"indexing_notes\": TextAreaWidget(),\n \"manuscript_full_text_std_proofread\": CheckboxWidget(),\n \"manuscript_full_text_proofread\": CheckboxWidget(),\n \"volpiano_proofread\": CheckboxWidget(),\n \"chant_range\": VolpianoAreaWidget(),\n }\n\n manuscript_full_text_std_spelling = forms.CharField(\n required=True,\n widget=AdminTextAreaWidget,\n help_text=\"Manuscript full text with standardized spelling. Enter the words \"\n \"according to the manuscript but normalize their spellings following \"\n \"Classical Latin forms. Use upper-case letters for proper nouns, \"\n 'the first word of each chant, and the first word after \"Alleluia\" for '\n \"Mass Alleluias. Punctuation is omitted.\",\n )\n\n folio = forms.CharField(\n required=True,\n widget=AdminTextInputWidget,\n help_text=\"Binding order\",\n )\n\n c_sequence = forms.CharField(\n required=True,\n widget=AdminTextInputWidget,\n help_text=\"Each folio starts with '1'.\",\n label=\"Sequence\",\n )\n\n # We use NameModelChoiceField here so the dropdown list of office/mass displays the name\n # instead of [name] + description\n office = NameModelChoiceField(\n queryset=Office.objects.all().order_by(\"name\"),\n required=False,\n )\n # We use NameModelChoiceField here so the dropdown list of genres displays the name\n # instead of [name] + description\n genre = NameModelChoiceField(\n queryset=Genre.objects.all().order_by(\"name\"), required=False\n )\n\n proofread_by = forms.ModelMultipleChoiceField(\n queryset=get_user_model()\n .objects.filter(Q(groups__name=\"project manager\") | Q(groups__name=\"editor\"))\n .order_by(\"last_name\"),\n required=False,\n widget=FilteredSelectMultiple(verbose_name=\"proofread by\", is_stacked=False),\n )\n\n\nclass AdminFeastForm(forms.ModelForm):\n class Meta:\n model = Feast\n fields = \"__all__\"\n\n name = forms.CharField(required=True, widget=AdminTextInputWidget)\n\n\nclass AdminGenreForm(forms.ModelForm):\n class Meta:\n model = Genre\n fields = \"__all__\"\n\n name = forms.CharField(required=True, widget=AdminTextInputWidget)\n description = forms.CharField(required=True, widget=AdminTextAreaWidget)\n\n\nclass AdminNotationForm(forms.ModelForm):\n class Meta:\n model = Notation\n fields = \"__all__\"\n\n name = forms.CharField(required=True, widget=AdminTextInputWidget)\n name.widget.attrs.update({\"style\": \"width: 400px;\"})\n\n\nclass AdminOfficeForm(forms.ModelForm):\n class Meta:\n model = Office\n fields = \"__all__\"\n\n name = forms.CharField(required=True, widget=AdminTextInputWidget)\n description = forms.CharField(required=True, widget=AdminTextAreaWidget)\n\n\nclass AdminProvenanceForm(forms.ModelForm):\n class Meta:\n model = Provenance\n fields = \"__all__\"\n\n name = forms.CharField(required=True, widget=AdminTextInputWidget)\n\n\nclass AdminRismSiglumForm(forms.ModelForm):\n class Meta:\n model = RismSiglum\n fields = \"__all__\"\n\n name = forms.CharField(required=True, widget=AdminTextInputWidget)\n\n\nclass AdminSegmentForm(forms.ModelForm):\n class Meta:\n model = Segment\n fields = \"__all__\"\n\n name = forms.CharField(required=True, widget=AdminTextInputWidget)\n name.widget.attrs.update({\"style\": \"width: 400px;\"})\n\n\nclass AdminSequenceForm(forms.ModelForm):\n class Meta:\n model = Sequence\n fields = \"__all__\"\n widgets = {\n \"volpiano\": VolpianoAreaWidget(),\n \"indexing_notes\": TextAreaWidget(),\n \"manuscript_full_text_std_proofread\": CheckboxWidget(),\n \"manuscript_full_text_proofread\": CheckboxWidget(),\n \"volpiano_proofread\": CheckboxWidget(),\n \"chant_range\": VolpianoAreaWidget(),\n }\n\n # We use NameModelChoiceField here so the dropdown list of office/mass displays the name\n # instead of [name] + description\n office = NameModelChoiceField(\n queryset=Office.objects.all().order_by(\"name\"),\n required=False,\n )\n # We use NameModelChoiceField here so the dropdown list of genres displays the name\n # instead of [name] + description\n genre = NameModelChoiceField(\n queryset=Genre.objects.all().order_by(\"name\"), required=False\n )\n\n proofread_by = forms.ModelMultipleChoiceField(\n queryset=get_user_model()\n .objects.filter(Q(groups__name=\"project manager\") | Q(groups__name=\"editor\"))\n .order_by(\"last_name\"),\n required=False,\n widget=FilteredSelectMultiple(verbose_name=\"proofread by\", is_stacked=False),\n )\n\n\nclass AdminSourceForm(forms.ModelForm):\n class Meta:\n model = Source\n fields = \"__all__\"\n\n title = forms.CharField(\n required=True,\n widget=AdminTextInputWidget,\n help_text=\"Full Manuscript Identification (City, Archive, Shelf-mark)\",\n )\n\n siglum = forms.CharField(\n required=True,\n widget=AdminTextInputWidget,\n help_text=\"RISM-style siglum + Shelf-mark (e.g. GB-Ob 202).\",\n )\n\n rism_siglum = forms.ModelChoiceField(\n queryset=RismSiglum.objects.all().order_by(\"name\"),\n required=False,\n )\n\n provenance = forms.ModelChoiceField(\n queryset=Provenance.objects.all().order_by(\"name\"),\n required=False,\n )\n TRUE_FALSE_CHOICES_SOURCE = (\n (True, \"Full source\"),\n (False, \"Fragment or Fragmented\"),\n )\n\n full_source = forms.ChoiceField(choices=TRUE_FALSE_CHOICES_SOURCE, required=False)\n\n century = forms.ModelMultipleChoiceField(\n queryset=Century.objects.all().order_by(\"name\"),\n required=False,\n widget=FilteredSelectMultiple(verbose_name=\"Century\", is_stacked=False),\n )\n\n current_editors = forms.ModelMultipleChoiceField(\n queryset=get_user_model()\n .objects.filter(\n Q(groups__name=\"project manager\")\n | Q(groups__name=\"editor\")\n | Q(groups__name=\"contributor\")\n )\n .order_by(\"last_name\"),\n required=False,\n widget=FilteredSelectMultiple(verbose_name=\"Century\", is_stacked=False),\n )\n\n melodies_entered_by = forms.ModelMultipleChoiceField(\n queryset=get_user_model().objects.all().order_by(\"full_name\"),\n required=False,\n widget=FilteredSelectMultiple(verbose_name=\"Century\", is_stacked=False),\n )\n\n TRUE_FALSE_CHOICES_INVEN = ((True, \"Complete\"), (False, \"Incomplete\"))\n\n complete_inventory = forms.ChoiceField(\n choices=TRUE_FALSE_CHOICES_INVEN, required=False\n )\n",
"path": "django/cantusdb_project/main_app/forms.py"
}
] | [
{
"content": "from django import forms\nfrom .models import (\n Chant,\n Office,\n Genre,\n Notation,\n Feast,\n Source,\n RismSiglum,\n Segment,\n Provenance,\n Century,\n Sequence,\n)\nfrom .widgets import (\n TextInputWidget,\n VolpianoInputWidget,\n TextAreaWidget,\n VolpianoAreaWidget,\n SelectWidget,\n CheckboxWidget,\n AdminTextAreaWidget,\n AdminTextInputWidget,\n)\nfrom django.contrib.auth import get_user_model\nfrom django.db.models import Q\nfrom django.contrib.admin.widgets import (\n FilteredSelectMultiple,\n)\n\n# ModelForm allows to build a form directly from a model\n# see https://docs.djangoproject.com/en/3.0/topics/forms/modelforms/\n\n\"\"\"\n# 3 ways of doing it\n#1 worst, helptext in the model will be missing\nclass CommetnForm(forms.Form):\n marginalia = forms.CharField(\n label=\"Marginalia\", widget=forms.TextInput(), help_text=\"help\"\n )\n url = forms.URLField()\n comment = forms.CharField()\n\n url.widget.attrs.update({'class': 'special'})\n comment.widget.attrs.update(size='40')\n#2\nclass CommentForm(forms.ModelForm):\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.fields['name'].widget.attrs.update({'class': 'special'})\n self.fields['comment'].widget.attrs.update(size='40')\n\"\"\"\n\n\nclass NameModelChoiceField(forms.ModelChoiceField):\n \"\"\"\n A custom ModelChoiceField that overrides the label_from_instance method\n to display the object's name attribute instead of str(object).\n This field is specifically designed for handling genre and office objects.\n Rather than displaying the name along with its description, sometimes we\n only want the shorthand notation for the genre and office objects.\n (Eg. [AV] Antiphon verse --> AV)\n \"\"\"\n\n def label_from_instance(self, obj):\n return obj.name\n\n\n# 3 best\nclass ChantCreateForm(forms.ModelForm):\n class Meta:\n model = Chant\n # specify either 'fields' or 'excludes' so that django knows which fields to use\n fields = [\n \"marginalia\",\n \"folio\",\n \"c_sequence\",\n \"office\",\n \"genre\",\n \"position\",\n \"cantus_id\",\n \"feast\",\n \"mode\",\n \"differentia\",\n \"differentia_new\",\n \"finalis\",\n \"extra\",\n \"chant_range\",\n \"manuscript_full_text_std_spelling\",\n \"manuscript_full_text\",\n \"volpiano\",\n \"image_link\",\n \"melody_id\",\n \"content_structure\",\n \"indexing_notes\",\n \"addendum\",\n ]\n # the widgets dictionary is ignored for a model field with a non-empty\n # choices attribute. In this case, you must override the form field to\n # use a different widget. this goes for all foreignkey and required fields\n # here, which are written explicitly below to override form field\n widgets = {\n \"marginalia\": TextInputWidget(),\n # folio: defined below (required)\n # c_sequence: defined below (required)\n # office: defined below (foreignkey)\n # genre: defined below (foreignkey)\n \"position\": TextInputWidget(),\n \"cantus_id\": TextInputWidget(),\n #'feast': defined below (foreignkey)\n \"mode\": TextInputWidget(),\n \"differentia\": TextInputWidget(),\n \"differentia_new\": TextInputWidget(),\n \"finalis\": TextInputWidget(),\n \"extra\": TextInputWidget(),\n \"chant_range\": VolpianoInputWidget(),\n # manuscript_full_text_std_spelling: defined below (required)\n \"manuscript_full_text\": TextAreaWidget(),\n \"volpiano\": VolpianoAreaWidget(),\n \"image_link\": TextInputWidget(),\n \"melody_id\": TextInputWidget(),\n \"content_structure\": TextInputWidget(),\n \"indexing_notes\": TextAreaWidget(),\n \"addendum\": TextInputWidget(),\n }\n\n folio = forms.CharField(\n required=True,\n widget=TextInputWidget,\n help_text=\"Binding order\",\n )\n\n c_sequence = forms.CharField(\n required=True,\n widget=TextInputWidget,\n help_text=\"Each folio starts with '1'.\",\n )\n\n # We use NameModelChoiceField here so the dropdown list of office/mass displays the name\n # instead of [name] + description\n office = NameModelChoiceField(\n queryset=Office.objects.all().order_by(\"name\"),\n required=False,\n )\n office.widget.attrs.update({\"class\": \"form-control custom-select custom-select-sm\"})\n\n # We use NameModelChoiceField here so the dropdown list of genres displays the name\n # instead of [name] + description\n genre = NameModelChoiceField(\n queryset=Genre.objects.all().order_by(\"name\"),\n required=False,\n )\n genre.widget.attrs.update({\"class\": \"form-control custom-select custom-select-sm\"})\n\n feast = forms.ModelChoiceField(\n queryset=Feast.objects.all().order_by(\"name\"), required=False\n )\n feast.widget.attrs.update({\"class\": \"form-control custom-select custom-select-sm\"})\n\n manuscript_full_text_std_spelling = forms.CharField(\n required=True,\n widget=TextAreaWidget,\n help_text=\"Manuscript full text with standardized spelling. Enter the words \"\n \"according to the manuscript but normalize their spellings following \"\n \"Classical Latin forms. Use upper-case letters for proper nouns, \"\n 'the first word of each chant, and the first word after \"Alleluia\" for '\n \"Mass Alleluias. Punctuation is omitted.\",\n )\n\n # automatically computed fields\n # source and incipit are mandatory fields in model,\n # but have to be optional in the form, otherwise the field validation won't pass\n source = forms.ModelChoiceField(\n queryset=Source.objects.all().order_by(\"title\"),\n required=False,\n error_messages={\n \"invalid_choice\": \"This source does not exist, please switch to a different source.\"\n },\n )\n incipit = forms.CharField(required=False)\n\n\nclass SourceCreateForm(forms.ModelForm):\n class Meta:\n model = Source\n fields = [\n \"title\",\n \"rism_siglum\",\n \"siglum\",\n \"provenance\",\n \"provenance_notes\",\n \"full_source\",\n \"date\",\n \"century\",\n \"cursus\",\n \"current_editors\",\n \"melodies_entered_by\",\n \"complete_inventory\",\n \"summary\",\n \"description\",\n \"selected_bibliography\",\n \"image_link\",\n \"fragmentarium_id\",\n \"dact_id\",\n \"indexing_notes\",\n ]\n widgets = {\n \"title\": TextInputWidget(),\n \"siglum\": TextInputWidget(),\n \"provenance_notes\": TextInputWidget(),\n \"date\": TextInputWidget(),\n \"cursus\": SelectWidget(),\n \"summary\": TextAreaWidget(),\n \"description\": TextAreaWidget(),\n \"selected_bibliography\": TextAreaWidget(),\n \"image_link\": TextInputWidget(),\n \"fragmentarium_id\": TextInputWidget(),\n \"dact_id\": TextInputWidget(),\n \"indexing_notes\": TextAreaWidget(),\n }\n\n rism_siglum = forms.ModelChoiceField(\n queryset=RismSiglum.objects.all().order_by(\"name\"), required=False\n )\n rism_siglum.widget.attrs.update(\n {\"class\": \"form-control custom-select custom-select-sm\"}\n )\n\n provenance = forms.ModelChoiceField(\n queryset=Provenance.objects.all().order_by(\"name\"), required=False\n )\n provenance.widget.attrs.update(\n {\"class\": \"form-control custom-select custom-select-sm\"}\n )\n\n TRUE_FALSE_CHOICES_SOURCE = (\n (True, \"Full source\"),\n (False, \"Fragment or Fragmented\"),\n )\n\n full_source = forms.ChoiceField(choices=TRUE_FALSE_CHOICES_SOURCE, required=False)\n full_source.widget.attrs.update(\n {\"class\": \"form-control custom-select custom-select-sm\"}\n )\n\n century = forms.ModelMultipleChoiceField(\n queryset=Century.objects.all().order_by(\"name\"), required=False\n )\n century.widget.attrs.update(\n {\"class\": \"form-control custom-select custom-select-sm\"}\n )\n\n current_editors = forms.ModelMultipleChoiceField(\n queryset=get_user_model()\n .objects.filter(\n Q(groups__name=\"project manager\")\n | Q(groups__name=\"editor\")\n | Q(groups__name=\"contributor\")\n )\n .order_by(\"last_name\"),\n required=False,\n )\n current_editors.widget.attrs.update(\n {\"class\": \"form-control custom-select custom-select-sm\"}\n )\n\n melodies_entered_by = forms.ModelMultipleChoiceField(\n queryset=get_user_model().objects.all().order_by(\"full_name\"), required=False\n )\n melodies_entered_by.widget.attrs.update(\n {\"class\": \"form-control custom-select custom-select-sm\"}\n )\n\n TRUE_FALSE_CHOICES_INVEN = ((True, \"Complete\"), (False, \"Incomplete\"))\n\n complete_inventory = forms.ChoiceField(\n choices=TRUE_FALSE_CHOICES_INVEN, required=False\n )\n complete_inventory.widget.attrs.update(\n {\"class\": \"form-control custom-select custom-select-sm\"}\n )\n\n\nclass ChantEditForm(forms.ModelForm):\n class Meta:\n model = Chant\n fields = [\n \"manuscript_full_text_std_spelling\",\n \"manuscript_full_text\",\n \"volpiano\",\n \"marginalia\",\n \"folio\",\n \"c_sequence\",\n \"feast\",\n \"office\",\n \"genre\",\n \"position\",\n \"cantus_id\",\n \"melody_id\",\n \"mode\",\n \"finalis\",\n \"differentia\",\n \"differentia_new\",\n \"extra\",\n \"image_link\",\n \"indexing_notes\",\n \"addendum\",\n ]\n widgets = {\n # manuscript_full_text_std_spelling: defined below (required)\n \"manuscript_full_text\": TextAreaWidget(),\n \"volpiano\": VolpianoAreaWidget(),\n \"marginalia\": TextInputWidget(),\n # folio: defined below (required)\n # c_sequence: defined below (required)\n # feast: defined below (foreignkey)\n # office: defined below (foreignkey)\n # genre: defined below (foreignkey)\n \"position\": TextInputWidget(),\n \"cantus_id\": TextInputWidget(),\n \"melody_id\": TextInputWidget(),\n \"mode\": TextInputWidget(),\n \"finalis\": TextInputWidget(),\n \"differentia\": TextInputWidget(),\n \"differentia_new\": TextInputWidget(),\n \"extra\": TextInputWidget(),\n \"image_link\": TextInputWidget(),\n \"indexing_notes\": TextAreaWidget(),\n \"addendum\": TextInputWidget(),\n }\n\n manuscript_full_text_std_spelling = forms.CharField(\n required=True,\n widget=TextAreaWidget,\n help_text=\"Manuscript full text with standardized spelling. Enter the words \"\n \"according to the manuscript but normalize their spellings following \"\n \"Classical Latin forms. Use upper-case letters for proper nouns, \"\n 'the first word of each chant, and the first word after \"Alleluia\" for '\n \"Mass Alleluias. Punctuation is omitted.\",\n )\n\n folio = forms.CharField(\n required=True,\n widget=TextInputWidget,\n help_text=\"Binding order\",\n )\n\n c_sequence = forms.CharField(\n required=True,\n widget=TextInputWidget,\n help_text=\"Each folio starts with '1'.\",\n )\n\n feast = forms.ModelChoiceField(\n queryset=Feast.objects.all().order_by(\"name\"), required=False\n )\n feast.widget.attrs.update({\"class\": \"form-control custom-select custom-select-sm\"})\n\n # We use NameModelChoiceField here so the dropdown list of office/mass displays the name\n # instead of [name] + description\n office = NameModelChoiceField(\n queryset=Office.objects.all().order_by(\"name\"),\n required=False,\n )\n office.widget.attrs.update({\"class\": \"form-control custom-select custom-select-sm\"})\n\n # We use NameModelChoiceField here so the dropdown list of genres displays the name\n # instead of [name] + description\n genre = NameModelChoiceField(\n queryset=Genre.objects.all().order_by(\"name\"),\n required=False,\n )\n genre.widget.attrs.update({\"class\": \"form-control custom-select custom-select-sm\"})\n\n\nclass ChantProofreadForm(forms.ModelForm):\n class Meta:\n model = Chant\n fields = [\n \"manuscript_full_text_std_spelling\",\n \"manuscript_full_text\",\n \"volpiano\",\n \"marginalia\",\n \"folio\",\n \"c_sequence\",\n \"feast\",\n \"office\",\n \"genre\",\n \"position\",\n \"cantus_id\",\n \"melody_id\",\n \"mode\",\n \"finalis\",\n \"differentia\",\n \"extra\",\n \"image_link\",\n \"indexing_notes\",\n # additional fields for the proofreading form\n \"manuscript_full_text_std_proofread\",\n \"manuscript_full_text_proofread\",\n \"volpiano_proofread\",\n \"proofread_by\",\n \"manuscript_syllabized_full_text\",\n \"chant_range\",\n \"siglum\",\n \"addendum\",\n \"differentia_new\",\n ]\n widgets = {\n # manuscript_full_text_std_spelling: defined below (required)\n \"manuscript_full_text\": TextAreaWidget(),\n \"volpiano\": VolpianoAreaWidget(),\n \"marginalia\": TextInputWidget(),\n # folio: defined below (required)\n # c_sequence: defined below (required)\n # \"office\": defined below (foreignkey)\n # \"genre\": defined below (foreignkey)\n \"position\": TextInputWidget(),\n \"cantus_id\": TextInputWidget(),\n \"melody_id\": TextInputWidget(),\n \"mode\": TextInputWidget(),\n \"finalis\": TextInputWidget(),\n \"differentia\": TextInputWidget(),\n \"extra\": TextInputWidget(),\n \"image_link\": TextInputWidget(),\n \"indexing_notes\": TextAreaWidget(),\n # additional fields for the proofreading form\n \"manuscript_full_text_std_proofread\": CheckboxWidget(),\n \"manuscript_full_text_proofread\": CheckboxWidget(),\n \"volpiano_proofread\": CheckboxWidget(),\n \"manuscript_syllabized_full_text\": TextAreaWidget(),\n \"chant_range\": VolpianoAreaWidget(),\n \"siglum\": TextInputWidget(),\n \"addendum\": TextInputWidget(),\n \"differentia_new\": TextInputWidget(),\n }\n\n manuscript_full_text_std_spelling = forms.CharField(\n required=True,\n widget=TextAreaWidget,\n help_text=\"Manuscript full text with standardized spelling. Enter the words \"\n \"according to the manuscript but normalize their spellings following \"\n \"Classical Latin forms. Use upper-case letters for proper nouns, \"\n 'the first word of each chant, and the first word after \"Alleluia\" for '\n \"Mass Alleluias. Punctuation is omitted.\",\n )\n\n folio = forms.CharField(\n required=True,\n widget=TextInputWidget,\n help_text=\"Binding order\",\n )\n\n c_sequence = forms.CharField(\n required=True,\n widget=TextInputWidget,\n help_text=\"Each folio starts with '1'.\",\n )\n\n feast = forms.ModelChoiceField(\n queryset=Feast.objects.all().order_by(\"name\"),\n required=False,\n )\n feast.widget.attrs.update({\"class\": \"form-control custom-select custom-select-sm\"})\n\n # We use NameModelChoiceField here so the dropdown list of office/mass displays the name\n # instead of [name] + description\n office = NameModelChoiceField(\n queryset=Office.objects.all().order_by(\"name\"),\n required=False,\n )\n office.widget.attrs.update({\"class\": \"form-control custom-select custom-select-sm\"})\n\n # We use NameModelChoiceField here so the dropdown list of genres displays the name\n # instead of [name] + description\n genre = NameModelChoiceField(\n queryset=Genre.objects.all().order_by(\"name\"), required=False\n )\n genre.widget.attrs.update({\"class\": \"form-control custom-select custom-select-sm\"})\n\n proofread_by = forms.ModelMultipleChoiceField(\n queryset=get_user_model()\n .objects.filter(Q(groups__name=\"project manager\") | Q(groups__name=\"editor\"))\n .order_by(\"last_name\"),\n required=False,\n )\n proofread_by.widget.attrs.update(\n {\"class\": \"form-control custom-select custom-select-sm\"}\n )\n\n\nclass SourceEditForm(forms.ModelForm):\n class Meta:\n model = Source\n fields = [\n \"title\",\n \"rism_siglum\",\n \"siglum\",\n \"provenance\",\n \"provenance_notes\",\n \"full_source\",\n \"date\",\n \"century\",\n \"cursus\",\n \"current_editors\",\n \"melodies_entered_by\",\n \"complete_inventory\",\n \"summary\",\n \"description\",\n \"selected_bibliography\",\n \"image_link\",\n \"fragmentarium_id\",\n \"dact_id\",\n \"indexing_notes\",\n ]\n widgets = {\n \"title\": TextInputWidget(),\n \"rism_siglum\": TextInputWidget(),\n \"siglum\": TextInputWidget(),\n \"provenance_notes\": TextInputWidget(),\n \"date\": TextInputWidget(),\n \"summary\": TextAreaWidget(),\n \"description\": TextAreaWidget(),\n \"selected_bibliography\": TextAreaWidget(),\n \"image_link\": TextInputWidget(),\n \"fragmentarium_id\": TextInputWidget(),\n \"dact_id\": TextInputWidget(),\n \"indexing_notes\": TextAreaWidget(),\n }\n\n provenance = forms.ModelChoiceField(\n queryset=Provenance.objects.all().order_by(\"name\"), required=False\n )\n provenance.widget.attrs.update(\n {\"class\": \"form-control custom-select custom-select-sm\"}\n ) # adds styling\n\n century = forms.ModelMultipleChoiceField(\n queryset=Century.objects.all().order_by(\"name\"), required=False\n )\n century.widget.attrs.update(\n {\"class\": \"form-control custom-select custom-select-sm\"}\n )\n\n CHOICES_FULL_SOURCE = (\n (None, \"None\"),\n (True, \"Full source\"),\n (False, \"Fragment or Fragmented\"),\n )\n full_source = forms.ChoiceField(choices=CHOICES_FULL_SOURCE, required=False)\n full_source.widget.attrs.update(\n {\"class\": \"form-control custom-select custom-select-sm\"}\n )\n\n CHOICES_CURSUS = (\n (None, \"None\"),\n (\"Monastic\", \"Monastic\"),\n (\"Secular\", \"Secular\"),\n )\n cursus = forms.ChoiceField(choices=CHOICES_CURSUS, required=False)\n cursus.widget.attrs.update({\"class\": \"form-control custom-select custom-select-sm\"})\n\n current_editors = forms.ModelMultipleChoiceField(\n queryset=get_user_model()\n .objects.filter(\n Q(groups__name=\"project manager\")\n | Q(groups__name=\"editor\")\n | Q(groups__name=\"contributor\")\n )\n .order_by(\"last_name\"),\n required=False,\n )\n current_editors.widget.attrs.update(\n {\"class\": \"form-control custom-select custom-select-sm\"}\n )\n\n melodies_entered_by = forms.ModelMultipleChoiceField(\n queryset=get_user_model().objects.all().order_by(\"full_name\"), required=False\n )\n melodies_entered_by.widget.attrs.update(\n {\"class\": \"form-control custom-select custom-select-sm\"}\n )\n\n CHOICES_COMPLETE_INV = (\n (True, \"complete inventory\"),\n (False, \"partial inventory\"),\n )\n complete_inventory = forms.ChoiceField(choices=CHOICES_COMPLETE_INV, required=False)\n complete_inventory.widget.attrs.update(\n {\"class\": \"form-control custom-select custom-select-sm\"}\n )\n\n\nclass SequenceEditForm(forms.ModelForm):\n class Meta:\n model = Sequence\n fields = [\n \"title\",\n \"siglum\",\n \"incipit\",\n \"folio\",\n \"s_sequence\",\n \"genre\",\n \"rubrics\",\n \"analecta_hymnica\",\n \"indexing_notes\",\n \"date\",\n \"col1\",\n \"col2\",\n \"col3\",\n \"ah_volume\",\n \"source\",\n \"cantus_id\",\n \"image_link\",\n ]\n widgets = {\n \"title\": TextInputWidget(),\n \"siglum\": TextInputWidget(),\n \"incipit\": TextInputWidget(),\n \"folio\": TextInputWidget(),\n \"s_sequence\": TextInputWidget(),\n \"rubrics\": TextInputWidget(),\n \"analecta_hymnica\": TextInputWidget(),\n \"indexing_notes\": TextAreaWidget(),\n \"date\": TextInputWidget(),\n \"col1\": TextInputWidget(),\n \"col2\": TextInputWidget(),\n \"col3\": TextInputWidget(),\n \"ah_volume\": TextInputWidget(),\n \"cantus_id\": TextInputWidget(),\n \"image_link\": TextInputWidget(),\n }\n\n # We use NameModelChoiceField here so the dropdown list of genres displays the name\n # instead of [name] + description\n genre = NameModelChoiceField(\n queryset=Genre.objects.all().order_by(\"name\"), required=False\n )\n genre.widget.attrs.update({\"class\": \"form-control custom-select custom-select-sm\"})\n\n source = forms.ModelChoiceField(\n queryset=Source.objects.all().order_by(\"title\"), required=False\n )\n source.widget.attrs.update({\"class\": \"form-control custom-select custom-select-sm\"})\n\n\nclass ChantEditSyllabificationForm(forms.ModelForm):\n class Meta:\n model = Chant\n fields = [\n \"manuscript_full_text\",\n \"manuscript_syllabized_full_text\",\n ]\n widgets = {\n \"manuscript_full_text\": TextAreaWidget(),\n \"manuscript_syllabized_full_text\": TextAreaWidget(),\n }\n\n\nclass AdminCenturyForm(forms.ModelForm):\n class Meta:\n model = Century\n fields = \"__all__\"\n\n name = forms.CharField(required=True, widget=AdminTextInputWidget)\n\n\nclass AdminChantForm(forms.ModelForm):\n class Meta:\n model = Chant\n fields = \"__all__\"\n widgets = {\n \"volpiano\": VolpianoAreaWidget(),\n \"indexing_notes\": TextAreaWidget(),\n \"manuscript_full_text_std_proofread\": CheckboxWidget(),\n \"manuscript_full_text_proofread\": CheckboxWidget(),\n \"volpiano_proofread\": CheckboxWidget(),\n \"chant_range\": VolpianoAreaWidget(),\n }\n\n manuscript_full_text_std_spelling = forms.CharField(\n required=True,\n widget=AdminTextAreaWidget,\n help_text=\"Manuscript full text with standardized spelling. Enter the words \"\n \"according to the manuscript but normalize their spellings following \"\n \"Classical Latin forms. Use upper-case letters for proper nouns, \"\n 'the first word of each chant, and the first word after \"Alleluia\" for '\n \"Mass Alleluias. Punctuation is omitted.\",\n )\n\n folio = forms.CharField(\n required=True,\n widget=AdminTextInputWidget,\n help_text=\"Binding order\",\n )\n\n c_sequence = forms.CharField(\n required=True,\n widget=AdminTextInputWidget,\n help_text=\"Each folio starts with '1'.\",\n label=\"Sequence\",\n )\n\n # We use NameModelChoiceField here so the dropdown list of office/mass displays the name\n # instead of [name] + description\n office = NameModelChoiceField(\n queryset=Office.objects.all().order_by(\"name\"),\n required=False,\n )\n # We use NameModelChoiceField here so the dropdown list of genres displays the name\n # instead of [name] + description\n genre = NameModelChoiceField(\n queryset=Genre.objects.all().order_by(\"name\"), required=False\n )\n\n proofread_by = forms.ModelMultipleChoiceField(\n queryset=get_user_model()\n .objects.filter(Q(groups__name=\"project manager\") | Q(groups__name=\"editor\"))\n .order_by(\"last_name\"),\n required=False,\n widget=FilteredSelectMultiple(verbose_name=\"proofread by\", is_stacked=False),\n )\n\n\nclass AdminFeastForm(forms.ModelForm):\n class Meta:\n model = Feast\n fields = \"__all__\"\n\n name = forms.CharField(required=True, widget=AdminTextInputWidget)\n\n\nclass AdminGenreForm(forms.ModelForm):\n class Meta:\n model = Genre\n fields = \"__all__\"\n\n name = forms.CharField(required=True, widget=AdminTextInputWidget)\n description = forms.CharField(required=True, widget=AdminTextAreaWidget)\n\n\nclass AdminNotationForm(forms.ModelForm):\n class Meta:\n model = Notation\n fields = \"__all__\"\n\n name = forms.CharField(required=True, widget=AdminTextInputWidget)\n name.widget.attrs.update({\"style\": \"width: 400px;\"})\n\n\nclass AdminOfficeForm(forms.ModelForm):\n class Meta:\n model = Office\n fields = \"__all__\"\n\n name = forms.CharField(required=True, widget=AdminTextInputWidget)\n description = forms.CharField(required=True, widget=AdminTextAreaWidget)\n\n\nclass AdminProvenanceForm(forms.ModelForm):\n class Meta:\n model = Provenance\n fields = \"__all__\"\n\n name = forms.CharField(required=True, widget=AdminTextInputWidget)\n\n\nclass AdminRismSiglumForm(forms.ModelForm):\n class Meta:\n model = RismSiglum\n fields = \"__all__\"\n\n name = forms.CharField(required=True, widget=AdminTextInputWidget)\n\n\nclass AdminSegmentForm(forms.ModelForm):\n class Meta:\n model = Segment\n fields = \"__all__\"\n\n name = forms.CharField(required=True, widget=AdminTextInputWidget)\n name.widget.attrs.update({\"style\": \"width: 400px;\"})\n\n\nclass AdminSequenceForm(forms.ModelForm):\n class Meta:\n model = Sequence\n fields = \"__all__\"\n widgets = {\n \"volpiano\": VolpianoAreaWidget(),\n \"indexing_notes\": TextAreaWidget(),\n \"manuscript_full_text_std_proofread\": CheckboxWidget(),\n \"manuscript_full_text_proofread\": CheckboxWidget(),\n \"volpiano_proofread\": CheckboxWidget(),\n \"chant_range\": VolpianoAreaWidget(),\n }\n\n # We use NameModelChoiceField here so the dropdown list of office/mass displays the name\n # instead of [name] + description\n office = NameModelChoiceField(\n queryset=Office.objects.all().order_by(\"name\"),\n required=False,\n )\n # We use NameModelChoiceField here so the dropdown list of genres displays the name\n # instead of [name] + description\n genre = NameModelChoiceField(\n queryset=Genre.objects.all().order_by(\"name\"), required=False\n )\n\n proofread_by = forms.ModelMultipleChoiceField(\n queryset=get_user_model()\n .objects.filter(Q(groups__name=\"project manager\") | Q(groups__name=\"editor\"))\n .order_by(\"last_name\"),\n required=False,\n widget=FilteredSelectMultiple(verbose_name=\"proofread by\", is_stacked=False),\n )\n\n\nclass AdminSourceForm(forms.ModelForm):\n class Meta:\n model = Source\n fields = \"__all__\"\n\n title = forms.CharField(\n required=True,\n widget=AdminTextInputWidget,\n help_text=\"Full Manuscript Identification (City, Archive, Shelf-mark)\",\n )\n title.widget.attrs.update({\"style\": \"width: 610px;\"})\n\n siglum = forms.CharField(\n required=True,\n widget=AdminTextInputWidget,\n help_text=\"RISM-style siglum + Shelf-mark (e.g. GB-Ob 202).\",\n )\n\n rism_siglum = forms.ModelChoiceField(\n queryset=RismSiglum.objects.all().order_by(\"name\"),\n required=False,\n )\n\n provenance = forms.ModelChoiceField(\n queryset=Provenance.objects.all().order_by(\"name\"),\n required=False,\n )\n TRUE_FALSE_CHOICES_SOURCE = (\n (True, \"Full source\"),\n (False, \"Fragment or Fragmented\"),\n )\n\n full_source = forms.ChoiceField(choices=TRUE_FALSE_CHOICES_SOURCE, required=False)\n\n century = forms.ModelMultipleChoiceField(\n queryset=Century.objects.all().order_by(\"name\"),\n required=False,\n widget=FilteredSelectMultiple(verbose_name=\"Century\", is_stacked=False),\n )\n\n current_editors = forms.ModelMultipleChoiceField(\n queryset=get_user_model()\n .objects.filter(\n Q(groups__name=\"project manager\")\n | Q(groups__name=\"editor\")\n | Q(groups__name=\"contributor\")\n )\n .order_by(\"last_name\"),\n required=False,\n widget=FilteredSelectMultiple(verbose_name=\"Century\", is_stacked=False),\n )\n\n melodies_entered_by = forms.ModelMultipleChoiceField(\n queryset=get_user_model().objects.all().order_by(\"full_name\"),\n required=False,\n widget=FilteredSelectMultiple(verbose_name=\"Century\", is_stacked=False),\n )\n\n TRUE_FALSE_CHOICES_INVEN = ((True, \"Complete\"), (False, \"Incomplete\"))\n\n complete_inventory = forms.ChoiceField(\n choices=TRUE_FALSE_CHOICES_INVEN, required=False\n )\n",
"path": "django/cantusdb_project/main_app/forms.py"
}
] | diff --git a/django/cantusdb_project/main_app/forms.py b/django/cantusdb_project/main_app/forms.py
index d595973c3..d106d4284 100644
--- a/django/cantusdb_project/main_app/forms.py
+++ b/django/cantusdb_project/main_app/forms.py
@@ -826,6 +826,7 @@ class Meta:
widget=AdminTextInputWidget,
help_text="Full Manuscript Identification (City, Archive, Shelf-mark)",
)
+ title.widget.attrs.update({"style": "width: 610px;"})
siglum = forms.CharField(
required=True,
|
webkom__lego-3128 | Broken link on weekly mails
The link used to unsubscribe from the mail is broken, because `frontend_url` is undefined. Probably due to the weekly mails being handled differently than all other notifications.
| [
{
"content": "from datetime import timedelta\n\nfrom django.conf import settings\nfrom django.template.loader import render_to_string\nfrom django.utils import timezone\n\nfrom premailer import transform\nfrom structlog import get_logger\n\nfrom lego import celery_app\nfrom lego.apps.events.constants import EVENT_TYPE_TRANSLATIONS\nfrom lego.apps.events.models import Event\nfrom lego.apps.joblistings.constants import JOB_TYPE_TRANSLATIONS\nfrom lego.apps.joblistings.models import Joblisting\nfrom lego.apps.notifications.constants import EMAIL, WEEKLY_MAIL\nfrom lego.apps.notifications.models import NotificationSetting\nfrom lego.apps.permissions.utils import get_permission_handler\nfrom lego.apps.restricted.message_processor import MessageProcessor\nfrom lego.apps.tags.models import Tag\nfrom lego.apps.users.models import AbakusGroup\nfrom lego.utils.tasks import AbakusTask\n\nlog = get_logger()\n\n\ndef create_weekly_mail(user):\n three_days_ago_timestamp = timezone.now() - timedelta(days=3)\n last_sunday_timestamp = timezone.now() - timedelta(days=7)\n\n weekly_tag = Tag.objects.filter(tag=\"weekly\").first()\n # Check if weekly tag exists so it does not crash if some idiot deletes the weekly tag\n todays_weekly = (\n weekly_tag.article_set.filter(created_at__gt=three_days_ago_timestamp).first()\n if weekly_tag\n else None\n )\n\n events_next_week = Event.objects.filter(\n pools__activation_date__gt=timezone.now(),\n pools__activation_date__lt=timezone.now() + timedelta(days=7),\n ).distinct()\n\n permission_handler = get_permission_handler(events_next_week.model)\n filtered_events = permission_handler.filter_queryset(user, events_next_week)\n\n filtered_events = filter(\n lambda event: event.get_possible_pools(user, True) or event.is_admitted(user),\n filtered_events,\n )\n\n joblistings_last_week = Joblisting.objects.filter(\n created_at__gt=last_sunday_timestamp, visible_from__lt=timezone.now()\n )\n\n joblistings = []\n for joblisting in joblistings_last_week:\n joblistings.append(\n {\n \"id\": joblisting.id,\n \"company_name\": joblisting.company.name,\n \"type\": JOB_TYPE_TRANSLATIONS[joblisting.job_type],\n \"title\": joblisting.title,\n }\n )\n\n events = []\n for event in filtered_events:\n pools = []\n for pool in event.pools.all():\n pools.append(\n {\n \"name\": pool.name,\n \"activation_date\": pool.activation_date.strftime(\"%d/%m kl. %H:%M\"),\n }\n )\n\n events.append(\n {\n \"title\": event.title,\n \"id\": event.id,\n \"pools\": pools,\n \"start_time\": event.start_time.strftime(\"%d/%m kl %H:%M\"),\n \"url\": event.get_absolute_url(),\n \"type\": EVENT_TYPE_TRANSLATIONS[event.event_type],\n }\n )\n\n html_body = render_to_string(\n \"email/email/weekly_mail.html\",\n {\n \"events\": events,\n \"todays_weekly\": \"\"\n if todays_weekly is None\n else todays_weekly.get_absolute_url(),\n \"joblistings\": joblistings,\n },\n )\n if events or joblistings or todays_weekly:\n return html_body\n return None\n\n\n@celery_app.task(serializer=\"json\", bind=True, base=AbakusTask)\ndef send_weekly_email(self, logger_context=None):\n self.setup_logger(logger_context)\n\n week_number = timezone.now().isocalendar().week\n\n # Set to just PR and Webkom for testing purposes\n all_users = set(\n AbakusGroup.objects.get(name=\"Webkom\").restricted_lookup()[0]\n + AbakusGroup.objects.get(name=\"PR\").restricted_lookup()[0]\n )\n recipients = []\n\n for user in all_users:\n if not user.email_lists_enabled:\n # Don't send emails to users that don't want mail.\n continue\n\n if EMAIL not in NotificationSetting.active_channels(user, WEEKLY_MAIL):\n continue\n recipients.append(user)\n\n datatuple = (\n (\n f\"Ukesmail uke {week_number}\",\n transform(html) if (html := create_weekly_mail(user)) is not None else None,\n settings.DEFAULT_FROM_EMAIL,\n [user.email],\n )\n for user in recipients\n )\n datatuple = tuple(tuppel for tuppel in datatuple if tuppel[1] is not None)\n if datatuple:\n MessageProcessor.send_mass_mail_html(datatuple)\n",
"path": "lego/apps/email/tasks.py"
}
] | [
{
"content": "from datetime import timedelta\n\nfrom django.conf import settings\nfrom django.template.loader import render_to_string\nfrom django.utils import timezone\n\nfrom premailer import transform\nfrom structlog import get_logger\n\nfrom lego import celery_app\nfrom lego.apps.events.constants import EVENT_TYPE_TRANSLATIONS\nfrom lego.apps.events.models import Event\nfrom lego.apps.joblistings.constants import JOB_TYPE_TRANSLATIONS\nfrom lego.apps.joblistings.models import Joblisting\nfrom lego.apps.notifications.constants import EMAIL, WEEKLY_MAIL\nfrom lego.apps.notifications.models import NotificationSetting\nfrom lego.apps.permissions.utils import get_permission_handler\nfrom lego.apps.restricted.message_processor import MessageProcessor\nfrom lego.apps.tags.models import Tag\nfrom lego.apps.users.models import AbakusGroup\nfrom lego.utils.tasks import AbakusTask\n\nlog = get_logger()\n\n\ndef create_weekly_mail(user):\n three_days_ago_timestamp = timezone.now() - timedelta(days=3)\n last_sunday_timestamp = timezone.now() - timedelta(days=7)\n\n weekly_tag = Tag.objects.filter(tag=\"weekly\").first()\n # Check if weekly tag exists so it does not crash if some idiot deletes the weekly tag\n todays_weekly = (\n weekly_tag.article_set.filter(created_at__gt=three_days_ago_timestamp).first()\n if weekly_tag\n else None\n )\n\n events_next_week = Event.objects.filter(\n pools__activation_date__gt=timezone.now(),\n pools__activation_date__lt=timezone.now() + timedelta(days=7),\n ).distinct()\n\n permission_handler = get_permission_handler(events_next_week.model)\n filtered_events = permission_handler.filter_queryset(user, events_next_week)\n\n filtered_events = filter(\n lambda event: event.get_possible_pools(user, True) or event.is_admitted(user),\n filtered_events,\n )\n\n joblistings_last_week = Joblisting.objects.filter(\n created_at__gt=last_sunday_timestamp, visible_from__lt=timezone.now()\n )\n\n joblistings = []\n for joblisting in joblistings_last_week:\n joblistings.append(\n {\n \"id\": joblisting.id,\n \"company_name\": joblisting.company.name,\n \"type\": JOB_TYPE_TRANSLATIONS[joblisting.job_type],\n \"title\": joblisting.title,\n }\n )\n\n events = []\n for event in filtered_events:\n pools = []\n for pool in event.pools.all():\n pools.append(\n {\n \"name\": pool.name,\n \"activation_date\": pool.activation_date.strftime(\"%d/%m kl. %H:%M\"),\n }\n )\n\n events.append(\n {\n \"title\": event.title,\n \"id\": event.id,\n \"pools\": pools,\n \"start_time\": event.start_time.strftime(\"%d/%m kl %H:%M\"),\n \"url\": event.get_absolute_url(),\n \"type\": EVENT_TYPE_TRANSLATIONS[event.event_type],\n }\n )\n\n html_body = render_to_string(\n \"email/email/weekly_mail.html\",\n {\n \"events\": events,\n \"todays_weekly\": \"\"\n if todays_weekly is None\n else todays_weekly.get_absolute_url(),\n \"joblistings\": joblistings,\n \"frontend_url\": settings.FRONTEND_URL,\n },\n )\n if events or joblistings or todays_weekly:\n return html_body\n return None\n\n\n@celery_app.task(serializer=\"json\", bind=True, base=AbakusTask)\ndef send_weekly_email(self, logger_context=None):\n self.setup_logger(logger_context)\n\n week_number = timezone.now().isocalendar().week\n\n # Set to just PR and Webkom for testing purposes\n all_users = set(\n AbakusGroup.objects.get(name=\"Webkom\").restricted_lookup()[0]\n + AbakusGroup.objects.get(name=\"PR\").restricted_lookup()[0]\n )\n recipients = []\n\n for user in all_users:\n if not user.email_lists_enabled:\n # Don't send emails to users that don't want mail.\n continue\n\n if EMAIL not in NotificationSetting.active_channels(user, WEEKLY_MAIL):\n continue\n recipients.append(user)\n\n datatuple = (\n (\n f\"Ukesmail uke {week_number}\",\n transform(html) if (html := create_weekly_mail(user)) is not None else None,\n settings.DEFAULT_FROM_EMAIL,\n [user.email],\n )\n for user in recipients\n )\n datatuple = tuple(tuppel for tuppel in datatuple if tuppel[1] is not None)\n if datatuple:\n MessageProcessor.send_mass_mail_html(datatuple)\n",
"path": "lego/apps/email/tasks.py"
}
] | diff --git a/lego/apps/email/tasks.py b/lego/apps/email/tasks.py
index a9b946bc7..3270a3e2b 100644
--- a/lego/apps/email/tasks.py
+++ b/lego/apps/email/tasks.py
@@ -93,6 +93,7 @@ def create_weekly_mail(user):
if todays_weekly is None
else todays_weekly.get_absolute_url(),
"joblistings": joblistings,
+ "frontend_url": settings.FRONTEND_URL,
},
)
if events or joblistings or todays_weekly:
|
OpenMined__PySyft-4923 | Small correction in Sample Code Block of sy.core.pointer.pointer
## Where?
Where are you looking to add documentation? Which file? Which feature?
In directory PySyft/src/syft/core/pointer directory, there is a python file, pointer.py.
The example code in the python file can be viewed by the user by executing the following command:
import syft as sy
sy.core.pointer.pointer?
The example code in the output of above command is as follows:
Example:
.. code-block::
# creating the data holder domain
domain_1 = Domain(name="Data holder domain")
# creating dummy data
tensor = th.tensor([1, 2, 3])
# creating the data holder client
domain_1_client = domain_1.get_root_client()
# sending the data to the client and receiving a pointer of that data.
data_ptr_domain_1 = tensor.send(domain_1_client) # or tensor.send_to(domain_1_client)
# creating the data user domain
domain_2 = Domain(name="Data user domain")
# creating a request to access the data
data_ptr_domain_1.request(
name="My Request", reason="I'd lke to see this pointer"
)
# getting the remote id of the object
requested_object = data_ptr_domain_1.id_at_location
message_request_id = domain_1_client.request_queue.get_request_id_from_object_id(
object_id=requested_object
)
# the data holder accepts the request
domain_1.requests[0].owner_client_if_available = domain_1_client
domain_1.requests[0].accept()
# the data user checks if the data holder approved his request
response = data_ptr_domain_1.check_access(node=domain_2, request_id=message_request_id)
**The following line is where the issue is:**
# getting the request id
message_request_id = domain_1_client.request_queue.get_request_id_from_object_id(
object_id=requested_object
)
**Error Generated by the line is:**
AttributeError: 'DomainClient' object has no attribute 'request_queue'
**Solution** :
Replacing "request_queue" to "requests" solves the issue.
| [
{
"content": "\"\"\"A Pointer is the main handler when interacting with remote data.\nA Pointer object represents an API for interacting with data (of any type)\nat a specific location. The pointer should never be instantiated, only subclassed.\n\nThe relation between pointers and data is many to one,\nthere can be multiple pointers pointing to the same piece of data, meanwhile,\na pointer cannot point to multiple data sources.\n\nA pointer is just an object id on a remote location and a set of methods that can be\nexecuted on the remote machine directly on that object. One note that has to be made\nis that all operations between pointers will return a pointer, the only way to have access\nto the result is by calling .get() on the pointer.\n\nThere are two proper ways of receiving a pointer on some data:\n 1. When sending that data on a remote machine the user receives a pointer.\n 2. When the user searches for the data in an object store it receives a pointer to that data,\n if it has the correct permissions for that.\n\nAfter receiving a pointer, one might want to get the data behind the pointer locally. For that the\nuser should:\n 1. Request access by calling .request().\n Example:\n\n .. code-block::\n\n pointer_object.request(name = \"Request name\", reason = \"Request reason\")\n\n 2.1 - The data owner has to approve the request (check the domain node docs).\n 2.2 - The data user checks if the request has been approved (check the domain node docs).\n 3. After the request has been approved, the data user can call .get() on the pointer to get the\n data locally.\n Example:\n\n .. code-block::\n\n pointer_object.get()\n\nPointers are being generated for most types of objects in the data science scene, but what you can\ndo on them is not the pointers job, see the lib module for more details. One can see the pointer\nas a proxy to the actual data, the filtering and the security being applied where the data is being\nheld.\n\nExample:\n\n.. code-block::\n\n # creating the data holder domain\n domain_1 = Domain(name=\"Data holder domain\")\n\n # creating dummy data\n tensor = th.tensor([1, 2, 3])\n\n # creating the data holder client\n domain_1_client = domain_1.get_root_client()\n\n # sending the data to the client and receiving a pointer of that data.\n data_ptr_domain_1 = tensor.send(domain_1_client) # or tensor.send_to(domain_1_client)\n\n # creating the data user domain\n domain_2 = Domain(name=\"Data user domain\")\n\n # creating a request to access the data\n data_ptr_domain_1.request(\n name=\"My Request\", reason=\"I'd lke to see this pointer\"\n )\n\n # getting the remote id of the object\n requested_object = data_ptr_domain_1.id_at_location\n\n # getting the request id\n message_request_id = domain_1_client.request_queue.get_request_id_from_object_id(\n object_id=requested_object\n )\n\n # the data holder accepts the request\n domain_1.requests[0].owner_client_if_available = domain_1_client\n domain_1.requests[0].accept()\n\n # the data user checks if the data holder approved his request\n response = data_ptr_domain_1.check_access(node=domain_2, request_id=message_request_id)\n\n\"\"\"\n# stdlib\nimport time\nfrom typing import Any\nfrom typing import List\nfrom typing import Optional\n\n# third party\nfrom google.protobuf.reflection import GeneratedProtocolMessageType\nfrom loguru import logger\nfrom nacl.signing import VerifyKey\n\n# syft absolute\nimport syft as sy\n\n# syft relative\nfrom ...decorators.syft_decorator_impl import syft_decorator\nfrom ...proto.core.pointer.pointer_pb2 import Pointer as Pointer_PB\nfrom ..common.pointer import AbstractPointer\nfrom ..common.serde.deserialize import _deserialize\nfrom ..common.uid import UID\nfrom ..io.address import Address\nfrom ..node.abstract.node import AbstractNode\nfrom ..node.common.action.garbage_collect_object_action import (\n GarbageCollectObjectAction,\n)\nfrom ..node.common.action.get_object_action import GetObjectAction\nfrom ..node.common.service.obj_search_permission_service import (\n ObjectSearchPermissionUpdateMessage,\n)\nfrom ..store.storeable_object import StorableObject\n\n\n# TODO: Fix the Client, Address, Location confusion\nclass Pointer(AbstractPointer):\n \"\"\"\n The pointer is the handler when interacting with remote data.\n\n Automatically generated subclasses of Pointer need to be able to look up\n the path and name of the object type they point to as a part of serde. For more\n information on how subclasses are automatically generated, please check the ast\n module.\n\n :param location: The location where the data is being held.\n :type location: Address\n :param id_at_location: The UID of the object on the remote location.\n :type id_at_location: UID\n \"\"\"\n\n path_and_name: str\n _searchable: bool = False\n\n def __init__(\n self,\n client: Any,\n id_at_location: Optional[UID] = None,\n tags: Optional[List[str]] = None,\n description: str = \"\",\n ) -> None:\n if id_at_location is None:\n id_at_location = UID()\n\n if tags is None:\n tags = []\n\n self.client = client\n self.id_at_location = id_at_location\n self.tags = tags\n self.description = description\n self.gc_enabled = True\n\n def _get(self, delete_obj: bool = True, verbose: bool = False) -> StorableObject:\n \"\"\"Method to download a remote object from a pointer object if you have the right\n permissions.\n\n :return: returns the downloaded data\n :rtype: StorableObject\n \"\"\"\n\n logger.debug(\n f\"> GetObjectAction for id_at_location={self.id_at_location} \"\n + f\"with delete_obj={delete_obj}\"\n )\n obj_msg = GetObjectAction(\n id_at_location=self.id_at_location,\n address=self.client.address,\n reply_to=self.client.address,\n delete_obj=delete_obj,\n )\n\n response = self.client.send_immediate_msg_with_reply(msg=obj_msg)\n\n return response.obj\n\n def get_copy(\n self,\n request_block: bool = False,\n timeout_secs: int = 20,\n name: str = \"\",\n reason: str = \"\",\n verbose: bool = False,\n ) -> Optional[StorableObject]:\n \"\"\"Method to download a remote object from a pointer object if you have the right\n permissions. Optionally can block while waiting for approval.\n\n :return: returns the downloaded data\n :rtype: Optional[StorableObject]\n \"\"\"\n return self.get(\n request_block=request_block,\n timeout_secs=timeout_secs,\n name=name,\n reason=reason,\n delete_obj=False,\n verbose=verbose,\n )\n\n def get(\n self,\n request_block: bool = False,\n timeout_secs: int = 20,\n name: str = \"\",\n reason: str = \"\",\n delete_obj: bool = True,\n verbose: bool = False,\n ) -> Optional[StorableObject]:\n \"\"\"Method to download a remote object from a pointer object if you have the right\n permissions. Optionally can block while waiting for approval.\n\n :return: returns the downloaded data\n :rtype: Optional[StorableObject]\n \"\"\"\n # syft relative\n from ..node.domain.service import RequestStatus\n\n if not request_block:\n return self._get(delete_obj=delete_obj, verbose=verbose)\n else:\n response_status = self.request(\n name=name,\n reason=reason,\n block=True,\n timeout_secs=timeout_secs,\n verbose=verbose,\n )\n if (\n response_status is not None\n and response_status == RequestStatus.Accepted\n ):\n return self._get(delete_obj=delete_obj, verbose=verbose)\n\n return None\n\n @syft_decorator(typechecking=True)\n def _object2proto(self) -> Pointer_PB:\n \"\"\"Returns a protobuf serialization of self.\n\n As a requirement of all objects which inherit from Serializable,\n this method transforms the current object into the corresponding\n Protobuf object so that it can be further serialized.\n\n :return: returns a protobuf object\n :rtype: Pointer_PB\n\n .. note::\n This method is purely an internal method. Please use object.serialize() or one of\n the other public serialization methods if you wish to serialize an\n object.\n \"\"\"\n return Pointer_PB(\n points_to_object_with_path=self.path_and_name,\n pointer_name=type(self).__name__,\n id_at_location=self.id_at_location.serialize(),\n location=self.client.address.serialize(),\n tags=self.tags,\n description=self.description,\n )\n\n @staticmethod\n def _proto2object(proto: Pointer_PB) -> \"Pointer\":\n \"\"\"Creates a Pointer from a protobuf\n\n As a requirement of all objects which inherit from Serializable,\n this method transforms a protobuf object into an instance of this class.\n\n :return: returns an instance of Pointer\n :rtype: Pointer\n\n .. note::\n This method is purely an internal method. Please use syft.deserialize()\n if you wish to deserialize an object.\n \"\"\"\n # TODO: we need _proto2object to include a reference to the node doing the\n # deserialization so that we can convert location into a client object. At present\n # it is an address object which will cause things to break later.\n\n points_to_type = sy.lib_ast(\n proto.points_to_object_with_path, return_callable=True\n )\n pointer_type = getattr(points_to_type, proto.pointer_name)\n # WARNING: This is sending a serialized Address back to the constructor\n # which currently depends on a Client for send_immediate_msg_with_reply\n return pointer_type(\n id_at_location=_deserialize(blob=proto.id_at_location),\n client=_deserialize(blob=proto.location),\n tags=proto.tags,\n description=proto.description,\n )\n\n @staticmethod\n def get_protobuf_schema() -> GeneratedProtocolMessageType:\n \"\"\"Return the type of protobuf object which stores a class of this type\n\n As a part of serialization and deserialization, we need the ability to\n lookup the protobuf object type directly from the object type. This\n static method allows us to do this.\n\n Importantly, this method is also used to create the reverse lookup ability within\n the metaclass of Serializable. In the metaclass, it calls this method and then\n it takes whatever type is returned from this method and adds an attribute to it\n with the type of this class attached to it. See the MetaSerializable class for details.\n\n :return: the type of protobuf object which corresponds to this class.\n :rtype: GeneratedProtocolMessageType\n\n \"\"\"\n\n return Pointer_PB\n\n def request(\n self,\n name: str = \"\",\n reason: str = \"\",\n block: bool = False,\n timeout_secs: Optional[int] = None,\n verbose: bool = False,\n ) -> Any:\n \"\"\"Method that requests access to the data on which the pointer points to.\n\n Example:\n\n .. code-block::\n\n # data holder domain\n domain_1 = Domain(name=\"Data holder\")\n\n # data\n tensor = th.tensor([1, 2, 3])\n\n # generating the client for the domain\n domain_1_client = domain_1.get_root_client()\n\n # sending the data and receiving a pointer\n data_ptr_domain_1 = tensor.send(domain_1_client) # or tensor.send_to(domain_1_client)\n\n # requesting access to the pointer\n data_ptr_domain_1.request(name=\"My Request\", reason=\"Research project.\")\n\n :param name: The title of the request that the data owner is going to see.\n :type name: str\n :param reason: The description of the request. This is the reason why you want to have\n access to the data.\n :type reason: str\n\n .. note::\n This method should be used when the remote data associated with the pointer wants to be\n downloaded locally (or use .get() on the pointer).\n \"\"\"\n # syft relative\n from ..node.domain.service import RequestMessage\n\n # if you request non-blocking you don't need a timeout\n # if you request blocking you need a timeout, so lets set a default on here\n # a timeout of 0 would be a way to say don't block my local notebook but if the\n # duet partner has a rule configured it will get executed first before the\n # request would time out\n if timeout_secs is None and block is False:\n timeout_secs = -1 # forever\n\n msg = RequestMessage(\n name=name,\n request_description=reason,\n address=self.client.address,\n owner_address=self.client.address,\n object_id=self.id_at_location,\n requester_verify_key=self.client.verify_key,\n timeout_secs=timeout_secs,\n )\n\n self.client.send_immediate_msg_without_reply(msg=msg)\n\n # wait long enough for it to arrive and trigger a handler\n time.sleep(0.1)\n\n if not block:\n return None\n else:\n if timeout_secs is None:\n timeout_secs = 30 # default if not explicitly set\n\n # syft relative\n from ..node.domain.service import RequestAnswerMessage\n from ..node.domain.service import RequestStatus\n\n output_string = \"> Waiting for Blocking Request: \"\n if len(name) > 0:\n output_string += f\" {name}\"\n if len(reason) > 0:\n output_string += f\": {reason}\"\n if len(name) > 0 or len(name) > 0:\n if len(output_string) > 0 and output_string[-1] != \".\":\n output_string += \".\"\n logger.debug(output_string)\n if verbose:\n print(f\"\\n{output_string}\", end=\"\")\n status = None\n start = time.time()\n\n last_check: float = 0.0\n while True:\n now = time.time()\n try:\n # won't run on the first pass because status is None which allows\n # for remote request handlers to auto respond before timeout\n if now - start > timeout_secs:\n log = (\n f\"\\n> Blocking Request Timeout after {timeout_secs} seconds\"\n )\n logger.debug(log)\n if verbose:\n print(log)\n return status\n\n # only check once every second\n if now - last_check > 1:\n last_check = now\n logger.debug(f\"> Sending another Request Message {now - start}\")\n status_msg = RequestAnswerMessage(\n request_id=msg.id,\n address=self.client.address,\n reply_to=self.client.address,\n )\n response = self.client.send_immediate_msg_with_reply(\n msg=status_msg\n )\n status = response.status\n if response.status == RequestStatus.Pending:\n time.sleep(0.1)\n if verbose:\n print(\".\", end=\"\")\n continue\n else:\n # accepted or rejected lets exit\n status_text = \"REJECTED\"\n if status == RequestStatus.Accepted:\n status_text = \"ACCEPTED\"\n log = f\" {status_text}\"\n logger.debug(log)\n if verbose:\n print(log)\n return status\n except Exception as e:\n logger.error(f\"Exception while running blocking request. {e}\")\n # escape the while loop\n return status\n\n @property\n def searchable(self) -> bool:\n return self._searchable\n\n @searchable.setter\n def searchable(self, value: bool) -> None:\n if value != self._searchable:\n self.update_searchability(not self._searchable)\n\n def update_searchability(\n self, searchable: bool = True, target_verify_key: Optional[VerifyKey] = None\n ) -> None:\n \"\"\"Make the object pointed at searchable or not for other people. If\n target_verify_key is not specified, the searchability for the VerifyAll group\n will be toggled.\n\n :param searchable: If the target object should be made searchable or not.\n :type target_verify_key: bool\n :param target_verify_key: The verify_key of the client to which we want to give\n search permission.\n :type target_verify_key: Optional[VerifyKey]\n \"\"\"\n self._searchable = searchable\n msg = ObjectSearchPermissionUpdateMessage(\n add_instead_of_remove=searchable,\n target_verify_key=target_verify_key,\n target_object_id=self.id_at_location,\n address=self.client.address,\n )\n self.client.send_immediate_msg_without_reply(msg=msg)\n\n def check_access(self, node: AbstractNode, request_id: UID) -> any: # type: ignore\n \"\"\"Method that checks the status of an already made request. There are three\n possible outcomes when requesting access:\n 1. RequestStatus.Accepted - your request has been approved, you can not\n .get() your data.\n 2. RequestStatus.Pending - your request has not been reviewed yet.\n 3. RequestStatus.Rejected - your request has been rejected.\n\n :param node: The node that queries the request status.\n :type node: AbstractNode\n :param request_id: The request on which you are querying the status.\n :type request_id: UID\n \"\"\"\n # syft relative\n from ..node.domain.service import RequestAnswerMessage\n\n msg = RequestAnswerMessage(\n request_id=request_id, address=self.client.address, reply_to=node.address\n )\n response = self.client.send_immediate_msg_with_reply(msg=msg)\n\n return response.status\n\n def __del__(self) -> None:\n _client_type = type(self.client)\n if (_client_type == Address) or issubclass(_client_type, AbstractNode):\n # it is a serialized pointer that we receive from another client do nothing\n return\n\n if self.gc_enabled:\n # Create the delete message\n msg = GarbageCollectObjectAction(\n id_at_location=self.id_at_location, address=self.client.address\n )\n\n # Send the message\n self.client.send_eventual_msg_without_reply(msg=msg)\n",
"path": "src/syft/core/pointer/pointer.py"
}
] | [
{
"content": "\"\"\"A Pointer is the main handler when interacting with remote data.\nA Pointer object represents an API for interacting with data (of any type)\nat a specific location. The pointer should never be instantiated, only subclassed.\n\nThe relation between pointers and data is many to one,\nthere can be multiple pointers pointing to the same piece of data, meanwhile,\na pointer cannot point to multiple data sources.\n\nA pointer is just an object id on a remote location and a set of methods that can be\nexecuted on the remote machine directly on that object. One note that has to be made\nis that all operations between pointers will return a pointer, the only way to have access\nto the result is by calling .get() on the pointer.\n\nThere are two proper ways of receiving a pointer on some data:\n 1. When sending that data on a remote machine the user receives a pointer.\n 2. When the user searches for the data in an object store it receives a pointer to that data,\n if it has the correct permissions for that.\n\nAfter receiving a pointer, one might want to get the data behind the pointer locally. For that the\nuser should:\n 1. Request access by calling .request().\n Example:\n\n .. code-block::\n\n pointer_object.request(name = \"Request name\", reason = \"Request reason\")\n\n 2.1 - The data owner has to approve the request (check the domain node docs).\n 2.2 - The data user checks if the request has been approved (check the domain node docs).\n 3. After the request has been approved, the data user can call .get() on the pointer to get the\n data locally.\n Example:\n\n .. code-block::\n\n pointer_object.get()\n\nPointers are being generated for most types of objects in the data science scene, but what you can\ndo on them is not the pointers job, see the lib module for more details. One can see the pointer\nas a proxy to the actual data, the filtering and the security being applied where the data is being\nheld.\n\nExample:\n\n.. code-block::\n\n # creating the data holder domain\n domain_1 = Domain(name=\"Data holder domain\")\n\n # creating dummy data\n tensor = th.tensor([1, 2, 3])\n\n # creating the data holder client\n domain_1_client = domain_1.get_root_client()\n\n # sending the data to the client and receiving a pointer of that data.\n data_ptr_domain_1 = tensor.send(domain_1_client) # or tensor.send_to(domain_1_client)\n\n # creating the data user domain\n domain_2 = Domain(name=\"Data user domain\")\n\n # creating a request to access the data\n data_ptr_domain_1.request(\n name=\"My Request\", reason=\"I'd lke to see this pointer\"\n )\n\n # getting the remote id of the object\n requested_object = data_ptr_domain_1.id_at_location\n\n # getting the request id\n message_request_id = domain_1_client.requests.get_request_id_from_object_id(\n object_id=requested_object\n )\n\n # the data holder accepts the request\n domain_1.requests[0].owner_client_if_available = domain_1_client\n domain_1.requests[0].accept()\n\n # the data user checks if the data holder approved his request\n response = data_ptr_domain_1.check_access(node=domain_2, request_id=message_request_id)\n\n\"\"\"\n# stdlib\nimport time\nfrom typing import Any\nfrom typing import List\nfrom typing import Optional\n\n# third party\nfrom google.protobuf.reflection import GeneratedProtocolMessageType\nfrom loguru import logger\nfrom nacl.signing import VerifyKey\n\n# syft absolute\nimport syft as sy\n\n# syft relative\nfrom ...decorators.syft_decorator_impl import syft_decorator\nfrom ...proto.core.pointer.pointer_pb2 import Pointer as Pointer_PB\nfrom ..common.pointer import AbstractPointer\nfrom ..common.serde.deserialize import _deserialize\nfrom ..common.uid import UID\nfrom ..io.address import Address\nfrom ..node.abstract.node import AbstractNode\nfrom ..node.common.action.garbage_collect_object_action import (\n GarbageCollectObjectAction,\n)\nfrom ..node.common.action.get_object_action import GetObjectAction\nfrom ..node.common.service.obj_search_permission_service import (\n ObjectSearchPermissionUpdateMessage,\n)\nfrom ..store.storeable_object import StorableObject\n\n\n# TODO: Fix the Client, Address, Location confusion\nclass Pointer(AbstractPointer):\n \"\"\"\n The pointer is the handler when interacting with remote data.\n\n Automatically generated subclasses of Pointer need to be able to look up\n the path and name of the object type they point to as a part of serde. For more\n information on how subclasses are automatically generated, please check the ast\n module.\n\n :param location: The location where the data is being held.\n :type location: Address\n :param id_at_location: The UID of the object on the remote location.\n :type id_at_location: UID\n \"\"\"\n\n path_and_name: str\n _searchable: bool = False\n\n def __init__(\n self,\n client: Any,\n id_at_location: Optional[UID] = None,\n tags: Optional[List[str]] = None,\n description: str = \"\",\n ) -> None:\n if id_at_location is None:\n id_at_location = UID()\n\n if tags is None:\n tags = []\n\n self.client = client\n self.id_at_location = id_at_location\n self.tags = tags\n self.description = description\n self.gc_enabled = True\n\n def _get(self, delete_obj: bool = True, verbose: bool = False) -> StorableObject:\n \"\"\"Method to download a remote object from a pointer object if you have the right\n permissions.\n\n :return: returns the downloaded data\n :rtype: StorableObject\n \"\"\"\n\n logger.debug(\n f\"> GetObjectAction for id_at_location={self.id_at_location} \"\n + f\"with delete_obj={delete_obj}\"\n )\n obj_msg = GetObjectAction(\n id_at_location=self.id_at_location,\n address=self.client.address,\n reply_to=self.client.address,\n delete_obj=delete_obj,\n )\n\n response = self.client.send_immediate_msg_with_reply(msg=obj_msg)\n\n return response.obj\n\n def get_copy(\n self,\n request_block: bool = False,\n timeout_secs: int = 20,\n name: str = \"\",\n reason: str = \"\",\n verbose: bool = False,\n ) -> Optional[StorableObject]:\n \"\"\"Method to download a remote object from a pointer object if you have the right\n permissions. Optionally can block while waiting for approval.\n\n :return: returns the downloaded data\n :rtype: Optional[StorableObject]\n \"\"\"\n return self.get(\n request_block=request_block,\n timeout_secs=timeout_secs,\n name=name,\n reason=reason,\n delete_obj=False,\n verbose=verbose,\n )\n\n def get(\n self,\n request_block: bool = False,\n timeout_secs: int = 20,\n name: str = \"\",\n reason: str = \"\",\n delete_obj: bool = True,\n verbose: bool = False,\n ) -> Optional[StorableObject]:\n \"\"\"Method to download a remote object from a pointer object if you have the right\n permissions. Optionally can block while waiting for approval.\n\n :return: returns the downloaded data\n :rtype: Optional[StorableObject]\n \"\"\"\n # syft relative\n from ..node.domain.service import RequestStatus\n\n if not request_block:\n return self._get(delete_obj=delete_obj, verbose=verbose)\n else:\n response_status = self.request(\n name=name,\n reason=reason,\n block=True,\n timeout_secs=timeout_secs,\n verbose=verbose,\n )\n if (\n response_status is not None\n and response_status == RequestStatus.Accepted\n ):\n return self._get(delete_obj=delete_obj, verbose=verbose)\n\n return None\n\n @syft_decorator(typechecking=True)\n def _object2proto(self) -> Pointer_PB:\n \"\"\"Returns a protobuf serialization of self.\n\n As a requirement of all objects which inherit from Serializable,\n this method transforms the current object into the corresponding\n Protobuf object so that it can be further serialized.\n\n :return: returns a protobuf object\n :rtype: Pointer_PB\n\n .. note::\n This method is purely an internal method. Please use object.serialize() or one of\n the other public serialization methods if you wish to serialize an\n object.\n \"\"\"\n return Pointer_PB(\n points_to_object_with_path=self.path_and_name,\n pointer_name=type(self).__name__,\n id_at_location=self.id_at_location.serialize(),\n location=self.client.address.serialize(),\n tags=self.tags,\n description=self.description,\n )\n\n @staticmethod\n def _proto2object(proto: Pointer_PB) -> \"Pointer\":\n \"\"\"Creates a Pointer from a protobuf\n\n As a requirement of all objects which inherit from Serializable,\n this method transforms a protobuf object into an instance of this class.\n\n :return: returns an instance of Pointer\n :rtype: Pointer\n\n .. note::\n This method is purely an internal method. Please use syft.deserialize()\n if you wish to deserialize an object.\n \"\"\"\n # TODO: we need _proto2object to include a reference to the node doing the\n # deserialization so that we can convert location into a client object. At present\n # it is an address object which will cause things to break later.\n\n points_to_type = sy.lib_ast(\n proto.points_to_object_with_path, return_callable=True\n )\n pointer_type = getattr(points_to_type, proto.pointer_name)\n # WARNING: This is sending a serialized Address back to the constructor\n # which currently depends on a Client for send_immediate_msg_with_reply\n return pointer_type(\n id_at_location=_deserialize(blob=proto.id_at_location),\n client=_deserialize(blob=proto.location),\n tags=proto.tags,\n description=proto.description,\n )\n\n @staticmethod\n def get_protobuf_schema() -> GeneratedProtocolMessageType:\n \"\"\"Return the type of protobuf object which stores a class of this type\n\n As a part of serialization and deserialization, we need the ability to\n lookup the protobuf object type directly from the object type. This\n static method allows us to do this.\n\n Importantly, this method is also used to create the reverse lookup ability within\n the metaclass of Serializable. In the metaclass, it calls this method and then\n it takes whatever type is returned from this method and adds an attribute to it\n with the type of this class attached to it. See the MetaSerializable class for details.\n\n :return: the type of protobuf object which corresponds to this class.\n :rtype: GeneratedProtocolMessageType\n\n \"\"\"\n\n return Pointer_PB\n\n def request(\n self,\n name: str = \"\",\n reason: str = \"\",\n block: bool = False,\n timeout_secs: Optional[int] = None,\n verbose: bool = False,\n ) -> Any:\n \"\"\"Method that requests access to the data on which the pointer points to.\n\n Example:\n\n .. code-block::\n\n # data holder domain\n domain_1 = Domain(name=\"Data holder\")\n\n # data\n tensor = th.tensor([1, 2, 3])\n\n # generating the client for the domain\n domain_1_client = domain_1.get_root_client()\n\n # sending the data and receiving a pointer\n data_ptr_domain_1 = tensor.send(domain_1_client) # or tensor.send_to(domain_1_client)\n\n # requesting access to the pointer\n data_ptr_domain_1.request(name=\"My Request\", reason=\"Research project.\")\n\n :param name: The title of the request that the data owner is going to see.\n :type name: str\n :param reason: The description of the request. This is the reason why you want to have\n access to the data.\n :type reason: str\n\n .. note::\n This method should be used when the remote data associated with the pointer wants to be\n downloaded locally (or use .get() on the pointer).\n \"\"\"\n # syft relative\n from ..node.domain.service import RequestMessage\n\n # if you request non-blocking you don't need a timeout\n # if you request blocking you need a timeout, so lets set a default on here\n # a timeout of 0 would be a way to say don't block my local notebook but if the\n # duet partner has a rule configured it will get executed first before the\n # request would time out\n if timeout_secs is None and block is False:\n timeout_secs = -1 # forever\n\n msg = RequestMessage(\n name=name,\n request_description=reason,\n address=self.client.address,\n owner_address=self.client.address,\n object_id=self.id_at_location,\n requester_verify_key=self.client.verify_key,\n timeout_secs=timeout_secs,\n )\n\n self.client.send_immediate_msg_without_reply(msg=msg)\n\n # wait long enough for it to arrive and trigger a handler\n time.sleep(0.1)\n\n if not block:\n return None\n else:\n if timeout_secs is None:\n timeout_secs = 30 # default if not explicitly set\n\n # syft relative\n from ..node.domain.service import RequestAnswerMessage\n from ..node.domain.service import RequestStatus\n\n output_string = \"> Waiting for Blocking Request: \"\n if len(name) > 0:\n output_string += f\" {name}\"\n if len(reason) > 0:\n output_string += f\": {reason}\"\n if len(name) > 0 or len(name) > 0:\n if len(output_string) > 0 and output_string[-1] != \".\":\n output_string += \".\"\n logger.debug(output_string)\n if verbose:\n print(f\"\\n{output_string}\", end=\"\")\n status = None\n start = time.time()\n\n last_check: float = 0.0\n while True:\n now = time.time()\n try:\n # won't run on the first pass because status is None which allows\n # for remote request handlers to auto respond before timeout\n if now - start > timeout_secs:\n log = (\n f\"\\n> Blocking Request Timeout after {timeout_secs} seconds\"\n )\n logger.debug(log)\n if verbose:\n print(log)\n return status\n\n # only check once every second\n if now - last_check > 1:\n last_check = now\n logger.debug(f\"> Sending another Request Message {now - start}\")\n status_msg = RequestAnswerMessage(\n request_id=msg.id,\n address=self.client.address,\n reply_to=self.client.address,\n )\n response = self.client.send_immediate_msg_with_reply(\n msg=status_msg\n )\n status = response.status\n if response.status == RequestStatus.Pending:\n time.sleep(0.1)\n if verbose:\n print(\".\", end=\"\")\n continue\n else:\n # accepted or rejected lets exit\n status_text = \"REJECTED\"\n if status == RequestStatus.Accepted:\n status_text = \"ACCEPTED\"\n log = f\" {status_text}\"\n logger.debug(log)\n if verbose:\n print(log)\n return status\n except Exception as e:\n logger.error(f\"Exception while running blocking request. {e}\")\n # escape the while loop\n return status\n\n @property\n def searchable(self) -> bool:\n return self._searchable\n\n @searchable.setter\n def searchable(self, value: bool) -> None:\n if value != self._searchable:\n self.update_searchability(not self._searchable)\n\n def update_searchability(\n self, searchable: bool = True, target_verify_key: Optional[VerifyKey] = None\n ) -> None:\n \"\"\"Make the object pointed at searchable or not for other people. If\n target_verify_key is not specified, the searchability for the VerifyAll group\n will be toggled.\n\n :param searchable: If the target object should be made searchable or not.\n :type target_verify_key: bool\n :param target_verify_key: The verify_key of the client to which we want to give\n search permission.\n :type target_verify_key: Optional[VerifyKey]\n \"\"\"\n self._searchable = searchable\n msg = ObjectSearchPermissionUpdateMessage(\n add_instead_of_remove=searchable,\n target_verify_key=target_verify_key,\n target_object_id=self.id_at_location,\n address=self.client.address,\n )\n self.client.send_immediate_msg_without_reply(msg=msg)\n\n def check_access(self, node: AbstractNode, request_id: UID) -> any: # type: ignore\n \"\"\"Method that checks the status of an already made request. There are three\n possible outcomes when requesting access:\n 1. RequestStatus.Accepted - your request has been approved, you can not\n .get() your data.\n 2. RequestStatus.Pending - your request has not been reviewed yet.\n 3. RequestStatus.Rejected - your request has been rejected.\n\n :param node: The node that queries the request status.\n :type node: AbstractNode\n :param request_id: The request on which you are querying the status.\n :type request_id: UID\n \"\"\"\n # syft relative\n from ..node.domain.service import RequestAnswerMessage\n\n msg = RequestAnswerMessage(\n request_id=request_id, address=self.client.address, reply_to=node.address\n )\n response = self.client.send_immediate_msg_with_reply(msg=msg)\n\n return response.status\n\n def __del__(self) -> None:\n _client_type = type(self.client)\n if (_client_type == Address) or issubclass(_client_type, AbstractNode):\n # it is a serialized pointer that we receive from another client do nothing\n return\n\n if self.gc_enabled:\n # Create the delete message\n msg = GarbageCollectObjectAction(\n id_at_location=self.id_at_location, address=self.client.address\n )\n\n # Send the message\n self.client.send_eventual_msg_without_reply(msg=msg)\n",
"path": "src/syft/core/pointer/pointer.py"
}
] | diff --git a/src/syft/core/pointer/pointer.py b/src/syft/core/pointer/pointer.py
index da8206d191d..6e94230b000 100644
--- a/src/syft/core/pointer/pointer.py
+++ b/src/syft/core/pointer/pointer.py
@@ -68,7 +68,7 @@
requested_object = data_ptr_domain_1.id_at_location
# getting the request id
- message_request_id = domain_1_client.request_queue.get_request_id_from_object_id(
+ message_request_id = domain_1_client.requests.get_request_id_from_object_id(
object_id=requested_object
)
|
cloud-custodian__cloud-custodian-2513 | S3Output should grant bucket owner full control
Currently `S3Output` misses the `ACL` option. In a cross account setup it is desirable to give the bucket owner full control.
Would you change the code like this?:
```
diff --git a/c7n/output.py b/c7n/output.py
index c3839c2f..5fb06f59 100644
--- a/c7n/output.py
+++ b/c7n/output.py
@@ -268,6 +268,7 @@ class S3Output(FSOutput):
self.transfer.upload_file(
os.path.join(root, f), self.bucket, key,
extra_args={
+ 'ACL': 'bucket-owner-full-control',
'ServerSideEncryption': 'AES256'})
def use_s3(self):
```
| [
{
"content": "# Copyright 2015-2017 Capital One Services, LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"\nOutputs metrics, logs, structured records across\na variety of sources.\n\nSee docs/usage/outputs.rst\n\n\"\"\"\nfrom __future__ import absolute_import, division, print_function, unicode_literals\n\nimport datetime\nimport gzip\nimport logging\nimport shutil\nimport tempfile\n\nimport os\n\nfrom boto3.s3.transfer import S3Transfer\n\nfrom c7n.registry import PluginRegistry\nfrom c7n.log import CloudWatchLogHandler\nfrom c7n.utils import local_session, parse_s3, get_retry\n\nDEFAULT_NAMESPACE = \"CloudMaid\"\n\nlog = logging.getLogger('custodian.output')\n\n\nblob_outputs = PluginRegistry('c7n.blob-outputs')\n\n\nclass MetricsOutput(object):\n \"\"\"Send metrics data to cloudwatch\n \"\"\"\n\n permissions = (\"cloudWatch:PutMetricData\",)\n\n retry = staticmethod(get_retry(('Throttling',)))\n\n @staticmethod\n def select(metrics_enabled):\n if metrics_enabled:\n return MetricsOutput\n return NullMetricsOutput\n\n def __init__(self, ctx, namespace=DEFAULT_NAMESPACE):\n self.ctx = ctx\n self.namespace = namespace\n self.buf = []\n\n def get_timestamp(self):\n \"\"\"\n Now, if C7N_METRICS_TZ is set to TRUE, UTC timestamp will be used.\n For backwards compatibility, if it is not set, UTC will be the default.\n To disable this and use the system's time zone, C7N_METRICS_TZ shoule be set to FALSE.\n \"\"\"\n\n if os.getenv(\"C7N_METRICS_TZ\", '').upper() in ('TRUE', ''):\n return datetime.datetime.utcnow()\n else:\n return datetime.datetime.now()\n\n def flush(self):\n if self.buf:\n self._put_metrics(self.namespace, self.buf)\n self.buf = []\n\n def put_metric(self, key, value, unit, buffer=False, **dimensions):\n d = {\n \"MetricName\": key,\n \"Timestamp\": self.get_timestamp(),\n \"Value\": value,\n \"Unit\": unit}\n d[\"Dimensions\"] = [\n {\"Name\": \"Policy\", \"Value\": self.ctx.policy.name},\n {\"Name\": \"ResType\", \"Value\": self.ctx.policy.resource_type}]\n for k, v in dimensions.items():\n d['Dimensions'].append({\"Name\": k, \"Value\": v})\n\n if buffer:\n self.buf.append(d)\n # Max metrics in a single request\n if len(self.buf) == 20:\n self.flush()\n else:\n self._put_metrics(self.namespace, [d])\n\n def _put_metrics(self, ns, metrics):\n watch = local_session(self.ctx.session_factory).client('cloudwatch')\n return self.retry(\n watch.put_metric_data, Namespace=ns, MetricData=metrics)\n\n\nclass NullMetricsOutput(MetricsOutput):\n\n permissions = ()\n\n def __init__(self, ctx, namespace=DEFAULT_NAMESPACE):\n super(NullMetricsOutput, self).__init__(ctx, namespace)\n self.data = []\n\n def _put_metrics(self, ns, metrics):\n self.data.append({'Namespace': ns, 'MetricData': metrics})\n for m in metrics:\n if m['MetricName'] not in ('ActionTime', 'ResourceTime'):\n log.debug(self.format_metric(m))\n\n def format_metric(self, m):\n label = \"metric:%s %s:%s\" % (m['MetricName'], m['Unit'], m['Value'])\n for d in m['Dimensions']:\n label += \" %s:%s\" % (d['Name'].lower(), d['Value'].lower())\n return label\n\n\nclass LogOutput(object):\n\n log_format = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'\n\n def __init__(self, ctx):\n self.ctx = ctx\n\n def get_handler(self):\n raise NotImplementedError()\n\n def __enter__(self):\n log.debug(\"Storing output with %s\" % repr(self))\n self.join_log()\n return self\n\n def __exit__(self, exc_type=None, exc_value=None, exc_traceback=None):\n self.leave_log()\n if exc_type is not None:\n log.exception(\"Error while executing policy\")\n\n def join_log(self):\n self.handler = self.get_handler()\n self.handler.setLevel(logging.DEBUG)\n self.handler.setFormatter(logging.Formatter(self.log_format))\n mlog = logging.getLogger('custodian')\n mlog.addHandler(self.handler)\n\n def leave_log(self):\n mlog = logging.getLogger('custodian')\n mlog.removeHandler(self.handler)\n self.handler.flush()\n self.handler.close()\n\n\nclass CloudWatchLogOutput(LogOutput):\n\n log_format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s'\n\n def get_handler(self):\n return CloudWatchLogHandler(\n log_group=self.ctx.options.log_group,\n log_stream=self.ctx.policy.name,\n session_factory=lambda x=None: self.ctx.session_factory(\n assume=False))\n\n def __repr__(self):\n return \"<%s to group:%s stream:%s>\" % (\n self.__class__.__name__,\n self.ctx.options.log_group,\n self.ctx.policy.name)\n\n\nclass FSOutput(LogOutput):\n\n @staticmethod\n def select(path):\n for k in blob_outputs.keys():\n if path.startswith('%s://' % k):\n return blob_outputs[k]\n # Fall back local disk\n return blob_outputs['file']\n\n @staticmethod\n def join(*parts):\n return os.path.join(*parts)\n\n def __init__(self, ctx):\n super(FSOutput, self).__init__(ctx)\n self.root_dir = self.ctx.output_path or tempfile.mkdtemp()\n\n def get_handler(self):\n return logging.FileHandler(\n os.path.join(self.root_dir, 'custodian-run.log'))\n\n def compress(self):\n # Compress files individually so thats easy to walk them, without\n # downloading tar and extracting.\n for root, dirs, files in os.walk(self.root_dir):\n for f in files:\n fp = os.path.join(root, f)\n with gzip.open(fp + \".gz\", \"wb\", compresslevel=7) as zfh:\n with open(fp, \"rb\") as sfh:\n shutil.copyfileobj(sfh, zfh, length=2**15)\n os.remove(fp)\n\n\n@blob_outputs.register('file')\nclass DirectoryOutput(FSOutput):\n\n permissions = ()\n\n def __init__(self, ctx):\n super(DirectoryOutput, self).__init__(ctx)\n if self.root_dir.startswith('file://'):\n self.root_dir = self.root_dir[len('file://'):]\n if self.ctx.output_path is not None:\n if not os.path.exists(self.root_dir):\n os.makedirs(self.root_dir)\n\n def __repr__(self):\n return \"<%s to dir:%s>\" % (self.__class__.__name__, self.root_dir)\n\n\n@blob_outputs.register('s3')\nclass S3Output(FSOutput):\n \"\"\"\n Usage:\n\n .. code-block:: python\n\n with S3Output(session_factory, 's3://bucket/prefix'):\n log.info('xyz') # -> log messages sent to custodian-run.log.gz\n\n \"\"\"\n\n permissions = ('S3:PutObject',)\n\n def __init__(self, ctx):\n super(S3Output, self).__init__(ctx)\n self.date_path = datetime.datetime.now().strftime('%Y/%m/%d/%H')\n self.s3_path, self.bucket, self.key_prefix = parse_s3(\n self.ctx.output_path)\n self.root_dir = tempfile.mkdtemp()\n self.transfer = None\n\n def __repr__(self):\n return \"<%s to bucket:%s prefix:%s>\" % (\n self.__class__.__name__,\n self.bucket,\n \"%s/%s\" % (self.key_prefix, self.date_path))\n\n @staticmethod\n def join(*parts):\n return \"/\".join([s.strip('/') for s in parts])\n\n def __exit__(self, exc_type=None, exc_value=None, exc_traceback=None):\n if exc_type is not None:\n log.exception(\"Error while executing policy\")\n log.debug(\"Uploading policy logs\")\n self.leave_log()\n self.compress()\n self.transfer = S3Transfer(\n self.ctx.session_factory(assume=False).client('s3'))\n self.upload()\n shutil.rmtree(self.root_dir)\n log.debug(\"Policy Logs uploaded\")\n\n def upload(self):\n for root, dirs, files in os.walk(self.root_dir):\n for f in files:\n key = \"%s/%s%s\" % (\n self.key_prefix,\n self.date_path,\n \"%s/%s\" % (\n root[len(self.root_dir):], f))\n key = key.strip('/')\n self.transfer.upload_file(\n os.path.join(root, f), self.bucket, key,\n extra_args={\n 'ServerSideEncryption': 'AES256'})\n",
"path": "c7n/output.py"
}
] | [
{
"content": "# Copyright 2015-2017 Capital One Services, LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"\nOutputs metrics, logs, structured records across\na variety of sources.\n\nSee docs/usage/outputs.rst\n\n\"\"\"\nfrom __future__ import absolute_import, division, print_function, unicode_literals\n\nimport datetime\nimport gzip\nimport logging\nimport shutil\nimport tempfile\n\nimport os\n\nfrom boto3.s3.transfer import S3Transfer\n\nfrom c7n.registry import PluginRegistry\nfrom c7n.log import CloudWatchLogHandler\nfrom c7n.utils import local_session, parse_s3, get_retry\n\nDEFAULT_NAMESPACE = \"CloudMaid\"\n\nlog = logging.getLogger('custodian.output')\n\n\nblob_outputs = PluginRegistry('c7n.blob-outputs')\n\n\nclass MetricsOutput(object):\n \"\"\"Send metrics data to cloudwatch\n \"\"\"\n\n permissions = (\"cloudWatch:PutMetricData\",)\n\n retry = staticmethod(get_retry(('Throttling',)))\n\n @staticmethod\n def select(metrics_enabled):\n if metrics_enabled:\n return MetricsOutput\n return NullMetricsOutput\n\n def __init__(self, ctx, namespace=DEFAULT_NAMESPACE):\n self.ctx = ctx\n self.namespace = namespace\n self.buf = []\n\n def get_timestamp(self):\n \"\"\"\n Now, if C7N_METRICS_TZ is set to TRUE, UTC timestamp will be used.\n For backwards compatibility, if it is not set, UTC will be the default.\n To disable this and use the system's time zone, C7N_METRICS_TZ shoule be set to FALSE.\n \"\"\"\n\n if os.getenv(\"C7N_METRICS_TZ\", '').upper() in ('TRUE', ''):\n return datetime.datetime.utcnow()\n else:\n return datetime.datetime.now()\n\n def flush(self):\n if self.buf:\n self._put_metrics(self.namespace, self.buf)\n self.buf = []\n\n def put_metric(self, key, value, unit, buffer=False, **dimensions):\n d = {\n \"MetricName\": key,\n \"Timestamp\": self.get_timestamp(),\n \"Value\": value,\n \"Unit\": unit}\n d[\"Dimensions\"] = [\n {\"Name\": \"Policy\", \"Value\": self.ctx.policy.name},\n {\"Name\": \"ResType\", \"Value\": self.ctx.policy.resource_type}]\n for k, v in dimensions.items():\n d['Dimensions'].append({\"Name\": k, \"Value\": v})\n\n if buffer:\n self.buf.append(d)\n # Max metrics in a single request\n if len(self.buf) == 20:\n self.flush()\n else:\n self._put_metrics(self.namespace, [d])\n\n def _put_metrics(self, ns, metrics):\n watch = local_session(self.ctx.session_factory).client('cloudwatch')\n return self.retry(\n watch.put_metric_data, Namespace=ns, MetricData=metrics)\n\n\nclass NullMetricsOutput(MetricsOutput):\n\n permissions = ()\n\n def __init__(self, ctx, namespace=DEFAULT_NAMESPACE):\n super(NullMetricsOutput, self).__init__(ctx, namespace)\n self.data = []\n\n def _put_metrics(self, ns, metrics):\n self.data.append({'Namespace': ns, 'MetricData': metrics})\n for m in metrics:\n if m['MetricName'] not in ('ActionTime', 'ResourceTime'):\n log.debug(self.format_metric(m))\n\n def format_metric(self, m):\n label = \"metric:%s %s:%s\" % (m['MetricName'], m['Unit'], m['Value'])\n for d in m['Dimensions']:\n label += \" %s:%s\" % (d['Name'].lower(), d['Value'].lower())\n return label\n\n\nclass LogOutput(object):\n\n log_format = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'\n\n def __init__(self, ctx):\n self.ctx = ctx\n\n def get_handler(self):\n raise NotImplementedError()\n\n def __enter__(self):\n log.debug(\"Storing output with %s\" % repr(self))\n self.join_log()\n return self\n\n def __exit__(self, exc_type=None, exc_value=None, exc_traceback=None):\n self.leave_log()\n if exc_type is not None:\n log.exception(\"Error while executing policy\")\n\n def join_log(self):\n self.handler = self.get_handler()\n self.handler.setLevel(logging.DEBUG)\n self.handler.setFormatter(logging.Formatter(self.log_format))\n mlog = logging.getLogger('custodian')\n mlog.addHandler(self.handler)\n\n def leave_log(self):\n mlog = logging.getLogger('custodian')\n mlog.removeHandler(self.handler)\n self.handler.flush()\n self.handler.close()\n\n\nclass CloudWatchLogOutput(LogOutput):\n\n log_format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s'\n\n def get_handler(self):\n return CloudWatchLogHandler(\n log_group=self.ctx.options.log_group,\n log_stream=self.ctx.policy.name,\n session_factory=lambda x=None: self.ctx.session_factory(\n assume=False))\n\n def __repr__(self):\n return \"<%s to group:%s stream:%s>\" % (\n self.__class__.__name__,\n self.ctx.options.log_group,\n self.ctx.policy.name)\n\n\nclass FSOutput(LogOutput):\n\n @staticmethod\n def select(path):\n for k in blob_outputs.keys():\n if path.startswith('%s://' % k):\n return blob_outputs[k]\n # Fall back local disk\n return blob_outputs['file']\n\n @staticmethod\n def join(*parts):\n return os.path.join(*parts)\n\n def __init__(self, ctx):\n super(FSOutput, self).__init__(ctx)\n self.root_dir = self.ctx.output_path or tempfile.mkdtemp()\n\n def get_handler(self):\n return logging.FileHandler(\n os.path.join(self.root_dir, 'custodian-run.log'))\n\n def compress(self):\n # Compress files individually so thats easy to walk them, without\n # downloading tar and extracting.\n for root, dirs, files in os.walk(self.root_dir):\n for f in files:\n fp = os.path.join(root, f)\n with gzip.open(fp + \".gz\", \"wb\", compresslevel=7) as zfh:\n with open(fp, \"rb\") as sfh:\n shutil.copyfileobj(sfh, zfh, length=2**15)\n os.remove(fp)\n\n\n@blob_outputs.register('file')\nclass DirectoryOutput(FSOutput):\n\n permissions = ()\n\n def __init__(self, ctx):\n super(DirectoryOutput, self).__init__(ctx)\n if self.root_dir.startswith('file://'):\n self.root_dir = self.root_dir[len('file://'):]\n if self.ctx.output_path is not None:\n if not os.path.exists(self.root_dir):\n os.makedirs(self.root_dir)\n\n def __repr__(self):\n return \"<%s to dir:%s>\" % (self.__class__.__name__, self.root_dir)\n\n\n@blob_outputs.register('s3')\nclass S3Output(FSOutput):\n \"\"\"\n Usage:\n\n .. code-block:: python\n\n with S3Output(session_factory, 's3://bucket/prefix'):\n log.info('xyz') # -> log messages sent to custodian-run.log.gz\n\n \"\"\"\n\n permissions = ('S3:PutObject',)\n\n def __init__(self, ctx):\n super(S3Output, self).__init__(ctx)\n self.date_path = datetime.datetime.now().strftime('%Y/%m/%d/%H')\n self.s3_path, self.bucket, self.key_prefix = parse_s3(\n self.ctx.output_path)\n self.root_dir = tempfile.mkdtemp()\n self.transfer = None\n\n def __repr__(self):\n return \"<%s to bucket:%s prefix:%s>\" % (\n self.__class__.__name__,\n self.bucket,\n \"%s/%s\" % (self.key_prefix, self.date_path))\n\n @staticmethod\n def join(*parts):\n return \"/\".join([s.strip('/') for s in parts])\n\n def __exit__(self, exc_type=None, exc_value=None, exc_traceback=None):\n if exc_type is not None:\n log.exception(\"Error while executing policy\")\n log.debug(\"Uploading policy logs\")\n self.leave_log()\n self.compress()\n self.transfer = S3Transfer(\n self.ctx.session_factory(assume=False).client('s3'))\n self.upload()\n shutil.rmtree(self.root_dir)\n log.debug(\"Policy Logs uploaded\")\n\n def upload(self):\n for root, dirs, files in os.walk(self.root_dir):\n for f in files:\n key = \"%s/%s%s\" % (\n self.key_prefix,\n self.date_path,\n \"%s/%s\" % (\n root[len(self.root_dir):], f))\n key = key.strip('/')\n self.transfer.upload_file(\n os.path.join(root, f), self.bucket, key,\n extra_args={\n 'ACL': 'bucket-owner-full-control',\n 'ServerSideEncryption': 'AES256'})\n",
"path": "c7n/output.py"
}
] | diff --git a/c7n/output.py b/c7n/output.py
index 47109950cd7..406f40c2287 100644
--- a/c7n/output.py
+++ b/c7n/output.py
@@ -284,4 +284,5 @@ def upload(self):
self.transfer.upload_file(
os.path.join(root, f), self.bucket, key,
extra_args={
+ 'ACL': 'bucket-owner-full-control',
'ServerSideEncryption': 'AES256'})
diff --git a/tests/test_output.py b/tests/test_output.py
index be4436b8294..05a3238e391 100644
--- a/tests/test_output.py
+++ b/tests/test_output.py
@@ -125,7 +125,7 @@ def test_upload(self):
fh.name,
"cloud-custodian",
"policies/xyz/%s/foo.txt" % output.date_path,
- extra_args={"ServerSideEncryption": "AES256"},
+ extra_args={"ACL": "bucket-owner-full-control", "ServerSideEncryption": "AES256"},
)
def test_sans_prefix(self):
@@ -143,5 +143,5 @@ def test_sans_prefix(self):
fh.name,
"cloud-custodian",
"policies/xyz/%s/foo.txt" % output.date_path,
- extra_args={"ServerSideEncryption": "AES256"},
+ extra_args={"ACL": "bucket-owner-full-control", "ServerSideEncryption": "AES256"},
)
|
bokeh__bokeh-6954 | length_units has no effect for rays
# READ AND FOLLOW THESE INSTRUCTIONS CAREFULLY
*ISSUES THAT DO NOT CONTAIN NECESSARY INFORMATION MAY BE CLOSED, IMMEDIATELY*
The issue tracker is NOT the place for general support. For questions and
technical assistance, come ask the [Bokeh mailing list](https://groups.google.com/a/continuum.io/forum/#!forum/bokeh) or join the chat on [Gitter](https://gitter.im/bokeh/bokeh). For feature requests, please provide a detailed description or proposal of the new capability or behavior.
For defects or deficiencies, please provide ALL OF THE FOLLOWING:
#### ALL software version info (bokeh, python, notebook, OS, browser, any other relevant packages)
Using Bokeh 0.12.9 in Chrome 59.0 on Fedora 25
#### Description of expected behavior and the observed behavior
The ``length_units`` attribute to ``Figure.ray`` has no effect. All rays are plotted in ``data`` units. Furthermore, the documentation has conflicting messages claiming the length defaults to screen units while length_units defaults to data
```
length (DistanceSpec) –
The length to extend the ray. Note that this length defaults to screen units.
```
while
```
length_units (Enum ( SpatialUnits )) – (default: ‘data’)
```
#### Complete, minimal, self-contained example code that reproduces the issue
```python
from bokeh.plotting import figure, curdoc
fig = figure(width=500, height=500)
fig.ray(x=[0, 0], y=[0, 0], angle=[0, 0.5], length=[100, 100],
length_units='screen', color='red')
fig.ray(x=[0, 0], y=[0, 0], angle=[-0.5, -1.5], length=[100, 100],
length_units='data', color='blue')
curdoc().add_root(fig)
```
#### Stack traceback and/or browser JavaScript console output
No errors
#### Screenshots or screencasts of the bug in action
All rays are equal length at all zoom levels
| [
{
"content": "# -*- coding: utf-8 -*-\n''' Display a variety of visual shapes whose attributes can be associated\nwith data columns from ``ColumnDataSources``.\n\nThe full list of glyphs built into Bokeh is given below:\n\n* :class:`~bokeh.models.glyphs.AnnularWedge`\n* :class:`~bokeh.models.glyphs.Annulus`\n* :class:`~bokeh.models.glyphs.Arc`\n* :class:`~bokeh.models.glyphs.Bezier`\n* :class:`~bokeh.models.glyphs.Ellipse`\n* :class:`~bokeh.models.glyphs.HBar`\n* :class:`~bokeh.models.glyphs.Image`\n* :class:`~bokeh.models.glyphs.ImageRGBA`\n* :class:`~bokeh.models.glyphs.ImageURL`\n* :class:`~bokeh.models.glyphs.Line`\n* :class:`~bokeh.models.glyphs.MultiLine`\n* :class:`~bokeh.models.glyphs.Oval`\n* :class:`~bokeh.models.glyphs.Patch`\n* :class:`~bokeh.models.glyphs.Patches`\n* :class:`~bokeh.models.glyphs.Quad`\n* :class:`~bokeh.models.glyphs.Quadratic`\n* :class:`~bokeh.models.glyphs.Ray`\n* :class:`~bokeh.models.glyphs.Rect`\n* :class:`~bokeh.models.glyphs.Segment`\n* :class:`~bokeh.models.glyphs.Text`\n* :class:`~bokeh.models.glyphs.VBar`\n* :class:`~bokeh.models.glyphs.Wedge`\n\nAll these glyphs share a minimal common interface through their base class\n``Glyph``:\n\n.. autoclass:: Glyph\n :members:\n\n'''\nfrom __future__ import absolute_import\n\nfrom ..core.enums import Anchor, Direction\nfrom ..core.has_props import abstract\nfrom ..core.properties import (AngleSpec, Bool, DistanceSpec, Enum, Float,\n Include, Instance, Int, NumberSpec, StringSpec)\nfrom ..core.property_mixins import FillProps, LineProps, TextProps\nfrom ..model import Model\n\nfrom .mappers import ColorMapper, LinearColorMapper\n\n@abstract\nclass Glyph(Model):\n ''' Base class for all glyph models.\n\n '''\n\n@abstract\nclass XYGlyph(Glyph):\n ''' Base class of glyphs with `x` and `y` coordinate attributes.\n\n '''\n\nclass AnnularWedge(XYGlyph):\n ''' Render annular wedges.\n\n '''\n\n __example__ = \"examples/reference/models/AnnularWedge.py\"\n\n # a canonical order for positional args that can be used for any\n # functions derived from this class\n _args = ('x', 'y', 'inner_radius', 'outer_radius', 'start_angle', 'end_angle', 'direction')\n\n x = NumberSpec(help=\"\"\"\n The x-coordinates of the center of the annular wedges.\n \"\"\")\n\n y = NumberSpec(help=\"\"\"\n The y-coordinates of the center of the annular wedges.\n \"\"\")\n\n inner_radius = DistanceSpec(help=\"\"\"\n The inner radii of the annular wedges.\n \"\"\")\n\n outer_radius = DistanceSpec(help=\"\"\"\n The outer radii of the annular wedges.\n \"\"\")\n\n start_angle = AngleSpec(help=\"\"\"\n The angles to start the annular wedges, as measured from the horizontal.\n \"\"\")\n\n end_angle = AngleSpec(help=\"\"\"\n The angles to end the annular wedges, as measured from the horizontal.\n \"\"\")\n\n direction = Enum(Direction, default=Direction.anticlock, help=\"\"\"\n Which direction to stroke between the start and end angles.\n \"\"\")\n\n line_props = Include(LineProps, use_prefix=False, help=\"\"\"\n The %s values for the annular wedges.\n \"\"\")\n\n fill_props = Include(FillProps, use_prefix=False, help=\"\"\"\n The %s values for the annular wedges.\n \"\"\")\n\nclass Annulus(XYGlyph):\n ''' Render annuli.\n\n '''\n\n __example__ = \"examples/reference/models/Annulus.py\"\n\n # a canonical order for positional args that can be used for any\n # functions derived from this class\n _args = ('x', 'y', 'inner_radius', 'outer_radius')\n\n x = NumberSpec(help=\"\"\"\n The x-coordinates of the center of the annuli.\n \"\"\")\n\n y = NumberSpec(help=\"\"\"\n The y-coordinates of the center of the annuli.\n \"\"\")\n\n inner_radius = DistanceSpec(help=\"\"\"\n The inner radii of the annuli.\n \"\"\")\n\n outer_radius = DistanceSpec(help=\"\"\"\n The outer radii of the annuli.\n \"\"\")\n\n line_props = Include(LineProps, use_prefix=False, help=\"\"\"\n The %s values for the annuli.\n \"\"\")\n\n fill_props = Include(FillProps, use_prefix=False, help=\"\"\"\n The %s values for the annuli.\n \"\"\")\n\nclass Arc(XYGlyph):\n ''' Render arcs.\n\n '''\n\n __example__ = \"examples/reference/models/Arc.py\"\n\n # a canonical order for positional args that can be used for any\n # functions derived from this class\n _args = ('x', 'y', 'radius', 'start_angle', 'end_angle', 'direction')\n\n x = NumberSpec(help=\"\"\"\n The x-coordinates of the center of the arcs.\n \"\"\")\n\n y = NumberSpec(help=\"\"\"\n The y-coordinates of the center of the arcs.\n \"\"\")\n\n radius = DistanceSpec(help=\"\"\"\n Radius of the arc.\n \"\"\")\n\n start_angle = AngleSpec(help=\"\"\"\n The angles to start the arcs, as measured from the horizontal.\n \"\"\")\n\n end_angle = AngleSpec(help=\"\"\"\n The angles to end the arcs, as measured from the horizontal.\n \"\"\")\n\n direction = Enum(Direction, default='anticlock', help=\"\"\"\n Which direction to stroke between the start and end angles.\n \"\"\")\n\n line_props = Include(LineProps, use_prefix=False, help=\"\"\"\n The %s values for the arcs.\n \"\"\")\n\nclass Bezier(Glyph):\n u''' Render Bézier curves.\n\n For more information consult the `Wikipedia article for Bézier curve`_.\n\n .. _Wikipedia article for Bézier curve: http://en.wikipedia.org/wiki/Bézier_curve\n\n '''\n\n __example__ = \"examples/reference/models/Bezier.py\"\n\n # a canonical order for positional args that can be used for any\n # functions derived from this class\n _args = ('x0', 'y0', 'x1', 'y1', 'cx0', 'cy0', 'cx1', 'cy1')\n\n x0 = NumberSpec(help=\"\"\"\n The x-coordinates of the starting points.\n \"\"\")\n\n y0 = NumberSpec(help=\"\"\"\n The y-coordinates of the starting points.\n \"\"\")\n\n x1 = NumberSpec(help=\"\"\"\n The x-coordinates of the ending points.\n \"\"\")\n\n y1 = NumberSpec(help=\"\"\"\n The y-coordinates of the ending points.\n \"\"\")\n\n cx0 = NumberSpec(help=\"\"\"\n The x-coordinates of first control points.\n \"\"\")\n\n cy0 = NumberSpec(help=\"\"\"\n The y-coordinates of first control points.\n \"\"\")\n\n cx1 = NumberSpec(help=\"\"\"\n The x-coordinates of second control points.\n \"\"\")\n\n cy1 = NumberSpec(help=\"\"\"\n The y-coordinates of second control points.\n \"\"\")\n\n line_props = Include(LineProps, use_prefix=False, help=u\"\"\"\n The %s values for the Bézier curves.\n \"\"\")\n\nclass Ellipse(XYGlyph):\n u''' Render ellipses.\n\n '''\n\n __example__ = \"examples/reference/models/Ellipse.py\"\n\n # a canonical order for positional args that can be used for any\n # functions derived from this class\n _args = ('x', 'y', 'width', 'height', 'angle')\n\n x = NumberSpec(help=\"\"\"\n The x-coordinates of the centers of the ellipses.\n \"\"\")\n\n y = NumberSpec(help=\"\"\"\n The y-coordinates of the centers of the ellipses.\n \"\"\")\n\n width = DistanceSpec(help=\"\"\"\n The widths of each ellipse.\n \"\"\")\n\n height = DistanceSpec(help=\"\"\"\n The heights of each ellipse.\n \"\"\")\n\n angle = AngleSpec(default=0.0, help=\"\"\"\n The angle the ellipses are rotated from horizontal. [rad]\n \"\"\")\n\n line_props = Include(LineProps, use_prefix=False, help=\"\"\"\n The %s values for the ovals.\n \"\"\")\n\n fill_props = Include(FillProps, use_prefix=False, help=\"\"\"\n The %s values for the ovals.\n \"\"\")\n\nclass HBar(Glyph):\n ''' Render horizontal bars, given a center coordinate, ``height`` and\n (``left``, ``right``) coordinates.\n\n '''\n\n __example__ = \"examples/reference/models/HBar.py\"\n\n # a canonical order for positional args that can be used for any\n # functions derived from this class\n _args = ('y', 'height', 'right', 'left')\n\n y = NumberSpec(help=\"\"\"\n The y-coordinates of the centers of the horizontal bars.\n \"\"\")\n\n height = NumberSpec(help=\"\"\"\n The heights of the vertical bars.\n \"\"\")\n\n left = NumberSpec(default=0, help=\"\"\"\n The x-coordinates of the left edges.\n \"\"\")\n\n right = NumberSpec(help=\"\"\"\n The x-coordinates of the right edges.\n \"\"\")\n\n line_props = Include(LineProps, use_prefix=False, help=\"\"\"\n The %s values for the horizontal bars.\n \"\"\")\n\n fill_props = Include(FillProps, use_prefix=False, help=\"\"\"\n The %s values for the horizontal bars.\n \"\"\")\n\nclass Image(XYGlyph):\n ''' Render images given as scalar data together with a color mapper.\n\n In addition to the defined model properties, ``Image`` also can accept\n a keyword argument ``palette`` in place of an explicit ``color_mapper``.\n The value should be a list of colors, or the name of one of the built-in\n palettes in ``bokeh.palettes``. This palette will be used to automatically\n construct a ``ColorMapper`` model for the ``color_mapper`` property.\n\n If both ``palette`` and ``color_mapper`` are passed, a ``ValueError``\n exception will be raised. If neither is passed, then the ``Greys9``\n palette will be used as a default.\n\n '''\n\n def __init__(self, **kwargs):\n if 'palette' in kwargs and 'color_mapper' in kwargs:\n raise ValueError(\"only one of 'palette' and 'color_mapper' may be specified\")\n elif 'color_mapper' not in kwargs:\n # Use a palette (given or default)\n palette = kwargs.pop('palette', 'Greys9')\n mapper = LinearColorMapper(palette)\n kwargs['color_mapper'] = mapper\n\n super(Image, self).__init__(**kwargs)\n\n # a canonical order for positional args that can be used for any\n # functions derived from this class\n _args = ('image', 'x', 'y', 'dw', 'dh', 'dilate')\n\n # a hook to specify any additional kwargs handled by an initializer\n _extra_kws = {\n 'palette': (\n 'str or list[color value]',\n 'a palette to construct a value for the color mapper property from'\n )\n }\n\n image = NumberSpec(help=\"\"\"\n The arrays of scalar data for the images to be colormapped.\n \"\"\")\n\n x = NumberSpec(help=\"\"\"\n The x-coordinates to locate the image anchors.\n \"\"\")\n\n y = NumberSpec(help=\"\"\"\n The y-coordinates to locate the image anchors.\n \"\"\")\n\n dw = DistanceSpec(help=\"\"\"\n The widths of the plot regions that the images will occupy.\n\n .. note::\n This is not the number of pixels that an image is wide.\n That number is fixed by the image itself.\n \"\"\")\n\n dh = DistanceSpec(help=\"\"\"\n The height of the plot region that the image will occupy.\n\n .. note::\n This is not the number of pixels that an image is tall.\n That number is fixed by the image itself.\n \"\"\")\n\n dilate = Bool(False, help=\"\"\"\n Whether to always round fractional pixel locations in such a way\n as to make the images bigger.\n\n This setting may be useful if pixel rounding errors are causing\n images to have a gap between them, when they should appear flush.\n \"\"\")\n\n color_mapper = Instance(ColorMapper, help=\"\"\"\n A ``ColorMapper`` to use to map the scalar data from ``image``\n into RGBA values for display.\n\n .. note::\n The color mapping step happens on the client.\n \"\"\")\n\n # TODO: (bev) support anchor property for Image\n # ref: https://github.com/bokeh/bokeh/issues/1763\n\nclass ImageRGBA(XYGlyph):\n ''' Render images given as RGBA data.\n\n '''\n\n # a canonical order for positional args that can be used for any\n # functions derived from this class\n _args = ('image', 'x', 'y', 'dw', 'dh', 'dilate')\n\n image = NumberSpec(help=\"\"\"\n The arrays of RGBA data for the images.\n \"\"\")\n\n x = NumberSpec(help=\"\"\"\n The x-coordinates to locate the image anchors.\n \"\"\")\n\n y = NumberSpec(help=\"\"\"\n The y-coordinates to locate the image anchors.\n \"\"\")\n\n dw = DistanceSpec(help=\"\"\"\n The widths of the plot regions that the images will occupy.\n\n .. note::\n This is not the number of pixels that an image is wide.\n That number is fixed by the image itself.\n \"\"\")\n\n dh = DistanceSpec(help=\"\"\"\n The height of the plot region that the image will occupy.\n\n .. note::\n This is not the number of pixels that an image is tall.\n That number is fixed by the image itself.\n \"\"\")\n\n dilate = Bool(False, help=\"\"\"\n Whether to always round fractional pixel locations in such a way\n as to make the images bigger.\n\n This setting may be useful if pixel rounding errors are causing\n images to have a gap between them, when they should appear flush.\n \"\"\")\n\n # TODO: (bev) support anchor property for ImageRGBA\n # ref: https://github.com/bokeh/bokeh/issues/1763\n\nclass ImageURL(XYGlyph):\n ''' Render images loaded from given URLs.\n\n '''\n\n __example__ = \"examples/reference/models/ImageURL.py\"\n\n # a canonical order for positional args that can be used for any\n # functions derived from this class\n _args = ('url', 'x', 'y', 'w', 'h', 'angle', 'global_alpha', 'dilate')\n\n # TODO (bev) Why is this a NumberSpec??\n url = NumberSpec(accept_datetime=False, help=\"\"\"\n The URLs to retrieve images from.\n\n .. note::\n The actual retrieving and loading of the images happens on\n the client.\n \"\"\")\n\n x = NumberSpec(help=\"\"\"\n The x-coordinates to locate the image anchors.\n \"\"\")\n\n y = NumberSpec(help=\"\"\"\n The y-coordinates to locate the image anchors.\n \"\"\")\n\n w = DistanceSpec(default=None, help=\"\"\"\n The height of the plot region that the image will occupy in data space.\n\n The default value is ``None``, in which case the image will be displayed\n at its actual image size (regardless of the units specified here).\n \"\"\")\n\n h = DistanceSpec(default=None, help=\"\"\"\n The height of the plot region that the image will occupy in data space.\n\n The default value is ``None``, in which case the image will be displayed\n at its actual image size (regardless of the units specified here).\n \"\"\")\n\n angle = AngleSpec(default=0, help=\"\"\"\n The angles to rotate the images, as measured from the horizontal.\n \"\"\")\n\n global_alpha = Float(1.0, help=\"\"\"\n An overall opacity that each image is rendered with (in addition\n to any inherent alpha values in the image itself).\n \"\"\")\n\n dilate = Bool(False, help=\"\"\"\n Whether to always round fractional pixel locations in such a way\n as to make the images bigger.\n\n This setting may be useful if pixel rounding errors are causing\n images to have a gap between them, when they should appear flush.\n \"\"\")\n\n anchor = Enum(Anchor, help=\"\"\"\n What position of the image should be anchored at the `x`, `y`\n coordinates.\n \"\"\")\n\n retry_attempts = Int(0, help=\"\"\"\n Number of attempts to retry loading the images from the specified URL.\n Default is zero.\n \"\"\")\n\n retry_timeout = Int(0, help=\"\"\"\n Timeout (in ms) between retry attempts to load the image from the\n specified URL. Default is zero ms.\n \"\"\")\n\nclass Line(XYGlyph):\n ''' Render a single line.\n\n The ``Line`` glyph is different from most other glyphs in that the vector\n of values only produces one glyph on the Plot.\n\n '''\n # a canonical order for positional args that can be used for any\n # functions derived from this class\n _args = ('x', 'y')\n\n __example__ = \"examples/reference/models/Line.py\"\n\n x = NumberSpec(help=\"\"\"\n The x-coordinates for the points of the line.\n \"\"\")\n\n y = NumberSpec(help=\"\"\"\n The y-coordinates for the points of the line.\n \"\"\")\n\n line_props = Include(LineProps, use_prefix=False, help=\"\"\"\n The %s values for the line.\n \"\"\")\n\nclass MultiLine(Glyph):\n ''' Render several lines.\n\n The data for the ``MultiLine`` glyph is different in that the vector of\n values is not a vector of scalars. Rather, it is a \"list of lists\".\n\n '''\n\n __example__ = \"examples/reference/models/MultiLine.py\"\n\n # a canonical order for positional args that can be used for any\n # functions derived from this class\n _args = ('xs', 'ys')\n\n xs = NumberSpec(help=\"\"\"\n The x-coordinates for all the lines, given as a \"list of lists\".\n \"\"\")\n\n ys = NumberSpec(help=\"\"\"\n The y-coordinates for all the lines, given as a \"list of lists\".\n \"\"\")\n\n line_props = Include(LineProps, use_prefix=False, help=\"\"\"\n The %s values for the lines.\n \"\"\")\n\nclass Oval(XYGlyph):\n u''' Render ovals.\n\n This glyph renders ovals using Bézier curves, which are similar,\n but not identical to ellipses. In particular, widths equal to heights\n will not render circles. Use the ``Ellipse`` glyph for that.\n\n '''\n\n __example__ = \"examples/reference/models/Oval.py\"\n\n # a canonical order for positional args that can be used for any\n # functions derived from this class\n _args = ('x', 'y', 'width', 'height', 'angle')\n\n x = NumberSpec(help=\"\"\"\n The x-coordinates of the centers of the ovals.\n \"\"\")\n\n y = NumberSpec(help=\"\"\"\n The y-coordinates of the centers of the ovals.\n \"\"\")\n\n width = DistanceSpec(help=\"\"\"\n The overall widths of each oval.\n \"\"\")\n\n height = DistanceSpec(help=\"\"\"\n The overall height of each oval.\n \"\"\")\n\n angle = AngleSpec(default=0.0, help=\"\"\"\n The angle the ovals are rotated from horizontal. [rad]\n \"\"\")\n\n line_props = Include(LineProps, use_prefix=False, help=\"\"\"\n The %s values for the ovals.\n \"\"\")\n\n fill_props = Include(FillProps, use_prefix=False, help=\"\"\"\n The %s values for the ovals.\n \"\"\")\n\nclass Patch(XYGlyph):\n ''' Render a single patch.\n\n The ``Patch`` glyph is different from most other glyphs in that the vector\n of values only produces one glyph on the Plot.\n\n '''\n\n __example__ = \"examples/reference/models/Patch.py\"\n\n # a canonical order for positional args that can be used for any\n # functions derived from this class\n _args = ('x', 'y')\n\n x = NumberSpec(help=\"\"\"\n The x-coordinates for the points of the patch.\n\n .. note::\n A patch may comprise multiple polygons. In this case the\n x-coordinates for each polygon should be separated by NaN\n values in the sequence.\n \"\"\")\n\n y = NumberSpec(help=\"\"\"\n The y-coordinates for the points of the patch.\n\n .. note::\n A patch may comprise multiple polygons. In this case the\n y-coordinates for each polygon should be separated by NaN\n values in the sequence.\n \"\"\")\n\n line_props = Include(LineProps, use_prefix=False, help=\"\"\"\n The %s values for the patch.\n \"\"\")\n\n fill_props = Include(FillProps, use_prefix=False, help=\"\"\"\n The %s values for the patch.\n \"\"\")\n\nclass Patches(Glyph):\n ''' Render several patches.\n\n The data for the ``Patches`` glyph is different in that the vector of\n values is not a vector of scalars. Rather, it is a \"list of lists\".\n\n '''\n\n __example__ = \"examples/reference/models/Patches.py\"\n\n # a canonical order for positional args that can be used for any\n # functions derived from this class\n _args = ('xs', 'ys')\n\n xs = NumberSpec(help=\"\"\"\n The x-coordinates for all the patches, given as a \"list of lists\".\n\n .. note::\n Individual patches may comprise multiple polygons. In this case\n the x-coordinates for each polygon should be separated by NaN\n values in the sublists.\n \"\"\")\n\n ys = NumberSpec(help=\"\"\"\n The y-coordinates for all the patches, given as a \"list of lists\".\n\n .. note::\n Individual patches may comprise multiple polygons. In this case\n the y-coordinates for each polygon should be separated by NaN\n values in the sublists.\n \"\"\")\n\n line_props = Include(LineProps, use_prefix=False, help=\"\"\"\n The %s values for the patches.\n \"\"\")\n\n fill_props = Include(FillProps, use_prefix=False, help=\"\"\"\n The %s values for the patches.\n \"\"\")\n\nclass Quad(Glyph):\n ''' Render axis-aligned quads.\n\n '''\n\n __example__ = \"examples/reference/models/Quad.py\"\n\n # a canonical order for positional args that can be used for any\n # functions derived from this class\n _args = ('left', 'right', 'top', 'bottom')\n\n left = NumberSpec(help=\"\"\"\n The x-coordinates of the left edges.\n \"\"\")\n\n right = NumberSpec(help=\"\"\"\n The x-coordinates of the right edges.\n \"\"\")\n\n bottom = NumberSpec(help=\"\"\"\n The y-coordinates of the bottom edges.\n \"\"\")\n\n top = NumberSpec(help=\"\"\"\n The y-coordinates of the top edges.\n \"\"\")\n\n line_props = Include(LineProps, use_prefix=False, help=\"\"\"\n The %s values for the quads.\n \"\"\")\n\n fill_props = Include(FillProps, use_prefix=False, help=\"\"\"\n The %s values for the quads.\n \"\"\")\n\nclass Quadratic(Glyph):\n ''' Render parabolas.\n\n '''\n\n __example__ = \"examples/reference/models/Quadratic.py\"\n\n # a canonical order for positional args that can be used for any\n # functions derived from this class\n _args = ('x0', 'y0', 'x1', 'y1', 'cx', 'cy')\n\n x0 = NumberSpec(help=\"\"\"\n The x-coordinates of the starting points.\n \"\"\")\n\n y0 = NumberSpec(help=\"\"\"\n The y-coordinates of the starting points.\n \"\"\")\n\n x1 = NumberSpec(help=\"\"\"\n The x-coordinates of the ending points.\n \"\"\")\n\n y1 = NumberSpec(help=\"\"\"\n The y-coordinates of the ending points.\n \"\"\")\n\n cx = NumberSpec(help=\"\"\"\n The x-coordinates of the control points.\n \"\"\")\n\n cy = NumberSpec(help=\"\"\"\n The y-coordinates of the control points.\n \"\"\")\n\n line_props = Include(LineProps, use_prefix=False, help=\"\"\"\n The %s values for the parabolas.\n \"\"\")\n\nclass Ray(XYGlyph):\n ''' Render rays.\n\n '''\n\n __example__ = \"examples/reference/models/Ray.py\"\n\n # a canonical order for positional args that can be used for any\n # functions derived from this class\n _args = ('x', 'y', 'length', 'angle')\n\n x = NumberSpec(help=\"\"\"\n The x-coordinates to start the rays.\n \"\"\")\n\n y = NumberSpec(help=\"\"\"\n The y-coordinates to start the rays.\n \"\"\")\n\n angle = AngleSpec(help=\"\"\"\n The angles in radians to extend the rays, as measured from the horizontal.\n \"\"\")\n\n length = DistanceSpec(help=\"\"\"\n The length to extend the ray. Note that this ``length`` defaults\n to screen units.\n \"\"\")\n\n line_props = Include(LineProps, use_prefix=False, help=\"\"\"\n The %s values for the rays.\n \"\"\")\n\nclass Rect(XYGlyph):\n ''' Render rectangles.\n\n '''\n\n __example__ = \"examples/reference/models/Rect.py\"\n\n # a canonical order for positional args that can be used for any\n # functions derived from this class\n _args = ('x', 'y', 'width', 'height', 'angle', 'dilate')\n\n x = NumberSpec(help=\"\"\"\n The x-coordinates of the centers of the rectangles.\n \"\"\")\n\n y = NumberSpec(help=\"\"\"\n The y-coordinates of the centers of the rectangles.\n \"\"\")\n\n width = DistanceSpec(help=\"\"\"\n The overall widths of the rectangles.\n \"\"\")\n\n height = DistanceSpec(help=\"\"\"\n The overall heights of the rectangles.\n \"\"\")\n\n angle = AngleSpec(default=0.0, help=\"\"\"\n The angles to rotate the rectangles, as measured from the horizontal.\n \"\"\")\n\n dilate = Bool(False, help=\"\"\"\n Whether to always round fractional pixel locations in such a way\n as to make the rectangles bigger.\n\n This setting may be useful if pixel rounding errors are causing\n rectangles to have a gap between them, when they should appear\n flush.\n \"\"\")\n\n line_props = Include(LineProps, use_prefix=False, help=\"\"\"\n The %s values for the rectangles.\n \"\"\")\n\n fill_props = Include(FillProps, use_prefix=False, help=\"\"\"\n The %s values for the rectangles.\n \"\"\")\n\nclass Segment(Glyph):\n ''' Render segments.\n\n '''\n\n __example__ = \"examples/reference/models/Segment.py\"\n\n # a canonical order for positional args that can be used for any\n # functions derived from this class\n _args = ('x0', 'y0', 'x1', 'y1')\n\n x0 = NumberSpec(help=\"\"\"\n The x-coordinates of the starting points.\n \"\"\")\n\n y0 = NumberSpec(help=\"\"\"\n The y-coordinates of the starting points.\n \"\"\")\n\n x1 = NumberSpec(help=\"\"\"\n The x-coordinates of the ending points.\n \"\"\")\n\n y1 = NumberSpec(help=\"\"\"\n The y-coordinates of the ending points.\n \"\"\")\n\n line_props = Include(LineProps, use_prefix=False, help=\"\"\"\n The %s values for the segments.\n \"\"\")\n\nclass Text(XYGlyph):\n ''' Render text.\n\n '''\n\n __example__ = \"examples/reference/models/Text.py\"\n\n # a canonical order for positional args that can be used for any\n # functions derived from this class\n _args = ('x', 'y', 'text', 'angle', 'x_offset', 'y_offset')\n\n x = NumberSpec(help=\"\"\"\n The x-coordinates to locate the text anchors.\n \"\"\")\n\n y = NumberSpec(help=\"\"\"\n The y-coordinates to locate the text anchors.\n \"\"\")\n\n text = StringSpec(\"text\", help=\"\"\"\n The text values to render.\n \"\"\")\n\n angle = AngleSpec(default=0, help=\"\"\"\n The angles to rotate the text, as measured from the horizontal.\n \"\"\")\n\n x_offset = NumberSpec(default=0, help=\"\"\"\n Offset values to apply to the x-coordinates.\n\n This is useful, for instance, if it is desired to \"float\" text a fixed\n distance in screen units from a given data position.\n \"\"\")\n\n y_offset = NumberSpec(default=0, help=\"\"\"\n Offset values to apply to the y-coordinates.\n\n This is useful, for instance, if it is desired to \"float\" text a fixed\n distance in screen units from a given data position.\n \"\"\")\n\n text_props = Include(TextProps, use_prefix=False, help=\"\"\"\n The %s values for the text.\n \"\"\")\n\nclass VBar(Glyph):\n ''' Render vertical bars, given a center coordinate, width and (top, bottom) coordinates.\n\n '''\n\n __example__ = \"examples/reference/models/VBar.py\"\n\n # a canonical order for positional args that can be used for any\n # functions derived from this class\n _args = ('x', 'width', 'top', 'bottom')\n\n x = NumberSpec(help=\"\"\"\n The x-coordinates of the centers of the vertical bars.\n \"\"\")\n\n width = NumberSpec(help=\"\"\"\n The widths of the vertical bars.\n \"\"\")\n\n bottom = NumberSpec(default=0, help=\"\"\"\n The y-coordinates of the bottom edges.\n \"\"\")\n\n top = NumberSpec(help=\"\"\"\n The y-coordinates of the top edges.\n \"\"\")\n\n line_props = Include(LineProps, use_prefix=False, help=\"\"\"\n The %s values for the vertical bars.\n \"\"\")\n\n fill_props = Include(FillProps, use_prefix=False, help=\"\"\"\n The %s values for the vertical bars.\n \"\"\")\n\nclass Wedge(XYGlyph):\n ''' Render wedges.\n\n '''\n\n __example__ = \"examples/reference/models/Wedge.py\"\n\n # a canonical order for positional args that can be used for any\n # functions derived from this class\n _args = ('x', 'y', 'radius', 'start_angle', 'end_angle', 'direction')\n\n x = NumberSpec(help=\"\"\"\n The x-coordinates of the points of the wedges.\n \"\"\")\n\n y = NumberSpec(help=\"\"\"\n The y-coordinates of the points of the wedges.\n \"\"\")\n\n radius = DistanceSpec(help=\"\"\"\n Radii of the wedges.\n \"\"\")\n\n start_angle = AngleSpec(help=\"\"\"\n The angles to start the wedges, as measured from the horizontal.\n \"\"\")\n\n end_angle = AngleSpec(help=\"\"\"\n The angles to end the wedges, as measured from the horizontal.\n \"\"\")\n\n direction = Enum(Direction, default='anticlock', help=\"\"\"\n Which direction to stroke between the start and end angles.\n \"\"\")\n\n line_props = Include(LineProps, use_prefix=False, help=\"\"\"\n The %s values for the wedges.\n \"\"\")\n\n fill_props = Include(FillProps, use_prefix=False, help=\"\"\"\n The %s values for the wedges.\n \"\"\")\n\n# XXX: allow `from bokeh.models.glyphs import *`\nfrom .markers import (Asterisk, Circle, CircleCross, CircleX, Cross, Diamond, DiamondCross,\n InvertedTriangle, Marker, Square, SquareCross, SquareX, Triangle, X)\n\n# Fool pyflakes\n(Asterisk, Circle, CircleCross, CircleX, Cross, Diamond, DiamondCross,\nInvertedTriangle, Marker, Square, SquareCross, SquareX, Triangle, X)\n",
"path": "bokeh/models/glyphs.py"
}
] | [
{
"content": "# -*- coding: utf-8 -*-\n''' Display a variety of visual shapes whose attributes can be associated\nwith data columns from ``ColumnDataSources``.\n\nThe full list of glyphs built into Bokeh is given below:\n\n* :class:`~bokeh.models.glyphs.AnnularWedge`\n* :class:`~bokeh.models.glyphs.Annulus`\n* :class:`~bokeh.models.glyphs.Arc`\n* :class:`~bokeh.models.glyphs.Bezier`\n* :class:`~bokeh.models.glyphs.Ellipse`\n* :class:`~bokeh.models.glyphs.HBar`\n* :class:`~bokeh.models.glyphs.Image`\n* :class:`~bokeh.models.glyphs.ImageRGBA`\n* :class:`~bokeh.models.glyphs.ImageURL`\n* :class:`~bokeh.models.glyphs.Line`\n* :class:`~bokeh.models.glyphs.MultiLine`\n* :class:`~bokeh.models.glyphs.Oval`\n* :class:`~bokeh.models.glyphs.Patch`\n* :class:`~bokeh.models.glyphs.Patches`\n* :class:`~bokeh.models.glyphs.Quad`\n* :class:`~bokeh.models.glyphs.Quadratic`\n* :class:`~bokeh.models.glyphs.Ray`\n* :class:`~bokeh.models.glyphs.Rect`\n* :class:`~bokeh.models.glyphs.Segment`\n* :class:`~bokeh.models.glyphs.Text`\n* :class:`~bokeh.models.glyphs.VBar`\n* :class:`~bokeh.models.glyphs.Wedge`\n\nAll these glyphs share a minimal common interface through their base class\n``Glyph``:\n\n.. autoclass:: Glyph\n :members:\n\n'''\nfrom __future__ import absolute_import\n\nfrom ..core.enums import Anchor, Direction\nfrom ..core.has_props import abstract\nfrom ..core.properties import (AngleSpec, Bool, DistanceSpec, Enum, Float,\n Include, Instance, Int, NumberSpec, StringSpec)\nfrom ..core.property_mixins import FillProps, LineProps, TextProps\nfrom ..model import Model\n\nfrom .mappers import ColorMapper, LinearColorMapper\n\n@abstract\nclass Glyph(Model):\n ''' Base class for all glyph models.\n\n '''\n\n@abstract\nclass XYGlyph(Glyph):\n ''' Base class of glyphs with `x` and `y` coordinate attributes.\n\n '''\n\nclass AnnularWedge(XYGlyph):\n ''' Render annular wedges.\n\n '''\n\n __example__ = \"examples/reference/models/AnnularWedge.py\"\n\n # a canonical order for positional args that can be used for any\n # functions derived from this class\n _args = ('x', 'y', 'inner_radius', 'outer_radius', 'start_angle', 'end_angle', 'direction')\n\n x = NumberSpec(help=\"\"\"\n The x-coordinates of the center of the annular wedges.\n \"\"\")\n\n y = NumberSpec(help=\"\"\"\n The y-coordinates of the center of the annular wedges.\n \"\"\")\n\n inner_radius = DistanceSpec(help=\"\"\"\n The inner radii of the annular wedges.\n \"\"\")\n\n outer_radius = DistanceSpec(help=\"\"\"\n The outer radii of the annular wedges.\n \"\"\")\n\n start_angle = AngleSpec(help=\"\"\"\n The angles to start the annular wedges, as measured from the horizontal.\n \"\"\")\n\n end_angle = AngleSpec(help=\"\"\"\n The angles to end the annular wedges, as measured from the horizontal.\n \"\"\")\n\n direction = Enum(Direction, default=Direction.anticlock, help=\"\"\"\n Which direction to stroke between the start and end angles.\n \"\"\")\n\n line_props = Include(LineProps, use_prefix=False, help=\"\"\"\n The %s values for the annular wedges.\n \"\"\")\n\n fill_props = Include(FillProps, use_prefix=False, help=\"\"\"\n The %s values for the annular wedges.\n \"\"\")\n\nclass Annulus(XYGlyph):\n ''' Render annuli.\n\n '''\n\n __example__ = \"examples/reference/models/Annulus.py\"\n\n # a canonical order for positional args that can be used for any\n # functions derived from this class\n _args = ('x', 'y', 'inner_radius', 'outer_radius')\n\n x = NumberSpec(help=\"\"\"\n The x-coordinates of the center of the annuli.\n \"\"\")\n\n y = NumberSpec(help=\"\"\"\n The y-coordinates of the center of the annuli.\n \"\"\")\n\n inner_radius = DistanceSpec(help=\"\"\"\n The inner radii of the annuli.\n \"\"\")\n\n outer_radius = DistanceSpec(help=\"\"\"\n The outer radii of the annuli.\n \"\"\")\n\n line_props = Include(LineProps, use_prefix=False, help=\"\"\"\n The %s values for the annuli.\n \"\"\")\n\n fill_props = Include(FillProps, use_prefix=False, help=\"\"\"\n The %s values for the annuli.\n \"\"\")\n\nclass Arc(XYGlyph):\n ''' Render arcs.\n\n '''\n\n __example__ = \"examples/reference/models/Arc.py\"\n\n # a canonical order for positional args that can be used for any\n # functions derived from this class\n _args = ('x', 'y', 'radius', 'start_angle', 'end_angle', 'direction')\n\n x = NumberSpec(help=\"\"\"\n The x-coordinates of the center of the arcs.\n \"\"\")\n\n y = NumberSpec(help=\"\"\"\n The y-coordinates of the center of the arcs.\n \"\"\")\n\n radius = DistanceSpec(help=\"\"\"\n Radius of the arc.\n \"\"\")\n\n start_angle = AngleSpec(help=\"\"\"\n The angles to start the arcs, as measured from the horizontal.\n \"\"\")\n\n end_angle = AngleSpec(help=\"\"\"\n The angles to end the arcs, as measured from the horizontal.\n \"\"\")\n\n direction = Enum(Direction, default='anticlock', help=\"\"\"\n Which direction to stroke between the start and end angles.\n \"\"\")\n\n line_props = Include(LineProps, use_prefix=False, help=\"\"\"\n The %s values for the arcs.\n \"\"\")\n\nclass Bezier(Glyph):\n u''' Render Bézier curves.\n\n For more information consult the `Wikipedia article for Bézier curve`_.\n\n .. _Wikipedia article for Bézier curve: http://en.wikipedia.org/wiki/Bézier_curve\n\n '''\n\n __example__ = \"examples/reference/models/Bezier.py\"\n\n # a canonical order for positional args that can be used for any\n # functions derived from this class\n _args = ('x0', 'y0', 'x1', 'y1', 'cx0', 'cy0', 'cx1', 'cy1')\n\n x0 = NumberSpec(help=\"\"\"\n The x-coordinates of the starting points.\n \"\"\")\n\n y0 = NumberSpec(help=\"\"\"\n The y-coordinates of the starting points.\n \"\"\")\n\n x1 = NumberSpec(help=\"\"\"\n The x-coordinates of the ending points.\n \"\"\")\n\n y1 = NumberSpec(help=\"\"\"\n The y-coordinates of the ending points.\n \"\"\")\n\n cx0 = NumberSpec(help=\"\"\"\n The x-coordinates of first control points.\n \"\"\")\n\n cy0 = NumberSpec(help=\"\"\"\n The y-coordinates of first control points.\n \"\"\")\n\n cx1 = NumberSpec(help=\"\"\"\n The x-coordinates of second control points.\n \"\"\")\n\n cy1 = NumberSpec(help=\"\"\"\n The y-coordinates of second control points.\n \"\"\")\n\n line_props = Include(LineProps, use_prefix=False, help=u\"\"\"\n The %s values for the Bézier curves.\n \"\"\")\n\nclass Ellipse(XYGlyph):\n u''' Render ellipses.\n\n '''\n\n __example__ = \"examples/reference/models/Ellipse.py\"\n\n # a canonical order for positional args that can be used for any\n # functions derived from this class\n _args = ('x', 'y', 'width', 'height', 'angle')\n\n x = NumberSpec(help=\"\"\"\n The x-coordinates of the centers of the ellipses.\n \"\"\")\n\n y = NumberSpec(help=\"\"\"\n The y-coordinates of the centers of the ellipses.\n \"\"\")\n\n width = DistanceSpec(help=\"\"\"\n The widths of each ellipse.\n \"\"\")\n\n height = DistanceSpec(help=\"\"\"\n The heights of each ellipse.\n \"\"\")\n\n angle = AngleSpec(default=0.0, help=\"\"\"\n The angle the ellipses are rotated from horizontal. [rad]\n \"\"\")\n\n line_props = Include(LineProps, use_prefix=False, help=\"\"\"\n The %s values for the ovals.\n \"\"\")\n\n fill_props = Include(FillProps, use_prefix=False, help=\"\"\"\n The %s values for the ovals.\n \"\"\")\n\nclass HBar(Glyph):\n ''' Render horizontal bars, given a center coordinate, ``height`` and\n (``left``, ``right``) coordinates.\n\n '''\n\n __example__ = \"examples/reference/models/HBar.py\"\n\n # a canonical order for positional args that can be used for any\n # functions derived from this class\n _args = ('y', 'height', 'right', 'left')\n\n y = NumberSpec(help=\"\"\"\n The y-coordinates of the centers of the horizontal bars.\n \"\"\")\n\n height = NumberSpec(help=\"\"\"\n The heights of the vertical bars.\n \"\"\")\n\n left = NumberSpec(default=0, help=\"\"\"\n The x-coordinates of the left edges.\n \"\"\")\n\n right = NumberSpec(help=\"\"\"\n The x-coordinates of the right edges.\n \"\"\")\n\n line_props = Include(LineProps, use_prefix=False, help=\"\"\"\n The %s values for the horizontal bars.\n \"\"\")\n\n fill_props = Include(FillProps, use_prefix=False, help=\"\"\"\n The %s values for the horizontal bars.\n \"\"\")\n\nclass Image(XYGlyph):\n ''' Render images given as scalar data together with a color mapper.\n\n In addition to the defined model properties, ``Image`` also can accept\n a keyword argument ``palette`` in place of an explicit ``color_mapper``.\n The value should be a list of colors, or the name of one of the built-in\n palettes in ``bokeh.palettes``. This palette will be used to automatically\n construct a ``ColorMapper`` model for the ``color_mapper`` property.\n\n If both ``palette`` and ``color_mapper`` are passed, a ``ValueError``\n exception will be raised. If neither is passed, then the ``Greys9``\n palette will be used as a default.\n\n '''\n\n def __init__(self, **kwargs):\n if 'palette' in kwargs and 'color_mapper' in kwargs:\n raise ValueError(\"only one of 'palette' and 'color_mapper' may be specified\")\n elif 'color_mapper' not in kwargs:\n # Use a palette (given or default)\n palette = kwargs.pop('palette', 'Greys9')\n mapper = LinearColorMapper(palette)\n kwargs['color_mapper'] = mapper\n\n super(Image, self).__init__(**kwargs)\n\n # a canonical order for positional args that can be used for any\n # functions derived from this class\n _args = ('image', 'x', 'y', 'dw', 'dh', 'dilate')\n\n # a hook to specify any additional kwargs handled by an initializer\n _extra_kws = {\n 'palette': (\n 'str or list[color value]',\n 'a palette to construct a value for the color mapper property from'\n )\n }\n\n image = NumberSpec(help=\"\"\"\n The arrays of scalar data for the images to be colormapped.\n \"\"\")\n\n x = NumberSpec(help=\"\"\"\n The x-coordinates to locate the image anchors.\n \"\"\")\n\n y = NumberSpec(help=\"\"\"\n The y-coordinates to locate the image anchors.\n \"\"\")\n\n dw = DistanceSpec(help=\"\"\"\n The widths of the plot regions that the images will occupy.\n\n .. note::\n This is not the number of pixels that an image is wide.\n That number is fixed by the image itself.\n \"\"\")\n\n dh = DistanceSpec(help=\"\"\"\n The height of the plot region that the image will occupy.\n\n .. note::\n This is not the number of pixels that an image is tall.\n That number is fixed by the image itself.\n \"\"\")\n\n dilate = Bool(False, help=\"\"\"\n Whether to always round fractional pixel locations in such a way\n as to make the images bigger.\n\n This setting may be useful if pixel rounding errors are causing\n images to have a gap between them, when they should appear flush.\n \"\"\")\n\n color_mapper = Instance(ColorMapper, help=\"\"\"\n A ``ColorMapper`` to use to map the scalar data from ``image``\n into RGBA values for display.\n\n .. note::\n The color mapping step happens on the client.\n \"\"\")\n\n # TODO: (bev) support anchor property for Image\n # ref: https://github.com/bokeh/bokeh/issues/1763\n\nclass ImageRGBA(XYGlyph):\n ''' Render images given as RGBA data.\n\n '''\n\n # a canonical order for positional args that can be used for any\n # functions derived from this class\n _args = ('image', 'x', 'y', 'dw', 'dh', 'dilate')\n\n image = NumberSpec(help=\"\"\"\n The arrays of RGBA data for the images.\n \"\"\")\n\n x = NumberSpec(help=\"\"\"\n The x-coordinates to locate the image anchors.\n \"\"\")\n\n y = NumberSpec(help=\"\"\"\n The y-coordinates to locate the image anchors.\n \"\"\")\n\n dw = DistanceSpec(help=\"\"\"\n The widths of the plot regions that the images will occupy.\n\n .. note::\n This is not the number of pixels that an image is wide.\n That number is fixed by the image itself.\n \"\"\")\n\n dh = DistanceSpec(help=\"\"\"\n The height of the plot region that the image will occupy.\n\n .. note::\n This is not the number of pixels that an image is tall.\n That number is fixed by the image itself.\n \"\"\")\n\n dilate = Bool(False, help=\"\"\"\n Whether to always round fractional pixel locations in such a way\n as to make the images bigger.\n\n This setting may be useful if pixel rounding errors are causing\n images to have a gap between them, when they should appear flush.\n \"\"\")\n\n # TODO: (bev) support anchor property for ImageRGBA\n # ref: https://github.com/bokeh/bokeh/issues/1763\n\nclass ImageURL(XYGlyph):\n ''' Render images loaded from given URLs.\n\n '''\n\n __example__ = \"examples/reference/models/ImageURL.py\"\n\n # a canonical order for positional args that can be used for any\n # functions derived from this class\n _args = ('url', 'x', 'y', 'w', 'h', 'angle', 'global_alpha', 'dilate')\n\n # TODO (bev) Why is this a NumberSpec??\n url = NumberSpec(accept_datetime=False, help=\"\"\"\n The URLs to retrieve images from.\n\n .. note::\n The actual retrieving and loading of the images happens on\n the client.\n \"\"\")\n\n x = NumberSpec(help=\"\"\"\n The x-coordinates to locate the image anchors.\n \"\"\")\n\n y = NumberSpec(help=\"\"\"\n The y-coordinates to locate the image anchors.\n \"\"\")\n\n w = DistanceSpec(default=None, help=\"\"\"\n The height of the plot region that the image will occupy in data space.\n\n The default value is ``None``, in which case the image will be displayed\n at its actual image size (regardless of the units specified here).\n \"\"\")\n\n h = DistanceSpec(default=None, help=\"\"\"\n The height of the plot region that the image will occupy in data space.\n\n The default value is ``None``, in which case the image will be displayed\n at its actual image size (regardless of the units specified here).\n \"\"\")\n\n angle = AngleSpec(default=0, help=\"\"\"\n The angles to rotate the images, as measured from the horizontal.\n \"\"\")\n\n global_alpha = Float(1.0, help=\"\"\"\n An overall opacity that each image is rendered with (in addition\n to any inherent alpha values in the image itself).\n \"\"\")\n\n dilate = Bool(False, help=\"\"\"\n Whether to always round fractional pixel locations in such a way\n as to make the images bigger.\n\n This setting may be useful if pixel rounding errors are causing\n images to have a gap between them, when they should appear flush.\n \"\"\")\n\n anchor = Enum(Anchor, help=\"\"\"\n What position of the image should be anchored at the `x`, `y`\n coordinates.\n \"\"\")\n\n retry_attempts = Int(0, help=\"\"\"\n Number of attempts to retry loading the images from the specified URL.\n Default is zero.\n \"\"\")\n\n retry_timeout = Int(0, help=\"\"\"\n Timeout (in ms) between retry attempts to load the image from the\n specified URL. Default is zero ms.\n \"\"\")\n\nclass Line(XYGlyph):\n ''' Render a single line.\n\n The ``Line`` glyph is different from most other glyphs in that the vector\n of values only produces one glyph on the Plot.\n\n '''\n # a canonical order for positional args that can be used for any\n # functions derived from this class\n _args = ('x', 'y')\n\n __example__ = \"examples/reference/models/Line.py\"\n\n x = NumberSpec(help=\"\"\"\n The x-coordinates for the points of the line.\n \"\"\")\n\n y = NumberSpec(help=\"\"\"\n The y-coordinates for the points of the line.\n \"\"\")\n\n line_props = Include(LineProps, use_prefix=False, help=\"\"\"\n The %s values for the line.\n \"\"\")\n\nclass MultiLine(Glyph):\n ''' Render several lines.\n\n The data for the ``MultiLine`` glyph is different in that the vector of\n values is not a vector of scalars. Rather, it is a \"list of lists\".\n\n '''\n\n __example__ = \"examples/reference/models/MultiLine.py\"\n\n # a canonical order for positional args that can be used for any\n # functions derived from this class\n _args = ('xs', 'ys')\n\n xs = NumberSpec(help=\"\"\"\n The x-coordinates for all the lines, given as a \"list of lists\".\n \"\"\")\n\n ys = NumberSpec(help=\"\"\"\n The y-coordinates for all the lines, given as a \"list of lists\".\n \"\"\")\n\n line_props = Include(LineProps, use_prefix=False, help=\"\"\"\n The %s values for the lines.\n \"\"\")\n\nclass Oval(XYGlyph):\n u''' Render ovals.\n\n This glyph renders ovals using Bézier curves, which are similar,\n but not identical to ellipses. In particular, widths equal to heights\n will not render circles. Use the ``Ellipse`` glyph for that.\n\n '''\n\n __example__ = \"examples/reference/models/Oval.py\"\n\n # a canonical order for positional args that can be used for any\n # functions derived from this class\n _args = ('x', 'y', 'width', 'height', 'angle')\n\n x = NumberSpec(help=\"\"\"\n The x-coordinates of the centers of the ovals.\n \"\"\")\n\n y = NumberSpec(help=\"\"\"\n The y-coordinates of the centers of the ovals.\n \"\"\")\n\n width = DistanceSpec(help=\"\"\"\n The overall widths of each oval.\n \"\"\")\n\n height = DistanceSpec(help=\"\"\"\n The overall height of each oval.\n \"\"\")\n\n angle = AngleSpec(default=0.0, help=\"\"\"\n The angle the ovals are rotated from horizontal. [rad]\n \"\"\")\n\n line_props = Include(LineProps, use_prefix=False, help=\"\"\"\n The %s values for the ovals.\n \"\"\")\n\n fill_props = Include(FillProps, use_prefix=False, help=\"\"\"\n The %s values for the ovals.\n \"\"\")\n\nclass Patch(XYGlyph):\n ''' Render a single patch.\n\n The ``Patch`` glyph is different from most other glyphs in that the vector\n of values only produces one glyph on the Plot.\n\n '''\n\n __example__ = \"examples/reference/models/Patch.py\"\n\n # a canonical order for positional args that can be used for any\n # functions derived from this class\n _args = ('x', 'y')\n\n x = NumberSpec(help=\"\"\"\n The x-coordinates for the points of the patch.\n\n .. note::\n A patch may comprise multiple polygons. In this case the\n x-coordinates for each polygon should be separated by NaN\n values in the sequence.\n \"\"\")\n\n y = NumberSpec(help=\"\"\"\n The y-coordinates for the points of the patch.\n\n .. note::\n A patch may comprise multiple polygons. In this case the\n y-coordinates for each polygon should be separated by NaN\n values in the sequence.\n \"\"\")\n\n line_props = Include(LineProps, use_prefix=False, help=\"\"\"\n The %s values for the patch.\n \"\"\")\n\n fill_props = Include(FillProps, use_prefix=False, help=\"\"\"\n The %s values for the patch.\n \"\"\")\n\nclass Patches(Glyph):\n ''' Render several patches.\n\n The data for the ``Patches`` glyph is different in that the vector of\n values is not a vector of scalars. Rather, it is a \"list of lists\".\n\n '''\n\n __example__ = \"examples/reference/models/Patches.py\"\n\n # a canonical order for positional args that can be used for any\n # functions derived from this class\n _args = ('xs', 'ys')\n\n xs = NumberSpec(help=\"\"\"\n The x-coordinates for all the patches, given as a \"list of lists\".\n\n .. note::\n Individual patches may comprise multiple polygons. In this case\n the x-coordinates for each polygon should be separated by NaN\n values in the sublists.\n \"\"\")\n\n ys = NumberSpec(help=\"\"\"\n The y-coordinates for all the patches, given as a \"list of lists\".\n\n .. note::\n Individual patches may comprise multiple polygons. In this case\n the y-coordinates for each polygon should be separated by NaN\n values in the sublists.\n \"\"\")\n\n line_props = Include(LineProps, use_prefix=False, help=\"\"\"\n The %s values for the patches.\n \"\"\")\n\n fill_props = Include(FillProps, use_prefix=False, help=\"\"\"\n The %s values for the patches.\n \"\"\")\n\nclass Quad(Glyph):\n ''' Render axis-aligned quads.\n\n '''\n\n __example__ = \"examples/reference/models/Quad.py\"\n\n # a canonical order for positional args that can be used for any\n # functions derived from this class\n _args = ('left', 'right', 'top', 'bottom')\n\n left = NumberSpec(help=\"\"\"\n The x-coordinates of the left edges.\n \"\"\")\n\n right = NumberSpec(help=\"\"\"\n The x-coordinates of the right edges.\n \"\"\")\n\n bottom = NumberSpec(help=\"\"\"\n The y-coordinates of the bottom edges.\n \"\"\")\n\n top = NumberSpec(help=\"\"\"\n The y-coordinates of the top edges.\n \"\"\")\n\n line_props = Include(LineProps, use_prefix=False, help=\"\"\"\n The %s values for the quads.\n \"\"\")\n\n fill_props = Include(FillProps, use_prefix=False, help=\"\"\"\n The %s values for the quads.\n \"\"\")\n\nclass Quadratic(Glyph):\n ''' Render parabolas.\n\n '''\n\n __example__ = \"examples/reference/models/Quadratic.py\"\n\n # a canonical order for positional args that can be used for any\n # functions derived from this class\n _args = ('x0', 'y0', 'x1', 'y1', 'cx', 'cy')\n\n x0 = NumberSpec(help=\"\"\"\n The x-coordinates of the starting points.\n \"\"\")\n\n y0 = NumberSpec(help=\"\"\"\n The y-coordinates of the starting points.\n \"\"\")\n\n x1 = NumberSpec(help=\"\"\"\n The x-coordinates of the ending points.\n \"\"\")\n\n y1 = NumberSpec(help=\"\"\"\n The y-coordinates of the ending points.\n \"\"\")\n\n cx = NumberSpec(help=\"\"\"\n The x-coordinates of the control points.\n \"\"\")\n\n cy = NumberSpec(help=\"\"\"\n The y-coordinates of the control points.\n \"\"\")\n\n line_props = Include(LineProps, use_prefix=False, help=\"\"\"\n The %s values for the parabolas.\n \"\"\")\n\nclass Ray(XYGlyph):\n ''' Render rays.\n\n '''\n\n __example__ = \"examples/reference/models/Ray.py\"\n\n # a canonical order for positional args that can be used for any\n # functions derived from this class\n _args = ('x', 'y', 'length', 'angle')\n\n x = NumberSpec(help=\"\"\"\n The x-coordinates to start the rays.\n \"\"\")\n\n y = NumberSpec(help=\"\"\"\n The y-coordinates to start the rays.\n \"\"\")\n\n angle = AngleSpec(help=\"\"\"\n The angles in radians to extend the rays, as measured from the horizontal.\n \"\"\")\n\n length = DistanceSpec(help=\"\"\"\n The length to extend the ray. Note that this ``length`` defaults\n to data units (measured in the x-direction).\n \"\"\")\n\n line_props = Include(LineProps, use_prefix=False, help=\"\"\"\n The %s values for the rays.\n \"\"\")\n\nclass Rect(XYGlyph):\n ''' Render rectangles.\n\n '''\n\n __example__ = \"examples/reference/models/Rect.py\"\n\n # a canonical order for positional args that can be used for any\n # functions derived from this class\n _args = ('x', 'y', 'width', 'height', 'angle', 'dilate')\n\n x = NumberSpec(help=\"\"\"\n The x-coordinates of the centers of the rectangles.\n \"\"\")\n\n y = NumberSpec(help=\"\"\"\n The y-coordinates of the centers of the rectangles.\n \"\"\")\n\n width = DistanceSpec(help=\"\"\"\n The overall widths of the rectangles.\n \"\"\")\n\n height = DistanceSpec(help=\"\"\"\n The overall heights of the rectangles.\n \"\"\")\n\n angle = AngleSpec(default=0.0, help=\"\"\"\n The angles to rotate the rectangles, as measured from the horizontal.\n \"\"\")\n\n dilate = Bool(False, help=\"\"\"\n Whether to always round fractional pixel locations in such a way\n as to make the rectangles bigger.\n\n This setting may be useful if pixel rounding errors are causing\n rectangles to have a gap between them, when they should appear\n flush.\n \"\"\")\n\n line_props = Include(LineProps, use_prefix=False, help=\"\"\"\n The %s values for the rectangles.\n \"\"\")\n\n fill_props = Include(FillProps, use_prefix=False, help=\"\"\"\n The %s values for the rectangles.\n \"\"\")\n\nclass Segment(Glyph):\n ''' Render segments.\n\n '''\n\n __example__ = \"examples/reference/models/Segment.py\"\n\n # a canonical order for positional args that can be used for any\n # functions derived from this class\n _args = ('x0', 'y0', 'x1', 'y1')\n\n x0 = NumberSpec(help=\"\"\"\n The x-coordinates of the starting points.\n \"\"\")\n\n y0 = NumberSpec(help=\"\"\"\n The y-coordinates of the starting points.\n \"\"\")\n\n x1 = NumberSpec(help=\"\"\"\n The x-coordinates of the ending points.\n \"\"\")\n\n y1 = NumberSpec(help=\"\"\"\n The y-coordinates of the ending points.\n \"\"\")\n\n line_props = Include(LineProps, use_prefix=False, help=\"\"\"\n The %s values for the segments.\n \"\"\")\n\nclass Text(XYGlyph):\n ''' Render text.\n\n '''\n\n __example__ = \"examples/reference/models/Text.py\"\n\n # a canonical order for positional args that can be used for any\n # functions derived from this class\n _args = ('x', 'y', 'text', 'angle', 'x_offset', 'y_offset')\n\n x = NumberSpec(help=\"\"\"\n The x-coordinates to locate the text anchors.\n \"\"\")\n\n y = NumberSpec(help=\"\"\"\n The y-coordinates to locate the text anchors.\n \"\"\")\n\n text = StringSpec(\"text\", help=\"\"\"\n The text values to render.\n \"\"\")\n\n angle = AngleSpec(default=0, help=\"\"\"\n The angles to rotate the text, as measured from the horizontal.\n \"\"\")\n\n x_offset = NumberSpec(default=0, help=\"\"\"\n Offset values to apply to the x-coordinates.\n\n This is useful, for instance, if it is desired to \"float\" text a fixed\n distance in screen units from a given data position.\n \"\"\")\n\n y_offset = NumberSpec(default=0, help=\"\"\"\n Offset values to apply to the y-coordinates.\n\n This is useful, for instance, if it is desired to \"float\" text a fixed\n distance in screen units from a given data position.\n \"\"\")\n\n text_props = Include(TextProps, use_prefix=False, help=\"\"\"\n The %s values for the text.\n \"\"\")\n\nclass VBar(Glyph):\n ''' Render vertical bars, given a center coordinate, width and (top, bottom) coordinates.\n\n '''\n\n __example__ = \"examples/reference/models/VBar.py\"\n\n # a canonical order for positional args that can be used for any\n # functions derived from this class\n _args = ('x', 'width', 'top', 'bottom')\n\n x = NumberSpec(help=\"\"\"\n The x-coordinates of the centers of the vertical bars.\n \"\"\")\n\n width = NumberSpec(help=\"\"\"\n The widths of the vertical bars.\n \"\"\")\n\n bottom = NumberSpec(default=0, help=\"\"\"\n The y-coordinates of the bottom edges.\n \"\"\")\n\n top = NumberSpec(help=\"\"\"\n The y-coordinates of the top edges.\n \"\"\")\n\n line_props = Include(LineProps, use_prefix=False, help=\"\"\"\n The %s values for the vertical bars.\n \"\"\")\n\n fill_props = Include(FillProps, use_prefix=False, help=\"\"\"\n The %s values for the vertical bars.\n \"\"\")\n\nclass Wedge(XYGlyph):\n ''' Render wedges.\n\n '''\n\n __example__ = \"examples/reference/models/Wedge.py\"\n\n # a canonical order for positional args that can be used for any\n # functions derived from this class\n _args = ('x', 'y', 'radius', 'start_angle', 'end_angle', 'direction')\n\n x = NumberSpec(help=\"\"\"\n The x-coordinates of the points of the wedges.\n \"\"\")\n\n y = NumberSpec(help=\"\"\"\n The y-coordinates of the points of the wedges.\n \"\"\")\n\n radius = DistanceSpec(help=\"\"\"\n Radii of the wedges.\n \"\"\")\n\n start_angle = AngleSpec(help=\"\"\"\n The angles to start the wedges, as measured from the horizontal.\n \"\"\")\n\n end_angle = AngleSpec(help=\"\"\"\n The angles to end the wedges, as measured from the horizontal.\n \"\"\")\n\n direction = Enum(Direction, default='anticlock', help=\"\"\"\n Which direction to stroke between the start and end angles.\n \"\"\")\n\n line_props = Include(LineProps, use_prefix=False, help=\"\"\"\n The %s values for the wedges.\n \"\"\")\n\n fill_props = Include(FillProps, use_prefix=False, help=\"\"\"\n The %s values for the wedges.\n \"\"\")\n\n# XXX: allow `from bokeh.models.glyphs import *`\nfrom .markers import (Asterisk, Circle, CircleCross, CircleX, Cross, Diamond, DiamondCross,\n InvertedTriangle, Marker, Square, SquareCross, SquareX, Triangle, X)\n\n# Fool pyflakes\n(Asterisk, Circle, CircleCross, CircleX, Cross, Diamond, DiamondCross,\nInvertedTriangle, Marker, Square, SquareCross, SquareX, Triangle, X)\n",
"path": "bokeh/models/glyphs.py"
}
] | diff --git a/bokeh/models/glyphs.py b/bokeh/models/glyphs.py
index b0842d0d381..fc54d1a6e86 100644
--- a/bokeh/models/glyphs.py
+++ b/bokeh/models/glyphs.py
@@ -784,7 +784,7 @@ class Ray(XYGlyph):
length = DistanceSpec(help="""
The length to extend the ray. Note that this ``length`` defaults
- to screen units.
+ to data units (measured in the x-direction).
""")
line_props = Include(LineProps, use_prefix=False, help="""
diff --git a/bokehjs/src/coffee/models/glyphs/ray.coffee b/bokehjs/src/coffee/models/glyphs/ray.coffee
index 0394e5fa3a5..9bdbf8f1f21 100644
--- a/bokehjs/src/coffee/models/glyphs/ray.coffee
+++ b/bokehjs/src/coffee/models/glyphs/ray.coffee
@@ -4,7 +4,10 @@ import * as p from "core/properties"
export class RayView extends XYGlyphView
_map_data: () ->
- @slength = @sdist(@renderer.xscale, @_x, @_length)
+ if @model.properties.length.units == "data"
+ @slength = @sdist(@renderer.xscale, @_x, @_length)
+ else
+ @slength = @_length
_render: (ctx, indices, {sx, sy, slength, _angle}) ->
if @visuals.line.doit
diff --git a/bokehjs/test/models/glyphs/index.coffee b/bokehjs/test/models/glyphs/index.coffee
index dd777789a8d..95f9752297a 100644
--- a/bokehjs/test/models/glyphs/index.coffee
+++ b/bokehjs/test/models/glyphs/index.coffee
@@ -1,4 +1,5 @@
require "./image"
require "./image_rgba"
require "./image_url"
+require "./ray"
require "./rect"
diff --git a/bokehjs/test/models/glyphs/ray.coffee b/bokehjs/test/models/glyphs/ray.coffee
new file mode 100644
index 00000000000..e71597886f4
--- /dev/null
+++ b/bokehjs/test/models/glyphs/ray.coffee
@@ -0,0 +1,90 @@
+{expect} = require "chai"
+utils = require "../../utils"
+sinon = require "sinon"
+
+{create_glyph_view} = require("./glyph_utils")
+{Ray, RayView} = utils.require("models/glyphs/ray")
+{LinearScale} = utils.require("models/scales/linear_scale")
+{LogScale} = utils.require("models/scales/log_scale")
+{Range1d} = utils.require("models/ranges/range1d")
+
+describe "Ray", ->
+
+ describe "RayView", ->
+
+ afterEach ->
+ utils.unstub_canvas()
+
+ beforeEach ->
+ utils.stub_canvas()
+
+ @glyph = new Ray({
+ x: {field: "x"}
+ y: {field: "y"}
+ length: {value: 10}
+ })
+
+ @set_scales = (glyph_view, type="linear") ->
+ if type == "linear"
+ scale = new LinearScale({
+ source_range: new Range1d({start: 0, end: 100})
+ target_range: new Range1d({start: 0, end: 200})
+ })
+ else if type == "reverse"
+ scale = new LinearScale({
+ source_range: new Range1d({start: 0, end: 100})
+ target_range: new Range1d({start: 200, end: 0})
+ })
+ else if type == "log"
+ scale = new LogScale({
+ source_range: new Range1d({start: 1, end: 1000})
+ target_range: new Range1d({start: 0, end: 200})
+ })
+ glyph_view.renderer.xscale = scale
+ glyph_view.renderer.yscale = scale
+ glyph_view.renderer.plot_view.frame.xscales['default'] = scale
+ glyph_view.renderer.plot_view.frame.yscales['default'] = scale
+
+ it "`_map_data` should correctly map data if length units are 'data'", ->
+ for angle in [0,1,2,3]
+ data = {x: [1], y: [2], length: [10]}
+ glyph_view = create_glyph_view(@glyph, data)
+
+ glyph_view.model.properties.length.units = "data"
+
+ @set_scales(glyph_view)
+ glyph_view.map_data()
+ expect(glyph_view.slength).to.be.deep.equal([20])
+
+ it "`_map_data` should correctly map data if length units are 'screen'", ->
+ for angle in [0,1,2,3]
+ data = {x: [1], y: [2], angle: [angle], length: [10]}
+ glyph_view = create_glyph_view(@glyph, data)
+
+ glyph_view.model.properties.length.units = "screen"
+
+ @set_scales(glyph_view)
+ glyph_view.map_data()
+ expect(glyph_view.slength).to.be.deep.equal([10])
+
+ it "`_map_data` should correctly map data if length units are 'data' and scale is reversed", ->
+ for angle in [0,1,2,3]
+ data = {x: [1], y: [2], length: [10]}
+ glyph_view = create_glyph_view(@glyph, data)
+
+ glyph_view.model.properties.length.units = "data"
+
+ @set_scales(glyph_view, "reverse")
+ glyph_view.map_data()
+ expect(glyph_view.slength).to.be.deep.equal([20])
+
+ it "`_map_data` should correctly map data if length units are 'screen' and scale is reversed", ->
+ for angle in [0,1,2,3]
+ data = {x: [1], y: [2], angle: [angle], length: [10]}
+ glyph_view = create_glyph_view(@glyph, data)
+
+ glyph_view.model.properties.length.units = "screen"
+
+ @set_scales(glyph_view, "reverse")
+ glyph_view.map_data()
+ expect(glyph_view.slength).to.be.deep.equal([10])
|
esphome__esphome-docs-1148 | Update docs for new fan speed
## Description:
**Related issue (if applicable):** fixes https://github.com/esphome/issues/issues/1278
**Pull request in [esphome](https://github.com/esphome/esphome) with YAML changes (if applicable):** esphome/esphome#https://github.com/esphome/esphome/pull/1391
## Checklist:
- [ ] Branch: `next` is for changes and new documentation that will go public with the next ESPHome release. Fixes, changes and adjustments for the current release should be created against `current`.
- [ ] Link added in `/index.rst` when creating new documents for new components or cookbook.
| [
{
"content": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n#\n# esphome documentation build configuration file, created by\n# sphinx-quickstart on Mon Jan 22 21:44:07 2018.\n#\n# This file is execfile()d with the current directory set to its\n# containing dir.\n#\n# Note that not all possible configuration values are present in this\n# autogenerated file.\n#\n# All configuration values have a default; values that are commented out\n# serve to show the default.\n\n# If extensions (or modules to document with autodoc) are in another directory,\n# add these directories to sys.path here. If the directory is relative to the\n# documentation root, use os.path.abspath to make it absolute, like shown here.\n#\n# import os\n# import sys\n# sys.path.insert(0, os.path.abspath('.'))\nimport hashlib\nimport os\nimport sys\n\n\nsys.path.append(os.path.abspath(\".\"))\n\n# -- General configuration ------------------------------------------------\n\n# If your documentation needs a minimal Sphinx version, state it here.\n#\n# needs_sphinx = '1.0'\n\n# Add any Sphinx extension module names here, as strings. They can be\n# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom\n# ones.\nextensions = [\n \"github\",\n \"seo\",\n \"sitemap\",\n \"schema_doc\",\n]\n\n# Add any paths that contain templates here, relative to this directory.\ntemplates_path = [\"_templates\"]\n\n# The suffix(es) of source filenames.\n# You can specify multiple suffix as a list of string:\n#\n# source_suffix = ['.rst', '.md']\nsource_suffix = \".rst\"\n\n# The master toctree document.\nmaster_doc = \"index\"\n\n# General information about the project.\nproject = \"ESPHome\"\ncopyright = \"2019, Otto Winter\"\nhtml_show_copyright = False\nhtml_show_sphinx = False\nauthor = \"Otto Winter\"\n\n# The version info for the project you're documenting, acts as replacement for\n# |version| and |release|, also used in various other places throughout the\n# built documents.\n#\n# The short X.Y version.\nversion = \"1.17\"\n# The full version, including alpha/beta/rc tags.\nrelease = \"1.17.1\"\n\n# The language for content autogenerated by Sphinx. Refer to documentation\n# for a list of supported languages.\n#\n# This is also used if you do content translation via gettext catalogs.\n# Usually you set \"language\" from the command line for these cases.\nlanguage = \"en\"\n\n# List of patterns, relative to source directory, that match files and\n# directories to ignore when looking for source files.\n# This patterns also effect to html_static_path and html_extra_path\nexclude_patterns = [\"_build\", \"Thumbs.db\", \".DS_Store\"]\n\n# The reST default role (used for this markup: `text`) to use for all documents.\n# default_role = 'cpp:any'\n\n# The name of the Pygments (syntax highlighting) style to use.\npygments_style = \"xcode\"\n\nhighlight_language = \"yaml\"\n\nprimary_domain = None\n\n# If true, `todo` and `todoList` produce output, else they produce nothing.\ntodo_include_todos = False\n\n\n# -- Options for HTML output ----------------------------------------------\n\n# The theme to use for HTML and HTML Help pages. See the documentation for\n# a list of builtin themes.\n#\nhtml_theme = \"alabaster\"\n\n# Theme options are theme-specific and customize the look and feel of a theme\n# further. For a list of options available for each theme, see the\n# documentation.\n#\nhtml_baseurl = os.getenv(\"BASE_URL\", \"https://esphome.io\")\nwith open(\"_static/custom.css\", \"rb\") as f:\n custom_css_hash = hashlib.md5(f.read()).hexdigest()[:8]\n\nhtml_theme_options = {\n # 'logo': 'logo-full.png',\n \"logo_name\": False,\n \"show_related\": False,\n \"sidebar_collapse\": True,\n \"fixed_sidebar\": True,\n \"show_powered_by\": False,\n}\n\nhtml_context = {\n \"custom_css_hash\": custom_css_hash,\n}\n\nhtml_logo = \"images/logo-text.svg\"\nhtml_copy_source = True\nhtml_show_sourcelink = False\nhtml_last_updated_fmt = None\nhtml_use_smartypants = False\nhtml_title = \"ESPHome\"\n\n# Add any paths that contain custom static files (such as style sheets) here,\n# relative to this directory. They are copied after the builtin static files,\n# so a file named \"default.css\" will overwrite the builtin \"default.css\".\nhtml_static_path = [\"_static\"]\n\n# Custom sidebar templates, must be a dictionary that maps document names\n# to template names.\n#\n# This is required for the alabaster theme\n# refs: http://alabaster.readthedocs.io/en/latest/installation.html#sidebars\nhtml_sidebars = {\n \"**\": [\n # 'about.html',\n \"searchbox.html\",\n \"localtoc.html\",\n ]\n}\n\n\n# -- Options for HTMLHelp output ------------------------------------------\n\n# Output file base name for HTML help builder.\nhtmlhelp_basename = \"esphomedoc\"\n\n\n# -- Options for LaTeX output ---------------------------------------------\n\nlatex_elements = {\n # The paper size ('letterpaper' or 'a4paper').\n #\n # 'papersize': 'letterpaper',\n # The font size ('10pt', '11pt' or '12pt').\n #\n # 'pointsize': '10pt',\n # Additional stuff for the LaTeX preamble.\n #\n # 'preamble': '',\n # Latex figure (float) alignment\n #\n # 'figure_align': 'htbp',\n}\n\n# Grouping the document tree into LaTeX files. List of tuples\n# (source start file, target name, title,\n# author, documentclass [howto, manual, or own class]).\nlatex_documents = [\n (master_doc, \"esphome.tex\", \"ESPHome Documentation\", \"Otto Winter\", \"manual\"),\n]\n\nlatex_engine = \"xelatex\"\n\n\n# -- Options for manual page output ---------------------------------------\n\n# One entry per manual page. List of tuples\n# (source start file, name, description, authors, manual section).\nman_pages = [(master_doc, \"esphome\", \"ESPHome Documentation\", [author], 1)]\n\n\n# -- Options for Texinfo output -------------------------------------------\n\n# Grouping the document tree into Texinfo files. List of tuples\n# (source start file, target name, title, author,\n# dir menu entry, description, category)\ntexinfo_documents = [\n (\n master_doc,\n \"esphome\",\n \"ESPHome Documentation\",\n author,\n \"esphome\",\n \"One line description of project.\",\n \"Miscellaneous\",\n ),\n]\nlinkcheck_ignore = [r\"https://github.com/.*\", r\"https://discord.gg/.*\"]\n",
"path": "conf.py"
}
] | [
{
"content": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n#\n# esphome documentation build configuration file, created by\n# sphinx-quickstart on Mon Jan 22 21:44:07 2018.\n#\n# This file is execfile()d with the current directory set to its\n# containing dir.\n#\n# Note that not all possible configuration values are present in this\n# autogenerated file.\n#\n# All configuration values have a default; values that are commented out\n# serve to show the default.\n\n# If extensions (or modules to document with autodoc) are in another directory,\n# add these directories to sys.path here. If the directory is relative to the\n# documentation root, use os.path.abspath to make it absolute, like shown here.\n#\n# import os\n# import sys\n# sys.path.insert(0, os.path.abspath('.'))\nimport hashlib\nimport os\nimport sys\n\n\nsys.path.append(os.path.abspath(\".\"))\n\n# -- General configuration ------------------------------------------------\n\n# If your documentation needs a minimal Sphinx version, state it here.\n#\n# needs_sphinx = '1.0'\n\n# Add any Sphinx extension module names here, as strings. They can be\n# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom\n# ones.\nextensions = [\n \"github\",\n \"seo\",\n \"sitemap\",\n \"schema_doc\",\n]\n\n# Add any paths that contain templates here, relative to this directory.\ntemplates_path = [\"_templates\"]\n\n# The suffix(es) of source filenames.\n# You can specify multiple suffix as a list of string:\n#\n# source_suffix = ['.rst', '.md']\nsource_suffix = \".rst\"\n\n# The master toctree document.\nmaster_doc = \"index\"\n\n# General information about the project.\nproject = \"ESPHome\"\ncopyright = \"2019, Otto Winter\"\nhtml_show_copyright = False\nhtml_show_sphinx = False\nauthor = \"Otto Winter\"\n\n# The version info for the project you're documenting, acts as replacement for\n# |version| and |release|, also used in various other places throughout the\n# built documents.\n#\n# The short X.Y version.\nversion = \"1.17\"\n# The full version, including alpha/beta/rc tags.\nrelease = \"1.17.2\"\n\n# The language for content autogenerated by Sphinx. Refer to documentation\n# for a list of supported languages.\n#\n# This is also used if you do content translation via gettext catalogs.\n# Usually you set \"language\" from the command line for these cases.\nlanguage = \"en\"\n\n# List of patterns, relative to source directory, that match files and\n# directories to ignore when looking for source files.\n# This patterns also effect to html_static_path and html_extra_path\nexclude_patterns = [\"_build\", \"Thumbs.db\", \".DS_Store\"]\n\n# The reST default role (used for this markup: `text`) to use for all documents.\n# default_role = 'cpp:any'\n\n# The name of the Pygments (syntax highlighting) style to use.\npygments_style = \"xcode\"\n\nhighlight_language = \"yaml\"\n\nprimary_domain = None\n\n# If true, `todo` and `todoList` produce output, else they produce nothing.\ntodo_include_todos = False\n\n\n# -- Options for HTML output ----------------------------------------------\n\n# The theme to use for HTML and HTML Help pages. See the documentation for\n# a list of builtin themes.\n#\nhtml_theme = \"alabaster\"\n\n# Theme options are theme-specific and customize the look and feel of a theme\n# further. For a list of options available for each theme, see the\n# documentation.\n#\nhtml_baseurl = os.getenv(\"BASE_URL\", \"https://esphome.io\")\nwith open(\"_static/custom.css\", \"rb\") as f:\n custom_css_hash = hashlib.md5(f.read()).hexdigest()[:8]\n\nhtml_theme_options = {\n # 'logo': 'logo-full.png',\n \"logo_name\": False,\n \"show_related\": False,\n \"sidebar_collapse\": True,\n \"fixed_sidebar\": True,\n \"show_powered_by\": False,\n}\n\nhtml_context = {\n \"custom_css_hash\": custom_css_hash,\n}\n\nhtml_logo = \"images/logo-text.svg\"\nhtml_copy_source = True\nhtml_show_sourcelink = False\nhtml_last_updated_fmt = None\nhtml_use_smartypants = False\nhtml_title = \"ESPHome\"\n\n# Add any paths that contain custom static files (such as style sheets) here,\n# relative to this directory. They are copied after the builtin static files,\n# so a file named \"default.css\" will overwrite the builtin \"default.css\".\nhtml_static_path = [\"_static\"]\n\n# Custom sidebar templates, must be a dictionary that maps document names\n# to template names.\n#\n# This is required for the alabaster theme\n# refs: http://alabaster.readthedocs.io/en/latest/installation.html#sidebars\nhtml_sidebars = {\n \"**\": [\n # 'about.html',\n \"searchbox.html\",\n \"localtoc.html\",\n ]\n}\n\n\n# -- Options for HTMLHelp output ------------------------------------------\n\n# Output file base name for HTML help builder.\nhtmlhelp_basename = \"esphomedoc\"\n\n\n# -- Options for LaTeX output ---------------------------------------------\n\nlatex_elements = {\n # The paper size ('letterpaper' or 'a4paper').\n #\n # 'papersize': 'letterpaper',\n # The font size ('10pt', '11pt' or '12pt').\n #\n # 'pointsize': '10pt',\n # Additional stuff for the LaTeX preamble.\n #\n # 'preamble': '',\n # Latex figure (float) alignment\n #\n # 'figure_align': 'htbp',\n}\n\n# Grouping the document tree into LaTeX files. List of tuples\n# (source start file, target name, title,\n# author, documentclass [howto, manual, or own class]).\nlatex_documents = [\n (master_doc, \"esphome.tex\", \"ESPHome Documentation\", \"Otto Winter\", \"manual\"),\n]\n\nlatex_engine = \"xelatex\"\n\n\n# -- Options for manual page output ---------------------------------------\n\n# One entry per manual page. List of tuples\n# (source start file, name, description, authors, manual section).\nman_pages = [(master_doc, \"esphome\", \"ESPHome Documentation\", [author], 1)]\n\n\n# -- Options for Texinfo output -------------------------------------------\n\n# Grouping the document tree into Texinfo files. List of tuples\n# (source start file, target name, title, author,\n# dir menu entry, description, category)\ntexinfo_documents = [\n (\n master_doc,\n \"esphome\",\n \"ESPHome Documentation\",\n author,\n \"esphome\",\n \"One line description of project.\",\n \"Miscellaneous\",\n ),\n]\nlinkcheck_ignore = [r\"https://github.com/.*\", r\"https://discord.gg/.*\"]\n",
"path": "conf.py"
}
] | diff --git a/Doxygen b/Doxygen
index 5d6b6d9846..31fa5d55d9 100644
--- a/Doxygen
+++ b/Doxygen
@@ -38,7 +38,7 @@ PROJECT_NAME = "ESPHome"
# could be handy for archiving the generated documentation or if some version
# control system is used.
-PROJECT_NUMBER = 1.17.1
+PROJECT_NUMBER = 1.17.2
# Using the PROJECT_BRIEF tag one can provide an optional one line description
# for a project that appears at the top of each page and should give viewer a
diff --git a/Makefile b/Makefile
index 3cf5f3a7d3..06150d7ba3 100644
--- a/Makefile
+++ b/Makefile
@@ -1,5 +1,5 @@
ESPHOME_PATH = ../esphome
-ESPHOME_REF = v1.17.1
+ESPHOME_REF = v1.17.2
.PHONY: html html-strict cleanhtml deploy help webserver Makefile netlify netlify-api api netlify-dependencies svg2png copy-svg2png
diff --git a/_static/version b/_static/version
index 507266ba01..0e1f39b86c 100644
--- a/_static/version
+++ b/_static/version
@@ -1 +1 @@
-1.17.1
\ No newline at end of file
+1.17.2
\ No newline at end of file
diff --git a/changelog/v1.17.0.rst b/changelog/v1.17.0.rst
index fddac3d815..d9ccf51782 100644
--- a/changelog/v1.17.0.rst
+++ b/changelog/v1.17.0.rst
@@ -79,6 +79,13 @@ Release 1.17.1 - May 5
- docs: Fixed typo in 1.17.0 changelogs :docspr:`1132` by :ghuser:`spacegaier`
- esphome: Do not call component update on failed components :esphomepr:`1392` by :ghuser:`alexyao2015`
+Release 1.17.2 - May 9
+----------------------
+
+- esphome: fixes #858 - esphome crashes with neolightbus and RMT :esphomepr:`1667` by :ghuser:`angelnu`
+- docs: Fix abundant apostrophes :docspr:`1137` by :ghuser:`jmartens`
+- docs: Add output part to binary light example :docspr:`1061` by :ghuser:`klaasnicolaas`
+
All changes
-----------
diff --git a/conf.py b/conf.py
index 993bcec6dc..f088451878 100644
--- a/conf.py
+++ b/conf.py
@@ -69,7 +69,7 @@
# The short X.Y version.
version = "1.17"
# The full version, including alpha/beta/rc tags.
-release = "1.17.1"
+release = "1.17.2"
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
diff --git a/guides/supporters.rst b/guides/supporters.rst
index 427837f9d3..cc302280d8 100644
--- a/guides/supporters.rst
+++ b/guides/supporters.rst
@@ -129,6 +129,7 @@ Contributors
- `Marcos Pérez Ferro (@djwmarcx) <https://github.com/djwmarcx>`__
- `Dan Mannock (@dmannock) <https://github.com/dmannock>`__
- `dmkif (@dmkif) <https://github.com/dmkif>`__
+- `Farzad E. (@dnetguru) <https://github.com/dnetguru>`__
- `DrZoid (@docteurzoidberg) <https://github.com/docteurzoidberg>`__
- `Jiang Sheng (@doskoi) <https://github.com/doskoi>`__
- `Robert Schütz (@dotlambda) <https://github.com/dotlambda>`__
@@ -528,4 +529,4 @@ Contributors
- `San (@zhujunsan) <https://github.com/zhujunsan>`__
- `Christian Zufferey (@zuzu59) <https://github.com/zuzu59>`__
-*This page was last updated May 5, 2021.*
+*This page was last updated May 9, 2021.*
|
microsoft__nni-5155 | Unclear what extras to install: `import nni.retiarii.execution.api` fails due to missing `pytorch_lightning`
**Describe the issue**:
I want to use `nni.retiarii.execution.api` module. I've installed it as below:
```
Collecting nni>=2.3
Downloading nni-2.9-py3-none-manylinux1_x86_64.whl (56.0 MB)
```
**Environment**:
- NNI version: 2.9
- Python version: 3.8
**Log message**:
```
_________________ ERROR collecting test/3rd_party/test_nni.py __________________
ImportError while importing test module '/__w/ai4cl-tianshou/ai4cl-tianshou/test/3rd_party/test_nni.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/usr/local/lib/python3.8/importlib/__init__.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
test/3rd_party/test_nni.py:8: in <module>
import nni.retiarii.execution.api
/usr/local/lib/python3.8/site-packages/nni/retiarii/__init__.py:4: in <module>
from .operation import Operation
/usr/local/lib/python3.8/site-packages/nni/retiarii/operation.py:6: in <module>
from nni.nas.execution.common.graph_op import *
/usr/local/lib/python3.8/site-packages/nni/nas/__init__.py:4: in <module>
from .execution import *
/usr/local/lib/python3.8/site-packages/nni/nas/execution/__init__.py:4: in <module>
from .api import *
/usr/local/lib/python3.8/site-packages/nni/nas/execution/api.py:9: in <module>
from nni.nas.execution.common import (
/usr/local/lib/python3.8/site-packages/nni/nas/execution/common/__init__.py:4: in <module>
from .engine import *
/usr/local/lib/python3.8/site-packages/nni/nas/execution/common/engine.py:7: in <module>
from .graph import Model, MetricData
/usr/local/lib/python3.8/site-packages/nni/nas/execution/common/graph.py:18: in <module>
from nni.nas.evaluator import Evaluator
/usr/local/lib/python3.8/site-packages/nni/nas/evaluator/__init__.py:9: in <module>
shortcut_framework(__name__)
/usr/local/lib/python3.8/site-packages/nni/common/framework.py:93: in shortcut_framework
shortcut_module(current, '.' + get_default_framework(), current)
/usr/local/lib/python3.8/site-packages/nni/common/framework.py:83: in shortcut_module
mod = importlib.import_module(target, package)
/usr/local/lib/python3.8/importlib/__init__.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
/usr/local/lib/python3.8/site-packages/nni/nas/evaluator/pytorch/__init__.py:4: in <module>
from .lightning import *
/usr/local/lib/python3.8/site-packages/nni/nas/evaluator/pytorch/lightning.py:10: in <module>
import pytorch_lightning as pl
E ModuleNotFoundError: No module named 'pytorch_lightning'
```
**How to reproduce it?**:
```
pip install nni==2.9
python -c "import nni.retiarii.execution.api"
```
| [
{
"content": "# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nfrom .lightning import *\n",
"path": "nni/nas/evaluator/pytorch/__init__.py"
}
] | [
{
"content": "# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nimport warnings\n\ntry:\n from .lightning import *\nexcept ImportError:\n warnings.warn(\"PyTorch-Lightning must be installed to use PyTorch in NAS. \"\n \"If you are not using PyTorch, please `nni.set_default_framework('none')`\")\n raise\n",
"path": "nni/nas/evaluator/pytorch/__init__.py"
}
] | diff --git a/nni/nas/evaluator/pytorch/__init__.py b/nni/nas/evaluator/pytorch/__init__.py
index 1b2a52c886..2a7b5d2a1b 100644
--- a/nni/nas/evaluator/pytorch/__init__.py
+++ b/nni/nas/evaluator/pytorch/__init__.py
@@ -1,4 +1,11 @@
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
-from .lightning import *
+import warnings
+
+try:
+ from .lightning import *
+except ImportError:
+ warnings.warn("PyTorch-Lightning must be installed to use PyTorch in NAS. "
+ "If you are not using PyTorch, please `nni.set_default_framework('none')`")
+ raise
|
dotkom__onlineweb4-1524 | Hide irrelevant information if not applicable to the event
The event dashboard should provide useful information to the administrators of the events, and I think it should refrain from displaying information that's not relevant.
For example, all events include a column about "Extras" for the event, even though the event has no extras assigned. The column for "Paid" could be stripped if the event does not have a payment relation. If it's not an attendance event, the column for "showed up" could be removed.
This way we could add more information for events where this would be useful, without the table being too cramped.
| [
{
"content": "# -*- coding: utf-8 -*-\n\nfrom collections import OrderedDict\nfrom datetime import datetime, timedelta\nfrom functools import reduce\n\nfrom django.conf import settings\nfrom django.contrib.auth.models import Group\nfrom django.contrib.contenttypes.models import ContentType\nfrom django.db import models\nfrom django.db.models import Case, Q, When\nfrom django.template.defaultfilters import slugify\nfrom django.utils import timezone\nfrom django.utils.translation import ugettext as _\nfrom filebrowser.fields import FileBrowseField\nfrom unidecode import unidecode\n\nfrom apps.authentication.models import FIELD_OF_STUDY_CHOICES\nfrom apps.companyprofile.models import Company\nfrom apps.marks.models import get_expiration_date\n\nUser = settings.AUTH_USER_MODEL\n\nTYPE_CHOICES = (\n (1, 'Sosialt'),\n (2, 'Bedriftspresentasjon'),\n (3, 'Kurs'),\n (4, 'Utflukt'),\n (5, 'Ekskursjon'),\n (6, 'Internt'),\n (7, 'Annet')\n)\n\n\n# Managers\n\nclass EventOrderedByRegistration(models.Manager):\n \"\"\"\n Order events by registration start if registration start is within 7 days of today.\n \"\"\"\n def get_queryset(self):\n DELTA_FUTURE = settings.OW4_SETTINGS.get('events').get('OW4_EVENTS_FEATURED_DAYS_FUTURE')\n DELTA_PAST = settings.OW4_SETTINGS.get('events').get('OW4_EVENTS_FEATURED_DAYS_PAST')\n week_back = timezone.now() - timedelta(days=DELTA_PAST)\n week_in_future = timezone.now() + timedelta(days=DELTA_FUTURE)\n\n return super(EventOrderedByRegistration, self).get_queryset().\\\n annotate(registration_filtered=Case(\n When(Q(attendance_event__registration_start__gte=week_back) &\n Q(attendance_event__registration_start__lte=week_in_future),\n then='attendance_event__registration_start'),\n default='event_end',\n output_field=models.DateTimeField()\n )\n ).order_by('registration_filtered')\n\n\nclass Event(models.Model):\n \"\"\"\n Base class for Event-objects.\n \"\"\"\n\n IMAGE_FOLDER = \"images/events\"\n IMAGE_EXTENSIONS = ['.jpg', '.jpeg', '.gif', '.png', '.tif', '.tiff']\n\n # Managers\n objects = models.Manager()\n by_registration = EventOrderedByRegistration()\n\n author = models.ForeignKey(User, related_name='oppretter')\n title = models.CharField(_('tittel'), max_length=60)\n event_start = models.DateTimeField(_('start-dato'))\n event_end = models.DateTimeField(_('slutt-dato'))\n location = models.CharField(_('lokasjon'), max_length=100)\n ingress_short = models.CharField(_(\"kort ingress\"), max_length=150)\n ingress = models.TextField(_('ingress'))\n description = models.TextField(_('beskrivelse'))\n image = FileBrowseField(_(\"bilde\"), max_length=200,\n directory=IMAGE_FOLDER, extensions=IMAGE_EXTENSIONS, null=True, blank=True)\n event_type = models.SmallIntegerField(_('type'), choices=TYPE_CHOICES, null=False)\n\n def is_attendance_event(self):\n \"\"\" Returns true if the event is an attendance event \"\"\"\n try:\n return True if self.attendance_event else False\n except AttendanceEvent.DoesNotExist:\n return False\n\n def images(self):\n if not self.image:\n return []\n from apps.events.utils import find_image_versions\n return find_image_versions(self)\n\n # TODO move payment and feedback stuff to attendance event when dasboard is done\n\n def feedback_users(self):\n if self.is_attendance_event:\n return [a.user for a in self.attendance_event.attendees.filter(attended=True)]\n return []\n\n def feedback_date(self):\n return self.event_end\n\n def feedback_title(self):\n return self.title\n\n def feedback_info(self):\n info = OrderedDict()\n if self.is_attendance_event():\n info[_('Påmeldte')] = self.attendance_event.number_of_attendees\n info[_('Oppmøtte')] = self.attendance_event.number_of_attendees - len(self.attendance_event.not_attended())\n info[_('Venteliste')] = self.attendance_event.number_on_waitlist\n\n return info\n\n @property\n def company_event(self):\n try:\n return CompanyEvent.objects.filter(event=self)\n except CompanyEvent.DoesNotExist:\n return None\n\n def feedback_mail(self):\n if self.event_type == 1 or self.event_type == 4: # Sosialt & Utflukt\n return settings.EMAIL_ARRKOM\n elif self.event_type == 2: # Bedpres\n return settings.EMAIL_BEDKOM\n elif self.event_type == 3: # Kurs\n return settings.EMAIL_FAGKOM\n elif self.event_type == 5: # Ekskursjon\n return settings.EMAIL_EKSKOM\n else:\n return settings.DEFAULT_FROM_EMAIL\n\n def can_display(self, user):\n restriction = GroupRestriction.objects.filter(event=self)\n\n if not restriction:\n return True\n\n if not user:\n return False\n\n groups = restriction[0].groups\n\n # returns True if any of the users groups are in one of the accepted groups\n return any(group in user.groups.all() for group in groups.all())\n\n @property\n def slug(self):\n return slugify(unidecode(self.title))\n\n @models.permalink\n def get_absolute_url(self):\n return 'events_details', None, {'event_id': self.id, 'event_slug': self.slug}\n\n def __str__(self):\n return self.title\n\n class Meta:\n verbose_name = _('arrangement')\n verbose_name_plural = _('arrangement')\n permissions = (\n ('view_event', 'View Event'),\n )\n\n\n\"\"\"\n BEGIN ACCESS RESTRICTION --------------------------------------------------------------------------\n\"\"\"\n\n\nclass Rule(models.Model):\n \"\"\"\n Super class for a rule object\n \"\"\"\n offset = models.PositiveSmallIntegerField(_('utsettelse'), help_text=_('utsettelse oppgis i timer'), default=0)\n\n def get_offset_time(self, time):\n if type(time) is not datetime:\n raise TypeError('time must be a datetime, not %s' % type(time))\n else:\n return time + timedelta(hours=self.offset)\n\n def satisfied(self, user):\n \"\"\" Checks if a user satisfies the rules \"\"\"\n return True\n\n def __str__(self):\n return 'Rule'\n\n class Meta:\n permissions = (\n ('view_rule', 'View Rule'),\n )\n\n\nclass FieldOfStudyRule(Rule):\n field_of_study = models.SmallIntegerField(_('studieretning'), choices=FIELD_OF_STUDY_CHOICES)\n\n def satisfied(self, user, registration_start):\n \"\"\" Override method \"\"\"\n\n # If the user has the same FOS as this rule\n if self.field_of_study == user.field_of_study:\n offset_datetime = self.get_offset_time(registration_start)\n # If the offset is in the past, it means you can attend even with the offset\n if offset_datetime < timezone.now():\n return {\"status\": True, \"message\": None, \"status_code\": 210}\n # If there is no offset, the signup just hasn't started yet\n elif self.offset == 0:\n return {\"status\": False, \"message\": _(\"Påmeldingen er ikke åpnet enda.\"), \"status_code\": 402}\n # In the last case there is a delayed signup\n else:\n return {\"status\": False, \"message\": _(\"Din studieretning har utsatt påmelding.\"),\n \"offset\": offset_datetime, \"status_code\": 420}\n return {\n \"status\": False, \"message\":\n _(\"Din studieretning er en annen enn de som har tilgang til dette arrangementet.\"), \"status_code\": 410}\n\n def __str__(self):\n if self.offset > 0:\n time_unit = _('timer') if self.offset > 1 else _('time')\n return _(\"%s etter %d %s\") % (str(self.get_field_of_study_display()), self.offset, time_unit)\n return str(self.get_field_of_study_display())\n\n class Meta:\n permissions = (\n ('view_fieldofstudyrule', 'View FieldOfStudyRule'),\n )\n\n\nclass GradeRule(Rule):\n grade = models.SmallIntegerField(_('klassetrinn'), null=False)\n\n def satisfied(self, user, registration_start):\n \"\"\" Override method \"\"\"\n\n # If the user has the same FOS as this rule\n if self.grade == user.year:\n offset_datetime = self.get_offset_time(registration_start)\n # If the offset is in the past, it means you can attend even with the offset\n if offset_datetime < timezone.now():\n return {\"status\": True, \"message\": None, \"status_code\": 211}\n # If there is no offset, the signup just hasn't started yet\n elif self.offset == 0:\n return {\"status\": False, \"message\": _(\"Påmeldingen er ikke åpnet enda.\"), \"status_code\": 402}\n # In the last case there is a delayed signup\n else:\n return {\n \"status\": False, \"message\":\n _(\"Ditt klassetrinn har utsatt påmelding.\"), \"offset\": offset_datetime, \"status_code\": 421}\n return {\n \"status\": False, \"message\":\n _(\"Du er ikke i et klassetrinn som har tilgang til dette arrangementet.\"), \"status_code\": 411}\n\n def __str__(self):\n if self.offset > 0:\n time_unit = _('timer') if self.offset > 1 else _('time')\n return _(\"%s. klasse etter %d %s\") % (self.grade, self.offset, time_unit)\n return _(\"%s. klasse\") % self.grade\n\n class Meta:\n permissions = (\n ('view_graderule', 'View GradeRule'),\n )\n\n\nclass UserGroupRule(Rule):\n group = models.ForeignKey(Group, blank=False, null=False)\n\n def satisfied(self, user, registration_start):\n \"\"\" Override method \"\"\"\n if self.group in user.groups.all():\n offset_datetime = self.get_offset_time(registration_start)\n # If the offset is in the past, it means you can attend even with the offset\n if offset_datetime < timezone.now():\n return {\"status\": True, \"message\": None, \"status_code\": 212}\n # If there is no offset, the signup just hasn't started yet\n elif self.offset == 0:\n return {\"status\": False, \"message\": _(\"Påmeldingen er ikke åpnet enda.\"), \"status_code\": 402}\n # In the last case there is a delayed signup\n else:\n return {\"status\": False, \"message\": _(\"%s har utsatt påmelding.\") % self.group,\n \"offset\": offset_datetime, \"status_code\": 422}\n return {\n \"status\": False, \"message\":\n _(\"Du er ikke i en brukergruppe som har tilgang til dette arrangmentet.\"), \"status_code\": 412}\n\n def __str__(self):\n if self.offset > 0:\n time_unit = _('timer') if self.offset > 1 else _('time')\n return _(\"%s etter %d %s\") % (str(self.group), self.offset, time_unit)\n return str(self.group)\n\n class Meta:\n permissions = (\n ('view_usergrouprule', 'View UserGroupRule'),\n )\n\n\nclass RuleBundle(models.Model):\n \"\"\"\n Access restriction rule object\n \"\"\"\n description = models.CharField(_('beskrivelse'), max_length=100, blank=True, null=True)\n field_of_study_rules = models.ManyToManyField(FieldOfStudyRule, blank=True)\n grade_rules = models.ManyToManyField(GradeRule, blank=True)\n user_group_rules = models.ManyToManyField(UserGroupRule, blank=True)\n\n def get_all_rules(self):\n rules = []\n rules.extend(self.field_of_study_rules.all())\n rules.extend(self.grade_rules.all())\n rules.extend(self.user_group_rules.all())\n return rules\n\n def get_rule_strings(self):\n return [str(rule) for rule in self.get_all_rules()]\n\n def satisfied(self, user, registration_start):\n\n errors = []\n\n for rule in self.get_all_rules():\n response = rule.satisfied(user, registration_start)\n if response['status']:\n return [response]\n else:\n errors.append(response)\n\n return errors\n\n def __str__(self):\n if self.description:\n return self.description\n elif self.get_rule_strings():\n return \", \".join(self.get_rule_strings())\n else:\n return _(\"Tom rule bundle.\")\n\n class Meta:\n permissions = (\n ('view_rulebundle', 'View RuleBundle'),\n )\n\n\n\"\"\"\n END ACCESS RESTRICTION --------------------------------------------------------------------------\n\"\"\"\n\n\nclass Extras(models.Model):\n \"\"\"\n Choices for events\n \"\"\"\n\n choice = models.CharField('valg', max_length=69)\n note = models.CharField('notat', max_length=200, blank=True, null=True)\n\n def __str__(self):\n return self.choice\n\n class Meta:\n verbose_name = _(\"ekstra valg\")\n verbose_name_plural = _(\"ekstra valg\")\n ordering = ['choice']\n\n\nclass AttendanceEvent(models.Model):\n \"\"\"\n Events that require special considerations regarding attendance.\n \"\"\"\n event = models.OneToOneField(\n Event,\n primary_key=True,\n related_name='attendance_event')\n\n max_capacity = models.PositiveIntegerField(_('maks-kapasitet'), null=False, blank=False)\n waitlist = models.BooleanField(_('venteliste'), default=False)\n guest_attendance = models.BooleanField(_('gjestepåmelding'), null=False, blank=False, default=False)\n registration_start = models.DateTimeField(_('registrerings-start'), null=False, blank=False)\n unattend_deadline = models.DateTimeField(_('avmeldings-frist'), null=False, blank=False)\n registration_end = models.DateTimeField(_('registrerings-slutt'), null=False, blank=False)\n\n # Automatic mark setting for not attending\n automatically_set_marks = models.BooleanField(_('automatisk prikk'), default=False,\n help_text=_('Påmeldte som ikke har møtt vil automatisk få prikk'))\n marks_has_been_set = models.BooleanField(default=False)\n\n # Access rules\n rule_bundles = models.ManyToManyField(RuleBundle, blank=True)\n\n # Extra choices\n extras = models.ManyToManyField(Extras, blank=True)\n\n @property\n def has_reservation(self):\n \"\"\" Returns whether this event has an attached reservation \"\"\"\n try:\n return True if self.reserved_seats else False\n except Reservation.DoesNotExist:\n return False\n\n @property\n def attendees_qs(self):\n \"\"\" Queryset with all attendees not on waiting list \"\"\"\n return self.attendees.all()[:self.max_capacity - self.number_of_reserved_seats]\n\n def not_attended(self):\n \"\"\" Queryset with all attendees not attended \"\"\"\n # .filter does apperantly not work on sliced querysets\n # return self.attendees_qs.filter(attended=False)\n\n not_attended = []\n\n for attendee in self.attendees_qs:\n if not attendee.attended:\n not_attended.append(attendee.user)\n\n return not_attended\n\n @property\n def waitlist_qs(self):\n \"\"\" Queryset with all attendees in waiting list \"\"\"\n return self.attendees.all()[self.max_capacity - self.number_of_reserved_seats:]\n\n @property\n def reservees_qs(self):\n \"\"\" Queryset with all reserved seats which have been filled \"\"\"\n if self.has_reservation:\n return self.reserved_seats.reservees.all()\n return []\n\n @property\n def attendees_not_paid(self):\n return [a for a in self.attendees_qs if a.paid]\n\n @property\n def number_of_attendees(self):\n \"\"\" Count of all attendees not in waiting list \"\"\"\n # We need to use len() instead of .count() here, because of the prefetched event archive\n return len(self.attendees_qs)\n\n @property\n def number_on_waitlist(self):\n \"\"\" Count of all attendees on waiting list \"\"\"\n # We need to use len() instead of .count() here, because of the prefetched event archive\n return len(self.waitlist_qs)\n\n @property\n def number_of_reserved_seats(self):\n \"\"\"\n Total number of seats for this event that are reserved\n \"\"\"\n return self.reserved_seats.seats if self.has_reservation else 0\n\n @property\n def number_of_reserved_seats_taken(self):\n \"\"\"\n Returns number of reserved seats which have been filled\n \"\"\"\n return self.reserved_seats.number_of_seats_taken if self.has_reservation else 0\n\n @property\n def number_of_seats_taken(self):\n \"\"\"\n Returns the total amount of taken seats for an attendance_event.\n \"\"\"\n # This includes all attendees + reserved seats for the event, if any.\n # Always use the total number of reserved seats here, because they are not\n # available for regular users to claim.\n return self.number_of_attendees + self.number_of_reserved_seats\n\n @property\n def free_seats(self):\n \"\"\"\n Integer representing the number of free seats for an event\n \"\"\"\n return 0 if self.number_of_seats_taken == self.max_capacity else self.max_capacity - self.number_of_seats_taken\n\n @property\n def room_on_event(self):\n \"\"\"\n Returns True if there are free seats or an open waiting list\n \"\"\"\n return True if self.free_seats > 0 or self.waitlist else False\n\n @property\n def registration_open(self):\n return timezone.now() < self.registration_start\n\n def has_delayed_signup(self, user):\n pass\n\n def is_marked(self, user):\n expiry_date = get_expiration_date(user)\n return expiry_date and expiry_date > timezone.now().date()\n\n def has_postponed_registration(self, user):\n if not self.is_marked(user):\n return False\n expiry_date = get_expiration_date(user)\n mark_offset = timedelta(days=1)\n postponed_registration_start = self.registration_start + mark_offset\n\n before_expiry = self.registration_start.date() < expiry_date\n\n if postponed_registration_start > timezone.now() and before_expiry:\n return postponed_registration_start\n\n def is_suspended(self, user):\n for suspension in user.get_active_suspensions():\n if not suspension.expiration_date or suspension.expiration_date > timezone.now().date():\n return True\n\n return False\n\n @property\n def will_i_be_on_wait_list(self):\n return True if self.free_seats == 0 and self.waitlist else False\n\n @property\n def waitlist_enabled(self):\n return self.waitlist\n\n def payment(self):\n # Importing here to awoid circular dependency error\n from apps.payment.models import Payment\n try:\n payment = Payment.objects.get(content_type=ContentType.objects.get_for_model(AttendanceEvent),\n object_id=self.event.id)\n except Payment.DoesNotExist:\n payment = None\n\n return payment\n\n def notify_waiting_list(self, host, unattended_user=None, extra_capacity=1):\n from apps.events.utils import handle_waitlist_bump # Imported here to avoid circular import\n # Notify next user on waiting list\n wait_list = self.waitlist_qs\n if wait_list:\n # Checking if user is on the wait list\n on_wait_list = False\n if unattended_user:\n for waiting_user in wait_list:\n if waiting_user.user == unattended_user:\n on_wait_list = True\n break\n if not on_wait_list:\n # Send mail to first user on waiting list\n attendees = wait_list[:extra_capacity]\n\n handle_waitlist_bump(self.event, host, attendees, self.payment())\n\n def is_eligible_for_signup(self, user):\n \"\"\"\n Checks if a user can attend a specific event\n This method checks for:\n Waitlist\n Room on event\n Rules\n Marks\n Suspension\n @param User object\n The returned dict contains a key called 'status_code'. These codes follow the HTTP\n standard in terms of overlying scheme.\n 2XX = successful\n 4XX = client error (user related)\n 5XX = server error (event related)\n These codes are meant as a debugging tool only. The eligibility checking is quite\n extensive, and tracking where it's going wrong is much needed.\n TODO:\n Exception handling\n \"\"\"\n\n response = {'status': False, 'message': '', 'status_code': None}\n\n # Registration closed\n if timezone.now() > self.registration_end:\n response['message'] = _('Påmeldingen er ikke lenger åpen.')\n response['status_code'] = 502\n return response\n\n # Room for me on the event?\n if not self.room_on_event:\n response['message'] = _(\"Det er ikke mer plass på dette arrangementet.\")\n response['status_code'] = 503\n return response\n\n #\n # Offset calculations.\n #\n\n # Are there any rules preventing me from attending?\n # This should be checked last of the offsets, because it can completely deny you access.\n response = self.rules_satisfied(user)\n if not response['status']:\n if 'offset' not in response:\n return response\n\n # Do I have any marks that postpone my registration date?\n response = self._check_marks(response, user)\n\n # Return response if offset was set.\n if 'offset' in response and response['offset'] > timezone.now():\n return response\n\n #\n # Offset calculations end\n #\n\n # Registration not open\n if timezone.now() < self.registration_start:\n response['status'] = False\n response['message'] = _('Påmeldingen har ikke åpnet enda.')\n response['status_code'] = 501\n return response\n\n # Is suspended\n if self.is_suspended(user):\n response['status'] = False\n response['message'] = _(\"Du er suspandert og kan ikke melde deg på.\")\n response['status_code'] = 402\n\n return response\n\n # Checks if the event is group restricted and if the user is in the right group\n if not self.event.can_display(user):\n response['status'] = False\n response['message'] = _(\"Du har ikke tilgang til å melde deg på dette arrangementet.\")\n response['status_code'] = 403\n\n return response\n\n # No objections, set eligible.\n response['status'] = True\n return response\n\n def _check_marks(self, response, user):\n expiry_date = get_expiration_date(user)\n if expiry_date and expiry_date > timezone.now().date():\n # Offset is currently 1 day if you have marks, regardless of amount.\n mark_offset = timedelta(days=1)\n postponed_registration_start = self.registration_start + mark_offset\n\n before_expiry = self.registration_start.date() < expiry_date\n\n if postponed_registration_start > timezone.now() and before_expiry:\n if 'offset' in response and response['offset'] < postponed_registration_start \\\n or 'offset' not in response:\n response['status'] = False\n response['status_code'] = 401\n response['message'] = _(\"Din påmelding er utsatt grunnet prikker.\")\n response['offset'] = postponed_registration_start\n return response\n\n def _process_rulebundle_satisfaction_responses(self, responses):\n # Put the smallest offset faaar into the future.\n smallest_offset = timezone.now() + timedelta(days=365)\n offset_response = {}\n future_response = {}\n errors = []\n\n for response in responses:\n if response['status']:\n return response\n elif 'offset' in response:\n if response['offset'] < smallest_offset:\n smallest_offset = response['offset']\n offset_response = response\n elif response['status_code'] == 402:\n future_response = response\n else:\n errors.append(response)\n\n if future_response:\n return future_response\n if smallest_offset > timezone.now() and offset_response:\n return offset_response\n if errors:\n return errors[0]\n\n def rules_satisfied(self, user):\n \"\"\"\n Checks a user against rules applied to an attendance event\n \"\"\"\n # If the event has guest attendance, allow absolutely anyone\n if self.guest_attendance:\n return {'status': True, 'status_code': 201}\n\n # If the user is not a member, return False right away\n # TODO check for guest list\n if not user.is_member:\n return {\n 'status': False, 'message':\n _(\"Dette arrangementet er kun åpent for medlemmer.\"), 'status_code': 400}\n\n # If there are no rule_bundles on this object, all members of Online are allowed.\n if not self.rule_bundles.exists() and user.is_member:\n return {'status': True, 'status_code': 200}\n\n # Check all rule bundles\n responses = []\n\n # If one satisfies, return true, else append to the error list\n for rule_bundle in self.rule_bundles.all():\n responses.extend(rule_bundle.satisfied(user, self.registration_start))\n\n return self._process_rulebundle_satisfaction_responses(responses)\n\n def is_attendee(self, user):\n return self.attendees.filter(user=user)\n\n def is_on_waitlist(self, user):\n return reduce(lambda x, y: x or y.user == user, self.waitlist_qs, False)\n\n def what_place_is_user_on_wait_list(self, user):\n if self.waitlist:\n waitlist = self.waitlist_qs\n if waitlist:\n for attendee_object in waitlist:\n if attendee_object.user == user:\n return list(waitlist).index(attendee_object) + 1\n return 0\n\n def __str__(self):\n return self.event.title\n\n class Meta:\n verbose_name = _('påmelding')\n verbose_name_plural = _('påmeldinger')\n permissions = (\n ('view_attendanceevent', 'View AttendanceEvent'),\n )\n\n\nclass CompanyEvent(models.Model):\n \"\"\"\n Company relation to AttendanceEvent\n \"\"\"\n company = models.ForeignKey(Company, verbose_name=_('bedrifter'))\n event = models.ForeignKey(Event, verbose_name=_('arrangement'), related_name='companies')\n\n class Meta:\n verbose_name = _('bedrift')\n verbose_name_plural = _('bedrifter')\n permissions = (\n ('view_companyevent', 'View CompanyEvent'),\n )\n\n\nclass Attendee(models.Model):\n \"\"\"\n User relation to AttendanceEvent.\n \"\"\"\n event = models.ForeignKey(AttendanceEvent, related_name=\"attendees\")\n user = models.ForeignKey(User)\n\n timestamp = models.DateTimeField(auto_now_add=True, editable=False)\n attended = models.BooleanField(_('var tilstede'), default=False)\n paid = models.BooleanField(_('har betalt'), default=False)\n note = models.CharField(_('notat'), max_length=100, blank=True, default='')\n extras = models.ForeignKey(Extras, blank=True, null=True)\n\n def __str__(self):\n return self.user.get_full_name()\n\n def delete(self):\n # Importing here to prevent circular dependencies\n from apps.payment.models import PaymentDelay\n try:\n PaymentDelay.objects.filter(user=self.user, payment=self.event.payment()).delete()\n except PaymentDelay.DoesNotExist:\n # Do nothing\n False\n\n super(Attendee, self).delete()\n\n class Meta:\n ordering = ['timestamp']\n unique_together = (('event', 'user'),)\n permissions = (\n ('view_attendee', 'View Attendee'),\n )\n\n\nclass Reservation(models.Model):\n attendance_event = models.OneToOneField(AttendanceEvent, related_name=\"reserved_seats\")\n seats = models.PositiveIntegerField(\"reserverte plasser\", blank=False, null=False)\n\n @property\n def number_of_seats_taken(self):\n return self.reservees.count()\n\n def __str__(self):\n return \"Reservasjoner for %s\" % self.attendance_event.event.title\n\n class Meta:\n verbose_name = _(\"reservasjon\")\n verbose_name_plural = _(\"reservasjoner\")\n permissions = (\n ('view_reservation', 'View Reservation'),\n )\n\n\nclass Reservee(models.Model):\n \"\"\"\n Reservation entry\n \"\"\"\n reservation = models.ForeignKey(Reservation, related_name='reservees')\n # I 2014 var norges lengste navn på 69 tegn;\n # julius andreas gimli arn macgyver chewbacka highlander elessar-jankov\n name = models.CharField('navn', max_length=69)\n note = models.CharField('notat', max_length=100)\n allergies = models.CharField('allergier', max_length=200, blank=True, null=True)\n\n def __str__(self):\n return self.name\n\n class Meta:\n verbose_name = _(\"reservasjon\")\n verbose_name_plural = _(\"reservasjoner\")\n ordering = ['id']\n permissions = (\n ('view_reservee', 'View Reservee'),\n )\n\n\nclass GroupRestriction(models.Model):\n event = models.OneToOneField(\n Event,\n primary_key=True,\n related_name='group_restriction')\n\n groups = models.ManyToManyField(Group, blank=True,\n help_text=_('Legg til de gruppene som skal ha tilgang til arrangementet'))\n\n class Meta:\n verbose_name = _(\"restriksjon\")\n verbose_name_plural = _(\"restriksjoner\")\n permissions = (\n ('view_restriction', 'View Restriction'),\n )\n",
"path": "apps/events/models.py"
}
] | [
{
"content": "# -*- coding: utf-8 -*-\n\nfrom collections import OrderedDict\nfrom datetime import datetime, timedelta\nfrom functools import reduce\n\nfrom django.conf import settings\nfrom django.contrib.auth.models import Group\nfrom django.contrib.contenttypes.models import ContentType\nfrom django.db import models\nfrom django.template.defaultfilters import slugify\nfrom django.utils import timezone\nfrom django.utils.translation import ugettext as _\nfrom filebrowser.fields import FileBrowseField\nfrom unidecode import unidecode\n\nfrom apps.authentication.models import FIELD_OF_STUDY_CHOICES\nfrom apps.companyprofile.models import Company\nfrom apps.marks.models import get_expiration_date\n\nUser = settings.AUTH_USER_MODEL\n\nTYPE_CHOICES = (\n (1, 'Sosialt'),\n (2, 'Bedriftspresentasjon'),\n (3, 'Kurs'),\n (4, 'Utflukt'),\n (5, 'Ekskursjon'),\n (6, 'Internt'),\n (7, 'Annet')\n)\n\n\nclass Event(models.Model):\n \"\"\"\n Base class for Event-objects.\n \"\"\"\n\n IMAGE_FOLDER = \"images/events\"\n IMAGE_EXTENSIONS = ['.jpg', '.jpeg', '.gif', '.png', '.tif', '.tiff']\n\n author = models.ForeignKey(User, related_name='oppretter')\n title = models.CharField(_('tittel'), max_length=60)\n event_start = models.DateTimeField(_('start-dato'))\n event_end = models.DateTimeField(_('slutt-dato'))\n location = models.CharField(_('lokasjon'), max_length=100)\n ingress_short = models.CharField(_(\"kort ingress\"), max_length=150)\n ingress = models.TextField(_('ingress'))\n description = models.TextField(_('beskrivelse'))\n image = FileBrowseField(_(\"bilde\"), max_length=200,\n directory=IMAGE_FOLDER, extensions=IMAGE_EXTENSIONS, null=True, blank=True)\n event_type = models.SmallIntegerField(_('type'), choices=TYPE_CHOICES, null=False)\n\n def is_attendance_event(self):\n \"\"\" Returns true if the event is an attendance event \"\"\"\n try:\n return True if self.attendance_event else False\n except AttendanceEvent.DoesNotExist:\n return False\n\n def images(self):\n if not self.image:\n return []\n from apps.events.utils import find_image_versions\n return find_image_versions(self)\n\n # TODO move payment and feedback stuff to attendance event when dasboard is done\n\n def feedback_users(self):\n if self.is_attendance_event:\n return [a.user for a in self.attendance_event.attendees.filter(attended=True)]\n return []\n\n def feedback_date(self):\n return self.event_end\n\n def feedback_title(self):\n return self.title\n\n def feedback_info(self):\n info = OrderedDict()\n if self.is_attendance_event():\n info[_('Påmeldte')] = self.attendance_event.number_of_attendees\n info[_('Oppmøtte')] = self.attendance_event.number_of_attendees - len(self.attendance_event.not_attended())\n info[_('Venteliste')] = self.attendance_event.number_on_waitlist\n\n return info\n\n @property\n def company_event(self):\n try:\n return CompanyEvent.objects.filter(event=self)\n except CompanyEvent.DoesNotExist:\n return None\n\n def feedback_mail(self):\n if self.event_type == 1 or self.event_type == 4: # Sosialt & Utflukt\n return settings.EMAIL_ARRKOM\n elif self.event_type == 2: # Bedpres\n return settings.EMAIL_BEDKOM\n elif self.event_type == 3: # Kurs\n return settings.EMAIL_FAGKOM\n elif self.event_type == 5: # Ekskursjon\n return settings.EMAIL_EKSKOM\n else:\n return settings.DEFAULT_FROM_EMAIL\n\n def can_display(self, user):\n restriction = GroupRestriction.objects.filter(event=self)\n\n if not restriction:\n return True\n\n if not user:\n return False\n\n groups = restriction[0].groups\n\n # returns True if any of the users groups are in one of the accepted groups\n return any(group in user.groups.all() for group in groups.all())\n\n @property\n def slug(self):\n return slugify(unidecode(self.title))\n\n @models.permalink\n def get_absolute_url(self):\n return 'events_details', None, {'event_id': self.id, 'event_slug': self.slug}\n\n def __str__(self):\n return self.title\n\n class Meta:\n verbose_name = _('arrangement')\n verbose_name_plural = _('arrangement')\n permissions = (\n ('view_event', 'View Event'),\n )\n\n\n\"\"\"\n BEGIN ACCESS RESTRICTION --------------------------------------------------------------------------\n\"\"\"\n\n\nclass Rule(models.Model):\n \"\"\"\n Super class for a rule object\n \"\"\"\n offset = models.PositiveSmallIntegerField(_('utsettelse'), help_text=_('utsettelse oppgis i timer'), default=0)\n\n def get_offset_time(self, time):\n if type(time) is not datetime:\n raise TypeError('time must be a datetime, not %s' % type(time))\n else:\n return time + timedelta(hours=self.offset)\n\n def satisfied(self, user):\n \"\"\" Checks if a user satisfies the rules \"\"\"\n return True\n\n def __str__(self):\n return 'Rule'\n\n class Meta:\n permissions = (\n ('view_rule', 'View Rule'),\n )\n\n\nclass FieldOfStudyRule(Rule):\n field_of_study = models.SmallIntegerField(_('studieretning'), choices=FIELD_OF_STUDY_CHOICES)\n\n def satisfied(self, user, registration_start):\n \"\"\" Override method \"\"\"\n\n # If the user has the same FOS as this rule\n if self.field_of_study == user.field_of_study:\n offset_datetime = self.get_offset_time(registration_start)\n # If the offset is in the past, it means you can attend even with the offset\n if offset_datetime < timezone.now():\n return {\"status\": True, \"message\": None, \"status_code\": 210}\n # If there is no offset, the signup just hasn't started yet\n elif self.offset == 0:\n return {\"status\": False, \"message\": _(\"Påmeldingen er ikke åpnet enda.\"), \"status_code\": 402}\n # In the last case there is a delayed signup\n else:\n return {\"status\": False, \"message\": _(\"Din studieretning har utsatt påmelding.\"),\n \"offset\": offset_datetime, \"status_code\": 420}\n return {\n \"status\": False, \"message\":\n _(\"Din studieretning er en annen enn de som har tilgang til dette arrangementet.\"), \"status_code\": 410}\n\n def __str__(self):\n if self.offset > 0:\n time_unit = _('timer') if self.offset > 1 else _('time')\n return _(\"%s etter %d %s\") % (str(self.get_field_of_study_display()), self.offset, time_unit)\n return str(self.get_field_of_study_display())\n\n class Meta:\n permissions = (\n ('view_fieldofstudyrule', 'View FieldOfStudyRule'),\n )\n\n\nclass GradeRule(Rule):\n grade = models.SmallIntegerField(_('klassetrinn'), null=False)\n\n def satisfied(self, user, registration_start):\n \"\"\" Override method \"\"\"\n\n # If the user has the same FOS as this rule\n if self.grade == user.year:\n offset_datetime = self.get_offset_time(registration_start)\n # If the offset is in the past, it means you can attend even with the offset\n if offset_datetime < timezone.now():\n return {\"status\": True, \"message\": None, \"status_code\": 211}\n # If there is no offset, the signup just hasn't started yet\n elif self.offset == 0:\n return {\"status\": False, \"message\": _(\"Påmeldingen er ikke åpnet enda.\"), \"status_code\": 402}\n # In the last case there is a delayed signup\n else:\n return {\n \"status\": False, \"message\":\n _(\"Ditt klassetrinn har utsatt påmelding.\"), \"offset\": offset_datetime, \"status_code\": 421}\n return {\n \"status\": False, \"message\":\n _(\"Du er ikke i et klassetrinn som har tilgang til dette arrangementet.\"), \"status_code\": 411}\n\n def __str__(self):\n if self.offset > 0:\n time_unit = _('timer') if self.offset > 1 else _('time')\n return _(\"%s. klasse etter %d %s\") % (self.grade, self.offset, time_unit)\n return _(\"%s. klasse\") % self.grade\n\n class Meta:\n permissions = (\n ('view_graderule', 'View GradeRule'),\n )\n\n\nclass UserGroupRule(Rule):\n group = models.ForeignKey(Group, blank=False, null=False)\n\n def satisfied(self, user, registration_start):\n \"\"\" Override method \"\"\"\n if self.group in user.groups.all():\n offset_datetime = self.get_offset_time(registration_start)\n # If the offset is in the past, it means you can attend even with the offset\n if offset_datetime < timezone.now():\n return {\"status\": True, \"message\": None, \"status_code\": 212}\n # If there is no offset, the signup just hasn't started yet\n elif self.offset == 0:\n return {\"status\": False, \"message\": _(\"Påmeldingen er ikke åpnet enda.\"), \"status_code\": 402}\n # In the last case there is a delayed signup\n else:\n return {\"status\": False, \"message\": _(\"%s har utsatt påmelding.\") % self.group,\n \"offset\": offset_datetime, \"status_code\": 422}\n return {\n \"status\": False, \"message\":\n _(\"Du er ikke i en brukergruppe som har tilgang til dette arrangmentet.\"), \"status_code\": 412}\n\n def __str__(self):\n if self.offset > 0:\n time_unit = _('timer') if self.offset > 1 else _('time')\n return _(\"%s etter %d %s\") % (str(self.group), self.offset, time_unit)\n return str(self.group)\n\n class Meta:\n permissions = (\n ('view_usergrouprule', 'View UserGroupRule'),\n )\n\n\nclass RuleBundle(models.Model):\n \"\"\"\n Access restriction rule object\n \"\"\"\n description = models.CharField(_('beskrivelse'), max_length=100, blank=True, null=True)\n field_of_study_rules = models.ManyToManyField(FieldOfStudyRule, blank=True)\n grade_rules = models.ManyToManyField(GradeRule, blank=True)\n user_group_rules = models.ManyToManyField(UserGroupRule, blank=True)\n\n def get_all_rules(self):\n rules = []\n rules.extend(self.field_of_study_rules.all())\n rules.extend(self.grade_rules.all())\n rules.extend(self.user_group_rules.all())\n return rules\n\n def get_rule_strings(self):\n return [str(rule) for rule in self.get_all_rules()]\n\n def satisfied(self, user, registration_start):\n\n errors = []\n\n for rule in self.get_all_rules():\n response = rule.satisfied(user, registration_start)\n if response['status']:\n return [response]\n else:\n errors.append(response)\n\n return errors\n\n def __str__(self):\n if self.description:\n return self.description\n elif self.get_rule_strings():\n return \", \".join(self.get_rule_strings())\n else:\n return _(\"Tom rule bundle.\")\n\n class Meta:\n permissions = (\n ('view_rulebundle', 'View RuleBundle'),\n )\n\n\n\"\"\"\n END ACCESS RESTRICTION --------------------------------------------------------------------------\n\"\"\"\n\n\nclass Extras(models.Model):\n \"\"\"\n Choices for events\n \"\"\"\n\n choice = models.CharField('valg', max_length=69)\n note = models.CharField('notat', max_length=200, blank=True, null=True)\n\n def __str__(self):\n return self.choice\n\n class Meta:\n verbose_name = _(\"ekstra valg\")\n verbose_name_plural = _(\"ekstra valg\")\n ordering = ['choice']\n\n\nclass AttendanceEvent(models.Model):\n \"\"\"\n Events that require special considerations regarding attendance.\n \"\"\"\n event = models.OneToOneField(\n Event,\n primary_key=True,\n related_name='attendance_event')\n\n max_capacity = models.PositiveIntegerField(_('maks-kapasitet'), null=False, blank=False)\n waitlist = models.BooleanField(_('venteliste'), default=False)\n guest_attendance = models.BooleanField(_('gjestepåmelding'), null=False, blank=False, default=False)\n registration_start = models.DateTimeField(_('registrerings-start'), null=False, blank=False)\n unattend_deadline = models.DateTimeField(_('avmeldings-frist'), null=False, blank=False)\n registration_end = models.DateTimeField(_('registrerings-slutt'), null=False, blank=False)\n\n # Automatic mark setting for not attending\n automatically_set_marks = models.BooleanField(_('automatisk prikk'), default=False,\n help_text=_('Påmeldte som ikke har møtt vil automatisk få prikk'))\n marks_has_been_set = models.BooleanField(default=False)\n\n # Access rules\n rule_bundles = models.ManyToManyField(RuleBundle, blank=True)\n\n # Extra choices\n extras = models.ManyToManyField(Extras, blank=True)\n\n @property\n def has_reservation(self):\n \"\"\" Returns whether this event has an attached reservation \"\"\"\n try:\n return True if self.reserved_seats else False\n except Reservation.DoesNotExist:\n return False\n\n @property\n def has_extras(self):\n return bool(self.extras.exists())\n\n @property\n def attendees_qs(self):\n \"\"\" Queryset with all attendees not on waiting list \"\"\"\n return self.attendees.all()[:self.max_capacity - self.number_of_reserved_seats]\n\n def not_attended(self):\n \"\"\" Queryset with all attendees not attended \"\"\"\n # .filter does apperantly not work on sliced querysets\n # return self.attendees_qs.filter(attended=False)\n\n not_attended = []\n\n for attendee in self.attendees_qs:\n if not attendee.attended:\n not_attended.append(attendee.user)\n\n return not_attended\n\n @property\n def waitlist_qs(self):\n \"\"\" Queryset with all attendees in waiting list \"\"\"\n return self.attendees.all()[self.max_capacity - self.number_of_reserved_seats:]\n\n @property\n def reservees_qs(self):\n \"\"\" Queryset with all reserved seats which have been filled \"\"\"\n if self.has_reservation:\n return self.reserved_seats.reservees.all()\n return []\n\n @property\n def attendees_not_paid(self):\n return [a for a in self.attendees_qs if a.paid]\n\n @property\n def number_of_attendees(self):\n \"\"\" Count of all attendees not in waiting list \"\"\"\n # We need to use len() instead of .count() here, because of the prefetched event archive\n return len(self.attendees_qs)\n\n @property\n def number_on_waitlist(self):\n \"\"\" Count of all attendees on waiting list \"\"\"\n # We need to use len() instead of .count() here, because of the prefetched event archive\n return len(self.waitlist_qs)\n\n @property\n def number_of_reserved_seats(self):\n \"\"\"\n Total number of seats for this event that are reserved\n \"\"\"\n return self.reserved_seats.seats if self.has_reservation else 0\n\n @property\n def number_of_reserved_seats_taken(self):\n \"\"\"\n Returns number of reserved seats which have been filled\n \"\"\"\n return self.reserved_seats.number_of_seats_taken if self.has_reservation else 0\n\n @property\n def number_of_seats_taken(self):\n \"\"\"\n Returns the total amount of taken seats for an attendance_event.\n \"\"\"\n # This includes all attendees + reserved seats for the event, if any.\n # Always use the total number of reserved seats here, because they are not\n # available for regular users to claim.\n return self.number_of_attendees + self.number_of_reserved_seats\n\n @property\n def free_seats(self):\n \"\"\"\n Integer representing the number of free seats for an event\n \"\"\"\n return 0 if self.number_of_seats_taken == self.max_capacity else self.max_capacity - self.number_of_seats_taken\n\n @property\n def room_on_event(self):\n \"\"\"\n Returns True if there are free seats or an open waiting list\n \"\"\"\n return True if self.free_seats > 0 or self.waitlist else False\n\n @property\n def registration_open(self):\n return timezone.now() < self.registration_start\n\n def has_delayed_signup(self, user):\n pass\n\n def is_marked(self, user):\n expiry_date = get_expiration_date(user)\n return expiry_date and expiry_date > timezone.now().date()\n\n def has_postponed_registration(self, user):\n if not self.is_marked(user):\n return False\n expiry_date = get_expiration_date(user)\n mark_offset = timedelta(days=1)\n postponed_registration_start = self.registration_start + mark_offset\n\n before_expiry = self.registration_start.date() < expiry_date\n\n if postponed_registration_start > timezone.now() and before_expiry:\n return postponed_registration_start\n\n def is_suspended(self, user):\n for suspension in user.get_active_suspensions():\n if not suspension.expiration_date or suspension.expiration_date > timezone.now().date():\n return True\n\n return False\n\n @property\n def will_i_be_on_wait_list(self):\n return True if self.free_seats == 0 and self.waitlist else False\n\n @property\n def waitlist_enabled(self):\n return self.waitlist\n\n def payment(self):\n # Importing here to awoid circular dependency error\n from apps.payment.models import Payment\n try:\n payment = Payment.objects.get(content_type=ContentType.objects.get_for_model(AttendanceEvent),\n object_id=self.event.id)\n except Payment.DoesNotExist:\n payment = None\n\n return payment\n\n def notify_waiting_list(self, host, unattended_user=None, extra_capacity=1):\n from apps.events.utils import handle_waitlist_bump # Imported here to avoid circular import\n # Notify next user on waiting list\n wait_list = self.waitlist_qs\n if wait_list:\n # Checking if user is on the wait list\n on_wait_list = False\n if unattended_user:\n for waiting_user in wait_list:\n if waiting_user.user == unattended_user:\n on_wait_list = True\n break\n if not on_wait_list:\n # Send mail to first user on waiting list\n attendees = wait_list[:extra_capacity]\n\n handle_waitlist_bump(self.event, host, attendees, self.payment())\n\n def is_eligible_for_signup(self, user):\n \"\"\"\n Checks if a user can attend a specific event\n This method checks for:\n Waitlist\n Room on event\n Rules\n Marks\n Suspension\n @param User object\n The returned dict contains a key called 'status_code'. These codes follow the HTTP\n standard in terms of overlying scheme.\n 2XX = successful\n 4XX = client error (user related)\n 5XX = server error (event related)\n These codes are meant as a debugging tool only. The eligibility checking is quite\n extensive, and tracking where it's going wrong is much needed.\n TODO:\n Exception handling\n \"\"\"\n\n response = {'status': False, 'message': '', 'status_code': None}\n\n # Registration closed\n if timezone.now() > self.registration_end:\n response['message'] = _('Påmeldingen er ikke lenger åpen.')\n response['status_code'] = 502\n return response\n\n # Room for me on the event?\n if not self.room_on_event:\n response['message'] = _(\"Det er ikke mer plass på dette arrangementet.\")\n response['status_code'] = 503\n return response\n\n #\n # Offset calculations.\n #\n\n # Are there any rules preventing me from attending?\n # This should be checked last of the offsets, because it can completely deny you access.\n response = self.rules_satisfied(user)\n if not response['status']:\n if 'offset' not in response:\n return response\n\n # Do I have any marks that postpone my registration date?\n response = self._check_marks(response, user)\n\n # Return response if offset was set.\n if 'offset' in response and response['offset'] > timezone.now():\n return response\n\n #\n # Offset calculations end\n #\n\n # Registration not open\n if timezone.now() < self.registration_start:\n response['status'] = False\n response['message'] = _('Påmeldingen har ikke åpnet enda.')\n response['status_code'] = 501\n return response\n\n # Is suspended\n if self.is_suspended(user):\n response['status'] = False\n response['message'] = _(\"Du er suspandert og kan ikke melde deg på.\")\n response['status_code'] = 402\n\n return response\n\n # Checks if the event is group restricted and if the user is in the right group\n if not self.event.can_display(user):\n response['status'] = False\n response['message'] = _(\"Du har ikke tilgang til å melde deg på dette arrangementet.\")\n response['status_code'] = 403\n\n return response\n\n # No objections, set eligible.\n response['status'] = True\n return response\n\n def _check_marks(self, response, user):\n expiry_date = get_expiration_date(user)\n if expiry_date and expiry_date > timezone.now().date():\n # Offset is currently 1 day if you have marks, regardless of amount.\n mark_offset = timedelta(days=1)\n postponed_registration_start = self.registration_start + mark_offset\n\n before_expiry = self.registration_start.date() < expiry_date\n\n if postponed_registration_start > timezone.now() and before_expiry:\n if 'offset' in response and response['offset'] < postponed_registration_start \\\n or 'offset' not in response:\n response['status'] = False\n response['status_code'] = 401\n response['message'] = _(\"Din påmelding er utsatt grunnet prikker.\")\n response['offset'] = postponed_registration_start\n return response\n\n def _process_rulebundle_satisfaction_responses(self, responses):\n # Put the smallest offset faaar into the future.\n smallest_offset = timezone.now() + timedelta(days=365)\n offset_response = {}\n future_response = {}\n errors = []\n\n for response in responses:\n if response['status']:\n return response\n elif 'offset' in response:\n if response['offset'] < smallest_offset:\n smallest_offset = response['offset']\n offset_response = response\n elif response['status_code'] == 402:\n future_response = response\n else:\n errors.append(response)\n\n if future_response:\n return future_response\n if smallest_offset > timezone.now() and offset_response:\n return offset_response\n if errors:\n return errors[0]\n\n def rules_satisfied(self, user):\n \"\"\"\n Checks a user against rules applied to an attendance event\n \"\"\"\n # If the event has guest attendance, allow absolutely anyone\n if self.guest_attendance:\n return {'status': True, 'status_code': 201}\n\n # If the user is not a member, return False right away\n # TODO check for guest list\n if not user.is_member:\n return {\n 'status': False, 'message':\n _(\"Dette arrangementet er kun åpent for medlemmer.\"), 'status_code': 400}\n\n # If there are no rule_bundles on this object, all members of Online are allowed.\n if not self.rule_bundles.exists() and user.is_member:\n return {'status': True, 'status_code': 200}\n\n # Check all rule bundles\n responses = []\n\n # If one satisfies, return true, else append to the error list\n for rule_bundle in self.rule_bundles.all():\n responses.extend(rule_bundle.satisfied(user, self.registration_start))\n\n return self._process_rulebundle_satisfaction_responses(responses)\n\n def is_attendee(self, user):\n return self.attendees.filter(user=user)\n\n def is_on_waitlist(self, user):\n return reduce(lambda x, y: x or y.user == user, self.waitlist_qs, False)\n\n def what_place_is_user_on_wait_list(self, user):\n if self.waitlist:\n waitlist = self.waitlist_qs\n if waitlist:\n for attendee_object in waitlist:\n if attendee_object.user == user:\n return list(waitlist).index(attendee_object) + 1\n return 0\n\n def __str__(self):\n return self.event.title\n\n class Meta:\n verbose_name = _('påmelding')\n verbose_name_plural = _('påmeldinger')\n permissions = (\n ('view_attendanceevent', 'View AttendanceEvent'),\n )\n\n\nclass CompanyEvent(models.Model):\n \"\"\"\n Company relation to AttendanceEvent\n \"\"\"\n company = models.ForeignKey(Company, verbose_name=_('bedrifter'))\n event = models.ForeignKey(Event, verbose_name=_('arrangement'), related_name='companies')\n\n class Meta:\n verbose_name = _('bedrift')\n verbose_name_plural = _('bedrifter')\n permissions = (\n ('view_companyevent', 'View CompanyEvent'),\n )\n\n\nclass Attendee(models.Model):\n \"\"\"\n User relation to AttendanceEvent.\n \"\"\"\n event = models.ForeignKey(AttendanceEvent, related_name=\"attendees\")\n user = models.ForeignKey(User)\n\n timestamp = models.DateTimeField(auto_now_add=True, editable=False)\n attended = models.BooleanField(_('var tilstede'), default=False)\n paid = models.BooleanField(_('har betalt'), default=False)\n note = models.CharField(_('notat'), max_length=100, blank=True, default='')\n extras = models.ForeignKey(Extras, blank=True, null=True)\n\n def __str__(self):\n return self.user.get_full_name()\n\n def delete(self):\n # Importing here to prevent circular dependencies\n from apps.payment.models import PaymentDelay\n try:\n PaymentDelay.objects.filter(user=self.user, payment=self.event.payment()).delete()\n except PaymentDelay.DoesNotExist:\n # Do nothing\n False\n\n super(Attendee, self).delete()\n\n class Meta:\n ordering = ['timestamp']\n unique_together = (('event', 'user'),)\n permissions = (\n ('view_attendee', 'View Attendee'),\n )\n\n\nclass Reservation(models.Model):\n attendance_event = models.OneToOneField(AttendanceEvent, related_name=\"reserved_seats\")\n seats = models.PositiveIntegerField(\"reserverte plasser\", blank=False, null=False)\n\n @property\n def number_of_seats_taken(self):\n return self.reservees.count()\n\n def __str__(self):\n return \"Reservasjoner for %s\" % self.attendance_event.event.title\n\n class Meta:\n verbose_name = _(\"reservasjon\")\n verbose_name_plural = _(\"reservasjoner\")\n permissions = (\n ('view_reservation', 'View Reservation'),\n )\n\n\nclass Reservee(models.Model):\n \"\"\"\n Reservation entry\n \"\"\"\n reservation = models.ForeignKey(Reservation, related_name='reservees')\n # I 2014 var norges lengste navn på 69 tegn;\n # julius andreas gimli arn macgyver chewbacka highlander elessar-jankov\n name = models.CharField('navn', max_length=69)\n note = models.CharField('notat', max_length=100)\n allergies = models.CharField('allergier', max_length=200, blank=True, null=True)\n\n def __str__(self):\n return self.name\n\n class Meta:\n verbose_name = _(\"reservasjon\")\n verbose_name_plural = _(\"reservasjoner\")\n ordering = ['id']\n permissions = (\n ('view_reservee', 'View Reservee'),\n )\n\n\nclass GroupRestriction(models.Model):\n event = models.OneToOneField(\n Event,\n primary_key=True,\n related_name='group_restriction')\n\n groups = models.ManyToManyField(Group, blank=True,\n help_text=_('Legg til de gruppene som skal ha tilgang til arrangementet'))\n\n class Meta:\n verbose_name = _(\"restriksjon\")\n verbose_name_plural = _(\"restriksjoner\")\n permissions = (\n ('view_restriction', 'View Restriction'),\n )\n",
"path": "apps/events/models.py"
}
] | diff --git a/apps/events/models.py b/apps/events/models.py
index feead4710..93f2fed4f 100644
--- a/apps/events/models.py
+++ b/apps/events/models.py
@@ -375,6 +375,10 @@ def has_reservation(self):
except Reservation.DoesNotExist:
return False
+ @property
+ def has_extras(self):
+ return bool(self.extras.exists())
+
@property
def attendees_qs(self):
""" Queryset with all attendees not on waiting list """
diff --git a/templates/events/dashboard/details/attendees.html b/templates/events/dashboard/details/attendees.html
index 8dbf777ad..1eac4fbd9 100644
--- a/templates/events/dashboard/details/attendees.html
+++ b/templates/events/dashboard/details/attendees.html
@@ -24,9 +24,13 @@ <h3 class="panel-title">Påmeldte (<span id="attendees-count">{{ event.attendanc
<th>#</th>
<th>Fornavn</th>
<th>Etternavn</th>
+ {% if event.attendance_event.payment %}
<th>Betalt</th>
+ {% endif %}
<th>Møtt</th>
+ {% if event.attendance_event.has_extras %}
<th>Extra</th>
+ {% endif %}
<th>Fjern</th>
</tr>
</thead>
@@ -36,22 +40,26 @@ <h3 class="panel-title">Påmeldte (<span id="attendees-count">{{ event.attendanc
<td>{{ forloop.counter }}</td>
<td><a href="{% url 'dashboard_attendee_details' attendee.id %}">{{ attendee.user.first_name }}</a></td>
<td><a href="{% url 'dashboard_attendee_details' attendee.id %}">{{ attendee.user.last_name }}</a></td>
+ {% if event.attendance_event.payment %}
<td>
<a href="#" data-id="{{ attendee.id }}" class="toggle-attendee paid">
<i class="fa fa-lg {% if attendee.paid %}fa-check-square-o checked{% else %}fa-square-o{% endif %}"></i>
</a>
</td>
+ {% endif %}
<td>
<a href="#" data-id="{{ attendee.id }}" class="toggle-attendee attended">
<i class="fa fa-lg {% if attendee.attended %}fa-check-square-o checked{% else %}fa-square-o{% endif %}"></i>
</a>
</td>
+ {% if event.attendance_event.has_extras %}
<td>
{% if attendee.extras %}{{ attendee.extras }}{% else %}-{% endif %}
</td>
+ {% endif %}
<td>
<a href="#modal-delete-attendee" data-toggle="modal" data-id="{{ attendee.id }}" data-name="{{ attendee.user.get_full_name }}" class="remove-user">
- <i class="fa fa-times fa-lg pull-right red"></i>
+ <i class="fa fa-times fa-lg red"></i>
</a>
</td>
</tr>
@@ -81,9 +89,13 @@ <h3 class="panel-title">Venteliste (<span id="waitlist-count">{{ event.attendanc
<th>#</th>
<th>Fornavn</th>
<th>Etternavn</th>
+ {% if event.attendance_event.payment %}
<th>Betalt</th>
+ {% endif %}
<th>Møtt</th>
+ {% if event.attendance_event.has_extras %}
<th>Extra</th>
+ {% endif %}
<th>Fjern</th>
</tr>
</thead>
@@ -93,22 +105,26 @@ <h3 class="panel-title">Venteliste (<span id="waitlist-count">{{ event.attendanc
<td>{{ forloop.counter }}</td>
<td><a href="{% url 'dashboard_attendee_details' attendee.id %}">{{ attendee.user.first_name }}</a></td>
<td><a href="{% url 'dashboard_attendee_details' attendee.id %}">{{ attendee.user.last_name }}</a></td>
+ {% if event.attendance_event.payment %}
<td>
<a href="#" data-id="{{ attendee.id }}" class="toggle-attendee paid">
<i class="fa fa-lg {% if attendee.paid %}fa-check-square-o checked{% else %}fa-square-o{% endif %}"></i>
</a>
</td>
+ {% endif %}
<td>
<a href="#" data-id="{{ attendee.id }}" class="toggle-attendee attended">
<i class="fa fa-lg {% if attendee.attended %}fa-check-square-o checked{% else %}fa-square-o{% endif %}"></i>
</a>
</td>
+ {% if event.attendance_event.has_extras %}
<td>
{% if attendee.extras %}{{ attendee.extras }}{% else %}-{% endif %}
</td>
+ {% endif %}
<td>
<a href="#modal-delete-attendee" data-toggle="modal" data-id="{{ attendee.id }}" data-name="{{ attendee.user.get_full_name }}" class="remove-user">
- <i class="fa fa-times fa-lg pull-right red"></i>
+ <i class="fa fa-times fa-lg red"></i>
</a>
</td>
</tr>
@@ -127,7 +143,7 @@ <h3 class="panel-title">Venteliste (<span id="waitlist-count">{{ event.attendanc
<br />
<!-- extras -->
-{% if event.attendance_event.extras %}
+{% if event.attendance_event.has_extras %}
<div class="row">
<div class="col-lg-12">
<div class="panel panel-default">
|
nipy__nipype-3455 | matplotlib "normed" argument deprecated
### Summary
When trying to use the nipype.algorithms.metrics.Distance with method='eucl_mean', get the following error
`AttributeError: 'Rectangle' object has no property 'normed'`
My two input volumes are z-stat map outputs from fitlins of the same individual (both are in same shape/dimensions).
It seems like the normed argument is deprecated in matplotlib.
### Actual behavior
The following error
```
~/miniconda3/envs/fmri_analysis/lib/python3.9/site-packages/nipype/interfaces/base/core.py in run(self, cwd, ignore_exception, **inputs)
426 try:
427 runtime = self._pre_run_hook(runtime)
--> 428 runtime = self._run_interface(runtime)
429 runtime = self._post_run_hook(runtime)
430 outputs = self.aggregate_outputs(runtime)
~/miniconda3/envs/fmri_analysis/lib/python3.9/site-packages/nipype/algorithms/metrics.py in _run_interface(self, runtime)
200
201 elif self.inputs.method == "eucl_mean":
--> 202 self._distance = self._eucl_mean(nii1, nii2)
203
204 elif self.inputs.method == "eucl_wmean":
~/miniconda3/envs/fmri_analysis/lib/python3.9/site-packages/nipype/algorithms/metrics.py in _eucl_mean(self, nii1, nii2, weighted)
151
152 plt.figure()
--> 153 plt.hist(min_dist_matrix, 50, normed=1, facecolor="green")
154 plt.savefig(self._hist_filename)
155 plt.clf()
~/miniconda3/envs/fmri_analysis/lib/python3.9/site-packages/matplotlib/pyplot.py in hist(x, bins, range, density, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, data, **kwargs)
2683 orientation='vertical', rwidth=None, log=False, color=None,
2684 label=None, stacked=False, *, data=None, **kwargs):
-> 2685 return gca().hist(
2686 x, bins=bins, range=range, density=density, weights=weights,
2687 cumulative=cumulative, bottom=bottom, histtype=histtype,
~/miniconda3/envs/fmri_analysis/lib/python3.9/site-packages/matplotlib/__init__.py in inner(ax, data, *args, **kwargs)
1445 def inner(ax, *args, data=None, **kwargs):
1446 if data is None:
-> 1447 return func(ax, *map(sanitize_sequence, args), **kwargs)
1448
1449 bound = new_sig.bind(ax, *args, **kwargs)
~/miniconda3/envs/fmri_analysis/lib/python3.9/site-packages/matplotlib/axes/_axes.py in hist(self, x, bins, range, density, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, **kwargs)
6813 if patch:
6814 p = patch[0]
-> 6815 p.update(kwargs)
6816 if lbl is not None:
6817 p.set_label(lbl)
~/miniconda3/envs/fmri_analysis/lib/python3.9/site-packages/matplotlib/artist.py in update(self, props)
994 func = getattr(self, f"set_{k}", None)
995 if not callable(func):
--> 996 raise AttributeError(f"{type(self).__name__!r} object "
997 f"has no property {k!r}")
998 ret.append(func(v))
AttributeError: 'Rectangle' object has no property 'normed'
```
### Expected behavior
Histogram output
### Platform details:
<!-- Please run the following code from your shell and place the output between the triple ticks, below.
python -c "import nipype; from pprint import pprint; pprint(nipype.get_info())"
-->
```
{'commit_hash': '0289137',
'commit_source': 'installation',
'networkx_version': '2.5.1',
'nibabel_version': '2.5.1',
'nipype_version': '1.6.1',
'numpy_version': '1.20.3',
'pkg_path': '/home/users/sjshim/miniconda3/envs/fmri_analysis/lib/python3.9/site-packages/nipype',
'scipy_version': '1.5.3',
'sys_executable': '/home/users/sjshim/miniconda3/envs/fmri_analysis/bin/python',
'sys_platform': 'linux',
'sys_version': '3.9.7 (default, Sep 16 2021, 13:09:58) \n[GCC 7.5.0]',
'traits_version': '6.2.0'}
```
| [
{
"content": "# -*- coding: utf-8 -*-\n# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-\n# vi: set ft=python sts=4 ts=4 sw=4 et:\n\"\"\"\nImage assessment algorithms. Typical overlap and error computation\nmeasures to evaluate results from other processing units.\n\"\"\"\nimport os\nimport os.path as op\n\nimport nibabel as nb\nimport numpy as np\n\nfrom .. import config, logging\n\nfrom ..interfaces.base import (\n SimpleInterface,\n BaseInterface,\n traits,\n TraitedSpec,\n File,\n InputMultiPath,\n BaseInterfaceInputSpec,\n isdefined,\n)\nfrom ..interfaces.nipy.base import NipyBaseInterface\n\niflogger = logging.getLogger(\"nipype.interface\")\n\n\nclass DistanceInputSpec(BaseInterfaceInputSpec):\n volume1 = File(\n exists=True, mandatory=True, desc=\"Has to have the same dimensions as volume2.\"\n )\n volume2 = File(\n exists=True, mandatory=True, desc=\"Has to have the same dimensions as volume1.\"\n )\n method = traits.Enum(\n \"eucl_min\",\n \"eucl_cog\",\n \"eucl_mean\",\n \"eucl_wmean\",\n \"eucl_max\",\n desc='\"\"eucl_min\": Euclidean distance between two closest points\\\n \"eucl_cog\": mean Euclidian distance between the Center of Gravity\\\n of volume1 and CoGs of volume2\\\n \"eucl_mean\": mean Euclidian minimum distance of all volume2 voxels\\\n to volume1\\\n \"eucl_wmean\": mean Euclidian minimum distance of all volume2 voxels\\\n to volume1 weighted by their values\\\n \"eucl_max\": maximum over minimum Euclidian distances of all volume2\\\n voxels to volume1 (also known as the Hausdorff distance)',\n usedefault=True,\n )\n mask_volume = File(exists=True, desc=\"calculate overlap only within this mask.\")\n\n\nclass DistanceOutputSpec(TraitedSpec):\n distance = traits.Float()\n point1 = traits.Array(shape=(3,))\n point2 = traits.Array(shape=(3,))\n histogram = File()\n\n\nclass Distance(BaseInterface):\n \"\"\"Calculates distance between two volumes.\"\"\"\n\n input_spec = DistanceInputSpec\n output_spec = DistanceOutputSpec\n\n _hist_filename = \"hist.pdf\"\n\n def _find_border(self, data):\n from scipy.ndimage.morphology import binary_erosion\n\n eroded = binary_erosion(data)\n border = np.logical_and(data, np.logical_not(eroded))\n return border\n\n def _get_coordinates(self, data, affine):\n if len(data.shape) == 4:\n data = data[:, :, :, 0]\n indices = np.vstack(np.nonzero(data))\n indices = np.vstack((indices, np.ones(indices.shape[1])))\n coordinates = np.dot(affine, indices)\n return coordinates[:3, :]\n\n def _eucl_min(self, nii1, nii2):\n from scipy.spatial.distance import cdist, euclidean\n\n origdata1 = np.asanyarray(nii1.dataobj).astype(bool)\n border1 = self._find_border(origdata1)\n\n origdata2 = np.asanyarray(nii2.dataobj).astype(bool)\n border2 = self._find_border(origdata2)\n\n set1_coordinates = self._get_coordinates(border1, nii1.affine)\n\n set2_coordinates = self._get_coordinates(border2, nii2.affine)\n\n dist_matrix = cdist(set1_coordinates.T, set2_coordinates.T)\n (point1, point2) = np.unravel_index(np.argmin(dist_matrix), dist_matrix.shape)\n return (\n euclidean(set1_coordinates.T[point1, :], set2_coordinates.T[point2, :]),\n set1_coordinates.T[point1, :],\n set2_coordinates.T[point2, :],\n )\n\n def _eucl_cog(self, nii1, nii2):\n from scipy.spatial.distance import cdist\n from scipy.ndimage.measurements import center_of_mass, label\n\n origdata1 = np.asanyarray(nii1.dataobj)\n origdata1 = (np.rint(origdata1) != 0) & ~np.isnan(origdata1)\n cog_t = np.array(center_of_mass(origdata1)).reshape(-1, 1)\n cog_t = np.vstack((cog_t, np.array([1])))\n cog_t_coor = np.dot(nii1.affine, cog_t)[:3, :]\n\n origdata2 = np.asanyarray(nii2.dataobj)\n origdata2 = (np.rint(origdata2) != 0) & ~np.isnan(origdata2)\n (labeled_data, n_labels) = label(origdata2)\n\n cogs = np.ones((4, n_labels))\n\n for i in range(n_labels):\n cogs[:3, i] = np.array(center_of_mass(origdata2, labeled_data, i + 1))\n\n cogs_coor = np.dot(nii2.affine, cogs)[:3, :]\n\n dist_matrix = cdist(cog_t_coor.T, cogs_coor.T)\n\n return np.mean(dist_matrix)\n\n def _eucl_mean(self, nii1, nii2, weighted=False):\n from scipy.spatial.distance import cdist\n\n origdata1 = np.asanyarray(nii1.dataobj).astype(bool)\n border1 = self._find_border(origdata1)\n\n origdata2 = np.asanyarray(nii2.dataobj).astype(bool)\n\n set1_coordinates = self._get_coordinates(border1, nii1.affine)\n set2_coordinates = self._get_coordinates(origdata2, nii2.affine)\n\n dist_matrix = cdist(set1_coordinates.T, set2_coordinates.T)\n min_dist_matrix = np.amin(dist_matrix, axis=0)\n import matplotlib\n\n matplotlib.use(config.get(\"execution\", \"matplotlib_backend\"))\n import matplotlib.pyplot as plt\n\n plt.figure()\n plt.hist(min_dist_matrix, 50, normed=1, facecolor=\"green\")\n plt.savefig(self._hist_filename)\n plt.clf()\n plt.close()\n\n if weighted:\n return np.average(min_dist_matrix, weights=nii2.dataobj[origdata2].flat)\n else:\n return np.mean(min_dist_matrix)\n\n def _eucl_max(self, nii1, nii2):\n from scipy.spatial.distance import cdist\n\n origdata1 = np.asanyarray(nii1.dataobj)\n origdata1 = (np.rint(origdata1) != 0) & ~np.isnan(origdata1)\n origdata2 = np.asanyarray(nii2.dataobj)\n origdata2 = (np.rint(origdata2) != 0) & ~np.isnan(origdata2)\n\n if isdefined(self.inputs.mask_volume):\n maskdata = np.asanyarray(nb.load(self.inputs.mask_volume).dataobj)\n maskdata = (np.rint(maskdata) != 0) & ~np.isnan(maskdata)\n origdata1 = np.logical_and(maskdata, origdata1)\n origdata2 = np.logical_and(maskdata, origdata2)\n\n if origdata1.max() == 0 or origdata2.max() == 0:\n return np.nan\n\n border1 = self._find_border(origdata1)\n border2 = self._find_border(origdata2)\n\n set1_coordinates = self._get_coordinates(border1, nii1.affine)\n set2_coordinates = self._get_coordinates(border2, nii2.affine)\n distances = cdist(set1_coordinates.T, set2_coordinates.T)\n mins = np.concatenate((np.amin(distances, axis=0), np.amin(distances, axis=1)))\n\n return np.max(mins)\n\n def _run_interface(self, runtime):\n # there is a bug in some scipy ndimage methods that gets tripped by memory mapped objects\n nii1 = nb.load(self.inputs.volume1, mmap=False)\n nii2 = nb.load(self.inputs.volume2, mmap=False)\n\n if self.inputs.method == \"eucl_min\":\n self._distance, self._point1, self._point2 = self._eucl_min(nii1, nii2)\n\n elif self.inputs.method == \"eucl_cog\":\n self._distance = self._eucl_cog(nii1, nii2)\n\n elif self.inputs.method == \"eucl_mean\":\n self._distance = self._eucl_mean(nii1, nii2)\n\n elif self.inputs.method == \"eucl_wmean\":\n self._distance = self._eucl_mean(nii1, nii2, weighted=True)\n elif self.inputs.method == \"eucl_max\":\n self._distance = self._eucl_max(nii1, nii2)\n\n return runtime\n\n def _list_outputs(self):\n outputs = self._outputs().get()\n outputs[\"distance\"] = self._distance\n if self.inputs.method == \"eucl_min\":\n outputs[\"point1\"] = self._point1\n outputs[\"point2\"] = self._point2\n elif self.inputs.method in [\"eucl_mean\", \"eucl_wmean\"]:\n outputs[\"histogram\"] = os.path.abspath(self._hist_filename)\n return outputs\n\n\nclass OverlapInputSpec(BaseInterfaceInputSpec):\n volume1 = File(\n exists=True, mandatory=True, desc=\"Has to have the same dimensions as volume2.\"\n )\n volume2 = File(\n exists=True, mandatory=True, desc=\"Has to have the same dimensions as volume1.\"\n )\n mask_volume = File(exists=True, desc=\"calculate overlap only within this mask.\")\n bg_overlap = traits.Bool(\n False, usedefault=True, mandatory=True, desc=\"consider zeros as a label\"\n )\n out_file = File(\"diff.nii\", usedefault=True)\n weighting = traits.Enum(\n \"none\",\n \"volume\",\n \"squared_vol\",\n usedefault=True,\n desc=(\n \"'none': no class-overlap weighting is \"\n \"performed. 'volume': computed class-\"\n \"overlaps are weighted by class volume \"\n \"'squared_vol': computed class-overlaps \"\n \"are weighted by the squared volume of \"\n \"the class\"\n ),\n )\n vol_units = traits.Enum(\n \"voxel\", \"mm\", mandatory=True, usedefault=True, desc=\"units for volumes\"\n )\n\n\nclass OverlapOutputSpec(TraitedSpec):\n jaccard = traits.Float(desc=\"averaged jaccard index\")\n dice = traits.Float(desc=\"averaged dice index\")\n roi_ji = traits.List(traits.Float(), desc=(\"the Jaccard index (JI) per ROI\"))\n roi_di = traits.List(traits.Float(), desc=(\"the Dice index (DI) per ROI\"))\n volume_difference = traits.Float(desc=(\"averaged volume difference\"))\n roi_voldiff = traits.List(traits.Float(), desc=(\"volume differences of ROIs\"))\n labels = traits.List(traits.Int(), desc=(\"detected labels\"))\n diff_file = File(exists=True, desc=\"error map of differences\")\n\n\nclass Overlap(BaseInterface):\n \"\"\"\n Calculates Dice and Jaccard's overlap measures between two ROI maps.\n The interface is backwards compatible with the former version in\n which only binary files were accepted.\n\n The averaged values of overlap indices can be weighted. Volumes\n now can be reported in :math:`mm^3`, although they are given in voxels\n to keep backwards compatibility.\n\n Example\n -------\n\n >>> overlap = Overlap()\n >>> overlap.inputs.volume1 = 'cont1.nii'\n >>> overlap.inputs.volume2 = 'cont2.nii'\n >>> res = overlap.run() # doctest: +SKIP\n\n \"\"\"\n\n input_spec = OverlapInputSpec\n output_spec = OverlapOutputSpec\n\n def _bool_vec_dissimilarity(self, booldata1, booldata2, method):\n from scipy.spatial.distance import dice, jaccard\n\n methods = {\"dice\": dice, \"jaccard\": jaccard}\n if not (np.any(booldata1) or np.any(booldata2)):\n return 0\n return 1 - methods[method](booldata1.flat, booldata2.flat)\n\n def _run_interface(self, runtime):\n nii1 = nb.load(self.inputs.volume1)\n nii2 = nb.load(self.inputs.volume2)\n\n scale = 1.0\n\n if self.inputs.vol_units == \"mm\":\n scale = np.prod(nii1.header.get_zooms()[:3])\n\n data1 = np.asanyarray(nii1.dataobj)\n data1[np.logical_or(data1 < 0, np.isnan(data1))] = 0\n max1 = int(data1.max())\n data1 = data1.astype(np.min_scalar_type(max1))\n data2 = np.asanyarray(nii2.dataobj).astype(np.min_scalar_type(max1))\n data2[np.logical_or(data1 < 0, np.isnan(data1))] = 0\n\n if isdefined(self.inputs.mask_volume):\n maskdata = np.asanyarray(nb.load(self.inputs.mask_volume).dataobj)\n maskdata = ~np.logical_or(maskdata == 0, np.isnan(maskdata))\n data1[~maskdata] = 0\n data2[~maskdata] = 0\n\n res = []\n volumes1 = []\n volumes2 = []\n\n labels = np.unique(data1[data1 > 0].reshape(-1)).tolist()\n if self.inputs.bg_overlap:\n labels.insert(0, 0)\n\n for l in labels:\n res.append(\n self._bool_vec_dissimilarity(data1 == l, data2 == l, method=\"jaccard\")\n )\n volumes1.append(scale * len(data1[data1 == l]))\n volumes2.append(scale * len(data2[data2 == l]))\n\n results = dict(jaccard=[], dice=[])\n results[\"jaccard\"] = np.array(res)\n results[\"dice\"] = 2.0 * results[\"jaccard\"] / (results[\"jaccard\"] + 1.0)\n\n weights = np.ones((len(volumes1),), dtype=np.float32)\n if self.inputs.weighting != \"none\":\n weights = weights / np.array(volumes1)\n if self.inputs.weighting == \"squared_vol\":\n weights = weights**2\n weights = weights / np.sum(weights)\n\n both_data = np.zeros(data1.shape)\n both_data[(data1 - data2) != 0] = 1\n\n nb.save(\n nb.Nifti1Image(both_data, nii1.affine, nii1.header), self.inputs.out_file\n )\n\n self._labels = labels\n self._ove_rois = results\n self._vol_rois = (np.array(volumes1) - np.array(volumes2)) / np.array(volumes1)\n\n self._dice = round(np.sum(weights * results[\"dice\"]), 5)\n self._jaccard = round(np.sum(weights * results[\"jaccard\"]), 5)\n self._volume = np.sum(weights * self._vol_rois)\n\n return runtime\n\n def _list_outputs(self):\n outputs = self._outputs().get()\n outputs[\"labels\"] = self._labels\n outputs[\"jaccard\"] = self._jaccard\n outputs[\"dice\"] = self._dice\n outputs[\"volume_difference\"] = self._volume\n\n outputs[\"roi_ji\"] = self._ove_rois[\"jaccard\"].tolist()\n outputs[\"roi_di\"] = self._ove_rois[\"dice\"].tolist()\n outputs[\"roi_voldiff\"] = self._vol_rois.tolist()\n outputs[\"diff_file\"] = os.path.abspath(self.inputs.out_file)\n return outputs\n\n\nclass FuzzyOverlapInputSpec(BaseInterfaceInputSpec):\n in_ref = InputMultiPath(\n File(exists=True),\n mandatory=True,\n desc=\"Reference image. Requires the same dimensions as in_tst.\",\n )\n in_tst = InputMultiPath(\n File(exists=True),\n mandatory=True,\n desc=\"Test image. Requires the same dimensions as in_ref.\",\n )\n in_mask = File(exists=True, desc=\"calculate overlap only within mask\")\n weighting = traits.Enum(\n \"none\",\n \"volume\",\n \"squared_vol\",\n usedefault=True,\n desc=(\n \"'none': no class-overlap weighting is \"\n \"performed. 'volume': computed class-\"\n \"overlaps are weighted by class volume \"\n \"'squared_vol': computed class-overlaps \"\n \"are weighted by the squared volume of \"\n \"the class\"\n ),\n )\n out_file = File(\n \"diff.nii\",\n desc=\"alternative name for resulting difference-map\",\n usedefault=True,\n )\n\n\nclass FuzzyOverlapOutputSpec(TraitedSpec):\n jaccard = traits.Float(desc=\"Fuzzy Jaccard Index (fJI), all the classes\")\n dice = traits.Float(desc=\"Fuzzy Dice Index (fDI), all the classes\")\n class_fji = traits.List(\n traits.Float(), desc=\"Array containing the fJIs of each computed class\"\n )\n class_fdi = traits.List(\n traits.Float(), desc=\"Array containing the fDIs of each computed class\"\n )\n\n\nclass FuzzyOverlap(SimpleInterface):\n \"\"\"Calculates various overlap measures between two maps, using the fuzzy\n definition proposed in: Crum et al., Generalized Overlap Measures for\n Evaluation and Validation in Medical Image Analysis, IEEE Trans. Med.\n Ima. 25(11),pp 1451-1461, Nov. 2006.\n\n in_ref and in_tst are lists of 2/3D images, each element on the list\n containing one volume fraction map of a class in a fuzzy partition\n of the domain.\n\n Example\n -------\n\n >>> overlap = FuzzyOverlap()\n >>> overlap.inputs.in_ref = [ 'ref_class0.nii', 'ref_class1.nii' ]\n >>> overlap.inputs.in_tst = [ 'tst_class0.nii', 'tst_class1.nii' ]\n >>> overlap.inputs.weighting = 'volume'\n >>> res = overlap.run() # doctest: +SKIP\n \"\"\"\n\n input_spec = FuzzyOverlapInputSpec\n output_spec = FuzzyOverlapOutputSpec\n\n def _run_interface(self, runtime):\n # Load data\n refdata = nb.concat_images(self.inputs.in_ref).dataobj\n tstdata = nb.concat_images(self.inputs.in_tst).dataobj\n\n # Data must have same shape\n if not refdata.shape == tstdata.shape:\n raise RuntimeError(\n 'Size of \"in_tst\" %s must match that of \"in_ref\" %s.'\n % (tstdata.shape, refdata.shape)\n )\n\n ncomp = refdata.shape[-1]\n\n # Load mask\n mask = np.ones_like(refdata, dtype=bool)\n if isdefined(self.inputs.in_mask):\n mask = np.asanyarray(nb.load(self.inputs.in_mask).dataobj) > 0\n mask = np.repeat(mask[..., np.newaxis], ncomp, -1)\n assert mask.shape == refdata.shape\n\n # Drop data outside mask\n refdata = refdata[mask]\n tstdata = tstdata[mask]\n\n if np.any(refdata < 0.0):\n iflogger.warning(\n 'Negative values encountered in \"in_ref\" input, '\n \"taking absolute values.\"\n )\n refdata = np.abs(refdata)\n\n if np.any(tstdata < 0.0):\n iflogger.warning(\n 'Negative values encountered in \"in_tst\" input, '\n \"taking absolute values.\"\n )\n tstdata = np.abs(tstdata)\n\n if np.any(refdata > 1.0):\n iflogger.warning(\n 'Values greater than 1.0 found in \"in_ref\" input, ' \"scaling values.\"\n )\n refdata /= refdata.max()\n\n if np.any(tstdata > 1.0):\n iflogger.warning(\n 'Values greater than 1.0 found in \"in_tst\" input, ' \"scaling values.\"\n )\n tstdata /= tstdata.max()\n\n numerators = np.atleast_2d(np.minimum(refdata, tstdata).reshape((-1, ncomp)))\n denominators = np.atleast_2d(np.maximum(refdata, tstdata).reshape((-1, ncomp)))\n\n jaccards = numerators.sum(axis=0) / denominators.sum(axis=0)\n\n # Calculate weights\n weights = np.ones_like(jaccards, dtype=float)\n if self.inputs.weighting != \"none\":\n volumes = np.sum((refdata + tstdata) > 0, axis=1).reshape((-1, ncomp))\n weights = 1.0 / volumes\n if self.inputs.weighting == \"squared_vol\":\n weights = weights**2\n\n weights = weights / np.sum(weights)\n dices = 2.0 * jaccards / (jaccards + 1.0)\n\n # Fill-in the results object\n self._results[\"jaccard\"] = float(weights.dot(jaccards))\n self._results[\"dice\"] = float(weights.dot(dices))\n self._results[\"class_fji\"] = [float(v) for v in jaccards]\n self._results[\"class_fdi\"] = [float(v) for v in dices]\n return runtime\n\n\nclass ErrorMapInputSpec(BaseInterfaceInputSpec):\n in_ref = File(\n exists=True,\n mandatory=True,\n desc=\"Reference image. Requires the same dimensions as in_tst.\",\n )\n in_tst = File(\n exists=True,\n mandatory=True,\n desc=\"Test image. Requires the same dimensions as in_ref.\",\n )\n mask = File(exists=True, desc=\"calculate overlap only within this mask.\")\n metric = traits.Enum(\n \"sqeuclidean\",\n \"euclidean\",\n desc=\"error map metric (as implemented in scipy cdist)\",\n usedefault=True,\n mandatory=True,\n )\n out_map = File(desc=\"Name for the output file\")\n\n\nclass ErrorMapOutputSpec(TraitedSpec):\n out_map = File(exists=True, desc=\"resulting error map\")\n distance = traits.Float(desc=\"Average distance between volume 1 and 2\")\n\n\nclass ErrorMap(BaseInterface):\n \"\"\"Calculates the error (distance) map between two input volumes.\n\n Example\n -------\n\n >>> errormap = ErrorMap()\n >>> errormap.inputs.in_ref = 'cont1.nii'\n >>> errormap.inputs.in_tst = 'cont2.nii'\n >>> res = errormap.run() # doctest: +SKIP\n \"\"\"\n\n input_spec = ErrorMapInputSpec\n output_spec = ErrorMapOutputSpec\n _out_file = \"\"\n\n def _run_interface(self, runtime):\n # Get two numpy data matrices\n nii_ref = nb.load(self.inputs.in_ref)\n ref_data = np.squeeze(nii_ref.dataobj)\n tst_data = np.squeeze(nb.load(self.inputs.in_tst).dataobj)\n assert ref_data.ndim == tst_data.ndim\n\n # Load mask\n comps = 1\n mapshape = ref_data.shape\n\n if ref_data.ndim == 4:\n comps = ref_data.shape[-1]\n mapshape = ref_data.shape[:-1]\n\n if isdefined(self.inputs.mask):\n msk = np.asanyarray(nb.load(self.inputs.mask).dataobj)\n if mapshape != msk.shape:\n raise RuntimeError(\n \"Mask should match volume shape, \\\n mask is %s and volumes are %s\"\n % (list(msk.shape), list(mapshape))\n )\n else:\n msk = np.ones(shape=mapshape)\n\n # Flatten both volumes and make the pixel differennce\n mskvector = msk.reshape(-1)\n msk_idxs = np.where(mskvector == 1)\n refvector = ref_data.reshape(-1, comps)[msk_idxs].astype(np.float32)\n tstvector = tst_data.reshape(-1, comps)[msk_idxs].astype(np.float32)\n diffvector = refvector - tstvector\n\n # Scale the difference\n if self.inputs.metric == \"sqeuclidean\":\n errvector = diffvector**2\n if comps > 1:\n errvector = np.sum(errvector, axis=1)\n else:\n errvector = np.squeeze(errvector)\n elif self.inputs.metric == \"euclidean\":\n errvector = np.linalg.norm(diffvector, axis=1)\n\n errvectorexp = np.zeros_like(\n mskvector, dtype=np.float32\n ) # The default type is uint8\n errvectorexp[msk_idxs] = errvector\n\n # Get averaged error\n self._distance = np.average(errvector) # Only average the masked voxels\n\n errmap = errvectorexp.reshape(mapshape)\n\n hdr = nii_ref.header.copy()\n hdr.set_data_dtype(np.float32)\n hdr[\"data_type\"] = 16\n hdr.set_data_shape(mapshape)\n\n if not isdefined(self.inputs.out_map):\n fname, ext = op.splitext(op.basename(self.inputs.in_tst))\n if ext == \".gz\":\n fname, ext2 = op.splitext(fname)\n ext = ext2 + ext\n self._out_file = op.abspath(fname + \"_errmap\" + ext)\n else:\n self._out_file = self.inputs.out_map\n\n nb.Nifti1Image(errmap.astype(np.float32), nii_ref.affine, hdr).to_filename(\n self._out_file\n )\n\n return runtime\n\n def _list_outputs(self):\n outputs = self.output_spec().get()\n outputs[\"out_map\"] = self._out_file\n outputs[\"distance\"] = self._distance\n return outputs\n\n\nclass SimilarityInputSpec(BaseInterfaceInputSpec):\n volume1 = File(exists=True, desc=\"3D/4D volume\", mandatory=True)\n volume2 = File(exists=True, desc=\"3D/4D volume\", mandatory=True)\n mask1 = File(exists=True, desc=\"3D volume\")\n mask2 = File(exists=True, desc=\"3D volume\")\n metric = traits.Either(\n traits.Enum(\"cc\", \"cr\", \"crl1\", \"mi\", \"nmi\", \"slr\"),\n traits.Callable(),\n desc=\"\"\"str or callable\nCost-function for assessing image similarity. If a string,\none of 'cc': correlation coefficient, 'cr': correlation\nratio, 'crl1': L1-norm based correlation ratio, 'mi': mutual\ninformation, 'nmi': normalized mutual information, 'slr':\nsupervised log-likelihood ratio. If a callable, it should\ntake a two-dimensional array representing the image joint\nhistogram as an input and return a float.\"\"\",\n usedefault=True,\n )\n\n\nclass SimilarityOutputSpec(TraitedSpec):\n similarity = traits.List(\n traits.Float(desc=\"Similarity between volume 1 and 2, frame by frame\")\n )\n\n\nclass Similarity(NipyBaseInterface):\n \"\"\"Calculates similarity between two 3D or 4D volumes. Both volumes have to be in\n the same coordinate system, same space within that coordinate system and\n with the same voxel dimensions.\n\n .. note:: This interface is an extension of\n :py:class:`nipype.interfaces.nipy.utils.Similarity` to support 4D files.\n Requires :py:mod:`nipy`\n\n Example\n -------\n >>> from nipype.algorithms.metrics import Similarity\n >>> similarity = Similarity()\n >>> similarity.inputs.volume1 = 'rc1s1.nii'\n >>> similarity.inputs.volume2 = 'rc1s2.nii'\n >>> similarity.inputs.mask1 = 'mask.nii'\n >>> similarity.inputs.mask2 = 'mask.nii'\n >>> similarity.inputs.metric = 'cr'\n >>> res = similarity.run() # doctest: +SKIP\n \"\"\"\n\n input_spec = SimilarityInputSpec\n output_spec = SimilarityOutputSpec\n\n def _run_interface(self, runtime):\n from nipy.algorithms.registration.histogram_registration import (\n HistogramRegistration,\n )\n from nipy.algorithms.registration.affine import Affine\n\n vol1_nii = nb.load(self.inputs.volume1)\n vol2_nii = nb.load(self.inputs.volume2)\n\n dims = len(vol1_nii.shape)\n\n if dims == 3 or dims == 2:\n vols1 = [vol1_nii]\n vols2 = [vol2_nii]\n if dims == 4:\n vols1 = nb.four_to_three(vol1_nii)\n vols2 = nb.four_to_three(vol2_nii)\n\n if dims < 2 or dims > 4:\n raise RuntimeError(\n \"Image dimensions not supported (detected %dD file)\" % dims\n )\n\n if isdefined(self.inputs.mask1):\n mask1 = np.asanyarray(nb.load(self.inputs.mask1).dataobj) == 1\n else:\n mask1 = None\n\n if isdefined(self.inputs.mask2):\n mask2 = np.asanyarray(nb.load(self.inputs.mask2).dataobj) == 1\n else:\n mask2 = None\n\n self._similarity = []\n\n for ts1, ts2 in zip(vols1, vols2):\n histreg = HistogramRegistration(\n from_img=ts1,\n to_img=ts2,\n similarity=self.inputs.metric,\n from_mask=mask1,\n to_mask=mask2,\n )\n self._similarity.append(histreg.eval(Affine()))\n\n return runtime\n\n def _list_outputs(self):\n outputs = self._outputs().get()\n outputs[\"similarity\"] = self._similarity\n return outputs\n",
"path": "nipype/algorithms/metrics.py"
}
] | [
{
"content": "# -*- coding: utf-8 -*-\n# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-\n# vi: set ft=python sts=4 ts=4 sw=4 et:\n\"\"\"\nImage assessment algorithms. Typical overlap and error computation\nmeasures to evaluate results from other processing units.\n\"\"\"\nimport os\nimport os.path as op\n\nimport nibabel as nb\nimport numpy as np\n\nfrom .. import config, logging\n\nfrom ..interfaces.base import (\n SimpleInterface,\n BaseInterface,\n traits,\n TraitedSpec,\n File,\n InputMultiPath,\n BaseInterfaceInputSpec,\n isdefined,\n)\nfrom ..interfaces.nipy.base import NipyBaseInterface\n\niflogger = logging.getLogger(\"nipype.interface\")\n\n\nclass DistanceInputSpec(BaseInterfaceInputSpec):\n volume1 = File(\n exists=True, mandatory=True, desc=\"Has to have the same dimensions as volume2.\"\n )\n volume2 = File(\n exists=True, mandatory=True, desc=\"Has to have the same dimensions as volume1.\"\n )\n method = traits.Enum(\n \"eucl_min\",\n \"eucl_cog\",\n \"eucl_mean\",\n \"eucl_wmean\",\n \"eucl_max\",\n desc='\"\"eucl_min\": Euclidean distance between two closest points\\\n \"eucl_cog\": mean Euclidian distance between the Center of Gravity\\\n of volume1 and CoGs of volume2\\\n \"eucl_mean\": mean Euclidian minimum distance of all volume2 voxels\\\n to volume1\\\n \"eucl_wmean\": mean Euclidian minimum distance of all volume2 voxels\\\n to volume1 weighted by their values\\\n \"eucl_max\": maximum over minimum Euclidian distances of all volume2\\\n voxels to volume1 (also known as the Hausdorff distance)',\n usedefault=True,\n )\n mask_volume = File(exists=True, desc=\"calculate overlap only within this mask.\")\n\n\nclass DistanceOutputSpec(TraitedSpec):\n distance = traits.Float()\n point1 = traits.Array(shape=(3,))\n point2 = traits.Array(shape=(3,))\n histogram = File()\n\n\nclass Distance(BaseInterface):\n \"\"\"Calculates distance between two volumes.\"\"\"\n\n input_spec = DistanceInputSpec\n output_spec = DistanceOutputSpec\n\n _hist_filename = \"hist.pdf\"\n\n def _find_border(self, data):\n from scipy.ndimage.morphology import binary_erosion\n\n eroded = binary_erosion(data)\n border = np.logical_and(data, np.logical_not(eroded))\n return border\n\n def _get_coordinates(self, data, affine):\n if len(data.shape) == 4:\n data = data[:, :, :, 0]\n indices = np.vstack(np.nonzero(data))\n indices = np.vstack((indices, np.ones(indices.shape[1])))\n coordinates = np.dot(affine, indices)\n return coordinates[:3, :]\n\n def _eucl_min(self, nii1, nii2):\n from scipy.spatial.distance import cdist, euclidean\n\n origdata1 = np.asanyarray(nii1.dataobj).astype(bool)\n border1 = self._find_border(origdata1)\n\n origdata2 = np.asanyarray(nii2.dataobj).astype(bool)\n border2 = self._find_border(origdata2)\n\n set1_coordinates = self._get_coordinates(border1, nii1.affine)\n\n set2_coordinates = self._get_coordinates(border2, nii2.affine)\n\n dist_matrix = cdist(set1_coordinates.T, set2_coordinates.T)\n (point1, point2) = np.unravel_index(np.argmin(dist_matrix), dist_matrix.shape)\n return (\n euclidean(set1_coordinates.T[point1, :], set2_coordinates.T[point2, :]),\n set1_coordinates.T[point1, :],\n set2_coordinates.T[point2, :],\n )\n\n def _eucl_cog(self, nii1, nii2):\n from scipy.spatial.distance import cdist\n from scipy.ndimage.measurements import center_of_mass, label\n\n origdata1 = np.asanyarray(nii1.dataobj)\n origdata1 = (np.rint(origdata1) != 0) & ~np.isnan(origdata1)\n cog_t = np.array(center_of_mass(origdata1)).reshape(-1, 1)\n cog_t = np.vstack((cog_t, np.array([1])))\n cog_t_coor = np.dot(nii1.affine, cog_t)[:3, :]\n\n origdata2 = np.asanyarray(nii2.dataobj)\n origdata2 = (np.rint(origdata2) != 0) & ~np.isnan(origdata2)\n (labeled_data, n_labels) = label(origdata2)\n\n cogs = np.ones((4, n_labels))\n\n for i in range(n_labels):\n cogs[:3, i] = np.array(center_of_mass(origdata2, labeled_data, i + 1))\n\n cogs_coor = np.dot(nii2.affine, cogs)[:3, :]\n\n dist_matrix = cdist(cog_t_coor.T, cogs_coor.T)\n\n return np.mean(dist_matrix)\n\n def _eucl_mean(self, nii1, nii2, weighted=False):\n from scipy.spatial.distance import cdist\n\n origdata1 = np.asanyarray(nii1.dataobj).astype(bool)\n border1 = self._find_border(origdata1)\n\n origdata2 = np.asanyarray(nii2.dataobj).astype(bool)\n\n set1_coordinates = self._get_coordinates(border1, nii1.affine)\n set2_coordinates = self._get_coordinates(origdata2, nii2.affine)\n\n dist_matrix = cdist(set1_coordinates.T, set2_coordinates.T)\n min_dist_matrix = np.amin(dist_matrix, axis=0)\n import matplotlib\n\n matplotlib.use(config.get(\"execution\", \"matplotlib_backend\"))\n import matplotlib.pyplot as plt\n\n plt.figure()\n plt.hist(min_dist_matrix, 50, density=True, facecolor=\"green\")\n plt.savefig(self._hist_filename)\n plt.clf()\n plt.close()\n\n if weighted:\n return np.average(min_dist_matrix, weights=nii2.dataobj[origdata2].flat)\n else:\n return np.mean(min_dist_matrix)\n\n def _eucl_max(self, nii1, nii2):\n from scipy.spatial.distance import cdist\n\n origdata1 = np.asanyarray(nii1.dataobj)\n origdata1 = (np.rint(origdata1) != 0) & ~np.isnan(origdata1)\n origdata2 = np.asanyarray(nii2.dataobj)\n origdata2 = (np.rint(origdata2) != 0) & ~np.isnan(origdata2)\n\n if isdefined(self.inputs.mask_volume):\n maskdata = np.asanyarray(nb.load(self.inputs.mask_volume).dataobj)\n maskdata = (np.rint(maskdata) != 0) & ~np.isnan(maskdata)\n origdata1 = np.logical_and(maskdata, origdata1)\n origdata2 = np.logical_and(maskdata, origdata2)\n\n if origdata1.max() == 0 or origdata2.max() == 0:\n return np.nan\n\n border1 = self._find_border(origdata1)\n border2 = self._find_border(origdata2)\n\n set1_coordinates = self._get_coordinates(border1, nii1.affine)\n set2_coordinates = self._get_coordinates(border2, nii2.affine)\n distances = cdist(set1_coordinates.T, set2_coordinates.T)\n mins = np.concatenate((np.amin(distances, axis=0), np.amin(distances, axis=1)))\n\n return np.max(mins)\n\n def _run_interface(self, runtime):\n # there is a bug in some scipy ndimage methods that gets tripped by memory mapped objects\n nii1 = nb.load(self.inputs.volume1, mmap=False)\n nii2 = nb.load(self.inputs.volume2, mmap=False)\n\n if self.inputs.method == \"eucl_min\":\n self._distance, self._point1, self._point2 = self._eucl_min(nii1, nii2)\n\n elif self.inputs.method == \"eucl_cog\":\n self._distance = self._eucl_cog(nii1, nii2)\n\n elif self.inputs.method == \"eucl_mean\":\n self._distance = self._eucl_mean(nii1, nii2)\n\n elif self.inputs.method == \"eucl_wmean\":\n self._distance = self._eucl_mean(nii1, nii2, weighted=True)\n elif self.inputs.method == \"eucl_max\":\n self._distance = self._eucl_max(nii1, nii2)\n\n return runtime\n\n def _list_outputs(self):\n outputs = self._outputs().get()\n outputs[\"distance\"] = self._distance\n if self.inputs.method == \"eucl_min\":\n outputs[\"point1\"] = self._point1\n outputs[\"point2\"] = self._point2\n elif self.inputs.method in [\"eucl_mean\", \"eucl_wmean\"]:\n outputs[\"histogram\"] = os.path.abspath(self._hist_filename)\n return outputs\n\n\nclass OverlapInputSpec(BaseInterfaceInputSpec):\n volume1 = File(\n exists=True, mandatory=True, desc=\"Has to have the same dimensions as volume2.\"\n )\n volume2 = File(\n exists=True, mandatory=True, desc=\"Has to have the same dimensions as volume1.\"\n )\n mask_volume = File(exists=True, desc=\"calculate overlap only within this mask.\")\n bg_overlap = traits.Bool(\n False, usedefault=True, mandatory=True, desc=\"consider zeros as a label\"\n )\n out_file = File(\"diff.nii\", usedefault=True)\n weighting = traits.Enum(\n \"none\",\n \"volume\",\n \"squared_vol\",\n usedefault=True,\n desc=(\n \"'none': no class-overlap weighting is \"\n \"performed. 'volume': computed class-\"\n \"overlaps are weighted by class volume \"\n \"'squared_vol': computed class-overlaps \"\n \"are weighted by the squared volume of \"\n \"the class\"\n ),\n )\n vol_units = traits.Enum(\n \"voxel\", \"mm\", mandatory=True, usedefault=True, desc=\"units for volumes\"\n )\n\n\nclass OverlapOutputSpec(TraitedSpec):\n jaccard = traits.Float(desc=\"averaged jaccard index\")\n dice = traits.Float(desc=\"averaged dice index\")\n roi_ji = traits.List(traits.Float(), desc=(\"the Jaccard index (JI) per ROI\"))\n roi_di = traits.List(traits.Float(), desc=(\"the Dice index (DI) per ROI\"))\n volume_difference = traits.Float(desc=(\"averaged volume difference\"))\n roi_voldiff = traits.List(traits.Float(), desc=(\"volume differences of ROIs\"))\n labels = traits.List(traits.Int(), desc=(\"detected labels\"))\n diff_file = File(exists=True, desc=\"error map of differences\")\n\n\nclass Overlap(BaseInterface):\n \"\"\"\n Calculates Dice and Jaccard's overlap measures between two ROI maps.\n The interface is backwards compatible with the former version in\n which only binary files were accepted.\n\n The averaged values of overlap indices can be weighted. Volumes\n now can be reported in :math:`mm^3`, although they are given in voxels\n to keep backwards compatibility.\n\n Example\n -------\n\n >>> overlap = Overlap()\n >>> overlap.inputs.volume1 = 'cont1.nii'\n >>> overlap.inputs.volume2 = 'cont2.nii'\n >>> res = overlap.run() # doctest: +SKIP\n\n \"\"\"\n\n input_spec = OverlapInputSpec\n output_spec = OverlapOutputSpec\n\n def _bool_vec_dissimilarity(self, booldata1, booldata2, method):\n from scipy.spatial.distance import dice, jaccard\n\n methods = {\"dice\": dice, \"jaccard\": jaccard}\n if not (np.any(booldata1) or np.any(booldata2)):\n return 0\n return 1 - methods[method](booldata1.flat, booldata2.flat)\n\n def _run_interface(self, runtime):\n nii1 = nb.load(self.inputs.volume1)\n nii2 = nb.load(self.inputs.volume2)\n\n scale = 1.0\n\n if self.inputs.vol_units == \"mm\":\n scale = np.prod(nii1.header.get_zooms()[:3])\n\n data1 = np.asanyarray(nii1.dataobj)\n data1[np.logical_or(data1 < 0, np.isnan(data1))] = 0\n max1 = int(data1.max())\n data1 = data1.astype(np.min_scalar_type(max1))\n data2 = np.asanyarray(nii2.dataobj).astype(np.min_scalar_type(max1))\n data2[np.logical_or(data1 < 0, np.isnan(data1))] = 0\n\n if isdefined(self.inputs.mask_volume):\n maskdata = np.asanyarray(nb.load(self.inputs.mask_volume).dataobj)\n maskdata = ~np.logical_or(maskdata == 0, np.isnan(maskdata))\n data1[~maskdata] = 0\n data2[~maskdata] = 0\n\n res = []\n volumes1 = []\n volumes2 = []\n\n labels = np.unique(data1[data1 > 0].reshape(-1)).tolist()\n if self.inputs.bg_overlap:\n labels.insert(0, 0)\n\n for l in labels:\n res.append(\n self._bool_vec_dissimilarity(data1 == l, data2 == l, method=\"jaccard\")\n )\n volumes1.append(scale * len(data1[data1 == l]))\n volumes2.append(scale * len(data2[data2 == l]))\n\n results = dict(jaccard=[], dice=[])\n results[\"jaccard\"] = np.array(res)\n results[\"dice\"] = 2.0 * results[\"jaccard\"] / (results[\"jaccard\"] + 1.0)\n\n weights = np.ones((len(volumes1),), dtype=np.float32)\n if self.inputs.weighting != \"none\":\n weights = weights / np.array(volumes1)\n if self.inputs.weighting == \"squared_vol\":\n weights = weights**2\n weights = weights / np.sum(weights)\n\n both_data = np.zeros(data1.shape)\n both_data[(data1 - data2) != 0] = 1\n\n nb.save(\n nb.Nifti1Image(both_data, nii1.affine, nii1.header), self.inputs.out_file\n )\n\n self._labels = labels\n self._ove_rois = results\n self._vol_rois = (np.array(volumes1) - np.array(volumes2)) / np.array(volumes1)\n\n self._dice = round(np.sum(weights * results[\"dice\"]), 5)\n self._jaccard = round(np.sum(weights * results[\"jaccard\"]), 5)\n self._volume = np.sum(weights * self._vol_rois)\n\n return runtime\n\n def _list_outputs(self):\n outputs = self._outputs().get()\n outputs[\"labels\"] = self._labels\n outputs[\"jaccard\"] = self._jaccard\n outputs[\"dice\"] = self._dice\n outputs[\"volume_difference\"] = self._volume\n\n outputs[\"roi_ji\"] = self._ove_rois[\"jaccard\"].tolist()\n outputs[\"roi_di\"] = self._ove_rois[\"dice\"].tolist()\n outputs[\"roi_voldiff\"] = self._vol_rois.tolist()\n outputs[\"diff_file\"] = os.path.abspath(self.inputs.out_file)\n return outputs\n\n\nclass FuzzyOverlapInputSpec(BaseInterfaceInputSpec):\n in_ref = InputMultiPath(\n File(exists=True),\n mandatory=True,\n desc=\"Reference image. Requires the same dimensions as in_tst.\",\n )\n in_tst = InputMultiPath(\n File(exists=True),\n mandatory=True,\n desc=\"Test image. Requires the same dimensions as in_ref.\",\n )\n in_mask = File(exists=True, desc=\"calculate overlap only within mask\")\n weighting = traits.Enum(\n \"none\",\n \"volume\",\n \"squared_vol\",\n usedefault=True,\n desc=(\n \"'none': no class-overlap weighting is \"\n \"performed. 'volume': computed class-\"\n \"overlaps are weighted by class volume \"\n \"'squared_vol': computed class-overlaps \"\n \"are weighted by the squared volume of \"\n \"the class\"\n ),\n )\n out_file = File(\n \"diff.nii\",\n desc=\"alternative name for resulting difference-map\",\n usedefault=True,\n )\n\n\nclass FuzzyOverlapOutputSpec(TraitedSpec):\n jaccard = traits.Float(desc=\"Fuzzy Jaccard Index (fJI), all the classes\")\n dice = traits.Float(desc=\"Fuzzy Dice Index (fDI), all the classes\")\n class_fji = traits.List(\n traits.Float(), desc=\"Array containing the fJIs of each computed class\"\n )\n class_fdi = traits.List(\n traits.Float(), desc=\"Array containing the fDIs of each computed class\"\n )\n\n\nclass FuzzyOverlap(SimpleInterface):\n \"\"\"Calculates various overlap measures between two maps, using the fuzzy\n definition proposed in: Crum et al., Generalized Overlap Measures for\n Evaluation and Validation in Medical Image Analysis, IEEE Trans. Med.\n Ima. 25(11),pp 1451-1461, Nov. 2006.\n\n in_ref and in_tst are lists of 2/3D images, each element on the list\n containing one volume fraction map of a class in a fuzzy partition\n of the domain.\n\n Example\n -------\n\n >>> overlap = FuzzyOverlap()\n >>> overlap.inputs.in_ref = [ 'ref_class0.nii', 'ref_class1.nii' ]\n >>> overlap.inputs.in_tst = [ 'tst_class0.nii', 'tst_class1.nii' ]\n >>> overlap.inputs.weighting = 'volume'\n >>> res = overlap.run() # doctest: +SKIP\n \"\"\"\n\n input_spec = FuzzyOverlapInputSpec\n output_spec = FuzzyOverlapOutputSpec\n\n def _run_interface(self, runtime):\n # Load data\n refdata = nb.concat_images(self.inputs.in_ref).dataobj\n tstdata = nb.concat_images(self.inputs.in_tst).dataobj\n\n # Data must have same shape\n if not refdata.shape == tstdata.shape:\n raise RuntimeError(\n 'Size of \"in_tst\" %s must match that of \"in_ref\" %s.'\n % (tstdata.shape, refdata.shape)\n )\n\n ncomp = refdata.shape[-1]\n\n # Load mask\n mask = np.ones_like(refdata, dtype=bool)\n if isdefined(self.inputs.in_mask):\n mask = np.asanyarray(nb.load(self.inputs.in_mask).dataobj) > 0\n mask = np.repeat(mask[..., np.newaxis], ncomp, -1)\n assert mask.shape == refdata.shape\n\n # Drop data outside mask\n refdata = refdata[mask]\n tstdata = tstdata[mask]\n\n if np.any(refdata < 0.0):\n iflogger.warning(\n 'Negative values encountered in \"in_ref\" input, '\n \"taking absolute values.\"\n )\n refdata = np.abs(refdata)\n\n if np.any(tstdata < 0.0):\n iflogger.warning(\n 'Negative values encountered in \"in_tst\" input, '\n \"taking absolute values.\"\n )\n tstdata = np.abs(tstdata)\n\n if np.any(refdata > 1.0):\n iflogger.warning(\n 'Values greater than 1.0 found in \"in_ref\" input, ' \"scaling values.\"\n )\n refdata /= refdata.max()\n\n if np.any(tstdata > 1.0):\n iflogger.warning(\n 'Values greater than 1.0 found in \"in_tst\" input, ' \"scaling values.\"\n )\n tstdata /= tstdata.max()\n\n numerators = np.atleast_2d(np.minimum(refdata, tstdata).reshape((-1, ncomp)))\n denominators = np.atleast_2d(np.maximum(refdata, tstdata).reshape((-1, ncomp)))\n\n jaccards = numerators.sum(axis=0) / denominators.sum(axis=0)\n\n # Calculate weights\n weights = np.ones_like(jaccards, dtype=float)\n if self.inputs.weighting != \"none\":\n volumes = np.sum((refdata + tstdata) > 0, axis=1).reshape((-1, ncomp))\n weights = 1.0 / volumes\n if self.inputs.weighting == \"squared_vol\":\n weights = weights**2\n\n weights = weights / np.sum(weights)\n dices = 2.0 * jaccards / (jaccards + 1.0)\n\n # Fill-in the results object\n self._results[\"jaccard\"] = float(weights.dot(jaccards))\n self._results[\"dice\"] = float(weights.dot(dices))\n self._results[\"class_fji\"] = [float(v) for v in jaccards]\n self._results[\"class_fdi\"] = [float(v) for v in dices]\n return runtime\n\n\nclass ErrorMapInputSpec(BaseInterfaceInputSpec):\n in_ref = File(\n exists=True,\n mandatory=True,\n desc=\"Reference image. Requires the same dimensions as in_tst.\",\n )\n in_tst = File(\n exists=True,\n mandatory=True,\n desc=\"Test image. Requires the same dimensions as in_ref.\",\n )\n mask = File(exists=True, desc=\"calculate overlap only within this mask.\")\n metric = traits.Enum(\n \"sqeuclidean\",\n \"euclidean\",\n desc=\"error map metric (as implemented in scipy cdist)\",\n usedefault=True,\n mandatory=True,\n )\n out_map = File(desc=\"Name for the output file\")\n\n\nclass ErrorMapOutputSpec(TraitedSpec):\n out_map = File(exists=True, desc=\"resulting error map\")\n distance = traits.Float(desc=\"Average distance between volume 1 and 2\")\n\n\nclass ErrorMap(BaseInterface):\n \"\"\"Calculates the error (distance) map between two input volumes.\n\n Example\n -------\n\n >>> errormap = ErrorMap()\n >>> errormap.inputs.in_ref = 'cont1.nii'\n >>> errormap.inputs.in_tst = 'cont2.nii'\n >>> res = errormap.run() # doctest: +SKIP\n \"\"\"\n\n input_spec = ErrorMapInputSpec\n output_spec = ErrorMapOutputSpec\n _out_file = \"\"\n\n def _run_interface(self, runtime):\n # Get two numpy data matrices\n nii_ref = nb.load(self.inputs.in_ref)\n ref_data = np.squeeze(nii_ref.dataobj)\n tst_data = np.squeeze(nb.load(self.inputs.in_tst).dataobj)\n assert ref_data.ndim == tst_data.ndim\n\n # Load mask\n comps = 1\n mapshape = ref_data.shape\n\n if ref_data.ndim == 4:\n comps = ref_data.shape[-1]\n mapshape = ref_data.shape[:-1]\n\n if isdefined(self.inputs.mask):\n msk = np.asanyarray(nb.load(self.inputs.mask).dataobj)\n if mapshape != msk.shape:\n raise RuntimeError(\n \"Mask should match volume shape, \\\n mask is %s and volumes are %s\"\n % (list(msk.shape), list(mapshape))\n )\n else:\n msk = np.ones(shape=mapshape)\n\n # Flatten both volumes and make the pixel differennce\n mskvector = msk.reshape(-1)\n msk_idxs = np.where(mskvector == 1)\n refvector = ref_data.reshape(-1, comps)[msk_idxs].astype(np.float32)\n tstvector = tst_data.reshape(-1, comps)[msk_idxs].astype(np.float32)\n diffvector = refvector - tstvector\n\n # Scale the difference\n if self.inputs.metric == \"sqeuclidean\":\n errvector = diffvector**2\n if comps > 1:\n errvector = np.sum(errvector, axis=1)\n else:\n errvector = np.squeeze(errvector)\n elif self.inputs.metric == \"euclidean\":\n errvector = np.linalg.norm(diffvector, axis=1)\n\n errvectorexp = np.zeros_like(\n mskvector, dtype=np.float32\n ) # The default type is uint8\n errvectorexp[msk_idxs] = errvector\n\n # Get averaged error\n self._distance = np.average(errvector) # Only average the masked voxels\n\n errmap = errvectorexp.reshape(mapshape)\n\n hdr = nii_ref.header.copy()\n hdr.set_data_dtype(np.float32)\n hdr[\"data_type\"] = 16\n hdr.set_data_shape(mapshape)\n\n if not isdefined(self.inputs.out_map):\n fname, ext = op.splitext(op.basename(self.inputs.in_tst))\n if ext == \".gz\":\n fname, ext2 = op.splitext(fname)\n ext = ext2 + ext\n self._out_file = op.abspath(fname + \"_errmap\" + ext)\n else:\n self._out_file = self.inputs.out_map\n\n nb.Nifti1Image(errmap.astype(np.float32), nii_ref.affine, hdr).to_filename(\n self._out_file\n )\n\n return runtime\n\n def _list_outputs(self):\n outputs = self.output_spec().get()\n outputs[\"out_map\"] = self._out_file\n outputs[\"distance\"] = self._distance\n return outputs\n\n\nclass SimilarityInputSpec(BaseInterfaceInputSpec):\n volume1 = File(exists=True, desc=\"3D/4D volume\", mandatory=True)\n volume2 = File(exists=True, desc=\"3D/4D volume\", mandatory=True)\n mask1 = File(exists=True, desc=\"3D volume\")\n mask2 = File(exists=True, desc=\"3D volume\")\n metric = traits.Either(\n traits.Enum(\"cc\", \"cr\", \"crl1\", \"mi\", \"nmi\", \"slr\"),\n traits.Callable(),\n desc=\"\"\"str or callable\nCost-function for assessing image similarity. If a string,\none of 'cc': correlation coefficient, 'cr': correlation\nratio, 'crl1': L1-norm based correlation ratio, 'mi': mutual\ninformation, 'nmi': normalized mutual information, 'slr':\nsupervised log-likelihood ratio. If a callable, it should\ntake a two-dimensional array representing the image joint\nhistogram as an input and return a float.\"\"\",\n usedefault=True,\n )\n\n\nclass SimilarityOutputSpec(TraitedSpec):\n similarity = traits.List(\n traits.Float(desc=\"Similarity between volume 1 and 2, frame by frame\")\n )\n\n\nclass Similarity(NipyBaseInterface):\n \"\"\"Calculates similarity between two 3D or 4D volumes. Both volumes have to be in\n the same coordinate system, same space within that coordinate system and\n with the same voxel dimensions.\n\n .. note:: This interface is an extension of\n :py:class:`nipype.interfaces.nipy.utils.Similarity` to support 4D files.\n Requires :py:mod:`nipy`\n\n Example\n -------\n >>> from nipype.algorithms.metrics import Similarity\n >>> similarity = Similarity()\n >>> similarity.inputs.volume1 = 'rc1s1.nii'\n >>> similarity.inputs.volume2 = 'rc1s2.nii'\n >>> similarity.inputs.mask1 = 'mask.nii'\n >>> similarity.inputs.mask2 = 'mask.nii'\n >>> similarity.inputs.metric = 'cr'\n >>> res = similarity.run() # doctest: +SKIP\n \"\"\"\n\n input_spec = SimilarityInputSpec\n output_spec = SimilarityOutputSpec\n\n def _run_interface(self, runtime):\n from nipy.algorithms.registration.histogram_registration import (\n HistogramRegistration,\n )\n from nipy.algorithms.registration.affine import Affine\n\n vol1_nii = nb.load(self.inputs.volume1)\n vol2_nii = nb.load(self.inputs.volume2)\n\n dims = len(vol1_nii.shape)\n\n if dims == 3 or dims == 2:\n vols1 = [vol1_nii]\n vols2 = [vol2_nii]\n if dims == 4:\n vols1 = nb.four_to_three(vol1_nii)\n vols2 = nb.four_to_three(vol2_nii)\n\n if dims < 2 or dims > 4:\n raise RuntimeError(\n \"Image dimensions not supported (detected %dD file)\" % dims\n )\n\n if isdefined(self.inputs.mask1):\n mask1 = np.asanyarray(nb.load(self.inputs.mask1).dataobj) == 1\n else:\n mask1 = None\n\n if isdefined(self.inputs.mask2):\n mask2 = np.asanyarray(nb.load(self.inputs.mask2).dataobj) == 1\n else:\n mask2 = None\n\n self._similarity = []\n\n for ts1, ts2 in zip(vols1, vols2):\n histreg = HistogramRegistration(\n from_img=ts1,\n to_img=ts2,\n similarity=self.inputs.metric,\n from_mask=mask1,\n to_mask=mask2,\n )\n self._similarity.append(histreg.eval(Affine()))\n\n return runtime\n\n def _list_outputs(self):\n outputs = self._outputs().get()\n outputs[\"similarity\"] = self._similarity\n return outputs\n",
"path": "nipype/algorithms/metrics.py"
}
] | diff --git a/nipype/algorithms/metrics.py b/nipype/algorithms/metrics.py
index fc209a9d27..b58e7fc59b 100644
--- a/nipype/algorithms/metrics.py
+++ b/nipype/algorithms/metrics.py
@@ -150,7 +150,7 @@ def _eucl_mean(self, nii1, nii2, weighted=False):
import matplotlib.pyplot as plt
plt.figure()
- plt.hist(min_dist_matrix, 50, normed=1, facecolor="green")
+ plt.hist(min_dist_matrix, 50, density=True, facecolor="green")
plt.savefig(self._hist_filename)
plt.clf()
plt.close()
|
privacyidea__privacyidea-3786 | %' is not properly decoded by the Privacyidea server
Using credential provider 3.4.0
Using Privacyidea 3.8 or 3.9
Ticket 3554
When there is a % in the password of the user, Authentication failed.
If the % is at the beginning or the end of the password, the authentication is successful.
The issue on the side of the credential provider is fixed since the 3.4
https://github.com/privacyidea/privacyidea-credential-provider/releases/tag/v3.4.0
_Fixed a bug where the '%' was not properly encoded when communicating with Privacyidea.
So it must be the decoding of the password on the side of the server that goes wrong
| [
{
"content": "# -*- coding: utf-8 -*-\n# privacyIDEA is a fork of LinOTP\n# May 08, 2014 Cornelius Kölbel\n# License: AGPLv3\n# contact: http://www.privacyidea.org\n#\n# Copyright (C) 2010 - 2014 LSE Leading Security Experts GmbH\n# License: AGPLv3\n# contact: http://www.linotp.org\n# http://www.lsexperts.de\n# [email protected]\n#\n# This code is free software; you can redistribute it and/or\n# modify it under the terms of the GNU AFFERO GENERAL PUBLIC LICENSE\n# License as published by the Free Software Foundation; either\n# version 3 of the License, or any later version.\n#\n# This code is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU AFFERO GENERAL PUBLIC LICENSE for more details.\n#\n# You should have received a copy of the GNU Affero General Public\n# License along with this program. If not, see <http://www.gnu.org/licenses/>.\n#\nimport string\n\nfrom ...lib.error import (ParameterError,\n AuthError, ERROR)\nfrom ...lib.log import log_with\nfrom privacyidea.lib import _\nfrom privacyidea.lib.utils import prepare_result, get_version, to_unicode\nimport time\nimport logging\nimport json\nimport jwt\nimport threading\nimport re\nfrom copy import copy\nfrom urllib.parse import unquote\nfrom flask import (jsonify,\n current_app)\n\nlog = logging.getLogger(__name__)\nENCODING = \"utf-8\"\nTRUSTED_JWT_ALGOS = [\"ES256\", \"ES384\", \"ES512\",\n \"RS256\", \"RS384\", \"RS512\",\n \"PS256\", \"PS384\", \"PS512\"]\n\n# The following user-agents (with versions) do not need extra unquoting\n# TODO: we should probably switch this when we do not do the extra unquote anymore\nNO_UNQUOTE_USER_AGENTS = {\n 'privacyIDEA-LDAP-Proxy': None,\n 'simpleSAMLphp': None\n}\n\nSESSION_KEY_LENGTH = 32\n\noptional = True\nrequired = False\n\n\ndef getParam(param, key, optional=True, default=None, allow_empty=True, allowed_values=None):\n \"\"\"\n returns a parameter from the request parameters.\n\n :param param: the dictionary of parameters\n :type param: dict\n :param key: the name of the parameter\n :param optional: defines if this parameter is optional or not\n an exception is thrown if the parameter is required\n otherwise: nothing done!\n :type optional: bool\n :param default: The value to assign to the parameter, if it is not\n contained in the param.\n :param allow_empty: Set to False is the parameter is a string and is\n not allowed to be empty\n :param allowed_values: A list of allowed values. If another value is given,\n then the default value is returned\n :type allow_empty: bool\n\n :return: the value (literal) of the parameter if exists or nothing\n in case the parameter is optional, otherwise throw an exception\n \"\"\"\n ret = None\n\n if key in param:\n ret = param[key]\n elif default:\n ret = default\n elif not optional:\n raise ParameterError(\"Missing parameter: {0!r}\".format(key), id=905)\n\n if not allow_empty and ret == \"\":\n raise ParameterError(\"Parameter {0!r} must not be empty\".format(key), id=905)\n\n if allowed_values and ret not in allowed_values:\n ret = default\n\n return ret\n\n\ndef send_result(obj, rid=1, details=None):\n \"\"\"\n sendResult - return a json result document\n\n :param obj: simple result object like dict, sting or list\n :type obj: dict or list or string/unicode\n :param rid: id value, for future versions\n :type rid: int\n :param details: optional parameter, which allows to provide more detail\n :type details: None or simple type like dict, list or string/unicode\n\n :return: json rendered sting result\n :rtype: string\n \"\"\"\n return jsonify(prepare_result(obj, rid, details))\n\n\ndef send_error(errstring, rid=1, context=None, error_code=-311, details=None):\n \"\"\"\n sendError - return a json error result document\n\n remark:\n the 'context' is especially required to catch errors from the _before_\n methods. The return of a _before_ must be of type response and\n must have the attribute response._exception set, to stop further\n processing, which otherwise will have ugly results!!\n\n :param errstring: An error message\n :type errstring: basestring\n :param rid: id value, for future versions\n :type rid: int\n :param context: default is None or 'before'\n :type context: string\n :param error_code: The error code in the JSON object along with the error\n message.\n :type error_code: int\n :param details: dict with additional details about the error (like\n challenges)\n :type details: dict\n\n :return: json rendered sting result\n :rtype: string\n\n \"\"\"\n if details:\n details[\"threadid\"] = threading.current_thread().ident\n res = {\"jsonrpc\": \"2.0\",\n \"detail\": details,\n \"result\": {\"status\": False,\n \"error\": {\"code\": error_code,\n \"message\": errstring}\n },\n \"version\": get_version(),\n \"id\": rid,\n \"time\": time.time()\n }\n\n ret = jsonify(res)\n return ret\n\n\ndef send_html(output):\n \"\"\"\n Send the output as HTML to the client with the correct mimetype.\n\n :param output: The HTML to send to the client\n :type output: str\n :return: The generated response\n :rtype: flask.Response\n \"\"\"\n return current_app.response_class(output, mimetype='text/html')\n\n\ndef send_file(output, filename, content_type='text/csv'):\n \"\"\"\n Send the output to the client with the \"Content-disposition\" header to\n declare it as a downloadable file.\n\n :param output: The data that should be sent as a file\n :type output: str\n :param filename: The proposed filename\n :type filename: str\n :param content_type: The proposed content type of the data\n :type content_type: str (should be something from this list:\n https://www.iana.org/assignments/media-types/media-types.xhtml)\n :return: The generated response\n :rtype: flask.Response\n \"\"\"\n headers = {'Content-disposition': 'attachment; filename={0!s}'.format(filename)}\n return current_app.response_class(output, headers=headers, mimetype=content_type)\n\n\ndef send_csv_result(obj, data_key=\"tokens\",\n filename=\"privacyidea-tokendata.csv\"):\n \"\"\"\n returns a CSV document of the input data (like in /token/list)\n\n It takes an obj as a dict like:\n { \"tokens\": [ { ...token1... }, { ...token2....}, ... ],\n \"count\": 100,\n \"....\": .... }\n\n :param obj: The data, that gets serialized as CSV\n :type obj: dict\n :param data_key: The key, from which the list should be returned as CSV.\n Usually this is \"tokens\".\n :type data_key: basestring\n :param filename: The filename to save the CSV to.\n :type filename: basestring\n :return: The result serialized as a CSV\n :rtype: Response object\n \"\"\"\n delim = \"'\"\n output = \"\"\n # check if there is any data\n if data_key in obj and len(obj[data_key]) > 0:\n # Do the header\n for k, _v in obj.get(data_key)[0].items():\n output += \"{0!s}{1!s}{2!s}, \".format(delim, k, delim)\n output += \"\\n\"\n\n # Do the data\n for row in obj.get(data_key):\n for val in row.values():\n if isinstance(val, str):\n value = val.replace(\"\\n\", \" \")\n else:\n value = val\n output += \"{0!s}{1!s}{2!s}, \".format(delim, value, delim)\n output += \"\\n\"\n\n return send_file(output, filename)\n\n\n@log_with(log)\ndef getLowerParams(param):\n ret = {}\n for key in param:\n lkey = key.lower()\n # strip the session parameter!\n if \"session\" != lkey:\n lval = param[key]\n ret[lkey] = lval\n return ret\n\n\ndef check_unquote(request, data):\n \"\"\"\n Check if we need to unquote the given data.\n Based on the user-agent header of the request we unquote the given values\n in `data`. The user-agent string parsing is based on\n https://httpwg.org/specs/rfc9110.html#field.user-agent\n\n :param request: The Flask request context\n :type request: Flask.Request\n :param data: The dictionary containing the requested data\n :type data: dict\n :return: New dictionary with the possibly unquoted values\n :rtype: dict\n \"\"\"\n # if no user agent is available, we assume that we must unquote the data\n if not request.user_agent.string:\n return {key: unquote(value) for (key, value) in data.items()}\n\n ua_match = re.match(r'^(?P<agent>[a-zA-Z0-9_-]+)(/(?P<version>\\d+[\\d.]*)(\\s.*)?)?',\n request.user_agent.string)\n if ua_match and not ua_match.group('agent') in NO_UNQUOTE_USER_AGENTS:\n return {key: unquote(value) for (key, value) in data.items()}\n else:\n return copy(data)\n\n\ndef get_all_params(request):\n \"\"\"\n Retrieve all parameters from a request, no matter if these are GET or POST requests\n or parameters are contained as viewargs like the serial in DELETE /token/<serial>\n\n :param request: The flask request object\n \"\"\"\n param = request.values\n body = request.data\n return_param = {}\n if param:\n log.debug(\"Update params in request {0!s} {1!s} with values.\".format(request.method,\n request.base_url))\n # Add the unquoted HTML and form parameters\n return_param = check_unquote(request, request.values)\n\n if request.is_json:\n log.debug(\"Update params in request {0!s} {1!s} with JSON data.\".format(request.method,\n request.base_url))\n # Add the original JSON data\n return_param.update(request.json)\n elif body:\n # In case of serialized JSON data in the body, add these to the values.\n try:\n json_data = json.loads(to_unicode(body))\n for k, v in json_data.items():\n return_param[k] = v\n except Exception as exx:\n log.debug(\"Can not get param: {0!s}\".format(exx))\n\n if request.view_args:\n log.debug(\"Update params in request {0!s} {1!s} with view_args.\".format(request.method,\n request.base_url))\n # We add the unquoted view_args\n return_param.update(check_unquote(request, request.view_args))\n\n return return_param\n\n\ndef get_priority_from_param(param):\n \"\"\"\n Return a dictionary of priorities as int from params like\n priority.key1=value1\n\n :param param: The params dictionary\n :type param: dict\n :return: dict\n \"\"\"\n priority = {}\n for k, v in param.items():\n if k.startswith(\"priority.\") and isinstance(v, int):\n priority[k[len(\"priority.\"):]] = int(v)\n return priority\n\n\ndef verify_auth_token(auth_token, required_role=None):\n \"\"\"\n Check if a given auth token is valid.\n\n Return a dictionary describing the authenticated user.\n\n :param auth_token: The Auth Token\n :param required_role: list of \"user\" and \"admin\"\n :return: dict with authtype, realm, rights, role, username, exp, nonce\n :rtype: dict\n \"\"\"\n r = None\n if required_role is None:\n required_role = [\"admin\", \"user\"]\n if auth_token is None:\n raise AuthError(_(\"Authentication failure. Missing Authorization header.\"),\n id=ERROR.AUTHENTICATE_AUTH_HEADER)\n\n try:\n headers = jwt.get_unverified_header(auth_token)\n except jwt.DecodeError as err:\n raise AuthError(_(\"Authentication failure. Error during decoding your token: {0!s}\").format(err),\n id=ERROR.AUTHENTICATE_DECODING_ERROR)\n algorithm = headers.get(\"alg\")\n wrong_username = None\n if algorithm in TRUSTED_JWT_ALGOS:\n # The trusted JWTs are RSA, PSS or elliptic curve signed\n trusted_jwts = current_app.config.get(\"PI_TRUSTED_JWT\", [])\n for trusted_jwt in trusted_jwts:\n try:\n if trusted_jwt.get(\"algorithm\") in TRUSTED_JWT_ALGOS:\n j = jwt.decode(auth_token,\n trusted_jwt.get(\"public_key\"),\n algorithms=[trusted_jwt.get(\"algorithm\")])\n if dict((k, j.get(k)) for k in (\"role\", \"resolver\", \"realm\")) == \\\n dict((k, trusted_jwt.get(k)) for k in (\"role\", \"resolver\", \"realm\")):\n if re.match(trusted_jwt.get(\"username\") + \"$\", j.get(\"username\")):\n r = j\n break\n else:\n r = wrong_username = j.get(\"username\")\n else:\n log.warning(\"Unsupported JWT algorithm in PI_TRUSTED_JWT.\")\n except jwt.DecodeError as _e:\n log.info(\"A given JWT definition does not match.\")\n except jwt.ExpiredSignatureError as err:\n # We have the correct token. It expired, so we raise an error\n raise AuthError(_(\"Authentication failure. Your token has expired: {0!s}\").format(err),\n id=ERROR.AUTHENTICATE_TOKEN_EXPIRED)\n\n if not r:\n try:\n r = jwt.decode(auth_token, current_app.secret_key, algorithms=['HS256'])\n except jwt.DecodeError as err:\n raise AuthError(_(\"Authentication failure. Error during decoding your token: {0!s}\").format(err),\n id=ERROR.AUTHENTICATE_DECODING_ERROR)\n except jwt.ExpiredSignatureError as err:\n raise AuthError(_(\"Authentication failure. Your token has expired: {0!s}\").format(err),\n id=ERROR.AUTHENTICATE_TOKEN_EXPIRED)\n if wrong_username:\n raise AuthError(_(\"Authentication failure. The username {0!s} is not allowed to \"\n \"impersonate via JWT.\".format(wrong_username)))\n if required_role and r.get(\"role\") not in required_role:\n # If we require a certain role like \"admin\", but the users role does\n # not match\n raise AuthError(_(\"Authentication failure. \"\n \"You do not have the necessary role ({0!s}) to access \"\n \"this resource!\").format(required_role),\n id=ERROR.AUTHENTICATE_MISSING_RIGHT)\n return r\n\n\ndef check_policy_name(name):\n \"\"\"\n This function checks, if the given name is a valid policy name.\n\n :param name: The name of the policy\n :return: Raises a ParameterError in case of an invalid name\n \"\"\"\n disallowed_patterns = [(\"^check$\", re.IGNORECASE),\n (\"^pi-update-policy-\", re.IGNORECASE)]\n for disallowed_pattern in disallowed_patterns:\n if re.search(disallowed_pattern[0], name, flags=disallowed_pattern[1]):\n raise ParameterError(_(\"'{0!s}' is an invalid policy name.\").format(name))\n\n if not re.match(r'^[a-zA-Z0-9_.\\- ]*$', name):\n raise ParameterError(_(\"The name of the policy may only contain \"\n \"the characters a-zA-Z0-9_. -\"))\n\n\ndef attestation_certificate_allowed(cert_info, allowed_certs_pols):\n \"\"\"\n Check a certificate against a set of policies.\n\n This will check an attestation certificate of a U2F-, or WebAuthn-Token,\n against a list of policies. It is used to verify, whether a token with the\n given attestation may be enrolled, or authorized, respectively.\n\n The certificate info may be None, in which case, true will be returned if\n the policies are also empty.\n\n :param cert_info: The `attestation_issuer`, `attestation_serial`, and `attestation_subject` of the cert.\n :type cert_info: dict or None\n :param allowed_certs_pols: The policies restricting enrollment, or authorization.\n :type allowed_certs_pols: dict or None\n :return: Whether the token should be allowed to complete enrollment, or authorization, based on its attestation.\n :rtype: bool\n \"\"\"\n\n if not cert_info:\n return not allowed_certs_pols\n\n if allowed_certs_pols:\n for allowed_cert in allowed_certs_pols:\n tag, matching, _rest = allowed_cert.split(\"/\", 3)\n tag_value = cert_info.get(\"attestation_{0!s}\".format(tag))\n # if we do not get a match, we bail out\n m = re.search(matching, tag_value) if matching and tag_value else None\n if matching and not m:\n return False\n\n return True\n\n\ndef is_fqdn(x):\n \"\"\"\n Check whether a given string could plausibly be a FQDN.\n\n This checks, whether a string could be a FQDN. Please note, that this\n function will currently return true for plenty of strings, that are not\n actually valid FQDNs. This is expected. This function performs a simple\n plausibility check to ward against obvious mistakes, like a user\n accidentally putting in a full url with protocol. The caller should not\n rely on this function, if it is absolutely crucial, that the checked\n string is a valid FQDN. It is solely intended to be used to implement user\n convenience, by alerting the user early on, if they have misunderstood\n a particular fields purpose.\n\n :param x: String to check.\n :type x: basestring\n :return: Whether the given string may plausibly be a FQDN.\n :rtype: bool\n \"\"\"\n return set(string.punctuation).intersection(x).issubset({'-', '.'})\n",
"path": "privacyidea/api/lib/utils.py"
}
] | [
{
"content": "# -*- coding: utf-8 -*-\n# privacyIDEA is a fork of LinOTP\n# May 08, 2014 Cornelius Kölbel\n# License: AGPLv3\n# contact: http://www.privacyidea.org\n#\n# Copyright (C) 2010 - 2014 LSE Leading Security Experts GmbH\n# License: AGPLv3\n# contact: http://www.linotp.org\n# http://www.lsexperts.de\n# [email protected]\n#\n# This code is free software; you can redistribute it and/or\n# modify it under the terms of the GNU AFFERO GENERAL PUBLIC LICENSE\n# License as published by the Free Software Foundation; either\n# version 3 of the License, or any later version.\n#\n# This code is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU AFFERO GENERAL PUBLIC LICENSE for more details.\n#\n# You should have received a copy of the GNU Affero General Public\n# License along with this program. If not, see <http://www.gnu.org/licenses/>.\n#\nimport string\n\nfrom ...lib.error import (ParameterError,\n AuthError, ERROR)\nfrom ...lib.log import log_with\nfrom privacyidea.lib import _\nfrom privacyidea.lib.utils import prepare_result, get_version, to_unicode\nimport time\nimport logging\nimport json\nimport jwt\nimport threading\nimport re\nfrom copy import copy\nfrom urllib.parse import unquote\nfrom flask import (jsonify,\n current_app)\n\nlog = logging.getLogger(__name__)\nENCODING = \"utf-8\"\nTRUSTED_JWT_ALGOS = [\"ES256\", \"ES384\", \"ES512\",\n \"RS256\", \"RS384\", \"RS512\",\n \"PS256\", \"PS384\", \"PS512\"]\n\n# The following user-agents (with versions) do not need extra unquoting\n# TODO: we should probably switch this when we do not do the extra unquote anymore\nNO_UNQUOTE_USER_AGENTS = {\n 'privacyIDEA-LDAP-Proxy': None,\n 'simpleSAMLphp': None,\n 'privacyidea-cp': None\n}\n\nSESSION_KEY_LENGTH = 32\n\noptional = True\nrequired = False\n\n\ndef getParam(param, key, optional=True, default=None, allow_empty=True, allowed_values=None):\n \"\"\"\n returns a parameter from the request parameters.\n\n :param param: the dictionary of parameters\n :type param: dict\n :param key: the name of the parameter\n :param optional: defines if this parameter is optional or not\n an exception is thrown if the parameter is required\n otherwise: nothing done!\n :type optional: bool\n :param default: The value to assign to the parameter, if it is not\n contained in the param.\n :param allow_empty: Set to False is the parameter is a string and is\n not allowed to be empty\n :param allowed_values: A list of allowed values. If another value is given,\n then the default value is returned\n :type allow_empty: bool\n\n :return: the value (literal) of the parameter if exists or nothing\n in case the parameter is optional, otherwise throw an exception\n \"\"\"\n ret = None\n\n if key in param:\n ret = param[key]\n elif default:\n ret = default\n elif not optional:\n raise ParameterError(\"Missing parameter: {0!r}\".format(key), id=905)\n\n if not allow_empty and ret == \"\":\n raise ParameterError(\"Parameter {0!r} must not be empty\".format(key), id=905)\n\n if allowed_values and ret not in allowed_values:\n ret = default\n\n return ret\n\n\ndef send_result(obj, rid=1, details=None):\n \"\"\"\n sendResult - return a json result document\n\n :param obj: simple result object like dict, sting or list\n :type obj: dict or list or string/unicode\n :param rid: id value, for future versions\n :type rid: int\n :param details: optional parameter, which allows to provide more detail\n :type details: None or simple type like dict, list or string/unicode\n\n :return: json rendered sting result\n :rtype: string\n \"\"\"\n return jsonify(prepare_result(obj, rid, details))\n\n\ndef send_error(errstring, rid=1, context=None, error_code=-311, details=None):\n \"\"\"\n sendError - return a json error result document\n\n remark:\n the 'context' is especially required to catch errors from the _before_\n methods. The return of a _before_ must be of type response and\n must have the attribute response._exception set, to stop further\n processing, which otherwise will have ugly results!!\n\n :param errstring: An error message\n :type errstring: basestring\n :param rid: id value, for future versions\n :type rid: int\n :param context: default is None or 'before'\n :type context: string\n :param error_code: The error code in the JSON object along with the error\n message.\n :type error_code: int\n :param details: dict with additional details about the error (like\n challenges)\n :type details: dict\n\n :return: json rendered sting result\n :rtype: string\n\n \"\"\"\n if details:\n details[\"threadid\"] = threading.current_thread().ident\n res = {\"jsonrpc\": \"2.0\",\n \"detail\": details,\n \"result\": {\"status\": False,\n \"error\": {\"code\": error_code,\n \"message\": errstring}\n },\n \"version\": get_version(),\n \"id\": rid,\n \"time\": time.time()\n }\n\n ret = jsonify(res)\n return ret\n\n\ndef send_html(output):\n \"\"\"\n Send the output as HTML to the client with the correct mimetype.\n\n :param output: The HTML to send to the client\n :type output: str\n :return: The generated response\n :rtype: flask.Response\n \"\"\"\n return current_app.response_class(output, mimetype='text/html')\n\n\ndef send_file(output, filename, content_type='text/csv'):\n \"\"\"\n Send the output to the client with the \"Content-disposition\" header to\n declare it as a downloadable file.\n\n :param output: The data that should be sent as a file\n :type output: str\n :param filename: The proposed filename\n :type filename: str\n :param content_type: The proposed content type of the data\n :type content_type: str (should be something from this list:\n https://www.iana.org/assignments/media-types/media-types.xhtml)\n :return: The generated response\n :rtype: flask.Response\n \"\"\"\n headers = {'Content-disposition': 'attachment; filename={0!s}'.format(filename)}\n return current_app.response_class(output, headers=headers, mimetype=content_type)\n\n\ndef send_csv_result(obj, data_key=\"tokens\",\n filename=\"privacyidea-tokendata.csv\"):\n \"\"\"\n returns a CSV document of the input data (like in /token/list)\n\n It takes an obj as a dict like:\n { \"tokens\": [ { ...token1... }, { ...token2....}, ... ],\n \"count\": 100,\n \"....\": .... }\n\n :param obj: The data, that gets serialized as CSV\n :type obj: dict\n :param data_key: The key, from which the list should be returned as CSV.\n Usually this is \"tokens\".\n :type data_key: basestring\n :param filename: The filename to save the CSV to.\n :type filename: basestring\n :return: The result serialized as a CSV\n :rtype: Response object\n \"\"\"\n delim = \"'\"\n output = \"\"\n # check if there is any data\n if data_key in obj and len(obj[data_key]) > 0:\n # Do the header\n for k, _v in obj.get(data_key)[0].items():\n output += \"{0!s}{1!s}{2!s}, \".format(delim, k, delim)\n output += \"\\n\"\n\n # Do the data\n for row in obj.get(data_key):\n for val in row.values():\n if isinstance(val, str):\n value = val.replace(\"\\n\", \" \")\n else:\n value = val\n output += \"{0!s}{1!s}{2!s}, \".format(delim, value, delim)\n output += \"\\n\"\n\n return send_file(output, filename)\n\n\n@log_with(log)\ndef getLowerParams(param):\n ret = {}\n for key in param:\n lkey = key.lower()\n # strip the session parameter!\n if \"session\" != lkey:\n lval = param[key]\n ret[lkey] = lval\n return ret\n\n\ndef check_unquote(request, data):\n \"\"\"\n Check if we need to unquote the given data.\n Based on the user-agent header of the request we unquote the given values\n in `data`. The user-agent string parsing is based on\n https://httpwg.org/specs/rfc9110.html#field.user-agent\n\n :param request: The Flask request context\n :type request: Flask.Request\n :param data: The dictionary containing the requested data\n :type data: dict\n :return: New dictionary with the possibly unquoted values\n :rtype: dict\n \"\"\"\n # if no user agent is available, we assume that we must unquote the data\n if not request.user_agent.string:\n return {key: unquote(value) for (key, value) in data.items()}\n\n ua_match = re.match(r'^(?P<agent>[a-zA-Z0-9_-]+)(/(?P<version>\\d+[\\d.]*)(\\s.*)?)?',\n request.user_agent.string)\n if ua_match and not ua_match.group('agent') in NO_UNQUOTE_USER_AGENTS:\n return {key: unquote(value) for (key, value) in data.items()}\n else:\n return copy(data)\n\n\ndef get_all_params(request):\n \"\"\"\n Retrieve all parameters from a request, no matter if these are GET or POST requests\n or parameters are contained as viewargs like the serial in DELETE /token/<serial>\n\n :param request: The flask request object\n \"\"\"\n param = request.values\n body = request.data\n return_param = {}\n if param:\n log.debug(\"Update params in request {0!s} {1!s} with values.\".format(request.method,\n request.base_url))\n # Add the unquoted HTML and form parameters\n return_param = check_unquote(request, request.values)\n\n if request.is_json:\n log.debug(\"Update params in request {0!s} {1!s} with JSON data.\".format(request.method,\n request.base_url))\n # Add the original JSON data\n return_param.update(request.json)\n elif body:\n # In case of serialized JSON data in the body, add these to the values.\n try:\n json_data = json.loads(to_unicode(body))\n for k, v in json_data.items():\n return_param[k] = v\n except Exception as exx:\n log.debug(\"Can not get param: {0!s}\".format(exx))\n\n if request.view_args:\n log.debug(\"Update params in request {0!s} {1!s} with view_args.\".format(request.method,\n request.base_url))\n # We add the unquoted view_args\n return_param.update(check_unquote(request, request.view_args))\n\n return return_param\n\n\ndef get_priority_from_param(param):\n \"\"\"\n Return a dictionary of priorities as int from params like\n priority.key1=value1\n\n :param param: The params dictionary\n :type param: dict\n :return: dict\n \"\"\"\n priority = {}\n for k, v in param.items():\n if k.startswith(\"priority.\") and isinstance(v, int):\n priority[k[len(\"priority.\"):]] = int(v)\n return priority\n\n\ndef verify_auth_token(auth_token, required_role=None):\n \"\"\"\n Check if a given auth token is valid.\n\n Return a dictionary describing the authenticated user.\n\n :param auth_token: The Auth Token\n :param required_role: list of \"user\" and \"admin\"\n :return: dict with authtype, realm, rights, role, username, exp, nonce\n :rtype: dict\n \"\"\"\n r = None\n if required_role is None:\n required_role = [\"admin\", \"user\"]\n if auth_token is None:\n raise AuthError(_(\"Authentication failure. Missing Authorization header.\"),\n id=ERROR.AUTHENTICATE_AUTH_HEADER)\n\n try:\n headers = jwt.get_unverified_header(auth_token)\n except jwt.DecodeError as err:\n raise AuthError(_(\"Authentication failure. Error during decoding your token: {0!s}\").format(err),\n id=ERROR.AUTHENTICATE_DECODING_ERROR)\n algorithm = headers.get(\"alg\")\n wrong_username = None\n if algorithm in TRUSTED_JWT_ALGOS:\n # The trusted JWTs are RSA, PSS or elliptic curve signed\n trusted_jwts = current_app.config.get(\"PI_TRUSTED_JWT\", [])\n for trusted_jwt in trusted_jwts:\n try:\n if trusted_jwt.get(\"algorithm\") in TRUSTED_JWT_ALGOS:\n j = jwt.decode(auth_token,\n trusted_jwt.get(\"public_key\"),\n algorithms=[trusted_jwt.get(\"algorithm\")])\n if dict((k, j.get(k)) for k in (\"role\", \"resolver\", \"realm\")) == \\\n dict((k, trusted_jwt.get(k)) for k in (\"role\", \"resolver\", \"realm\")):\n if re.match(trusted_jwt.get(\"username\") + \"$\", j.get(\"username\")):\n r = j\n break\n else:\n r = wrong_username = j.get(\"username\")\n else:\n log.warning(\"Unsupported JWT algorithm in PI_TRUSTED_JWT.\")\n except jwt.DecodeError as _e:\n log.info(\"A given JWT definition does not match.\")\n except jwt.ExpiredSignatureError as err:\n # We have the correct token. It expired, so we raise an error\n raise AuthError(_(\"Authentication failure. Your token has expired: {0!s}\").format(err),\n id=ERROR.AUTHENTICATE_TOKEN_EXPIRED)\n\n if not r:\n try:\n r = jwt.decode(auth_token, current_app.secret_key, algorithms=['HS256'])\n except jwt.DecodeError as err:\n raise AuthError(_(\"Authentication failure. Error during decoding your token: {0!s}\").format(err),\n id=ERROR.AUTHENTICATE_DECODING_ERROR)\n except jwt.ExpiredSignatureError as err:\n raise AuthError(_(\"Authentication failure. Your token has expired: {0!s}\").format(err),\n id=ERROR.AUTHENTICATE_TOKEN_EXPIRED)\n if wrong_username:\n raise AuthError(_(\"Authentication failure. The username {0!s} is not allowed to \"\n \"impersonate via JWT.\".format(wrong_username)))\n if required_role and r.get(\"role\") not in required_role:\n # If we require a certain role like \"admin\", but the users role does\n # not match\n raise AuthError(_(\"Authentication failure. \"\n \"You do not have the necessary role ({0!s}) to access \"\n \"this resource!\").format(required_role),\n id=ERROR.AUTHENTICATE_MISSING_RIGHT)\n return r\n\n\ndef check_policy_name(name):\n \"\"\"\n This function checks, if the given name is a valid policy name.\n\n :param name: The name of the policy\n :return: Raises a ParameterError in case of an invalid name\n \"\"\"\n disallowed_patterns = [(\"^check$\", re.IGNORECASE),\n (\"^pi-update-policy-\", re.IGNORECASE)]\n for disallowed_pattern in disallowed_patterns:\n if re.search(disallowed_pattern[0], name, flags=disallowed_pattern[1]):\n raise ParameterError(_(\"'{0!s}' is an invalid policy name.\").format(name))\n\n if not re.match(r'^[a-zA-Z0-9_.\\- ]*$', name):\n raise ParameterError(_(\"The name of the policy may only contain \"\n \"the characters a-zA-Z0-9_. -\"))\n\n\ndef attestation_certificate_allowed(cert_info, allowed_certs_pols):\n \"\"\"\n Check a certificate against a set of policies.\n\n This will check an attestation certificate of a U2F-, or WebAuthn-Token,\n against a list of policies. It is used to verify, whether a token with the\n given attestation may be enrolled, or authorized, respectively.\n\n The certificate info may be None, in which case, true will be returned if\n the policies are also empty.\n\n :param cert_info: The `attestation_issuer`, `attestation_serial`, and `attestation_subject` of the cert.\n :type cert_info: dict or None\n :param allowed_certs_pols: The policies restricting enrollment, or authorization.\n :type allowed_certs_pols: dict or None\n :return: Whether the token should be allowed to complete enrollment, or authorization, based on its attestation.\n :rtype: bool\n \"\"\"\n\n if not cert_info:\n return not allowed_certs_pols\n\n if allowed_certs_pols:\n for allowed_cert in allowed_certs_pols:\n tag, matching, _rest = allowed_cert.split(\"/\", 3)\n tag_value = cert_info.get(\"attestation_{0!s}\".format(tag))\n # if we do not get a match, we bail out\n m = re.search(matching, tag_value) if matching and tag_value else None\n if matching and not m:\n return False\n\n return True\n\n\ndef is_fqdn(x):\n \"\"\"\n Check whether a given string could plausibly be a FQDN.\n\n This checks, whether a string could be a FQDN. Please note, that this\n function will currently return true for plenty of strings, that are not\n actually valid FQDNs. This is expected. This function performs a simple\n plausibility check to ward against obvious mistakes, like a user\n accidentally putting in a full url with protocol. The caller should not\n rely on this function, if it is absolutely crucial, that the checked\n string is a valid FQDN. It is solely intended to be used to implement user\n convenience, by alerting the user early on, if they have misunderstood\n a particular fields purpose.\n\n :param x: String to check.\n :type x: basestring\n :return: Whether the given string may plausibly be a FQDN.\n :rtype: bool\n \"\"\"\n return set(string.punctuation).intersection(x).issubset({'-', '.'})\n",
"path": "privacyidea/api/lib/utils.py"
}
] | diff --git a/privacyidea/api/lib/utils.py b/privacyidea/api/lib/utils.py
index e11d45ad02..4043c81909 100644
--- a/privacyidea/api/lib/utils.py
+++ b/privacyidea/api/lib/utils.py
@@ -51,7 +51,8 @@
# TODO: we should probably switch this when we do not do the extra unquote anymore
NO_UNQUOTE_USER_AGENTS = {
'privacyIDEA-LDAP-Proxy': None,
- 'simpleSAMLphp': None
+ 'simpleSAMLphp': None,
+ 'privacyidea-cp': None
}
SESSION_KEY_LENGTH = 32
diff --git a/tests/test_api_lib_utils.py b/tests/test_api_lib_utils.py
index 674b5dd4c9..416aa45c78 100644
--- a/tests/test_api_lib_utils.py
+++ b/tests/test_api_lib_utils.py
@@ -305,6 +305,15 @@ def test_09_check_unquote(self):
res = self.app.full_dispatch_request()
self.assertTrue(res.status_code == 200, res)
+ # And check the Credential Provider plugin
+ with self.app.test_request_context('/auth',
+ method='POST',
+ data={'username': 'pwpercent',
+ 'password': 'pw%45#test'},
+ headers={'User-Agent': 'privacyidea-cp/2.0'}):
+ res = self.app.full_dispatch_request()
+ self.assertTrue(res.status_code == 200, res)
+
# now check the /validate/check endpoint
set_policy(name="otppin",
scope=SCOPE.AUTH,
@@ -346,6 +355,18 @@ def test_09_check_unquote(self):
result = res.json.get("result")
self.assertTrue(result.get("value"), res.json)
+ # when using the correct user-agent (CredentialProvider), quoting is not necessary
+ with self.app.test_request_context('/validate/check',
+ method='POST',
+ data={"user": "pwpercent",
+ "realm": self.realm1,
+ "pass": "pw%45#test"},
+ headers={'User-Agent': 'privacyidea-cp/3.0'}):
+ res = self.app.full_dispatch_request()
+ self.assertEqual(res.status_code, 200, res)
+ result = res.json.get("result")
+ self.assertTrue(result.get("value"), res.json)
+
# cleanup
remove_token(serial='spass1d')
delete_policy('otppin')
|
dynaconf__dynaconf-953 | [bug] reload() function does not clear old hooks
**Describe the bug**
the `reload()` function of the `LazySettings` class does not clear the `_loaded_hooks` attribute. Because of that, when trying to reload and rerun the loaders, the hook won't run again.
**To Reproduce**
Steps to reproduce the behavior:
Configure a post hook just like in the docs. Create a `DynaConf` object and then run `reload()` on the object.
**Expected behavior**
The post hook should run after every reload.
**Additional context**
I'll be glad to make a PR for this myself.
| [
{
"content": "from __future__ import annotations\n\nimport copy\nimport glob\nimport importlib\nimport inspect\nimport os\nimport warnings\nfrom collections import defaultdict\nfrom contextlib import contextmanager\nfrom contextlib import suppress\nfrom pathlib import Path\nfrom typing import Any\nfrom typing import Callable\n\nfrom dynaconf import default_settings\nfrom dynaconf.loaders import default_loader\nfrom dynaconf.loaders import enable_external_loaders\nfrom dynaconf.loaders import env_loader\nfrom dynaconf.loaders import execute_instance_hooks\nfrom dynaconf.loaders import execute_module_hooks\nfrom dynaconf.loaders import py_loader\nfrom dynaconf.loaders import settings_loader\nfrom dynaconf.loaders import yaml_loader\nfrom dynaconf.loaders.base import SourceMetadata\nfrom dynaconf.utils import BANNER\nfrom dynaconf.utils import compat_kwargs\nfrom dynaconf.utils import ensure_a_list\nfrom dynaconf.utils import missing\nfrom dynaconf.utils import object_merge\nfrom dynaconf.utils import recursively_evaluate_lazy_format\nfrom dynaconf.utils import RENAMED_VARS\nfrom dynaconf.utils import upperfy\nfrom dynaconf.utils.boxing import DynaBox\nfrom dynaconf.utils.files import find_file\nfrom dynaconf.utils.functional import empty\nfrom dynaconf.utils.functional import LazyObject\nfrom dynaconf.utils.parse_conf import apply_converter\nfrom dynaconf.utils.parse_conf import converters\nfrom dynaconf.utils.parse_conf import Lazy\nfrom dynaconf.utils.parse_conf import parse_conf_data\nfrom dynaconf.utils.parse_conf import true_values\nfrom dynaconf.validator import ValidationError\nfrom dynaconf.validator import ValidatorList\nfrom dynaconf.vendor.box.box_list import BoxList\n\n\nclass LazySettings(LazyObject):\n \"\"\"Loads settings lazily from multiple sources::\n\n settings = Dynaconf(\n settings_files=[\"settings.toml\"], # path/glob\n environments=True, # activate layered environments\n envvar_prefix=\"MYAPP\", # `export MYAPP_FOO=bar`\n env_switcher=\"MYAPP_MODE\", # `export MYAPP_MODE=production`\n load_dotenv=True, # read a .env file\n )\n\n More options available on https://www.dynaconf.com/configuration/\n \"\"\"\n\n def __init__(self, wrapped=None, **kwargs):\n \"\"\"\n handle initialization for the customization cases\n\n :param wrapped: a deepcopy of this object will be wrapped (issue #596)\n :param kwargs: values that overrides default_settings\n \"\"\"\n\n self._warn_dynaconf_global_settings = kwargs.pop(\n \"warn_dynaconf_global_settings\", None\n ) # in 3.0.0 global settings is deprecated\n\n self.__resolve_config_aliases(kwargs)\n compat_kwargs(kwargs)\n self._kwargs = kwargs\n super().__init__()\n\n if wrapped:\n if self._django_override:\n # This fixes django issue #596\n self._wrapped = copy.deepcopy(wrapped)\n else:\n self._wrapped = wrapped\n\n def __resolve_config_aliases(self, kwargs):\n \"\"\"takes aliases for _FOR_DYNACONF configurations\n\n e.g: ROOT_PATH='/' is transformed into `ROOT_PATH_FOR_DYNACONF`\n \"\"\"\n\n mispells = {\n \"settings_files\": \"settings_file\",\n \"SETTINGS_FILES\": \"SETTINGS_FILE\",\n \"environment\": \"environments\",\n \"ENVIRONMENT\": \"ENVIRONMENTS\",\n }\n for misspell, correct in mispells.items():\n if misspell in kwargs:\n kwargs[correct] = kwargs.pop(misspell)\n\n for_dynaconf_keys = {\n key\n for key in UPPER_DEFAULT_SETTINGS\n if key.endswith(\"_FOR_DYNACONF\")\n }\n aliases = {\n key.upper()\n for key in kwargs\n if f\"{key.upper()}_FOR_DYNACONF\" in for_dynaconf_keys\n }\n for alias in aliases:\n value = kwargs.pop(alias, empty)\n if value is empty:\n value = kwargs.pop(alias.lower())\n kwargs[f\"{alias}_FOR_DYNACONF\"] = value\n\n def __getattr__(self, name):\n \"\"\"Allow getting keys from self.store using dot notation\"\"\"\n if self._wrapped is empty:\n self._setup()\n if name in self._wrapped._deleted: # noqa\n raise AttributeError(\n f\"Attribute {name} was deleted, \" \"or belongs to different env\"\n )\n\n if name not in RESERVED_ATTRS:\n lowercase_mode = self._kwargs.get(\n \"LOWERCASE_READ_FOR_DYNACONF\",\n default_settings.LOWERCASE_READ_FOR_DYNACONF,\n )\n if lowercase_mode is True:\n name = name.upper()\n\n if (\n name.isupper()\n and (\n self._wrapped._fresh\n or name in self._wrapped.FRESH_VARS_FOR_DYNACONF\n )\n and name not in UPPER_DEFAULT_SETTINGS\n ):\n return self._wrapped.get_fresh(name)\n value = getattr(self._wrapped, name)\n if name not in RESERVED_ATTRS:\n return recursively_evaluate_lazy_format(value, self)\n return value\n\n def __call__(self, *args, **kwargs):\n \"\"\"Allow direct call of settings('val')\n in place of settings.get('val')\n \"\"\"\n return self.get(*args, **kwargs)\n\n @property\n def _should_load_dotenv(self):\n \"\"\"Chicken and egg problem, we must manually check envvar\n before deciding if we are loading envvars :)\"\"\"\n _environ_load_dotenv = parse_conf_data(\n os.environ.get(\"LOAD_DOTENV_FOR_DYNACONF\"), tomlfy=True\n )\n return self._kwargs.get(\"load_dotenv\", _environ_load_dotenv)\n\n def _setup(self):\n \"\"\"Initial setup, run once.\"\"\"\n\n if self._warn_dynaconf_global_settings:\n warnings.warn(\n \"Usage of `from dynaconf import settings` is now \"\n \"DEPRECATED in 3.0.0+. You are encouraged to change it to \"\n \"your own instance e.g: `settings = Dynaconf(*options)`\",\n DeprecationWarning,\n )\n\n default_settings.reload(self._should_load_dotenv)\n environment_variable = self._kwargs.get(\n \"ENVVAR_FOR_DYNACONF\", default_settings.ENVVAR_FOR_DYNACONF\n )\n settings_module = os.environ.get(environment_variable)\n self._wrapped = Settings(\n settings_module=settings_module, **self._kwargs\n )\n\n def configure(self, settings_module=None, **kwargs):\n \"\"\"\n Allows user to reconfigure settings object passing a new settings\n module or separated kwargs\n\n :param settings_module: defines the settings file\n :param kwargs: override default settings\n \"\"\"\n default_settings.reload(self._should_load_dotenv)\n environment_var = self._kwargs.get(\n \"ENVVAR_FOR_DYNACONF\", default_settings.ENVVAR_FOR_DYNACONF\n )\n settings_module = settings_module or os.environ.get(environment_var)\n compat_kwargs(kwargs)\n kwargs.update(self._kwargs)\n self._wrapped = Settings(settings_module=settings_module, **kwargs)\n\n @property\n def configured(self):\n \"\"\"If wrapped is configured\"\"\"\n return self._wrapped is not empty\n\n\nclass Settings:\n \"\"\"\n Common logic for settings whether set by a module or by the user.\n \"\"\"\n\n dynaconf_banner = BANNER\n _store = DynaBox()\n\n def __init__(self, settings_module=None, **kwargs): # pragma: no cover\n \"\"\"Execute loaders and custom initialization\n\n :param settings_module: defines the settings file\n :param kwargs: override default settings\n \"\"\"\n self._fresh = False\n self._loaded_envs = []\n self._loaded_hooks = defaultdict(dict)\n self._loaded_py_modules = []\n self._loaded_files = []\n self._deleted = set()\n self._store = DynaBox(box_settings=self)\n self._env_cache = {}\n self._loaded_by_loaders: dict[SourceMetadata, Any] = {}\n self._loaders = []\n self._defaults = DynaBox(box_settings=self)\n self.environ = os.environ\n self.SETTINGS_MODULE = None\n self.filter_strategy = kwargs.get(\"filter_strategy\", None)\n self._not_installed_warnings = []\n self._validate_only = kwargs.pop(\"validate_only\", None)\n self._validate_exclude = kwargs.pop(\"validate_exclude\", None)\n self._validate_only_current_env = kwargs.pop(\n \"validate_only_current_env\", False\n )\n\n self.validators = ValidatorList(\n self, validators=kwargs.pop(\"validators\", None)\n )\n self._post_hooks: list[Callable] = ensure_a_list(\n kwargs.get(\"post_hooks\", [])\n )\n\n compat_kwargs(kwargs)\n if settings_module:\n self.set(\"SETTINGS_FILE_FOR_DYNACONF\", settings_module)\n for key, value in kwargs.items():\n self.set(key, value)\n # execute loaders only after setting defaults got from kwargs\n self._defaults = kwargs\n\n # The following flags are used for when copying of settings is done\n skip_loaders = kwargs.get(\"dynaconf_skip_loaders\", False)\n skip_validators = kwargs.get(\"dynaconf_skip_validators\", False)\n\n if not skip_loaders:\n self.execute_loaders()\n\n if not skip_validators:\n self.validators.validate(\n only=self._validate_only,\n exclude=self._validate_exclude,\n only_current_env=self._validate_only_current_env,\n )\n\n def __call__(self, *args, **kwargs):\n \"\"\"Allow direct call of `settings('val')`\n in place of `settings.get('val')`\n \"\"\"\n return self.get(*args, **kwargs)\n\n def __setattr__(self, name, value):\n \"\"\"Allow `settings.FOO = 'value'` while keeping internal attrs.\"\"\"\n\n if name in RESERVED_ATTRS:\n super().__setattr__(name, value)\n else:\n self.set(name, value)\n\n def __delattr__(self, name):\n \"\"\"stores reference in `_deleted` for proper error management\"\"\"\n self._deleted.add(name)\n if hasattr(self, name):\n super().__delattr__(name)\n\n def __contains__(self, item):\n \"\"\"Respond to `item in settings`\"\"\"\n return item.upper() in self.store or item.lower() in self.store\n\n def __getattribute__(self, name):\n if name not in RESERVED_ATTRS and name not in UPPER_DEFAULT_SETTINGS:\n with suppress(KeyError):\n # self._store has Lazy values already evaluated\n if (\n name.islower()\n and self._store.get(\"LOWERCASE_READ_FOR_DYNACONF\", empty)\n is False\n ):\n # only matches exact casing, first levels always upper\n return self._store.__getattribute__(name)\n # perform lookups for upper, and casefold\n return self._store[name]\n # in case of RESERVED_ATTRS or KeyError above, keep default behaviour\n return super().__getattribute__(name)\n\n def __getitem__(self, item):\n \"\"\"Allow getting variables as dict keys `settings['KEY']`\"\"\"\n value = self.get(item, default=empty)\n if value is empty:\n raise KeyError(f\"{item} does not exist\")\n return value\n\n def __setitem__(self, key, value):\n \"\"\"Allow `settings['KEY'] = 'value'`\"\"\"\n self.set(key, value)\n\n @property\n def store(self):\n \"\"\"Gets internal storage\"\"\"\n return self._store\n\n def __dir__(self):\n \"\"\"Enable auto-complete for code editors\"\"\"\n return (\n RESERVED_ATTRS\n + [k.lower() for k in self.keys()]\n + list(self.keys())\n )\n\n def __iter__(self):\n \"\"\"Redirects to store object\"\"\"\n yield from self._store\n\n def items(self):\n \"\"\"Redirects to store object\"\"\"\n return self._store.items()\n\n def keys(self):\n \"\"\"Redirects to store object\"\"\"\n return self.store.keys()\n\n def values(self):\n \"\"\"Redirects to store object\"\"\"\n return self.store.values()\n\n def setdefault(\n self, item, default, apply_default_on_none=False, env: str = \"unknown\"\n ):\n \"\"\"Returns value if exists or set it as the given default\n\n apply_default_on_none: if True, default is set when value is None\n env: used to create the source identifier\n \"\"\"\n value = self.get(item, empty)\n\n # Yaml loader reads empty values as None, would we apply defaults?\n global_apply_default = (\n self.get(\"APPLY_DEFAULT_ON_NONE_FOR_DYNACONF\") is not None\n )\n apply_default = default is not empty and (\n value is empty\n or (\n value is None\n and (\n apply_default_on_none is True\n or global_apply_default is True\n )\n )\n )\n loader_identifier = SourceMetadata(\n \"validation_default\", \"unique\", env.lower()\n )\n\n if apply_default:\n self.set(\n item,\n default,\n loader_identifier=loader_identifier,\n tomlfy=True,\n )\n return default\n\n return value\n\n def as_dict(self, env=None, internal=False):\n \"\"\"Returns a dictionary with set key and values.\n\n :param env: Str env name, default self.current_env `DEVELOPMENT`\n :param internal: bool - should include dynaconf internal vars?\n \"\"\"\n ctx_mgr = suppress() if env is None else self.using_env(env)\n with ctx_mgr:\n data = self.store.to_dict().copy()\n # if not internal remove internal settings\n if not internal:\n for name in UPPER_DEFAULT_SETTINGS:\n data.pop(name, None)\n return data\n\n to_dict = as_dict # backwards compatibility\n\n def _dotted_get(\n self, dotted_key, default=None, parent=None, cast=None, **kwargs\n ):\n \"\"\"\n Perform dotted key lookups and keep track of where we are.\n :param key: The name of the setting value, will always be upper case\n :param default: In case of not found it will be returned\n :param parent: Is there a pre-loaded parent in a nested data?\n \"\"\"\n split_key = dotted_key.split(\".\")\n name, keys = split_key[0], split_key[1:]\n result = self.get(name, default=default, parent=parent, **kwargs)\n\n # If we've reached the end, or parent key not found, then return result\n if not keys or result == default:\n if cast and cast in converters:\n return apply_converter(cast, result, box_settings=self)\n elif cast is True:\n return parse_conf_data(result, tomlfy=True, box_settings=self)\n return result\n\n # If we've still got key elements to traverse, let's do that.\n return self._dotted_get(\n \".\".join(keys), default=default, parent=result, cast=cast, **kwargs\n )\n\n def get(\n self,\n key,\n default=None,\n cast=None,\n fresh=False,\n dotted_lookup=empty,\n parent=None,\n sysenv_fallback=None,\n ):\n \"\"\"\n Get a value from settings store, this is the preferred way to access::\n\n >>> from dynaconf import settings\n >>> settings.get('KEY')\n\n :param key: The name of the setting value, will always be upper case\n :param default: In case of not found it will be returned\n :param cast: Should cast in to @int, @float, @bool or @json ?\n :param fresh: Should reload from loaders store before access?\n :param dotted_lookup: Should perform dotted-path lookup?\n :param parent: Is there a pre-loaded parent in a nested data?\n :param sysenv_fallback: Should fallback to system environ if not found?\n :return: The value if found, default or None\n \"\"\"\n if sysenv_fallback is None:\n sysenv_fallback = self._store.get(\"SYSENV_FALLBACK_FOR_DYNACONF\")\n\n nested_sep = self._store.get(\"NESTED_SEPARATOR_FOR_DYNACONF\")\n if nested_sep and nested_sep in key:\n # turn FOO__bar__ZAZ in `FOO.bar.ZAZ`\n key = key.replace(nested_sep, \".\")\n\n if dotted_lookup is empty:\n dotted_lookup = self._store.get(\"DOTTED_LOOKUP_FOR_DYNACONF\")\n\n if \".\" in key and dotted_lookup:\n return self._dotted_get(\n dotted_key=key,\n default=default,\n cast=cast,\n fresh=fresh,\n parent=parent,\n )\n\n key = upperfy(key)\n\n # handles system environment fallback\n if default is None:\n key_in_sysenv_fallback_list = isinstance(\n sysenv_fallback, list\n ) and key in [upperfy(k) for k in sysenv_fallback]\n if sysenv_fallback is True or key_in_sysenv_fallback_list:\n default = self.environ.get(key)\n\n # default values should behave exactly Dynaconf parsed values\n if default is not None:\n if isinstance(default, list):\n default = BoxList(default)\n elif isinstance(default, dict):\n default = DynaBox(default)\n\n if key in self._deleted:\n return default\n\n if (\n fresh\n or self._fresh\n or key in getattr(self, \"FRESH_VARS_FOR_DYNACONF\", ())\n ) and key not in UPPER_DEFAULT_SETTINGS:\n self.unset(key)\n self.execute_loaders(key=key)\n\n data = (parent or self.store).get(key, default)\n if cast:\n data = apply_converter(cast, data, box_settings=self)\n return data\n\n def exists(self, key, fresh=False):\n \"\"\"Check if key exists\n\n :param key: the name of setting variable\n :param fresh: if key should be taken from source directly\n :return: Boolean\n \"\"\"\n key = upperfy(key)\n if key in self._deleted:\n return False\n return self.get(key, fresh=fresh, default=missing) is not missing\n\n def get_fresh(self, key, default=None, cast=None):\n \"\"\"This is a shortcut to `get(key, fresh=True)`. always reload from\n loaders store before getting the var.\n\n :param key: The name of the setting value, will always be upper case\n :param default: In case of not found it will be returned\n :param cast: Should cast in to @int, @float, @bool or @json ?\n :return: The value if found, default or None\n \"\"\"\n return self.get(key, default=default, cast=cast, fresh=True)\n\n def get_environ(self, key, default=None, cast=None):\n \"\"\"Get value from environment variable using os.environ.get\n\n :param key: The name of the setting value, will always be upper case\n :param default: In case of not found it will be returned\n :param cast: Should cast in to @int, @float, @bool or @json ?\n or cast must be true to use cast inference\n :return: The value if found, default or None\n \"\"\"\n key = upperfy(key)\n data = self.environ.get(key, default)\n if data:\n if cast in converters:\n data = apply_converter(cast, data, box_settings=self)\n elif cast is True:\n data = parse_conf_data(data, tomlfy=True, box_settings=self)\n return data\n\n def exists_in_environ(self, key):\n \"\"\"Return True if env variable is exported\"\"\"\n return upperfy(key) in self.environ\n\n def as_bool(self, key):\n \"\"\"Partial method for get with bool cast\"\"\"\n return self.get(key, cast=\"@bool\")\n\n def as_int(self, key):\n \"\"\"Partial method for get with int cast\"\"\"\n return self.get(key, cast=\"@int\")\n\n def as_float(self, key):\n \"\"\"Partial method for get with float cast\"\"\"\n return self.get(key, cast=\"@float\")\n\n def as_json(self, key):\n \"\"\"Partial method for get with json cast\"\"\"\n return self.get(key, cast=\"@json\")\n\n @property\n def loaded_envs(self):\n \"\"\"Get or create internal loaded envs list\"\"\"\n if not self._loaded_envs:\n self._loaded_envs = []\n return self._loaded_envs\n\n @loaded_envs.setter\n def loaded_envs(self, value):\n \"\"\"Setter for env list\"\"\"\n self._loaded_envs = value\n\n # compat\n loaded_namespaces = loaded_envs\n\n @property\n def loaded_by_loaders(self):\n \"\"\"Gets the internal mapping of LOADER -> values\"\"\"\n # return {k.loader:data for k, data in self._loaded_by_loaders}\n return self._loaded_by_loaders\n\n def from_env(self, env=\"\", keep=False, **kwargs):\n \"\"\"Return a new isolated settings object pointing to specified env.\n\n Example of settings.toml::\n\n [development]\n message = 'This is in dev'\n [other]\n message = 'this is in other env'\n\n Program::\n\n >>> from dynaconf import settings\n >>> print(settings.MESSAGE)\n 'This is in dev'\n >>> print(settings.from_env('other').MESSAGE)\n 'This is in other env'\n # The existing settings object remains the same.\n >>> print(settings.MESSAGE)\n 'This is in dev'\n\n Arguments:\n env {str} -- Env to load (development, production, custom)\n\n Keyword Arguments:\n keep {bool} -- Keep pre-existing values (default: {False})\n kwargs {dict} -- Passed directly to new instance.\n \"\"\"\n cache_key = f\"{env}_{keep}_{kwargs}\"\n if cache_key in self._env_cache:\n return self._env_cache[cache_key]\n\n new_data = {\n key: self.get(key)\n for key in UPPER_DEFAULT_SETTINGS\n if key not in RENAMED_VARS\n }\n\n if self.filter_strategy:\n # Retain the filtering strategy when switching environments\n new_data[\"filter_strategy\"] = self.filter_strategy\n\n # This is here for backwards compatibility\n # To be removed on 4.x.x\n default_settings_paths = self.get(\"default_settings_paths\")\n if default_settings_paths: # pragma: no cover\n new_data[\"default_settings_paths\"] = default_settings_paths\n\n if keep:\n # keep existing values from current env\n new_data.update(\n {\n key: value\n for key, value in self.store.to_dict().copy().items()\n if key.isupper() and key not in RENAMED_VARS\n }\n )\n\n new_data.update(kwargs)\n new_data[\"FORCE_ENV_FOR_DYNACONF\"] = env\n new_settings = LazySettings(**new_data)\n self._env_cache[cache_key] = new_settings\n\n # update source metadata for inspecting\n self._loaded_by_loaders.update(new_settings._loaded_by_loaders)\n\n return new_settings\n\n @contextmanager\n def using_env(self, env, clean=True, silent=True, filename=None):\n \"\"\"\n This context manager allows the contextual use of a different env\n Example of settings.toml::\n\n [development]\n message = 'This is in dev'\n [other]\n message = 'this is in other env'\n\n Program::\n\n >>> from dynaconf import settings\n >>> print settings.MESSAGE\n 'This is in dev'\n >>> with settings.using_env('OTHER'):\n ... print settings.MESSAGE\n 'this is in other env'\n\n :param env: Upper case name of env without any _\n :param clean: If preloaded vars should be cleaned\n :param silent: Silence errors\n :param filename: Custom filename to load (optional)\n :return: context\n \"\"\"\n try:\n self.setenv(env, clean=clean, silent=silent, filename=filename)\n yield\n finally:\n if env.lower() != self.ENV_FOR_DYNACONF.lower():\n del self.loaded_envs[-1]\n self.setenv(self.current_env, clean=clean, filename=filename)\n\n # compat\n using_namespace = using_env\n\n @contextmanager\n def fresh(self):\n \"\"\"\n this context manager force the load of a key direct from the store::\n\n $ export DYNACONF_VALUE='Original'\n >>> from dynaconf import settings\n >>> print settings.VALUE\n 'Original'\n $ export DYNACONF_VALUE='Changed Value'\n >>> print settings.VALUE # will not be reloaded from env vars\n 'Original\n >>> with settings.fresh(): # inside this context all is reloaded\n ... print settings.VALUE\n 'Changed Value'\n\n an alternative is using `settings.get_fresh(key)`\n\n :return: context\n \"\"\"\n\n self._fresh = True\n yield\n self._fresh = False\n\n @property\n def current_env(self):\n \"\"\"Return the current active env\"\"\"\n\n if self.ENVIRONMENTS_FOR_DYNACONF is False:\n return self.MAIN_ENV_FOR_DYNACONF.lower()\n\n if self.FORCE_ENV_FOR_DYNACONF is not None:\n self.ENV_FOR_DYNACONF = self.FORCE_ENV_FOR_DYNACONF\n return self.FORCE_ENV_FOR_DYNACONF\n\n try:\n return self.loaded_envs[-1]\n except IndexError:\n return self.ENV_FOR_DYNACONF\n\n # compat\n current_namespace = current_env\n\n @property\n def settings_module(self):\n \"\"\"Gets SETTINGS_MODULE variable\"\"\"\n settings_module = parse_conf_data(\n os.environ.get(\n self.ENVVAR_FOR_DYNACONF, self.SETTINGS_FILE_FOR_DYNACONF\n ),\n tomlfy=True,\n box_settings=self,\n )\n if settings_module != getattr(self, \"SETTINGS_MODULE\", None):\n self.set(\"SETTINGS_MODULE\", settings_module)\n\n # This is for backewards compatibility, to be removed on 4.x.x\n if not self.SETTINGS_MODULE and self.get(\"default_settings_paths\"):\n self.SETTINGS_MODULE = self.get(\"default_settings_paths\")\n\n return self.SETTINGS_MODULE\n\n # Backwards compatibility see #169\n settings_file = settings_module\n\n def setenv(self, env=None, clean=True, silent=True, filename=None):\n \"\"\"Used to interactively change the env\n Example of settings.toml::\n\n [development]\n message = 'This is in dev'\n [other]\n message = 'this is in other env'\n\n Program::\n\n >>> from dynaconf import settings\n >>> print settings.MESSAGE\n 'This is in dev'\n >>> with settings.using_env('OTHER'):\n ... print settings.MESSAGE\n 'this is in other env'\n\n :param env: Upper case name of env without any _\n :param clean: If preloaded vars should be cleaned\n :param silent: Silence errors\n :param filename: Custom filename to load (optional)\n :return: context\n \"\"\"\n env = env or self.ENV_FOR_DYNACONF\n\n if not isinstance(env, str) or \"_\" in env or \" \" in env:\n raise ValueError(\"env should be a string without _ or spaces\")\n\n env = env.upper()\n\n if env != self.ENV_FOR_DYNACONF:\n self.loaded_envs.append(env)\n else:\n self.loaded_envs = []\n\n if clean:\n self.clean(env=env)\n self.execute_loaders(env=env, silent=silent, filename=filename)\n\n # compat\n namespace = setenv\n\n def clean(self, *args, **kwargs):\n \"\"\"Clean all loaded values to reload when switching envs\"\"\"\n for key in list(self.store.keys()):\n self.unset(key)\n\n def unset(self, key, force=False):\n \"\"\"Unset on all references\n\n :param key: The key to be unset\n :param force: Bypass default checks and force unset\n \"\"\"\n key = upperfy(key.strip())\n if (\n key not in UPPER_DEFAULT_SETTINGS\n and key not in self._defaults\n or force\n ):\n with suppress(KeyError, AttributeError):\n # AttributeError can happen when a LazyValue consumes\n # a previously deleted key\n delattr(self, key)\n del self.store[key]\n\n def unset_all(self, keys, force=False): # pragma: no cover\n \"\"\"Unset based on a list of keys\n\n :param keys: a list of keys\n :param force: Bypass default checks and force unset\n \"\"\"\n for key in keys:\n self.unset(key, force=force)\n\n def _dotted_set(\n self, dotted_key, value, tomlfy=False, validate=empty, **kwargs\n ):\n \"\"\"Sets dotted keys as nested dictionaries.\n\n Dotted set will always reassign the value, to merge use `@merge` token\n\n Arguments:\n dotted_key {str} -- A traversal name e.g: foo.bar.zaz\n value {Any} -- The value to set to the nested value.\n\n Keyword Arguments:\n tomlfy {bool} -- Perform toml parsing (default: {False})\n validate {bool} --\n \"\"\"\n if validate is empty:\n validate = self.get(\n \"VALIDATE_ON_UPDATE_FOR_DYNACONF\"\n ) # pragma: nocover\n\n split_keys = dotted_key.split(\".\")\n existing_data = self.get(split_keys[0], {})\n new_data = tree = DynaBox(box_settings=self)\n\n for k in split_keys[:-1]:\n tree = tree.setdefault(k, {})\n\n value = parse_conf_data(value, tomlfy=tomlfy, box_settings=self)\n tree[split_keys[-1]] = value\n\n if existing_data:\n old_data = DynaBox(\n {split_keys[0]: existing_data}, box_settings=self\n )\n new_data = object_merge(\n old=old_data,\n new=new_data,\n full_path=split_keys,\n )\n self.update(data=new_data, tomlfy=tomlfy, validate=validate, **kwargs)\n\n def set(\n self,\n key,\n value,\n loader_identifier: SourceMetadata | None = None,\n tomlfy=False,\n dotted_lookup=empty,\n is_secret=\"DeprecatedArgument\", # noqa\n validate=empty,\n merge=False,\n ):\n \"\"\"Set a value storing references for the loader\n\n :param key: The key to store\n :param value: The value to store\n :param loader_identifier: Optional loader name e.g: toml, yaml etc.\n :param tomlfy: Bool define if value is parsed by toml (defaults False)\n :param merge: Bool define if existing nested data will be merged.\n :param validate: Bool define if validation will be triggered\n \"\"\"\n if validate is empty:\n validate = self.get(\"VALIDATE_ON_UPDATE_FOR_DYNACONF\")\n\n if dotted_lookup is empty:\n dotted_lookup = self.get(\"DOTTED_LOOKUP_FOR_DYNACONF\")\n\n nested_sep = self.get(\"NESTED_SEPARATOR_FOR_DYNACONF\")\n if nested_sep and nested_sep in key:\n # turn FOO__bar__ZAZ in `FOO.bar.ZAZ`\n key = key.replace(nested_sep, \".\")\n\n if \".\" in key and dotted_lookup is True:\n return self._dotted_set(\n key,\n value,\n loader_identifier=loader_identifier,\n tomlfy=tomlfy,\n validate=validate,\n )\n\n # Fix for #905\n # parsed_conf default value was causing duplication\n value_not_parsed = (\n value\n if loader_identifier\n and loader_identifier.loader == \"validation_default\"\n else None\n )\n\n value = parse_conf_data(value, tomlfy=tomlfy, box_settings=self)\n key = upperfy(key.strip())\n\n # Fix for #869 - The call to getattr trigger early evaluation\n existing = (\n getattr(self, key, None) if not isinstance(value, Lazy) else None\n )\n\n if getattr(value, \"_dynaconf_del\", None):\n # just in case someone use a `@del` in a first level var.\n self.unset(key, force=True)\n return\n\n if getattr(value, \"_dynaconf_reset\", False): # pragma: no cover\n # just in case someone use a `@reset` in a first level var.\n value = value.unwrap()\n\n if getattr(value, \"_dynaconf_merge_unique\", False):\n # just in case someone use a `@merge_unique` in a first level var\n if existing:\n # update SourceMetadata (for inspecting purposes)\n loader_identifier = (\n loader_identifier._replace(merged=True)\n if loader_identifier\n else None\n )\n value = object_merge(existing, value.unwrap(), unique=True)\n else:\n value = value.unwrap()\n\n if getattr(value, \"_dynaconf_merge\", False):\n # just in case someone use a `@merge` in a first level var\n if existing:\n # update SourceMetadata (for inspecting purposes)\n loader_identifier = (\n loader_identifier._replace(merged=True)\n if loader_identifier\n else None\n )\n value = object_merge(existing, value.unwrap())\n else:\n value = value.unwrap()\n\n if existing is not None and existing != value:\n # `dynaconf_merge` used in file root `merge=True`\n if merge:\n loader_identifier = (\n loader_identifier._replace(merged=True)\n if loader_identifier\n else None\n )\n value = object_merge(existing, value)\n else:\n # Fix for #905\n if (\n loader_identifier\n and loader_identifier.loader == \"validation_default\"\n ):\n value = value_not_parsed\n else:\n # `dynaconf_merge` may be used within the key structure\n # Or merge_enabled is set to True\n value, updated_identifier = self._merge_before_set(\n existing, value, loader_identifier\n )\n loader_identifier = updated_identifier\n\n if isinstance(value, dict) and not isinstance(value, DynaBox):\n value = DynaBox(value, box_settings=self)\n\n self.store[key] = value\n self._deleted.discard(key)\n super().__setattr__(key, value)\n\n # set loader identifiers so cleaners know which keys to clean\n if loader_identifier and loader_identifier in self._loaded_by_loaders:\n self._loaded_by_loaders[loader_identifier][key] = value\n elif loader_identifier:\n self._loaded_by_loaders[loader_identifier] = {key: value}\n elif loader_identifier is None:\n # if .set is called without loader identifier it becomes\n # a default value and goes away only when explicitly unset\n self._defaults[key] = value\n\n if validate is True:\n self.validators.validate()\n\n def update(\n self,\n data=None,\n loader_identifier=None,\n tomlfy=False,\n merge=False,\n is_secret=\"DeprecatedArgument\", # noqa\n dotted_lookup=empty,\n validate=empty,\n **kwargs,\n ):\n \"\"\"\n Update values in the current settings object without saving in stores::\n\n >>> from dynaconf import settings\n >>> print settings.NAME\n 'Bruno'\n >>> settings.update({'NAME': 'John'}, other_value=1)\n >>> print settings.NAME\n 'John'\n >>> print settings.OTHER_VALUE\n 1\n\n :param data: Data to be updated\n :param loader_identifier: Only to be used by custom loaders\n :param tomlfy: Bool define if value is parsed by toml (defaults False)\n :param merge: Bool define if existing nested data will be merged.\n :param validate: Bool define if validators will trigger automatically\n :param kwargs: extra values to update\n :return: None\n \"\"\"\n\n if validate is empty:\n validate = self.get(\"VALIDATE_ON_UPDATE_FOR_DYNACONF\")\n\n data = data or {}\n data.update(kwargs)\n for key, value in data.items():\n # update() will handle validation later\n with suppress(ValidationError):\n self.set(\n key,\n value,\n loader_identifier=loader_identifier,\n tomlfy=tomlfy,\n merge=merge,\n dotted_lookup=dotted_lookup,\n validate=validate,\n )\n\n # handle param `validate`\n if validate is True:\n self.validators.validate()\n elif validate == \"all\":\n self.validators.validate_all()\n\n def _merge_before_set(\n self, existing, value, identifier: SourceMetadata | None = None\n ):\n \"\"\"\n Merge the new value being set with the existing value before set\n Returns the merged value and the updated identifier (for inspecting).\n \"\"\"\n global_merge = getattr(self, \"MERGE_ENABLED_FOR_DYNACONF\", False)\n if isinstance(value, dict):\n local_merge = value.pop(\n \"dynaconf_merge\", value.pop(\"dynaconf_merge_unique\", None)\n )\n if local_merge not in (True, False, None) and not value:\n # In case `dynaconf_merge:` holds value not boolean - ref #241\n value = local_merge\n\n if global_merge or local_merge:\n identifier = (\n identifier._replace(merged=True) if identifier else None\n )\n value = object_merge(existing, value)\n\n if isinstance(value, (list, tuple)):\n local_merge = (\n \"dynaconf_merge\" in value or \"dynaconf_merge_unique\" in value\n )\n if global_merge or local_merge:\n value = list(value)\n unique = False\n if local_merge:\n try:\n value.remove(\"dynaconf_merge\")\n except ValueError: # EAFP\n value.remove(\"dynaconf_merge_unique\")\n unique = True\n identifier = (\n identifier._replace(merged=True) if identifier else None\n )\n value = object_merge(existing, value, unique=unique)\n return value, identifier\n\n @property\n def loaders(self): # pragma: no cover\n \"\"\"Return available loaders\"\"\"\n if self.LOADERS_FOR_DYNACONF in (None, 0, \"0\", \"false\", False):\n return []\n\n if not self._loaders:\n self._loaders = self.LOADERS_FOR_DYNACONF\n\n return [importlib.import_module(loader) for loader in self._loaders]\n\n def reload(self, env=None, silent=None): # pragma: no cover\n \"\"\"Clean end Execute all loaders\"\"\"\n self.clean()\n self.execute_loaders(env, silent)\n\n def execute_loaders(\n self, env=None, silent=None, key=None, filename=None, loaders=None\n ):\n \"\"\"Execute all internal and registered loaders\n\n :param env: The environment to load\n :param silent: If loading errors is silenced\n :param key: if provided load a single key\n :param filename: optional custom filename to load\n :param loaders: optional list of loader modules\n \"\"\"\n if key is None:\n default_loader(self, self._defaults)\n\n env = (env or self.current_env).upper()\n silent = silent or self.SILENT_ERRORS_FOR_DYNACONF\n\n if loaders is None:\n self.pre_load(env, silent=silent, key=key)\n settings_loader(\n self, env=env, silent=silent, key=key, filename=filename\n )\n self.load_extra_yaml(env, silent, key) # DEPRECATED\n enable_external_loaders(self)\n\n loaders = self.loaders\n # non setting_file or py_module loaders\n for core_loader in loaders:\n core_loader.load(self, env, silent=silent, key=key)\n\n self.load_includes(env, silent=silent, key=key)\n self._store._box_config[\"_bypass_evaluation\"] = True\n\n # execute hooks\n execute_module_hooks(\"post\", self, env, silent=silent, key=key)\n execute_instance_hooks(self, \"post\", self._post_hooks)\n\n self._store._box_config[\"_bypass_evaluation\"] = False\n\n def pre_load(self, env, silent, key):\n \"\"\"Do we have any file to pre-load before main settings file?\"\"\"\n preloads = self.get(\"PRELOAD_FOR_DYNACONF\", [])\n if preloads:\n self.load_file(path=preloads, env=env, silent=silent, key=key)\n\n def load_includes(self, env, silent, key):\n \"\"\"Do we have any nested includes we need to process?\"\"\"\n includes = ensure_a_list(self.get(\"DYNACONF_INCLUDE\"))\n includes.extend(ensure_a_list(self.get(\"INCLUDES_FOR_DYNACONF\")))\n if includes:\n self.load_file(path=includes, env=env, silent=silent, key=key)\n # ensure env vars are the last thing loaded after all includes\n last_loader = self.loaders and self.loaders[-1]\n if last_loader and last_loader == env_loader:\n last_loader.load(self, env, silent, key)\n\n def load_file(\n self, path=None, env=None, silent=True, key=None, validate=empty\n ):\n \"\"\"Programmatically load files from ``path``.\n\n When using relative paths, the basedir fallbacks in this order:\n - ROOT_PATH_FOR_DYNACONF\n - Directory of the last loaded file\n - CWD\n\n :param path: A single filename or a file list\n :param env: Which env to load from file (default current_env)\n :param silent: Should raise errors?\n :param key: Load a single key?\n :param validate: Should trigger validation?\n \"\"\"\n if validate is empty:\n validate = self.get(\"VALIDATE_ON_UPDATE_FOR_DYNACONF\")\n\n env = (env or self.current_env).upper()\n files = ensure_a_list(path)\n if files:\n already_loaded = set()\n for _filename in files:\n\n # load_file() will handle validation later\n with suppress(ValidationError):\n if py_loader.try_to_load_from_py_module_name(\n obj=self, name=_filename, silent=True\n ):\n # if it was possible to load from module name\n # continue the loop.\n continue\n\n root_dir = str(self._root_path or os.getcwd())\n\n # Issue #494\n if (\n isinstance(_filename, Path)\n and str(_filename.parent) in root_dir\n ): # pragma: no cover\n filepath = str(_filename)\n else:\n filepath = os.path.join(root_dir, str(_filename))\n\n paths = [\n p\n for p in sorted(glob.glob(filepath))\n if \".local.\" not in p\n ]\n local_paths = [\n p for p in sorted(glob.glob(filepath)) if \".local.\" in p\n ]\n\n # Handle possible *.globs sorted alphanumeric\n for path in paths + local_paths:\n if path in already_loaded: # pragma: no cover\n continue\n\n # load_file() will handle validation later\n with suppress(ValidationError):\n settings_loader(\n obj=self,\n env=env,\n silent=silent,\n key=key,\n filename=path,\n validate=validate,\n )\n already_loaded.add(path)\n\n # handle param `validate`\n if validate is True:\n self.validators.validate()\n elif validate == \"all\":\n self.validators.validate_all()\n\n @property\n def _root_path(self):\n \"\"\"ROOT_PATH_FOR_DYNACONF or the path of first loaded file or '.'\"\"\"\n\n if self.ROOT_PATH_FOR_DYNACONF is not None:\n return self.ROOT_PATH_FOR_DYNACONF\n\n if self._loaded_files: # called once\n root_path = os.path.dirname(self._loaded_files[0])\n self.set(\"ROOT_PATH_FOR_DYNACONF\", root_path)\n return root_path\n\n def load_extra_yaml(self, env, silent, key):\n \"\"\"This is deprecated, kept for compat\n\n .. deprecated:: 1.0.0\n Use multiple settings or INCLUDES_FOR_DYNACONF files instead.\n \"\"\"\n if self.get(\"YAML\") is not None:\n warnings.warn(\n \"The use of YAML var is deprecated, please define multiple \"\n \"filepaths instead: \"\n \"e.g: SETTINGS_FILE_FOR_DYNACONF = \"\n \"'settings.py,settings.yaml,settings.toml' or \"\n \"INCLUDES_FOR_DYNACONF=['path.toml', 'folder/*']\"\n )\n yaml_loader.load(\n self,\n env=env,\n filename=self.find_file(self.get(\"YAML\")),\n silent=silent,\n key=key,\n )\n\n def path_for(self, *args):\n \"\"\"Path containing _root_path\"\"\"\n if args and args[0].startswith(os.path.sep):\n return os.path.join(*args)\n return os.path.join(self._root_path or os.getcwd(), *args)\n\n def find_file(self, *args, **kwargs):\n kwargs.setdefault(\"project_root\", self._root_path)\n kwargs.setdefault(\n \"skip_files\", self.get(\"SKIP_FILES_FOR_DYNACONF\", [])\n )\n return find_file(*args, **kwargs)\n\n def flag(self, key, env=None):\n \"\"\"Feature flagging system\n write flags to redis\n $ dynaconf write redis -s DASHBOARD=1 -e premiumuser\n meaning: Any premium user has DASHBOARD feature enabled\n\n In your program do::\n\n # premium user has access to dashboard?\n >>> if settings.flag('dashboard', 'premiumuser'):\n ... activate_dashboard()\n\n The value is ensured to be loaded fresh from redis server\n\n It also works with file settings but the recommended is redis\n as the data can be loaded once it is updated.\n\n :param key: The flag name\n :param env: The env to look for\n \"\"\"\n env = env or self.ENVVAR_PREFIX_FOR_DYNACONF or \"DYNACONF\"\n with self.using_env(env):\n value = self.get_fresh(key)\n return value is True or value in true_values\n\n def populate_obj(self, obj, keys=None, ignore=None):\n \"\"\"Given the `obj` populate it using self.store items.\n\n :param obj: An object to be populated, a class instance.\n :param keys: A list of keys to be included.\n :param ignore: A list of keys to be excluded.\n \"\"\"\n keys = keys or self.keys()\n for key in keys:\n key = upperfy(key)\n if ignore and key in ignore:\n continue\n value = self.get(key, empty)\n if value is not empty:\n setattr(obj, key, value)\n\n def dynaconf_clone(self):\n \"\"\"Clone the current settings object.\"\"\"\n try:\n return copy.deepcopy(self)\n except TypeError:\n # can't deepcopy settings object because of module object\n # being set as value in the settings dict\n new_data = self.to_dict(internal=True)\n new_data[\"dynaconf_skip_loaders\"] = True\n new_data[\"dynaconf_skip_validators\"] = True\n return Settings(**new_data)\n\n @property\n def dynaconf(self):\n \"\"\"A proxy to access internal methods and attributes\n\n Starting in 3.0.0 Dynaconf now allows first level lower case\n keys that are not reserved keyword, so this is a proxy to\n internal methods and attrs.\n \"\"\"\n\n class AttrProxy:\n def __init__(self, obj):\n self.obj = obj\n\n def __getattr__(self, name):\n return getattr(self.obj, f\"dynaconf_{name}\")\n\n return AttrProxy(self)\n\n @property\n def logger(self): # pragma: no cover\n \"\"\"backwards compatibility with pre 3.0 loaders\n In dynaconf 3.0.0 logger and debug messages has been removed.\n \"\"\"\n warnings.warn(\n \"logger and DEBUG messages has been removed on dynaconf 3.0.0\"\n )\n import logging # noqa\n\n return logging.getLogger(\"dynaconf\")\n\n def is_overridden(self, setting): # noqa\n \"\"\"This is to provide Django DJDT support: issue 382\"\"\"\n return False\n\n\n\"\"\"Upper case default settings\"\"\"\nUPPER_DEFAULT_SETTINGS = [k for k in dir(default_settings) if k.isupper()]\n\n\"\"\"Attributes created on Settings before 3.0.0\"\"\"\nRESERVED_ATTRS = (\n [\n item[0]\n for item in inspect.getmembers(LazySettings)\n if not item[0].startswith(\"__\")\n ]\n + [\n item[0]\n for item in inspect.getmembers(Settings)\n if not item[0].startswith(\"__\")\n ]\n + [\n \"_defaults\",\n \"_deleted\",\n \"_env_cache\",\n \"_fresh\",\n \"_kwargs\",\n \"_loaded_by_loaders\",\n \"_loaded_envs\",\n \"_loaded_hooks\",\n \"_loaded_py_modules\",\n \"_loaded_files\",\n \"_loaders\",\n \"_not_installed_warnings\",\n \"_store\",\n \"_warn_dynaconf_global_settings\",\n \"_should_load_dotenv\",\n \"environ\",\n \"SETTINGS_MODULE\",\n \"filter_strategy\",\n \"validators\",\n \"_validate_only\",\n \"_validate_exclude\",\n \"_validate_only_current_env\",\n \"_post_hooks\",\n ]\n)\n",
"path": "dynaconf/base.py"
}
] | [
{
"content": "from __future__ import annotations\n\nimport copy\nimport glob\nimport importlib\nimport inspect\nimport os\nimport warnings\nfrom collections import defaultdict\nfrom contextlib import contextmanager\nfrom contextlib import suppress\nfrom pathlib import Path\nfrom typing import Any\nfrom typing import Callable\n\nfrom dynaconf import default_settings\nfrom dynaconf.loaders import default_loader\nfrom dynaconf.loaders import enable_external_loaders\nfrom dynaconf.loaders import env_loader\nfrom dynaconf.loaders import execute_instance_hooks\nfrom dynaconf.loaders import execute_module_hooks\nfrom dynaconf.loaders import py_loader\nfrom dynaconf.loaders import settings_loader\nfrom dynaconf.loaders import yaml_loader\nfrom dynaconf.loaders.base import SourceMetadata\nfrom dynaconf.utils import BANNER\nfrom dynaconf.utils import compat_kwargs\nfrom dynaconf.utils import ensure_a_list\nfrom dynaconf.utils import missing\nfrom dynaconf.utils import object_merge\nfrom dynaconf.utils import recursively_evaluate_lazy_format\nfrom dynaconf.utils import RENAMED_VARS\nfrom dynaconf.utils import upperfy\nfrom dynaconf.utils.boxing import DynaBox\nfrom dynaconf.utils.files import find_file\nfrom dynaconf.utils.functional import empty\nfrom dynaconf.utils.functional import LazyObject\nfrom dynaconf.utils.parse_conf import apply_converter\nfrom dynaconf.utils.parse_conf import converters\nfrom dynaconf.utils.parse_conf import Lazy\nfrom dynaconf.utils.parse_conf import parse_conf_data\nfrom dynaconf.utils.parse_conf import true_values\nfrom dynaconf.validator import ValidationError\nfrom dynaconf.validator import ValidatorList\nfrom dynaconf.vendor.box.box_list import BoxList\n\n\nclass LazySettings(LazyObject):\n \"\"\"Loads settings lazily from multiple sources::\n\n settings = Dynaconf(\n settings_files=[\"settings.toml\"], # path/glob\n environments=True, # activate layered environments\n envvar_prefix=\"MYAPP\", # `export MYAPP_FOO=bar`\n env_switcher=\"MYAPP_MODE\", # `export MYAPP_MODE=production`\n load_dotenv=True, # read a .env file\n )\n\n More options available on https://www.dynaconf.com/configuration/\n \"\"\"\n\n def __init__(self, wrapped=None, **kwargs):\n \"\"\"\n handle initialization for the customization cases\n\n :param wrapped: a deepcopy of this object will be wrapped (issue #596)\n :param kwargs: values that overrides default_settings\n \"\"\"\n\n self._warn_dynaconf_global_settings = kwargs.pop(\n \"warn_dynaconf_global_settings\", None\n ) # in 3.0.0 global settings is deprecated\n\n self.__resolve_config_aliases(kwargs)\n compat_kwargs(kwargs)\n self._kwargs = kwargs\n super().__init__()\n\n if wrapped:\n if self._django_override:\n # This fixes django issue #596\n self._wrapped = copy.deepcopy(wrapped)\n else:\n self._wrapped = wrapped\n\n def __resolve_config_aliases(self, kwargs):\n \"\"\"takes aliases for _FOR_DYNACONF configurations\n\n e.g: ROOT_PATH='/' is transformed into `ROOT_PATH_FOR_DYNACONF`\n \"\"\"\n\n mispells = {\n \"settings_files\": \"settings_file\",\n \"SETTINGS_FILES\": \"SETTINGS_FILE\",\n \"environment\": \"environments\",\n \"ENVIRONMENT\": \"ENVIRONMENTS\",\n }\n for misspell, correct in mispells.items():\n if misspell in kwargs:\n kwargs[correct] = kwargs.pop(misspell)\n\n for_dynaconf_keys = {\n key\n for key in UPPER_DEFAULT_SETTINGS\n if key.endswith(\"_FOR_DYNACONF\")\n }\n aliases = {\n key.upper()\n for key in kwargs\n if f\"{key.upper()}_FOR_DYNACONF\" in for_dynaconf_keys\n }\n for alias in aliases:\n value = kwargs.pop(alias, empty)\n if value is empty:\n value = kwargs.pop(alias.lower())\n kwargs[f\"{alias}_FOR_DYNACONF\"] = value\n\n def __getattr__(self, name):\n \"\"\"Allow getting keys from self.store using dot notation\"\"\"\n if self._wrapped is empty:\n self._setup()\n if name in self._wrapped._deleted: # noqa\n raise AttributeError(\n f\"Attribute {name} was deleted, \" \"or belongs to different env\"\n )\n\n if name not in RESERVED_ATTRS:\n lowercase_mode = self._kwargs.get(\n \"LOWERCASE_READ_FOR_DYNACONF\",\n default_settings.LOWERCASE_READ_FOR_DYNACONF,\n )\n if lowercase_mode is True:\n name = name.upper()\n\n if (\n name.isupper()\n and (\n self._wrapped._fresh\n or name in self._wrapped.FRESH_VARS_FOR_DYNACONF\n )\n and name not in UPPER_DEFAULT_SETTINGS\n ):\n return self._wrapped.get_fresh(name)\n value = getattr(self._wrapped, name)\n if name not in RESERVED_ATTRS:\n return recursively_evaluate_lazy_format(value, self)\n return value\n\n def __call__(self, *args, **kwargs):\n \"\"\"Allow direct call of settings('val')\n in place of settings.get('val')\n \"\"\"\n return self.get(*args, **kwargs)\n\n @property\n def _should_load_dotenv(self):\n \"\"\"Chicken and egg problem, we must manually check envvar\n before deciding if we are loading envvars :)\"\"\"\n _environ_load_dotenv = parse_conf_data(\n os.environ.get(\"LOAD_DOTENV_FOR_DYNACONF\"), tomlfy=True\n )\n return self._kwargs.get(\"load_dotenv\", _environ_load_dotenv)\n\n def _setup(self):\n \"\"\"Initial setup, run once.\"\"\"\n\n if self._warn_dynaconf_global_settings:\n warnings.warn(\n \"Usage of `from dynaconf import settings` is now \"\n \"DEPRECATED in 3.0.0+. You are encouraged to change it to \"\n \"your own instance e.g: `settings = Dynaconf(*options)`\",\n DeprecationWarning,\n )\n\n default_settings.reload(self._should_load_dotenv)\n environment_variable = self._kwargs.get(\n \"ENVVAR_FOR_DYNACONF\", default_settings.ENVVAR_FOR_DYNACONF\n )\n settings_module = os.environ.get(environment_variable)\n self._wrapped = Settings(\n settings_module=settings_module, **self._kwargs\n )\n\n def configure(self, settings_module=None, **kwargs):\n \"\"\"\n Allows user to reconfigure settings object passing a new settings\n module or separated kwargs\n\n :param settings_module: defines the settings file\n :param kwargs: override default settings\n \"\"\"\n default_settings.reload(self._should_load_dotenv)\n environment_var = self._kwargs.get(\n \"ENVVAR_FOR_DYNACONF\", default_settings.ENVVAR_FOR_DYNACONF\n )\n settings_module = settings_module or os.environ.get(environment_var)\n compat_kwargs(kwargs)\n kwargs.update(self._kwargs)\n self._wrapped = Settings(settings_module=settings_module, **kwargs)\n\n @property\n def configured(self):\n \"\"\"If wrapped is configured\"\"\"\n return self._wrapped is not empty\n\n\nclass Settings:\n \"\"\"\n Common logic for settings whether set by a module or by the user.\n \"\"\"\n\n dynaconf_banner = BANNER\n _store = DynaBox()\n\n def __init__(self, settings_module=None, **kwargs): # pragma: no cover\n \"\"\"Execute loaders and custom initialization\n\n :param settings_module: defines the settings file\n :param kwargs: override default settings\n \"\"\"\n self._fresh = False\n self._loaded_envs = []\n self._loaded_hooks = defaultdict(dict)\n self._loaded_py_modules = []\n self._loaded_files = []\n self._deleted = set()\n self._store = DynaBox(box_settings=self)\n self._env_cache = {}\n self._loaded_by_loaders: dict[SourceMetadata, Any] = {}\n self._loaders = []\n self._defaults = DynaBox(box_settings=self)\n self.environ = os.environ\n self.SETTINGS_MODULE = None\n self.filter_strategy = kwargs.get(\"filter_strategy\", None)\n self._not_installed_warnings = []\n self._validate_only = kwargs.pop(\"validate_only\", None)\n self._validate_exclude = kwargs.pop(\"validate_exclude\", None)\n self._validate_only_current_env = kwargs.pop(\n \"validate_only_current_env\", False\n )\n\n self.validators = ValidatorList(\n self, validators=kwargs.pop(\"validators\", None)\n )\n self._post_hooks: list[Callable] = ensure_a_list(\n kwargs.get(\"post_hooks\", [])\n )\n\n compat_kwargs(kwargs)\n if settings_module:\n self.set(\"SETTINGS_FILE_FOR_DYNACONF\", settings_module)\n for key, value in kwargs.items():\n self.set(key, value)\n # execute loaders only after setting defaults got from kwargs\n self._defaults = kwargs\n\n # The following flags are used for when copying of settings is done\n skip_loaders = kwargs.get(\"dynaconf_skip_loaders\", False)\n skip_validators = kwargs.get(\"dynaconf_skip_validators\", False)\n\n if not skip_loaders:\n self.execute_loaders()\n\n if not skip_validators:\n self.validators.validate(\n only=self._validate_only,\n exclude=self._validate_exclude,\n only_current_env=self._validate_only_current_env,\n )\n\n def __call__(self, *args, **kwargs):\n \"\"\"Allow direct call of `settings('val')`\n in place of `settings.get('val')`\n \"\"\"\n return self.get(*args, **kwargs)\n\n def __setattr__(self, name, value):\n \"\"\"Allow `settings.FOO = 'value'` while keeping internal attrs.\"\"\"\n\n if name in RESERVED_ATTRS:\n super().__setattr__(name, value)\n else:\n self.set(name, value)\n\n def __delattr__(self, name):\n \"\"\"stores reference in `_deleted` for proper error management\"\"\"\n self._deleted.add(name)\n if hasattr(self, name):\n super().__delattr__(name)\n\n def __contains__(self, item):\n \"\"\"Respond to `item in settings`\"\"\"\n return item.upper() in self.store or item.lower() in self.store\n\n def __getattribute__(self, name):\n if name not in RESERVED_ATTRS and name not in UPPER_DEFAULT_SETTINGS:\n with suppress(KeyError):\n # self._store has Lazy values already evaluated\n if (\n name.islower()\n and self._store.get(\"LOWERCASE_READ_FOR_DYNACONF\", empty)\n is False\n ):\n # only matches exact casing, first levels always upper\n return self._store.__getattribute__(name)\n # perform lookups for upper, and casefold\n return self._store[name]\n # in case of RESERVED_ATTRS or KeyError above, keep default behaviour\n return super().__getattribute__(name)\n\n def __getitem__(self, item):\n \"\"\"Allow getting variables as dict keys `settings['KEY']`\"\"\"\n value = self.get(item, default=empty)\n if value is empty:\n raise KeyError(f\"{item} does not exist\")\n return value\n\n def __setitem__(self, key, value):\n \"\"\"Allow `settings['KEY'] = 'value'`\"\"\"\n self.set(key, value)\n\n @property\n def store(self):\n \"\"\"Gets internal storage\"\"\"\n return self._store\n\n def __dir__(self):\n \"\"\"Enable auto-complete for code editors\"\"\"\n return (\n RESERVED_ATTRS\n + [k.lower() for k in self.keys()]\n + list(self.keys())\n )\n\n def __iter__(self):\n \"\"\"Redirects to store object\"\"\"\n yield from self._store\n\n def items(self):\n \"\"\"Redirects to store object\"\"\"\n return self._store.items()\n\n def keys(self):\n \"\"\"Redirects to store object\"\"\"\n return self.store.keys()\n\n def values(self):\n \"\"\"Redirects to store object\"\"\"\n return self.store.values()\n\n def setdefault(\n self, item, default, apply_default_on_none=False, env: str = \"unknown\"\n ):\n \"\"\"Returns value if exists or set it as the given default\n\n apply_default_on_none: if True, default is set when value is None\n env: used to create the source identifier\n \"\"\"\n value = self.get(item, empty)\n\n # Yaml loader reads empty values as None, would we apply defaults?\n global_apply_default = (\n self.get(\"APPLY_DEFAULT_ON_NONE_FOR_DYNACONF\") is not None\n )\n apply_default = default is not empty and (\n value is empty\n or (\n value is None\n and (\n apply_default_on_none is True\n or global_apply_default is True\n )\n )\n )\n loader_identifier = SourceMetadata(\n \"validation_default\", \"unique\", env.lower()\n )\n\n if apply_default:\n self.set(\n item,\n default,\n loader_identifier=loader_identifier,\n tomlfy=True,\n )\n return default\n\n return value\n\n def as_dict(self, env=None, internal=False):\n \"\"\"Returns a dictionary with set key and values.\n\n :param env: Str env name, default self.current_env `DEVELOPMENT`\n :param internal: bool - should include dynaconf internal vars?\n \"\"\"\n ctx_mgr = suppress() if env is None else self.using_env(env)\n with ctx_mgr:\n data = self.store.to_dict().copy()\n # if not internal remove internal settings\n if not internal:\n for name in UPPER_DEFAULT_SETTINGS:\n data.pop(name, None)\n return data\n\n to_dict = as_dict # backwards compatibility\n\n def _dotted_get(\n self, dotted_key, default=None, parent=None, cast=None, **kwargs\n ):\n \"\"\"\n Perform dotted key lookups and keep track of where we are.\n :param key: The name of the setting value, will always be upper case\n :param default: In case of not found it will be returned\n :param parent: Is there a pre-loaded parent in a nested data?\n \"\"\"\n split_key = dotted_key.split(\".\")\n name, keys = split_key[0], split_key[1:]\n result = self.get(name, default=default, parent=parent, **kwargs)\n\n # If we've reached the end, or parent key not found, then return result\n if not keys or result == default:\n if cast and cast in converters:\n return apply_converter(cast, result, box_settings=self)\n elif cast is True:\n return parse_conf_data(result, tomlfy=True, box_settings=self)\n return result\n\n # If we've still got key elements to traverse, let's do that.\n return self._dotted_get(\n \".\".join(keys), default=default, parent=result, cast=cast, **kwargs\n )\n\n def get(\n self,\n key,\n default=None,\n cast=None,\n fresh=False,\n dotted_lookup=empty,\n parent=None,\n sysenv_fallback=None,\n ):\n \"\"\"\n Get a value from settings store, this is the preferred way to access::\n\n >>> from dynaconf import settings\n >>> settings.get('KEY')\n\n :param key: The name of the setting value, will always be upper case\n :param default: In case of not found it will be returned\n :param cast: Should cast in to @int, @float, @bool or @json ?\n :param fresh: Should reload from loaders store before access?\n :param dotted_lookup: Should perform dotted-path lookup?\n :param parent: Is there a pre-loaded parent in a nested data?\n :param sysenv_fallback: Should fallback to system environ if not found?\n :return: The value if found, default or None\n \"\"\"\n if sysenv_fallback is None:\n sysenv_fallback = self._store.get(\"SYSENV_FALLBACK_FOR_DYNACONF\")\n\n nested_sep = self._store.get(\"NESTED_SEPARATOR_FOR_DYNACONF\")\n if nested_sep and nested_sep in key:\n # turn FOO__bar__ZAZ in `FOO.bar.ZAZ`\n key = key.replace(nested_sep, \".\")\n\n if dotted_lookup is empty:\n dotted_lookup = self._store.get(\"DOTTED_LOOKUP_FOR_DYNACONF\")\n\n if \".\" in key and dotted_lookup:\n return self._dotted_get(\n dotted_key=key,\n default=default,\n cast=cast,\n fresh=fresh,\n parent=parent,\n )\n\n key = upperfy(key)\n\n # handles system environment fallback\n if default is None:\n key_in_sysenv_fallback_list = isinstance(\n sysenv_fallback, list\n ) and key in [upperfy(k) for k in sysenv_fallback]\n if sysenv_fallback is True or key_in_sysenv_fallback_list:\n default = self.environ.get(key)\n\n # default values should behave exactly Dynaconf parsed values\n if default is not None:\n if isinstance(default, list):\n default = BoxList(default)\n elif isinstance(default, dict):\n default = DynaBox(default)\n\n if key in self._deleted:\n return default\n\n if (\n fresh\n or self._fresh\n or key in getattr(self, \"FRESH_VARS_FOR_DYNACONF\", ())\n ) and key not in UPPER_DEFAULT_SETTINGS:\n self.unset(key)\n self.execute_loaders(key=key)\n\n data = (parent or self.store).get(key, default)\n if cast:\n data = apply_converter(cast, data, box_settings=self)\n return data\n\n def exists(self, key, fresh=False):\n \"\"\"Check if key exists\n\n :param key: the name of setting variable\n :param fresh: if key should be taken from source directly\n :return: Boolean\n \"\"\"\n key = upperfy(key)\n if key in self._deleted:\n return False\n return self.get(key, fresh=fresh, default=missing) is not missing\n\n def get_fresh(self, key, default=None, cast=None):\n \"\"\"This is a shortcut to `get(key, fresh=True)`. always reload from\n loaders store before getting the var.\n\n :param key: The name of the setting value, will always be upper case\n :param default: In case of not found it will be returned\n :param cast: Should cast in to @int, @float, @bool or @json ?\n :return: The value if found, default or None\n \"\"\"\n return self.get(key, default=default, cast=cast, fresh=True)\n\n def get_environ(self, key, default=None, cast=None):\n \"\"\"Get value from environment variable using os.environ.get\n\n :param key: The name of the setting value, will always be upper case\n :param default: In case of not found it will be returned\n :param cast: Should cast in to @int, @float, @bool or @json ?\n or cast must be true to use cast inference\n :return: The value if found, default or None\n \"\"\"\n key = upperfy(key)\n data = self.environ.get(key, default)\n if data:\n if cast in converters:\n data = apply_converter(cast, data, box_settings=self)\n elif cast is True:\n data = parse_conf_data(data, tomlfy=True, box_settings=self)\n return data\n\n def exists_in_environ(self, key):\n \"\"\"Return True if env variable is exported\"\"\"\n return upperfy(key) in self.environ\n\n def as_bool(self, key):\n \"\"\"Partial method for get with bool cast\"\"\"\n return self.get(key, cast=\"@bool\")\n\n def as_int(self, key):\n \"\"\"Partial method for get with int cast\"\"\"\n return self.get(key, cast=\"@int\")\n\n def as_float(self, key):\n \"\"\"Partial method for get with float cast\"\"\"\n return self.get(key, cast=\"@float\")\n\n def as_json(self, key):\n \"\"\"Partial method for get with json cast\"\"\"\n return self.get(key, cast=\"@json\")\n\n @property\n def loaded_envs(self):\n \"\"\"Get or create internal loaded envs list\"\"\"\n if not self._loaded_envs:\n self._loaded_envs = []\n return self._loaded_envs\n\n @loaded_envs.setter\n def loaded_envs(self, value):\n \"\"\"Setter for env list\"\"\"\n self._loaded_envs = value\n\n # compat\n loaded_namespaces = loaded_envs\n\n @property\n def loaded_by_loaders(self):\n \"\"\"Gets the internal mapping of LOADER -> values\"\"\"\n # return {k.loader:data for k, data in self._loaded_by_loaders}\n return self._loaded_by_loaders\n\n def from_env(self, env=\"\", keep=False, **kwargs):\n \"\"\"Return a new isolated settings object pointing to specified env.\n\n Example of settings.toml::\n\n [development]\n message = 'This is in dev'\n [other]\n message = 'this is in other env'\n\n Program::\n\n >>> from dynaconf import settings\n >>> print(settings.MESSAGE)\n 'This is in dev'\n >>> print(settings.from_env('other').MESSAGE)\n 'This is in other env'\n # The existing settings object remains the same.\n >>> print(settings.MESSAGE)\n 'This is in dev'\n\n Arguments:\n env {str} -- Env to load (development, production, custom)\n\n Keyword Arguments:\n keep {bool} -- Keep pre-existing values (default: {False})\n kwargs {dict} -- Passed directly to new instance.\n \"\"\"\n cache_key = f\"{env}_{keep}_{kwargs}\"\n if cache_key in self._env_cache:\n return self._env_cache[cache_key]\n\n new_data = {\n key: self.get(key)\n for key in UPPER_DEFAULT_SETTINGS\n if key not in RENAMED_VARS\n }\n\n if self.filter_strategy:\n # Retain the filtering strategy when switching environments\n new_data[\"filter_strategy\"] = self.filter_strategy\n\n # This is here for backwards compatibility\n # To be removed on 4.x.x\n default_settings_paths = self.get(\"default_settings_paths\")\n if default_settings_paths: # pragma: no cover\n new_data[\"default_settings_paths\"] = default_settings_paths\n\n if keep:\n # keep existing values from current env\n new_data.update(\n {\n key: value\n for key, value in self.store.to_dict().copy().items()\n if key.isupper() and key not in RENAMED_VARS\n }\n )\n\n new_data.update(kwargs)\n new_data[\"FORCE_ENV_FOR_DYNACONF\"] = env\n new_settings = LazySettings(**new_data)\n self._env_cache[cache_key] = new_settings\n\n # update source metadata for inspecting\n self._loaded_by_loaders.update(new_settings._loaded_by_loaders)\n\n return new_settings\n\n @contextmanager\n def using_env(self, env, clean=True, silent=True, filename=None):\n \"\"\"\n This context manager allows the contextual use of a different env\n Example of settings.toml::\n\n [development]\n message = 'This is in dev'\n [other]\n message = 'this is in other env'\n\n Program::\n\n >>> from dynaconf import settings\n >>> print settings.MESSAGE\n 'This is in dev'\n >>> with settings.using_env('OTHER'):\n ... print settings.MESSAGE\n 'this is in other env'\n\n :param env: Upper case name of env without any _\n :param clean: If preloaded vars should be cleaned\n :param silent: Silence errors\n :param filename: Custom filename to load (optional)\n :return: context\n \"\"\"\n try:\n self.setenv(env, clean=clean, silent=silent, filename=filename)\n yield\n finally:\n if env.lower() != self.ENV_FOR_DYNACONF.lower():\n del self.loaded_envs[-1]\n self.setenv(self.current_env, clean=clean, filename=filename)\n\n # compat\n using_namespace = using_env\n\n @contextmanager\n def fresh(self):\n \"\"\"\n this context manager force the load of a key direct from the store::\n\n $ export DYNACONF_VALUE='Original'\n >>> from dynaconf import settings\n >>> print settings.VALUE\n 'Original'\n $ export DYNACONF_VALUE='Changed Value'\n >>> print settings.VALUE # will not be reloaded from env vars\n 'Original\n >>> with settings.fresh(): # inside this context all is reloaded\n ... print settings.VALUE\n 'Changed Value'\n\n an alternative is using `settings.get_fresh(key)`\n\n :return: context\n \"\"\"\n\n self._fresh = True\n yield\n self._fresh = False\n\n @property\n def current_env(self):\n \"\"\"Return the current active env\"\"\"\n\n if self.ENVIRONMENTS_FOR_DYNACONF is False:\n return self.MAIN_ENV_FOR_DYNACONF.lower()\n\n if self.FORCE_ENV_FOR_DYNACONF is not None:\n self.ENV_FOR_DYNACONF = self.FORCE_ENV_FOR_DYNACONF\n return self.FORCE_ENV_FOR_DYNACONF\n\n try:\n return self.loaded_envs[-1]\n except IndexError:\n return self.ENV_FOR_DYNACONF\n\n # compat\n current_namespace = current_env\n\n @property\n def settings_module(self):\n \"\"\"Gets SETTINGS_MODULE variable\"\"\"\n settings_module = parse_conf_data(\n os.environ.get(\n self.ENVVAR_FOR_DYNACONF, self.SETTINGS_FILE_FOR_DYNACONF\n ),\n tomlfy=True,\n box_settings=self,\n )\n if settings_module != getattr(self, \"SETTINGS_MODULE\", None):\n self.set(\"SETTINGS_MODULE\", settings_module)\n\n # This is for backewards compatibility, to be removed on 4.x.x\n if not self.SETTINGS_MODULE and self.get(\"default_settings_paths\"):\n self.SETTINGS_MODULE = self.get(\"default_settings_paths\")\n\n return self.SETTINGS_MODULE\n\n # Backwards compatibility see #169\n settings_file = settings_module\n\n def setenv(self, env=None, clean=True, silent=True, filename=None):\n \"\"\"Used to interactively change the env\n Example of settings.toml::\n\n [development]\n message = 'This is in dev'\n [other]\n message = 'this is in other env'\n\n Program::\n\n >>> from dynaconf import settings\n >>> print settings.MESSAGE\n 'This is in dev'\n >>> with settings.using_env('OTHER'):\n ... print settings.MESSAGE\n 'this is in other env'\n\n :param env: Upper case name of env without any _\n :param clean: If preloaded vars should be cleaned\n :param silent: Silence errors\n :param filename: Custom filename to load (optional)\n :return: context\n \"\"\"\n env = env or self.ENV_FOR_DYNACONF\n\n if not isinstance(env, str) or \"_\" in env or \" \" in env:\n raise ValueError(\"env should be a string without _ or spaces\")\n\n env = env.upper()\n\n if env != self.ENV_FOR_DYNACONF:\n self.loaded_envs.append(env)\n else:\n self.loaded_envs = []\n\n if clean:\n self.clean(env=env)\n self.execute_loaders(env=env, silent=silent, filename=filename)\n\n # compat\n namespace = setenv\n\n def clean(self, *args, **kwargs):\n \"\"\"Clean all loaded values to reload when switching envs\"\"\"\n for key in list(self.store.keys()):\n self.unset(key)\n\n def unset(self, key, force=False):\n \"\"\"Unset on all references\n\n :param key: The key to be unset\n :param force: Bypass default checks and force unset\n \"\"\"\n key = upperfy(key.strip())\n if (\n key not in UPPER_DEFAULT_SETTINGS\n and key not in self._defaults\n or force\n ):\n with suppress(KeyError, AttributeError):\n # AttributeError can happen when a LazyValue consumes\n # a previously deleted key\n delattr(self, key)\n del self.store[key]\n\n def unset_all(self, keys, force=False): # pragma: no cover\n \"\"\"Unset based on a list of keys\n\n :param keys: a list of keys\n :param force: Bypass default checks and force unset\n \"\"\"\n for key in keys:\n self.unset(key, force=force)\n\n def _dotted_set(\n self, dotted_key, value, tomlfy=False, validate=empty, **kwargs\n ):\n \"\"\"Sets dotted keys as nested dictionaries.\n\n Dotted set will always reassign the value, to merge use `@merge` token\n\n Arguments:\n dotted_key {str} -- A traversal name e.g: foo.bar.zaz\n value {Any} -- The value to set to the nested value.\n\n Keyword Arguments:\n tomlfy {bool} -- Perform toml parsing (default: {False})\n validate {bool} --\n \"\"\"\n if validate is empty:\n validate = self.get(\n \"VALIDATE_ON_UPDATE_FOR_DYNACONF\"\n ) # pragma: nocover\n\n split_keys = dotted_key.split(\".\")\n existing_data = self.get(split_keys[0], {})\n new_data = tree = DynaBox(box_settings=self)\n\n for k in split_keys[:-1]:\n tree = tree.setdefault(k, {})\n\n value = parse_conf_data(value, tomlfy=tomlfy, box_settings=self)\n tree[split_keys[-1]] = value\n\n if existing_data:\n old_data = DynaBox(\n {split_keys[0]: existing_data}, box_settings=self\n )\n new_data = object_merge(\n old=old_data,\n new=new_data,\n full_path=split_keys,\n )\n self.update(data=new_data, tomlfy=tomlfy, validate=validate, **kwargs)\n\n def set(\n self,\n key,\n value,\n loader_identifier: SourceMetadata | None = None,\n tomlfy=False,\n dotted_lookup=empty,\n is_secret=\"DeprecatedArgument\", # noqa\n validate=empty,\n merge=False,\n ):\n \"\"\"Set a value storing references for the loader\n\n :param key: The key to store\n :param value: The value to store\n :param loader_identifier: Optional loader name e.g: toml, yaml etc.\n :param tomlfy: Bool define if value is parsed by toml (defaults False)\n :param merge: Bool define if existing nested data will be merged.\n :param validate: Bool define if validation will be triggered\n \"\"\"\n if validate is empty:\n validate = self.get(\"VALIDATE_ON_UPDATE_FOR_DYNACONF\")\n\n if dotted_lookup is empty:\n dotted_lookup = self.get(\"DOTTED_LOOKUP_FOR_DYNACONF\")\n\n nested_sep = self.get(\"NESTED_SEPARATOR_FOR_DYNACONF\")\n if nested_sep and nested_sep in key:\n # turn FOO__bar__ZAZ in `FOO.bar.ZAZ`\n key = key.replace(nested_sep, \".\")\n\n if \".\" in key and dotted_lookup is True:\n return self._dotted_set(\n key,\n value,\n loader_identifier=loader_identifier,\n tomlfy=tomlfy,\n validate=validate,\n )\n\n # Fix for #905\n # parsed_conf default value was causing duplication\n value_not_parsed = (\n value\n if loader_identifier\n and loader_identifier.loader == \"validation_default\"\n else None\n )\n\n value = parse_conf_data(value, tomlfy=tomlfy, box_settings=self)\n key = upperfy(key.strip())\n\n # Fix for #869 - The call to getattr trigger early evaluation\n existing = (\n getattr(self, key, None) if not isinstance(value, Lazy) else None\n )\n\n if getattr(value, \"_dynaconf_del\", None):\n # just in case someone use a `@del` in a first level var.\n self.unset(key, force=True)\n return\n\n if getattr(value, \"_dynaconf_reset\", False): # pragma: no cover\n # just in case someone use a `@reset` in a first level var.\n value = value.unwrap()\n\n if getattr(value, \"_dynaconf_merge_unique\", False):\n # just in case someone use a `@merge_unique` in a first level var\n if existing:\n # update SourceMetadata (for inspecting purposes)\n loader_identifier = (\n loader_identifier._replace(merged=True)\n if loader_identifier\n else None\n )\n value = object_merge(existing, value.unwrap(), unique=True)\n else:\n value = value.unwrap()\n\n if getattr(value, \"_dynaconf_merge\", False):\n # just in case someone use a `@merge` in a first level var\n if existing:\n # update SourceMetadata (for inspecting purposes)\n loader_identifier = (\n loader_identifier._replace(merged=True)\n if loader_identifier\n else None\n )\n value = object_merge(existing, value.unwrap())\n else:\n value = value.unwrap()\n\n if existing is not None and existing != value:\n # `dynaconf_merge` used in file root `merge=True`\n if merge:\n loader_identifier = (\n loader_identifier._replace(merged=True)\n if loader_identifier\n else None\n )\n value = object_merge(existing, value)\n else:\n # Fix for #905\n if (\n loader_identifier\n and loader_identifier.loader == \"validation_default\"\n ):\n value = value_not_parsed\n else:\n # `dynaconf_merge` may be used within the key structure\n # Or merge_enabled is set to True\n value, updated_identifier = self._merge_before_set(\n existing, value, loader_identifier\n )\n loader_identifier = updated_identifier\n\n if isinstance(value, dict) and not isinstance(value, DynaBox):\n value = DynaBox(value, box_settings=self)\n\n self.store[key] = value\n self._deleted.discard(key)\n super().__setattr__(key, value)\n\n # set loader identifiers so cleaners know which keys to clean\n if loader_identifier and loader_identifier in self._loaded_by_loaders:\n self._loaded_by_loaders[loader_identifier][key] = value\n elif loader_identifier:\n self._loaded_by_loaders[loader_identifier] = {key: value}\n elif loader_identifier is None:\n # if .set is called without loader identifier it becomes\n # a default value and goes away only when explicitly unset\n self._defaults[key] = value\n\n if validate is True:\n self.validators.validate()\n\n def update(\n self,\n data=None,\n loader_identifier=None,\n tomlfy=False,\n merge=False,\n is_secret=\"DeprecatedArgument\", # noqa\n dotted_lookup=empty,\n validate=empty,\n **kwargs,\n ):\n \"\"\"\n Update values in the current settings object without saving in stores::\n\n >>> from dynaconf import settings\n >>> print settings.NAME\n 'Bruno'\n >>> settings.update({'NAME': 'John'}, other_value=1)\n >>> print settings.NAME\n 'John'\n >>> print settings.OTHER_VALUE\n 1\n\n :param data: Data to be updated\n :param loader_identifier: Only to be used by custom loaders\n :param tomlfy: Bool define if value is parsed by toml (defaults False)\n :param merge: Bool define if existing nested data will be merged.\n :param validate: Bool define if validators will trigger automatically\n :param kwargs: extra values to update\n :return: None\n \"\"\"\n\n if validate is empty:\n validate = self.get(\"VALIDATE_ON_UPDATE_FOR_DYNACONF\")\n\n data = data or {}\n data.update(kwargs)\n for key, value in data.items():\n # update() will handle validation later\n with suppress(ValidationError):\n self.set(\n key,\n value,\n loader_identifier=loader_identifier,\n tomlfy=tomlfy,\n merge=merge,\n dotted_lookup=dotted_lookup,\n validate=validate,\n )\n\n # handle param `validate`\n if validate is True:\n self.validators.validate()\n elif validate == \"all\":\n self.validators.validate_all()\n\n def _merge_before_set(\n self, existing, value, identifier: SourceMetadata | None = None\n ):\n \"\"\"\n Merge the new value being set with the existing value before set\n Returns the merged value and the updated identifier (for inspecting).\n \"\"\"\n global_merge = getattr(self, \"MERGE_ENABLED_FOR_DYNACONF\", False)\n if isinstance(value, dict):\n local_merge = value.pop(\n \"dynaconf_merge\", value.pop(\"dynaconf_merge_unique\", None)\n )\n if local_merge not in (True, False, None) and not value:\n # In case `dynaconf_merge:` holds value not boolean - ref #241\n value = local_merge\n\n if global_merge or local_merge:\n identifier = (\n identifier._replace(merged=True) if identifier else None\n )\n value = object_merge(existing, value)\n\n if isinstance(value, (list, tuple)):\n local_merge = (\n \"dynaconf_merge\" in value or \"dynaconf_merge_unique\" in value\n )\n if global_merge or local_merge:\n value = list(value)\n unique = False\n if local_merge:\n try:\n value.remove(\"dynaconf_merge\")\n except ValueError: # EAFP\n value.remove(\"dynaconf_merge_unique\")\n unique = True\n identifier = (\n identifier._replace(merged=True) if identifier else None\n )\n value = object_merge(existing, value, unique=unique)\n return value, identifier\n\n @property\n def loaders(self): # pragma: no cover\n \"\"\"Return available loaders\"\"\"\n if self.LOADERS_FOR_DYNACONF in (None, 0, \"0\", \"false\", False):\n return []\n\n if not self._loaders:\n self._loaders = self.LOADERS_FOR_DYNACONF\n\n return [importlib.import_module(loader) for loader in self._loaders]\n\n def reload(self, env=None, silent=None): # pragma: no cover\n \"\"\"Clean end Execute all loaders\"\"\"\n self.clean()\n self._loaded_hooks.clear()\n self.execute_loaders(env, silent)\n\n def execute_loaders(\n self, env=None, silent=None, key=None, filename=None, loaders=None\n ):\n \"\"\"Execute all internal and registered loaders\n\n :param env: The environment to load\n :param silent: If loading errors is silenced\n :param key: if provided load a single key\n :param filename: optional custom filename to load\n :param loaders: optional list of loader modules\n \"\"\"\n if key is None:\n default_loader(self, self._defaults)\n\n env = (env or self.current_env).upper()\n silent = silent or self.SILENT_ERRORS_FOR_DYNACONF\n\n if loaders is None:\n self.pre_load(env, silent=silent, key=key)\n settings_loader(\n self, env=env, silent=silent, key=key, filename=filename\n )\n self.load_extra_yaml(env, silent, key) # DEPRECATED\n enable_external_loaders(self)\n\n loaders = self.loaders\n # non setting_file or py_module loaders\n for core_loader in loaders:\n core_loader.load(self, env, silent=silent, key=key)\n\n self.load_includes(env, silent=silent, key=key)\n self._store._box_config[\"_bypass_evaluation\"] = True\n\n # execute hooks\n execute_module_hooks(\"post\", self, env, silent=silent, key=key)\n execute_instance_hooks(self, \"post\", self._post_hooks)\n\n self._store._box_config[\"_bypass_evaluation\"] = False\n\n def pre_load(self, env, silent, key):\n \"\"\"Do we have any file to pre-load before main settings file?\"\"\"\n preloads = self.get(\"PRELOAD_FOR_DYNACONF\", [])\n if preloads:\n self.load_file(path=preloads, env=env, silent=silent, key=key)\n\n def load_includes(self, env, silent, key):\n \"\"\"Do we have any nested includes we need to process?\"\"\"\n includes = ensure_a_list(self.get(\"DYNACONF_INCLUDE\"))\n includes.extend(ensure_a_list(self.get(\"INCLUDES_FOR_DYNACONF\")))\n if includes:\n self.load_file(path=includes, env=env, silent=silent, key=key)\n # ensure env vars are the last thing loaded after all includes\n last_loader = self.loaders and self.loaders[-1]\n if last_loader and last_loader == env_loader:\n last_loader.load(self, env, silent, key)\n\n def load_file(\n self, path=None, env=None, silent=True, key=None, validate=empty\n ):\n \"\"\"Programmatically load files from ``path``.\n\n When using relative paths, the basedir fallbacks in this order:\n - ROOT_PATH_FOR_DYNACONF\n - Directory of the last loaded file\n - CWD\n\n :param path: A single filename or a file list\n :param env: Which env to load from file (default current_env)\n :param silent: Should raise errors?\n :param key: Load a single key?\n :param validate: Should trigger validation?\n \"\"\"\n if validate is empty:\n validate = self.get(\"VALIDATE_ON_UPDATE_FOR_DYNACONF\")\n\n env = (env or self.current_env).upper()\n files = ensure_a_list(path)\n if files:\n already_loaded = set()\n for _filename in files:\n\n # load_file() will handle validation later\n with suppress(ValidationError):\n if py_loader.try_to_load_from_py_module_name(\n obj=self, name=_filename, silent=True\n ):\n # if it was possible to load from module name\n # continue the loop.\n continue\n\n root_dir = str(self._root_path or os.getcwd())\n\n # Issue #494\n if (\n isinstance(_filename, Path)\n and str(_filename.parent) in root_dir\n ): # pragma: no cover\n filepath = str(_filename)\n else:\n filepath = os.path.join(root_dir, str(_filename))\n\n paths = [\n p\n for p in sorted(glob.glob(filepath))\n if \".local.\" not in p\n ]\n local_paths = [\n p for p in sorted(glob.glob(filepath)) if \".local.\" in p\n ]\n\n # Handle possible *.globs sorted alphanumeric\n for path in paths + local_paths:\n if path in already_loaded: # pragma: no cover\n continue\n\n # load_file() will handle validation later\n with suppress(ValidationError):\n settings_loader(\n obj=self,\n env=env,\n silent=silent,\n key=key,\n filename=path,\n validate=validate,\n )\n already_loaded.add(path)\n\n # handle param `validate`\n if validate is True:\n self.validators.validate()\n elif validate == \"all\":\n self.validators.validate_all()\n\n @property\n def _root_path(self):\n \"\"\"ROOT_PATH_FOR_DYNACONF or the path of first loaded file or '.'\"\"\"\n\n if self.ROOT_PATH_FOR_DYNACONF is not None:\n return self.ROOT_PATH_FOR_DYNACONF\n\n if self._loaded_files: # called once\n root_path = os.path.dirname(self._loaded_files[0])\n self.set(\"ROOT_PATH_FOR_DYNACONF\", root_path)\n return root_path\n\n def load_extra_yaml(self, env, silent, key):\n \"\"\"This is deprecated, kept for compat\n\n .. deprecated:: 1.0.0\n Use multiple settings or INCLUDES_FOR_DYNACONF files instead.\n \"\"\"\n if self.get(\"YAML\") is not None:\n warnings.warn(\n \"The use of YAML var is deprecated, please define multiple \"\n \"filepaths instead: \"\n \"e.g: SETTINGS_FILE_FOR_DYNACONF = \"\n \"'settings.py,settings.yaml,settings.toml' or \"\n \"INCLUDES_FOR_DYNACONF=['path.toml', 'folder/*']\"\n )\n yaml_loader.load(\n self,\n env=env,\n filename=self.find_file(self.get(\"YAML\")),\n silent=silent,\n key=key,\n )\n\n def path_for(self, *args):\n \"\"\"Path containing _root_path\"\"\"\n if args and args[0].startswith(os.path.sep):\n return os.path.join(*args)\n return os.path.join(self._root_path or os.getcwd(), *args)\n\n def find_file(self, *args, **kwargs):\n kwargs.setdefault(\"project_root\", self._root_path)\n kwargs.setdefault(\n \"skip_files\", self.get(\"SKIP_FILES_FOR_DYNACONF\", [])\n )\n return find_file(*args, **kwargs)\n\n def flag(self, key, env=None):\n \"\"\"Feature flagging system\n write flags to redis\n $ dynaconf write redis -s DASHBOARD=1 -e premiumuser\n meaning: Any premium user has DASHBOARD feature enabled\n\n In your program do::\n\n # premium user has access to dashboard?\n >>> if settings.flag('dashboard', 'premiumuser'):\n ... activate_dashboard()\n\n The value is ensured to be loaded fresh from redis server\n\n It also works with file settings but the recommended is redis\n as the data can be loaded once it is updated.\n\n :param key: The flag name\n :param env: The env to look for\n \"\"\"\n env = env or self.ENVVAR_PREFIX_FOR_DYNACONF or \"DYNACONF\"\n with self.using_env(env):\n value = self.get_fresh(key)\n return value is True or value in true_values\n\n def populate_obj(self, obj, keys=None, ignore=None):\n \"\"\"Given the `obj` populate it using self.store items.\n\n :param obj: An object to be populated, a class instance.\n :param keys: A list of keys to be included.\n :param ignore: A list of keys to be excluded.\n \"\"\"\n keys = keys or self.keys()\n for key in keys:\n key = upperfy(key)\n if ignore and key in ignore:\n continue\n value = self.get(key, empty)\n if value is not empty:\n setattr(obj, key, value)\n\n def dynaconf_clone(self):\n \"\"\"Clone the current settings object.\"\"\"\n try:\n return copy.deepcopy(self)\n except TypeError:\n # can't deepcopy settings object because of module object\n # being set as value in the settings dict\n new_data = self.to_dict(internal=True)\n new_data[\"dynaconf_skip_loaders\"] = True\n new_data[\"dynaconf_skip_validators\"] = True\n return Settings(**new_data)\n\n @property\n def dynaconf(self):\n \"\"\"A proxy to access internal methods and attributes\n\n Starting in 3.0.0 Dynaconf now allows first level lower case\n keys that are not reserved keyword, so this is a proxy to\n internal methods and attrs.\n \"\"\"\n\n class AttrProxy:\n def __init__(self, obj):\n self.obj = obj\n\n def __getattr__(self, name):\n return getattr(self.obj, f\"dynaconf_{name}\")\n\n return AttrProxy(self)\n\n @property\n def logger(self): # pragma: no cover\n \"\"\"backwards compatibility with pre 3.0 loaders\n In dynaconf 3.0.0 logger and debug messages has been removed.\n \"\"\"\n warnings.warn(\n \"logger and DEBUG messages has been removed on dynaconf 3.0.0\"\n )\n import logging # noqa\n\n return logging.getLogger(\"dynaconf\")\n\n def is_overridden(self, setting): # noqa\n \"\"\"This is to provide Django DJDT support: issue 382\"\"\"\n return False\n\n\n\"\"\"Upper case default settings\"\"\"\nUPPER_DEFAULT_SETTINGS = [k for k in dir(default_settings) if k.isupper()]\n\n\"\"\"Attributes created on Settings before 3.0.0\"\"\"\nRESERVED_ATTRS = (\n [\n item[0]\n for item in inspect.getmembers(LazySettings)\n if not item[0].startswith(\"__\")\n ]\n + [\n item[0]\n for item in inspect.getmembers(Settings)\n if not item[0].startswith(\"__\")\n ]\n + [\n \"_defaults\",\n \"_deleted\",\n \"_env_cache\",\n \"_fresh\",\n \"_kwargs\",\n \"_loaded_by_loaders\",\n \"_loaded_envs\",\n \"_loaded_hooks\",\n \"_loaded_py_modules\",\n \"_loaded_files\",\n \"_loaders\",\n \"_not_installed_warnings\",\n \"_store\",\n \"_warn_dynaconf_global_settings\",\n \"_should_load_dotenv\",\n \"environ\",\n \"SETTINGS_MODULE\",\n \"filter_strategy\",\n \"validators\",\n \"_validate_only\",\n \"_validate_exclude\",\n \"_validate_only_current_env\",\n \"_post_hooks\",\n ]\n)\n",
"path": "dynaconf/base.py"
}
] | diff --git a/dynaconf/base.py b/dynaconf/base.py
index 355760a9d..39cffe9ad 100644
--- a/dynaconf/base.py
+++ b/dynaconf/base.py
@@ -1124,6 +1124,7 @@ def loaders(self): # pragma: no cover
def reload(self, env=None, silent=None): # pragma: no cover
"""Clean end Execute all loaders"""
self.clean()
+ self._loaded_hooks.clear()
self.execute_loaders(env, silent)
def execute_loaders(
diff --git a/tests_functional/issues/850_reload-hooks/app.py b/tests_functional/issues/850_reload-hooks/app.py
new file mode 100644
index 000000000..884854956
--- /dev/null
+++ b/tests_functional/issues/850_reload-hooks/app.py
@@ -0,0 +1,12 @@
+"""
+Assert settings.reload() re-reruns hooks.
+"""
+from __future__ import annotations
+
+from dynaconf import Dynaconf
+
+settings = Dynaconf(settings_file="settings.toml")
+
+assert settings.counter == 1
+settings.reload()
+assert settings.counter == 1
diff --git a/tests_functional/issues/850_reload-hooks/dynaconf_hooks.py b/tests_functional/issues/850_reload-hooks/dynaconf_hooks.py
new file mode 100644
index 000000000..e07f2df85
--- /dev/null
+++ b/tests_functional/issues/850_reload-hooks/dynaconf_hooks.py
@@ -0,0 +1,5 @@
+from __future__ import annotations
+
+
+def post(settings):
+ return {"counter": settings.counter + 1}
diff --git a/tests_functional/issues/850_reload-hooks/settings.toml b/tests_functional/issues/850_reload-hooks/settings.toml
new file mode 100644
index 000000000..343a3f3e7
--- /dev/null
+++ b/tests_functional/issues/850_reload-hooks/settings.toml
@@ -0,0 +1 @@
+counter=0
|
pytorch__rl-1910 | [BUG] I had to patch these 2 methods in order to run my script
## Describe the bug
(1)
`DoubleToFloat` transform cannot transform to `float` if env to be transformed is already a `float` (e.g. it is a `float` and the input dtype was declared as `double`).
I mean, all it has to do is call `.to(torch.float)` or something, which can be called regardless of the `dtype` of the input tensor, so why not?
(2)
The `torchrl.objectives.utils._cache_values` function is also bugged I think. If there is no `_cache` attribute in `self.__dict__`, it raises an error, but the function seems to be implemented in a way that it can work anyway. In fact, when I manually do `self.__dict__["_cache"] = {}`, all is good.
This happens with the `torchrl.objectives.ClipPPOLoss`.
## To Reproduce
I do not know how to reproduce, although my project is fully available at: https://gitlab.com/svnv-svsv-jm/chess-rl/-/tree/develop?ref_type=heads
For more context, I am trying to combine `torchrl` with `pytorch-lightning`.
## Reason and Possible fixes
For the cache issue with `torchrl.objectives.ClipPPOLoss`, I patched this:
```python
# in torchrl.objectives.utils
def _cache_values(fun):
"""Caches the tensordict returned by a property."""
name = fun.__name__
def new_fun(self, netname=None):
__dict__ = self.__dict__
# MY PATCH START
try:
_cache = __dict__["_cache"]
except KeyError as ex:
__dict__["_cache"] = {}
_cache = __dict__["_cache"]
# MY PATCH END
attr_name = name
if netname is not None:
attr_name += "_" + netname
if attr_name in _cache:
out = _cache[attr_name]
return out
if netname is not None:
out = fun(self, netname)
else:
out = fun(self)
# TODO: decide what to do with locked tds in functional calls
# if is_tensor_collection(out):
# out.lock_()
_cache[attr_name] = out
return out
return new_fun
```
For the `torch.envs.DoubleToFloat`, I patched `DTypeCastTransform`:
```python
# class DTypeCastTransform
def transform_observation_spec(self, observation_spec):
full_observation_spec = observation_spec
for observation_key, observation_spec in list(
full_observation_spec.items(True, True)
):
# find out_key that match the in_key
for in_key, out_key in zip(self.in_keys, self.out_keys):
if observation_key == in_key:
if observation_spec.dtype != self.dtype_in:
# PATCH START
self.dtype_in = observation_spec.dtype
# raise TypeError(
# f"observation_spec.dtype is not {self.dtype_in}"
# )
# PATCH END
full_observation_spec[out_key] = self._transform_spec(
observation_spec
)
return full_observation_spec
```
After these patches, all runs smoothly again.
Sorry for not providing a minimal example, but I really wouldn't know how to create one from my code. These are the tests where I encountered the errors: https://gitlab.com/svnv-svsv-jm/chess-rl/-/tree/develop/tests/shark/models/ppo?ref_type=heads
| [
{
"content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n#\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport functools\nimport re\nimport warnings\nfrom enum import Enum\nfrom typing import Iterable, Optional, Union\n\nimport torch\nfrom tensordict import TensorDict, TensorDictBase\nfrom tensordict.nn import TensorDictModule\nfrom torch import nn, Tensor\nfrom torch.nn import functional as F\nfrom torch.nn.modules import dropout\n\ntry:\n from torch import vmap\nexcept ImportError as err:\n try:\n from functorch import vmap\n except ImportError as err_ft:\n raise err_ft from err\nfrom torchrl.envs.utils import step_mdp\n\n_GAMMA_LMBDA_DEPREC_ERROR = (\n \"Passing gamma / lambda parameters through the loss constructor \"\n \"is a deprecated feature. To customize your value function, \"\n \"run `loss_module.make_value_estimator(ValueEstimators.<value_fun>, gamma=val)`.\"\n)\n\nRANDOM_MODULE_LIST = (dropout._DropoutNd,)\n\n\nclass ValueEstimators(Enum):\n \"\"\"Value function enumerator for custom-built estimators.\n\n Allows for a flexible usage of various value functions when the loss module\n allows it.\n\n Examples:\n >>> dqn_loss = DQNLoss(actor)\n >>> dqn_loss.make_value_estimator(ValueEstimators.TD0, gamma=0.9)\n\n \"\"\"\n\n TD0 = \"Bootstrapped TD (1-step return)\"\n TD1 = \"TD(1) (infinity-step return)\"\n TDLambda = \"TD(lambda)\"\n GAE = \"Generalized advantage estimate\"\n VTrace = \"V-trace\"\n\n\ndef default_value_kwargs(value_type: ValueEstimators):\n \"\"\"Default value function keyword argument generator.\n\n Args:\n value_type (Enum.value): the value function type, from the\n :class:`~torchrl.objectives.utils.ValueEstimators` class.\n\n Examples:\n >>> kwargs = default_value_kwargs(ValueEstimators.TDLambda)\n {\"gamma\": 0.99, \"lmbda\": 0.95}\n\n \"\"\"\n if value_type == ValueEstimators.TD1:\n return {\"gamma\": 0.99, \"differentiable\": True}\n elif value_type == ValueEstimators.TD0:\n return {\"gamma\": 0.99, \"differentiable\": True}\n elif value_type == ValueEstimators.GAE:\n return {\"gamma\": 0.99, \"lmbda\": 0.95, \"differentiable\": True}\n elif value_type == ValueEstimators.TDLambda:\n return {\"gamma\": 0.99, \"lmbda\": 0.95, \"differentiable\": True}\n elif value_type == ValueEstimators.VTrace:\n return {\"gamma\": 0.99, \"differentiable\": True}\n else:\n raise NotImplementedError(f\"Unknown value type {value_type}.\")\n\n\nclass _context_manager:\n def __init__(self, value=True):\n self.value = value\n self.prev = []\n\n def __call__(self, func):\n @functools.wraps(func)\n def decorate_context(*args, **kwargs):\n with self:\n return func(*args, **kwargs)\n\n return decorate_context\n\n\ndef distance_loss(\n v1: torch.Tensor,\n v2: torch.Tensor,\n loss_function: str,\n strict_shape: bool = True,\n) -> torch.Tensor:\n \"\"\"Computes a distance loss between two tensors.\n\n Args:\n v1 (Tensor): a tensor with a shape compatible with v2\n v2 (Tensor): a tensor with a shape compatible with v1\n loss_function (str): One of \"l2\", \"l1\" or \"smooth_l1\" representing which loss function is to be used.\n strict_shape (bool): if False, v1 and v2 are allowed to have a different shape.\n Default is ``True``.\n\n Returns:\n A tensor of the shape v1.view_as(v2) or v2.view_as(v1) with values equal to the distance loss between the\n two.\n\n \"\"\"\n if v1.shape != v2.shape and strict_shape:\n raise RuntimeError(\n f\"The input tensors have shapes {v1.shape} and {v2.shape} which are incompatible.\"\n )\n\n if loss_function == \"l2\":\n value_loss = F.mse_loss(\n v1,\n v2,\n reduction=\"none\",\n )\n\n elif loss_function == \"l1\":\n value_loss = F.l1_loss(\n v1,\n v2,\n reduction=\"none\",\n )\n\n elif loss_function == \"smooth_l1\":\n value_loss = F.smooth_l1_loss(\n v1,\n v2,\n reduction=\"none\",\n )\n else:\n raise NotImplementedError(f\"Unknown loss {loss_function}\")\n return value_loss\n\n\nclass TargetNetUpdater:\n \"\"\"An abstract class for target network update in Double DQN/DDPG.\n\n Args:\n loss_module (DQNLoss or DDPGLoss): loss module where the target network should be updated.\n\n \"\"\"\n\n def __init__(\n self,\n loss_module: \"LossModule\", # noqa: F821\n ):\n from torchrl.objectives.common import LossModule\n\n if not isinstance(loss_module, LossModule):\n raise ValueError(\"The loss_module must be a LossModule instance.\")\n _has_update_associated = getattr(loss_module, \"_has_update_associated\", None)\n for k in loss_module._has_update_associated.keys():\n loss_module._has_update_associated[k] = True\n try:\n _target_names = []\n for name, _ in loss_module.named_children():\n # the TensorDictParams is a nn.Module instance\n if name.startswith(\"target_\") and name.endswith(\"_params\"):\n _target_names.append(name)\n\n if len(_target_names) == 0:\n raise RuntimeError(\n \"Did not find any target parameters or buffers in the loss module.\"\n )\n\n _source_names = [\"\".join(name.split(\"target_\")) for name in _target_names]\n\n for _source in _source_names:\n try:\n getattr(loss_module, _source)\n except AttributeError as err:\n raise RuntimeError(\n f\"Incongruent target and source parameter lists: \"\n f\"{_source} is not an attribute of the loss_module\"\n ) from err\n\n self._target_names = _target_names\n self._source_names = _source_names\n self.loss_module = loss_module\n self.initialized = False\n self.init_()\n _has_update_associated = True\n finally:\n for k in loss_module._has_update_associated.keys():\n loss_module._has_update_associated[k] = _has_update_associated\n\n @property\n def _targets(self):\n return TensorDict(\n {name: getattr(self.loss_module, name) for name in self._target_names},\n [],\n )\n\n @property\n def _sources(self):\n return TensorDict(\n {name: getattr(self.loss_module, name) for name in self._source_names},\n [],\n )\n\n def init_(self) -> None:\n if self.initialized:\n warnings.warn(\"Updated already initialized.\")\n found_distinct = False\n self._distinct = {}\n for key, source in self._sources.items(True, True):\n if not isinstance(key, tuple):\n key = (key,)\n key = (\"target_\" + key[0], *key[1:])\n target = self._targets[key]\n # for p_source, p_target in zip(source, target):\n if target.requires_grad:\n raise RuntimeError(\"the target parameter is part of a graph.\")\n self._distinct[key] = target.data_ptr() != source.data.data_ptr()\n found_distinct = found_distinct or self._distinct[key]\n target.data.copy_(source.data)\n if not found_distinct:\n raise RuntimeError(\n f\"The target and source data are identical for all params. \"\n \"Have you created proper target parameters? \"\n \"If the loss has a ``delay_value`` kwarg, make sure to set it \"\n \"to True if it is not done by default. \"\n f\"If no target parameter is needed, do not use a target updater such as {type(self)}.\"\n )\n\n self.initialized = True\n\n def step(self) -> None:\n if not self.initialized:\n raise Exception(\n f\"{self.__class__.__name__} must be \"\n f\"initialized (`{self.__class__.__name__}.init_()`) before calling step()\"\n )\n for key, source in self._sources.items(True, True):\n if not isinstance(key, tuple):\n key = (key,)\n key = (\"target_\" + key[0], *key[1:])\n if not self._distinct[key]:\n continue\n target = self._targets[key]\n if target.requires_grad:\n raise RuntimeError(\"the target parameter is part of a graph.\")\n if target.is_leaf:\n self._step(source, target)\n else:\n target.copy_(source)\n\n def _step(self, p_source: Tensor, p_target: Tensor) -> None:\n raise NotImplementedError\n\n def __repr__(self) -> str:\n string = (\n f\"{self.__class__.__name__}(sources={self._sources}, targets=\"\n f\"{self._targets})\"\n )\n return string\n\n\nclass SoftUpdate(TargetNetUpdater):\n r\"\"\"A soft-update class for target network update in Double DQN/DDPG.\n\n This was proposed in \"CONTINUOUS CONTROL WITH DEEP REINFORCEMENT LEARNING\", https://arxiv.org/pdf/1509.02971.pdf\n\n One and only one decay factor (tau or eps) must be specified.\n\n Args:\n loss_module (DQNLoss or DDPGLoss): loss module where the target network should be updated.\n eps (scalar): epsilon in the update equation:\n .. math::\n\n \\theta_t = \\theta_{t-1} * \\epsilon + \\theta_t * (1-\\epsilon)\n\n Exclusive with ``tau``.\n tau (scalar): Polyak tau. It is equal to ``1-eps``, and exclusive with it.\n \"\"\"\n\n def __init__(\n self,\n loss_module: Union[\n \"DQNLoss\", # noqa: F821\n \"DDPGLoss\", # noqa: F821\n \"SACLoss\", # noqa: F821\n \"REDQLoss\", # noqa: F821\n \"TD3Loss\", # noqa: F821\n ],\n *,\n eps: float = None,\n tau: Optional[float] = None,\n ):\n if eps is None and tau is None:\n raise RuntimeError(\n \"Neither eps nor tau was provided. This behaviour is deprecated.\",\n )\n eps = 0.999\n if (eps is None) ^ (tau is None):\n if eps is None:\n eps = 1 - tau\n else:\n raise ValueError(\"One and only one argument (tau or eps) can be specified.\")\n if eps < 0.5:\n warnings.warn(\n \"Found an eps value < 0.5, which is unexpected. \"\n \"You may want to use the `tau` keyword argument instead.\"\n )\n if not (eps <= 1.0 and eps >= 0.0):\n raise ValueError(\n f\"Got eps = {eps} when it was supposed to be between 0 and 1.\"\n )\n super(SoftUpdate, self).__init__(loss_module)\n self.eps = eps\n\n def _step(self, p_source: Tensor, p_target: Tensor) -> None:\n p_target.data.copy_(p_target.data * self.eps + p_source.data * (1 - self.eps))\n\n\nclass HardUpdate(TargetNetUpdater):\n \"\"\"A hard-update class for target network update in Double DQN/DDPG (by contrast with soft updates).\n\n This was proposed in the original Double DQN paper: \"Deep Reinforcement Learning with Double Q-learning\",\n https://arxiv.org/abs/1509.06461.\n\n Args:\n loss_module (DQNLoss or DDPGLoss): loss module where the target network should be updated.\n\n Keyword Args:\n value_network_update_interval (scalar): how often the target network should be updated.\n default: 1000\n \"\"\"\n\n def __init__(\n self,\n loss_module: Union[\"DQNLoss\", \"DDPGLoss\", \"SACLoss\", \"TD3Loss\"], # noqa: F821\n *,\n value_network_update_interval: float = 1000,\n ):\n super(HardUpdate, self).__init__(loss_module)\n self.value_network_update_interval = value_network_update_interval\n self.counter = 0\n\n def _step(self, p_source: Tensor, p_target: Tensor) -> None:\n if self.counter == self.value_network_update_interval:\n p_target.data.copy_(p_source.data)\n\n def step(self) -> None:\n super().step()\n if self.counter == self.value_network_update_interval:\n self.counter = 0\n else:\n self.counter += 1\n\n\nclass hold_out_net(_context_manager):\n \"\"\"Context manager to hold a network out of a computational graph.\"\"\"\n\n def __init__(self, network: nn.Module) -> None:\n self.network = network\n for p in network.parameters():\n self.mode = p.requires_grad\n break\n else:\n self.mode = True\n\n def __enter__(self) -> None:\n if self.mode:\n self.network.requires_grad_(False)\n\n def __exit__(self, exc_type, exc_val, exc_tb) -> None:\n if self.mode:\n self.network.requires_grad_()\n\n\nclass hold_out_params(_context_manager):\n \"\"\"Context manager to hold a list of parameters out of a computational graph.\"\"\"\n\n def __init__(self, params: Iterable[Tensor]) -> None:\n if isinstance(params, TensorDictBase):\n self.params = params.detach()\n else:\n self.params = tuple(p.detach() for p in params)\n\n def __enter__(self) -> None:\n return self.params\n\n def __exit__(self, exc_type, exc_val, exc_tb) -> None:\n pass\n\n\[email protected]_grad()\ndef next_state_value(\n tensordict: TensorDictBase,\n operator: Optional[TensorDictModule] = None,\n next_val_key: str = \"state_action_value\",\n gamma: float = 0.99,\n pred_next_val: Optional[Tensor] = None,\n **kwargs,\n) -> torch.Tensor:\n \"\"\"Computes the next state value (without gradient) to compute a target value.\n\n The target value is ususally used to compute a distance loss (e.g. MSE):\n L = Sum[ (q_value - target_value)^2 ]\n The target value is computed as\n r + gamma ** n_steps_to_next * value_next_state\n If the reward is the immediate reward, n_steps_to_next=1. If N-steps rewards are used, n_steps_to_next is gathered\n from the input tensordict.\n\n Args:\n tensordict (TensorDictBase): Tensordict containing a reward and done key (and a n_steps_to_next key for n-steps\n rewards).\n operator (ProbabilisticTDModule, optional): the value function operator. Should write a 'next_val_key'\n key-value in the input tensordict when called. It does not need to be provided if pred_next_val is given.\n next_val_key (str, optional): key where the next value will be written.\n Default: 'state_action_value'\n gamma (float, optional): return discount rate.\n default: 0.99\n pred_next_val (Tensor, optional): the next state value can be provided if it is not computed with the operator.\n\n Returns:\n a Tensor of the size of the input tensordict containing the predicted value state.\n\n \"\"\"\n if \"steps_to_next_obs\" in tensordict.keys():\n steps_to_next_obs = tensordict.get(\"steps_to_next_obs\").squeeze(-1)\n else:\n steps_to_next_obs = 1\n\n rewards = tensordict.get((\"next\", \"reward\")).squeeze(-1)\n done = tensordict.get((\"next\", \"done\")).squeeze(-1)\n if done.all() or gamma == 0:\n return rewards\n\n if pred_next_val is None:\n next_td = step_mdp(tensordict) # next_observation -> observation\n next_td = next_td.select(*operator.in_keys)\n operator(next_td, **kwargs)\n pred_next_val_detach = next_td.get(next_val_key).squeeze(-1)\n else:\n pred_next_val_detach = pred_next_val.squeeze(-1)\n done = done.to(torch.float)\n target_value = (1 - done) * pred_next_val_detach\n rewards = rewards.to(torch.float)\n target_value = rewards + (gamma**steps_to_next_obs) * target_value\n return target_value\n\n\ndef _cache_values(fun):\n \"\"\"Caches the tensordict returned by a property.\"\"\"\n name = fun.__name__\n\n def new_fun(self, netname=None):\n __dict__ = self.__dict__\n _cache = __dict__[\"_cache\"]\n attr_name = name\n if netname is not None:\n attr_name += \"_\" + netname\n if attr_name in _cache:\n out = _cache[attr_name]\n return out\n if netname is not None:\n out = fun(self, netname)\n else:\n out = fun(self)\n # TODO: decide what to do with locked tds in functional calls\n # if is_tensor_collection(out):\n # out.lock_()\n _cache[attr_name] = out\n return out\n\n return new_fun\n\n\ndef _vmap_func(module, *args, func=None, **kwargs):\n try:\n\n def decorated_module(*module_args_params):\n params = module_args_params[-1]\n module_args = module_args_params[:-1]\n with params.to_module(module):\n if func is None:\n return module(*module_args)\n else:\n return getattr(module, func)(*module_args)\n\n return vmap(decorated_module, *args, **kwargs) # noqa: TOR101\n\n except RuntimeError as err:\n if re.match(\n r\"vmap: called random operation while in randomness error mode\", str(err)\n ):\n raise RuntimeError(\n \"Please use <loss_module>.set_vmap_randomness('different') to handle random operations during vmap.\"\n ) from err\n",
"path": "torchrl/objectives/utils.py"
}
] | [
{
"content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n#\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport functools\nimport re\nimport warnings\nfrom enum import Enum\nfrom typing import Iterable, Optional, Union\n\nimport torch\nfrom tensordict import TensorDict, TensorDictBase\nfrom tensordict.nn import TensorDictModule\nfrom torch import nn, Tensor\nfrom torch.nn import functional as F\nfrom torch.nn.modules import dropout\n\ntry:\n from torch import vmap\nexcept ImportError as err:\n try:\n from functorch import vmap\n except ImportError as err_ft:\n raise err_ft from err\nfrom torchrl.envs.utils import step_mdp\n\n_GAMMA_LMBDA_DEPREC_ERROR = (\n \"Passing gamma / lambda parameters through the loss constructor \"\n \"is a deprecated feature. To customize your value function, \"\n \"run `loss_module.make_value_estimator(ValueEstimators.<value_fun>, gamma=val)`.\"\n)\n\nRANDOM_MODULE_LIST = (dropout._DropoutNd,)\n\n\nclass ValueEstimators(Enum):\n \"\"\"Value function enumerator for custom-built estimators.\n\n Allows for a flexible usage of various value functions when the loss module\n allows it.\n\n Examples:\n >>> dqn_loss = DQNLoss(actor)\n >>> dqn_loss.make_value_estimator(ValueEstimators.TD0, gamma=0.9)\n\n \"\"\"\n\n TD0 = \"Bootstrapped TD (1-step return)\"\n TD1 = \"TD(1) (infinity-step return)\"\n TDLambda = \"TD(lambda)\"\n GAE = \"Generalized advantage estimate\"\n VTrace = \"V-trace\"\n\n\ndef default_value_kwargs(value_type: ValueEstimators):\n \"\"\"Default value function keyword argument generator.\n\n Args:\n value_type (Enum.value): the value function type, from the\n :class:`~torchrl.objectives.utils.ValueEstimators` class.\n\n Examples:\n >>> kwargs = default_value_kwargs(ValueEstimators.TDLambda)\n {\"gamma\": 0.99, \"lmbda\": 0.95}\n\n \"\"\"\n if value_type == ValueEstimators.TD1:\n return {\"gamma\": 0.99, \"differentiable\": True}\n elif value_type == ValueEstimators.TD0:\n return {\"gamma\": 0.99, \"differentiable\": True}\n elif value_type == ValueEstimators.GAE:\n return {\"gamma\": 0.99, \"lmbda\": 0.95, \"differentiable\": True}\n elif value_type == ValueEstimators.TDLambda:\n return {\"gamma\": 0.99, \"lmbda\": 0.95, \"differentiable\": True}\n elif value_type == ValueEstimators.VTrace:\n return {\"gamma\": 0.99, \"differentiable\": True}\n else:\n raise NotImplementedError(f\"Unknown value type {value_type}.\")\n\n\nclass _context_manager:\n def __init__(self, value=True):\n self.value = value\n self.prev = []\n\n def __call__(self, func):\n @functools.wraps(func)\n def decorate_context(*args, **kwargs):\n with self:\n return func(*args, **kwargs)\n\n return decorate_context\n\n\ndef distance_loss(\n v1: torch.Tensor,\n v2: torch.Tensor,\n loss_function: str,\n strict_shape: bool = True,\n) -> torch.Tensor:\n \"\"\"Computes a distance loss between two tensors.\n\n Args:\n v1 (Tensor): a tensor with a shape compatible with v2\n v2 (Tensor): a tensor with a shape compatible with v1\n loss_function (str): One of \"l2\", \"l1\" or \"smooth_l1\" representing which loss function is to be used.\n strict_shape (bool): if False, v1 and v2 are allowed to have a different shape.\n Default is ``True``.\n\n Returns:\n A tensor of the shape v1.view_as(v2) or v2.view_as(v1) with values equal to the distance loss between the\n two.\n\n \"\"\"\n if v1.shape != v2.shape and strict_shape:\n raise RuntimeError(\n f\"The input tensors have shapes {v1.shape} and {v2.shape} which are incompatible.\"\n )\n\n if loss_function == \"l2\":\n value_loss = F.mse_loss(\n v1,\n v2,\n reduction=\"none\",\n )\n\n elif loss_function == \"l1\":\n value_loss = F.l1_loss(\n v1,\n v2,\n reduction=\"none\",\n )\n\n elif loss_function == \"smooth_l1\":\n value_loss = F.smooth_l1_loss(\n v1,\n v2,\n reduction=\"none\",\n )\n else:\n raise NotImplementedError(f\"Unknown loss {loss_function}\")\n return value_loss\n\n\nclass TargetNetUpdater:\n \"\"\"An abstract class for target network update in Double DQN/DDPG.\n\n Args:\n loss_module (DQNLoss or DDPGLoss): loss module where the target network should be updated.\n\n \"\"\"\n\n def __init__(\n self,\n loss_module: \"LossModule\", # noqa: F821\n ):\n from torchrl.objectives.common import LossModule\n\n if not isinstance(loss_module, LossModule):\n raise ValueError(\"The loss_module must be a LossModule instance.\")\n _has_update_associated = getattr(loss_module, \"_has_update_associated\", None)\n for k in loss_module._has_update_associated.keys():\n loss_module._has_update_associated[k] = True\n try:\n _target_names = []\n for name, _ in loss_module.named_children():\n # the TensorDictParams is a nn.Module instance\n if name.startswith(\"target_\") and name.endswith(\"_params\"):\n _target_names.append(name)\n\n if len(_target_names) == 0:\n raise RuntimeError(\n \"Did not find any target parameters or buffers in the loss module.\"\n )\n\n _source_names = [\"\".join(name.split(\"target_\")) for name in _target_names]\n\n for _source in _source_names:\n try:\n getattr(loss_module, _source)\n except AttributeError as err:\n raise RuntimeError(\n f\"Incongruent target and source parameter lists: \"\n f\"{_source} is not an attribute of the loss_module\"\n ) from err\n\n self._target_names = _target_names\n self._source_names = _source_names\n self.loss_module = loss_module\n self.initialized = False\n self.init_()\n _has_update_associated = True\n finally:\n for k in loss_module._has_update_associated.keys():\n loss_module._has_update_associated[k] = _has_update_associated\n\n @property\n def _targets(self):\n return TensorDict(\n {name: getattr(self.loss_module, name) for name in self._target_names},\n [],\n )\n\n @property\n def _sources(self):\n return TensorDict(\n {name: getattr(self.loss_module, name) for name in self._source_names},\n [],\n )\n\n def init_(self) -> None:\n if self.initialized:\n warnings.warn(\"Updated already initialized.\")\n found_distinct = False\n self._distinct = {}\n for key, source in self._sources.items(True, True):\n if not isinstance(key, tuple):\n key = (key,)\n key = (\"target_\" + key[0], *key[1:])\n target = self._targets[key]\n # for p_source, p_target in zip(source, target):\n if target.requires_grad:\n raise RuntimeError(\"the target parameter is part of a graph.\")\n self._distinct[key] = target.data_ptr() != source.data.data_ptr()\n found_distinct = found_distinct or self._distinct[key]\n target.data.copy_(source.data)\n if not found_distinct:\n raise RuntimeError(\n f\"The target and source data are identical for all params. \"\n \"Have you created proper target parameters? \"\n \"If the loss has a ``delay_value`` kwarg, make sure to set it \"\n \"to True if it is not done by default. \"\n f\"If no target parameter is needed, do not use a target updater such as {type(self)}.\"\n )\n\n self.initialized = True\n\n def step(self) -> None:\n if not self.initialized:\n raise Exception(\n f\"{self.__class__.__name__} must be \"\n f\"initialized (`{self.__class__.__name__}.init_()`) before calling step()\"\n )\n for key, source in self._sources.items(True, True):\n if not isinstance(key, tuple):\n key = (key,)\n key = (\"target_\" + key[0], *key[1:])\n if not self._distinct[key]:\n continue\n target = self._targets[key]\n if target.requires_grad:\n raise RuntimeError(\"the target parameter is part of a graph.\")\n if target.is_leaf:\n self._step(source, target)\n else:\n target.copy_(source)\n\n def _step(self, p_source: Tensor, p_target: Tensor) -> None:\n raise NotImplementedError\n\n def __repr__(self) -> str:\n string = (\n f\"{self.__class__.__name__}(sources={self._sources}, targets=\"\n f\"{self._targets})\"\n )\n return string\n\n\nclass SoftUpdate(TargetNetUpdater):\n r\"\"\"A soft-update class for target network update in Double DQN/DDPG.\n\n This was proposed in \"CONTINUOUS CONTROL WITH DEEP REINFORCEMENT LEARNING\", https://arxiv.org/pdf/1509.02971.pdf\n\n One and only one decay factor (tau or eps) must be specified.\n\n Args:\n loss_module (DQNLoss or DDPGLoss): loss module where the target network should be updated.\n eps (scalar): epsilon in the update equation:\n .. math::\n\n \\theta_t = \\theta_{t-1} * \\epsilon + \\theta_t * (1-\\epsilon)\n\n Exclusive with ``tau``.\n tau (scalar): Polyak tau. It is equal to ``1-eps``, and exclusive with it.\n \"\"\"\n\n def __init__(\n self,\n loss_module: Union[\n \"DQNLoss\", # noqa: F821\n \"DDPGLoss\", # noqa: F821\n \"SACLoss\", # noqa: F821\n \"REDQLoss\", # noqa: F821\n \"TD3Loss\", # noqa: F821\n ],\n *,\n eps: float = None,\n tau: Optional[float] = None,\n ):\n if eps is None and tau is None:\n raise RuntimeError(\n \"Neither eps nor tau was provided. This behaviour is deprecated.\",\n )\n eps = 0.999\n if (eps is None) ^ (tau is None):\n if eps is None:\n eps = 1 - tau\n else:\n raise ValueError(\"One and only one argument (tau or eps) can be specified.\")\n if eps < 0.5:\n warnings.warn(\n \"Found an eps value < 0.5, which is unexpected. \"\n \"You may want to use the `tau` keyword argument instead.\"\n )\n if not (eps <= 1.0 and eps >= 0.0):\n raise ValueError(\n f\"Got eps = {eps} when it was supposed to be between 0 and 1.\"\n )\n super(SoftUpdate, self).__init__(loss_module)\n self.eps = eps\n\n def _step(self, p_source: Tensor, p_target: Tensor) -> None:\n p_target.data.copy_(p_target.data * self.eps + p_source.data * (1 - self.eps))\n\n\nclass HardUpdate(TargetNetUpdater):\n \"\"\"A hard-update class for target network update in Double DQN/DDPG (by contrast with soft updates).\n\n This was proposed in the original Double DQN paper: \"Deep Reinforcement Learning with Double Q-learning\",\n https://arxiv.org/abs/1509.06461.\n\n Args:\n loss_module (DQNLoss or DDPGLoss): loss module where the target network should be updated.\n\n Keyword Args:\n value_network_update_interval (scalar): how often the target network should be updated.\n default: 1000\n \"\"\"\n\n def __init__(\n self,\n loss_module: Union[\"DQNLoss\", \"DDPGLoss\", \"SACLoss\", \"TD3Loss\"], # noqa: F821\n *,\n value_network_update_interval: float = 1000,\n ):\n super(HardUpdate, self).__init__(loss_module)\n self.value_network_update_interval = value_network_update_interval\n self.counter = 0\n\n def _step(self, p_source: Tensor, p_target: Tensor) -> None:\n if self.counter == self.value_network_update_interval:\n p_target.data.copy_(p_source.data)\n\n def step(self) -> None:\n super().step()\n if self.counter == self.value_network_update_interval:\n self.counter = 0\n else:\n self.counter += 1\n\n\nclass hold_out_net(_context_manager):\n \"\"\"Context manager to hold a network out of a computational graph.\"\"\"\n\n def __init__(self, network: nn.Module) -> None:\n self.network = network\n for p in network.parameters():\n self.mode = p.requires_grad\n break\n else:\n self.mode = True\n\n def __enter__(self) -> None:\n if self.mode:\n self.network.requires_grad_(False)\n\n def __exit__(self, exc_type, exc_val, exc_tb) -> None:\n if self.mode:\n self.network.requires_grad_()\n\n\nclass hold_out_params(_context_manager):\n \"\"\"Context manager to hold a list of parameters out of a computational graph.\"\"\"\n\n def __init__(self, params: Iterable[Tensor]) -> None:\n if isinstance(params, TensorDictBase):\n self.params = params.detach()\n else:\n self.params = tuple(p.detach() for p in params)\n\n def __enter__(self) -> None:\n return self.params\n\n def __exit__(self, exc_type, exc_val, exc_tb) -> None:\n pass\n\n\[email protected]_grad()\ndef next_state_value(\n tensordict: TensorDictBase,\n operator: Optional[TensorDictModule] = None,\n next_val_key: str = \"state_action_value\",\n gamma: float = 0.99,\n pred_next_val: Optional[Tensor] = None,\n **kwargs,\n) -> torch.Tensor:\n \"\"\"Computes the next state value (without gradient) to compute a target value.\n\n The target value is ususally used to compute a distance loss (e.g. MSE):\n L = Sum[ (q_value - target_value)^2 ]\n The target value is computed as\n r + gamma ** n_steps_to_next * value_next_state\n If the reward is the immediate reward, n_steps_to_next=1. If N-steps rewards are used, n_steps_to_next is gathered\n from the input tensordict.\n\n Args:\n tensordict (TensorDictBase): Tensordict containing a reward and done key (and a n_steps_to_next key for n-steps\n rewards).\n operator (ProbabilisticTDModule, optional): the value function operator. Should write a 'next_val_key'\n key-value in the input tensordict when called. It does not need to be provided if pred_next_val is given.\n next_val_key (str, optional): key where the next value will be written.\n Default: 'state_action_value'\n gamma (float, optional): return discount rate.\n default: 0.99\n pred_next_val (Tensor, optional): the next state value can be provided if it is not computed with the operator.\n\n Returns:\n a Tensor of the size of the input tensordict containing the predicted value state.\n\n \"\"\"\n if \"steps_to_next_obs\" in tensordict.keys():\n steps_to_next_obs = tensordict.get(\"steps_to_next_obs\").squeeze(-1)\n else:\n steps_to_next_obs = 1\n\n rewards = tensordict.get((\"next\", \"reward\")).squeeze(-1)\n done = tensordict.get((\"next\", \"done\")).squeeze(-1)\n if done.all() or gamma == 0:\n return rewards\n\n if pred_next_val is None:\n next_td = step_mdp(tensordict) # next_observation -> observation\n next_td = next_td.select(*operator.in_keys)\n operator(next_td, **kwargs)\n pred_next_val_detach = next_td.get(next_val_key).squeeze(-1)\n else:\n pred_next_val_detach = pred_next_val.squeeze(-1)\n done = done.to(torch.float)\n target_value = (1 - done) * pred_next_val_detach\n rewards = rewards.to(torch.float)\n target_value = rewards + (gamma**steps_to_next_obs) * target_value\n return target_value\n\n\ndef _cache_values(fun):\n \"\"\"Caches the tensordict returned by a property.\"\"\"\n name = fun.__name__\n\n def new_fun(self, netname=None):\n __dict__ = self.__dict__\n _cache = __dict__.setdefault(\"_cache\", {})\n attr_name = name\n if netname is not None:\n attr_name += \"_\" + netname\n if attr_name in _cache:\n out = _cache[attr_name]\n return out\n if netname is not None:\n out = fun(self, netname)\n else:\n out = fun(self)\n # TODO: decide what to do with locked tds in functional calls\n # if is_tensor_collection(out):\n # out.lock_()\n _cache[attr_name] = out\n return out\n\n return new_fun\n\n\ndef _vmap_func(module, *args, func=None, **kwargs):\n try:\n\n def decorated_module(*module_args_params):\n params = module_args_params[-1]\n module_args = module_args_params[:-1]\n with params.to_module(module):\n if func is None:\n return module(*module_args)\n else:\n return getattr(module, func)(*module_args)\n\n return vmap(decorated_module, *args, **kwargs) # noqa: TOR101\n\n except RuntimeError as err:\n if re.match(\n r\"vmap: called random operation while in randomness error mode\", str(err)\n ):\n raise RuntimeError(\n \"Please use <loss_module>.set_vmap_randomness('different') to handle random operations during vmap.\"\n ) from err\n",
"path": "torchrl/objectives/utils.py"
}
] | diff --git a/torchrl/objectives/utils.py b/torchrl/objectives/utils.py
index b234af6a804..9afbf8095f0 100644
--- a/torchrl/objectives/utils.py
+++ b/torchrl/objectives/utils.py
@@ -459,7 +459,7 @@ def _cache_values(fun):
def new_fun(self, netname=None):
__dict__ = self.__dict__
- _cache = __dict__["_cache"]
+ _cache = __dict__.setdefault("_cache", {})
attr_name = name
if netname is not None:
attr_name += "_" + netname
|
techmatters__terraso-backend-81 | Add photo field to the User model
## Description
The user profile photo might be automatically fetched from the third-party account system (Google or Apple), or it can also be uploaded from by the user. Since the file itself might be stored on an external storage service, this field will be used to store the location of the file.
In this issue, it's important to consider the flow front-end → back-end for photo upload.
## Suggested subtasks
- [ ] Design the overall flow to upload photo considering front-end → back-end flow
- [ ] Add the new field on model with proper support to the external storage service (upload) and update DB migrations
- [ ] Implement upload feature to update photo
- [ ] Add support to present the proper photo URL from external services
- [ ] Add the new photo field on user API
This issue depends on:
- #21
| [
{
"content": "import graphene\nfrom graphene import relay\nfrom graphene_django import DjangoObjectType\n\nfrom apps.core.models import User\n\nfrom .commons import BaseDeleteMutation\n\n\nclass UserNode(DjangoObjectType):\n id = graphene.ID(source=\"pk\", required=True)\n\n class Meta:\n model = User\n filter_fields = {\n \"email\": [\"exact\", \"icontains\"],\n \"first_name\": [\"icontains\"],\n \"last_name\": [\"icontains\"],\n }\n fields = (\"email\", \"first_name\", \"last_name\", \"memberships\")\n interfaces = (relay.Node,)\n\n\nclass UserAddMutation(relay.ClientIDMutation):\n user = graphene.Field(UserNode)\n\n class Input:\n first_name = graphene.String()\n last_name = graphene.String()\n email = graphene.String(required=True)\n password = graphene.String(required=True)\n\n @classmethod\n def mutate_and_get_payload(cls, root, info, **kwargs):\n user = User.objects.create_user(\n kwargs.pop(\"email\"), password=kwargs.pop(\"password\"), **kwargs\n )\n\n return cls(user=user)\n\n\nclass UserUpdateMutation(relay.ClientIDMutation):\n user = graphene.Field(UserNode)\n\n model_class = User\n\n class Input:\n id = graphene.ID(required=True)\n first_name = graphene.String()\n last_name = graphene.String()\n email = graphene.String()\n password = graphene.String()\n\n @classmethod\n def mutate_and_get_payload(cls, root, info, **kwargs):\n _id = kwargs.pop(\"id\")\n\n user = User.objects.get(pk=_id)\n new_password = kwargs.pop(\"password\", None)\n\n if new_password:\n user.set_password(new_password)\n\n for attr, value in kwargs.items():\n setattr(user, attr, value)\n\n user.save()\n\n return cls(user=user)\n\n\nclass UserDeleteMutation(BaseDeleteMutation):\n user = graphene.Field(UserNode)\n model_class = User\n\n class Input:\n id = graphene.ID()\n",
"path": "terraso_backend/apps/graphql/schema/users.py"
}
] | [
{
"content": "import graphene\nfrom graphene import relay\nfrom graphene_django import DjangoObjectType\n\nfrom apps.core.models import User\n\nfrom .commons import BaseDeleteMutation\n\n\nclass UserNode(DjangoObjectType):\n id = graphene.ID(source=\"pk\", required=True)\n\n class Meta:\n model = User\n filter_fields = {\n \"email\": [\"exact\", \"icontains\"],\n \"first_name\": [\"icontains\"],\n \"last_name\": [\"icontains\"],\n }\n fields = (\"email\", \"first_name\", \"last_name\", \"profile_image\", \"memberships\")\n interfaces = (relay.Node,)\n\n\nclass UserAddMutation(relay.ClientIDMutation):\n user = graphene.Field(UserNode)\n\n class Input:\n first_name = graphene.String()\n last_name = graphene.String()\n email = graphene.String(required=True)\n password = graphene.String(required=True)\n\n @classmethod\n def mutate_and_get_payload(cls, root, info, **kwargs):\n user = User.objects.create_user(\n kwargs.pop(\"email\"), password=kwargs.pop(\"password\"), **kwargs\n )\n\n return cls(user=user)\n\n\nclass UserUpdateMutation(relay.ClientIDMutation):\n user = graphene.Field(UserNode)\n\n model_class = User\n\n class Input:\n id = graphene.ID(required=True)\n first_name = graphene.String()\n last_name = graphene.String()\n email = graphene.String()\n password = graphene.String()\n\n @classmethod\n def mutate_and_get_payload(cls, root, info, **kwargs):\n _id = kwargs.pop(\"id\")\n\n user = User.objects.get(pk=_id)\n new_password = kwargs.pop(\"password\", None)\n\n if new_password:\n user.set_password(new_password)\n\n for attr, value in kwargs.items():\n setattr(user, attr, value)\n\n user.save()\n\n return cls(user=user)\n\n\nclass UserDeleteMutation(BaseDeleteMutation):\n user = graphene.Field(UserNode)\n model_class = User\n\n class Input:\n id = graphene.ID()\n",
"path": "terraso_backend/apps/graphql/schema/users.py"
}
] | diff --git a/terraso_backend/apps/graphql/schema/users.py b/terraso_backend/apps/graphql/schema/users.py
index f8c8c8b88..9018ff1bf 100644
--- a/terraso_backend/apps/graphql/schema/users.py
+++ b/terraso_backend/apps/graphql/schema/users.py
@@ -17,7 +17,7 @@ class Meta:
"first_name": ["icontains"],
"last_name": ["icontains"],
}
- fields = ("email", "first_name", "last_name", "memberships")
+ fields = ("email", "first_name", "last_name", "profile_image", "memberships")
interfaces = (relay.Node,)
diff --git a/terraso_backend/tests/graphql/test_core_request.py b/terraso_backend/tests/graphql/test_core_request.py
index 56204c23d..deba83f96 100644
--- a/terraso_backend/tests/graphql/test_core_request.py
+++ b/terraso_backend/tests/graphql/test_core_request.py
@@ -144,6 +144,7 @@ def test_users_query(client_query, users):
edges {
node {
email
+ profileImage
}
}
}
@@ -151,6 +152,8 @@ def test_users_query(client_query, users):
"""
)
edges = response.json()["data"]["users"]["edges"]
- users_result = [edge["node"]["email"] for edge in edges]
+ users_result_nodes = [edge["node"] for edge in edges]
for user in users:
- assert user.email in users_result
+ user_node = next(item for item in users_result_nodes if item["email"] == user.email)
+ assert user_node
+ assert user.profile_image == user_node["profileImage"]
|
cowrie__cowrie-920 | output_localsyslog exceptions.KeyError: 'isError'
After pulling the most recent version of cowrie to some of my honeypots, I get this error when a new connection I enabled [output_localsyslog] with configuration below:
```
[output_localsyslog]
enabled = true
facility = LOCAL5
format = text
```
The log error shows this:
```
2018-10-11T18:29:01.778300+0000 [twisted.logger._observer#critical] Temporarily disabling observer LegacyLogObserverWrapper(<bound method Output.emit of <cowrie.output.localsyslog.Output object at 0xb55ae7b0>>) due to exception: [Failure instance: Traceback: <type 'exceptions.KeyError'>: 'isError'
/opt/cowrie/src/cowrie/core/checkers.py:110:checkUserPass
/opt/cowrie/cowrie-env/local/lib/python2.7/site-packages/twisted/python/threadable.py:53:sync
/opt/cowrie/cowrie-env/local/lib/python2.7/site-packages/twisted/python/log.py:286:msg
/opt/cowrie/cowrie-env/local/lib/python2.7/site-packages/twisted/logger/_legacy.py:154:publishToNewObserver
--- <exception caught here> ---
/opt/cowrie/cowrie-env/local/lib/python2.7/site-packages/twisted/logger/_observer.py:131:__call__
/opt/cowrie/cowrie-env/local/lib/python2.7/site-packages/twisted/logger/_legacy.py:93:__call__
/opt/cowrie/src/cowrie/core/output.py:209:emit
/opt/cowrie/src/cowrie/output/localsyslog.py:65:write
/opt/cowrie/cowrie-env/local/lib/python2.7/site-packages/twisted/python/syslog.py:76:emit
]
Traceback (most recent call last):
File "/opt/cowrie/src/cowrie/core/checkers.py", line 110, in checkUserPass
password=thepassword)
File "/opt/cowrie/cowrie-env/local/lib/python2.7/site-packages/twisted/python/threadable.py", line 53, in sync
return function(self, *args, **kwargs)
File "/opt/cowrie/cowrie-env/local/lib/python2.7/site-packages/twisted/python/log.py", line 286, in msg
_publishNew(self._publishPublisher, actualEventDict, textFromEventDict)
File "/opt/cowrie/cowrie-env/local/lib/python2.7/site-packages/twisted/logger/_legacy.py", line 154, in publishToNewObserver
observer(eventDict)
--- <exception caught here> ---
File "/opt/cowrie/cowrie-env/local/lib/python2.7/site-packages/twisted/logger/_observer.py", line 131, in __call__
observer(event)
File "/opt/cowrie/cowrie-env/local/lib/python2.7/site-packages/twisted/logger/_legacy.py", line 93, in __call__
self.legacyObserver(event)
File "/opt/cowrie/src/cowrie/core/output.py", line 209, in emit
self.write(ev)
File "/opt/cowrie/src/cowrie/output/localsyslog.py", line 65, in write
self.syslog.emit(logentry)
File "/opt/cowrie/cowrie-env/local/lib/python2.7/site-packages/twisted/python/syslog.py", line 76, in emit
if eventDict['isError']:
exceptions.KeyError: 'isError'
```
| [
{
"content": "# Copyright (c) 2015 Michel Oosterhof <[email protected]>\n# All rights reserved.\n#\n# Redistribution and use in source and binary forms, with or without\n# modification, are permitted provided that the following conditions\n# are met:\n#\n# 1. Redistributions of source code must retain the above copyright\n# notice, this list of conditions and the following disclaimer.\n# 2. Redistributions in binary form must reproduce the above copyright\n# notice, this list of conditions and the following disclaimer in the\n# documentation and/or other materials provided with the distribution.\n# 3. The names of the author(s) may not be used to endorse or promote\n# products derived from this software without specific prior written\n# permission.\n#\n# THIS SOFTWARE IS PROVIDED BY THE AUTHORS ``AS IS'' AND ANY EXPRESS OR\n# IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES\n# OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.\n# IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY DIRECT, INDIRECT,\n# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,\n# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;\n# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED\n# AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\n# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY\n# OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF\n# SUCH DAMAGE.\n\nfrom __future__ import absolute_import, division\n\nimport syslog\n\nimport twisted.python.syslog\n\nimport cowrie.core.cef\nimport cowrie.core.output\nfrom cowrie.core.config import CONFIG\n\n\nclass Output(cowrie.core.output.Output):\n\n def __init__(self):\n facilityString = CONFIG.get('output_localsyslog', 'facility')\n self.format = CONFIG.get('output_localsyslog', 'format')\n self.facility = vars(syslog)['LOG_' + facilityString]\n self.syslog = twisted.python.syslog.SyslogObserver(prefix='cowrie', facility=self.facility)\n cowrie.core.output.Output.__init__(self)\n\n def start(self):\n pass\n\n def stop(self):\n pass\n\n def write(self, logentry):\n if self.format == 'cef':\n self.syslog.emit({\n 'message': cowrie.core.cef.formatCef(logentry),\n 'isError': False,\n 'system': 'cowrie'\n })\n else:\n # message appears with additional spaces if message key is defined\n logentry['message'] = [logentry['message']]\n self.syslog.emit(logentry)\n",
"path": "src/cowrie/output/localsyslog.py"
}
] | [
{
"content": "# Copyright (c) 2015 Michel Oosterhof <[email protected]>\n# All rights reserved.\n#\n# Redistribution and use in source and binary forms, with or without\n# modification, are permitted provided that the following conditions\n# are met:\n#\n# 1. Redistributions of source code must retain the above copyright\n# notice, this list of conditions and the following disclaimer.\n# 2. Redistributions in binary form must reproduce the above copyright\n# notice, this list of conditions and the following disclaimer in the\n# documentation and/or other materials provided with the distribution.\n# 3. The names of the author(s) may not be used to endorse or promote\n# products derived from this software without specific prior written\n# permission.\n#\n# THIS SOFTWARE IS PROVIDED BY THE AUTHORS ``AS IS'' AND ANY EXPRESS OR\n# IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES\n# OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.\n# IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY DIRECT, INDIRECT,\n# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,\n# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;\n# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED\n# AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\n# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY\n# OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF\n# SUCH DAMAGE.\n\nfrom __future__ import absolute_import, division\n\nimport syslog\n\nimport twisted.python.syslog\n\nimport cowrie.core.cef\nimport cowrie.core.output\nfrom cowrie.core.config import CONFIG\n\n\nclass Output(cowrie.core.output.Output):\n\n def __init__(self):\n facilityString = CONFIG.get('output_localsyslog', 'facility')\n self.format = CONFIG.get('output_localsyslog', 'format')\n self.facility = vars(syslog)['LOG_' + facilityString]\n self.syslog = twisted.python.syslog.SyslogObserver(prefix='cowrie', facility=self.facility)\n cowrie.core.output.Output.__init__(self)\n\n def start(self):\n pass\n\n def stop(self):\n pass\n\n def write(self, logentry):\n if 'isError' not in logentry:\n logentry['isError'] = False\n\n if self.format == 'cef':\n self.syslog.emit({\n 'message': cowrie.core.cef.formatCef(logentry),\n 'isError': False,\n 'system': 'cowrie'\n })\n else:\n # message appears with additional spaces if message key is defined\n logentry['message'] = [logentry['message']]\n self.syslog.emit(logentry)\n",
"path": "src/cowrie/output/localsyslog.py"
}
] | diff --git a/src/cowrie/output/localsyslog.py b/src/cowrie/output/localsyslog.py
index 751e8d835c..1156a82d30 100644
--- a/src/cowrie/output/localsyslog.py
+++ b/src/cowrie/output/localsyslog.py
@@ -53,6 +53,9 @@ def stop(self):
pass
def write(self, logentry):
+ if 'isError' not in logentry:
+ logentry['isError'] = False
+
if self.format == 'cef':
self.syslog.emit({
'message': cowrie.core.cef.formatCef(logentry),
|
scikit-image__scikit-image-6343 | imageIO warnings due to v2 -> v3 migration
## Description
As of imageIO 2.16.0 (Feb22) there are now a v2 and v3 namespaces in addition to the top-level namespace. As of 2.16.2 (released Apr22) directly using the top-level namespace results in warnings to either explicitly opt-into the v3 API or opt-out and import the v2.
This in turn causes warnings when using `skimage.io.imread`.
I suggest that this is a good first issue as there is no API design choices here (at least to start) and only needs the
```python
try:
import newway
except ImportError:
import old way
```
dance.
The warnings look like (lifted from a test suite):
```
____________________________________________________________________________ ReaderSequence.test_slice_of_slice ____________________________________________________________________________
pims/tests/test_imseq.py:256: in setUp
self.v = self.klass(self.filename, **self.kwargs)
pims/image_sequence.py:217: in __init__
with self.reader_cls(self._filepaths[0], **self.kwargs) as reader:
pims/image_reader.py:60: in __init__
self._data = Frame(imread(filename, **kwargs), frame_no=0)
../../../../.pybuild/bleeding/lib/python3.11/contextlib.py:155: in __exit__
self.gen.throw(typ, value, traceback)
../../../../.virtualenvs/bleeding/lib/python3.11/site-packages/skimage/io/util.py:43: in file_or_url_context
yield resource_name
../../../../.virtualenvs/bleeding/lib/python3.11/site-packages/skimage/io/_io.py:53: in imread
img = call_plugin('imread', fname, plugin=plugin, **plugin_args)
../../../../.virtualenvs/bleeding/lib/python3.11/site-packages/skimage/io/manage_plugins.py:207: in call_plugin
return func(*args, **kwargs)
../../../../.virtualenvs/bleeding/lib/python3.11/site-packages/skimage/io/_plugins/imageio_plugin.py:10: in imread
return np.asarray(imageio_imread(*args, **kwargs))
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
uri = '/home/tcaswell/source/bnl/soft-matter/pims/pims/tests/data/image_sequence3d/file001.png', format = None, kwargs = {}
def imread(uri, format=None, **kwargs):
"""imread(uri, format=None, **kwargs)
Reads an image from the specified file. Returns a numpy array, which
comes with a dict of meta data at its 'meta' attribute.
Note that the image data is returned as-is, and may not always have
a dtype of uint8 (and thus may differ from what e.g. PIL returns).
Parameters
----------
uri : {str, pathlib.Path, bytes, file}
The resource to load the image from, e.g. a filename, pathlib.Path,
http address or file object, see the docs for more info.
format : str
The format to use to read the file. By default imageio selects
the appropriate for you based on the filename and its contents.
kwargs : ...
Further keyword arguments are passed to the reader. See :func:`.help`
to see what arguments are available for a particular format.
"""
> warnings.warn(
"Starting with ImageIO v3 the behavior of this function will switch to that of"
" iio.v3.imread. To keep the current behavior (and make this warning dissapear)"
" use `import imageio.v2 as imageio` or call `imageio.v2.imread` directly.",
DeprecationWarning,
)
E DeprecationWarning: Starting with ImageIO v3 the behavior of this function will switch to that of iio.v3.imread. To keep the current behavior (and make this warning dissapear) use `import imageio.v2 as imageio` or call `imageio.v2.imread` directly.
../../../../.virtualenvs/bleeding/lib/python3.11/site-packages/imageio/__init__.py:89: DeprecationWarning
```
| [
{
"content": "__all__ = ['imread', 'imsave']\n\nfrom functools import wraps\nimport numpy as np\nfrom imageio import imread as imageio_imread, imsave\n\n\n@wraps(imageio_imread)\ndef imread(*args, **kwargs):\n return np.asarray(imageio_imread(*args, **kwargs))\n",
"path": "skimage/io/_plugins/imageio_plugin.py"
}
] | [
{
"content": "__all__ = ['imread', 'imsave']\n\nfrom functools import wraps\nimport numpy as np\n\ntry:\n # Try using the v2 API directly to avoid a warning from imageio >= 2.16.2\n from imageio.v2 import imread as imageio_imread, imsave\nexcept ImportError:\n from imageio import imread as imageio_imread, imsave\n\n\n@wraps(imageio_imread)\ndef imread(*args, **kwargs):\n return np.asarray(imageio_imread(*args, **kwargs))\n",
"path": "skimage/io/_plugins/imageio_plugin.py"
}
] | diff --git a/skimage/io/_plugins/imageio_plugin.py b/skimage/io/_plugins/imageio_plugin.py
index c8831e785eb..567d45dd78c 100644
--- a/skimage/io/_plugins/imageio_plugin.py
+++ b/skimage/io/_plugins/imageio_plugin.py
@@ -2,7 +2,12 @@
from functools import wraps
import numpy as np
-from imageio import imread as imageio_imread, imsave
+
+try:
+ # Try using the v2 API directly to avoid a warning from imageio >= 2.16.2
+ from imageio.v2 import imread as imageio_imread, imsave
+except ImportError:
+ from imageio import imread as imageio_imread, imsave
@wraps(imageio_imread)
|
litestar-org__litestar-2681 | Docs: Build errors
### Summary
```
/home/peter/PycharmProjects/litestar/litestar/plugins/base.py:docstring of litestar.plugins.base.InitPluginProtocol.on_app_init:5: ERROR: Error in "code-block" directive:
maximum 1 argument(s) allowed, 14 supplied.
.. code-block:: python
from litestar import Litestar, get
from litestar.di import Provide
from litestar.plugins import InitPluginProtocol
def get_name() -> str:
return "world"
@get("/my-path")
def my_route_handler(name: str) -> dict[str, str]:
return {"hello": name}
class MyPlugin(InitPluginProtocol):
def on_app_init(self, app_config: AppConfig) -> AppConfig:
app_config.dependencies["name"] = Provide(get_name)
app_config.route_handlers.append(my_route_handler)
return app_config
app = Litestar(plugins=[MyPlugin()])
```
And
```
/home/peter/PycharmProjects/litestar/docs/topics/deployment/manually-with-asgi-server.rst:2: WARNING: Title underline too short.
Manually with ASGI server
==========
```
<!-- POLAR PLEDGE BADGE START -->
---
> [!NOTE]
> While we are open for sponsoring on [GitHub Sponsors](https://github.com/sponsors/litestar-org/) and
> [OpenCollective](https://opencollective.com/litestar), we also utilize [Polar.sh](https://polar.sh/) to engage in pledge-based sponsorship.
>
> Check out all issues funded or available for funding [on our Polar.sh Litestar dashboard](https://polar.sh/litestar-org)
> * If you would like to see an issue prioritized, make a pledge towards it!
> * We receive the pledge once the issue is completed & verified
> * This, along with engagement in the community, helps us know which features are a priority to our users.
<a href="https://polar.sh/litestar-org/litestar/issues/2680">
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://polar.sh/api/github/litestar-org/litestar/issues/2680/pledge.svg?darkmode=1">
<img alt="Fund with Polar" src="https://polar.sh/api/github/litestar-org/litestar/issues/2680/pledge.svg">
</picture>
</a>
<!-- POLAR PLEDGE BADGE END -->
| [
{
"content": "from __future__ import annotations\n\nfrom typing import TYPE_CHECKING, Any, Iterator, Protocol, TypeVar, Union, cast, runtime_checkable\n\nif TYPE_CHECKING:\n from click import Group\n\n from litestar._openapi.schema_generation import SchemaCreator\n from litestar.config.app import AppConfig\n from litestar.dto import AbstractDTO\n from litestar.openapi.spec import Schema\n from litestar.typing import FieldDefinition\n\n__all__ = (\n \"SerializationPluginProtocol\",\n \"InitPluginProtocol\",\n \"OpenAPISchemaPluginProtocol\",\n \"OpenAPISchemaPlugin\",\n \"PluginProtocol\",\n \"CLIPluginProtocol\",\n \"PluginRegistry\",\n)\n\n\n@runtime_checkable\nclass InitPluginProtocol(Protocol):\n \"\"\"Protocol used to define plugins that affect the application's init process.\"\"\"\n\n __slots__ = ()\n\n def on_app_init(self, app_config: AppConfig) -> AppConfig:\n \"\"\"Receive the :class:`AppConfig<.config.app.AppConfig>` instance after `on_app_init` hooks have been called.\n\n Examples:\n .. code-block:: python\n from litestar import Litestar, get\n from litestar.di import Provide\n from litestar.plugins import InitPluginProtocol\n\n\n def get_name() -> str:\n return \"world\"\n\n\n @get(\"/my-path\")\n def my_route_handler(name: str) -> dict[str, str]:\n return {\"hello\": name}\n\n\n class MyPlugin(InitPluginProtocol):\n def on_app_init(self, app_config: AppConfig) -> AppConfig:\n app_config.dependencies[\"name\"] = Provide(get_name)\n app_config.route_handlers.append(my_route_handler)\n return app_config\n\n\n app = Litestar(plugins=[MyPlugin()])\n\n Args:\n app_config: The :class:`AppConfig <litestar.config.app.AppConfig>` instance.\n\n Returns:\n The app config object.\n \"\"\"\n return app_config # pragma: no cover\n\n\n@runtime_checkable\nclass CLIPluginProtocol(Protocol):\n \"\"\"Plugin protocol to extend the CLI.\"\"\"\n\n def on_cli_init(self, cli: Group) -> None:\n \"\"\"Called when the CLI is initialized.\n\n This can be used to extend or override existing commands.\n\n Args:\n cli: The root :class:`click.Group` of the Litestar CLI\n\n Examples:\n .. code-block:: python\n\n from litestar import Litestar\n from litestar.plugins import CLIPluginProtocol\n from click import Group\n\n\n class CLIPlugin(CLIPluginProtocol):\n def on_cli_init(self, cli: Group) -> None:\n @cli.command()\n def is_debug_mode(app: Litestar):\n print(app.debug)\n\n\n app = Litestar(plugins=[CLIPlugin()])\n \"\"\"\n\n\n@runtime_checkable\nclass SerializationPluginProtocol(Protocol):\n \"\"\"Protocol used to define a serialization plugin for DTOs.\"\"\"\n\n __slots__ = ()\n\n def supports_type(self, field_definition: FieldDefinition) -> bool:\n \"\"\"Given a value of indeterminate type, determine if this value is supported by the plugin.\n\n Args:\n field_definition: A parsed type.\n\n Returns:\n Whether the type is supported by the plugin.\n \"\"\"\n raise NotImplementedError()\n\n def create_dto_for_type(self, field_definition: FieldDefinition) -> type[AbstractDTO]:\n \"\"\"Given a parsed type, create a DTO class.\n\n Args:\n field_definition: A parsed type.\n\n Returns:\n A DTO class.\n \"\"\"\n raise NotImplementedError()\n\n\n@runtime_checkable\nclass OpenAPISchemaPluginProtocol(Protocol):\n \"\"\"Plugin protocol to extend the support of OpenAPI schema generation for non-library types.\"\"\"\n\n __slots__ = ()\n\n @staticmethod\n def is_plugin_supported_type(value: Any) -> bool:\n \"\"\"Given a value of indeterminate type, determine if this value is supported by the plugin.\n\n Args:\n value: An arbitrary value.\n\n Returns:\n A typeguard dictating whether the value is supported by the plugin.\n \"\"\"\n raise NotImplementedError()\n\n def to_openapi_schema(self, field_definition: FieldDefinition, schema_creator: SchemaCreator) -> Schema:\n \"\"\"Given a type annotation, transform it into an OpenAPI schema class.\n\n Args:\n field_definition: An :class:`OpenAPI <litestar.openapi.spec.schema.Schema>` instance.\n schema_creator: An instance of the openapi SchemaCreator.\n\n Returns:\n An :class:`OpenAPI <litestar.openapi.spec.schema.Schema>` instance.\n \"\"\"\n raise NotImplementedError()\n\n\nclass OpenAPISchemaPlugin(OpenAPISchemaPluginProtocol):\n \"\"\"Plugin to extend the support of OpenAPI schema generation for non-library types.\"\"\"\n\n @staticmethod\n def is_undefined_sentinel(value: Any) -> bool:\n \"\"\"Return ``True`` if ``value`` should be treated as an undefined field\"\"\"\n return False\n\n @staticmethod\n def is_constrained_field(field_definition: FieldDefinition) -> bool:\n \"\"\"Return ``True`` if the field should be treated as constrained. If returning\n ``True``, constraints should be defined in the field's extras\n \"\"\"\n return False\n\n\nPluginProtocol = Union[\n SerializationPluginProtocol,\n InitPluginProtocol,\n OpenAPISchemaPluginProtocol,\n OpenAPISchemaPlugin,\n CLIPluginProtocol,\n]\n\nPluginT = TypeVar(\"PluginT\", bound=PluginProtocol)\n\n\nclass PluginRegistry:\n __slots__ = {\n \"init\": \"Plugins that implement the InitPluginProtocol\",\n \"openapi\": \"Plugins that implement the OpenAPISchemaPluginProtocol\",\n \"serialization\": \"Plugins that implement the SerializationPluginProtocol\",\n \"cli\": \"Plugins that implement the CLIPluginProtocol\",\n \"_plugins_by_type\": None,\n \"_plugins\": None,\n \"_get_plugins_of_type\": None,\n }\n\n def __init__(self, plugins: list[PluginProtocol]) -> None:\n self._plugins_by_type = {type(p): p for p in plugins}\n self._plugins = frozenset(plugins)\n self.init = tuple(p for p in plugins if isinstance(p, InitPluginProtocol))\n self.openapi = tuple(p for p in plugins if isinstance(p, OpenAPISchemaPluginProtocol))\n self.serialization = tuple(p for p in plugins if isinstance(p, SerializationPluginProtocol))\n self.cli = tuple(p for p in plugins if isinstance(p, CLIPluginProtocol))\n\n def get(self, type_: type[PluginT]) -> PluginT:\n \"\"\"Return the registered plugin of ``type_``.\n\n This should be used with subclasses of the plugin protocols.\n \"\"\"\n try:\n return cast(PluginT, self._plugins_by_type[type_]) # type: ignore[index]\n except KeyError as e:\n raise KeyError(f\"No plugin of type {type_.__name__!r} registered\") from e\n\n def __iter__(self) -> Iterator[PluginProtocol]:\n return iter(self._plugins)\n\n def __contains__(self, item: PluginProtocol) -> bool:\n return item in self._plugins\n",
"path": "litestar/plugins/base.py"
}
] | [
{
"content": "from __future__ import annotations\n\nfrom typing import TYPE_CHECKING, Any, Iterator, Protocol, TypeVar, Union, cast, runtime_checkable\n\nif TYPE_CHECKING:\n from click import Group\n\n from litestar._openapi.schema_generation import SchemaCreator\n from litestar.config.app import AppConfig\n from litestar.dto import AbstractDTO\n from litestar.openapi.spec import Schema\n from litestar.typing import FieldDefinition\n\n__all__ = (\n \"SerializationPluginProtocol\",\n \"InitPluginProtocol\",\n \"OpenAPISchemaPluginProtocol\",\n \"OpenAPISchemaPlugin\",\n \"PluginProtocol\",\n \"CLIPluginProtocol\",\n \"PluginRegistry\",\n)\n\n\n@runtime_checkable\nclass InitPluginProtocol(Protocol):\n \"\"\"Protocol used to define plugins that affect the application's init process.\"\"\"\n\n __slots__ = ()\n\n def on_app_init(self, app_config: AppConfig) -> AppConfig:\n \"\"\"Receive the :class:`AppConfig<.config.app.AppConfig>` instance after `on_app_init` hooks have been called.\n\n Examples:\n .. code-block:: python\n\n from litestar import Litestar, get\n from litestar.di import Provide\n from litestar.plugins import InitPluginProtocol\n\n\n def get_name() -> str:\n return \"world\"\n\n\n @get(\"/my-path\")\n def my_route_handler(name: str) -> dict[str, str]:\n return {\"hello\": name}\n\n\n class MyPlugin(InitPluginProtocol):\n def on_app_init(self, app_config: AppConfig) -> AppConfig:\n app_config.dependencies[\"name\"] = Provide(get_name)\n app_config.route_handlers.append(my_route_handler)\n return app_config\n\n\n app = Litestar(plugins=[MyPlugin()])\n\n Args:\n app_config: The :class:`AppConfig <litestar.config.app.AppConfig>` instance.\n\n Returns:\n The app config object.\n \"\"\"\n return app_config # pragma: no cover\n\n\n@runtime_checkable\nclass CLIPluginProtocol(Protocol):\n \"\"\"Plugin protocol to extend the CLI.\"\"\"\n\n def on_cli_init(self, cli: Group) -> None:\n \"\"\"Called when the CLI is initialized.\n\n This can be used to extend or override existing commands.\n\n Args:\n cli: The root :class:`click.Group` of the Litestar CLI\n\n Examples:\n .. code-block:: python\n\n from litestar import Litestar\n from litestar.plugins import CLIPluginProtocol\n from click import Group\n\n\n class CLIPlugin(CLIPluginProtocol):\n def on_cli_init(self, cli: Group) -> None:\n @cli.command()\n def is_debug_mode(app: Litestar):\n print(app.debug)\n\n\n app = Litestar(plugins=[CLIPlugin()])\n \"\"\"\n\n\n@runtime_checkable\nclass SerializationPluginProtocol(Protocol):\n \"\"\"Protocol used to define a serialization plugin for DTOs.\"\"\"\n\n __slots__ = ()\n\n def supports_type(self, field_definition: FieldDefinition) -> bool:\n \"\"\"Given a value of indeterminate type, determine if this value is supported by the plugin.\n\n Args:\n field_definition: A parsed type.\n\n Returns:\n Whether the type is supported by the plugin.\n \"\"\"\n raise NotImplementedError()\n\n def create_dto_for_type(self, field_definition: FieldDefinition) -> type[AbstractDTO]:\n \"\"\"Given a parsed type, create a DTO class.\n\n Args:\n field_definition: A parsed type.\n\n Returns:\n A DTO class.\n \"\"\"\n raise NotImplementedError()\n\n\n@runtime_checkable\nclass OpenAPISchemaPluginProtocol(Protocol):\n \"\"\"Plugin protocol to extend the support of OpenAPI schema generation for non-library types.\"\"\"\n\n __slots__ = ()\n\n @staticmethod\n def is_plugin_supported_type(value: Any) -> bool:\n \"\"\"Given a value of indeterminate type, determine if this value is supported by the plugin.\n\n Args:\n value: An arbitrary value.\n\n Returns:\n A typeguard dictating whether the value is supported by the plugin.\n \"\"\"\n raise NotImplementedError()\n\n def to_openapi_schema(self, field_definition: FieldDefinition, schema_creator: SchemaCreator) -> Schema:\n \"\"\"Given a type annotation, transform it into an OpenAPI schema class.\n\n Args:\n field_definition: An :class:`OpenAPI <litestar.openapi.spec.schema.Schema>` instance.\n schema_creator: An instance of the openapi SchemaCreator.\n\n Returns:\n An :class:`OpenAPI <litestar.openapi.spec.schema.Schema>` instance.\n \"\"\"\n raise NotImplementedError()\n\n\nclass OpenAPISchemaPlugin(OpenAPISchemaPluginProtocol):\n \"\"\"Plugin to extend the support of OpenAPI schema generation for non-library types.\"\"\"\n\n @staticmethod\n def is_undefined_sentinel(value: Any) -> bool:\n \"\"\"Return ``True`` if ``value`` should be treated as an undefined field\"\"\"\n return False\n\n @staticmethod\n def is_constrained_field(field_definition: FieldDefinition) -> bool:\n \"\"\"Return ``True`` if the field should be treated as constrained. If returning\n ``True``, constraints should be defined in the field's extras\n \"\"\"\n return False\n\n\nPluginProtocol = Union[\n SerializationPluginProtocol,\n InitPluginProtocol,\n OpenAPISchemaPluginProtocol,\n OpenAPISchemaPlugin,\n CLIPluginProtocol,\n]\n\nPluginT = TypeVar(\"PluginT\", bound=PluginProtocol)\n\n\nclass PluginRegistry:\n __slots__ = {\n \"init\": \"Plugins that implement the InitPluginProtocol\",\n \"openapi\": \"Plugins that implement the OpenAPISchemaPluginProtocol\",\n \"serialization\": \"Plugins that implement the SerializationPluginProtocol\",\n \"cli\": \"Plugins that implement the CLIPluginProtocol\",\n \"_plugins_by_type\": None,\n \"_plugins\": None,\n \"_get_plugins_of_type\": None,\n }\n\n def __init__(self, plugins: list[PluginProtocol]) -> None:\n self._plugins_by_type = {type(p): p for p in plugins}\n self._plugins = frozenset(plugins)\n self.init = tuple(p for p in plugins if isinstance(p, InitPluginProtocol))\n self.openapi = tuple(p for p in plugins if isinstance(p, OpenAPISchemaPluginProtocol))\n self.serialization = tuple(p for p in plugins if isinstance(p, SerializationPluginProtocol))\n self.cli = tuple(p for p in plugins if isinstance(p, CLIPluginProtocol))\n\n def get(self, type_: type[PluginT]) -> PluginT:\n \"\"\"Return the registered plugin of ``type_``.\n\n This should be used with subclasses of the plugin protocols.\n \"\"\"\n try:\n return cast(PluginT, self._plugins_by_type[type_]) # type: ignore[index]\n except KeyError as e:\n raise KeyError(f\"No plugin of type {type_.__name__!r} registered\") from e\n\n def __iter__(self) -> Iterator[PluginProtocol]:\n return iter(self._plugins)\n\n def __contains__(self, item: PluginProtocol) -> bool:\n return item in self._plugins\n",
"path": "litestar/plugins/base.py"
}
] | diff --git a/docs/topics/deployment/manually-with-asgi-server.rst b/docs/topics/deployment/manually-with-asgi-server.rst
index 11f6b89b24..11f00e21a2 100644
--- a/docs/topics/deployment/manually-with-asgi-server.rst
+++ b/docs/topics/deployment/manually-with-asgi-server.rst
@@ -1,5 +1,5 @@
Manually with ASGI server
-==========
+=========================
ASGI (Asynchronous Server Gateway Interface) is intended to provide a standard interface between async Python web frameworks like Litestar, and async web servers. There are several popular ASGI servers available, and you can choose the one that best fits your application's needs.
diff --git a/litestar/plugins/base.py b/litestar/plugins/base.py
index c2214b533f..8b6d536bb2 100644
--- a/litestar/plugins/base.py
+++ b/litestar/plugins/base.py
@@ -33,6 +33,7 @@ def on_app_init(self, app_config: AppConfig) -> AppConfig:
Examples:
.. code-block:: python
+
from litestar import Litestar, get
from litestar.di import Provide
from litestar.plugins import InitPluginProtocol
|
opsdroid__opsdroid-1408 | Duplicated shell prompt
# Description
When I run the hello skill from the shell, I found duplicated shell prompt output. I think there's some issue with the shell connector.
## Steps to Reproduce
```
qidong@ubuntu:~/Documents/opsdroid$ opsdroid start
mybot> hello
Hello qidong
mybot> mybot>
```
## Expected Functionality
There should be only one prompt printed after the skill response.
## Experienced Functionality
One extra prompt is printed.
## Configuration File
```yaml
logging:
console: false
connectors:
websocket:
bot-name: "mybot"
max-connections: 10
connection-timeout: 10
shell:
bot-name: "mybot"
skills:
## Hello (https://github.com/opsdroid/skill-hello)
hello: {}
```
| [
{
"content": "\"\"\"A connector to send messages using the command line.\"\"\"\nimport logging\nimport os\nimport sys\nimport platform\nimport asyncio\n\nfrom opsdroid.connector import Connector, register_event\nfrom opsdroid.events import Message\n\n_LOGGER = logging.getLogger(__name__)\nCONFIG_SCHEMA = {\"bot-name\": str}\n\n\nclass ConnectorShell(Connector):\n \"\"\"A connector to send messages using the command line.\"\"\"\n\n def __init__(self, config, opsdroid=None):\n \"\"\"Create the connector.\"\"\"\n _LOGGER.debug(_(\"Loaded shell Connector.\"))\n super().__init__(config, opsdroid=opsdroid)\n self.name = \"shell\"\n self.config = config\n self.bot_name = config.get(\"bot-name\", \"opsdroid\")\n self.prompt_length = None\n self.listening = True\n self.reader = None\n self._closing = asyncio.Event()\n self.loop = asyncio.get_event_loop()\n\n for name in (\"LOGNAME\", \"USER\", \"LNAME\", \"USERNAME\"):\n user = os.environ.get(name)\n if user:\n self.user = user\n\n @property\n def is_listening(self):\n \"\"\"Get listening status.\"\"\"\n return self.listening\n\n @is_listening.setter\n def is_listening(self, val):\n \"\"\"Set listening status.\"\"\"\n self.listening = val\n\n async def read_stdin(self):\n \"\"\"Create a stream reader to read stdin asynchronously.\n\n Returns:\n class: asyncio.streams.StreamReader\n\n \"\"\"\n self.reader = asyncio.StreamReader(loop=self.loop)\n reader_protocol = asyncio.StreamReaderProtocol(self.reader)\n\n await self.loop.connect_read_pipe(lambda: reader_protocol, sys.stdin)\n\n return self.reader\n\n async def async_input(self):\n \"\"\"Read user input asynchronously from stdin.\n\n Returns:\n string: A decoded string from user input.\n\n \"\"\"\n if not self.reader:\n self.reader = await self.read_stdin()\n line = await self.reader.readline()\n\n return line.decode(\"utf8\").replace(\"\\r\", \"\").replace(\"\\n\", \"\")\n\n def draw_prompt(self):\n \"\"\"Draw the user input prompt.\"\"\"\n prompt = self.bot_name + \"> \"\n self.prompt_length = len(prompt)\n print(prompt, end=\"\", flush=True)\n\n def clear_prompt(self):\n \"\"\"Clear the prompt.\"\"\"\n print(\"\\r\" + (\" \" * self.prompt_length) + \"\\r\", end=\"\", flush=True)\n\n async def parseloop(self):\n \"\"\"Parseloop moved out for testing.\"\"\"\n self.draw_prompt()\n user_input = await self.async_input()\n message = Message(text=user_input, user=self.user, target=None, connector=self)\n await self.opsdroid.parse(message)\n\n async def _parse_message(self):\n \"\"\"Parse user input.\"\"\"\n while self.is_listening:\n await self.parseloop()\n\n async def connect(self):\n \"\"\"Connect to the shell.\n\n There is nothing to do here since stdin is already available.\n\n Since this is the first method called when opsdroid starts, a logging\n message is shown if the user is using windows.\n\n \"\"\"\n if platform.system() == \"Windows\":\n _LOGGER.warning(\n \"The shell connector does not work on windows. Please install the Opsdroid Desktop App.\"\n )\n pass\n\n async def listen(self):\n \"\"\"Listen for and parse new user input.\"\"\"\n _LOGGER.debug(_(\"Connecting to shell.\"))\n message_processor = self.loop.create_task(self._parse_message())\n await self._closing.wait()\n message_processor.cancel()\n\n @register_event(Message)\n async def respond(self, message):\n \"\"\"Respond with a message.\n\n Args:\n message (object): An instance of Message\n\n \"\"\"\n _LOGGER.debug(_(\"Responding with: %s.\"), message.text)\n self.clear_prompt()\n print(message.text)\n self.draw_prompt()\n\n async def disconnect(self):\n \"\"\"Disconnects the connector.\"\"\"\n self._closing.set()\n",
"path": "opsdroid/connector/shell/__init__.py"
}
] | [
{
"content": "\"\"\"A connector to send messages using the command line.\"\"\"\nimport logging\nimport os\nimport sys\nimport platform\nimport asyncio\n\nfrom opsdroid.connector import Connector, register_event\nfrom opsdroid.events import Message\n\n_LOGGER = logging.getLogger(__name__)\nCONFIG_SCHEMA = {\"bot-name\": str}\n\n\nclass ConnectorShell(Connector):\n \"\"\"A connector to send messages using the command line.\"\"\"\n\n def __init__(self, config, opsdroid=None):\n \"\"\"Create the connector.\"\"\"\n _LOGGER.debug(_(\"Loaded shell Connector.\"))\n super().__init__(config, opsdroid=opsdroid)\n self.name = \"shell\"\n self.config = config\n self.bot_name = config.get(\"bot-name\", \"opsdroid\")\n self.prompt_length = None\n self.listening = True\n self.reader = None\n self._closing = asyncio.Event()\n self.loop = asyncio.get_event_loop()\n\n for name in (\"LOGNAME\", \"USER\", \"LNAME\", \"USERNAME\"):\n user = os.environ.get(name)\n if user:\n self.user = user\n\n @property\n def is_listening(self):\n \"\"\"Get listening status.\"\"\"\n return self.listening\n\n @is_listening.setter\n def is_listening(self, val):\n \"\"\"Set listening status.\"\"\"\n self.listening = val\n\n async def read_stdin(self):\n \"\"\"Create a stream reader to read stdin asynchronously.\n\n Returns:\n class: asyncio.streams.StreamReader\n\n \"\"\"\n self.reader = asyncio.StreamReader(loop=self.loop)\n reader_protocol = asyncio.StreamReaderProtocol(self.reader)\n\n await self.loop.connect_read_pipe(lambda: reader_protocol, sys.stdin)\n\n return self.reader\n\n async def async_input(self):\n \"\"\"Read user input asynchronously from stdin.\n\n Returns:\n string: A decoded string from user input.\n\n \"\"\"\n if not self.reader:\n self.reader = await self.read_stdin()\n line = await self.reader.readline()\n\n return line.decode(\"utf8\").replace(\"\\r\", \"\").replace(\"\\n\", \"\")\n\n def draw_prompt(self):\n \"\"\"Draw the user input prompt.\"\"\"\n prompt = self.bot_name + \"> \"\n self.prompt_length = len(prompt)\n print(prompt, end=\"\", flush=True)\n\n def clear_prompt(self):\n \"\"\"Clear the prompt.\"\"\"\n print(\"\\r\" + (\" \" * self.prompt_length) + \"\\r\", end=\"\", flush=True)\n\n async def parseloop(self):\n \"\"\"Parseloop moved out for testing.\"\"\"\n self.draw_prompt()\n user_input = await self.async_input()\n message = Message(text=user_input, user=self.user, target=None, connector=self)\n await self.opsdroid.parse(message)\n\n async def _parse_message(self):\n \"\"\"Parse user input.\"\"\"\n while self.is_listening:\n await self.parseloop()\n\n async def connect(self):\n \"\"\"Connect to the shell.\n\n There is nothing to do here since stdin is already available.\n\n Since this is the first method called when opsdroid starts, a logging\n message is shown if the user is using windows.\n\n \"\"\"\n if platform.system() == \"Windows\":\n _LOGGER.warning(\n \"The shell connector does not work on windows. Please install the Opsdroid Desktop App.\"\n )\n pass\n\n async def listen(self):\n \"\"\"Listen for and parse new user input.\"\"\"\n _LOGGER.debug(_(\"Connecting to shell.\"))\n message_processor = self.loop.create_task(self._parse_message())\n await self._closing.wait()\n message_processor.cancel()\n\n @register_event(Message)\n async def respond(self, message):\n \"\"\"Respond with a message.\n\n Args:\n message (object): An instance of Message\n\n \"\"\"\n _LOGGER.debug(_(\"Responding with: %s.\"), message.text)\n self.clear_prompt()\n print(message.text)\n\n async def disconnect(self):\n \"\"\"Disconnects the connector.\"\"\"\n self._closing.set()\n",
"path": "opsdroid/connector/shell/__init__.py"
}
] | diff --git a/opsdroid/connector/shell/__init__.py b/opsdroid/connector/shell/__init__.py
index be6ea62e9..4adfea5a2 100644
--- a/opsdroid/connector/shell/__init__.py
+++ b/opsdroid/connector/shell/__init__.py
@@ -125,7 +125,6 @@ async def respond(self, message):
_LOGGER.debug(_("Responding with: %s."), message.text)
self.clear_prompt()
print(message.text)
- self.draw_prompt()
async def disconnect(self):
"""Disconnects the connector."""
diff --git a/tests/test_connector_shell.py b/tests/test_connector_shell.py
index 70e000aeb..b1a040a6d 100644
--- a/tests/test_connector_shell.py
+++ b/tests/test_connector_shell.py
@@ -161,7 +161,7 @@ async def test_respond(self):
with contextlib.redirect_stdout(f):
await self.connector.respond(message)
prompt = f.getvalue()
- self.assertEqual(prompt.strip(), "Hi\nopsdroid>")
+ self.assertEqual(prompt.strip(), "Hi")
async def test_disconnect(self):
connector = ConnectorShell({}, opsdroid=OpsDroid())
|
opsdroid__opsdroid-946 | PyPI deployments are failing
Looks like PyPI deployments are failing. `v0.15.1` and `v0.15.2` haven't gone out.
```
HTTPError: 400 Client Error: The description failed to render in the default format of reStructuredText. See https://pypi.org/help/#description-content-type for more information. for url: https://upload.pypi.org/legacy/
```
PyPI deployments are failing
Looks like PyPI deployments are failing. `v0.15.1` and `v0.15.2` haven't gone out.
```
HTTPError: 400 Client Error: The description failed to render in the default format of reStructuredText. See https://pypi.org/help/#description-content-type for more information. for url: https://upload.pypi.org/legacy/
```
| [
{
"content": "#!/usr/bin/env python3\nimport os\nfrom setuptools import setup, find_packages\nfrom setuptools.command.build_py import build_py\nfrom setuptools.command.sdist import sdist\nfrom setuptools.command.develop import develop\nimport versioneer\n\nPACKAGE_NAME = 'opsdroid'\nHERE = os.path.abspath(os.path.dirname(__file__))\nREADME = open(os.path.join(HERE, 'README.md'), encoding=\"utf8\").read()\n\nPACKAGES = find_packages(exclude=['tests', 'tests.*', 'modules',\n 'modules.*', 'docs', 'docs.*'])\n\n\n# For now we simply define the install_requires based on the contents\n# of requirements.txt. In the future, install_requires may become much\n# looser than the (automatically) resolved requirements.txt.\nwith open(os.path.join(HERE, 'requirements.txt'), 'r') as fh:\n REQUIRES = [line.strip() for line in fh]\n\n\nclass Develop(develop):\n \"\"\"Custom `develop` command to always build mo files on install -e.\"\"\"\n\n def run(self):\n self.run_command('compile_catalog')\n develop.run(self) # old style class\n\n\nclass BuildPy(build_py):\n \"\"\"Custom `build_py` command to always build mo files for wheels.\"\"\"\n\n def run(self):\n self.run_command('compile_catalog')\n build_py.run(self) # old style class\n\n\nclass Sdist(sdist):\n \"\"\"Custom `sdist` command to ensure that mo files are always created.\"\"\"\n\n def run(self):\n self.run_command('compile_catalog')\n sdist.run(self) # old style class\n\n\nsetup(\n name=PACKAGE_NAME,\n version=versioneer.get_version(),\n license='Apache License 2.0',\n url='https://opsdroid.github.io/',\n download_url='https://github.com/opsdroid/opsdroid/releases',\n author='Jacob Tomlinson',\n author_email='[email protected]',\n description='An open source ChatOps bot framework.',\n long_description=README,\n packages=PACKAGES,\n include_package_data=True,\n zip_safe=False,\n platforms='any',\n classifiers=[\n 'Development Status :: 4 - Beta',\n 'Environment :: Console',\n 'Framework :: AsyncIO',\n 'Intended Audience :: Developers',\n 'Intended Audience :: System Administrators',\n 'Intended Audience :: Information Technology',\n 'License :: OSI Approved :: Apache Software License',\n 'Programming Language :: Python',\n 'Programming Language :: Python :: 3',\n 'Programming Language :: Python :: 3 :: Only',\n 'Programming Language :: Python :: 3.5',\n 'Programming Language :: Python :: 3.6',\n 'Programming Language :: Python :: 3.7',\n 'Topic :: Communications :: Chat',\n 'Topic :: Scientific/Engineering :: Artificial Intelligence',\n 'Topic :: Software Development :: Libraries :: Python Modules'\n ],\n install_requires=REQUIRES,\n test_suite='tests',\n keywords=[\n 'bot',\n 'bot-framework',\n 'opsdroid',\n 'botkit',\n 'python3',\n 'asyncio',\n 'chatops',\n 'devops',\n 'nlu'\n ],\n setup_requires=['Babel'],\n cmdclass=versioneer.get_cmdclass({'sdist': Sdist,\n 'build_py': BuildPy,\n 'develop': Develop}),\n entry_points={\n 'console_scripts': [\n 'opsdroid = opsdroid.__main__:main'\n ]\n },\n)\n",
"path": "setup.py"
}
] | [
{
"content": "#!/usr/bin/env python3\nimport os\nfrom setuptools import setup, find_packages\nfrom setuptools.command.build_py import build_py\nfrom setuptools.command.sdist import sdist\nfrom setuptools.command.develop import develop\nimport versioneer\n\nPACKAGE_NAME = 'opsdroid'\nHERE = os.path.abspath(os.path.dirname(__file__))\nREADME = open(os.path.join(HERE, 'README.md'), encoding=\"utf8\").read()\n\nPACKAGES = find_packages(exclude=['tests', 'tests.*', 'modules',\n 'modules.*', 'docs', 'docs.*'])\n\n\n# For now we simply define the install_requires based on the contents\n# of requirements.txt. In the future, install_requires may become much\n# looser than the (automatically) resolved requirements.txt.\nwith open(os.path.join(HERE, 'requirements.txt'), 'r') as fh:\n REQUIRES = [line.strip() for line in fh]\n\n\nclass Develop(develop):\n \"\"\"Custom `develop` command to always build mo files on install -e.\"\"\"\n\n def run(self):\n self.run_command('compile_catalog')\n develop.run(self) # old style class\n\n\nclass BuildPy(build_py):\n \"\"\"Custom `build_py` command to always build mo files for wheels.\"\"\"\n\n def run(self):\n self.run_command('compile_catalog')\n build_py.run(self) # old style class\n\n\nclass Sdist(sdist):\n \"\"\"Custom `sdist` command to ensure that mo files are always created.\"\"\"\n\n def run(self):\n self.run_command('compile_catalog')\n sdist.run(self) # old style class\n\n\nsetup(\n name=PACKAGE_NAME,\n version=versioneer.get_version(),\n license='Apache License 2.0',\n url='https://opsdroid.github.io/',\n download_url='https://github.com/opsdroid/opsdroid/releases',\n author='Jacob Tomlinson',\n author_email='[email protected]',\n description='An open source ChatOps bot framework.',\n long_description=README,\n long_description_content_type='text/markdown',\n packages=PACKAGES,\n include_package_data=True,\n zip_safe=False,\n platforms='any',\n classifiers=[\n 'Development Status :: 4 - Beta',\n 'Environment :: Console',\n 'Framework :: AsyncIO',\n 'Intended Audience :: Developers',\n 'Intended Audience :: System Administrators',\n 'Intended Audience :: Information Technology',\n 'License :: OSI Approved :: Apache Software License',\n 'Programming Language :: Python',\n 'Programming Language :: Python :: 3',\n 'Programming Language :: Python :: 3 :: Only',\n 'Programming Language :: Python :: 3.5',\n 'Programming Language :: Python :: 3.6',\n 'Programming Language :: Python :: 3.7',\n 'Topic :: Communications :: Chat',\n 'Topic :: Scientific/Engineering :: Artificial Intelligence',\n 'Topic :: Software Development :: Libraries :: Python Modules'\n ],\n install_requires=REQUIRES,\n test_suite='tests',\n keywords=[\n 'bot',\n 'bot-framework',\n 'opsdroid',\n 'botkit',\n 'python3',\n 'asyncio',\n 'chatops',\n 'devops',\n 'nlu'\n ],\n setup_requires=['Babel'],\n cmdclass=versioneer.get_cmdclass({'sdist': Sdist,\n 'build_py': BuildPy,\n 'develop': Develop}),\n entry_points={\n 'console_scripts': [\n 'opsdroid = opsdroid.__main__:main'\n ]\n },\n)\n",
"path": "setup.py"
}
] | diff --git a/setup.py b/setup.py
index 588670838..f9ed22303 100644
--- a/setup.py
+++ b/setup.py
@@ -55,6 +55,7 @@ def run(self):
author_email='[email protected]',
description='An open source ChatOps bot framework.',
long_description=README,
+ long_description_content_type='text/markdown',
packages=PACKAGES,
include_package_data=True,
zip_safe=False,
|
netbox-community__netbox-14935 | Typo in DataSourceBulkEditForm
### Deployment Type
Self-hosted
### NetBox Version
v3.7.1
### Python Version
3.8
### Steps to Reproduce
"lavel" is defined as "Enforce unique space", but I think the correct definition is "Enabled".
https://github.com/netbox-community/netbox/blob/487f1ccfde26ef3c1f8a28089826acc0cd6fadb2/netbox/core/forms/bulk_edit.py#L21-L25
- Add a new data source
- Editing 1 Data Sources
### Expected Behavior
Enabled
### Observed Behavior
Enforce unique space
| [
{
"content": "from django import forms\nfrom django.utils.translation import gettext_lazy as _\n\nfrom core.models import *\nfrom netbox.forms import NetBoxModelBulkEditForm\nfrom netbox.utils import get_data_backend_choices\nfrom utilities.forms.fields import CommentField\nfrom utilities.forms.widgets import BulkEditNullBooleanSelect\n\n__all__ = (\n 'DataSourceBulkEditForm',\n)\n\n\nclass DataSourceBulkEditForm(NetBoxModelBulkEditForm):\n type = forms.ChoiceField(\n label=_('Type'),\n choices=get_data_backend_choices,\n required=False\n )\n enabled = forms.NullBooleanField(\n required=False,\n widget=BulkEditNullBooleanSelect(),\n label=_('Enforce unique space')\n )\n description = forms.CharField(\n label=_('Description'),\n max_length=200,\n required=False\n )\n comments = CommentField()\n parameters = forms.JSONField(\n label=_('Parameters'),\n required=False\n )\n ignore_rules = forms.CharField(\n label=_('Ignore rules'),\n required=False,\n widget=forms.Textarea()\n )\n\n model = DataSource\n fieldsets = (\n (None, ('type', 'enabled', 'description', 'comments', 'parameters', 'ignore_rules')),\n )\n nullable_fields = (\n 'description', 'description', 'parameters', 'comments', 'parameters', 'ignore_rules',\n )\n",
"path": "netbox/core/forms/bulk_edit.py"
}
] | [
{
"content": "from django import forms\nfrom django.utils.translation import gettext_lazy as _\n\nfrom core.models import *\nfrom netbox.forms import NetBoxModelBulkEditForm\nfrom netbox.utils import get_data_backend_choices\nfrom utilities.forms.fields import CommentField\nfrom utilities.forms.widgets import BulkEditNullBooleanSelect\n\n__all__ = (\n 'DataSourceBulkEditForm',\n)\n\n\nclass DataSourceBulkEditForm(NetBoxModelBulkEditForm):\n type = forms.ChoiceField(\n label=_('Type'),\n choices=get_data_backend_choices,\n required=False\n )\n enabled = forms.NullBooleanField(\n required=False,\n widget=BulkEditNullBooleanSelect(),\n label=_('Enabled')\n )\n description = forms.CharField(\n label=_('Description'),\n max_length=200,\n required=False\n )\n comments = CommentField()\n parameters = forms.JSONField(\n label=_('Parameters'),\n required=False\n )\n ignore_rules = forms.CharField(\n label=_('Ignore rules'),\n required=False,\n widget=forms.Textarea()\n )\n\n model = DataSource\n fieldsets = (\n (None, ('type', 'enabled', 'description', 'comments', 'parameters', 'ignore_rules')),\n )\n nullable_fields = (\n 'description', 'description', 'parameters', 'comments', 'parameters', 'ignore_rules',\n )\n",
"path": "netbox/core/forms/bulk_edit.py"
}
] | diff --git a/netbox/core/forms/bulk_edit.py b/netbox/core/forms/bulk_edit.py
index dcc92c6f07c..bc2ef8fc92e 100644
--- a/netbox/core/forms/bulk_edit.py
+++ b/netbox/core/forms/bulk_edit.py
@@ -21,7 +21,7 @@ class DataSourceBulkEditForm(NetBoxModelBulkEditForm):
enabled = forms.NullBooleanField(
required=False,
widget=BulkEditNullBooleanSelect(),
- label=_('Enforce unique space')
+ label=_('Enabled')
)
description = forms.CharField(
label=_('Description'),
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.